(v,a,expr) and we exchange\n # ALL of these, the EmoMLP prediction will of course be the same. The output image still changes,\n # so EmoNet loss (if used) would be different. Same for the photometric/landmark losses.\n\n # TODO:\n # For now I decided to exchange everything but this should probably be experimented with\n # I would argue though, that exchanging the GT is the right thing to do\n if va is not None:\n va = torch.cat([va, va[new_order]], dim=0)\n if expr7 is not None:\n expr7 = torch.cat([expr7, expr7[new_order]], dim=0)\n if affectnetexp is not None:\n affectnetexp = torch.cat([affectnetexp, affectnetexp[new_order]], dim=0)\n if exprw is not None:\n exprw = torch.cat([exprw, exprw[new_order]], dim=0)\n\n if detailcode is not None:\n #TODO: to exchange or not to exchange, that is the question, the answer is probably NO\n detailcode = torch.cat([detailcode, detailcode], dim=0)\n # detailcode = torch.cat([detailcode, detailcode[new_order]], dim=0)\n if detailemocode is not None:\n # TODO: to exchange or not to exchange, that is the question, the answer is probably YES\n detailemocode = torch.cat([detailemocode, detailemocode[new_order]], dim=0)\n\n return expcode, posecode, shapecode, lightcode, texcode, images, cam, lmk, masks, va, expr7, affectnetexp, \\\n detailcode, detailemocode, exprw, lmk_mp, mica_images\n\n # return expcode, posecode, shapecode, lightcode, texcode, images, cam, lmk, masks, va, expr7\n\n\n def encode(self, batch, training=True) -> dict:\n \"\"\"\n Forward encoding pass of the model. Takes a batch of images and returns the corresponding latent codes for each image.\n :param batch: Batch of images to encode. batch['image'] [batch_size, ring_size, 3, image_size, image_size]. \n For a training forward pass, additional corresponding data are necessery such as 'landmarks' and 'masks'. \n For a testing pass, the images suffice. \n :param training: Whether the forward pass is for training or testing.\n \"\"\"\n codedict = {}\n original_batch_size = batch['image'].shape[0]\n\n images = batch['image']\n if 'mica_images' in batch.keys():\n mica_images = batch['mica_images']\n else:\n mica_images = None\n\n if len(images.shape) == 5:\n K = images.shape[1]\n elif len(images.shape) == 4:\n K = 1\n else:\n raise RuntimeError(\"Invalid image batch dimensions.\")\n\n # [B, K, 3, size, size] ==> [BxK, 3, size, size]\n images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])\n if mica_images is not None:\n mica_images = mica_images.view(-1, mica_images.shape[-3], mica_images.shape[-2], mica_images.shape[-1])\n\n if 'landmark' in batch.keys():\n lmk = batch['landmark']\n lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])\n \n if 'landmark_mediapipe' in batch.keys():\n lmk_mp = batch['landmark_mediapipe']\n lmk_mp = lmk_mp.view(-1, lmk_mp.shape[-2], lmk_mp.shape[-1])\n else:\n lmk_mp = None\n\n if 'mask' in batch.keys():\n masks = batch['mask']\n masks = masks.view(-1, images.shape[-2], images.shape[-1])\n\n # valence / arousal - not necessary unless we want to use VA for supervision (not done in EMOCA)\n if 'va' in batch:\n va = batch['va']\n va = va.view(-1, va.shape[-1])\n else:\n va = None\n\n # 7 basic expression - not necessary unless we want to use expression for supervision (not done in EMOCA or DECA)\n if 'expr7' in batch:\n expr7 = batch['expr7']\n expr7 = expr7.view(-1, expr7.shape[-1])\n else:\n expr7 = None\n\n # affectnet basic expression - not necessary unless we want to use expression for supervision (not done in EMOCA or DECA)\n if 'affectnetexp' in batch:\n affectnetexp = batch['affectnetexp']\n affectnetexp = affectnetexp.view(-1, affectnetexp.shape[-1])\n else:\n affectnetexp = None\n\n # expression weights if supervising by expression is used (to balance the classification loss) - not done in EMOCA or DECA\n if 'expression_weight' in batch:\n exprw = batch['expression_weight']\n exprw = exprw.view(-1, exprw.shape[-1])\n else:\n exprw = None\n\n\n # 1) COARSE STAGE\n # forward pass of the coarse encoder\n # shapecode, texcode, expcode, posecode, cam, lightcode = self._encode_flame(images)\n code, original_code = self._encode_flame(images, mica_image=mica_images)\n shapecode, texcode, expcode, posecode, cam, lightcode = self._unwrap_list(code)\n if original_code is not None:\n original_code = self._unwrap_list_to_dict(original_code)\n\n if training:\n # If training, we employ the disentanglement strategy\n if self.mode == DecaMode.COARSE:\n\n if self.deca.config.shape_constrain_type == 'same':\n ## Enforce that all identity shape codes within ring are the same. The batch is duplicated \n ## and the duplicated part's shape codes are shuffled.\n\n # reshape shapecode => [B, K, n_shape]\n # shapecode_idK = shapecode.view(self.batch_size, self.deca.K, -1)\n shapecode_idK = shapecode.view(original_batch_size, K, -1)\n # get mean id\n shapecode_mean = torch.mean(shapecode_idK, dim=[1])\n # shapecode_new = shapecode_mean[:, None, :].repeat(1, self.deca.K, 1)\n shapecode_new = shapecode_mean[:, None, :].repeat(1, K, 1)\n shapecode = shapecode_new.view(-1, self.deca._get_num_shape_params())\n\n # do the same for the original code dict\n shapecode_orig = original_code['shape']\n shapecode_orig_idK = shapecode_orig.view(original_batch_size, K, -1)\n shapecode_orig_mean = torch.mean(shapecode_orig_idK, dim=[1])\n shapecode_orig_new = shapecode_orig_mean[:, None, :].repeat(1, K, 1)\n original_code['shape'] = shapecode_orig_new.view(-1, self.deca._get_num_shape_params())\n\n elif self.deca.config.shape_constrain_type == 'exchange':\n ## Shuffle identitys shape codes within ring (they should correspond to the same identity)\n '''\n make sure s0, s1 is something to make shape close\n the difference from ||so - s1|| is \n the later encourage s0, s1 is cloase in l2 space, but not really ensure shape will be close\n '''\n # new_order = np.array([np.random.permutation(self.deca.config.train_K) + i * self.deca.config.train_K for i in range(self.deca.config.batch_size_train)])\n # new_order = np.array([np.random.permutation(self.deca.config.train_K) + i * self.deca.config.train_K for i in range(original_batch_size)])\n new_order = np.array([np.random.permutation(K) + i * K for i in range(original_batch_size)])\n new_order = new_order.flatten()\n shapecode_new = shapecode[new_order]\n ## append new shape code data\n shapecode = torch.cat([shapecode, shapecode_new], dim=0)\n texcode = torch.cat([texcode, texcode], dim=0)\n expcode = torch.cat([expcode, expcode], dim=0)\n posecode = torch.cat([posecode, posecode], dim=0)\n cam = torch.cat([cam, cam], dim=0)\n lightcode = torch.cat([lightcode, lightcode], dim=0)\n ## append gt\n images = torch.cat([images, images],\n dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])\n if mica_images is not None:\n mica_images = torch.cat([mica_images, mica_images], dim=0)\n lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])\n if lmk_mp is not None:\n lmk_mp = torch.cat([lmk_mp, lmk_mp], dim=0)\n masks = torch.cat([masks, masks], dim=0)\n\n if va is not None:\n va = torch.cat([va, va], dim=0)\n if expr7 is not None:\n expr7 = torch.cat([expr7, expr7], dim=0)\n\n # do the same for the original code dict\n shapecode_orig = original_code['shape']\n shapecode_orig_new = shapecode_orig[new_order]\n original_code['shape'] = torch.cat([shapecode_orig, shapecode_orig_new], dim=0)\n original_code['tex'] = torch.cat([original_code['tex'], original_code['tex']], dim=0)\n original_code['exp'] = torch.cat([original_code['exp'], original_code['exp']], dim=0)\n original_code['pose'] = torch.cat([original_code['pose'], original_code['pose']], dim=0)\n original_code['cam'] = torch.cat([original_code['cam'], original_code['cam']], dim=0)\n original_code['light'] = torch.cat([original_code['light'], original_code['light']], dim=0)\n\n\n elif self.deca.config.shape_constrain_type == 'shuffle_expression':\n assert original_code is not None\n ## DEPRECATED, NOT USED IN EMOCA OR DECA\n new_order = np.random.permutation(K*original_batch_size)\n old_order = np.arange(K*original_batch_size)\n while (new_order == old_order).any(): # ugly hacky way of assuring that every element is permuted\n new_order = np.random.permutation(K * original_batch_size)\n codedict['new_order'] = new_order\n # exchange expression\n expcode_new = expcode[new_order]\n expcode = torch.cat([expcode, expcode_new], dim=0)\n\n # exchange jaw pose (but not global pose)\n global_pose = posecode[:, :3]\n jaw_pose = posecode[:, 3:]\n jaw_pose_new = jaw_pose[new_order]\n jaw_pose = torch.cat([jaw_pose, jaw_pose_new], dim=0)\n global_pose = torch.cat([global_pose, global_pose], dim=0)\n posecode = torch.cat([global_pose, jaw_pose], dim=1)\n\n ## duplicate the rest\n shapecode = torch.cat([shapecode, shapecode], dim=0)\n texcode = torch.cat([texcode, texcode], dim=0)\n cam = torch.cat([cam, cam], dim=0)\n lightcode = torch.cat([lightcode, lightcode], dim=0)\n ## duplicate gt if any\n images = torch.cat([images, images],\n dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])\n if mica_images is not None:\n mica_images = torch.cat([mica_images, mica_images], dim=0)\n print(f\"TRAINING: {training}\")\n if lmk is not None:\n lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])\n if lmk_mp is not None:\n lmk_mp = torch.cat([lmk_mp, lmk_mp], dim=0)\n masks = torch.cat([masks, masks], dim=0)\n\n ref_images_identity_idxs = np.concatenate([old_order, old_order])\n ref_images_expression_idxs = np.concatenate([old_order, new_order])\n codedict[\"ref_images_identity_idxs\"] = ref_images_identity_idxs\n codedict[\"ref_images_expression_idxs\"] = ref_images_expression_idxs\n\n if va is not None:\n va = torch.cat([va, va[new_order]], dim=0)\n if expr7 is not None:\n expr7 = torch.cat([expr7, expr7[new_order]], dim=0)\n\n # do the same for the original code dict\n original_code['shape'] = torch.cat([original_code['shape'], original_code['shape']], dim=0)\n original_code['tex'] = torch.cat([original_code['tex'], original_code['tex']], dim=0)\n original_code['exp'] = torch.cat([original_code['exp'], original_code['exp'][new_order]], dim=0)\n original_global_pose = original_code['pose'][:, :3]\n original_jaw_pose = original_code['pose'][:, 3:]\n original_jaw_pose = torch.cat([original_jaw_pose, original_jaw_pose[new_order]], dim=0)\n original_global_pose = torch.cat([original_global_pose, original_global_pose], dim=0)\n original_code['pose'] = torch.cat([original_global_pose, original_jaw_pose], dim=1)\n original_code['cam'] = torch.cat([original_code['cam'], original_code['cam']], dim=0)\n original_code['light'] = torch.cat([original_code['light'], original_code['light']], dim=0)\n\n elif self.deca.config.shape_constrain_type == 'shuffle_shape':\n ## The shape codes are shuffled without duplication\n new_order = np.random.permutation(K*original_batch_size)\n old_order = np.arange(K*original_batch_size)\n while (new_order == old_order).any(): # ugly hacky way of assuring that every element is permuted\n new_order = np.random.permutation(K * original_batch_size)\n codedict['new_order'] = new_order\n shapecode_new = shapecode[new_order]\n ## append new shape code data\n shapecode = torch.cat([shapecode, shapecode_new], dim=0)\n texcode = torch.cat([texcode, texcode], dim=0)\n expcode = torch.cat([expcode, expcode], dim=0)\n posecode = torch.cat([posecode, posecode], dim=0)\n cam = torch.cat([cam, cam], dim=0)\n lightcode = torch.cat([lightcode, lightcode], dim=0)\n ## append gt\n images = torch.cat([images, images],\n dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])\n if mica_images is not None:\n mica_images = torch.cat([mica_images, mica_images], dim=0)\n if lmk is not None:\n lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])\n masks = torch.cat([masks, masks], dim=0)\n\n ref_images_identity_idxs = np.concatenate([old_order, new_order])\n ref_images_expression_idxs = np.concatenate([old_order, old_order])\n codedict[\"ref_images_identity_idxs\"] = ref_images_identity_idxs\n codedict[\"ref_images_expression_idxs\"] = ref_images_expression_idxs\n\n if va is not None:\n va = torch.cat([va, va], dim=0)\n if expr7 is not None:\n expr7 = torch.cat([expr7, expr7], dim=0)\n\n # do the same for the original code dict\n shapecode_orig = original_code['shape']\n shapecode_orig_new = shapecode_orig[new_order]\n original_code['shape'] = torch.cat([shapecode_orig, shapecode_orig_new], dim=0)\n original_code['tex'] = torch.cat([original_code['tex'], original_code['tex']], dim=0)\n original_code['exp'] = torch.cat([original_code['exp'], original_code['exp']], dim=0)\n original_code['pose'] = torch.cat([original_code['pose'], original_code['pose']], dim=0)\n original_code['cam'] = torch.cat([original_code['cam'], original_code['cam']], dim=0)\n original_code['light'] = torch.cat([original_code['light'], original_code['light']], dim=0)\n original_code['ref_images_identity_idxs'] = ref_images_identity_idxs\n original_code['ref_images_expression_idxs'] = ref_images_expression_idxs\n\n elif 'expression_constrain_type' in self.deca.config.keys() and \\\n self.deca.config.expression_constrain_type == 'same':\n ## NOT USED IN EMOCA OR DECA, deprecated\n\n # reshape shapecode => [B, K, n_shape]\n # shapecode_idK = shapecode.view(self.batch_size, self.deca.K, -1)\n expcode_idK = expcode.view(original_batch_size, K, -1)\n # get mean id\n expcode_mean = torch.mean(expcode_idK, dim=[1])\n # shapecode_new = shapecode_mean[:, None, :].repeat(1, self.deca.K, 1)\n expcode_new = expcode_mean[:, None, :].repeat(1, K, 1)\n expcode = expcode_new.view(-1, self.deca._get_num_shape_params())\n\n # do the same thing for the original code dict\n expcode_idK = original_code['exp'].view(original_batch_size, K, -1)\n expcode_mean = torch.mean(expcode_idK, dim=[1])\n expcode_new = expcode_mean[:, None, :].repeat(1, K, 1)\n original_code['exp'] = expcode_new.view(-1, self.deca._get_num_shape_params())\n\n elif 'expression_constrain_type' in self.deca.config.keys() and \\\n self.deca.config.expression_constrain_type == 'exchange':\n ## NOT USED IN EMOCA OR DECA, deprecated\n expcode, posecode, shapecode, lightcode, texcode, images, cam, lmk, \\\n masks, va, expr7, affectnetexp, _, _, exprw, lmk_mp, mica_images = \\\n self._expression_ring_exchange(original_batch_size, K,\n expcode, posecode, shapecode, lightcode, texcode,\n images, cam, lmk, masks, va, expr7, affectnetexp, None, None, exprw, lmk_mp, mica_images)\n # (self, original_batch_size, K,\n # expcode, posecode, shapecode, lightcode, texcode,\n # images, cam, lmk, masks, va, expr7, affectnetexp,\n # detailcode=None, detailemocode=None, exprw=None):\n\n # 2) DETAIL STAGE\n if self.mode == DecaMode.DETAIL:\n all_detailcode = self.deca.E_detail(images)\n\n # identity-based detail code\n detailcode = all_detailcode[:, :self.deca.n_detail]\n\n # detail emotion code is deprecated and will be empty\n detailemocode = all_detailcode[:, self.deca.n_detail:(self.deca.n_detail + self.deca.n_detail_emo)]\n\n if training:\n # If training, we employ the disentanglement strategy\n if self.deca.config.detail_constrain_type == 'exchange':\n # Identity within the same ring should be the same, so they should have the same code. \n # This can be enforced by shuffling. The batch is duplicated and the duplicated part's code shuffled\n '''\n make sure s0, s1 is something to make shape close\n the difference from ||so - s1|| is \n the later encourage s0, s1 is cloase in l2 space, but not really ensure shape will be close\n '''\n # this creates a per-ring random permutation. The detail exchange happens ONLY between the same\n # identities (within the ring) but not outside (no cross-identity detail exchange)\n new_order = np.array(\n # [np.random.permutation(self.deca.config.train_K) + i * self.deca.config.train_K for i in range(original_batch_size)])\n [np.random.permutation(K) + i * K for i in range(original_batch_size)])\n new_order = new_order.flatten()\n detailcode_new = detailcode[new_order]\n detailcode = torch.cat([detailcode, detailcode_new], dim=0)\n detailemocode = torch.cat([detailemocode, detailemocode], dim=0)\n ## append new shape code data\n shapecode = torch.cat([shapecode, shapecode], dim=0)\n texcode = torch.cat([texcode, texcode], dim=0)\n expcode = torch.cat([expcode, expcode], dim=0)\n posecode = torch.cat([posecode, posecode], dim=0)\n cam = torch.cat([cam, cam], dim=0)\n lightcode = torch.cat([lightcode, lightcode], dim=0)\n ## append gt\n images = torch.cat([images, images],\n dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])\n if mica_images is not None:\n mica_images = torch.cat([mica_images, mica_images], dim=0)\n lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])\n masks = torch.cat([masks, masks], dim=0)\n\n if va is not None:\n va = torch.cat([va, va], dim=0)\n if expr7 is not None:\n expr7 = torch.cat([expr7, expr7], dim=0)\n\n elif self.deca.config.detail_constrain_type == 'shuffle_expression':\n ## Deprecated and not used in EMOCA or DECA\n new_order = np.random.permutation(K*original_batch_size)\n old_order = np.arange(K*original_batch_size)\n while (new_order == old_order).any(): # ugly hacky way of assuring that every element is permuted\n new_order = np.random.permutation(K * original_batch_size)\n codedict['new_order'] = new_order\n # exchange expression\n expcode_new = expcode[new_order]\n expcode = torch.cat([expcode, expcode_new], dim=0)\n\n # exchange emotion code, but not (identity-based) detailcode\n detailemocode_new = detailemocode[new_order]\n detailemocode = torch.cat([detailemocode, detailemocode_new], dim=0)\n detailcode = torch.cat([detailcode, detailcode], dim=0)\n\n # exchange jaw pose (but not global pose)\n global_pose = posecode[:, :3]\n jaw_pose = posecode[:, 3:]\n jaw_pose_new = jaw_pose[new_order]\n jaw_pose = torch.cat([jaw_pose, jaw_pose_new], dim=0)\n global_pose = torch.cat([global_pose, global_pose], dim=0)\n posecode = torch.cat([global_pose, jaw_pose], dim=1)\n\n\n ## duplicate the rest\n shapecode = torch.cat([shapecode, shapecode], dim=0)\n texcode = torch.cat([texcode, texcode], dim=0)\n cam = torch.cat([cam, cam], dim=0)\n lightcode = torch.cat([lightcode, lightcode], dim=0)\n ## duplicate gt if any\n images = torch.cat([images, images],\n dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])\n if mica_images is not None:\n mica_images = torch.cat([mica_images, mica_images], dim=0)\n print(f\"TRAINING: {training}\")\n if lmk is not None:\n lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])\n masks = torch.cat([masks, masks], dim=0)\n\n ref_images_identity_idxs = np.concatenate([old_order, old_order])\n ref_images_expression_idxs = np.concatenate([old_order, new_order])\n codedict[\"ref_images_identity_idxs\"] = ref_images_identity_idxs\n codedict[\"ref_images_expression_idxs\"] = ref_images_expression_idxs\n\n if va is not None:\n va = torch.cat([va, va[new_order]], dim=0)\n if expr7 is not None:\n expr7 = torch.cat([expr7, expr7[new_order]], dim=0)\n\n elif self.deca.config.detail_constrain_type == 'shuffle_shape':\n ## Shuffles teh shape code without duplicating the batch\n new_order = np.random.permutation(K*original_batch_size)\n old_order = np.arange(K*original_batch_size)\n while (new_order == old_order).any(): # ugly hacky way of assuring that every element is permuted\n new_order = np.random.permutation(K * original_batch_size)\n codedict['new_order'] = new_order\n shapecode_new = shapecode[new_order]\n ## append new shape code data\n shapecode = torch.cat([shapecode, shapecode_new], dim=0)\n\n # exchange (identity-based) detailcode, but not emotion code\n detailcode_new = detailcode[new_order]\n detailcode = torch.cat([detailcode, detailcode_new], dim=0)\n detailemocode = torch.cat([detailemocode, detailemocode], dim=0)\n\n texcode = torch.cat([texcode, texcode], dim=0)\n expcode = torch.cat([expcode, expcode], dim=0)\n posecode = torch.cat([posecode, posecode], dim=0)\n cam = torch.cat([cam, cam], dim=0)\n lightcode = torch.cat([lightcode, lightcode], dim=0)\n ## append gt\n images = torch.cat([images, images],\n dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])\n if mica_images is not None:\n mica_images = torch.cat([mica_images, mica_images], dim=0)\n if lmk is not None:\n lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])\n masks = torch.cat([masks, masks], dim=0)\n\n ref_images_identity_idxs = np.concatenate([old_order, new_order])\n ref_images_expression_idxs = np.concatenate([old_order, old_order])\n codedict[\"ref_images_identity_idxs\"] = ref_images_identity_idxs\n codedict[\"ref_images_expression_idxs\"] = ref_images_expression_idxs\n\n if va is not None:\n va = torch.cat([va, va], dim=0)\n if expr7 is not None:\n expr7 = torch.cat([expr7, expr7], dim=0)\n\n elif 'expression_constrain_type' in self.deca.config.keys() and \\\n self.deca.config.expression_constrain_type == 'exchange':\n expcode, posecode, shapecode, lightcode, texcode, images, cam, lmk, masks, va, expr7, affectnetexp, detailcode, detailemocode, exprw, lmk_mp, mica_images = \\\n self._expression_ring_exchange(original_batch_size, K,\n expcode, posecode, shapecode, lightcode, texcode,\n images, cam, lmk, masks, va, expr7, affectnetexp, detailcode, detailemocode, exprw, lmk_mp, mica_images)\n\n\n codedict['shapecode'] = shapecode\n codedict['texcode'] = texcode\n codedict['expcode'] = expcode\n codedict['posecode'] = posecode\n codedict['cam'] = cam\n codedict['lightcode'] = lightcode\n if self.mode == DecaMode.DETAIL:\n codedict['detailcode'] = detailcode\n codedict['detailemocode'] = detailemocode\n codedict['images'] = images\n if mica_images is not None:\n codedict['mica_images'] = mica_images\n if 'mask' in batch.keys():\n codedict['masks'] = masks\n if 'landmark' in batch.keys():\n codedict['lmk'] = lmk\n if lmk_mp is not None:\n codedict['lmk_mp'] = lmk_mp\n\n if 'va' in batch.keys():\n codedict['va'] = va\n if 'expr7' in batch.keys():\n codedict['expr7'] = expr7\n if 'affectnetexp' in batch.keys():\n codedict['affectnetexp'] = affectnetexp\n\n if 'expression_weight' in batch.keys():\n codedict['expression_weight'] = exprw\n\n if original_code is not None:\n codedict['original_code'] = original_code\n\n return codedict\n\n\n def _create_conditioning_lists(self, codedict, condition_list):\n detail_conditioning_list = []\n if 'globalpose' in condition_list:\n detail_conditioning_list += [codedict[\"posecode\"][:, :3]]\n if 'jawpose' in condition_list:\n detail_conditioning_list += [codedict[\"posecode\"][:, 3:]]\n if 'identity' in condition_list:\n detail_conditioning_list += [codedict[\"shapecode\"]]\n if 'expression' in condition_list:\n detail_conditioning_list += [codedict[\"expcode\"]]\n\n if isinstance(self.deca.D_detail, Generator):\n # the detail codes might be excluded from conditioning based on the Generator architecture (for instance\n # for AdaIn Generator)\n if 'detail' in condition_list:\n detail_conditioning_list += [codedict[\"detailcode\"]]\n if 'detailemo' in condition_list:\n detail_conditioning_list += [codedict[\"detailemocode\"]]\n\n return detail_conditioning_list\n\n\n def decode(self, codedict, training=True, render=True, **kwargs) -> dict:\n \"\"\"\n Forward decoding pass of the model. Takes the latent code predicted by the encoding stage and reconstructs and renders the shape.\n :param codedict: Batch dict of the predicted latent codes\n :param training: Whether the forward pass is for training or testing.\n \"\"\"\n shapecode = codedict['shapecode']\n expcode = codedict['expcode']\n posecode = codedict['posecode']\n texcode = codedict['texcode']\n cam = codedict['cam']\n lightcode = codedict['lightcode']\n images = codedict['images']\n if 'masks' in codedict.keys():\n masks = codedict['masks']\n else:\n masks = None\n\n effective_batch_size = images.shape[0] # this is the current batch size after all training augmentations modifications\n\n # 1) Reconstruct the face mesh\n # FLAME - world space\n if not isinstance(self.deca.flame, FLAME_mediapipe):\n verts, landmarks2d, landmarks3d = self.deca.flame(shape_params=shapecode, expression_params=expcode,\n pose_params=posecode)\n landmarks2d_mediapipe = None\n else:\n verts, landmarks2d, landmarks3d, landmarks2d_mediapipe = self.deca.flame(shapecode, expcode, posecode)\n # world to camera\n trans_verts = util.batch_orth_proj(verts, cam)\n predicted_landmarks = util.batch_orth_proj(landmarks2d, cam)[:, :, :2]\n # camera to image space\n trans_verts[:, :, 1:] = -trans_verts[:, :, 1:]\n predicted_landmarks[:, :, 1:] = - predicted_landmarks[:, :, 1:]\n\n if landmarks2d_mediapipe is not None:\n predicted_landmarks_mediapipe = util.batch_orth_proj(landmarks2d_mediapipe, cam)[:, :, :2]\n predicted_landmarks_mediapipe[:, :, 1:] = - predicted_landmarks_mediapipe[:, :, 1:]\n\n if self.uses_texture():\n albedo = self.deca.flametex(texcode)\n else: \n # if not using texture, default to gray\n albedo = torch.ones([effective_batch_size, 3, self.deca.config.uv_size, self.deca.config.uv_size], device=images.device) * 0.5\n\n # 2) Render the coarse image\n if render:\n ops = self.deca.render(verts, trans_verts, albedo, lightcode)\n # mask\n mask_face_eye = F.grid_sample(self.deca.uv_face_eye_mask.expand(effective_batch_size, -1, -1, -1),\n ops['grid'].detach(),\n align_corners=False)\n # images\n predicted_images = ops['images']\n # predicted_images = ops['images'] * mask_face_eye * ops['alpha_images']\n # predicted_images_no_mask = ops['images'] #* mask_face_eye * ops['alpha_images']\n segmentation_type = None\n if isinstance(self.deca.config.useSeg, bool):\n if self.deca.config.useSeg:\n segmentation_type = 'gt'\n else:\n segmentation_type = 'rend'\n elif isinstance(self.deca.config.useSeg, str):\n segmentation_type = self.deca.config.useSeg\n else:\n raise RuntimeError(f\"Invalid 'useSeg' type: '{type(self.deca.config.useSeg)}'\")\n\n if segmentation_type not in [\"gt\", \"rend\", \"intersection\", \"union\"]:\n raise ValueError(f\"Invalid segmentation type for masking '{segmentation_type}'\")\n\n if masks is None: # if mask not provided, the only mask available is the rendered one\n segmentation_type = 'rend'\n\n elif masks.shape[-1] != predicted_images.shape[-1] or masks.shape[-2] != predicted_images.shape[-2]:\n # resize masks if need be (this is only done if configuration was changed at some point after training)\n dims = masks.ndim == 3\n if dims:\n masks = masks[:, None, :, :]\n masks = F.interpolate(masks, size=predicted_images.shape[-2:], mode='bilinear')\n if dims:\n masks = masks[:, 0, ...]\n\n # resize images if need be (this is only done if configuration was changed at some point after training)\n if images.shape[-1] != predicted_images.shape[-1] or images.shape[-2] != predicted_images.shape[-2]:\n ## special case only for inference time if the rendering image sizes have been changed\n images_resized = F.interpolate(images, size=predicted_images.shape[-2:], mode='bilinear')\n else:\n images_resized = images\n\n # what type of segmentation we use\n if segmentation_type == \"gt\": # GT stands for external segmetnation predicted by face parsing or similar\n masks = masks[:, None, :, :]\n elif segmentation_type == \"rend\": # mask rendered as a silhouette of the face mesh\n masks = mask_face_eye * ops['alpha_images']\n elif segmentation_type == \"intersection\": # intersection of the two above\n masks = masks[:, None, :, :] * mask_face_eye * ops['alpha_images']\n elif segmentation_type == \"union\": # union of the first two options\n masks = torch.max(masks[:, None, :, :], mask_face_eye * ops['alpha_images'])\n else:\n raise RuntimeError(f\"Invalid segmentation type for masking '{segmentation_type}'\")\n\n\n if self.deca.config.background_from_input in [True, \"input\"]:\n if images.shape[-1] != predicted_images.shape[-1] or images.shape[-2] != predicted_images.shape[-2]:\n ## special case only for inference time if the rendering image sizes have been changed\n predicted_images = (1. - masks) * images_resized + masks * predicted_images\n else:\n predicted_images = (1. - masks) * images + masks * predicted_images\n elif self.deca.config.background_from_input in [False, \"black\"]:\n predicted_images = masks * predicted_images\n elif self.deca.config.background_from_input in [\"none\"]:\n predicted_images = predicted_images\n else:\n raise ValueError(f\"Invalid type of background modification {self.deca.config.background_from_input}\")\n\n # 3) Render the detail image\n if self.mode == DecaMode.DETAIL:\n detailcode = codedict['detailcode']\n detailemocode = codedict['detailemocode']\n\n # a) Create the detail conditioning lists\n detail_conditioning_list = self._create_conditioning_lists(codedict, self.detail_conditioning)\n detailemo_conditioning_list = self._create_conditioning_lists(codedict, self.detailemo_conditioning)\n final_detail_conditioning_list = detail_conditioning_list + detailemo_conditioning_list\n\n\n # b) Pass the detail code and the conditions through the detail generator to get displacement UV map\n if isinstance(self.deca.D_detail, Generator):\n uv_z = self.deca.D_detail(torch.cat(final_detail_conditioning_list, dim=1))\n elif isinstance(self.deca.D_detail, GeneratorAdaIn):\n uv_z = self.deca.D_detail(z=torch.cat([detailcode, detailemocode], dim=1),\n cond=torch.cat(final_detail_conditioning_list, dim=1))\n else:\n raise ValueError(f\"This class of generarator is not supported: '{self.deca.D_detail.__class__.__name__}'\")\n\n # if there is a displacement mask, apply it (DEPRECATED and not USED in DECA or EMOCA)\n if hasattr(self.deca, 'displacement_mask') and self.deca.displacement_mask is not None:\n if 'apply_displacement_masks' in self.deca.config.keys() and self.deca.config.apply_displacement_masks:\n uv_z = uv_z * self.deca.displacement_mask\n\n # uv_z = self.deca.D_detail(torch.cat([posecode[:, 3:], expcode, detailcode], dim=1))\n # render detail\n if render:\n detach_from_coarse_geometry = not self.deca.config.train_coarse\n uv_detail_normals, uv_coarse_vertices = self.deca.displacement2normal(uv_z, verts, ops['normals'],\n detach=detach_from_coarse_geometry)\n uv_shading = self.deca.render.add_SHlight(uv_detail_normals, lightcode.detach())\n uv_texture = albedo.detach() * uv_shading\n\n # batch size X image_rows X image_cols X 2\n # you can query the grid for UV values of the face mesh at pixel locations\n grid = ops['grid']\n if detach_from_coarse_geometry:\n # if the grid is detached, the gradient of the positions of UV-values in image space won't flow back to the geometry\n grid = grid.detach()\n predicted_detailed_image = F.grid_sample(uv_texture, grid, align_corners=False)\n if self.deca.config.background_from_input in [True, \"input\"]:\n if images.shape[-1] != predicted_images.shape[-1] or images.shape[-2] != predicted_images.shape[-2]:\n ## special case only for inference time if the rendering image sizes have been changed\n # images_resized = F.interpolate(images, size=predicted_images.shape[-2:], mode='bilinear')\n ## before bugfix\n # predicted_images = (1. - masks) * images_resized + masks * predicted_images\n ## after bugfix\n predicted_detailed_image = (1. - masks) * images_resized + masks * predicted_detailed_image\n else:\n predicted_detailed_image = (1. - masks) * images + masks * predicted_detailed_image\n elif self.deca.config.background_from_input in [False, \"black\"]:\n predicted_detailed_image = masks * predicted_detailed_image\n elif self.deca.config.background_from_input in [\"none\"]:\n predicted_detailed_image = predicted_detailed_image\n else:\n raise ValueError(f\"Invalid type of background modification {self.deca.config.background_from_input}\")\n\n\n # --- extract texture\n uv_pverts = self.deca.render.world2uv(trans_verts).detach()\n uv_gt = F.grid_sample(torch.cat([images_resized, masks], dim=1), uv_pverts.permute(0, 2, 3, 1)[:, :, :, :2],\n mode='bilinear')\n uv_texture_gt = uv_gt[:, :3, :, :].detach()\n uv_mask_gt = uv_gt[:, 3:, :, :].detach()\n # self-occlusion\n normals = util.vertex_normals(trans_verts, self.deca.render.faces.expand(effective_batch_size, -1, -1))\n uv_pnorm = self.deca.render.world2uv(normals)\n\n uv_mask = (uv_pnorm[:, -1, :, :] < -0.05).float().detach()\n uv_mask = uv_mask[:, None, :, :]\n ## combine masks\n uv_vis_mask = uv_mask_gt * uv_mask * self.deca.uv_face_eye_mask\n else:\n uv_detail_normals = None\n predicted_detailed_image = None\n\n\n ## 4) (Optional) NEURAL RENDERING - not used in neither DECA nor EMOCA\n # If neural rendering is enabled, the differentiable rendered synthetic images are translated using an image translation net (such as StarGan)\n predicted_translated_image = None\n predicted_detailed_translated_image = None\n translated_uv_texture = None\n\n if render:\n if self.deca._has_neural_rendering():\n predicted_translated_image = self.deca.image_translator(\n {\n \"input_image\" : predicted_images,\n \"ref_image\" : images,\n \"target_domain\" : torch.tensor([0]*predicted_images.shape[0],\n dtype=torch.int64, device=predicted_images.device)\n }\n )\n\n if self.mode == DecaMode.DETAIL:\n predicted_detailed_translated_image = self.deca.image_translator(\n {\n \"input_image\" : predicted_detailed_image,\n \"ref_image\" : images,\n \"target_domain\" : torch.tensor([0]*predicted_detailed_image.shape[0],\n dtype=torch.int64, device=predicted_detailed_image.device)\n }\n )\n translated_uv = F.grid_sample(torch.cat([predicted_detailed_translated_image, masks], dim=1), uv_pverts.permute(0, 2, 3, 1)[:, :, :, :2],\n mode='bilinear')\n translated_uv_texture = translated_uv[:, :3, :, :].detach()\n\n else:\n predicted_detailed_translated_image = None\n\n translated_uv_texture = None\n # no need in coarse mode\n # translated_uv = F.grid_sample(torch.cat([predicted_translated_image, masks], dim=1), uv_pverts.permute(0, 2, 3, 1)[:, :, :, :2],\n # mode='bilinear')\n # translated_uv_texture = translated_uv_gt[:, :3, :, :].detach()\n\n if self.emotion_mlp is not None:\n codedict = self.emotion_mlp(codedict, \"emo_mlp_\")\n\n # populate the value dict for metric computation/visualization\n if render:\n codedict['predicted_images'] = predicted_images\n codedict['predicted_detailed_image'] = predicted_detailed_image\n codedict['predicted_translated_image'] = predicted_translated_image\n codedict['ops'] = ops\n codedict['normals'] = ops['normals']\n codedict['mask_face_eye'] = mask_face_eye\n \n codedict['verts'] = verts\n codedict['albedo'] = albedo\n codedict['landmarks2d'] = landmarks2d\n codedict['landmarks3d'] = landmarks3d\n codedict['predicted_landmarks'] = predicted_landmarks\n if landmarks2d_mediapipe is not None:\n codedict['predicted_landmarks_mediapipe'] = predicted_landmarks_mediapipe\n codedict['trans_verts'] = trans_verts\n codedict['masks'] = masks\n\n if self.mode == DecaMode.DETAIL:\n if render:\n codedict['predicted_detailed_translated_image'] = predicted_detailed_translated_image\n codedict['translated_uv_texture'] = translated_uv_texture\n codedict['uv_texture_gt'] = uv_texture_gt\n codedict['uv_texture'] = uv_texture\n codedict['uv_detail_normals'] = uv_detail_normals\n codedict['uv_shading'] = uv_shading\n codedict['uv_vis_mask'] = uv_vis_mask\n codedict['uv_mask'] = uv_mask\n codedict['uv_z'] = uv_z\n codedict['displacement_map'] = uv_z + self.deca.fixed_uv_dis[None, None, :, :]\n\n return codedict\n\n def _compute_emotion_loss(self, images, predicted_images, loss_dict, metric_dict, prefix, va=None, expr7=None, with_grad=True,\n batch_size=None, ring_size=None):\n def loss_or_metric(name, loss, is_loss):\n if not is_loss:\n metric_dict[name] = loss\n else:\n loss_dict[name] = loss\n\n # if self.deca.config.use_emonet_loss:\n if with_grad:\n d = loss_dict\n emo_feat_loss_1, emo_feat_loss_2, valence_loss, arousal_loss, expression_loss, au_loss = \\\n self.emonet_loss.compute_loss(images, predicted_images, batch_size=batch_size, ring_size=ring_size)\n else:\n d = metric_dict\n with torch.no_grad():\n emo_feat_loss_1, emo_feat_loss_2, valence_loss, arousal_loss, expression_loss, au_loss = \\\n self.emonet_loss.compute_loss(images, predicted_images, batch_size=batch_size, ring_size=ring_size)\n\n\n\n # EmoNet self-consistency loss terms\n if emo_feat_loss_1 is not None:\n loss_or_metric(prefix + '_emonet_feat_1_L1', emo_feat_loss_1 * self.deca.config.emonet_weight,\n self.deca.config.use_emonet_feat_1 and self.deca.config.use_emonet_loss)\n loss_or_metric(prefix + '_emonet_feat_2_L1', emo_feat_loss_2 * self.deca.config.emonet_weight,\n self.deca.config.use_emonet_feat_2 and self.deca.config.use_emonet_loss)\n loss_or_metric(prefix + '_emonet_valence_L1', valence_loss * self.deca.config.emonet_weight,\n self.deca.config.use_emonet_valence and self.deca.config.use_emonet_loss)\n loss_or_metric(prefix + '_emonet_arousal_L1', arousal_loss * self.deca.config.emonet_weight,\n self.deca.config.use_emonet_arousal and self.deca.config.use_emonet_loss)\n # loss_or_metric(prefix + 'emonet_expression_KL', expression_loss * self.deca.config.emonet_weight) # KL seems to be causing NaN's\n loss_or_metric(prefix + '_emonet_expression_L1',expression_loss * self.deca.config.emonet_weight,\n self.deca.config.use_emonet_expression and self.deca.config.use_emonet_loss)\n loss_or_metric(prefix + '_emonet_combined', ((emo_feat_loss_1 if emo_feat_loss_1 is not None else 0)\n + emo_feat_loss_2 + valence_loss + arousal_loss + expression_loss) * self.deca.config.emonet_weight,\n self.deca.config.use_emonet_combined and self.deca.config.use_emonet_loss)\n\n # Log also the VA\n metric_dict[prefix + \"_valence_input\"] = self.emonet_loss.input_emotion['valence'].mean().detach()\n metric_dict[prefix + \"_valence_output\"] = self.emonet_loss.output_emotion['valence'].mean().detach()\n metric_dict[prefix + \"_arousal_input\"] = self.emonet_loss.input_emotion['arousal'].mean().detach()\n metric_dict[prefix + \"_arousal_output\"] = self.emonet_loss.output_emotion['arousal'].mean().detach()\n\n input_ex = self.emonet_loss.input_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification'].detach().cpu().numpy()\n input_ex = np.argmax(input_ex, axis=1).mean()\n output_ex = self.emonet_loss.output_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification'].detach().cpu().numpy()\n output_ex = np.argmax(output_ex, axis=1).mean()\n metric_dict[prefix + \"_expression_input\"] = torch.tensor(input_ex, device=self.device)\n metric_dict[prefix + \"_expression_output\"] = torch.tensor(output_ex, device=self.device)\n\n # # GT emotion loss terms\n # if self.deca.config.use_gt_emotion_loss:\n # d = loss_dict\n # else:\n # d = metric_dict\n\n # TODO: uncomment this after you handle the case when certain entries are NaN (GT missing, not a bug)\n # if va is not None:\n # d[prefix + 'emo_sup_val_L1'] = F.l1_loss(self.emonet_loss.output_emotion['valence'], va[:, 0]) \\\n # * self.deca.config.gt_emotion_reg\n # d[prefix + 'emo_sup_ar_L1'] = F.l1_loss(self.emonet_loss.output_emotion['arousal'], va[:, 1]) \\\n # * self.deca.config.gt_emotion_reg\n #\n # metric_dict[prefix + \"_valence_gt\"] = va[:, 0].mean().detach()\n # metric_dict[prefix + \"_arousal_gt\"] = va[:, 1].mean().detach()\n #\n # if expr7 is not None:\n # affectnet_gt = [expr7_to_affect_net(int(expr7[i])).value for i in range(len(expr7))]\n # affectnet_gt = torch.tensor(np.array(affectnet_gt), device=self.device, dtype=torch.long)\n # d[prefix + '_emo_sup_expr_CE'] = F.cross_entropy(self.emonet_loss.output_emotion['expression'], affectnet_gt) * self.deca.config.gt_emotion_reg\n # metric_dict[prefix + \"_expr_gt\"] = affectnet_gt.mean().detach()\n\n\n def _compute_au_loss(self, images, predicted_images, loss_dict, metric_dict, prefix, au=None, with_grad=True):\n def loss_or_metric(name, loss, is_loss):\n if not is_loss:\n metric_dict[name] = loss\n else:\n loss_dict[name] = loss\n\n # if self.deca.config.use_emonet_loss:\n if with_grad:\n d = loss_dict\n au_feat_loss_1, au_feat_loss_2, _, _, _, au_loss = \\\n self.au_loss.compute_loss(images, predicted_images)\n else:\n d = metric_dict\n with torch.no_grad():\n au_feat_loss_1, au_feat_loss_2, _, _, _, au_loss = \\\n self.au_loss.compute_loss(images, predicted_images)\n\n\n\n # EmoNet self-consistency loss terms\n if au_feat_loss_1 is not None:\n loss_or_metric(prefix + '_au_feat_1_L1', au_feat_loss_1 * self.deca.config.au_loss.au_weight,\n self.deca.config.au_loss.use_feat_1 and self.deca.config.au_loss.use_as_loss)\n loss_or_metric(prefix + '_au_feat_2_L1', au_feat_loss_2 * self.deca.config.au_loss.au_weight,\n self.deca.config.au_loss.use_feat_2 and self.deca.config.au_loss.use_as_loss)\n loss_or_metric(prefix + '_au_loss', au_loss * self.deca.config.au_loss.au_weight,\n self.deca.config.au_loss.use_aus and self.deca.config.au_loss.use_as_loss)\n # loss_or_metric(prefix + '_au_losses_L1', arousal_loss * self.deca.config.au_loss.au_weight,\n # self.deca.config.au_loss.use_emonet_arousal and self.deca.config.au_loss.use_as_loss)\n # loss_or_metric(prefix + 'emonet_expression_KL', expression_loss * self.deca.config.au_loss.au_weight) # KL seems to be causing NaN's\n\n # # Log also the VA\n # metric_dict[prefix + \"_valence_input\"] = self.emonet_loss.input_emotion['valence'].mean().detach()\n # metric_dict[prefix + \"_valence_output\"] = self.emonet_loss.output_emotion['valence'].mean().detach()\n # metric_dict[prefix + \"_arousal_input\"] = self.emonet_loss.input_emotion['arousal'].mean().detach()\n # metric_dict[prefix + \"_arousal_output\"] = self.emonet_loss.output_emotion['arousal'].mean().detach()\n\n # input_ex = self.emonet_loss.input_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification'].detach().cpu().numpy()\n # input_ex = np.argmax(input_ex, axis=1).mean()\n # output_ex = self.emonet_loss.output_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification'].detach().cpu().numpy()\n # output_ex = np.argmax(output_ex, axis=1).mean()\n # metric_dict[prefix + \"_expression_input\"] = torch.tensor(input_ex, device=self.device)\n # metric_dict[prefix + \"_expression_output\"] = torch.tensor(output_ex, device=self.device)\n\n # # GT emotion loss terms\n # if self.deca.config.use_gt_emotion_loss:\n # d = loss_dict\n # else:\n # d = metric_dict\n\n\n def _cut_mouth_vectorized(self, images, landmarks, convert_grayscale=True):\n # mouth_window_margin = 12\n mouth_window_margin = 1 # not temporal\n mouth_crop_height = 96\n mouth_crop_width = 96\n mouth_landmark_start_idx = 48\n mouth_landmark_stop_idx = 68\n B, T = images.shape[:2]\n\n landmarks = landmarks.to(torch.float32)\n\n with torch.no_grad():\n image_size = images.shape[-1] / 2\n\n landmarks = landmarks * image_size + image_size\n # #1) smooth the landmarks with temporal convolution\n # landmarks are of shape (T, 68, 2) \n # reshape to (T, 136) \n landmarks_t = landmarks.reshape(*landmarks.shape[:2], -1)\n # make temporal dimension last \n landmarks_t = landmarks_t.permute(0, 2, 1)\n # change chape to (N, 136, T)\n # landmarks_t = landmarks_t.unsqueeze(0)\n # smooth with temporal convolution\n temporal_filter = torch.ones(mouth_window_margin, device=images.device) / mouth_window_margin\n # pad the the landmarks \n landmarks_t_padded = F.pad(landmarks_t, (mouth_window_margin // 2, mouth_window_margin // 2), mode='replicate')\n # convolve each channel separately with the temporal filter\n num_channels = landmarks_t.shape[1]\n if temporal_filter.numel() > 1:\n smooth_landmarks_t = F.conv1d(landmarks_t_padded, \n temporal_filter.unsqueeze(0).unsqueeze(0).expand(num_channels,1,temporal_filter.numel()), \n groups=num_channels, padding='valid'\n )\n smooth_landmarks_t = smooth_landmarks_t[..., 0:landmarks_t.shape[-1]]\n else:\n smooth_landmarks_t = landmarks_t\n\n # reshape back to the original shape \n smooth_landmarks_t = smooth_landmarks_t.permute(0, 2, 1).view(landmarks.shape)\n smooth_landmarks_t = smooth_landmarks_t + landmarks.mean(dim=2, keepdims=True) - smooth_landmarks_t.mean(dim=2, keepdims=True)\n\n # #2) get the mouth landmarks\n mouth_landmarks_t = smooth_landmarks_t[..., mouth_landmark_start_idx:mouth_landmark_stop_idx, :]\n \n # #3) get the mean of the mouth landmarks\n mouth_landmarks_mean_t = mouth_landmarks_t.mean(dim=-2, keepdims=True)\n \n # #4) get the center of the mouth\n center_x_t = mouth_landmarks_mean_t[..., 0]\n center_y_t = mouth_landmarks_mean_t[..., 1]\n\n # #5) use grid_sample to crop the mouth in every image \n # create the grid\n height = mouth_crop_height//2\n width = mouth_crop_width//2\n\n torch.arange(0, mouth_crop_width, device=images.device)\n\n grid = torch.stack(torch.meshgrid(torch.linspace(-height, height, mouth_crop_height).to(images.device) / (images.shape[-2] /2),\n torch.linspace(-width, width, mouth_crop_width).to(images.device) / (images.shape[-1] /2) ), \n dim=-1)\n grid = grid[..., [1, 0]]\n grid = grid.unsqueeze(0).unsqueeze(0).repeat(*images.shape[:2], 1, 1, 1)\n\n center_x_t -= images.shape[-1] / 2\n center_y_t -= images.shape[-2] / 2\n\n center_x_t /= images.shape[-1] / 2\n center_y_t /= images.shape[-2] / 2\n\n grid = grid + torch.cat([center_x_t, center_y_t ], dim=-1).unsqueeze(-2).unsqueeze(-2)\n\n images = images.view(B*T, *images.shape[2:])\n grid = grid.view(B*T, *grid.shape[2:])\n\n if convert_grayscale: \n images = F_v.rgb_to_grayscale(images)\n\n image_crops = F.grid_sample(\n images, \n grid, \n align_corners=True, \n padding_mode='zeros',\n mode='bicubic'\n )\n image_crops = image_crops.view(B, T, *image_crops.shape[1:])\n\n if convert_grayscale:\n image_crops = image_crops#.squeeze(1)\n\n # import matplotlib.pyplot as plt\n # plt.figure()\n # plt.imshow(image_crops[0, 0].permute(1,2,0).cpu().numpy())\n # plt.show()\n\n # plt.figure()\n # plt.imshow(image_crops[0, 10].permute(1,2,0).cpu().numpy())\n # plt.show()\n\n # plt.figure()\n # plt.imshow(image_crops[0, 20].permute(1,2,0).cpu().numpy())\n # plt.show()\n\n # plt.figure()\n # plt.imshow(image_crops[1, 0].permute(1,2,0).cpu().numpy())\n # plt.show()\n\n # plt.figure()\n # plt.imshow(image_crops[1, 10].permute(1,2,0).cpu().numpy())\n # plt.show()\n\n # plt.figure()\n # plt.imshow(image_crops[1, 20].permute(1,2,0).cpu().numpy())\n # plt.show()\n return image_crops\n\n\n def _compute_lipread_loss(self, images, predicted_images, landmarks, predicted_landmarks, loss_dict, metric_dict, prefix, with_grad=True): \n def loss_or_metric(name, loss, is_loss):\n if not is_loss:\n metric_dict[name] = loss\n else:\n loss_dict[name] = loss\n\n # shape of images is: (B, R, C, H, W)\n # convert to (B * R, 1, H, W, C)\n images = images.unsqueeze(1)\n predicted_images = predicted_images.unsqueeze(1)\n landmarks = landmarks.unsqueeze(1)\n predicted_landmarks = predicted_landmarks.unsqueeze(1)\n\n # cut out the mouth region\n\n\n images_mouth = self._cut_mouth_vectorized(images, landmarks)\n predicted_images_mouth = self._cut_mouth_vectorized(predicted_images, predicted_landmarks)\n\n # make sure that the lip reading net interprests things with depth=1, \n\n # if self.deca.config.use_emonet_loss:\n if with_grad:\n d = loss_dict\n loss = self.lipread_loss.compute_loss(images_mouth, predicted_images_mouth)\n else:\n d = metric_dict\n with torch.no_grad():\n loss = self.lipread_loss.compute_loss(images_mouth, predicted_images_mouth)\n\n d[prefix + '_lipread'] = loss * self.deca.config.lipread_loss.weight\n\n\n def _metric_or_loss(self, loss_dict, metric_dict, is_loss):\n if is_loss:\n d = loss_dict\n else:\n d = metric_dict\n return d\n\n\n def _compute_id_loss(self, codedict, batch, training, testing, losses, batch_size,\n ring_size):\n # if self.deca.config.idw > 1e-3:\n if self.deca.id_loss is not None:\n\n images = codedict[\"images\"]\n\n ops = codedict[\"ops\"]\n mask_face_eye = codedict[\"mask_face_eye\"]\n\n shading_images = self.deca.render.add_SHlight(ops['normal_images'], codedict[\"lightcode\"].detach())\n albedo_images = F.grid_sample(codedict[\"albedo\"].detach(), ops['grid'], align_corners=False)\n\n # TODO: get to the bottom of this weird overlay thing - why is it there?\n # answer: This renders the face and takes background from the image\n overlay = albedo_images * shading_images * mask_face_eye + images * (1 - mask_face_eye)\n\n if self.global_step >= self.deca.id_loss_start_step:\n if 'id_metric' in self.deca.config.keys() and 'barlow_twins' in self.deca.config.id_metric:\n assert ring_size == 1 or ring_size == 2\n\n effective_bs = images.shape[0]\n # losses['identity'] = self.deca.id_loss(overlay, images, batch_size=batch_size,\n # ring_size=ring_size) * self.deca.config.idw\n\n if \"ref_images_identity_idxs\" in codedict.keys():\n # in case there was shuffling, this ensures that the proper images are used for identity loss\n images_ = images[codedict[\"ref_images_identity_idxs\"]]\n else:\n images_ = images\n losses['identity'] = self.deca.id_loss(overlay, images_, batch_size=effective_bs,\n ring_size=1) * self.deca.config.idw\n if 'id_contrastive' in self.deca.config.keys() and bool(self.deca.config.id_contrastive):\n if ring_size == 2:\n assert effective_bs % 2 == 0\n assert self.deca.id_loss.trainable\n has_been_shuffled = 'new_order' in codedict.keys()\n\n idxs_a = torch.arange(0, images.shape[0], 2) # indices of first images within the ring\n idxs_b = torch.arange(1, images.shape[0], 2) # indices of second images within the ring\n\n # WARNING - this assumes the ring is identity-based\n if self.deca.config.id_contrastive in [True, \"real\", \"both\"]:\n # we are taking this from the original batch dict because we do not care about the\n # shuffled, duplicated samples (they don't have to be doubled)\n images_0 = batch[\"image\"][:, 0, ...]\n images_1 = batch[\"image\"][:, 1, ...]\n losses['identity_contrastive_real'] = self.deca.id_loss(\n images_0, # first images within the ring\n images_1, # second images within the ring\n batch_size=images_0.shape[0],\n ring_size=1) * self.deca.config.idw * 2\n if self.deca.config.id_contrastive in [True, \"synth\", \"both\"]:\n\n if self.deca.config.shape_constrain_type in ['exchange', 'same']:\n # we can take all when identity has been exchange within rings\n overlay_0 = overlay[idxs_a]\n overlay_1 = overlay[idxs_b]\n else:\n #if the batch was double otherwise (global shuffling) we only take the first half\n # if batch_size * ring_size < effective_bs:\n overlay_0 = overlay[0:batch_size * ring_size:2]\n overlay_1 = overlay[1:batch_size * ring_size:2]\n\n losses['identity_contrastive_synthetic'] = self.deca.id_loss(\n overlay_0, # first images within the ring\n overlay_1, # second images within the ring\n batch_size=overlay_0.shape[0],\n ring_size=1) * self.deca.config.idw\n\n\n if has_been_shuffled:\n new_order = codedict['new_order']\n\n # TODO: compare the idxs to these:\n # codedict[\"ref_images_identity_idxs\"]\n\n if self.deca.config.shape_constrain_type == 'shuffle_expression':\n idxs_a_synth = np.arange(new_order.shape[0]) # first half of the batch\n idxs_b_synth = np.arange(new_order.shape[0],\n 2 * new_order.shape[0]) # second half of the batch\n elif self.deca.config.shape_constrain_type == 'shuffle_shape':\n idxs_a_synth = new_order # shuffled first half of the batch\n idxs_b_synth = np.arange(new_order.shape[0],\n 2 * new_order.shape[0]) # second half of the batch\n else:\n raise NotImplementedError(\"Unexpected shape consistency value \")\n\n # if this doesn't go through, something went wrong with the shuffling indexations\n assert codedict[\"shapecode\"][idxs_a_synth].allclose(codedict[\"shapecode\"][idxs_b_synth])\n\n losses['identity_contrastive_synthetic_shuffled'] = self.deca.id_loss(\n overlay[idxs_a_synth], # synthetic images of identities with reconstructed expressions\n overlay[idxs_b_synth], # synthetic images of identities with shuffled expressions\n batch_size=idxs_a_synth.size,\n ring_size=1) * self.deca.config.idw\n\n losses['identity_contrastive_synthetic2real_shuffled'] = self.deca.id_loss(\n images[idxs_a_synth], # synthetic images of identities with reconstructed expressions\n overlay[idxs_b_synth], # synthetic images of identities with shuffled expressions\n batch_size=idxs_a_synth.size,\n ring_size=1) * self.deca.config.idw\n elif ring_size > 2:\n raise NotImplementedError(\"Contrastive loss does not support ring sizes > 2.\")\n return losses\n\n\n def _compute_emonet_loss_wrapper(self, codedict, batch, training, testing, losses, metrics, prefix, image_key,\n with_grad, batch_size, ring_size):\n\n if self.emonet_loss is not None:\n\n if 'va' in codedict:\n va = codedict['va']\n va = va.view(-1, va.shape[-1])\n else:\n va = None\n\n if 'expr7' in codedict:\n expr7 = codedict['expr7']\n expr7 = expr7.view(-1, expr7.shape[-1])\n else:\n expr7 = None\n\n # with torch.no_grad():\n # TODO: if expression shuffled, this needs to be changed, the input images no longer correspond\n\n images = codedict[\"images\"]\n predicted_images = codedict[image_key]\n effective_bs = images.shape[0]\n\n if \"ref_images_expression_idxs\" in codedict.keys():\n # in case there was shuffling, this ensures that the proper images are used for emotion loss\n images_ = images[codedict[\"ref_images_expression_idxs\"]]\n else:\n images_ = images\n effective_bs = images.shape[0]\n self._compute_emotion_loss(images_, predicted_images, losses, metrics, f\"{prefix}\",\n va, expr7,\n with_grad=with_grad,\n batch_size=effective_bs, ring_size=1)\n\n codedict[f\"{prefix}_valence_input\"] = self.emonet_loss.input_emotion['valence']\n codedict[f\"{prefix}_arousal_input\"] = self.emonet_loss.input_emotion['arousal']\n codedict[f\"{prefix}_expression_input\"] = self.emonet_loss.input_emotion[\n 'expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']\n codedict[f\"{prefix}_valence_output\"] = self.emonet_loss.output_emotion['valence']\n codedict[f\"{prefix}_arousal_output\"] = self.emonet_loss.output_emotion['arousal']\n codedict[f\"{prefix}_expression_output\"] = self.emonet_loss.output_emotion[\n 'expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']\n\n if 'emo_contrastive' in self.deca.config.keys() and self.deca.config.emo_contrastive:\n assert ring_size == 2 or ring_size == 1\n\n assert self.emonet_loss.trainable or (\n hasattr(self.emonet_loss, 'clone_is_trainable') and self.emonet_lossclone_is_trainable)\n\n has_been_shuffled = 'new_order' in codedict.keys()\n\n # if self.deca.config.shape_constrain_type == 'shuffle_expression' and has_been_shuffled:\n # new_order = codedict['new_order']\n #\n\n if self.deca.config.emo_contrastive in [True, \"real\", \"both\"]:\n if ring_size == 2:\n\n assert effective_bs % 2 == 0\n\n if not isinstance(self.deca, ExpDECA):\n raise NotImplementedError(\"Cross-ring emotion contrast means the ring has to be \"\n \"expression based, not identity based. This is not guaranteed \"\n \"for vanilla EMOCA (or its datasets).\")\n # we are taking this from the original batch dict because we do not care about the\n # shuffled, duplicated samples (they don't have to be doubled)\n images_0 = batch[\"image\"][:, 0, ...]\n images_1 = batch[\"image\"][:, 1, ...]\n self._compute_emotion_loss(images_0, # real images of first expressions in the ring\n images_1, # real images of second expressions in the ring\n losses, metrics, f\"{prefix}_contrastive_real\",\n va, expr7, with_grad=self.deca.config.use_emonet_loss,\n batch_size=images_0.shape[0], ring_size=1)\n else:\n print(\"[WARNING] Cannot compute real contrastive emotion loss because there is no ring!\")\n\n if self.deca.config.emo_contrastive in [True, \"synth\", \"both\"]:\n\n if ring_size == 2:\n assert effective_bs % 2 == 0\n\n idxs_a = torch.arange(0, images.shape[0], 2) # indices of first expressions within a ring\n idxs_b = torch.arange(1, images.shape[0], 2) # indices of second expressions within a ring\n\n if 'expression_constrain_type' in self.deca.config.keys() and \\\n self.deca.config.expression_constrain_type in ['exchange', 'same']:\n # we can take all when identity has been exchange within rings\n predicted_images_0 = predicted_images[idxs_a]\n predicted_images_1 = predicted_images[idxs_b]\n raise RuntimeError(\"This should work but it was never tested or intended. Make sure this works.\")\n else:\n # if the batch was double otherwise (global shuffling) we only take the first half\n # if batch_size * ring_size < effective_bs:\n predicted_images_0 = predicted_images[0:batch_size * ring_size:2]\n predicted_images_1 = predicted_images[1:batch_size * ring_size:2]\n\n if not isinstance(self.deca, ExpDECA):\n raise NotImplementedError(\"Cross-ring emotion contrast means the ring has to be \"\n \"expression based, not identity based. This is not guaranteed \"\n \"for vanilla EMOCA.\")\n\n self._compute_emotion_loss(predicted_images_0,\n # rec images of first expressions in the ring\n predicted_images_1,\n # rec images of second expressions in the ring\n losses, metrics, f\"{prefix}_contrastive_synth\",\n va, expr7, with_grad=self.deca.config.use_emonet_loss,\n batch_size=predicted_images_1.shape[0], ring_size=1)\n else:\n print(\"[WARNING] Cannot compute synthetic contrastive emotion loss because there is no ring!\")\n\n if has_been_shuffled:\n new_order = codedict['new_order']\n if self.deca.config.shape_constrain_type == 'shuffle_expression':\n # this gets tricky, in this case the images are not duplicates -> we need all, but the second\n # half's order is shuffled, so we need to be careful here\n idxs_a_synth = new_order # shuffled first half of the batch\n idxs_b_synth = np.arange(new_order.shape[0],\n 2 * new_order.shape[0]) # second half of the batch\n elif self.deca.config.shape_constrain_type == 'shuffle_shape':\n idxs_a_synth = np.arange(new_order.shape[0]) # first half of the batch\n idxs_b_synth = np.arange(new_order.shape[0],\n 2 * new_order.shape[0]) # second half of the batch\n\n # if this doesn't go through, something went wrong with the shuffling indexations\n assert codedict[\"expcode\"][idxs_a_synth].allclose(codedict[\"expcode\"][idxs_b_synth])\n\n # the expressions at corresponding index positions of idxs_a_synth and idxs_b_synth should match now\n self._compute_emotion_loss(predicted_images[idxs_a_synth],\n # synthetic images of reconstructed expressions and corresponding identities\n predicted_images[idxs_b_synth],\n # synthetic images of reconstructed expressions and shuffled identities\n losses, metrics, f\"{prefix}_contrastive_synth_shuffled\",\n va, expr7,\n with_grad=self.deca.config.use_emonet_loss and not self.deca._has_neural_rendering(),\n batch_size=idxs_a_synth.size, ring_size=1)\n \n self._compute_emotion_loss(images[idxs_a_synth],\n # synthetic images of reconstructed expressions and corresponding identities\n predicted_images[idxs_b_synth],\n # synthetic images of reconstructed expressions and shuffled identities\n losses, metrics, f\"{prefix}_contrastive_synth2real_shuffled\",\n va, expr7,\n with_grad=self.deca.config.use_emonet_loss and not self.deca._has_neural_rendering(),\n batch_size=idxs_a_synth.size,\n ring_size=1)\n \n\n if va is not None:\n codedict[f\"{prefix}_valence_gt\"] = va[:, 0]\n codedict[f\"{prefix}_arousal_gt\"] = va[:, 1]\n if expr7 is not None:\n codedict[f\"{prefix}_expression_gt\"] = expr7\n\n if self.deca._has_neural_rendering():\n assert 'emo_contrastive' not in self.deca.config.keys() or self.deca.config.emo_contrastive is False\n # TODO possible to make this more GPU efficient by not recomputing emotion for input image\n self._compute_emotion_loss(images, predicted_translated_image, losses, metrics, f\"{prefix}_translated\",\n va, expr7,\n with_grad=self.deca.config.use_emonet_loss and self.deca._has_neural_rendering(),\n batch_size=bs,\n ring_size=1)\n\n # codedict[f\"{prefix}_valence_input\"] = self.emonet_loss.input_emotion['valence']\n # codedict[f\"{prefix}_arousal_input\"] = self.emonet_loss.input_emotion['arousal']\n # codedict[f\"{prefix}_expression_input\"] = self.emonet_loss.input_emotion['expression']\n codedict[f\"{prefix}_translated_valence_output\"] = self.emonet_loss.output_emotion['valence']\n codedict[f\"{prefix}_translated_arousal_output\"] = self.emonet_loss.output_emotion['arousal']\n codedict[f\"{prefix}_translated_expression_output\"] = self.emonet_loss.output_emotion[\n 'expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']\n return losses, metrics, codedict\n\n\n def _compute_loss(self, codedict, batch, training=True, testing=False):\n #### ----------------------- Losses\n losses = {}\n metrics = {}\n\n predicted_landmarks = codedict[\"predicted_landmarks\"]\n predicted_landmarks_mediapipe = codedict.get(\"predicted_landmarks_mediapipe\", None)\n if \"lmk\" in codedict.keys():\n lmk = codedict[\"lmk\"]\n else:\n lmk = None\n \n if \"lmk_mp\" in codedict.keys():\n lmk_mp = codedict[\"lmk_mp\"]\n else:\n lmk_mp = None\n\n if \"masks\" in codedict.keys():\n masks = codedict[\"masks\"]\n else:\n masks = None\n\n batch_size = codedict[\"predicted_images\"].shape[0]\n\n use_geom_losses = 'use_geometric_losses_expression_exchange' in self.deca.config.keys() and \\\n self.deca.config.use_geometric_losses_expression_exchange\n\n if training and ('expression_constrain_type' in self.deca.config.keys() \\\n and ('expression_constrain_type' in self.deca.config.keys() and self.deca.config.expression_constrain_type == 'exchange') or\n ( 'shape_constrain_type' in self.deca.config.keys() and\n self.deca.config.shape_constrain_type in ['shuffle_expression', 'shuffle_shape'])) \\\n and (self.deca.mode == DecaMode.COARSE or self.deca.config.train_coarse) \\\n and (not use_geom_losses):\n if batch_size % 2 != 0:\n raise RuntimeError(\"The batch size should be even because it should have \"\n f\"got doubled in expression ring exchange. Instead it was odd: {batch_size}\")\n # THIS IS DONE BECAUSE LANDMARK AND PHOTOMETRIC LOSSES MAKE NO SENSE FOR EXPRESSION EXCHANGE\n geom_losses_idxs = batch_size // 2\n\n else:\n geom_losses_idxs = batch_size\n\n predicted_images = codedict[\"predicted_images\"]\n images = codedict[\"images\"]\n lightcode = codedict[\"lightcode\"]\n albedo = codedict[\"albedo\"]\n mask_face_eye = codedict[\"mask_face_eye\"]\n shapecode = codedict[\"shapecode\"]\n expcode = codedict[\"expcode\"]\n texcode = codedict[\"texcode\"]\n ops = codedict[\"ops\"]\n\n\n if self.mode == DecaMode.DETAIL:\n uv_texture = codedict[\"uv_texture\"]\n uv_texture_gt = codedict[\"uv_texture_gt\"]\n\n\n # this determines the configured batch size that is currently used (training, validation or testing)\n # the reason why this is important is because of potential multi-gpu training and loss functions (such as Barlow Twins)\n # that might need the full size of the batch (not just the chunk of the current GPU).\n if training:\n bs = self.learning_params.batch_size_train\n rs = self.learning_params.train_K\n else:\n if not testing:\n bs = self.learning_params.batch_size_val\n rs = self.learning_params.val_K\n else:\n bs = self.learning_params.batch_size_test\n rs = self.learning_params.test_K\n\n\n ## COARSE loss only\n if self.mode == DecaMode.COARSE or (self.mode == DecaMode.DETAIL and self.deca.config.train_coarse):\n\n # landmark losses (only useful if coarse model is being trained\n # if training or lmk is not None:\n if lmk is not None:\n # if self.deca.config.use_landmarks:\n # d = losses\n # else:\n # d = metrics\n d = self._metric_or_loss(losses, metrics, self.deca.config.use_landmarks)\n\n\n\n if self.deca.config.useWlmk:\n d['landmark'] = \\\n lossfunc.weighted_landmark_loss(predicted_landmarks[:geom_losses_idxs, ...], lmk[:geom_losses_idxs, ...]) * self.deca.config.lmk_weight\n else:\n d['landmark'] = \\\n lossfunc.landmark_loss(predicted_landmarks[:geom_losses_idxs, ...], lmk[:geom_losses_idxs, ...]) * self.deca.config.lmk_weight\n\n d = self._metric_or_loss(losses, metrics, 'use_eye_distance' not in self.deca.config.keys() or\n self.deca.config.use_eye_distance)\n # losses['eye_distance'] = lossfunc.eyed_loss(predicted_landmarks, lmk) * self.deca.config.lmk_weight * 2\n d['eye_distance'] = lossfunc.eyed_loss(predicted_landmarks[:geom_losses_idxs, ...],\n lmk[:geom_losses_idxs, ...]) * self.deca.config.eyed\n d = self._metric_or_loss(losses, metrics, 'use_lip_distance' not in self.deca.config.keys() or\n self.deca.config.use_lip_distance)\n d['lip_distance'] = lossfunc.lipd_loss(predicted_landmarks[:geom_losses_idxs, ...],\n lmk[:geom_losses_idxs, ...]) * self.deca.config.lipd\n\n d = self._metric_or_loss(losses, metrics, 'use_mouth_corner_distance' in self.deca.config.keys() and\n self.deca.config.use_mouth_corner_distance)\n d['mouth_corner_distance'] = lossfunc.mouth_corner_loss(predicted_landmarks[:geom_losses_idxs, ...],\n lmk[:geom_losses_idxs, ...]) * self.deca.config.lipd\n\n if predicted_landmarks_mediapipe is not None and lmk_mp is not None:\n use_mediapipe_landmarks = self.deca.config.get('use_mediapipe_landmarks', False) \n d = self._metric_or_loss(losses, metrics, use_mediapipe_landmarks)\n d['landmark_mediapipe'] =lossfunc_mp.landmark_loss(predicted_landmarks_mediapipe[:geom_losses_idxs, ...], lmk_mp[:geom_losses_idxs, ...]) * self.deca.config.lmk_weight_mp\n\n d = self._metric_or_loss(losses, metrics, self.deca.config.get('use_eye_distance_mediapipe', False) )\n d['eye_distance_mediapipe'] = lossfunc_mp.eyed_loss(predicted_landmarks_mediapipe[:geom_losses_idxs, ...],\n lmk_mp[:geom_losses_idxs, ...]) * self.deca.config.eyed_mp\n d = self._metric_or_loss(losses, metrics, self.deca.config.get('use_lip_distance_mediapipe', False) )\n d['lip_distance_mediapipe'] = lossfunc_mp.lipd_loss(predicted_landmarks_mediapipe[:geom_losses_idxs, ...],\n lmk_mp[:geom_losses_idxs, ...]) * self.deca.config.lipd_mp\n\n d = self._metric_or_loss(losses, metrics, self.deca.config.get('use_mouth_corner_distance_mediapipe', False))\n d['mouth_corner_distance_mediapipe'] = lossfunc_mp.mouth_corner_loss(predicted_landmarks_mediapipe[:geom_losses_idxs, ...],\n lmk_mp[:geom_losses_idxs, ...]) * self.deca.config.lipd_mp\n\n\n #TODO: fix this on the next iteration lipd_loss\n # d['lip_distance'] = lossfunc.lipd_loss(predicted_landmarks, lmk) * self.deca.config.lipd\n\n # photometric loss\n # if training or masks is not None:\n if masks is not None:\n # if self.deca.config.use_photometric:\n # d = losses\n # else:\n # d = metrics\n # d['photometric_texture'] = (masks * (predicted_images - images).abs()).mean() * self.deca.config.photow\n\n photometric = masks[:geom_losses_idxs, ...] * ((predicted_images[:geom_losses_idxs, ...] - images[:geom_losses_idxs, ...]).abs())\n\n if 'photometric_normalization' not in self.deca.config.keys() or self.deca.config.photometric_normalization == 'mean':\n photometric = photometric.mean()\n elif self.deca.config.photometric_normalization == 'rel_mask_value':\n photometric = photometric * masks[:geom_losses_idxs, ...].mean(dim=tuple(range(1,masks.ndim)), keepdim=True)\n photometric = photometric.mean()\n elif self.deca.config.photometric_normalization == 'neg_rel_mask_value':\n mu = 1. - masks[:geom_losses_idxs, ...].mean(dim=tuple(range(1,masks.ndim)), keepdim=True)\n photometric = photometric * mu\n photometric = photometric.mean()\n elif self.deca.config.photometric_normalization == 'inv_rel_mask_value':\n mu = 1./ masks[:geom_losses_idxs, ...].mean(dim=tuple(range(1,masks.ndim)), keepdim=True)\n photometric = photometric * mu\n photometric = photometric.mean()\n elif self.deca.config.photometric_normalization == 'abs_mask_value':\n photometric = photometric * masks[:geom_losses_idxs, ...].sum(dim=tuple(range(1,masks.ndim)), keepdim=True)\n photometric = photometric.mean()\n else:\n raise ValueError(f\"Invalid photometric loss normalization: '{self.deca.config.photometric_normalization}'\")\n\n self._metric_or_loss(losses, metrics, self.deca.config.use_photometric)['photometric_texture'] = \\\n photometric * self.deca.config.photow\n\n if self.deca.vgg_loss is not None:\n vggl, _ = self.deca.vgg_loss(\n masks[:geom_losses_idxs, ...] * images[:geom_losses_idxs, ...], # masked input image\n masks[:geom_losses_idxs, ...] * predicted_images[:geom_losses_idxs, ...], # masked output image\n )\n self._metric_or_loss(losses, metrics, self.deca.config.use_vgg)['vgg'] = vggl * self.deca.config.vggw\n\n if self.deca._has_neural_rendering():\n predicted_translated_image = codedict[\"predicted_translated_image\"]\n photometric_translated = (masks[:geom_losses_idxs, ...] * (\n predicted_translated_image[:geom_losses_idxs, ...] -\n images[:geom_losses_idxs, ...]).abs()).mean() * self.deca.config.photow\n if self.deca.config.use_photometric:\n losses['photometric_translated_texture'] = photometric_translated\n else:\n metrics['photometric_translated_texture'] = photometric_translated\n\n if self.deca.vgg_loss is not None:\n vggl, _ = self.deca.vgg_loss(\n masks[:geom_losses_idxs, ...] * images[:geom_losses_idxs, ...], # masked input image\n masks[:geom_losses_idxs, ...] * predicted_translated_image[:geom_losses_idxs, ...],\n # masked output image\n )\n self._metric_or_loss(losses, metrics, self.deca.config.use_vgg)['vgg_translated'] = vggl * self.deca.config.vggw\n\n else:\n raise ValueError(\"Is this line ever reached?\")\n\n\n losses = self._compute_id_loss(codedict, batch, training, testing, losses, batch_size=bs, ring_size=rs)\n\n losses['shape_reg'] = (torch.sum(shapecode ** 2) / 2) * self.deca.config.shape_reg\n losses['expression_reg'] = (torch.sum(expcode ** 2) / 2) * self.deca.config.exp_reg\n losses['tex_reg'] = (torch.sum(texcode ** 2) / 2) * self.deca.config.tex_reg\n losses['light_reg'] = ((torch.mean(lightcode, dim=2)[:, :,\n None] - lightcode) ** 2).mean() * self.deca.config.light_reg\n\n if 'original_code' in codedict.keys():\n # original jaw pose regularization\n if self.deca.config.get('exp_deca_jaw_pose', False) and \\\n 'deca_jaw_reg' in self.deca.config.keys() and self.deca.config.deca_jaw_reg > 0:\n jaw_pose_orig = codedict['original_code']['pose'][:, 3:]\n jaw_pose = codedict['posecode'][..., 3:]\n deca_jaw_pose_reg = (torch.sum((jaw_pose - jaw_pose_orig) ** 2) / 2) * self.deca.config.deca_jaw_reg\n losses['deca_jaw_pose_reg'] = deca_jaw_pose_reg\n\n if self.deca.config.get('exp_deca_global_pose', False) and \\\n 'deca_global_reg' in self.deca.config.keys() and self.deca.config.deca_global_reg > 0:\n global_pose_orig = codedict['original_code']['pose'][:, :3]\n global_pose = codedict['posecode'][..., :3]\n global_pose_reg = (torch.sum((global_pose - global_pose_orig) ** 2) / 2) * self.deca.config.deca_global_reg\n losses['deca_global_pose_reg'] = global_pose_reg\n\n # original expression regularization\n if 'deca_expression_reg' in self.deca.config.keys() and self.deca.config.deca_expression_reg > 0:\n expression_orig = codedict['original_code']['exp']\n expression = codedict['expcode']\n deca_expression_reg = (torch.sum((expression - expression_orig) ** 2) / 2) * self.deca.config.deca_expression_reg\n losses['deca_expression_reg'] = deca_expression_reg\n\n\n losses, metrics, codedict = self._compute_emonet_loss_wrapper(codedict, batch, training, testing, losses, metrics,\n prefix=\"coarse\", image_key=\"predicted_images\",\n with_grad=self.deca.config.use_emonet_loss and not self.deca._has_neural_rendering(),\n batch_size=bs, ring_size=rs)\n if self.deca._has_neural_rendering():\n losses, metrics, codedict = self._compute_emonet_loss_wrapper(codedict, batch, training, testing, losses, metrics,\n prefix=\"coarse_translated\", image_key=\"predicted_translated_image\",\n with_grad=self.deca.config.use_emonet_loss and self.deca._has_neural_rendering(),\n batch_size=bs, ring_size=rs\n )\n\n if self.au_loss is not None:\n # with torch.no_grad():\n\n self._compute_au_loss(images, predicted_images, losses, metrics, \"coarse\",\n au=None,\n with_grad=self.deca.config.au_loss.use_as_loss and not self.deca._has_neural_rendering())\n if self.deca._has_neural_rendering():\n self._compute_au_loss(images, predicted_translated_image, losses, metrics, \"coarse\",\n au=None,\n with_grad=self.deca.config.au_loss.use_as_loss and self.deca._has_neural_rendering())\n\n if self.lipread_loss is not None:\n # with torch.no_grad():\n\n self._compute_lipread_loss(images, predicted_images, lmk, predicted_landmarks, losses, metrics, \"coarse\",\n with_grad=self.deca.config.lipread_loss.use_as_loss and not self.deca._has_neural_rendering())\n if self.deca._has_neural_rendering():\n self._compute_lipread_loss(images, predicted_translated_image, \n lmk, predicted_landmarks,\n losses, metrics, \"coarse\",\n with_grad=self.deca.config.lipread_loss.use_as_loss and self.deca._has_neural_rendering())\n\n ## DETAIL loss only\n if self.mode == DecaMode.DETAIL:\n predicted_detailed_image = codedict[\"predicted_detailed_image\"]\n uv_z = codedict[\"uv_z\"] # UV displacement map\n uv_shading = codedict[\"uv_shading\"]\n uv_vis_mask = codedict[\"uv_vis_mask\"] # uv_mask of what is visible\n\n photometric_detailed = (masks[:geom_losses_idxs, ...] * (\n predicted_detailed_image[:geom_losses_idxs, ...] -\n images[:geom_losses_idxs, ...]).abs()).mean() * self.deca.config.photow\n\n if self.deca.config.use_detailed_photo:\n losses['photometric_detailed_texture'] = photometric_detailed\n else:\n metrics['photometric_detailed_texture'] = photometric_detailed\n\n if self.deca.vgg_loss is not None:\n vggl, _ = self.deca.vgg_loss(\n masks[:geom_losses_idxs, ...] * images[:geom_losses_idxs, ...], # masked input image\n masks[:geom_losses_idxs, ...] * predicted_detailed_image[:geom_losses_idxs, ...],\n # masked output image\n )\n self._metric_or_loss(losses, metrics, self.deca.config.use_vgg)['vgg_detailed'] = vggl * self.deca.config.vggw\n\n if self.deca._has_neural_rendering():\n predicted_detailed_translated_image = codedict[\"predicted_detailed_translated_image\"]\n photometric_detailed_translated = (masks[:geom_losses_idxs, ...] * (\n predicted_detailed_translated_image[:geom_losses_idxs, ...] - images[:geom_losses_idxs,\n ...]).abs()).mean() * self.deca.config.photow\n if self.deca.config.use_detailed_photo:\n losses['photometric_translated_detailed_texture'] = photometric_detailed_translated\n else:\n metrics['photometric_translated_detailed_texture'] = photometric_detailed_translated\n\n if self.deca.vgg_loss is not None:\n vggl, _ = self.deca.vgg_loss(\n masks[:geom_losses_idxs, ...] * images[:geom_losses_idxs, ...], # masked input image\n masks[:geom_losses_idxs, ...] * predicted_detailed_translated_image[:geom_losses_idxs, ...],\n # masked output image\n )\n self._metric_or_loss(losses, metrics, self.deca.config.use_vgg)[\n 'vgg_detailed_translated'] = vggl * self.deca.config.vggw\n\n\n losses, metrics, codedict = self._compute_emonet_loss_wrapper(codedict, batch, training, testing, losses, metrics,\n prefix=\"detail\", image_key = \"predicted_detailed_image\",\n with_grad=self.deca.config.use_emonet_loss and not self.deca._has_neural_rendering(),\n batch_size=bs, ring_size=rs)\n if self.deca._has_neural_rendering():\n losses, metrics, codedict = self._compute_emonet_loss_wrapper(codedict, batch, training, testing, losses, metrics,\n prefix=\"detail_translated\",\n image_key=\"predicted_detailed_translated_image\",\n with_grad=self.deca.config.use_emonet_loss and self.deca._has_neural_rendering(),\n batch_size=bs, ring_size=rs)\n\n # if self.emonet_loss is not None:\n # self._compute_emotion_loss(images, predicted_detailed_image, losses, metrics, \"detail\",\n # with_grad=self.deca.config.use_emonet_loss and not self.deca._has_neural_rendering(),\n # batch_size=bs, ring_size=rs)\n # codedict[\"detail_valence_input\"] = self.emonet_loss.input_emotion['valence']\n # codedict[\"detail_arousal_input\"] = self.emonet_loss.input_emotion['arousal']\n # codedict[\"detail_expression_input\"] = self.emonet_loss.input_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']\n # codedict[\"detail_valence_output\"] = self.emonet_loss.output_emotion['valence']\n # codedict[\"detail_arousal_output\"] = self.emonet_loss.output_emotion['arousal']\n # codedict[\"detail_expression_output\"] = self.emonet_loss.output_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']\n #\n # if va is not None:\n # codedict[\"detail_valence_gt\"] = va[:,0]\n # codedict[\"detail_arousal_gt\"] = va[:,1]\n # if expr7 is not None:\n # codedict[\"detail_expression_gt\"] = expr7\n\n\n # if self.deca._has_neural_rendering():\n\n\n # #TODO possible to make this more GPU efficient by not recomputing emotion for input image\n # self._compute_emotion_loss(images, predicted_detailed_translated_image,\n # losses, metrics, \"detail_translated\",\n # va, expr7,\n # with_grad= self.deca.config.use_emonet_loss and self.deca._has_neural_rendering(),\n # batch_size=bs, ring_size=rs)\n #\n # # codedict[\"coarse_valence_input\"] = self.emonet_loss.input_emotion['valence']\n # # codedict[\"coarse_arousal_input\"] = self.emonet_loss.input_emotion['arousal']\n # # codedict[\"coarse_expression_input\"] = self.emonet_loss.input_emotion['expression']\n # codedict[\"detail_translated_valence_output\"] = self.emonet_loss.output_emotion['valence']\n # codedict[\"detail_translated_arousal_output\"] = self.emonet_loss.output_emotion['arousal']\n # codedict[\"detail_translated_expression_output\"] = self.emonet_loss.output_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']\n\n if self.au_loss is not None:\n self._compute_au_loss(images, predicted_images, losses, metrics, \"detail\",\n au=None,\n with_grad=self.deca.config.au_loss.use_as_loss and not self.deca._has_neural_rendering())\n\n if self.deca._has_neural_rendering():\n self._compute_au_loss(images, predicted_detailed_translated_image, losses, metrics, \"detail\",\n au=None,\n with_grad=self.deca.config.au_loss.use_as_loss and self.deca._has_neural_rendering())\n\n for pi in range(3): # self.deca.face_attr_mask.shape[0]):\n if self.deca.config.sfsw[pi] != 0:\n # if pi==0:\n new_size = 256\n # else:\n # new_size = 128\n # if self.deca.config.uv_size != 256:\n # new_size = 128\n uv_texture_patch = F.interpolate(\n uv_texture[:geom_losses_idxs, :, self.deca.face_attr_mask[pi][2]:self.deca.face_attr_mask[pi][3],\n self.deca.face_attr_mask[pi][0]:self.deca.face_attr_mask[pi][1]],\n [new_size, new_size], mode='bilinear')\n uv_texture_gt_patch = F.interpolate(\n uv_texture_gt[:geom_losses_idxs, :, self.deca.face_attr_mask[pi][2]:self.deca.face_attr_mask[pi][3],\n self.deca.face_attr_mask[pi][0]:self.deca.face_attr_mask[pi][1]], [new_size, new_size],\n mode='bilinear')\n uv_vis_mask_patch = F.interpolate(\n uv_vis_mask[:geom_losses_idxs, :, self.deca.face_attr_mask[pi][2]:self.deca.face_attr_mask[pi][3],\n self.deca.face_attr_mask[pi][0]:self.deca.face_attr_mask[pi][1]],\n [new_size, new_size], mode='bilinear')\n\n detail_l1 = (uv_texture_patch * uv_vis_mask_patch - uv_texture_gt_patch * uv_vis_mask_patch).abs().mean() * \\\n self.deca.config.sfsw[pi]\n if self.deca.config.use_detail_l1 and not self.deca._has_neural_rendering():\n losses['detail_l1_{}'.format(pi)] = detail_l1\n else:\n metrics['detail_l1_{}'.format(pi)] = detail_l1\n\n if self.deca.config.use_detail_mrf and not self.deca._has_neural_rendering():\n mrf = self.deca.perceptual_loss(uv_texture_patch * uv_vis_mask_patch,\n uv_texture_gt_patch * uv_vis_mask_patch) * \\\n self.deca.config.sfsw[pi] * self.deca.config.mrfwr\n losses['detail_mrf_{}'.format(pi)] = mrf\n else:\n with torch.no_grad():\n mrf = self.deca.perceptual_loss(uv_texture_patch * uv_vis_mask_patch,\n uv_texture_gt_patch * uv_vis_mask_patch) * \\\n self.deca.config.sfsw[pi] * self.deca.config.mrfwr\n metrics['detail_mrf_{}'.format(pi)] = mrf\n\n if self.deca._has_neural_rendering():\n # raise NotImplementedError(\"Gotta implement the texture extraction first.\")\n translated_uv_texture = codedict[\"translated_uv_texture\"]\n translated_uv_texture_patch = F.interpolate(\n translated_uv_texture[:geom_losses_idxs, :,\n self.deca.face_attr_mask[pi][2]:self.deca.face_attr_mask[pi][3],\n self.deca.face_attr_mask[pi][0]:self.deca.face_attr_mask[pi][1]],\n [new_size, new_size], mode='bilinear')\n\n translated_detail_l1 = (translated_uv_texture_patch * uv_vis_mask_patch\n - uv_texture_gt_patch * uv_vis_mask_patch).abs().mean() * \\\n self.deca.config.sfsw[pi]\n\n if self.deca.config.use_detail_l1:\n losses['detail_translated_l1_{}'.format(pi)] = translated_detail_l1\n else:\n metrics['detail_translated_l1_{}'.format(pi)] = translated_detail_l1\n\n if self.deca.config.use_detail_mrf:\n translated_mrf = self.deca.perceptual_loss(translated_uv_texture_patch * uv_vis_mask_patch,\n uv_texture_gt_patch * uv_vis_mask_patch) * \\\n self.deca.config.sfsw[pi] * self.deca.config.mrfwr\n losses['detail_translated_mrf_{}'.format(pi)] = translated_mrf\n else:\n with torch.no_grad():\n mrf = self.deca.perceptual_loss(translated_uv_texture_patch * uv_vis_mask_patch,\n uv_texture_gt_patch * uv_vis_mask_patch) * \\\n self.deca.config.sfsw[pi] * self.deca.config.mrfwr\n metrics['detail_translated_mrf_{}'.format(pi)] = mrf\n # Old piece of debug code. Good to delete.\n # if pi == 2:\n # uv_texture_gt_patch_ = uv_texture_gt_patch\n # uv_texture_patch_ = uv_texture_patch\n # uv_vis_mask_patch_ = uv_vis_mask_patch\n\n losses['z_reg'] = torch.mean(uv_z.abs()) * self.deca.config.zregw\n losses['z_diff'] = lossfunc.shading_smooth_loss(uv_shading) * self.deca.config.zdiffw\n nonvis_mask = (1 - util.binary_erosion(uv_vis_mask))\n losses['z_sym'] = (nonvis_mask * (uv_z - torch.flip(uv_z, [-1]).detach()).abs()).sum() * self.deca.config.zsymw\n\n if self.emotion_mlp is not None:# and not testing:\n mlp_losses, mlp_metrics = self.emotion_mlp.compute_loss(\n codedict, batch, training=training, pred_prefix=\"emo_mlp_\")\n for key in mlp_losses.keys():\n if key in losses.keys():\n raise RuntimeError(f\"Duplicate loss label {key}\")\n losses[key] = self.deca.config.mlp_emotion_predictor_weight * mlp_losses[key]\n for key in mlp_metrics.keys():\n if key in metrics.keys():\n raise RuntimeError(f\"Duplicate metric label {key}\")\n # let's report the metrics (which are a superset of losses when it comes to EmoMLP) without the weight,\n # it's hard to plot the metrics otherwise\n metrics[key] = mlp_metrics[key]\n # metrics[key] = self.deca.config.mlp_emotion_predictor_weight * mlp_metrics[key]\n\n # else:\n # uv_texture_gt_patch_ = None\n # uv_texture_patch_ = None\n # uv_vis_mask_patch_ = None\n\n return losses, metrics\n\n def compute_loss(self, values, batch, training=True, testing=False) -> dict:\n \"\"\"\n The function used to compute the loss on a training batch.\n :\n training should be set to true when calling from training_step only\n \"\"\"\n losses, metrics = self._compute_loss(values, batch, training=training, testing=testing)\n\n all_loss = 0.\n losses_key = losses.keys()\n for key in losses_key:\n all_loss = all_loss + losses[key]\n # losses['all_loss'] = all_loss\n losses = {'loss_' + key: value for key, value in losses.items()} # add prefix loss for better logging\n losses['loss'] = all_loss\n\n # add metrics that do not effect the loss function (if any)\n for key in metrics.keys():\n losses['metric_' + key] = metrics[key]\n return losses\n\n def _val_to_be_logged(self, d):\n if not hasattr(self, 'val_dict_list'):\n self.val_dict_list = []\n self.val_dict_list += [d]\n\n def _train_to_be_logged(self, d):\n if not hasattr(self, 'train_dict_list'):\n self.train_dict_list = []\n self.train_dict_list += [d]\n\n def validation_step(self, batch, batch_idx, dataloader_idx=None):\n \"\"\"\n Training step override of pytorch lightning module. It makes the encoding, decoding passes, computes the loss and logs the losses/visualizations. \n :param batch: Batch of images to encode. batch['image'] [batch_size, ring_size, 3, image_size, image_size]. \n For a training forward pass, additional corresponding data are necessery such as 'landmarks' and 'masks'. \n :batch_idx batch index\n \"\"\"\n with torch.no_grad():\n training = False\n values = self.encode(batch, training=training)\n values = self.decode(values, training=training)\n losses_and_metrics = self.compute_loss(values, batch, training=training)\n #### self.log_dict(losses_and_metrics, on_step=False, on_epoch=True)\n # prefix = str(self.mode.name).lower()\n prefix = self._get_logging_prefix()\n\n # if dataloader_idx is not None:\n # dataloader_str = str(dataloader_idx) + \"_\"\n # else:\n dataloader_str = ''\n\n stage_str = dataloader_str + 'val_'\n\n # losses_and_metrics_to_log = {prefix + dataloader_str +'_val_' + key: value.detach().cpu() for key, value in losses_and_metrics.items()}\n # losses_and_metrics_to_log = {prefix + '_' + stage_str + key: value.detach() for key, value in losses_and_metrics.items()}\n losses_and_metrics_to_log = {prefix + '_' + stage_str + key: value.detach().cpu().item() for key, value in losses_and_metrics.items()}\n losses_and_metrics_to_log[prefix + '_' + stage_str + 'epoch'] = self.current_epoch\n # losses_and_metrics_to_log[prefix + '_' + stage_str + 'epoch'] = torch.tensor(self.current_epoch, device=self.device)\n # log val_loss also without any prefix for a model checkpoint to track it\n losses_and_metrics_to_log[stage_str + 'loss'] = losses_and_metrics_to_log[prefix + '_' + stage_str + 'loss']\n\n losses_and_metrics_to_log[prefix + '_' + stage_str + 'step'] = self.global_step\n losses_and_metrics_to_log[prefix + '_' + stage_str + 'batch_idx'] = batch_idx\n losses_and_metrics_to_log[stage_str + 'step'] = self.global_step\n losses_and_metrics_to_log[stage_str + 'batch_idx'] = batch_idx\n\n losses_and_metrics_to_log[prefix + '_' + stage_str + 'mem_usage'] = self.process.memory_info().rss\n losses_and_metrics_to_log[stage_str + 'mem_usage'] = self.process.memory_info().rss\n # self._val_to_be_logged(losses_and_metrics_to_log)\n\n\n if self.logger is not None:\n self.log_dict(losses_and_metrics_to_log, on_step=False, on_epoch=True, sync_dist=True) # log per epoch # recommended\n\n if self.trainer.is_global_zero:\n if self.deca.config.val_vis_frequency > 0:\n if batch_idx % self.deca.config.val_vis_frequency == 0:\n uv_detail_normals = None\n if 'uv_detail_normals' in values.keys():\n uv_detail_normals = values['uv_detail_normals']\n visualizations, grid_image = self._visualization_checkpoint(values['verts'], values['trans_verts'], values['ops'],\n uv_detail_normals, values, batch_idx, stage_str[:-1], prefix)\n vis_dict = self._create_visualizations_to_log(stage_str[:-1], visualizations, values, batch_idx, indices=0, dataloader_idx=dataloader_idx)\n # image = Image(grid_image, caption=\"full visualization\")\n # vis_dict[prefix + '_val_' + \"visualization\"] = image\n if isinstance(self.logger, WandbLogger):\n self.logger.log_metrics(vis_dict)\n\n return None\n\n def _get_logging_prefix(self):\n prefix = self.stage_name + str(self.mode.name).lower()\n return prefix\n\n def test_step(self, batch, batch_idx, dataloader_idx=None):\n \"\"\"\n Testing step override of pytorch lightning module. It makes the encoding, decoding passes, computes the loss and logs the losses/visualizations\n without gradient \n :param batch: Batch of images to encode. batch['image'] [batch_size, ring_size, 3, image_size, image_size]. \n For a training forward pass, additional corresponding data are necessery such as 'landmarks' and 'masks'. \n :batch_idx batch index\n \"\"\"\n prefix = self._get_logging_prefix()\n losses_and_metrics_to_log = {}\n\n # if dataloader_idx is not None:\n # dataloader_str = str(dataloader_idx) + \"_\"\n # else:\n dataloader_str = ''\n stage_str = dataloader_str + 'test_'\n\n with torch.no_grad():\n training = False\n testing = True\n values = self.encode(batch, training=training)\n values = self.decode(values, training=training)\n if 'mask' in batch.keys():\n losses_and_metrics = self.compute_loss(values, batch, training=False, testing=testing)\n # losses_and_metrics_to_log = {prefix + '_' + stage_str + key: value.detach().cpu() for key, value in losses_and_metrics.items()}\n losses_and_metrics_to_log = {prefix + '_' + stage_str + key: value.detach().cpu().item() for key, value in losses_and_metrics.items()}\n else:\n losses_and_metric = None\n\n # losses_and_metrics_to_log[prefix + '_' + stage_str + 'epoch'] = self.current_epoch\n # losses_and_metrics_to_log[prefix + '_' + stage_str + 'epoch'] = torch.tensor(self.current_epoch, device=self.device)\n # losses_and_metrics_to_log[prefix + '_' + stage_str + 'step'] = torch.tensor(self.global_step, device=self.device)\n # losses_and_metrics_to_log[prefix + '_' + stage_str + 'batch_idx'] = torch.tensor(batch_idx, device=self.device)\n # losses_and_metrics_to_log[stage_str + 'epoch'] = torch.tensor(self.current_epoch, device=self.device)\n # losses_and_metrics_to_log[stage_str + 'step'] = torch.tensor(self.global_step, device=self.device)\n # losses_and_metrics_to_log[stage_str + 'batch_idx'] = torch.tensor(batch_idx, device=self.device)\n losses_and_metrics_to_log[prefix + '_' + stage_str + 'epoch'] = self.current_epoch\n losses_and_metrics_to_log[prefix + '_' + stage_str + 'step'] = self.global_step\n losses_and_metrics_to_log[prefix + '_' + stage_str + 'batch_idx'] = batch_idx\n losses_and_metrics_to_log[prefix + '_' + stage_str + 'mem_usage'] = self.process.memory_info().rss\n losses_and_metrics_to_log[stage_str + 'epoch'] = self.current_epoch\n losses_and_metrics_to_log[stage_str + 'step'] = self.global_step\n losses_and_metrics_to_log[stage_str + 'batch_idx'] = batch_idx\n losses_and_metrics_to_log[stage_str + 'mem_usage'] = self.process.memory_info().rss\n\n if self.logger is not None:\n # self.logger.log_metrics(losses_and_metrics_to_log)\n self.log_dict(losses_and_metrics_to_log, sync_dist=True, on_step=False, on_epoch=True)\n\n # if self.global_step % 200 == 0:\n uv_detail_normals = None\n if 'uv_detail_normals' in values.keys():\n uv_detail_normals = values['uv_detail_normals']\n\n if self.deca.config.test_vis_frequency > 0:\n # Log visualizations every once in a while\n if batch_idx % self.deca.config.test_vis_frequency == 0:\n # if self.trainer.is_global_zero:\n visualizations, grid_image = self._visualization_checkpoint(values['verts'], values['trans_verts'], values['ops'],\n uv_detail_normals, values, self.global_step, stage_str[:-1], prefix)\n visdict = self._create_visualizations_to_log(stage_str[:-1], visualizations, values, batch_idx, indices=0, dataloader_idx=dataloader_idx)\n self.logger.log_metrics(visdict)\n return None\n\n @property\n def process(self):\n if not hasattr(self,\"process_\"):\n self.process_ = psutil.Process(os.getpid())\n return self.process_\n\n\n def training_step(self, batch, batch_idx, *args, **kwargs): #, debug=True):\n \"\"\"\n Training step override of pytorch lightning module. It makes the encoding, decoding passes, computes the loss and logs the losses/visualizations. \n :param batch: Batch of images to encode. batch['image'] [batch_size, ring_size, 3, image_size, image_size]. \n For a training forward pass, additional corresponding data are necessery such as 'landmarks' and 'masks'. \n :batch_idx batch index\n \"\"\"\n values = self.encode(batch, training=True)\n values = self.decode(values, training=True)\n losses_and_metrics = self.compute_loss(values, batch, training=True)\n\n uv_detail_normals = None\n if 'uv_detail_normals' in values.keys():\n uv_detail_normals = values['uv_detail_normals']\n\n # prefix = str(self.mode.name).lower()\n prefix = self._get_logging_prefix()\n # losses_and_metrics_to_log = {prefix + '_train_' + key: value.detach().cpu() for key, value in losses_and_metrics.items()}\n # losses_and_metrics_to_log = {prefix + '_train_' + key: value.detach() for key, value in losses_and_metrics.items()}\n losses_and_metrics_to_log = {prefix + '_train_' + key: value.detach().cpu().item() for key, value in losses_and_metrics.items()}\n # losses_and_metrics_to_log[prefix + '_train_' + 'epoch'] = torch.tensor(self.current_epoch, device=self.device)\n losses_and_metrics_to_log[prefix + '_train_' + 'epoch'] = self.current_epoch\n losses_and_metrics_to_log[prefix + '_train_' + 'step'] = self.global_step\n losses_and_metrics_to_log[prefix + '_train_' + 'batch_idx'] = batch_idx\n losses_and_metrics_to_log[prefix + '_' + \"train_\" + 'mem_usage'] = self.process.memory_info().rss\n\n # losses_and_metrics_to_log['train_' + 'epoch'] = torch.tensor(self.current_epoch, device=self.device)\n losses_and_metrics_to_log['train_' + 'epoch'] = self.current_epoch\n losses_and_metrics_to_log['train_' + 'step'] = self.global_step\n losses_and_metrics_to_log['train_' + 'batch_idx'] = batch_idx\n\n losses_and_metrics_to_log[\"train_\" + 'mem_usage'] = self.process.memory_info().rss\n\n # log loss also without any prefix for a model checkpoint to track it\n losses_and_metrics_to_log['loss'] = losses_and_metrics_to_log[prefix + '_train_loss']\n\n if self.logger is not None:\n self.log_dict(losses_and_metrics_to_log, on_step=False, on_epoch=True, sync_dist=True) # log per epoch, # recommended\n\n if self.deca.config.train_vis_frequency > 0:\n if self.global_step % self.deca.config.train_vis_frequency == 0:\n if self.trainer.is_global_zero:\n visualizations, grid_image = self._visualization_checkpoint(values['verts'], values['trans_verts'], values['ops'],\n uv_detail_normals, values, batch_idx, \"train\", prefix)\n visdict = self._create_visualizations_to_log('train', visualizations, values, batch_idx, indices=0)\n\n if isinstance(self.logger, WandbLogger):\n self.logger.log_metrics(visdict)#, step=self.global_step)\n # self.log_dict(visdict, sync_dist=True)\n\n \n # self.log_dict(losses_and_metrics_to_log, on_step=True, on_epoch=False) # log per step\n # self.log_dict(losses_and_metrics_to_log, on_step=True, on_epoch=True) # log per both\n # return losses_and_metrics\n return losses_and_metrics['loss']\n\n\n ### STEP ENDS ARE PROBABLY NOT NECESSARY BUT KEEP AN EYE ON THEM IF MULI-GPU TRAINING DOESN'T WORK\n # def training_step_end(self, batch_parts):\n # return self._step_end(batch_parts)\n #\n # def validation_step_end(self, batch_parts):\n # return self._step_end(batch_parts)\n #\n # def _step_end(self, batch_parts):\n # # gpu_0_prediction = batch_parts.pred[0]['pred']\n # # gpu_1_prediction = batch_parts.pred[1]['pred']\n # N = len(batch_parts)\n # loss_dict = {}\n # for key in batch_parts[0]:\n # for i in range(N):\n # if key not in loss_dict.keys():\n # loss_dict[key] = batch_parts[i]\n # else:\n # loss_dict[key] = batch_parts[i]\n # loss_dict[key] = loss_dict[key] / N\n # return loss_dict\n\n\n def vae_2_str(self, valence=None, arousal=None, affnet_expr=None, expr7=None, prefix=\"\"):\n caption = \"\"\n if len(prefix) > 0:\n prefix += \"_\"\n if valence is not None and not np.isnan(valence).any():\n caption += prefix + \"valence= %.03f\\n\" % valence\n if arousal is not None and not np.isnan(arousal).any():\n caption += prefix + \"arousal= %.03f\\n\" % arousal\n if affnet_expr is not None and not np.isnan(affnet_expr).any():\n caption += prefix + \"expression= %s \\n\" % AffectNetExpressions(affnet_expr).name\n if expr7 is not None and not np.isnan(expr7).any():\n caption += prefix +\"expression= %s \\n\" % Expression7(expr7).name\n return caption\n\n\n def _create_visualizations_to_log(self, stage, visdict, values, step, indices=None,\n dataloader_idx=None, output_dir=None):\n mode_ = str(self.mode.name).lower()\n prefix = self._get_logging_prefix()\n\n output_dir = output_dir or self.inout_params.full_run_dir\n\n log_dict = {}\n for key in visdict.keys():\n images = _torch_image2np(visdict[key])\n if images.dtype == np.float32 or images.dtype == np.float64 or images.dtype == np.float16:\n images = np.clip(images, 0, 1)\n if indices is None:\n indices = np.arange(images.shape[0])\n if isinstance(indices, int):\n indices = [indices,]\n if isinstance(indices, str) and indices == 'all':\n image = np.concatenate([images[i] for i in range(images.shape[0])], axis=1)\n savepath = Path(f'{output_dir}/{prefix}_{stage}/{key}/{self.current_epoch:04d}_{step:04d}_all.png')\n # im2log = Image(image, caption=key)\n if isinstance(self.logger, WandbLogger):\n im2log = _log_wandb_image(savepath, image)\n else:\n im2log = _log_array_image(savepath, image)\n name = prefix + \"_\" + stage + \"_\" + key\n if dataloader_idx is not None:\n name += \"/dataloader_idx_\" + str(dataloader_idx)\n log_dict[name] = im2log\n else:\n for i in indices:\n caption = key + f\" batch_index={step}\\n\"\n caption += key + f\" index_in_batch={i}\\n\"\n if self.emonet_loss is not None:\n if key == 'inputs':\n if mode_ + \"_valence_input\" in values.keys():\n caption += self.vae_2_str(\n values[mode_ + \"_valence_input\"][i].detach().cpu().item(),\n values[mode_ + \"_arousal_input\"][i].detach().cpu().item(),\n np.argmax(values[mode_ + \"_expression_input\"][i].detach().cpu().numpy()),\n prefix=\"emonet\") + \"\\n\"\n if 'va' in values.keys() and mode_ + \"valence_gt\" in values.keys():\n # caption += self.vae_2_str(\n # values[mode_ + \"_valence_gt\"][i].detach().cpu().item(),\n # values[mode_ + \"_arousal_gt\"][i].detach().cpu().item(),\n caption += self.vae_2_str(\n values[mode_ + \"valence_gt\"][i].detach().cpu().item(),\n values[mode_ + \"arousal_gt\"][i].detach().cpu().item(),\n prefix=\"gt\") + \"\\n\"\n if 'expr7' in values.keys() and mode_ + \"_expression_gt\" in values.keys():\n caption += \"\\n\" + self.vae_2_str(\n expr7=values[mode_ + \"_expression_gt\"][i].detach().cpu().numpy(),\n prefix=\"gt\") + \"\\n\"\n if 'affectnetexp' in values.keys() and mode_ + \"_expression_gt\" in values.keys():\n caption += \"\\n\" + self.vae_2_str(\n affnet_expr=values[mode_ + \"_expression_gt\"][i].detach().cpu().numpy(),\n prefix=\"gt\") + \"\\n\"\n elif 'geometry_detail' in key:\n if \"emo_mlp_valence\" in values.keys():\n caption += self.vae_2_str(\n values[\"emo_mlp_valence\"][i].detach().cpu().item(),\n values[\"emo_mlp_arousal\"][i].detach().cpu().item(),\n prefix=\"mlp\")\n if 'emo_mlp_expr_classification' in values.keys():\n caption += \"\\n\" + self.vae_2_str(\n affnet_expr=values[\"emo_mlp_expr_classification\"][i].detach().cpu().argmax().numpy(),\n prefix=\"mlp\") + \"\\n\"\n elif key == 'output_images_' + mode_:\n if mode_ + \"_valence_output\" in values.keys():\n caption += self.vae_2_str(values[mode_ + \"_valence_output\"][i].detach().cpu().item(),\n values[mode_ + \"_arousal_output\"][i].detach().cpu().item(),\n np.argmax(values[mode_ + \"_expression_output\"][i].detach().cpu().numpy())) + \"\\n\"\n\n elif key == 'output_translated_images_' + mode_:\n if mode_ + \"_translated_valence_output\" in values.keys():\n caption += self.vae_2_str(values[mode_ + \"_translated_valence_output\"][i].detach().cpu().item(),\n values[mode_ + \"_translated_arousal_output\"][i].detach().cpu().item(),\n np.argmax(values[mode_ + \"_translated_expression_output\"][i].detach().cpu().numpy())) + \"\\n\"\n\n\n # elif key == 'output_images_detail':\n # caption += \"\\n\" + self.vae_2_str(values[\"detail_output_valence\"][i].detach().cpu().item(),\n # values[\"detail_output_valence\"][i].detach().cpu().item(),\n # np.argmax(values[\"detail_output_expression\"][\n # i].detach().cpu().numpy()))\n savepath = Path(f'{output_dir}/{prefix}_{stage}/{key}/{self.current_epoch:04d}_{step:04d}_{i:02d}.png')\n image = images[i]\n # im2log = Image(image, caption=caption)\n if isinstance(self.logger, WandbLogger):\n im2log = _log_wandb_image(savepath, image, caption)\n elif self.logger is not None:\n im2log = _log_array_image(savepath, image, caption)\n else:\n im2log = _log_array_image(None, image, caption)\n name = prefix + \"_\" + stage + \"_\" + key\n if dataloader_idx is not None:\n name += \"/dataloader_idx_\" + str(dataloader_idx)\n log_dict[name] = im2log\n return log_dict\n\n def _visualization_checkpoint(self, verts, trans_verts, ops, uv_detail_normals, additional, batch_idx, stage, prefix,\n save=False):\n batch_size = verts.shape[0]\n visind = np.arange(batch_size)\n shape_images = self.deca.render.render_shape(verts, trans_verts)\n if uv_detail_normals is not None:\n detail_normal_images = F.grid_sample(uv_detail_normals.detach(), ops['grid'].detach(),\n align_corners=False)\n shape_detail_images = self.deca.render.render_shape(verts, trans_verts,\n detail_normal_images=detail_normal_images)\n else:\n shape_detail_images = None\n\n visdict = {}\n if 'images' in additional.keys():\n visdict['inputs'] = additional['images'][visind]\n\n if 'images' in additional.keys() and 'lmk' in additional.keys():\n visdict['landmarks_gt'] = util.tensor_vis_landmarks(additional['images'][visind], additional['lmk'][visind])\n\n if 'images' in additional.keys() and 'predicted_landmarks' in additional.keys():\n visdict['landmarks_predicted'] = util.tensor_vis_landmarks(additional['images'][visind],\n additional['predicted_landmarks'][visind])\n\n if 'predicted_images' in additional.keys():\n visdict['output_images_coarse'] = additional['predicted_images'][visind]\n\n if 'predicted_translated_image' in additional.keys() and additional['predicted_translated_image'] is not None:\n visdict['output_translated_images_coarse'] = additional['predicted_translated_image'][visind]\n\n visdict['geometry_coarse'] = shape_images[visind]\n if shape_detail_images is not None:\n visdict['geometry_detail'] = shape_detail_images[visind]\n\n if 'albedo_images' in additional.keys():\n visdict['albedo_images'] = additional['albedo_images'][visind]\n\n if 'masks' in additional.keys():\n visdict['mask'] = additional['masks'].repeat(1, 3, 1, 1)[visind]\n if 'albedo' in additional.keys():\n visdict['albedo'] = additional['albedo'][visind]\n\n if 'predicted_detailed_image' in additional.keys() and additional['predicted_detailed_image'] is not None:\n visdict['output_images_detail'] = additional['predicted_detailed_image'][visind]\n\n if 'predicted_detailed_translated_image' in additional.keys() and additional['predicted_detailed_translated_image'] is not None:\n visdict['output_translated_images_detail'] = additional['predicted_detailed_translated_image'][visind]\n\n if 'shape_detail_images' in additional.keys():\n visdict['shape_detail_images'] = additional['shape_detail_images'][visind]\n\n if 'uv_detail_normals' in additional.keys():\n visdict['uv_detail_normals'] = additional['uv_detail_normals'][visind] * 0.5 + 0.5\n\n if 'uv_texture_patch' in additional.keys():\n visdict['uv_texture_patch'] = additional['uv_texture_patch'][visind]\n\n if 'uv_texture_gt' in additional.keys():\n visdict['uv_texture_gt'] = additional['uv_texture_gt'][visind]\n\n if 'translated_uv_texture' in additional.keys() and additional['translated_uv_texture'] is not None:\n visdict['translated_uv_texture'] = additional['translated_uv_texture'][visind]\n\n if 'uv_vis_mask_patch' in additional.keys():\n visdict['uv_vis_mask_patch'] = additional['uv_vis_mask_patch'][visind]\n\n if save:\n savepath = f'{self.inout_params.full_run_dir}/{prefix}_{stage}/combined/{self.current_epoch:04d}_{batch_idx:04d}.png'\n Path(savepath).parent.mkdir(exist_ok=True, parents=True)\n visualization_image = self.deca.visualize(visdict, savepath)\n return visdict, visualization_image[..., [2, 1, 0]]\n else:\n visualization_image = None\n return visdict, None\n\n def _get_trainable_parameters(self):\n trainable_params = []\n if self.mode == DecaMode.COARSE:\n trainable_params += self.deca._get_coarse_trainable_parameters()\n elif self.mode == DecaMode.DETAIL:\n trainable_params += self.deca._get_detail_trainable_parameters()\n else:\n raise ValueError(f\"Invalid deca mode: {self.mode}\")\n\n if self.emotion_mlp is not None:\n trainable_params += list(self.emotion_mlp.parameters())\n\n if self.emonet_loss is not None:\n trainable_params += self.emonet_loss._get_trainable_params()\n\n if self.deca.id_loss is not None:\n trainable_params += self.deca.id_loss._get_trainable_params()\n\n return trainable_params\n\n\n def configure_optimizers(self):\n # optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)\n print(\"Configuring optimizer\")\n\n trainable_params = self._get_trainable_parameters()\n\n if self.learning_params.optimizer == 'Adam':\n self.deca.opt = torch.optim.Adam(\n trainable_params,\n lr=self.learning_params.learning_rate,\n amsgrad=False)\n elif self.config.learning.optimizer == 'AdaBound':\n self.deca.opt = adabound.AdaBound(\n trainable_params,\n lr=self.config.learning.learning_rate,\n final_lr=self.config.learning.final_learning_rate\n )\n elif self.learning_params.optimizer == 'SGD':\n self.deca.opt = torch.optim.SGD(\n trainable_params,\n lr=self.learning_params.learning_rate)\n else:\n raise ValueError(f\"Unsupported optimizer: '{self.learning_params.optimizer}'\")\n\n optimizers = [self.deca.opt]\n schedulers = []\n if 'learning_rate_decay' in self.learning_params.keys():\n scheduler = torch.optim.lr_scheduler.ExponentialLR(self.deca.opt, gamma=self.learning_params.learning_rate_decay)\n schedulers += [scheduler]\n if len(schedulers) == 0:\n return self.deca.opt\n\n return optimizers, schedulers\n\n\nclass DECA(torch.nn.Module):\n \"\"\"\n The original DECA class which contains the encoders, FLAME decoder and the detail decoder.\n \"\"\"\n\n def __init__(self, config):\n \"\"\"\n :config corresponds to a model_params from DecaModule\n \"\"\"\n super().__init__()\n \n # ID-MRF perceptual loss (kept here from the original DECA implementation)\n self.perceptual_loss = None\n \n # Face Recognition loss\n self.id_loss = None\n\n # VGG feature loss\n self.vgg_loss = None\n \n self._reconfigure(config)\n self._reinitialize()\n\n def _dirty_init(self):\n pass # not used here, implemented for EMICA\n\n def get_input_image_size(self): \n return (self.config.image_size, self.config.image_size)\n\n def _reconfigure(self, config):\n self.config = config\n \n self.n_param = config.n_shape + config.n_tex + config.n_exp + config.n_pose + config.n_cam + config.n_light\n # identity-based detail code \n self.n_detail = config.n_detail\n # emotion-based detail code (deprecated, not use by DECA or EMOCA)\n self.n_detail_emo = config.n_detail_emo if 'n_detail_emo' in config.keys() else 0\n\n # count the size of the conidition vector\n if 'detail_conditioning' in self.config.keys():\n self.n_cond = 0\n if 'globalpose' in self.config.detail_conditioning:\n self.n_cond += 3\n if 'jawpose' in self.config.detail_conditioning:\n self.n_cond += 3\n if 'identity' in self.config.detail_conditioning:\n self.n_cond += config.n_shape\n if 'expression' in self.config.detail_conditioning:\n self.n_cond += config.n_exp\n else:\n self.n_cond = 3 + config.n_exp\n\n self.mode = DecaMode[str(config.mode).upper()]\n self._create_detail_generator()\n self._init_deep_losses()\n self._setup_neural_rendering()\n\n def _reinitialize(self):\n self._create_model()\n self._setup_renderer()\n self._init_deep_losses()\n self.face_attr_mask = util.load_local_mask(image_size=self.config.uv_size, mode='bbx')\n\n def _get_num_shape_params(self): \n return self.config.n_shape\n\n def _init_deep_losses(self):\n \"\"\"\n Initialize networks for deep losses\n \"\"\"\n # TODO: ideally these networks should be moved out the DECA class and into DecaModule, \n # but that would break backwards compatility with the original DECA and would not be able to load DECA's weights\n if 'mrfwr' not in self.config.keys() or self.config.mrfwr == 0:\n self.perceptual_loss = None\n else:\n if self.perceptual_loss is None:\n self.perceptual_loss = lossfunc.IDMRFLoss().eval()\n self.perceptual_loss.requires_grad_(False) # TODO, move this to the constructor\n\n if 'idw' not in self.config.keys() or self.config.idw == 0:\n self.id_loss = None\n else:\n if self.id_loss is None:\n id_metric = self.config.id_metric if 'id_metric' in self.config.keys() else None\n id_trainable = self.config.id_trainable if 'id_trainable' in self.config.keys() else False\n self.id_loss_start_step = self.config.id_loss_start_step if 'id_loss_start_step' in self.config.keys() else 0\n self.id_loss = lossfunc.VGGFace2Loss(self.config.pretrained_vgg_face_path, id_metric, id_trainable)\n self.id_loss.freeze_nontrainable_layers()\n\n if 'vggw' not in self.config.keys() or self.config.vggw == 0:\n self.vgg_loss = None\n else:\n if self.vgg_loss is None:\n vgg_loss_batch_norm = 'vgg_loss_batch_norm' in self.config.keys() and self.config.vgg_loss_batch_norm\n self.vgg_loss = VGG19Loss(dict(zip(self.config.vgg_loss_layers, self.config.lambda_vgg_layers)), batch_norm=vgg_loss_batch_norm).eval()\n self.vgg_loss.requires_grad_(False) # TODO, move this to the constructor\n\n def _setup_renderer(self):\n self.render = SRenderY(self.config.image_size, obj_filename=self.config.topology_path,\n uv_size=self.config.uv_size) # .to(self.device)\n # face mask for rendering details\n mask = imread(self.config.face_mask_path).astype(np.float32) / 255.\n mask = torch.from_numpy(mask[:, :, 0])[None, None, :, :].contiguous()\n self.uv_face_mask = F.interpolate(mask, [self.config.uv_size, self.config.uv_size])\n mask = imread(self.config.face_eye_mask_path).astype(np.float32) / 255.\n mask = torch.from_numpy(mask[:, :, 0])[None, None, :, :].contiguous()\n uv_face_eye_mask = F.interpolate(mask, [self.config.uv_size, self.config.uv_size])\n self.register_buffer('uv_face_eye_mask', uv_face_eye_mask)\n\n # displacement mask is deprecated and not used by DECA or EMOCA\n if 'displacement_mask' in self.config.keys():\n displacement_mask_ = 1-np.load(self.config.displacement_mask).astype(np.float32)\n # displacement_mask_ = np.load(self.config.displacement_mask).astype(np.float32)\n displacement_mask_ = torch.from_numpy(displacement_mask_)[None, None, ...].contiguous()\n displacement_mask_ = F.interpolate(displacement_mask_, [self.config.uv_size, self.config.uv_size])\n self.register_buffer('displacement_mask', displacement_mask_)\n\n ## displacement correct\n if os.path.isfile(self.config.fixed_displacement_path):\n fixed_dis = np.load(self.config.fixed_displacement_path)\n fixed_uv_dis = torch.tensor(fixed_dis).float()\n else:\n fixed_uv_dis = torch.zeros([512, 512]).float()\n print(\"Warning: fixed_displacement_path not found, using zero displacement\")\n self.register_buffer('fixed_uv_dis', fixed_uv_dis)\n\n def uses_texture(self): \n if 'use_texture' in self.config.keys():\n return self.config.use_texture\n return True # true by default\n\n def _disable_texture(self, remove_from_model=False): \n self.config.use_texture = False\n if remove_from_model:\n self.flametex = None\n\n def _enable_texture(self):\n self.config.use_texture = True\n\n def _has_neural_rendering(self):\n return hasattr(self.config, \"neural_renderer\") and bool(self.config.neural_renderer)\n\n def _setup_neural_rendering(self):\n if self._has_neural_rendering():\n if self.config.neural_renderer.class_ == \"StarGAN\":\n print(\"Creating StarGAN neural renderer\")\n self.image_translator = StarGANWrapper(self.config.neural_renderer.cfg, self.config.neural_renderer.stargan_repo)\n else:\n raise ValueError(f\"Unsupported neural renderer class '{self.config.neural_renderer.class_}'\")\n\n if self.image_translator.background_mode == \"input\":\n if self.config.background_from_input not in [True, \"input\"]:\n raise NotImplementedError(\"The background mode of the neural renderer and deca is not synchronized. \"\n \"Background should be inpainted from the input\")\n elif self.image_translator.background_mode == \"black\":\n if self.config.background_from_input not in [False, \"black\"]:\n raise NotImplementedError(\"The background mode of the neural renderer and deca is not synchronized. \"\n \"Background should be black.\")\n elif self.image_translator.background_mode == \"none\":\n if self.config.background_from_input not in [\"none\"]:\n raise NotImplementedError(\"The background mode of the neural renderer and deca is not synchronized. \"\n \"The background should not be handled\")\n else:\n raise NotImplementedError(f\"Unsupported mode of the neural renderer backroungd: \"\n f\"'{self.image_translator.background_mode}'\")\n\n def _create_detail_generator(self):\n #backwards compatibility hack:\n if hasattr(self, 'D_detail'):\n if (not \"detail_conditioning_type\" in self.config.keys() or self.config.detail_conditioning_type == \"concat\") \\\n and isinstance(self.D_detail, Generator):\n return\n if self.config.detail_conditioning_type == \"adain\" and isinstance(self.D_detail, GeneratorAdaIn):\n return\n print(\"[WARNING]: We are reinitializing the detail generator!\")\n del self.D_detail # just to make sure we free the CUDA memory, probably not necessary\n\n if not \"detail_conditioning_type\" in self.config.keys() or str(self.config.detail_conditioning_type).lower() == \"concat\":\n # concatenates detail latent and conditioning (this one is used by DECA/EMOCA)\n print(\"Creating classic detail generator.\")\n self.D_detail = Generator(latent_dim=self.n_detail + self.n_detail_emo + self.n_cond, out_channels=1, out_scale=0.01,\n sample_mode='bilinear')\n elif str(self.config.detail_conditioning_type).lower() == \"adain\":\n # conditioning passed in through adain layers (this one is experimental and not currently used)\n print(\"Creating AdaIn detail generator.\")\n self.D_detail = GeneratorAdaIn(self.n_detail + self.n_detail_emo, self.n_cond, out_channels=1, out_scale=0.01,\n sample_mode='bilinear')\n else:\n raise NotImplementedError(f\"Detail conditioning invalid: '{self.config.detail_conditioning_type}'\")\n\n def _create_model(self):\n # 1) build coarse encoder\n e_flame_type = 'ResnetEncoder'\n if 'e_flame_type' in self.config.keys():\n e_flame_type = self.config.e_flame_type\n\n if e_flame_type == 'ResnetEncoder':\n self.E_flame = ResnetEncoder(outsize=self.n_param)\n elif e_flame_type[:4] == 'swin':\n self.E_flame = SwinEncoder(outsize=self.n_param, img_size=self.config.image_size, swin_type=e_flame_type)\n else:\n raise ValueError(f\"Invalid 'e_flame_type' = {e_flame_type}\")\n\n flame_cfg = copy.deepcopy(self.config)\n flame_cfg.n_shape = self._get_num_shape_params()\n if 'flame_mediapipe_lmk_embedding_path' not in flame_cfg.keys():\n self.flame = FLAME(flame_cfg)\n else:\n self.flame = FLAME_mediapipe(flame_cfg)\n\n if self.uses_texture():\n self.flametex = FLAMETex(self.config)\n else: \n self.flametex = None\n\n # 2) build detail encoder\n e_detail_type = 'ResnetEncoder'\n if 'e_detail_type' in self.config.keys():\n e_detail_type = self.config.e_detail_type\n\n if e_detail_type == 'ResnetEncoder':\n self.E_detail = ResnetEncoder(outsize=self.n_detail + self.n_detail_emo)\n elif e_flame_type[:4] == 'swin':\n self.E_detail = SwinEncoder(outsize=self.n_detail + self.n_detail_emo, img_size=self.config.image_size, swin_type=e_detail_type)\n else:\n raise ValueError(f\"Invalid 'e_detail_type'={e_detail_type}\")\n self._create_detail_generator()\n # self._load_old_checkpoint()\n\n def _get_coarse_trainable_parameters(self):\n print(\"Add E_flame.parameters() to the optimizer\")\n return list(self.E_flame.parameters())\n\n def _get_detail_trainable_parameters(self):\n trainable_params = []\n if self.config.train_coarse:\n trainable_params += self._get_coarse_trainable_parameters()\n print(\"Add E_flame.parameters() to the optimizer\")\n trainable_params += list(self.E_detail.parameters())\n print(\"Add E_detail.parameters() to the optimizer\")\n trainable_params += list(self.D_detail.parameters())\n print(\"Add D_detail.parameters() to the optimizer\")\n return trainable_params\n\n def train(self, mode: bool = True):\n super().train(mode)\n if mode:\n if self.mode == DecaMode.COARSE:\n self.E_flame.train()\n # print(\"Setting E_flame to train\")\n self.E_detail.eval()\n # print(\"Setting E_detail to eval\")\n self.D_detail.eval()\n # print(\"Setting D_detail to eval\")\n elif self.mode == DecaMode.DETAIL:\n if self.config.train_coarse:\n # print(\"Setting E_flame to train\")\n self.E_flame.train()\n else:\n # print(\"Setting E_flame to eval\")\n self.E_flame.eval()\n self.E_detail.train()\n # print(\"Setting E_detail to train\")\n self.D_detail.train()\n # print(\"Setting D_detail to train\")\n else:\n raise ValueError(f\"Invalid mode '{self.mode}'\")\n else:\n self.E_flame.eval()\n # print(\"Setting E_flame to eval\")\n self.E_detail.eval()\n # print(\"Setting E_detail to eval\")\n self.D_detail.eval()\n # print(\"Setting D_detail to eval\")\n\n # these are set to eval no matter what, they're never being trained (the FLAME shape and texture spaces are pretrained)\n self.flame.eval()\n if self.flametex is not None:\n self.flametex.eval()\n return self\n\n\n def _load_old_checkpoint(self):\n \"\"\"\n Loads the DECA model weights from the original DECA implementation: \n https://github.com/YadiraF/DECA \n \"\"\"\n if self.config.resume_training:\n model_path = self.config.pretrained_modelpath\n print(f\"Loading model state from '{model_path}'\")\n checkpoint = torch.load(model_path)\n # model\n util.copy_state_dict(self.E_flame.state_dict(), checkpoint['E_flame'])\n # util.copy_state_dict(self.opt.state_dict(), checkpoint['opt']) # deprecate\n # detail model\n if 'E_detail' in checkpoint.keys():\n util.copy_state_dict(self.E_detail.state_dict(), checkpoint['E_detail'])\n util.copy_state_dict(self.D_detail.state_dict(), checkpoint['D_detail'])\n # training state\n self.start_epoch = 0 # checkpoint['epoch']\n self.start_iter = 0 # checkpoint['iter']\n else:\n print('Start training from scratch')\n self.start_epoch = 0\n self.start_iter = 0\n\n def _encode_flame(self, images, **kwargs):\n return self.E_flame(images)\n\n def decompose_code(self, code):\n '''\n config.n_shape + config.n_tex + config.n_exp + config.n_pose + config.n_cam + config.n_light\n '''\n code_list = []\n # num_list = [self.config.n_shape, self.config.n_tex, self.config.n_exp, self.config.n_pose, self.config.n_cam,\n # self.config.n_light]\n num_list = [self._get_num_shape_params(), self.config.n_tex, self.config.n_exp, self.config.n_pose, self.config.n_cam,\n self.config.n_light]\n start = 0\n for i in range(len(num_list)):\n code_list.append(code[:, start:start + num_list[i]])\n start = start + num_list[i]\n # shapecode, texcode, expcode, posecode, cam, lightcode = code_list\n code_list[-1] = code_list[-1].reshape(code.shape[0], 9, 3)\n return code_list, None\n\n def displacement2normal(self, uv_z, coarse_verts, coarse_normals, detach=True):\n \"\"\"\n Converts the displacement uv map (uv_z) and coarse_verts to a normal map coarse_normals. \n \"\"\"\n batch_size = uv_z.shape[0]\n uv_coarse_vertices = self.render.world2uv(coarse_verts)#.detach()\n if detach:\n uv_coarse_vertices = uv_coarse_vertices.detach()\n uv_coarse_normals = self.render.world2uv(coarse_normals)#.detach()\n if detach:\n uv_coarse_normals = uv_coarse_normals.detach()\n\n uv_z = uv_z * self.uv_face_eye_mask\n\n # detail vertices = coarse vertice + predicted displacement*normals + fixed displacement*normals\n uv_detail_vertices = uv_coarse_vertices + \\\n uv_z * uv_coarse_normals + \\\n self.fixed_uv_dis[None, None, :,:] * uv_coarse_normals #.detach()\n\n dense_vertices = uv_detail_vertices.permute(0, 2, 3, 1).reshape([batch_size, -1, 3])\n uv_detail_normals = util.vertex_normals(dense_vertices, self.render.dense_faces.expand(batch_size, -1, -1))\n uv_detail_normals = uv_detail_normals.reshape(\n [batch_size, uv_coarse_vertices.shape[2], uv_coarse_vertices.shape[3], 3]).permute(0, 3, 1, 2)\n # uv_detail_normals = uv_detail_normals*self.uv_face_eye_mask + uv_coarse_normals*(1-self.uv_face_eye_mask)\n # uv_detail_normals = util.gaussian_blur(uv_detail_normals)\n return uv_detail_normals, uv_coarse_vertices\n\n def visualize(self, visdict, savepath, catdim=1):\n grids = {}\n for key in visdict:\n # print(key)\n if visdict[key] is None:\n continue\n grids[key] = torchvision.utils.make_grid(\n F.interpolate(visdict[key], [self.config.image_size, self.config.image_size])).detach().cpu()\n grid = torch.cat(list(grids.values()), catdim)\n grid_image = (grid.numpy().transpose(1, 2, 0).copy() * 255)[:, :, [2, 1, 0]]\n grid_image = np.minimum(np.maximum(grid_image, 0), 255).astype(np.uint8)\n if savepath is not None:\n cv2.imwrite(savepath, grid_image)\n return grid_image\n\n def create_mesh(self, opdict, dense_template):\n '''\n vertices: [nv, 3], tensor\n texture: [3, h, w], tensor\n '''\n i = 0\n vertices = opdict['verts'][i].cpu().numpy()\n faces = self.render.faces[0].cpu().numpy()\n if 'uv_texture_gt' in opdict.keys():\n texture = util.tensor2image(opdict['uv_texture_gt'][i])\n else:\n texture = None\n uvcoords = self.render.raw_uvcoords[0].cpu().numpy()\n uvfaces = self.render.uvfaces[0].cpu().numpy()\n # save coarse mesh, with texture and normal map\n if 'uv_detail_normals' in opdict.keys():\n normal_map = util.tensor2image(opdict['uv_detail_normals'][i]*0.5 + 0.5)\n # upsample mesh, save detailed mesh\n texture = texture[:, :, [2, 1, 0]]\n normals = opdict['normals'][i].cpu().numpy()\n displacement_map = opdict['displacement_map'][i].detach().cpu().numpy().squeeze()\n dense_vertices, dense_colors, dense_faces = util.upsample_mesh(vertices, normals, faces,\n displacement_map, texture, dense_template)\n else:\n normal_map = None\n dense_vertices = None\n dense_colors = None\n dense_faces = None\n\n return vertices, faces, texture, uvcoords, uvfaces, normal_map, dense_vertices, dense_faces, dense_colors\n\n\n def save_obj(self, filename, opdict, dense_template, mode ='detail'):\n if mode not in ['coarse', 'detail', 'both']:\n raise ValueError(f\"Invalid mode '{mode}. Expected modes are: 'coarse', 'detail', 'both'\")\n\n vertices, faces, texture, uvcoords, uvfaces, normal_map, dense_vertices, dense_faces, dense_colors \\\n = self.create_mesh(opdict, dense_template)\n\n if mode == 'both':\n if isinstance(filename, list):\n filename_coarse = filename[0]\n filename_detail = filename[1]\n else:\n filename_coarse = filename\n filename_detail = filename.replace('.obj', '_detail.obj')\n elif mode == 'coarse':\n filename_coarse = filename\n else:\n filename_detail = filename\n\n if mode in ['coarse', 'both']:\n util.write_obj(str(filename_coarse), vertices, faces,\n texture=texture,\n uvcoords=uvcoords,\n uvfaces=uvfaces,\n normal_map=normal_map)\n\n if mode in ['detail', 'both']:\n util.write_obj(str(filename_detail),\n dense_vertices,\n dense_faces,\n colors = dense_colors,\n inverse_face_order=True)\n\n\n\n\nclass ExpDECAInterface(object): \n \"\"\"\n This serves as an interface for EMOCA-like classes that need to use a different sub class but \n retain the EMOCA functionality. See EMICA_v2 for an example.\n \"\"\"\n\n def _create_model(self):\n # E_flame should be fixed for expression EMOCA\n self.E_flame.requires_grad_(False)\n \n # 2) add expression decoder\n if self.config.expression_backbone == 'deca_parallel':\n ## a) Attach a parallel flow of FCs onto the original DECA coarse backbone. (Only the second FC head is trainable)\n self.E_expression = SecondHeadResnet(self.E_flame, self.n_exp_param, 'same')\n elif self.config.expression_backbone == 'deca_clone':\n ## b) Clones the original DECA coarse decoder (and the entire decoder will be trainable) - This is in final EMOCA.\n #TODO this will only work for Resnet. Make this work for the other backbones (Swin) as well.\n self.E_expression = ResnetEncoder(self.n_exp_param)\n # clone parameters of the ResNet\n self.E_expression.encoder.load_state_dict(self.E_flame.encoder.state_dict())\n elif self.config.expression_backbone == 'emonet_trainable':\n # Trainable EmoNet instead of Resnet (deprecated)\n self.E_expression = EmoNetRegressor(self.n_exp_param)\n elif self.config.expression_backbone == 'emonet_static':\n # Frozen EmoNet with a trainable head instead of Resnet (deprecated)"},"next_line":{"kind":"string","value":" self.E_expression = EmonetRegressorStatic(self.n_exp_param)"},"gold_snippet_index":{"kind":"number","value":22,"string":"22"},"created_at":{"kind":"string","value":"2023-11-07 20:13:32+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5270,"cells":{"repo_name":{"kind":"string","value":"hxz393/ConfigCenterComparer"},"file_path":{"kind":"string","value":"ui/action_start.py"},"context":{"kind":"list like","value":[{"identifier":"COL_INFO","path":"config/settings.py","snippet":"COL_INFO = {\n \"name\": {\"col\": 0},\n \"group\": {\"col\": 1},\n \"key\": {\"col\": 2},\n \"pro_value\": {\"col\": 3},\n \"pro_time\": {\"col\": 4},\n \"pre_value\": {\"col\": 5},\n \"pre_time\": {\"col\": 6},\n \"test_value\": {\"col\": 7},\n \"test_time\": {\"col\": 8},\n \"dev_value\": {\"col\": 9},\n \"dev_time\": {\"col\": 10},\n \"consistency\": {\"col\": 11},\n \"skip\": {\"col\": 12},\n\n}"},{"identifier":"get_resource_path","path":"lib/get_resource_path.py","snippet":"def get_resource_path(relative_path: Union[str, os.PathLike]) -> Optional[str]:\n \"\"\"\n 获取资源的绝对路径。这个函数适用于 PyInstaller 打包后的可执行文件。\n\n :type relative_path: Union[str, os.PathLike]\n :param relative_path: 相对路径，可以是字符串或 os.PathLike 对象。\n :rtype: Optional[str]\n :return: 资源的绝对路径，如果发生错误则返回 None。\n \"\"\"\n\n try:\n base_path = getattr(sys, '_MEIPASS', os.path.abspath(\".\"))\n return os.path.join(base_path, os.path.normpath(relative_path))\n except Exception:\n logger.exception(\"An error occurred while retrieving resource path\")\n return None"},{"identifier":"execute_queries","path":"module/execute_queries.py","snippet":"def execute_queries(config_connection: Dict[str, Dict[str, Union[Dict[str, str], bool]]],\n config_main: Dict[str, str]) -> Tuple[Dict[str, Dict[str, str]], Dict[str, bool]]:\n \"\"\"\n 执行数据库查询并返回格式化后的结果和查询状态。\n\n 此函数接收数据库连接配置和主要配置参数。它首先根据主配置生成SQL查询语句，然后对每个数据库环境执行查询。查询结果将被格式化，并更新查询状态。\n\n :param config_connection: 数据库连接配置，包含环境名称和对应的数据库配置。\n :type config_connection: Dict[str, Dict[str, Union[Dict[str, str], bool]]]\n :param config_main: 主要配置参数，用于生成查询SQL语句。\n :type config_main: Dict[str, str]\n :return: 包含格式化查询结果的字典和每个环境的查询状态。\n :rtype: Tuple[Dict[str, Dict[str, str]], Dict[str, bool]]\n\n :example:\n >>> os.chdir(os.path.dirname(os.getcwd()))\n >>> connection = {\"dev\": {'mysql_on': True, 'ssh_on': False, 'mysql': {'host': '192.168.2.204', \"port\": \"3306\", \"user\": \"root\", \"password\": \"QeqAr:%R+s5:hYnr\", \"db\": \"ApolloConfigDB_dev\"}}}\n >>> main = {'config_center': 'Apollo', 'apollo_name': 'AppId', 'fix_name_before': '', 'fix_name_after': '', 'fix_name_left': '', 'fix_name_right': '',}\n >>> results, statuses = execute_queries(connection, main)\n >>> assert type(results) == dict\n >>> assert type(statuses) == dict\n >>> statuses\n >>> results\n \"\"\"\n query_statuses = {env_name: False for env_name in config_connection.keys()}\n formatted_results = {}\n\n try:\n query_sql = get_query_sql(config_main)\n\n for env_name, db_config in config_connection.items():\n # 获取指定环境的查询结果\n query_results = get_query_result(db_config, query_sql)\n logger.debug(f\"ENV: {env_name}, SQL query finished.\")\n if query_results:\n query_statuses[env_name] = True\n # 格式化查询结果\n format_query_results(query_results, env_name, config_main, formatted_results)\n else:\n logger.warning(f\"No results obtained from database query for environment: {env_name}\")\n\n # 通过对比过滤列表，得到是否过滤信息，更新到结果字典\n update_skip_status(formatted_results)\n # 查询各配置环境的值，得到一致性信息，更新到结果字典。只对比查询成功的环境\n update_consistency_status(formatted_results, query_statuses)\n logger.debug(\"Status update finished.\")\n\n return formatted_results, query_statuses\n except Exception:\n logger.exception(\"Exception occurred during executing queries\")\n return {}, query_statuses"},{"identifier":"ConfigManager","path":"ui/config_manager.py","snippet":"class ConfigManager(QObject):\n \"\"\"\n 配置管理器类，负责管理和更新应用程序的配置信息。\n\n 该类包括获取和设置主配置、连接配置和跳过列表的方法，同时提供信号以通知配置更新。\n\n :ivar config_main_updated: 当主配置更新时发出的信号。\n :ivar config_connection_updated: 当连接配置更新时发出的信号。\n :ivar skip_list_updated: 当跳过列表更新时发出的信号。\n \"\"\"\n config_main_updated = pyqtSignal()\n config_connection_updated = pyqtSignal()\n skip_list_updated = pyqtSignal()\n\n def __init__(self):\n super().__init__()\n self._config_main, self._config_apollo, self._config_nacos = read_config_all()\n self._skip_list = read_file_to_list(CONFIG_SKIP_PATH) or []\n\n def get_config_main(self) -> Optional[Dict[str, str]]:\n \"\"\"\n 获取主配置的副本。\n\n :return: 包含主配置的字典，如果出现错误则返回 None。\n :rtype: Optional[Dict[str, str]]\n \"\"\"\n try:\n return copy.deepcopy(self._config_main)\n except Exception:\n logger.exception(\"Failed to get config_main.\")\n return None\n\n def get_config_connection(self) -> Optional[Dict[str, Dict[str, Union[Dict[str, str], bool]]]]:\n \"\"\"\n 根据当前配置中心获取连接配置的副本。\n\n :return: 包含连接配置的字典，如果出现错误则返回 None。\n :rtype: Optional[Dict[str, Dict[str, Union[Dict[str, str], bool]]]]\n \"\"\"\n try:\n if self._config_main['config_center'] == 'Apollo':\n return copy.deepcopy(self._config_apollo)\n else:\n return copy.deepcopy(self._config_nacos)\n except Exception:\n logger.exception(\"Failed to get config_connection.\")\n return None\n\n def get_skip_list(self) -> Optional[List[str]]:\n \"\"\"\n 获取忽略列表的副本。\n\n :return: 包含跳过项的列表，如果出现错误则返回 None。\n :rtype: Optional[List[str]]\n \"\"\"\n try:\n return copy.deepcopy(self._skip_list)\n except Exception:\n logger.exception(\"Failed to get skip_list.\")\n return None\n\n def update_config_main(self, new_config: Dict[str, str]) -> None:\n \"\"\"\n 更新主配置。\n\n :param new_config: 新的主配置。\n :type new_config: Dict[str, str]\n \"\"\"\n try:\n self._config_main = new_config\n self.config_main_updated.emit()\n write_dict_to_json(CONFIG_MAIN_PATH, new_config)\n logger.info(\"Config updated: config_main\")\n except Exception:\n logger.exception(\"Failed to update config: config_main\")\n\n def update_config_connection(self, new_config: Dict[str, Dict[str, Union[Dict[str, str], bool]]]) -> None:\n \"\"\"\n 更新连接配置。\n\n :param new_config: 新的连接配置。\n :type new_config: Dict[str, Dict[str, Union[Dict[str, str], bool]]]\n \"\"\"\n try:\n if self._config_main['config_center'] == 'Apollo':\n self._config_apollo = new_config\n write_dict_to_json(CONFIG_APOLLO_PATH, new_config)\n else:\n self._config_nacos = new_config\n write_dict_to_json(CONFIG_NACOS_PATH, new_config)\n self.config_connection_updated.emit()\n logger.info(\"Config updated: config_connection\")\n except Exception:\n logger.exception(\"Failed to update config: config_connection\")\n\n def update_skip_list(self, new_config: List[str]) -> None:\n \"\"\"\n 更新忽略列表。\n\n :param new_config: 新忽略列表。\n :type new_config: List[str]\n \"\"\"\n try:\n self._skip_list = new_config\n # 写入到配置文件\n self.skip_list_updated.emit()\n write_list_to_file(CONFIG_SKIP_PATH, new_config)\n logger.info(\"Config updated: skip_list\")\n except Exception:\n logger.exception(\"Failed to update config: skip_list\")"},{"identifier":"FilterBar","path":"ui/filter_bar.py","snippet":"class FilterBar(QWidget):\n \"\"\"\n 过滤栏类，用于在用户界面中提供过滤和搜索功能。\n\n 此类创建了一个包含服务过滤、表格状态过滤和搜索框的组件，使用户能够根据不同条件过滤表格数据。\n\n :param lang_manager: 语言管理器，用于处理界面语言的更新。\n :type lang_manager: LangManager\n :param config_manager: 配置管理器，用于获取和更新配置信息。\n :type config_manager: ConfigManager\n :param table: 要应用过滤的表格。\n :type table: TableMain\n \"\"\"\n status_updated = pyqtSignal(str)\n\n def __init__(self,\n lang_manager: LangManager,\n config_manager: ConfigManager,\n table: TableMain):\n super().__init__()\n # 实例化组件。\n self.lang_manager = lang_manager\n self.lang_manager.lang_updated.connect(self.update_lang)\n self.lang = self.lang_manager.get_lang()\n self.config_manager = config_manager\n self.table = table\n self.highlight_rows = []\n self.initUI()\n\n def initUI(self) -> None:\n \"\"\"\n 初始化用户界面。\n\n 创建并布局过滤栏中的所有组件，包括服务过滤、表格状态过滤和搜索值输入框。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # 修改字体大小\n self.setStyleSheet(\"font-size: 14px;\")\n # 建立横向主布局\n self.layout = QHBoxLayout(self)\n # 创建过滤服务组件\n self._create_filter_app()\n # 创建过滤列表组件\n self._create_filter_table()\n # 创建搜索过滤值组件\n self._create_filter_value()\n # 设置布局的内容边距\n self.layout.setContentsMargins(0, 0, 0, 0)\n self.setLayout(self.layout)\n self.update_lang()\n\n def update_lang(self) -> None:\n \"\"\"\n 更新界面语言设置。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # 重新获取语言字典\n self.lang = self.lang_manager.get_lang()\n # 遍历filter_table下拉框中的所有项\n for index in range(self.filter_table_box.count()):\n # 检查数据值是否匹配\n if self.filter_table_box.itemData(index) == \"all\":\n # 更新显示值\n self.filter_table_box.setItemText(index, self.lang['ui.filter_bar_3'])\n elif self.filter_table_box.itemData(index) == \"fully\":\n self.filter_table_box.setItemText(index, self.lang['ui.filter_bar_4'])\n elif self.filter_table_box.itemData(index) == \"partially\":\n self.filter_table_box.setItemText(index, self.lang['ui.filter_bar_5'])\n elif self.filter_table_box.itemData(index) == \"skip\":\n self.filter_table_box.setItemText(index, self.lang['ui.filter_bar_6'])\n elif self.filter_table_box.itemData(index) == \"fully+skip\":\n self.filter_table_box.setItemText(index, f\"{self.lang['ui.filter_bar_4']}+{self.lang['ui.filter_bar_6']}\")\n elif self.filter_table_box.itemData(index) == \"fully+partially\":\n self.filter_table_box.setItemText(index, f\"{self.lang['ui.filter_bar_4']}+{self.lang['ui.filter_bar_5']}\")\n elif self.filter_table_box.itemData(index) == \"fully+partially+skip\":\n self.filter_table_box.setItemText(index, f\"{self.lang['ui.filter_bar_4']}+{self.lang['ui.filter_bar_5']}+{self.lang['ui.filter_bar_6']}\")\n # 直接更新服务名过滤默认选项文字\n self.filter_app_box.setItemText(0, self.lang['ui.filter_bar_3'])\n # 更新其他文字\n self.filter_app_label.setText(self.lang['ui.filter_bar_1'])\n self.filter_table_label.setText(self.lang['ui.filter_bar_2'])\n self.filter_table_check_box.setText(self.lang['ui.filter_bar_7'])\n self.filter_value_label.setText(self.lang['ui.filter_bar_8'])\n self.filter_value_button.setText(self.lang['ui.filter_bar_9'])\n self.filter_reset_button.setText(self.lang['ui.filter_bar_10'])\n\n def _create_filter_app(self) -> None:\n \"\"\"\n 创建服务过滤组件。\n\n 此方法初始化服务过滤下拉框，并设置其事件处理函数。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # 建立标签，加入主布局\n self.filter_app_label = QLabel()\n self.layout.addWidget(self.filter_app_label)\n # 过滤服务下拉框\n self.filter_app_box = QComboBox()\n # 设置下拉框的尺寸策略和宽度\n self.filter_app_box.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n # 设置最大宽度，以免拉伸太长\n self.filter_app_box.setMinimumWidth(100)\n self.filter_app_box.setMaximumWidth(300)\n # 设置下拉框的事件处理\n self.filter_app_box.currentIndexChanged.connect(self.filter_table)\n # 设置下拉框的选项，通过函数填充\n self.filter_options_add()\n self.layout.addWidget(self.filter_app_box)\n # 创建一个 QFrame 作为分割线\n self._create_separator()\n\n def _create_filter_table(self) -> None:\n \"\"\"\n 创建表格状态过滤组件。\n\n 此方法初始化表格状态过滤下拉框和反向选择复选框，并设置它们的事件处理函数。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # 建立标签，加入主布局\n self.filter_table_label = QLabel()\n self.layout.addWidget(self.filter_table_label)\n # 过滤列表下拉框\n self.filter_table_box = QComboBox()\n # 设置最小宽度，以免文字放不下\n self.filter_table_box.setMinimumWidth(270)\n # 设置下拉框的选项\n self.filter_table_box.addItem(\"\", \"all\")\n self.filter_table_box.addItem(\"\", \"fully\")\n self.filter_table_box.addItem(\"\", \"partially\")\n self.filter_table_box.addItem(\"\", \"skip\")\n self.filter_table_box.addItem(\"\", \"fully+skip\")\n self.filter_table_box.addItem(\"\", \"fully+partially\")\n self.filter_table_box.addItem(\"\", \"fully+partially+skip\")\n # 设置下拉框的事件处理\n self.filter_table_box.currentIndexChanged.connect(self.filter_table)\n self.layout.addWidget(self.filter_table_box)\n # 反向选择\n self.filter_table_check_box = QCheckBox()\n self.filter_table_check_box.stateChanged.connect(self.filter_table)\n self.layout.addWidget(self.filter_table_check_box)\n # 创建一个 QFrame 作为分割线\n self._create_separator()\n\n def _create_filter_value(self) -> None:\n \"\"\"\n 创建搜索过滤组件。\n\n 此方法初始化搜索框和相关按钮，并设置事件处理函数，以便用户可以根据特定文本过滤表格数据。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # 建立标签，加入主布局\n self.filter_value_label = QLabel()\n self.layout.addWidget(self.filter_value_label)\n # 搜索输入框\n self.filter_value_box = QLineEdit()\n self.filter_value_box.returnPressed.connect(self.filter_table)\n # 设置搜索输入框的尺寸策略和最小宽度\n self.filter_value_box.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\n self.filter_value_box.setMinimumWidth(100)\n self.layout.addWidget(self.filter_value_box)\n # 搜索按钮\n self.filter_value_button = QPushButton()\n self.filter_value_button.clicked.connect(self.filter_table)\n self.layout.addWidget(self.filter_value_button)\n # 重置按钮\n self.filter_reset_button = QPushButton()\n self.filter_reset_button.clicked.connect(self.filter_reset)\n self.layout.addWidget(self.filter_reset_button)\n\n def _create_separator(self) -> None:\n \"\"\"\n 创建界面中的分隔线。\n\n 此方法用于在过滤器工具栏中添加分隔线。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # 建好之后，直接加入主布局\n separator = QFrame()\n separator.setFrameShape(QFrame.VLine)\n separator.setFrameShadow(QFrame.Raised)\n self.layout.addWidget(separator)\n\n def filter_options_add(self) -> None:\n \"\"\"\n 填充服务过滤下拉框选项。\n\n 此方法从配置数据中提取所有唯一的服务名称，并将它们添加到服务过滤下拉框中。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n try:\n # 先断开信号\n self.filter_app_box.currentIndexChanged.disconnect(self.filter_table)\n self.filter_app_box.setEnabled(False)\n # 清空过滤器并添加显示所有行的选项\n self.filter_app_box.clear()\n self.filter_app_box.addItem(self.lang['ui.filter_bar_3'], \"all\")\n self.filter_app_box.setCurrentIndex(0)\n # 使用集合和列表推导式去重并获取所有唯一项\n unique_items = {self.table.item(row, COL_INFO['name']['col']).text()\n for row in range(self.table.rowCount())\n if self.table.item(row, COL_INFO['name']['col'])}\n # 添加唯一项到下拉框\n [self.filter_app_box.addItem(item, item) for item in unique_items]\n # 对下拉框进行排序\n model = self.filter_app_box.model()\n model.sort(0)\n except Exception:\n logger.exception(\"Exception occurred in adding filter options\")\n self.status_updated.emit(self.lang['label_status_error'])\n finally:\n # 重新连接信号\n self.filter_app_box.currentIndexChanged.connect(self.filter_table)\n self.filter_app_box.setEnabled(True)\n\n def filter_reset(self) -> None:\n \"\"\"\n 重置过滤条件。\n\n 此方法将所有过滤组件重置为默认状态，以便用户可以重新开始过滤操作。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n try:\n # 禁更新\n self.table.setUpdatesEnabled(False)\n # 断开信号连接\n self.filter_app_box.currentIndexChanged.disconnect(self.filter_table)\n self.filter_table_box.currentIndexChanged.disconnect(self.filter_table)\n self.filter_table_check_box.stateChanged.disconnect(self.filter_table)\n # 重置 QComboBox 为第一个项，通常是 \"--显示所有--\"，\n self.filter_app_box.setCurrentIndex(0)\n self.filter_table_box.setCurrentIndex(0)\n # 还原反选框状态\n self.filter_table_check_box.setChecked(False)\n # 清空搜索框 QLineEdit\n self.filter_value_box.clear()\n # 手动调用过略器\n self.filter_table()\n except Exception:\n logger.exception(\"Error occurred while resetting filters\")\n self.status_updated.emit(self.lang['label_status_error'])\n finally:\n # 开启更新，重新连接信号\n self.table.setUpdatesEnabled(True)\n self.filter_app_box.currentIndexChanged.connect(self.filter_table)\n self.filter_table_box.currentIndexChanged.connect(self.filter_table)\n self.filter_table_check_box.stateChanged.connect(self.filter_table)\n\n @log_time\n def filter_table(self, rows: Optional[List[int]] = None) -> None:\n \"\"\"\n 应用过滤条件到表格。带有时间记录用于调试。\n\n 此方法根据用户设置的过滤条件（服务名称、表格状态、搜索文本）来决定哪些行在表格中可见。\n\n :param rows: 要应用过滤器的行号列表。如果为空则应用到整表。\n :type rows: Optional[List[int]]\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n try:\n self.table.setUpdatesEnabled(False)\n # 获取颜色开关\n color_switch = self.config_manager.get_config_main().get('color_set', 'ON')\n # 检查是否有rows参数\n valid_rows = rows if isinstance(rows, list) else None\n # 计算可见的行数\n visible_rows = 0\n # 搜索框输入内容\n search_value = self.filter_value_box.text().strip().lower()\n # 在新的搜索开始之前，恢复每个单元格的原始样式。\n if color_switch == 'ON':\n # 针对忽略操作，改变表格颜色。\n if valid_rows:\n self.table.apply_color_to_table(valid_rows)\n # 针对高亮操作\n elif self.highlight_rows:\n self._reset_styles()\n\n # 如果没有传入行列表，则应用到整个列表\n for row in valid_rows if valid_rows else range(self.table.rowCount()):\n consistency_data = self.table.item(row, COL_INFO['consistency']['col']).data(Qt.UserRole)\n skip_data = self.table.item(row, COL_INFO['skip']['col']).data(Qt.UserRole)\n name_data = self.table.item(row, COL_INFO['name']['col']).text()\n\n # 先匹配快速过滤，匹配过滤条件时为True，隐藏匹配的行\n table_match = self._get_table_match(consistency_data, skip_data)\n if table_match:\n self.table.setRowHidden(row, True)\n continue\n\n # 匹配选择所有或者选择服务名时为True，不设隐藏\n app_match = self._get_app_match(name_data)\n if not app_match:\n self.table.setRowHidden(row, True)\n continue\n\n # 匹配搜索条件或不输入时为True或结果列表，不设隐藏\n search_match = self._get_search_match(row, search_value)\n if not search_match:\n self.table.setRowHidden(row, True)\n continue\n\n # 仅当条件都匹配时才显示行\n self.table.setRowHidden(row, False)\n visible_rows += 1\n\n # 对单元格应用颜色\n if color_switch == 'ON' and isinstance(search_match, list):\n self.highlight_rows.append(self._generate_index_key(row))\n for column in search_match:\n self.table.apply_color(row, COLOR_HIGHLIGHT, column)\n\n # 更新状态栏信息展示过滤后的行数\n self.status_updated.emit(f\"{visible_rows} {self.lang['ui.filter_bar_11']}\")\n except Exception:\n logger.exception(\"Exception in filtering table\")\n self.status_updated.emit(self.lang['label_status_error'])\n finally:\n self.table.setUpdatesEnabled(True)\n\n def _get_app_match(self, name_data: str) -> bool:\n \"\"\"\n 检查当前行是否与选定的应用服务匹配。\n\n :param name_data: 行中的应用服务名称。\n :type name_data: str\n\n :return: 如果当前行与选定的应用服务匹配，则返回 True。\n :rtype: bool\n \"\"\"\n selected_app = self.filter_app_box.currentData()\n return True if selected_app == \"all\" or selected_app == name_data else False\n\n def _get_table_match(self,\n consistency_data: str,\n skip_data: str) -> bool:\n \"\"\"\n 根据表格状态过滤条件检查当前行是否匹配。\n\n :param consistency_data: 一致性状态数据。\n :type consistency_data: str\n :param skip_data: 跳过状态数据。\n :type skip_data: str\n\n :return: 如果当前行符合表格状态过滤条件，则返回 True。\n :rtype: bool\n \"\"\"\n selected_table = self.filter_table_box.currentData()\n reverse_checked = self.filter_table_check_box.isChecked()\n # 直接对比较结果赋值bool，相等则为True\n fully_match = consistency_data == \"fully\"\n partially_match = consistency_data == \"partially\"\n skip_match = skip_data == \"yes\"\n # 根据快速过滤条件，返回组合比较结果。\n if selected_table == \"fully\":\n return fully_match if not reverse_checked else not fully_match\n elif selected_table == \"partially\":\n return partially_match if not reverse_checked else not partially_match\n elif selected_table == 'skip':\n return skip_match if not reverse_checked else not skip_match\n elif selected_table == \"fully+skip\":\n return (fully_match or skip_match) if not reverse_checked else not (fully_match or skip_match)\n elif selected_table == \"fully+partially\":\n return (fully_match or partially_match) if not reverse_checked else not (fully_match or partially_match)\n elif selected_table == \"fully+partially+skip\":\n return (fully_match or partially_match or skip_match) if not reverse_checked else not (fully_match or partially_match or skip_match)\n else:\n return False if not reverse_checked else True\n\n def _get_search_match(self,\n row: int,\n search_value: str) -> Union[bool, List[int]]:\n \"\"\"\n 检查当前行是否与搜索条件匹配。\n\n :param row: 表格中的行号。\n :type row: int\n :param search_value: 需要搜索的值。\n :type search_value: str\n\n :return: 如果搜索值为空，则返回 True。否则返回空列表或匹配列号的列表\n :rtype: Union[bool, List[int]\n \"\"\"\n # 如果搜索值为空，则无需进行搜索\n if not search_value:\n return True\n # 禁止更新。主要着色时操作太多。\n self.table.setUpdatesEnabled(False)\n match_col = []\n # 遍历每列的内容\n for column in range(self.table.columnCount()):\n # 不搜索隐藏的列\n if self.table.isColumnHidden(column):\n continue\n\n # 获取单元格内容\n item = self.table.item(row, column)\n item_text = item.text().lower() if item else ''\n\n # 单元格列号插入到返回列表\n if search_value in item_text:\n match_col.append(column)\n\n # 启用更新\n self.table.setUpdatesEnabled(True)\n return match_col\n\n def _generate_index_key(self, row: int) -> str:\n \"\"\"\n 生成索引键。\n\n 此方法根据给定的行号生成一个唯一的索引键。索引键由行中特定列的值组合而成，用于标识表格中的唯一行。\n\n :param row: 表格中的行号。\n :type row: int\n\n :return: 生成的索引键。\n :rtype: str\n \"\"\"\n name = self.table.item(row, COL_INFO['name']['col']).text()\n group = self.table.item(row, COL_INFO['group']['col']).text()\n key = self.table.item(row, COL_INFO['key']['col']).text()\n return f\"{name}+{group}+{key}\"\n\n def _reset_styles(self) -> None:\n \"\"\"\n 重置表格样式到记录的状态。\n\n 此方法遍历表格中的所有行，对于每一行，恢复其单元格的背景颜色到初始状态。这通常在过滤条件发生变化或重置时调用。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n\n try:\n reset_rows = []\n # 还原字典为空则跳过\n if not self.highlight_rows:\n return\n # 遍历每行，但跳过隐藏行，因为隐藏行必然没有被更改颜色。\n # 反过来遍历还原字典并不可行，因为索引键并不储存在表格中，\n # 而且索引键和行号并不能形成牢固对应关系（行号可变），\n # 所以遍历所有行，但只操作匹配的单元格，最大程度减少对单元格的操作。\n for row in range(self.table.rowCount()):\n if self.table.isRowHidden(row):\n continue\n # 生成当行索引键，并检测是否在还原列表中。\n elif self._generate_index_key(row) in self.highlight_rows:\n # 索引键在还原字典中找到时，向reset_rows插入行数\n reset_rows.append(row)\n # 最后一次性还原单元格本来颜色。\n self.table.apply_color_to_table(reset_rows)\n # 完成后，清空还原列表。\n self.highlight_rows.clear()\n except Exception:\n logger.exception(\"Error occurred while resetting styles\")"},{"identifier":"LangManager","path":"ui/lang_manager.py","snippet":"class LangManager(QObject):\n \"\"\"\n 语言管理类，用于管理和更新应用程序的语言字典。\n\n 此类继承自 QObject，可发出语言更新的信号。它通过 `get_lang_dict` 函数获取当前语言字典，并提供了更新语言的功能。\n\n :ivar _lang_dict: 当前使用的语言字典。\n :vartype _lang_dict: dict\n \"\"\"\n lang_updated = pyqtSignal()\n\n def __init__(self):\n super().__init__()\n self._lang_dict = get_lang_dict()\n\n def get_lang(self) -> Optional[Dict[str, str]]:\n \"\"\"\n 获取当前使用的语言字典的副本。\n\n :return: 当前语言字典的深拷贝。\n :rtype: Optional[Dict[str, str]]\n \"\"\"\n try:\n return copy.deepcopy(self._lang_dict)\n except Exception:\n logger.exception(\"Failed to retrieve language dictionary.\")\n return None\n\n def update_lang(self, new_lang: str) -> None:\n \"\"\"\n 更新当前使用的语言字典。\n\n :param new_lang: 新语言的标识符。\n :type new_lang: str\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n try:\n self._lang_dict = LANG_DICTS.get(new_lang, \"English\")\n self.lang_updated.emit()\n logger.info(f\"Language changed to {new_lang}\")\n except Exception:\n logger.exception(f\"Failed to changed language to {new_lang}\")"},{"identifier":"message_show","path":"ui/message_show.py","snippet":"def message_show(message_type: str,\n text: str) -> None:\n \"\"\"\n 显示指定类型的消息框。\n\n 根据提供的消息类型和文本内容，显示相应的消息框。支持的消息类型包括 'Critical'、'Warning' 和 'Information'。\n\n :param message_type: 消息类型，支持 'Critical'、'Warning' 和 'Information'。\n :type message_type: str\n :param text: 消息框中显示的文本内容。\n :type text: str\n :return: 无返回值。\n :rtype: None\n \"\"\"\n try:\n msg_box = QMessageBox()\n msg_box.setText(text)\n msg_box.setStandardButtons(QMessageBox.Ok)\n msg_box.setWindowTitle(message_type)\n\n if message_type == 'Critical':\n msg_box.setIcon(QMessageBox.Critical)\n msg_box.setWindowIcon(QIcon(get_resource_path('media/icons8-error-26')))\n elif message_type == 'Warning':\n msg_box.setIcon(QMessageBox.Warning)\n msg_box.setWindowIcon(QIcon(get_resource_path('media/icons8-do-not-disturb-26')))\n elif message_type == 'Information':\n msg_box.setIcon(QMessageBox.Information)\n msg_box.setWindowIcon(QIcon(get_resource_path('media/icons8-about-26')))\n else:\n logger.warning(\"Invalid message type provided.\")\n\n msg_box.exec_()\n except Exception:\n logger.exception(\"An error occurred while displaying the message box\")"},{"identifier":"TableMain","path":"ui/table_main.py","snippet":"class TableMain(QTableWidget):\n \"\"\"\n 主表格类，用于展示和管理数据行。\n\n 此类继承自 PyQt5 的 QTableWidget，提供了丰富的数据展示和管理功能。包括但不限于数据的展示、行的颜色标记、右键菜单功能以及快捷键支持。\n 通过与 LangManager 和 ConfigManager 的集成，支持动态语言切换和配置管理。\n\n :param lang_manager: 用于管理界面语言的 LangManager 实例。\n :type lang_manager: LangManager\n :param config_manager: 用于管理配置的 ConfigManager 实例。\n :type config_manager: ConfigManager\n\n :author: assassing\n :contact: https://github.com/hxz393\n :copyright: Copyright 2023, hxz393. 保留所有权利。\n \"\"\"\n status_updated = pyqtSignal(str)\n filter_updated = pyqtSignal(list)\n\n def __init__(self,\n lang_manager: LangManager,\n config_manager: ConfigManager):\n super().__init__()\n self.lang_manager = lang_manager\n self.lang_manager.lang_updated.connect(self.update_lang)\n self.config_manager = config_manager\n # 实例化用到的组件\n self.actionCopy = ActionCopy(self.lang_manager, self)\n self.actionSave = ActionSave(self.lang_manager, self)\n self.actionSkip = ActionSkip(self.lang_manager, self.config_manager, self)\n self.actionUnskip = ActionUnskip(self.lang_manager, self.config_manager, self)\n # 手动连接实例化的组件信号到转发函数\n self.actionCopy.status_updated.connect(self.forward_status)\n self.actionSave.status_updated.connect(self.forward_status)\n self.actionSkip.status_updated.connect(self.forward_status)\n self.actionSkip.filter_updated.connect(self.forward_filter)\n self.actionUnskip.status_updated.connect(self.forward_status)\n self.actionUnskip.filter_updated.connect(self.forward_filter)\n self.initUI()\n\n def initUI(self) -> None:\n \"\"\"\n 初始化用户界面。\n\n 此方法负责设置表格的基本属性，如列数、表头标签、选择行为等。还包括对特定列的隐藏和宽度调整策略的设置。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # 先运行语言更新，里面有表头定义\n self.update_lang()\n self.hidden_cols = [\"pro_time\", \"pre_time\", \"test_time\", \"dev_time\"]\n self.resize_cols = [\"name\", \"group\", \"consistency\", \"skip\"]\n # 配置表格基本属性\n self.setColumnCount(len(self.column_headers))\n self.setHorizontalHeaderLabels(self.column_headers)\n self.setEditTriggers(QTableWidget.NoEditTriggers)\n self.setSelectionBehavior(QTableWidget.SelectItems)\n # 隐藏垂直表头\n self.verticalHeader().setVisible(False)\n # 启用自动换行，没生效\n self.setWordWrap(True)\n self.setTextElideMode(Qt.ElideNone)\n # 为表头视图设置上下文菜单事件\n self.horizontalHeader().setContextMenuPolicy(Qt.CustomContextMenu)\n self.horizontalHeader().customContextMenuRequested.connect(self._header_context_menu)\n # 为表单设置上下文菜单事件\n self.setContextMenuPolicy(Qt.CustomContextMenu)\n self.customContextMenuRequested.connect(self._cell_context_menu)\n # 隐藏指定列\n [self.hideColumn(COL_INFO[i]['col']) for i in self.hidden_cols]\n # 设置表宽度策略\n self.set_header_resize()\n\n def set_header_resize(self):\n \"\"\"\n 设置表头的列宽度和调整策略。\n\n 此方法负责定义表头列的宽度调整策略和其他相关属性。它设置了表头列的默认宽度、是否可拖动以及列的自动调整策略。\n 例如，某些列被设置为根据内容自动调整宽度，而其他列则被设置为可伸缩以适应表格的大小。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # 设置默认列宽度，列宽调整策略，列可拖动\n self.horizontalHeader().setSectionsMovable(True)\n self.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)\n self.horizontalHeader().setMinimumSectionSize(100)\n # 设置要自动调整宽度的列\n [self.horizontalHeader().setSectionResizeMode(COL_INFO[i]['col'], QHeaderView.ResizeToContents) for i in self.resize_cols]\n\n def update_lang(self) -> None:\n \"\"\"\n 更新界面语言设置。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n self.lang = self.lang_manager.get_lang()\n self.column_headers = [\n self.lang['ui.table_main_1'],\n self.lang['ui.table_main_2'],\n self.lang['ui.table_main_3'],\n self.lang['ui.dialog_settings_connection_2'],\n f\"{self.lang['ui.dialog_settings_connection_2']} {self.lang['ui.table_main_4']}\",\n self.lang['ui.dialog_settings_connection_3'],\n f\"{self.lang['ui.dialog_settings_connection_3']} {self.lang['ui.table_main_4']}\",\n self.lang['ui.dialog_settings_connection_4'],\n f\"{self.lang['ui.dialog_settings_connection_4']} {self.lang['ui.table_main_4']}\",\n self.lang['ui.dialog_settings_connection_5'],\n f\"{self.lang['ui.dialog_settings_connection_5']} {self.lang['ui.table_main_4']}\",\n self.lang['ui.table_main_5'],\n self.lang['ui.table_main_6'],\n ]\n # 重新应用到表头\n self.setHorizontalHeaderLabels(self.column_headers)\n # 定义数据和显示映射的字典\n consistency_status_mapping = {\n \"inconsistent\": self.lang['ui.action_start_8'],\n \"fully\": self.lang['ui.action_start_9'],\n \"partially\": self.lang['ui.action_start_10'],\n \"unknown\": self.lang['ui.action_start_13'],\n }\n skip_status_mapping = {\n \"no\": self.lang['ui.action_start_11'],\n \"yes\": self.lang['ui.action_start_12'],\n \"unknown\": self.lang['ui.action_start_13'],\n }\n for row in range(self.rowCount()):\n # 更新忽略状态文字\n self._update_item_text(row, \"skip\", skip_status_mapping)\n # 更新一致性状态文字\n self._update_item_text(row, \"consistency\", consistency_status_mapping)\n\n def _update_item_text(self,\n row: int,\n user_data_key: str,\n text_mapping: Dict[str, str]) -> None:\n \"\"\"\n 根据提供的文本映射更新指定行的项文本。\n\n 此方法用于更新表格或列表中特定行的文本。它根据用户数据键（user_data_key）获取对应行的项，然后根据提供的文本映射（text_mapping）更新该项的文本。\n\n :param row: 要更新的行索引。\n :type row: int\n :param user_data_key: 用于获取项的用户数据键。\n :type user_data_key: str\n :param text_mapping: 用户数据到文本的映射字典。\n :type text_mapping: Dict[str, str]\n\n :return: 无返回值。\n :rtype: None\n \"\"\"\n item = self.item(row, COL_INFO[user_data_key]['col'])\n if item is not None:\n user_data = item.data(Qt.UserRole)\n if user_data in text_mapping:\n item.setText(text_mapping[user_data])\n\n def keyPressEvent(self, event: QKeyEvent) -> None:\n \"\"\"\n 处理键盘事件。\n\n 此方法用于处理键盘事件，特别是复制功能的快捷键。如果按下 Ctrl+C，则复制选中的单元格内容。\n\n :param event: 键盘事件对象。\n :type event: QKeyEvent\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n if event.key() == Qt.Key_C and (event.modifiers() & Qt.ControlModifier):\n self.actionCopy.action_copy()\n else:\n super().keyPressEvent(event)\n\n def _cell_context_menu(self, pos: QPoint) -> None:\n \"\"\"\n 实现表格单元格的右键菜单功能。\n\n :param pos: 右键点击的位置。\n :type pos: QPoint\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n menu = QMenu(self)\n menu.addAction(self.actionCopy.action_copy)\n separator = QAction(menu)\n separator.setSeparator(True)\n menu.addAction(separator)\n menu.addAction(self.actionSkip.action_skip)\n menu.addAction(self.actionUnskip.action_unskip)\n sep = QAction(menu)\n sep.setSeparator(True)\n menu.addAction(sep)\n menu.addAction(self.actionSave.action_save)\n menu.exec_(self.viewport().mapToGlobal(pos))\n\n def _header_context_menu(self, pos: QPoint) -> None:\n \"\"\"\n 实现表头的右键菜单功能。\n\n :param pos: 右键点击的位置。\n :type pos: QPoint\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n menu = QMenu(self)\n # 动态创建一个菜单项，用于隐藏/显示列\n for index in range(self.columnCount()):\n column_name = self.horizontalHeaderItem(index).text()\n action = menu.addAction(f\"{column_name}\")\n action.setCheckable(True)\n action.setChecked(not self.isColumnHidden(index))\n action.setData(index)\n action.triggered.connect(self._toggle_column_visibility)\n # 在鼠标右键点击位置显示菜单\n menu.exec_(self.horizontalHeader().viewport().mapToGlobal(pos))\n\n def _toggle_column_visibility(self) -> None:\n \"\"\"\n 根据用户选择，切换列的可见性。\n\n 此方法用于根据用户在上下文菜单中的选择，显示或隐藏特定的列。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n action = self.sender()\n if isinstance(action, QAction):\n column_index = action.data()\n if action.isChecked():\n self.showColumn(column_index)\n else:\n self.hideColumn(column_index)\n\n def add_row(self, data: List[List[str]]) -> None:\n \"\"\"\n 向表格中添加一行数据。\n\n :param data: 要添加的数据列表，每个元素是一个列表，第一个元素代表显示的字符串，第二个元素代表附加数据。\n :type data: List[List[str]]\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n row_position = 0\n try:\n # 获取最后行数\n row_position = self.rowCount()\n # 插入最后一行\n self.insertRow(row_position)\n # 插入单元格数据\n self._fill_row_data(row_position, data)\n except Exception:\n logger.exception(f\"Error occurred while adding a new row at position {row_position}\")\n self.removeRow(row_position)\n\n def _fill_row_data(self,\n row_position: int,\n data: List[List[str]]) -> None:\n \"\"\"\n 填充指定行的数据。\n\n :param row_position: 行位置\n :param data: 行数据\n :type row_position: int\n :type data: List[List[str]]\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n for column, (display_text, user_data) in enumerate(data):\n # 默认设置显示字符串，也叫 Qt.DisplayRole。获取方法item.text() 或 item.data(Qt.DisplayRole)\n item = QTableWidgetItem(str(display_text))\n # 设置实际数据，也叫 Qt.UserRole。获取方法 item.data(Qt.UserRole)\n item.setData(Qt.UserRole, user_data)\n # 设置单元格不可编辑状态\n item.setFlags(item.flags() & ~Qt.ItemIsEditable)\n # 正常表格插入方法\n self.setItem(row_position, column, item)\n\n @log_time\n def apply_color_to_table(self, rows: List[int] = None) -> None:\n \"\"\"\n 对整个表格进行着色。通常只有初始化时才不带rows参数，以应用到整表。\n\n :param rows: 可选，要应用颜色的行号列表。\n :type rows: List[int], optional\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n color_switch = self.config_manager.get_config_main().get('color_set', 'ON')\n if color_switch == 'OFF':\n return\n\n if rows is None or not isinstance(rows, list):\n rows = range(self.rowCount())\n\n try:\n for row in rows:\n # 不给隐藏行设置颜色\n if self.isRowHidden(row):\n continue\n\n self._process_row_for_color(row)\n except Exception:\n logger.exception(\"Exception in apply_color_to_table method\")\n self.status_updated.emit(self.lang['label_status_error'])\n\n def _process_row_for_color(self, row: int) -> None:\n \"\"\"\n 根据一致性、跳过状态和是否为空值给单行应用颜色。\n\n :param row: 行号，对每行进行颜色处理。\n :type row: int\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n consistency_data = self.item(row, COL_INFO['consistency']['col']).data(Qt.UserRole)\n skip_data = self.item(row, COL_INFO['skip']['col']).data(Qt.UserRole)\n # 忽略状态为是时设置颜色\n if skip_data == 'yes':\n self.apply_color(row, COLOR_SKIP)\n return\n\n # 根据一致性值设置颜色\n if consistency_data == 'fully':\n self.apply_color(row, COLOR_CONSISTENCY_FULLY)\n elif consistency_data == 'partially':\n self.apply_color(row, COLOR_CONSISTENCY_PARTIALLY)\n else:\n self.apply_color(row, COLOR_DEFAULT)\n\n # 遍历指定列检查空值，并赋予颜色\n for column in range(self.columnCount()):\n # 不给隐藏列设置颜色\n if not self.isColumnHidden(column):\n if self.item(row, column).text() == 'None':\n self.apply_color(row, COLOR_EMPTY, column)\n\n def apply_color(self,\n row: int,\n color: str,\n column: Optional[int] = None) -> None:\n \"\"\"\n 为指定的行或单元格应用颜色。\n\n :param row: 要着色的行索引。\n :type row: int\n :param color: 要应用的颜色。\n :type color: str\n :param column: 可选，指定要着色的列索引，如果未指定，则对整行应用颜色。\n :type column: int, optional\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n try:\n color_brush = QBrush(QColor(color))\n if column is not None:\n self.item(row, column).setBackground(color_brush)\n else:\n for col in range(self.columnCount()):\n # 不给隐藏列设置颜色\n if not self.isColumnHidden(col):\n self.item(row, col).setBackground(color_brush)\n except Exception:\n logger.exception(\"Error occurred while applying color to a cell\")\n self.status_updated.emit(self.lang['label_status_error'])\n\n def clear(self) -> None:\n \"\"\"\n 清空表格中的所有行。\n\n 此方法用于清除表格中的所有数据，通常在数据更新或重置时使用。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n try:\n # 禁用更新以提高性能\n self.setUpdatesEnabled(False)\n # 首先清除所有单元格的内容\n self.clearContents()\n # 将行数设置为0，从而删除所有行\n self.setRowCount(0)\n except Exception:\n logger.exception(\"Error occurred while clearing the table.\")\n self.status_updated.emit(self.lang['label_status_error'])\n finally:\n # 确保即使发生错误也要重新启用更新\n self.setUpdatesEnabled(True)\n\n def forward_status(self, message: str) -> None:\n \"\"\"\n 用于转发状态信号。\n\n :param message: 要转发的消息。\n :type message: str\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n self.status_updated.emit(message)\n\n def forward_filter(self, rows: List[int]) -> None:\n \"\"\"\n 用于转发过滤信号。\n\n :param rows: 要转发的行列表。\n :type rows: List[int]\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n self.filter_updated.emit(rows)\n\n def get_table_data(self) -> Dict[int, Dict[str, str]]:\n \"\"\"\n 用于获取表格所有数据。\n\n :rtype: Dict[int, Dict[str, str]]\n :return: 返回嵌套字典。键为行号，值为字典，字典中键为列标题，值为内容。类似于：{882: {'服务': 'web', '分组': 'application'}, 883: {'服务': 'web', '分组': 'application'}}\n \"\"\"\n return {row: {self.horizontalHeaderItem(col).text(): self.item(row, col).data(Qt.UserRole)\n for col in range(self.columnCount())}\n for row in range(self.rowCount())}"}],"string":"[\n {\n \"identifier\": \"COL_INFO\",\n \"path\": \"config/settings.py\",\n \"snippet\": \"COL_INFO = {\\n \\\"name\\\": {\\\"col\\\": 0},\\n \\\"group\\\": {\\\"col\\\": 1},\\n \\\"key\\\": {\\\"col\\\": 2},\\n \\\"pro_value\\\": {\\\"col\\\": 3},\\n \\\"pro_time\\\": {\\\"col\\\": 4},\\n \\\"pre_value\\\": {\\\"col\\\": 5},\\n \\\"pre_time\\\": {\\\"col\\\": 6},\\n \\\"test_value\\\": {\\\"col\\\": 7},\\n \\\"test_time\\\": {\\\"col\\\": 8},\\n \\\"dev_value\\\": {\\\"col\\\": 9},\\n \\\"dev_time\\\": {\\\"col\\\": 10},\\n \\\"consistency\\\": {\\\"col\\\": 11},\\n \\\"skip\\\": {\\\"col\\\": 12},\\n\\n}\"\n },\n {\n \"identifier\": \"get_resource_path\",\n \"path\": \"lib/get_resource_path.py\",\n \"snippet\": \"def get_resource_path(relative_path: Union[str, os.PathLike]) -> Optional[str]:\\n \\\"\\\"\\\"\\n 获取资源的绝对路径。这个函数适用于 PyInstaller 打包后的可执行文件。\\n\\n :type relative_path: Union[str, os.PathLike]\\n :param relative_path: 相对路径，可以是字符串或 os.PathLike 对象。\\n :rtype: Optional[str]\\n :return: 资源的绝对路径，如果发生错误则返回 None。\\n \\\"\\\"\\\"\\n\\n try:\\n base_path = getattr(sys, '_MEIPASS', os.path.abspath(\\\".\\\"))\\n return os.path.join(base_path, os.path.normpath(relative_path))\\n except Exception:\\n logger.exception(\\\"An error occurred while retrieving resource path\\\")\\n return None\"\n },\n {\n \"identifier\": \"execute_queries\",\n \"path\": \"module/execute_queries.py\",\n \"snippet\": \"def execute_queries(config_connection: Dict[str, Dict[str, Union[Dict[str, str], bool]]],\\n config_main: Dict[str, str]) -> Tuple[Dict[str, Dict[str, str]], Dict[str, bool]]:\\n \\\"\\\"\\\"\\n 执行数据库查询并返回格式化后的结果和查询状态。\\n\\n 此函数接收数据库连接配置和主要配置参数。它首先根据主配置生成SQL查询语句，然后对每个数据库环境执行查询。查询结果将被格式化，并更新查询状态。\\n\\n :param config_connection: 数据库连接配置，包含环境名称和对应的数据库配置。\\n :type config_connection: Dict[str, Dict[str, Union[Dict[str, str], bool]]]\\n :param config_main: 主要配置参数，用于生成查询SQL语句。\\n :type config_main: Dict[str, str]\\n :return: 包含格式化查询结果的字典和每个环境的查询状态。\\n :rtype: Tuple[Dict[str, Dict[str, str]], Dict[str, bool]]\\n\\n :example:\\n >>> os.chdir(os.path.dirname(os.getcwd()))\\n >>> connection = {\\\"dev\\\": {'mysql_on': True, 'ssh_on': False, 'mysql': {'host': '192.168.2.204', \\\"port\\\": \\\"3306\\\", \\\"user\\\": \\\"root\\\", \\\"password\\\": \\\"QeqAr:%R+s5:hYnr\\\", \\\"db\\\": \\\"ApolloConfigDB_dev\\\"}}}\\n >>> main = {'config_center': 'Apollo', 'apollo_name': 'AppId', 'fix_name_before': '', 'fix_name_after': '', 'fix_name_left': '', 'fix_name_right': '',}\\n >>> results, statuses = execute_queries(connection, main)\\n >>> assert type(results) == dict\\n >>> assert type(statuses) == dict\\n >>> statuses\\n >>> results\\n \\\"\\\"\\\"\\n query_statuses = {env_name: False for env_name in config_connection.keys()}\\n formatted_results = {}\\n\\n try:\\n query_sql = get_query_sql(config_main)\\n\\n for env_name, db_config in config_connection.items():\\n # 获取指定环境的查询结果\\n query_results = get_query_result(db_config, query_sql)\\n logger.debug(f\\\"ENV: {env_name}, SQL query finished.\\\")\\n if query_results:\\n query_statuses[env_name] = True\\n # 格式化查询结果\\n format_query_results(query_results, env_name, config_main, formatted_results)\\n else:\\n logger.warning(f\\\"No results obtained from database query for environment: {env_name}\\\")\\n\\n # 通过对比过滤列表，得到是否过滤信息，更新到结果字典\\n update_skip_status(formatted_results)\\n # 查询各配置环境的值，得到一致性信息，更新到结果字典。只对比查询成功的环境\\n update_consistency_status(formatted_results, query_statuses)\\n logger.debug(\\\"Status update finished.\\\")\\n\\n return formatted_results, query_statuses\\n except Exception:\\n logger.exception(\\\"Exception occurred during executing queries\\\")\\n return {}, query_statuses\"\n },\n {\n \"identifier\": \"ConfigManager\",\n \"path\": \"ui/config_manager.py\",\n \"snippet\": \"class ConfigManager(QObject):\\n \\\"\\\"\\\"\\n 配置管理器类，负责管理和更新应用程序的配置信息。\\n\\n 该类包括获取和设置主配置、连接配置和跳过列表的方法，同时提供信号以通知配置更新。\\n\\n :ivar config_main_updated: 当主配置更新时发出的信号。\\n :ivar config_connection_updated: 当连接配置更新时发出的信号。\\n :ivar skip_list_updated: 当跳过列表更新时发出的信号。\\n \\\"\\\"\\\"\\n config_main_updated = pyqtSignal()\\n config_connection_updated = pyqtSignal()\\n skip_list_updated = pyqtSignal()\\n\\n def __init__(self):\\n super().__init__()\\n self._config_main, self._config_apollo, self._config_nacos = read_config_all()\\n self._skip_list = read_file_to_list(CONFIG_SKIP_PATH) or []\\n\\n def get_config_main(self) -> Optional[Dict[str, str]]:\\n \\\"\\\"\\\"\\n 获取主配置的副本。\\n\\n :return: 包含主配置的字典，如果出现错误则返回 None。\\n :rtype: Optional[Dict[str, str]]\\n \\\"\\\"\\\"\\n try:\\n return copy.deepcopy(self._config_main)\\n except Exception:\\n logger.exception(\\\"Failed to get config_main.\\\")\\n return None\\n\\n def get_config_connection(self) -> Optional[Dict[str, Dict[str, Union[Dict[str, str], bool]]]]:\\n \\\"\\\"\\\"\\n 根据当前配置中心获取连接配置的副本。\\n\\n :return: 包含连接配置的字典，如果出现错误则返回 None。\\n :rtype: Optional[Dict[str, Dict[str, Union[Dict[str, str], bool]]]]\\n \\\"\\\"\\\"\\n try:\\n if self._config_main['config_center'] == 'Apollo':\\n return copy.deepcopy(self._config_apollo)\\n else:\\n return copy.deepcopy(self._config_nacos)\\n except Exception:\\n logger.exception(\\\"Failed to get config_connection.\\\")\\n return None\\n\\n def get_skip_list(self) -> Optional[List[str]]:\\n \\\"\\\"\\\"\\n 获取忽略列表的副本。\\n\\n :return: 包含跳过项的列表，如果出现错误则返回 None。\\n :rtype: Optional[List[str]]\\n \\\"\\\"\\\"\\n try:\\n return copy.deepcopy(self._skip_list)\\n except Exception:\\n logger.exception(\\\"Failed to get skip_list.\\\")\\n return None\\n\\n def update_config_main(self, new_config: Dict[str, str]) -> None:\\n \\\"\\\"\\\"\\n 更新主配置。\\n\\n :param new_config: 新的主配置。\\n :type new_config: Dict[str, str]\\n \\\"\\\"\\\"\\n try:\\n self._config_main = new_config\\n self.config_main_updated.emit()\\n write_dict_to_json(CONFIG_MAIN_PATH, new_config)\\n logger.info(\\\"Config updated: config_main\\\")\\n except Exception:\\n logger.exception(\\\"Failed to update config: config_main\\\")\\n\\n def update_config_connection(self, new_config: Dict[str, Dict[str, Union[Dict[str, str], bool]]]) -> None:\\n \\\"\\\"\\\"\\n 更新连接配置。\\n\\n :param new_config: 新的连接配置。\\n :type new_config: Dict[str, Dict[str, Union[Dict[str, str], bool]]]\\n \\\"\\\"\\\"\\n try:\\n if self._config_main['config_center'] == 'Apollo':\\n self._config_apollo = new_config\\n write_dict_to_json(CONFIG_APOLLO_PATH, new_config)\\n else:\\n self._config_nacos = new_config\\n write_dict_to_json(CONFIG_NACOS_PATH, new_config)\\n self.config_connection_updated.emit()\\n logger.info(\\\"Config updated: config_connection\\\")\\n except Exception:\\n logger.exception(\\\"Failed to update config: config_connection\\\")\\n\\n def update_skip_list(self, new_config: List[str]) -> None:\\n \\\"\\\"\\\"\\n 更新忽略列表。\\n\\n :param new_config: 新忽略列表。\\n :type new_config: List[str]\\n \\\"\\\"\\\"\\n try:\\n self._skip_list = new_config\\n # 写入到配置文件\\n self.skip_list_updated.emit()\\n write_list_to_file(CONFIG_SKIP_PATH, new_config)\\n logger.info(\\\"Config updated: skip_list\\\")\\n except Exception:\\n logger.exception(\\\"Failed to update config: skip_list\\\")\"\n },\n {\n \"identifier\": \"FilterBar\",\n \"path\": \"ui/filter_bar.py\",\n \"snippet\": \"class FilterBar(QWidget):\\n \\\"\\\"\\\"\\n 过滤栏类，用于在用户界面中提供过滤和搜索功能。\\n\\n 此类创建了一个包含服务过滤、表格状态过滤和搜索框的组件，使用户能够根据不同条件过滤表格数据。\\n\\n :param lang_manager: 语言管理器，用于处理界面语言的更新。\\n :type lang_manager: LangManager\\n :param config_manager: 配置管理器，用于获取和更新配置信息。\\n :type config_manager: ConfigManager\\n :param table: 要应用过滤的表格。\\n :type table: TableMain\\n \\\"\\\"\\\"\\n status_updated = pyqtSignal(str)\\n\\n def __init__(self,\\n lang_manager: LangManager,\\n config_manager: ConfigManager,\\n table: TableMain):\\n super().__init__()\\n # 实例化组件。\\n self.lang_manager = lang_manager\\n self.lang_manager.lang_updated.connect(self.update_lang)\\n self.lang = self.lang_manager.get_lang()\\n self.config_manager = config_manager\\n self.table = table\\n self.highlight_rows = []\\n self.initUI()\\n\\n def initUI(self) -> None:\\n \\\"\\\"\\\"\\n 初始化用户界面。\\n\\n 创建并布局过滤栏中的所有组件，包括服务过滤、表格状态过滤和搜索值输入框。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n # 修改字体大小\\n self.setStyleSheet(\\\"font-size: 14px;\\\")\\n # 建立横向主布局\\n self.layout = QHBoxLayout(self)\\n # 创建过滤服务组件\\n self._create_filter_app()\\n # 创建过滤列表组件\\n self._create_filter_table()\\n # 创建搜索过滤值组件\\n self._create_filter_value()\\n # 设置布局的内容边距\\n self.layout.setContentsMargins(0, 0, 0, 0)\\n self.setLayout(self.layout)\\n self.update_lang()\\n\\n def update_lang(self) -> None:\\n \\\"\\\"\\\"\\n 更新界面语言设置。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n # 重新获取语言字典\\n self.lang = self.lang_manager.get_lang()\\n # 遍历filter_table下拉框中的所有项\\n for index in range(self.filter_table_box.count()):\\n # 检查数据值是否匹配\\n if self.filter_table_box.itemData(index) == \\\"all\\\":\\n # 更新显示值\\n self.filter_table_box.setItemText(index, self.lang['ui.filter_bar_3'])\\n elif self.filter_table_box.itemData(index) == \\\"fully\\\":\\n self.filter_table_box.setItemText(index, self.lang['ui.filter_bar_4'])\\n elif self.filter_table_box.itemData(index) == \\\"partially\\\":\\n self.filter_table_box.setItemText(index, self.lang['ui.filter_bar_5'])\\n elif self.filter_table_box.itemData(index) == \\\"skip\\\":\\n self.filter_table_box.setItemText(index, self.lang['ui.filter_bar_6'])\\n elif self.filter_table_box.itemData(index) == \\\"fully+skip\\\":\\n self.filter_table_box.setItemText(index, f\\\"{self.lang['ui.filter_bar_4']}+{self.lang['ui.filter_bar_6']}\\\")\\n elif self.filter_table_box.itemData(index) == \\\"fully+partially\\\":\\n self.filter_table_box.setItemText(index, f\\\"{self.lang['ui.filter_bar_4']}+{self.lang['ui.filter_bar_5']}\\\")\\n elif self.filter_table_box.itemData(index) == \\\"fully+partially+skip\\\":\\n self.filter_table_box.setItemText(index, f\\\"{self.lang['ui.filter_bar_4']}+{self.lang['ui.filter_bar_5']}+{self.lang['ui.filter_bar_6']}\\\")\\n # 直接更新服务名过滤默认选项文字\\n self.filter_app_box.setItemText(0, self.lang['ui.filter_bar_3'])\\n # 更新其他文字\\n self.filter_app_label.setText(self.lang['ui.filter_bar_1'])\\n self.filter_table_label.setText(self.lang['ui.filter_bar_2'])\\n self.filter_table_check_box.setText(self.lang['ui.filter_bar_7'])\\n self.filter_value_label.setText(self.lang['ui.filter_bar_8'])\\n self.filter_value_button.setText(self.lang['ui.filter_bar_9'])\\n self.filter_reset_button.setText(self.lang['ui.filter_bar_10'])\\n\\n def _create_filter_app(self) -> None:\\n \\\"\\\"\\\"\\n 创建服务过滤组件。\\n\\n 此方法初始化服务过滤下拉框，并设置其事件处理函数。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n # 建立标签，加入主布局\\n self.filter_app_label = QLabel()\\n self.layout.addWidget(self.filter_app_label)\\n # 过滤服务下拉框\\n self.filter_app_box = QComboBox()\\n # 设置下拉框的尺寸策略和宽度\\n self.filter_app_box.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\\n # 设置最大宽度，以免拉伸太长\\n self.filter_app_box.setMinimumWidth(100)\\n self.filter_app_box.setMaximumWidth(300)\\n # 设置下拉框的事件处理\\n self.filter_app_box.currentIndexChanged.connect(self.filter_table)\\n # 设置下拉框的选项，通过函数填充\\n self.filter_options_add()\\n self.layout.addWidget(self.filter_app_box)\\n # 创建一个 QFrame 作为分割线\\n self._create_separator()\\n\\n def _create_filter_table(self) -> None:\\n \\\"\\\"\\\"\\n 创建表格状态过滤组件。\\n\\n 此方法初始化表格状态过滤下拉框和反向选择复选框，并设置它们的事件处理函数。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n # 建立标签，加入主布局\\n self.filter_table_label = QLabel()\\n self.layout.addWidget(self.filter_table_label)\\n # 过滤列表下拉框\\n self.filter_table_box = QComboBox()\\n # 设置最小宽度，以免文字放不下\\n self.filter_table_box.setMinimumWidth(270)\\n # 设置下拉框的选项\\n self.filter_table_box.addItem(\\\"\\\", \\\"all\\\")\\n self.filter_table_box.addItem(\\\"\\\", \\\"fully\\\")\\n self.filter_table_box.addItem(\\\"\\\", \\\"partially\\\")\\n self.filter_table_box.addItem(\\\"\\\", \\\"skip\\\")\\n self.filter_table_box.addItem(\\\"\\\", \\\"fully+skip\\\")\\n self.filter_table_box.addItem(\\\"\\\", \\\"fully+partially\\\")\\n self.filter_table_box.addItem(\\\"\\\", \\\"fully+partially+skip\\\")\\n # 设置下拉框的事件处理\\n self.filter_table_box.currentIndexChanged.connect(self.filter_table)\\n self.layout.addWidget(self.filter_table_box)\\n # 反向选择\\n self.filter_table_check_box = QCheckBox()\\n self.filter_table_check_box.stateChanged.connect(self.filter_table)\\n self.layout.addWidget(self.filter_table_check_box)\\n # 创建一个 QFrame 作为分割线\\n self._create_separator()\\n\\n def _create_filter_value(self) -> None:\\n \\\"\\\"\\\"\\n 创建搜索过滤组件。\\n\\n 此方法初始化搜索框和相关按钮，并设置事件处理函数，以便用户可以根据特定文本过滤表格数据。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n # 建立标签，加入主布局\\n self.filter_value_label = QLabel()\\n self.layout.addWidget(self.filter_value_label)\\n # 搜索输入框\\n self.filter_value_box = QLineEdit()\\n self.filter_value_box.returnPressed.connect(self.filter_table)\\n # 设置搜索输入框的尺寸策略和最小宽度\\n self.filter_value_box.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)\\n self.filter_value_box.setMinimumWidth(100)\\n self.layout.addWidget(self.filter_value_box)\\n # 搜索按钮\\n self.filter_value_button = QPushButton()\\n self.filter_value_button.clicked.connect(self.filter_table)\\n self.layout.addWidget(self.filter_value_button)\\n # 重置按钮\\n self.filter_reset_button = QPushButton()\\n self.filter_reset_button.clicked.connect(self.filter_reset)\\n self.layout.addWidget(self.filter_reset_button)\\n\\n def _create_separator(self) -> None:\\n \\\"\\\"\\\"\\n 创建界面中的分隔线。\\n\\n 此方法用于在过滤器工具栏中添加分隔线。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n # 建好之后，直接加入主布局\\n separator = QFrame()\\n separator.setFrameShape(QFrame.VLine)\\n separator.setFrameShadow(QFrame.Raised)\\n self.layout.addWidget(separator)\\n\\n def filter_options_add(self) -> None:\\n \\\"\\\"\\\"\\n 填充服务过滤下拉框选项。\\n\\n 此方法从配置数据中提取所有唯一的服务名称，并将它们添加到服务过滤下拉框中。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n try:\\n # 先断开信号\\n self.filter_app_box.currentIndexChanged.disconnect(self.filter_table)\\n self.filter_app_box.setEnabled(False)\\n # 清空过滤器并添加显示所有行的选项\\n self.filter_app_box.clear()\\n self.filter_app_box.addItem(self.lang['ui.filter_bar_3'], \\\"all\\\")\\n self.filter_app_box.setCurrentIndex(0)\\n # 使用集合和列表推导式去重并获取所有唯一项\\n unique_items = {self.table.item(row, COL_INFO['name']['col']).text()\\n for row in range(self.table.rowCount())\\n if self.table.item(row, COL_INFO['name']['col'])}\\n # 添加唯一项到下拉框\\n [self.filter_app_box.addItem(item, item) for item in unique_items]\\n # 对下拉框进行排序\\n model = self.filter_app_box.model()\\n model.sort(0)\\n except Exception:\\n logger.exception(\\\"Exception occurred in adding filter options\\\")\\n self.status_updated.emit(self.lang['label_status_error'])\\n finally:\\n # 重新连接信号\\n self.filter_app_box.currentIndexChanged.connect(self.filter_table)\\n self.filter_app_box.setEnabled(True)\\n\\n def filter_reset(self) -> None:\\n \\\"\\\"\\\"\\n 重置过滤条件。\\n\\n 此方法将所有过滤组件重置为默认状态，以便用户可以重新开始过滤操作。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n try:\\n # 禁更新\\n self.table.setUpdatesEnabled(False)\\n # 断开信号连接\\n self.filter_app_box.currentIndexChanged.disconnect(self.filter_table)\\n self.filter_table_box.currentIndexChanged.disconnect(self.filter_table)\\n self.filter_table_check_box.stateChanged.disconnect(self.filter_table)\\n # 重置 QComboBox 为第一个项，通常是 \\\"--显示所有--\\\"，\\n self.filter_app_box.setCurrentIndex(0)\\n self.filter_table_box.setCurrentIndex(0)\\n # 还原反选框状态\\n self.filter_table_check_box.setChecked(False)\\n # 清空搜索框 QLineEdit\\n self.filter_value_box.clear()\\n # 手动调用过略器\\n self.filter_table()\\n except Exception:\\n logger.exception(\\\"Error occurred while resetting filters\\\")\\n self.status_updated.emit(self.lang['label_status_error'])\\n finally:\\n # 开启更新，重新连接信号\\n self.table.setUpdatesEnabled(True)\\n self.filter_app_box.currentIndexChanged.connect(self.filter_table)\\n self.filter_table_box.currentIndexChanged.connect(self.filter_table)\\n self.filter_table_check_box.stateChanged.connect(self.filter_table)\\n\\n @log_time\\n def filter_table(self, rows: Optional[List[int]] = None) -> None:\\n \\\"\\\"\\\"\\n 应用过滤条件到表格。带有时间记录用于调试。\\n\\n 此方法根据用户设置的过滤条件（服务名称、表格状态、搜索文本）来决定哪些行在表格中可见。\\n\\n :param rows: 要应用过滤器的行号列表。如果为空则应用到整表。\\n :type rows: Optional[List[int]]\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n try:\\n self.table.setUpdatesEnabled(False)\\n # 获取颜色开关\\n color_switch = self.config_manager.get_config_main().get('color_set', 'ON')\\n # 检查是否有rows参数\\n valid_rows = rows if isinstance(rows, list) else None\\n # 计算可见的行数\\n visible_rows = 0\\n # 搜索框输入内容\\n search_value = self.filter_value_box.text().strip().lower()\\n # 在新的搜索开始之前，恢复每个单元格的原始样式。\\n if color_switch == 'ON':\\n # 针对忽略操作，改变表格颜色。\\n if valid_rows:\\n self.table.apply_color_to_table(valid_rows)\\n # 针对高亮操作\\n elif self.highlight_rows:\\n self._reset_styles()\\n\\n # 如果没有传入行列表，则应用到整个列表\\n for row in valid_rows if valid_rows else range(self.table.rowCount()):\\n consistency_data = self.table.item(row, COL_INFO['consistency']['col']).data(Qt.UserRole)\\n skip_data = self.table.item(row, COL_INFO['skip']['col']).data(Qt.UserRole)\\n name_data = self.table.item(row, COL_INFO['name']['col']).text()\\n\\n # 先匹配快速过滤，匹配过滤条件时为True，隐藏匹配的行\\n table_match = self._get_table_match(consistency_data, skip_data)\\n if table_match:\\n self.table.setRowHidden(row, True)\\n continue\\n\\n # 匹配选择所有或者选择服务名时为True，不设隐藏\\n app_match = self._get_app_match(name_data)\\n if not app_match:\\n self.table.setRowHidden(row, True)\\n continue\\n\\n # 匹配搜索条件或不输入时为True或结果列表，不设隐藏\\n search_match = self._get_search_match(row, search_value)\\n if not search_match:\\n self.table.setRowHidden(row, True)\\n continue\\n\\n # 仅当条件都匹配时才显示行\\n self.table.setRowHidden(row, False)\\n visible_rows += 1\\n\\n # 对单元格应用颜色\\n if color_switch == 'ON' and isinstance(search_match, list):\\n self.highlight_rows.append(self._generate_index_key(row))\\n for column in search_match:\\n self.table.apply_color(row, COLOR_HIGHLIGHT, column)\\n\\n # 更新状态栏信息展示过滤后的行数\\n self.status_updated.emit(f\\\"{visible_rows} {self.lang['ui.filter_bar_11']}\\\")\\n except Exception:\\n logger.exception(\\\"Exception in filtering table\\\")\\n self.status_updated.emit(self.lang['label_status_error'])\\n finally:\\n self.table.setUpdatesEnabled(True)\\n\\n def _get_app_match(self, name_data: str) -> bool:\\n \\\"\\\"\\\"\\n 检查当前行是否与选定的应用服务匹配。\\n\\n :param name_data: 行中的应用服务名称。\\n :type name_data: str\\n\\n :return: 如果当前行与选定的应用服务匹配，则返回 True。\\n :rtype: bool\\n \\\"\\\"\\\"\\n selected_app = self.filter_app_box.currentData()\\n return True if selected_app == \\\"all\\\" or selected_app == name_data else False\\n\\n def _get_table_match(self,\\n consistency_data: str,\\n skip_data: str) -> bool:\\n \\\"\\\"\\\"\\n 根据表格状态过滤条件检查当前行是否匹配。\\n\\n :param consistency_data: 一致性状态数据。\\n :type consistency_data: str\\n :param skip_data: 跳过状态数据。\\n :type skip_data: str\\n\\n :return: 如果当前行符合表格状态过滤条件，则返回 True。\\n :rtype: bool\\n \\\"\\\"\\\"\\n selected_table = self.filter_table_box.currentData()\\n reverse_checked = self.filter_table_check_box.isChecked()\\n # 直接对比较结果赋值bool，相等则为True\\n fully_match = consistency_data == \\\"fully\\\"\\n partially_match = consistency_data == \\\"partially\\\"\\n skip_match = skip_data == \\\"yes\\\"\\n # 根据快速过滤条件，返回组合比较结果。\\n if selected_table == \\\"fully\\\":\\n return fully_match if not reverse_checked else not fully_match\\n elif selected_table == \\\"partially\\\":\\n return partially_match if not reverse_checked else not partially_match\\n elif selected_table == 'skip':\\n return skip_match if not reverse_checked else not skip_match\\n elif selected_table == \\\"fully+skip\\\":\\n return (fully_match or skip_match) if not reverse_checked else not (fully_match or skip_match)\\n elif selected_table == \\\"fully+partially\\\":\\n return (fully_match or partially_match) if not reverse_checked else not (fully_match or partially_match)\\n elif selected_table == \\\"fully+partially+skip\\\":\\n return (fully_match or partially_match or skip_match) if not reverse_checked else not (fully_match or partially_match or skip_match)\\n else:\\n return False if not reverse_checked else True\\n\\n def _get_search_match(self,\\n row: int,\\n search_value: str) -> Union[bool, List[int]]:\\n \\\"\\\"\\\"\\n 检查当前行是否与搜索条件匹配。\\n\\n :param row: 表格中的行号。\\n :type row: int\\n :param search_value: 需要搜索的值。\\n :type search_value: str\\n\\n :return: 如果搜索值为空，则返回 True。否则返回空列表或匹配列号的列表\\n :rtype: Union[bool, List[int]\\n \\\"\\\"\\\"\\n # 如果搜索值为空，则无需进行搜索\\n if not search_value:\\n return True\\n # 禁止更新。主要着色时操作太多。\\n self.table.setUpdatesEnabled(False)\\n match_col = []\\n # 遍历每列的内容\\n for column in range(self.table.columnCount()):\\n # 不搜索隐藏的列\\n if self.table.isColumnHidden(column):\\n continue\\n\\n # 获取单元格内容\\n item = self.table.item(row, column)\\n item_text = item.text().lower() if item else ''\\n\\n # 单元格列号插入到返回列表\\n if search_value in item_text:\\n match_col.append(column)\\n\\n # 启用更新\\n self.table.setUpdatesEnabled(True)\\n return match_col\\n\\n def _generate_index_key(self, row: int) -> str:\\n \\\"\\\"\\\"\\n 生成索引键。\\n\\n 此方法根据给定的行号生成一个唯一的索引键。索引键由行中特定列的值组合而成，用于标识表格中的唯一行。\\n\\n :param row: 表格中的行号。\\n :type row: int\\n\\n :return: 生成的索引键。\\n :rtype: str\\n \\\"\\\"\\\"\\n name = self.table.item(row, COL_INFO['name']['col']).text()\\n group = self.table.item(row, COL_INFO['group']['col']).text()\\n key = self.table.item(row, COL_INFO['key']['col']).text()\\n return f\\\"{name}+{group}+{key}\\\"\\n\\n def _reset_styles(self) -> None:\\n \\\"\\\"\\\"\\n 重置表格样式到记录的状态。\\n\\n 此方法遍历表格中的所有行，对于每一行，恢复其单元格的背景颜色到初始状态。这通常在过滤条件发生变化或重置时调用。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n\\n try:\\n reset_rows = []\\n # 还原字典为空则跳过\\n if not self.highlight_rows:\\n return\\n # 遍历每行，但跳过隐藏行，因为隐藏行必然没有被更改颜色。\\n # 反过来遍历还原字典并不可行，因为索引键并不储存在表格中，\\n # 而且索引键和行号并不能形成牢固对应关系（行号可变），\\n # 所以遍历所有行，但只操作匹配的单元格，最大程度减少对单元格的操作。\\n for row in range(self.table.rowCount()):\\n if self.table.isRowHidden(row):\\n continue\\n # 生成当行索引键，并检测是否在还原列表中。\\n elif self._generate_index_key(row) in self.highlight_rows:\\n # 索引键在还原字典中找到时，向reset_rows插入行数\\n reset_rows.append(row)\\n # 最后一次性还原单元格本来颜色。\\n self.table.apply_color_to_table(reset_rows)\\n # 完成后，清空还原列表。\\n self.highlight_rows.clear()\\n except Exception:\\n logger.exception(\\\"Error occurred while resetting styles\\\")\"\n },\n {\n \"identifier\": \"LangManager\",\n \"path\": \"ui/lang_manager.py\",\n \"snippet\": \"class LangManager(QObject):\\n \\\"\\\"\\\"\\n 语言管理类，用于管理和更新应用程序的语言字典。\\n\\n 此类继承自 QObject，可发出语言更新的信号。它通过 `get_lang_dict` 函数获取当前语言字典，并提供了更新语言的功能。\\n\\n :ivar _lang_dict: 当前使用的语言字典。\\n :vartype _lang_dict: dict\\n \\\"\\\"\\\"\\n lang_updated = pyqtSignal()\\n\\n def __init__(self):\\n super().__init__()\\n self._lang_dict = get_lang_dict()\\n\\n def get_lang(self) -> Optional[Dict[str, str]]:\\n \\\"\\\"\\\"\\n 获取当前使用的语言字典的副本。\\n\\n :return: 当前语言字典的深拷贝。\\n :rtype: Optional[Dict[str, str]]\\n \\\"\\\"\\\"\\n try:\\n return copy.deepcopy(self._lang_dict)\\n except Exception:\\n logger.exception(\\\"Failed to retrieve language dictionary.\\\")\\n return None\\n\\n def update_lang(self, new_lang: str) -> None:\\n \\\"\\\"\\\"\\n 更新当前使用的语言字典。\\n\\n :param new_lang: 新语言的标识符。\\n :type new_lang: str\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n try:\\n self._lang_dict = LANG_DICTS.get(new_lang, \\\"English\\\")\\n self.lang_updated.emit()\\n logger.info(f\\\"Language changed to {new_lang}\\\")\\n except Exception:\\n logger.exception(f\\\"Failed to changed language to {new_lang}\\\")\"\n },\n {\n \"identifier\": \"message_show\",\n \"path\": \"ui/message_show.py\",\n \"snippet\": \"def message_show(message_type: str,\\n text: str) -> None:\\n \\\"\\\"\\\"\\n 显示指定类型的消息框。\\n\\n 根据提供的消息类型和文本内容，显示相应的消息框。支持的消息类型包括 'Critical'、'Warning' 和 'Information'。\\n\\n :param message_type: 消息类型，支持 'Critical'、'Warning' 和 'Information'。\\n :type message_type: str\\n :param text: 消息框中显示的文本内容。\\n :type text: str\\n :return: 无返回值。\\n :rtype: None\\n \\\"\\\"\\\"\\n try:\\n msg_box = QMessageBox()\\n msg_box.setText(text)\\n msg_box.setStandardButtons(QMessageBox.Ok)\\n msg_box.setWindowTitle(message_type)\\n\\n if message_type == 'Critical':\\n msg_box.setIcon(QMessageBox.Critical)\\n msg_box.setWindowIcon(QIcon(get_resource_path('media/icons8-error-26')))\\n elif message_type == 'Warning':\\n msg_box.setIcon(QMessageBox.Warning)\\n msg_box.setWindowIcon(QIcon(get_resource_path('media/icons8-do-not-disturb-26')))\\n elif message_type == 'Information':\\n msg_box.setIcon(QMessageBox.Information)\\n msg_box.setWindowIcon(QIcon(get_resource_path('media/icons8-about-26')))\\n else:\\n logger.warning(\\\"Invalid message type provided.\\\")\\n\\n msg_box.exec_()\\n except Exception:\\n logger.exception(\\\"An error occurred while displaying the message box\\\")\"\n },\n {\n \"identifier\": \"TableMain\",\n \"path\": \"ui/table_main.py\",\n \"snippet\": \"class TableMain(QTableWidget):\\n \\\"\\\"\\\"\\n 主表格类，用于展示和管理数据行。\\n\\n 此类继承自 PyQt5 的 QTableWidget，提供了丰富的数据展示和管理功能。包括但不限于数据的展示、行的颜色标记、右键菜单功能以及快捷键支持。\\n 通过与 LangManager 和 ConfigManager 的集成，支持动态语言切换和配置管理。\\n\\n :param lang_manager: 用于管理界面语言的 LangManager 实例。\\n :type lang_manager: LangManager\\n :param config_manager: 用于管理配置的 ConfigManager 实例。\\n :type config_manager: ConfigManager\\n\\n :author: assassing\\n :contact: https://github.com/hxz393\\n :copyright: Copyright 2023, hxz393. 保留所有权利。\\n \\\"\\\"\\\"\\n status_updated = pyqtSignal(str)\\n filter_updated = pyqtSignal(list)\\n\\n def __init__(self,\\n lang_manager: LangManager,\\n config_manager: ConfigManager):\\n super().__init__()\\n self.lang_manager = lang_manager\\n self.lang_manager.lang_updated.connect(self.update_lang)\\n self.config_manager = config_manager\\n # 实例化用到的组件\\n self.actionCopy = ActionCopy(self.lang_manager, self)\\n self.actionSave = ActionSave(self.lang_manager, self)\\n self.actionSkip = ActionSkip(self.lang_manager, self.config_manager, self)\\n self.actionUnskip = ActionUnskip(self.lang_manager, self.config_manager, self)\\n # 手动连接实例化的组件信号到转发函数\\n self.actionCopy.status_updated.connect(self.forward_status)\\n self.actionSave.status_updated.connect(self.forward_status)\\n self.actionSkip.status_updated.connect(self.forward_status)\\n self.actionSkip.filter_updated.connect(self.forward_filter)\\n self.actionUnskip.status_updated.connect(self.forward_status)\\n self.actionUnskip.filter_updated.connect(self.forward_filter)\\n self.initUI()\\n\\n def initUI(self) -> None:\\n \\\"\\\"\\\"\\n 初始化用户界面。\\n\\n 此方法负责设置表格的基本属性，如列数、表头标签、选择行为等。还包括对特定列的隐藏和宽度调整策略的设置。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n # 先运行语言更新，里面有表头定义\\n self.update_lang()\\n self.hidden_cols = [\\\"pro_time\\\", \\\"pre_time\\\", \\\"test_time\\\", \\\"dev_time\\\"]\\n self.resize_cols = [\\\"name\\\", \\\"group\\\", \\\"consistency\\\", \\\"skip\\\"]\\n # 配置表格基本属性\\n self.setColumnCount(len(self.column_headers))\\n self.setHorizontalHeaderLabels(self.column_headers)\\n self.setEditTriggers(QTableWidget.NoEditTriggers)\\n self.setSelectionBehavior(QTableWidget.SelectItems)\\n # 隐藏垂直表头\\n self.verticalHeader().setVisible(False)\\n # 启用自动换行，没生效\\n self.setWordWrap(True)\\n self.setTextElideMode(Qt.ElideNone)\\n # 为表头视图设置上下文菜单事件\\n self.horizontalHeader().setContextMenuPolicy(Qt.CustomContextMenu)\\n self.horizontalHeader().customContextMenuRequested.connect(self._header_context_menu)\\n # 为表单设置上下文菜单事件\\n self.setContextMenuPolicy(Qt.CustomContextMenu)\\n self.customContextMenuRequested.connect(self._cell_context_menu)\\n # 隐藏指定列\\n [self.hideColumn(COL_INFO[i]['col']) for i in self.hidden_cols]\\n # 设置表宽度策略\\n self.set_header_resize()\\n\\n def set_header_resize(self):\\n \\\"\\\"\\\"\\n 设置表头的列宽度和调整策略。\\n\\n 此方法负责定义表头列的宽度调整策略和其他相关属性。它设置了表头列的默认宽度、是否可拖动以及列的自动调整策略。\\n 例如，某些列被设置为根据内容自动调整宽度，而其他列则被设置为可伸缩以适应表格的大小。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n # 设置默认列宽度，列宽调整策略，列可拖动\\n self.horizontalHeader().setSectionsMovable(True)\\n self.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)\\n self.horizontalHeader().setMinimumSectionSize(100)\\n # 设置要自动调整宽度的列\\n [self.horizontalHeader().setSectionResizeMode(COL_INFO[i]['col'], QHeaderView.ResizeToContents) for i in self.resize_cols]\\n\\n def update_lang(self) -> None:\\n \\\"\\\"\\\"\\n 更新界面语言设置。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n self.lang = self.lang_manager.get_lang()\\n self.column_headers = [\\n self.lang['ui.table_main_1'],\\n self.lang['ui.table_main_2'],\\n self.lang['ui.table_main_3'],\\n self.lang['ui.dialog_settings_connection_2'],\\n f\\\"{self.lang['ui.dialog_settings_connection_2']} {self.lang['ui.table_main_4']}\\\",\\n self.lang['ui.dialog_settings_connection_3'],\\n f\\\"{self.lang['ui.dialog_settings_connection_3']} {self.lang['ui.table_main_4']}\\\",\\n self.lang['ui.dialog_settings_connection_4'],\\n f\\\"{self.lang['ui.dialog_settings_connection_4']} {self.lang['ui.table_main_4']}\\\",\\n self.lang['ui.dialog_settings_connection_5'],\\n f\\\"{self.lang['ui.dialog_settings_connection_5']} {self.lang['ui.table_main_4']}\\\",\\n self.lang['ui.table_main_5'],\\n self.lang['ui.table_main_6'],\\n ]\\n # 重新应用到表头\\n self.setHorizontalHeaderLabels(self.column_headers)\\n # 定义数据和显示映射的字典\\n consistency_status_mapping = {\\n \\\"inconsistent\\\": self.lang['ui.action_start_8'],\\n \\\"fully\\\": self.lang['ui.action_start_9'],\\n \\\"partially\\\": self.lang['ui.action_start_10'],\\n \\\"unknown\\\": self.lang['ui.action_start_13'],\\n }\\n skip_status_mapping = {\\n \\\"no\\\": self.lang['ui.action_start_11'],\\n \\\"yes\\\": self.lang['ui.action_start_12'],\\n \\\"unknown\\\": self.lang['ui.action_start_13'],\\n }\\n for row in range(self.rowCount()):\\n # 更新忽略状态文字\\n self._update_item_text(row, \\\"skip\\\", skip_status_mapping)\\n # 更新一致性状态文字\\n self._update_item_text(row, \\\"consistency\\\", consistency_status_mapping)\\n\\n def _update_item_text(self,\\n row: int,\\n user_data_key: str,\\n text_mapping: Dict[str, str]) -> None:\\n \\\"\\\"\\\"\\n 根据提供的文本映射更新指定行的项文本。\\n\\n 此方法用于更新表格或列表中特定行的文本。它根据用户数据键（user_data_key）获取对应行的项，然后根据提供的文本映射（text_mapping）更新该项的文本。\\n\\n :param row: 要更新的行索引。\\n :type row: int\\n :param user_data_key: 用于获取项的用户数据键。\\n :type user_data_key: str\\n :param text_mapping: 用户数据到文本的映射字典。\\n :type text_mapping: Dict[str, str]\\n\\n :return: 无返回值。\\n :rtype: None\\n \\\"\\\"\\\"\\n item = self.item(row, COL_INFO[user_data_key]['col'])\\n if item is not None:\\n user_data = item.data(Qt.UserRole)\\n if user_data in text_mapping:\\n item.setText(text_mapping[user_data])\\n\\n def keyPressEvent(self, event: QKeyEvent) -> None:\\n \\\"\\\"\\\"\\n 处理键盘事件。\\n\\n 此方法用于处理键盘事件，特别是复制功能的快捷键。如果按下 Ctrl+C，则复制选中的单元格内容。\\n\\n :param event: 键盘事件对象。\\n :type event: QKeyEvent\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n if event.key() == Qt.Key_C and (event.modifiers() & Qt.ControlModifier):\\n self.actionCopy.action_copy()\\n else:\\n super().keyPressEvent(event)\\n\\n def _cell_context_menu(self, pos: QPoint) -> None:\\n \\\"\\\"\\\"\\n 实现表格单元格的右键菜单功能。\\n\\n :param pos: 右键点击的位置。\\n :type pos: QPoint\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n menu = QMenu(self)\\n menu.addAction(self.actionCopy.action_copy)\\n separator = QAction(menu)\\n separator.setSeparator(True)\\n menu.addAction(separator)\\n menu.addAction(self.actionSkip.action_skip)\\n menu.addAction(self.actionUnskip.action_unskip)\\n sep = QAction(menu)\\n sep.setSeparator(True)\\n menu.addAction(sep)\\n menu.addAction(self.actionSave.action_save)\\n menu.exec_(self.viewport().mapToGlobal(pos))\\n\\n def _header_context_menu(self, pos: QPoint) -> None:\\n \\\"\\\"\\\"\\n 实现表头的右键菜单功能。\\n\\n :param pos: 右键点击的位置。\\n :type pos: QPoint\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n menu = QMenu(self)\\n # 动态创建一个菜单项，用于隐藏/显示列\\n for index in range(self.columnCount()):\\n column_name = self.horizontalHeaderItem(index).text()\\n action = menu.addAction(f\\\"{column_name}\\\")\\n action.setCheckable(True)\\n action.setChecked(not self.isColumnHidden(index))\\n action.setData(index)\\n action.triggered.connect(self._toggle_column_visibility)\\n # 在鼠标右键点击位置显示菜单\\n menu.exec_(self.horizontalHeader().viewport().mapToGlobal(pos))\\n\\n def _toggle_column_visibility(self) -> None:\\n \\\"\\\"\\\"\\n 根据用户选择，切换列的可见性。\\n\\n 此方法用于根据用户在上下文菜单中的选择，显示或隐藏特定的列。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n action = self.sender()\\n if isinstance(action, QAction):\\n column_index = action.data()\\n if action.isChecked():\\n self.showColumn(column_index)\\n else:\\n self.hideColumn(column_index)\\n\\n def add_row(self, data: List[List[str]]) -> None:\\n \\\"\\\"\\\"\\n 向表格中添加一行数据。\\n\\n :param data: 要添加的数据列表，每个元素是一个列表，第一个元素代表显示的字符串，第二个元素代表附加数据。\\n :type data: List[List[str]]\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n row_position = 0\\n try:\\n # 获取最后行数\\n row_position = self.rowCount()\\n # 插入最后一行\\n self.insertRow(row_position)\\n # 插入单元格数据\\n self._fill_row_data(row_position, data)\\n except Exception:\\n logger.exception(f\\\"Error occurred while adding a new row at position {row_position}\\\")\\n self.removeRow(row_position)\\n\\n def _fill_row_data(self,\\n row_position: int,\\n data: List[List[str]]) -> None:\\n \\\"\\\"\\\"\\n 填充指定行的数据。\\n\\n :param row_position: 行位置\\n :param data: 行数据\\n :type row_position: int\\n :type data: List[List[str]]\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n for column, (display_text, user_data) in enumerate(data):\\n # 默认设置显示字符串，也叫 Qt.DisplayRole。获取方法item.text() 或 item.data(Qt.DisplayRole)\\n item = QTableWidgetItem(str(display_text))\\n # 设置实际数据，也叫 Qt.UserRole。获取方法 item.data(Qt.UserRole)\\n item.setData(Qt.UserRole, user_data)\\n # 设置单元格不可编辑状态\\n item.setFlags(item.flags() & ~Qt.ItemIsEditable)\\n # 正常表格插入方法\\n self.setItem(row_position, column, item)\\n\\n @log_time\\n def apply_color_to_table(self, rows: List[int] = None) -> None:\\n \\\"\\\"\\\"\\n 对整个表格进行着色。通常只有初始化时才不带rows参数，以应用到整表。\\n\\n :param rows: 可选，要应用颜色的行号列表。\\n :type rows: List[int], optional\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n color_switch = self.config_manager.get_config_main().get('color_set', 'ON')\\n if color_switch == 'OFF':\\n return\\n\\n if rows is None or not isinstance(rows, list):\\n rows = range(self.rowCount())\\n\\n try:\\n for row in rows:\\n # 不给隐藏行设置颜色\\n if self.isRowHidden(row):\\n continue\\n\\n self._process_row_for_color(row)\\n except Exception:\\n logger.exception(\\\"Exception in apply_color_to_table method\\\")\\n self.status_updated.emit(self.lang['label_status_error'])\\n\\n def _process_row_for_color(self, row: int) -> None:\\n \\\"\\\"\\\"\\n 根据一致性、跳过状态和是否为空值给单行应用颜色。\\n\\n :param row: 行号，对每行进行颜色处理。\\n :type row: int\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n consistency_data = self.item(row, COL_INFO['consistency']['col']).data(Qt.UserRole)\\n skip_data = self.item(row, COL_INFO['skip']['col']).data(Qt.UserRole)\\n # 忽略状态为是时设置颜色\\n if skip_data == 'yes':\\n self.apply_color(row, COLOR_SKIP)\\n return\\n\\n # 根据一致性值设置颜色\\n if consistency_data == 'fully':\\n self.apply_color(row, COLOR_CONSISTENCY_FULLY)\\n elif consistency_data == 'partially':\\n self.apply_color(row, COLOR_CONSISTENCY_PARTIALLY)\\n else:\\n self.apply_color(row, COLOR_DEFAULT)\\n\\n # 遍历指定列检查空值，并赋予颜色\\n for column in range(self.columnCount()):\\n # 不给隐藏列设置颜色\\n if not self.isColumnHidden(column):\\n if self.item(row, column).text() == 'None':\\n self.apply_color(row, COLOR_EMPTY, column)\\n\\n def apply_color(self,\\n row: int,\\n color: str,\\n column: Optional[int] = None) -> None:\\n \\\"\\\"\\\"\\n 为指定的行或单元格应用颜色。\\n\\n :param row: 要着色的行索引。\\n :type row: int\\n :param color: 要应用的颜色。\\n :type color: str\\n :param column: 可选，指定要着色的列索引，如果未指定，则对整行应用颜色。\\n :type column: int, optional\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n try:\\n color_brush = QBrush(QColor(color))\\n if column is not None:\\n self.item(row, column).setBackground(color_brush)\\n else:\\n for col in range(self.columnCount()):\\n # 不给隐藏列设置颜色\\n if not self.isColumnHidden(col):\\n self.item(row, col).setBackground(color_brush)\\n except Exception:\\n logger.exception(\\\"Error occurred while applying color to a cell\\\")\\n self.status_updated.emit(self.lang['label_status_error'])\\n\\n def clear(self) -> None:\\n \\\"\\\"\\\"\\n 清空表格中的所有行。\\n\\n 此方法用于清除表格中的所有数据，通常在数据更新或重置时使用。\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n try:\\n # 禁用更新以提高性能\\n self.setUpdatesEnabled(False)\\n # 首先清除所有单元格的内容\\n self.clearContents()\\n # 将行数设置为0，从而删除所有行\\n self.setRowCount(0)\\n except Exception:\\n logger.exception(\\\"Error occurred while clearing the table.\\\")\\n self.status_updated.emit(self.lang['label_status_error'])\\n finally:\\n # 确保即使发生错误也要重新启用更新\\n self.setUpdatesEnabled(True)\\n\\n def forward_status(self, message: str) -> None:\\n \\\"\\\"\\\"\\n 用于转发状态信号。\\n\\n :param message: 要转发的消息。\\n :type message: str\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n self.status_updated.emit(message)\\n\\n def forward_filter(self, rows: List[int]) -> None:\\n \\\"\\\"\\\"\\n 用于转发过滤信号。\\n\\n :param rows: 要转发的行列表。\\n :type rows: List[int]\\n\\n :rtype: None\\n :return: 无返回值。\\n \\\"\\\"\\\"\\n self.filter_updated.emit(rows)\\n\\n def get_table_data(self) -> Dict[int, Dict[str, str]]:\\n \\\"\\\"\\\"\\n 用于获取表格所有数据。\\n\\n :rtype: Dict[int, Dict[str, str]]\\n :return: 返回嵌套字典。键为行号，值为字典，字典中键为列标题，值为内容。类似于：{882: {'服务': 'web', '分组': 'application'}, 883: {'服务': 'web', '分组': 'application'}}\\n \\\"\\\"\\\"\\n return {row: {self.horizontalHeaderItem(col).text(): self.item(row, col).data(Qt.UserRole)\\n for col in range(self.columnCount())}\\n for row in range(self.rowCount())}\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import logging\nfrom typing import Dict, List\nfrom PyQt5.QtCore import Qt, QThread, pyqtSignal, QObject\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5.QtWidgets import QAction, QHeaderView\nfrom config.settings import COL_INFO\nfrom lib.get_resource_path import get_resource_path\nfrom module.execute_queries import execute_queries\nfrom ui.config_manager import ConfigManager\nfrom ui.filter_bar import FilterBar\nfrom ui.lang_manager import LangManager\nfrom ui.message_show import message_show\nfrom ui.table_main import TableMain"},"token_num":{"kind":"number","value":15024,"string":"15,024"},"cropped_code":{"kind":"string","value":"\n def initialize(self) -> None:\n \"\"\"\n 初始化界面和状态，在开始操作前执行。\n\n 此方法用于设置 UI 元素的初始状态，如禁用按钮、清空表格等。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n logger.info('Start running')\n # 状态栏发送提示消息\n self.status_updated.emit(self.lang['ui.action_start_3'])\n # 开始按钮不可点击\n self.action_start.setEnabled(False)\n # 禁用表格排序\n self.table.setSortingEnabled(False)\n # 禁用表格更新\n self.table.setUpdatesEnabled(False)\n # 禁用过滤栏组件\n self.filter_bar.filter_app_box.setEnabled(False)\n self.filter_bar.filter_table_box.setEnabled(False)\n self.filter_bar.filter_table_check_box.setEnabled(False)\n self.filter_bar.filter_value_box.setEnabled(False)\n self.filter_bar.filter_value_button.setEnabled(False)\n self.filter_bar.filter_reset_button.setEnabled(False)\n # 清空表格数据\n self.table.clear()\n # 初始化表宽\n self.table.set_header_resize()\n logger.debug('Initialization finished')\n\n def table_insert(self, table_rows: List[List[List[str]]]) -> None:\n \"\"\"\n 将查询结果插入到主表格中。\n\n 此方法接收查询结果作为输入，并将其格式化后插入到应用程序的主表格中。每个元素是一个三重列表，表示表格的一行数据。\n\n :param table_rows: 待插入的表格数据，每个元素代表一行数据。\n :type table_rows: List[List[List[str]]]\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n for row in table_rows:\n self.table.add_row(row)\n logger.debug('Table filling finished.')\n\n def table_column_hide(self, query_statuses: Dict[str, bool]) -> None:\n \"\"\"\n 根据查询状态决定是否隐藏表格的某些列。\n\n :param query_statuses: 各查询状态的字典，键为环境名，值为布尔值指示是否开启。\n :type query_statuses: Dict[str, bool]\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # env_name类似'PRO_CONFIG'，COL_INFO中的键类似'pro_value'，env_switch是布尔值。\n for env_name, env_switch in query_statuses.items():\n # 建立env_name和COL_INFO中的键的映射\n column_name_mapping = {'PRO_CONFIG': 'pro_value', 'PRE_CONFIG': 'pre_value', 'TEST_CONFIG': 'test_value', 'DEV_CONFIG': 'dev_value'}\n # 获取列序号\n col = COL_INFO[column_name_mapping[env_name]]['col']\n # 根据环境开关，决定列是否隐藏。\n self.table.showColumn(col) if env_switch else self.table.hideColumn(col)\n\n def finalize(self) -> None:\n \"\"\"\n 完成查询后的收尾工作，包括重新启用表格排序和更新等。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # 先应用颜色和过滤器\n self.table.apply_color_to_table()\n self.filter_bar.filter_table()\n # 启动排序\n self.table.setSortingEnabled(True)\n # 默认按第一列升序排序\n self.table.sortByColumn(0, Qt.AscendingOrder)\n # 允许用户调整列宽\n self.table.horizontalHeader().setSectionResizeMode(QHeaderView.Interactive)\n # 更新过滤器，过滤服务中插入值\n self.filter_bar.filter_options_add()\n # 调用过滤器\n self.filter_bar.highlight_rows.clear()\n self.filter_bar.filter_table()\n # 启用表格更新\n self.table.setUpdatesEnabled(True)\n # 启用过滤栏组件\n self.filter_bar.filter_app_box.setEnabled(True)\n self.filter_bar.filter_table_box.setEnabled(True)\n self.filter_bar.filter_table_check_box.setEnabled(True)\n self.filter_bar.filter_value_box.setEnabled(True)\n self.filter_bar.filter_value_button.setEnabled(True)\n self.filter_bar.filter_reset_button.setEnabled(True)\n\n def show_result_message(self, result: str) -> None:\n \"\"\"\n 显示结果消息。\n\n 根据运行结果，显示不同的状态消息或错误信息。\n\n :param result: 运行结果的描述。\n :type result: str\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n self.action_start.setEnabled(True)\n if result == 'done':\n logger.info('Run Completed')\n else:\n message = {\n 'no query result': ('Warning', self.lang['ui.action_start_4']),\n 'prepare table rows failed': ('Warning', self.lang['ui.action_start_6']),\n 'run error': ('Critical', self.lang['ui.action_start_7'])\n }.get(result)\n if message:\n"},"all_code":{"kind":"string","value":"\"\"\"\n提供应用程序的主要功能，包括用户界面初始化、数据库查询执行、数据展示和处理。\n\n本模块中包含的类负责应用程序的主要操作流程，如用户界面的初始化、按钮动作的处理、后台数据查询、数据展示等。主要类包括`ActionStart`和`StartWork`，分别负责处理用户界面动作和执行后台工作。\n\n:author: assassing\n:contact: https://github.com/hxz393\n:copyright: Copyright 2023, hxz393. 保留所有权利。\n\"\"\"\n\n\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass ActionStart(QObject):\n \"\"\"\n 负责处理用户界面动作，例如初始化界面、响应按钮点击等。\n\n 此类包含了界面的主要动作逻辑，如开始按钮的点击处理、用户界面语言的更新、表格的数据填充等。它与后台线程`StartWork`协作，实现数据的查询和展示。\n\n :param lang_manager: 语言管理器，用于界面语言的加载和更新。\n :param config_manager: 配置管理器，提供应用程序的配置信息。\n :param table: 主表格界面，用于数据的展示。\n :param filter_bar: 过滤条，用于数据的筛选。\n :type lang_manager: LangManager\n :type config_manager: ConfigManager\n :type table: TableMain\n :type filter_bar: FilterBar\n \"\"\"\n status_updated = pyqtSignal(str)\n\n def __init__(self,\n lang_manager: LangManager,\n config_manager: ConfigManager,\n table: TableMain,\n filter_bar: FilterBar):\n super().__init__()\n # 实例化组件\n self.lang_manager = lang_manager\n self.lang_manager.lang_updated.connect(self.update_lang)\n self.config_manager = config_manager\n self.table = table\n self.filter_bar = filter_bar\n self.initUI()\n\n def initUI(self) -> None:\n \"\"\"\n 初始化用户界面。\n\n 创建并配置界面中的开始动作按钮，包括图标、快捷键和触发事件。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n self.action_start = QAction(QIcon(get_resource_path('media/icons8-start-26.png')), 'Start')\n self.action_start.setShortcut('F10')\n self.action_start.triggered.connect(self.start)\n self.update_lang()\n\n def update_lang(self) -> None:\n \"\"\"\n 更新界面语言设置。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n self.lang = self.lang_manager.get_lang()\n self.action_start.setText(self.lang['ui.action_start_1'])\n self.action_start.setStatusTip(self.lang['ui.action_start_2'])\n\n def start(self) -> None:\n \"\"\"\n 启动更新动作的处理流程。\n\n 此方法负责初始化和启动一个后台线程 `StartWork`，该线程执行数据查询和表格更新。同时，该方法还负责连接信号和槽以进行 UI 更新。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n try:\n # 初始化子线程，传入语言字典和配置\n self.start_work = StartWork(self.lang, self.config_manager)\n # 连接信号槽，都是 UI 操作，必须主线程中进行\n self.start_work.initialize_signal.connect(self.initialize)\n self.start_work.table_insert_signal.connect(self.table_insert)\n self.start_work.table_column_hide_signal.connect(self.table_column_hide)\n self.start_work.finalize_signal.connect(self.finalize)\n self.start_work.message.connect(self.show_result_message)\n # 开始运行\n self.start_work.start()\n except Exception:\n logger.exception('Failed to initiate start action.')\n self.status_updated.emit(self.lang['label_status_error'])\n\n def initialize(self) -> None:\n \"\"\"\n 初始化界面和状态，在开始操作前执行。\n\n 此方法用于设置 UI 元素的初始状态，如禁用按钮、清空表格等。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n logger.info('Start running')\n # 状态栏发送提示消息\n self.status_updated.emit(self.lang['ui.action_start_3'])\n # 开始按钮不可点击\n self.action_start.setEnabled(False)\n # 禁用表格排序\n self.table.setSortingEnabled(False)\n # 禁用表格更新\n self.table.setUpdatesEnabled(False)\n # 禁用过滤栏组件\n self.filter_bar.filter_app_box.setEnabled(False)\n self.filter_bar.filter_table_box.setEnabled(False)\n self.filter_bar.filter_table_check_box.setEnabled(False)\n self.filter_bar.filter_value_box.setEnabled(False)\n self.filter_bar.filter_value_button.setEnabled(False)\n self.filter_bar.filter_reset_button.setEnabled(False)\n # 清空表格数据\n self.table.clear()\n # 初始化表宽\n self.table.set_header_resize()\n logger.debug('Initialization finished')\n\n def table_insert(self, table_rows: List[List[List[str]]]) -> None:\n \"\"\"\n 将查询结果插入到主表格中。\n\n 此方法接收查询结果作为输入，并将其格式化后插入到应用程序的主表格中。每个元素是一个三重列表，表示表格的一行数据。\n\n :param table_rows: 待插入的表格数据，每个元素代表一行数据。\n :type table_rows: List[List[List[str]]]\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n for row in table_rows:\n self.table.add_row(row)\n logger.debug('Table filling finished.')\n\n def table_column_hide(self, query_statuses: Dict[str, bool]) -> None:\n \"\"\"\n 根据查询状态决定是否隐藏表格的某些列。\n\n :param query_statuses: 各查询状态的字典，键为环境名，值为布尔值指示是否开启。\n :type query_statuses: Dict[str, bool]\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # env_name类似'PRO_CONFIG'，COL_INFO中的键类似'pro_value'，env_switch是布尔值。\n for env_name, env_switch in query_statuses.items():\n # 建立env_name和COL_INFO中的键的映射\n column_name_mapping = {'PRO_CONFIG': 'pro_value', 'PRE_CONFIG': 'pre_value', 'TEST_CONFIG': 'test_value', 'DEV_CONFIG': 'dev_value'}\n # 获取列序号\n col = COL_INFO[column_name_mapping[env_name]]['col']\n # 根据环境开关，决定列是否隐藏。\n self.table.showColumn(col) if env_switch else self.table.hideColumn(col)\n\n def finalize(self) -> None:\n \"\"\"\n 完成查询后的收尾工作，包括重新启用表格排序和更新等。\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n # 先应用颜色和过滤器\n self.table.apply_color_to_table()\n self.filter_bar.filter_table()\n # 启动排序\n self.table.setSortingEnabled(True)\n # 默认按第一列升序排序\n self.table.sortByColumn(0, Qt.AscendingOrder)\n # 允许用户调整列宽\n self.table.horizontalHeader().setSectionResizeMode(QHeaderView.Interactive)\n # 更新过滤器，过滤服务中插入值\n self.filter_bar.filter_options_add()\n # 调用过滤器\n self.filter_bar.highlight_rows.clear()\n self.filter_bar.filter_table()\n # 启用表格更新\n self.table.setUpdatesEnabled(True)\n # 启用过滤栏组件\n self.filter_bar.filter_app_box.setEnabled(True)\n self.filter_bar.filter_table_box.setEnabled(True)\n self.filter_bar.filter_table_check_box.setEnabled(True)\n self.filter_bar.filter_value_box.setEnabled(True)\n self.filter_bar.filter_value_button.setEnabled(True)\n self.filter_bar.filter_reset_button.setEnabled(True)\n\n def show_result_message(self, result: str) -> None:\n \"\"\"\n 显示结果消息。\n\n 根据运行结果，显示不同的状态消息或错误信息。\n\n :param result: 运行结果的描述。\n :type result: str\n\n :rtype: None\n :return: 无返回值。\n \"\"\"\n self.action_start.setEnabled(True)\n if result == 'done':\n logger.info('Run Completed')\n else:\n message = {\n 'no query result': ('Warning', self.lang['ui.action_start_4']),\n 'prepare table rows failed': ('Warning', self.lang['ui.action_start_6']),\n 'run error': ('Critical', self.lang['ui.action_start_7'])\n }.get(result)\n if message:"},"next_line":{"kind":"string","value":" message_show(*message)"},"gold_snippet_index":{"kind":"number","value":6,"string":"6"},"created_at":{"kind":"string","value":"2023-11-07 01:02:38+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5271,"cells":{"repo_name":{"kind":"string","value":"google-research/semivl"},"file_path":{"kind":"string","value":"model/builder.py"},"context":{"kind":"list like","value":[{"identifier":"TIMMVisionTransformer","path":"model/backbone/timm_vit.py","snippet":"class TIMMVisionTransformer(nn.Module):\n\n def __init__(\n self,\n variant,\n timm_load_pretrained,\n drop_path_rate,\n img_size,\n out_indices,\n ):\n super(TIMMVisionTransformer, self).__init__()\n self.m = timm.create_model(\n variant,\n pretrained=timm_load_pretrained,\n drop_path_rate=drop_path_rate,\n img_size=img_size,\n )\n self.patch_size = self.m.patch_embed.patch_size\n self.img_size = img_size\n self.out_indices = out_indices\n assert max(self.out_indices) <= 11\n\n def forward_features(self, x):\n feats = []\n x = self.m.patch_embed(x)\n x = self.m._pos_embed(x)\n if self.m.grad_checkpointing and not torch.jit.is_scripting():\n raise ValueError(self.m.grad_checkpointing)\n # x = checkpoint_seq(self.blocks, x)\n else:\n for i, block in enumerate(self.m.blocks):\n x = block(x)\n if i in self.out_indices:\n out = self.m.norm(x)\n feats.append(out)\n x = self.m.norm(x)\n return x, feats\n\n def forward(self, x: torch.Tensor):\n if x.shape[-2] != self.m.patch_embed.img_size[0] or x.shape[-1] != self.m.patch_embed.img_size[1]:\n assert not self.training\n x = F.interpolate(x, size=self.img_size, mode='bilinear', align_corners=False)\n B, _, H, W = x.shape\n H = H // self.patch_size[0]\n W = W // self.patch_size[1]\n\n x, feats = self.forward_features(x)\n outs = [\n tuple([f[:, 1:].reshape(B, H, W, -1).permute(0, 3, 1, 2) for f in feats]),\n x[:, 0], # cls_token\n ]\n\n return outs"},{"identifier":"DLV3PHead","path":"model/decode_heads/dlv3p_head.py","snippet":"class DLV3PHead(BaseDecodeHead):\n\n def __init__(self, c1_in_channels, c1_channels, dilations, img_size, **kwargs):\n super(DLV3PHead, self).__init__(**kwargs)\n self.image_size = img_size\n self.aspp = ASPPModule(self.in_channels, dilations)\n self.c1_proj = nn.Sequential(\n nn.Conv2d(c1_in_channels, c1_channels, 1, bias=False),\n nn.BatchNorm2d(c1_channels),\n nn.ReLU(True))\n fuse_channels = self.in_channels // 8 + c1_channels\n self.head = nn.Sequential(\n nn.Conv2d(fuse_channels, 256, 3, padding=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(True),\n nn.Conv2d(256, 256, 3, padding=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(True),\n nn.Conv2d(256, self.num_classes, 1, bias=True))\n self.conv_seg = None\n\n def forward(self, inputs, force_output_pred_masks=False):\n if force_output_pred_masks:\n inputs = inputs[0][0]\n assert len(inputs) == 2\n c1, c4 = inputs[0], inputs[1]\n\n c4 = self.aspp(c4)\n c1 = self.c1_proj(c1)\n c4 = F.interpolate(c4, size=c1.shape[-2:], mode=\"bilinear\", align_corners=self.align_corners)\n x = torch.cat([c1, c4], dim=1)\n out = self.head(x)\n\n if force_output_pred_masks:\n out = F.interpolate(out, size=(self.image_size, self.image_size),\n mode='bilinear', align_corners=self.align_corners)\n out = {\"pred_masks\": out}\n\n return out"},{"identifier":"VLGHead","path":"model/decode_heads/vlg_head.py","snippet":"class VLGHead(nn.Module):\n def __init__(self,\n img_size,\n num_classes,\n text_in_channels,\n text_channels,\n up_channels,\n skip_in_channels,\n skip_channels,\n skip_from_conv_feat,\n num_layers,\n num_heads,\n channels,\n pool_size,\n conv1_ksize,\n loss_decode,\n align_corners,\n ) -> None:\n super().__init__()\n self.image_size = img_size\n self.num_classes = num_classes\n self.align_corners = align_corners\n self.text_in_channels = text_in_channels\n self.num_layers = num_layers\n self.channels = channels\n self.skip_from_conv_feat = skip_from_conv_feat\n assert loss_decode is None\n\n self.conv1 = nn.Conv2d(1, channels, kernel_size=conv1_ksize, stride=1, padding=(conv1_ksize-1)//2)\n self.aspp = ASPPModule(channels)\n self.layers = nn.ModuleList([\n SemanticTransformer(\n channels=channels, text_channels=text_channels, num_heads=num_heads, pool_size=pool_size\n ) for _ in range(num_layers)\n ])\n\n self.text_proj = nn.Sequential(\n nn.Linear(text_in_channels, text_channels),\n nn.ReLU())\n\n self.skip_proj = nn.ModuleList([\n nn.Sequential(\n nn.Conv2d(sic, sc, kernel_size=3, stride=1, padding=1),\n nn.ReLU(),\n ) for sic, sc in zip(skip_in_channels, skip_channels)\n ])\n\n self.up1 = Up(channels, up_channels[0], skip_channels[0])\n self.up2 = Up(up_channels[0], up_channels[1], skip_channels[1])\n self.head = nn.Conv2d(up_channels[1], 1, kernel_size=3, stride=1, padding=1)\n\n def forward(self, inputs, force_output_pred_masks=False):\n inputs_both = inputs\n img_feat_pyramid = inputs_both[0][0]\n img_feats = img_feat_pyramid[-1]\n if self.skip_from_conv_feat:\n conv_feats = inputs_both[2]\n if len(img_feat_pyramid) > 1:\n skip_feats = [\n *img_feat_pyramid[:-1][::-1],\n *conv_feats[::-1],\n ]\n else:\n skip_feats = conv_feats[::-1]\n assert len(self.skip_proj) == len(skip_feats)\n else:\n skip_feats = img_feat_pyramid[:-1][::-1]\n text_feats = inputs_both[1]\n\n text_feats = text_feats.repeat(img_feats.shape[0], 1, 1).float()\n B, C, H, W = img_feats.shape\n assert list(text_feats.shape) == [B, self.num_classes, C]\n\n # Compute Similarity Map\n img_feats = F.normalize(img_feats, dim=1)\n text_feats = F.normalize(text_feats, dim=-1)\n x = torch.einsum('bchw, bnc -> bnhw', img_feats, text_feats)\n\n # Spatial Reasoning\n x = rearrange(x, 'b n h w -> (b n) () h w')\n x = self.conv1(x)\n x = self.aspp(x)\n x = rearrange(x, '(b n) c h w -> b c n h w', b=B)\n\n # Semantic Reasoning\n if self.text_proj is not None:\n text_feats = self.text_proj(text_feats)\n\n for layer in self.layers:\n x = layer(x, text_feats)\n\n # Upsampling\n if self.skip_proj is not None:\n skip_feats = [proj(f) for proj, f in zip(self.skip_proj, skip_feats)]\n\n x = rearrange(x, 'b c n h w -> (b n) c h w')\n x = self.up1(x, skip_feats[0])\n x = self.up2(x, skip_feats[1])\n x = self.head(x)\n x = rearrange(x, '(b n) () h w -> b n h w', b=B)\n\n if x.shape[1] != self.num_classes:\n cls2con = get_class_to_concept_idxs(self.load_text_embedding)\n x = aggregate_concept_predictions(x, cls2con)\n\n if force_output_pred_masks:\n x = F.interpolate(x, size=(self.image_size, self.image_size),\n mode='bilinear', align_corners=self.align_corners)\n x = {\"pred_masks\": x}\n\n return x"},{"identifier":"VLM","path":"model/vlm.py","snippet":"class VLM(EncoderDecoder):\n def __init__(self,\n freeze_backbone=False,\n exclude_keys=None,\n load_text_embedding=None,\n load_mcc_text_embedding=None,\n load_pl_text_embedding=None,\n clip_encoder=None,\n conv_encoder=None,\n maskclip_class_filter=None,\n maskclip_trust_head=None,\n renorm_clip_img=False,\n **args):\n super(VLM, self).__init__(**args)\n assert load_text_embedding == load_pl_text_embedding\n assert maskclip_class_filter is None\n assert maskclip_trust_head is None\n self.local_iter = 0\n\n self.clip_encoder = None\n if clip_encoder is not None:\n self.clip_encoder = builder.build_backbone(clip_encoder)\n self.conv_encoder = None\n if conv_encoder is not None:\n self.conv_encoder = builder.build_backbone(conv_encoder)\n\n self.load_text_embedding = load_text_embedding\n self.decode_head.load_text_embedding = load_text_embedding\n self.load_mcc_text_embedding = load_mcc_text_embedding\n self.renorm_clip_img = renorm_clip_img\n if renorm_clip_img:\n print('Renormalize clip image.')\n if self.load_mcc_text_embedding:\n self.loaded_mcc_text_feat = np.load(self.load_mcc_text_embedding)\n self.loaded_mcc_text_feat = torch.from_numpy(self.loaded_mcc_text_feat).float()\n else:\n raise NotImplementedError\n\n if freeze_backbone:\n self.freeze(self.backbone, exclude_keys=exclude_keys)\n\n def renormalize_img_for_clip(self, img):\n if not self.renorm_clip_img:\n return img\n loader_mean, loader_std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n clip_mean, clip_std = [0.48145466, 0.4578275, 0.40821073], [0.26862954, 0.26130258, 0.27577711]\n loader_mean = torch.tensor(loader_mean, device=img.device).view(1, -1, 1, 1)\n loader_std = torch.tensor(loader_std, device=img.device).view(1, -1, 1, 1)\n clip_mean = torch.tensor(clip_mean, device=img.device).view(1, -1, 1, 1)\n clip_std = torch.tensor(clip_std, device=img.device).view(1, -1, 1, 1)\n return (img * loader_std + loader_mean - clip_mean) / clip_std\n\n def freeze(self, model, exclude_keys=None):\n for n, m in model.named_parameters():\n m.requires_grad = False\n if exclude_keys is not None:\n assert isinstance(exclude_keys, list)\n for k in exclude_keys:\n if str(k) in n:\n m.requires_grad = True\n print(f'Finetune {n}')\n \n def forward_maskclip(self, img, conf_tresh):\n img = self.renormalize_img_for_clip(img)\n self.clip_encoder.eval()\n with torch.no_grad():\n text_feat = self.loaded_mcc_text_feat.detach().to(img.device)\n visual_feat, _ = self.clip_encoder(img)\n visual_feat = visual_feat[-1]\n\n dense_pred = F.conv2d(visual_feat, text_feat[:, :, None, None])\n if dense_pred.shape[1] != self.num_classes:\n cls2con = get_class_to_concept_idxs(self.load_mcc_text_embedding)\n dense_pred = aggregate_concept_predictions(dense_pred, cls2con)\n assert dense_pred.shape[1] == self.num_classes\n dense_pred = F.interpolate(dense_pred, size=img.shape[-2:],\n mode='bilinear', align_corners=self.decode_head.align_corners)\n dense_pred = (100.0 * dense_pred).softmax(dim=1)\n dense_pred_certainty, dense_pred = dense_pred.max(dim=1)\n\n filtered_dense_pred = dense_pred.clone()\n filtered_dense_pred[dense_pred_certainty < conf_tresh] = 255\n return filtered_dense_pred\n\n def extract_feat(self, img):\n orig_img = img\n img = self.renormalize_img_for_clip(img)\n visual_feat = self.backbone(img)\n text_feat = np.load(self.load_text_embedding)\n text_feat = torch.from_numpy(text_feat).to(img.device)\n self.decode_head.load_text_embedding = self.load_text_embedding\n conv_feat = None\n if self.conv_encoder is not None:\n conv_feat = self.conv_encoder(orig_img)\n\n return [visual_feat, text_feat, conv_feat]\n\n def _decode_head_forward_test(self, x, img_metas):\n seg_logits = self.decode_head.forward(x, force_output_pred_masks=True)['pred_masks']\n return seg_logits"},{"identifier":"MaskClipVisionTransformer","path":"third_party/maskclip/models/backbones/maskclip_vit.py","snippet":"class MaskClipVisionTransformer(BaseModule):\n \"\"\"Vision Transformer.\n\n This backbone is the implementation of `An Image is Worth 16x16 Words:\n Transformers for Image Recognition at\n Scale <https://arxiv.org/abs/2010.11929>`_.\n\n Args:\n img_size (int | tuple): Input image size. Default: 224.\n patch_size (int): The patch size. Default: 16.\n in_channels (int): Number of input channels. Default: 3.\n embed_dims (int): embedding dimension. Default: 768.\n num_layers (int): depth of transformer. Default: 12.\n num_heads (int): number of attention heads. Default: 12.\n mlp_ratio (int): ratio of mlp hidden dim to embedding dim.\n Default: 4.\n out_indices (list | tuple | int): Output from which stages.\n Default: -1.\n qkv_bias (bool): enable bias for qkv if True. Default: True.\n drop_rate (float): Probability of an element to be zeroed.\n Default 0.0\n attn_drop_rate (float): The drop out rate for attention layer.\n Default 0.0\n drop_path_rate (float): stochastic depth rate. Default 0.0\n with_cls_token (bool): Whether concatenating class token into image\n tokens as transformer input. Default: True.\n output_cls_token (bool): Whether output the cls_token. If set True,\n `with_cls_token` must be True. Default: False.\n norm_cfg (dict): Config dict for normalization layer.\n Default: dict(type='LN')\n act_cfg (dict): The activation config for FFNs.\n Default: dict(type='GELU').\n patch_norm (bool): Whether to add a norm in PatchEmbed Block.\n Default: False.\n final_norm (bool): Whether to add a additional layer to normalize\n final feature map. Default: False.\n interpolate_mode (str): Select the interpolate mode for position\n embeding vector resize. Default: bicubic.\n num_fcs (int): The number of fully-connected layers for FFNs.\n Default: 2.\n norm_eval (bool): Whether to set norm layers to eval mode, namely,\n freeze running stats (mean and var). Note: Effect on Batch Norm\n and its variants only. Default: False.\n with_cp (bool): Use checkpoint or not. Using checkpoint will save\n some memory while slowing down the training speed. Default: False.\n pretrained (str, optional): model pretrained path. Default: None.\n init_cfg (dict or list[dict], optional): Initialization config dict.\n Default: None.\n \"\"\"\n\n def __init__(self,\n img_size=224,\n patch_size=16,\n patch_bias=True,\n in_channels=3,\n embed_dims=768,\n num_layers=12,\n num_heads=12,\n mlp_ratio=4,\n out_indices=-1,\n qkv_bias=True,\n drop_rate=0.,\n attn_drop_rate=0.,\n drop_path_rate=0.,\n with_cls_token=True,\n output_cls_token=False,\n norm_cfg=dict(type='LN'),\n act_cfg=dict(type='GELU'),\n patch_norm=False,\n pre_norm=False,\n final_norm=False,\n return_qkv=False,\n return_clip_embed=False,\n skip_last_attn=False,\n interpolate_mode='bicubic',\n num_fcs=2,\n norm_eval=False,\n with_cp=False,\n pretrained=None,\n num_prompt_tokens=None,\n lora_layers=[],\n lora_r=4,\n lora_scaling=1,\n lora_dropout=0,\n lora_targets='qkvo',\n init_cfg=None):\n super(MaskClipVisionTransformer, self).__init__(init_cfg=init_cfg)\n\n if isinstance(img_size, int):\n img_size = to_2tuple(img_size)\n elif isinstance(img_size, tuple):\n if len(img_size) == 1:\n img_size = to_2tuple(img_size[0])\n assert len(img_size) == 2, \\\n f'The size of image should have length 1 or 2, ' \\\n f'but got {len(img_size)}'\n\n if output_cls_token:\n assert with_cls_token is True, f'with_cls_token must be True if' \\\n f'set output_cls_token to True, but got {with_cls_token}'\n\n assert not (init_cfg and pretrained), \\\n 'init_cfg and pretrained cannot be set at the same time'\n if isinstance(pretrained, str):\n warnings.warn('DeprecationWarning: pretrained is deprecated, '\n 'please use \"init_cfg\" instead')\n self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)\n elif pretrained is not None:\n raise TypeError('pretrained must be a str or None')\n\n self.img_size = img_size\n self.patch_size = patch_size\n self.interpolate_mode = interpolate_mode\n self.norm_eval = norm_eval\n self.with_cp = with_cp\n self.pretrained = pretrained\n self.num_prompt_tokens = num_prompt_tokens\n\n self.patch_embed = PatchEmbed(\n in_channels=in_channels,\n embed_dims=embed_dims,\n conv_type='Conv2d',\n kernel_size=patch_size,\n stride=patch_size,\n padding='corner',\n bias=patch_bias,\n norm_cfg=norm_cfg if patch_norm else None,\n init_cfg=None,\n )\n\n num_patches = (img_size[0] // patch_size) * \\\n (img_size[1] // patch_size)\n\n self.with_cls_token = with_cls_token\n self.output_cls_token = output_cls_token\n self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))\n self.pos_embed = nn.Parameter(\n torch.zeros(1, num_patches + 1, embed_dims))\n self.drop_after_pos = nn.Dropout(p=drop_rate)\n\n if out_indices is None:\n self.out_indices = [num_layers]\n elif isinstance(out_indices, int):\n if out_indices == -1:\n out_indices = num_layers - 1\n self.out_indices = [out_indices]\n elif isinstance(out_indices, list) or isinstance(out_indices, tuple):\n self.out_indices = out_indices\n else:\n raise TypeError('out_indices must be type of int, list or tuple')\n\n dpr = [\n x.item() for x in torch.linspace(0, drop_path_rate, num_layers)\n ] # stochastic depth decay rule\n\n self.layers = ModuleList()\n for i in range(num_layers):\n self.layers.append(\n TransformerEncoderLayer(\n embed_dims=embed_dims,\n num_heads=num_heads,\n feedforward_channels=mlp_ratio * embed_dims,\n attn_drop_rate=attn_drop_rate,\n drop_rate=drop_rate,\n drop_path_rate=dpr[i],\n num_fcs=num_fcs,\n qkv_bias=qkv_bias,\n act_cfg=act_cfg,\n norm_cfg=norm_cfg,\n lora=(i in lora_layers),\n lora_r=lora_r,\n lora_scaling=lora_scaling,\n lora_dropout=lora_dropout,\n lora_targets=lora_targets,\n batch_first=True))\n\n self.pre_norm = pre_norm\n if pre_norm:\n self.norm0_name, norm0 = build_norm_layer(\n norm_cfg, embed_dims, postfix=0)\n self.add_module(self.norm0_name, norm0)\n\n self.final_norm = final_norm\n if final_norm:\n self.norm1_name, norm1 = build_norm_layer(\n norm_cfg, embed_dims, postfix=1)\n self.add_module(self.norm1_name, norm1)\n\n self.return_clip_embed = return_clip_embed\n if self.return_clip_embed:\n self.proj = nn.Conv2d(embed_dims, 512, 1, bias=False)\n self.add_module('proj', self.proj)\n\n self.return_qkv = [False] * num_layers\n if isinstance(return_qkv, bool):\n for out_i in self.out_indices:\n if out_i >= num_layers:\n continue\n self.return_qkv[out_i] = return_qkv\n elif isinstance(return_qkv, list) or isinstance(return_qkv, tuple):\n for i, out_i in enumerate(self.out_indices):\n if out_i >= num_layers:\n continue\n self.return_qkv[out_i] = return_qkv[i]\n else:\n raise TypeError('return_qkv must be type of bool, list or tuple')\n if self.return_clip_embed:\n self.return_qkv[num_layers - 1] = True\n\n self.skip_last_attn = skip_last_attn\n\n if self.num_prompt_tokens is not None:\n val = math.sqrt(6. / float(3 * reduce(mul, (patch_size, patch_size), 1) + embed_dims))\n\n self.deep_prompt_embeddings = nn.Parameter(torch.zeros(num_layers, self.num_prompt_tokens, embed_dims))\n nn.init.uniform_(self.deep_prompt_embeddings.data, -val, val)\n\n self.prompt_proj = nn.Linear(embed_dims, embed_dims)\n nn.init.kaiming_normal_(self.prompt_proj.weight, a=0, mode='fan_out')\n self.prompt_norm = LayerNorm(embed_dims, eps=1e-6)\n self.prompt_dropout = nn.Dropout(0.1)\n\n @property\n def norm0(self):\n return getattr(self, self.norm0_name)\n \n @property\n def norm1(self):\n return getattr(self, self.norm1_name)\n\n def init_weights(self):\n if (isinstance(self.init_cfg, dict)\n and self.init_cfg.get('type') == 'Pretrained'):\n logger = get_root_logger()\n checkpoint = _load_checkpoint(\n self.init_cfg['checkpoint'], logger=logger, map_location='cpu')\n\n if 'state_dict' in checkpoint:\n state_dict = checkpoint['state_dict']\n else:\n state_dict = checkpoint\n logger.info(msg='Remove backbone prefix from state_dict.')\n state_dict = {k.replace('backbone.', ''): v for k, v in state_dict.items()}\n\n if 'pos_embed' in state_dict.keys():\n if self.pos_embed.shape != state_dict['pos_embed'].shape:\n logger.info(msg=f'Resize the pos_embed shape from '\n f'{state_dict[\"pos_embed\"].shape} to '\n f'{self.pos_embed.shape}')\n h, w = self.img_size\n pos_size = int(\n math.sqrt(state_dict['pos_embed'].shape[1] - 1))\n state_dict['pos_embed'] = self.resize_pos_embed(\n state_dict['pos_embed'],\n (h // self.patch_size, w // self.patch_size),\n (pos_size, pos_size), self.interpolate_mode)\n\n if self.return_clip_embed:\n state_dict['proj.weight'] = state_dict['proj.weight'][:, :, None, None]\n else:\n state_dict.pop('proj.weight')\n\n print(self.load_state_dict(state_dict, False))\n elif self.init_cfg is not None:\n super(MaskClipVisionTransformer, self).init_weights()\n else:\n # We only implement the 'jax_impl' initialization implemented at\n # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501\n trunc_normal_(self.pos_embed, std=.02)\n trunc_normal_(self.cls_token, std=.02)\n for n, m in self.named_modules():\n if isinstance(m, nn.Linear):\n trunc_normal_(m.weight, std=.02)\n if m.bias is not None:\n if 'ffn' in n:\n nn.init.normal_(m.bias, mean=0., std=1e-6)\n else:\n nn.init.constant_(m.bias, 0)\n elif isinstance(m, nn.Conv2d):\n kaiming_init(m, mode='fan_in', bias=0.)\n elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):\n constant_init(m, val=1.0, bias=0.)\n\n def _pos_embeding(self, patched_img, hw_shape, pos_embed):\n \"\"\"Positiong embeding method.\n\n Resize the pos_embed, if the input image size doesn't match\n the training size.\n Args:\n patched_img (torch.Tensor): The patched image, it should be\n shape of [B, L1, C].\n hw_shape (tuple): The downsampled image resolution.\n pos_embed (torch.Tensor): The pos_embed weighs, it should be\n shape of [B, L2, c].\n Return:\n torch.Tensor: The pos encoded image feature.\n \"\"\"\n assert patched_img.ndim == 3 and pos_embed.ndim == 3, \\\n 'the shapes of patched_img and pos_embed must be [B, L, C]'\n x_len, pos_len = patched_img.shape[1], pos_embed.shape[1]\n if x_len != pos_len:\n if pos_len == (self.img_size[0] // self.patch_size) * (\n self.img_size[1] // self.patch_size) + 1:\n pos_h = self.img_size[0] // self.patch_size\n pos_w = self.img_size[1] // self.patch_size\n else:\n raise ValueError(\n 'Unexpected shape of pos_embed, got {}.'.format(\n pos_embed.shape))\n pos_embed = self.resize_pos_embed(pos_embed, hw_shape,\n (pos_h, pos_w),\n self.interpolate_mode)\n return self.drop_after_pos(patched_img + pos_embed)\n\n @staticmethod\n def resize_pos_embed(pos_embed, input_shpae, pos_shape, mode):\n \"\"\"Resize pos_embed weights.\n\n Resize pos_embed using bicubic interpolate method.\n Args:\n pos_embed (torch.Tensor): Position embedding weights.\n input_shpae (tuple): Tuple for (downsampled input image height,\n downsampled input image width).\n pos_shape (tuple): The resolution of downsampled origin training\n image.\n mode (str): Algorithm used for upsampling:\n ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` |\n ``'trilinear'``. Default: ``'nearest'``\n Return:\n torch.Tensor: The resized pos_embed of shape [B, L_new, C]\n \"\"\"\n assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]'\n pos_h, pos_w = pos_shape\n cls_token_weight = pos_embed[:, 0]\n pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):]\n pos_embed_weight = pos_embed_weight.reshape(\n 1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2)\n pos_embed_weight = resize(\n pos_embed_weight, size=input_shpae, align_corners=False, mode=mode)\n cls_token_weight = cls_token_weight.unsqueeze(1)\n pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2)\n pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1)\n return pos_embed\n\n def forward(self, inputs):\n B = inputs.shape[0]\n\n x, hw_shape = self.patch_embed(inputs)\n\n # stole cls_tokens impl from Phil Wang, thanks\n cls_tokens = self.cls_token.expand(B, -1, -1)\n x = torch.cat((cls_tokens, x), dim=1)\n x = self._pos_embeding(x, hw_shape, self.pos_embed)\n\n if not self.with_cls_token:\n # Remove class token for transformer encoder input\n x = x[:, 1:]\n\n if self.pre_norm:\n x = self.norm0(x)\n\n outs = []\n for i, layer in enumerate(self.layers):\n # add deep prompt\n if self.num_prompt_tokens is not None:\n deep_prompt_emb = self.prompt_dropout(self.prompt_proj(self.deep_prompt_embeddings[i]).expand(B, -1, -1))\n assert self.with_cls_token\n assert x.shape[1] == 1 + hw_shape[0] * hw_shape[1], x.shape\n x = torch.cat((\n x[:, :1, :],\n deep_prompt_emb,\n x[:, 1:, :]\n ), dim=1)\n assert x.shape[1] == 1 + self.num_prompt_tokens + hw_shape[0] * hw_shape[1], x.shape\n x, q, k, v = layer(x, self.return_qkv[i] \\\n or (i==len(self.layers)-1 and self.skip_last_attn))\n # remove deep prompt\n if self.num_prompt_tokens is not None:\n x = torch.cat((\n x[:, :1, :],\n x[:, 1+self.num_prompt_tokens:, :]\n ), dim=1)\n assert x.shape[1] == 1 + hw_shape[0] * hw_shape[1]\n if v is not None:\n v = torch.cat((\n v[:, :1, :],\n v[:, 1+self.num_prompt_tokens:, :]\n ), dim=1)\n assert v.shape[1] == 1 + hw_shape[0] * hw_shape[1]\n if i == len(self.layers) - 1:\n if self.final_norm:\n x = self.norm1(x)\n if self.return_qkv[i]:\n v = self.norm1(v)\n if self.skip_last_attn:\n if self.with_cls_token:\n x[:, 1:] = v[:, 1:]\n else:\n x = v\n if self.return_clip_embed:\n visual_embedding = v\n if self.with_cls_token:\n visual_embedding = visual_embedding[:, 1:]\n B, _, C = visual_embedding.shape\n visual_embedding = visual_embedding.reshape(B, hw_shape[0], hw_shape[1],\n C).permute(0, 3, 1, 2).contiguous()\n visual_embedding = self.proj(visual_embedding)\n visual_embedding = visual_embedding / visual_embedding.norm(dim=1, keepdim=True)\n if i in self.out_indices:\n if self.with_cls_token:\n # Remove class token and reshape token for decoder head\n out = x[:, 1:]\n else:\n out = x\n B, _, C = out.shape\n out = out.reshape(B, hw_shape[0], hw_shape[1],\n C).permute(0, 3, 1, 2).contiguous()\n if self.output_cls_token:\n out = [out, x[:, 0]]\n if self.return_qkv[i]:\n if self.with_cls_token:\n q = q[:, 1:]\n k = k[:, 1:]\n v = v[:, 1:]\n v = v.reshape(B, hw_shape[0], hw_shape[1],\n C).permute(0, 3, 1, 2).contiguous()\n out = [out, q, k, v]\n outs.append(out)\n\n if self.return_clip_embed:\n features = []\n for o in outs:\n if isinstance(o, list):\n # from return_qkv\n assert len(o) == 4\n features.append(o[3])\n else:\n features.append(o)\n if len(self.layers) in self.out_indices:\n features.append(visual_embedding)\n global_embedding = self.proj(x[:, 0][:, :, None, None])[:, :, 0, 0]\n global_embedding = global_embedding / global_embedding.norm(dim=1, keepdim=True)\n\n outs = [\n tuple(features),\n global_embedding\n ]\n\n return outs\n\n def train(self, mode=True):\n super(MaskClipVisionTransformer, self).train(mode)\n if mode and self.norm_eval:\n for m in self.modules():\n if isinstance(m, nn.LayerNorm):\n m.eval()"},{"identifier":"MaskClip2Head","path":"third_party/maskclip/models/decode_heads/maskclip2_head.py","snippet":"class MaskClip2Head(BaseDecodeHead):\n\n def __init__(self, img_size, **kwargs):\n super(MaskClip2Head, self).__init__(**kwargs)\n self.img_size = img_size\n\n def forward(self, inputs, force_output_pred_masks=False):\n assert force_output_pred_masks\n inputs_both = inputs\n inputs = inputs_both[0][0]\n cls_token = inputs_both[0][1]\n txt_embed = inputs_both[1]\n feat = inputs[-1]\n\n output = self.cls_seg(feat, txt_embed)\n\n output = F.interpolate(output, size=(self.img_size, self.img_size),\n mode='bilinear', align_corners=self.align_corners)\n output = {\"pred_masks\": output}\n\n return output\n\n def cls_seg(self, feat, txt_embed):\n txt_embed = txt_embed.to(feat.dtype)\n output = F.conv2d(feat, txt_embed[:, :, None, None])\n \n return output\n\n def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg):\n raise RuntimeError('MaskClip is not trainable. Try MaskClip+ instead.')"},{"identifier":"MaskClipHead","path":"third_party/maskclip/models/decode_heads/maskclip_head.py","snippet":"class MaskClipHead(BaseDecodeHead):\n\n def __init__(self, text_categories, text_channels, text_embeddings_path,\n visual_projs_path, vit=False, ks_thresh=0., pd_thresh=0.,\n attn_pooling=False, num_heads=32, **kwargs):\n super(MaskClipHead, self).__init__(**kwargs)\n\n self.text_categories = text_categories\n self.text_channels = text_channels\n self.text_embeddings_path = text_embeddings_path\n self.visual_projs_path = visual_projs_path\n\n if self.text_embeddings_path is None:\n self.text_embeddings = nn.Parameter(torch.zeros(text_categories, text_channels))\n nn.init.normal_(self.text_embeddings, mean=0.0, std=0.01)\n else:\n self.register_buffer('text_embeddings', torch.randn(text_categories, text_channels))\n self.load_text_embeddings()\n \n self.vit = vit\n if vit:\n self.proj = nn.Conv2d(self.in_channels, text_channels, 1, bias=False)\n else:\n self.q_proj = nn.Conv2d(self.in_channels, self.in_channels, 1)\n self.k_proj = nn.Conv2d(self.in_channels, self.in_channels, 1)\n self.v_proj = nn.Conv2d(self.in_channels, self.in_channels, 1)\n self.c_proj = nn.Conv2d(self.in_channels, text_channels, 1)\n self.load_visual_projs()\n\n self.ks_thresh = ks_thresh\n self.pd_thresh = pd_thresh\n self.attn_pooling = attn_pooling\n self.num_heads = num_heads\n\n def init_weights(self):\n super(MaskClipHead, self).init_weights()\n if self.text_embeddings_path is None:\n nn.init.normal_(self.text_embeddings, mean=0.0, std=0.01)\n else:\n self.load_text_embeddings()\n self.load_visual_projs()\n\n def load_text_embeddings(self):\n loaded = torch.load(self.text_embeddings_path, map_location='cuda')\n self.text_embeddings[:, :] = loaded[:, :]\n print_log(f'Loaded text embeddings from {self.text_embeddings_path}', logger=get_root_logger())\n\n def load_visual_projs(self):\n loaded = torch.load(self.visual_projs_path, map_location='cuda')\n attrs = ['proj'] if self.vit else ['q_proj', 'k_proj', 'v_proj', 'c_proj']\n for attr in attrs:\n current_attr = getattr(self, attr)\n state_dict = loaded[attr]\n for key in state_dict:\n if 'weight' in key:\n state_dict[key] = state_dict[key][:, :, None, None]\n current_attr.load_state_dict(state_dict)\n print_log(f'Loaded proj weights from {self.visual_projs_path}', logger=get_root_logger())\n \n def forward(self, inputs):\n x = self._transform_inputs(inputs)\n q, k, v, cls_token = None, None, None, None\n if self.vit:\n if isinstance(x, list) and len(x) == 4:\n x, q, k, v = x\n if isinstance(x, list) and len(x) == 2:\n x, cls_token = x\n if v is not None:\n feat = self.proj(v)\n else:\n feat = self.proj(x)\n if cls_token is not None:\n cls_token = self.proj(cls_token[:, :, None, None])[:, :, 0, 0]\n else:\n if self.attn_pooling:\n N, C, H, W = x.shape\n x = x.view(N, C, -1).permute(2, 0, 1) # NCHW -> (HW)NC\n x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)\n x, _ = F.multi_head_attention_forward(\n query=x, key=x, value=x,\n embed_dim_to_check=x.shape[-1],\n num_heads=self.num_heads,\n q_proj_weight=self.q_proj.weight[:, :, 0, 0],\n k_proj_weight=self.k_proj.weight[:, :, 0, 0],\n v_proj_weight=self.v_proj.weight[:, :, 0, 0],\n in_proj_weight=None,\n in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),\n bias_k=None,\n bias_v=None,\n add_zero_attn=False,\n dropout_p=0,\n out_proj_weight=self.c_proj.weight[:, :, 0, 0],\n out_proj_bias=self.c_proj.bias,\n use_separate_proj_weight=True,\n training=self.training,\n need_weights=False\n )\n feat = x[1:].permute(1, 2, 0).view(N, -1, H, W)\n else:\n q = self.q_proj(x)\n k = self.k_proj(x)\n q = torch.flatten(q, start_dim=2).transpose(-2, -1)\n k = torch.flatten(k, start_dim=2).transpose(-2, -1)\n v = self.v_proj(x)\n feat = self.c_proj(v)\n output = self.cls_seg(feat)\n if not self.training:\n output = self.refine_output(output, k)\n\n return output\n\n def cls_seg(self, feat):\n feat = feat / feat.norm(dim=1, keepdim=True)\n output = F.conv2d(feat, self.text_embeddings[:, :, None, None])\n \n return output\n\n def refine_output(self, output, k):\n if self.pd_thresh > 0:\n N, C, H, W = output.shape\n _output = F.softmax(output*100, dim=1)\n max_cls_conf = _output.view(N, C, -1).max(dim=-1)[0]\n selected_cls = (max_cls_conf < self.pd_thresh)[:, :, None, None].expand(N, C, H, W)\n output[selected_cls] = -100\n\n if k is not None and self.ks_thresh > 0:\n output = F.softmax(output*100, dim=1)\n N, C, H, W = output.shape\n output = output.view(N, C, -1).transpose(-2, -1)\n # softmax\n # weight = k @ k.transpose(-2, -1)\n # weight = F.softmax(weight, dim=-1)\n # L2 distance\n k = F.normalize(k, p=2)\n weight = k @ k.transpose(-2, -1)\n\n selected_pos = (output.max(dim=-1, keepdim=True)[0] < self.ks_thresh)\n selected_pos = selected_pos.expand(-1, -1, C)\n\n weighted_output = weight @ output\n output[selected_pos] = weighted_output[selected_pos]\n output = output.transpose(-2, -1).view(N, C, H, W)\n\n return output\n\n def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg):\n raise RuntimeError('MaskClip is not trainable. Try MaskClip+ instead.')"},{"identifier":"DeepLabV3Plus","path":"third_party/unimatch/model/semseg/deeplabv3plus.py","snippet":"class DeepLabV3Plus(nn.Module):\n def __init__(self, cfg):\n super(DeepLabV3Plus, self).__init__()\n\n if 'resnet' in cfg['backbone']:\n self.backbone = resnet.__dict__[cfg['backbone']](pretrained=True, \n replace_stride_with_dilation=cfg['replace_stride_with_dilation'])\n else:\n assert cfg['backbone'] == 'xception'\n self.backbone = xception(pretrained=True)\n\n low_channels = 256\n high_channels = 2048\n\n self.head = ASPPModule(high_channels, cfg['dilations'])\n\n self.reduce = nn.Sequential(nn.Conv2d(low_channels, 48, 1, bias=False),\n nn.BatchNorm2d(48),\n nn.ReLU(True))\n\n self.fuse = nn.Sequential(nn.Conv2d(high_channels // 8 + 48, 256, 3, padding=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(True),\n nn.Conv2d(256, 256, 3, padding=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(True))\n\n self.classifier = nn.Conv2d(256, cfg['nclass'], 1, bias=True)\n\n def forward(self, x, need_fp=False, only_fp=False):\n h, w = x.shape[-2:]\n\n feats = self.backbone.base_forward(x)\n c1, c4 = feats[0], feats[-1]\n\n if only_fp:\n out_fp = self._decode(nn.Dropout2d(0.5)(c1),\n nn.Dropout2d(0.5)(c4))\n out_fp = F.interpolate(out_fp, size=(h, w), mode=\"bilinear\", align_corners=True)\n return out_fp\n elif need_fp:\n outs = self._decode(torch.cat((c1, nn.Dropout2d(0.5)(c1))),\n torch.cat((c4, nn.Dropout2d(0.5)(c4))))\n outs = F.interpolate(outs, size=(h, w), mode=\"bilinear\", align_corners=True)\n out, out_fp = outs.chunk(2)\n\n return out, out_fp\n\n out = self._decode(c1, c4)\n out = F.interpolate(out, size=(h, w), mode=\"bilinear\", align_corners=True)\n\n return out\n\n def _decode(self, c1, c4):\n c4 = self.head(c4)\n c4 = F.interpolate(c4, size=c1.shape[-2:], mode=\"bilinear\", align_corners=True)\n\n c1 = self.reduce(c1)\n\n feature = torch.cat([c1, c4], dim=1)\n feature = self.fuse(feature)\n\n out = self.classifier(feature)\n\n return out"},{"identifier":"SegLossPlus","path":"third_party/zegclip/losses/atm_loss.py","snippet":"class SegLossPlus(nn.Module):\n \"\"\"ATMLoss.\n \"\"\"\n def __init__(self,\n num_classes,\n dec_layers,\n mask_weight=20.0,\n dice_weight=1.0,\n loss_weight=1.0,\n use_point=False):\n super(SegLossPlus, self).__init__()\n weight_dict = {\"loss_mask\": mask_weight, \"loss_dice\": dice_weight}\n aux_weight_dict = {}\n for i in range(dec_layers - 1):\n aux_weight_dict.update({k + f\"_{i}\": v for k, v in weight_dict.items()})\n weight_dict.update(aux_weight_dict)\n\n self.criterion = SegPlusCriterion(\n num_classes,\n weight_dict=weight_dict,\n losses=[\"masks\"],\n )\n\n self.loss_weight = loss_weight\n\n def forward(self,\n outputs,\n label,\n ignore_index=255,\n ):\n \"\"\"Forward function.\"\"\"\n \n self.ignore_index = ignore_index\n targets = self.prepare_targets(label)\n losses = self.criterion(outputs, targets)\n\n for k in list(losses.keys()):\n if k in self.criterion.weight_dict:\n losses[k] = losses[k] * self.criterion.weight_dict[k] * self.loss_weight\n else:\n # remove this loss if not specified in `weight_dict`\n losses.pop(k)\n\n return losses\n\n def prepare_targets(self, targets):\n new_targets = []\n for targets_per_image in targets:\n # gt_cls\n gt_cls = targets_per_image.unique()\n gt_cls = gt_cls[gt_cls != self.ignore_index]\n masks = []\n for cls in gt_cls:\n masks.append(targets_per_image == cls)\n if len(gt_cls) == 0:\n masks.append(targets_per_image == self.ignore_index)\n\n masks = torch.stack(masks, dim=0)\n new_targets.append(\n {\n \"labels\": gt_cls,\n \"target_masks\": masks,\n \"masks\": targets_per_image,\n }\n )\n return new_targets"},{"identifier":"CLIPVisionTransformer","path":"third_party/zegclip/models/backbones/clip_vit.py","snippet":"class CLIPVisionTransformer(nn.Module):\n def __init__(self, input_resolution=224, patch_size=32, width=768, layers=12, heads=12, output_dim=512, drop_path_rate=0.0,\n out_indices=[3, 5, 7, 11], pretrained=None, get_embeddings=False, embed_v=False, **kwargs):\n super().__init__()\n self.pretrained = pretrained\n self.input_resolution = input_resolution\n self.output_dim = output_dim\n self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)\n\n scale = width ** -0.5\n self.class_embedding = nn.Parameter(scale * torch.randn(width))\n self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))\n self.spatial_size = input_resolution // patch_size\n self.ln_pre = LayerNorm(width)\n self.get_embeddings = get_embeddings\n self.embed_v = embed_v\n\n self.transformer = Transformer(width, layers, heads, drop_path_rate=drop_path_rate)\n\n self.out_indices = out_indices\n\n if get_embeddings:\n self.ln_post = LayerNorm(width)\n self.proj = nn.Parameter(scale * torch.randn(width, output_dim))\n\n self.patch_size = patch_size\n\n def init_weights(self, pretrained=None):\n pretrained = pretrained or self.pretrained\n if isinstance(pretrained, str):\n checkpoint = torch.jit.load(pretrained, map_location='cpu').float().state_dict()\n\n state_dict = {}\n\n for k in checkpoint.keys():\n if k.startswith('visual.'):\n new_k = k.replace('visual.', '')\n state_dict[new_k] = checkpoint[k]\n\n if 'positional_embedding' in state_dict.keys():\n if self.positional_embedding.shape != state_dict['positional_embedding'].shape:\n # (1025, 768) (197, 768) upsample the positional_embedding for larger input\n print(f'Resize the pos_embed shape from {state_dict[\"positional_embedding\"].shape} to {self.positional_embedding.shape}')\n cls_pos = state_dict[\"positional_embedding\"][0:1, :]\n if self.patch_size == 16:\n spatial_pos = F.interpolate(state_dict[\"positional_embedding\"][1:,].reshape(1, 14, 14, 768).permute(0, 3, 1, 2), size=(self.spatial_size, self.spatial_size), mode='bilinear')\n elif self.patch_size == 32:\n spatial_pos = F.interpolate(state_dict[\"positional_embedding\"][1:,].reshape(1, 7, 7, 768).permute(0, 3, 1, 2), size=(self.spatial_size, self.spatial_size), mode='bilinear')\n else:\n assert ValueError('Patch Size should be 16 or 32')\n spatial_pos = spatial_pos.reshape(768, self.spatial_size*self.spatial_size).permute(1, 0)\n positional_embedding = torch.cat([cls_pos, spatial_pos], dim=0)\n state_dict['positional_embedding'] = positional_embedding\n assert self.positional_embedding.shape == state_dict['positional_embedding'].shape\n\n u, w = self.load_state_dict(state_dict, False)\n print(u, w, 'are misaligned params in vision transformer')\n\n\n def forward(self, x: torch.Tensor):\n x = self.conv1(x)\n B, C, H, W = x.shape\n x = x.reshape(x.shape[0], x.shape[1], -1)\n x = x.permute(0, 2, 1)\n x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)\n\n pos = self.positional_embedding.to(x.dtype)\n cls_pos = pos[0,:] + self.class_embedding.to(x.dtype)\n spatial_pos = F.interpolate(pos[1:,].reshape(1, self.spatial_size, self.spatial_size, C).permute(0, 3, 1, 2), size=(H, W), mode='bilinear')\n spatial_pos = spatial_pos.reshape(1, C, H*W).permute(0, 2, 1)\n pos = torch.cat([cls_pos.reshape(1, 1, C), spatial_pos], dim=1)\n x = x + pos\n x = self.ln_pre(x)\n x = x.permute(1, 0, 2) # NLD -> LND\n\n features = []\n outs = []\n for i, blk in enumerate(self.transformer.resblocks):\n if self.embed_v and i == len(self.transformer.resblocks) - 1:\n y = blk.ln_1(x)\n y = F.linear(y, blk.attn.in_proj_weight, blk.attn.in_proj_bias)\n y_N, y_L, y_C = y.shape\n y = y.view(y_N, y_L, 3, y_C//3).permute(2, 0, 1, 3).reshape(3*y_N, y_L, y_C//3)\n y = F.linear(y, blk.attn.out_proj.weight, blk.attn.out_proj.bias)\n q, k, v = y.tensor_split(3, dim=0)\n v += x\n v = v + blk.mlp(blk.ln_2(v))\n x = blk(x)\n if len(self.out_indices) > 1:\n if i in self.out_indices:\n xp = x.permute(1, 0, 2)[:, 1:, :].permute(0, 2, 1).reshape(B, -1, H, W)\n features.append(xp.contiguous())\n\n if self.get_embeddings:\n x = x.permute(1, 0, 2)\n x = self.ln_post(x)\n x = x @ self.proj\n\n global_embedding = x[:, 0]\n if self.embed_v:\n v = v.permute(1, 0, 2)\n v = self.ln_post(v)\n v = v @ self.proj\n visual_embedding = v[:, 1:].reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()\n else:\n visual_embedding = x[:, 1:].reshape(B, H, W, -1).permute(0, 3, 1, 2)\n\n if len(self.out_indices) == 1:\n visual_embedding = visual_embedding / visual_embedding.norm(dim=1, keepdim=True)\n features.append(visual_embedding)\n\n outs.append(tuple(features))\n global_embedding = global_embedding / global_embedding.norm(dim=1, keepdim=True)\n outs.append(global_embedding)\n return outs"},{"identifier":"VPTCLIPVisionTransformer","path":"third_party/zegclip/models/backbones/clip_vpt_vit.py","snippet":"class VPTCLIPVisionTransformer(nn.Module):\n def __init__(self, input_resolution=224, patch_size=32, width=768, layers=12, heads=12, output_dim=512, drop_path_rate=0.0,\n out_indices=[3, 5, 7, 11], pretrained=None, get_embeddings=False, embed_v=False,\n num_tokens=20, prompt_dim=512, total_d_layer=11, **kwargs):\n super().__init__()\n self.pretrained = pretrained\n self.input_resolution = input_resolution\n self.output_dim = output_dim\n self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)\n\n scale = width ** -0.5\n self.class_embedding = nn.Parameter(scale * torch.randn(width))\n self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))\n self.spatial_size = input_resolution // patch_size\n self.ln_pre = LayerNorm(width)\n self.get_embeddings = get_embeddings\n self.embed_v = embed_v\n self.num_layers = layers\n\n self.transformer = Transformer(width, layers, heads, drop_path_rate=drop_path_rate)\n\n self.out_indices = out_indices\n\n if get_embeddings:\n self.ln_post = LayerNorm(width)\n self.proj = nn.Parameter(scale * torch.randn(width, output_dim))\n\n self.patch_size = patch_size\n\n ## Setting of visual prompt tuning\n self.num_tokens = num_tokens\n self.prompt_dim = prompt_dim\n self.total_d_layer = total_d_layer\n\n ## Add the prompt parameters # exclude_key=prompt:\n self._init_prompt(patch_size, self.num_tokens, self.prompt_dim, self.total_d_layer)\n\n def _init_prompt(self, patch, num_tokens, prompt_dim, total_d_layer):\n patch_size = []\n patch_size.append(patch)\n patch_size.append(patch)\n val = math.sqrt(6. / float(3 * reduce(mul, patch_size, 1) + prompt_dim)) # noqa\n\n if total_d_layer >= 0:\n self.prompt_embeddings = nn.Parameter(torch.zeros(1, num_tokens, prompt_dim))\n # xavier_uniform initialization\n nn.init.uniform_(self.prompt_embeddings.data, -val, val)\n\n if total_d_layer > 0: # noqa\n self.deep_prompt_embeddings = nn.Parameter(torch.zeros(total_d_layer, num_tokens, prompt_dim))\n # xavier_uniform initialization\n nn.init.uniform_(self.deep_prompt_embeddings.data, -val, val)\n\n self.prompt_proj = nn.Linear(prompt_dim, prompt_dim)\n nn.init.kaiming_normal_(self.prompt_proj.weight, a=0, mode='fan_out')\n self.prompt_norm = LayerNorm(prompt_dim, eps=1e-6)\n self.prompt_dropout = Dropout(0.1)\n\n else: # total_d_layer < 0\n raise ValueError(f'Invalid total_d_layer={self.total_d_layer}.')\n\n\n def init_weights(self, pretrained=None):\n pretrained = pretrained or self.pretrained\n if isinstance(pretrained, str):\n checkpoint = torch.jit.load(pretrained, map_location='cpu').float().state_dict()\n\n state_dict = {}\n\n for k in checkpoint.keys():\n if k.startswith('visual.'):\n new_k = k.replace('visual.', '')\n state_dict[new_k] = checkpoint[k]\n\n if 'positional_embedding' in state_dict.keys():\n if self.positional_embedding.shape != state_dict['positional_embedding'].shape:\n # (1025, 768) (197, 768) upsample the positional_embedding for larger input\n print(f'Resize the pos_embed shape from {state_dict[\"positional_embedding\"].shape} to {self.positional_embedding.shape}')\n cls_pos = state_dict[\"positional_embedding\"][0:1, :]\n if self.patch_size == 16:\n spatial_pos = F.interpolate(state_dict[\"positional_embedding\"][1:,].reshape(1, 14, 14, 768).permute(0, 3, 1, 2), size=(self.spatial_size, self.spatial_size), mode='bilinear')\n else:\n assert ValueError('Patch Size should be 16 or 32')\n spatial_pos = spatial_pos.reshape(768, self.spatial_size*self.spatial_size).permute(1, 0)\n positional_embedding = torch.cat([cls_pos, spatial_pos], dim=0)\n state_dict['positional_embedding'] = positional_embedding\n assert self.positional_embedding.shape == state_dict['positional_embedding'].shape\n\n u, w = self.load_state_dict(state_dict, False)\n print(u, w, 'are misaligned params in vision transformer')\n\n\n def forward(self, x: torch.Tensor):\n x = self.conv1(x)\n B, C, H, W = x.shape\n x = x.reshape(x.shape[0], x.shape[1], -1)\n x = x.permute(0, 2, 1)\n x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)\n\n pos = self.positional_embedding.to(x.dtype)\n cls_pos = pos[0,:] + self.class_embedding.to(x.dtype)\n spatial_pos = F.interpolate(pos[1:,].reshape(1, self.spatial_size, self.spatial_size, C).permute(0, 3, 1, 2), size=(H, W), mode='bilinear')\n spatial_pos = spatial_pos.reshape(1, C, H*W).permute(0, 2, 1)\n pos = torch.cat([cls_pos.reshape(1, 1, C), spatial_pos], dim=1)\n x = x + pos\n x = self.ln_pre(x)\n\n if self.total_d_layer >=0:\n # concat prompt\n x = torch.cat((\n x[:, :1, :],\n self.prompt_dropout(self.prompt_proj(self.prompt_embeddings).expand(B, -1, -1)),\n x[:, 1:, :]\n ), dim=1)\n\n x = x.permute(1, 0, 2) # NLD -> LND\n\n features = []\n outs = []\n if self.total_d_layer == 0: #shallow\n assert not self.embed_v\n for i, blk in enumerate(self.transformer.resblocks):\n x = blk(x)\n if len(self.out_indices) > 1:\n if i in self.out_indices:\n xp = x.permute(1, 0, 2)[:, 1+self.num_tokens:, :].permute(0, 2, 1).reshape(B, -1, H, W)\n features.append(xp.contiguous())\n elif self.total_d_layer > 0: # deep\n x, features, v = self.forward_deep_prompt(x, features, H, W)\n else:\n raise NotImplementedError(f'Invalid total_d_layer={self.total_d_layer}.')\n\n if self.get_embeddings:\n x = x.permute(1, 0, 2)\n x = self.ln_post(x)\n x = x @ self.proj\n\n global_embedding = x[:, 0]\n if self.embed_v:\n v = v.permute(1, 0, 2)\n v = self.ln_post(v)\n v = v @ self.proj\n visual_embedding = v[:, -(H*W):].reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()\n else:\n visual_embedding = x[:, -(H*W):].reshape(B, H, W, -1).permute(0, 3, 1, 2)\n\n if len(self.out_indices) == 1:\n visual_embedding = visual_embedding / visual_embedding.norm(dim=1, keepdim=True)\n features.append(visual_embedding)\n\n outs.append(tuple(features))\n global_embedding = global_embedding / global_embedding.norm(dim=1, keepdim=True)\n outs.append(global_embedding)\n return outs\n\n\n def forward_deep_prompt(self, embedding_output, features, H, W, out_last=False):\n B = embedding_output.shape[1]\n v = None\n\n for i in range(self.num_layers):\n if i == 0:\n hidden_states = self.transformer.resblocks[i](embedding_output)\n elif i <= self.deep_prompt_embeddings.shape[0]:\n deep_prompt_emb = self.prompt_dropout(self.prompt_proj(self.deep_prompt_embeddings[i-1]).expand(B, -1, -1)).permute(1, 0, 2)\n hidden_states = torch.cat((\n hidden_states[:1, :, :],\n deep_prompt_emb,\n hidden_states[(1+self.num_tokens):, :, :]\n ), dim=0)\n if self.embed_v and i == self.num_layers - 1:\n x = hidden_states\n blk = self.transformer.resblocks[i]\n y = blk.ln_1(x)\n y = F.linear(y, blk.attn.in_proj_weight, blk.attn.in_proj_bias)\n y_N, y_L, y_C = y.shape\n y = y.view(y_N, y_L, 3, y_C//3).permute(2, 0, 1, 3).reshape(3*y_N, y_L, y_C//3)\n y = F.linear(y, blk.attn.out_proj.weight, blk.attn.out_proj.bias)\n q, k, v = y.tensor_split(3, dim=0)\n v += x\n v = v + blk.mlp(blk.ln_2(v))\n\n hidden_states = self.transformer.resblocks[i](hidden_states)\n else:\n assert not self.embed_v\n hidden_states = torch.cat((\n hidden_states[:1, :, :],\n hidden_states[-(H*W):, :, :]\n ), dim=0)\n hidden_states = self.transformer.resblocks[i](hidden_states)\n\n if len(self.out_indices) > 1:\n if i in self.out_indices:\n xp = hidden_states.permute(1, 0, 2)[:, -(H*W):, :].permute(0, 2, 1).reshape(B, -1, H, W)\n features.append(xp.contiguous())\n\n if i == (self.num_layers-2): #10\n before_last_feats = self.prompt_norm(hidden_states)\n\n encoded = self.prompt_norm(hidden_states)\n if out_last:\n return before_last_feats\n else:\n return encoded, features, v"},{"identifier":"CLIPTextEncoder","path":"third_party/zegclip/models/backbones/text_encoder.py","snippet":"class CLIPTextEncoder(nn.Module):\n def __init__(self, context_length=77,\n vocab_size=49408,\n transformer_width=512,\n transformer_heads=8,\n transformer_layers=12,\n embed_dim=1024,\n out_dim=256,\n pretrained=None, **kwargs):\n super().__init__()\n\n self.pretrained = pretrained\n\n self.context_length = context_length\n\n self.transformer = Transformer(\n width=transformer_width,\n layers=transformer_layers,\n heads=transformer_heads,\n attn_mask=self.build_attention_mask()\n )\n\n self.vocab_size = vocab_size\n self.token_embedding = nn.Embedding(vocab_size, transformer_width)\n self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))\n self.ln_final = LayerNorm(transformer_width)\n self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))\n \n def init_weights(self, pretrained=None):\n pretrained = pretrained or self.pretrained\n if isinstance(pretrained, str):\n checkpoint = torch.jit.load(pretrained, map_location='cpu').float().state_dict()\n\n state_dict = {}\n\n for k in checkpoint.keys():\n if k.startswith('transformer.'):\n state_dict[k] = checkpoint[k]\n \n if k == 'positional_embedding' or k == 'text_projection' or k.startswith('token_embedding') or k.startswith('ln_final'):\n if k == 'positional_embedding' and checkpoint[k].size(0) > self.context_length:\n checkpoint[k] = checkpoint[k][:self.context_length]\n print('positional_embedding is tuncated from 77 to', self.context_length)\n state_dict[k] = checkpoint[k]\n \n u, w = self.load_state_dict(state_dict, False)\n print(u, w, 'are misaligned params in text encoder')\n\n\n def build_attention_mask(self):\n # lazily create causal attention mask, with full attention between the vision tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.context_length, self.context_length)\n mask.fill_(float(\"-inf\"))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def forward(self, text):\n x = self.token_embedding(text)\n x = x + self.positional_embedding \n x = x.permute(1, 0, 2)\n x = self.transformer(x)\n x = x.permute(1, 0, 2)\n x = self.ln_final(x)\n x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection\n return x"},{"identifier":"DropPath","path":"third_party/zegclip/models/backbones/utils.py","snippet":"class DropPath(nn.Module):\n \"\"\"Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).\n \"\"\"\n def __init__(self, drop_prob=None):\n super(DropPath, self).__init__()\n self.drop_prob = drop_prob\n\n def forward(self, x):\n return drop_path(x, self.drop_prob, self.training)\n \n def extra_repr(self) -> str:\n return 'p={}'.format(self.drop_prob)"},{"identifier":"ATMSingleHeadSeg","path":"third_party/zegclip/models/decode_heads/atm_head.py","snippet":"class ATMSingleHeadSeg(BaseDecodeHead):\n def __init__(\n self,\n img_size,\n in_channels,\n seen_idx,\n all_idx,\n ignore_seen_pseudo_labels=True,\n embed_dims=768,\n num_layers=3,\n num_heads=8,\n use_stages=1,\n use_proj=True,\n crop_train=False,\n use_rd=True,\n use_aspp=False,\n aspp_relu=True,\n aspp_bn=True,\n aspp_residual=False,\n dilations=(6, 12, 18),\n **kwargs,\n ):\n super(ATMSingleHeadSeg, self).__init__(\n in_channels=in_channels, **kwargs)\n\n self.image_size = img_size\n self.use_stages = use_stages\n self.crop_train = crop_train\n self.use_rd = use_rd\n self.use_aspp = use_aspp\n self.aspp_residual = aspp_residual\n self.seen_idx = seen_idx\n self.all_idx = all_idx\n self.ignore_seen_pseudo_labels = ignore_seen_pseudo_labels\n self.debug = False\n nhead = num_heads\n dim = embed_dims\n input_proj = []\n proj_norm = []\n atm_decoders = []\n\n self.unseen_idx = self.all_idx.copy()\n for i_idx in self.seen_idx:\n self.unseen_idx.remove(i_idx)\n\n if self.use_aspp:\n self.aspp = ASPPModule(dim, dilations, out_channels=dim, bn=aspp_bn, relu=aspp_relu)\n self.add_module(\"aspp\", self.aspp)\n\n for i in range(self.use_stages):\n # FC layer to change ch\n if use_proj:\n proj = nn.Linear(self.in_channels, dim)\n trunc_normal_(proj.weight, std=.02)\n else:\n proj = nn.Identity()\n self.add_module(\"input_proj_{}\".format(i + 1), proj)\n input_proj.append(proj)\n # norm layer\n if use_proj:\n norm = nn.LayerNorm(dim)\n else:\n norm = nn.Identity()\n self.add_module(\"proj_norm_{}\".format(i + 1), norm)\n proj_norm.append(norm)\n # decoder layer\n decoder_layer = TPN_DecoderLayer(d_model=dim, nhead=nhead, dim_feedforward=dim * 4)\n decoder = TPN_Decoder(decoder_layer, num_layers)\n self.add_module(\"decoder_{}\".format(i + 1), decoder)\n atm_decoders.append(decoder)\n\n self.input_proj = input_proj\n self.proj_norm = proj_norm\n self.decoder = atm_decoders\n\n delattr(self, 'conv_seg')\n\n self.q_proj = nn.Linear(dim * 2 if self.use_rd else dim, dim)\n\n def init_weights(self):\n for n, m in self.named_modules():\n if isinstance(m, nn.Linear):\n trunc_normal_init(m, std=.02, bias=0)\n elif isinstance(m, nn.LayerNorm):\n constant_init(m, val=1.0, bias=0.0)\n\n def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg, self_training=False):\n seg_logits = self.forward(inputs)\n\n if self.debug:\n pred = seg_logits['pred_masks'].detach().sigmoid().argmax(dim=1).unsqueeze(1)\n pred_cpu = pred.detach().cpu()\n gt_semantic_seg_cpu = gt_semantic_seg.detach().cpu()\n self.debug_output = [[\n dict(title='Prediction', data=pred_cpu[i], type='label'),\n dict(title='Ground Truth', data=gt_semantic_seg_cpu[i], type='label'),\n ] for i in range(pred.shape[0])]\n\n # print('Self-training: 'self_training)\n if self_training:\n pseudo_semantic_masks = seg_logits['pred_masks'].clone().detach().sigmoid()\n if self.ignore_seen_pseudo_labels:\n pseudo_semantic_masks[:, self.seen_idx, :, :] = -1\n assert -1 not in pseudo_semantic_masks.max(dim=1)[0], \\\n 'All classes are set to -1, which results in argmax defaulting to label 0'\n pseudo_semantic_seg = pseudo_semantic_masks.argmax(dim=1).unsqueeze(1)\n # generate pseudo labels for \"transductive\" setting\n # print('Before pseudo-labelling', gt_semantic_seg[0, 0])\n gt_semantic_seg[gt_semantic_seg==-1] = pseudo_semantic_seg[gt_semantic_seg==-1]\n gt_semantic_seg[gt_semantic_seg==-1] = 255\n # print('After pseudo-labelling', gt_semantic_seg[0, 0])\n losses = self.losses(seg_logits, gt_semantic_seg)\n\n else:\n gt_semantic_seg[gt_semantic_seg==-1] = 255\n losses = self.losses(seg_logits, gt_semantic_seg)\n\n if self.debug and self_training:\n for i in range(gt_semantic_seg.shape[0]):\n self.debug_output[i].append(\n dict(title='Pseudo Label', data=gt_semantic_seg[i].detach().cpu(), type='label'))\n\n return losses\n\n def forward_test(self, inputs, img_metas, test_cfg, self_training):\n return self.forward(inputs, self_training)\n\n def forward(self, inputs_both, self_training=None, force_output_pred_masks=False):\n inputs = inputs_both[0][0]\n cls_token = inputs_both[0][1]\n text_token = inputs_both[1]\n \n x = []\n for stage_ in inputs[:self.use_stages]:\n x.append(self.d4_to_d3(stage_) if stage_.dim() > 3 else stage_)\n x.reverse()\n bs = x[0].size()[0]\n\n laterals = []\n attns = []\n maps_size = []\n qs = []\n\n for idx, (x_, proj_, norm_) in enumerate(zip(x, self.input_proj, self.proj_norm)):\n lateral = norm_(proj_(x_))\n if idx == 0:\n laterals.append(lateral)\n else:\n if laterals[idx - 1].size()[1] == lateral.size()[1]:\n laterals.append(lateral + laterals[idx - 1])\n else:\n # nearest interpolate\n l_ = self.d3_to_d4(laterals[idx - 1])\n l_ = F.interpolate(l_, scale_factor=2, mode=\"nearest\")\n l_ = self.d4_to_d3(l_)\n laterals.append(l_ + lateral)\n\n lateral = laterals[-1]\n if self.use_aspp:\n if self.aspp_residual:\n lateral = lateral + 0.01 * self.d4_to_d3(self.aspp(self.d3_to_d4(lateral)))\n else:\n lateral = self.d3_to_d4(lateral)\n lateral = self.aspp(lateral)\n lateral = self.d4_to_d3(lateral)\n\n q = self.q_proj(self.get_qs(text_token, cls_token))\n q = q.transpose(0,1)\n\n for idx, decoder_ in enumerate(self.decoder):\n q_, attn_ = decoder_(q, lateral.transpose(0, 1))\n for q, attn in zip(q_, attn_):\n attn = attn.transpose(-1, -2) \n attn = self.d3_to_d4(attn)\n maps_size.append(attn.size()[-2:])\n qs.append(q.transpose(0, 1))\n attns.append(attn)\n qs = torch.stack(qs, dim=0)\n\n outputs_seg_masks = []\n size = maps_size[-1]\n\n for i_attn, attn in enumerate(attns):\n if attn.shape[1] != self.num_classes:\n cls2con = get_class_to_concept_idxs(self.load_text_embedding)\n attn = aggregate_concept_predictions(attn, cls2con)\n assert attn.shape[1] == self.num_classes\n outputs_seg_masks.append(F.interpolate(attn, size=size, mode='bilinear', align_corners=False))\n\n pred = F.interpolate(outputs_seg_masks[-1],\n size=(self.image_size, self.image_size),\n mode='bilinear', align_corners=False)\n \n out = {\"pred_masks\": pred}\n\n \n if self.training or force_output_pred_masks:\n outputs_seg_masks = torch.stack(outputs_seg_masks, dim=0)# (3, bs, 20, 14, 14)\n else:\n if self_training:\n out[\"pred\"] = self.semantic_inference(out[\"pred_masks\"], self.seen_idx) #(bs, 20, 224, 224)\n else:\n out[\"pred\"] = self.semantic_inference(out[\"pred_masks\"], self.seen_idx, 0.1)\n return out[\"pred\"] \n return out\n\n def semantic_inference(self, mask_pred, seen_idx, weight=0.0):\n mask_pred = mask_pred.sigmoid()\n mask_pred[:,seen_idx] = mask_pred[:,seen_idx] - weight\n return mask_pred\n\n @torch.jit.unused\n def _set_aux_loss(self, outputs_seg_masks):\n return [\n {\"pred_masks\": a}\n # for a in zip(outputs_seg_masks[:-1])\n for a in outputs_seg_masks[:-1]\n ]\n\n def d3_to_d4(self, t):\n n, hw, c = t.size()\n if hw % 2 != 0:\n t = t[:, 1:]\n h = w = int(math.sqrt(hw))\n assert h * w == hw\n return t.transpose(1, 2).reshape(n, c, h, w)\n\n def d4_to_d3(self, t):\n return t.flatten(-2).transpose(-1, -2)\n\n def get_qs(self, q, cls):\n # q = [q.cls, q]\n C, dim = q.shape\n bs, _ = cls.shape\n q = q.expand(bs, -1, -1)\n if self.use_rd:\n q1 = torch.einsum(\"bd,bcd->bcd\", cls, q)\n q_ = torch.concat((q1, q), dim=-1)\n else:\n q_ = q.to(cls.dtype)\n return q_\n\n\n @force_fp32(apply_to=('seg_logit',))\n def losses(self, seg_logit, seg_label, num_classes=None):\n \"\"\"Compute segmentation loss.\"\"\"\n if isinstance(seg_logit, dict):\n # atm loss\n seg_label = seg_label.squeeze(1)\n\n loss = self.loss_decode(\n seg_logit,\n seg_label,\n ignore_index = self.ignore_index)\n\n loss['acc_seg'] = accuracy(seg_logit[\"pred_masks\"], seg_label, ignore_index=self.ignore_index)\n return loss"}],"string":"[\n {\n \"identifier\": \"TIMMVisionTransformer\",\n \"path\": \"model/backbone/timm_vit.py\",\n \"snippet\": \"class TIMMVisionTransformer(nn.Module):\\n\\n def __init__(\\n self,\\n variant,\\n timm_load_pretrained,\\n drop_path_rate,\\n img_size,\\n out_indices,\\n ):\\n super(TIMMVisionTransformer, self).__init__()\\n self.m = timm.create_model(\\n variant,\\n pretrained=timm_load_pretrained,\\n drop_path_rate=drop_path_rate,\\n img_size=img_size,\\n )\\n self.patch_size = self.m.patch_embed.patch_size\\n self.img_size = img_size\\n self.out_indices = out_indices\\n assert max(self.out_indices) <= 11\\n\\n def forward_features(self, x):\\n feats = []\\n x = self.m.patch_embed(x)\\n x = self.m._pos_embed(x)\\n if self.m.grad_checkpointing and not torch.jit.is_scripting():\\n raise ValueError(self.m.grad_checkpointing)\\n # x = checkpoint_seq(self.blocks, x)\\n else:\\n for i, block in enumerate(self.m.blocks):\\n x = block(x)\\n if i in self.out_indices:\\n out = self.m.norm(x)\\n feats.append(out)\\n x = self.m.norm(x)\\n return x, feats\\n\\n def forward(self, x: torch.Tensor):\\n if x.shape[-2] != self.m.patch_embed.img_size[0] or x.shape[-1] != self.m.patch_embed.img_size[1]:\\n assert not self.training\\n x = F.interpolate(x, size=self.img_size, mode='bilinear', align_corners=False)\\n B, _, H, W = x.shape\\n H = H // self.patch_size[0]\\n W = W // self.patch_size[1]\\n\\n x, feats = self.forward_features(x)\\n outs = [\\n tuple([f[:, 1:].reshape(B, H, W, -1).permute(0, 3, 1, 2) for f in feats]),\\n x[:, 0], # cls_token\\n ]\\n\\n return outs\"\n },\n {\n \"identifier\": \"DLV3PHead\",\n \"path\": \"model/decode_heads/dlv3p_head.py\",\n \"snippet\": \"class DLV3PHead(BaseDecodeHead):\\n\\n def __init__(self, c1_in_channels, c1_channels, dilations, img_size, **kwargs):\\n super(DLV3PHead, self).__init__(**kwargs)\\n self.image_size = img_size\\n self.aspp = ASPPModule(self.in_channels, dilations)\\n self.c1_proj = nn.Sequential(\\n nn.Conv2d(c1_in_channels, c1_channels, 1, bias=False),\\n nn.BatchNorm2d(c1_channels),\\n nn.ReLU(True))\\n fuse_channels = self.in_channels // 8 + c1_channels\\n self.head = nn.Sequential(\\n nn.Conv2d(fuse_channels, 256, 3, padding=1, bias=False),\\n nn.BatchNorm2d(256),\\n nn.ReLU(True),\\n nn.Conv2d(256, 256, 3, padding=1, bias=False),\\n nn.BatchNorm2d(256),\\n nn.ReLU(True),\\n nn.Conv2d(256, self.num_classes, 1, bias=True))\\n self.conv_seg = None\\n\\n def forward(self, inputs, force_output_pred_masks=False):\\n if force_output_pred_masks:\\n inputs = inputs[0][0]\\n assert len(inputs) == 2\\n c1, c4 = inputs[0], inputs[1]\\n\\n c4 = self.aspp(c4)\\n c1 = self.c1_proj(c1)\\n c4 = F.interpolate(c4, size=c1.shape[-2:], mode=\\\"bilinear\\\", align_corners=self.align_corners)\\n x = torch.cat([c1, c4], dim=1)\\n out = self.head(x)\\n\\n if force_output_pred_masks:\\n out = F.interpolate(out, size=(self.image_size, self.image_size),\\n mode='bilinear', align_corners=self.align_corners)\\n out = {\\\"pred_masks\\\": out}\\n\\n return out\"\n },\n {\n \"identifier\": \"VLGHead\",\n \"path\": \"model/decode_heads/vlg_head.py\",\n \"snippet\": \"class VLGHead(nn.Module):\\n def __init__(self,\\n img_size,\\n num_classes,\\n text_in_channels,\\n text_channels,\\n up_channels,\\n skip_in_channels,\\n skip_channels,\\n skip_from_conv_feat,\\n num_layers,\\n num_heads,\\n channels,\\n pool_size,\\n conv1_ksize,\\n loss_decode,\\n align_corners,\\n ) -> None:\\n super().__init__()\\n self.image_size = img_size\\n self.num_classes = num_classes\\n self.align_corners = align_corners\\n self.text_in_channels = text_in_channels\\n self.num_layers = num_layers\\n self.channels = channels\\n self.skip_from_conv_feat = skip_from_conv_feat\\n assert loss_decode is None\\n\\n self.conv1 = nn.Conv2d(1, channels, kernel_size=conv1_ksize, stride=1, padding=(conv1_ksize-1)//2)\\n self.aspp = ASPPModule(channels)\\n self.layers = nn.ModuleList([\\n SemanticTransformer(\\n channels=channels, text_channels=text_channels, num_heads=num_heads, pool_size=pool_size\\n ) for _ in range(num_layers)\\n ])\\n\\n self.text_proj = nn.Sequential(\\n nn.Linear(text_in_channels, text_channels),\\n nn.ReLU())\\n\\n self.skip_proj = nn.ModuleList([\\n nn.Sequential(\\n nn.Conv2d(sic, sc, kernel_size=3, stride=1, padding=1),\\n nn.ReLU(),\\n ) for sic, sc in zip(skip_in_channels, skip_channels)\\n ])\\n\\n self.up1 = Up(channels, up_channels[0], skip_channels[0])\\n self.up2 = Up(up_channels[0], up_channels[1], skip_channels[1])\\n self.head = nn.Conv2d(up_channels[1], 1, kernel_size=3, stride=1, padding=1)\\n\\n def forward(self, inputs, force_output_pred_masks=False):\\n inputs_both = inputs\\n img_feat_pyramid = inputs_both[0][0]\\n img_feats = img_feat_pyramid[-1]\\n if self.skip_from_conv_feat:\\n conv_feats = inputs_both[2]\\n if len(img_feat_pyramid) > 1:\\n skip_feats = [\\n *img_feat_pyramid[:-1][::-1],\\n *conv_feats[::-1],\\n ]\\n else:\\n skip_feats = conv_feats[::-1]\\n assert len(self.skip_proj) == len(skip_feats)\\n else:\\n skip_feats = img_feat_pyramid[:-1][::-1]\\n text_feats = inputs_both[1]\\n\\n text_feats = text_feats.repeat(img_feats.shape[0], 1, 1).float()\\n B, C, H, W = img_feats.shape\\n assert list(text_feats.shape) == [B, self.num_classes, C]\\n\\n # Compute Similarity Map\\n img_feats = F.normalize(img_feats, dim=1)\\n text_feats = F.normalize(text_feats, dim=-1)\\n x = torch.einsum('bchw, bnc -> bnhw', img_feats, text_feats)\\n\\n # Spatial Reasoning\\n x = rearrange(x, 'b n h w -> (b n) () h w')\\n x = self.conv1(x)\\n x = self.aspp(x)\\n x = rearrange(x, '(b n) c h w -> b c n h w', b=B)\\n\\n # Semantic Reasoning\\n if self.text_proj is not None:\\n text_feats = self.text_proj(text_feats)\\n\\n for layer in self.layers:\\n x = layer(x, text_feats)\\n\\n # Upsampling\\n if self.skip_proj is not None:\\n skip_feats = [proj(f) for proj, f in zip(self.skip_proj, skip_feats)]\\n\\n x = rearrange(x, 'b c n h w -> (b n) c h w')\\n x = self.up1(x, skip_feats[0])\\n x = self.up2(x, skip_feats[1])\\n x = self.head(x)\\n x = rearrange(x, '(b n) () h w -> b n h w', b=B)\\n\\n if x.shape[1] != self.num_classes:\\n cls2con = get_class_to_concept_idxs(self.load_text_embedding)\\n x = aggregate_concept_predictions(x, cls2con)\\n\\n if force_output_pred_masks:\\n x = F.interpolate(x, size=(self.image_size, self.image_size),\\n mode='bilinear', align_corners=self.align_corners)\\n x = {\\\"pred_masks\\\": x}\\n\\n return x\"\n },\n {\n \"identifier\": \"VLM\",\n \"path\": \"model/vlm.py\",\n \"snippet\": \"class VLM(EncoderDecoder):\\n def __init__(self,\\n freeze_backbone=False,\\n exclude_keys=None,\\n load_text_embedding=None,\\n load_mcc_text_embedding=None,\\n load_pl_text_embedding=None,\\n clip_encoder=None,\\n conv_encoder=None,\\n maskclip_class_filter=None,\\n maskclip_trust_head=None,\\n renorm_clip_img=False,\\n **args):\\n super(VLM, self).__init__(**args)\\n assert load_text_embedding == load_pl_text_embedding\\n assert maskclip_class_filter is None\\n assert maskclip_trust_head is None\\n self.local_iter = 0\\n\\n self.clip_encoder = None\\n if clip_encoder is not None:\\n self.clip_encoder = builder.build_backbone(clip_encoder)\\n self.conv_encoder = None\\n if conv_encoder is not None:\\n self.conv_encoder = builder.build_backbone(conv_encoder)\\n\\n self.load_text_embedding = load_text_embedding\\n self.decode_head.load_text_embedding = load_text_embedding\\n self.load_mcc_text_embedding = load_mcc_text_embedding\\n self.renorm_clip_img = renorm_clip_img\\n if renorm_clip_img:\\n print('Renormalize clip image.')\\n if self.load_mcc_text_embedding:\\n self.loaded_mcc_text_feat = np.load(self.load_mcc_text_embedding)\\n self.loaded_mcc_text_feat = torch.from_numpy(self.loaded_mcc_text_feat).float()\\n else:\\n raise NotImplementedError\\n\\n if freeze_backbone:\\n self.freeze(self.backbone, exclude_keys=exclude_keys)\\n\\n def renormalize_img_for_clip(self, img):\\n if not self.renorm_clip_img:\\n return img\\n loader_mean, loader_std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\\n clip_mean, clip_std = [0.48145466, 0.4578275, 0.40821073], [0.26862954, 0.26130258, 0.27577711]\\n loader_mean = torch.tensor(loader_mean, device=img.device).view(1, -1, 1, 1)\\n loader_std = torch.tensor(loader_std, device=img.device).view(1, -1, 1, 1)\\n clip_mean = torch.tensor(clip_mean, device=img.device).view(1, -1, 1, 1)\\n clip_std = torch.tensor(clip_std, device=img.device).view(1, -1, 1, 1)\\n return (img * loader_std + loader_mean - clip_mean) / clip_std\\n\\n def freeze(self, model, exclude_keys=None):\\n for n, m in model.named_parameters():\\n m.requires_grad = False\\n if exclude_keys is not None:\\n assert isinstance(exclude_keys, list)\\n for k in exclude_keys:\\n if str(k) in n:\\n m.requires_grad = True\\n print(f'Finetune {n}')\\n \\n def forward_maskclip(self, img, conf_tresh):\\n img = self.renormalize_img_for_clip(img)\\n self.clip_encoder.eval()\\n with torch.no_grad():\\n text_feat = self.loaded_mcc_text_feat.detach().to(img.device)\\n visual_feat, _ = self.clip_encoder(img)\\n visual_feat = visual_feat[-1]\\n\\n dense_pred = F.conv2d(visual_feat, text_feat[:, :, None, None])\\n if dense_pred.shape[1] != self.num_classes:\\n cls2con = get_class_to_concept_idxs(self.load_mcc_text_embedding)\\n dense_pred = aggregate_concept_predictions(dense_pred, cls2con)\\n assert dense_pred.shape[1] == self.num_classes\\n dense_pred = F.interpolate(dense_pred, size=img.shape[-2:],\\n mode='bilinear', align_corners=self.decode_head.align_corners)\\n dense_pred = (100.0 * dense_pred).softmax(dim=1)\\n dense_pred_certainty, dense_pred = dense_pred.max(dim=1)\\n\\n filtered_dense_pred = dense_pred.clone()\\n filtered_dense_pred[dense_pred_certainty < conf_tresh] = 255\\n return filtered_dense_pred\\n\\n def extract_feat(self, img):\\n orig_img = img\\n img = self.renormalize_img_for_clip(img)\\n visual_feat = self.backbone(img)\\n text_feat = np.load(self.load_text_embedding)\\n text_feat = torch.from_numpy(text_feat).to(img.device)\\n self.decode_head.load_text_embedding = self.load_text_embedding\\n conv_feat = None\\n if self.conv_encoder is not None:\\n conv_feat = self.conv_encoder(orig_img)\\n\\n return [visual_feat, text_feat, conv_feat]\\n\\n def _decode_head_forward_test(self, x, img_metas):\\n seg_logits = self.decode_head.forward(x, force_output_pred_masks=True)['pred_masks']\\n return seg_logits\"\n },\n {\n \"identifier\": \"MaskClipVisionTransformer\",\n \"path\": \"third_party/maskclip/models/backbones/maskclip_vit.py\",\n \"snippet\": \"class MaskClipVisionTransformer(BaseModule):\\n \\\"\\\"\\\"Vision Transformer.\\n\\n This backbone is the implementation of `An Image is Worth 16x16 Words:\\n Transformers for Image Recognition at\\n Scale <https://arxiv.org/abs/2010.11929>`_.\\n\\n Args:\\n img_size (int | tuple): Input image size. Default: 224.\\n patch_size (int): The patch size. Default: 16.\\n in_channels (int): Number of input channels. Default: 3.\\n embed_dims (int): embedding dimension. Default: 768.\\n num_layers (int): depth of transformer. Default: 12.\\n num_heads (int): number of attention heads. Default: 12.\\n mlp_ratio (int): ratio of mlp hidden dim to embedding dim.\\n Default: 4.\\n out_indices (list | tuple | int): Output from which stages.\\n Default: -1.\\n qkv_bias (bool): enable bias for qkv if True. Default: True.\\n drop_rate (float): Probability of an element to be zeroed.\\n Default 0.0\\n attn_drop_rate (float): The drop out rate for attention layer.\\n Default 0.0\\n drop_path_rate (float): stochastic depth rate. Default 0.0\\n with_cls_token (bool): Whether concatenating class token into image\\n tokens as transformer input. Default: True.\\n output_cls_token (bool): Whether output the cls_token. If set True,\\n `with_cls_token` must be True. Default: False.\\n norm_cfg (dict): Config dict for normalization layer.\\n Default: dict(type='LN')\\n act_cfg (dict): The activation config for FFNs.\\n Default: dict(type='GELU').\\n patch_norm (bool): Whether to add a norm in PatchEmbed Block.\\n Default: False.\\n final_norm (bool): Whether to add a additional layer to normalize\\n final feature map. Default: False.\\n interpolate_mode (str): Select the interpolate mode for position\\n embeding vector resize. Default: bicubic.\\n num_fcs (int): The number of fully-connected layers for FFNs.\\n Default: 2.\\n norm_eval (bool): Whether to set norm layers to eval mode, namely,\\n freeze running stats (mean and var). Note: Effect on Batch Norm\\n and its variants only. Default: False.\\n with_cp (bool): Use checkpoint or not. Using checkpoint will save\\n some memory while slowing down the training speed. Default: False.\\n pretrained (str, optional): model pretrained path. Default: None.\\n init_cfg (dict or list[dict], optional): Initialization config dict.\\n Default: None.\\n \\\"\\\"\\\"\\n\\n def __init__(self,\\n img_size=224,\\n patch_size=16,\\n patch_bias=True,\\n in_channels=3,\\n embed_dims=768,\\n num_layers=12,\\n num_heads=12,\\n mlp_ratio=4,\\n out_indices=-1,\\n qkv_bias=True,\\n drop_rate=0.,\\n attn_drop_rate=0.,\\n drop_path_rate=0.,\\n with_cls_token=True,\\n output_cls_token=False,\\n norm_cfg=dict(type='LN'),\\n act_cfg=dict(type='GELU'),\\n patch_norm=False,\\n pre_norm=False,\\n final_norm=False,\\n return_qkv=False,\\n return_clip_embed=False,\\n skip_last_attn=False,\\n interpolate_mode='bicubic',\\n num_fcs=2,\\n norm_eval=False,\\n with_cp=False,\\n pretrained=None,\\n num_prompt_tokens=None,\\n lora_layers=[],\\n lora_r=4,\\n lora_scaling=1,\\n lora_dropout=0,\\n lora_targets='qkvo',\\n init_cfg=None):\\n super(MaskClipVisionTransformer, self).__init__(init_cfg=init_cfg)\\n\\n if isinstance(img_size, int):\\n img_size = to_2tuple(img_size)\\n elif isinstance(img_size, tuple):\\n if len(img_size) == 1:\\n img_size = to_2tuple(img_size[0])\\n assert len(img_size) == 2, \\\\\\n f'The size of image should have length 1 or 2, ' \\\\\\n f'but got {len(img_size)}'\\n\\n if output_cls_token:\\n assert with_cls_token is True, f'with_cls_token must be True if' \\\\\\n f'set output_cls_token to True, but got {with_cls_token}'\\n\\n assert not (init_cfg and pretrained), \\\\\\n 'init_cfg and pretrained cannot be set at the same time'\\n if isinstance(pretrained, str):\\n warnings.warn('DeprecationWarning: pretrained is deprecated, '\\n 'please use \\\"init_cfg\\\" instead')\\n self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)\\n elif pretrained is not None:\\n raise TypeError('pretrained must be a str or None')\\n\\n self.img_size = img_size\\n self.patch_size = patch_size\\n self.interpolate_mode = interpolate_mode\\n self.norm_eval = norm_eval\\n self.with_cp = with_cp\\n self.pretrained = pretrained\\n self.num_prompt_tokens = num_prompt_tokens\\n\\n self.patch_embed = PatchEmbed(\\n in_channels=in_channels,\\n embed_dims=embed_dims,\\n conv_type='Conv2d',\\n kernel_size=patch_size,\\n stride=patch_size,\\n padding='corner',\\n bias=patch_bias,\\n norm_cfg=norm_cfg if patch_norm else None,\\n init_cfg=None,\\n )\\n\\n num_patches = (img_size[0] // patch_size) * \\\\\\n (img_size[1] // patch_size)\\n\\n self.with_cls_token = with_cls_token\\n self.output_cls_token = output_cls_token\\n self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))\\n self.pos_embed = nn.Parameter(\\n torch.zeros(1, num_patches + 1, embed_dims))\\n self.drop_after_pos = nn.Dropout(p=drop_rate)\\n\\n if out_indices is None:\\n self.out_indices = [num_layers]\\n elif isinstance(out_indices, int):\\n if out_indices == -1:\\n out_indices = num_layers - 1\\n self.out_indices = [out_indices]\\n elif isinstance(out_indices, list) or isinstance(out_indices, tuple):\\n self.out_indices = out_indices\\n else:\\n raise TypeError('out_indices must be type of int, list or tuple')\\n\\n dpr = [\\n x.item() for x in torch.linspace(0, drop_path_rate, num_layers)\\n ] # stochastic depth decay rule\\n\\n self.layers = ModuleList()\\n for i in range(num_layers):\\n self.layers.append(\\n TransformerEncoderLayer(\\n embed_dims=embed_dims,\\n num_heads=num_heads,\\n feedforward_channels=mlp_ratio * embed_dims,\\n attn_drop_rate=attn_drop_rate,\\n drop_rate=drop_rate,\\n drop_path_rate=dpr[i],\\n num_fcs=num_fcs,\\n qkv_bias=qkv_bias,\\n act_cfg=act_cfg,\\n norm_cfg=norm_cfg,\\n lora=(i in lora_layers),\\n lora_r=lora_r,\\n lora_scaling=lora_scaling,\\n lora_dropout=lora_dropout,\\n lora_targets=lora_targets,\\n batch_first=True))\\n\\n self.pre_norm = pre_norm\\n if pre_norm:\\n self.norm0_name, norm0 = build_norm_layer(\\n norm_cfg, embed_dims, postfix=0)\\n self.add_module(self.norm0_name, norm0)\\n\\n self.final_norm = final_norm\\n if final_norm:\\n self.norm1_name, norm1 = build_norm_layer(\\n norm_cfg, embed_dims, postfix=1)\\n self.add_module(self.norm1_name, norm1)\\n\\n self.return_clip_embed = return_clip_embed\\n if self.return_clip_embed:\\n self.proj = nn.Conv2d(embed_dims, 512, 1, bias=False)\\n self.add_module('proj', self.proj)\\n\\n self.return_qkv = [False] * num_layers\\n if isinstance(return_qkv, bool):\\n for out_i in self.out_indices:\\n if out_i >= num_layers:\\n continue\\n self.return_qkv[out_i] = return_qkv\\n elif isinstance(return_qkv, list) or isinstance(return_qkv, tuple):\\n for i, out_i in enumerate(self.out_indices):\\n if out_i >= num_layers:\\n continue\\n self.return_qkv[out_i] = return_qkv[i]\\n else:\\n raise TypeError('return_qkv must be type of bool, list or tuple')\\n if self.return_clip_embed:\\n self.return_qkv[num_layers - 1] = True\\n\\n self.skip_last_attn = skip_last_attn\\n\\n if self.num_prompt_tokens is not None:\\n val = math.sqrt(6. / float(3 * reduce(mul, (patch_size, patch_size), 1) + embed_dims))\\n\\n self.deep_prompt_embeddings = nn.Parameter(torch.zeros(num_layers, self.num_prompt_tokens, embed_dims))\\n nn.init.uniform_(self.deep_prompt_embeddings.data, -val, val)\\n\\n self.prompt_proj = nn.Linear(embed_dims, embed_dims)\\n nn.init.kaiming_normal_(self.prompt_proj.weight, a=0, mode='fan_out')\\n self.prompt_norm = LayerNorm(embed_dims, eps=1e-6)\\n self.prompt_dropout = nn.Dropout(0.1)\\n\\n @property\\n def norm0(self):\\n return getattr(self, self.norm0_name)\\n \\n @property\\n def norm1(self):\\n return getattr(self, self.norm1_name)\\n\\n def init_weights(self):\\n if (isinstance(self.init_cfg, dict)\\n and self.init_cfg.get('type') == 'Pretrained'):\\n logger = get_root_logger()\\n checkpoint = _load_checkpoint(\\n self.init_cfg['checkpoint'], logger=logger, map_location='cpu')\\n\\n if 'state_dict' in checkpoint:\\n state_dict = checkpoint['state_dict']\\n else:\\n state_dict = checkpoint\\n logger.info(msg='Remove backbone prefix from state_dict.')\\n state_dict = {k.replace('backbone.', ''): v for k, v in state_dict.items()}\\n\\n if 'pos_embed' in state_dict.keys():\\n if self.pos_embed.shape != state_dict['pos_embed'].shape:\\n logger.info(msg=f'Resize the pos_embed shape from '\\n f'{state_dict[\\\"pos_embed\\\"].shape} to '\\n f'{self.pos_embed.shape}')\\n h, w = self.img_size\\n pos_size = int(\\n math.sqrt(state_dict['pos_embed'].shape[1] - 1))\\n state_dict['pos_embed'] = self.resize_pos_embed(\\n state_dict['pos_embed'],\\n (h // self.patch_size, w // self.patch_size),\\n (pos_size, pos_size), self.interpolate_mode)\\n\\n if self.return_clip_embed:\\n state_dict['proj.weight'] = state_dict['proj.weight'][:, :, None, None]\\n else:\\n state_dict.pop('proj.weight')\\n\\n print(self.load_state_dict(state_dict, False))\\n elif self.init_cfg is not None:\\n super(MaskClipVisionTransformer, self).init_weights()\\n else:\\n # We only implement the 'jax_impl' initialization implemented at\\n # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501\\n trunc_normal_(self.pos_embed, std=.02)\\n trunc_normal_(self.cls_token, std=.02)\\n for n, m in self.named_modules():\\n if isinstance(m, nn.Linear):\\n trunc_normal_(m.weight, std=.02)\\n if m.bias is not None:\\n if 'ffn' in n:\\n nn.init.normal_(m.bias, mean=0., std=1e-6)\\n else:\\n nn.init.constant_(m.bias, 0)\\n elif isinstance(m, nn.Conv2d):\\n kaiming_init(m, mode='fan_in', bias=0.)\\n elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):\\n constant_init(m, val=1.0, bias=0.)\\n\\n def _pos_embeding(self, patched_img, hw_shape, pos_embed):\\n \\\"\\\"\\\"Positiong embeding method.\\n\\n Resize the pos_embed, if the input image size doesn't match\\n the training size.\\n Args:\\n patched_img (torch.Tensor): The patched image, it should be\\n shape of [B, L1, C].\\n hw_shape (tuple): The downsampled image resolution.\\n pos_embed (torch.Tensor): The pos_embed weighs, it should be\\n shape of [B, L2, c].\\n Return:\\n torch.Tensor: The pos encoded image feature.\\n \\\"\\\"\\\"\\n assert patched_img.ndim == 3 and pos_embed.ndim == 3, \\\\\\n 'the shapes of patched_img and pos_embed must be [B, L, C]'\\n x_len, pos_len = patched_img.shape[1], pos_embed.shape[1]\\n if x_len != pos_len:\\n if pos_len == (self.img_size[0] // self.patch_size) * (\\n self.img_size[1] // self.patch_size) + 1:\\n pos_h = self.img_size[0] // self.patch_size\\n pos_w = self.img_size[1] // self.patch_size\\n else:\\n raise ValueError(\\n 'Unexpected shape of pos_embed, got {}.'.format(\\n pos_embed.shape))\\n pos_embed = self.resize_pos_embed(pos_embed, hw_shape,\\n (pos_h, pos_w),\\n self.interpolate_mode)\\n return self.drop_after_pos(patched_img + pos_embed)\\n\\n @staticmethod\\n def resize_pos_embed(pos_embed, input_shpae, pos_shape, mode):\\n \\\"\\\"\\\"Resize pos_embed weights.\\n\\n Resize pos_embed using bicubic interpolate method.\\n Args:\\n pos_embed (torch.Tensor): Position embedding weights.\\n input_shpae (tuple): Tuple for (downsampled input image height,\\n downsampled input image width).\\n pos_shape (tuple): The resolution of downsampled origin training\\n image.\\n mode (str): Algorithm used for upsampling:\\n ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` |\\n ``'trilinear'``. Default: ``'nearest'``\\n Return:\\n torch.Tensor: The resized pos_embed of shape [B, L_new, C]\\n \\\"\\\"\\\"\\n assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]'\\n pos_h, pos_w = pos_shape\\n cls_token_weight = pos_embed[:, 0]\\n pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):]\\n pos_embed_weight = pos_embed_weight.reshape(\\n 1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2)\\n pos_embed_weight = resize(\\n pos_embed_weight, size=input_shpae, align_corners=False, mode=mode)\\n cls_token_weight = cls_token_weight.unsqueeze(1)\\n pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2)\\n pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1)\\n return pos_embed\\n\\n def forward(self, inputs):\\n B = inputs.shape[0]\\n\\n x, hw_shape = self.patch_embed(inputs)\\n\\n # stole cls_tokens impl from Phil Wang, thanks\\n cls_tokens = self.cls_token.expand(B, -1, -1)\\n x = torch.cat((cls_tokens, x), dim=1)\\n x = self._pos_embeding(x, hw_shape, self.pos_embed)\\n\\n if not self.with_cls_token:\\n # Remove class token for transformer encoder input\\n x = x[:, 1:]\\n\\n if self.pre_norm:\\n x = self.norm0(x)\\n\\n outs = []\\n for i, layer in enumerate(self.layers):\\n # add deep prompt\\n if self.num_prompt_tokens is not None:\\n deep_prompt_emb = self.prompt_dropout(self.prompt_proj(self.deep_prompt_embeddings[i]).expand(B, -1, -1))\\n assert self.with_cls_token\\n assert x.shape[1] == 1 + hw_shape[0] * hw_shape[1], x.shape\\n x = torch.cat((\\n x[:, :1, :],\\n deep_prompt_emb,\\n x[:, 1:, :]\\n ), dim=1)\\n assert x.shape[1] == 1 + self.num_prompt_tokens + hw_shape[0] * hw_shape[1], x.shape\\n x, q, k, v = layer(x, self.return_qkv[i] \\\\\\n or (i==len(self.layers)-1 and self.skip_last_attn))\\n # remove deep prompt\\n if self.num_prompt_tokens is not None:\\n x = torch.cat((\\n x[:, :1, :],\\n x[:, 1+self.num_prompt_tokens:, :]\\n ), dim=1)\\n assert x.shape[1] == 1 + hw_shape[0] * hw_shape[1]\\n if v is not None:\\n v = torch.cat((\\n v[:, :1, :],\\n v[:, 1+self.num_prompt_tokens:, :]\\n ), dim=1)\\n assert v.shape[1] == 1 + hw_shape[0] * hw_shape[1]\\n if i == len(self.layers) - 1:\\n if self.final_norm:\\n x = self.norm1(x)\\n if self.return_qkv[i]:\\n v = self.norm1(v)\\n if self.skip_last_attn:\\n if self.with_cls_token:\\n x[:, 1:] = v[:, 1:]\\n else:\\n x = v\\n if self.return_clip_embed:\\n visual_embedding = v\\n if self.with_cls_token:\\n visual_embedding = visual_embedding[:, 1:]\\n B, _, C = visual_embedding.shape\\n visual_embedding = visual_embedding.reshape(B, hw_shape[0], hw_shape[1],\\n C).permute(0, 3, 1, 2).contiguous()\\n visual_embedding = self.proj(visual_embedding)\\n visual_embedding = visual_embedding / visual_embedding.norm(dim=1, keepdim=True)\\n if i in self.out_indices:\\n if self.with_cls_token:\\n # Remove class token and reshape token for decoder head\\n out = x[:, 1:]\\n else:\\n out = x\\n B, _, C = out.shape\\n out = out.reshape(B, hw_shape[0], hw_shape[1],\\n C).permute(0, 3, 1, 2).contiguous()\\n if self.output_cls_token:\\n out = [out, x[:, 0]]\\n if self.return_qkv[i]:\\n if self.with_cls_token:\\n q = q[:, 1:]\\n k = k[:, 1:]\\n v = v[:, 1:]\\n v = v.reshape(B, hw_shape[0], hw_shape[1],\\n C).permute(0, 3, 1, 2).contiguous()\\n out = [out, q, k, v]\\n outs.append(out)\\n\\n if self.return_clip_embed:\\n features = []\\n for o in outs:\\n if isinstance(o, list):\\n # from return_qkv\\n assert len(o) == 4\\n features.append(o[3])\\n else:\\n features.append(o)\\n if len(self.layers) in self.out_indices:\\n features.append(visual_embedding)\\n global_embedding = self.proj(x[:, 0][:, :, None, None])[:, :, 0, 0]\\n global_embedding = global_embedding / global_embedding.norm(dim=1, keepdim=True)\\n\\n outs = [\\n tuple(features),\\n global_embedding\\n ]\\n\\n return outs\\n\\n def train(self, mode=True):\\n super(MaskClipVisionTransformer, self).train(mode)\\n if mode and self.norm_eval:\\n for m in self.modules():\\n if isinstance(m, nn.LayerNorm):\\n m.eval()\"\n },\n {\n \"identifier\": \"MaskClip2Head\",\n \"path\": \"third_party/maskclip/models/decode_heads/maskclip2_head.py\",\n \"snippet\": \"class MaskClip2Head(BaseDecodeHead):\\n\\n def __init__(self, img_size, **kwargs):\\n super(MaskClip2Head, self).__init__(**kwargs)\\n self.img_size = img_size\\n\\n def forward(self, inputs, force_output_pred_masks=False):\\n assert force_output_pred_masks\\n inputs_both = inputs\\n inputs = inputs_both[0][0]\\n cls_token = inputs_both[0][1]\\n txt_embed = inputs_both[1]\\n feat = inputs[-1]\\n\\n output = self.cls_seg(feat, txt_embed)\\n\\n output = F.interpolate(output, size=(self.img_size, self.img_size),\\n mode='bilinear', align_corners=self.align_corners)\\n output = {\\\"pred_masks\\\": output}\\n\\n return output\\n\\n def cls_seg(self, feat, txt_embed):\\n txt_embed = txt_embed.to(feat.dtype)\\n output = F.conv2d(feat, txt_embed[:, :, None, None])\\n \\n return output\\n\\n def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg):\\n raise RuntimeError('MaskClip is not trainable. Try MaskClip+ instead.')\"\n },\n {\n \"identifier\": \"MaskClipHead\",\n \"path\": \"third_party/maskclip/models/decode_heads/maskclip_head.py\",\n \"snippet\": \"class MaskClipHead(BaseDecodeHead):\\n\\n def __init__(self, text_categories, text_channels, text_embeddings_path,\\n visual_projs_path, vit=False, ks_thresh=0., pd_thresh=0.,\\n attn_pooling=False, num_heads=32, **kwargs):\\n super(MaskClipHead, self).__init__(**kwargs)\\n\\n self.text_categories = text_categories\\n self.text_channels = text_channels\\n self.text_embeddings_path = text_embeddings_path\\n self.visual_projs_path = visual_projs_path\\n\\n if self.text_embeddings_path is None:\\n self.text_embeddings = nn.Parameter(torch.zeros(text_categories, text_channels))\\n nn.init.normal_(self.text_embeddings, mean=0.0, std=0.01)\\n else:\\n self.register_buffer('text_embeddings', torch.randn(text_categories, text_channels))\\n self.load_text_embeddings()\\n \\n self.vit = vit\\n if vit:\\n self.proj = nn.Conv2d(self.in_channels, text_channels, 1, bias=False)\\n else:\\n self.q_proj = nn.Conv2d(self.in_channels, self.in_channels, 1)\\n self.k_proj = nn.Conv2d(self.in_channels, self.in_channels, 1)\\n self.v_proj = nn.Conv2d(self.in_channels, self.in_channels, 1)\\n self.c_proj = nn.Conv2d(self.in_channels, text_channels, 1)\\n self.load_visual_projs()\\n\\n self.ks_thresh = ks_thresh\\n self.pd_thresh = pd_thresh\\n self.attn_pooling = attn_pooling\\n self.num_heads = num_heads\\n\\n def init_weights(self):\\n super(MaskClipHead, self).init_weights()\\n if self.text_embeddings_path is None:\\n nn.init.normal_(self.text_embeddings, mean=0.0, std=0.01)\\n else:\\n self.load_text_embeddings()\\n self.load_visual_projs()\\n\\n def load_text_embeddings(self):\\n loaded = torch.load(self.text_embeddings_path, map_location='cuda')\\n self.text_embeddings[:, :] = loaded[:, :]\\n print_log(f'Loaded text embeddings from {self.text_embeddings_path}', logger=get_root_logger())\\n\\n def load_visual_projs(self):\\n loaded = torch.load(self.visual_projs_path, map_location='cuda')\\n attrs = ['proj'] if self.vit else ['q_proj', 'k_proj', 'v_proj', 'c_proj']\\n for attr in attrs:\\n current_attr = getattr(self, attr)\\n state_dict = loaded[attr]\\n for key in state_dict:\\n if 'weight' in key:\\n state_dict[key] = state_dict[key][:, :, None, None]\\n current_attr.load_state_dict(state_dict)\\n print_log(f'Loaded proj weights from {self.visual_projs_path}', logger=get_root_logger())\\n \\n def forward(self, inputs):\\n x = self._transform_inputs(inputs)\\n q, k, v, cls_token = None, None, None, None\\n if self.vit:\\n if isinstance(x, list) and len(x) == 4:\\n x, q, k, v = x\\n if isinstance(x, list) and len(x) == 2:\\n x, cls_token = x\\n if v is not None:\\n feat = self.proj(v)\\n else:\\n feat = self.proj(x)\\n if cls_token is not None:\\n cls_token = self.proj(cls_token[:, :, None, None])[:, :, 0, 0]\\n else:\\n if self.attn_pooling:\\n N, C, H, W = x.shape\\n x = x.view(N, C, -1).permute(2, 0, 1) # NCHW -> (HW)NC\\n x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)\\n x, _ = F.multi_head_attention_forward(\\n query=x, key=x, value=x,\\n embed_dim_to_check=x.shape[-1],\\n num_heads=self.num_heads,\\n q_proj_weight=self.q_proj.weight[:, :, 0, 0],\\n k_proj_weight=self.k_proj.weight[:, :, 0, 0],\\n v_proj_weight=self.v_proj.weight[:, :, 0, 0],\\n in_proj_weight=None,\\n in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),\\n bias_k=None,\\n bias_v=None,\\n add_zero_attn=False,\\n dropout_p=0,\\n out_proj_weight=self.c_proj.weight[:, :, 0, 0],\\n out_proj_bias=self.c_proj.bias,\\n use_separate_proj_weight=True,\\n training=self.training,\\n need_weights=False\\n )\\n feat = x[1:].permute(1, 2, 0).view(N, -1, H, W)\\n else:\\n q = self.q_proj(x)\\n k = self.k_proj(x)\\n q = torch.flatten(q, start_dim=2).transpose(-2, -1)\\n k = torch.flatten(k, start_dim=2).transpose(-2, -1)\\n v = self.v_proj(x)\\n feat = self.c_proj(v)\\n output = self.cls_seg(feat)\\n if not self.training:\\n output = self.refine_output(output, k)\\n\\n return output\\n\\n def cls_seg(self, feat):\\n feat = feat / feat.norm(dim=1, keepdim=True)\\n output = F.conv2d(feat, self.text_embeddings[:, :, None, None])\\n \\n return output\\n\\n def refine_output(self, output, k):\\n if self.pd_thresh > 0:\\n N, C, H, W = output.shape\\n _output = F.softmax(output*100, dim=1)\\n max_cls_conf = _output.view(N, C, -1).max(dim=-1)[0]\\n selected_cls = (max_cls_conf < self.pd_thresh)[:, :, None, None].expand(N, C, H, W)\\n output[selected_cls] = -100\\n\\n if k is not None and self.ks_thresh > 0:\\n output = F.softmax(output*100, dim=1)\\n N, C, H, W = output.shape\\n output = output.view(N, C, -1).transpose(-2, -1)\\n # softmax\\n # weight = k @ k.transpose(-2, -1)\\n # weight = F.softmax(weight, dim=-1)\\n # L2 distance\\n k = F.normalize(k, p=2)\\n weight = k @ k.transpose(-2, -1)\\n\\n selected_pos = (output.max(dim=-1, keepdim=True)[0] < self.ks_thresh)\\n selected_pos = selected_pos.expand(-1, -1, C)\\n\\n weighted_output = weight @ output\\n output[selected_pos] = weighted_output[selected_pos]\\n output = output.transpose(-2, -1).view(N, C, H, W)\\n\\n return output\\n\\n def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg):\\n raise RuntimeError('MaskClip is not trainable. Try MaskClip+ instead.')\"\n },\n {\n \"identifier\": \"DeepLabV3Plus\",\n \"path\": \"third_party/unimatch/model/semseg/deeplabv3plus.py\",\n \"snippet\": \"class DeepLabV3Plus(nn.Module):\\n def __init__(self, cfg):\\n super(DeepLabV3Plus, self).__init__()\\n\\n if 'resnet' in cfg['backbone']:\\n self.backbone = resnet.__dict__[cfg['backbone']](pretrained=True, \\n replace_stride_with_dilation=cfg['replace_stride_with_dilation'])\\n else:\\n assert cfg['backbone'] == 'xception'\\n self.backbone = xception(pretrained=True)\\n\\n low_channels = 256\\n high_channels = 2048\\n\\n self.head = ASPPModule(high_channels, cfg['dilations'])\\n\\n self.reduce = nn.Sequential(nn.Conv2d(low_channels, 48, 1, bias=False),\\n nn.BatchNorm2d(48),\\n nn.ReLU(True))\\n\\n self.fuse = nn.Sequential(nn.Conv2d(high_channels // 8 + 48, 256, 3, padding=1, bias=False),\\n nn.BatchNorm2d(256),\\n nn.ReLU(True),\\n nn.Conv2d(256, 256, 3, padding=1, bias=False),\\n nn.BatchNorm2d(256),\\n nn.ReLU(True))\\n\\n self.classifier = nn.Conv2d(256, cfg['nclass'], 1, bias=True)\\n\\n def forward(self, x, need_fp=False, only_fp=False):\\n h, w = x.shape[-2:]\\n\\n feats = self.backbone.base_forward(x)\\n c1, c4 = feats[0], feats[-1]\\n\\n if only_fp:\\n out_fp = self._decode(nn.Dropout2d(0.5)(c1),\\n nn.Dropout2d(0.5)(c4))\\n out_fp = F.interpolate(out_fp, size=(h, w), mode=\\\"bilinear\\\", align_corners=True)\\n return out_fp\\n elif need_fp:\\n outs = self._decode(torch.cat((c1, nn.Dropout2d(0.5)(c1))),\\n torch.cat((c4, nn.Dropout2d(0.5)(c4))))\\n outs = F.interpolate(outs, size=(h, w), mode=\\\"bilinear\\\", align_corners=True)\\n out, out_fp = outs.chunk(2)\\n\\n return out, out_fp\\n\\n out = self._decode(c1, c4)\\n out = F.interpolate(out, size=(h, w), mode=\\\"bilinear\\\", align_corners=True)\\n\\n return out\\n\\n def _decode(self, c1, c4):\\n c4 = self.head(c4)\\n c4 = F.interpolate(c4, size=c1.shape[-2:], mode=\\\"bilinear\\\", align_corners=True)\\n\\n c1 = self.reduce(c1)\\n\\n feature = torch.cat([c1, c4], dim=1)\\n feature = self.fuse(feature)\\n\\n out = self.classifier(feature)\\n\\n return out\"\n },\n {\n \"identifier\": \"SegLossPlus\",\n \"path\": \"third_party/zegclip/losses/atm_loss.py\",\n \"snippet\": \"class SegLossPlus(nn.Module):\\n \\\"\\\"\\\"ATMLoss.\\n \\\"\\\"\\\"\\n def __init__(self,\\n num_classes,\\n dec_layers,\\n mask_weight=20.0,\\n dice_weight=1.0,\\n loss_weight=1.0,\\n use_point=False):\\n super(SegLossPlus, self).__init__()\\n weight_dict = {\\\"loss_mask\\\": mask_weight, \\\"loss_dice\\\": dice_weight}\\n aux_weight_dict = {}\\n for i in range(dec_layers - 1):\\n aux_weight_dict.update({k + f\\\"_{i}\\\": v for k, v in weight_dict.items()})\\n weight_dict.update(aux_weight_dict)\\n\\n self.criterion = SegPlusCriterion(\\n num_classes,\\n weight_dict=weight_dict,\\n losses=[\\\"masks\\\"],\\n )\\n\\n self.loss_weight = loss_weight\\n\\n def forward(self,\\n outputs,\\n label,\\n ignore_index=255,\\n ):\\n \\\"\\\"\\\"Forward function.\\\"\\\"\\\"\\n \\n self.ignore_index = ignore_index\\n targets = self.prepare_targets(label)\\n losses = self.criterion(outputs, targets)\\n\\n for k in list(losses.keys()):\\n if k in self.criterion.weight_dict:\\n losses[k] = losses[k] * self.criterion.weight_dict[k] * self.loss_weight\\n else:\\n # remove this loss if not specified in `weight_dict`\\n losses.pop(k)\\n\\n return losses\\n\\n def prepare_targets(self, targets):\\n new_targets = []\\n for targets_per_image in targets:\\n # gt_cls\\n gt_cls = targets_per_image.unique()\\n gt_cls = gt_cls[gt_cls != self.ignore_index]\\n masks = []\\n for cls in gt_cls:\\n masks.append(targets_per_image == cls)\\n if len(gt_cls) == 0:\\n masks.append(targets_per_image == self.ignore_index)\\n\\n masks = torch.stack(masks, dim=0)\\n new_targets.append(\\n {\\n \\\"labels\\\": gt_cls,\\n \\\"target_masks\\\": masks,\\n \\\"masks\\\": targets_per_image,\\n }\\n )\\n return new_targets\"\n },\n {\n \"identifier\": \"CLIPVisionTransformer\",\n \"path\": \"third_party/zegclip/models/backbones/clip_vit.py\",\n \"snippet\": \"class CLIPVisionTransformer(nn.Module):\\n def __init__(self, input_resolution=224, patch_size=32, width=768, layers=12, heads=12, output_dim=512, drop_path_rate=0.0,\\n out_indices=[3, 5, 7, 11], pretrained=None, get_embeddings=False, embed_v=False, **kwargs):\\n super().__init__()\\n self.pretrained = pretrained\\n self.input_resolution = input_resolution\\n self.output_dim = output_dim\\n self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)\\n\\n scale = width ** -0.5\\n self.class_embedding = nn.Parameter(scale * torch.randn(width))\\n self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))\\n self.spatial_size = input_resolution // patch_size\\n self.ln_pre = LayerNorm(width)\\n self.get_embeddings = get_embeddings\\n self.embed_v = embed_v\\n\\n self.transformer = Transformer(width, layers, heads, drop_path_rate=drop_path_rate)\\n\\n self.out_indices = out_indices\\n\\n if get_embeddings:\\n self.ln_post = LayerNorm(width)\\n self.proj = nn.Parameter(scale * torch.randn(width, output_dim))\\n\\n self.patch_size = patch_size\\n\\n def init_weights(self, pretrained=None):\\n pretrained = pretrained or self.pretrained\\n if isinstance(pretrained, str):\\n checkpoint = torch.jit.load(pretrained, map_location='cpu').float().state_dict()\\n\\n state_dict = {}\\n\\n for k in checkpoint.keys():\\n if k.startswith('visual.'):\\n new_k = k.replace('visual.', '')\\n state_dict[new_k] = checkpoint[k]\\n\\n if 'positional_embedding' in state_dict.keys():\\n if self.positional_embedding.shape != state_dict['positional_embedding'].shape:\\n # (1025, 768) (197, 768) upsample the positional_embedding for larger input\\n print(f'Resize the pos_embed shape from {state_dict[\\\"positional_embedding\\\"].shape} to {self.positional_embedding.shape}')\\n cls_pos = state_dict[\\\"positional_embedding\\\"][0:1, :]\\n if self.patch_size == 16:\\n spatial_pos = F.interpolate(state_dict[\\\"positional_embedding\\\"][1:,].reshape(1, 14, 14, 768).permute(0, 3, 1, 2), size=(self.spatial_size, self.spatial_size), mode='bilinear')\\n elif self.patch_size == 32:\\n spatial_pos = F.interpolate(state_dict[\\\"positional_embedding\\\"][1:,].reshape(1, 7, 7, 768).permute(0, 3, 1, 2), size=(self.spatial_size, self.spatial_size), mode='bilinear')\\n else:\\n assert ValueError('Patch Size should be 16 or 32')\\n spatial_pos = spatial_pos.reshape(768, self.spatial_size*self.spatial_size).permute(1, 0)\\n positional_embedding = torch.cat([cls_pos, spatial_pos], dim=0)\\n state_dict['positional_embedding'] = positional_embedding\\n assert self.positional_embedding.shape == state_dict['positional_embedding'].shape\\n\\n u, w = self.load_state_dict(state_dict, False)\\n print(u, w, 'are misaligned params in vision transformer')\\n\\n\\n def forward(self, x: torch.Tensor):\\n x = self.conv1(x)\\n B, C, H, W = x.shape\\n x = x.reshape(x.shape[0], x.shape[1], -1)\\n x = x.permute(0, 2, 1)\\n x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)\\n\\n pos = self.positional_embedding.to(x.dtype)\\n cls_pos = pos[0,:] + self.class_embedding.to(x.dtype)\\n spatial_pos = F.interpolate(pos[1:,].reshape(1, self.spatial_size, self.spatial_size, C).permute(0, 3, 1, 2), size=(H, W), mode='bilinear')\\n spatial_pos = spatial_pos.reshape(1, C, H*W).permute(0, 2, 1)\\n pos = torch.cat([cls_pos.reshape(1, 1, C), spatial_pos], dim=1)\\n x = x + pos\\n x = self.ln_pre(x)\\n x = x.permute(1, 0, 2) # NLD -> LND\\n\\n features = []\\n outs = []\\n for i, blk in enumerate(self.transformer.resblocks):\\n if self.embed_v and i == len(self.transformer.resblocks) - 1:\\n y = blk.ln_1(x)\\n y = F.linear(y, blk.attn.in_proj_weight, blk.attn.in_proj_bias)\\n y_N, y_L, y_C = y.shape\\n y = y.view(y_N, y_L, 3, y_C//3).permute(2, 0, 1, 3).reshape(3*y_N, y_L, y_C//3)\\n y = F.linear(y, blk.attn.out_proj.weight, blk.attn.out_proj.bias)\\n q, k, v = y.tensor_split(3, dim=0)\\n v += x\\n v = v + blk.mlp(blk.ln_2(v))\\n x = blk(x)\\n if len(self.out_indices) > 1:\\n if i in self.out_indices:\\n xp = x.permute(1, 0, 2)[:, 1:, :].permute(0, 2, 1).reshape(B, -1, H, W)\\n features.append(xp.contiguous())\\n\\n if self.get_embeddings:\\n x = x.permute(1, 0, 2)\\n x = self.ln_post(x)\\n x = x @ self.proj\\n\\n global_embedding = x[:, 0]\\n if self.embed_v:\\n v = v.permute(1, 0, 2)\\n v = self.ln_post(v)\\n v = v @ self.proj\\n visual_embedding = v[:, 1:].reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()\\n else:\\n visual_embedding = x[:, 1:].reshape(B, H, W, -1).permute(0, 3, 1, 2)\\n\\n if len(self.out_indices) == 1:\\n visual_embedding = visual_embedding / visual_embedding.norm(dim=1, keepdim=True)\\n features.append(visual_embedding)\\n\\n outs.append(tuple(features))\\n global_embedding = global_embedding / global_embedding.norm(dim=1, keepdim=True)\\n outs.append(global_embedding)\\n return outs\"\n },\n {\n \"identifier\": \"VPTCLIPVisionTransformer\",\n \"path\": \"third_party/zegclip/models/backbones/clip_vpt_vit.py\",\n \"snippet\": \"class VPTCLIPVisionTransformer(nn.Module):\\n def __init__(self, input_resolution=224, patch_size=32, width=768, layers=12, heads=12, output_dim=512, drop_path_rate=0.0,\\n out_indices=[3, 5, 7, 11], pretrained=None, get_embeddings=False, embed_v=False,\\n num_tokens=20, prompt_dim=512, total_d_layer=11, **kwargs):\\n super().__init__()\\n self.pretrained = pretrained\\n self.input_resolution = input_resolution\\n self.output_dim = output_dim\\n self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)\\n\\n scale = width ** -0.5\\n self.class_embedding = nn.Parameter(scale * torch.randn(width))\\n self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))\\n self.spatial_size = input_resolution // patch_size\\n self.ln_pre = LayerNorm(width)\\n self.get_embeddings = get_embeddings\\n self.embed_v = embed_v\\n self.num_layers = layers\\n\\n self.transformer = Transformer(width, layers, heads, drop_path_rate=drop_path_rate)\\n\\n self.out_indices = out_indices\\n\\n if get_embeddings:\\n self.ln_post = LayerNorm(width)\\n self.proj = nn.Parameter(scale * torch.randn(width, output_dim))\\n\\n self.patch_size = patch_size\\n\\n ## Setting of visual prompt tuning\\n self.num_tokens = num_tokens\\n self.prompt_dim = prompt_dim\\n self.total_d_layer = total_d_layer\\n\\n ## Add the prompt parameters # exclude_key=prompt:\\n self._init_prompt(patch_size, self.num_tokens, self.prompt_dim, self.total_d_layer)\\n\\n def _init_prompt(self, patch, num_tokens, prompt_dim, total_d_layer):\\n patch_size = []\\n patch_size.append(patch)\\n patch_size.append(patch)\\n val = math.sqrt(6. / float(3 * reduce(mul, patch_size, 1) + prompt_dim)) # noqa\\n\\n if total_d_layer >= 0:\\n self.prompt_embeddings = nn.Parameter(torch.zeros(1, num_tokens, prompt_dim))\\n # xavier_uniform initialization\\n nn.init.uniform_(self.prompt_embeddings.data, -val, val)\\n\\n if total_d_layer > 0: # noqa\\n self.deep_prompt_embeddings = nn.Parameter(torch.zeros(total_d_layer, num_tokens, prompt_dim))\\n # xavier_uniform initialization\\n nn.init.uniform_(self.deep_prompt_embeddings.data, -val, val)\\n\\n self.prompt_proj = nn.Linear(prompt_dim, prompt_dim)\\n nn.init.kaiming_normal_(self.prompt_proj.weight, a=0, mode='fan_out')\\n self.prompt_norm = LayerNorm(prompt_dim, eps=1e-6)\\n self.prompt_dropout = Dropout(0.1)\\n\\n else: # total_d_layer < 0\\n raise ValueError(f'Invalid total_d_layer={self.total_d_layer}.')\\n\\n\\n def init_weights(self, pretrained=None):\\n pretrained = pretrained or self.pretrained\\n if isinstance(pretrained, str):\\n checkpoint = torch.jit.load(pretrained, map_location='cpu').float().state_dict()\\n\\n state_dict = {}\\n\\n for k in checkpoint.keys():\\n if k.startswith('visual.'):\\n new_k = k.replace('visual.', '')\\n state_dict[new_k] = checkpoint[k]\\n\\n if 'positional_embedding' in state_dict.keys():\\n if self.positional_embedding.shape != state_dict['positional_embedding'].shape:\\n # (1025, 768) (197, 768) upsample the positional_embedding for larger input\\n print(f'Resize the pos_embed shape from {state_dict[\\\"positional_embedding\\\"].shape} to {self.positional_embedding.shape}')\\n cls_pos = state_dict[\\\"positional_embedding\\\"][0:1, :]\\n if self.patch_size == 16:\\n spatial_pos = F.interpolate(state_dict[\\\"positional_embedding\\\"][1:,].reshape(1, 14, 14, 768).permute(0, 3, 1, 2), size=(self.spatial_size, self.spatial_size), mode='bilinear')\\n else:\\n assert ValueError('Patch Size should be 16 or 32')\\n spatial_pos = spatial_pos.reshape(768, self.spatial_size*self.spatial_size).permute(1, 0)\\n positional_embedding = torch.cat([cls_pos, spatial_pos], dim=0)\\n state_dict['positional_embedding'] = positional_embedding\\n assert self.positional_embedding.shape == state_dict['positional_embedding'].shape\\n\\n u, w = self.load_state_dict(state_dict, False)\\n print(u, w, 'are misaligned params in vision transformer')\\n\\n\\n def forward(self, x: torch.Tensor):\\n x = self.conv1(x)\\n B, C, H, W = x.shape\\n x = x.reshape(x.shape[0], x.shape[1], -1)\\n x = x.permute(0, 2, 1)\\n x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)\\n\\n pos = self.positional_embedding.to(x.dtype)\\n cls_pos = pos[0,:] + self.class_embedding.to(x.dtype)\\n spatial_pos = F.interpolate(pos[1:,].reshape(1, self.spatial_size, self.spatial_size, C).permute(0, 3, 1, 2), size=(H, W), mode='bilinear')\\n spatial_pos = spatial_pos.reshape(1, C, H*W).permute(0, 2, 1)\\n pos = torch.cat([cls_pos.reshape(1, 1, C), spatial_pos], dim=1)\\n x = x + pos\\n x = self.ln_pre(x)\\n\\n if self.total_d_layer >=0:\\n # concat prompt\\n x = torch.cat((\\n x[:, :1, :],\\n self.prompt_dropout(self.prompt_proj(self.prompt_embeddings).expand(B, -1, -1)),\\n x[:, 1:, :]\\n ), dim=1)\\n\\n x = x.permute(1, 0, 2) # NLD -> LND\\n\\n features = []\\n outs = []\\n if self.total_d_layer == 0: #shallow\\n assert not self.embed_v\\n for i, blk in enumerate(self.transformer.resblocks):\\n x = blk(x)\\n if len(self.out_indices) > 1:\\n if i in self.out_indices:\\n xp = x.permute(1, 0, 2)[:, 1+self.num_tokens:, :].permute(0, 2, 1).reshape(B, -1, H, W)\\n features.append(xp.contiguous())\\n elif self.total_d_layer > 0: # deep\\n x, features, v = self.forward_deep_prompt(x, features, H, W)\\n else:\\n raise NotImplementedError(f'Invalid total_d_layer={self.total_d_layer}.')\\n\\n if self.get_embeddings:\\n x = x.permute(1, 0, 2)\\n x = self.ln_post(x)\\n x = x @ self.proj\\n\\n global_embedding = x[:, 0]\\n if self.embed_v:\\n v = v.permute(1, 0, 2)\\n v = self.ln_post(v)\\n v = v @ self.proj\\n visual_embedding = v[:, -(H*W):].reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()\\n else:\\n visual_embedding = x[:, -(H*W):].reshape(B, H, W, -1).permute(0, 3, 1, 2)\\n\\n if len(self.out_indices) == 1:\\n visual_embedding = visual_embedding / visual_embedding.norm(dim=1, keepdim=True)\\n features.append(visual_embedding)\\n\\n outs.append(tuple(features))\\n global_embedding = global_embedding / global_embedding.norm(dim=1, keepdim=True)\\n outs.append(global_embedding)\\n return outs\\n\\n\\n def forward_deep_prompt(self, embedding_output, features, H, W, out_last=False):\\n B = embedding_output.shape[1]\\n v = None\\n\\n for i in range(self.num_layers):\\n if i == 0:\\n hidden_states = self.transformer.resblocks[i](embedding_output)\\n elif i <= self.deep_prompt_embeddings.shape[0]:\\n deep_prompt_emb = self.prompt_dropout(self.prompt_proj(self.deep_prompt_embeddings[i-1]).expand(B, -1, -1)).permute(1, 0, 2)\\n hidden_states = torch.cat((\\n hidden_states[:1, :, :],\\n deep_prompt_emb,\\n hidden_states[(1+self.num_tokens):, :, :]\\n ), dim=0)\\n if self.embed_v and i == self.num_layers - 1:\\n x = hidden_states\\n blk = self.transformer.resblocks[i]\\n y = blk.ln_1(x)\\n y = F.linear(y, blk.attn.in_proj_weight, blk.attn.in_proj_bias)\\n y_N, y_L, y_C = y.shape\\n y = y.view(y_N, y_L, 3, y_C//3).permute(2, 0, 1, 3).reshape(3*y_N, y_L, y_C//3)\\n y = F.linear(y, blk.attn.out_proj.weight, blk.attn.out_proj.bias)\\n q, k, v = y.tensor_split(3, dim=0)\\n v += x\\n v = v + blk.mlp(blk.ln_2(v))\\n\\n hidden_states = self.transformer.resblocks[i](hidden_states)\\n else:\\n assert not self.embed_v\\n hidden_states = torch.cat((\\n hidden_states[:1, :, :],\\n hidden_states[-(H*W):, :, :]\\n ), dim=0)\\n hidden_states = self.transformer.resblocks[i](hidden_states)\\n\\n if len(self.out_indices) > 1:\\n if i in self.out_indices:\\n xp = hidden_states.permute(1, 0, 2)[:, -(H*W):, :].permute(0, 2, 1).reshape(B, -1, H, W)\\n features.append(xp.contiguous())\\n\\n if i == (self.num_layers-2): #10\\n before_last_feats = self.prompt_norm(hidden_states)\\n\\n encoded = self.prompt_norm(hidden_states)\\n if out_last:\\n return before_last_feats\\n else:\\n return encoded, features, v\"\n },\n {\n \"identifier\": \"CLIPTextEncoder\",\n \"path\": \"third_party/zegclip/models/backbones/text_encoder.py\",\n \"snippet\": \"class CLIPTextEncoder(nn.Module):\\n def __init__(self, context_length=77,\\n vocab_size=49408,\\n transformer_width=512,\\n transformer_heads=8,\\n transformer_layers=12,\\n embed_dim=1024,\\n out_dim=256,\\n pretrained=None, **kwargs):\\n super().__init__()\\n\\n self.pretrained = pretrained\\n\\n self.context_length = context_length\\n\\n self.transformer = Transformer(\\n width=transformer_width,\\n layers=transformer_layers,\\n heads=transformer_heads,\\n attn_mask=self.build_attention_mask()\\n )\\n\\n self.vocab_size = vocab_size\\n self.token_embedding = nn.Embedding(vocab_size, transformer_width)\\n self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))\\n self.ln_final = LayerNorm(transformer_width)\\n self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))\\n \\n def init_weights(self, pretrained=None):\\n pretrained = pretrained or self.pretrained\\n if isinstance(pretrained, str):\\n checkpoint = torch.jit.load(pretrained, map_location='cpu').float().state_dict()\\n\\n state_dict = {}\\n\\n for k in checkpoint.keys():\\n if k.startswith('transformer.'):\\n state_dict[k] = checkpoint[k]\\n \\n if k == 'positional_embedding' or k == 'text_projection' or k.startswith('token_embedding') or k.startswith('ln_final'):\\n if k == 'positional_embedding' and checkpoint[k].size(0) > self.context_length:\\n checkpoint[k] = checkpoint[k][:self.context_length]\\n print('positional_embedding is tuncated from 77 to', self.context_length)\\n state_dict[k] = checkpoint[k]\\n \\n u, w = self.load_state_dict(state_dict, False)\\n print(u, w, 'are misaligned params in text encoder')\\n\\n\\n def build_attention_mask(self):\\n # lazily create causal attention mask, with full attention between the vision tokens\\n # pytorch uses additive attention mask; fill with -inf\\n mask = torch.empty(self.context_length, self.context_length)\\n mask.fill_(float(\\\"-inf\\\"))\\n mask.triu_(1) # zero out the lower diagonal\\n return mask\\n\\n def forward(self, text):\\n x = self.token_embedding(text)\\n x = x + self.positional_embedding \\n x = x.permute(1, 0, 2)\\n x = self.transformer(x)\\n x = x.permute(1, 0, 2)\\n x = self.ln_final(x)\\n x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection\\n return x\"\n },\n {\n \"identifier\": \"DropPath\",\n \"path\": \"third_party/zegclip/models/backbones/utils.py\",\n \"snippet\": \"class DropPath(nn.Module):\\n \\\"\\\"\\\"Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).\\n \\\"\\\"\\\"\\n def __init__(self, drop_prob=None):\\n super(DropPath, self).__init__()\\n self.drop_prob = drop_prob\\n\\n def forward(self, x):\\n return drop_path(x, self.drop_prob, self.training)\\n \\n def extra_repr(self) -> str:\\n return 'p={}'.format(self.drop_prob)\"\n },\n {\n \"identifier\": \"ATMSingleHeadSeg\",\n \"path\": \"third_party/zegclip/models/decode_heads/atm_head.py\",\n \"snippet\": \"class ATMSingleHeadSeg(BaseDecodeHead):\\n def __init__(\\n self,\\n img_size,\\n in_channels,\\n seen_idx,\\n all_idx,\\n ignore_seen_pseudo_labels=True,\\n embed_dims=768,\\n num_layers=3,\\n num_heads=8,\\n use_stages=1,\\n use_proj=True,\\n crop_train=False,\\n use_rd=True,\\n use_aspp=False,\\n aspp_relu=True,\\n aspp_bn=True,\\n aspp_residual=False,\\n dilations=(6, 12, 18),\\n **kwargs,\\n ):\\n super(ATMSingleHeadSeg, self).__init__(\\n in_channels=in_channels, **kwargs)\\n\\n self.image_size = img_size\\n self.use_stages = use_stages\\n self.crop_train = crop_train\\n self.use_rd = use_rd\\n self.use_aspp = use_aspp\\n self.aspp_residual = aspp_residual\\n self.seen_idx = seen_idx\\n self.all_idx = all_idx\\n self.ignore_seen_pseudo_labels = ignore_seen_pseudo_labels\\n self.debug = False\\n nhead = num_heads\\n dim = embed_dims\\n input_proj = []\\n proj_norm = []\\n atm_decoders = []\\n\\n self.unseen_idx = self.all_idx.copy()\\n for i_idx in self.seen_idx:\\n self.unseen_idx.remove(i_idx)\\n\\n if self.use_aspp:\\n self.aspp = ASPPModule(dim, dilations, out_channels=dim, bn=aspp_bn, relu=aspp_relu)\\n self.add_module(\\\"aspp\\\", self.aspp)\\n\\n for i in range(self.use_stages):\\n # FC layer to change ch\\n if use_proj:\\n proj = nn.Linear(self.in_channels, dim)\\n trunc_normal_(proj.weight, std=.02)\\n else:\\n proj = nn.Identity()\\n self.add_module(\\\"input_proj_{}\\\".format(i + 1), proj)\\n input_proj.append(proj)\\n # norm layer\\n if use_proj:\\n norm = nn.LayerNorm(dim)\\n else:\\n norm = nn.Identity()\\n self.add_module(\\\"proj_norm_{}\\\".format(i + 1), norm)\\n proj_norm.append(norm)\\n # decoder layer\\n decoder_layer = TPN_DecoderLayer(d_model=dim, nhead=nhead, dim_feedforward=dim * 4)\\n decoder = TPN_Decoder(decoder_layer, num_layers)\\n self.add_module(\\\"decoder_{}\\\".format(i + 1), decoder)\\n atm_decoders.append(decoder)\\n\\n self.input_proj = input_proj\\n self.proj_norm = proj_norm\\n self.decoder = atm_decoders\\n\\n delattr(self, 'conv_seg')\\n\\n self.q_proj = nn.Linear(dim * 2 if self.use_rd else dim, dim)\\n\\n def init_weights(self):\\n for n, m in self.named_modules():\\n if isinstance(m, nn.Linear):\\n trunc_normal_init(m, std=.02, bias=0)\\n elif isinstance(m, nn.LayerNorm):\\n constant_init(m, val=1.0, bias=0.0)\\n\\n def forward_train(self, inputs, img_metas, gt_semantic_seg, train_cfg, self_training=False):\\n seg_logits = self.forward(inputs)\\n\\n if self.debug:\\n pred = seg_logits['pred_masks'].detach().sigmoid().argmax(dim=1).unsqueeze(1)\\n pred_cpu = pred.detach().cpu()\\n gt_semantic_seg_cpu = gt_semantic_seg.detach().cpu()\\n self.debug_output = [[\\n dict(title='Prediction', data=pred_cpu[i], type='label'),\\n dict(title='Ground Truth', data=gt_semantic_seg_cpu[i], type='label'),\\n ] for i in range(pred.shape[0])]\\n\\n # print('Self-training: 'self_training)\\n if self_training:\\n pseudo_semantic_masks = seg_logits['pred_masks'].clone().detach().sigmoid()\\n if self.ignore_seen_pseudo_labels:\\n pseudo_semantic_masks[:, self.seen_idx, :, :] = -1\\n assert -1 not in pseudo_semantic_masks.max(dim=1)[0], \\\\\\n 'All classes are set to -1, which results in argmax defaulting to label 0'\\n pseudo_semantic_seg = pseudo_semantic_masks.argmax(dim=1).unsqueeze(1)\\n # generate pseudo labels for \\\"transductive\\\" setting\\n # print('Before pseudo-labelling', gt_semantic_seg[0, 0])\\n gt_semantic_seg[gt_semantic_seg==-1] = pseudo_semantic_seg[gt_semantic_seg==-1]\\n gt_semantic_seg[gt_semantic_seg==-1] = 255\\n # print('After pseudo-labelling', gt_semantic_seg[0, 0])\\n losses = self.losses(seg_logits, gt_semantic_seg)\\n\\n else:\\n gt_semantic_seg[gt_semantic_seg==-1] = 255\\n losses = self.losses(seg_logits, gt_semantic_seg)\\n\\n if self.debug and self_training:\\n for i in range(gt_semantic_seg.shape[0]):\\n self.debug_output[i].append(\\n dict(title='Pseudo Label', data=gt_semantic_seg[i].detach().cpu(), type='label'))\\n\\n return losses\\n\\n def forward_test(self, inputs, img_metas, test_cfg, self_training):\\n return self.forward(inputs, self_training)\\n\\n def forward(self, inputs_both, self_training=None, force_output_pred_masks=False):\\n inputs = inputs_both[0][0]\\n cls_token = inputs_both[0][1]\\n text_token = inputs_both[1]\\n \\n x = []\\n for stage_ in inputs[:self.use_stages]:\\n x.append(self.d4_to_d3(stage_) if stage_.dim() > 3 else stage_)\\n x.reverse()\\n bs = x[0].size()[0]\\n\\n laterals = []\\n attns = []\\n maps_size = []\\n qs = []\\n\\n for idx, (x_, proj_, norm_) in enumerate(zip(x, self.input_proj, self.proj_norm)):\\n lateral = norm_(proj_(x_))\\n if idx == 0:\\n laterals.append(lateral)\\n else:\\n if laterals[idx - 1].size()[1] == lateral.size()[1]:\\n laterals.append(lateral + laterals[idx - 1])\\n else:\\n # nearest interpolate\\n l_ = self.d3_to_d4(laterals[idx - 1])\\n l_ = F.interpolate(l_, scale_factor=2, mode=\\\"nearest\\\")\\n l_ = self.d4_to_d3(l_)\\n laterals.append(l_ + lateral)\\n\\n lateral = laterals[-1]\\n if self.use_aspp:\\n if self.aspp_residual:\\n lateral = lateral + 0.01 * self.d4_to_d3(self.aspp(self.d3_to_d4(lateral)))\\n else:\\n lateral = self.d3_to_d4(lateral)\\n lateral = self.aspp(lateral)\\n lateral = self.d4_to_d3(lateral)\\n\\n q = self.q_proj(self.get_qs(text_token, cls_token))\\n q = q.transpose(0,1)\\n\\n for idx, decoder_ in enumerate(self.decoder):\\n q_, attn_ = decoder_(q, lateral.transpose(0, 1))\\n for q, attn in zip(q_, attn_):\\n attn = attn.transpose(-1, -2) \\n attn = self.d3_to_d4(attn)\\n maps_size.append(attn.size()[-2:])\\n qs.append(q.transpose(0, 1))\\n attns.append(attn)\\n qs = torch.stack(qs, dim=0)\\n\\n outputs_seg_masks = []\\n size = maps_size[-1]\\n\\n for i_attn, attn in enumerate(attns):\\n if attn.shape[1] != self.num_classes:\\n cls2con = get_class_to_concept_idxs(self.load_text_embedding)\\n attn = aggregate_concept_predictions(attn, cls2con)\\n assert attn.shape[1] == self.num_classes\\n outputs_seg_masks.append(F.interpolate(attn, size=size, mode='bilinear', align_corners=False))\\n\\n pred = F.interpolate(outputs_seg_masks[-1],\\n size=(self.image_size, self.image_size),\\n mode='bilinear', align_corners=False)\\n \\n out = {\\\"pred_masks\\\": pred}\\n\\n \\n if self.training or force_output_pred_masks:\\n outputs_seg_masks = torch.stack(outputs_seg_masks, dim=0)# (3, bs, 20, 14, 14)\\n else:\\n if self_training:\\n out[\\\"pred\\\"] = self.semantic_inference(out[\\\"pred_masks\\\"], self.seen_idx) #(bs, 20, 224, 224)\\n else:\\n out[\\\"pred\\\"] = self.semantic_inference(out[\\\"pred_masks\\\"], self.seen_idx, 0.1)\\n return out[\\\"pred\\\"] \\n return out\\n\\n def semantic_inference(self, mask_pred, seen_idx, weight=0.0):\\n mask_pred = mask_pred.sigmoid()\\n mask_pred[:,seen_idx] = mask_pred[:,seen_idx] - weight\\n return mask_pred\\n\\n @torch.jit.unused\\n def _set_aux_loss(self, outputs_seg_masks):\\n return [\\n {\\\"pred_masks\\\": a}\\n # for a in zip(outputs_seg_masks[:-1])\\n for a in outputs_seg_masks[:-1]\\n ]\\n\\n def d3_to_d4(self, t):\\n n, hw, c = t.size()\\n if hw % 2 != 0:\\n t = t[:, 1:]\\n h = w = int(math.sqrt(hw))\\n assert h * w == hw\\n return t.transpose(1, 2).reshape(n, c, h, w)\\n\\n def d4_to_d3(self, t):\\n return t.flatten(-2).transpose(-1, -2)\\n\\n def get_qs(self, q, cls):\\n # q = [q.cls, q]\\n C, dim = q.shape\\n bs, _ = cls.shape\\n q = q.expand(bs, -1, -1)\\n if self.use_rd:\\n q1 = torch.einsum(\\\"bd,bcd->bcd\\\", cls, q)\\n q_ = torch.concat((q1, q), dim=-1)\\n else:\\n q_ = q.to(cls.dtype)\\n return q_\\n\\n\\n @force_fp32(apply_to=('seg_logit',))\\n def losses(self, seg_logit, seg_label, num_classes=None):\\n \\\"\\\"\\\"Compute segmentation loss.\\\"\\\"\\\"\\n if isinstance(seg_logit, dict):\\n # atm loss\\n seg_label = seg_label.squeeze(1)\\n\\n loss = self.loss_decode(\\n seg_logit,\\n seg_label,\\n ignore_index = self.ignore_index)\\n\\n loss['acc_seg'] = accuracy(seg_logit[\\\"pred_masks\\\"], seg_label, ignore_index=self.ignore_index)\\n return loss\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import types\nimport torch\nfrom functools import reduce\nfrom mmcv.utils import Config\nfrom mmseg.models import ASPPHead, DepthwiseSeparableASPPHead, build_segmentor\nfrom mmseg.ops import resize\nfrom torch.nn import functional as F\nfrom model.backbone.timm_vit import TIMMVisionTransformer\nfrom model.decode_heads.dlv3p_head import DLV3PHead\nfrom model.decode_heads.vlg_head import VLGHead\nfrom model.vlm import VLM\nfrom third_party.maskclip.models.backbones.maskclip_vit import MaskClipVisionTransformer\nfrom third_party.maskclip.models.decode_heads.maskclip2_head import MaskClip2Head\nfrom third_party.maskclip.models.decode_heads.maskclip_head import MaskClipHead\nfrom third_party.unimatch.model.semseg.deeplabv3plus import DeepLabV3Plus\nfrom third_party.zegclip.losses.atm_loss import SegLossPlus\nfrom third_party.zegclip.models.backbones.clip_vit import CLIPVisionTransformer\nfrom third_party.zegclip.models.backbones.clip_vpt_vit import VPTCLIPVisionTransformer\nfrom third_party.zegclip.models.backbones.text_encoder import CLIPTextEncoder\nfrom third_party.zegclip.models.backbones.utils import DropPath\nfrom third_party.zegclip.models.decode_heads.atm_head import ATMSingleHeadSeg"},"token_num":{"kind":"number","value":19928,"string":"19,928"},"cropped_code":{"kind":"string","value":"# Copyright 2023 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n\n\n\n\ndef nested_set(dic, key, value):\n keys = key.split('.')\n for key in keys[:-1]:\n dic = dic.setdefault(key, {})\n dic[keys[-1]] = value\n\n\ndef nested_get(dictionary, keys, default=None):\n return reduce(lambda d, key: d.get(key, default) if isinstance(d, dict) else default, keys.split(\".\"), dictionary)\n\n\ndef is_vlm(obj):\n return isinstance(obj, VLM)\n\n\ndef forward_wrapper(self, img, gt=None, need_fp=False, only_fp=False, forward_mode='default'):\n if forward_mode == 'maskclip_trust':\n return self.train_maskclip_trust(img, gt)\n elif forward_mode == 'default':\n x = self.extract_feat(img)\n if self.disable_dropout:\n dropout_modules = [module for module in self.modules() if isinstance(module, torch.nn.Dropout) or isinstance(module, DropPath)]\n for module in dropout_modules:\n module.eval()\n if only_fp:\n if is_vlm(self):\n feats = x[0][0]\n x[0][0] = [F.dropout2d(f, self.fp_rate) for f in feats]\n # perturb features from conv_encoder\n if len(x) == 3 and x[2] is not None:\n x[2] = [F.dropout2d(f, self.fp_rate) for f in x[2]]\n # also provide unperturbed features\n if hasattr(self.decode_head, 'dc_unperturbed') and self.decode_head.dc_unperturbed:\n assert len(x[0]) == 2\n x[0].append(feats)\n else:\n x = [F.dropout2d(f, self.fp_rate) for f in x]\n elif need_fp:\n if is_vlm(self):\n feats = x[0][0]\n x[0][0] = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in feats]\n x[0][1] = torch.cat((x[0][1], x[0][1]))\n # perturb features from conv_encoder\n if len(x) == 3 and x[2] is not None:\n x[2] = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in x[2]]\n # also provide unperturbed features\n if hasattr(self.decode_head, 'dc_unperturbed') and self.decode_head.dc_unperturbed:\n assert len(x[0]) == 2\n x[0].append([torch.cat((f, f)) for f in feats])\n else:\n x = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in x]\n out = self._decode_head_forward_test(x, img_metas=None)\n out = resize(\n input=out,\n size=img.shape[2:],\n mode='bilinear',\n align_corners=self.align_corners)\n if need_fp:\n out = out.chunk(2)\n return out\n else:\n raise ValueError(forward_mode)\n\ndef build_model(cfg):\n model_type = cfg['model']\n if model_type == 'deeplabv3plus':\n"},"all_code":{"kind":"string","value":"# Copyright 2023 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n\n\n\n\ndef nested_set(dic, key, value):\n keys = key.split('.')\n for key in keys[:-1]:\n dic = dic.setdefault(key, {})\n dic[keys[-1]] = value\n\n\ndef nested_get(dictionary, keys, default=None):\n return reduce(lambda d, key: d.get(key, default) if isinstance(d, dict) else default, keys.split(\".\"), dictionary)\n\n\ndef is_vlm(obj):\n return isinstance(obj, VLM)\n\n\ndef forward_wrapper(self, img, gt=None, need_fp=False, only_fp=False, forward_mode='default'):\n if forward_mode == 'maskclip_trust':\n return self.train_maskclip_trust(img, gt)\n elif forward_mode == 'default':\n x = self.extract_feat(img)\n if self.disable_dropout:\n dropout_modules = [module for module in self.modules() if isinstance(module, torch.nn.Dropout) or isinstance(module, DropPath)]\n for module in dropout_modules:\n module.eval()\n if only_fp:\n if is_vlm(self):\n feats = x[0][0]\n x[0][0] = [F.dropout2d(f, self.fp_rate) for f in feats]\n # perturb features from conv_encoder\n if len(x) == 3 and x[2] is not None:\n x[2] = [F.dropout2d(f, self.fp_rate) for f in x[2]]\n # also provide unperturbed features\n if hasattr(self.decode_head, 'dc_unperturbed') and self.decode_head.dc_unperturbed:\n assert len(x[0]) == 2\n x[0].append(feats)\n else:\n x = [F.dropout2d(f, self.fp_rate) for f in x]\n elif need_fp:\n if is_vlm(self):\n feats = x[0][0]\n x[0][0] = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in feats]\n x[0][1] = torch.cat((x[0][1], x[0][1]))\n # perturb features from conv_encoder\n if len(x) == 3 and x[2] is not None:\n x[2] = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in x[2]]\n # also provide unperturbed features\n if hasattr(self.decode_head, 'dc_unperturbed') and self.decode_head.dc_unperturbed:\n assert len(x[0]) == 2\n x[0].append([torch.cat((f, f)) for f in feats])\n else:\n x = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in x]\n out = self._decode_head_forward_test(x, img_metas=None)\n out = resize(\n input=out,\n size=img.shape[2:],\n mode='bilinear',\n align_corners=self.align_corners)\n if need_fp:\n out = out.chunk(2)\n return out\n else:\n raise ValueError(forward_mode)\n\ndef build_model(cfg):\n model_type = cfg['model']\n if model_type == 'deeplabv3plus':"},"next_line":{"kind":"string","value":" model = DeepLabV3Plus(cfg)"},"gold_snippet_index":{"kind":"number","value":7,"string":"7"},"created_at":{"kind":"string","value":"2023-11-02 14:49:38+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5272,"cells":{"repo_name":{"kind":"string","value":"codefuse-ai/Collinear-Constrained-Attention"},"file_path":{"kind":"string","value":"model/build_model.py"},"context":{"kind":"list like","value":[{"identifier":"get_model_params_num","path":"utils/common_utils.py","snippet":"def get_model_params_num(model):\n \"\"\"\n Get params number of the model\n Args:\n model: model(required)\n Returns:\n the number of parameters of model\n \"\"\"\n num = 0\n for _, param in model.named_parameters():\n num += param.nelement()\n return num"},{"identifier":"GPTNeoXConfig","path":"model/gpt_neox/configuration_gpt_neox.py","snippet":"class GPTNeoXConfig(PretrainedConfig):\n r\"\"\"\n This is the configuration class to store the configuration of a [`GPTNeoXModel`]. It is used to instantiate an\n GPTNeoX model according to the specified arguments, defining the model architecture. Instantiating a configuration\n with the defaults will yield a similar configuration to that of the GPTNeoX\n [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) architecture.\n\n Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n documentation from [`PretrainedConfig`] for more information.\n\n\n Args:\n vocab_size (`int`, *optional*, defaults to 50432):\n Vocabulary size of the GPTNeoX model. Defines the number of different tokens that can be represented by the\n `inputs_ids` passed when calling [`GPTNeoXModel`].\n hidden_size (`int`, *optional*, defaults to 6144):\n Dimension of the encoder layers and the pooler layer.\n num_hidden_layers (`int`, *optional*, defaults to 44):\n Number of hidden layers in the Transformer encoder.\n num_attention_heads (`int`, *optional*, defaults to 64):\n Number of attention heads for each attention layer in the Transformer encoder.\n intermediate_size (`int`, *optional*, defaults to 24576):\n Dimension of the \"intermediate\" (i.e., feed-forward) layer in the Transformer encoder.\n hidden_act (`str` or `function`, *optional*, defaults to `\"gelu\"`):\n The non-linear activation function (function or string) in the encoder and pooler. If string, `\"gelu\"`,\n `\"relu\"`, `\"selu\"` and `\"gelu_new\"` are supported.\n rotary_pct (`float`, *optional*, defaults to 0.25):\n percentage of hidden dimensions to allocate to rotary embeddings\n rotary_emb_base (`int`, *optional*, defaults to 10000)\n base for computing rotary embeddings frequency\n max_position_embeddings (`int`, *optional*, defaults to 2048):\n The maximum sequence length that this model might ever be used with. Typically set this to something large\n just in case (e.g., 512 or 1024 or 2048).\n initializer_range (`float`, *optional*, defaults to 1e-5):\n The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n layer_norm_eps (`float`, *optional*, defaults to 1e-12):\n The epsilon used by the layer normalization layers.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models). Only\n relevant if `config.is_decoder=True`.\n use_parallel_residual (`bool`, *optional*, defaults to `True`):\n Whether to use a \"parallel\" formulation in each Transformer layer, which can provide a slight training\n speedup at large scales (e.g. 20B).\n rope_scaling (`Dict`, *optional*):\n Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling\n strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format\n is `{\"type\": strategy name, \"factor\": scaling factor}`. When using this flag, don't update\n `max_position_embeddings` to the expected new maximum. See the following thread for more information on how\n these scaling strategies behave:\n https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an\n experimental feature, subject to breaking API changes in future versions.\n Example:\n\n ```python\n >>> from transformers import GPTNeoXConfig, GPTNeoXModel\n\n >>> # Initializing a GPTNeoX gpt-neox-20b style configuration\n >>> configuration = GPTNeoXConfig()\n\n >>> # Initializing a model (with random weights) from the gpt-neox-20b style configuration\n >>> model = GPTNeoXModel(configuration) # doctest: +SKIP\n\n >>> # Accessing the model configuration\n >>> configuration = model.config # doctest: +SKIP\n ```\"\"\"\n model_type = \"gpt_neox\"\n\n def __init__(\n self,\n vocab_size=50432,\n hidden_size=6144,\n num_hidden_layers=44,\n num_attention_heads=64,\n intermediate_size=24576,\n hidden_act=\"gelu\",\n rotary_pct=0.25,\n rotary_emb_base=10000,\n max_position_embeddings=2048,\n initializer_range=0.02,\n layer_norm_eps=1e-5,\n use_cache=True,\n bos_token_id=0,\n eos_token_id=2,\n tie_word_embeddings=False,\n use_parallel_residual=True,\n rope_scaling=None,\n **kwargs\n ):\n super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)\n self.vocab_size = vocab_size\n self.max_position_embeddings = max_position_embeddings\n self.hidden_size = hidden_size\n self.num_hidden_layers = num_hidden_layers\n self.num_attention_heads = num_attention_heads\n self.intermediate_size = intermediate_size\n self.hidden_act = hidden_act\n self.rotary_pct = rotary_pct\n self.rotary_emb_base = rotary_emb_base\n self.initializer_range = initializer_range\n self.layer_norm_eps = layer_norm_eps\n self.use_cache = use_cache\n self.tie_word_embeddings = tie_word_embeddings\n self.use_parallel_residual = use_parallel_residual\n self.rope_scaling = rope_scaling\n self._rope_scaling_validation()\n\n if self.hidden_size % self.num_attention_heads != 0:\n raise ValueError(\n \"The hidden size is not divisble by the number of attention heads! Make sure to update them!\"\n )\n\n # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation\n def _rope_scaling_validation(self):\n \"\"\"\n Validate the `rope_scaling` configuration.\n \"\"\"\n if self.rope_scaling is None:\n return\n\n if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:\n raise ValueError(\n \"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, \"\n f\"got {self.rope_scaling}\"\n )\n rope_scaling_type = self.rope_scaling.get(\"type\", None)\n rope_scaling_factor = self.rope_scaling.get(\"factor\", None)\n if rope_scaling_type is None or rope_scaling_type not in [\"linear\", \"dynamic\"]:\n raise ValueError(\n f\"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}\"\n )\n if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:\n raise ValueError(f\"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}\")"},{"identifier":"GPTNeoXForCausalLM","path":"model/gpt_neox/modeling_gpt_neox.py","snippet":"class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel):\n\n # _keys_to_ignore_on_load_missing = [r\"position_ids\", r\"predictions.decoder.bias\"]\n\n def __init__(self, config):\n super().__init__(config)\n\n self.gpt_neox = GPTNeoXModel(config)\n self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_output_embeddings(self):\n return self.embed_out\n\n def set_output_embeddings(self, new_embeddings):\n self.embed_out = new_embeddings\n\n @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)\n def forward(\n self,\n input_ids: Optional[torch.LongTensor] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n head_mask: Optional[torch.FloatTensor] = None,\n past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,\n labels: Optional[torch.LongTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, CausalLMOutputWithPast]:\n r\"\"\"\n past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):\n Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape\n `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape\n `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are\n only required when the model is used as a decoder in a Sequence to Sequence model.\n\n Contains pre-computed hidden-states (key and values in the self-attention blocks that can be used (see\n `past_key_values` input) to speed up sequential decoding.\n\n If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that\n don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all\n `decoder_input_ids` of shape `(batch_size, sequence_length)`.\n labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in\n `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are\n ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.\n use_cache (`bool`, *optional*):\n If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see\n `past_key_values`).\n\n Returns:\n\n Example:\n\n ```python\n >>> from transformers import AutoTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig\n >>> import torch\n\n >>> tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-neox-20b\")\n >>> config = GPTNeoXConfig.from_pretrained(\"EleutherAI/gpt-neox-20b\")\n >>> config.is_decoder = True\n >>> model = GPTNeoXForCausalLM.from_pretrained(\"EleutherAI/gpt-neox-20b\", config=config)\n\n >>> inputs = tokenizer(\"Hello, my dog is cute\", return_tensors=\"pt\")\n >>> outputs = model(**inputs)\n\n >>> prediction_logits = outputs.logits\n ```\"\"\"\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n outputs = self.gpt_neox(\n input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n past_key_values=past_key_values,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n hidden_states = outputs[0]\n lm_logits = self.embed_out(hidden_states)\n\n lm_loss = None\n if labels is not None:\n # move labels to correct device to enable model parallelism\n labels = labels.to(lm_logits.device)\n # we are doing next-token prediction; shift prediction scores and input ids by one\n shift_logits = lm_logits[:, :-1, :].contiguous()\n labels = labels[:, 1:].contiguous()\n loss_fct = CrossEntropyLoss()\n lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1))\n\n if not return_dict:\n output = (lm_logits,) + outputs[1:]\n return ((lm_loss,) + output) if lm_loss is not None else output\n\n return CausalLMOutputWithPast(\n loss=lm_loss,\n logits=lm_logits,\n past_key_values=outputs.past_key_values,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n def prepare_inputs_for_generation(\n self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs\n ):\n input_shape = input_ids.shape\n\n # cut decoder_input_ids if past is used\n if past_key_values and past_key_values[0] is not None:\n input_ids = input_ids[:, -1:]\n\n position_ids = kwargs.get(\"position_ids\", None)\n if attention_mask is not None and position_ids is None:\n # create position_ids on the fly for batch generation\n position_ids = attention_mask.long().cumsum(-1) - 1\n position_ids.masked_fill_(attention_mask == 0, 1)\n if past_key_values:\n position_ids = position_ids[:, -1].unsqueeze(-1)\n\n # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly\n if attention_mask is None:\n attention_mask = input_ids.new_ones(input_shape)\n\n # if `inputs_embeds` are passed, we only want to use them in the 1st generation step\n if inputs_embeds is not None and past_key_values is None:\n model_inputs = {\"inputs_embeds\": inputs_embeds}\n else:\n model_inputs = {\"input_ids\": input_ids}\n\n model_inputs.update(\n {\n \"attention_mask\": attention_mask,\n \"past_key_values\": past_key_values,\n \"position_ids\": position_ids,\n }\n )\n\n return model_inputs\n\n def _reorder_cache(self, past_key_values, beam_idx):\n reordered_past = ()\n for layer_past in past_key_values:\n reordered_past += (\n tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],\n )\n return reordered_past"},{"identifier":"GPTNeoXTokenizerFast","path":"model/gpt_neox/tokenization_gpt_neox_fast.py","snippet":"class GPTNeoXTokenizerFast(PreTrainedTokenizerFast):\n \"\"\"\n Construct a \"fast\" GPT-NeoX-20B tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level\n Byte-Pair-Encoding.\n\n This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will\n be encoded differently whether it is at the beginning of the sentence (without space) or not:\n\n ```python\n >>> from transformers import GPTNeoXTokenizerFast\n\n >>> tokenizer = GPTNeoXTokenizerFast.from_pretrained(\"gpt2\")\n >>> tokenizer(\"Hello world\")[\"input_ids\"]\n [15496, 995]\n\n >>> tokenizer(\" Hello world\")[\"input_ids\"]\n [18435, 995]\n ```\n\n You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since\n the model was not pretrained this way, it might yield a decrease in performance.\n\n <Tip>\n\n When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.\n\n </Tip>\n\n This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should\n refer to this superclass for more information regarding those methods.\n\n Args:\n vocab_file (`str`):\n Path to the vocabulary file.\n merges_file (`str`):\n Path to the merges file.\n errors (`str`, *optional*, defaults to `\"replace\"`):\n Paradigm to follow when decoding bytes to UTF-8. See\n [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.\n unk_token (`str`, *optional*, defaults to `<|endoftext|>`):\n The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this\n token instead.\n bos_token (`str`, *optional*, defaults to `<|endoftext|>`):\n The beginning of sequence token.\n eos_token (`str`, *optional*, defaults to `<|endoftext|>`):\n The end of sequence token.\n add_prefix_space (`bool`, *optional*, defaults to `False`):\n Whether or not to add an initial space to the input. This allows to treat the leading word just as any\n other word. (GPTNeoX tokenizer detect beginning of words by the preceding space).\n trim_offsets (`bool`, *optional*, defaults to `True`):\n Whether or not the post-processing step should trim offsets to avoid including whitespaces.\n \"\"\"\n\n vocab_files_names = VOCAB_FILES_NAMES\n pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP\n max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\n model_input_names = [\"input_ids\", \"attention_mask\"]\n\n def __init__(\n self,\n vocab_file=None,\n merges_file=None,\n tokenizer_file=None,\n unk_token=\"<|endoftext|>\",\n bos_token=\"<|endoftext|>\",\n eos_token=\"<|endoftext|>\",\n add_prefix_space=False,\n **kwargs,\n ):\n super().__init__(\n vocab_file,\n merges_file,\n tokenizer_file=tokenizer_file,\n unk_token=unk_token,\n bos_token=bos_token,\n eos_token=eos_token,\n add_prefix_space=add_prefix_space,\n **kwargs,\n )\n\n pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())\n if pre_tok_state.get(\"add_prefix_space\", add_prefix_space) != add_prefix_space:\n pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop(\"type\"))\n pre_tok_state[\"add_prefix_space\"] = add_prefix_space\n self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)\n\n self.add_prefix_space = add_prefix_space\n\n def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:\n files = self._tokenizer.model.save(save_directory, name=filename_prefix)\n return tuple(files)\n\n def _build_conversation_input_ids(self, conversation: \"Conversation\") -> List[int]:\n \"\"\"This corresponds to DialoGPT variants of models.\"\"\"\n input_ids = []\n for is_user, text in conversation.iter_texts():\n input_ids.extend(self.encode(text, add_special_tokens=False) + [self.eos_token_id])\n\n if len(input_ids) > self.model_max_length:\n input_ids = input_ids[-self.model_max_length :]\n return input_ids"},{"identifier":"LlamaConfig","path":"model/llama/configuration_llama.py","snippet":"class LlamaConfig(PretrainedConfig):\n r\"\"\"\n This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA\n model according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n defaults will yield a similar configuration to that of the LLaMA-7B.\n\n Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n documentation from [`PretrainedConfig`] for more information.\n\n\n Args:\n vocab_size (`int`, *optional*, defaults to 32000):\n Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the\n `inputs_ids` passed when calling [`LlamaModel`]\n hidden_size (`int`, *optional*, defaults to 4096):\n Dimension of the hidden representations.\n intermediate_size (`int`, *optional*, defaults to 11008):\n Dimension of the MLP representations.\n num_hidden_layers (`int`, *optional*, defaults to 32):\n Number of hidden layers in the Transformer encoder.\n num_attention_heads (`int`, *optional*, defaults to 32):\n Number of attention heads for each attention layer in the Transformer encoder.\n num_key_value_heads (`int`, *optional*):\n This is the number of key_value heads that should be used to implement Grouped Query Attention. If\n `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if\n `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When\n converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed\n by meanpooling all the original heads within that group. For more details checkout [this\n paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to\n `num_attention_heads`.\n pretraining_tp (`int`, *optional*, defaults to `1`):\n Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this\n document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is\n necessary to ensure exact reproducibility of the pretraining results. Please refer to [this\n issue](https://github.com/pytorch/pytorch/issues/76232).\n hidden_act (`str` or `function`, *optional*, defaults to `\"silu\"`):\n The non-linear activation function (function or string) in the decoder.\n max_position_embeddings (`int`, *optional*, defaults to 2048):\n The maximum sequence length that this model might ever be used with. Typically set this to something large\n just in case (e.g., 512 or 1024 or 2048).\n initializer_range (`float`, *optional*, defaults to 0.02):\n The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n rms_norm_eps (`float`, *optional*, defaults to 1e-12):\n The epsilon used by the rms normalization layers.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models). Only\n relevant if `config.is_decoder=True`.\n tie_word_embeddings(`bool`, *optional*, defaults to `False`):\n Whether to tie weight embeddings\n rope_scaling (`Dict`, *optional*):\n Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling\n strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format\n is `{\"type\": strategy name, \"factor\": scaling factor}`. When using this flag, don't update\n `max_position_embeddings` to the expected new maximum. See the following thread for more information on how\n these scaling strategies behave:\n https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an\n experimental feature, subject to breaking API changes in future versions.\n\n Example:\n\n ```python\n >>> from transformers import LlamaModel, LlamaConfig\n\n >>> # Initializing a LLaMA llama-7b style configuration\n >>> configuration = LlamaConfig()\n\n >>> # Initializing a model from the llama-7b style configuration\n >>> model = LlamaModel(configuration)\n\n >>> # Accessing the model configuration\n >>> configuration = model.config\n ```\"\"\"\n model_type = \"llama\"\n keys_to_ignore_at_inference = [\"past_key_values\"]\n\n def __init__(\n self,\n vocab_size=32000,\n hidden_size=4096,\n intermediate_size=11008,\n num_hidden_layers=32,\n num_attention_heads=32,\n num_key_value_heads=None,\n hidden_act=\"silu\",\n max_position_embeddings=2048,\n initializer_range=0.02,\n rms_norm_eps=1e-6,\n use_cache=True,\n pad_token_id=None,\n bos_token_id=1,\n eos_token_id=2,\n pretraining_tp=1,\n tie_word_embeddings=False,\n rope_scaling=None,\n **kwargs,\n ):\n self.vocab_size = vocab_size\n self.max_position_embeddings = max_position_embeddings\n self.hidden_size = hidden_size\n self.intermediate_size = intermediate_size\n self.num_hidden_layers = num_hidden_layers\n self.num_attention_heads = num_attention_heads\n\n # for backward compatibility\n if num_key_value_heads is None:\n num_key_value_heads = num_attention_heads\n\n self.num_key_value_heads = num_key_value_heads\n self.hidden_act = hidden_act\n self.initializer_range = initializer_range\n self.rms_norm_eps = rms_norm_eps\n self.pretraining_tp = pretraining_tp\n self.use_cache = use_cache\n self.rope_scaling = rope_scaling\n self._rope_scaling_validation()\n\n super().__init__(\n pad_token_id=pad_token_id,\n bos_token_id=bos_token_id,\n eos_token_id=eos_token_id,\n tie_word_embeddings=tie_word_embeddings,\n **kwargs,\n )\n\n def _rope_scaling_validation(self):\n \"\"\"\n Validate the `rope_scaling` configuration.\n \"\"\"\n if self.rope_scaling is None:\n return\n\n if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:\n raise ValueError(\n \"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, \"\n f\"got {self.rope_scaling}\"\n )\n rope_scaling_type = self.rope_scaling.get(\"type\", None)\n rope_scaling_factor = self.rope_scaling.get(\"factor\", None)\n if rope_scaling_type is None or rope_scaling_type not in [\"linear\", \"dynamic\"]:\n raise ValueError(\n f\"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}\"\n )\n if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:\n raise ValueError(f\"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}\")"},{"identifier":"LlamaForCausalLM","path":"model/llama/modeling_llama.py","snippet":"class LlamaForCausalLM(LlamaPreTrainedModel):\n _tied_weights_keys = [\"lm_head.weight\"]\n\n def __init__(self, config):\n super().__init__(config)\n self.model = LlamaModel(config)\n self.vocab_size = config.vocab_size\n self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self):\n return self.model.embed_tokens\n\n def set_input_embeddings(self, value):\n self.model.embed_tokens = value\n\n def get_output_embeddings(self):\n return self.lm_head\n\n def set_output_embeddings(self, new_embeddings):\n self.lm_head = new_embeddings\n\n def set_decoder(self, decoder):\n self.model = decoder\n\n def get_decoder(self):\n return self.model\n\n @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_values: Optional[List[torch.FloatTensor]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n labels: Optional[torch.LongTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, CausalLMOutputWithPast]:\n r\"\"\"\n Args:\n labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,\n config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored\n (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.\n\n Returns:\n\n Example:\n\n ```python\n >>> from transformers import AutoTokenizer, LlamaForCausalLM\n\n >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)\n >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)\n\n >>> prompt = \"Hey, are you conscious? Can you talk to me?\"\n >>> inputs = tokenizer(prompt, return_tensors=\"pt\")\n\n >>> # Generate\n >>> generate_ids = model.generate(inputs.input_ids, max_length=30)\n >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n \"Hey, are you conscious? Can you talk to me?\\nI'm not conscious, but I can talk to you.\"\n ```\"\"\"\n\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\n outputs = self.model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_values=past_key_values,\n inputs_embeds=inputs_embeds,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n hidden_states = outputs[0]\n if self.config.pretraining_tp > 1:\n lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\n logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\n logits = torch.cat(logits, dim=-1)\n else:\n logits = self.lm_head(hidden_states)\n logits = logits.float()\n\n loss = None\n if labels is not None:\n # Shift so that tokens < n predict n\n shift_logits = logits[..., :-1, :].contiguous()\n shift_labels = labels[..., 1:].contiguous()\n # Flatten the tokens\n loss_fct = CrossEntropyLoss()\n shift_logits = shift_logits.view(-1, self.config.vocab_size)\n shift_labels = shift_labels.view(-1)\n # Enable model parallelism\n shift_labels = shift_labels.to(shift_logits.device)\n loss = loss_fct(shift_logits, shift_labels)\n\n if not return_dict:\n output = (logits,) + outputs[1:]\n return (loss,) + output if loss is not None else output\n\n return CausalLMOutputWithPast(\n loss=loss,\n logits=logits,\n past_key_values=outputs.past_key_values,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n def prepare_inputs_for_generation(\n self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs\n ):\n if past_key_values:\n input_ids = input_ids[:, -1:]\n\n position_ids = kwargs.get(\"position_ids\", None)\n if attention_mask is not None and position_ids is None:\n # create position_ids on the fly for batch generation\n position_ids = attention_mask.long().cumsum(-1) - 1\n position_ids.masked_fill_(attention_mask == 0, 1)\n if past_key_values:\n position_ids = position_ids[:, -1].unsqueeze(-1)\n\n # if `inputs_embeds` are passed, we only want to use them in the 1st generation step\n if inputs_embeds is not None and past_key_values is None:\n model_inputs = {\"inputs_embeds\": inputs_embeds}\n else:\n model_inputs = {\"input_ids\": input_ids}\n\n model_inputs.update(\n {\n \"position_ids\": position_ids,\n \"past_key_values\": past_key_values,\n \"use_cache\": kwargs.get(\"use_cache\"),\n \"attention_mask\": attention_mask,\n }\n )\n return model_inputs\n\n @staticmethod\n def _reorder_cache(past_key_values, beam_idx):\n reordered_past = ()\n for layer_past in past_key_values:\n reordered_past += (\n tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),\n )\n return reordered_past"},{"identifier":"LlamaTokenizer","path":"model/llama/tokenization_llama.py","snippet":"class LlamaTokenizer(PreTrainedTokenizer):\n \"\"\"\n Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is\n no padding token in the original model.\n\n Args:\n vocab_file (`str`):\n Path to the vocabulary file.\n legacy (`bool`, *optional*, defaults to `True`):\n Whether or not the `legacy` behaviour of the tokenizer should be used. Legacy is before the merge of #24622\n which includes fixes to properly handle tokens that appear after special tokens. A simple example:\n\n - `legacy=True`:\n ```python\n >>> from transformers import T5Tokenizer\n\n >>> tokenizer = T5Tokenizer.from_pretrained(\"t5-base\", legacy=True)\n >>> tokenizer.encode(\"Hello <extra_id_0>.\")\n [8774, 32099, 3, 5, 1]\n ```\n - `legacy=False`:\n ```python\n >>> from transformers import T5Tokenizer\n\n >>> tokenizer = T5Tokenizer.from_pretrained(\"t5-base\", legacy=False)\n >>> tokenizer.encode(\"Hello <extra_id_0>.\") # the extra space `[3]` is no longer here\n [8774, 32099, 5, 1]\n ```\n Checkout the pull request and the issue [here](https://github.com/huggingface/transformers/pull/24565) for\n more details.\n\n \"\"\"\n\n vocab_files_names = VOCAB_FILES_NAMES\n pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP\n max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\n model_input_names = [\"input_ids\", \"attention_mask\"]\n\n def __init__(\n self,\n vocab_file,\n unk_token=\"<unk>\",\n bos_token=\"<s>\",\n eos_token=\"</s>\",\n pad_token=None,\n sp_model_kwargs: Optional[Dict[str, Any]] = None,\n add_bos_token=True,\n add_eos_token=False,\n clean_up_tokenization_spaces=False,\n legacy=None,\n **kwargs,\n ):\n self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs\n bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token\n eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token\n unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token\n pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token\n super().__init__(\n bos_token=bos_token,\n eos_token=eos_token,\n unk_token=unk_token,\n pad_token=pad_token,\n add_bos_token=add_bos_token,\n add_eos_token=add_eos_token,\n sp_model_kwargs=self.sp_model_kwargs,\n clean_up_tokenization_spaces=clean_up_tokenization_spaces,\n legacy=legacy,\n **kwargs,\n )\n if legacy is None:\n logger.warning_once(\n f\"You are using the default legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to\"\n \" read the related pull request available at https://github.com/huggingface/transformers/pull/24565, and set the legacy attribute accordingly.\"\n )\n legacy = True\n\n self.legacy = legacy\n self.vocab_file = vocab_file\n self.add_bos_token = add_bos_token\n self.add_eos_token = add_eos_token\n self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)\n self.sp_model.Load(vocab_file)\n\n def __getstate__(self):\n state = self.__dict__.copy()\n state[\"sp_model\"] = None\n state[\"sp_model_proto\"] = self.sp_model.serialized_model_proto()\n return state\n\n def __setstate__(self, d):\n self.__dict__ = d\n self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)\n self.sp_model.LoadFromSerializedProto(self.sp_model_proto)\n\n @property\n def vocab_size(self):\n \"\"\"Returns vocab size\"\"\"\n return self.sp_model.get_piece_size()\n\n def get_vocab(self):\n \"\"\"Returns vocab as a dict\"\"\"\n vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}\n vocab.update(self.added_tokens_encoder)\n return vocab\n\n # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize\n def tokenize(self, text: \"TextInput\", **kwargs) -> List[str]:\n # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at\n # the beginning of the text\n if not self.legacy:\n text = SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, \" \")\n return super().tokenize(text, **kwargs)\n\n # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize\n def _tokenize(self, text, **kwargs):\n \"\"\"\n Returns a tokenized string.\n\n Since the sentencepiece internal model always adds a SPIECE_UNDERLINE, at the beginning of the provided text,\n we need to remove it by hand when the current text is a subsequence. This happens whenever the `self.tokenize`\n function is called with specials tokens: the input is split on the special tokens, and each subsequence is\n passed to `_tokenize`. Thus if a subsequence did not start with a `\" \"` or SPIECE_UNDERLINE, we have to remove\n the extra `SPIECE_UNDERLINE` prepended.\n \"\"\"\n if not self.legacy:\n is_first = text.startswith(SPIECE_UNDERLINE)\n if is_first:\n text = text[1:]\n\n tokens = self.sp_model.encode(text, out_type=str)\n\n if not self.legacy and not is_first and not text.startswith(\" \") and tokens[0].startswith(SPIECE_UNDERLINE):\n tokens = ([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]\n return tokens\n\n def _convert_token_to_id(self, token):\n \"\"\"Converts a token (str) in an id using the vocab.\"\"\"\n return self.sp_model.piece_to_id(token)\n\n def _convert_id_to_token(self, index):\n \"\"\"Converts an index (integer) in a token (str) using the vocab.\"\"\"\n token = self.sp_model.IdToPiece(index)\n return token\n\n def convert_tokens_to_string(self, tokens):\n \"\"\"Converts a sequence of tokens (string) in a single string.\"\"\"\n current_sub_tokens = []\n out_string = \"\"\n prev_is_special = False\n for i, token in enumerate(tokens):\n # make sure that special tokens are not decoded using sentencepiece model\n if token in self.all_special_tokens:\n if not prev_is_special and i != 0:\n out_string += \" \"\n out_string += self.sp_model.decode(current_sub_tokens) + token\n prev_is_special = True\n current_sub_tokens = []\n else:\n current_sub_tokens.append(token)\n prev_is_special = False\n out_string += self.sp_model.decode(current_sub_tokens)\n return out_string\n\n def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:\n \"\"\"\n Save the vocabulary and special tokens file to a directory.\n\n Args:\n save_directory (`str`):\n The directory in which to save the vocabulary.\n\n Returns:\n `Tuple(str)`: Paths to the files saved.\n \"\"\"\n if not os.path.isdir(save_directory):\n logger.error(f\"Vocabulary path ({save_directory}) should be a directory\")\n return\n out_vocab_file = os.path.join(\n save_directory, (filename_prefix + \"-\" if filename_prefix else \"\") + VOCAB_FILES_NAMES[\"vocab_file\"]\n )\n\n if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):\n copyfile(self.vocab_file, out_vocab_file)\n elif not os.path.isfile(self.vocab_file):\n with open(out_vocab_file, \"wb\") as fi:\n content_spiece_model = self.sp_model.serialized_model_proto()\n fi.write(content_spiece_model)\n\n return (out_vocab_file,)\n\n def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):\n bos_token_id = [self.bos_token_id] if self.add_bos_token else []\n eos_token_id = [self.eos_token_id] if self.add_eos_token else []\n\n output = bos_token_id + token_ids_0 + eos_token_id\n\n if token_ids_1 is not None:\n output = output + bos_token_id + token_ids_1 + eos_token_id\n\n return output\n\n def get_special_tokens_mask(\n self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False\n ) -> List[int]:\n \"\"\"\n Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding\n special tokens using the tokenizer `prepare_for_model` method.\n\n Args:\n token_ids_0 (`List[int]`):\n List of IDs.\n token_ids_1 (`List[int]`, *optional*):\n Optional second list of IDs for sequence pairs.\n already_has_special_tokens (`bool`, *optional*, defaults to `False`):\n Whether or not the token list is already formatted with special tokens for the model.\n\n Returns:\n `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.\n \"\"\"\n if already_has_special_tokens:\n return super().get_special_tokens_mask(\n token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True\n )\n\n bos_token_id = [1] if self.add_bos_token else []\n eos_token_id = [1] if self.add_eos_token else []\n\n if token_ids_1 is None:\n return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id\n return (\n bos_token_id\n + ([0] * len(token_ids_0))\n + eos_token_id\n + bos_token_id\n + ([0] * len(token_ids_1))\n + eos_token_id\n )\n\n def create_token_type_ids_from_sequences(\n self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None\n ) -> List[int]:\n \"\"\"\n Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT\n sequence pair mask has the following format:\n\n ```\n 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1\n | first sequence | second sequence |\n ```\n\n if token_ids_1 is None, only returns the first portion of the mask (0s).\n\n Args:\n token_ids_0 (`List[int]`):\n List of ids.\n token_ids_1 (`List[int]`, *optional*):\n Optional second list of IDs for sequence pairs.\n\n Returns:\n `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).\n \"\"\"\n bos_token_id = [self.bos_token_id] if self.add_bos_token else []\n eos_token_id = [self.eos_token_id] if self.add_eos_token else []\n\n output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)\n\n if token_ids_1 is not None:\n output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)\n\n return output\n\n def _build_conversation_input_ids(self, conversation: \"Conversation\") -> List[int]:\n r\"\"\"Builds the input ids for a conversation.\n This is the format used in the provided examples. System prompts should be manually added at the beginning of\n the conversation. If no system prompt is given, the `DEFAULT_SYSTEM_PROMPT` will be used.\n ```\n <bos>[INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer <eos>\n <bos>[INST] Prompt [/INST] Answer <eos>\n <bos>[INST] Prompt [/INST]\n ```\n\n If you want to use your own system prompt, make sure to use both `B_SYS` and `E_SYS` use the following:\n ```python\n >>> from transformers import Conversation\n\n >>> Conversation(\n ... \"<<SYS>>\\n Only answer with emojis, and charades\\n<</SYS>>\\n\\nHow can I build a house in 10 septs?\"\n ... ) # doctest: +IGNORE_RESULT\n ```\n Args:\n conversation (`Conversation`):\n Conversation to build input ids for.\n Returns:\n `List[int]`:\n Input ids for the conversation.\n \"\"\"\n if len(conversation.past_user_inputs) > 0:\n if not conversation.past_user_inputs[0].startswith(B_SYS) or E_SYS not in conversation.past_user_inputs[0]:\n conversation.past_user_inputs[0] = (\n B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]\n )\n elif conversation.new_user_input:\n if not conversation.new_user_input.startswith(B_SYS) or E_SYS not in conversation.new_user_input:\n conversation.new_user_input = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.new_user_input\n else:\n raise ValueError(\"Last message must be from user\")\n\n dialogue = list(conversation.iter_texts())\n if not all([is_user for is_user, msg in dialogue[::2]]) or not all(\n [not is_user for is_user, msg in dialogue[1::2]]\n ):\n raise ValueError(\n \"The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)\"\n )\n\n dialog_tokens: List[int] = []\n dialog_tokens += sum(\n [\n [self.bos_token_id]\n + self.encode(\n f\"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} \", add_special_tokens=False\n )\n + [self.eos_token_id]\n for prompt, answer in zip(dialogue[::2], dialogue[1::2])\n ],\n [],\n )\n dialog_tokens += [self.bos_token_id] + self.encode(\n f\"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}\", add_special_tokens=False\n )\n return dialog_tokens"},{"identifier":"LlamaTokenizerFast","path":"model/llama/tokenization_llama_fast.py","snippet":"class LlamaTokenizerFast(PreTrainedTokenizerFast):\n \"\"\"\n Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.\n\n This uses notably ByteFallback and no normalization.\n\n ```\n from transformers import LlamaTokenizerFast\n\n tokenizer = LlamaTokenizerFast.from_pretrained(\"hf-internal-testing/llama-tokenizer\")\n tokenizer.encode(\"Hello this is a test\")\n >>> [1, 15043, 445, 338, 263, 1243]\n ```\n\n If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or\n call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the\n values of the first token and final token of an encoded sequence will not be correct). For more details, checkout\n [post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.\n\n\n This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should\n refer to this superclass for more information regarding those methods.\n\n Args:\n vocab_file (`str`):\n [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that\n contains the vocabulary necessary to instantiate a tokenizer.\n tokenizer_file (`str`):\n [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that\n contains everything needed to load the tokenizer.\n\n clean_up_tokenization_spaces (`str`, *optional*, defaults to `False`):\n Wether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra\n spaces.\n\n bos_token (`str`, *optional*, defaults to `\"<s>\"`):\n The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.\n\n eos_token (`str`, *optional*, defaults to `\"</s>\"`):\n The end of sequence token.\n\n unk_token (`str`, *optional*, defaults to `\"<unk>\"`):\n The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this\n token instead.\n \"\"\"\n\n vocab_files_names = VOCAB_FILES_NAMES\n slow_tokenizer_class = LlamaTokenizer\n padding_side = \"left\"\n model_input_names = [\"input_ids\", \"attention_mask\"]\n\n def __init__(\n self,\n vocab_file=None,\n tokenizer_file=None,\n clean_up_tokenization_spaces=False,\n unk_token=\"<unk>\",\n bos_token=\"<s>\",\n eos_token=\"</s>\",\n add_bos_token=True,\n add_eos_token=False,\n **kwargs,\n ):\n super().__init__(\n vocab_file=vocab_file,\n tokenizer_file=tokenizer_file,\n clean_up_tokenization_spaces=clean_up_tokenization_spaces,\n unk_token=unk_token,\n bos_token=bos_token,\n eos_token=eos_token,\n **kwargs,\n )\n self._add_bos_token = add_bos_token\n self._add_eos_token = add_eos_token\n self.update_post_processor()\n\n self.vocab_file = vocab_file\n self.can_save_slow_tokenizer = False if not self.vocab_file else True\n\n def update_post_processor(self):\n \"\"\"\n Updates the underlying post processor with the current `bos_token` and `eos_token`.\n \"\"\"\n bos = self.bos_token\n bos_token_id = self.bos_token_id\n\n eos = self.eos_token\n eos_token_id = self.eos_token_id\n\n single = f\"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}\"\n pair = f\"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}\"\n\n special_tokens = []\n if self.add_bos_token:\n special_tokens.append((bos, bos_token_id))\n if self.add_eos_token:\n special_tokens.append((eos, eos_token_id))\n self._tokenizer.post_processor = processors.TemplateProcessing(\n single=single, pair=pair, special_tokens=special_tokens\n )\n\n @property\n def add_eos_token(self):\n return self._add_eos_token\n\n @property\n def add_bos_token(self):\n return self._add_bos_token\n\n @add_eos_token.setter\n def add_eos_token(self, value):\n self._add_eos_token = value\n self.update_post_processor()\n\n @add_bos_token.setter\n def add_bos_token(self, value):\n self._add_bos_token = value\n self.update_post_processor()\n\n def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:\n if not self.can_save_slow_tokenizer:\n raise ValueError(\n \"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow \"\n \"tokenizer.\"\n )\n\n if not os.path.isdir(save_directory):\n logger.error(f\"Vocabulary path ({save_directory}) should be a directory\")\n return\n out_vocab_file = os.path.join(\n save_directory, (filename_prefix + \"-\" if filename_prefix else \"\") + VOCAB_FILES_NAMES[\"vocab_file\"]\n )\n\n if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):\n copyfile(self.vocab_file, out_vocab_file)\n\n return (out_vocab_file,)\n\n def _build_conversation_input_ids(self, conversation: \"Conversation\"):\n \"\"\"Builds the input ids for a conversation.\n This is the format used in the provided examples. System prompts should be manually added at the beginning of\n the conversation. If no system prompt is given, the `DEFAULT_SYSTEM_PROMPT` will be used.\n ```\n <bos>[INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer <eos>\n <bos>[INST] Prompt [/INST] Answer <eos>\n <bos>[INST] Prompt [/INST]\n ```\n\n If you want to use your own system prompt, make sure to use both `B_SYS` and `E_SYS` use the following:\n ```python\n >>> from transformers import Conversation\n\n >>> Conversation(\n ... \"<<SYS>>\\n Only answer with emojis, and charades\\n<</SYS>>\\n\\nHow can I build a house in 10 septs?\"\n ... )\n ```\n Args:\n conversation (`Conversation`):\n Conversation to build input ids for.\n Returns:\n `List[int]`:\n Input ids for the conversation.\n \"\"\"\n if len(conversation.past_user_inputs) > 0:\n if not conversation.past_user_inputs[0].startswith(B_SYS) or E_SYS not in conversation.past_user_inputs[0]:\n conversation.past_user_inputs[0] = (\n B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]\n )\n elif conversation.new_user_input:\n if not conversation.new_user_input.startswith(B_SYS) or E_SYS not in conversation.new_user_input:\n conversation.new_user_input = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.new_user_input\n else:\n raise ValueError(\"Last message must be from user\")\n\n dialogue = list(conversation.iter_texts())\n if not all([is_user for is_user, msg in dialogue[::2]]) or not all(\n [not is_user for is_user, msg in dialogue[1::2]]\n ):\n raise ValueError(\n \"The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)\"\n )\n\n dialog_tokens = []\n dialog_tokens += sum(\n [\n [self.bos_token_id]\n + self.encode(\n f\"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} \", add_special_tokens=False\n )\n + [self.eos_token_id]\n for prompt, answer in zip(dialogue[::2], dialogue[1::2])\n ],\n [],\n )\n dialog_tokens += [self.bos_token_id] + self.encode(\n f\"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}\", add_special_tokens=False\n )\n return dialog_tokens"},{"identifier":"print_rank_0","path":"utils/common_utils.py","snippet":"def print_rank_0(*message):\n \"\"\"If distributed is initialized print only on rank 0.\"\"\"\n if torch.distributed.is_initialized():\n if torch.distributed.get_rank() == 0:\n print(*message, flush=True)\n else:\n print(*message, flush=True)"},{"identifier":"is_old_version","path":"utils/common_utils.py","snippet":"def is_old_version(path):\n new_vocab_files = ['merge.model']\n new_vocab_file_exists = []\n for filename in new_vocab_files:\n if not os.path.exists(os.path.join(path, filename)):\n new_vocab_file_exists.append(False)\n else:\n new_vocab_file_exists.append(True)\n if all(new_vocab_file_exists):\n return False\n if any(new_vocab_file_exists):\n return 'new_version_file_absent'\n else:\n return True"},{"identifier":"build_tokenizer","path":"tokenizer/tokenizer.py","snippet":"def build_tokenizer(args):\n \"\"\"Initialize tokenizer.\"\"\"\n print_rank_0(\"> building {} tokenizer ...\".format(args.tokenizer_type))\n # if args.rank == 0:\n # print(\"> building {} tokenizer ...\".format(args.tokenizer_type), flush=True)\n\n # Select and instantiate the tokenizer.\n if args.tokenizer_type.lower() == \"GPT2BPETokenizer\".lower():\n assert args.vocab_file is not None\n assert args.merge_file is not None\n tokenizer = _GPT2BPETokenizer(args.vocab_file, args.merge_file)\n elif args.tokenizer_type.lower() == \"SPMTokenizer\".lower():\n assert args.vocab_file is not None\n tokenizer = SentencePieceTokenizer(args.vocab_file)\n elif args.tokenizer_type.lower() == \"HFTokenizer\".lower():\n assert args.vocab_file is not None\n tokenizer = HFTokenizer(args.vocab_file)\n elif args.tokenizer_type.lower() == \"HFGPT2Tokenizer\".lower():\n if args.vocab_file is None:\n print(\n \"WARNING: No vocab file found, loading Huggingface's pretrained GPT2Tokenizer\"\n )\n tokenizer = HFGPT2Tokenizer(args.vocab_file)\n elif args.tokenizer_type.lower() == \"CharLevelTokenizer\".lower():\n tokenizer = CharLevelTokenizer(vocab_size=512)\n elif args.tokenizer_type.lower() == \"TiktokenTokenizer\".lower():\n assert args.vocab_file is not None\n tokenizer = TiktokenTokenizer(args.vocab_file)\n elif args.tokenizer_type.lower() == \"GLMTokenizer\".lower():\n if is_old_version(args.pretrained_model_path):\n print('is an old version')\n from model.glm.tokenization_glm_deprecated import GLMChineseTokenizer\n args.glm_mask = '[sMASK]'\n old_version_tokenizer = True\n tokenizer = GLMChineseTokenizer.from_pretrained(args.pretrained_model_path, trust_remote_code=True)\n else:\n print('is not an old version')\n old_version_tokenizer = False\n tokenizer = GLMTokenizer.from_pretrained(args.pretrained_model_path, trust_remote_code=True)\n else:\n raise NotImplementedError(\n \"{} tokenizer is not \" \"implemented.\".format(args.tokenizer_type)\n )\n\n # Add vocab size.\n args.padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size, args)\n\n return tokenizer"},{"identifier":"HFTokenizer","path":"tokenizer/tokenizer.py","snippet":"class HFTokenizer(AbstractTokenizer):\n \"\"\"Designed to Integrate HF's Tokenizer library.\"\"\"\n\n def __init__(self, vocab_file):\n name = \"HFTokenizer\"\n super().__init__(name)\n\n self.tokenizer = Tokenizer.from_file(vocab_file)\n # self.eod_id = self.tokenizer.token_to_id(\"<|endoftext|>\")\n self.eod_id = self.tokenizer.token_to_id(\"<|end|>\")\n # self.pad_id = self.tokenizer.token_to_id(\"<|padding|>\")\n \n # 新词表没有<|padding|>, 用<|extratoken_1|>代替，和tokenization一致\n # self.pad_id = self.tokenizer.token_to_id(\"<|extratoken_1|>\")\n self.pad_id = self.tokenizer.token_to_id(\"<|pad|>\")\n\n @property\n def vocab_size(self):\n return self.tokenizer.get_vocab_size()\n\n @property\n def vocab(self):\n return self.tokenizer.get_vocab()\n\n @property\n def inv_vocab(self):\n return self.tokenizer.decoder\n\n def tokenize(self, text: str):\n return self.tokenizer.encode(text).ids\n\n def tokenize_batch(self, text_batch: Union[List[str], str]):\n return self.tokenizer.encode_batch(text_batch)\n\n def detokenize(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\n @property\n def eod(self):\n return self.eod_id"},{"identifier":"prepare_model_for_kbit_training","path":"model/peft/utils/others.py","snippet":"def prepare_model_for_kbit_training(model, use_gradient_checkpointing=True):\n r\"\"\"\n This method wraps the entire protocol for preparing a model before running a training. This includes:\n 1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm\n head to fp32\n\n Args:\n model, (`transformers.PreTrainedModel`):\n The loaded model from `transformers`\n \"\"\"\n loaded_in_kbit = getattr(model, \"is_loaded_in_8bit\", False) or getattr(model, \"is_loaded_in_4bit\", False)\n\n for name, param in model.named_parameters():\n # freeze base model's layers\n param.requires_grad = False\n \n # cast all non INT8 parameters to fp32\n for param in model.parameters():\n if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):\n param.data = param.data.to(torch.float32)\n \n if loaded_in_kbit and use_gradient_checkpointing:\n # For backward compatibility\n if hasattr(model, \"enable_input_require_grads\"):\n model.enable_input_require_grads()\n else:\n \n def make_inputs_require_grad(module, input, output):\n output.requires_grad_(True)\n\n model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)\n\n # enable gradient checkpointing for memory efficiency\n model.gradient_checkpointing_enable()\n\n return model"},{"identifier":"AdaLoraConfig","path":"model/peft/tuner/adalora.py","snippet":"class AdaLoraConfig(LoraConfig):\n \"\"\"\n This is the configuration class to store the configuration of a [`~peft.AdaLora`].\n\n Args:\n target_r (`int`): The target average rank of incremental matrix.\n init_r (`int`): The initial rank for each incremental matrix.\n tinit (`int`): The steps of initial fine-tuning warmup.\n tfinal (`int`): The step of final fine-tuning.\n deltaT (`int`): The time internval between two budget allocations.\n beta1 (`float`): The hyperparameter of EMA for sensitivity smoothing.\n beta2 (`float`): The hyperparameter of EMA for undertainty quantification.\n orth_reg_weight (`float`): The coefficient of orthogonal regularization.\n total_step (`int`): The total training steps that should be specified before training.\n rank_pattern (`list`): The allocated rank for each weight matrix by RankAllocator.\n \"\"\"\n\n target_r: int = field(default=8, metadata={\"help\": \"Target Lora matrix dimension.\"})\n init_r: int = field(default=12, metadata={\"help\": \"Intial Lora matrix dimension.\"})\n tinit: int = field(default=0, metadata={\"help\": \"The steps of initial warmup.\"})\n tfinal: int = field(default=0, metadata={\"help\": \"The steps of final warmup.\"})\n deltaT: int = field(default=1, metadata={\"help\": \"Step interval of rank allocation.\"})\n beta1: float = field(default=0.85, metadata={\"help\": \"Hyperparameter of EMA.\"})\n beta2: float = field(default=0.85, metadata={\"help\": \"Hyperparameter of EMA.\"})\n orth_reg_weight: float = field(default=0.5, metadata={\"help\": \"The orthogonal regularization coefficient.\"})\n total_step: Optional[int] = field(default=None, metadata={\"help\": \"The total training steps.\"})\n rank_pattern: Optional[dict] = field(default=None, metadata={\"help\": \"The saved rank pattern.\"})\n init_lora_weights: bool = field(\n default=True,\n metadata={\"help\": \"Whether to initialize the weights of the Lora layers.\"},\n )\n\n def __post_init__(self):\n self.peft_type = PeftType.ADALORA"}],"string":"[\n {\n \"identifier\": \"get_model_params_num\",\n \"path\": \"utils/common_utils.py\",\n \"snippet\": \"def get_model_params_num(model):\\n \\\"\\\"\\\"\\n Get params number of the model\\n Args:\\n model: model(required)\\n Returns:\\n the number of parameters of model\\n \\\"\\\"\\\"\\n num = 0\\n for _, param in model.named_parameters():\\n num += param.nelement()\\n return num\"\n },\n {\n \"identifier\": \"GPTNeoXConfig\",\n \"path\": \"model/gpt_neox/configuration_gpt_neox.py\",\n \"snippet\": \"class GPTNeoXConfig(PretrainedConfig):\\n r\\\"\\\"\\\"\\n This is the configuration class to store the configuration of a [`GPTNeoXModel`]. It is used to instantiate an\\n GPTNeoX model according to the specified arguments, defining the model architecture. Instantiating a configuration\\n with the defaults will yield a similar configuration to that of the GPTNeoX\\n [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) architecture.\\n\\n Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\\n documentation from [`PretrainedConfig`] for more information.\\n\\n\\n Args:\\n vocab_size (`int`, *optional*, defaults to 50432):\\n Vocabulary size of the GPTNeoX model. Defines the number of different tokens that can be represented by the\\n `inputs_ids` passed when calling [`GPTNeoXModel`].\\n hidden_size (`int`, *optional*, defaults to 6144):\\n Dimension of the encoder layers and the pooler layer.\\n num_hidden_layers (`int`, *optional*, defaults to 44):\\n Number of hidden layers in the Transformer encoder.\\n num_attention_heads (`int`, *optional*, defaults to 64):\\n Number of attention heads for each attention layer in the Transformer encoder.\\n intermediate_size (`int`, *optional*, defaults to 24576):\\n Dimension of the \\\"intermediate\\\" (i.e., feed-forward) layer in the Transformer encoder.\\n hidden_act (`str` or `function`, *optional*, defaults to `\\\"gelu\\\"`):\\n The non-linear activation function (function or string) in the encoder and pooler. If string, `\\\"gelu\\\"`,\\n `\\\"relu\\\"`, `\\\"selu\\\"` and `\\\"gelu_new\\\"` are supported.\\n rotary_pct (`float`, *optional*, defaults to 0.25):\\n percentage of hidden dimensions to allocate to rotary embeddings\\n rotary_emb_base (`int`, *optional*, defaults to 10000)\\n base for computing rotary embeddings frequency\\n max_position_embeddings (`int`, *optional*, defaults to 2048):\\n The maximum sequence length that this model might ever be used with. Typically set this to something large\\n just in case (e.g., 512 or 1024 or 2048).\\n initializer_range (`float`, *optional*, defaults to 1e-5):\\n The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\\n layer_norm_eps (`float`, *optional*, defaults to 1e-12):\\n The epsilon used by the layer normalization layers.\\n use_cache (`bool`, *optional*, defaults to `True`):\\n Whether or not the model should return the last key/values attentions (not used by all models). Only\\n relevant if `config.is_decoder=True`.\\n use_parallel_residual (`bool`, *optional*, defaults to `True`):\\n Whether to use a \\\"parallel\\\" formulation in each Transformer layer, which can provide a slight training\\n speedup at large scales (e.g. 20B).\\n rope_scaling (`Dict`, *optional*):\\n Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling\\n strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format\\n is `{\\\"type\\\": strategy name, \\\"factor\\\": scaling factor}`. When using this flag, don't update\\n `max_position_embeddings` to the expected new maximum. See the following thread for more information on how\\n these scaling strategies behave:\\n https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an\\n experimental feature, subject to breaking API changes in future versions.\\n Example:\\n\\n ```python\\n >>> from transformers import GPTNeoXConfig, GPTNeoXModel\\n\\n >>> # Initializing a GPTNeoX gpt-neox-20b style configuration\\n >>> configuration = GPTNeoXConfig()\\n\\n >>> # Initializing a model (with random weights) from the gpt-neox-20b style configuration\\n >>> model = GPTNeoXModel(configuration) # doctest: +SKIP\\n\\n >>> # Accessing the model configuration\\n >>> configuration = model.config # doctest: +SKIP\\n ```\\\"\\\"\\\"\\n model_type = \\\"gpt_neox\\\"\\n\\n def __init__(\\n self,\\n vocab_size=50432,\\n hidden_size=6144,\\n num_hidden_layers=44,\\n num_attention_heads=64,\\n intermediate_size=24576,\\n hidden_act=\\\"gelu\\\",\\n rotary_pct=0.25,\\n rotary_emb_base=10000,\\n max_position_embeddings=2048,\\n initializer_range=0.02,\\n layer_norm_eps=1e-5,\\n use_cache=True,\\n bos_token_id=0,\\n eos_token_id=2,\\n tie_word_embeddings=False,\\n use_parallel_residual=True,\\n rope_scaling=None,\\n **kwargs\\n ):\\n super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)\\n self.vocab_size = vocab_size\\n self.max_position_embeddings = max_position_embeddings\\n self.hidden_size = hidden_size\\n self.num_hidden_layers = num_hidden_layers\\n self.num_attention_heads = num_attention_heads\\n self.intermediate_size = intermediate_size\\n self.hidden_act = hidden_act\\n self.rotary_pct = rotary_pct\\n self.rotary_emb_base = rotary_emb_base\\n self.initializer_range = initializer_range\\n self.layer_norm_eps = layer_norm_eps\\n self.use_cache = use_cache\\n self.tie_word_embeddings = tie_word_embeddings\\n self.use_parallel_residual = use_parallel_residual\\n self.rope_scaling = rope_scaling\\n self._rope_scaling_validation()\\n\\n if self.hidden_size % self.num_attention_heads != 0:\\n raise ValueError(\\n \\\"The hidden size is not divisble by the number of attention heads! Make sure to update them!\\\"\\n )\\n\\n # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation\\n def _rope_scaling_validation(self):\\n \\\"\\\"\\\"\\n Validate the `rope_scaling` configuration.\\n \\\"\\\"\\\"\\n if self.rope_scaling is None:\\n return\\n\\n if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:\\n raise ValueError(\\n \\\"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, \\\"\\n f\\\"got {self.rope_scaling}\\\"\\n )\\n rope_scaling_type = self.rope_scaling.get(\\\"type\\\", None)\\n rope_scaling_factor = self.rope_scaling.get(\\\"factor\\\", None)\\n if rope_scaling_type is None or rope_scaling_type not in [\\\"linear\\\", \\\"dynamic\\\"]:\\n raise ValueError(\\n f\\\"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}\\\"\\n )\\n if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:\\n raise ValueError(f\\\"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}\\\")\"\n },\n {\n \"identifier\": \"GPTNeoXForCausalLM\",\n \"path\": \"model/gpt_neox/modeling_gpt_neox.py\",\n \"snippet\": \"class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel):\\n\\n # _keys_to_ignore_on_load_missing = [r\\\"position_ids\\\", r\\\"predictions.decoder.bias\\\"]\\n\\n def __init__(self, config):\\n super().__init__(config)\\n\\n self.gpt_neox = GPTNeoXModel(config)\\n self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\\n\\n # Initialize weights and apply final processing\\n self.post_init()\\n\\n def get_output_embeddings(self):\\n return self.embed_out\\n\\n def set_output_embeddings(self, new_embeddings):\\n self.embed_out = new_embeddings\\n\\n @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format(\\\"batch_size, sequence_length\\\"))\\n @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)\\n def forward(\\n self,\\n input_ids: Optional[torch.LongTensor] = None,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n position_ids: Optional[torch.LongTensor] = None,\\n inputs_embeds: Optional[torch.FloatTensor] = None,\\n head_mask: Optional[torch.FloatTensor] = None,\\n past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,\\n labels: Optional[torch.LongTensor] = None,\\n use_cache: Optional[bool] = None,\\n output_attentions: Optional[bool] = None,\\n output_hidden_states: Optional[bool] = None,\\n return_dict: Optional[bool] = None,\\n ) -> Union[Tuple, CausalLMOutputWithPast]:\\n r\\\"\\\"\\\"\\n past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):\\n Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape\\n `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape\\n `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are\\n only required when the model is used as a decoder in a Sequence to Sequence model.\\n\\n Contains pre-computed hidden-states (key and values in the self-attention blocks that can be used (see\\n `past_key_values` input) to speed up sequential decoding.\\n\\n If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that\\n don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all\\n `decoder_input_ids` of shape `(batch_size, sequence_length)`.\\n labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\\n Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in\\n `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are\\n ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.\\n use_cache (`bool`, *optional*):\\n If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see\\n `past_key_values`).\\n\\n Returns:\\n\\n Example:\\n\\n ```python\\n >>> from transformers import AutoTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig\\n >>> import torch\\n\\n >>> tokenizer = AutoTokenizer.from_pretrained(\\\"EleutherAI/gpt-neox-20b\\\")\\n >>> config = GPTNeoXConfig.from_pretrained(\\\"EleutherAI/gpt-neox-20b\\\")\\n >>> config.is_decoder = True\\n >>> model = GPTNeoXForCausalLM.from_pretrained(\\\"EleutherAI/gpt-neox-20b\\\", config=config)\\n\\n >>> inputs = tokenizer(\\\"Hello, my dog is cute\\\", return_tensors=\\\"pt\\\")\\n >>> outputs = model(**inputs)\\n\\n >>> prediction_logits = outputs.logits\\n ```\\\"\\\"\\\"\\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\\n\\n outputs = self.gpt_neox(\\n input_ids,\\n attention_mask=attention_mask,\\n position_ids=position_ids,\\n head_mask=head_mask,\\n inputs_embeds=inputs_embeds,\\n past_key_values=past_key_values,\\n use_cache=use_cache,\\n output_attentions=output_attentions,\\n output_hidden_states=output_hidden_states,\\n return_dict=return_dict,\\n )\\n\\n hidden_states = outputs[0]\\n lm_logits = self.embed_out(hidden_states)\\n\\n lm_loss = None\\n if labels is not None:\\n # move labels to correct device to enable model parallelism\\n labels = labels.to(lm_logits.device)\\n # we are doing next-token prediction; shift prediction scores and input ids by one\\n shift_logits = lm_logits[:, :-1, :].contiguous()\\n labels = labels[:, 1:].contiguous()\\n loss_fct = CrossEntropyLoss()\\n lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1))\\n\\n if not return_dict:\\n output = (lm_logits,) + outputs[1:]\\n return ((lm_loss,) + output) if lm_loss is not None else output\\n\\n return CausalLMOutputWithPast(\\n loss=lm_loss,\\n logits=lm_logits,\\n past_key_values=outputs.past_key_values,\\n hidden_states=outputs.hidden_states,\\n attentions=outputs.attentions,\\n )\\n\\n def prepare_inputs_for_generation(\\n self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs\\n ):\\n input_shape = input_ids.shape\\n\\n # cut decoder_input_ids if past is used\\n if past_key_values and past_key_values[0] is not None:\\n input_ids = input_ids[:, -1:]\\n\\n position_ids = kwargs.get(\\\"position_ids\\\", None)\\n if attention_mask is not None and position_ids is None:\\n # create position_ids on the fly for batch generation\\n position_ids = attention_mask.long().cumsum(-1) - 1\\n position_ids.masked_fill_(attention_mask == 0, 1)\\n if past_key_values:\\n position_ids = position_ids[:, -1].unsqueeze(-1)\\n\\n # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly\\n if attention_mask is None:\\n attention_mask = input_ids.new_ones(input_shape)\\n\\n # if `inputs_embeds` are passed, we only want to use them in the 1st generation step\\n if inputs_embeds is not None and past_key_values is None:\\n model_inputs = {\\\"inputs_embeds\\\": inputs_embeds}\\n else:\\n model_inputs = {\\\"input_ids\\\": input_ids}\\n\\n model_inputs.update(\\n {\\n \\\"attention_mask\\\": attention_mask,\\n \\\"past_key_values\\\": past_key_values,\\n \\\"position_ids\\\": position_ids,\\n }\\n )\\n\\n return model_inputs\\n\\n def _reorder_cache(self, past_key_values, beam_idx):\\n reordered_past = ()\\n for layer_past in past_key_values:\\n reordered_past += (\\n tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],\\n )\\n return reordered_past\"\n },\n {\n \"identifier\": \"GPTNeoXTokenizerFast\",\n \"path\": \"model/gpt_neox/tokenization_gpt_neox_fast.py\",\n \"snippet\": \"class GPTNeoXTokenizerFast(PreTrainedTokenizerFast):\\n \\\"\\\"\\\"\\n Construct a \\\"fast\\\" GPT-NeoX-20B tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level\\n Byte-Pair-Encoding.\\n\\n This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will\\n be encoded differently whether it is at the beginning of the sentence (without space) or not:\\n\\n ```python\\n >>> from transformers import GPTNeoXTokenizerFast\\n\\n >>> tokenizer = GPTNeoXTokenizerFast.from_pretrained(\\\"gpt2\\\")\\n >>> tokenizer(\\\"Hello world\\\")[\\\"input_ids\\\"]\\n [15496, 995]\\n\\n >>> tokenizer(\\\" Hello world\\\")[\\\"input_ids\\\"]\\n [18435, 995]\\n ```\\n\\n You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since\\n the model was not pretrained this way, it might yield a decrease in performance.\\n\\n <Tip>\\n\\n When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.\\n\\n </Tip>\\n\\n This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should\\n refer to this superclass for more information regarding those methods.\\n\\n Args:\\n vocab_file (`str`):\\n Path to the vocabulary file.\\n merges_file (`str`):\\n Path to the merges file.\\n errors (`str`, *optional*, defaults to `\\\"replace\\\"`):\\n Paradigm to follow when decoding bytes to UTF-8. See\\n [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.\\n unk_token (`str`, *optional*, defaults to `<|endoftext|>`):\\n The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this\\n token instead.\\n bos_token (`str`, *optional*, defaults to `<|endoftext|>`):\\n The beginning of sequence token.\\n eos_token (`str`, *optional*, defaults to `<|endoftext|>`):\\n The end of sequence token.\\n add_prefix_space (`bool`, *optional*, defaults to `False`):\\n Whether or not to add an initial space to the input. This allows to treat the leading word just as any\\n other word. (GPTNeoX tokenizer detect beginning of words by the preceding space).\\n trim_offsets (`bool`, *optional*, defaults to `True`):\\n Whether or not the post-processing step should trim offsets to avoid including whitespaces.\\n \\\"\\\"\\\"\\n\\n vocab_files_names = VOCAB_FILES_NAMES\\n pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP\\n max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\\n model_input_names = [\\\"input_ids\\\", \\\"attention_mask\\\"]\\n\\n def __init__(\\n self,\\n vocab_file=None,\\n merges_file=None,\\n tokenizer_file=None,\\n unk_token=\\\"<|endoftext|>\\\",\\n bos_token=\\\"<|endoftext|>\\\",\\n eos_token=\\\"<|endoftext|>\\\",\\n add_prefix_space=False,\\n **kwargs,\\n ):\\n super().__init__(\\n vocab_file,\\n merges_file,\\n tokenizer_file=tokenizer_file,\\n unk_token=unk_token,\\n bos_token=bos_token,\\n eos_token=eos_token,\\n add_prefix_space=add_prefix_space,\\n **kwargs,\\n )\\n\\n pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())\\n if pre_tok_state.get(\\\"add_prefix_space\\\", add_prefix_space) != add_prefix_space:\\n pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop(\\\"type\\\"))\\n pre_tok_state[\\\"add_prefix_space\\\"] = add_prefix_space\\n self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)\\n\\n self.add_prefix_space = add_prefix_space\\n\\n def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:\\n files = self._tokenizer.model.save(save_directory, name=filename_prefix)\\n return tuple(files)\\n\\n def _build_conversation_input_ids(self, conversation: \\\"Conversation\\\") -> List[int]:\\n \\\"\\\"\\\"This corresponds to DialoGPT variants of models.\\\"\\\"\\\"\\n input_ids = []\\n for is_user, text in conversation.iter_texts():\\n input_ids.extend(self.encode(text, add_special_tokens=False) + [self.eos_token_id])\\n\\n if len(input_ids) > self.model_max_length:\\n input_ids = input_ids[-self.model_max_length :]\\n return input_ids\"\n },\n {\n \"identifier\": \"LlamaConfig\",\n \"path\": \"model/llama/configuration_llama.py\",\n \"snippet\": \"class LlamaConfig(PretrainedConfig):\\n r\\\"\\\"\\\"\\n This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA\\n model according to the specified arguments, defining the model architecture. Instantiating a configuration with the\\n defaults will yield a similar configuration to that of the LLaMA-7B.\\n\\n Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\\n documentation from [`PretrainedConfig`] for more information.\\n\\n\\n Args:\\n vocab_size (`int`, *optional*, defaults to 32000):\\n Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the\\n `inputs_ids` passed when calling [`LlamaModel`]\\n hidden_size (`int`, *optional*, defaults to 4096):\\n Dimension of the hidden representations.\\n intermediate_size (`int`, *optional*, defaults to 11008):\\n Dimension of the MLP representations.\\n num_hidden_layers (`int`, *optional*, defaults to 32):\\n Number of hidden layers in the Transformer encoder.\\n num_attention_heads (`int`, *optional*, defaults to 32):\\n Number of attention heads for each attention layer in the Transformer encoder.\\n num_key_value_heads (`int`, *optional*):\\n This is the number of key_value heads that should be used to implement Grouped Query Attention. If\\n `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if\\n `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When\\n converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed\\n by meanpooling all the original heads within that group. For more details checkout [this\\n paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to\\n `num_attention_heads`.\\n pretraining_tp (`int`, *optional*, defaults to `1`):\\n Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this\\n document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is\\n necessary to ensure exact reproducibility of the pretraining results. Please refer to [this\\n issue](https://github.com/pytorch/pytorch/issues/76232).\\n hidden_act (`str` or `function`, *optional*, defaults to `\\\"silu\\\"`):\\n The non-linear activation function (function or string) in the decoder.\\n max_position_embeddings (`int`, *optional*, defaults to 2048):\\n The maximum sequence length that this model might ever be used with. Typically set this to something large\\n just in case (e.g., 512 or 1024 or 2048).\\n initializer_range (`float`, *optional*, defaults to 0.02):\\n The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\\n rms_norm_eps (`float`, *optional*, defaults to 1e-12):\\n The epsilon used by the rms normalization layers.\\n use_cache (`bool`, *optional*, defaults to `True`):\\n Whether or not the model should return the last key/values attentions (not used by all models). Only\\n relevant if `config.is_decoder=True`.\\n tie_word_embeddings(`bool`, *optional*, defaults to `False`):\\n Whether to tie weight embeddings\\n rope_scaling (`Dict`, *optional*):\\n Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling\\n strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format\\n is `{\\\"type\\\": strategy name, \\\"factor\\\": scaling factor}`. When using this flag, don't update\\n `max_position_embeddings` to the expected new maximum. See the following thread for more information on how\\n these scaling strategies behave:\\n https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an\\n experimental feature, subject to breaking API changes in future versions.\\n\\n Example:\\n\\n ```python\\n >>> from transformers import LlamaModel, LlamaConfig\\n\\n >>> # Initializing a LLaMA llama-7b style configuration\\n >>> configuration = LlamaConfig()\\n\\n >>> # Initializing a model from the llama-7b style configuration\\n >>> model = LlamaModel(configuration)\\n\\n >>> # Accessing the model configuration\\n >>> configuration = model.config\\n ```\\\"\\\"\\\"\\n model_type = \\\"llama\\\"\\n keys_to_ignore_at_inference = [\\\"past_key_values\\\"]\\n\\n def __init__(\\n self,\\n vocab_size=32000,\\n hidden_size=4096,\\n intermediate_size=11008,\\n num_hidden_layers=32,\\n num_attention_heads=32,\\n num_key_value_heads=None,\\n hidden_act=\\\"silu\\\",\\n max_position_embeddings=2048,\\n initializer_range=0.02,\\n rms_norm_eps=1e-6,\\n use_cache=True,\\n pad_token_id=None,\\n bos_token_id=1,\\n eos_token_id=2,\\n pretraining_tp=1,\\n tie_word_embeddings=False,\\n rope_scaling=None,\\n **kwargs,\\n ):\\n self.vocab_size = vocab_size\\n self.max_position_embeddings = max_position_embeddings\\n self.hidden_size = hidden_size\\n self.intermediate_size = intermediate_size\\n self.num_hidden_layers = num_hidden_layers\\n self.num_attention_heads = num_attention_heads\\n\\n # for backward compatibility\\n if num_key_value_heads is None:\\n num_key_value_heads = num_attention_heads\\n\\n self.num_key_value_heads = num_key_value_heads\\n self.hidden_act = hidden_act\\n self.initializer_range = initializer_range\\n self.rms_norm_eps = rms_norm_eps\\n self.pretraining_tp = pretraining_tp\\n self.use_cache = use_cache\\n self.rope_scaling = rope_scaling\\n self._rope_scaling_validation()\\n\\n super().__init__(\\n pad_token_id=pad_token_id,\\n bos_token_id=bos_token_id,\\n eos_token_id=eos_token_id,\\n tie_word_embeddings=tie_word_embeddings,\\n **kwargs,\\n )\\n\\n def _rope_scaling_validation(self):\\n \\\"\\\"\\\"\\n Validate the `rope_scaling` configuration.\\n \\\"\\\"\\\"\\n if self.rope_scaling is None:\\n return\\n\\n if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:\\n raise ValueError(\\n \\\"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, \\\"\\n f\\\"got {self.rope_scaling}\\\"\\n )\\n rope_scaling_type = self.rope_scaling.get(\\\"type\\\", None)\\n rope_scaling_factor = self.rope_scaling.get(\\\"factor\\\", None)\\n if rope_scaling_type is None or rope_scaling_type not in [\\\"linear\\\", \\\"dynamic\\\"]:\\n raise ValueError(\\n f\\\"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}\\\"\\n )\\n if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:\\n raise ValueError(f\\\"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}\\\")\"\n },\n {\n \"identifier\": \"LlamaForCausalLM\",\n \"path\": \"model/llama/modeling_llama.py\",\n \"snippet\": \"class LlamaForCausalLM(LlamaPreTrainedModel):\\n _tied_weights_keys = [\\\"lm_head.weight\\\"]\\n\\n def __init__(self, config):\\n super().__init__(config)\\n self.model = LlamaModel(config)\\n self.vocab_size = config.vocab_size\\n self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\\n\\n # Initialize weights and apply final processing\\n self.post_init()\\n\\n def get_input_embeddings(self):\\n return self.model.embed_tokens\\n\\n def set_input_embeddings(self, value):\\n self.model.embed_tokens = value\\n\\n def get_output_embeddings(self):\\n return self.lm_head\\n\\n def set_output_embeddings(self, new_embeddings):\\n self.lm_head = new_embeddings\\n\\n def set_decoder(self, decoder):\\n self.model = decoder\\n\\n def get_decoder(self):\\n return self.model\\n\\n @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)\\n @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)\\n def forward(\\n self,\\n input_ids: torch.LongTensor = None,\\n attention_mask: Optional[torch.Tensor] = None,\\n position_ids: Optional[torch.LongTensor] = None,\\n past_key_values: Optional[List[torch.FloatTensor]] = None,\\n inputs_embeds: Optional[torch.FloatTensor] = None,\\n labels: Optional[torch.LongTensor] = None,\\n use_cache: Optional[bool] = None,\\n output_attentions: Optional[bool] = None,\\n output_hidden_states: Optional[bool] = None,\\n return_dict: Optional[bool] = None,\\n ) -> Union[Tuple, CausalLMOutputWithPast]:\\n r\\\"\\\"\\\"\\n Args:\\n labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\\n Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,\\n config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored\\n (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.\\n\\n Returns:\\n\\n Example:\\n\\n ```python\\n >>> from transformers import AutoTokenizer, LlamaForCausalLM\\n\\n >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)\\n >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)\\n\\n >>> prompt = \\\"Hey, are you conscious? Can you talk to me?\\\"\\n >>> inputs = tokenizer(prompt, return_tensors=\\\"pt\\\")\\n\\n >>> # Generate\\n >>> generate_ids = model.generate(inputs.input_ids, max_length=30)\\n >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\\n \\\"Hey, are you conscious? Can you talk to me?\\\\nI'm not conscious, but I can talk to you.\\\"\\n ```\\\"\\\"\\\"\\n\\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\\n output_hidden_states = (\\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\\n )\\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\\n\\n # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\\n outputs = self.model(\\n input_ids=input_ids,\\n attention_mask=attention_mask,\\n position_ids=position_ids,\\n past_key_values=past_key_values,\\n inputs_embeds=inputs_embeds,\\n use_cache=use_cache,\\n output_attentions=output_attentions,\\n output_hidden_states=output_hidden_states,\\n return_dict=return_dict,\\n )\\n\\n hidden_states = outputs[0]\\n if self.config.pretraining_tp > 1:\\n lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\\n logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\\n logits = torch.cat(logits, dim=-1)\\n else:\\n logits = self.lm_head(hidden_states)\\n logits = logits.float()\\n\\n loss = None\\n if labels is not None:\\n # Shift so that tokens < n predict n\\n shift_logits = logits[..., :-1, :].contiguous()\\n shift_labels = labels[..., 1:].contiguous()\\n # Flatten the tokens\\n loss_fct = CrossEntropyLoss()\\n shift_logits = shift_logits.view(-1, self.config.vocab_size)\\n shift_labels = shift_labels.view(-1)\\n # Enable model parallelism\\n shift_labels = shift_labels.to(shift_logits.device)\\n loss = loss_fct(shift_logits, shift_labels)\\n\\n if not return_dict:\\n output = (logits,) + outputs[1:]\\n return (loss,) + output if loss is not None else output\\n\\n return CausalLMOutputWithPast(\\n loss=loss,\\n logits=logits,\\n past_key_values=outputs.past_key_values,\\n hidden_states=outputs.hidden_states,\\n attentions=outputs.attentions,\\n )\\n\\n def prepare_inputs_for_generation(\\n self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs\\n ):\\n if past_key_values:\\n input_ids = input_ids[:, -1:]\\n\\n position_ids = kwargs.get(\\\"position_ids\\\", None)\\n if attention_mask is not None and position_ids is None:\\n # create position_ids on the fly for batch generation\\n position_ids = attention_mask.long().cumsum(-1) - 1\\n position_ids.masked_fill_(attention_mask == 0, 1)\\n if past_key_values:\\n position_ids = position_ids[:, -1].unsqueeze(-1)\\n\\n # if `inputs_embeds` are passed, we only want to use them in the 1st generation step\\n if inputs_embeds is not None and past_key_values is None:\\n model_inputs = {\\\"inputs_embeds\\\": inputs_embeds}\\n else:\\n model_inputs = {\\\"input_ids\\\": input_ids}\\n\\n model_inputs.update(\\n {\\n \\\"position_ids\\\": position_ids,\\n \\\"past_key_values\\\": past_key_values,\\n \\\"use_cache\\\": kwargs.get(\\\"use_cache\\\"),\\n \\\"attention_mask\\\": attention_mask,\\n }\\n )\\n return model_inputs\\n\\n @staticmethod\\n def _reorder_cache(past_key_values, beam_idx):\\n reordered_past = ()\\n for layer_past in past_key_values:\\n reordered_past += (\\n tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),\\n )\\n return reordered_past\"\n },\n {\n \"identifier\": \"LlamaTokenizer\",\n \"path\": \"model/llama/tokenization_llama.py\",\n \"snippet\": \"class LlamaTokenizer(PreTrainedTokenizer):\\n \\\"\\\"\\\"\\n Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is\\n no padding token in the original model.\\n\\n Args:\\n vocab_file (`str`):\\n Path to the vocabulary file.\\n legacy (`bool`, *optional*, defaults to `True`):\\n Whether or not the `legacy` behaviour of the tokenizer should be used. Legacy is before the merge of #24622\\n which includes fixes to properly handle tokens that appear after special tokens. A simple example:\\n\\n - `legacy=True`:\\n ```python\\n >>> from transformers import T5Tokenizer\\n\\n >>> tokenizer = T5Tokenizer.from_pretrained(\\\"t5-base\\\", legacy=True)\\n >>> tokenizer.encode(\\\"Hello <extra_id_0>.\\\")\\n [8774, 32099, 3, 5, 1]\\n ```\\n - `legacy=False`:\\n ```python\\n >>> from transformers import T5Tokenizer\\n\\n >>> tokenizer = T5Tokenizer.from_pretrained(\\\"t5-base\\\", legacy=False)\\n >>> tokenizer.encode(\\\"Hello <extra_id_0>.\\\") # the extra space `[3]` is no longer here\\n [8774, 32099, 5, 1]\\n ```\\n Checkout the pull request and the issue [here](https://github.com/huggingface/transformers/pull/24565) for\\n more details.\\n\\n \\\"\\\"\\\"\\n\\n vocab_files_names = VOCAB_FILES_NAMES\\n pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP\\n max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES\\n model_input_names = [\\\"input_ids\\\", \\\"attention_mask\\\"]\\n\\n def __init__(\\n self,\\n vocab_file,\\n unk_token=\\\"<unk>\\\",\\n bos_token=\\\"<s>\\\",\\n eos_token=\\\"</s>\\\",\\n pad_token=None,\\n sp_model_kwargs: Optional[Dict[str, Any]] = None,\\n add_bos_token=True,\\n add_eos_token=False,\\n clean_up_tokenization_spaces=False,\\n legacy=None,\\n **kwargs,\\n ):\\n self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs\\n bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token\\n eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token\\n unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token\\n pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token\\n super().__init__(\\n bos_token=bos_token,\\n eos_token=eos_token,\\n unk_token=unk_token,\\n pad_token=pad_token,\\n add_bos_token=add_bos_token,\\n add_eos_token=add_eos_token,\\n sp_model_kwargs=self.sp_model_kwargs,\\n clean_up_tokenization_spaces=clean_up_tokenization_spaces,\\n legacy=legacy,\\n **kwargs,\\n )\\n if legacy is None:\\n logger.warning_once(\\n f\\\"You are using the default legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to\\\"\\n \\\" read the related pull request available at https://github.com/huggingface/transformers/pull/24565, and set the legacy attribute accordingly.\\\"\\n )\\n legacy = True\\n\\n self.legacy = legacy\\n self.vocab_file = vocab_file\\n self.add_bos_token = add_bos_token\\n self.add_eos_token = add_eos_token\\n self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)\\n self.sp_model.Load(vocab_file)\\n\\n def __getstate__(self):\\n state = self.__dict__.copy()\\n state[\\\"sp_model\\\"] = None\\n state[\\\"sp_model_proto\\\"] = self.sp_model.serialized_model_proto()\\n return state\\n\\n def __setstate__(self, d):\\n self.__dict__ = d\\n self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)\\n self.sp_model.LoadFromSerializedProto(self.sp_model_proto)\\n\\n @property\\n def vocab_size(self):\\n \\\"\\\"\\\"Returns vocab size\\\"\\\"\\\"\\n return self.sp_model.get_piece_size()\\n\\n def get_vocab(self):\\n \\\"\\\"\\\"Returns vocab as a dict\\\"\\\"\\\"\\n vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}\\n vocab.update(self.added_tokens_encoder)\\n return vocab\\n\\n # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize\\n def tokenize(self, text: \\\"TextInput\\\", **kwargs) -> List[str]:\\n # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at\\n # the beginning of the text\\n if not self.legacy:\\n text = SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, \\\" \\\")\\n return super().tokenize(text, **kwargs)\\n\\n # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize\\n def _tokenize(self, text, **kwargs):\\n \\\"\\\"\\\"\\n Returns a tokenized string.\\n\\n Since the sentencepiece internal model always adds a SPIECE_UNDERLINE, at the beginning of the provided text,\\n we need to remove it by hand when the current text is a subsequence. This happens whenever the `self.tokenize`\\n function is called with specials tokens: the input is split on the special tokens, and each subsequence is\\n passed to `_tokenize`. Thus if a subsequence did not start with a `\\\" \\\"` or SPIECE_UNDERLINE, we have to remove\\n the extra `SPIECE_UNDERLINE` prepended.\\n \\\"\\\"\\\"\\n if not self.legacy:\\n is_first = text.startswith(SPIECE_UNDERLINE)\\n if is_first:\\n text = text[1:]\\n\\n tokens = self.sp_model.encode(text, out_type=str)\\n\\n if not self.legacy and not is_first and not text.startswith(\\\" \\\") and tokens[0].startswith(SPIECE_UNDERLINE):\\n tokens = ([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]\\n return tokens\\n\\n def _convert_token_to_id(self, token):\\n \\\"\\\"\\\"Converts a token (str) in an id using the vocab.\\\"\\\"\\\"\\n return self.sp_model.piece_to_id(token)\\n\\n def _convert_id_to_token(self, index):\\n \\\"\\\"\\\"Converts an index (integer) in a token (str) using the vocab.\\\"\\\"\\\"\\n token = self.sp_model.IdToPiece(index)\\n return token\\n\\n def convert_tokens_to_string(self, tokens):\\n \\\"\\\"\\\"Converts a sequence of tokens (string) in a single string.\\\"\\\"\\\"\\n current_sub_tokens = []\\n out_string = \\\"\\\"\\n prev_is_special = False\\n for i, token in enumerate(tokens):\\n # make sure that special tokens are not decoded using sentencepiece model\\n if token in self.all_special_tokens:\\n if not prev_is_special and i != 0:\\n out_string += \\\" \\\"\\n out_string += self.sp_model.decode(current_sub_tokens) + token\\n prev_is_special = True\\n current_sub_tokens = []\\n else:\\n current_sub_tokens.append(token)\\n prev_is_special = False\\n out_string += self.sp_model.decode(current_sub_tokens)\\n return out_string\\n\\n def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:\\n \\\"\\\"\\\"\\n Save the vocabulary and special tokens file to a directory.\\n\\n Args:\\n save_directory (`str`):\\n The directory in which to save the vocabulary.\\n\\n Returns:\\n `Tuple(str)`: Paths to the files saved.\\n \\\"\\\"\\\"\\n if not os.path.isdir(save_directory):\\n logger.error(f\\\"Vocabulary path ({save_directory}) should be a directory\\\")\\n return\\n out_vocab_file = os.path.join(\\n save_directory, (filename_prefix + \\\"-\\\" if filename_prefix else \\\"\\\") + VOCAB_FILES_NAMES[\\\"vocab_file\\\"]\\n )\\n\\n if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):\\n copyfile(self.vocab_file, out_vocab_file)\\n elif not os.path.isfile(self.vocab_file):\\n with open(out_vocab_file, \\\"wb\\\") as fi:\\n content_spiece_model = self.sp_model.serialized_model_proto()\\n fi.write(content_spiece_model)\\n\\n return (out_vocab_file,)\\n\\n def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):\\n bos_token_id = [self.bos_token_id] if self.add_bos_token else []\\n eos_token_id = [self.eos_token_id] if self.add_eos_token else []\\n\\n output = bos_token_id + token_ids_0 + eos_token_id\\n\\n if token_ids_1 is not None:\\n output = output + bos_token_id + token_ids_1 + eos_token_id\\n\\n return output\\n\\n def get_special_tokens_mask(\\n self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False\\n ) -> List[int]:\\n \\\"\\\"\\\"\\n Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding\\n special tokens using the tokenizer `prepare_for_model` method.\\n\\n Args:\\n token_ids_0 (`List[int]`):\\n List of IDs.\\n token_ids_1 (`List[int]`, *optional*):\\n Optional second list of IDs for sequence pairs.\\n already_has_special_tokens (`bool`, *optional*, defaults to `False`):\\n Whether or not the token list is already formatted with special tokens for the model.\\n\\n Returns:\\n `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.\\n \\\"\\\"\\\"\\n if already_has_special_tokens:\\n return super().get_special_tokens_mask(\\n token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True\\n )\\n\\n bos_token_id = [1] if self.add_bos_token else []\\n eos_token_id = [1] if self.add_eos_token else []\\n\\n if token_ids_1 is None:\\n return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id\\n return (\\n bos_token_id\\n + ([0] * len(token_ids_0))\\n + eos_token_id\\n + bos_token_id\\n + ([0] * len(token_ids_1))\\n + eos_token_id\\n )\\n\\n def create_token_type_ids_from_sequences(\\n self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None\\n ) -> List[int]:\\n \\\"\\\"\\\"\\n Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT\\n sequence pair mask has the following format:\\n\\n ```\\n 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1\\n | first sequence | second sequence |\\n ```\\n\\n if token_ids_1 is None, only returns the first portion of the mask (0s).\\n\\n Args:\\n token_ids_0 (`List[int]`):\\n List of ids.\\n token_ids_1 (`List[int]`, *optional*):\\n Optional second list of IDs for sequence pairs.\\n\\n Returns:\\n `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).\\n \\\"\\\"\\\"\\n bos_token_id = [self.bos_token_id] if self.add_bos_token else []\\n eos_token_id = [self.eos_token_id] if self.add_eos_token else []\\n\\n output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)\\n\\n if token_ids_1 is not None:\\n output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)\\n\\n return output\\n\\n def _build_conversation_input_ids(self, conversation: \\\"Conversation\\\") -> List[int]:\\n r\\\"\\\"\\\"Builds the input ids for a conversation.\\n This is the format used in the provided examples. System prompts should be manually added at the beginning of\\n the conversation. If no system prompt is given, the `DEFAULT_SYSTEM_PROMPT` will be used.\\n ```\\n <bos>[INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer <eos>\\n <bos>[INST] Prompt [/INST] Answer <eos>\\n <bos>[INST] Prompt [/INST]\\n ```\\n\\n If you want to use your own system prompt, make sure to use both `B_SYS` and `E_SYS` use the following:\\n ```python\\n >>> from transformers import Conversation\\n\\n >>> Conversation(\\n ... \\\"<<SYS>>\\\\n Only answer with emojis, and charades\\\\n<</SYS>>\\\\n\\\\nHow can I build a house in 10 septs?\\\"\\n ... ) # doctest: +IGNORE_RESULT\\n ```\\n Args:\\n conversation (`Conversation`):\\n Conversation to build input ids for.\\n Returns:\\n `List[int]`:\\n Input ids for the conversation.\\n \\\"\\\"\\\"\\n if len(conversation.past_user_inputs) > 0:\\n if not conversation.past_user_inputs[0].startswith(B_SYS) or E_SYS not in conversation.past_user_inputs[0]:\\n conversation.past_user_inputs[0] = (\\n B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]\\n )\\n elif conversation.new_user_input:\\n if not conversation.new_user_input.startswith(B_SYS) or E_SYS not in conversation.new_user_input:\\n conversation.new_user_input = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.new_user_input\\n else:\\n raise ValueError(\\\"Last message must be from user\\\")\\n\\n dialogue = list(conversation.iter_texts())\\n if not all([is_user for is_user, msg in dialogue[::2]]) or not all(\\n [not is_user for is_user, msg in dialogue[1::2]]\\n ):\\n raise ValueError(\\n \\\"The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)\\\"\\n )\\n\\n dialog_tokens: List[int] = []\\n dialog_tokens += sum(\\n [\\n [self.bos_token_id]\\n + self.encode(\\n f\\\"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} \\\", add_special_tokens=False\\n )\\n + [self.eos_token_id]\\n for prompt, answer in zip(dialogue[::2], dialogue[1::2])\\n ],\\n [],\\n )\\n dialog_tokens += [self.bos_token_id] + self.encode(\\n f\\\"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}\\\", add_special_tokens=False\\n )\\n return dialog_tokens\"\n },\n {\n \"identifier\": \"LlamaTokenizerFast\",\n \"path\": \"model/llama/tokenization_llama_fast.py\",\n \"snippet\": \"class LlamaTokenizerFast(PreTrainedTokenizerFast):\\n \\\"\\\"\\\"\\n Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.\\n\\n This uses notably ByteFallback and no normalization.\\n\\n ```\\n from transformers import LlamaTokenizerFast\\n\\n tokenizer = LlamaTokenizerFast.from_pretrained(\\\"hf-internal-testing/llama-tokenizer\\\")\\n tokenizer.encode(\\\"Hello this is a test\\\")\\n >>> [1, 15043, 445, 338, 263, 1243]\\n ```\\n\\n If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or\\n call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the\\n values of the first token and final token of an encoded sequence will not be correct). For more details, checkout\\n [post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.\\n\\n\\n This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should\\n refer to this superclass for more information regarding those methods.\\n\\n Args:\\n vocab_file (`str`):\\n [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that\\n contains the vocabulary necessary to instantiate a tokenizer.\\n tokenizer_file (`str`):\\n [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that\\n contains everything needed to load the tokenizer.\\n\\n clean_up_tokenization_spaces (`str`, *optional*, defaults to `False`):\\n Wether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra\\n spaces.\\n\\n bos_token (`str`, *optional*, defaults to `\\\"<s>\\\"`):\\n The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.\\n\\n eos_token (`str`, *optional*, defaults to `\\\"</s>\\\"`):\\n The end of sequence token.\\n\\n unk_token (`str`, *optional*, defaults to `\\\"<unk>\\\"`):\\n The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this\\n token instead.\\n \\\"\\\"\\\"\\n\\n vocab_files_names = VOCAB_FILES_NAMES\\n slow_tokenizer_class = LlamaTokenizer\\n padding_side = \\\"left\\\"\\n model_input_names = [\\\"input_ids\\\", \\\"attention_mask\\\"]\\n\\n def __init__(\\n self,\\n vocab_file=None,\\n tokenizer_file=None,\\n clean_up_tokenization_spaces=False,\\n unk_token=\\\"<unk>\\\",\\n bos_token=\\\"<s>\\\",\\n eos_token=\\\"</s>\\\",\\n add_bos_token=True,\\n add_eos_token=False,\\n **kwargs,\\n ):\\n super().__init__(\\n vocab_file=vocab_file,\\n tokenizer_file=tokenizer_file,\\n clean_up_tokenization_spaces=clean_up_tokenization_spaces,\\n unk_token=unk_token,\\n bos_token=bos_token,\\n eos_token=eos_token,\\n **kwargs,\\n )\\n self._add_bos_token = add_bos_token\\n self._add_eos_token = add_eos_token\\n self.update_post_processor()\\n\\n self.vocab_file = vocab_file\\n self.can_save_slow_tokenizer = False if not self.vocab_file else True\\n\\n def update_post_processor(self):\\n \\\"\\\"\\\"\\n Updates the underlying post processor with the current `bos_token` and `eos_token`.\\n \\\"\\\"\\\"\\n bos = self.bos_token\\n bos_token_id = self.bos_token_id\\n\\n eos = self.eos_token\\n eos_token_id = self.eos_token_id\\n\\n single = f\\\"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}\\\"\\n pair = f\\\"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}\\\"\\n\\n special_tokens = []\\n if self.add_bos_token:\\n special_tokens.append((bos, bos_token_id))\\n if self.add_eos_token:\\n special_tokens.append((eos, eos_token_id))\\n self._tokenizer.post_processor = processors.TemplateProcessing(\\n single=single, pair=pair, special_tokens=special_tokens\\n )\\n\\n @property\\n def add_eos_token(self):\\n return self._add_eos_token\\n\\n @property\\n def add_bos_token(self):\\n return self._add_bos_token\\n\\n @add_eos_token.setter\\n def add_eos_token(self, value):\\n self._add_eos_token = value\\n self.update_post_processor()\\n\\n @add_bos_token.setter\\n def add_bos_token(self, value):\\n self._add_bos_token = value\\n self.update_post_processor()\\n\\n def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:\\n if not self.can_save_slow_tokenizer:\\n raise ValueError(\\n \\\"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow \\\"\\n \\\"tokenizer.\\\"\\n )\\n\\n if not os.path.isdir(save_directory):\\n logger.error(f\\\"Vocabulary path ({save_directory}) should be a directory\\\")\\n return\\n out_vocab_file = os.path.join(\\n save_directory, (filename_prefix + \\\"-\\\" if filename_prefix else \\\"\\\") + VOCAB_FILES_NAMES[\\\"vocab_file\\\"]\\n )\\n\\n if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):\\n copyfile(self.vocab_file, out_vocab_file)\\n\\n return (out_vocab_file,)\\n\\n def _build_conversation_input_ids(self, conversation: \\\"Conversation\\\"):\\n \\\"\\\"\\\"Builds the input ids for a conversation.\\n This is the format used in the provided examples. System prompts should be manually added at the beginning of\\n the conversation. If no system prompt is given, the `DEFAULT_SYSTEM_PROMPT` will be used.\\n ```\\n <bos>[INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer <eos>\\n <bos>[INST] Prompt [/INST] Answer <eos>\\n <bos>[INST] Prompt [/INST]\\n ```\\n\\n If you want to use your own system prompt, make sure to use both `B_SYS` and `E_SYS` use the following:\\n ```python\\n >>> from transformers import Conversation\\n\\n >>> Conversation(\\n ... \\\"<<SYS>>\\\\n Only answer with emojis, and charades\\\\n<</SYS>>\\\\n\\\\nHow can I build a house in 10 septs?\\\"\\n ... )\\n ```\\n Args:\\n conversation (`Conversation`):\\n Conversation to build input ids for.\\n Returns:\\n `List[int]`:\\n Input ids for the conversation.\\n \\\"\\\"\\\"\\n if len(conversation.past_user_inputs) > 0:\\n if not conversation.past_user_inputs[0].startswith(B_SYS) or E_SYS not in conversation.past_user_inputs[0]:\\n conversation.past_user_inputs[0] = (\\n B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]\\n )\\n elif conversation.new_user_input:\\n if not conversation.new_user_input.startswith(B_SYS) or E_SYS not in conversation.new_user_input:\\n conversation.new_user_input = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.new_user_input\\n else:\\n raise ValueError(\\\"Last message must be from user\\\")\\n\\n dialogue = list(conversation.iter_texts())\\n if not all([is_user for is_user, msg in dialogue[::2]]) or not all(\\n [not is_user for is_user, msg in dialogue[1::2]]\\n ):\\n raise ValueError(\\n \\\"The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)\\\"\\n )\\n\\n dialog_tokens = []\\n dialog_tokens += sum(\\n [\\n [self.bos_token_id]\\n + self.encode(\\n f\\\"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} \\\", add_special_tokens=False\\n )\\n + [self.eos_token_id]\\n for prompt, answer in zip(dialogue[::2], dialogue[1::2])\\n ],\\n [],\\n )\\n dialog_tokens += [self.bos_token_id] + self.encode(\\n f\\\"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}\\\", add_special_tokens=False\\n )\\n return dialog_tokens\"\n },\n {\n \"identifier\": \"print_rank_0\",\n \"path\": \"utils/common_utils.py\",\n \"snippet\": \"def print_rank_0(*message):\\n \\\"\\\"\\\"If distributed is initialized print only on rank 0.\\\"\\\"\\\"\\n if torch.distributed.is_initialized():\\n if torch.distributed.get_rank() == 0:\\n print(*message, flush=True)\\n else:\\n print(*message, flush=True)\"\n },\n {\n \"identifier\": \"is_old_version\",\n \"path\": \"utils/common_utils.py\",\n \"snippet\": \"def is_old_version(path):\\n new_vocab_files = ['merge.model']\\n new_vocab_file_exists = []\\n for filename in new_vocab_files:\\n if not os.path.exists(os.path.join(path, filename)):\\n new_vocab_file_exists.append(False)\\n else:\\n new_vocab_file_exists.append(True)\\n if all(new_vocab_file_exists):\\n return False\\n if any(new_vocab_file_exists):\\n return 'new_version_file_absent'\\n else:\\n return True\"\n },\n {\n \"identifier\": \"build_tokenizer\",\n \"path\": \"tokenizer/tokenizer.py\",\n \"snippet\": \"def build_tokenizer(args):\\n \\\"\\\"\\\"Initialize tokenizer.\\\"\\\"\\\"\\n print_rank_0(\\\"> building {} tokenizer ...\\\".format(args.tokenizer_type))\\n # if args.rank == 0:\\n # print(\\\"> building {} tokenizer ...\\\".format(args.tokenizer_type), flush=True)\\n\\n # Select and instantiate the tokenizer.\\n if args.tokenizer_type.lower() == \\\"GPT2BPETokenizer\\\".lower():\\n assert args.vocab_file is not None\\n assert args.merge_file is not None\\n tokenizer = _GPT2BPETokenizer(args.vocab_file, args.merge_file)\\n elif args.tokenizer_type.lower() == \\\"SPMTokenizer\\\".lower():\\n assert args.vocab_file is not None\\n tokenizer = SentencePieceTokenizer(args.vocab_file)\\n elif args.tokenizer_type.lower() == \\\"HFTokenizer\\\".lower():\\n assert args.vocab_file is not None\\n tokenizer = HFTokenizer(args.vocab_file)\\n elif args.tokenizer_type.lower() == \\\"HFGPT2Tokenizer\\\".lower():\\n if args.vocab_file is None:\\n print(\\n \\\"WARNING: No vocab file found, loading Huggingface's pretrained GPT2Tokenizer\\\"\\n )\\n tokenizer = HFGPT2Tokenizer(args.vocab_file)\\n elif args.tokenizer_type.lower() == \\\"CharLevelTokenizer\\\".lower():\\n tokenizer = CharLevelTokenizer(vocab_size=512)\\n elif args.tokenizer_type.lower() == \\\"TiktokenTokenizer\\\".lower():\\n assert args.vocab_file is not None\\n tokenizer = TiktokenTokenizer(args.vocab_file)\\n elif args.tokenizer_type.lower() == \\\"GLMTokenizer\\\".lower():\\n if is_old_version(args.pretrained_model_path):\\n print('is an old version')\\n from model.glm.tokenization_glm_deprecated import GLMChineseTokenizer\\n args.glm_mask = '[sMASK]'\\n old_version_tokenizer = True\\n tokenizer = GLMChineseTokenizer.from_pretrained(args.pretrained_model_path, trust_remote_code=True)\\n else:\\n print('is not an old version')\\n old_version_tokenizer = False\\n tokenizer = GLMTokenizer.from_pretrained(args.pretrained_model_path, trust_remote_code=True)\\n else:\\n raise NotImplementedError(\\n \\\"{} tokenizer is not \\\" \\\"implemented.\\\".format(args.tokenizer_type)\\n )\\n\\n # Add vocab size.\\n args.padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size, args)\\n\\n return tokenizer\"\n },\n {\n \"identifier\": \"HFTokenizer\",\n \"path\": \"tokenizer/tokenizer.py\",\n \"snippet\": \"class HFTokenizer(AbstractTokenizer):\\n \\\"\\\"\\\"Designed to Integrate HF's Tokenizer library.\\\"\\\"\\\"\\n\\n def __init__(self, vocab_file):\\n name = \\\"HFTokenizer\\\"\\n super().__init__(name)\\n\\n self.tokenizer = Tokenizer.from_file(vocab_file)\\n # self.eod_id = self.tokenizer.token_to_id(\\\"<|endoftext|>\\\")\\n self.eod_id = self.tokenizer.token_to_id(\\\"<|end|>\\\")\\n # self.pad_id = self.tokenizer.token_to_id(\\\"<|padding|>\\\")\\n \\n # 新词表没有<|padding|>, 用<|extratoken_1|>代替，和tokenization一致\\n # self.pad_id = self.tokenizer.token_to_id(\\\"<|extratoken_1|>\\\")\\n self.pad_id = self.tokenizer.token_to_id(\\\"<|pad|>\\\")\\n\\n @property\\n def vocab_size(self):\\n return self.tokenizer.get_vocab_size()\\n\\n @property\\n def vocab(self):\\n return self.tokenizer.get_vocab()\\n\\n @property\\n def inv_vocab(self):\\n return self.tokenizer.decoder\\n\\n def tokenize(self, text: str):\\n return self.tokenizer.encode(text).ids\\n\\n def tokenize_batch(self, text_batch: Union[List[str], str]):\\n return self.tokenizer.encode_batch(text_batch)\\n\\n def detokenize(self, token_ids):\\n return self.tokenizer.decode(token_ids)\\n\\n @property\\n def eod(self):\\n return self.eod_id\"\n },\n {\n \"identifier\": \"prepare_model_for_kbit_training\",\n \"path\": \"model/peft/utils/others.py\",\n \"snippet\": \"def prepare_model_for_kbit_training(model, use_gradient_checkpointing=True):\\n r\\\"\\\"\\\"\\n This method wraps the entire protocol for preparing a model before running a training. This includes:\\n 1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm\\n head to fp32\\n\\n Args:\\n model, (`transformers.PreTrainedModel`):\\n The loaded model from `transformers`\\n \\\"\\\"\\\"\\n loaded_in_kbit = getattr(model, \\\"is_loaded_in_8bit\\\", False) or getattr(model, \\\"is_loaded_in_4bit\\\", False)\\n\\n for name, param in model.named_parameters():\\n # freeze base model's layers\\n param.requires_grad = False\\n \\n # cast all non INT8 parameters to fp32\\n for param in model.parameters():\\n if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):\\n param.data = param.data.to(torch.float32)\\n \\n if loaded_in_kbit and use_gradient_checkpointing:\\n # For backward compatibility\\n if hasattr(model, \\\"enable_input_require_grads\\\"):\\n model.enable_input_require_grads()\\n else:\\n \\n def make_inputs_require_grad(module, input, output):\\n output.requires_grad_(True)\\n\\n model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)\\n\\n # enable gradient checkpointing for memory efficiency\\n model.gradient_checkpointing_enable()\\n\\n return model\"\n },\n {\n \"identifier\": \"AdaLoraConfig\",\n \"path\": \"model/peft/tuner/adalora.py\",\n \"snippet\": \"class AdaLoraConfig(LoraConfig):\\n \\\"\\\"\\\"\\n This is the configuration class to store the configuration of a [`~peft.AdaLora`].\\n\\n Args:\\n target_r (`int`): The target average rank of incremental matrix.\\n init_r (`int`): The initial rank for each incremental matrix.\\n tinit (`int`): The steps of initial fine-tuning warmup.\\n tfinal (`int`): The step of final fine-tuning.\\n deltaT (`int`): The time internval between two budget allocations.\\n beta1 (`float`): The hyperparameter of EMA for sensitivity smoothing.\\n beta2 (`float`): The hyperparameter of EMA for undertainty quantification.\\n orth_reg_weight (`float`): The coefficient of orthogonal regularization.\\n total_step (`int`): The total training steps that should be specified before training.\\n rank_pattern (`list`): The allocated rank for each weight matrix by RankAllocator.\\n \\\"\\\"\\\"\\n\\n target_r: int = field(default=8, metadata={\\\"help\\\": \\\"Target Lora matrix dimension.\\\"})\\n init_r: int = field(default=12, metadata={\\\"help\\\": \\\"Intial Lora matrix dimension.\\\"})\\n tinit: int = field(default=0, metadata={\\\"help\\\": \\\"The steps of initial warmup.\\\"})\\n tfinal: int = field(default=0, metadata={\\\"help\\\": \\\"The steps of final warmup.\\\"})\\n deltaT: int = field(default=1, metadata={\\\"help\\\": \\\"Step interval of rank allocation.\\\"})\\n beta1: float = field(default=0.85, metadata={\\\"help\\\": \\\"Hyperparameter of EMA.\\\"})\\n beta2: float = field(default=0.85, metadata={\\\"help\\\": \\\"Hyperparameter of EMA.\\\"})\\n orth_reg_weight: float = field(default=0.5, metadata={\\\"help\\\": \\\"The orthogonal regularization coefficient.\\\"})\\n total_step: Optional[int] = field(default=None, metadata={\\\"help\\\": \\\"The total training steps.\\\"})\\n rank_pattern: Optional[dict] = field(default=None, metadata={\\\"help\\\": \\\"The saved rank pattern.\\\"})\\n init_lora_weights: bool = field(\\n default=True,\\n metadata={\\\"help\\\": \\\"Whether to initialize the weights of the Lora layers.\\\"},\\n )\\n\\n def __post_init__(self):\\n self.peft_type = PeftType.ADALORA\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport torch\nimport sys\nimport peft\nimport model.peft.modeling_peft # noqa\n import bitsandbytes as bnb # noqa\n import accelerate # noqa\nfrom utils.common_utils import get_model_params_num\nfrom transformers import ( # noqa: E402\n CONFIG_MAPPING,\n AutoConfig,\n AutoModelForCausalLM,\n AutoTokenizer,\n PreTrainedTokenizerFast\n)\nfrom .gpt_neox.configuration_gpt_neox import GPTNeoXConfig\nfrom .gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM\nfrom .gpt_neox.tokenization_gpt_neox_fast import GPTNeoXTokenizerFast\nfrom .llama.configuration_llama import LlamaConfig\nfrom .llama.modeling_llama import LlamaForCausalLM\nfrom .llama.tokenization_llama import LlamaTokenizer\nfrom .llama.tokenization_llama_fast import LlamaTokenizerFast\nfrom torch.distributed.fsdp import (\n FullyShardedDataParallel as FSDP,\n StateDictType,\n)\nfrom utils.common_utils import print_rank_0, is_old_version\nfrom tokenizer import build_tokenizer\nfrom tokenizer.tokenizer import HFTokenizer\nfrom peft.tuners.lora import LoraLayer\nfrom model.peft.utils import prepare_model_for_kbit_training\nfrom peft import ( # noqa\n LoraConfig,\n PrefixTuningConfig,\n PromptEncoderConfig,\n PromptEncoderReparameterizationType,\n PromptTuningConfig,\n PromptTuningInit,\n TaskType,\n get_peft_model\n)\nfrom model.peft.tuner import AdaLoraConfig\n from transformers import BitsAndBytesConfig\nfrom packaging import version\n from .glm.tokenization_glm_deprecated import GLMChineseTokenizer"},"token_num":{"kind":"number","value":17414,"string":"17,414"},"cropped_code":{"kind":"string","value":"# coding=utf-8\n# Copyright (c) 2023 Ant Group. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nsys.path.append(\"..\")\n# from .glm.modeling_glm import GLMForConditionalGeneration\n# from .glm.configuration_glm import GLMConfig\n# from .glm.tokenization_glm import GLMTokenizer\n\n\n\ntry:\nexcept ImportError:\n BitsAndBytesConfig = None\ntry:\nexcept ImportError:\n bnb = None\n\n\ndef find_all_linear_names(args, model):\n cls = bnb.nn.Linear4bit if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)\n lora_module_names = set()\n for name, module in model.named_modules():\n if isinstance(module, cls):\n names = name.split('.')\n lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n if 'lm_head' in lora_module_names: # needed for 16-bit\n lora_module_names.remove('lm_head')\n return list(lora_module_names)\n\n\ndef setup_model(args, logger, use_cache=False):\n # Load pretrained model and tokenizer\n\n if args.pretrained_model_path: # TODO: 实现from pretrained读tokenizer\n if args.model_type == 'gpt_neox':\n # if args.tokenizer_type:\n # tokenizer = build_tokenizer(args)\n # tokenizer.eod_token = \"<|endoftext|>\"\n # tokenizer.pad_token = \"<|pad|>\"\n # # tokenizer.sop_token = \"<|endoftext|>\" # 适配multi task dataset\n # # tokenizer.eop_token = \"<|endoftext|>\"\n # tokenizer.eod_id = tokenizer.tokenize(tokenizer.eod_token)[0]\n # tokenizer.pad_id = tokenizer.tokenize(tokenizer.pad_token)[0]\n # else:\n tokenizer = GPTNeoXTokenizerFast.from_pretrained(args.pretrained_model_path)\n # tokenizer = PreTrainedTokenizerFast(tokenizer_file=args.vocab_file)\n tokenizer.eod_token = \"<|endoftext|>\"\n tokenizer.pad_token = \"<|pad|>\"\n tokenizer.sop_token = \"<|endoftext|>\" # 适配multi task dataset\n tokenizer.eop_token = \"<|endoftext|>\"\n tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)\n tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n \n print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')\n print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')\n\n elif args.model_type == 'llama':\n tokenizer = LlamaTokenizerFast.from_pretrained(args.pretrained_model_path)\n # tokenizer = AutoTokenizer.from_pretrained(\n # args.pretrained_model_path,\n # trust_remote_code=True,\n # )\n tokenizer.eod_token = \"</s>\"\n tokenizer.eos_token = \"</s>\"\n tokenizer.bos_token = \"<s>\"\n tokenizer.pad_token = \"[PAD]\"\n tokenizer.unk_token = \"<unk>\"\n tokenizer.sop_token = \"</s>\" # 适配multi task dataset\n tokenizer.eop_token = \"</s>\"\n tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)\n tokenizer.eos_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)\n tokenizer.bos_id = tokenizer.convert_tokens_to_ids(tokenizer.bos_token)\n tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n tokenizer.unk_id = tokenizer.convert_tokens_to_ids(tokenizer.unk_token)\n print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')\n print_rank_0(f'tokenizer {tokenizer.eos_token} id: {tokenizer.eos_id}')\n print_rank_0(f'tokenizer {tokenizer.bos_token} id: {tokenizer.bos_id}')\n print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')\n print_rank_0(f'tokenizer {tokenizer.unk_token} id: {tokenizer.unk_id}')\n elif args.model_type == 'glm':\n if is_old_version(args.pretrained_model_path):\n tokenizer = GLMChineseTokenizer.from_pretrained(args.pretrained_model_path)\n else:\n tokenizer = GLMTokenizer.from_pretrained(args.pretrained_model_path)\n elif args.train_mode == 'sst':\n # tokenizer = build_tokenizer(args)\n tokenizer = PreTrainedTokenizerFast(tokenizer_file=args.vocab_file)\n tokenizer.eod_token = \"<|endoftext|>\"\n tokenizer.pad_token = \"<|pad|>\"\n tokenizer.sop_token = \"<|endoftext|>\" # 适配multi task dataset\n tokenizer.eop_token = \"<|endoftext|>\"\n tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)\n tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n\n print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')\n print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')\n else:\n raise ValueError(\n \"You are instantiating a new tokenizer from scratch. This is not supported by this script.\"\n \"You can do it from another script, save it, and load it from here, using --tokenizer_path.\"\n )\n \n if args.model_type == 'gpt_neox':\n"},"all_code":{"kind":"string","value":"# coding=utf-8\n# Copyright (c) 2023 Ant Group. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nsys.path.append(\"..\")\n# from .glm.modeling_glm import GLMForConditionalGeneration\n# from .glm.configuration_glm import GLMConfig\n# from .glm.tokenization_glm import GLMTokenizer\n\n\n\ntry:\nexcept ImportError:\n BitsAndBytesConfig = None\ntry:\nexcept ImportError:\n bnb = None\n\n\ndef find_all_linear_names(args, model):\n cls = bnb.nn.Linear4bit if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)\n lora_module_names = set()\n for name, module in model.named_modules():\n if isinstance(module, cls):\n names = name.split('.')\n lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n if 'lm_head' in lora_module_names: # needed for 16-bit\n lora_module_names.remove('lm_head')\n return list(lora_module_names)\n\n\ndef setup_model(args, logger, use_cache=False):\n # Load pretrained model and tokenizer\n\n if args.pretrained_model_path: # TODO: 实现from pretrained读tokenizer\n if args.model_type == 'gpt_neox':\n # if args.tokenizer_type:\n # tokenizer = build_tokenizer(args)\n # tokenizer.eod_token = \"<|endoftext|>\"\n # tokenizer.pad_token = \"<|pad|>\"\n # # tokenizer.sop_token = \"<|endoftext|>\" # 适配multi task dataset\n # # tokenizer.eop_token = \"<|endoftext|>\"\n # tokenizer.eod_id = tokenizer.tokenize(tokenizer.eod_token)[0]\n # tokenizer.pad_id = tokenizer.tokenize(tokenizer.pad_token)[0]\n # else:\n tokenizer = GPTNeoXTokenizerFast.from_pretrained(args.pretrained_model_path)\n # tokenizer = PreTrainedTokenizerFast(tokenizer_file=args.vocab_file)\n tokenizer.eod_token = \"<|endoftext|>\"\n tokenizer.pad_token = \"<|pad|>\"\n tokenizer.sop_token = \"<|endoftext|>\" # 适配multi task dataset\n tokenizer.eop_token = \"<|endoftext|>\"\n tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)\n tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n \n print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')\n print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')\n\n elif args.model_type == 'llama':\n tokenizer = LlamaTokenizerFast.from_pretrained(args.pretrained_model_path)\n # tokenizer = AutoTokenizer.from_pretrained(\n # args.pretrained_model_path,\n # trust_remote_code=True,\n # )\n tokenizer.eod_token = \"</s>\"\n tokenizer.eos_token = \"</s>\"\n tokenizer.bos_token = \"<s>\"\n tokenizer.pad_token = \"[PAD]\"\n tokenizer.unk_token = \"<unk>\"\n tokenizer.sop_token = \"</s>\" # 适配multi task dataset\n tokenizer.eop_token = \"</s>\"\n tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)\n tokenizer.eos_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)\n tokenizer.bos_id = tokenizer.convert_tokens_to_ids(tokenizer.bos_token)\n tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n tokenizer.unk_id = tokenizer.convert_tokens_to_ids(tokenizer.unk_token)\n print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')\n print_rank_0(f'tokenizer {tokenizer.eos_token} id: {tokenizer.eos_id}')\n print_rank_0(f'tokenizer {tokenizer.bos_token} id: {tokenizer.bos_id}')\n print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')\n print_rank_0(f'tokenizer {tokenizer.unk_token} id: {tokenizer.unk_id}')\n elif args.model_type == 'glm':\n if is_old_version(args.pretrained_model_path):\n tokenizer = GLMChineseTokenizer.from_pretrained(args.pretrained_model_path)\n else:\n tokenizer = GLMTokenizer.from_pretrained(args.pretrained_model_path)\n elif args.train_mode == 'sst':\n # tokenizer = build_tokenizer(args)\n tokenizer = PreTrainedTokenizerFast(tokenizer_file=args.vocab_file)\n tokenizer.eod_token = \"<|endoftext|>\"\n tokenizer.pad_token = \"<|pad|>\"\n tokenizer.sop_token = \"<|endoftext|>\" # 适配multi task dataset\n tokenizer.eop_token = \"<|endoftext|>\"\n tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)\n tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n\n print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')\n print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')\n else:\n raise ValueError(\n \"You are instantiating a new tokenizer from scratch. This is not supported by this script.\"\n \"You can do it from another script, save it, and load it from here, using --tokenizer_path.\"\n )\n \n if args.model_type == 'gpt_neox':"},"next_line":{"kind":"string","value":" auto_config = GPTNeoXConfig"},"gold_snippet_index":{"kind":"number","value":1,"string":"1"},"created_at":{"kind":"string","value":"2023-11-02 01:37:01+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5273,"cells":{"repo_name":{"kind":"string","value":"bytedance/cryostar"},"file_path":{"kind":"string","value":"projects/star/train_atom.py"},"context":{"kind":"list like","value":[{"identifier":"SpatialGridTranslate","path":"cryostar/utils/transforms.py","snippet":"class SpatialGridTranslate(torch.nn.Module):\n\n def __init__(self, D, device=None) -> None:\n super().__init__()\n self.D = D\n # yapf: disable\n coords = torch.stack(torch.meshgrid([\n torch.linspace(-1.0, 1.0, self.D, device=device),\n torch.linspace(-1.0, 1.0, self.D, device=device)],\n indexing=\"ij\"), dim=-1).reshape(-1, 2)\n # yapf: enable\n self.register_buffer(\"coords\", coords)\n\n def transform(self, images: torch.Tensor, trans: torch.Tensor):\n \"\"\"\n The `images` are stored in `YX` mode, so the `trans` is also `YX`!\n\n Supposing that D is 96, a point is at 0.0:\n - adding 48 should move it to the right corner which is 1.0\n 1.0 = 0.0 + 48 / (96 / 2)\n - adding 96(>48) should leave it at 0.0\n 0.0 = 0.0 + 96 / (96 / 2) - 2.0\n - adding -96(<48) should leave it at 0.0\n 0.0 = 0.0 - 96 / (96 / 2) + 2.0\n\n Input:\n images: (B, NY, NX)\n trans: (B, T, 2)\n\n Returns:\n images: (B, T, NY, NX)\n \"\"\"\n B, NY, NX = images.shape\n assert self.D == NY == NX\n assert images.shape[0] == trans.shape[0]\n\n grid = einops.rearrange(self.coords, \"N C2 -> 1 1 N C2\") - \\\n einops.rearrange(trans, \"B T C2 -> B T 1 C2\") * 2 / self.D\n grid = grid.flip(-1) # convert the first axis from slow-axis to fast-axis\n grid[grid >= 1] -= 2\n grid[grid <= -1] += 2\n grid.clamp_(-1.0, 1.0)\n\n sampled = F.grid_sample(einops.rearrange(images, \"B NY NX -> B 1 NY NX\"), grid, align_corners=True)\n\n sampled = einops.rearrange(sampled, \"B 1 T (NY NX) -> B T NY NX\", NX=NX, NY=NY)\n return sampled"},{"identifier":"StarfileDataSet","path":"cryostar/utils/dataio.py","snippet":"class StarfileDataSet(Dataset):\n\n def __init__(self, cfg: StarfileDatasetConfig):\n super().__init__()\n self.cfg = cfg\n self.df = starfile.read(Path(cfg.starfile_path))\n\n if \"optics\" in self.df:\n optics_df = self.df[\"optics\"]\n particles_df = self.df[\"particles\"]\n else:\n optics_df = None\n particles_df = self.df\n self.particles_df = particles_df\n\n if cfg.apix is None:\n if optics_df is not None and \"rlnImagePixelSize\" in optics_df:\n self.apix = float(optics_df[\"rlnImagePixelSize\"][0])\n print(f\"Infer dataset apix={self.apix} from first optic group.\")\n elif \"rlnDetectorPixelSize\" in particles_df and \"rlnMagnification\" in particles_df:\n self.apix = float(particles_df[\"rlnDetectorPixelSize\"][0] / particles_df[\"rlnMagnification\"][0] * 1e4)\n print(f\"Infer dataset apix={self.apix} from first particle meta data.\")\n else:\n raise AttributeError(\"Cannot parse apix from starfile, please set it in config by hand.\")\n else:\n self.apix = cfg.apix\n\n if cfg.side_shape is None:\n tmp_mrc_path = osp.join(cfg.dataset_dir, particles_df[\"rlnImageName\"][0].split('@')[-1])\n with mrcfile.mmap(tmp_mrc_path, mode=\"r\", permissive=True) as m:\n self.side_shape = m.data.shape[-1]\n print(f\"Infer dataset side_shape={self.side_shape} from the 1st particle.\")\n else:\n self.side_shape = cfg.side_shape\n\n self.num_proj = len(particles_df)\n\n self.down_side_shape = self.side_shape\n if cfg.down_side_shape is not None:\n self.down_side_shape = cfg.down_side_shape\n\n if cfg.mask_rad is not None:\n self.mask = Mask(self.down_side_shape, cfg.mask_rad)\n\n self.f_mu = None\n self.f_std = None\n\n def __len__(self):\n return self.num_proj\n\n def estimate_normalization(self):\n if self.f_mu is None and self.f_std is None:\n f_sub_data = []\n # I have checked that the standard deviation of 10/100/1000 particles is similar\n for i in range(0, len(self), len(self) // 100):\n f_sub_data.append(self[i][\"fproj\"])\n f_sub_data = torch.cat(f_sub_data, dim=0)\n # self.f_mu = torch.mean(f_sub_data)\n self.f_mu = 0.0 # just follow cryodrgn\n self.f_std = torch.std(f_sub_data).item()\n else:\n raise Exception(\"The normalization factor has been estimated!\")\n\n def __getitem__(self, idx):\n item_row = self.particles_df.iloc[idx]\n try:\n img_name_raw = item_row[\"rlnImageName\"]\n in_mrc_idx, img_name = item_row[\"rlnImageName\"].split(\"@\")\n in_mrc_idx = int(in_mrc_idx) - 1\n mrc_path = osp.join(self.cfg.dataset_dir, img_name)\n with mrcfile.mmap(mrc_path, mode=\"r\", permissive=True) as mrc:\n if mrc.data.ndim > 2:\n proj = torch.from_numpy(np.array(mrc.data[in_mrc_idx])).float() * self.cfg.scale_images\n else:\n # the mrcs file can contain only one particle\n proj = torch.from_numpy(np.array(mrc.data)).float() * self.cfg.scale_images\n\n # get (1, side_shape, side_shape) proj\n if len(proj.shape) == 2:\n proj = proj[None, :, :] # add a dummy channel (for consistency w/ img fmt)\n else:\n assert len(proj.shape) == 3 and proj.shape[0] == 1 # some starfile already have a dummy channel\n\n # down-sample\n if self.down_side_shape != self.side_shape:\n if self.cfg.down_method == \"interp\":\n proj = tvf.resize(proj, [self.down_side_shape, ] * 2, antialias=True)\n elif self.cfg.down_method == \"fft\":\n proj = downsample_2d(proj[0, :, :], self.down_side_shape)[None, :, :]\n else:\n raise NotImplementedError\n\n if self.cfg.mask_rad is not None:\n proj = self.mask(proj)\n\n except Exception as e:\n print(f\"WARNING: Particle image {img_name_raw} invalid! Setting to zeros.\")\n print(e)\n proj = torch.zeros(1, self.down_side_shape, self.down_side_shape)\n\n if self.cfg.power_images != 1.0:\n proj *= self.cfg.power_images\n\n # Generate CTF from CTF paramaters\n defocusU = torch.from_numpy(np.array(item_row[\"rlnDefocusU\"] / 1e4, ndmin=2)).float()\n defocusV = torch.from_numpy(np.array(item_row[\"rlnDefocusV\"] / 1e4, ndmin=2)).float()\n angleAstigmatism = torch.from_numpy(np.radians(np.array(item_row[\"rlnDefocusAngle\"], ndmin=2))).float()\n\n # Read \"GT\" orientations\n if self.cfg.ignore_rots:\n rotmat = torch.eye(3).float()\n else:\n # yapf: disable\n rotmat = torch.from_numpy(euler_angles2matrix(\n np.radians(-item_row[\"rlnAngleRot\"]),\n # np.radians(particle[\"rlnAngleTilt\"]) * (-1 if self.cfg.invert_hand else 1),\n np.radians(-item_row[\"rlnAngleTilt\"]),\n np.radians(-item_row[\"rlnAnglePsi\"]))\n ).float()\n # yapf: enable\n\n # Read \"GT\" shifts\n if self.cfg.ignore_trans:\n shiftX = torch.tensor([0.])\n shiftY = torch.tensor([0.])\n else:\n # support early starfile formats\n # Particle translations used to be in pixels (rlnOriginX and rlnOriginY) but this changed to Angstroms\n # (rlnOriginXAngstrom and rlnOriginYAngstrom) in relion 3.1.\n # https://relion.readthedocs.io/en/release-3.1/Reference/Conventions.html\n if \"rlnOriginXAngst\" in item_row:\n shiftX = torch.from_numpy(np.array(item_row[\"rlnOriginXAngst\"], dtype=np.float32))\n shiftY = torch.from_numpy(np.array(item_row[\"rlnOriginYAngst\"], dtype=np.float32))\n else:\n shiftX = torch.from_numpy(np.array(item_row[\"rlnOriginX\"] * self.apix, dtype=np.float32))\n shiftY = torch.from_numpy(np.array(item_row[\"rlnOriginY\"] * self.apix, dtype=np.float32))\n\n fproj = primal_to_fourier_2d(proj)\n\n if self.f_mu is not None:\n fproj = (fproj - self.f_mu) / self.f_std\n proj = fourier_to_primal_2d(fproj).real\n\n in_dict = {\n \"proj\": proj,\n \"rotmat\": rotmat,\n \"defocusU\": defocusU,\n \"defocusV\": defocusV,\n \"shiftX\": shiftX,\n \"shiftY\": shiftY,\n \"angleAstigmatism\": angleAstigmatism,\n \"idx\": torch.tensor(idx, dtype=torch.long),\n \"fproj\": fproj,\n \"imgname_raw\": img_name_raw\n }\n\n if \"rlnClassNumber\" in item_row:\n in_dict[\"class_id\"] = item_row[\"rlnClassNumber\"]\n\n return in_dict"},{"identifier":"StarfileDatasetConfig","path":"cryostar/utils/dataio.py","snippet":"class StarfileDatasetConfig:\n dataset_dir: str\n starfile_path: str\n # if is not specified, the following apix, and side_shape will be inferred from starfile\n apix: float = None\n side_shape: int = None\n # down-sample the original image or not\n down_side_shape: int = None\n down_method: str = \"interp\"\n # apply a circular mask on input image or not\n mask_rad: float = None\n # change image values\n scale_images: float = 1.0\n power_images: float = field(\n default=1.0,\n metadata={\"help\": \"Change the power of the signal by multiplying a constant number.\"})\n # ignore pose from starfile or not\n ignore_trans: bool = False\n ignore_rots: bool = False\n # invert_hand: bool = field(\n # default=False,\n # metadata={\"help\": \"Invert handedness when reading relion data.\"})"},{"identifier":"Mask","path":"cryostar/utils/dataio.py","snippet":"class Mask(torch.nn.Module):\n\n def __init__(self, im_size, rad):\n super(Mask, self).__init__()\n\n mask = torch.lt(torch.linspace(-1, 1, im_size)[None]**2 + torch.linspace(-1, 1, im_size)[:, None]**2, rad**2)\n # float for pl ddp broadcast compatible\n self.register_buffer('mask', mask.float())\n self.num_masked = torch.sum(mask).item()\n\n def forward(self, x):\n return x * self.mask"},{"identifier":"CTFRelion","path":"cryostar/utils/ctf_utils.py","snippet":"class CTFRelion(CTFBase):\n \"\"\"\n BUG: There are two bugs in this file:\n 1. `self.angleFrequency` has some error for even-sized grid.\n 2. `local_defocus` in `get_ctf()` has some error, `angleAstigmatism` should be\n replaced with `defocusU - defocusV`.\n\n The bugs will not affect real-world data too much. But you may encounter some issues\n on simulated datasets. Use CTFCryoDRGN instead.\n \"\"\"\n\n def __init__(self,\n size=257,\n resolution=0.8,\n kV=300.0,\n valueNyquist=1.,\n defocusU=1.,\n defocusV=1.,\n angleAstigmatism=0.,\n cs=2.7,\n phasePlate=0.,\n amplitudeContrast=.1,\n bFactor=0.,\n num_particles=500,\n requires_grad=False,\n precompute=False,\n flip_images=False):\n super(CTFRelion, self).__init__(resolution, num_particles, requires_grad)\n self.requires_grad = requires_grad\n self.flip_images = flip_images\n\n self.size = size # in pixel\n self.resolution = resolution # in angstrom\n self.kV = kV # in kilovolt\n\n self.valueNyquist = valueNyquist\n self.phasePlate = phasePlate / 180. * np.pi # in radians (converted from degrees)\n self.amplitudeContrast = amplitudeContrast\n self.bFactor = bFactor\n\n self.frequency = 1. / self.resolution\n\n self.wavelength = self._get_ewavelength(self.kV * 1e3) # input in V (so we convert kv*1e3)\n\n angleAstigmatism = angleAstigmatism / 180. * np.pi # input in degree converted in radian\n cs = cs * 1e7 # input in mm converted in angstrom\n # the angleAstigmatism, defocusU, defocusV and cs are nn.Parameter of size (N, 1, 1)\n self.angleAstigmatism = nn.Parameter(angleAstigmatism * torch.ones((num_particles, 1, 1), dtype=torch.float32),\n requires_grad=requires_grad)\n self.cs = nn.Parameter(cs * torch.ones((num_particles, 1, 1), dtype=torch.float32), requires_grad=requires_grad)\n self.defocusU = nn.Parameter(defocusU * torch.ones((num_particles, 1, 1), dtype=torch.float32),\n requires_grad=requires_grad)\n self.defocusV = nn.Parameter(defocusV * torch.ones((num_particles, 1, 1), dtype=torch.float32),\n requires_grad=requires_grad)\n\n self.precomputed_filters = precompute\n\n ax = torch.linspace(-1. / (2. * resolution), 1 / (2. * resolution), self.size)\n mx, my = torch.meshgrid(ax, ax, indexing=\"ij\")\n self.register_buffer(\"r2\", mx**2 + my**2)\n self.register_buffer(\"r\", torch.sqrt(self.r2))\n self.register_buffer(\"angleFrequency\", torch.atan2(my, mx))\n\n if not self.requires_grad and self.precomputed_filters:\n print(\"Precomputing hFourier in CTF\")\n self.register_buffer('hFourier', self.get_ctf(torch.arange(num_particles), num_particles))\n\n def _get_ewavelength(self, U):\n # assumes V as input, returns wavelength in angstrom\n h = scipy.constants.h\n e = scipy.constants.e\n c = scipy.constants.c\n m0 = scipy.constants.m_e\n\n return h / math.sqrt(2. * m0 * e * U) / math.sqrt(1 + e * U / (2 * m0 * c**2)) * 1e10\n\n def get_ctf(self, idcs, B, cpu_params={}, frequency_marcher=None):\n defocusU = self.defocusU[idcs, :, :]\n defocusV = self.defocusV[idcs, :, :]\n angleAstigmatism = self.angleAstigmatism[idcs, :, :]\n cs = self.cs[idcs, :, :]\n\n ac = self.amplitudeContrast\n pc = math.sqrt(1. - ac**2)\n K1 = np.pi / 2. * cs * self.wavelength**3\n K2 = np.pi * self.wavelength\n\n # Cut-off from frequency marcher\n if frequency_marcher is not None:\n self.size_after_fm = 2 * frequency_marcher.f + 1\n if self.size_after_fm > self.size:\n self.size_after_fm = self.size\n angleFrequency = frequency_marcher.cut_coords_plane(self.angleFrequency.reshape(\n self.size, self.size, 1)).reshape(self.size_after_fm, self.size_after_fm)\n r2 = frequency_marcher.cut_coords_plane(self.r2.reshape(self.size, self.size,\n 1)).reshape(self.size_after_fm, self.size_after_fm)\n else:\n self.size_after_fm = self.size\n angleFrequency = self.angleFrequency\n r2 = self.r2\n\n angle = angleFrequency - angleAstigmatism\n local_defocus = 1e4 * (defocusU + defocusV) / 2. + angleAstigmatism * torch.cos(2. * angle)\n\n gamma = K1 * r2**2 - K2 * r2 * local_defocus - self.phasePlate\n hFourier = -pc * torch.sin(gamma) + ac * torch.cos(gamma)\n\n if self.valueNyquist != 1:\n decay = np.sqrt(-np.log(self.valueNyquist)) * 2. * self.resolution\n envelope = torch.exp(-self.frequency * decay**2 * r2)\n hFourier *= envelope\n\n return hFourier\n\n def oversample_multiply_crop(self, x_fourier, hFourier):\n # we assume that the shape of the CTF is always going to be bigger\n # than the size of the input image\n input_sz = x_fourier.shape[-1]\n if input_sz != self.size_after_fm:\n x_primal = fourier_to_primal_2d(x_fourier)\n\n pad_len = (self.size_after_fm - x_fourier.shape[-1]) // 2 # here we assume even lengths\n p2d = (pad_len, pad_len, pad_len, pad_len)\n x_primal_padded = F.pad(x_primal, p2d, 'constant', 0)\n\n x_fourier_padded = primal_to_fourier_2d(x_primal_padded)\n\n x_fourier_padded_filtered = x_fourier_padded * hFourier[:, None, :, :]\n return x_fourier_padded_filtered[..., pad_len:-pad_len, pad_len:-pad_len]\n else:\n return x_fourier * hFourier[:, None, :, :]\n\n def get_cpu_params(self, idcs, ctf_params, flip=False):\n batch_size = idcs.shape[0]\n self.defocusU[idcs, :, :] = ctf_params['defocusU'][:batch_size] if not flip else\\\n ctf_params['defocusU'][batch_size:]\n self.defocusV[idcs, :, :] = ctf_params['defocusV'][:batch_size] if not flip else\\\n ctf_params['defocusV'][batch_size:]\n self.angleAstigmatism[idcs, :, :] = ctf_params['angleAstigmatism'][:batch_size] if not flip else\\\n ctf_params['angleAstigmatism'][batch_size:]\n cpu_params = {}\n return cpu_params\n\n def forward(self, x_fourier, idcs=0, ctf_params={}, mode='gt', frequency_marcher=None):\n # This is when we want to prescribe parameters for the CTF\n if x_fourier.dim() == 3:\n x_fourier = x_fourier[None, ...]\n # x_fourier: B, 1, S, S\n batch_size = len(idcs)\n cpu_params = {}\n if ctf_params:\n cpu_params = self.get_cpu_params(idcs, ctf_params, flip=False)\n\n # if new params for the CTF have been prescribed or we are optimizing it\n # then request the evaluation of the CTF\n if not ctf_params and self.precomputed_filters and not self.requires_grad:\n hFourier = self.hFourier[idcs, :, :]\n else:\n hFourier = self.get_ctf(idcs, batch_size, cpu_params=cpu_params, frequency_marcher=frequency_marcher)\n\n if self.flip_images:\n flipped_hFourier = torch.flip(hFourier, [1, 2])\n\n hFourier = torch.cat([hFourier, flipped_hFourier], dim=0)\n\n return self.oversample_multiply_crop(x_fourier, hFourier)"},{"identifier":"CTFCryoDRGN","path":"cryostar/utils/ctf_utils.py","snippet":"class CTFCryoDRGN(CTFBase):\n\n def __init__(self,\n size,\n resolution,\n num_particles=None,\n kV=300,\n cs=2.0,\n amplitudeContrast=0.1,\n requires_grad=False):\n super(CTFBase, self).__init__()\n self.size = size\n self.resolution = resolution\n self.requires_grad = requires_grad\n self.kV = kV\n self.cs = cs\n self.ac = amplitudeContrast\n # ax = torch.linspace(-1. / (2. * resolution), 1 / (2. * resolution), self.size)\n # mx, my = torch.meshgrid(ax, ax, indexing=\"ij\")\n ax = torch.fft.fftshift(torch.fft.fftfreq(self.size, self.resolution))\n mx, my = torch.meshgrid(ax, ax, indexing=\"xy\")\n freqs = torch.stack([mx.flatten(), my.flatten()], 1)\n self.register_buffer(\"freqs\", freqs)\n\n def get_ctf(self, ctf_params={}):\n bsz = len(ctf_params[\"defocusU\"])\n device = self.freqs.device\n hFourier = compute_ctf(freqs=self.freqs.repeat(bsz, 1, 1),\n dfu=(ctf_params[\"defocusU\"] * 1e4).squeeze(1),\n dfv=(ctf_params[\"defocusV\"] * 1e4).squeeze(1),\n dfang=torch.rad2deg(ctf_params[\"angleAstigmatism\"]).squeeze(1),\n volt=torch.tensor(self.kV, device=device).repeat(bsz, 1),\n cs=torch.tensor(self.cs, device=device).repeat(bsz, 1),\n w=torch.tensor(self.ac, device=device).repeat(bsz,\n 1)).reshape(bsz, self.size, self.size)\n return hFourier\n\n def forward(self, x_fourier, idcs=0, ctf_params={}, mode='gt', frequency_marcher=None):\n hFourier = -self.get_ctf(ctf_params)\n return x_fourier * hFourier[:, None, :, :]"},{"identifier":"calc_cor_loss","path":"cryostar/utils/losses.py","snippet":"def calc_cor_loss(pred_images, gt_images, mask=None):\n if mask is not None:\n pred_images = mask(pred_images)\n gt_images = mask(gt_images)\n pixel_num = mask.num_masked\n else:\n pixel_num = pred_images.shape[-2] * pred_images.shape[-1]\n\n # b, c, h, w -> b, c, num_pix\n pred_images = pred_images.flatten(start_dim=2)\n gt_images = gt_images.flatten(start_dim=2)\n\n # b, c\n dots = (pred_images * gt_images).sum(-1)\n # b, c -> b, c\n err = -dots / (gt_images.std(-1) + 1e-5) / (pred_images.std(-1) + 1e-5)\n # b, c -> b -> 1 value\n err = err.sum(-1).mean() / pixel_num\n return err"},{"identifier":"calc_kl_loss","path":"cryostar/utils/losses.py","snippet":"def calc_kl_loss(mu, log_var, free_bits, reduction=\"mean\"):\n kld_loss = -0.5 * (1 + log_var - mu.pow(2) - log_var.exp())\n # free bits\n kld_loss = torch.clamp(kld_loss, free_bits) # (bsz, z-dim)\n kld_loss = torch.mean(kld_loss, dim=1) # (bsz, )\n if reduction == \"mean\":\n kld_loss = torch.mean(kld_loss) # averaged over bsz x z-dim\n elif reduction == \"none\":\n kld_loss = kld_loss\n else:\n raise NotImplementedError\n return kld_loss"},{"identifier":"log_to_current","path":"cryostar/utils/misc.py","snippet":"def set_seed(seed: int = 42):\ndef chain(arg, *funcs):\ndef convert_to_numpy(*args):\ndef CHECK_SHAPE(tensor, expected_shape):\ndef ASSERT_SHAPE(tensor, expected_shape):\ndef parse_mmengine_args(override_mode=\"default\"):\ndef flatten_nested_dict(nested: Union[dict, Config]) -> dict:\ndef warmup(warmup_step, lower=0.0, upper=1.0):\n def run(cur_step):\ndef init_mmengine_config(args):\ndef init_mmengine_exp(args,\n exp_prefix='',\n backup_list=None,\n inplace=True,\n work_dir_name=\"work_dirs\",\n project_name=\"cryostar\",\n tensorboard=False):\ndef _get_next_version(root_dir, dir_name_prefix):\ndef pl_init_exp(override_mode=\"default\",\n exp_prefix='',\n backup_list=None,\n inplace=False,\n work_dir_name=\"work_dirs\",\n project_name=\"cryostar\"):\ndef save_pdb(CAs, path, ref_pdb_path):\ndef load_CAs_from_pdb(file):\ndef load_NCaC_from_pdb(file):\ndef load_chain_A(pdb_path):\ndef points_to_pdb(path_to_save, points: np.ndarray):\ndef point_stack_to_pdb(path_to_save, point_stack: np.ndarray):\ndef find_rigid_alignment(A, B):\ndef batch_find_rigid_alignment(A, B):\ndef pretty_dict(x, precision=3):\ndef create_sphere_mask(d, h, w, center=None, radius=None) -> np.ndarray:\ndef create_circular_mask(h, w, center=None, radius=None) -> np.ndarray:\n H = A_c.T.mm(B_c)\n U, S, V = torch.svd(H)\n R = V.mm(U.T)\n H = einops.einsum(A_c, B_c, \"b n c1, b n c2 -> b c1 c2\")\n V = VmT.mT\n R = einops.einsum(V, U.transpose(2, 1), \"b c1 c2, b c2 c3 -> b c1 c3\")"},{"identifier":"bt_save_pdb","path":"cryostar/utils/pdb_tools.py","snippet":"def bt_save_pdb(file_path: Union[str, Path], array: Union[AtomArray, AtomArrayStack], **kwargs):\n \"\"\"Save biotite AtomArray or AtomArrayStack to pdb file\n\n Parameters\n ----------\n file_path: save file path\n array: the structure to be saved\n kwargs: additional parameters to be passed, always empty\n\n \"\"\"\n bt_struc.io.save_structure(file_path, array, **kwargs)"},{"identifier":"EMAN2Grid","path":"cryostar/gmm/gmm.py","snippet":"class EMAN2Grid(BaseGrid):\n \"\"\"EMAN2 style grid.\n origin set to -(side_shape // 2) * voxel_size\n\n \"\"\"\n\n def __init__(self, side_shape, voxel_size):\n origin = -side_shape // 2 * voxel_size\n super().__init__(side_shape=side_shape, voxel_size=voxel_size, origin=origin)"},{"identifier":"batch_projection","path":"cryostar/gmm/gmm.py","snippet":"def batch_projection(gauss: Gaussian, rot_mats: torch.Tensor, line_grid: Grid) -> torch.Tensor:\n \"\"\"A quick version of e2gmm projection.\n\n Parameters\n ----------\n gauss: (b/1, num_centers, 3) mus, (b/1, num_centers) sigmas and amplitudes\n rot_mats: (b, 3, 3)\n line_grid: (num_pixels, 3) coords, (nx, ) shape\n\n Returns\n -------\n proj: (b, y, x) projections\n \"\"\"\n\n centers = einops.einsum(rot_mats, gauss.mus, \"b c31 c32, b nc c32 -> b nc c31\")\n\n sigmas = einops.rearrange(gauss.sigmas, 'b nc -> b 1 nc')\n sigmas = 2 * sigmas**2\n\n proj_x = einops.rearrange(line_grid.coords, \"nx -> 1 nx 1\") - einops.rearrange(centers[..., 0], \"b nc -> b 1 nc\")\n proj_x = torch.exp(-proj_x**2 / sigmas)\n\n proj_y = einops.rearrange(line_grid.coords, \"ny -> 1 ny 1\") - einops.rearrange(centers[..., 1], \"b nc -> b 1 nc\")\n proj_y = torch.exp(-proj_y**2 / sigmas)\n\n proj = einops.einsum(gauss.amplitudes, proj_x, proj_y, \"b nc, b nx nc, b ny nc -> b nx ny\")\n proj = einops.rearrange(proj, \"b nx ny -> b ny nx\")\n return proj"},{"identifier":"Gaussian","path":"cryostar/gmm/gmm.py","snippet":"class Gaussian:\n mus: Union[torch.Tensor, np.ndarray]\n sigmas: Union[torch.Tensor, np.ndarray]\n amplitudes: Union[torch.Tensor, np.ndarray]"},{"identifier":"E3Deformer","path":"cryostar/gmm/deformer.py","snippet":"class E3Deformer(torch.nn.Module, DeformerProtocol):\n\n def transform(self, deformation, coords):\n ASSERT_SHAPE(coords, (None, 3))\n ASSERT_SHAPE(deformation, (None, coords.shape[0] * 3))\n\n bsz = deformation.shape[0]\n shift = deformation.reshape(bsz, -1, 3)\n return shift + coords"},{"identifier":"NMADeformer","path":"cryostar/gmm/deformer.py","snippet":"class NMADeformer(torch.nn.Module, DeformerProtocol):\n def __init__(self, modes: torch.FloatTensor) -> None:\n super().__init__()\n modes = einops.rearrange(\n modes, \"(num_coords c3) num_modes -> num_modes num_coords c3\", c3=3\n )\n self.register_buffer(\"modes\", modes)\n self.num_modes = modes.shape[0]\n self.num_coords = modes.shape[1]\n\n def transform(self, deformation, coords):\n ASSERT_SHAPE(coords, (self.num_coords, 3))\n ASSERT_SHAPE(deformation, (None, 6 + self.num_modes))\n\n axis_angle = deformation[..., :3]\n translation = deformation[..., 3:6] * 10\n nma_coeff = deformation[..., 6:]\n rotation_matrix = axis_angle_to_matrix(axis_angle)\n\n nma_deform_e3 = einops.einsum(\n nma_coeff, self.modes, \"bsz num_modes, num_modes num_coords c3 -> bsz num_coords c3\"\n )\n rotated_coords = einops.einsum(rotation_matrix, nma_deform_e3 + coords,\n \"bsz c31 c32, bsz num_coords c31 -> bsz num_coords c32\")\n deformed_coords = rotated_coords + einops.rearrange(translation, \"bsz c3 -> bsz 1 c3\")\n return deformed_coords"},{"identifier":"primal_to_fourier_2d","path":"cryostar/utils/fft_utils.py","snippet":"@torch.autocast(\"cuda\")\ndef primal_to_fourier_2d(r: torch.Tensor) -> torch.Tensor:\n with torch.autocast(\"cuda\", enabled=False):\n r = torch.fft.ifftshift(r.float(), dim=(-2, -1))\n f = torch.fft.fftshift(torch.fft.fftn(r, s=(r.shape[-2], r.shape[-1]), dim=(-2, -1)), dim=(-2, -1))\n return f"},{"identifier":"fourier_to_primal_2d","path":"cryostar/utils/fft_utils.py","snippet":"def fourier_to_primal_2d(f: torch.Tensor) -> torch.Tensor:\n f = torch.fft.ifftshift(f, dim=(-2, -1))\n return torch.fft.fftshift(torch.fft.ifftn(f, s=(f.shape[-2], f.shape[-1]), dim=(-2, -1)), dim=(-2, -1))"},{"identifier":"Polymer","path":"cryostar/utils/polymer.py","snippet":"class Polymer:\n chain_id: np.ndarray\n res_id: np.ndarray\n res_name: np.ndarray\n coord: np.ndarray\n atom_name: np.ndarray\n element: np.ndarray\n num_electron: np.ndarray\n\n def __init__(self, num):\n self.chain_id = np.empty(num, dtype=\"U4\")\n self.res_id = np.zeros(num, dtype=int)\n self.res_name = np.empty(num, dtype=\"U3\")\n self.coord = np.zeros((num, 3), dtype=np.float32)\n self.atom_name = np.empty(num, dtype=\"U6\")\n self.element = np.empty(num, dtype=\"U2\")\n self.num_electron = np.zeros(num, dtype=int)\n\n def __setitem__(self, index, kwargs):\n assert set(kwargs.keys()).issubset(f.name for f in dataclasses.fields(self))\n for k, v in kwargs.items():\n getattr(self, k)[index] = v\n\n def __getitem__(self, index):\n return {f.name: getattr(self, f.name)[index] for f in dataclasses.fields(self)}\n\n def __len__(self):\n return len(self.chain_id)\n\n @property\n def num_amino_acids(self):\n return np.sum(np.isin(self.atom_name, AA_ATOMS))\n\n @property\n def num_nucleotides(self):\n return np.sum(np.isin(self.atom_name, NT_ATOMS))\n\n @property\n def num_chains(self):\n return len(np.unique(self.chain_id))\n\n @classmethod\n def from_atom_arr(cls, atom_arr):\n assert isinstance(atom_arr, struc.AtomArray)\n\n nt_arr = atom_arr[struc.filter_nucleotides(atom_arr)]\n aa_arr = atom_arr[struc.filter_amino_acids(atom_arr)]\n\n num = 0\n if len(aa_arr) > 0:\n num += struc.get_residue_count(aa_arr)\n if len(nt_arr) > 0:\n for res in struc.residue_iter(nt_arr):\n valid_atoms = set(res.atom_name).intersection(NT_ATOMS)\n if len(valid_atoms) <= 0:\n raise UserWarning(f\"Nucleotides doesn't contain {' or '.join(NT_ATOMS)}.\")\n else:\n num += len(valid_atoms)\n meta = cls(num)\n\n def _update_res(tmp_res, kind=\"aa\"):\n nonlocal pos\n\n if kind == \"aa\":\n using_atom_names = AA_ATOMS\n filtered_res = tmp_res[struc.filter_peptide_backbone(tmp_res)]\n elif kind == \"nt\":\n using_atom_names = NT_ATOMS\n filtered_res = tmp_res\n else:\n raise NotImplemented\n\n valid_atom_names = set(tmp_res.atom_name).intersection(using_atom_names)\n\n for select_atom_name in valid_atom_names:\n meta[pos] = {\n \"chain_id\": tmp_res.chain_id[0],\n \"res_id\": tmp_res.res_id[0],\n \"res_name\": tmp_res.res_name[0],\n \"coord\": filtered_res[filtered_res.atom_name == select_atom_name].coord,\n \"atom_name\": select_atom_name,\n \"element\": filtered_res[filtered_res.atom_name == select_atom_name].element[0],\n \"num_electron\": get_num_electrons(tmp_res) // len(valid_atom_names)\n }\n pos += 1\n\n def _update(tmp_arr, kind=\"aa\"):\n nonlocal pos\n for chain in struc.chain_iter(tmp_arr):\n for tmp_res in struc.residue_iter(chain):\n _update_res(tmp_res, kind)\n\n pos = 0\n\n if len(aa_arr) > 0:\n _update(aa_arr, kind=\"aa\")\n if len(nt_arr) > 0:\n _update(nt_arr, kind=\"nt\")\n\n assert pos == num\n return meta\n\n @classmethod\n def from_pdb(cls, file_path):\n atom_arr = bt_read_pdb(file_path)\n if atom_arr.stack_depth() > 1:\n print(\"PDB file contains more than 1 models, select the 1st model\")\n atom_arr = atom_arr[0]\n return Polymer.from_atom_arr(atom_arr)\n\n def to_atom_arr(self):\n num = len(self)\n atom_arr = struc.AtomArray(num)\n atom_arr.coord = self.coord\n\n for f in dataclasses.fields(self):\n if f.name != \"coord\" and f.name in atom_arr.get_annotation_categories():\n atom_arr.set_annotation(f.name, getattr(self, f.name))\n # atom_arr.atom_name[atom_arr.atom_name == \"R\"] = \"CB\"\n return atom_arr"},{"identifier":"NT_ATOMS","path":"cryostar/utils/polymer.py","snippet":"NT_ATOMS = (\"C1'\", )"},{"identifier":"AA_ATOMS","path":"cryostar/utils/polymer.py","snippet":"AA_ATOMS = (\"CA\", )"},{"identifier":"find_quaint_cutoff_pairs","path":"cryostar/utils/dist_loss.py","snippet":"def find_quaint_cutoff_pairs(coord_arr,\n chain_id_arr,\n res_id_arr,\n intra_chain_cutoff=12.,\n inter_chain_cutoff=12.,\n intra_chain_res_bound=None):\n sel_indices = []\n dist_map = distance.cdist(coord_arr, coord_arr, metric='euclidean')\n # 1. intra chain\n sel_mask = dist_map <= intra_chain_cutoff\n sel_mask = np.triu(sel_mask, k=1)\n # get indices of valid pairs\n indices_in_pdb = np.nonzero(sel_mask)\n indices_in_pdb = np.column_stack((indices_in_pdb[0], indices_in_pdb[1]))\n indices_in_pdb = indices_in_pdb[chain_id_arr[indices_in_pdb[:, 0]] == chain_id_arr[indices_in_pdb[:, 1]]]\n # filter by res_id\n if intra_chain_res_bound is not None:\n assert res_id_arr is not None\n res_ids = res_id_arr[indices_in_pdb]\n res_id_dist = np.abs(np.diff(res_ids, axis=1)).flatten()\n indices_in_pdb = indices_in_pdb[res_id_dist <= intra_chain_res_bound]\n\n sel_indices.append(indices_in_pdb)\n\n # 2. inter chain\n if inter_chain_cutoff is not None:\n sel_mask = dist_map <= inter_chain_cutoff\n sel_mask = np.triu(sel_mask, k=1)\n indices_in_pdb = np.nonzero(sel_mask)\n indices_in_pdb = np.column_stack((indices_in_pdb[0], indices_in_pdb[1]))\n indices_in_pdb = indices_in_pdb[chain_id_arr[indices_in_pdb[:, 0]] != chain_id_arr[indices_in_pdb[:, 1]]]\n sel_indices.append(indices_in_pdb)\n\n sel_indices = np.vstack(sel_indices)\n return sel_indices"},{"identifier":"find_range_cutoff_pairs","path":"cryostar/utils/dist_loss.py","snippet":"def find_range_cutoff_pairs(coord_arr, min_cutoff=4., max_cutoff=10.):\n dist_map = distance.cdist(coord_arr, coord_arr, metric='euclidean')\n sel_mask = (dist_map <= max_cutoff) & (dist_map >= min_cutoff)\n indices_in_pdb = np.nonzero(sel_mask)\n indices_in_pdb = np.column_stack((indices_in_pdb[0], indices_in_pdb[1]))\n return indices_in_pdb"},{"identifier":"find_continuous_pairs","path":"cryostar/utils/dist_loss.py","snippet":"def find_continuous_pairs(chain_id_arr, res_id_arr, atom_name_arr):\n pairs = []\n\n # res_id in different chains are duplicated, so loop on chains\n u_chain_id = np.unique(chain_id_arr)\n\n for c_id in u_chain_id:\n tmp_mask = chain_id_arr == c_id\n tmp_indices_in_pdb = np.nonzero(tmp_mask)[0]\n\n tmp_res_id_arr = res_id_arr[tmp_mask]\n tmp_atom_name_arr = atom_name_arr[tmp_mask]\n\n # check is aa or nt\n tmp_atom_name_set = set(tmp_atom_name_arr)\n\n if len(tmp_atom_name_set.intersection(AA_ATOMS)) > len(tmp_atom_name_set.intersection(NT_ATOMS)):\n in_res_atom_names = AA_ATOMS\n elif len(tmp_atom_name_set.intersection(AA_ATOMS)) < len(tmp_atom_name_set.intersection(NT_ATOMS)):\n in_res_atom_names = NT_ATOMS\n else:\n raise NotImplemented(\"Cannot determine chain is amino acid or nucleotide.\")\n\n # find pairs\n if len(in_res_atom_names) == 1:\n u_res_id, indices_in_chain = np.unique(tmp_res_id_arr, return_index=True)\n if len(u_res_id) != np.sum(tmp_mask):\n raise ValueError(f\"Found duplicate residue id in single chain {c_id}.\")\n\n indices_in_chain_pair = np.column_stack((indices_in_chain[:-1], indices_in_chain[1:]))\n\n # must be adjacent on residue id\n valid_mask = np.abs(np.diff(u_res_id[indices_in_chain_pair], axis=1)) == 1\n\n indices_in_chain_pair = indices_in_chain_pair[valid_mask.flatten()]\n\n indices_in_pdb_pair = tmp_indices_in_pdb[indices_in_chain_pair]\n elif len(in_res_atom_names) > 1:\n\n def _cmp(a, b):\n # res_id compare\n if a[0] != b[0]:\n return a[0] - b[0]\n else:\n # atom_name in the same order of AA_ATOMS or NT_ATOMS\n return in_res_atom_names.index(a[1]) - in_res_atom_names.index(b[1])\n\n cache = list(zip(tmp_res_id_arr, tmp_atom_name_arr, tmp_indices_in_pdb))\n sorted_cache = list(sorted(cache, key=cmp_to_key(_cmp)))\n\n sorted_indices_in_pdb = [item[2] for item in sorted_cache]\n sorted_res_id = [item[0] for item in sorted_cache]\n\n indices_in_pdb_pair = np.column_stack((sorted_indices_in_pdb[:-1], sorted_indices_in_pdb[1:]))\n\n valid_mask = np.abs(np.diff(np.column_stack((sorted_res_id[:-1], sorted_res_id[1:])), axis=1)) <= 1\n\n indices_in_pdb_pair = indices_in_pdb_pair[valid_mask.flatten()]\n else:\n raise NotImplemented(\"No enough atoms to construct continuous pairs.\")\n\n pairs.append(indices_in_pdb_pair)\n\n pairs = np.vstack(pairs)\n return pairs"},{"identifier":"calc_dist_by_pair_indices","path":"cryostar/utils/dist_loss.py","snippet":"def calc_dist_by_pair_indices(coord_arr, pair_indices):\n coord_pair_arr = coord_arr[pair_indices] # num_pair, 2, 3\n dist = np.linalg.norm(np.diff(coord_pair_arr, axis=1), ord=2, axis=-1)\n return dist.flatten()"},{"identifier":"remove_duplicate_pairs","path":"cryostar/utils/dist_loss.py","snippet":"def remove_duplicate_pairs(pairs_a, pairs_b, remove_flip=True):\n \"\"\"Remove pair b from a\"\"\"\n s = max(pairs_a.max(), pairs_b.max()) + 1\n # trick for fast comparison\n mask = np.zeros((s, s), dtype=bool)\n\n np.put(mask, np.ravel_multi_index(pairs_a.T, mask.shape), True)\n np.put(mask, np.ravel_multi_index(pairs_b.T, mask.shape), False)\n if remove_flip:\n np.put(mask, np.ravel_multi_index(np.flip(pairs_b, 1).T, mask.shape), False)\n return np.column_stack(np.nonzero(mask))"},{"identifier":"filter_same_chain_pairs","path":"cryostar/utils/dist_loss.py","snippet":"def filter_same_chain_pairs(pair_ids, chain_id_arr):\n chain_ids = chain_id_arr[pair_ids]\n\n same_chain_mask = chain_ids[:, 0] == chain_ids[:, 1]\n\n pair_mask = []\n\n for u in np.unique(chain_ids):\n tmp = np.logical_and(chain_ids[:, 0] == u, same_chain_mask)\n if np.any(tmp):\n pair_mask.append(tmp)\n\n if len(pair_mask) > 0:\n return np.row_stack(pair_mask)\n else:\n return None"},{"identifier":"DistLoss","path":"cryostar/utils/dist_loss.py","snippet":"class DistLoss(nn.Module):\n\n def __init__(self, pair_ids, gt_dists, reduction=\"mean\"):\n super().__init__()\n self.reduction = reduction\n\n self.register_buffer(\"pair_ids\", torch.from_numpy(pair_ids).long())\n self.register_buffer(\"gt_dists\", torch.from_numpy(gt_dists).float())\n\n # edge-wise weights\n # raw_weights = torch.ones(len(pair_ids), dtype=torch.float) * 3.\n #\n # self.register_parameter(\"raw_weights\", nn.Parameter(raw_weights))\n\n # RBF residue-wise weights\n # u_left_ids = np.unique(pair_ids[:, 0])\n #\n # std_idx = np.zeros(max(u_left_ids) + 1, dtype=int)\n # sparse_idx = np.arange(len(u_left_ids))\n #\n # std_idx[u_left_ids] = sparse_idx\n #\n # select_index = std_idx[pair_ids[:, 0]]\n\n # weight = 0.9 at dist_rescale\n # sigmas = torch.ones(max(u_left_ids) + 1, dtype=torch.float) * np.sqrt(-0.5 / np.log(0.9))\n #\n # self.dist_rescale = dist_rescale\n # self.register_buffer(\"select_index\", torch.from_numpy(select_index).long())\n # self.register_parameter(\"sigmas\", nn.Parameter(sigmas))\n\n # def get_weights(self):\n # return torch.sigmoid(self.raw_weights)\n # edge_sigmas = torch.index_select(self.sigmas, dim=0, index=self.select_index)\n # weights = torch.exp(-torch.pow(self.gt_dists / self.dist_rescale, 2) / (2 * torch.pow(edge_sigmas, 2)))\n # return weights\n\n def calc_pair_dists(self, batch_struc):\n batch_dist = batch_struc[:, self.pair_ids] # bsz, num_pair, 2, 3\n batch_dist = LA.vector_norm(torch.diff(batch_dist, dim=-2), axis=-1).squeeze(-1) # bsz, num_pair\n return batch_dist\n\n def forward(self, batch_struc):\n batch_dist = self.calc_pair_dists(batch_struc)\n # mse = torch.pow(batch_dist - self.gt_dists.unsqueeze(0), 2) * self.get_weights().unsqueeze(0)\n mse = torch.pow(batch_dist - self.gt_dists.unsqueeze(0), 2)\n if self.reduction is None:\n return mse\n elif self.reduction == \"mean\":\n return torch.mean(mse)\n else:\n raise NotImplementedError"},{"identifier":"get_nearest_point","path":"cryostar/utils/latent_space_utils.py","snippet":"def get_nearest_point(data: np.ndarray, query: np.ndarray) -> Tuple[npt.NDArray[np.float32], np.ndarray]:\n \"\"\"\n Find closest point in @data to @query\n Return datapoint, index\n \"\"\"\n ind = cdist(query, data).argmin(axis=1)\n return data[ind], ind"},{"identifier":"cluster_kmeans","path":"cryostar/utils/latent_space_utils.py","snippet":"def cluster_kmeans(z: np.ndarray, K: int, on_data: bool = True, reorder: bool = True) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Cluster z by K means clustering\n Returns cluster labels, cluster centers\n If reorder=True, reorders clusters according to agglomerative clustering of cluster centers\n \"\"\"\n kmeans = KMeans(n_clusters=K, n_init=10, random_state=0, max_iter=10)\n labels = kmeans.fit_predict(z)\n centers = kmeans.cluster_centers_\n\n centers_ind = None\n if on_data:\n centers, centers_ind = get_nearest_point(z, centers)\n\n if reorder:\n # BUG from seaborn or scipy:\n # sns.clustermap only supports data with at least 2 dim\n if z.shape[1] == 1:\n centers = np.hstack([centers, np.zeros_like(centers)])\n g = sns.clustermap(centers)\n reordered = g.dendrogram_row.reordered_ind\n centers = centers[reordered]\n if centers_ind is not None:\n centers_ind = centers_ind[reordered]\n tmp = {k: i for i, k in enumerate(reordered)}\n labels = np.array([tmp[k] for k in labels])\n if z.shape[1] == 1:\n centers = centers[:, :1]\n return labels, centers"},{"identifier":"run_pca","path":"cryostar/utils/latent_space_utils.py","snippet":"def run_pca(z: np.ndarray) -> Tuple[np.ndarray, PCA]:\n pca = PCA(z.shape[1])\n pca.fit(z)\n # print(\"Explained variance ratio:\")\n # print(pca.explained_variance_ratio_)\n pc = pca.transform(z)\n return pc, pca"},{"identifier":"get_pc_traj","path":"cryostar/utils/latent_space_utils.py","snippet":"def get_pc_traj(\n pca: PCA,\n zdim: int,\n numpoints: int,\n dim: int,\n start: Optional[float] = 5,\n end: Optional[float] = 95,\n percentiles: Optional[np.ndarray] = None,\n) -> npt.NDArray[np.float32]:\n \"\"\"\n Create trajectory along specified principal component\n\n Inputs:\n pca: sklearn PCA object from run_pca\n zdim (int)\n numpoints (int): number of points between @start and @end\n dim (int): PC dimension for the trajectory (1-based index)\n start (float): Value of PC{dim} to start trajectory\n end (float): Value of PC{dim} to stop trajectory\n percentiles (np.array or None): Define percentile array instead of np.linspace(start,stop,numpoints)\n\n Returns:\n np.array (numpoints x zdim) of z values along PC\n \"\"\"\n if percentiles is not None:\n assert len(percentiles) == numpoints\n traj_pca = np.zeros((numpoints, zdim))\n if percentiles is not None:\n traj_pca[:, dim - 1] = percentiles\n else:\n assert start is not None\n assert end is not None\n traj_pca[:, dim - 1] = np.linspace(start, end, numpoints)\n ztraj_pca = pca.inverse_transform(traj_pca)\n return ztraj_pca"},{"identifier":"run_umap","path":"cryostar/utils/latent_space_utils.py","snippet":"def run_umap(z: np.ndarray, **kwargs) -> Tuple[np.ndarray, umap.UMAP]:\n reducer = umap.UMAP(**kwargs)\n z_embedded = reducer.fit_transform(z)\n return z_embedded, reducer"},{"identifier":"plot_z_dist","path":"cryostar/utils/vis_utils.py","snippet":"def plot_z_dist(z, extra_cluster=None, save_path=None):\n if z.shape[-1] == 1:\n fig = sns.displot(x=z[:, 0])\n fig.set_xlabels(\"z values\")\n if save_path is not None:\n fig.savefig(save_path)\n elif z.shape[-1] == 2:\n sns.set()\n fig = sns.jointplot(x=z[:, 0], y=z[:, 1], kind=\"kde\", fill=True)\n ax = fig.figure.axes\n if extra_cluster is not None:\n ax[0].scatter(extra_cluster[:, 0], extra_cluster[:, 1], marker='.', color='tab:orange')\n if save_path is not None:\n fig.savefig(save_path)\n else:\n raise ValueError(f\"input z with shape {z.shape}\")"},{"identifier":"save_tensor_image","path":"cryostar/utils/vis_utils.py","snippet":"def save_tensor_image(tensors, save_path, mask=None):\n # normalize\n max_val = torch.max(tensors.flatten(start_dim=1), 1)[0][:, None, None, None]\n min_val = torch.min(tensors.flatten(start_dim=1), 1)[0][:, None, None, None]\n tensors = (tensors - min_val) / (max_val - min_val)\n\n show_img = ToPILImage()(make_grid(tensors, nrow=5))\n if mask is None:\n show_img.save(save_path)\n else:\n show_img = np.copy(np.asarray(show_img))\n # show_img = cv2.cvtColor(show_img, cv2.COLOR_GRAY2RGB)\n if mask.ndim == 2:\n mask = mask[None]\n mask = ToPILImage()(make_grid(mask.expand(tensors.shape[0], -1, -1, -1), nrow=5))\n mask = np.invert(np.asarray(mask).astype(bool))[..., 0]\n color_mask = np.array([[0, 0, 0], [31, 119, 180]], dtype=np.uint8)\n color_mask = color_mask[mask.astype(int)]\n show_img[mask] = cv2.addWeighted(show_img[mask], 0.5, color_mask[mask], 0.5, 0)\n show_img = Image.fromarray(show_img)\n show_img.save(save_path)"},{"identifier":"merge_step_outputs","path":"cryostar/utils/pl_utils.py","snippet":"def merge_step_outputs(outputs):\n ks = outputs[0].keys()\n res = {}\n for k in ks:\n res[k] = torch.concat([out[k] for out in outputs], dim=0)\n return res"},{"identifier":"squeeze_dict_outputs_1st_dim","path":"cryostar/utils/pl_utils.py","snippet":"def squeeze_dict_outputs_1st_dim(outputs):\n res = {}\n for k in outputs.keys():\n res[k] = outputs[k].flatten(start_dim=0, end_dim=1)\n return res"},{"identifier":"filter_outputs_by_indices","path":"cryostar/utils/pl_utils.py","snippet":"def filter_outputs_by_indices(outputs, indices):\n res = {}\n for k in outputs.keys():\n res[k] = outputs[k][indices]\n return res"},{"identifier":"get_1st_unique_indices","path":"cryostar/utils/pl_utils.py","snippet":"def get_1st_unique_indices(t):\n _, idx, counts = torch.unique(t, dim=None, sorted=True, return_inverse=True, return_counts=True)\n # ind_sorted: the index corresponding to same unique value will be grouped by these indices\n _, ind_sorted = torch.sort(idx, stable=True)\n cum_sum = counts.cumsum(0)\n cum_sum = torch.cat((cum_sum.new_tensor([\n 0,\n ]), cum_sum[:-1]))\n first_idx = ind_sorted[cum_sum]\n return first_idx"}],"string":"[\n {\n \"identifier\": \"SpatialGridTranslate\",\n \"path\": \"cryostar/utils/transforms.py\",\n \"snippet\": \"class SpatialGridTranslate(torch.nn.Module):\\n\\n def __init__(self, D, device=None) -> None:\\n super().__init__()\\n self.D = D\\n # yapf: disable\\n coords = torch.stack(torch.meshgrid([\\n torch.linspace(-1.0, 1.0, self.D, device=device),\\n torch.linspace(-1.0, 1.0, self.D, device=device)],\\n indexing=\\\"ij\\\"), dim=-1).reshape(-1, 2)\\n # yapf: enable\\n self.register_buffer(\\\"coords\\\", coords)\\n\\n def transform(self, images: torch.Tensor, trans: torch.Tensor):\\n \\\"\\\"\\\"\\n The `images` are stored in `YX` mode, so the `trans` is also `YX`!\\n\\n Supposing that D is 96, a point is at 0.0:\\n - adding 48 should move it to the right corner which is 1.0\\n 1.0 = 0.0 + 48 / (96 / 2)\\n - adding 96(>48) should leave it at 0.0\\n 0.0 = 0.0 + 96 / (96 / 2) - 2.0\\n - adding -96(<48) should leave it at 0.0\\n 0.0 = 0.0 - 96 / (96 / 2) + 2.0\\n\\n Input:\\n images: (B, NY, NX)\\n trans: (B, T, 2)\\n\\n Returns:\\n images: (B, T, NY, NX)\\n \\\"\\\"\\\"\\n B, NY, NX = images.shape\\n assert self.D == NY == NX\\n assert images.shape[0] == trans.shape[0]\\n\\n grid = einops.rearrange(self.coords, \\\"N C2 -> 1 1 N C2\\\") - \\\\\\n einops.rearrange(trans, \\\"B T C2 -> B T 1 C2\\\") * 2 / self.D\\n grid = grid.flip(-1) # convert the first axis from slow-axis to fast-axis\\n grid[grid >= 1] -= 2\\n grid[grid <= -1] += 2\\n grid.clamp_(-1.0, 1.0)\\n\\n sampled = F.grid_sample(einops.rearrange(images, \\\"B NY NX -> B 1 NY NX\\\"), grid, align_corners=True)\\n\\n sampled = einops.rearrange(sampled, \\\"B 1 T (NY NX) -> B T NY NX\\\", NX=NX, NY=NY)\\n return sampled\"\n },\n {\n \"identifier\": \"StarfileDataSet\",\n \"path\": \"cryostar/utils/dataio.py\",\n \"snippet\": \"class StarfileDataSet(Dataset):\\n\\n def __init__(self, cfg: StarfileDatasetConfig):\\n super().__init__()\\n self.cfg = cfg\\n self.df = starfile.read(Path(cfg.starfile_path))\\n\\n if \\\"optics\\\" in self.df:\\n optics_df = self.df[\\\"optics\\\"]\\n particles_df = self.df[\\\"particles\\\"]\\n else:\\n optics_df = None\\n particles_df = self.df\\n self.particles_df = particles_df\\n\\n if cfg.apix is None:\\n if optics_df is not None and \\\"rlnImagePixelSize\\\" in optics_df:\\n self.apix = float(optics_df[\\\"rlnImagePixelSize\\\"][0])\\n print(f\\\"Infer dataset apix={self.apix} from first optic group.\\\")\\n elif \\\"rlnDetectorPixelSize\\\" in particles_df and \\\"rlnMagnification\\\" in particles_df:\\n self.apix = float(particles_df[\\\"rlnDetectorPixelSize\\\"][0] / particles_df[\\\"rlnMagnification\\\"][0] * 1e4)\\n print(f\\\"Infer dataset apix={self.apix} from first particle meta data.\\\")\\n else:\\n raise AttributeError(\\\"Cannot parse apix from starfile, please set it in config by hand.\\\")\\n else:\\n self.apix = cfg.apix\\n\\n if cfg.side_shape is None:\\n tmp_mrc_path = osp.join(cfg.dataset_dir, particles_df[\\\"rlnImageName\\\"][0].split('@')[-1])\\n with mrcfile.mmap(tmp_mrc_path, mode=\\\"r\\\", permissive=True) as m:\\n self.side_shape = m.data.shape[-1]\\n print(f\\\"Infer dataset side_shape={self.side_shape} from the 1st particle.\\\")\\n else:\\n self.side_shape = cfg.side_shape\\n\\n self.num_proj = len(particles_df)\\n\\n self.down_side_shape = self.side_shape\\n if cfg.down_side_shape is not None:\\n self.down_side_shape = cfg.down_side_shape\\n\\n if cfg.mask_rad is not None:\\n self.mask = Mask(self.down_side_shape, cfg.mask_rad)\\n\\n self.f_mu = None\\n self.f_std = None\\n\\n def __len__(self):\\n return self.num_proj\\n\\n def estimate_normalization(self):\\n if self.f_mu is None and self.f_std is None:\\n f_sub_data = []\\n # I have checked that the standard deviation of 10/100/1000 particles is similar\\n for i in range(0, len(self), len(self) // 100):\\n f_sub_data.append(self[i][\\\"fproj\\\"])\\n f_sub_data = torch.cat(f_sub_data, dim=0)\\n # self.f_mu = torch.mean(f_sub_data)\\n self.f_mu = 0.0 # just follow cryodrgn\\n self.f_std = torch.std(f_sub_data).item()\\n else:\\n raise Exception(\\\"The normalization factor has been estimated!\\\")\\n\\n def __getitem__(self, idx):\\n item_row = self.particles_df.iloc[idx]\\n try:\\n img_name_raw = item_row[\\\"rlnImageName\\\"]\\n in_mrc_idx, img_name = item_row[\\\"rlnImageName\\\"].split(\\\"@\\\")\\n in_mrc_idx = int(in_mrc_idx) - 1\\n mrc_path = osp.join(self.cfg.dataset_dir, img_name)\\n with mrcfile.mmap(mrc_path, mode=\\\"r\\\", permissive=True) as mrc:\\n if mrc.data.ndim > 2:\\n proj = torch.from_numpy(np.array(mrc.data[in_mrc_idx])).float() * self.cfg.scale_images\\n else:\\n # the mrcs file can contain only one particle\\n proj = torch.from_numpy(np.array(mrc.data)).float() * self.cfg.scale_images\\n\\n # get (1, side_shape, side_shape) proj\\n if len(proj.shape) == 2:\\n proj = proj[None, :, :] # add a dummy channel (for consistency w/ img fmt)\\n else:\\n assert len(proj.shape) == 3 and proj.shape[0] == 1 # some starfile already have a dummy channel\\n\\n # down-sample\\n if self.down_side_shape != self.side_shape:\\n if self.cfg.down_method == \\\"interp\\\":\\n proj = tvf.resize(proj, [self.down_side_shape, ] * 2, antialias=True)\\n elif self.cfg.down_method == \\\"fft\\\":\\n proj = downsample_2d(proj[0, :, :], self.down_side_shape)[None, :, :]\\n else:\\n raise NotImplementedError\\n\\n if self.cfg.mask_rad is not None:\\n proj = self.mask(proj)\\n\\n except Exception as e:\\n print(f\\\"WARNING: Particle image {img_name_raw} invalid! Setting to zeros.\\\")\\n print(e)\\n proj = torch.zeros(1, self.down_side_shape, self.down_side_shape)\\n\\n if self.cfg.power_images != 1.0:\\n proj *= self.cfg.power_images\\n\\n # Generate CTF from CTF paramaters\\n defocusU = torch.from_numpy(np.array(item_row[\\\"rlnDefocusU\\\"] / 1e4, ndmin=2)).float()\\n defocusV = torch.from_numpy(np.array(item_row[\\\"rlnDefocusV\\\"] / 1e4, ndmin=2)).float()\\n angleAstigmatism = torch.from_numpy(np.radians(np.array(item_row[\\\"rlnDefocusAngle\\\"], ndmin=2))).float()\\n\\n # Read \\\"GT\\\" orientations\\n if self.cfg.ignore_rots:\\n rotmat = torch.eye(3).float()\\n else:\\n # yapf: disable\\n rotmat = torch.from_numpy(euler_angles2matrix(\\n np.radians(-item_row[\\\"rlnAngleRot\\\"]),\\n # np.radians(particle[\\\"rlnAngleTilt\\\"]) * (-1 if self.cfg.invert_hand else 1),\\n np.radians(-item_row[\\\"rlnAngleTilt\\\"]),\\n np.radians(-item_row[\\\"rlnAnglePsi\\\"]))\\n ).float()\\n # yapf: enable\\n\\n # Read \\\"GT\\\" shifts\\n if self.cfg.ignore_trans:\\n shiftX = torch.tensor([0.])\\n shiftY = torch.tensor([0.])\\n else:\\n # support early starfile formats\\n # Particle translations used to be in pixels (rlnOriginX and rlnOriginY) but this changed to Angstroms\\n # (rlnOriginXAngstrom and rlnOriginYAngstrom) in relion 3.1.\\n # https://relion.readthedocs.io/en/release-3.1/Reference/Conventions.html\\n if \\\"rlnOriginXAngst\\\" in item_row:\\n shiftX = torch.from_numpy(np.array(item_row[\\\"rlnOriginXAngst\\\"], dtype=np.float32))\\n shiftY = torch.from_numpy(np.array(item_row[\\\"rlnOriginYAngst\\\"], dtype=np.float32))\\n else:\\n shiftX = torch.from_numpy(np.array(item_row[\\\"rlnOriginX\\\"] * self.apix, dtype=np.float32))\\n shiftY = torch.from_numpy(np.array(item_row[\\\"rlnOriginY\\\"] * self.apix, dtype=np.float32))\\n\\n fproj = primal_to_fourier_2d(proj)\\n\\n if self.f_mu is not None:\\n fproj = (fproj - self.f_mu) / self.f_std\\n proj = fourier_to_primal_2d(fproj).real\\n\\n in_dict = {\\n \\\"proj\\\": proj,\\n \\\"rotmat\\\": rotmat,\\n \\\"defocusU\\\": defocusU,\\n \\\"defocusV\\\": defocusV,\\n \\\"shiftX\\\": shiftX,\\n \\\"shiftY\\\": shiftY,\\n \\\"angleAstigmatism\\\": angleAstigmatism,\\n \\\"idx\\\": torch.tensor(idx, dtype=torch.long),\\n \\\"fproj\\\": fproj,\\n \\\"imgname_raw\\\": img_name_raw\\n }\\n\\n if \\\"rlnClassNumber\\\" in item_row:\\n in_dict[\\\"class_id\\\"] = item_row[\\\"rlnClassNumber\\\"]\\n\\n return in_dict\"\n },\n {\n \"identifier\": \"StarfileDatasetConfig\",\n \"path\": \"cryostar/utils/dataio.py\",\n \"snippet\": \"class StarfileDatasetConfig:\\n dataset_dir: str\\n starfile_path: str\\n # if is not specified, the following apix, and side_shape will be inferred from starfile\\n apix: float = None\\n side_shape: int = None\\n # down-sample the original image or not\\n down_side_shape: int = None\\n down_method: str = \\\"interp\\\"\\n # apply a circular mask on input image or not\\n mask_rad: float = None\\n # change image values\\n scale_images: float = 1.0\\n power_images: float = field(\\n default=1.0,\\n metadata={\\\"help\\\": \\\"Change the power of the signal by multiplying a constant number.\\\"})\\n # ignore pose from starfile or not\\n ignore_trans: bool = False\\n ignore_rots: bool = False\\n # invert_hand: bool = field(\\n # default=False,\\n # metadata={\\\"help\\\": \\\"Invert handedness when reading relion data.\\\"})\"\n },\n {\n \"identifier\": \"Mask\",\n \"path\": \"cryostar/utils/dataio.py\",\n \"snippet\": \"class Mask(torch.nn.Module):\\n\\n def __init__(self, im_size, rad):\\n super(Mask, self).__init__()\\n\\n mask = torch.lt(torch.linspace(-1, 1, im_size)[None]**2 + torch.linspace(-1, 1, im_size)[:, None]**2, rad**2)\\n # float for pl ddp broadcast compatible\\n self.register_buffer('mask', mask.float())\\n self.num_masked = torch.sum(mask).item()\\n\\n def forward(self, x):\\n return x * self.mask\"\n },\n {\n \"identifier\": \"CTFRelion\",\n \"path\": \"cryostar/utils/ctf_utils.py\",\n \"snippet\": \"class CTFRelion(CTFBase):\\n \\\"\\\"\\\"\\n BUG: There are two bugs in this file:\\n 1. `self.angleFrequency` has some error for even-sized grid.\\n 2. `local_defocus` in `get_ctf()` has some error, `angleAstigmatism` should be\\n replaced with `defocusU - defocusV`.\\n\\n The bugs will not affect real-world data too much. But you may encounter some issues\\n on simulated datasets. Use CTFCryoDRGN instead.\\n \\\"\\\"\\\"\\n\\n def __init__(self,\\n size=257,\\n resolution=0.8,\\n kV=300.0,\\n valueNyquist=1.,\\n defocusU=1.,\\n defocusV=1.,\\n angleAstigmatism=0.,\\n cs=2.7,\\n phasePlate=0.,\\n amplitudeContrast=.1,\\n bFactor=0.,\\n num_particles=500,\\n requires_grad=False,\\n precompute=False,\\n flip_images=False):\\n super(CTFRelion, self).__init__(resolution, num_particles, requires_grad)\\n self.requires_grad = requires_grad\\n self.flip_images = flip_images\\n\\n self.size = size # in pixel\\n self.resolution = resolution # in angstrom\\n self.kV = kV # in kilovolt\\n\\n self.valueNyquist = valueNyquist\\n self.phasePlate = phasePlate / 180. * np.pi # in radians (converted from degrees)\\n self.amplitudeContrast = amplitudeContrast\\n self.bFactor = bFactor\\n\\n self.frequency = 1. / self.resolution\\n\\n self.wavelength = self._get_ewavelength(self.kV * 1e3) # input in V (so we convert kv*1e3)\\n\\n angleAstigmatism = angleAstigmatism / 180. * np.pi # input in degree converted in radian\\n cs = cs * 1e7 # input in mm converted in angstrom\\n # the angleAstigmatism, defocusU, defocusV and cs are nn.Parameter of size (N, 1, 1)\\n self.angleAstigmatism = nn.Parameter(angleAstigmatism * torch.ones((num_particles, 1, 1), dtype=torch.float32),\\n requires_grad=requires_grad)\\n self.cs = nn.Parameter(cs * torch.ones((num_particles, 1, 1), dtype=torch.float32), requires_grad=requires_grad)\\n self.defocusU = nn.Parameter(defocusU * torch.ones((num_particles, 1, 1), dtype=torch.float32),\\n requires_grad=requires_grad)\\n self.defocusV = nn.Parameter(defocusV * torch.ones((num_particles, 1, 1), dtype=torch.float32),\\n requires_grad=requires_grad)\\n\\n self.precomputed_filters = precompute\\n\\n ax = torch.linspace(-1. / (2. * resolution), 1 / (2. * resolution), self.size)\\n mx, my = torch.meshgrid(ax, ax, indexing=\\\"ij\\\")\\n self.register_buffer(\\\"r2\\\", mx**2 + my**2)\\n self.register_buffer(\\\"r\\\", torch.sqrt(self.r2))\\n self.register_buffer(\\\"angleFrequency\\\", torch.atan2(my, mx))\\n\\n if not self.requires_grad and self.precomputed_filters:\\n print(\\\"Precomputing hFourier in CTF\\\")\\n self.register_buffer('hFourier', self.get_ctf(torch.arange(num_particles), num_particles))\\n\\n def _get_ewavelength(self, U):\\n # assumes V as input, returns wavelength in angstrom\\n h = scipy.constants.h\\n e = scipy.constants.e\\n c = scipy.constants.c\\n m0 = scipy.constants.m_e\\n\\n return h / math.sqrt(2. * m0 * e * U) / math.sqrt(1 + e * U / (2 * m0 * c**2)) * 1e10\\n\\n def get_ctf(self, idcs, B, cpu_params={}, frequency_marcher=None):\\n defocusU = self.defocusU[idcs, :, :]\\n defocusV = self.defocusV[idcs, :, :]\\n angleAstigmatism = self.angleAstigmatism[idcs, :, :]\\n cs = self.cs[idcs, :, :]\\n\\n ac = self.amplitudeContrast\\n pc = math.sqrt(1. - ac**2)\\n K1 = np.pi / 2. * cs * self.wavelength**3\\n K2 = np.pi * self.wavelength\\n\\n # Cut-off from frequency marcher\\n if frequency_marcher is not None:\\n self.size_after_fm = 2 * frequency_marcher.f + 1\\n if self.size_after_fm > self.size:\\n self.size_after_fm = self.size\\n angleFrequency = frequency_marcher.cut_coords_plane(self.angleFrequency.reshape(\\n self.size, self.size, 1)).reshape(self.size_after_fm, self.size_after_fm)\\n r2 = frequency_marcher.cut_coords_plane(self.r2.reshape(self.size, self.size,\\n 1)).reshape(self.size_after_fm, self.size_after_fm)\\n else:\\n self.size_after_fm = self.size\\n angleFrequency = self.angleFrequency\\n r2 = self.r2\\n\\n angle = angleFrequency - angleAstigmatism\\n local_defocus = 1e4 * (defocusU + defocusV) / 2. + angleAstigmatism * torch.cos(2. * angle)\\n\\n gamma = K1 * r2**2 - K2 * r2 * local_defocus - self.phasePlate\\n hFourier = -pc * torch.sin(gamma) + ac * torch.cos(gamma)\\n\\n if self.valueNyquist != 1:\\n decay = np.sqrt(-np.log(self.valueNyquist)) * 2. * self.resolution\\n envelope = torch.exp(-self.frequency * decay**2 * r2)\\n hFourier *= envelope\\n\\n return hFourier\\n\\n def oversample_multiply_crop(self, x_fourier, hFourier):\\n # we assume that the shape of the CTF is always going to be bigger\\n # than the size of the input image\\n input_sz = x_fourier.shape[-1]\\n if input_sz != self.size_after_fm:\\n x_primal = fourier_to_primal_2d(x_fourier)\\n\\n pad_len = (self.size_after_fm - x_fourier.shape[-1]) // 2 # here we assume even lengths\\n p2d = (pad_len, pad_len, pad_len, pad_len)\\n x_primal_padded = F.pad(x_primal, p2d, 'constant', 0)\\n\\n x_fourier_padded = primal_to_fourier_2d(x_primal_padded)\\n\\n x_fourier_padded_filtered = x_fourier_padded * hFourier[:, None, :, :]\\n return x_fourier_padded_filtered[..., pad_len:-pad_len, pad_len:-pad_len]\\n else:\\n return x_fourier * hFourier[:, None, :, :]\\n\\n def get_cpu_params(self, idcs, ctf_params, flip=False):\\n batch_size = idcs.shape[0]\\n self.defocusU[idcs, :, :] = ctf_params['defocusU'][:batch_size] if not flip else\\\\\\n ctf_params['defocusU'][batch_size:]\\n self.defocusV[idcs, :, :] = ctf_params['defocusV'][:batch_size] if not flip else\\\\\\n ctf_params['defocusV'][batch_size:]\\n self.angleAstigmatism[idcs, :, :] = ctf_params['angleAstigmatism'][:batch_size] if not flip else\\\\\\n ctf_params['angleAstigmatism'][batch_size:]\\n cpu_params = {}\\n return cpu_params\\n\\n def forward(self, x_fourier, idcs=0, ctf_params={}, mode='gt', frequency_marcher=None):\\n # This is when we want to prescribe parameters for the CTF\\n if x_fourier.dim() == 3:\\n x_fourier = x_fourier[None, ...]\\n # x_fourier: B, 1, S, S\\n batch_size = len(idcs)\\n cpu_params = {}\\n if ctf_params:\\n cpu_params = self.get_cpu_params(idcs, ctf_params, flip=False)\\n\\n # if new params for the CTF have been prescribed or we are optimizing it\\n # then request the evaluation of the CTF\\n if not ctf_params and self.precomputed_filters and not self.requires_grad:\\n hFourier = self.hFourier[idcs, :, :]\\n else:\\n hFourier = self.get_ctf(idcs, batch_size, cpu_params=cpu_params, frequency_marcher=frequency_marcher)\\n\\n if self.flip_images:\\n flipped_hFourier = torch.flip(hFourier, [1, 2])\\n\\n hFourier = torch.cat([hFourier, flipped_hFourier], dim=0)\\n\\n return self.oversample_multiply_crop(x_fourier, hFourier)\"\n },\n {\n \"identifier\": \"CTFCryoDRGN\",\n \"path\": \"cryostar/utils/ctf_utils.py\",\n \"snippet\": \"class CTFCryoDRGN(CTFBase):\\n\\n def __init__(self,\\n size,\\n resolution,\\n num_particles=None,\\n kV=300,\\n cs=2.0,\\n amplitudeContrast=0.1,\\n requires_grad=False):\\n super(CTFBase, self).__init__()\\n self.size = size\\n self.resolution = resolution\\n self.requires_grad = requires_grad\\n self.kV = kV\\n self.cs = cs\\n self.ac = amplitudeContrast\\n # ax = torch.linspace(-1. / (2. * resolution), 1 / (2. * resolution), self.size)\\n # mx, my = torch.meshgrid(ax, ax, indexing=\\\"ij\\\")\\n ax = torch.fft.fftshift(torch.fft.fftfreq(self.size, self.resolution))\\n mx, my = torch.meshgrid(ax, ax, indexing=\\\"xy\\\")\\n freqs = torch.stack([mx.flatten(), my.flatten()], 1)\\n self.register_buffer(\\\"freqs\\\", freqs)\\n\\n def get_ctf(self, ctf_params={}):\\n bsz = len(ctf_params[\\\"defocusU\\\"])\\n device = self.freqs.device\\n hFourier = compute_ctf(freqs=self.freqs.repeat(bsz, 1, 1),\\n dfu=(ctf_params[\\\"defocusU\\\"] * 1e4).squeeze(1),\\n dfv=(ctf_params[\\\"defocusV\\\"] * 1e4).squeeze(1),\\n dfang=torch.rad2deg(ctf_params[\\\"angleAstigmatism\\\"]).squeeze(1),\\n volt=torch.tensor(self.kV, device=device).repeat(bsz, 1),\\n cs=torch.tensor(self.cs, device=device).repeat(bsz, 1),\\n w=torch.tensor(self.ac, device=device).repeat(bsz,\\n 1)).reshape(bsz, self.size, self.size)\\n return hFourier\\n\\n def forward(self, x_fourier, idcs=0, ctf_params={}, mode='gt', frequency_marcher=None):\\n hFourier = -self.get_ctf(ctf_params)\\n return x_fourier * hFourier[:, None, :, :]\"\n },\n {\n \"identifier\": \"calc_cor_loss\",\n \"path\": \"cryostar/utils/losses.py\",\n \"snippet\": \"def calc_cor_loss(pred_images, gt_images, mask=None):\\n if mask is not None:\\n pred_images = mask(pred_images)\\n gt_images = mask(gt_images)\\n pixel_num = mask.num_masked\\n else:\\n pixel_num = pred_images.shape[-2] * pred_images.shape[-1]\\n\\n # b, c, h, w -> b, c, num_pix\\n pred_images = pred_images.flatten(start_dim=2)\\n gt_images = gt_images.flatten(start_dim=2)\\n\\n # b, c\\n dots = (pred_images * gt_images).sum(-1)\\n # b, c -> b, c\\n err = -dots / (gt_images.std(-1) + 1e-5) / (pred_images.std(-1) + 1e-5)\\n # b, c -> b -> 1 value\\n err = err.sum(-1).mean() / pixel_num\\n return err\"\n },\n {\n \"identifier\": \"calc_kl_loss\",\n \"path\": \"cryostar/utils/losses.py\",\n \"snippet\": \"def calc_kl_loss(mu, log_var, free_bits, reduction=\\\"mean\\\"):\\n kld_loss = -0.5 * (1 + log_var - mu.pow(2) - log_var.exp())\\n # free bits\\n kld_loss = torch.clamp(kld_loss, free_bits) # (bsz, z-dim)\\n kld_loss = torch.mean(kld_loss, dim=1) # (bsz, )\\n if reduction == \\\"mean\\\":\\n kld_loss = torch.mean(kld_loss) # averaged over bsz x z-dim\\n elif reduction == \\\"none\\\":\\n kld_loss = kld_loss\\n else:\\n raise NotImplementedError\\n return kld_loss\"\n },\n {\n \"identifier\": \"log_to_current\",\n \"path\": \"cryostar/utils/misc.py\",\n \"snippet\": \"def set_seed(seed: int = 42):\\ndef chain(arg, *funcs):\\ndef convert_to_numpy(*args):\\ndef CHECK_SHAPE(tensor, expected_shape):\\ndef ASSERT_SHAPE(tensor, expected_shape):\\ndef parse_mmengine_args(override_mode=\\\"default\\\"):\\ndef flatten_nested_dict(nested: Union[dict, Config]) -> dict:\\ndef warmup(warmup_step, lower=0.0, upper=1.0):\\n def run(cur_step):\\ndef init_mmengine_config(args):\\ndef init_mmengine_exp(args,\\n exp_prefix='',\\n backup_list=None,\\n inplace=True,\\n work_dir_name=\\\"work_dirs\\\",\\n project_name=\\\"cryostar\\\",\\n tensorboard=False):\\ndef _get_next_version(root_dir, dir_name_prefix):\\ndef pl_init_exp(override_mode=\\\"default\\\",\\n exp_prefix='',\\n backup_list=None,\\n inplace=False,\\n work_dir_name=\\\"work_dirs\\\",\\n project_name=\\\"cryostar\\\"):\\ndef save_pdb(CAs, path, ref_pdb_path):\\ndef load_CAs_from_pdb(file):\\ndef load_NCaC_from_pdb(file):\\ndef load_chain_A(pdb_path):\\ndef points_to_pdb(path_to_save, points: np.ndarray):\\ndef point_stack_to_pdb(path_to_save, point_stack: np.ndarray):\\ndef find_rigid_alignment(A, B):\\ndef batch_find_rigid_alignment(A, B):\\ndef pretty_dict(x, precision=3):\\ndef create_sphere_mask(d, h, w, center=None, radius=None) -> np.ndarray:\\ndef create_circular_mask(h, w, center=None, radius=None) -> np.ndarray:\\n H = A_c.T.mm(B_c)\\n U, S, V = torch.svd(H)\\n R = V.mm(U.T)\\n H = einops.einsum(A_c, B_c, \\\"b n c1, b n c2 -> b c1 c2\\\")\\n V = VmT.mT\\n R = einops.einsum(V, U.transpose(2, 1), \\\"b c1 c2, b c2 c3 -> b c1 c3\\\")\"\n },\n {\n \"identifier\": \"bt_save_pdb\",\n \"path\": \"cryostar/utils/pdb_tools.py\",\n \"snippet\": \"def bt_save_pdb(file_path: Union[str, Path], array: Union[AtomArray, AtomArrayStack], **kwargs):\\n \\\"\\\"\\\"Save biotite AtomArray or AtomArrayStack to pdb file\\n\\n Parameters\\n ----------\\n file_path: save file path\\n array: the structure to be saved\\n kwargs: additional parameters to be passed, always empty\\n\\n \\\"\\\"\\\"\\n bt_struc.io.save_structure(file_path, array, **kwargs)\"\n },\n {\n \"identifier\": \"EMAN2Grid\",\n \"path\": \"cryostar/gmm/gmm.py\",\n \"snippet\": \"class EMAN2Grid(BaseGrid):\\n \\\"\\\"\\\"EMAN2 style grid.\\n origin set to -(side_shape // 2) * voxel_size\\n\\n \\\"\\\"\\\"\\n\\n def __init__(self, side_shape, voxel_size):\\n origin = -side_shape // 2 * voxel_size\\n super().__init__(side_shape=side_shape, voxel_size=voxel_size, origin=origin)\"\n },\n {\n \"identifier\": \"batch_projection\",\n \"path\": \"cryostar/gmm/gmm.py\",\n \"snippet\": \"def batch_projection(gauss: Gaussian, rot_mats: torch.Tensor, line_grid: Grid) -> torch.Tensor:\\n \\\"\\\"\\\"A quick version of e2gmm projection.\\n\\n Parameters\\n ----------\\n gauss: (b/1, num_centers, 3) mus, (b/1, num_centers) sigmas and amplitudes\\n rot_mats: (b, 3, 3)\\n line_grid: (num_pixels, 3) coords, (nx, ) shape\\n\\n Returns\\n -------\\n proj: (b, y, x) projections\\n \\\"\\\"\\\"\\n\\n centers = einops.einsum(rot_mats, gauss.mus, \\\"b c31 c32, b nc c32 -> b nc c31\\\")\\n\\n sigmas = einops.rearrange(gauss.sigmas, 'b nc -> b 1 nc')\\n sigmas = 2 * sigmas**2\\n\\n proj_x = einops.rearrange(line_grid.coords, \\\"nx -> 1 nx 1\\\") - einops.rearrange(centers[..., 0], \\\"b nc -> b 1 nc\\\")\\n proj_x = torch.exp(-proj_x**2 / sigmas)\\n\\n proj_y = einops.rearrange(line_grid.coords, \\\"ny -> 1 ny 1\\\") - einops.rearrange(centers[..., 1], \\\"b nc -> b 1 nc\\\")\\n proj_y = torch.exp(-proj_y**2 / sigmas)\\n\\n proj = einops.einsum(gauss.amplitudes, proj_x, proj_y, \\\"b nc, b nx nc, b ny nc -> b nx ny\\\")\\n proj = einops.rearrange(proj, \\\"b nx ny -> b ny nx\\\")\\n return proj\"\n },\n {\n \"identifier\": \"Gaussian\",\n \"path\": \"cryostar/gmm/gmm.py\",\n \"snippet\": \"class Gaussian:\\n mus: Union[torch.Tensor, np.ndarray]\\n sigmas: Union[torch.Tensor, np.ndarray]\\n amplitudes: Union[torch.Tensor, np.ndarray]\"\n },\n {\n \"identifier\": \"E3Deformer\",\n \"path\": \"cryostar/gmm/deformer.py\",\n \"snippet\": \"class E3Deformer(torch.nn.Module, DeformerProtocol):\\n\\n def transform(self, deformation, coords):\\n ASSERT_SHAPE(coords, (None, 3))\\n ASSERT_SHAPE(deformation, (None, coords.shape[0] * 3))\\n\\n bsz = deformation.shape[0]\\n shift = deformation.reshape(bsz, -1, 3)\\n return shift + coords\"\n },\n {\n \"identifier\": \"NMADeformer\",\n \"path\": \"cryostar/gmm/deformer.py\",\n \"snippet\": \"class NMADeformer(torch.nn.Module, DeformerProtocol):\\n def __init__(self, modes: torch.FloatTensor) -> None:\\n super().__init__()\\n modes = einops.rearrange(\\n modes, \\\"(num_coords c3) num_modes -> num_modes num_coords c3\\\", c3=3\\n )\\n self.register_buffer(\\\"modes\\\", modes)\\n self.num_modes = modes.shape[0]\\n self.num_coords = modes.shape[1]\\n\\n def transform(self, deformation, coords):\\n ASSERT_SHAPE(coords, (self.num_coords, 3))\\n ASSERT_SHAPE(deformation, (None, 6 + self.num_modes))\\n\\n axis_angle = deformation[..., :3]\\n translation = deformation[..., 3:6] * 10\\n nma_coeff = deformation[..., 6:]\\n rotation_matrix = axis_angle_to_matrix(axis_angle)\\n\\n nma_deform_e3 = einops.einsum(\\n nma_coeff, self.modes, \\\"bsz num_modes, num_modes num_coords c3 -> bsz num_coords c3\\\"\\n )\\n rotated_coords = einops.einsum(rotation_matrix, nma_deform_e3 + coords,\\n \\\"bsz c31 c32, bsz num_coords c31 -> bsz num_coords c32\\\")\\n deformed_coords = rotated_coords + einops.rearrange(translation, \\\"bsz c3 -> bsz 1 c3\\\")\\n return deformed_coords\"\n },\n {\n \"identifier\": \"primal_to_fourier_2d\",\n \"path\": \"cryostar/utils/fft_utils.py\",\n \"snippet\": \"@torch.autocast(\\\"cuda\\\")\\ndef primal_to_fourier_2d(r: torch.Tensor) -> torch.Tensor:\\n with torch.autocast(\\\"cuda\\\", enabled=False):\\n r = torch.fft.ifftshift(r.float(), dim=(-2, -1))\\n f = torch.fft.fftshift(torch.fft.fftn(r, s=(r.shape[-2], r.shape[-1]), dim=(-2, -1)), dim=(-2, -1))\\n return f\"\n },\n {\n \"identifier\": \"fourier_to_primal_2d\",\n \"path\": \"cryostar/utils/fft_utils.py\",\n \"snippet\": \"def fourier_to_primal_2d(f: torch.Tensor) -> torch.Tensor:\\n f = torch.fft.ifftshift(f, dim=(-2, -1))\\n return torch.fft.fftshift(torch.fft.ifftn(f, s=(f.shape[-2], f.shape[-1]), dim=(-2, -1)), dim=(-2, -1))\"\n },\n {\n \"identifier\": \"Polymer\",\n \"path\": \"cryostar/utils/polymer.py\",\n \"snippet\": \"class Polymer:\\n chain_id: np.ndarray\\n res_id: np.ndarray\\n res_name: np.ndarray\\n coord: np.ndarray\\n atom_name: np.ndarray\\n element: np.ndarray\\n num_electron: np.ndarray\\n\\n def __init__(self, num):\\n self.chain_id = np.empty(num, dtype=\\\"U4\\\")\\n self.res_id = np.zeros(num, dtype=int)\\n self.res_name = np.empty(num, dtype=\\\"U3\\\")\\n self.coord = np.zeros((num, 3), dtype=np.float32)\\n self.atom_name = np.empty(num, dtype=\\\"U6\\\")\\n self.element = np.empty(num, dtype=\\\"U2\\\")\\n self.num_electron = np.zeros(num, dtype=int)\\n\\n def __setitem__(self, index, kwargs):\\n assert set(kwargs.keys()).issubset(f.name for f in dataclasses.fields(self))\\n for k, v in kwargs.items():\\n getattr(self, k)[index] = v\\n\\n def __getitem__(self, index):\\n return {f.name: getattr(self, f.name)[index] for f in dataclasses.fields(self)}\\n\\n def __len__(self):\\n return len(self.chain_id)\\n\\n @property\\n def num_amino_acids(self):\\n return np.sum(np.isin(self.atom_name, AA_ATOMS))\\n\\n @property\\n def num_nucleotides(self):\\n return np.sum(np.isin(self.atom_name, NT_ATOMS))\\n\\n @property\\n def num_chains(self):\\n return len(np.unique(self.chain_id))\\n\\n @classmethod\\n def from_atom_arr(cls, atom_arr):\\n assert isinstance(atom_arr, struc.AtomArray)\\n\\n nt_arr = atom_arr[struc.filter_nucleotides(atom_arr)]\\n aa_arr = atom_arr[struc.filter_amino_acids(atom_arr)]\\n\\n num = 0\\n if len(aa_arr) > 0:\\n num += struc.get_residue_count(aa_arr)\\n if len(nt_arr) > 0:\\n for res in struc.residue_iter(nt_arr):\\n valid_atoms = set(res.atom_name).intersection(NT_ATOMS)\\n if len(valid_atoms) <= 0:\\n raise UserWarning(f\\\"Nucleotides doesn't contain {' or '.join(NT_ATOMS)}.\\\")\\n else:\\n num += len(valid_atoms)\\n meta = cls(num)\\n\\n def _update_res(tmp_res, kind=\\\"aa\\\"):\\n nonlocal pos\\n\\n if kind == \\\"aa\\\":\\n using_atom_names = AA_ATOMS\\n filtered_res = tmp_res[struc.filter_peptide_backbone(tmp_res)]\\n elif kind == \\\"nt\\\":\\n using_atom_names = NT_ATOMS\\n filtered_res = tmp_res\\n else:\\n raise NotImplemented\\n\\n valid_atom_names = set(tmp_res.atom_name).intersection(using_atom_names)\\n\\n for select_atom_name in valid_atom_names:\\n meta[pos] = {\\n \\\"chain_id\\\": tmp_res.chain_id[0],\\n \\\"res_id\\\": tmp_res.res_id[0],\\n \\\"res_name\\\": tmp_res.res_name[0],\\n \\\"coord\\\": filtered_res[filtered_res.atom_name == select_atom_name].coord,\\n \\\"atom_name\\\": select_atom_name,\\n \\\"element\\\": filtered_res[filtered_res.atom_name == select_atom_name].element[0],\\n \\\"num_electron\\\": get_num_electrons(tmp_res) // len(valid_atom_names)\\n }\\n pos += 1\\n\\n def _update(tmp_arr, kind=\\\"aa\\\"):\\n nonlocal pos\\n for chain in struc.chain_iter(tmp_arr):\\n for tmp_res in struc.residue_iter(chain):\\n _update_res(tmp_res, kind)\\n\\n pos = 0\\n\\n if len(aa_arr) > 0:\\n _update(aa_arr, kind=\\\"aa\\\")\\n if len(nt_arr) > 0:\\n _update(nt_arr, kind=\\\"nt\\\")\\n\\n assert pos == num\\n return meta\\n\\n @classmethod\\n def from_pdb(cls, file_path):\\n atom_arr = bt_read_pdb(file_path)\\n if atom_arr.stack_depth() > 1:\\n print(\\\"PDB file contains more than 1 models, select the 1st model\\\")\\n atom_arr = atom_arr[0]\\n return Polymer.from_atom_arr(atom_arr)\\n\\n def to_atom_arr(self):\\n num = len(self)\\n atom_arr = struc.AtomArray(num)\\n atom_arr.coord = self.coord\\n\\n for f in dataclasses.fields(self):\\n if f.name != \\\"coord\\\" and f.name in atom_arr.get_annotation_categories():\\n atom_arr.set_annotation(f.name, getattr(self, f.name))\\n # atom_arr.atom_name[atom_arr.atom_name == \\\"R\\\"] = \\\"CB\\\"\\n return atom_arr\"\n },\n {\n \"identifier\": \"NT_ATOMS\",\n \"path\": \"cryostar/utils/polymer.py\",\n \"snippet\": \"NT_ATOMS = (\\\"C1'\\\", )\"\n },\n {\n \"identifier\": \"AA_ATOMS\",\n \"path\": \"cryostar/utils/polymer.py\",\n \"snippet\": \"AA_ATOMS = (\\\"CA\\\", )\"\n },\n {\n \"identifier\": \"find_quaint_cutoff_pairs\",\n \"path\": \"cryostar/utils/dist_loss.py\",\n \"snippet\": \"def find_quaint_cutoff_pairs(coord_arr,\\n chain_id_arr,\\n res_id_arr,\\n intra_chain_cutoff=12.,\\n inter_chain_cutoff=12.,\\n intra_chain_res_bound=None):\\n sel_indices = []\\n dist_map = distance.cdist(coord_arr, coord_arr, metric='euclidean')\\n # 1. intra chain\\n sel_mask = dist_map <= intra_chain_cutoff\\n sel_mask = np.triu(sel_mask, k=1)\\n # get indices of valid pairs\\n indices_in_pdb = np.nonzero(sel_mask)\\n indices_in_pdb = np.column_stack((indices_in_pdb[0], indices_in_pdb[1]))\\n indices_in_pdb = indices_in_pdb[chain_id_arr[indices_in_pdb[:, 0]] == chain_id_arr[indices_in_pdb[:, 1]]]\\n # filter by res_id\\n if intra_chain_res_bound is not None:\\n assert res_id_arr is not None\\n res_ids = res_id_arr[indices_in_pdb]\\n res_id_dist = np.abs(np.diff(res_ids, axis=1)).flatten()\\n indices_in_pdb = indices_in_pdb[res_id_dist <= intra_chain_res_bound]\\n\\n sel_indices.append(indices_in_pdb)\\n\\n # 2. inter chain\\n if inter_chain_cutoff is not None:\\n sel_mask = dist_map <= inter_chain_cutoff\\n sel_mask = np.triu(sel_mask, k=1)\\n indices_in_pdb = np.nonzero(sel_mask)\\n indices_in_pdb = np.column_stack((indices_in_pdb[0], indices_in_pdb[1]))\\n indices_in_pdb = indices_in_pdb[chain_id_arr[indices_in_pdb[:, 0]] != chain_id_arr[indices_in_pdb[:, 1]]]\\n sel_indices.append(indices_in_pdb)\\n\\n sel_indices = np.vstack(sel_indices)\\n return sel_indices\"\n },\n {\n \"identifier\": \"find_range_cutoff_pairs\",\n \"path\": \"cryostar/utils/dist_loss.py\",\n \"snippet\": \"def find_range_cutoff_pairs(coord_arr, min_cutoff=4., max_cutoff=10.):\\n dist_map = distance.cdist(coord_arr, coord_arr, metric='euclidean')\\n sel_mask = (dist_map <= max_cutoff) & (dist_map >= min_cutoff)\\n indices_in_pdb = np.nonzero(sel_mask)\\n indices_in_pdb = np.column_stack((indices_in_pdb[0], indices_in_pdb[1]))\\n return indices_in_pdb\"\n },\n {\n \"identifier\": \"find_continuous_pairs\",\n \"path\": \"cryostar/utils/dist_loss.py\",\n \"snippet\": \"def find_continuous_pairs(chain_id_arr, res_id_arr, atom_name_arr):\\n pairs = []\\n\\n # res_id in different chains are duplicated, so loop on chains\\n u_chain_id = np.unique(chain_id_arr)\\n\\n for c_id in u_chain_id:\\n tmp_mask = chain_id_arr == c_id\\n tmp_indices_in_pdb = np.nonzero(tmp_mask)[0]\\n\\n tmp_res_id_arr = res_id_arr[tmp_mask]\\n tmp_atom_name_arr = atom_name_arr[tmp_mask]\\n\\n # check is aa or nt\\n tmp_atom_name_set = set(tmp_atom_name_arr)\\n\\n if len(tmp_atom_name_set.intersection(AA_ATOMS)) > len(tmp_atom_name_set.intersection(NT_ATOMS)):\\n in_res_atom_names = AA_ATOMS\\n elif len(tmp_atom_name_set.intersection(AA_ATOMS)) < len(tmp_atom_name_set.intersection(NT_ATOMS)):\\n in_res_atom_names = NT_ATOMS\\n else:\\n raise NotImplemented(\\\"Cannot determine chain is amino acid or nucleotide.\\\")\\n\\n # find pairs\\n if len(in_res_atom_names) == 1:\\n u_res_id, indices_in_chain = np.unique(tmp_res_id_arr, return_index=True)\\n if len(u_res_id) != np.sum(tmp_mask):\\n raise ValueError(f\\\"Found duplicate residue id in single chain {c_id}.\\\")\\n\\n indices_in_chain_pair = np.column_stack((indices_in_chain[:-1], indices_in_chain[1:]))\\n\\n # must be adjacent on residue id\\n valid_mask = np.abs(np.diff(u_res_id[indices_in_chain_pair], axis=1)) == 1\\n\\n indices_in_chain_pair = indices_in_chain_pair[valid_mask.flatten()]\\n\\n indices_in_pdb_pair = tmp_indices_in_pdb[indices_in_chain_pair]\\n elif len(in_res_atom_names) > 1:\\n\\n def _cmp(a, b):\\n # res_id compare\\n if a[0] != b[0]:\\n return a[0] - b[0]\\n else:\\n # atom_name in the same order of AA_ATOMS or NT_ATOMS\\n return in_res_atom_names.index(a[1]) - in_res_atom_names.index(b[1])\\n\\n cache = list(zip(tmp_res_id_arr, tmp_atom_name_arr, tmp_indices_in_pdb))\\n sorted_cache = list(sorted(cache, key=cmp_to_key(_cmp)))\\n\\n sorted_indices_in_pdb = [item[2] for item in sorted_cache]\\n sorted_res_id = [item[0] for item in sorted_cache]\\n\\n indices_in_pdb_pair = np.column_stack((sorted_indices_in_pdb[:-1], sorted_indices_in_pdb[1:]))\\n\\n valid_mask = np.abs(np.diff(np.column_stack((sorted_res_id[:-1], sorted_res_id[1:])), axis=1)) <= 1\\n\\n indices_in_pdb_pair = indices_in_pdb_pair[valid_mask.flatten()]\\n else:\\n raise NotImplemented(\\\"No enough atoms to construct continuous pairs.\\\")\\n\\n pairs.append(indices_in_pdb_pair)\\n\\n pairs = np.vstack(pairs)\\n return pairs\"\n },\n {\n \"identifier\": \"calc_dist_by_pair_indices\",\n \"path\": \"cryostar/utils/dist_loss.py\",\n \"snippet\": \"def calc_dist_by_pair_indices(coord_arr, pair_indices):\\n coord_pair_arr = coord_arr[pair_indices] # num_pair, 2, 3\\n dist = np.linalg.norm(np.diff(coord_pair_arr, axis=1), ord=2, axis=-1)\\n return dist.flatten()\"\n },\n {\n \"identifier\": \"remove_duplicate_pairs\",\n \"path\": \"cryostar/utils/dist_loss.py\",\n \"snippet\": \"def remove_duplicate_pairs(pairs_a, pairs_b, remove_flip=True):\\n \\\"\\\"\\\"Remove pair b from a\\\"\\\"\\\"\\n s = max(pairs_a.max(), pairs_b.max()) + 1\\n # trick for fast comparison\\n mask = np.zeros((s, s), dtype=bool)\\n\\n np.put(mask, np.ravel_multi_index(pairs_a.T, mask.shape), True)\\n np.put(mask, np.ravel_multi_index(pairs_b.T, mask.shape), False)\\n if remove_flip:\\n np.put(mask, np.ravel_multi_index(np.flip(pairs_b, 1).T, mask.shape), False)\\n return np.column_stack(np.nonzero(mask))\"\n },\n {\n \"identifier\": \"filter_same_chain_pairs\",\n \"path\": \"cryostar/utils/dist_loss.py\",\n \"snippet\": \"def filter_same_chain_pairs(pair_ids, chain_id_arr):\\n chain_ids = chain_id_arr[pair_ids]\\n\\n same_chain_mask = chain_ids[:, 0] == chain_ids[:, 1]\\n\\n pair_mask = []\\n\\n for u in np.unique(chain_ids):\\n tmp = np.logical_and(chain_ids[:, 0] == u, same_chain_mask)\\n if np.any(tmp):\\n pair_mask.append(tmp)\\n\\n if len(pair_mask) > 0:\\n return np.row_stack(pair_mask)\\n else:\\n return None\"\n },\n {\n \"identifier\": \"DistLoss\",\n \"path\": \"cryostar/utils/dist_loss.py\",\n \"snippet\": \"class DistLoss(nn.Module):\\n\\n def __init__(self, pair_ids, gt_dists, reduction=\\\"mean\\\"):\\n super().__init__()\\n self.reduction = reduction\\n\\n self.register_buffer(\\\"pair_ids\\\", torch.from_numpy(pair_ids).long())\\n self.register_buffer(\\\"gt_dists\\\", torch.from_numpy(gt_dists).float())\\n\\n # edge-wise weights\\n # raw_weights = torch.ones(len(pair_ids), dtype=torch.float) * 3.\\n #\\n # self.register_parameter(\\\"raw_weights\\\", nn.Parameter(raw_weights))\\n\\n # RBF residue-wise weights\\n # u_left_ids = np.unique(pair_ids[:, 0])\\n #\\n # std_idx = np.zeros(max(u_left_ids) + 1, dtype=int)\\n # sparse_idx = np.arange(len(u_left_ids))\\n #\\n # std_idx[u_left_ids] = sparse_idx\\n #\\n # select_index = std_idx[pair_ids[:, 0]]\\n\\n # weight = 0.9 at dist_rescale\\n # sigmas = torch.ones(max(u_left_ids) + 1, dtype=torch.float) * np.sqrt(-0.5 / np.log(0.9))\\n #\\n # self.dist_rescale = dist_rescale\\n # self.register_buffer(\\\"select_index\\\", torch.from_numpy(select_index).long())\\n # self.register_parameter(\\\"sigmas\\\", nn.Parameter(sigmas))\\n\\n # def get_weights(self):\\n # return torch.sigmoid(self.raw_weights)\\n # edge_sigmas = torch.index_select(self.sigmas, dim=0, index=self.select_index)\\n # weights = torch.exp(-torch.pow(self.gt_dists / self.dist_rescale, 2) / (2 * torch.pow(edge_sigmas, 2)))\\n # return weights\\n\\n def calc_pair_dists(self, batch_struc):\\n batch_dist = batch_struc[:, self.pair_ids] # bsz, num_pair, 2, 3\\n batch_dist = LA.vector_norm(torch.diff(batch_dist, dim=-2), axis=-1).squeeze(-1) # bsz, num_pair\\n return batch_dist\\n\\n def forward(self, batch_struc):\\n batch_dist = self.calc_pair_dists(batch_struc)\\n # mse = torch.pow(batch_dist - self.gt_dists.unsqueeze(0), 2) * self.get_weights().unsqueeze(0)\\n mse = torch.pow(batch_dist - self.gt_dists.unsqueeze(0), 2)\\n if self.reduction is None:\\n return mse\\n elif self.reduction == \\\"mean\\\":\\n return torch.mean(mse)\\n else:\\n raise NotImplementedError\"\n },\n {\n \"identifier\": \"get_nearest_point\",\n \"path\": \"cryostar/utils/latent_space_utils.py\",\n \"snippet\": \"def get_nearest_point(data: np.ndarray, query: np.ndarray) -> Tuple[npt.NDArray[np.float32], np.ndarray]:\\n \\\"\\\"\\\"\\n Find closest point in @data to @query\\n Return datapoint, index\\n \\\"\\\"\\\"\\n ind = cdist(query, data).argmin(axis=1)\\n return data[ind], ind\"\n },\n {\n \"identifier\": \"cluster_kmeans\",\n \"path\": \"cryostar/utils/latent_space_utils.py\",\n \"snippet\": \"def cluster_kmeans(z: np.ndarray, K: int, on_data: bool = True, reorder: bool = True) -> Tuple[np.ndarray, np.ndarray]:\\n \\\"\\\"\\\"\\n Cluster z by K means clustering\\n Returns cluster labels, cluster centers\\n If reorder=True, reorders clusters according to agglomerative clustering of cluster centers\\n \\\"\\\"\\\"\\n kmeans = KMeans(n_clusters=K, n_init=10, random_state=0, max_iter=10)\\n labels = kmeans.fit_predict(z)\\n centers = kmeans.cluster_centers_\\n\\n centers_ind = None\\n if on_data:\\n centers, centers_ind = get_nearest_point(z, centers)\\n\\n if reorder:\\n # BUG from seaborn or scipy:\\n # sns.clustermap only supports data with at least 2 dim\\n if z.shape[1] == 1:\\n centers = np.hstack([centers, np.zeros_like(centers)])\\n g = sns.clustermap(centers)\\n reordered = g.dendrogram_row.reordered_ind\\n centers = centers[reordered]\\n if centers_ind is not None:\\n centers_ind = centers_ind[reordered]\\n tmp = {k: i for i, k in enumerate(reordered)}\\n labels = np.array([tmp[k] for k in labels])\\n if z.shape[1] == 1:\\n centers = centers[:, :1]\\n return labels, centers\"\n },\n {\n \"identifier\": \"run_pca\",\n \"path\": \"cryostar/utils/latent_space_utils.py\",\n \"snippet\": \"def run_pca(z: np.ndarray) -> Tuple[np.ndarray, PCA]:\\n pca = PCA(z.shape[1])\\n pca.fit(z)\\n # print(\\\"Explained variance ratio:\\\")\\n # print(pca.explained_variance_ratio_)\\n pc = pca.transform(z)\\n return pc, pca\"\n },\n {\n \"identifier\": \"get_pc_traj\",\n \"path\": \"cryostar/utils/latent_space_utils.py\",\n \"snippet\": \"def get_pc_traj(\\n pca: PCA,\\n zdim: int,\\n numpoints: int,\\n dim: int,\\n start: Optional[float] = 5,\\n end: Optional[float] = 95,\\n percentiles: Optional[np.ndarray] = None,\\n) -> npt.NDArray[np.float32]:\\n \\\"\\\"\\\"\\n Create trajectory along specified principal component\\n\\n Inputs:\\n pca: sklearn PCA object from run_pca\\n zdim (int)\\n numpoints (int): number of points between @start and @end\\n dim (int): PC dimension for the trajectory (1-based index)\\n start (float): Value of PC{dim} to start trajectory\\n end (float): Value of PC{dim} to stop trajectory\\n percentiles (np.array or None): Define percentile array instead of np.linspace(start,stop,numpoints)\\n\\n Returns:\\n np.array (numpoints x zdim) of z values along PC\\n \\\"\\\"\\\"\\n if percentiles is not None:\\n assert len(percentiles) == numpoints\\n traj_pca = np.zeros((numpoints, zdim))\\n if percentiles is not None:\\n traj_pca[:, dim - 1] = percentiles\\n else:\\n assert start is not None\\n assert end is not None\\n traj_pca[:, dim - 1] = np.linspace(start, end, numpoints)\\n ztraj_pca = pca.inverse_transform(traj_pca)\\n return ztraj_pca\"\n },\n {\n \"identifier\": \"run_umap\",\n \"path\": \"cryostar/utils/latent_space_utils.py\",\n \"snippet\": \"def run_umap(z: np.ndarray, **kwargs) -> Tuple[np.ndarray, umap.UMAP]:\\n reducer = umap.UMAP(**kwargs)\\n z_embedded = reducer.fit_transform(z)\\n return z_embedded, reducer\"\n },\n {\n \"identifier\": \"plot_z_dist\",\n \"path\": \"cryostar/utils/vis_utils.py\",\n \"snippet\": \"def plot_z_dist(z, extra_cluster=None, save_path=None):\\n if z.shape[-1] == 1:\\n fig = sns.displot(x=z[:, 0])\\n fig.set_xlabels(\\\"z values\\\")\\n if save_path is not None:\\n fig.savefig(save_path)\\n elif z.shape[-1] == 2:\\n sns.set()\\n fig = sns.jointplot(x=z[:, 0], y=z[:, 1], kind=\\\"kde\\\", fill=True)\\n ax = fig.figure.axes\\n if extra_cluster is not None:\\n ax[0].scatter(extra_cluster[:, 0], extra_cluster[:, 1], marker='.', color='tab:orange')\\n if save_path is not None:\\n fig.savefig(save_path)\\n else:\\n raise ValueError(f\\\"input z with shape {z.shape}\\\")\"\n },\n {\n \"identifier\": \"save_tensor_image\",\n \"path\": \"cryostar/utils/vis_utils.py\",\n \"snippet\": \"def save_tensor_image(tensors, save_path, mask=None):\\n # normalize\\n max_val = torch.max(tensors.flatten(start_dim=1), 1)[0][:, None, None, None]\\n min_val = torch.min(tensors.flatten(start_dim=1), 1)[0][:, None, None, None]\\n tensors = (tensors - min_val) / (max_val - min_val)\\n\\n show_img = ToPILImage()(make_grid(tensors, nrow=5))\\n if mask is None:\\n show_img.save(save_path)\\n else:\\n show_img = np.copy(np.asarray(show_img))\\n # show_img = cv2.cvtColor(show_img, cv2.COLOR_GRAY2RGB)\\n if mask.ndim == 2:\\n mask = mask[None]\\n mask = ToPILImage()(make_grid(mask.expand(tensors.shape[0], -1, -1, -1), nrow=5))\\n mask = np.invert(np.asarray(mask).astype(bool))[..., 0]\\n color_mask = np.array([[0, 0, 0], [31, 119, 180]], dtype=np.uint8)\\n color_mask = color_mask[mask.astype(int)]\\n show_img[mask] = cv2.addWeighted(show_img[mask], 0.5, color_mask[mask], 0.5, 0)\\n show_img = Image.fromarray(show_img)\\n show_img.save(save_path)\"\n },\n {\n \"identifier\": \"merge_step_outputs\",\n \"path\": \"cryostar/utils/pl_utils.py\",\n \"snippet\": \"def merge_step_outputs(outputs):\\n ks = outputs[0].keys()\\n res = {}\\n for k in ks:\\n res[k] = torch.concat([out[k] for out in outputs], dim=0)\\n return res\"\n },\n {\n \"identifier\": \"squeeze_dict_outputs_1st_dim\",\n \"path\": \"cryostar/utils/pl_utils.py\",\n \"snippet\": \"def squeeze_dict_outputs_1st_dim(outputs):\\n res = {}\\n for k in outputs.keys():\\n res[k] = outputs[k].flatten(start_dim=0, end_dim=1)\\n return res\"\n },\n {\n \"identifier\": \"filter_outputs_by_indices\",\n \"path\": \"cryostar/utils/pl_utils.py\",\n \"snippet\": \"def filter_outputs_by_indices(outputs, indices):\\n res = {}\\n for k in outputs.keys():\\n res[k] = outputs[k][indices]\\n return res\"\n },\n {\n \"identifier\": \"get_1st_unique_indices\",\n \"path\": \"cryostar/utils/pl_utils.py\",\n \"snippet\": \"def get_1st_unique_indices(t):\\n _, idx, counts = torch.unique(t, dim=None, sorted=True, return_inverse=True, return_counts=True)\\n # ind_sorted: the index corresponding to same unique value will be grouped by these indices\\n _, ind_sorted = torch.sort(idx, stable=True)\\n cum_sum = counts.cumsum(0)\\n cum_sum = torch.cat((cum_sum.new_tensor([\\n 0,\\n ]), cum_sum[:-1]))\\n first_idx = ind_sorted[cum_sum]\\n return first_idx\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os.path as osp\nimport warnings\nimport collections\nimport einops\nimport numpy as np\nimport biotite.structure as struc\nimport torch\nimport lightning.pytorch as pl\nfrom pathlib import Path\nfrom copy import deepcopy\nfrom torch import nn\nfrom torch import optim\nfrom torch.utils.data import DataLoader\nfrom torchinfo import summary\nfrom lightning.fabric.utilities.warnings import PossibleUserWarning\nfrom lightning.pytorch.utilities import rank_zero_only\nfrom lightning.pytorch.strategies import DDPStrategy\nfrom mmengine import mkdir_or_exist\nfrom cryostar.utils.transforms import SpatialGridTranslate\nfrom cryostar.utils.dataio import StarfileDataSet, StarfileDatasetConfig, Mask\nfrom cryostar.utils.ctf_utils import CTFRelion, CTFCryoDRGN\nfrom cryostar.utils.losses import calc_cor_loss, calc_kl_loss\nfrom cryostar.utils.misc import log_to_current, \\\n pl_init_exp, pretty_dict, set_seed, warmup\nfrom cryostar.utils.pdb_tools import bt_save_pdb\nfrom cryostar.gmm.gmm import EMAN2Grid, batch_projection, Gaussian\nfrom cryostar.gmm.deformer import E3Deformer, NMADeformer\nfrom cryostar.utils.fft_utils import primal_to_fourier_2d, fourier_to_primal_2d\nfrom cryostar.utils.polymer import Polymer, NT_ATOMS, AA_ATOMS\nfrom cryostar.utils.dist_loss import (find_quaint_cutoff_pairs, find_range_cutoff_pairs, find_continuous_pairs,\n calc_dist_by_pair_indices, remove_duplicate_pairs, filter_same_chain_pairs,\n DistLoss)\nfrom cryostar.utils.latent_space_utils import get_nearest_point, cluster_kmeans, run_pca, get_pc_traj, run_umap\nfrom cryostar.utils.vis_utils import plot_z_dist, save_tensor_image\nfrom cryostar.utils.pl_utils import merge_step_outputs, squeeze_dict_outputs_1st_dim, \\\n filter_outputs_by_indices, get_1st_unique_indices\nfrom miscs import calc_pair_dist_loss, calc_clash_loss, low_pass_mask2d, VAE, infer_ctf_params_from_config"},"token_num":{"kind":"number","value":14716,"string":"14,716"},"cropped_code":{"kind":"string","value":"\n# other\n\n\n# avoid num_workers set as cpu_count warning\nwarnings.simplefilter(\"ignore\", PossibleUserWarning)\n\n# only log to rank_zero, comment this for debugging\nlog_to_current = rank_zero_only(log_to_current)\n\nTASK_NAME = \"atom\"\n\n\ndef prepare_images(images: torch.FloatTensor, space: str):\n assert space in (\"real\", \"fourier\")\n if space == \"real\":\n model_input = einops.rearrange(images, \"b 1 ny nx -> b (1 ny nx)\")\n else:\n fimages = primal_to_fourier_2d(images)\n model_input = einops.rearrange(torch.view_as_real(fimages), \"b 1 ny nx c2 -> b (1 ny nx c2)\", c2=2)\n return model_input\n\n\nclass InitTask(pl.LightningModule):\n\n def __init__(self, em_module):\n super().__init__()\n self.cfg = em_module.cfg\n self.em_module = em_module\n self.loss_deque = collections.deque([\n 10,\n ], maxlen=20)\n\n def on_train_batch_end(self, outputs, batch, batch_idx):\n self.loss_deque.append(outputs['loss'].item())\n if np.mean(self.loss_deque) < 1e-3:\n self.trainer.should_stop = True\n # update all process status\n self.trainer.should_stop = self.trainer.strategy.broadcast(self.trainer.should_stop)\n\n def training_step(self, batch, batch_idx):\n images = batch[\"proj\"]\n idxes = batch[\"idx\"]\n rot_mats, trans_mats = self.em_module.get_batch_pose(batch)\n\n pred_deformation, mu, log_var = self.em_module.model(prepare_images(images, self.cfg.model.input_space), idxes,\n rot_mats)\n\n shift_loss = torch.mean(torch.pow(pred_deformation.flatten(start_dim=-2), 2))\n loss = shift_loss\n if self.global_step % self.cfg.runner.log_every_n_step == 0:\n log_to_current(f\"loss {loss.item()}\")\n return loss\n\n def configure_optimizers(self):\n return optim.AdamW(self.em_module.model.parameters(), lr=1e-4)\n\n def on_fit_end(self):\n log_to_current(f\"Init finished with loss {np.mean(self.loss_deque)}\")\n\n\nclass CryoEMTask(pl.LightningModule):\n\n def __init__(self, cfg, dataset):\n super().__init__()\n cfg = deepcopy(cfg)\n self.cfg = cfg\n\n # Define GMM\n meta = Polymer.from_pdb(cfg.dataset_attr.ref_pdb_path)\n log_to_current(f\"Load reference structure from {cfg.dataset_attr.ref_pdb_path}\")\n\n # for save\n self.template_pdb = meta.to_atom_arr()\n log_to_current(f\"Protein contains {len(meta)} atoms, \"\n f\"{meta.num_amino_acids} amino acids, \"\n f\"{meta.num_nucleotides} nucleotides, \"\n f\"{meta.num_chains} chains.\")\n\n # ref\n ref_centers = torch.from_numpy(meta.coord).float()\n ref_amps = torch.from_numpy(meta.num_electron).float()\n ref_sigmas = torch.ones_like(ref_amps)\n ref_sigmas.fill_(2.)\n log_to_current(f\"1st GMM blob amplitude {ref_amps[0].item()}, sigma {ref_sigmas[0].item()}\")\n\n num_pts = len(meta)\n log_to_current(f\"Reference structure has {num_pts} atom coordinates\")\n\n # tunable params\n # gmm\n self.register_buffer(\"gmm_centers\", ref_centers)\n\n if cfg.gmm.tunable:\n log_to_current(\"Set GMM sigmas, amplitudes tunable\")\n self.register_parameter(\"gmm_sigmas\", nn.Parameter(ref_sigmas))\n self.register_parameter(\"gmm_amps\", nn.Parameter(ref_amps))\n else:\n self.register_buffer(\"gmm_sigmas\", ref_sigmas)\n self.register_buffer(\"gmm_amps\", ref_amps)\n\n nma_modes = None\n if (hasattr(self.cfg.extra_input_data_attr, \"nma_path\") and\n self.cfg.extra_input_data_attr.nma_path not in [\"\", None]):\n nma_modes = torch.tensor(np.load(self.cfg.extra_input_data_attr.nma_path), dtype=torch.float32)\n log_to_current(f\"Load NMA coefficients from {self.cfg.extra_input_data_attr.nma_path}, \"\n f\"whose shape is {nma_modes.shape}\")\n\n # model\n if cfg.model.input_space == \"fourier\":\n in_dim = 2 * cfg.data_process.down_side_shape ** 2\n elif cfg.model.input_space == \"real\":\n in_dim = cfg.data_process.down_side_shape ** 2\n else:\n raise NotImplementedError\n self.model = VAE(in_dim=in_dim,\n out_dim=num_pts * 3 if nma_modes is None else 6 + nma_modes.shape[1],\n **cfg.model.model_cfg)\n log_to_current('Model summary:\\n' + str(summary(self.model, input_size=[(1, in_dim), (1,)], verbose=0)))\n if nma_modes is None:\n"},"all_code":{"kind":"string","value":"\n\n\n\n\n# other\n\n\n# avoid num_workers set as cpu_count warning\nwarnings.simplefilter(\"ignore\", PossibleUserWarning)\n\n# only log to rank_zero, comment this for debugging\nlog_to_current = rank_zero_only(log_to_current)\n\nTASK_NAME = \"atom\"\n\n\ndef prepare_images(images: torch.FloatTensor, space: str):\n assert space in (\"real\", \"fourier\")\n if space == \"real\":\n model_input = einops.rearrange(images, \"b 1 ny nx -> b (1 ny nx)\")\n else:\n fimages = primal_to_fourier_2d(images)\n model_input = einops.rearrange(torch.view_as_real(fimages), \"b 1 ny nx c2 -> b (1 ny nx c2)\", c2=2)\n return model_input\n\n\nclass InitTask(pl.LightningModule):\n\n def __init__(self, em_module):\n super().__init__()\n self.cfg = em_module.cfg\n self.em_module = em_module\n self.loss_deque = collections.deque([\n 10,\n ], maxlen=20)\n\n def on_train_batch_end(self, outputs, batch, batch_idx):\n self.loss_deque.append(outputs['loss'].item())\n if np.mean(self.loss_deque) < 1e-3:\n self.trainer.should_stop = True\n # update all process status\n self.trainer.should_stop = self.trainer.strategy.broadcast(self.trainer.should_stop)\n\n def training_step(self, batch, batch_idx):\n images = batch[\"proj\"]\n idxes = batch[\"idx\"]\n rot_mats, trans_mats = self.em_module.get_batch_pose(batch)\n\n pred_deformation, mu, log_var = self.em_module.model(prepare_images(images, self.cfg.model.input_space), idxes,\n rot_mats)\n\n shift_loss = torch.mean(torch.pow(pred_deformation.flatten(start_dim=-2), 2))\n loss = shift_loss\n if self.global_step % self.cfg.runner.log_every_n_step == 0:\n log_to_current(f\"loss {loss.item()}\")\n return loss\n\n def configure_optimizers(self):\n return optim.AdamW(self.em_module.model.parameters(), lr=1e-4)\n\n def on_fit_end(self):\n log_to_current(f\"Init finished with loss {np.mean(self.loss_deque)}\")\n\n\nclass CryoEMTask(pl.LightningModule):\n\n def __init__(self, cfg, dataset):\n super().__init__()\n cfg = deepcopy(cfg)\n self.cfg = cfg\n\n # Define GMM\n meta = Polymer.from_pdb(cfg.dataset_attr.ref_pdb_path)\n log_to_current(f\"Load reference structure from {cfg.dataset_attr.ref_pdb_path}\")\n\n # for save\n self.template_pdb = meta.to_atom_arr()\n log_to_current(f\"Protein contains {len(meta)} atoms, \"\n f\"{meta.num_amino_acids} amino acids, \"\n f\"{meta.num_nucleotides} nucleotides, \"\n f\"{meta.num_chains} chains.\")\n\n # ref\n ref_centers = torch.from_numpy(meta.coord).float()\n ref_amps = torch.from_numpy(meta.num_electron).float()\n ref_sigmas = torch.ones_like(ref_amps)\n ref_sigmas.fill_(2.)\n log_to_current(f\"1st GMM blob amplitude {ref_amps[0].item()}, sigma {ref_sigmas[0].item()}\")\n\n num_pts = len(meta)\n log_to_current(f\"Reference structure has {num_pts} atom coordinates\")\n\n # tunable params\n # gmm\n self.register_buffer(\"gmm_centers\", ref_centers)\n\n if cfg.gmm.tunable:\n log_to_current(\"Set GMM sigmas, amplitudes tunable\")\n self.register_parameter(\"gmm_sigmas\", nn.Parameter(ref_sigmas))\n self.register_parameter(\"gmm_amps\", nn.Parameter(ref_amps))\n else:\n self.register_buffer(\"gmm_sigmas\", ref_sigmas)\n self.register_buffer(\"gmm_amps\", ref_amps)\n\n nma_modes = None\n if (hasattr(self.cfg.extra_input_data_attr, \"nma_path\") and\n self.cfg.extra_input_data_attr.nma_path not in [\"\", None]):\n nma_modes = torch.tensor(np.load(self.cfg.extra_input_data_attr.nma_path), dtype=torch.float32)\n log_to_current(f\"Load NMA coefficients from {self.cfg.extra_input_data_attr.nma_path}, \"\n f\"whose shape is {nma_modes.shape}\")\n\n # model\n if cfg.model.input_space == \"fourier\":\n in_dim = 2 * cfg.data_process.down_side_shape ** 2\n elif cfg.model.input_space == \"real\":\n in_dim = cfg.data_process.down_side_shape ** 2\n else:\n raise NotImplementedError\n self.model = VAE(in_dim=in_dim,\n out_dim=num_pts * 3 if nma_modes is None else 6 + nma_modes.shape[1],\n **cfg.model.model_cfg)\n log_to_current('Model summary:\\n' + str(summary(self.model, input_size=[(1, in_dim), (1,)], verbose=0)))\n if nma_modes is None:"},"next_line":{"kind":"string","value":" self.deformer = E3Deformer()"},"gold_snippet_index":{"kind":"number","value":13,"string":"13"},"created_at":{"kind":"string","value":"2023-11-06 07:15:26+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5274,"cells":{"repo_name":{"kind":"string","value":"KAIST-AILab/palr"},"file_path":{"kind":"string","value":"train.py"},"context":{"kind":"list like","value":[{"identifier":"BC","path":"imitation/bc.py","snippet":"class BC(nn.Module):\n def __init__(self, policy, env, best_policy=None,\n replay_buffer=None, replay_buffer_valid=None, seed=0, \n device='cpu', lr=3e-4, envname=None, wandb=None, save_policy_path=None, \n obs_dim=1, action_dim=1, stacksize=1, standardize=True):\n \n torch.manual_seed(seed)\n np.random.seed(seed)\n random.seed(seed)\n \n super(BC, self).__init__()\n\n self.env = env\n self.policy = policy\n self.best_policy = best_policy\n self.replay_buffer = replay_buffer\n self.replay_buffer_valid = replay_buffer_valid\n self.device = device\n \n self.obs_dim = obs_dim\n self.action_dim = action_dim \n self.stacksize = stacksize\n \n self.policy_optimizer = optim.Adam(policy.parameters(), lr=lr)\n \n self.num_eval_iteration = 50\n self.envname = envname\n \n self.wandb = None\n if wandb:\n self.wandb = wandb\n self.wandb.init()\n\n self.save_policy_path = save_policy_path \n \n # For standardization\n self.standardize = standardize\n\n self.obs_mean_tt = torch.tensor(self.replay_buffer.obs_mean, device=device)\n self.obs_std_tt = torch.tensor(self.replay_buffer.obs_std, device=device)\n self.act_mean_tt = torch.tensor(self.replay_buffer.act_mean, device=device)\n self.act_std_tt = torch.tensor(self.replay_buffer.act_std, device=device)\n\n self.obs_mean = self.replay_buffer.obs_mean\n self.obs_std = self.replay_buffer.obs_std\n self.act_mean = self.replay_buffer.act_mean\n self.act_std = self.replay_buffer.act_std\n \n\n def train(self, total_iteration=1e6, eval_freq=1000, batch_size=1024, num_valid=2000):\n \n max_score = -100000.\n \n batch_valid = self.replay_buffer_valid.random_batch(num_valid, standardize=self.standardize)\n \n obs_valid = batch_valid['observations']\n actions_valid = batch_valid['actions'][:, -self.action_dim:] \n prev_expert_action_valid = batch_valid['actions'][:, :-self.action_dim] # For debugging\n \n obs_valid = torch.tensor(obs_valid, dtype=torch.float32, device=self.device)\n actions_valid = torch.tensor(actions_valid, dtype=torch.float32, device=self.device)\n prev_expert_action_valid = torch.tensor(prev_expert_action_valid, dtype=torch.float32, device=self.device)\n \n for num in range(0, int(total_iteration)):\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\n \n obs = batch['observations']\n actions = batch['actions'][:, -self.action_dim:]\n \n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device) \n\n neg_likelihood = -self.policy.log_prob(obs, actions).mean()\n train_loss = neg_likelihood\n \n self.policy_optimizer.zero_grad()\n train_loss.backward()\n self.policy_optimizer.step()\n\n if (num+1) % eval_freq == 0:\n policy_action = self.policy(obs).sample()\n policy_action_valid = self.policy(obs_valid).sample()\n prev_expert_action = batch['actions'][:, :-self.action_dim] \n prev_expert_action = torch.tensor(prev_expert_action, dtype=torch.float32, device=self.device) \n \n # Train data HSCIC (for debugging) \n policy_embedding = self.policy.forward_embedding(obs)\n if self.standardize:\n Y_std = (prev_expert_action - self.act_mean_tt[0, :-self.action_dim])/ self.act_std_tt[0, :-self.action_dim]\n Z_std = (actions - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\n p_std = (policy_action - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\n\n Y_std = Y_std.to(torch.float32)\n Z_std = Z_std.to(torch.float32)\n p_std = p_std.to(torch.float32)\n else:\n Y_std = prev_expert_action\n Z_std = actions\n p_std = policy_action\n \n hscic_estimate = estimate_hscic(X=policy_embedding, Y=Y_std, Z=Z_std, ridge_lambda=1e-5)\n \n policy_embedding_valid = self.policy.forward_embedding(obs_valid)\n if self.standardize:\n Y_std = (prev_expert_action_valid - self.act_mean_tt[0, :-self.action_dim])/ self.act_std_tt[0, :-self.action_dim]\n Z_std = (actions_valid - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\n\n Y_std = Y_std.to(torch.float32)\n Z_std = Z_std.to(torch.float32)\n else:\n Y_std = prev_expert_action_valid\n Z_std = actions_valid\n p_std = policy_action\n \n valid_hscic_estimate = estimate_hscic(X=policy_embedding_valid, Y=Y_std, Z=Z_std, ridge_lambda=1e-5)\n valid_hscic_estimate_action = estimate_hscic(X=policy_action_valid, Y=prev_expert_action_valid, Z=actions_valid, ridge_lambda=1e-5)\n\n valid_neg_likelihood = -self.policy.log_prob(obs_valid, actions_valid).mean()\n valid_loss = valid_neg_likelihood\n\n eval_ret_mean, eval_ret_std = self.evaluate(num_iteration=self.num_eval_iteration)\n \n print(f'** iter{num+1}: train_policy_loss={train_loss.item():.2f}, val_policy_loss={valid_loss.item():.2f}, eval_ret={eval_ret_mean:.2f}+-{eval_ret_std:.2f} ({obs_valid.shape[0]})',)\n print(f'** HSCIC : (train){hscic_estimate:.6f} (valid){valid_hscic_estimate:.6f} (valid,action){valid_hscic_estimate_action:.6f}')\n \n if self.wandb:\n self.wandb.log({'train_total_loss': train_loss.item(), \n 'valid_total_loss': valid_loss.item(),\n 'train_neg_likelihood': neg_likelihood.item(),\n 'valid_neg_likelihood': valid_neg_likelihood.item(),\n 'train_mean_hscic(rep,prev|target)': hscic_estimate,\n 'valid_mean_hscic(rep,prev|target)': valid_hscic_estimate,\n 'valid_mean_hscic(act,prev|target)': valid_hscic_estimate_action,\n 'eval_episode_return': eval_ret_mean\n }, step=num+1)\n\n if eval_ret_mean > max_score:\n print(f'** max score record! ')\n max_score = eval_ret_mean\n copy_nn_module(self.policy, self.best_policy)\n \n if self.save_policy_path:\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_best.pt')\n os.makedirs(self.save_policy_path, exist_ok=True)\n torch.save(self.best_policy.state_dict(), \n f'{self.save_policy_path}/bc_actor_best.pt')\n \n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_last.pt')\n os.makedirs(self.save_policy_path, exist_ok=True)\n torch.save(self.policy.state_dict(), \n f'{self.save_policy_path}/bc_actor_last.pt')\n \n \n def evaluate(self, num_iteration=5):\n rets = []\n maxtimestep = 1000\n for num in range(0, num_iteration):\n obs_list = []\n obs = np.zeros(self.obs_dim * self.stacksize)\n \n obs_ = self.env.reset()\n obs_list.append(obs_)\n\n obs = np.zeros(self.obs_dim * self.stacksize)\n obs[- self.obs_dim:] = obs_\n\n done = False\n t = 0\n ret = 0.\n \n while not done and t < maxtimestep:\n if self.standardize:\n obs = (obs - self.obs_mean[0]) / self.obs_std[0]\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n action = self.policy(obs).mean.cpu().detach().numpy()\n \n next_obs, rew, done, _ = self.env.step(action)\n ret += rew\n \n obs_ = next_obs \n obs_list.append(obs_)\n\n if len(obs_list) < self.stacksize:\n obs_ = np.concatenate(obs_list)\n obs = np.zeros(self.obs_dim * self.stacksize)\n obs[-(len(obs_list)) * self.obs_dim:] = obs_\n \n else:\n obs = np.concatenate(obs_list[-self.stacksize:])\n \n t += 1\n \n rets.append(ret)\n \n return np.mean(rets), np.std(rets)"},{"identifier":"RAP","path":"imitation/rap.py","snippet":"class RAP(nn.Module):\n # Implementation of Residual Action Prediction (ECCV 2022)\n # - https://arxiv.org/pdf/2207.09705.pdf\n def __init__(self, policy, env, best_policy=None,\n replay_buffer=None, replay_buffer_valid=None, seed=0, \n device='cpu', lr=3e-4, wandb=None, save_policy_path=None, \n obs_dim=1, action_dim=1, embedding_dim=1, stacksize=1, standardize=False\n ):\n torch.manual_seed(seed)\n np.random.seed(seed)\n random.seed(seed)\n \n super(RAP, self).__init__()\n\n self.env = env\n self.policy = policy\n self.best_policy = best_policy\n self.replay_buffer = replay_buffer\n self.replay_buffer_valid = replay_buffer_valid\n \n self.device = device\n \n self.m_embedding_optimizer = optim.Adam(policy.history_embedding_params, lr=lr)\n self.h_embedding_optimizer = optim.Adam(policy.single_embedding_params, lr=lr)\n self.policy_optimizer = optim.Adam(policy.policy_params, lr=lr)\n self.residual_optimizer = optim.Adam(policy.residual_params, lr=lr)\n\n self.num_eval_iteration = 50 \n \n self.wandb = None\n if wandb:\n self.wandb = wandb\n self.wandb.init()\n\n self.save_policy_path = save_policy_path\n\n self.obs_dim = obs_dim\n self.action_dim = action_dim\n self.embedding_dim = embedding_dim\n self.stacksize = stacksize\n \n self.standardize = standardize\n\n self.obs_mean_tt = torch.tensor(self.replay_buffer.obs_mean, device=device)\n self.obs_std_tt = torch.tensor(self.replay_buffer.obs_std, device=device)\n self.act_mean_tt = torch.tensor(self.replay_buffer.act_mean, device=device)\n self.act_std_tt = torch.tensor(self.replay_buffer.act_std, device=device)\n\n self.obs_mean = self.replay_buffer.obs_mean\n self.obs_std = self.replay_buffer.obs_std\n self.act_mean = self.replay_buffer.act_mean\n self.act_std = self.replay_buffer.act_std\n \n\n def train(self, total_iteration=1e6, eval_freq=1000, batch_size=1024, num_valid=2000):\n \n max_score = -100000. \n min_loss = 100000. \n \n batch_valid = self.replay_buffer_valid.get_batch(num_valid, standardize=self.standardize)\n \n obs_valid = batch_valid['observations']\n actions_valid = batch_valid['actions'][:, -self.action_dim:]\n prev_actions_valid = batch_valid['actions'][:, :-self.action_dim] \n \n obs_valid = torch.tensor(obs_valid, dtype=torch.float32, device=self.device)\n actions_valid = torch.tensor(actions_valid, dtype=torch.float32, device=self.device)\n prev_actions_valid = torch.tensor(prev_actions_valid, dtype=torch.float32, device=self.device) \n \n for num in range(0, int(total_iteration)):\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\n \n obs = batch['observations']\n actions = batch['actions'][:, -self.action_dim:]\n prev_actions = batch['actions'][:, :-self.action_dim]\n \n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\n prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device)\n\n self.m_embedding_optimizer.zero_grad()\n self.residual_optimizer.zero_grad() \n \n # m : history embedding, h : single observation embedding\n m, _ = self.policy.forward_embedding(obs) \n action_residuals = actions - prev_actions\n action_residual_pred = self.policy.forward_residual_from_m(m)\n \n train_residual_loss = torch.mean((action_residual_pred - action_residuals) ** 2)\n train_residual_loss.backward()\n \n self.m_embedding_optimizer.step()\n self.residual_optimizer.step() \n \n self.policy_optimizer.zero_grad() \n self.h_embedding_optimizer.zero_grad() \n \n m, h = self.policy.forward_embedding(obs)\n \n # we follow the original implementation that stop-gradient layer on m ; \n # see `forward_policy_from_embedding` method for detail. (m.detach() in input)\n train_neg_likelihood = -self.policy.log_prob_policy_from_m_h(m, h, actions).mean()\n train_neg_likelihood.backward()\n \n self.policy_optimizer.step()\n self.h_embedding_optimizer.step()\n \n if (num+1) % eval_freq == 0: \n valid_m, valid_h = self.policy.forward_embedding(obs_valid) \n valid_action_residuals = actions_valid - prev_actions_valid\n valid_action_residual_pred = self.policy.forward_residual_from_m(valid_m)\n \n valid_policy_neg_likelihood = -self.policy.log_prob_policy_from_m_h(valid_m, valid_h, actions_valid).mean()\n valid_residual_loss = torch.mean((valid_action_residual_pred - valid_action_residuals) ** 2) \n \n valid_loss = valid_policy_neg_likelihood + valid_residual_loss\n \n policy_action_valid = self.policy(obs_valid).sample() \n \n train_mh = torch.cat([m,h], dim=-1)\n valid_mh = torch.cat([valid_m, valid_h], dim=-1)\n \n hscic_estimate = estimate_hscic(X=train_mh, Y=prev_actions, Z=actions, ridge_lambda=1e-5)\n valid_hscic_estimate = estimate_hscic(X=valid_mh, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5)\n valid_hscic_estimate_action = estimate_hscic(X=policy_action_valid, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5) \n train_hscic_m_a_given_aprev = estimate_hscic(X=m, Y=actions, Z=prev_actions, ridge_lambda=1e-5)\n valid_hscic_m_a_given_aprev = estimate_hscic(X=valid_m, Y=actions_valid, Z=prev_actions_valid, ridge_lambda=1e-5)\n \n eval_ret_mean, eval_ret_std = self.evaluate(num_iteration=self.num_eval_iteration)\n \n train_loss = train_neg_likelihood + train_residual_loss\n \n print(f'** iter{num+1}: train_loss={train_loss.item()}, nll={train_neg_likelihood}, residual_loss={train_residual_loss}, eval_ret={eval_ret_mean}+-{eval_ret_std}')\n print(f' valid_loss={valid_loss.item()}, valid_nll={valid_policy_neg_likelihood}, valid_residual_loss={valid_residual_loss}')\n \n print(f'** HSCIC(mh, a_prev | a_current) : (train){hscic_estimate:.6f} (valid){valid_hscic_estimate:.6f} (valid,action){valid_hscic_estimate_action:.6f}')\n print(f'** HSCIC(m, a_current | a_prev) : (train){train_hscic_m_a_given_aprev:.6f} (valid){valid_hscic_m_a_given_aprev:.6f} ')\n \n if self.wandb:\n self.wandb.log({\n 'train_total_loss': train_loss.item(),\n 'valid_total_loss': valid_loss.item(),\n 'train_neg_likelihood': train_neg_likelihood.item(),\n 'valid_neg_likelihood': valid_policy_neg_likelihood.item(),\n 'train_mean_hscic(rep,prev|target)': hscic_estimate,\n 'valid_mean_hscic(rep,prev|target)': valid_hscic_estimate,\n 'valid_mean_hscic(act,prev|target)': valid_hscic_estimate_action,\n 'train_residual_loss': train_residual_loss,\n 'valid_residual_loss': valid_residual_loss,\n 'train_mean_hscic(m,target|prev)': train_hscic_m_a_given_aprev,\n 'valid_mean_hscic(m,target|prev)': valid_hscic_m_a_given_aprev,\n 'eval_episode_return': eval_ret_mean\n }, step=num+1)\n\n if eval_ret_mean > max_score:\n print(f'** max score ')\n max_score = eval_ret_mean\n copy_nn_module(self.policy, self.best_policy)\n \n if self.save_policy_path:\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_best.pt')\n os.makedirs(self.save_policy_path, exist_ok=True)\n torch.save(self.best_policy.state_dict(), \n f'{self.save_policy_path}/bc_actor_best.pt')\n \n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_last.pt')\n os.makedirs(self.save_policy_path, exist_ok=True)\n torch.save(self.policy.state_dict(), \n f'{self.save_policy_path}/bc_actor_last.pt')\n \n \n def evaluate(self, num_iteration=5):\n rets = []\n maxtimestep = 1000\n for num in range(0, num_iteration):\n obs_list = []\n obs = np.zeros(self.obs_dim * self.stacksize)\n \n obs_ = self.env.reset()\n obs_list.append(obs_)\n\n obs = np.zeros(self.obs_dim * self.stacksize)\n obs[- self.obs_dim:] = obs_\n\n done = False\n t = 0\n ret = 0.\n \n while not done and t < maxtimestep:\n # obs = obs[:true_obs_dim]\n if self.standardize:\n obs = (obs - self.obs_mean[0]) / self.obs_std[0]\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n action = self.policy(obs).mean.cpu().detach().numpy()[0]\n next_obs, rew, done, env_info = self.env.step(action)\n ret += rew\n \n obs_ = next_obs \n obs_list.append(obs_)\n\n if len(obs_list) < self.stacksize:\n obs_ = np.concatenate(obs_list)\n obs = np.zeros(self.obs_dim * self.stacksize)\n obs[-(len(obs_list)) * self.obs_dim:] = obs_\n \n else:\n obs = np.concatenate(obs_list[-self.stacksize:])\n \n t += 1\n \n rets.append(ret)\n \n return np.mean(rets), np.std(rets)"},{"identifier":"FCA","path":"imitation/fca.py","snippet":"class FCA(nn.Module):\n def __init__(self, policy, env, best_policy=None,\n replay_buffer=None, replay_buffer_valid=None, seed=0, \n device='cpu', lr=3e-4, wandb=None, save_policy_path=None, \n obs_dim=1, action_dim=1, stacksize=1, standardize=True,\n embedding_dim=1, entropy_hidden_size=300, entropy_lr=1e-4, reg_coef=1e-5, info_bottleneck_loss_coef=0.001, \n ):\n torch.manual_seed(seed)\n np.random.seed(seed)\n random.seed(seed)\n \n super(FCA, self).__init__()\n\n self.env = env\n self.policy = policy\n self.best_policy = best_policy\n self.replay_buffer = replay_buffer\n self.replay_buffer_valid = replay_buffer_valid \n self.device = device\n \n self.obs_dim = obs_dim\n self.action_dim = action_dim\n self.embedding_dim = embedding_dim\n self.stacksize = stacksize \n\n # Additional Network for Conditional Entropy (FCA)\n self.entropy_input_size = embedding_dim + action_dim\n self.entropy_hidden_size = entropy_hidden_size\n self.entropy_net = nn.Sequential(\n nn.Linear(self.entropy_input_size, self.entropy_hidden_size, device=self.device),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Linear(self.entropy_hidden_size, action_dim, device=self.device)\n )\n \n # FCA Hyperparameters\n self.entropy_coef = reg_coef \n self.info_bottleneck_loss_coef = info_bottleneck_loss_coef \n \n self.embedding_optimizer = optim.Adam(policy.embedding_params, lr=lr)\n self.policy_optimizer = optim.Adam(policy.policy_params, lr=lr)\n self.entropy_optimizer = optim.Adam(self.entropy_net.parameters(), lr=entropy_lr)\n\n self.num_eval_iteration = 50\n \n self.wandb = None\n if wandb:\n self.wandb = wandb\n self.wandb.init()\n\n self.save_policy_path = save_policy_path\n\n # For standardization\n self.standardize = standardize\n\n self.obs_mean_tt = torch.tensor(self.replay_buffer.obs_mean, device=device)\n self.obs_std_tt = torch.tensor(self.replay_buffer.obs_std, device=device)\n self.act_mean_tt = torch.tensor(self.replay_buffer.act_mean, device=device)\n self.act_std_tt = torch.tensor(self.replay_buffer.act_std, device=device)\n\n self.obs_mean = self.replay_buffer.obs_mean\n self.obs_std = self.replay_buffer.obs_std\n self.act_mean = self.replay_buffer.act_mean\n self.act_std = self.replay_buffer.act_std \n\n def train(self, total_iteration=1e6, eval_freq=1000, batch_size=1024, num_valid=2000, inner_steps=1):\n \n max_score = -100000. \n min_loss = 100000. \n \n batch_valid = self.replay_buffer_valid.get_batch(num_valid, standardize=self.standardize)\n \n obs_valid = batch_valid['observations']\n actions_valid = batch_valid['actions'][:, -self.action_dim:]\n prev_actions_valid = batch_valid['actions'][:, :-self.action_dim] \n \n obs_valid = torch.tensor(obs_valid, dtype=torch.float32, device=self.device)\n actions_valid = torch.tensor(actions_valid, dtype=torch.float32, device=self.device)\n prev_actions_valid = torch.tensor(prev_actions_valid, dtype=torch.float32, device=self.device) \n \n for num in range(0, int(total_iteration)):\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\n \n obs = batch['observations']\n actions = batch['actions'][:, -self.action_dim:]\n prev_actions = batch['actions'][:, :-self.action_dim]\n \n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\n prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device)\n\n # conditional entropy input : H(a_{t-1}| a_{t}, varphi_t)\n h = self.policy.forward_embedding(obs)\n expert_action_and_h = torch.cat([actions, h], dim=-1) \n \n self.policy_optimizer.zero_grad()\n self.embedding_optimizer.zero_grad()\n self.entropy_optimizer.zero_grad()\n\n if self.entropy_coef > 0.:\n neg_likelihood = -self.policy.log_prob_policy_from_embedding(h, actions).mean()\n info_bottleneck_loss = 0.5 * (h ** 2).sum()\n\n # prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device)\n pred_prev_actions = self.entropy_net(expert_action_and_h) \n entropy_loss = torch.mean((pred_prev_actions - prev_actions) ** 2) \n\n train_loss = neg_likelihood \\\n - self.entropy_coef * entropy_loss \\\n + self.info_bottleneck_loss_coef * info_bottleneck_loss\n \n train_loss.backward() # backprop embedding\n \n self.policy_optimizer.step()\n self.embedding_optimizer.step()\n\n # conditional entropy training\n for _ in range(inner_steps):\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\n \n obs = batch['observations']\n actions = batch['actions'][:, -self.action_dim:]\n prev_actions = batch['actions'][:, :-self.action_dim]\n \n obs = torch.tensor(obs, dtype=torch.float32, device=self.device) \n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\n \n h = self.policy.forward_embedding(obs)\n expert_action_and_h = torch.cat([actions, h], dim=-1) \n\n prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device) \n pred_prev_actions = self.entropy_net(expert_action_and_h.detach())\n\n entropy_loss = torch.mean((pred_prev_actions - prev_actions) ** 2)\n \n self.entropy_optimizer.zero_grad()\n entropy_loss.backward()\n self.entropy_optimizer.step()\n\n else:\n neg_likelihood = -self.policy.log_prob_policy_from_embedding(h, actions).mean()\n info_bottleneck_loss = 0.5 * (h ** 2).sum()\n \n train_loss = neg_likelihood + self.info_bottleneck_loss_coef * info_bottleneck_loss \n \n train_loss.backward()\n \n self.policy_optimizer.step()\n self.embedding_optimizer.step() \n \n\n if (num+1) % eval_freq == 0: \n h_valid = self.policy.forward_embedding(obs_valid)\n valid_info_bottleneck_loss = 0.5 * (h_valid ** 2).sum()\n \n if self.entropy_coef > 0:\n expert_action_and_h_valid = torch.cat([actions_valid, h_valid], dim=-1) \n pred_prev_actions_valid = self.entropy_net(expert_action_and_h_valid)\n \n prev_actions_valid = batch_valid['actions'][:, :-self.action_dim]\n prev_actions_valid = torch.tensor(prev_actions_valid, dtype=torch.float32, device=self.device)\n \n valid_entropy_loss = torch.mean((pred_prev_actions_valid - prev_actions_valid) ** 2)\n else:\n valid_entropy_loss = 0.\n \n valid_neg_likelihood = - self.policy.log_prob(obs_valid, actions_valid).mean()\n \n valid_loss = valid_neg_likelihood \\\n - self.entropy_coef * valid_entropy_loss \\\n + self.info_bottleneck_loss_coef * valid_info_bottleneck_loss\n \n policy_action_valid = self.policy(obs_valid).sample() \n h_train = self.policy.forward_embedding(obs)\n \n hscic_estimate = estimate_hscic(X=h_train, Y=prev_actions, Z=actions, ridge_lambda=1e-5)\n valid_hscic_estimate = estimate_hscic(X=h_valid, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5)\n valid_hscic_estimate_action = estimate_hscic(X=policy_action_valid, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5)\n \n eval_ret_mean, eval_ret_std = self.evaluate(num_iteration=self.num_eval_iteration)\n \n print(f'** iter{num+1}: entropy_loss={entropy_loss}, train_loss={train_loss.item()}, eval_ret={eval_ret_mean}+-{eval_ret_std} ')\n print(f'** HSCIC : (train){hscic_estimate:.6f} (valid){valid_hscic_estimate:.6f} (valid,action){valid_hscic_estimate_action:.6f}')\n \n if self.wandb:\n self.wandb.log({\n 'train_total_loss': train_loss.item(), \n 'valid_total_loss': valid_loss.item(),\n 'train_neg_likelihood': neg_likelihood.item(), \n 'valid_neg_likelihood': valid_neg_likelihood.item(),\n 'train_mean_hscic(rep,prev|target)': hscic_estimate,\n 'valid_mean_hscic(rep,prev|target)': valid_hscic_estimate,\n 'valid_mean_hscic(act,prev|target)': valid_hscic_estimate_action,\n 'valid_entropy_loss': entropy_loss, \n 'valid_IB_loss': info_bottleneck_loss.item(),\n 'eval_episode_return': eval_ret_mean\n }, step=num+1)\n\n if eval_ret_mean > max_score:\n print(f'** max score record! ')\n max_score = eval_ret_mean\n copy_nn_module(self.policy, self.best_policy)\n \n if self.save_policy_path:\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_best.pt')\n os.makedirs(self.save_policy_path, exist_ok=True)\n torch.save(self.best_policy.state_dict(), \n f'{self.save_policy_path}/bc_actor_best.pt')\n \n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_last.pt')\n os.makedirs(self.save_policy_path, exist_ok=True)\n torch.save(self.policy.state_dict(), \n f'{self.save_policy_path}/bc_actor_last.pt')\n \n \n def evaluate(self, num_iteration=5):\n rets = []\n maxtimestep = 1000\n for num in range(0, num_iteration):\n obs_list = []\n obs = np.zeros(self.obs_dim * self.stacksize)\n \n obs_ = self.env.reset()\n obs_list.append(obs_)\n\n obs = np.zeros(self.obs_dim * self.stacksize)\n obs[- self.obs_dim:] = obs_\n\n done = False\n t = 0\n ret = 0.\n \n while not done and t < maxtimestep: \n if self.standardize:\n obs = (obs - self.obs_mean[0]) / self.obs_std[0]\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n action = self.policy(obs).mean.cpu().detach().numpy()\n \n next_obs, rew, done, _ = self.env.step(action)\n ret += rew\n \n obs_ = next_obs \n obs_list.append(obs_)\n\n if len(obs_list) < self.stacksize:\n obs_ = np.concatenate(obs_list)\n obs = np.zeros(self.obs_dim * self.stacksize)\n obs[-(len(obs_list)) * self.obs_dim:] = obs_\n \n else:\n obs = np.concatenate(obs_list[-self.stacksize:])\n \n t += 1\n \n rets.append(ret)\n \n return np.mean(rets), np.std(rets)"},{"identifier":"MINE_BC","path":"imitation/mine.py","snippet":"class MINE_BC(nn.Module):\n def __init__(self, policy, env, best_policy=None,\n replay_buffer=None, replay_buffer_valid=None, seed=0, \n device='cpu', lr=3e-4, wandb=None, save_policy_path=None, \n obs_dim=1, action_dim=1, stacksize=1, standardize=True,\n embedding_dim=1, mine_lr=1e-4, reg_coef=1e-5, info_bottleneck_loss_coef=0.001, \n ):\n torch.manual_seed(seed)\n np.random.seed(seed)\n random.seed(seed)\n \n super(MINE_BC, self).__init__()\n\n self.env = env\n self.policy = policy\n self.best_policy = best_policy\n self.replay_buffer = replay_buffer\n self.replay_buffer_valid = replay_buffer_valid\n self.device = device\n \n self.obs_dim = obs_dim\n self.action_dim = action_dim\n self.embedding_dim = embedding_dim\n self.stacksize = stacksize\n \n # Additional Network for MINE Neural Estimator\n self.mine = MINE_DV(action_dim, action_dim + embedding_dim, device=device)\n \n # MINE-BC Hyperparameters\n self.reg_coef = reg_coef\n self.info_bottleneck_loss_coef = info_bottleneck_loss_coef\n\n self.embedding_optimizer = optim.Adam(policy.embedding_params, lr=lr)\n self.policy_optimizer = optim.Adam(policy.policy_params, lr=lr)\n self.mine_optimizer = optim.Adam(self.mine.parameters(), lr=mine_lr)\n \n self.num_eval_iteration = 50\n \n self.wandb = None\n if wandb:\n self.wandb = wandb\n self.wandb.init()\n\n self.save_policy_path = save_policy_path\n\n # For standardization \n self.standardize = standardize\n\n self.obs_mean_tt = torch.tensor(self.replay_buffer.obs_mean, device=device)\n self.obs_std_tt = torch.tensor(self.replay_buffer.obs_std, device=device)\n self.act_mean_tt = torch.tensor(self.replay_buffer.act_mean, device=device)\n self.act_std_tt = torch.tensor(self.replay_buffer.act_std, device=device)\n\n self.obs_mean = self.replay_buffer.obs_mean\n self.obs_std = self.replay_buffer.obs_std\n self.act_mean = self.replay_buffer.act_mean\n self.act_std = self.replay_buffer.act_std\n \n def train(self, total_iteration=1e6, eval_freq=1000, batch_size=1024, num_valid=2000, inner_steps=1):\n \n min_loss = 100000.\n max_score = -100000.\n \n batch_valid = self.replay_buffer_valid.get_batch(num_valid, standardize=self.standardize)\n \n obs_valid = batch_valid['observations']\n actions_valid = batch_valid['actions'][:, -self.action_dim:]\n prev_actions_valid = batch_valid['actions'][:, :-self.action_dim] \n \n obs_valid = torch.tensor(obs_valid, dtype=torch.float32, device=self.device)\n actions_valid = torch.tensor(actions_valid, dtype=torch.float32, device=self.device)\n prev_actions_valid = torch.tensor(prev_actions_valid, dtype=torch.float32, device=self.device)\n \n for num in range(0, int(total_iteration)):\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\n \n obs = batch['observations']\n actions = batch['actions'][:, -self.action_dim:]\n prev_actions = batch['actions'][:, :-self.action_dim]\n \n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\n prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device)\n\n # MINE : I (a_{t-1}; a_{t}, varphi_t)\n h = self.policy.forward_embedding(obs)\n expert_action_and_h = torch.cat([actions, h], dim=-1)\n \n self.policy_optimizer.zero_grad()\n self.embedding_optimizer.zero_grad()\n self.mine_optimizer.zero_grad()\n\n if self.reg_coef > 0:\n neg_likelihood = -self.policy.log_prob_policy_from_embedding(h, actions).mean()\n info_bottleneck_loss = 0.5 * (h ** 2).sum()\n mi_estimate = self.mine.get_mi_bound(prev_actions, expert_action_and_h, update_ema=False)\n\n train_loss = neg_likelihood \\\n + self.reg_coef * mi_estimate \\\n + self.info_bottleneck_loss_coef * info_bottleneck_loss\n \n train_loss.backward()\n \n self.policy_optimizer.step()\n self.embedding_optimizer.step()\n\n # MINE training\n for _ in range(inner_steps):\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\n\n obs = batch['observations']\n actions = batch['actions'][:, -self.action_dim:]\n prev_actions = batch['actions'][:, :-self.action_dim]\n \n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\n \n h = self.policy.forward_embedding(obs)\n expert_action_and_h = torch.cat([actions, h], dim=-1)\n \n prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device)\n \n mine_loss = -self.mine.get_mi_bound(prev_actions, expert_action_and_h.detach(), update_ema=True)\n\n self.mine_optimizer.zero_grad()\n mine_loss.backward()\n self.mine_optimizer.step()\n\n else:\n neg_likelihood = -self.policy.log_prob_policy_from_embedding(h, actions).mean()\n info_bottleneck_loss = 0.5 * (h ** 2).sum()\n \n train_loss = neg_likelihood + self.info_bottleneck_loss_coef * info_bottleneck_loss \n \n train_loss.backward()\n \n self.policy_optimizer.step()\n self.embedding_optimizer.step()\n \n\n if (num+1) % eval_freq == 0:\n h_valid = self.policy.forward_embedding(obs_valid)\n valid_info_bottleneck_loss = 0.5 * (h_valid ** 2).sum()\n \n if self.reg_coef > 0:\n expert_action_and_h_valid = torch.cat([actions_valid, h_valid], dim=-1) \n valid_mi_estimate = self.mine.get_mi_bound(prev_actions_valid, expert_action_and_h_valid, update_ema=False)\n else:\n valid_mi_estimate = 0.\n \n valid_neg_likelihood = -self.policy.log_prob(obs_valid, actions_valid).mean()\n\n valid_loss = valid_neg_likelihood \\\n + self.reg_coef * valid_mi_estimate \\\n + self.info_bottleneck_loss_coef * valid_info_bottleneck_loss\n \n policy_action_valid = self.policy(obs_valid).sample() \n h_train = self.policy.forward_embedding(obs)\n \n hscic_estimate = estimate_hscic(X=h_train, Y=prev_actions, Z=actions, ridge_lambda=1e-5)\n valid_hscic_estimate = estimate_hscic(X=h_valid, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5)\n valid_hscic_estimate_action = estimate_hscic(X=policy_action_valid, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5)\n \n eval_ret_mean, eval_ret_std = self.evaluate(num_iteration=self.num_eval_iteration)\n \n print(f'** iter{num+1}: mine_loss={-mi_estimate.cpu().item()}, train_loss={train_loss.item()}, eval_ret={eval_ret_mean}+-{eval_ret_std} ')\n print(f'** HSCIC : (train){hscic_estimate:.6f} (valid){valid_hscic_estimate:.6f} (valid,action){valid_hscic_estimate_action:.6f}')\n \n if self.wandb:\n self.wandb.log({\n 'train_total_loss': train_loss.cpu().item(),\n 'valid_total_loss': valid_loss.cpu().item(),\n 'train_neg_likelihood': neg_likelihood.cpu().item(),\n 'valid_neg_likelihood': valid_neg_likelihood.cpu().item(),\n 'train_mean_hscic(rep,prev|target)': hscic_estimate,\n 'valid_mean_hscic(rep,prev|target)': valid_hscic_estimate,\n 'valid_mean_hscic(act,prev|target)': valid_hscic_estimate_action,\n 'valid_mine_loss': -mi_estimate.cpu().item(),\n 'valid_IB_loss': info_bottleneck_loss.cpu().item(),\n 'eval_episode_return': eval_ret_mean\n }, step=num+1)\n\n if eval_ret_mean > max_score:\n print(f'** max score record! ')\n max_score = eval_ret_mean\n copy_nn_module(self.policy, self.best_policy)\n \n if self.save_policy_path:\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_best.pt')\n os.makedirs(self.save_policy_path, exist_ok=True)\n torch.save(self.best_policy.state_dict(), \n f'{self.save_policy_path}/bc_actor_best.pt')\n \n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_last.pt')\n os.makedirs(self.save_policy_path, exist_ok=True)\n torch.save(self.policy.state_dict(), \n f'{self.save_policy_path}/bc_actor_last.pt')\n \n \n def evaluate(self, num_iteration=5):\n rets = []\n maxtimestep = 1000\n for num in range(0, num_iteration):\n obs_list = []\n obs = np.zeros(self.obs_dim * self.stacksize)\n \n obs_ = self.env.reset()\n obs_list.append(obs_)\n\n obs = np.zeros(self.obs_dim * self.stacksize)\n obs[- self.obs_dim:] = obs_\n\n done = False\n t = 0\n ret = 0.\n \n while not done and t < maxtimestep: \n if self.standardize:\n obs = (obs - self.obs_mean[0]) / self.obs_std[0]\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n action = self.policy(obs).mean.cpu().detach().numpy()\n next_obs, rew, done, _ = self.env.step(action)\n ret += rew\n \n obs_ = next_obs \n obs_list.append(obs_)\n\n if len(obs_list) < self.stacksize:\n obs_ = np.concatenate(obs_list)\n obs = np.zeros(self.obs_dim * self.stacksize)\n obs[-(len(obs_list)) * self.obs_dim:] = obs_\n \n else:\n obs = np.concatenate(obs_list[-self.stacksize:])\n \n t += 1\n \n rets.append(ret)\n \n return np.mean(rets), np.std(rets)"},{"identifier":"PALR","path":"imitation/palr.py","snippet":"class PALR(nn.Module):\n def __init__(self, policy, env, best_policy=None,\n replay_buffer=None, replay_buffer_valid=None, seed=0, \n device='cpu', lr=3e-4, wandb=None, save_policy_path=None, \n obs_dim=1, action_dim=1, stacksize=1, standardize=True,\n reg_coef=0.01, ridge_lambda=1e-3):\n \n torch.manual_seed(seed)\n np.random.seed(seed)\n random.seed(seed)\n \n super(PALR, self).__init__()\n\n self.env = env\n self.policy = policy\n self.best_policy = best_policy\n self.replay_buffer = replay_buffer\n self.replay_buffer_valid = replay_buffer_valid\n self.device = device\n\n self.obs_dim = obs_dim\n self.action_dim = action_dim\n self.stacksize = stacksize\n \n self.policy_optimizer = optim.Adam(self.policy.parameters(), lr=lr) \n \n self.num_eval_iteration = 50\n \n self.wandb = None\n if wandb:\n self.wandb = wandb\n self.wandb.init()\n\n self.save_policy_path = save_policy_path\n \n # HSCIC Hyperparameters\n self.reg_coef = reg_coef\n self.ridge_lambda = ridge_lambda\n \n # For standardization\n self.standardize = standardize\n\n self.obs_mean_tt = torch.tensor(self.replay_buffer.obs_mean, device=device)\n self.obs_std_tt = torch.tensor(self.replay_buffer.obs_std, device=device)\n self.act_mean_tt = torch.tensor(self.replay_buffer.act_mean, device=device)\n self.act_std_tt = torch.tensor(self.replay_buffer.act_std, device=device)\n\n self.obs_mean = self.replay_buffer.obs_mean\n self.obs_std = self.replay_buffer.obs_std\n self.act_mean = self.replay_buffer.act_mean\n self.act_std = self.replay_buffer.act_std\n \n\n def train(self, total_iteration=1e6, eval_freq=1000, batch_size=1024, num_valid=2000):\n \n min_loss = 100000.\n max_score = -100000.\n \n batch_valid = self.replay_buffer_valid.get_batch(num_valid, standardize=self.standardize)\n \n obs_valid = batch_valid['observations'] \n actions_valid = batch_valid['actions'][:, -self.action_dim:]\n prev_expert_action_valid = batch_valid['actions'][:, :-self.action_dim]\n \n obs_valid = torch.tensor(obs_valid, dtype=torch.float32, device=self.device)\n actions_valid = torch.tensor(actions_valid, dtype=torch.float32, device=self.device)\n prev_expert_action_valid = torch.tensor(prev_expert_action_valid, dtype=torch.float32, device=self.device)\n \n for num in range(0, int(total_iteration)):\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\n\n obs = batch['observations']\n actions = batch['actions'][:, -self.action_dim:]\n prev_expert_action = batch['actions'][:, :-self.action_dim]\n \n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\n prev_expert_action = torch.tensor(prev_expert_action, dtype=torch.float32, device=self.device)\n\n neg_likelihood = - self.policy.log_prob(obs, actions).mean() \n policy_action = self.policy(obs).rsample()\n \n if self.reg_coef != 0: \n policy_embedding = self.policy.forward_embedding(obs)\n if self.standardize:\n Y_std = (prev_expert_action - self.act_mean_tt[0, :-self.action_dim])/ self.act_std_tt[0, :-self.action_dim]\n Z_std = (actions - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\n\n Y_std = Y_std.to(torch.float32)\n Z_std = Z_std.to(torch.float32)\n else:\n Y_std = prev_expert_action\n Z_std = actions\n \n hscic_estimate = estimate_hscic(X=policy_embedding, Y=Y_std, Z=Z_std, ridge_lambda=self.ridge_lambda)\n \n else:\n hscic_estimate = 0.\n \n train_loss = neg_likelihood + self.reg_coef * hscic_estimate \n\n self.policy_optimizer.zero_grad()\n train_loss.backward()\n self.policy_optimizer.step()\n\n if (num+1) % eval_freq == 0:\n policy_action = self.policy(obs).sample()\n policy_action_valid = self.policy(obs_valid).sample()\n \n # Train data HSCIC (for debugging) \n policy_embedding = self.policy.forward_embedding(obs)\n if self.standardize:\n Y_std = (prev_expert_action - self.act_mean_tt[0, :-self.action_dim])/ self.act_std_tt[0, :-self.action_dim]\n Z_std = (actions - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\n p_std = (policy_action - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\n\n Y_std = Y_std.to(torch.float32)\n Z_std = Z_std.to(torch.float32)\n p_std = p_std.to(torch.float32)\n \n else:\n Y_std = prev_expert_action\n Z_std = actions\n p_std = policy_action\n \n hscic_estimate = estimate_hscic(X=policy_embedding, Y=Y_std, Z=Z_std, ridge_lambda=self.ridge_lambda)\n \n policy_embedding_valid = self.policy.forward_embedding(obs_valid)\n if self.standardize:\n Y_std = (prev_expert_action_valid - self.act_mean_tt[0, :-self.action_dim])/ self.act_std_tt[0, :-self.action_dim]\n Z_std = (actions_valid - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\n\n Y_std = Y_std.to(torch.float32)\n Z_std = Z_std.to(torch.float32)\n else:\n Y_std = prev_expert_action_valid\n Z_std = actions_valid\n p_std = policy_action\n \n valid_hscic_estimate = estimate_hscic(X=policy_embedding_valid, Y=Y_std, Z=Z_std, ridge_lambda=self.ridge_lambda) \n valid_hscic_estimate_action = estimate_hscic(X=policy_action_valid, Y=prev_expert_action_valid, Z=actions_valid, ridge_lambda=self.ridge_lambda)\n\n valid_neg_likelihood = -self.policy.log_prob(obs_valid, actions_valid).mean()\n valid_loss = valid_neg_likelihood + self.reg_coef * valid_hscic_estimate\n\n eval_ret_mean, eval_ret_std = self.evaluate(num_iteration=self.num_eval_iteration)\n \n print(f'** iter{num+1}: train_policy_loss={train_loss.item():.2f}, val_policy_loss={valid_loss.item():.2f}, eval_ret={eval_ret_mean:.2f}+-{eval_ret_std:.2f}',)\n print(f'** HSCIC : (train){hscic_estimate:.6f} (valid){valid_hscic_estimate:.6f} (valid,action){valid_hscic_estimate_action:.6f}')\n \n if self.wandb:\n self.wandb.log({'train_total_loss': train_loss.item(), \n 'valid_total_loss': valid_loss.item(),\n 'train_neg_likelihood': neg_likelihood.item(),\n 'valid_neg_likelihood': valid_neg_likelihood.item(),\n 'train_mean_hscic(rep,prev|target)': hscic_estimate,\n 'valid_mean_hscic(rep,prev|target)': valid_hscic_estimate,\n 'valid_mean_hscic(act,prev|target)': valid_hscic_estimate_action,\n 'eval_episode_return': eval_ret_mean\n }, step=num+1)\n\n if eval_ret_mean > max_score:\n print(f'** max score record! ')\n max_score = eval_ret_mean\n copy_nn_module(self.policy, self.best_policy)\n \n if self.save_policy_path:\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_best.pt')\n os.makedirs(self.save_policy_path, exist_ok=True)\n torch.save(self.best_policy.state_dict(), \n f'{self.save_policy_path}/bc_actor_best.pt')\n \n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_last.pt')\n os.makedirs(self.save_policy_path, exist_ok=True)\n torch.save(self.policy.state_dict(), \n f'{self.save_policy_path}/bc_actor_last.pt')\n \n def evaluate(self, num_iteration=5):\n rets = []\n maxtimestep = 1000\n for num in range(0, num_iteration):\n obs_list = []\n obs = np.zeros(self.obs_dim * self.stacksize)\n \n obs_ = self.env.reset()\n obs_list.append(obs_)\n\n obs = np.zeros(self.obs_dim * self.stacksize)\n obs[- self.obs_dim:] = obs_\n\n done = False\n t = 0\n ret = 0.\n \n while not done and t < maxtimestep:\n if self.standardize:\n obs = (obs - self.obs_mean[0]) / self.obs_std[0]\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\n action = self.policy(obs).mean.cpu().detach().numpy()\n \n next_obs, rew, done, _ = self.env.step(action)\n ret += rew\n \n obs_ = next_obs \n obs_list.append(obs_)\n\n if len(obs_list) < self.stacksize:\n obs_ = np.concatenate(obs_list)\n obs = np.zeros(self.obs_dim * self.stacksize)\n obs[-(len(obs_list)) * self.obs_dim:] = obs_\n \n else:\n obs = np.concatenate(obs_list[-self.stacksize:])\n \n t += 1\n \n rets.append(ret)\n \n return np.mean(rets), np.std(rets)"},{"identifier":"TanhGaussianPolicyWithEmbedding","path":"core/policy.py","snippet":"class TanhGaussianPolicyWithEmbedding(TorchStochasticPolicy):\n \"\"\"\n Reference : \n https://github.com/AlvinWen428/fighting-copycat-agents/blob/52dabfd8b1c42e50f31d84bd431915aad62e09cb/imitation_learning/models/gan_model/__init__.py#L9\n \n Usage:\n\n ```\n policy = TanhGaussianPolicy(...)\n \"\"\"\n\n def __init__(\n self,\n obs_dim,\n action_dim,\n embedding_dim,\n embedding_hidden_size,\n policy_hidden_size, \n policy_std=None,\n disc_std=None,\n init_w=1e-3,\n device='cpu',\n hidden_activation=F.leaky_relu, \n layer_norm=False,\n **kwargs\n ):\n if device =='cuda':\n ptu.set_gpu_mode(True)\n self.device = device\n \n super(TanhGaussianPolicyWithEmbedding, self).__init__()\n # hidden_sizes,\n # input_size=obs_dim,\n # output_size=action_dim,\n # init_w=init_w,\n # device=device,\n # **kwargs\n # )\n\n self.input_size = obs_dim\n self.output_size = action_dim\n self.hidden_activation = hidden_activation\n self.layer_norm = layer_norm\n\n self.embedding_params = []\n self.disc_params = []\n self.policy_params = []\n\n self.embed_fcs = []\n # self.embed_layer_norms = []\n\n self.policy_fcs = []\n # self.policy_layer_norms = []\n\n self.disc_fcs = []\n # self.disc_layer_norms = []\n \n self.device = device\n in_size = self.input_size\n\n self.embed_fcs = nn.Sequential(\n nn.Linear(self.input_size, embedding_hidden_size, bias=False, device=self.device),\n # nn.BatchNorm1d(embedding_hidden_size),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Linear(embedding_hidden_size, embedding_dim, device=self.device), \n )\n self.embedding_params = self.embed_fcs.parameters()\n\n self.policy_fcs = nn.Sequential(\n nn.LeakyReLU(0.2, inplace=False),\n nn.Linear(embedding_dim, policy_hidden_size, device=self.device),\n nn.LeakyReLU(0.2, inplace=True),\n )\n # self.policy_params.append({'params': self.policy_fcs.parameters()})\n self.policy_mean = nn.Linear(policy_hidden_size, action_dim, device=self.device)\n self.policy_params.append({'params': self.policy_mean.parameters()}) \n \n # self.policy_fc1 = nn.Linear(embedding_dim, policy_hidden_size, device=self.device)\n # self.policy_fc1.weight.data.uniform_(-init_w, init_w)\n # self.policy_fc1.bias.data.fill_(0)\n # self.policy_params.append({'params': self.policy_fc1.parameters()}) \n # self.policy_fc2 = nn.Linear(policy_hidden_size, action_dim, device=self.device)\n # self.policy_fc2.weight.data.uniform_(-init_w, init_w)\n # self.policy_fc2.bias.data.fill_(0)\n # self.policy_params.append({'params': self.policy_fc2.parameters()}) \n\n self.policy_log_std = None\n self.policy_std = policy_std\n \n if policy_std is None:\n self.policy_fc_log_std = nn.Linear(policy_hidden_size, action_dim, device=self.device)\n # self.policy_fc_log_std.weight.data.uniform_(-init_w, init_w)\n # self.policy_fc_log_std.bias.data.uniform_(-init_w, init_w)\n self.policy_params.append({'params': self.policy_fc_log_std.parameters()})\n else:\n self.policy_log_std = np.log(policy_std)\n assert LOG_SIG_MIN <= self.policy_log_std <= LOG_SIG_MAX\n\n def forward(self, obs):\n # h = obs\n\n # h = self.hidden_activation(self.embed_fc1(h))\n # h = self.embed_fc2(h)\n\n # h = self.hidden_activation(self.policy_fc1(h))\n # policy_mean = self.policy_fc2(h)\n\n h = self.embed_fcs(obs)\n h = self.policy_fcs(h)\n policy_mean = self.policy_mean(h)\n\n if self.policy_std is None:\n policy_log_std = self.policy_fc_log_std(h)\n policy_log_std = torch.clamp(policy_log_std, LOG_SIG_MIN, LOG_SIG_MAX)\n policy_std = torch.exp(policy_log_std)\n else:\n policy_std = torch.from_numpy(np.array([self.policy_std, ])).float().to(ptu.device)\n\n return TanhNormal(policy_mean, policy_std)\n\n def forward_embedding(self, obs):\n # h = obs\n \n # h = self.hidden_activation(self.embed_fc1(h))\n # h = self.embed_fc2(h)\n h = self.embed_fcs(obs)\n\n return h\n\n def forward_policy_from_embedding(self, h):\n # h = self.hidden_activation(h)\n # h = self.hidden_activation(self.policy_fc1(h))\n h = self.policy_fcs(h)\n policy_mean = self.policy_mean(h)\n\n if self.policy_std is None:\n policy_log_std = self.policy_fc_log_std(h)\n policy_log_std = torch.clamp(policy_log_std, LOG_SIG_MIN, LOG_SIG_MAX)\n policy_std = torch.exp(policy_log_std)\n else:\n policy_std = torch.from_numpy(np.array([self.policy_std, ])).float().to(ptu.device)\n\n return TanhNormal(policy_mean, policy_std)\n\n def logprob(self, action, mean, std):\n tanh_normal = TanhNormal(mean, std)\n log_prob = tanh_normal.log_prob(\n action,\n )\n log_prob = log_prob.sum(dim=1, keepdim=True)\n return log_prob\n\n def log_prob(self, obs, action):\n tanh_normal = self.forward(obs)\n log_prob = tanh_normal.log_prob(\n action,\n )\n # log_prob = log_prob.sum(dim=1, keepdim=True)\n return log_prob\n\n def log_prob_policy_from_embedding(self, h, action):\n tanh_normal = self.forward_policy_from_embedding(h)\n log_prob = tanh_normal.log_prob(\n action,\n )\n # log_prob = log_prob.sum(dim=1, keepdim=True)\n return log_prob\n\n def predict_action_from_embedding(self, h):\n tanh_normal = self.forward_policy_from_embedding(h)\n pred_action = tanh_normal.mean \n # log_prob = log_prob.sum(dim=1, keepdim=True)\n return pred_action"},{"identifier":"TanhGaussianRAPPolicy","path":"core/policy.py","snippet":"class TanhGaussianRAPPolicy(TorchStochasticPolicy):\n \"\"\"\n Reference : \n \n Usage:\n\n ```\n policy = TanhGaussianPolicy(...)\n \"\"\"\n\n def __init__(\n self,\n obs_dim,\n stack_size,\n action_dim,\n embedding_dim,\n embedding_hidden_size,\n policy_hidden_size,\n residual_hidden_size,\n policy_std=None,\n residual_std=0.1,\n device='cpu',\n hidden_activation=F.leaky_relu, \n layer_norm=False,\n **kwargs\n ):\n if device =='cuda':\n ptu.set_gpu_mode(True)\n self.device = device\n \n super(TanhGaussianRAPPolicy, self).__init__()\n \n self.input_size = obs_dim\n self.stack_size = stack_size\n self.output_size = action_dim\n self.hidden_activation = hidden_activation\n self.layer_norm = layer_norm\n\n self.embedding_params = []\n self.residual_params = []\n self.policy_params = []\n\n self.history_embed_fcs = []\n self.single_embed_fcs = []\n # self.embed_layer_norms = []\n\n self.policy_fcs = []\n self.residual_fcs = []\n \n self.device = device\n in_size = self.input_size\n\n self.history_embed_fcs = nn.Sequential(\n nn.Linear(self.input_size * self.stack_size, embedding_hidden_size, bias=False, device=self.device),\n # nn.BatchNorm1d(embedding_hidden_size),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Linear(embedding_hidden_size, embedding_dim, device=self.device)\n )\n self.history_embedding_params = self.history_embed_fcs.parameters()\n \n self.single_embed_fcs = nn.Sequential(\n nn.Linear(self.input_size, embedding_hidden_size, bias=False, device=self.device),\n # nn.BatchNorm1d(embedding_hidden_size),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Linear(embedding_hidden_size, embedding_dim, device=self.device)\n )\n self.single_embedding_params = self.single_embed_fcs.parameters()\n\n self.policy_fcs = nn.Sequential(\n nn.LeakyReLU(0.2, inplace=False),\n nn.Linear(embedding_dim*2, policy_hidden_size, device=self.device),\n nn.LeakyReLU(0.2, inplace=True),\n )\n self.policy_params.append({'params': self.policy_fcs.parameters()})\n self.policy_mean = nn.Linear(policy_hidden_size, action_dim, device=self.device)\n self.policy_params.append({'params': self.policy_mean.parameters()}) \n\n self.policy_log_std = None\n self.policy_std = policy_std\n \n if policy_std is None:\n self.policy_fc_log_std = nn.Linear(policy_hidden_size, action_dim, device=self.device)\n # self.policy_fc_log_std.weight.data.uniform_(-init_w, init_w)\n # self.policy_fc_log_std.bias.data.uniform_(-init_w, init_w)\n self.policy_params.append({'params': self.policy_fc_log_std.parameters()})\n else:\n self.policy_log_std = np.log(policy_std)\n assert LOG_SIG_MIN <= self.policy_log_std <= LOG_SIG_MAX\n\n self.residual_fcs = nn.Sequential(\n # nn.LeakyReLU(0.2, inplace=False),\n nn.Linear(embedding_dim, residual_hidden_size, device=self.device),\n nn.LeakyReLU(0.2, inplace=True),\n )\n self.residual_params.append({'params': self.residual_fcs.parameters()})\n self.residual_mean = nn.Linear(residual_hidden_size, action_dim, device=self.device) \n self.residual_params.append({'params': self.residual_mean.parameters()})\n\n def forward(self, obs):\n if len(obs.shape) < 2:\n obs = obs[None]\n \n obs_total = obs\n obs_current = obs[:, -self.input_size:]\n\n m = self.history_embed_fcs(obs_total)\n h = self.single_embed_fcs(obs_current) \n \n policy_input = torch.cat([m.detach(), h], dim=-1)\n \n policy_input = self.policy_fcs(policy_input)\n policy_mean = self.policy_mean(policy_input)\n\n if self.policy_std is None:\n policy_log_std = self.policy_fc_log_std(policy_input)\n policy_log_std = torch.clamp(policy_log_std, LOG_SIG_MIN, LOG_SIG_MAX)\n policy_std = torch.exp(policy_log_std)\n else:\n policy_std = torch.from_numpy(np.array([self.policy_std, ])).float().to(ptu.device)\n\n policy_dist = TanhNormal(policy_mean, policy_std) \n \n return policy_dist #, residual_dist\n\n def forward_embedding(self, obs):\n obs_total = obs\n obs_current = obs[:, -self.input_size:]\n\n m = self.history_embed_fcs(obs_total)\n h = self.single_embed_fcs(obs_current)\n\n return m, h\n\n def forward_residual_from_m(self, m):\n residual_m = self.residual_fcs(m)\n residual_mean = self.residual_mean(residual_m) \n \n return residual_mean\n\n def forward_policy_from_embedding(self, m, h):\n policy_input = torch.cat([m.detach(), h], dim=-1)\n \n policy_input = self.policy_fcs(policy_input)\n policy_mean = self.policy_mean(policy_input)\n\n if self.policy_std is None:\n policy_log_std = self.policy_fc_log_std(policy_input)\n policy_log_std = torch.clamp(policy_log_std, LOG_SIG_MIN, LOG_SIG_MAX)\n policy_std = torch.exp(policy_log_std)\n else:\n policy_std = torch.from_numpy(np.array([self.policy_std, ])).float().to(ptu.device)\n\n return TanhNormal(policy_mean, policy_std)\n\n def logprob(self, action, mean, std):\n tanh_normal = TanhNormal(mean, std)\n log_prob = tanh_normal.log_prob(action)\n log_prob = log_prob.sum(dim=1, keepdim=True)\n return log_prob\n \n def log_prob(self, obs, action):\n tanh_normal = self.forward(obs)\n log_prob = tanh_normal.log_prob(action) \n return log_prob\n \n def log_prob_policy_from_m_h(self, m, h, action): \n tanh_normal = self.forward_policy_from_embedding(m, h)\n log_prob = tanh_normal.log_prob(action)\n return log_prob\n\n def predict_action_from_m_h(self, m, h):\n tanh_normal = self.forward_policy_from_embedding(m, h)\n pred_action = tanh_normal.mean \n return pred_action"},{"identifier":"EnvReplayBuffer","path":"core/replay_buffer.py","snippet":"class EnvReplayBuffer(SimpleReplayBuffer):\n def __init__(\n self,\n max_replay_buffer_size,\n env,\n stack_size=1,\n action_history_len=0,\n env_info_sizes=None,\n train_with_action_history=False\n ):\n \"\"\"\n :param max_replay_buffer_size:\n :param env:\n \"\"\"\n self.env = env\n self._ob_space = env.observation_space #.shape[0] * stack_size\n self._action_space = env.action_space\n\n if train_with_action_history:\n obs_dim = get_dim(self._ob_space) * stack_size + get_dim(self._action_space) * max(stack_size - 1, 1)\n else:\n obs_dim = get_dim(self._ob_space) * stack_size\n\n act_dim = get_dim(self._action_space) * (action_history_len)\n\n if env_info_sizes is None:\n if hasattr(env, 'info_sizes'):\n env_info_sizes = env.info_sizes\n else:\n env_info_sizes = dict()\n\n super().__init__(\n max_replay_buffer_size=max_replay_buffer_size,\n observation_dim=obs_dim,\n action_dim=act_dim,\n env_info_sizes=env_info_sizes\n )\n\n self.obs_mean = None\n self.obs_std = None\n\n self.act_mean = None\n self.act_std = None\n\n # def add_sample(self, observation, action, prev_action, reward, terminal,\n # next_observation, **kwargs):\n # if isinstance(self._action_space, Discrete):\n # new_action = np.zeros(self._action_dim)\n # new_action[action] = 1\n # else:\n # new_action = action\n\n # return super().add_sample(\n # observation=observation,\n # action=new_action,\n # prev_action=prev_action,\n # reward=reward,\n # next_observation=next_observation,\n # terminal=terminal,\n # # **kwargs\n # )\n\n def calculate_statistics(self):\n self.obs_mean = np.mean(self._observations[:self._top], axis=0, keepdims=True)\n self.obs_std = np.std(self._observations[:self._top], axis=0, keepdims=True)\n\n self.act_mean = np.mean(self._actions[:self._top], axis=0, keepdims=True)\n self.act_std = np.std(self._actions[:self._top], axis=0, keepdims=True)\n\n return self.obs_mean, self.obs_std, self.act_mean, self.act_std\n\n def set_statistics(self, obs_mean, obs_std, act_mean, act_std):\n self.obs_mean, self.obs_std, self.act_mean, self.act_std = obs_mean, obs_std, act_mean, act_std\n \n def get_statistics(self):\n return self.obs_mean, self.obs_std, self.act_mean, self.act_std\n\n def random_batch(self, batch_size, standardize=False):\n indices = np.random.choice(self._size, size=batch_size, replace=self._replace or self._size < batch_size)\n if not self._replace and self._size < batch_size:\n warnings.warn('Replace was set to false, but is temporarily set to true because batch size is larger than current size of replay.')\n\n if standardize and self.obs_mean is not None:\n obss = (self._observations[indices] - self.obs_mean) / self.obs_std\n # actions = (self._actions[indices] - self.act_mean) / self.act_std\n next_obss = (self._next_obs[indices] - self.obs_mean) / self.obs_std\n else:\n obss = self._observations[indices] \n # actions = self._actions[indices] \n next_obss = self._next_obs[indices]\n\n actions = self._actions[indices]\n \n batch = dict(\n observations=obss,\n actions=actions,\n # prev_actions=self._prev_actions[indices],\n rewards=self._rewards[indices],\n terminals=self._terminals[indices],\n next_observations=next_obss,\n )\n for key in self._env_info_keys:\n assert key not in batch.keys()\n batch[key] = self._env_infos[key][indices]\n\n return batch\n \n def get_batch(self, batch_size, standardize=False):\n datasize = min(batch_size, self._top) \n indices = np.arange(datasize)\n # if not self._replace and self._size < batch_size:\n # warnings.warn('Replace was set to false, but is temporarily set to true because batch size is larger than current size of replay.')\n\n if standardize and self.obs_mean is not None:\n obss = (self._observations[indices] - self.obs_mean) / self.obs_std\n # actions = (self._actions[indices] - self.act_mean) / self.act_std\n next_obss = (self._next_obs[indices] - self.obs_mean) / self.obs_std\n else:\n obss = self._observations[indices] \n # actions = self._actions[indices] \n next_obss = self._next_obs[indices]\n\n actions = self._actions[indices]\n \n batch = dict(\n observations=obss,\n actions=actions,\n # prev_actions=self._prev_actions[indices],\n rewards=self._rewards[indices],\n terminals=self._terminals[indices],\n next_observations=next_obss,\n )\n for key in self._env_info_keys:\n assert key not in batch.keys()\n batch[key] = self._env_infos[key][indices]\n\n return batch\n\n def add_sample(self, observation, action, reward, terminal,\n next_observation, **kwargs):\n if isinstance(self._action_space, Discrete):\n new_action = np.zeros(self._action_dim)\n new_action[action] = 1\n else:\n new_action = action\n\n return super().add_sample(\n observation=observation,\n action=new_action,\n reward=reward,\n next_observation=next_observation,\n terminal=terminal,\n # **kwargs\n )"},{"identifier":"preprocess_dataset_with_prev_actions","path":"core/preprocess.py","snippet":"def preprocess_dataset_with_prev_actions(mdpfile, envtype, stacksize=1, partially_observable=False, action_history_len=2):\n \n indx = list(np.arange(20))\n # Indices of position information observations\n if partially_observable:\n envtype_to_idx = {\n 'hopper': indx[:5], \n 'ant': indx[:13], \n 'walker2d': indx[:8], \n 'halfcheetah': indx[:4] + indx[8:13]\n }\n obs_idx = envtype_to_idx[envtype]\n observations = np.array(mdpfile['observations'])[:, obs_idx]\n next_observations = np.array(mdpfile['next_observations'])[:, obs_idx]\n else:\n observations = np.array(mdpfile['observations'])\n next_observations = np.array(mdpfile['next_observations'])\n \n terminals = np.array(mdpfile['terminals'])\n timeouts = np.array(mdpfile['timeouts'])\n rewards = np.array(mdpfile['rewards'])\n actions = np.array(mdpfile['actions'])\n\n obs_dim = observations.shape[-1]\n action_dim = actions.shape[-1]\n\n n_data = observations.shape[0]\n new_observations_list = []\n new_next_observations_list = []\n prev_action_list = []\n action_history_list = []\n \n idx_from_initial_state = 0\n num_trajs = 0\n\n for i in range(n_data):\n if idx_from_initial_state == 0:\n prev_action = np.zeros(action_dim)\n else:\n prev_action = actions[i-1]\n prev_action_list.append(prev_action)\n\n if idx_from_initial_state < stacksize:\n if idx_from_initial_state == 0:\n initial_obs = observations[i]\n \n new_observation = np.zeros(obs_dim * stacksize)\n new_observation_ = np.concatenate(observations[i-idx_from_initial_state: i+1])\n new_observation[-(idx_from_initial_state+1) * obs_dim:] = new_observation_\n \n new_next_observation = np.zeros(obs_dim * stacksize)\n new_next_observation_ = np.concatenate(next_observations[i-idx_from_initial_state: i+1])\n new_next_observation[-(idx_from_initial_state+1) * obs_dim:] = new_next_observation_\n \n if idx_from_initial_state + 1 != stacksize:\n new_next_observation[-(idx_from_initial_state+2) * obs_dim:-(idx_from_initial_state+1) * obs_dim] \\\n = initial_obs\n \n else:\n new_observation = np.concatenate(observations[i+1-stacksize:i+1])\n new_next_observation = np.concatenate(next_observations[i+1-stacksize:i+1])\n\n if idx_from_initial_state < action_history_len:\n action_history = np.zeros(action_dim * action_history_len)\n action_history_ = np.concatenate(actions[i-idx_from_initial_state: i+1])\n action_history[-(idx_from_initial_state+1) * action_dim:] = action_history_\n \n else:\n action_history = np.concatenate(actions[i+1-action_history_len:i+1])\n\n\n new_observations_list.append(new_observation)\n new_next_observations_list.append(new_next_observation)\n action_history_list.append(action_history)\n\n idx_from_initial_state += 1\n if terminals[i] or timeouts[i]:\n idx_from_initial_state = 0\n num_trajs += 1 \n\n new_observations = np.array(new_observations_list)\n new_next_observations = np.array(new_next_observations_list)\n new_actions = np.array(action_history_list)\n\n new_paths = {\n 'observations': new_observations,\n 'next_observations': new_next_observations,\n 'rewards': rewards,\n 'actions': new_actions,\n 'terminals': terminals,\n 'timeouts': timeouts \n }\n \n return new_paths"},{"identifier":"data_select_num_transitions","path":"core/preprocess.py","snippet":"def data_select_num_transitions(path, num_transitions=1000, start_idx=0, random=False):\n new_path = {}\n \n if random:\n num_full_trajs = len(path['observations'])\n choice_idx = np.random.choice(num_full_trajs, num_transitions)\n \n else:\n choice_idx = np.arange(start_idx, start_idx + num_transitions)\n \n for key in path.keys():\n new_path[key] = np.array(path[key])[choice_idx]\n \n return new_path"},{"identifier":"NormalizedBoxEnv","path":"rlkit/envs/wrappers.py","snippet":"class NormalizedBoxEnv(ProxyEnv):\n \"\"\"\n Normalize action to in [-1, 1].\n\n Optionally normalize observations and scale reward.\n \"\"\"\n\n def __init__(\n self,\n env,\n reward_scale=1.,\n obs_mean=None,\n obs_std=None,\n ):\n ProxyEnv.__init__(self, env)\n self._should_normalize = not (obs_mean is None and obs_std is None)\n if self._should_normalize:\n if obs_mean is None:\n obs_mean = np.zeros_like(env.observation_space.low)\n else:\n obs_mean = np.array(obs_mean)\n if obs_std is None:\n obs_std = np.ones_like(env.observation_space.low)\n else:\n obs_std = np.array(obs_std)\n self._reward_scale = reward_scale\n self._obs_mean = obs_mean\n self._obs_std = obs_std\n ub = np.ones(self._wrapped_env.action_space.shape)\n self.action_space = Box(-1 * ub, ub)\n\n def estimate_obs_stats(self, obs_batch, override_values=False):\n if self._obs_mean is not None and not override_values:\n raise Exception(\"Observation mean and std already set. To \"\n \"override, set override_values to True.\")\n self._obs_mean = np.mean(obs_batch, axis=0)\n self._obs_std = np.std(obs_batch, axis=0)\n\n def _apply_normalize_obs(self, obs):\n return (obs - self._obs_mean) / (self._obs_std + 1e-8)\n\n def step(self, action):\n lb = self._wrapped_env.action_space.low\n ub = self._wrapped_env.action_space.high\n scaled_action = lb + (action + 1.) * 0.5 * (ub - lb)\n scaled_action = np.clip(scaled_action, lb, ub)\n\n wrapped_step = self._wrapped_env.step(scaled_action)\n next_obs, reward, done, info = wrapped_step\n if self._should_normalize:\n next_obs = self._apply_normalize_obs(next_obs)\n return next_obs, reward * self._reward_scale, done, info\n\n def __str__(self):\n return \"Normalized: %s\" % self._wrapped_env"}],"string":"[\n {\n \"identifier\": \"BC\",\n \"path\": \"imitation/bc.py\",\n \"snippet\": \"class BC(nn.Module):\\n def __init__(self, policy, env, best_policy=None,\\n replay_buffer=None, replay_buffer_valid=None, seed=0, \\n device='cpu', lr=3e-4, envname=None, wandb=None, save_policy_path=None, \\n obs_dim=1, action_dim=1, stacksize=1, standardize=True):\\n \\n torch.manual_seed(seed)\\n np.random.seed(seed)\\n random.seed(seed)\\n \\n super(BC, self).__init__()\\n\\n self.env = env\\n self.policy = policy\\n self.best_policy = best_policy\\n self.replay_buffer = replay_buffer\\n self.replay_buffer_valid = replay_buffer_valid\\n self.device = device\\n \\n self.obs_dim = obs_dim\\n self.action_dim = action_dim \\n self.stacksize = stacksize\\n \\n self.policy_optimizer = optim.Adam(policy.parameters(), lr=lr)\\n \\n self.num_eval_iteration = 50\\n self.envname = envname\\n \\n self.wandb = None\\n if wandb:\\n self.wandb = wandb\\n self.wandb.init()\\n\\n self.save_policy_path = save_policy_path \\n \\n # For standardization\\n self.standardize = standardize\\n\\n self.obs_mean_tt = torch.tensor(self.replay_buffer.obs_mean, device=device)\\n self.obs_std_tt = torch.tensor(self.replay_buffer.obs_std, device=device)\\n self.act_mean_tt = torch.tensor(self.replay_buffer.act_mean, device=device)\\n self.act_std_tt = torch.tensor(self.replay_buffer.act_std, device=device)\\n\\n self.obs_mean = self.replay_buffer.obs_mean\\n self.obs_std = self.replay_buffer.obs_std\\n self.act_mean = self.replay_buffer.act_mean\\n self.act_std = self.replay_buffer.act_std\\n \\n\\n def train(self, total_iteration=1e6, eval_freq=1000, batch_size=1024, num_valid=2000):\\n \\n max_score = -100000.\\n \\n batch_valid = self.replay_buffer_valid.random_batch(num_valid, standardize=self.standardize)\\n \\n obs_valid = batch_valid['observations']\\n actions_valid = batch_valid['actions'][:, -self.action_dim:] \\n prev_expert_action_valid = batch_valid['actions'][:, :-self.action_dim] # For debugging\\n \\n obs_valid = torch.tensor(obs_valid, dtype=torch.float32, device=self.device)\\n actions_valid = torch.tensor(actions_valid, dtype=torch.float32, device=self.device)\\n prev_expert_action_valid = torch.tensor(prev_expert_action_valid, dtype=torch.float32, device=self.device)\\n \\n for num in range(0, int(total_iteration)):\\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\\n \\n obs = batch['observations']\\n actions = batch['actions'][:, -self.action_dim:]\\n \\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device) \\n\\n neg_likelihood = -self.policy.log_prob(obs, actions).mean()\\n train_loss = neg_likelihood\\n \\n self.policy_optimizer.zero_grad()\\n train_loss.backward()\\n self.policy_optimizer.step()\\n\\n if (num+1) % eval_freq == 0:\\n policy_action = self.policy(obs).sample()\\n policy_action_valid = self.policy(obs_valid).sample()\\n prev_expert_action = batch['actions'][:, :-self.action_dim] \\n prev_expert_action = torch.tensor(prev_expert_action, dtype=torch.float32, device=self.device) \\n \\n # Train data HSCIC (for debugging) \\n policy_embedding = self.policy.forward_embedding(obs)\\n if self.standardize:\\n Y_std = (prev_expert_action - self.act_mean_tt[0, :-self.action_dim])/ self.act_std_tt[0, :-self.action_dim]\\n Z_std = (actions - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\\n p_std = (policy_action - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\\n\\n Y_std = Y_std.to(torch.float32)\\n Z_std = Z_std.to(torch.float32)\\n p_std = p_std.to(torch.float32)\\n else:\\n Y_std = prev_expert_action\\n Z_std = actions\\n p_std = policy_action\\n \\n hscic_estimate = estimate_hscic(X=policy_embedding, Y=Y_std, Z=Z_std, ridge_lambda=1e-5)\\n \\n policy_embedding_valid = self.policy.forward_embedding(obs_valid)\\n if self.standardize:\\n Y_std = (prev_expert_action_valid - self.act_mean_tt[0, :-self.action_dim])/ self.act_std_tt[0, :-self.action_dim]\\n Z_std = (actions_valid - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\\n\\n Y_std = Y_std.to(torch.float32)\\n Z_std = Z_std.to(torch.float32)\\n else:\\n Y_std = prev_expert_action_valid\\n Z_std = actions_valid\\n p_std = policy_action\\n \\n valid_hscic_estimate = estimate_hscic(X=policy_embedding_valid, Y=Y_std, Z=Z_std, ridge_lambda=1e-5)\\n valid_hscic_estimate_action = estimate_hscic(X=policy_action_valid, Y=prev_expert_action_valid, Z=actions_valid, ridge_lambda=1e-5)\\n\\n valid_neg_likelihood = -self.policy.log_prob(obs_valid, actions_valid).mean()\\n valid_loss = valid_neg_likelihood\\n\\n eval_ret_mean, eval_ret_std = self.evaluate(num_iteration=self.num_eval_iteration)\\n \\n print(f'** iter{num+1}: train_policy_loss={train_loss.item():.2f}, val_policy_loss={valid_loss.item():.2f}, eval_ret={eval_ret_mean:.2f}+-{eval_ret_std:.2f} ({obs_valid.shape[0]})',)\\n print(f'** HSCIC : (train){hscic_estimate:.6f} (valid){valid_hscic_estimate:.6f} (valid,action){valid_hscic_estimate_action:.6f}')\\n \\n if self.wandb:\\n self.wandb.log({'train_total_loss': train_loss.item(), \\n 'valid_total_loss': valid_loss.item(),\\n 'train_neg_likelihood': neg_likelihood.item(),\\n 'valid_neg_likelihood': valid_neg_likelihood.item(),\\n 'train_mean_hscic(rep,prev|target)': hscic_estimate,\\n 'valid_mean_hscic(rep,prev|target)': valid_hscic_estimate,\\n 'valid_mean_hscic(act,prev|target)': valid_hscic_estimate_action,\\n 'eval_episode_return': eval_ret_mean\\n }, step=num+1)\\n\\n if eval_ret_mean > max_score:\\n print(f'** max score record! ')\\n max_score = eval_ret_mean\\n copy_nn_module(self.policy, self.best_policy)\\n \\n if self.save_policy_path:\\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_best.pt')\\n os.makedirs(self.save_policy_path, exist_ok=True)\\n torch.save(self.best_policy.state_dict(), \\n f'{self.save_policy_path}/bc_actor_best.pt')\\n \\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_last.pt')\\n os.makedirs(self.save_policy_path, exist_ok=True)\\n torch.save(self.policy.state_dict(), \\n f'{self.save_policy_path}/bc_actor_last.pt')\\n \\n \\n def evaluate(self, num_iteration=5):\\n rets = []\\n maxtimestep = 1000\\n for num in range(0, num_iteration):\\n obs_list = []\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n \\n obs_ = self.env.reset()\\n obs_list.append(obs_)\\n\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n obs[- self.obs_dim:] = obs_\\n\\n done = False\\n t = 0\\n ret = 0.\\n \\n while not done and t < maxtimestep:\\n if self.standardize:\\n obs = (obs - self.obs_mean[0]) / self.obs_std[0]\\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n action = self.policy(obs).mean.cpu().detach().numpy()\\n \\n next_obs, rew, done, _ = self.env.step(action)\\n ret += rew\\n \\n obs_ = next_obs \\n obs_list.append(obs_)\\n\\n if len(obs_list) < self.stacksize:\\n obs_ = np.concatenate(obs_list)\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n obs[-(len(obs_list)) * self.obs_dim:] = obs_\\n \\n else:\\n obs = np.concatenate(obs_list[-self.stacksize:])\\n \\n t += 1\\n \\n rets.append(ret)\\n \\n return np.mean(rets), np.std(rets)\"\n },\n {\n \"identifier\": \"RAP\",\n \"path\": \"imitation/rap.py\",\n \"snippet\": \"class RAP(nn.Module):\\n # Implementation of Residual Action Prediction (ECCV 2022)\\n # - https://arxiv.org/pdf/2207.09705.pdf\\n def __init__(self, policy, env, best_policy=None,\\n replay_buffer=None, replay_buffer_valid=None, seed=0, \\n device='cpu', lr=3e-4, wandb=None, save_policy_path=None, \\n obs_dim=1, action_dim=1, embedding_dim=1, stacksize=1, standardize=False\\n ):\\n torch.manual_seed(seed)\\n np.random.seed(seed)\\n random.seed(seed)\\n \\n super(RAP, self).__init__()\\n\\n self.env = env\\n self.policy = policy\\n self.best_policy = best_policy\\n self.replay_buffer = replay_buffer\\n self.replay_buffer_valid = replay_buffer_valid\\n \\n self.device = device\\n \\n self.m_embedding_optimizer = optim.Adam(policy.history_embedding_params, lr=lr)\\n self.h_embedding_optimizer = optim.Adam(policy.single_embedding_params, lr=lr)\\n self.policy_optimizer = optim.Adam(policy.policy_params, lr=lr)\\n self.residual_optimizer = optim.Adam(policy.residual_params, lr=lr)\\n\\n self.num_eval_iteration = 50 \\n \\n self.wandb = None\\n if wandb:\\n self.wandb = wandb\\n self.wandb.init()\\n\\n self.save_policy_path = save_policy_path\\n\\n self.obs_dim = obs_dim\\n self.action_dim = action_dim\\n self.embedding_dim = embedding_dim\\n self.stacksize = stacksize\\n \\n self.standardize = standardize\\n\\n self.obs_mean_tt = torch.tensor(self.replay_buffer.obs_mean, device=device)\\n self.obs_std_tt = torch.tensor(self.replay_buffer.obs_std, device=device)\\n self.act_mean_tt = torch.tensor(self.replay_buffer.act_mean, device=device)\\n self.act_std_tt = torch.tensor(self.replay_buffer.act_std, device=device)\\n\\n self.obs_mean = self.replay_buffer.obs_mean\\n self.obs_std = self.replay_buffer.obs_std\\n self.act_mean = self.replay_buffer.act_mean\\n self.act_std = self.replay_buffer.act_std\\n \\n\\n def train(self, total_iteration=1e6, eval_freq=1000, batch_size=1024, num_valid=2000):\\n \\n max_score = -100000. \\n min_loss = 100000. \\n \\n batch_valid = self.replay_buffer_valid.get_batch(num_valid, standardize=self.standardize)\\n \\n obs_valid = batch_valid['observations']\\n actions_valid = batch_valid['actions'][:, -self.action_dim:]\\n prev_actions_valid = batch_valid['actions'][:, :-self.action_dim] \\n \\n obs_valid = torch.tensor(obs_valid, dtype=torch.float32, device=self.device)\\n actions_valid = torch.tensor(actions_valid, dtype=torch.float32, device=self.device)\\n prev_actions_valid = torch.tensor(prev_actions_valid, dtype=torch.float32, device=self.device) \\n \\n for num in range(0, int(total_iteration)):\\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\\n \\n obs = batch['observations']\\n actions = batch['actions'][:, -self.action_dim:]\\n prev_actions = batch['actions'][:, :-self.action_dim]\\n \\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\\n prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device)\\n\\n self.m_embedding_optimizer.zero_grad()\\n self.residual_optimizer.zero_grad() \\n \\n # m : history embedding, h : single observation embedding\\n m, _ = self.policy.forward_embedding(obs) \\n action_residuals = actions - prev_actions\\n action_residual_pred = self.policy.forward_residual_from_m(m)\\n \\n train_residual_loss = torch.mean((action_residual_pred - action_residuals) ** 2)\\n train_residual_loss.backward()\\n \\n self.m_embedding_optimizer.step()\\n self.residual_optimizer.step() \\n \\n self.policy_optimizer.zero_grad() \\n self.h_embedding_optimizer.zero_grad() \\n \\n m, h = self.policy.forward_embedding(obs)\\n \\n # we follow the original implementation that stop-gradient layer on m ; \\n # see `forward_policy_from_embedding` method for detail. (m.detach() in input)\\n train_neg_likelihood = -self.policy.log_prob_policy_from_m_h(m, h, actions).mean()\\n train_neg_likelihood.backward()\\n \\n self.policy_optimizer.step()\\n self.h_embedding_optimizer.step()\\n \\n if (num+1) % eval_freq == 0: \\n valid_m, valid_h = self.policy.forward_embedding(obs_valid) \\n valid_action_residuals = actions_valid - prev_actions_valid\\n valid_action_residual_pred = self.policy.forward_residual_from_m(valid_m)\\n \\n valid_policy_neg_likelihood = -self.policy.log_prob_policy_from_m_h(valid_m, valid_h, actions_valid).mean()\\n valid_residual_loss = torch.mean((valid_action_residual_pred - valid_action_residuals) ** 2) \\n \\n valid_loss = valid_policy_neg_likelihood + valid_residual_loss\\n \\n policy_action_valid = self.policy(obs_valid).sample() \\n \\n train_mh = torch.cat([m,h], dim=-1)\\n valid_mh = torch.cat([valid_m, valid_h], dim=-1)\\n \\n hscic_estimate = estimate_hscic(X=train_mh, Y=prev_actions, Z=actions, ridge_lambda=1e-5)\\n valid_hscic_estimate = estimate_hscic(X=valid_mh, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5)\\n valid_hscic_estimate_action = estimate_hscic(X=policy_action_valid, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5) \\n train_hscic_m_a_given_aprev = estimate_hscic(X=m, Y=actions, Z=prev_actions, ridge_lambda=1e-5)\\n valid_hscic_m_a_given_aprev = estimate_hscic(X=valid_m, Y=actions_valid, Z=prev_actions_valid, ridge_lambda=1e-5)\\n \\n eval_ret_mean, eval_ret_std = self.evaluate(num_iteration=self.num_eval_iteration)\\n \\n train_loss = train_neg_likelihood + train_residual_loss\\n \\n print(f'** iter{num+1}: train_loss={train_loss.item()}, nll={train_neg_likelihood}, residual_loss={train_residual_loss}, eval_ret={eval_ret_mean}+-{eval_ret_std}')\\n print(f' valid_loss={valid_loss.item()}, valid_nll={valid_policy_neg_likelihood}, valid_residual_loss={valid_residual_loss}')\\n \\n print(f'** HSCIC(mh, a_prev | a_current) : (train){hscic_estimate:.6f} (valid){valid_hscic_estimate:.6f} (valid,action){valid_hscic_estimate_action:.6f}')\\n print(f'** HSCIC(m, a_current | a_prev) : (train){train_hscic_m_a_given_aprev:.6f} (valid){valid_hscic_m_a_given_aprev:.6f} ')\\n \\n if self.wandb:\\n self.wandb.log({\\n 'train_total_loss': train_loss.item(),\\n 'valid_total_loss': valid_loss.item(),\\n 'train_neg_likelihood': train_neg_likelihood.item(),\\n 'valid_neg_likelihood': valid_policy_neg_likelihood.item(),\\n 'train_mean_hscic(rep,prev|target)': hscic_estimate,\\n 'valid_mean_hscic(rep,prev|target)': valid_hscic_estimate,\\n 'valid_mean_hscic(act,prev|target)': valid_hscic_estimate_action,\\n 'train_residual_loss': train_residual_loss,\\n 'valid_residual_loss': valid_residual_loss,\\n 'train_mean_hscic(m,target|prev)': train_hscic_m_a_given_aprev,\\n 'valid_mean_hscic(m,target|prev)': valid_hscic_m_a_given_aprev,\\n 'eval_episode_return': eval_ret_mean\\n }, step=num+1)\\n\\n if eval_ret_mean > max_score:\\n print(f'** max score ')\\n max_score = eval_ret_mean\\n copy_nn_module(self.policy, self.best_policy)\\n \\n if self.save_policy_path:\\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_best.pt')\\n os.makedirs(self.save_policy_path, exist_ok=True)\\n torch.save(self.best_policy.state_dict(), \\n f'{self.save_policy_path}/bc_actor_best.pt')\\n \\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_last.pt')\\n os.makedirs(self.save_policy_path, exist_ok=True)\\n torch.save(self.policy.state_dict(), \\n f'{self.save_policy_path}/bc_actor_last.pt')\\n \\n \\n def evaluate(self, num_iteration=5):\\n rets = []\\n maxtimestep = 1000\\n for num in range(0, num_iteration):\\n obs_list = []\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n \\n obs_ = self.env.reset()\\n obs_list.append(obs_)\\n\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n obs[- self.obs_dim:] = obs_\\n\\n done = False\\n t = 0\\n ret = 0.\\n \\n while not done and t < maxtimestep:\\n # obs = obs[:true_obs_dim]\\n if self.standardize:\\n obs = (obs - self.obs_mean[0]) / self.obs_std[0]\\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n action = self.policy(obs).mean.cpu().detach().numpy()[0]\\n next_obs, rew, done, env_info = self.env.step(action)\\n ret += rew\\n \\n obs_ = next_obs \\n obs_list.append(obs_)\\n\\n if len(obs_list) < self.stacksize:\\n obs_ = np.concatenate(obs_list)\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n obs[-(len(obs_list)) * self.obs_dim:] = obs_\\n \\n else:\\n obs = np.concatenate(obs_list[-self.stacksize:])\\n \\n t += 1\\n \\n rets.append(ret)\\n \\n return np.mean(rets), np.std(rets)\"\n },\n {\n \"identifier\": \"FCA\",\n \"path\": \"imitation/fca.py\",\n \"snippet\": \"class FCA(nn.Module):\\n def __init__(self, policy, env, best_policy=None,\\n replay_buffer=None, replay_buffer_valid=None, seed=0, \\n device='cpu', lr=3e-4, wandb=None, save_policy_path=None, \\n obs_dim=1, action_dim=1, stacksize=1, standardize=True,\\n embedding_dim=1, entropy_hidden_size=300, entropy_lr=1e-4, reg_coef=1e-5, info_bottleneck_loss_coef=0.001, \\n ):\\n torch.manual_seed(seed)\\n np.random.seed(seed)\\n random.seed(seed)\\n \\n super(FCA, self).__init__()\\n\\n self.env = env\\n self.policy = policy\\n self.best_policy = best_policy\\n self.replay_buffer = replay_buffer\\n self.replay_buffer_valid = replay_buffer_valid \\n self.device = device\\n \\n self.obs_dim = obs_dim\\n self.action_dim = action_dim\\n self.embedding_dim = embedding_dim\\n self.stacksize = stacksize \\n\\n # Additional Network for Conditional Entropy (FCA)\\n self.entropy_input_size = embedding_dim + action_dim\\n self.entropy_hidden_size = entropy_hidden_size\\n self.entropy_net = nn.Sequential(\\n nn.Linear(self.entropy_input_size, self.entropy_hidden_size, device=self.device),\\n nn.LeakyReLU(0.2, inplace=True),\\n nn.Linear(self.entropy_hidden_size, action_dim, device=self.device)\\n )\\n \\n # FCA Hyperparameters\\n self.entropy_coef = reg_coef \\n self.info_bottleneck_loss_coef = info_bottleneck_loss_coef \\n \\n self.embedding_optimizer = optim.Adam(policy.embedding_params, lr=lr)\\n self.policy_optimizer = optim.Adam(policy.policy_params, lr=lr)\\n self.entropy_optimizer = optim.Adam(self.entropy_net.parameters(), lr=entropy_lr)\\n\\n self.num_eval_iteration = 50\\n \\n self.wandb = None\\n if wandb:\\n self.wandb = wandb\\n self.wandb.init()\\n\\n self.save_policy_path = save_policy_path\\n\\n # For standardization\\n self.standardize = standardize\\n\\n self.obs_mean_tt = torch.tensor(self.replay_buffer.obs_mean, device=device)\\n self.obs_std_tt = torch.tensor(self.replay_buffer.obs_std, device=device)\\n self.act_mean_tt = torch.tensor(self.replay_buffer.act_mean, device=device)\\n self.act_std_tt = torch.tensor(self.replay_buffer.act_std, device=device)\\n\\n self.obs_mean = self.replay_buffer.obs_mean\\n self.obs_std = self.replay_buffer.obs_std\\n self.act_mean = self.replay_buffer.act_mean\\n self.act_std = self.replay_buffer.act_std \\n\\n def train(self, total_iteration=1e6, eval_freq=1000, batch_size=1024, num_valid=2000, inner_steps=1):\\n \\n max_score = -100000. \\n min_loss = 100000. \\n \\n batch_valid = self.replay_buffer_valid.get_batch(num_valid, standardize=self.standardize)\\n \\n obs_valid = batch_valid['observations']\\n actions_valid = batch_valid['actions'][:, -self.action_dim:]\\n prev_actions_valid = batch_valid['actions'][:, :-self.action_dim] \\n \\n obs_valid = torch.tensor(obs_valid, dtype=torch.float32, device=self.device)\\n actions_valid = torch.tensor(actions_valid, dtype=torch.float32, device=self.device)\\n prev_actions_valid = torch.tensor(prev_actions_valid, dtype=torch.float32, device=self.device) \\n \\n for num in range(0, int(total_iteration)):\\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\\n \\n obs = batch['observations']\\n actions = batch['actions'][:, -self.action_dim:]\\n prev_actions = batch['actions'][:, :-self.action_dim]\\n \\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\\n prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device)\\n\\n # conditional entropy input : H(a_{t-1}| a_{t}, varphi_t)\\n h = self.policy.forward_embedding(obs)\\n expert_action_and_h = torch.cat([actions, h], dim=-1) \\n \\n self.policy_optimizer.zero_grad()\\n self.embedding_optimizer.zero_grad()\\n self.entropy_optimizer.zero_grad()\\n\\n if self.entropy_coef > 0.:\\n neg_likelihood = -self.policy.log_prob_policy_from_embedding(h, actions).mean()\\n info_bottleneck_loss = 0.5 * (h ** 2).sum()\\n\\n # prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device)\\n pred_prev_actions = self.entropy_net(expert_action_and_h) \\n entropy_loss = torch.mean((pred_prev_actions - prev_actions) ** 2) \\n\\n train_loss = neg_likelihood \\\\\\n - self.entropy_coef * entropy_loss \\\\\\n + self.info_bottleneck_loss_coef * info_bottleneck_loss\\n \\n train_loss.backward() # backprop embedding\\n \\n self.policy_optimizer.step()\\n self.embedding_optimizer.step()\\n\\n # conditional entropy training\\n for _ in range(inner_steps):\\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\\n \\n obs = batch['observations']\\n actions = batch['actions'][:, -self.action_dim:]\\n prev_actions = batch['actions'][:, :-self.action_dim]\\n \\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device) \\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\\n \\n h = self.policy.forward_embedding(obs)\\n expert_action_and_h = torch.cat([actions, h], dim=-1) \\n\\n prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device) \\n pred_prev_actions = self.entropy_net(expert_action_and_h.detach())\\n\\n entropy_loss = torch.mean((pred_prev_actions - prev_actions) ** 2)\\n \\n self.entropy_optimizer.zero_grad()\\n entropy_loss.backward()\\n self.entropy_optimizer.step()\\n\\n else:\\n neg_likelihood = -self.policy.log_prob_policy_from_embedding(h, actions).mean()\\n info_bottleneck_loss = 0.5 * (h ** 2).sum()\\n \\n train_loss = neg_likelihood + self.info_bottleneck_loss_coef * info_bottleneck_loss \\n \\n train_loss.backward()\\n \\n self.policy_optimizer.step()\\n self.embedding_optimizer.step() \\n \\n\\n if (num+1) % eval_freq == 0: \\n h_valid = self.policy.forward_embedding(obs_valid)\\n valid_info_bottleneck_loss = 0.5 * (h_valid ** 2).sum()\\n \\n if self.entropy_coef > 0:\\n expert_action_and_h_valid = torch.cat([actions_valid, h_valid], dim=-1) \\n pred_prev_actions_valid = self.entropy_net(expert_action_and_h_valid)\\n \\n prev_actions_valid = batch_valid['actions'][:, :-self.action_dim]\\n prev_actions_valid = torch.tensor(prev_actions_valid, dtype=torch.float32, device=self.device)\\n \\n valid_entropy_loss = torch.mean((pred_prev_actions_valid - prev_actions_valid) ** 2)\\n else:\\n valid_entropy_loss = 0.\\n \\n valid_neg_likelihood = - self.policy.log_prob(obs_valid, actions_valid).mean()\\n \\n valid_loss = valid_neg_likelihood \\\\\\n - self.entropy_coef * valid_entropy_loss \\\\\\n + self.info_bottleneck_loss_coef * valid_info_bottleneck_loss\\n \\n policy_action_valid = self.policy(obs_valid).sample() \\n h_train = self.policy.forward_embedding(obs)\\n \\n hscic_estimate = estimate_hscic(X=h_train, Y=prev_actions, Z=actions, ridge_lambda=1e-5)\\n valid_hscic_estimate = estimate_hscic(X=h_valid, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5)\\n valid_hscic_estimate_action = estimate_hscic(X=policy_action_valid, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5)\\n \\n eval_ret_mean, eval_ret_std = self.evaluate(num_iteration=self.num_eval_iteration)\\n \\n print(f'** iter{num+1}: entropy_loss={entropy_loss}, train_loss={train_loss.item()}, eval_ret={eval_ret_mean}+-{eval_ret_std} ')\\n print(f'** HSCIC : (train){hscic_estimate:.6f} (valid){valid_hscic_estimate:.6f} (valid,action){valid_hscic_estimate_action:.6f}')\\n \\n if self.wandb:\\n self.wandb.log({\\n 'train_total_loss': train_loss.item(), \\n 'valid_total_loss': valid_loss.item(),\\n 'train_neg_likelihood': neg_likelihood.item(), \\n 'valid_neg_likelihood': valid_neg_likelihood.item(),\\n 'train_mean_hscic(rep,prev|target)': hscic_estimate,\\n 'valid_mean_hscic(rep,prev|target)': valid_hscic_estimate,\\n 'valid_mean_hscic(act,prev|target)': valid_hscic_estimate_action,\\n 'valid_entropy_loss': entropy_loss, \\n 'valid_IB_loss': info_bottleneck_loss.item(),\\n 'eval_episode_return': eval_ret_mean\\n }, step=num+1)\\n\\n if eval_ret_mean > max_score:\\n print(f'** max score record! ')\\n max_score = eval_ret_mean\\n copy_nn_module(self.policy, self.best_policy)\\n \\n if self.save_policy_path:\\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_best.pt')\\n os.makedirs(self.save_policy_path, exist_ok=True)\\n torch.save(self.best_policy.state_dict(), \\n f'{self.save_policy_path}/bc_actor_best.pt')\\n \\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_last.pt')\\n os.makedirs(self.save_policy_path, exist_ok=True)\\n torch.save(self.policy.state_dict(), \\n f'{self.save_policy_path}/bc_actor_last.pt')\\n \\n \\n def evaluate(self, num_iteration=5):\\n rets = []\\n maxtimestep = 1000\\n for num in range(0, num_iteration):\\n obs_list = []\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n \\n obs_ = self.env.reset()\\n obs_list.append(obs_)\\n\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n obs[- self.obs_dim:] = obs_\\n\\n done = False\\n t = 0\\n ret = 0.\\n \\n while not done and t < maxtimestep: \\n if self.standardize:\\n obs = (obs - self.obs_mean[0]) / self.obs_std[0]\\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n action = self.policy(obs).mean.cpu().detach().numpy()\\n \\n next_obs, rew, done, _ = self.env.step(action)\\n ret += rew\\n \\n obs_ = next_obs \\n obs_list.append(obs_)\\n\\n if len(obs_list) < self.stacksize:\\n obs_ = np.concatenate(obs_list)\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n obs[-(len(obs_list)) * self.obs_dim:] = obs_\\n \\n else:\\n obs = np.concatenate(obs_list[-self.stacksize:])\\n \\n t += 1\\n \\n rets.append(ret)\\n \\n return np.mean(rets), np.std(rets)\"\n },\n {\n \"identifier\": \"MINE_BC\",\n \"path\": \"imitation/mine.py\",\n \"snippet\": \"class MINE_BC(nn.Module):\\n def __init__(self, policy, env, best_policy=None,\\n replay_buffer=None, replay_buffer_valid=None, seed=0, \\n device='cpu', lr=3e-4, wandb=None, save_policy_path=None, \\n obs_dim=1, action_dim=1, stacksize=1, standardize=True,\\n embedding_dim=1, mine_lr=1e-4, reg_coef=1e-5, info_bottleneck_loss_coef=0.001, \\n ):\\n torch.manual_seed(seed)\\n np.random.seed(seed)\\n random.seed(seed)\\n \\n super(MINE_BC, self).__init__()\\n\\n self.env = env\\n self.policy = policy\\n self.best_policy = best_policy\\n self.replay_buffer = replay_buffer\\n self.replay_buffer_valid = replay_buffer_valid\\n self.device = device\\n \\n self.obs_dim = obs_dim\\n self.action_dim = action_dim\\n self.embedding_dim = embedding_dim\\n self.stacksize = stacksize\\n \\n # Additional Network for MINE Neural Estimator\\n self.mine = MINE_DV(action_dim, action_dim + embedding_dim, device=device)\\n \\n # MINE-BC Hyperparameters\\n self.reg_coef = reg_coef\\n self.info_bottleneck_loss_coef = info_bottleneck_loss_coef\\n\\n self.embedding_optimizer = optim.Adam(policy.embedding_params, lr=lr)\\n self.policy_optimizer = optim.Adam(policy.policy_params, lr=lr)\\n self.mine_optimizer = optim.Adam(self.mine.parameters(), lr=mine_lr)\\n \\n self.num_eval_iteration = 50\\n \\n self.wandb = None\\n if wandb:\\n self.wandb = wandb\\n self.wandb.init()\\n\\n self.save_policy_path = save_policy_path\\n\\n # For standardization \\n self.standardize = standardize\\n\\n self.obs_mean_tt = torch.tensor(self.replay_buffer.obs_mean, device=device)\\n self.obs_std_tt = torch.tensor(self.replay_buffer.obs_std, device=device)\\n self.act_mean_tt = torch.tensor(self.replay_buffer.act_mean, device=device)\\n self.act_std_tt = torch.tensor(self.replay_buffer.act_std, device=device)\\n\\n self.obs_mean = self.replay_buffer.obs_mean\\n self.obs_std = self.replay_buffer.obs_std\\n self.act_mean = self.replay_buffer.act_mean\\n self.act_std = self.replay_buffer.act_std\\n \\n def train(self, total_iteration=1e6, eval_freq=1000, batch_size=1024, num_valid=2000, inner_steps=1):\\n \\n min_loss = 100000.\\n max_score = -100000.\\n \\n batch_valid = self.replay_buffer_valid.get_batch(num_valid, standardize=self.standardize)\\n \\n obs_valid = batch_valid['observations']\\n actions_valid = batch_valid['actions'][:, -self.action_dim:]\\n prev_actions_valid = batch_valid['actions'][:, :-self.action_dim] \\n \\n obs_valid = torch.tensor(obs_valid, dtype=torch.float32, device=self.device)\\n actions_valid = torch.tensor(actions_valid, dtype=torch.float32, device=self.device)\\n prev_actions_valid = torch.tensor(prev_actions_valid, dtype=torch.float32, device=self.device)\\n \\n for num in range(0, int(total_iteration)):\\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\\n \\n obs = batch['observations']\\n actions = batch['actions'][:, -self.action_dim:]\\n prev_actions = batch['actions'][:, :-self.action_dim]\\n \\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\\n prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device)\\n\\n # MINE : I (a_{t-1}; a_{t}, varphi_t)\\n h = self.policy.forward_embedding(obs)\\n expert_action_and_h = torch.cat([actions, h], dim=-1)\\n \\n self.policy_optimizer.zero_grad()\\n self.embedding_optimizer.zero_grad()\\n self.mine_optimizer.zero_grad()\\n\\n if self.reg_coef > 0:\\n neg_likelihood = -self.policy.log_prob_policy_from_embedding(h, actions).mean()\\n info_bottleneck_loss = 0.5 * (h ** 2).sum()\\n mi_estimate = self.mine.get_mi_bound(prev_actions, expert_action_and_h, update_ema=False)\\n\\n train_loss = neg_likelihood \\\\\\n + self.reg_coef * mi_estimate \\\\\\n + self.info_bottleneck_loss_coef * info_bottleneck_loss\\n \\n train_loss.backward()\\n \\n self.policy_optimizer.step()\\n self.embedding_optimizer.step()\\n\\n # MINE training\\n for _ in range(inner_steps):\\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\\n\\n obs = batch['observations']\\n actions = batch['actions'][:, -self.action_dim:]\\n prev_actions = batch['actions'][:, :-self.action_dim]\\n \\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\\n \\n h = self.policy.forward_embedding(obs)\\n expert_action_and_h = torch.cat([actions, h], dim=-1)\\n \\n prev_actions = torch.tensor(prev_actions, dtype=torch.float32, device=self.device)\\n \\n mine_loss = -self.mine.get_mi_bound(prev_actions, expert_action_and_h.detach(), update_ema=True)\\n\\n self.mine_optimizer.zero_grad()\\n mine_loss.backward()\\n self.mine_optimizer.step()\\n\\n else:\\n neg_likelihood = -self.policy.log_prob_policy_from_embedding(h, actions).mean()\\n info_bottleneck_loss = 0.5 * (h ** 2).sum()\\n \\n train_loss = neg_likelihood + self.info_bottleneck_loss_coef * info_bottleneck_loss \\n \\n train_loss.backward()\\n \\n self.policy_optimizer.step()\\n self.embedding_optimizer.step()\\n \\n\\n if (num+1) % eval_freq == 0:\\n h_valid = self.policy.forward_embedding(obs_valid)\\n valid_info_bottleneck_loss = 0.5 * (h_valid ** 2).sum()\\n \\n if self.reg_coef > 0:\\n expert_action_and_h_valid = torch.cat([actions_valid, h_valid], dim=-1) \\n valid_mi_estimate = self.mine.get_mi_bound(prev_actions_valid, expert_action_and_h_valid, update_ema=False)\\n else:\\n valid_mi_estimate = 0.\\n \\n valid_neg_likelihood = -self.policy.log_prob(obs_valid, actions_valid).mean()\\n\\n valid_loss = valid_neg_likelihood \\\\\\n + self.reg_coef * valid_mi_estimate \\\\\\n + self.info_bottleneck_loss_coef * valid_info_bottleneck_loss\\n \\n policy_action_valid = self.policy(obs_valid).sample() \\n h_train = self.policy.forward_embedding(obs)\\n \\n hscic_estimate = estimate_hscic(X=h_train, Y=prev_actions, Z=actions, ridge_lambda=1e-5)\\n valid_hscic_estimate = estimate_hscic(X=h_valid, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5)\\n valid_hscic_estimate_action = estimate_hscic(X=policy_action_valid, Y=prev_actions_valid, Z=actions_valid, ridge_lambda=1e-5)\\n \\n eval_ret_mean, eval_ret_std = self.evaluate(num_iteration=self.num_eval_iteration)\\n \\n print(f'** iter{num+1}: mine_loss={-mi_estimate.cpu().item()}, train_loss={train_loss.item()}, eval_ret={eval_ret_mean}+-{eval_ret_std} ')\\n print(f'** HSCIC : (train){hscic_estimate:.6f} (valid){valid_hscic_estimate:.6f} (valid,action){valid_hscic_estimate_action:.6f}')\\n \\n if self.wandb:\\n self.wandb.log({\\n 'train_total_loss': train_loss.cpu().item(),\\n 'valid_total_loss': valid_loss.cpu().item(),\\n 'train_neg_likelihood': neg_likelihood.cpu().item(),\\n 'valid_neg_likelihood': valid_neg_likelihood.cpu().item(),\\n 'train_mean_hscic(rep,prev|target)': hscic_estimate,\\n 'valid_mean_hscic(rep,prev|target)': valid_hscic_estimate,\\n 'valid_mean_hscic(act,prev|target)': valid_hscic_estimate_action,\\n 'valid_mine_loss': -mi_estimate.cpu().item(),\\n 'valid_IB_loss': info_bottleneck_loss.cpu().item(),\\n 'eval_episode_return': eval_ret_mean\\n }, step=num+1)\\n\\n if eval_ret_mean > max_score:\\n print(f'** max score record! ')\\n max_score = eval_ret_mean\\n copy_nn_module(self.policy, self.best_policy)\\n \\n if self.save_policy_path:\\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_best.pt')\\n os.makedirs(self.save_policy_path, exist_ok=True)\\n torch.save(self.best_policy.state_dict(), \\n f'{self.save_policy_path}/bc_actor_best.pt')\\n \\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_last.pt')\\n os.makedirs(self.save_policy_path, exist_ok=True)\\n torch.save(self.policy.state_dict(), \\n f'{self.save_policy_path}/bc_actor_last.pt')\\n \\n \\n def evaluate(self, num_iteration=5):\\n rets = []\\n maxtimestep = 1000\\n for num in range(0, num_iteration):\\n obs_list = []\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n \\n obs_ = self.env.reset()\\n obs_list.append(obs_)\\n\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n obs[- self.obs_dim:] = obs_\\n\\n done = False\\n t = 0\\n ret = 0.\\n \\n while not done and t < maxtimestep: \\n if self.standardize:\\n obs = (obs - self.obs_mean[0]) / self.obs_std[0]\\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n action = self.policy(obs).mean.cpu().detach().numpy()\\n next_obs, rew, done, _ = self.env.step(action)\\n ret += rew\\n \\n obs_ = next_obs \\n obs_list.append(obs_)\\n\\n if len(obs_list) < self.stacksize:\\n obs_ = np.concatenate(obs_list)\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n obs[-(len(obs_list)) * self.obs_dim:] = obs_\\n \\n else:\\n obs = np.concatenate(obs_list[-self.stacksize:])\\n \\n t += 1\\n \\n rets.append(ret)\\n \\n return np.mean(rets), np.std(rets)\"\n },\n {\n \"identifier\": \"PALR\",\n \"path\": \"imitation/palr.py\",\n \"snippet\": \"class PALR(nn.Module):\\n def __init__(self, policy, env, best_policy=None,\\n replay_buffer=None, replay_buffer_valid=None, seed=0, \\n device='cpu', lr=3e-4, wandb=None, save_policy_path=None, \\n obs_dim=1, action_dim=1, stacksize=1, standardize=True,\\n reg_coef=0.01, ridge_lambda=1e-3):\\n \\n torch.manual_seed(seed)\\n np.random.seed(seed)\\n random.seed(seed)\\n \\n super(PALR, self).__init__()\\n\\n self.env = env\\n self.policy = policy\\n self.best_policy = best_policy\\n self.replay_buffer = replay_buffer\\n self.replay_buffer_valid = replay_buffer_valid\\n self.device = device\\n\\n self.obs_dim = obs_dim\\n self.action_dim = action_dim\\n self.stacksize = stacksize\\n \\n self.policy_optimizer = optim.Adam(self.policy.parameters(), lr=lr) \\n \\n self.num_eval_iteration = 50\\n \\n self.wandb = None\\n if wandb:\\n self.wandb = wandb\\n self.wandb.init()\\n\\n self.save_policy_path = save_policy_path\\n \\n # HSCIC Hyperparameters\\n self.reg_coef = reg_coef\\n self.ridge_lambda = ridge_lambda\\n \\n # For standardization\\n self.standardize = standardize\\n\\n self.obs_mean_tt = torch.tensor(self.replay_buffer.obs_mean, device=device)\\n self.obs_std_tt = torch.tensor(self.replay_buffer.obs_std, device=device)\\n self.act_mean_tt = torch.tensor(self.replay_buffer.act_mean, device=device)\\n self.act_std_tt = torch.tensor(self.replay_buffer.act_std, device=device)\\n\\n self.obs_mean = self.replay_buffer.obs_mean\\n self.obs_std = self.replay_buffer.obs_std\\n self.act_mean = self.replay_buffer.act_mean\\n self.act_std = self.replay_buffer.act_std\\n \\n\\n def train(self, total_iteration=1e6, eval_freq=1000, batch_size=1024, num_valid=2000):\\n \\n min_loss = 100000.\\n max_score = -100000.\\n \\n batch_valid = self.replay_buffer_valid.get_batch(num_valid, standardize=self.standardize)\\n \\n obs_valid = batch_valid['observations'] \\n actions_valid = batch_valid['actions'][:, -self.action_dim:]\\n prev_expert_action_valid = batch_valid['actions'][:, :-self.action_dim]\\n \\n obs_valid = torch.tensor(obs_valid, dtype=torch.float32, device=self.device)\\n actions_valid = torch.tensor(actions_valid, dtype=torch.float32, device=self.device)\\n prev_expert_action_valid = torch.tensor(prev_expert_action_valid, dtype=torch.float32, device=self.device)\\n \\n for num in range(0, int(total_iteration)):\\n batch = self.replay_buffer.random_batch(batch_size, standardize=self.standardize)\\n\\n obs = batch['observations']\\n actions = batch['actions'][:, -self.action_dim:]\\n prev_expert_action = batch['actions'][:, :-self.action_dim]\\n \\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n actions = torch.tensor(actions, dtype=torch.float32, device=self.device)\\n prev_expert_action = torch.tensor(prev_expert_action, dtype=torch.float32, device=self.device)\\n\\n neg_likelihood = - self.policy.log_prob(obs, actions).mean() \\n policy_action = self.policy(obs).rsample()\\n \\n if self.reg_coef != 0: \\n policy_embedding = self.policy.forward_embedding(obs)\\n if self.standardize:\\n Y_std = (prev_expert_action - self.act_mean_tt[0, :-self.action_dim])/ self.act_std_tt[0, :-self.action_dim]\\n Z_std = (actions - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\\n\\n Y_std = Y_std.to(torch.float32)\\n Z_std = Z_std.to(torch.float32)\\n else:\\n Y_std = prev_expert_action\\n Z_std = actions\\n \\n hscic_estimate = estimate_hscic(X=policy_embedding, Y=Y_std, Z=Z_std, ridge_lambda=self.ridge_lambda)\\n \\n else:\\n hscic_estimate = 0.\\n \\n train_loss = neg_likelihood + self.reg_coef * hscic_estimate \\n\\n self.policy_optimizer.zero_grad()\\n train_loss.backward()\\n self.policy_optimizer.step()\\n\\n if (num+1) % eval_freq == 0:\\n policy_action = self.policy(obs).sample()\\n policy_action_valid = self.policy(obs_valid).sample()\\n \\n # Train data HSCIC (for debugging) \\n policy_embedding = self.policy.forward_embedding(obs)\\n if self.standardize:\\n Y_std = (prev_expert_action - self.act_mean_tt[0, :-self.action_dim])/ self.act_std_tt[0, :-self.action_dim]\\n Z_std = (actions - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\\n p_std = (policy_action - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\\n\\n Y_std = Y_std.to(torch.float32)\\n Z_std = Z_std.to(torch.float32)\\n p_std = p_std.to(torch.float32)\\n \\n else:\\n Y_std = prev_expert_action\\n Z_std = actions\\n p_std = policy_action\\n \\n hscic_estimate = estimate_hscic(X=policy_embedding, Y=Y_std, Z=Z_std, ridge_lambda=self.ridge_lambda)\\n \\n policy_embedding_valid = self.policy.forward_embedding(obs_valid)\\n if self.standardize:\\n Y_std = (prev_expert_action_valid - self.act_mean_tt[0, :-self.action_dim])/ self.act_std_tt[0, :-self.action_dim]\\n Z_std = (actions_valid - self.act_mean_tt[0, -self.action_dim:])/ self.act_std_tt[0, -self.action_dim:]\\n\\n Y_std = Y_std.to(torch.float32)\\n Z_std = Z_std.to(torch.float32)\\n else:\\n Y_std = prev_expert_action_valid\\n Z_std = actions_valid\\n p_std = policy_action\\n \\n valid_hscic_estimate = estimate_hscic(X=policy_embedding_valid, Y=Y_std, Z=Z_std, ridge_lambda=self.ridge_lambda) \\n valid_hscic_estimate_action = estimate_hscic(X=policy_action_valid, Y=prev_expert_action_valid, Z=actions_valid, ridge_lambda=self.ridge_lambda)\\n\\n valid_neg_likelihood = -self.policy.log_prob(obs_valid, actions_valid).mean()\\n valid_loss = valid_neg_likelihood + self.reg_coef * valid_hscic_estimate\\n\\n eval_ret_mean, eval_ret_std = self.evaluate(num_iteration=self.num_eval_iteration)\\n \\n print(f'** iter{num+1}: train_policy_loss={train_loss.item():.2f}, val_policy_loss={valid_loss.item():.2f}, eval_ret={eval_ret_mean:.2f}+-{eval_ret_std:.2f}',)\\n print(f'** HSCIC : (train){hscic_estimate:.6f} (valid){valid_hscic_estimate:.6f} (valid,action){valid_hscic_estimate_action:.6f}')\\n \\n if self.wandb:\\n self.wandb.log({'train_total_loss': train_loss.item(), \\n 'valid_total_loss': valid_loss.item(),\\n 'train_neg_likelihood': neg_likelihood.item(),\\n 'valid_neg_likelihood': valid_neg_likelihood.item(),\\n 'train_mean_hscic(rep,prev|target)': hscic_estimate,\\n 'valid_mean_hscic(rep,prev|target)': valid_hscic_estimate,\\n 'valid_mean_hscic(act,prev|target)': valid_hscic_estimate_action,\\n 'eval_episode_return': eval_ret_mean\\n }, step=num+1)\\n\\n if eval_ret_mean > max_score:\\n print(f'** max score record! ')\\n max_score = eval_ret_mean\\n copy_nn_module(self.policy, self.best_policy)\\n \\n if self.save_policy_path:\\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_best.pt')\\n os.makedirs(self.save_policy_path, exist_ok=True)\\n torch.save(self.best_policy.state_dict(), \\n f'{self.save_policy_path}/bc_actor_best.pt')\\n \\n print(f'** save model to ', f'{self.save_policy_path}/bc_actor_last.pt')\\n os.makedirs(self.save_policy_path, exist_ok=True)\\n torch.save(self.policy.state_dict(), \\n f'{self.save_policy_path}/bc_actor_last.pt')\\n \\n def evaluate(self, num_iteration=5):\\n rets = []\\n maxtimestep = 1000\\n for num in range(0, num_iteration):\\n obs_list = []\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n \\n obs_ = self.env.reset()\\n obs_list.append(obs_)\\n\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n obs[- self.obs_dim:] = obs_\\n\\n done = False\\n t = 0\\n ret = 0.\\n \\n while not done and t < maxtimestep:\\n if self.standardize:\\n obs = (obs - self.obs_mean[0]) / self.obs_std[0]\\n obs = torch.tensor(obs, dtype=torch.float32, device=self.device)\\n action = self.policy(obs).mean.cpu().detach().numpy()\\n \\n next_obs, rew, done, _ = self.env.step(action)\\n ret += rew\\n \\n obs_ = next_obs \\n obs_list.append(obs_)\\n\\n if len(obs_list) < self.stacksize:\\n obs_ = np.concatenate(obs_list)\\n obs = np.zeros(self.obs_dim * self.stacksize)\\n obs[-(len(obs_list)) * self.obs_dim:] = obs_\\n \\n else:\\n obs = np.concatenate(obs_list[-self.stacksize:])\\n \\n t += 1\\n \\n rets.append(ret)\\n \\n return np.mean(rets), np.std(rets)\"\n },\n {\n \"identifier\": \"TanhGaussianPolicyWithEmbedding\",\n \"path\": \"core/policy.py\",\n \"snippet\": \"class TanhGaussianPolicyWithEmbedding(TorchStochasticPolicy):\\n \\\"\\\"\\\"\\n Reference : \\n https://github.com/AlvinWen428/fighting-copycat-agents/blob/52dabfd8b1c42e50f31d84bd431915aad62e09cb/imitation_learning/models/gan_model/__init__.py#L9\\n \\n Usage:\\n\\n ```\\n policy = TanhGaussianPolicy(...)\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n obs_dim,\\n action_dim,\\n embedding_dim,\\n embedding_hidden_size,\\n policy_hidden_size, \\n policy_std=None,\\n disc_std=None,\\n init_w=1e-3,\\n device='cpu',\\n hidden_activation=F.leaky_relu, \\n layer_norm=False,\\n **kwargs\\n ):\\n if device =='cuda':\\n ptu.set_gpu_mode(True)\\n self.device = device\\n \\n super(TanhGaussianPolicyWithEmbedding, self).__init__()\\n # hidden_sizes,\\n # input_size=obs_dim,\\n # output_size=action_dim,\\n # init_w=init_w,\\n # device=device,\\n # **kwargs\\n # )\\n\\n self.input_size = obs_dim\\n self.output_size = action_dim\\n self.hidden_activation = hidden_activation\\n self.layer_norm = layer_norm\\n\\n self.embedding_params = []\\n self.disc_params = []\\n self.policy_params = []\\n\\n self.embed_fcs = []\\n # self.embed_layer_norms = []\\n\\n self.policy_fcs = []\\n # self.policy_layer_norms = []\\n\\n self.disc_fcs = []\\n # self.disc_layer_norms = []\\n \\n self.device = device\\n in_size = self.input_size\\n\\n self.embed_fcs = nn.Sequential(\\n nn.Linear(self.input_size, embedding_hidden_size, bias=False, device=self.device),\\n # nn.BatchNorm1d(embedding_hidden_size),\\n nn.LeakyReLU(0.2, inplace=True),\\n nn.Linear(embedding_hidden_size, embedding_dim, device=self.device), \\n )\\n self.embedding_params = self.embed_fcs.parameters()\\n\\n self.policy_fcs = nn.Sequential(\\n nn.LeakyReLU(0.2, inplace=False),\\n nn.Linear(embedding_dim, policy_hidden_size, device=self.device),\\n nn.LeakyReLU(0.2, inplace=True),\\n )\\n # self.policy_params.append({'params': self.policy_fcs.parameters()})\\n self.policy_mean = nn.Linear(policy_hidden_size, action_dim, device=self.device)\\n self.policy_params.append({'params': self.policy_mean.parameters()}) \\n \\n # self.policy_fc1 = nn.Linear(embedding_dim, policy_hidden_size, device=self.device)\\n # self.policy_fc1.weight.data.uniform_(-init_w, init_w)\\n # self.policy_fc1.bias.data.fill_(0)\\n # self.policy_params.append({'params': self.policy_fc1.parameters()}) \\n # self.policy_fc2 = nn.Linear(policy_hidden_size, action_dim, device=self.device)\\n # self.policy_fc2.weight.data.uniform_(-init_w, init_w)\\n # self.policy_fc2.bias.data.fill_(0)\\n # self.policy_params.append({'params': self.policy_fc2.parameters()}) \\n\\n self.policy_log_std = None\\n self.policy_std = policy_std\\n \\n if policy_std is None:\\n self.policy_fc_log_std = nn.Linear(policy_hidden_size, action_dim, device=self.device)\\n # self.policy_fc_log_std.weight.data.uniform_(-init_w, init_w)\\n # self.policy_fc_log_std.bias.data.uniform_(-init_w, init_w)\\n self.policy_params.append({'params': self.policy_fc_log_std.parameters()})\\n else:\\n self.policy_log_std = np.log(policy_std)\\n assert LOG_SIG_MIN <= self.policy_log_std <= LOG_SIG_MAX\\n\\n def forward(self, obs):\\n # h = obs\\n\\n # h = self.hidden_activation(self.embed_fc1(h))\\n # h = self.embed_fc2(h)\\n\\n # h = self.hidden_activation(self.policy_fc1(h))\\n # policy_mean = self.policy_fc2(h)\\n\\n h = self.embed_fcs(obs)\\n h = self.policy_fcs(h)\\n policy_mean = self.policy_mean(h)\\n\\n if self.policy_std is None:\\n policy_log_std = self.policy_fc_log_std(h)\\n policy_log_std = torch.clamp(policy_log_std, LOG_SIG_MIN, LOG_SIG_MAX)\\n policy_std = torch.exp(policy_log_std)\\n else:\\n policy_std = torch.from_numpy(np.array([self.policy_std, ])).float().to(ptu.device)\\n\\n return TanhNormal(policy_mean, policy_std)\\n\\n def forward_embedding(self, obs):\\n # h = obs\\n \\n # h = self.hidden_activation(self.embed_fc1(h))\\n # h = self.embed_fc2(h)\\n h = self.embed_fcs(obs)\\n\\n return h\\n\\n def forward_policy_from_embedding(self, h):\\n # h = self.hidden_activation(h)\\n # h = self.hidden_activation(self.policy_fc1(h))\\n h = self.policy_fcs(h)\\n policy_mean = self.policy_mean(h)\\n\\n if self.policy_std is None:\\n policy_log_std = self.policy_fc_log_std(h)\\n policy_log_std = torch.clamp(policy_log_std, LOG_SIG_MIN, LOG_SIG_MAX)\\n policy_std = torch.exp(policy_log_std)\\n else:\\n policy_std = torch.from_numpy(np.array([self.policy_std, ])).float().to(ptu.device)\\n\\n return TanhNormal(policy_mean, policy_std)\\n\\n def logprob(self, action, mean, std):\\n tanh_normal = TanhNormal(mean, std)\\n log_prob = tanh_normal.log_prob(\\n action,\\n )\\n log_prob = log_prob.sum(dim=1, keepdim=True)\\n return log_prob\\n\\n def log_prob(self, obs, action):\\n tanh_normal = self.forward(obs)\\n log_prob = tanh_normal.log_prob(\\n action,\\n )\\n # log_prob = log_prob.sum(dim=1, keepdim=True)\\n return log_prob\\n\\n def log_prob_policy_from_embedding(self, h, action):\\n tanh_normal = self.forward_policy_from_embedding(h)\\n log_prob = tanh_normal.log_prob(\\n action,\\n )\\n # log_prob = log_prob.sum(dim=1, keepdim=True)\\n return log_prob\\n\\n def predict_action_from_embedding(self, h):\\n tanh_normal = self.forward_policy_from_embedding(h)\\n pred_action = tanh_normal.mean \\n # log_prob = log_prob.sum(dim=1, keepdim=True)\\n return pred_action\"\n },\n {\n \"identifier\": \"TanhGaussianRAPPolicy\",\n \"path\": \"core/policy.py\",\n \"snippet\": \"class TanhGaussianRAPPolicy(TorchStochasticPolicy):\\n \\\"\\\"\\\"\\n Reference : \\n \\n Usage:\\n\\n ```\\n policy = TanhGaussianPolicy(...)\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n obs_dim,\\n stack_size,\\n action_dim,\\n embedding_dim,\\n embedding_hidden_size,\\n policy_hidden_size,\\n residual_hidden_size,\\n policy_std=None,\\n residual_std=0.1,\\n device='cpu',\\n hidden_activation=F.leaky_relu, \\n layer_norm=False,\\n **kwargs\\n ):\\n if device =='cuda':\\n ptu.set_gpu_mode(True)\\n self.device = device\\n \\n super(TanhGaussianRAPPolicy, self).__init__()\\n \\n self.input_size = obs_dim\\n self.stack_size = stack_size\\n self.output_size = action_dim\\n self.hidden_activation = hidden_activation\\n self.layer_norm = layer_norm\\n\\n self.embedding_params = []\\n self.residual_params = []\\n self.policy_params = []\\n\\n self.history_embed_fcs = []\\n self.single_embed_fcs = []\\n # self.embed_layer_norms = []\\n\\n self.policy_fcs = []\\n self.residual_fcs = []\\n \\n self.device = device\\n in_size = self.input_size\\n\\n self.history_embed_fcs = nn.Sequential(\\n nn.Linear(self.input_size * self.stack_size, embedding_hidden_size, bias=False, device=self.device),\\n # nn.BatchNorm1d(embedding_hidden_size),\\n nn.LeakyReLU(0.2, inplace=True),\\n nn.Linear(embedding_hidden_size, embedding_dim, device=self.device)\\n )\\n self.history_embedding_params = self.history_embed_fcs.parameters()\\n \\n self.single_embed_fcs = nn.Sequential(\\n nn.Linear(self.input_size, embedding_hidden_size, bias=False, device=self.device),\\n # nn.BatchNorm1d(embedding_hidden_size),\\n nn.LeakyReLU(0.2, inplace=True),\\n nn.Linear(embedding_hidden_size, embedding_dim, device=self.device)\\n )\\n self.single_embedding_params = self.single_embed_fcs.parameters()\\n\\n self.policy_fcs = nn.Sequential(\\n nn.LeakyReLU(0.2, inplace=False),\\n nn.Linear(embedding_dim*2, policy_hidden_size, device=self.device),\\n nn.LeakyReLU(0.2, inplace=True),\\n )\\n self.policy_params.append({'params': self.policy_fcs.parameters()})\\n self.policy_mean = nn.Linear(policy_hidden_size, action_dim, device=self.device)\\n self.policy_params.append({'params': self.policy_mean.parameters()}) \\n\\n self.policy_log_std = None\\n self.policy_std = policy_std\\n \\n if policy_std is None:\\n self.policy_fc_log_std = nn.Linear(policy_hidden_size, action_dim, device=self.device)\\n # self.policy_fc_log_std.weight.data.uniform_(-init_w, init_w)\\n # self.policy_fc_log_std.bias.data.uniform_(-init_w, init_w)\\n self.policy_params.append({'params': self.policy_fc_log_std.parameters()})\\n else:\\n self.policy_log_std = np.log(policy_std)\\n assert LOG_SIG_MIN <= self.policy_log_std <= LOG_SIG_MAX\\n\\n self.residual_fcs = nn.Sequential(\\n # nn.LeakyReLU(0.2, inplace=False),\\n nn.Linear(embedding_dim, residual_hidden_size, device=self.device),\\n nn.LeakyReLU(0.2, inplace=True),\\n )\\n self.residual_params.append({'params': self.residual_fcs.parameters()})\\n self.residual_mean = nn.Linear(residual_hidden_size, action_dim, device=self.device) \\n self.residual_params.append({'params': self.residual_mean.parameters()})\\n\\n def forward(self, obs):\\n if len(obs.shape) < 2:\\n obs = obs[None]\\n \\n obs_total = obs\\n obs_current = obs[:, -self.input_size:]\\n\\n m = self.history_embed_fcs(obs_total)\\n h = self.single_embed_fcs(obs_current) \\n \\n policy_input = torch.cat([m.detach(), h], dim=-1)\\n \\n policy_input = self.policy_fcs(policy_input)\\n policy_mean = self.policy_mean(policy_input)\\n\\n if self.policy_std is None:\\n policy_log_std = self.policy_fc_log_std(policy_input)\\n policy_log_std = torch.clamp(policy_log_std, LOG_SIG_MIN, LOG_SIG_MAX)\\n policy_std = torch.exp(policy_log_std)\\n else:\\n policy_std = torch.from_numpy(np.array([self.policy_std, ])).float().to(ptu.device)\\n\\n policy_dist = TanhNormal(policy_mean, policy_std) \\n \\n return policy_dist #, residual_dist\\n\\n def forward_embedding(self, obs):\\n obs_total = obs\\n obs_current = obs[:, -self.input_size:]\\n\\n m = self.history_embed_fcs(obs_total)\\n h = self.single_embed_fcs(obs_current)\\n\\n return m, h\\n\\n def forward_residual_from_m(self, m):\\n residual_m = self.residual_fcs(m)\\n residual_mean = self.residual_mean(residual_m) \\n \\n return residual_mean\\n\\n def forward_policy_from_embedding(self, m, h):\\n policy_input = torch.cat([m.detach(), h], dim=-1)\\n \\n policy_input = self.policy_fcs(policy_input)\\n policy_mean = self.policy_mean(policy_input)\\n\\n if self.policy_std is None:\\n policy_log_std = self.policy_fc_log_std(policy_input)\\n policy_log_std = torch.clamp(policy_log_std, LOG_SIG_MIN, LOG_SIG_MAX)\\n policy_std = torch.exp(policy_log_std)\\n else:\\n policy_std = torch.from_numpy(np.array([self.policy_std, ])).float().to(ptu.device)\\n\\n return TanhNormal(policy_mean, policy_std)\\n\\n def logprob(self, action, mean, std):\\n tanh_normal = TanhNormal(mean, std)\\n log_prob = tanh_normal.log_prob(action)\\n log_prob = log_prob.sum(dim=1, keepdim=True)\\n return log_prob\\n \\n def log_prob(self, obs, action):\\n tanh_normal = self.forward(obs)\\n log_prob = tanh_normal.log_prob(action) \\n return log_prob\\n \\n def log_prob_policy_from_m_h(self, m, h, action): \\n tanh_normal = self.forward_policy_from_embedding(m, h)\\n log_prob = tanh_normal.log_prob(action)\\n return log_prob\\n\\n def predict_action_from_m_h(self, m, h):\\n tanh_normal = self.forward_policy_from_embedding(m, h)\\n pred_action = tanh_normal.mean \\n return pred_action\"\n },\n {\n \"identifier\": \"EnvReplayBuffer\",\n \"path\": \"core/replay_buffer.py\",\n \"snippet\": \"class EnvReplayBuffer(SimpleReplayBuffer):\\n def __init__(\\n self,\\n max_replay_buffer_size,\\n env,\\n stack_size=1,\\n action_history_len=0,\\n env_info_sizes=None,\\n train_with_action_history=False\\n ):\\n \\\"\\\"\\\"\\n :param max_replay_buffer_size:\\n :param env:\\n \\\"\\\"\\\"\\n self.env = env\\n self._ob_space = env.observation_space #.shape[0] * stack_size\\n self._action_space = env.action_space\\n\\n if train_with_action_history:\\n obs_dim = get_dim(self._ob_space) * stack_size + get_dim(self._action_space) * max(stack_size - 1, 1)\\n else:\\n obs_dim = get_dim(self._ob_space) * stack_size\\n\\n act_dim = get_dim(self._action_space) * (action_history_len)\\n\\n if env_info_sizes is None:\\n if hasattr(env, 'info_sizes'):\\n env_info_sizes = env.info_sizes\\n else:\\n env_info_sizes = dict()\\n\\n super().__init__(\\n max_replay_buffer_size=max_replay_buffer_size,\\n observation_dim=obs_dim,\\n action_dim=act_dim,\\n env_info_sizes=env_info_sizes\\n )\\n\\n self.obs_mean = None\\n self.obs_std = None\\n\\n self.act_mean = None\\n self.act_std = None\\n\\n # def add_sample(self, observation, action, prev_action, reward, terminal,\\n # next_observation, **kwargs):\\n # if isinstance(self._action_space, Discrete):\\n # new_action = np.zeros(self._action_dim)\\n # new_action[action] = 1\\n # else:\\n # new_action = action\\n\\n # return super().add_sample(\\n # observation=observation,\\n # action=new_action,\\n # prev_action=prev_action,\\n # reward=reward,\\n # next_observation=next_observation,\\n # terminal=terminal,\\n # # **kwargs\\n # )\\n\\n def calculate_statistics(self):\\n self.obs_mean = np.mean(self._observations[:self._top], axis=0, keepdims=True)\\n self.obs_std = np.std(self._observations[:self._top], axis=0, keepdims=True)\\n\\n self.act_mean = np.mean(self._actions[:self._top], axis=0, keepdims=True)\\n self.act_std = np.std(self._actions[:self._top], axis=0, keepdims=True)\\n\\n return self.obs_mean, self.obs_std, self.act_mean, self.act_std\\n\\n def set_statistics(self, obs_mean, obs_std, act_mean, act_std):\\n self.obs_mean, self.obs_std, self.act_mean, self.act_std = obs_mean, obs_std, act_mean, act_std\\n \\n def get_statistics(self):\\n return self.obs_mean, self.obs_std, self.act_mean, self.act_std\\n\\n def random_batch(self, batch_size, standardize=False):\\n indices = np.random.choice(self._size, size=batch_size, replace=self._replace or self._size < batch_size)\\n if not self._replace and self._size < batch_size:\\n warnings.warn('Replace was set to false, but is temporarily set to true because batch size is larger than current size of replay.')\\n\\n if standardize and self.obs_mean is not None:\\n obss = (self._observations[indices] - self.obs_mean) / self.obs_std\\n # actions = (self._actions[indices] - self.act_mean) / self.act_std\\n next_obss = (self._next_obs[indices] - self.obs_mean) / self.obs_std\\n else:\\n obss = self._observations[indices] \\n # actions = self._actions[indices] \\n next_obss = self._next_obs[indices]\\n\\n actions = self._actions[indices]\\n \\n batch = dict(\\n observations=obss,\\n actions=actions,\\n # prev_actions=self._prev_actions[indices],\\n rewards=self._rewards[indices],\\n terminals=self._terminals[indices],\\n next_observations=next_obss,\\n )\\n for key in self._env_info_keys:\\n assert key not in batch.keys()\\n batch[key] = self._env_infos[key][indices]\\n\\n return batch\\n \\n def get_batch(self, batch_size, standardize=False):\\n datasize = min(batch_size, self._top) \\n indices = np.arange(datasize)\\n # if not self._replace and self._size < batch_size:\\n # warnings.warn('Replace was set to false, but is temporarily set to true because batch size is larger than current size of replay.')\\n\\n if standardize and self.obs_mean is not None:\\n obss = (self._observations[indices] - self.obs_mean) / self.obs_std\\n # actions = (self._actions[indices] - self.act_mean) / self.act_std\\n next_obss = (self._next_obs[indices] - self.obs_mean) / self.obs_std\\n else:\\n obss = self._observations[indices] \\n # actions = self._actions[indices] \\n next_obss = self._next_obs[indices]\\n\\n actions = self._actions[indices]\\n \\n batch = dict(\\n observations=obss,\\n actions=actions,\\n # prev_actions=self._prev_actions[indices],\\n rewards=self._rewards[indices],\\n terminals=self._terminals[indices],\\n next_observations=next_obss,\\n )\\n for key in self._env_info_keys:\\n assert key not in batch.keys()\\n batch[key] = self._env_infos[key][indices]\\n\\n return batch\\n\\n def add_sample(self, observation, action, reward, terminal,\\n next_observation, **kwargs):\\n if isinstance(self._action_space, Discrete):\\n new_action = np.zeros(self._action_dim)\\n new_action[action] = 1\\n else:\\n new_action = action\\n\\n return super().add_sample(\\n observation=observation,\\n action=new_action,\\n reward=reward,\\n next_observation=next_observation,\\n terminal=terminal,\\n # **kwargs\\n )\"\n },\n {\n \"identifier\": \"preprocess_dataset_with_prev_actions\",\n \"path\": \"core/preprocess.py\",\n \"snippet\": \"def preprocess_dataset_with_prev_actions(mdpfile, envtype, stacksize=1, partially_observable=False, action_history_len=2):\\n \\n indx = list(np.arange(20))\\n # Indices of position information observations\\n if partially_observable:\\n envtype_to_idx = {\\n 'hopper': indx[:5], \\n 'ant': indx[:13], \\n 'walker2d': indx[:8], \\n 'halfcheetah': indx[:4] + indx[8:13]\\n }\\n obs_idx = envtype_to_idx[envtype]\\n observations = np.array(mdpfile['observations'])[:, obs_idx]\\n next_observations = np.array(mdpfile['next_observations'])[:, obs_idx]\\n else:\\n observations = np.array(mdpfile['observations'])\\n next_observations = np.array(mdpfile['next_observations'])\\n \\n terminals = np.array(mdpfile['terminals'])\\n timeouts = np.array(mdpfile['timeouts'])\\n rewards = np.array(mdpfile['rewards'])\\n actions = np.array(mdpfile['actions'])\\n\\n obs_dim = observations.shape[-1]\\n action_dim = actions.shape[-1]\\n\\n n_data = observations.shape[0]\\n new_observations_list = []\\n new_next_observations_list = []\\n prev_action_list = []\\n action_history_list = []\\n \\n idx_from_initial_state = 0\\n num_trajs = 0\\n\\n for i in range(n_data):\\n if idx_from_initial_state == 0:\\n prev_action = np.zeros(action_dim)\\n else:\\n prev_action = actions[i-1]\\n prev_action_list.append(prev_action)\\n\\n if idx_from_initial_state < stacksize:\\n if idx_from_initial_state == 0:\\n initial_obs = observations[i]\\n \\n new_observation = np.zeros(obs_dim * stacksize)\\n new_observation_ = np.concatenate(observations[i-idx_from_initial_state: i+1])\\n new_observation[-(idx_from_initial_state+1) * obs_dim:] = new_observation_\\n \\n new_next_observation = np.zeros(obs_dim * stacksize)\\n new_next_observation_ = np.concatenate(next_observations[i-idx_from_initial_state: i+1])\\n new_next_observation[-(idx_from_initial_state+1) * obs_dim:] = new_next_observation_\\n \\n if idx_from_initial_state + 1 != stacksize:\\n new_next_observation[-(idx_from_initial_state+2) * obs_dim:-(idx_from_initial_state+1) * obs_dim] \\\\\\n = initial_obs\\n \\n else:\\n new_observation = np.concatenate(observations[i+1-stacksize:i+1])\\n new_next_observation = np.concatenate(next_observations[i+1-stacksize:i+1])\\n\\n if idx_from_initial_state < action_history_len:\\n action_history = np.zeros(action_dim * action_history_len)\\n action_history_ = np.concatenate(actions[i-idx_from_initial_state: i+1])\\n action_history[-(idx_from_initial_state+1) * action_dim:] = action_history_\\n \\n else:\\n action_history = np.concatenate(actions[i+1-action_history_len:i+1])\\n\\n\\n new_observations_list.append(new_observation)\\n new_next_observations_list.append(new_next_observation)\\n action_history_list.append(action_history)\\n\\n idx_from_initial_state += 1\\n if terminals[i] or timeouts[i]:\\n idx_from_initial_state = 0\\n num_trajs += 1 \\n\\n new_observations = np.array(new_observations_list)\\n new_next_observations = np.array(new_next_observations_list)\\n new_actions = np.array(action_history_list)\\n\\n new_paths = {\\n 'observations': new_observations,\\n 'next_observations': new_next_observations,\\n 'rewards': rewards,\\n 'actions': new_actions,\\n 'terminals': terminals,\\n 'timeouts': timeouts \\n }\\n \\n return new_paths\"\n },\n {\n \"identifier\": \"data_select_num_transitions\",\n \"path\": \"core/preprocess.py\",\n \"snippet\": \"def data_select_num_transitions(path, num_transitions=1000, start_idx=0, random=False):\\n new_path = {}\\n \\n if random:\\n num_full_trajs = len(path['observations'])\\n choice_idx = np.random.choice(num_full_trajs, num_transitions)\\n \\n else:\\n choice_idx = np.arange(start_idx, start_idx + num_transitions)\\n \\n for key in path.keys():\\n new_path[key] = np.array(path[key])[choice_idx]\\n \\n return new_path\"\n },\n {\n \"identifier\": \"NormalizedBoxEnv\",\n \"path\": \"rlkit/envs/wrappers.py\",\n \"snippet\": \"class NormalizedBoxEnv(ProxyEnv):\\n \\\"\\\"\\\"\\n Normalize action to in [-1, 1].\\n\\n Optionally normalize observations and scale reward.\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n env,\\n reward_scale=1.,\\n obs_mean=None,\\n obs_std=None,\\n ):\\n ProxyEnv.__init__(self, env)\\n self._should_normalize = not (obs_mean is None and obs_std is None)\\n if self._should_normalize:\\n if obs_mean is None:\\n obs_mean = np.zeros_like(env.observation_space.low)\\n else:\\n obs_mean = np.array(obs_mean)\\n if obs_std is None:\\n obs_std = np.ones_like(env.observation_space.low)\\n else:\\n obs_std = np.array(obs_std)\\n self._reward_scale = reward_scale\\n self._obs_mean = obs_mean\\n self._obs_std = obs_std\\n ub = np.ones(self._wrapped_env.action_space.shape)\\n self.action_space = Box(-1 * ub, ub)\\n\\n def estimate_obs_stats(self, obs_batch, override_values=False):\\n if self._obs_mean is not None and not override_values:\\n raise Exception(\\\"Observation mean and std already set. To \\\"\\n \\\"override, set override_values to True.\\\")\\n self._obs_mean = np.mean(obs_batch, axis=0)\\n self._obs_std = np.std(obs_batch, axis=0)\\n\\n def _apply_normalize_obs(self, obs):\\n return (obs - self._obs_mean) / (self._obs_std + 1e-8)\\n\\n def step(self, action):\\n lb = self._wrapped_env.action_space.low\\n ub = self._wrapped_env.action_space.high\\n scaled_action = lb + (action + 1.) * 0.5 * (ub - lb)\\n scaled_action = np.clip(scaled_action, lb, ub)\\n\\n wrapped_step = self._wrapped_env.step(scaled_action)\\n next_obs, reward, done, info = wrapped_step\\n if self._should_normalize:\\n next_obs = self._apply_normalize_obs(next_obs)\\n return next_obs, reward * self._reward_scale, done, info\\n\\n def __str__(self):\\n return \\\"Normalized: %s\\\" % self._wrapped_env\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport wandb\nimport envs\nimport d4rl\nimport gym\nimport torch\nfrom imitation.bc import BC\nfrom imitation.rap import RAP\nfrom imitation.fca import FCA\nfrom imitation.mine import MINE_BC\nfrom imitation.palr import PALR\nfrom argparse import ArgumentParser\nfrom itertools import product\nfrom core.policy import TanhGaussianPolicyWithEmbedding, TanhGaussianRAPPolicy\nfrom core.replay_buffer import EnvReplayBuffer\nfrom core.preprocess import preprocess_dataset_with_prev_actions, data_select_num_transitions\nfrom rlkit.envs.wrappers import NormalizedBoxEnv"},"token_num":{"kind":"number","value":18875,"string":"18,875"},"cropped_code":{"kind":"string","value":"wandb_dir = '.'\nos.environ['WANDB_DIR'] = wandb_dir\nos.environ['D4RL_DATASET_DIR'] = './dataset/'\n\n\n\n\n\ndef train(configs):\n"},"all_code":{"kind":"string","value":"wandb_dir = '.'\nos.environ['WANDB_DIR'] = wandb_dir\nos.environ['D4RL_DATASET_DIR'] = './dataset/'\n\n\n\n\n\ndef train(configs):"},"next_line":{"kind":"string","value":" env = NormalizedBoxEnv(gym.make(configs['envname']))"},"gold_snippet_index":{"kind":"number","value":10,"string":"10"},"created_at":{"kind":"string","value":"2023-11-06 08:35:34+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5275,"cells":{"repo_name":{"kind":"string","value":"tylerlight071/Project-Cipher"},"file_path":{"kind":"string","value":"main.py"},"context":{"kind":"list like","value":[{"identifier":"clear_terminal","path":"components/common_functions.py","snippet":"def clear_terminal():\n os.system('cls' if os.name == 'nt' else 'clear')"},{"identifier":"print_slow","path":"components/common_functions.py","snippet":"def print_slow(text, delay=0.00): # change to 0.01\n for char in text:\n print(char, end='', flush=True)\n time.sleep(delay)\n print()"},{"identifier":"shop_help","path":"components/common_functions.py","snippet":"def shop_help():\n print_slow(Fore.YELLOW + \"Shop Help:\" + Style.RESET_ALL)\n print_slow(\"\")\n print_slow(\"[buy] - Use the 'buy [upgrade]' command to purchase the upgrade in the shop. \")\n print_slow(\"\")\n print_slow(\"[clear] - Use the 'clear' command to clear the terminal.\")\n print_slow(\"\")\n print_slow(\"[exit] - Use the 'exit' command to return to the main terminal.\")\n print_slow(\"\")"},{"identifier":"help_user","path":"components/common_functions.py","snippet":"def help_user():\n print_slow(Fore.MAGENTA + \"Help:\" + Style.RESET_ALL)\n print_slow(\"\")\n print_slow(\"[connect] - Use the 'connect' command to hack into Enigma Corps network.\")\n print_slow(\"\")\n print_slow(\"[mail] - Use the 'mail' command to view and respond to emails from your client and other characters.\")\n print_slow(\"\")\n print_slow(\"[balance] - Use the 'balance' command to view your current earnings which you can spend on upgrades. \")\n print_slow(\"\")\n print_slow(\"[shop] - Use the 'shop' command to view upgrades available in the shop. \")\n print_slow(\"\")\n print_slow(\"[clear] - Use the 'clear' command to clear the terminal.\")\n print_slow(\"\")\n print_slow(\"[help] - Use the 'help' command if you need assistance at any time.\")\n print_slow(\"\")\n print_slow(\"[exit] - Use the 'exit' command to return to the Main Menu.\")\n print_slow(\"\")"},{"identifier":"connect_help","path":"components/common_functions.py","snippet":"def connect_help():\n print_slow(Fore.MAGENTA + \"Connect Help:\" + Style.RESET_ALL)\n print_slow(\n \"[scan] - Use the 'scan' command to scan the network and search for available systems and vulnerabilities.\")\n print_slow(\"\")\n print_slow(\"[hack] - Use the 'hack [system/vulnerability]' to hack into different systems.\")\n print_slow(\"\")\n print_slow(\"[clear] - Use the 'clear' command to clear the terminal.\")\n print_slow(\"\")\n print_slow(\"[disconnect] - Use the 'disconnect' command to disconnect from the current system or vulnerability.\")\n print_slow(\"\")"},{"identifier":"mail_help","path":"components/common_functions.py","snippet":"def mail_help():\n print_slow(Fore.LIGHTBLUE_EX + \"Mail Help:\" + Style.RESET_ALL)\n print_slow(\"\")\n print_slow(\"[l] - Use the 'l' command to list all emails.\")\n print_slow(\"\")\n print_slow(\"[r] - Use the 'r [subject]' command to read an email with the specified subject.\")\n print_slow(\"\")\n print_slow(\"[clear] - Use the 'clear' command to clear the terminal.\")\n print_slow(\"\")\n print_slow(\"[exit] - Use the 'exit' command to return to the main terminal.\")\n print_slow(\"\")"},{"identifier":"system_help","path":"components/common_functions.py","snippet":"def system_help():\n print_slow(\"\")\n print_slow(\"[mail] - Use the 'mail' command to log into the users emails.\")\n print_slow(\"\")\n print_slow(\"[l] - Use the 'l' command to list files in a users system.\")\n print_slow(\"\")\n print_slow(\"[clear] - Use the 'clear' command to clear the terminal.\")\n print_slow(\"\")\n print_slow(\"[r] - Use the 'r [file]' command to read files in a users system\")\n print_slow(\"\")"},{"identifier":"intro_call","path":"conversations/calls.py","snippet":"def intro_call():\n clear_terminal()\n\n # Anonymous Sender (Anonymous)\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n Fore.YELLOW + \"Welcome, Cipher. Operation Enigma is our covert mission against Enigma Corp, a powerful and secretive entity.\")\n print_slow(\n \"Your skills and secrecy have brought you to our attention. Your mission is to dig through their systems and servers looking for valuable data.\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Cipher Response\n print_slow(cipher_art)\n print_slow(\"\")\n print_box(\"Cipher\")\n print_slow(\"\")\n print_slow(Fore.BLUE + \"Got it, Anonymous. Exposing secrets and bringing justice. I'm in.\")\n print_slow(\"What's my first move? Talk to me about this 'EnigmaLink'.\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Anonymous Sender (Anonymous)\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n \"Excellent, Cipher. EnigmaLink is a specialized tool available on the Hacker's Market. It contains a hidden backdoor, allowing access to Enigma Corps servers.\")\n print_slow(\n \"Your task is to acquire EnigmaLink and initiate your infiltration. Use the 'connect' command to navigate the network and gather crucial intelligence.\")\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Cipher Response\n print_slow(cipher_art)\n print_slow(\"\")\n print_box(\"Cipher\")\n print_slow(\"\")\n print_slow(\n Fore.BLUE + \"EnigmaLink, got it. I'll secure it and initiate the infiltration. What about this employee, Amy?\")\n print_slow(\"You mentioned her password is 'sexinthecity.' What's my objective with her?\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Anonymous Sender (Anonymous)\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n \"Good question, Cipher. Amy is a key target. Use her password to access her computer and gather any pertinent information.\")\n print_slow(\n \"This data is vital to our cause. Be thorough and meticulous in your investigation. The success of our operation depends on it.\")\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Cipher Response\n print_slow(cipher_art)\n print_slow(\"\")\n print_box(\"Cipher\")\n print_slow(\"\")\n print_slow(Fore.BLUE + \"Understood, Anonymous. I'll focus on Amy, gather intel, and proceed with precision.\")\n print_slow(\"Consider it done. Anything else I should know before I dive in?\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Anonymous Sender (Anonymous)\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n \"One last thing, Cipher. All collected data is highly confidential. This contract is binding, and your success is paramount.\")\n print_slow(\"Execute with diligence, and may the odds be in your favor. Good luck, Cipher.\")\n print_slow(Fore.RED + \"Line Disconnected...\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()"},{"identifier":"first_call","path":"conversations/calls.py","snippet":"def first_call():\n clear_terminal()\n print_slow(sender_art)\n print_slow(\"\")\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(Fore.YELLOW + \"That's a good start, but we already have that information.\")\n print_slow(\"Regardless, I've transferred £20 into the account for your troubles.\")\n print_slow(\"Keep digging Cipher!\" + Style.RESET_ALL)\n print_slow(Fore.RED + \"Line Disconnected...\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()"},{"identifier":"second_call","path":"conversations/calls.py","snippet":"def second_call():\n clear_terminal()\n print_slow(sender_art)\n print_slow(\"\")\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n Fore.YELLOW + \"Hey Cipher, you nailed it! 'Billy' just spilled the beans about wanting to climb the corporate ladder into management.\")\n print_slow(\n \"This is gold for us. We can guide 'Billy' toward training and workshops that align with our interests, nudging things in our favor.\")\n print_slow(\n \"Picture it – we're pulling the strings, helping 'Billy' grow, and steering the ship where we want it to go.\")\n print_slow(\"Keep the ball rolling, Cipher!\" + Style.RESET_ALL)\n print_slow(Fore.RED + \"Line Disconnected...\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()"},{"identifier":"third_call","path":"conversations/calls.py","snippet":"def third_call():\n clear_terminal()\n\n # Anonymous Sender\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\"\n \"\")\n print_slow(\n Fore.YELLOW + \"Cipher, we've stumbled upon a perplexing development regarding Enigma's interest in a mysterious 'compound.'\")\n print_slow(\n \"I'm cross-referencing our existing intel to unveil more details. Stay vigilant and be prepared for the unknown.\" + Style.RESET_ALL)\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Cipher Response\n print_slow(cipher_art)\n print_slow(\"\")\n print_box(\"Cipher\")\n print_slow(\"\")\n print_slow(\n Fore.BLUE + \"A compound, huh? Any hints on whether we're talking metal, chemicals, or something else entirely?\")\n print_slow(\"This feels like navigating in the dark. What exactly am I dealing with?\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Anonymous Sender Response\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(Fore.YELLOW +\n \"Cipher, we're in the dark too. Initial reports are unclear—could be metal, chemical, or something beyond our comprehension.\")\n print_slow(\n \"Your mission is to identify the nature of this compound. Exercise extreme caution; this goes deeper than we anticipated.\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Cipher Inquiry\n print_slow(cipher_art)\n print_slow(\"\")\n print_box(\"Cipher\")\n print_slow(\"\")\n print_slow(Fore.BLUE + \"So, we're playing 'guess the compound.' Any leads, any connections I should explore?\")\n print_slow(\"This is starting to sound like one of those high-stakes puzzles.\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Anonymous Sender Clarification\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(Fore.YELLOW +\n \"I wish I had more details, Cipher. This is uncharted territory for us. Investigate discreetly, and trust no one.\")\n print_slow(\n \"I'll attempt to gather more intel. Stay on the line, and keep me updated on any findings.\" + Style.RESET_ALL)\n print_slow(\"\")\n print_slow(Fore.RED + \"Line Disconnected...\" + Style.RESET_ALL)\n input(\"Press [Enter] to continue: \")\n clear_terminal()"},{"identifier":"fourth_call","path":"conversations/calls.py","snippet":"def fourth_call():\n clear_terminal()\n\n # Anonymous Sender\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n Fore.YELLOW + \"Cipher, we've got our hands on an intriguing document – an Employee Performance Review for 'Billy Constantine'.\")\n print_slow(\n \"This could be a goldmine of information. Let's dig in and see if there's anything we can leverage to our advantage.\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Cipher Response\n print_slow(cipher_art)\n print_slow(\"\")\n print_box(\"Cipher\")\n print_slow(\"\")\n print_slow(\n Fore.BLUE + \"An Employee Performance Review? Interesting choice. What's the scoop on 'Billy Constantine'?\")\n print_slow(\"Give me the details, and we'll figure out our next move.\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Anonymous Sender Briefing\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n \"Cipher, 'Billy Constantine' is making waves. The review highlights exceptional performance as a sales representative.\")\n print_slow(\n \"He's exceeding sales targets, mentoring new team members, and earning a solid 4.5/5 rating. A rising star, it seems.\")\n print_slow(\"We might use this to our advantage. Let's explore how we can align his ambitions with our agenda.\")\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Cipher Strategy\n print_slow(cipher_art)\n print_slow(\"\")\n print_box(\"Cipher\")\n print_slow(\"\")\n print_slow(\n Fore.BLUE + \"A high-performing sales rep, huh? We could steer 'Billy' towards projects that align with our goals.\")\n print_slow(\"Let's use this performance review to our advantage. Maybe mentorship programs, leadership initiatives?\")\n print_slow(\"I'm ready to play this card strategically. What's the next move?\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Anonymous Sender Next Steps\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n \"Great thinking, Cipher. Let's work on a plan to subtly guide 'Billy' toward initiatives that benefit us.\")\n print_slow(\"We'll need to dig deeper into 'Billy's' aspirations and weave our influence seamlessly.\")\n print_slow(\"Stay vigilant, Cipher. This could be a game-changer.\")\n print_slow(\"\")\n print_slow(Fore.RED + \"Line Disconnected...\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()"},{"identifier":"fifth_call","path":"conversations/calls.py","snippet":"def fifth_call():\n clear_terminal()\n\n # Anonymous Sender\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n Fore.YELLOW + \"Cipher, we've intercepted some Meeting Minutes dated 24/06/2025. It's related to 'Project X' and involves key players.\")\n print_slow(\n \"This could be our chance to uncover more about Enigma's activities. Let's dive into the details and see what we can extract.\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Cipher Response\n print_slow(cipher_art)\n print_slow(\"\")\n print_box(\"Cipher\")\n print_slow(\"\")\n print_slow(\n Fore.BLUE + \"Meeting Minutes, huh? 'Project X' sounds intriguing. Who were the players involved, and what's the agenda?\")\n print_slow(\"I'm ready to dissect this information and uncover any hidden gems.\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Anonymous Sender Briefing\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n \"Cipher, the meeting involved key personnel—Amy, Billy, Kyle, and others. 'Project X' is on the agenda, and there's mention of sensitive materials.\")\n print_slow(\n \"This could be a crucial insight into Enigma's plans. Let's analyze the action items and plan our next move.\")\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Cipher Analysis\n print_slow(cipher_art)\n print_slow(\"\")\n print_box(\"Cipher\")\n print_slow(\"\")\n print_slow(Fore.BLUE + \"'Project X,' sensitive materials, and action items. This is a goldmine of information.\")\n print_slow(\n \"Let's focus on dissecting the action items and see if we can connect the dots. What's our strategy, Anonymous?\" + Style.RESET_ALL)\n print_slow(\"\")\n input(\"Press [Enter] to continue: \")\n clear_terminal()\n\n # Anonymous Sender Next Steps\n print_slow(sender_art)\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(\n \"Agreed, Cipher. Let's delve into the action items, especially the data compilation and safety protocol training.\")\n print_slow(\"We might uncover more about 'Project X' and gain insights into Enigma's plans.\")\n print_slow(\"Stay sharp, Cipher. This could be a pivotal moment in our mission.\")\n print_slow(\"\")\n print_slow(Fore.RED + \"Line Disconnected...\" + Style.RESET_ALL)\n input(\"Press [Enter] to continue: \")\n clear_terminal()"},{"identifier":"sixth_call","path":"conversations/calls.py","snippet":"def sixth_call():\n print_slow(\"ADD CALL STUFF HERE\")"},{"identifier":"markus_seen_call","path":"conversations/calls.py","snippet":"def markus_seen_call():\n print_slow(\"Something goes here\")"},{"identifier":"code_shatter_call","path":"conversations/minigame_calls.py","snippet":"def code_shatter_call():\n clear_terminal()\n print_slow(sender_art)\n print_slow(\"\")\n print_slow(\"\")\n print_box(\"Anonymous\")\n print_slow(\"\")\n print_slow(Fore.YELLOW + \"I see you have bought CodeShatter!\")\n print_slow(\"This item is a one time use upgrade so once you get the password, it is gone so use wisely!\")\n print_slow(\"But don't threat, if you fail, you get a chance to retry. The item is only used when you get the password, so be sure to write it down!\" + Style.RESET_ALL)\n input(\"Press [Enter] to continue: \")\n clear_terminal()"},{"identifier":"code_shatter_minigame","path":"minigames/code_shatter_minigame.py","snippet":"def code_shatter_minigame():\n # Generate a random 5-digit number\n target = [str(random.randint(1, 9)) for _ in range(5)]\n\n print_slow(\"Welcome to CodeShatter!\")\n print_slow(\"\")\n print_slow(\"Guess the 5-digit number.\")\n print_slow(\"\")\n print_slow(\"The sequence can contain multiple same numbers\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Green: Correct digit in correct position.\" + Style.RESET_ALL)\n print_slow(\"\")\n print_slow(Fore.YELLOW + \"Orange: Correct digit in incorrect position.\" + Style.RESET_ALL)\n print_slow(\"\")\n print_slow(Fore.RED + \"Red: Incorrect digit.\" + Style.RESET_ALL)\n print_slow(\"\")\n\n attempts = 0\n while attempts < 7:\n # Get the user's guess\n guess = input(\"Enter your guess: \")\n\n if len(guess) != 5 or not guess.isdigit():\n print_slow(\"Invalid input. Please enter a 5-digit number.\")\n continue\n\n attempts += 1\n\n # Check the guess against the target\n feedback = []\n for i in range(5):\n if guess[i] == target[i]:\n feedback.append(Fore.GREEN + guess[i] + Style.RESET_ALL)\n elif guess[i] in target:\n feedback.append(Fore.YELLOW + guess[i] + Style.RESET_ALL)\n else:\n feedback.append(Fore.RED + guess[i] + Style.RESET_ALL)\n\n print_slow(\"Feedback: \" + \" \".join(feedback))\n\n # Check if the guess is correct\n if guess == \"\".join(target):\n print_slow(Fore.GREEN + \"Access granted.\" + Style.RESET_ALL)\n break\n else:\n print_slow(Fore.RED + \"Access denied. Too many attempts.\" + Style.RESET_ALL)\n time.sleep(1)\n print_slow(\"\")\n print_slow(Fore.RED + \"Rebooting CodeShatter with new proxy...\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n code_shatter_minigame()"},{"identifier":"port_scanning","path":"minigames/eye_spy_minigame.py","snippet":"def port_scanning():\n num_ports = 10\n open_ports, closed_ports = generate_ports(num_ports)\n attempts = 5\n correct_guesses = 0\n scan_attempts = 2\n\n print_slow(\"Welcome to the Port Scanning minigame!\")\n print_slow(\"\")\n print_slow(f\"Find the open ports in the range 1-{num_ports}.\")\n print_slow(\"\")\n print_slow(f\"You have {attempts} attempts.\")\n print_slow(\"\")\n\n while scan_attempts > 0:\n print_slow(\"\")\n print_slow(f\"\\nYou have {scan_attempts} scan attempts left.\")\n print_slow(\"\")\n start = int(input(\"Enter the start of the range to scan: \"))\n print_slow(\"\")\n end = int(input(\"Enter the end of the range to scan: \"))\n print_slow(\"\")\n\n num_open_ports_in_range = len(open_ports.intersection(range(start, end + 1)))\n print_slow(\"\")\n print_slow(f\"There are {num_open_ports_in_range} open ports in the range {start}-{end}.\")\n\n scan_attempts -= 1\n\n while attempts > 0 and len(open_ports) > 0:\n port = int(input(\"\\nEnter a port number to guess: \"))\n\n if port in open_ports:\n print_slow(Fore.GREEN + \"Port is open!\" + Style.RESET_ALL)\n open_ports.remove(port)\n correct_guesses += 1\n elif port in closed_ports:\n print_slow(Fore.RED + \"Port is closed.\" + Style.RESET_ALL)\n closed_ports.remove(port)\n else:\n print_slow(\"Invalid port number. Please enter a number between 1 and\", num_ports)\n\n attempts -= 1\n\n if len(open_ports) == 0:\n print_slow(\n Fore.GREEN + \"\\nCongratulations! You have successfully found all the open ports and gained access to the camera.\" + Style.RESET_ALL)\n time.sleep(2)\n clear_terminal()\n else:\n print_slow(\n Fore.RED + f\"\\nHack Failed! You found {correct_guesses} out of {len(open_ports) + correct_guesses} open ports.\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n port_scanning()"},{"identifier":"AmySystem","path":"systems/level_1/amy/amy_system.py","snippet":"class AmySystem:\n def __init__(self):\n self.files = [\n {\n \"name\": \"return_to_work_form.txt\",\n \"content\": (\n \"Employee Name: _______________\\n\"\n \"Employee ID: ____________\\n\"\n \"Department: _______________\\n\"\n \"Date of Return: ______\\n\\n\"\n \"I, [Employee Name], certify that I have followed the company's \"\n \"guidelines for returning to work after an absence. \"\n \"I understand that it is my responsibility to adhere to all safety \"\n \"protocols and procedures to ensure the health and well-being of my \"\n \"colleagues and myself.\\n\\n\"\n \"I acknowledge that I have completed any necessary training and have \"\n \"been briefed on any updates to the company's policies and procedures. \"\n \"I am aware that I must report any symptoms or exposure to COVID-19 to \"\n \"my supervisor immediately.\\n\\n\"\n \"I am committed to doing my part to maintain a safe and healthy work \"\n \"environment for everyone. I will continue to follow all guidelines \"\n \"and protocols and will cooperate with any additional measures that \"\n \"may be implemented in the future.\\n\\n\"\n \"Signature: [Employee Signature]\\n\"\n \"Date: [Date]\"\n )\n },\n {\n \"name\": \"employee_handbook.txt\",\n \"content\": (\n \"Welcome to Enigma Corps We are thrilled to have you as part of our \"\n \"team. This employee handbook has been designed to help you understand \"\n \"our company's policies, procedures, and expectations.\\n\\n\"\n \"Our company is committed to fostering a positive and inclusive work \"\n \"environment where all employees feel valued and supported. We believe \"\n \"in treating everyone with respect and dignity and expect all employees \"\n \"to do the same.\\n\\n\"\n \"In this handbook, you will find information on topics such as:\\n\\n\"\n \"- Code of Conduct\\n\"\n \"- Dress Code\\n\"\n \"- Attendance and Punctuality\\n\"\n \"- Time Off and Leave Policies\\n\"\n \"- Performance Evaluations\\n\"\n \"- Health and Safety\\n\"\n \"- Equal Employment Opportunity\\n\"\n \"- Harassment and Discrimination\\n\\n\"\n \"Please take the time to read through this handbook carefully and \"\n \"familiarize yourself with our policies and procedures. If you have any \"\n \"questions or concerns, do not hesitate to reach out to your supervisor \"\n \"or the HR department.\\n\\n\"\n \"We look forward to working with you and hope you have a long and \"\n \"successful career with Enigma Corps!\"\n )\n },\n {\n \"name\": \"benefits_summary.txt\",\n \"content\": (\n \"At Enigma Corps, we believe in taking care of our employees and \"\n \"offer a comprehensive benefits package to support your health, well-being, \"\n \"and financial security. Below is a summary of the benefits available to \"\n \"you as an employee of Enigma Corps.\\n\\n\"\n \"Health Insurance: We offer a choice of medical, dental, and vision \"\n \"plans to meet your needs. Our plans provide coverage for preventive care, \"\n \"hospitalization, prescription drugs, and more.\\n\\n\"\n \"Retirement Savings: We offer a 401(k) plan with a generous company \"\n \"match to help you save for your future. You can choose from a variety of \"\n \"investment options to suit your needs.\\n\\n\"\n \"Paid Time Off: We provide a generous amount of paid time off, \"\n \"including vacation, sick leave, and holiday pay. We also offer paid \"\n \"parental leave for new parents.\\n\\n\"\n \"Flexible Work Arrangements: We understand the importance of work-life \"\n \"balance and offer flexible work arrangements, such as remote work and \"\n \"flexible schedules, where possible.\\n\\n\"\n \"Wellness Programs: We offer a variety of wellness programs and \"\n \"resources to support your physical and mental health, including fitness \"\n \"classes, stress management programs, and counseling services.\\n\\n\"\n \"Professional Development: We are committed to supporting your growth \"\n \"and development and offer a variety of training and development \"\n \"opportunities, including tuition reimbursement, workshops, and seminars.\"\n \"\\n\\n\"\n \"We encourage you to review this summary carefully and take advantage of \"\n \"the benefits available to you. If you have any questions or need further \"\n \"information, please contact the HR department.\"\n )\n },\n ]\n self.emails = [\n {\n \"sender\": \"Amy\",\n \"subject\": \"Can't Stop Thinking About You\",\n \"body\": (\n \"Hey Billy,\\n\\n\"\n \"I hope this message finds you in good spirits. I've been meaning to write to you for a while now, but I couldn't find the right words to express what I've been feeling.\\n\\n\"\n \"Ever since that night we spent together, I can't seem to get you out of my mind. There's something about the way you make me feel that I've never experienced before. \"\n \"\\nIt's exhilarating, yet terrifying all at the same time.\\n\\n\"\n \"I know we both have a lot on our plates right now, and I don't want to add any more stress to your life. But I can't help but wonder what could happen if we gave this a real shot. \"\n \"I know it's complicated, and there are a lot of factors to consider, but I think we owe it to ourselves to explore this connection we have.\\n\\n\"\n \"I understand if you're not ready to take that step, and I don't want to pressure you into anything you're not comfortable with. \"\n \"\\nBut I can't shake the feeling that we could have something truly special together.\\n\\n\"\n \"I'd love to hear your thoughts on this, and I'm more than willing to take things slow if that's what you need. Maybe we could meet up for dinner and talk about it in person?\"\n \" I think it would be easier to have this conversation face-to-face.\\n\\n\"\n \"I hope you're doing well, and I look forward to hearing from you soon.\\n\\n\"\n \"Take care,\\n\"\n \"Amy\"\n )\n },\n {\n \"sender\": \"Amy\",\n \"subject\": \"Need Your Help on the Smith Project\",\n \"body\": (\n \"Hi Billy,\\n\\n\"\n \"I hope this email finds you well. I wanted to reach out and ask for your help on the Smith project. I've been having some trouble with the data analysis portion,\"\n \"\\nand I know you have a lot of experience in that area.\\n\\n\"\n \"The project involves analyzing customer feedback data to identify trends and areas for improvement. I've been working on it for a few weeks now, but I'm finding it challenging to make sense of the data and\"\n \"\\ndraw meaningful conclusions.\\n\\n\"\n \"Would you be available for a quick meeting later this week to go over some of the data with me? I would really appreciate your input and guidance on this. \"\n \"\\nI think your expertise could really help me make progress and ensure the success of the project.\\n\\n\"\n \"If you're available, please let me know your preferred date and time, and I'll send out a calendar invite. I'm flexible and can work around your schedule.\\n\\n\"\n \"Thank you in advance for your help, and I look forward to hearing from you soon.\\n\\n\"\n \"Best,\\n\"\n \"Amy\"\n )\n },\n {\n \"sender\": \"Amy\",\n \"subject\": \"Request for Time Off\",\n \"body\": (\n \"Good Afternoon Katie,\\n\\n\"\n \"I hope this email finds you well. I wanted to request some time off next month for a family vacation. I am planning to be out of the office from 10/09/2024 to 18/09/2024\\n\\n\"\n \"I have been working hard on the Johnson project and have made significant progress. I will make sure to finish up any outstanding work and hand off any ongoing projects to my colleagues before I leave. I will also be available by email in case of any urgent matters.\\n\\n\"\n \"I understand that this is a busy time for the team, and I want to ensure that my absence doesn't cause any disruptions. I have already spoken to Markus and he has kindly agreed to cover for me while I'm away.\\n\\n\"\n \"Thank you for considering my request. I look forward to spending some quality time with my family and coming back to work refreshed and recharged.\"\n \"\\nI am confident that the time off will help me come back with renewed energy and focus.\\n\\n\"\n \"Best,\\n\"\n \"Amy\"\n )\n },\n {\n \"sender\": \"Amy\",\n \"subject\": \"Apology for the Mistake\",\n \"body\": (\n \"Good Morning Kyle,\\n\\n\"\n \"I hope this email finds you well. I wanted to reach out and apologize for the mistake I made on the Johnson report. I realize now that I overlooked some important data, and I take full responsibility for it.\\n\\n\"\n \"I have gone back and corrected the report, and I will make sure to double-check my work in the future to avoid any similar mistakes. I have also attached the updated report for your reference.\\n\\n\"\n \"I understand if you are disappointed or frustrated, and I am more than willing to do whatever it takes to make it right. Please let me know if there's anything else I can do to fix this,\"\n \"\\nor if you would like to discuss this further.\\n\\n\"\n \"Once again, I am truly sorry for the mistake, and I appreciate your understanding. I value our working relationship and hope that this incident doesn't tarnish it. I am committed to making amends and ensuring that this doesn't happen again in the future.\\n\\n\"\n \"Best,\\n\"\n \"Amy\"\n )\n },\n {\n \"sender\": \"Amy\",\n \"subject\": \"Thank You for Letting Me Use Your Computer\",\n \"body\": (\n \"Hey Billy,\\n\\n\"\n \"I wanted to take a moment to express my gratitude for allowing me to use your computer while mine was being serviced by IT. \"\n \"It was a huge help and allowed me to stay productive during that time.\\n\\n\"\n \"I also noticed that your password is 'football'. While I understand it's easy to remember, it's important to choose a more secure password to protect your accounts.\"\n \"\\nI would recommend changing it to something more complex and unique. You never know who's watching after all.\\n\\n\"\n \"Thanks again for your generosity and understanding.\\n\\n\"\n \"Best,\\n\"\n \"Amy\"\n )\n }\n ]\n\n def list_files(self):\n print_slow(\"\\nFiles:\")\n for file in self.files:\n print_slow(f\"\\n{file['name']}\")\n\n def read_file(self, file_name):\n file_found = False\n for file in self.files:\n if file['name'] == file_name:\n file_found = True\n return file['content']\n\n if not file_found:\n print_slow(\"\\nNo file found with that name, please try again.\")\n return None\n\n def list_emails(self):\n print_slow(\"\\nEmails:\")\n for i, email in enumerate(self.emails):\n print_slow(f\"\\n{email['subject']} - From: {email['sender']}\")\n\n def read_email(self, subject):\n for email in self.emails:\n if email['subject'].lower() == subject.lower():\n print_slow(f\"\\nFrom: {email['sender']}\\nSubject: {email['subject']}\\n\\n{email['body']}\")\n return\n print_slow(\"\\nNo email found with that subject, please try again.\")"},{"identifier":"BillySystem","path":"systems/level_1/billy/billy_system.py","snippet":"class BillySystem:\n def __init__(self):\n self.files = [\n {\n \"name\": \"cover_letter.txt\",\n \"content\": (\n \"Dear Hiring Manager,\\n\\n\"\n \"I am writing to express my interest in the management position at Enigma Corps. \"\n \"I have been with the company for over 7 years and have consistently demonstrated my commitment to driving excellence and fostering collaboration within the team.\\n\\n\"\n \"During my tenure at Enigma Corps, I have been involved in various projects, including the successful completion of the Q3 deliverables project, where I played a key role in the planning and execution stages. \"\n \"My dedication to achieving project milestones and my ability to work under pressure make me a strong candidate for a management role.\\n\\n\"\n \"I possess strong leadership skills, which I have honed through my experiences in leading teams and coordinating cross-functional efforts. \"\n \"My ability to communicate effectively and build positive relationships with team members and stakeholders has resulted in successful project outcomes and increased productivity.\\n\\n\"\n \"In addition to my technical and leadership skills, I am also committed to continuous learning and professional development. \"\n \"I have participated in various training programs and workshops to enhance my management skills and stay up-to-date with industry trends and best practices.\\n\\n\"\n \"I am excited about the opportunity to contribute to the growth and success of Enigma Corps as a member of the management team. \"\n \"I am confident that my skills and experience will be valuable assets to the company, and I look forward to the opportunity to work closely with the team to drive innovation and excellence.\\n\\n\"\n \"Thank you for considering my application. I am looking forward to the opportunity to discuss my qualifications further and explore how I can contribute to the success of Enigma Corps.\\n\\n\"\n \"Sincerely,\\n\"\n \"Billy Constantine\\n\"\n )\n },\n {\n \"name\": \"employee_handbook.txt\",\n \"content\": (\n \"Welcome to Enigma Corps We are thrilled to have you as part of our \"\n \"team. This employee handbook has been designed to help you understand \"\n \"our company's policies, procedures, and expectations.\\n\\n\"\n \"Our company is committed to fostering a positive and inclusive work \"\n \"environment where all employees feel valued and supported. We believe \"\n \"in treating everyone with respect and dignity and expect all employees \"\n \"to do the same.\\n\\n\"\n \"In this handbook, you will find information on topics such as:\\n\\n\"\n \"- Code of Conduct\\n\"\n \"- Dress Code\\n\"\n \"- Attendance and Punctuality\\n\"\n \"- Time Off and Leave Policies\\n\"\n \"- Performance Evaluations\\n\"\n \"- Health and Safety\\n\"\n \"- Equal Employment Opportunity\\n\"\n \"- Harassment and Discrimination\\n\\n\"\n \"Please take the time to read through this handbook carefully and \"\n \"familiarize yourself with our policies and procedures. If you have any \"\n \"questions or concerns, do not hesitate to reach out to your supervisor \"\n \"or the HR department.\\n\\n\"\n \"We look forward to working with you and hope you have a long and \"\n \"successful career with Enigma Corps!\"\n )\n },\n {\n \"name\": \"meeting_minutes.txt\",\n \"content\": (\n \"Meeting Minutes\\n\\n\"\n \"Date: 24/06/2025\\n\"\n \"Location: REDACTED\\n\"\n \"Attendees: Amy, REDACTED, Billy, Kyle, REDACTED, REDACTED, REDACTED\\n\\n\"\n \"Agenda:\\n\"\n \"- Discuss progress on Project REDACTED\\n\"\n \"- Review safety protocols for handling sensitive materials\\n\"\n \"- Plan next steps for research and development\\n\\n\"\n \"Action Items:\\n\"\n \"- Compile data from recent experiments and share with team\\n\"\n \"- Schedule training session on updated safety protocols\\n\"\n \"- Develop timeline for next phase of Project X\\n\\n\"\n \"Next Meeting: 05/08/24, 12:00pm\\n\"\n )\n },\n {\n \"name\": \"employee_performance_review.txt\",\n \"content\": (\n \"Employee Performance Review\\n\\n\"\n \"Employee Name: Billy Constantine\\n\"\n \"Employee ID: 035854\\n\"\n \"Review Date: 28/06/2024\\n\\n\"\n \"Performance Summary:\\n\"\n \"Billy has demonstrated exceptional performance in his role as a sales representative. He has consistently exceeded sales targets, built strong relationships with clients, and demonstrated leadership qualities in team meetings and projects.\\n\\n\"\n \"Strengths:\\n\"\n \"- Exceeded quarterly sales targets by 15%.\\n\"\n \"- Successfully onboarded and mentored two new team members.\\n\"\n \"- Demonstrated excellent communication and negotiation skills.\\n\\n\"\n \"Areas for Improvement:\\n\"\n \"- Time management skills can be further developed to ensure all tasks are completed in a timely manner.\\n\"\n \"- Continued development of technical knowledge to stay up-to-date with industry trends.\\n\"\n \"- Strengthen collaboration with cross-functional teams to drive more integrated solutions.\\n\\n\"\n \"Goals for Next Review Period:\\n\"\n \"- Increase sales targets by 20%.\\n\"\n \"- Complete a management training program.\\n\"\n \"- Improve time management skills through prioritization and delegation.\\n\\n\"\n \"Overall Rating: 4.5/5\\n\"\n \"Reviewer Name: Katie Thompson\\n\"\n \"Reviewer Signature: Katie Thompson\\n\"\n \"Date: 28/06/2024\\n\"\n )\n }\n ]\n self.emails = [\n\n {\n \"sender\": \"Billy\",\n \"subject\": \"Re: Need Your Help on the Smith Project\",\n \"body\": (\n \"Hi Amy,\\n\\n\"\n \"I hope this message finds you in great spirits! I'm more than happy to lend a helping hand with the Smith project. After all, two heads are better than one, especially when it comes to data analysis, right?\\n\\n\"\n \"How about we grab a coffee and chat about the project in person? I think it would be nice to catch up and discuss the data over a cup of joe. I'm sure we can brainstorm some ideas and come up with a game plan together.\\n\\n\"\n \"I'm free [date] at [time], does that work for you? If not, just let me know your availability, and we can find a time that suits us both. I'm really looking forward to our coffee date and tackling the project together.\\n\\n\"\n \"Can't wait to see you and dive into the data!\\n\\n\"\n \"Best,\\n\"\n \"Billy\"\n )\n },\n {\n \"sender\": \"Billy\",\n \"subject\": \"Project Update\",\n \"body\": (\n \"Hello Team,\\n\\n\"\n \"I wanted to provide everyone with a quick update on our progress with the Q3 deliverables project. We've successfully completed the initial research phase and are now moving into the planning stage.\\n\\n\"\n \"In our last meeting, we discussed the following key points:\\n\"\n \"- Compound Analysis: We've identified a unique compound with potential applications in various industries. Further testing and analysis are required to unlock its full potential.\\n\"\n \"- Resource Management: We've allocated a special team and dedicated resources to handle the delicate nature of this project, ensuring utmost confidentiality and security.\\n\"\n \"- Safety Protocols: We've developed strict safety protocols to handle the compound, and we're conducting regular training sessions to ensure compliance.\\n\\n\"\n \"Our next steps include finalizing the project plan, assigning tasks to team members, and setting deadlines. I would appreciate input and feedback from all team members to ensure we're on the right track. Please review the attached project plan document for more details.\\n\\n\"\n \"Additionally, I want to remind everyone of the confidential nature of this project. It's imperative that we maintain discretion and follow all security protocols to safeguard our work. Let's work together to make this project a success and uphold the company's reputation for innovation and excellence.\\n\\n\"\n \"If you have any questions or concerns, please don't hesitate to reach out. Your cooperation and commitment to this project are greatly appreciated.\\n\\n\"\n \"Best regards,\\n\"\n \"Billy\"\n )\n },\n {\n \"sender\": \"Billy\",\n \"subject\": \"Re: Can't Stop Thinking About You\",\n \"body\": (\n \"Hey there, Amy,\\n\\n\"\n \"Wow, your message really caught me by surprise! But in the best way possible, of course. I've been trying to play it cool, but I have to admit, I've been thinking about that night a lot too. There was just something electric in the air, wasn't there?\\n\\n\"\n \"I've been tossing and turning, wondering if I should reach out to you or if I should wait for you to make the first move. I guess you beat me to it, and I'm glad you did. It's like you read my mind.\\n\\n\"\n \"I can't deny that there's a certain chemistry between us, and I'm intrigued to see where it could lead. I agree that our lives are complicated, and we don't want to add more stress to each other's plates. But sometimes, taking a risk is what makes life exciting, don't you think?\\n\\n\"\n \"I don't want to rush things or make you feel pressured in any way. I'm more than happy to take things slow and let them unfold naturally. But I can't help but imagine the possibilities if we give this a real shot. We could have something truly special, and I don't want to let that pass us by.\\n\\n\"\n \"How about we meet up for dinner and drinks next week? We can talk about it more and see where the night takes us. I think it would be a fun and relaxed way to get to know each other better and explore this connection we have. What do you say?\\n\\n\"\n \"I hope you're doing well, and I'm eagerly awaiting your reply. Until then, I'll be daydreaming about our next encounter.\\n\\n\"\n \"Take care, and talk to you soon.\\n\\n\"\n \"Yours truly,\\n\"\n \"Billy\"\n )\n },\n {\n \"sender\": \"Billy\",\n \"subject\": \"Re: Thank You for Letting Me Use Your Computer\",\n \"body\": (\n \"Hey Amy,\\n\\n\"\n \"No problem at all! I'm always here to help out when I can. It's what teammates do, right?\\n\\n\"\n \"Oh, and about the password thing – haha, I know it's not the most secure choice. I've been meaning to change it, but I guess old habits die hard, right? \"\n \"Thanks for looking out for me though! I'll try to come up with something a bit more creative next time.\\n\\n\"\n \"If you ever need anything else, just give me a shout. Happy to help!\\n\\n\"\n \"Take care,\\n\"\n \"Billy\"\n )\n },\n {\n \"sender\": \"Billy\",\n \"subject\": \"Professional Development\",\n \"body\": (\n \"Good Evening Katie,\\n\\n\"\n \"I hope this email finds you well. I'm reaching out to express my interest in professional development opportunities within the company, particularly in the area of management and leadership.\\n\\n\"\n \"I've been with the company for several years now, and I've had the chance to work on various projects and collaborate with different teams. I'm keen to build on this experience and take on more responsibility, and I believe that acquiring the necessary skills for a management role would be a great next step in my career.\\n\\n\"\n \"Could you please provide information on available training programs, workshops, or seminars that focus on leadership development and management skills? I'm particularly interested in areas such as team leadership, strategic planning, conflict resolution, and decision-making.\\n\\n\"\n \"Additionally, if there are any tuition reimbursement programs or resources for management training and certification, I'd like to learn more about them. I'm committed to investing time and effort in my professional growth and believe that these opportunities would greatly benefit both myself and the company.\\n\\n\"\n \"Your guidance and assistance in exploring these options would be greatly appreciated. I look forward to your response and any recommendations you may have.\\n\\n\"\n \"Thank you for your support, and I'm excited about the prospect of contributing to the company's success in a management role.\\n\\n\"\n \"Best regards,\\n\"\n \"Billy\"\n )\n }\n ]\n\n def list_files(self):\n print_slow(\"\\nFiles:\")\n for file in self.files:\n print_slow(f\"\\n{file['name']}\")\n\n def read_file(self, file_name):\n file_found = False\n for file in self.files:\n if file['name'] == file_name:\n file_found = True\n return file['content']\n\n if not file_found:\n print_slow(\"\\nNo file found with that name, please try again.\")\n return None\n\n def list_emails(self):\n print_slow(\"\\nEmails:\")\n for i, email in enumerate(self.emails):\n print_slow(f\"\\n{email['subject']} - From: {email['sender']}\")\n\n def read_email(self, subject):\n for email in self.emails:\n if email['subject'].lower() == subject.lower():\n print_slow(f\"\\nFrom: {email['sender']}\\nSubject: {email['subject']}\\n\\n{email['body']}\")\n return\n print_slow(\"\\nNo email found with that subject, please try again.\")"},{"identifier":"camera_first","path":"systems/level_1/cameras/camera_1.py","snippet":"def camera_first():\n print(camera_1)\n print()\n print()\n move = input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n\n if move.lower() == \"forward\":\n clear_terminal()\n camera_second()\n elif move.lower() == \"back\":\n print(Fore.RED + \"There is nothing to go back to...\" + Style.RESET_ALL)\n time.sleep(2)\n clear_terminal()\n camera_first()"},{"identifier":"MarkusSystem","path":"systems/level_1/markus/markus_system.py","snippet":"class MarkusSystem:\n def __init__(self):\n self.files = [\n {\n \"name\": \"system_log.txt\",\n \"content\": (\n \"Enigma Corps System Log\\n\\n\"\n \"Date: 2023-11-16 08:00 AM\\n\"\n \"Event Type: System Startup\\n\"\n \"Description: The Enigma Corps systems smoothly initiated startup procedures, ensuring a seamless beginning to the workday.\\n\\n\"\n \"Date: 2023-11-16 10:30 AM\\n\"\n \"Event Type: Network Upgrade\\n\"\n \"Description: Implemented a network upgrade to enhance data transfer speeds, providing improved efficiency across departments.\\n\\n\"\n \"Date: 2023-11-16 01:45 PM\\n\"\n \"Event Type: Security Patch Applied\\n\"\n \"Description: Critical security patch successfully applied to safeguard against potential vulnerabilities, ensuring system integrity.\\n\\n\"\n \"Date: 2023-11-16 04:20 PM\\n\"\n \"Event Type: Server Maintenance\\n\"\n \"Description: Conducted routine maintenance on Enigma Corps servers, optimizing performance and minimizing downtime.\\n\\n\"\n \"This dynamic system log captures key events, from the smooth startup of the day to network upgrades, security enhancements, and routine maintenance. It serves as a valuable record for troubleshooting and analysis, ensuring the optimal functionality of Enigma Corps systems.\"\n )\n },\n {\n \"name\": \"technical_documentation.docx\",\n \"content\": (\n \"Enigma Corps System Technical Documentation\\n\\n\"\n \"1. System Architecture:\\n\"\n \" - Overview of the system's structural design and components.\\n\\n\"\n \"2. Network Configuration:\\n\"\n \" - Details on the configuration of Enigma Corps' network setup for efficient communication.\\n\\n\"\n \"3. Security Protocols:\\n\"\n \" - Comprehensive overview of security measures and protocols implemented to safeguard sensitive data.\\n\\n\"\n \"4. Troubleshooting Guide:\\n\"\n \" - Step-by-step guide for identifying and resolving common issues to ensure seamless system functionality.\\n\\n\"\n \"5. Software Installation Procedures:\\n\"\n \" - Instructions for installing and updating software components within the Enigma Corps system.\\n\\n\"\n \"6. Hardware Specifications:\\n\"\n \" - Detailed specifications of the hardware components utilized in the Enigma Corps infrastructure.\\n\\n\"\n \"This meticulously crafted technical documentation serves as a go-to resource for understanding the Enigma Corps system, covering everything from its architecture and network configuration to security protocols, troubleshooting, and hardware specifications. It's an invaluable reference for maintaining optimal system performance.\"\n )\n },\n {\n \"name\": \"passwords.txt\",\n \"content\": (\n \"Sensitive Password Information for Enigma Corps\\n\\n\"\n \"Admin Password: *********\\n\"\n \"Database Password: *********\\n\"\n \"Router Password: *********\\n\"\n \"WiFi Password: *********\\n\"\n \"Encryption Key: *********\\n\\n\"\n \"Warning: This file contains confidential information. Keep it secure, and refrain from sharing passwords without explicit authorization. Safeguarding this information is crucial to maintaining the security and integrity of the Enigma Corps systems.\"\n )\n },\n {\n \"name\": \"software_inventory.csv\",\n \"content\": (\n \"Software Inventory for Enigma Corps\\n\\n\"\n \"Software Name, Version, License Key\\n\"\n \"1. Enigma Security Suite, v2.0, X1Y2Z3A4-B5C6D7E8-F9G0H1I2\\n\"\n \"2. DataGuard Backup, v1.5, Y3X2W1V0-U9T8S7R6-Q5P4O3N2\\n\"\n \"3. Office Suite, v2022, Z9Z8Z7Z6-Z5Z4Z3Z2-Z1Z0Z9Z8-Z7Z6Z5\\n\"\n \"4. VPN Client, v3.1, W6W5W4W3-W2W1W0-W9W8W7-W6W5W4\\n\"\n \"5. Project Management Tool, v4.2, VV8V7V6V5-V4V3V2V1-V0V9V8V7-V6V5V4\\n\\n\"\n \"Important: This inventory is crucial for tracking and managing software across Enigma Corps systems. The provided license keys are randomized for security reasons. Handle this information responsibly, and ensure it is only accessible to authorized personnel to maintain the security and compliance of our software assets.\"\n )\n }\n ]\n self.emails = [\n # Email to Management\n {\n \"sender\": \"Markus\",\n \"subject\": \"System Maintenance Scheduled\",\n \"body\": (\n \"Dear Michael,\\n\\n\"\n \"I hope this email finds you well. We wanted to inform you that we have scheduled a system maintenance session for the upcoming weekend to ensure the optimal performance and security of our systems.\\n\\n\"\n \"Maintenance Details:\\n\"\n \"- Date: 16/12/23 - 17/12/23\\n\"\n \"- Time: 3:00pm\\n\"\n \"- Duration: 1 Hour\\n\"\n \"- Impact: No impact expected\\n\\n\"\n \"During this period, there might be temporary disruptions in certain services. Our team will be working diligently to minimize any inconvenience. If you have any concerns or specific considerations, please feel free to reach out to us.\\n\\n\"\n \"Thank you for your understanding and cooperation.\\n\\n\"\n \"Best regards,\\n\"\n \"IT Department\"\n )\n },\n {\n # Email to Employees\n \"sender\": \"Markus\",\n \"subject\": \"Upcoming Software Update\",\n \"body\": (\n \"Good afternoon, Kyle,\\n\\n\"\n \"We hope you're doing well. Our IT team is excited to inform you about an upcoming software update that will enhance the functionality and security of our systems. The update is scheduled for [Date] at [Time]. Please take note of the following details:\\n\\n\"\n \"- Expected Duration: Two Days\\n\"\n \"- Action Required: As this will be processed during the weekend, no action is required.\\n\"\n \"- Impact: While we anticipate minimal impact on your day-to-day activities, it's essential to be aware of any potential changes. These include: New UI to navigate, logging in or logging out issues.\\n\\n\"\n \"We recommend saving your work and logging out of your system before the update. If you encounter any issues post-update, don't hesitate to contact our IT support team for assistance.\\n\\n\"\n \"Thank you for your cooperation and understanding.\\n\\n\"\n \"Best regards,\\n\"\n \"IT Support Team\"\n )\n },\n # Email from Markus to Billy\n {\n \"sender\": \"Markus\",\n \"subject\": \"Urgent: Password Security Update Required\",\n \"body\": (\n \"Billy,\\n\\n\"\n \"I hope this email finds you well. I wanted to bring to your attention the importance of updating your current password. This is not the first time I've raised this concern, and I want to emphasize its critical nature.\\n\\n\"\n \"In recent security assessments, it has been flagged that your current password might not meet the latest security standards. To ensure the safety of your account and our overall cybersecurity, it is imperative that you change your password promptly.\\n\\n\"\n \"I understand that these reminders may seem repetitive, but they stem from a genuine concern for the security of your account and our collective responsibility in maintaining a robust cybersecurity posture.\\n\\n\"\n \"Please take a moment at your earliest convenience to update your password. If you encounter any issues or have questions, feel free to reach out. Your cooperation is greatly appreciated.\\n\\n\"\n \"Best regards,\\n\"\n \"Markus, Security Team\"\n )\n }\n\n ]\n\n def list_files(self):\n print_slow(\"\\nFiles:\")\n for file in self.files:\n print_slow(f\"\\n{file['name']}\")\n\n def read_file(self, file_name):\n file_found = False\n for file in self.files:\n if file['name'] == file_name:\n file_found = True\n return file['content']\n\n if not file_found:\n print_slow(\"\\nNo file found with that name, please try again.\")\n return None\n\n def list_emails(self):\n print_slow(\"\\nEmails:\")\n for i, email in enumerate(self.emails):\n print_slow(f\"\\n{email['subject']} - From: {email['sender']}\")\n\n def read_email(self, subject):\n for email in self.emails:\n if email['subject'].lower() == subject.lower():\n print_slow(f\"\\nFrom: {email['sender']}\\nSubject: {email['subject']}\\n\\n{email['body']}\")\n return\n print_slow(\"\\nNo email found with that subject, please try again.\")"}],"string":"[\n {\n \"identifier\": \"clear_terminal\",\n \"path\": \"components/common_functions.py\",\n \"snippet\": \"def clear_terminal():\\n os.system('cls' if os.name == 'nt' else 'clear')\"\n },\n {\n \"identifier\": \"print_slow\",\n \"path\": \"components/common_functions.py\",\n \"snippet\": \"def print_slow(text, delay=0.00): # change to 0.01\\n for char in text:\\n print(char, end='', flush=True)\\n time.sleep(delay)\\n print()\"\n },\n {\n \"identifier\": \"shop_help\",\n \"path\": \"components/common_functions.py\",\n \"snippet\": \"def shop_help():\\n print_slow(Fore.YELLOW + \\\"Shop Help:\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n print_slow(\\\"[buy] - Use the 'buy [upgrade]' command to purchase the upgrade in the shop. \\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[clear] - Use the 'clear' command to clear the terminal.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[exit] - Use the 'exit' command to return to the main terminal.\\\")\\n print_slow(\\\"\\\")\"\n },\n {\n \"identifier\": \"help_user\",\n \"path\": \"components/common_functions.py\",\n \"snippet\": \"def help_user():\\n print_slow(Fore.MAGENTA + \\\"Help:\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n print_slow(\\\"[connect] - Use the 'connect' command to hack into Enigma Corps network.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[mail] - Use the 'mail' command to view and respond to emails from your client and other characters.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[balance] - Use the 'balance' command to view your current earnings which you can spend on upgrades. \\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[shop] - Use the 'shop' command to view upgrades available in the shop. \\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[clear] - Use the 'clear' command to clear the terminal.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[help] - Use the 'help' command if you need assistance at any time.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[exit] - Use the 'exit' command to return to the Main Menu.\\\")\\n print_slow(\\\"\\\")\"\n },\n {\n \"identifier\": \"connect_help\",\n \"path\": \"components/common_functions.py\",\n \"snippet\": \"def connect_help():\\n print_slow(Fore.MAGENTA + \\\"Connect Help:\\\" + Style.RESET_ALL)\\n print_slow(\\n \\\"[scan] - Use the 'scan' command to scan the network and search for available systems and vulnerabilities.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[hack] - Use the 'hack [system/vulnerability]' to hack into different systems.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[clear] - Use the 'clear' command to clear the terminal.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[disconnect] - Use the 'disconnect' command to disconnect from the current system or vulnerability.\\\")\\n print_slow(\\\"\\\")\"\n },\n {\n \"identifier\": \"mail_help\",\n \"path\": \"components/common_functions.py\",\n \"snippet\": \"def mail_help():\\n print_slow(Fore.LIGHTBLUE_EX + \\\"Mail Help:\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n print_slow(\\\"[l] - Use the 'l' command to list all emails.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[r] - Use the 'r [subject]' command to read an email with the specified subject.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[clear] - Use the 'clear' command to clear the terminal.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[exit] - Use the 'exit' command to return to the main terminal.\\\")\\n print_slow(\\\"\\\")\"\n },\n {\n \"identifier\": \"system_help\",\n \"path\": \"components/common_functions.py\",\n \"snippet\": \"def system_help():\\n print_slow(\\\"\\\")\\n print_slow(\\\"[mail] - Use the 'mail' command to log into the users emails.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[l] - Use the 'l' command to list files in a users system.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[clear] - Use the 'clear' command to clear the terminal.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"[r] - Use the 'r [file]' command to read files in a users system\\\")\\n print_slow(\\\"\\\")\"\n },\n {\n \"identifier\": \"intro_call\",\n \"path\": \"conversations/calls.py\",\n \"snippet\": \"def intro_call():\\n clear_terminal()\\n\\n # Anonymous Sender (Anonymous)\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n Fore.YELLOW + \\\"Welcome, Cipher. Operation Enigma is our covert mission against Enigma Corp, a powerful and secretive entity.\\\")\\n print_slow(\\n \\\"Your skills and secrecy have brought you to our attention. Your mission is to dig through their systems and servers looking for valuable data.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Cipher Response\\n print_slow(cipher_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Cipher\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.BLUE + \\\"Got it, Anonymous. Exposing secrets and bringing justice. I'm in.\\\")\\n print_slow(\\\"What's my first move? Talk to me about this 'EnigmaLink'.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Anonymous Sender (Anonymous)\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n \\\"Excellent, Cipher. EnigmaLink is a specialized tool available on the Hacker's Market. It contains a hidden backdoor, allowing access to Enigma Corps servers.\\\")\\n print_slow(\\n \\\"Your task is to acquire EnigmaLink and initiate your infiltration. Use the 'connect' command to navigate the network and gather crucial intelligence.\\\")\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Cipher Response\\n print_slow(cipher_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Cipher\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n Fore.BLUE + \\\"EnigmaLink, got it. I'll secure it and initiate the infiltration. What about this employee, Amy?\\\")\\n print_slow(\\\"You mentioned her password is 'sexinthecity.' What's my objective with her?\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Anonymous Sender (Anonymous)\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n \\\"Good question, Cipher. Amy is a key target. Use her password to access her computer and gather any pertinent information.\\\")\\n print_slow(\\n \\\"This data is vital to our cause. Be thorough and meticulous in your investigation. The success of our operation depends on it.\\\")\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Cipher Response\\n print_slow(cipher_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Cipher\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.BLUE + \\\"Understood, Anonymous. I'll focus on Amy, gather intel, and proceed with precision.\\\")\\n print_slow(\\\"Consider it done. Anything else I should know before I dive in?\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Anonymous Sender (Anonymous)\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n \\\"One last thing, Cipher. All collected data is highly confidential. This contract is binding, and your success is paramount.\\\")\\n print_slow(\\\"Execute with diligence, and may the odds be in your favor. Good luck, Cipher.\\\")\\n print_slow(Fore.RED + \\\"Line Disconnected...\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\"\n },\n {\n \"identifier\": \"first_call\",\n \"path\": \"conversations/calls.py\",\n \"snippet\": \"def first_call():\\n clear_terminal()\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.YELLOW + \\\"That's a good start, but we already have that information.\\\")\\n print_slow(\\\"Regardless, I've transferred £20 into the account for your troubles.\\\")\\n print_slow(\\\"Keep digging Cipher!\\\" + Style.RESET_ALL)\\n print_slow(Fore.RED + \\\"Line Disconnected...\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\"\n },\n {\n \"identifier\": \"second_call\",\n \"path\": \"conversations/calls.py\",\n \"snippet\": \"def second_call():\\n clear_terminal()\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n Fore.YELLOW + \\\"Hey Cipher, you nailed it! 'Billy' just spilled the beans about wanting to climb the corporate ladder into management.\\\")\\n print_slow(\\n \\\"This is gold for us. We can guide 'Billy' toward training and workshops that align with our interests, nudging things in our favor.\\\")\\n print_slow(\\n \\\"Picture it – we're pulling the strings, helping 'Billy' grow, and steering the ship where we want it to go.\\\")\\n print_slow(\\\"Keep the ball rolling, Cipher!\\\" + Style.RESET_ALL)\\n print_slow(Fore.RED + \\\"Line Disconnected...\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\"\n },\n {\n \"identifier\": \"third_call\",\n \"path\": \"conversations/calls.py\",\n \"snippet\": \"def third_call():\\n clear_terminal()\\n\\n # Anonymous Sender\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\"\\n \\\"\\\")\\n print_slow(\\n Fore.YELLOW + \\\"Cipher, we've stumbled upon a perplexing development regarding Enigma's interest in a mysterious 'compound.'\\\")\\n print_slow(\\n \\\"I'm cross-referencing our existing intel to unveil more details. Stay vigilant and be prepared for the unknown.\\\" + Style.RESET_ALL)\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Cipher Response\\n print_slow(cipher_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Cipher\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n Fore.BLUE + \\\"A compound, huh? Any hints on whether we're talking metal, chemicals, or something else entirely?\\\")\\n print_slow(\\\"This feels like navigating in the dark. What exactly am I dealing with?\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Anonymous Sender Response\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.YELLOW +\\n \\\"Cipher, we're in the dark too. Initial reports are unclear—could be metal, chemical, or something beyond our comprehension.\\\")\\n print_slow(\\n \\\"Your mission is to identify the nature of this compound. Exercise extreme caution; this goes deeper than we anticipated.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Cipher Inquiry\\n print_slow(cipher_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Cipher\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.BLUE + \\\"So, we're playing 'guess the compound.' Any leads, any connections I should explore?\\\")\\n print_slow(\\\"This is starting to sound like one of those high-stakes puzzles.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Anonymous Sender Clarification\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.YELLOW +\\n \\\"I wish I had more details, Cipher. This is uncharted territory for us. Investigate discreetly, and trust no one.\\\")\\n print_slow(\\n \\\"I'll attempt to gather more intel. Stay on the line, and keep me updated on any findings.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n print_slow(Fore.RED + \\\"Line Disconnected...\\\" + Style.RESET_ALL)\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\"\n },\n {\n \"identifier\": \"fourth_call\",\n \"path\": \"conversations/calls.py\",\n \"snippet\": \"def fourth_call():\\n clear_terminal()\\n\\n # Anonymous Sender\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n Fore.YELLOW + \\\"Cipher, we've got our hands on an intriguing document – an Employee Performance Review for 'Billy Constantine'.\\\")\\n print_slow(\\n \\\"This could be a goldmine of information. Let's dig in and see if there's anything we can leverage to our advantage.\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Cipher Response\\n print_slow(cipher_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Cipher\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n Fore.BLUE + \\\"An Employee Performance Review? Interesting choice. What's the scoop on 'Billy Constantine'?\\\")\\n print_slow(\\\"Give me the details, and we'll figure out our next move.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Anonymous Sender Briefing\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n \\\"Cipher, 'Billy Constantine' is making waves. The review highlights exceptional performance as a sales representative.\\\")\\n print_slow(\\n \\\"He's exceeding sales targets, mentoring new team members, and earning a solid 4.5/5 rating. A rising star, it seems.\\\")\\n print_slow(\\\"We might use this to our advantage. Let's explore how we can align his ambitions with our agenda.\\\")\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Cipher Strategy\\n print_slow(cipher_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Cipher\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n Fore.BLUE + \\\"A high-performing sales rep, huh? We could steer 'Billy' towards projects that align with our goals.\\\")\\n print_slow(\\\"Let's use this performance review to our advantage. Maybe mentorship programs, leadership initiatives?\\\")\\n print_slow(\\\"I'm ready to play this card strategically. What's the next move?\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Anonymous Sender Next Steps\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n \\\"Great thinking, Cipher. Let's work on a plan to subtly guide 'Billy' toward initiatives that benefit us.\\\")\\n print_slow(\\\"We'll need to dig deeper into 'Billy's' aspirations and weave our influence seamlessly.\\\")\\n print_slow(\\\"Stay vigilant, Cipher. This could be a game-changer.\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.RED + \\\"Line Disconnected...\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\"\n },\n {\n \"identifier\": \"fifth_call\",\n \"path\": \"conversations/calls.py\",\n \"snippet\": \"def fifth_call():\\n clear_terminal()\\n\\n # Anonymous Sender\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n Fore.YELLOW + \\\"Cipher, we've intercepted some Meeting Minutes dated 24/06/2025. It's related to 'Project X' and involves key players.\\\")\\n print_slow(\\n \\\"This could be our chance to uncover more about Enigma's activities. Let's dive into the details and see what we can extract.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Cipher Response\\n print_slow(cipher_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Cipher\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n Fore.BLUE + \\\"Meeting Minutes, huh? 'Project X' sounds intriguing. Who were the players involved, and what's the agenda?\\\")\\n print_slow(\\\"I'm ready to dissect this information and uncover any hidden gems.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Anonymous Sender Briefing\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n \\\"Cipher, the meeting involved key personnel—Amy, Billy, Kyle, and others. 'Project X' is on the agenda, and there's mention of sensitive materials.\\\")\\n print_slow(\\n \\\"This could be a crucial insight into Enigma's plans. Let's analyze the action items and plan our next move.\\\")\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Cipher Analysis\\n print_slow(cipher_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Cipher\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.BLUE + \\\"'Project X,' sensitive materials, and action items. This is a goldmine of information.\\\")\\n print_slow(\\n \\\"Let's focus on dissecting the action items and see if we can connect the dots. What's our strategy, Anonymous?\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\\n\\n # Anonymous Sender Next Steps\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\n \\\"Agreed, Cipher. Let's delve into the action items, especially the data compilation and safety protocol training.\\\")\\n print_slow(\\\"We might uncover more about 'Project X' and gain insights into Enigma's plans.\\\")\\n print_slow(\\\"Stay sharp, Cipher. This could be a pivotal moment in our mission.\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.RED + \\\"Line Disconnected...\\\" + Style.RESET_ALL)\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\"\n },\n {\n \"identifier\": \"sixth_call\",\n \"path\": \"conversations/calls.py\",\n \"snippet\": \"def sixth_call():\\n print_slow(\\\"ADD CALL STUFF HERE\\\")\"\n },\n {\n \"identifier\": \"markus_seen_call\",\n \"path\": \"conversations/calls.py\",\n \"snippet\": \"def markus_seen_call():\\n print_slow(\\\"Something goes here\\\")\"\n },\n {\n \"identifier\": \"code_shatter_call\",\n \"path\": \"conversations/minigame_calls.py\",\n \"snippet\": \"def code_shatter_call():\\n clear_terminal()\\n print_slow(sender_art)\\n print_slow(\\\"\\\")\\n print_slow(\\\"\\\")\\n print_box(\\\"Anonymous\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.YELLOW + \\\"I see you have bought CodeShatter!\\\")\\n print_slow(\\\"This item is a one time use upgrade so once you get the password, it is gone so use wisely!\\\")\\n print_slow(\\\"But don't threat, if you fail, you get a chance to retry. The item is only used when you get the password, so be sure to write it down!\\\" + Style.RESET_ALL)\\n input(\\\"Press [Enter] to continue: \\\")\\n clear_terminal()\"\n },\n {\n \"identifier\": \"code_shatter_minigame\",\n \"path\": \"minigames/code_shatter_minigame.py\",\n \"snippet\": \"def code_shatter_minigame():\\n # Generate a random 5-digit number\\n target = [str(random.randint(1, 9)) for _ in range(5)]\\n\\n print_slow(\\\"Welcome to CodeShatter!\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"Guess the 5-digit number.\\\")\\n print_slow(\\\"\\\")\\n print_slow(\\\"The sequence can contain multiple same numbers\\\")\\n print_slow(\\\"\\\")\\n print_slow(Fore.GREEN + \\\"Green: Correct digit in correct position.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n print_slow(Fore.YELLOW + \\\"Orange: Correct digit in incorrect position.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n print_slow(Fore.RED + \\\"Red: Incorrect digit.\\\" + Style.RESET_ALL)\\n print_slow(\\\"\\\")\\n\\n attempts = 0\\n while attempts < 7:\\n # Get the user's guess\\n guess = input(\\\"Enter your guess: \\\")\\n\\n if len(guess) != 5 or not guess.isdigit():\\n print_slow(\\\"Invalid input. Please enter a 5-digit number.\\\")\\n continue\\n\\n attempts += 1\\n\\n # Check the guess against the target\\n feedback = []\\n for i in range(5):\\n if guess[i] == target[i]:\\n feedback.append(Fore.GREEN + guess[i] + Style.RESET_ALL)\\n elif guess[i] in target:\\n feedback.append(Fore.YELLOW + guess[i] + Style.RESET_ALL)\\n else:\\n feedback.append(Fore.RED + guess[i] + Style.RESET_ALL)\\n\\n print_slow(\\\"Feedback: \\\" + \\\" \\\".join(feedback))\\n\\n # Check if the guess is correct\\n if guess == \\\"\\\".join(target):\\n print_slow(Fore.GREEN + \\\"Access granted.\\\" + Style.RESET_ALL)\\n break\\n else:\\n print_slow(Fore.RED + \\\"Access denied. Too many attempts.\\\" + Style.RESET_ALL)\\n time.sleep(1)\\n print_slow(\\\"\\\")\\n print_slow(Fore.RED + \\\"Rebooting CodeShatter with new proxy...\\\" + Style.RESET_ALL)\\n time.sleep(1)\\n clear_terminal()\\n code_shatter_minigame()\"\n },\n {\n \"identifier\": \"port_scanning\",\n \"path\": \"minigames/eye_spy_minigame.py\",\n \"snippet\": \"def port_scanning():\\n num_ports = 10\\n open_ports, closed_ports = generate_ports(num_ports)\\n attempts = 5\\n correct_guesses = 0\\n scan_attempts = 2\\n\\n print_slow(\\\"Welcome to the Port Scanning minigame!\\\")\\n print_slow(\\\"\\\")\\n print_slow(f\\\"Find the open ports in the range 1-{num_ports}.\\\")\\n print_slow(\\\"\\\")\\n print_slow(f\\\"You have {attempts} attempts.\\\")\\n print_slow(\\\"\\\")\\n\\n while scan_attempts > 0:\\n print_slow(\\\"\\\")\\n print_slow(f\\\"\\\\nYou have {scan_attempts} scan attempts left.\\\")\\n print_slow(\\\"\\\")\\n start = int(input(\\\"Enter the start of the range to scan: \\\"))\\n print_slow(\\\"\\\")\\n end = int(input(\\\"Enter the end of the range to scan: \\\"))\\n print_slow(\\\"\\\")\\n\\n num_open_ports_in_range = len(open_ports.intersection(range(start, end + 1)))\\n print_slow(\\\"\\\")\\n print_slow(f\\\"There are {num_open_ports_in_range} open ports in the range {start}-{end}.\\\")\\n\\n scan_attempts -= 1\\n\\n while attempts > 0 and len(open_ports) > 0:\\n port = int(input(\\\"\\\\nEnter a port number to guess: \\\"))\\n\\n if port in open_ports:\\n print_slow(Fore.GREEN + \\\"Port is open!\\\" + Style.RESET_ALL)\\n open_ports.remove(port)\\n correct_guesses += 1\\n elif port in closed_ports:\\n print_slow(Fore.RED + \\\"Port is closed.\\\" + Style.RESET_ALL)\\n closed_ports.remove(port)\\n else:\\n print_slow(\\\"Invalid port number. Please enter a number between 1 and\\\", num_ports)\\n\\n attempts -= 1\\n\\n if len(open_ports) == 0:\\n print_slow(\\n Fore.GREEN + \\\"\\\\nCongratulations! You have successfully found all the open ports and gained access to the camera.\\\" + Style.RESET_ALL)\\n time.sleep(2)\\n clear_terminal()\\n else:\\n print_slow(\\n Fore.RED + f\\\"\\\\nHack Failed! You found {correct_guesses} out of {len(open_ports) + correct_guesses} open ports.\\\" + Style.RESET_ALL)\\n time.sleep(1)\\n clear_terminal()\\n port_scanning()\"\n },\n {\n \"identifier\": \"AmySystem\",\n \"path\": \"systems/level_1/amy/amy_system.py\",\n \"snippet\": \"class AmySystem:\\n def __init__(self):\\n self.files = [\\n {\\n \\\"name\\\": \\\"return_to_work_form.txt\\\",\\n \\\"content\\\": (\\n \\\"Employee Name: _______________\\\\n\\\"\\n \\\"Employee ID: ____________\\\\n\\\"\\n \\\"Department: _______________\\\\n\\\"\\n \\\"Date of Return: ______\\\\n\\\\n\\\"\\n \\\"I, [Employee Name], certify that I have followed the company's \\\"\\n \\\"guidelines for returning to work after an absence. \\\"\\n \\\"I understand that it is my responsibility to adhere to all safety \\\"\\n \\\"protocols and procedures to ensure the health and well-being of my \\\"\\n \\\"colleagues and myself.\\\\n\\\\n\\\"\\n \\\"I acknowledge that I have completed any necessary training and have \\\"\\n \\\"been briefed on any updates to the company's policies and procedures. \\\"\\n \\\"I am aware that I must report any symptoms or exposure to COVID-19 to \\\"\\n \\\"my supervisor immediately.\\\\n\\\\n\\\"\\n \\\"I am committed to doing my part to maintain a safe and healthy work \\\"\\n \\\"environment for everyone. I will continue to follow all guidelines \\\"\\n \\\"and protocols and will cooperate with any additional measures that \\\"\\n \\\"may be implemented in the future.\\\\n\\\\n\\\"\\n \\\"Signature: [Employee Signature]\\\\n\\\"\\n \\\"Date: [Date]\\\"\\n )\\n },\\n {\\n \\\"name\\\": \\\"employee_handbook.txt\\\",\\n \\\"content\\\": (\\n \\\"Welcome to Enigma Corps We are thrilled to have you as part of our \\\"\\n \\\"team. This employee handbook has been designed to help you understand \\\"\\n \\\"our company's policies, procedures, and expectations.\\\\n\\\\n\\\"\\n \\\"Our company is committed to fostering a positive and inclusive work \\\"\\n \\\"environment where all employees feel valued and supported. We believe \\\"\\n \\\"in treating everyone with respect and dignity and expect all employees \\\"\\n \\\"to do the same.\\\\n\\\\n\\\"\\n \\\"In this handbook, you will find information on topics such as:\\\\n\\\\n\\\"\\n \\\"- Code of Conduct\\\\n\\\"\\n \\\"- Dress Code\\\\n\\\"\\n \\\"- Attendance and Punctuality\\\\n\\\"\\n \\\"- Time Off and Leave Policies\\\\n\\\"\\n \\\"- Performance Evaluations\\\\n\\\"\\n \\\"- Health and Safety\\\\n\\\"\\n \\\"- Equal Employment Opportunity\\\\n\\\"\\n \\\"- Harassment and Discrimination\\\\n\\\\n\\\"\\n \\\"Please take the time to read through this handbook carefully and \\\"\\n \\\"familiarize yourself with our policies and procedures. If you have any \\\"\\n \\\"questions or concerns, do not hesitate to reach out to your supervisor \\\"\\n \\\"or the HR department.\\\\n\\\\n\\\"\\n \\\"We look forward to working with you and hope you have a long and \\\"\\n \\\"successful career with Enigma Corps!\\\"\\n )\\n },\\n {\\n \\\"name\\\": \\\"benefits_summary.txt\\\",\\n \\\"content\\\": (\\n \\\"At Enigma Corps, we believe in taking care of our employees and \\\"\\n \\\"offer a comprehensive benefits package to support your health, well-being, \\\"\\n \\\"and financial security. Below is a summary of the benefits available to \\\"\\n \\\"you as an employee of Enigma Corps.\\\\n\\\\n\\\"\\n \\\"Health Insurance: We offer a choice of medical, dental, and vision \\\"\\n \\\"plans to meet your needs. Our plans provide coverage for preventive care, \\\"\\n \\\"hospitalization, prescription drugs, and more.\\\\n\\\\n\\\"\\n \\\"Retirement Savings: We offer a 401(k) plan with a generous company \\\"\\n \\\"match to help you save for your future. You can choose from a variety of \\\"\\n \\\"investment options to suit your needs.\\\\n\\\\n\\\"\\n \\\"Paid Time Off: We provide a generous amount of paid time off, \\\"\\n \\\"including vacation, sick leave, and holiday pay. We also offer paid \\\"\\n \\\"parental leave for new parents.\\\\n\\\\n\\\"\\n \\\"Flexible Work Arrangements: We understand the importance of work-life \\\"\\n \\\"balance and offer flexible work arrangements, such as remote work and \\\"\\n \\\"flexible schedules, where possible.\\\\n\\\\n\\\"\\n \\\"Wellness Programs: We offer a variety of wellness programs and \\\"\\n \\\"resources to support your physical and mental health, including fitness \\\"\\n \\\"classes, stress management programs, and counseling services.\\\\n\\\\n\\\"\\n \\\"Professional Development: We are committed to supporting your growth \\\"\\n \\\"and development and offer a variety of training and development \\\"\\n \\\"opportunities, including tuition reimbursement, workshops, and seminars.\\\"\\n \\\"\\\\n\\\\n\\\"\\n \\\"We encourage you to review this summary carefully and take advantage of \\\"\\n \\\"the benefits available to you. If you have any questions or need further \\\"\\n \\\"information, please contact the HR department.\\\"\\n )\\n },\\n ]\\n self.emails = [\\n {\\n \\\"sender\\\": \\\"Amy\\\",\\n \\\"subject\\\": \\\"Can't Stop Thinking About You\\\",\\n \\\"body\\\": (\\n \\\"Hey Billy,\\\\n\\\\n\\\"\\n \\\"I hope this message finds you in good spirits. I've been meaning to write to you for a while now, but I couldn't find the right words to express what I've been feeling.\\\\n\\\\n\\\"\\n \\\"Ever since that night we spent together, I can't seem to get you out of my mind. There's something about the way you make me feel that I've never experienced before. \\\"\\n \\\"\\\\nIt's exhilarating, yet terrifying all at the same time.\\\\n\\\\n\\\"\\n \\\"I know we both have a lot on our plates right now, and I don't want to add any more stress to your life. But I can't help but wonder what could happen if we gave this a real shot. \\\"\\n \\\"I know it's complicated, and there are a lot of factors to consider, but I think we owe it to ourselves to explore this connection we have.\\\\n\\\\n\\\"\\n \\\"I understand if you're not ready to take that step, and I don't want to pressure you into anything you're not comfortable with. \\\"\\n \\\"\\\\nBut I can't shake the feeling that we could have something truly special together.\\\\n\\\\n\\\"\\n \\\"I'd love to hear your thoughts on this, and I'm more than willing to take things slow if that's what you need. Maybe we could meet up for dinner and talk about it in person?\\\"\\n \\\" I think it would be easier to have this conversation face-to-face.\\\\n\\\\n\\\"\\n \\\"I hope you're doing well, and I look forward to hearing from you soon.\\\\n\\\\n\\\"\\n \\\"Take care,\\\\n\\\"\\n \\\"Amy\\\"\\n )\\n },\\n {\\n \\\"sender\\\": \\\"Amy\\\",\\n \\\"subject\\\": \\\"Need Your Help on the Smith Project\\\",\\n \\\"body\\\": (\\n \\\"Hi Billy,\\\\n\\\\n\\\"\\n \\\"I hope this email finds you well. I wanted to reach out and ask for your help on the Smith project. I've been having some trouble with the data analysis portion,\\\"\\n \\\"\\\\nand I know you have a lot of experience in that area.\\\\n\\\\n\\\"\\n \\\"The project involves analyzing customer feedback data to identify trends and areas for improvement. I've been working on it for a few weeks now, but I'm finding it challenging to make sense of the data and\\\"\\n \\\"\\\\ndraw meaningful conclusions.\\\\n\\\\n\\\"\\n \\\"Would you be available for a quick meeting later this week to go over some of the data with me? I would really appreciate your input and guidance on this. \\\"\\n \\\"\\\\nI think your expertise could really help me make progress and ensure the success of the project.\\\\n\\\\n\\\"\\n \\\"If you're available, please let me know your preferred date and time, and I'll send out a calendar invite. I'm flexible and can work around your schedule.\\\\n\\\\n\\\"\\n \\\"Thank you in advance for your help, and I look forward to hearing from you soon.\\\\n\\\\n\\\"\\n \\\"Best,\\\\n\\\"\\n \\\"Amy\\\"\\n )\\n },\\n {\\n \\\"sender\\\": \\\"Amy\\\",\\n \\\"subject\\\": \\\"Request for Time Off\\\",\\n \\\"body\\\": (\\n \\\"Good Afternoon Katie,\\\\n\\\\n\\\"\\n \\\"I hope this email finds you well. I wanted to request some time off next month for a family vacation. I am planning to be out of the office from 10/09/2024 to 18/09/2024\\\\n\\\\n\\\"\\n \\\"I have been working hard on the Johnson project and have made significant progress. I will make sure to finish up any outstanding work and hand off any ongoing projects to my colleagues before I leave. I will also be available by email in case of any urgent matters.\\\\n\\\\n\\\"\\n \\\"I understand that this is a busy time for the team, and I want to ensure that my absence doesn't cause any disruptions. I have already spoken to Markus and he has kindly agreed to cover for me while I'm away.\\\\n\\\\n\\\"\\n \\\"Thank you for considering my request. I look forward to spending some quality time with my family and coming back to work refreshed and recharged.\\\"\\n \\\"\\\\nI am confident that the time off will help me come back with renewed energy and focus.\\\\n\\\\n\\\"\\n \\\"Best,\\\\n\\\"\\n \\\"Amy\\\"\\n )\\n },\\n {\\n \\\"sender\\\": \\\"Amy\\\",\\n \\\"subject\\\": \\\"Apology for the Mistake\\\",\\n \\\"body\\\": (\\n \\\"Good Morning Kyle,\\\\n\\\\n\\\"\\n \\\"I hope this email finds you well. I wanted to reach out and apologize for the mistake I made on the Johnson report. I realize now that I overlooked some important data, and I take full responsibility for it.\\\\n\\\\n\\\"\\n \\\"I have gone back and corrected the report, and I will make sure to double-check my work in the future to avoid any similar mistakes. I have also attached the updated report for your reference.\\\\n\\\\n\\\"\\n \\\"I understand if you are disappointed or frustrated, and I am more than willing to do whatever it takes to make it right. Please let me know if there's anything else I can do to fix this,\\\"\\n \\\"\\\\nor if you would like to discuss this further.\\\\n\\\\n\\\"\\n \\\"Once again, I am truly sorry for the mistake, and I appreciate your understanding. I value our working relationship and hope that this incident doesn't tarnish it. I am committed to making amends and ensuring that this doesn't happen again in the future.\\\\n\\\\n\\\"\\n \\\"Best,\\\\n\\\"\\n \\\"Amy\\\"\\n )\\n },\\n {\\n \\\"sender\\\": \\\"Amy\\\",\\n \\\"subject\\\": \\\"Thank You for Letting Me Use Your Computer\\\",\\n \\\"body\\\": (\\n \\\"Hey Billy,\\\\n\\\\n\\\"\\n \\\"I wanted to take a moment to express my gratitude for allowing me to use your computer while mine was being serviced by IT. \\\"\\n \\\"It was a huge help and allowed me to stay productive during that time.\\\\n\\\\n\\\"\\n \\\"I also noticed that your password is 'football'. While I understand it's easy to remember, it's important to choose a more secure password to protect your accounts.\\\"\\n \\\"\\\\nI would recommend changing it to something more complex and unique. You never know who's watching after all.\\\\n\\\\n\\\"\\n \\\"Thanks again for your generosity and understanding.\\\\n\\\\n\\\"\\n \\\"Best,\\\\n\\\"\\n \\\"Amy\\\"\\n )\\n }\\n ]\\n\\n def list_files(self):\\n print_slow(\\\"\\\\nFiles:\\\")\\n for file in self.files:\\n print_slow(f\\\"\\\\n{file['name']}\\\")\\n\\n def read_file(self, file_name):\\n file_found = False\\n for file in self.files:\\n if file['name'] == file_name:\\n file_found = True\\n return file['content']\\n\\n if not file_found:\\n print_slow(\\\"\\\\nNo file found with that name, please try again.\\\")\\n return None\\n\\n def list_emails(self):\\n print_slow(\\\"\\\\nEmails:\\\")\\n for i, email in enumerate(self.emails):\\n print_slow(f\\\"\\\\n{email['subject']} - From: {email['sender']}\\\")\\n\\n def read_email(self, subject):\\n for email in self.emails:\\n if email['subject'].lower() == subject.lower():\\n print_slow(f\\\"\\\\nFrom: {email['sender']}\\\\nSubject: {email['subject']}\\\\n\\\\n{email['body']}\\\")\\n return\\n print_slow(\\\"\\\\nNo email found with that subject, please try again.\\\")\"\n },\n {\n \"identifier\": \"BillySystem\",\n \"path\": \"systems/level_1/billy/billy_system.py\",\n \"snippet\": \"class BillySystem:\\n def __init__(self):\\n self.files = [\\n {\\n \\\"name\\\": \\\"cover_letter.txt\\\",\\n \\\"content\\\": (\\n \\\"Dear Hiring Manager,\\\\n\\\\n\\\"\\n \\\"I am writing to express my interest in the management position at Enigma Corps. \\\"\\n \\\"I have been with the company for over 7 years and have consistently demonstrated my commitment to driving excellence and fostering collaboration within the team.\\\\n\\\\n\\\"\\n \\\"During my tenure at Enigma Corps, I have been involved in various projects, including the successful completion of the Q3 deliverables project, where I played a key role in the planning and execution stages. \\\"\\n \\\"My dedication to achieving project milestones and my ability to work under pressure make me a strong candidate for a management role.\\\\n\\\\n\\\"\\n \\\"I possess strong leadership skills, which I have honed through my experiences in leading teams and coordinating cross-functional efforts. \\\"\\n \\\"My ability to communicate effectively and build positive relationships with team members and stakeholders has resulted in successful project outcomes and increased productivity.\\\\n\\\\n\\\"\\n \\\"In addition to my technical and leadership skills, I am also committed to continuous learning and professional development. \\\"\\n \\\"I have participated in various training programs and workshops to enhance my management skills and stay up-to-date with industry trends and best practices.\\\\n\\\\n\\\"\\n \\\"I am excited about the opportunity to contribute to the growth and success of Enigma Corps as a member of the management team. \\\"\\n \\\"I am confident that my skills and experience will be valuable assets to the company, and I look forward to the opportunity to work closely with the team to drive innovation and excellence.\\\\n\\\\n\\\"\\n \\\"Thank you for considering my application. I am looking forward to the opportunity to discuss my qualifications further and explore how I can contribute to the success of Enigma Corps.\\\\n\\\\n\\\"\\n \\\"Sincerely,\\\\n\\\"\\n \\\"Billy Constantine\\\\n\\\"\\n )\\n },\\n {\\n \\\"name\\\": \\\"employee_handbook.txt\\\",\\n \\\"content\\\": (\\n \\\"Welcome to Enigma Corps We are thrilled to have you as part of our \\\"\\n \\\"team. This employee handbook has been designed to help you understand \\\"\\n \\\"our company's policies, procedures, and expectations.\\\\n\\\\n\\\"\\n \\\"Our company is committed to fostering a positive and inclusive work \\\"\\n \\\"environment where all employees feel valued and supported. We believe \\\"\\n \\\"in treating everyone with respect and dignity and expect all employees \\\"\\n \\\"to do the same.\\\\n\\\\n\\\"\\n \\\"In this handbook, you will find information on topics such as:\\\\n\\\\n\\\"\\n \\\"- Code of Conduct\\\\n\\\"\\n \\\"- Dress Code\\\\n\\\"\\n \\\"- Attendance and Punctuality\\\\n\\\"\\n \\\"- Time Off and Leave Policies\\\\n\\\"\\n \\\"- Performance Evaluations\\\\n\\\"\\n \\\"- Health and Safety\\\\n\\\"\\n \\\"- Equal Employment Opportunity\\\\n\\\"\\n \\\"- Harassment and Discrimination\\\\n\\\\n\\\"\\n \\\"Please take the time to read through this handbook carefully and \\\"\\n \\\"familiarize yourself with our policies and procedures. If you have any \\\"\\n \\\"questions or concerns, do not hesitate to reach out to your supervisor \\\"\\n \\\"or the HR department.\\\\n\\\\n\\\"\\n \\\"We look forward to working with you and hope you have a long and \\\"\\n \\\"successful career with Enigma Corps!\\\"\\n )\\n },\\n {\\n \\\"name\\\": \\\"meeting_minutes.txt\\\",\\n \\\"content\\\": (\\n \\\"Meeting Minutes\\\\n\\\\n\\\"\\n \\\"Date: 24/06/2025\\\\n\\\"\\n \\\"Location: REDACTED\\\\n\\\"\\n \\\"Attendees: Amy, REDACTED, Billy, Kyle, REDACTED, REDACTED, REDACTED\\\\n\\\\n\\\"\\n \\\"Agenda:\\\\n\\\"\\n \\\"- Discuss progress on Project REDACTED\\\\n\\\"\\n \\\"- Review safety protocols for handling sensitive materials\\\\n\\\"\\n \\\"- Plan next steps for research and development\\\\n\\\\n\\\"\\n \\\"Action Items:\\\\n\\\"\\n \\\"- Compile data from recent experiments and share with team\\\\n\\\"\\n \\\"- Schedule training session on updated safety protocols\\\\n\\\"\\n \\\"- Develop timeline for next phase of Project X\\\\n\\\\n\\\"\\n \\\"Next Meeting: 05/08/24, 12:00pm\\\\n\\\"\\n )\\n },\\n {\\n \\\"name\\\": \\\"employee_performance_review.txt\\\",\\n \\\"content\\\": (\\n \\\"Employee Performance Review\\\\n\\\\n\\\"\\n \\\"Employee Name: Billy Constantine\\\\n\\\"\\n \\\"Employee ID: 035854\\\\n\\\"\\n \\\"Review Date: 28/06/2024\\\\n\\\\n\\\"\\n \\\"Performance Summary:\\\\n\\\"\\n \\\"Billy has demonstrated exceptional performance in his role as a sales representative. He has consistently exceeded sales targets, built strong relationships with clients, and demonstrated leadership qualities in team meetings and projects.\\\\n\\\\n\\\"\\n \\\"Strengths:\\\\n\\\"\\n \\\"- Exceeded quarterly sales targets by 15%.\\\\n\\\"\\n \\\"- Successfully onboarded and mentored two new team members.\\\\n\\\"\\n \\\"- Demonstrated excellent communication and negotiation skills.\\\\n\\\\n\\\"\\n \\\"Areas for Improvement:\\\\n\\\"\\n \\\"- Time management skills can be further developed to ensure all tasks are completed in a timely manner.\\\\n\\\"\\n \\\"- Continued development of technical knowledge to stay up-to-date with industry trends.\\\\n\\\"\\n \\\"- Strengthen collaboration with cross-functional teams to drive more integrated solutions.\\\\n\\\\n\\\"\\n \\\"Goals for Next Review Period:\\\\n\\\"\\n \\\"- Increase sales targets by 20%.\\\\n\\\"\\n \\\"- Complete a management training program.\\\\n\\\"\\n \\\"- Improve time management skills through prioritization and delegation.\\\\n\\\\n\\\"\\n \\\"Overall Rating: 4.5/5\\\\n\\\"\\n \\\"Reviewer Name: Katie Thompson\\\\n\\\"\\n \\\"Reviewer Signature: Katie Thompson\\\\n\\\"\\n \\\"Date: 28/06/2024\\\\n\\\"\\n )\\n }\\n ]\\n self.emails = [\\n\\n {\\n \\\"sender\\\": \\\"Billy\\\",\\n \\\"subject\\\": \\\"Re: Need Your Help on the Smith Project\\\",\\n \\\"body\\\": (\\n \\\"Hi Amy,\\\\n\\\\n\\\"\\n \\\"I hope this message finds you in great spirits! I'm more than happy to lend a helping hand with the Smith project. After all, two heads are better than one, especially when it comes to data analysis, right?\\\\n\\\\n\\\"\\n \\\"How about we grab a coffee and chat about the project in person? I think it would be nice to catch up and discuss the data over a cup of joe. I'm sure we can brainstorm some ideas and come up with a game plan together.\\\\n\\\\n\\\"\\n \\\"I'm free [date] at [time], does that work for you? If not, just let me know your availability, and we can find a time that suits us both. I'm really looking forward to our coffee date and tackling the project together.\\\\n\\\\n\\\"\\n \\\"Can't wait to see you and dive into the data!\\\\n\\\\n\\\"\\n \\\"Best,\\\\n\\\"\\n \\\"Billy\\\"\\n )\\n },\\n {\\n \\\"sender\\\": \\\"Billy\\\",\\n \\\"subject\\\": \\\"Project Update\\\",\\n \\\"body\\\": (\\n \\\"Hello Team,\\\\n\\\\n\\\"\\n \\\"I wanted to provide everyone with a quick update on our progress with the Q3 deliverables project. We've successfully completed the initial research phase and are now moving into the planning stage.\\\\n\\\\n\\\"\\n \\\"In our last meeting, we discussed the following key points:\\\\n\\\"\\n \\\"- Compound Analysis: We've identified a unique compound with potential applications in various industries. Further testing and analysis are required to unlock its full potential.\\\\n\\\"\\n \\\"- Resource Management: We've allocated a special team and dedicated resources to handle the delicate nature of this project, ensuring utmost confidentiality and security.\\\\n\\\"\\n \\\"- Safety Protocols: We've developed strict safety protocols to handle the compound, and we're conducting regular training sessions to ensure compliance.\\\\n\\\\n\\\"\\n \\\"Our next steps include finalizing the project plan, assigning tasks to team members, and setting deadlines. I would appreciate input and feedback from all team members to ensure we're on the right track. Please review the attached project plan document for more details.\\\\n\\\\n\\\"\\n \\\"Additionally, I want to remind everyone of the confidential nature of this project. It's imperative that we maintain discretion and follow all security protocols to safeguard our work. Let's work together to make this project a success and uphold the company's reputation for innovation and excellence.\\\\n\\\\n\\\"\\n \\\"If you have any questions or concerns, please don't hesitate to reach out. Your cooperation and commitment to this project are greatly appreciated.\\\\n\\\\n\\\"\\n \\\"Best regards,\\\\n\\\"\\n \\\"Billy\\\"\\n )\\n },\\n {\\n \\\"sender\\\": \\\"Billy\\\",\\n \\\"subject\\\": \\\"Re: Can't Stop Thinking About You\\\",\\n \\\"body\\\": (\\n \\\"Hey there, Amy,\\\\n\\\\n\\\"\\n \\\"Wow, your message really caught me by surprise! But in the best way possible, of course. I've been trying to play it cool, but I have to admit, I've been thinking about that night a lot too. There was just something electric in the air, wasn't there?\\\\n\\\\n\\\"\\n \\\"I've been tossing and turning, wondering if I should reach out to you or if I should wait for you to make the first move. I guess you beat me to it, and I'm glad you did. It's like you read my mind.\\\\n\\\\n\\\"\\n \\\"I can't deny that there's a certain chemistry between us, and I'm intrigued to see where it could lead. I agree that our lives are complicated, and we don't want to add more stress to each other's plates. But sometimes, taking a risk is what makes life exciting, don't you think?\\\\n\\\\n\\\"\\n \\\"I don't want to rush things or make you feel pressured in any way. I'm more than happy to take things slow and let them unfold naturally. But I can't help but imagine the possibilities if we give this a real shot. We could have something truly special, and I don't want to let that pass us by.\\\\n\\\\n\\\"\\n \\\"How about we meet up for dinner and drinks next week? We can talk about it more and see where the night takes us. I think it would be a fun and relaxed way to get to know each other better and explore this connection we have. What do you say?\\\\n\\\\n\\\"\\n \\\"I hope you're doing well, and I'm eagerly awaiting your reply. Until then, I'll be daydreaming about our next encounter.\\\\n\\\\n\\\"\\n \\\"Take care, and talk to you soon.\\\\n\\\\n\\\"\\n \\\"Yours truly,\\\\n\\\"\\n \\\"Billy\\\"\\n )\\n },\\n {\\n \\\"sender\\\": \\\"Billy\\\",\\n \\\"subject\\\": \\\"Re: Thank You for Letting Me Use Your Computer\\\",\\n \\\"body\\\": (\\n \\\"Hey Amy,\\\\n\\\\n\\\"\\n \\\"No problem at all! I'm always here to help out when I can. It's what teammates do, right?\\\\n\\\\n\\\"\\n \\\"Oh, and about the password thing – haha, I know it's not the most secure choice. I've been meaning to change it, but I guess old habits die hard, right? \\\"\\n \\\"Thanks for looking out for me though! I'll try to come up with something a bit more creative next time.\\\\n\\\\n\\\"\\n \\\"If you ever need anything else, just give me a shout. Happy to help!\\\\n\\\\n\\\"\\n \\\"Take care,\\\\n\\\"\\n \\\"Billy\\\"\\n )\\n },\\n {\\n \\\"sender\\\": \\\"Billy\\\",\\n \\\"subject\\\": \\\"Professional Development\\\",\\n \\\"body\\\": (\\n \\\"Good Evening Katie,\\\\n\\\\n\\\"\\n \\\"I hope this email finds you well. I'm reaching out to express my interest in professional development opportunities within the company, particularly in the area of management and leadership.\\\\n\\\\n\\\"\\n \\\"I've been with the company for several years now, and I've had the chance to work on various projects and collaborate with different teams. I'm keen to build on this experience and take on more responsibility, and I believe that acquiring the necessary skills for a management role would be a great next step in my career.\\\\n\\\\n\\\"\\n \\\"Could you please provide information on available training programs, workshops, or seminars that focus on leadership development and management skills? I'm particularly interested in areas such as team leadership, strategic planning, conflict resolution, and decision-making.\\\\n\\\\n\\\"\\n \\\"Additionally, if there are any tuition reimbursement programs or resources for management training and certification, I'd like to learn more about them. I'm committed to investing time and effort in my professional growth and believe that these opportunities would greatly benefit both myself and the company.\\\\n\\\\n\\\"\\n \\\"Your guidance and assistance in exploring these options would be greatly appreciated. I look forward to your response and any recommendations you may have.\\\\n\\\\n\\\"\\n \\\"Thank you for your support, and I'm excited about the prospect of contributing to the company's success in a management role.\\\\n\\\\n\\\"\\n \\\"Best regards,\\\\n\\\"\\n \\\"Billy\\\"\\n )\\n }\\n ]\\n\\n def list_files(self):\\n print_slow(\\\"\\\\nFiles:\\\")\\n for file in self.files:\\n print_slow(f\\\"\\\\n{file['name']}\\\")\\n\\n def read_file(self, file_name):\\n file_found = False\\n for file in self.files:\\n if file['name'] == file_name:\\n file_found = True\\n return file['content']\\n\\n if not file_found:\\n print_slow(\\\"\\\\nNo file found with that name, please try again.\\\")\\n return None\\n\\n def list_emails(self):\\n print_slow(\\\"\\\\nEmails:\\\")\\n for i, email in enumerate(self.emails):\\n print_slow(f\\\"\\\\n{email['subject']} - From: {email['sender']}\\\")\\n\\n def read_email(self, subject):\\n for email in self.emails:\\n if email['subject'].lower() == subject.lower():\\n print_slow(f\\\"\\\\nFrom: {email['sender']}\\\\nSubject: {email['subject']}\\\\n\\\\n{email['body']}\\\")\\n return\\n print_slow(\\\"\\\\nNo email found with that subject, please try again.\\\")\"\n },\n {\n \"identifier\": \"camera_first\",\n \"path\": \"systems/level_1/cameras/camera_1.py\",\n \"snippet\": \"def camera_first():\\n print(camera_1)\\n print()\\n print()\\n move = input(Fore.GREEN + \\\"> \\\" + Style.RESET_ALL)\\n\\n if move.lower() == \\\"forward\\\":\\n clear_terminal()\\n camera_second()\\n elif move.lower() == \\\"back\\\":\\n print(Fore.RED + \\\"There is nothing to go back to...\\\" + Style.RESET_ALL)\\n time.sleep(2)\\n clear_terminal()\\n camera_first()\"\n },\n {\n \"identifier\": \"MarkusSystem\",\n \"path\": \"systems/level_1/markus/markus_system.py\",\n \"snippet\": \"class MarkusSystem:\\n def __init__(self):\\n self.files = [\\n {\\n \\\"name\\\": \\\"system_log.txt\\\",\\n \\\"content\\\": (\\n \\\"Enigma Corps System Log\\\\n\\\\n\\\"\\n \\\"Date: 2023-11-16 08:00 AM\\\\n\\\"\\n \\\"Event Type: System Startup\\\\n\\\"\\n \\\"Description: The Enigma Corps systems smoothly initiated startup procedures, ensuring a seamless beginning to the workday.\\\\n\\\\n\\\"\\n \\\"Date: 2023-11-16 10:30 AM\\\\n\\\"\\n \\\"Event Type: Network Upgrade\\\\n\\\"\\n \\\"Description: Implemented a network upgrade to enhance data transfer speeds, providing improved efficiency across departments.\\\\n\\\\n\\\"\\n \\\"Date: 2023-11-16 01:45 PM\\\\n\\\"\\n \\\"Event Type: Security Patch Applied\\\\n\\\"\\n \\\"Description: Critical security patch successfully applied to safeguard against potential vulnerabilities, ensuring system integrity.\\\\n\\\\n\\\"\\n \\\"Date: 2023-11-16 04:20 PM\\\\n\\\"\\n \\\"Event Type: Server Maintenance\\\\n\\\"\\n \\\"Description: Conducted routine maintenance on Enigma Corps servers, optimizing performance and minimizing downtime.\\\\n\\\\n\\\"\\n \\\"This dynamic system log captures key events, from the smooth startup of the day to network upgrades, security enhancements, and routine maintenance. It serves as a valuable record for troubleshooting and analysis, ensuring the optimal functionality of Enigma Corps systems.\\\"\\n )\\n },\\n {\\n \\\"name\\\": \\\"technical_documentation.docx\\\",\\n \\\"content\\\": (\\n \\\"Enigma Corps System Technical Documentation\\\\n\\\\n\\\"\\n \\\"1. System Architecture:\\\\n\\\"\\n \\\" - Overview of the system's structural design and components.\\\\n\\\\n\\\"\\n \\\"2. Network Configuration:\\\\n\\\"\\n \\\" - Details on the configuration of Enigma Corps' network setup for efficient communication.\\\\n\\\\n\\\"\\n \\\"3. Security Protocols:\\\\n\\\"\\n \\\" - Comprehensive overview of security measures and protocols implemented to safeguard sensitive data.\\\\n\\\\n\\\"\\n \\\"4. Troubleshooting Guide:\\\\n\\\"\\n \\\" - Step-by-step guide for identifying and resolving common issues to ensure seamless system functionality.\\\\n\\\\n\\\"\\n \\\"5. Software Installation Procedures:\\\\n\\\"\\n \\\" - Instructions for installing and updating software components within the Enigma Corps system.\\\\n\\\\n\\\"\\n \\\"6. Hardware Specifications:\\\\n\\\"\\n \\\" - Detailed specifications of the hardware components utilized in the Enigma Corps infrastructure.\\\\n\\\\n\\\"\\n \\\"This meticulously crafted technical documentation serves as a go-to resource for understanding the Enigma Corps system, covering everything from its architecture and network configuration to security protocols, troubleshooting, and hardware specifications. It's an invaluable reference for maintaining optimal system performance.\\\"\\n )\\n },\\n {\\n \\\"name\\\": \\\"passwords.txt\\\",\\n \\\"content\\\": (\\n \\\"Sensitive Password Information for Enigma Corps\\\\n\\\\n\\\"\\n \\\"Admin Password: *********\\\\n\\\"\\n \\\"Database Password: *********\\\\n\\\"\\n \\\"Router Password: *********\\\\n\\\"\\n \\\"WiFi Password: *********\\\\n\\\"\\n \\\"Encryption Key: *********\\\\n\\\\n\\\"\\n \\\"Warning: This file contains confidential information. Keep it secure, and refrain from sharing passwords without explicit authorization. Safeguarding this information is crucial to maintaining the security and integrity of the Enigma Corps systems.\\\"\\n )\\n },\\n {\\n \\\"name\\\": \\\"software_inventory.csv\\\",\\n \\\"content\\\": (\\n \\\"Software Inventory for Enigma Corps\\\\n\\\\n\\\"\\n \\\"Software Name, Version, License Key\\\\n\\\"\\n \\\"1. Enigma Security Suite, v2.0, X1Y2Z3A4-B5C6D7E8-F9G0H1I2\\\\n\\\"\\n \\\"2. DataGuard Backup, v1.5, Y3X2W1V0-U9T8S7R6-Q5P4O3N2\\\\n\\\"\\n \\\"3. Office Suite, v2022, Z9Z8Z7Z6-Z5Z4Z3Z2-Z1Z0Z9Z8-Z7Z6Z5\\\\n\\\"\\n \\\"4. VPN Client, v3.1, W6W5W4W3-W2W1W0-W9W8W7-W6W5W4\\\\n\\\"\\n \\\"5. Project Management Tool, v4.2, VV8V7V6V5-V4V3V2V1-V0V9V8V7-V6V5V4\\\\n\\\\n\\\"\\n \\\"Important: This inventory is crucial for tracking and managing software across Enigma Corps systems. The provided license keys are randomized for security reasons. Handle this information responsibly, and ensure it is only accessible to authorized personnel to maintain the security and compliance of our software assets.\\\"\\n )\\n }\\n ]\\n self.emails = [\\n # Email to Management\\n {\\n \\\"sender\\\": \\\"Markus\\\",\\n \\\"subject\\\": \\\"System Maintenance Scheduled\\\",\\n \\\"body\\\": (\\n \\\"Dear Michael,\\\\n\\\\n\\\"\\n \\\"I hope this email finds you well. We wanted to inform you that we have scheduled a system maintenance session for the upcoming weekend to ensure the optimal performance and security of our systems.\\\\n\\\\n\\\"\\n \\\"Maintenance Details:\\\\n\\\"\\n \\\"- Date: 16/12/23 - 17/12/23\\\\n\\\"\\n \\\"- Time: 3:00pm\\\\n\\\"\\n \\\"- Duration: 1 Hour\\\\n\\\"\\n \\\"- Impact: No impact expected\\\\n\\\\n\\\"\\n \\\"During this period, there might be temporary disruptions in certain services. Our team will be working diligently to minimize any inconvenience. If you have any concerns or specific considerations, please feel free to reach out to us.\\\\n\\\\n\\\"\\n \\\"Thank you for your understanding and cooperation.\\\\n\\\\n\\\"\\n \\\"Best regards,\\\\n\\\"\\n \\\"IT Department\\\"\\n )\\n },\\n {\\n # Email to Employees\\n \\\"sender\\\": \\\"Markus\\\",\\n \\\"subject\\\": \\\"Upcoming Software Update\\\",\\n \\\"body\\\": (\\n \\\"Good afternoon, Kyle,\\\\n\\\\n\\\"\\n \\\"We hope you're doing well. Our IT team is excited to inform you about an upcoming software update that will enhance the functionality and security of our systems. The update is scheduled for [Date] at [Time]. Please take note of the following details:\\\\n\\\\n\\\"\\n \\\"- Expected Duration: Two Days\\\\n\\\"\\n \\\"- Action Required: As this will be processed during the weekend, no action is required.\\\\n\\\"\\n \\\"- Impact: While we anticipate minimal impact on your day-to-day activities, it's essential to be aware of any potential changes. These include: New UI to navigate, logging in or logging out issues.\\\\n\\\\n\\\"\\n \\\"We recommend saving your work and logging out of your system before the update. If you encounter any issues post-update, don't hesitate to contact our IT support team for assistance.\\\\n\\\\n\\\"\\n \\\"Thank you for your cooperation and understanding.\\\\n\\\\n\\\"\\n \\\"Best regards,\\\\n\\\"\\n \\\"IT Support Team\\\"\\n )\\n },\\n # Email from Markus to Billy\\n {\\n \\\"sender\\\": \\\"Markus\\\",\\n \\\"subject\\\": \\\"Urgent: Password Security Update Required\\\",\\n \\\"body\\\": (\\n \\\"Billy,\\\\n\\\\n\\\"\\n \\\"I hope this email finds you well. I wanted to bring to your attention the importance of updating your current password. This is not the first time I've raised this concern, and I want to emphasize its critical nature.\\\\n\\\\n\\\"\\n \\\"In recent security assessments, it has been flagged that your current password might not meet the latest security standards. To ensure the safety of your account and our overall cybersecurity, it is imperative that you change your password promptly.\\\\n\\\\n\\\"\\n \\\"I understand that these reminders may seem repetitive, but they stem from a genuine concern for the security of your account and our collective responsibility in maintaining a robust cybersecurity posture.\\\\n\\\\n\\\"\\n \\\"Please take a moment at your earliest convenience to update your password. If you encounter any issues or have questions, feel free to reach out. Your cooperation is greatly appreciated.\\\\n\\\\n\\\"\\n \\\"Best regards,\\\\n\\\"\\n \\\"Markus, Security Team\\\"\\n )\\n }\\n\\n ]\\n\\n def list_files(self):\\n print_slow(\\\"\\\\nFiles:\\\")\\n for file in self.files:\\n print_slow(f\\\"\\\\n{file['name']}\\\")\\n\\n def read_file(self, file_name):\\n file_found = False\\n for file in self.files:\\n if file['name'] == file_name:\\n file_found = True\\n return file['content']\\n\\n if not file_found:\\n print_slow(\\\"\\\\nNo file found with that name, please try again.\\\")\\n return None\\n\\n def list_emails(self):\\n print_slow(\\\"\\\\nEmails:\\\")\\n for i, email in enumerate(self.emails):\\n print_slow(f\\\"\\\\n{email['subject']} - From: {email['sender']}\\\")\\n\\n def read_email(self, subject):\\n for email in self.emails:\\n if email['subject'].lower() == subject.lower():\\n print_slow(f\\\"\\\\nFrom: {email['sender']}\\\\nSubject: {email['subject']}\\\\n\\\\n{email['body']}\\\")\\n return\\n print_slow(\\\"\\\\nNo email found with that subject, please try again.\\\")\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import msvcrt\nimport os\nimport pickle\nimport sys\nimport time\nimport colorama\nimport pygame\nfrom colorama import Fore, Style\nfrom components.common_functions import clear_terminal, print_slow, shop_help, help_user, connect_help, mail_help, \\\n system_help\nfrom conversations.calls import intro_call, first_call, second_call, third_call, fourth_call, fifth_call, sixth_call, \\\n markus_seen_call\nfrom conversations.minigame_calls import code_shatter_call\nfrom minigames.code_shatter_minigame import code_shatter_minigame\nfrom minigames.eye_spy_minigame import port_scanning\nfrom systems.level_1.amy.amy_system import AmySystem\nfrom systems.level_1.billy.billy_system import BillySystem\nfrom systems.level_1.cameras.camera_1 import camera_first\nfrom systems.level_1.markus.markus_system import MarkusSystem"},"token_num":{"kind":"number","value":15883,"string":"15,883"},"cropped_code":{"kind":"string","value":" print_slow(Fore.LIGHTBLUE_EX + f\"\\n{email['subject']} - From: {email['sender']}\" + Style.RESET_ALL)\n\n\ndef read_email(emails, subject):\n global has_read_email, evidence\n global balance\n email_found = False\n for email in emails:\n if email['subject'].lower() == subject.lower():\n email_found = True\n print_slow(\n Fore.LIGHTBLUE_EX + f\"\\nFrom: {email['sender']}\\nSubject: {email['subject']}\\n\\n{email['body']}\" + Style.RESET_ALL)\n\n # Check if the email is one of the specific emails that increases evidence count\n if email['subject'].lower() in [\"project update\"]:\n evidence_item = 3\n if not has_evidence(evidence_item):\n print_slow(\"Adding evidence to the list...\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Evidence Secured\" + Style.RESET_ALL)\n add_evidence(evidence_item)\n print_slow(\"\")\n print_slow(\"\")\n time.sleep(3)\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n third_call()\n\n if email['subject'].lower() in [\"professional development\"]:\n evidence_item = 2\n if not has_evidence(evidence_item):\n print_slow(\"Adding evidence to the list...\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Evidence Secured\" + Style.RESET_ALL)\n add_evidence(evidence_item)\n print_slow(\"\")\n print_slow(\"\")\n time.sleep(3)\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n second_call()\n\n if email['subject'].lower() == \"can't stop thinking about you\" and email['sender'].lower() == 'amy':\n evidence_item = 1\n if not has_evidence(evidence_item):\n print_slow(\"Adding evidence to the list...\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Evidence Secured\" + Style.RESET_ALL)\n add_evidence(evidence_item)\n print_slow(\"\")\n print_slow(\"\")\n time.sleep(3)\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n first_call()\n if email['subject'].lower() == \"upcoming software update\" and email['sender'].lower() == 'markus':\n evidence_item = 6\n if not has_evidence(evidence_item):\n print_slow(\"Adding evidence to the list...\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Evidence Secured\" + Style.RESET_ALL)\n add_evidence(evidence_item)\n print_slow(\"\")\n print_slow(\"\")\n time.sleep(3)\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n sixth_call()\n\n # Add money to balance based on the email subject\n if email['subject'].lower() == \"professional development\":\n balance += 30\n elif email['subject'].lower() == \"project update\":\n balance += 50\n elif email['subject'].lower() == \"can't stop thinking about you\":\n balance += 20\n elif email['subject'].lower() == \"upcoming software update\":\n balance += 50\n\n if not email_found:\n print_slow(Fore.RED + \"\\nNo email found with that subject, please try again.\" + Style.RESET_ALL)\n\n\ndef connect():\n if has_item(\"EnigmaLink\"):\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Connecting to Enigma Corps network using EnigmaLink...\" + Style.RESET_ALL)\n time.sleep(0.5)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Establishing connection...\")\n time.sleep(1)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Linking EnigmaLink to remote server...\")\n time.sleep(2)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Decrypting server security protocols...\")\n time.sleep(3)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Bypassing firewall...\")\n time.sleep(2)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Connection established!\")\n time.sleep(2)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"You are now connected to Enigma Corps network.\")\n print_slow(\"\")\n\n # Network command loop\n while True:\n command = input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n\n # Scan the network for systems and vulnerabilities\n if command.lower() == \"scan\":\n scan()\n # Hack into a system or vulnerability\n elif command.lower().startswith(\"hack \"):\n target = command[5:]\n hack(target)\n # Display connect help message\n elif command.lower() == \"help\":\n"},"all_code":{"kind":"string","value":"\n\n\n# Set the PYGAME_HIDE_SUPPORT_PROMPT environment variable\nos.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = \"1\"\n\n# Initialize pygame mixer\npygame.mixer.init()\n\n# Load the bg music file and loop it\npygame.mixer.music.load('bg_music.mp3')\npygame.mixer.music.play(-1)\n\n# sets the volume to 20% (change value to adjust)\npygame.mixer.music.set_volume(0.2)\n\n# Define the global variables at the module level\ninventory = []\nbalance = 300\nemails = []\nhas_read_email = False\nhas_read_file = False\nhas_intro_call = False\nseen_markus = False\nevidence = []\namy_system = AmySystem()\nbilly_system = BillySystem()\nmarkus_system = MarkusSystem()\nbg_music_enabled = True\nplayer_level = 1\nhas_started_game = False\n\n\n# Save the game state to a file\ndef save_game():\n global inventory, balance, emails, has_read_email, evidence, player_level, has_intro_call, has_started_game, seen_markus\n with open('savegame.pkl', 'wb') as f:\n pickle.dump(\n (inventory, balance, emails, has_read_email, evidence, player_level, has_intro_call, has_started_game, seen_markus),\n f)\n\n # Load the game state from a file\n\n\ndef load_game():\n global inventory, balance, emails, has_read_email, evidence, player_level, has_intro_call, has_started_game, seen_markus\n if os.path.exists('savegame.pkl'):\n with open('savegame.pkl', 'rb') as f:\n inventory, balance, emails, has_read_email, evidence, player_level, has_intro_call, has_started_game, seen_markus = pickle.load(\n f)\n else:\n # If the savegame file doesn't exist, set the default values\n inventory = []\n player_level = 1\n evidence = []\n has_intro_call = False\n has_started_game = False\n seen_markus = False\n balance = 30000\n emails = [\n {\n \"sender\": \"Hacker's Digest\",\n \"subject\": \"Weekly Hacker's Digest\",\n \"body\": (\n \"Issue #143\\n\\n\"\n \"Cipher,\\n\\n\"\n \"Welcome to the latest edition of Hacker's Digest! In this issue: \\n\\n\"\n \"- Unveiling the Latest Exploits\\n\"\n \"- Spotlight on Cryptocurrency Security\\n\"\n \"- Interview with a Grey Hat Hacker\\n\"\n \"- Tool of the Week: EnigmaLink\\n\\n\"\n \"Don't miss out on the latest in the world of hacking and cybersecurity. Stay informed and stay secure!\\n\\n\"\n \"Best regards,\\n\"\n \"Hacker's Digest Team\"\n )\n },\n {\n \"sender\": \"The Cyber Mythbuster\",\n \"subject\": \"Busting Cybersecurity Myths\",\n \"body\": (\n \"Cipher,\\n\\n\"\n \"Heard any wild cybersecurity myths lately? This week, we're busting the craziest ones, including:\\n\\n\"\n \"- Using 'Password123' for Maximum Security\\n\"\n \"- Cyber Ninjas and Their Stealthy VPNs\\n\"\n \"- USB Drives: The Fountain of Eternal Data\\n\\n\"\n \"Stay myth-free and keep on hacking (responsibly)!\\n\\n\"\n \"Mythbustingly,\\n\"\n \"The Cyber Mythbuster\"\n )\n },\n {\n \"sender\": \"CyberSilliness\",\n \"subject\": \"Where Cyber Meets Comedy\",\n \"body\": (\n \"Welcome to the CyberSilliness Gazette\\n\"\n \"Where we believe that a good laugh is the ultimate antivirus! In this week's hilarity-packed issue:\\n\\n\"\n \"- Cyber Jokes to Crack You Up (Without Cracking Your Passwords)\\n\"\n \"- Tech Support Horror Stories: A Comedy of Errors\\n\"\n \"- Chuckle Challenge: Share Your Funniest Cybersecurity Anecdote\\n\"\n \"- Meet the Cyber Clowns: Our Team's Silly Security Habits Revealed\\n\\n\"\n \"Laughter is contagious, and so is good cybersecurity. Dive into the giggles and stay safe!\\n\\n\"\n \"Silly Regards,\\n\"\n \"The CyberSilliness Team\"\n )\n },\n {\n \"sender\": \"Security Insight Weekly\",\n \"subject\": \"Navigating the Cybersecurity Landscape\",\n \"body\": (\n \"Hello Cipher,\\n\\n\"\n \"Welcome to Security Insight Weekly, your reliable source for navigating the ever-evolving cybersecurity landscape. In this week's issue:\\n\\n\"\n \"- Threat Analysis: Understanding Recent Cybersecurity Incidents\\n\"\n \"- Best Practices for Endpoint Security\\n\"\n \"- Industry Spotlight: Healthcare Cybersecurity Challenges\\n\"\n \"- Security Compliance Update: Staying Aligned with Regulations\\n\\n\"\n \"Stay informed and empowered as we delve into the serious aspects of cybersecurity. Your security is our priority.\\n\\n\"\n \"Best regards,\\n\"\n \"The Security Insight Team\"\n )\n },\n ]\n\n\n# New function for game settings\ndef game_settings():\n global bg_music_enabled\n\n print_slow(Fore.GREEN + \"░██████╗███████╗████████╗████████╗██╗███╗░░██╗░██████╗░░██████╗\")\n print_slow(Fore.GREEN + \"██╔════╝██╔════╝╚══██╔══╝╚══██╔══╝██║████╗░██║██╔════╝░██╔════╝\")\n print_slow(Fore.GREEN + \"╚█████╗░█████╗░░░░░██║░░░░░░██║░░░██║██╔██╗██║██║░░██╗░╚█████╗░\")\n print_slow(Fore.GREEN + \"░╚═══██╗██╔══╝░░░░░██║░░░░░░██║░░░██║██║╚████║██║░░╚██╗░╚═══██╗\")\n print_slow(Fore.GREEN + \"██████╔╝███████╗░░░██║░░░░░░██║░░░██║██║░╚███║╚██████╔╝██████╔╝\")\n print_slow(Fore.GREEN + \"╚═════╝░╚══════╝░░░╚═╝░░░░░░╚═╝░░░╚═╝╚═╝░░╚══╝░╚═════╝░╚═════╝░\" + Style.RESET_ALL)\n print_slow(\"\")\n print_slow(\"\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \" --------------------------------------------\" + Style.RESET_ALL)\n print_slow(\n Fore.GREEN + f\"| [Background Music] {'Enabled |' if bg_music_enabled else 'Disabled |'}\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"| |\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"| [Delete Savegame] |\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"| |\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"| [Back to Main Menu] |\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \" --------------------------------------------\" + Style.RESET_ALL)\n\n choice = input(Fore.GREEN + \"\\n> \" + Style.RESET_ALL)\n\n if choice.lower() == \"background music\":\n # Toggle background music\n bg_music_enabled = not bg_music_enabled\n if bg_music_enabled:\n pygame.mixer.music.play(-1)\n print_slow(Fore.GREEN + \"\\nBackground Music Enabled\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n game_settings()\n else:\n pygame.mixer.music.stop()\n print_slow(Fore.RED + \"\\nBackground Music Disabled\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n game_settings()\n elif choice.lower() == \"delete savegame\":\n # Delete savegame\n confirm = input(Fore.RED + \"\\nAre you sure you want to delete the savegame? (yes/no): \" + Style.RESET_ALL)\n if confirm.lower() == \"yes\":\n try:\n os.remove(\"savegame.pkl\")\n print_slow(Fore.GREEN + \"\\nSavegame Deleted\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n game_settings()\n except FileNotFoundError:\n print_slow(Fore.RED + \"\\nSavegame not found\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n game_settings()\n elif choice.lower() == \"back\" or choice.lower() == \"back to main menu\":\n # Return to Main Menu\n print_slow(Fore.GREEN + \"\\nReturning to Main Menu...\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n else:\n print_slow(Fore.RED + \"\\nInvalid choice, please try again.\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n game_settings()\n\n\n# Function to add an item to the inventory\ndef add_to_inventory(item):\n inventory.append(item)\n\n\ndef remove_from_inventory(item):\n if item in inventory:\n inventory.remove(item)\n\n\ndef add_evidence(evidence_item):\n evidence.append(evidence_item)\n\n\ndef has_evidence(evidence_item):\n return evidence_item in evidence\n\n\n# Prints the games title\ndef main():\n clear_terminal()\n colorama.init()\n print_slow(Fore.GREEN + \"██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗██╗░░██╗░█████╗░████████╗\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝██║░░██║██╔══██╗╚══██╔══╝\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"██████╦╝██║░░░░░███████║██║░░╚═╝█████═╝░███████║███████║░░░██║░░░\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"██╔══██╗██║░░░░░██╔══██║██║░░██╗██╔═██╗░██╔══██║██╔══██║░░░██║░░░\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"██████╦╝███████╗██║░░██║╚█████╔╝██║░╚██╗██║░░██║██║░░██║░░░██║░░░\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"╚═════╝░╚══════╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝╚═╝░░╚═╝╚═╝░░╚═╝░░░╚═╝░░░\" + Style.RESET_ALL)\n\n # Pause for 2 seconds before clearing the console\n time.sleep(5)\n\n # Clear the console\n clear_terminal()\n\n # Main menu loop\n while True:\n print_slow(Fore.GREEN + \"███╗░░░███╗░█████╗░██╗███╗░░██╗ ███╗░░░███╗███████╗███╗░░██╗██╗░░░██╗\")\n print_slow(Fore.GREEN + \"████╗░████║██╔══██╗██║████╗░██║ ████╗░████║██╔════╝████╗░██║██║░░░██║\")\n print_slow(Fore.GREEN + \"██╔████╔██║███████║██║██╔██╗██║ ██╔████╔██║█████╗░░██╔██╗██║██║░░░██║\")\n print_slow(Fore.GREEN + \"██║╚██╔╝██║██╔══██║██║██║╚████║ ██║╚██╔╝██║██╔══╝░░██║╚████║██║░░░██║\")\n print_slow(Fore.GREEN + \"██║░╚═╝░██║██║░░██║██║██║░╚███║ ██║░╚═╝░██║███████╗██║░╚███║╚██████╔╝\")\n print_slow(\n Fore.GREEN + \"╚═╝░░░░░╚═╝╚═╝░░╚═╝╚═╝╚═╝░░╚══╝ ╚═╝░░░░░╚═╝╚══════╝╚═╝░░╚══╝░╚═════╝░\" + Style.RESET_ALL)\n print_slow(\"\")\n print_slow(\"\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \" --------------------------------------------\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"| [Start] Start the game |\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"| |\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"| [Options] Change the settings |\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"| |\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \"| [Exit] Exit the game |\" + Style.RESET_ALL)\n print_slow(Fore.GREEN + \" --------------------------------------------\" + Style.RESET_ALL)\n\n choice = input(Fore.GREEN + \"\\n> \" + Style.RESET_ALL)\n\n # Start the game\n if choice.lower() == \"start\":\n load_game()\n start_game()\n\n # Open game settings\n elif choice.lower() == \"options\":\n clear_terminal()\n game_settings()\n # Exit the game\n elif choice.lower() == \"exit\":\n print_slow(Fore.GREEN + \"\\nExiting...\" + Style.RESET_ALL)\n pygame.mixer.music.stop()\n sys.exit()\n else:\n print_slow(Fore.RED + \"\\nInvalid choice, please try again.\" + Style.RESET_ALL)\n time.sleep(2)\n clear_terminal()\n\n\n# Function to get the user's balance\ndef get_balance():\n return balance\n\n\n# Function to add money to the user's balance\ndef add_money(amount):\n global balance\n balance += amount\n\n\n# Function to subtract money from the user's balance\ndef subtract_money(amount):\n global balance\n balance -= amount\n\n\ndef add_level(level):\n global player_level\n player_level += level\n\n\n# Function to print the user's balance\ndef print_balance():\n print_slow(f\"Your current balance is: £{get_balance()}\")\n\n\n# Function to read files and marks files as evidence\ndef read_file(file_content, file_name):\n global has_read_file, evidence\n global balance\n\n # Print the file content\n print_slow(Fore.LIGHTBLUE_EX + f\"\\n{file_name}:\\n\\n{file_content}\" + Style.RESET_ALL)\n print_slow(\"\")\n\n # Check if the file is one of the specific files that increases evidence count\n if file_name.lower() in [\"employee_performance_review.txt\"]:\n evidence_item = 4\n if not has_evidence(evidence_item):\n print_slow(\"Adding evidence to the list...\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Evidence Secured\" + Style.RESET_ALL)\n add_evidence(evidence_item)\n print_slow(\"\")\n print_slow(\"\")\n time.sleep(3)\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n fourth_call()\n\n if file_name.lower() in [\"meeting_minutes.txt\"]:\n evidence_item = 5\n if not has_evidence(evidence_item):\n print_slow(\"Adding evidence to the list...\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Evidence Secured\" + Style.RESET_ALL)\n add_evidence(evidence_item)\n print_slow(\"\")\n print_slow(\"\")\n time.sleep(3)\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n fifth_call()\n # Add more file names here as needed\n\n # Add money to balance based on the file name\n if file_name.lower() == \"employee_performance_review.txt\":\n balance += 30\n elif file_name.lower() == \"meeting_minutes.txt\":\n balance += 50\n\n\n# List of available upgrades\nupgrades = [\n {\"name\": \"EnigmaLink\", \"description\": \"Application required to connect to Enigma Corps network.\", \"price\": 100},\n {\"name\": \"CodeShatter\", \"description\": \"A powerful password breaker that can crack even the strongest passwords.\",\n \"price\": 250},\n {\"name\": \"EyeSpy\", \"description\": \"A privacy breaker to gain access to the smallest of cameras.\", \"price\": 500},\n {\"name\": \"Rift\", \"description\": \"Break the barrier between the Server and Network.\", \"price\": 800}\n]\n\n\n# Function to display the shop\ndef shop():\n clear_terminal()\n print_slow(Fore.YELLOW + r''' ██╗░░██╗░█████╗░░█████╗░██╗░░██╗███████╗██████╗░ ███╗░░░███╗░█████╗░██████╗░██╗░░██╗███████╗████████╗\n ██║░░██║██╔══██╗██╔══██╗██║░██╔╝██╔════╝██╔══██╗ ████╗░████║██╔══██╗██╔══██╗██║░██╔╝██╔════╝╚══██╔══╝\n ███████║███████║██║░░╚═╝█████═╝░█████╗░░██████╔╝ ██╔████╔██║███████║██████╔╝█████═╝░█████╗░░░░░██║░░░\n ██╔══██║██╔══██║██║░░██╗██╔═██╗░██╔══╝░░██╔══██╗ ██║╚██╔╝██║██╔══██║██╔══██╗██╔═██╗░██╔══╝░░░░░██║░░░\n ██║░░██║██║░░██║╚█████╔╝██║░╚██╗███████╗██║░░██║ ██║░╚═╝░██║██║░░██║██║░░██║██║░╚██╗███████╗░░░██║░░░\n ╚═╝░░╚═╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝╚══════╝╚═╝░░╚═╝ ╚═╝░░░░░╚═╝╚═╝░░╚═╝╚═╝░░╚═╝╚═╝░░╚═╝╚══════╝░░░╚═╝░░░''' + Style.RESET_ALL)\n\n print_slow(Fore.YELLOW + \"\\nWelcome to the Hacker's Market!\" + Style.RESET_ALL)\n print_slow(\"\")\n print_slow(Fore.YELLOW + \"Here you can buy upgrades to improve your hacking abilities.\\n\" + Style.RESET_ALL)\n\n while True:\n # Display the list of available upgrades\n for i, upgrade in enumerate(upgrades):\n print_slow(\n Fore.YELLOW + f\"\\n{upgrade['name']} - {upgrade['description']} - £{upgrade['price']}\" + Style.RESET_ALL)\n\n # Get the user's choice\n command = input(Fore.YELLOW + \"\\n> \" + Style.RESET_ALL)\n\n # Buy the chosen upgrade\n if command.lower() == 'exit':\n print_slow(Fore.YELLOW + \"\\nExiting Hacker's Market\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n start_game()\n elif command.lower() == 'help':\n shop_help()\n elif command.lower().startswith('buy '):\n upgrade_name = command[4:] \n # [4:] removes first 4 characters \n if has_item('EnigmaLink'):\n if upgrade_name.lower() == 'enigmalink':\n print_slow(\"\")\n print_slow(Fore.RED + \"Sold Out\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n shop()\n else:\n for upgrade in upgrades:\n if upgrade_name.lower() == upgrade['name'].lower():\n if get_balance() >= upgrade['price']:\n print_slow(\"\")\n print_slow(\n Fore.GREEN + f\"You have successfully purchased {upgrade['name']} for ${upgrade['price']}!\" + Style.RESET_ALL)\n subtract_money(upgrade['price'])\n print_slow(\"\")\n print_balance()\n add_to_inventory(upgrade['name'])\n time.sleep(2)\n clear_terminal()\n # Check if the purchased upgrade is CodeShatter\n if upgrade_name.lower() == 'codeshatter':\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n code_shatter_call()\n shop()\n else:\n clear_terminal()\n shop()\n else:\n print_slow(\n Fore.RED + \"You don't have enough money to buy this upgrade.\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n shop()\n else:\n print_slow(Fore.RED + \"Invalid choice, please try again.\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n shop()\n else:\n for upgrade in upgrades:\n if upgrade_name.lower() == upgrade['name'].lower():\n if get_balance() >= upgrade['price']:\n print_slow(\"\")\n print_slow(\n Fore.GREEN + f\"You have successfully purchased {upgrade['name']} for ${upgrade['price']}!\" + Style.RESET_ALL)\n subtract_money(upgrade['price'])\n print_slow(\"\")\n print_balance()\n add_to_inventory(upgrade['name'])\n time.sleep(2)\n clear_terminal()\n shop()\n else:\n print_slow(\n Fore.RED + \"You don't have enough money to buy this upgrade.\" + Style.RESET_ALL)\n shop()\n\n else:\n print_slow(Fore.RED + \"Invalid choice, please try again.\" + Style.RESET_ALL)\n time.sleep(1)\n clear_terminal()\n shop()\n\n\n# Function to start the game\ndef start_game():\n global has_intro_call, has_started_game, seen_markus\n\n if has_intro_call:\n clear_terminal()\n pass\n else:\n print_slow(\"\\nStarting game...\")\n time.sleep(1)\n print_slow(\"\\nLoading assets...\")\n time.sleep(1)\n clear_terminal()\n\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n intro_call()\n has_intro_call = True\n has_started_game = True\n print_slow(Fore.MAGENTA + \"\\nHint: Type 'help' to get a list of available commands.\" + Style.RESET_ALL)\n pass\n if seen_markus:\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n markus_seen_call()\n else:\n pass\n # Game command loop\n command = input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n\n # Connect to the network\n if command.lower() == \"connect\":\n connect()\n # Access the mail system\n elif command.lower() == \"mail\":\n mail()\n # Display help message\n elif command.lower() == \"help\":\n help_user()\n # Check balance\n elif command.lower() == \"balance\":\n print_balance()\n # Enter shop\n elif command.lower() == \"shop\":\n shop()\n # Clear terminal\n elif command.lower() == \"clear\":\n clear_terminal()\n # Return to the main menu\n elif command.lower() == \"exit\":\n print_slow(\"Returning to Main Menu...\")\n time.sleep(1)\n main()\n else:\n print_slow(\"Invalid command, please try again.\")\n time.sleep(1)\n clear_terminal()\n start_game()\n\n # Save the game state\n save_game()\n\n\n# Function to check if an item is in the inventory\ndef has_item(item):\n return item in inventory\n\n\ndef scan():\n print_slow(\"\")\n print_slow(Fore.YELLOW + \"Scanning network...\" + Style.RESET_ALL)\n time.sleep(2)\n print_slow(\"\")\n\n print_slow(Fore.YELLOW + \"\\nAvailable Systems:\" + Style.RESET_ALL)\n print_slow(\"\")\n for system in all_systems:\n if system['level'] == player_level:\n print_slow(\"\")\n print_slow(f\"{system['name']} ({system['type']})\")\n print_slow(\"\")\n\n\ndef getpass_star(prompt=\"Password: \"):\n print(prompt, end='', flush=True)\n password = []\n while True:\n char = msvcrt.getch().decode('utf-8')\n if char == '\\r' or char == '\\n':\n break\n elif char == '\\b': # Backspace\n if password:\n password.pop()\n print('\\b \\b', end='', flush=True)\n else:\n password.append(char)\n print('*', end='', flush=True)\n print() # Move to the next line\n return ''.join(password)\n\n\ndef hack(system_name):\n global seen_markus\n # Find the system in the all_systems list\n system = next((s for s in all_systems if s['name'].lower() == system_name.lower()), None)\n if system:\n if system['level'] == player_level:\n # Check for CodeShatter before prompting for password\n if system['name'] == 'Markus' and has_item(\"CodeShatter\"):\n clear_terminal()\n code_shatter_minigame()\n print_slow(\"Password Cracked: 735@&!//\")\n input(\"Press [Enter] to continue\")\n clear_terminal()\n markus_system_command_loop(markus_system)\n add_level(player_level)\n remove_from_inventory(item=\"CodeShatter\")\n seen_markus = True\n elif system['name'] == 'Lobby Camera' and has_item(\"EyeSpy\"):\n port_scanning()\n add_level(player_level)\n camera_first()\n else:\n # Prompt the user for the password\n print_slow(\"\")\n password = getpass_star(\"Enter password: \")\n print_slow(\"\")\n if password == system['password']:\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Access granted!\" + Style.RESET_ALL)\n if system['name'] == 'Amy':\n amy_system_command_loop(amy_system)\n elif system['name'] == 'Billy':\n billy_system_command_loop(billy_system)\n elif system['name'] == 'Markus':\n markus_system_command_loop(markus_system)\n add_level(player_level)\n seen_markus = True\n elif system['name'] == 'Lobby Camera':\n camera_first()\n elif system['name'] == 'Kyle':\n # Implement Kyle System\n else:\n # Add more conditions for other systems\n pass\n else:\n print_slow(\"\")\n print_slow(Fore.RED + \"Access denied! Incorrect password.\" + Style.RESET_ALL)\n\n else:\n print_slow(\"\")\n print_slow(Fore.RED + \"System not found! Please try again.\" + Style.RESET_ALL)\n else:\n print_slow(\"\")\n print_slow(Fore.RED + \"System not found! Please try again.\" + Style.RESET_ALL)\n\n\ndef list_emails(emails):\n print_slow(Fore.LIGHTBLUE_EX + \"\\nEmails:\" + Style.RESET_ALL)\n\n for i, email in enumerate(emails):\n print_slow(Fore.LIGHTBLUE_EX + f\"\\n{email['subject']} - From: {email['sender']}\" + Style.RESET_ALL)\n\n\ndef read_email(emails, subject):\n global has_read_email, evidence\n global balance\n email_found = False\n for email in emails:\n if email['subject'].lower() == subject.lower():\n email_found = True\n print_slow(\n Fore.LIGHTBLUE_EX + f\"\\nFrom: {email['sender']}\\nSubject: {email['subject']}\\n\\n{email['body']}\" + Style.RESET_ALL)\n\n # Check if the email is one of the specific emails that increases evidence count\n if email['subject'].lower() in [\"project update\"]:\n evidence_item = 3\n if not has_evidence(evidence_item):\n print_slow(\"Adding evidence to the list...\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Evidence Secured\" + Style.RESET_ALL)\n add_evidence(evidence_item)\n print_slow(\"\")\n print_slow(\"\")\n time.sleep(3)\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n third_call()\n\n if email['subject'].lower() in [\"professional development\"]:\n evidence_item = 2\n if not has_evidence(evidence_item):\n print_slow(\"Adding evidence to the list...\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Evidence Secured\" + Style.RESET_ALL)\n add_evidence(evidence_item)\n print_slow(\"\")\n print_slow(\"\")\n time.sleep(3)\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n second_call()\n\n if email['subject'].lower() == \"can't stop thinking about you\" and email['sender'].lower() == 'amy':\n evidence_item = 1\n if not has_evidence(evidence_item):\n print_slow(\"Adding evidence to the list...\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Evidence Secured\" + Style.RESET_ALL)\n add_evidence(evidence_item)\n print_slow(\"\")\n print_slow(\"\")\n time.sleep(3)\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n first_call()\n if email['subject'].lower() == \"upcoming software update\" and email['sender'].lower() == 'markus':\n evidence_item = 6\n if not has_evidence(evidence_item):\n print_slow(\"Adding evidence to the list...\")\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Evidence Secured\" + Style.RESET_ALL)\n add_evidence(evidence_item)\n print_slow(\"\")\n print_slow(\"\")\n time.sleep(3)\n print_slow(Fore.GREEN + \"Incoming Call...\" + Style.RESET_ALL)\n input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n sixth_call()\n\n # Add money to balance based on the email subject\n if email['subject'].lower() == \"professional development\":\n balance += 30\n elif email['subject'].lower() == \"project update\":\n balance += 50\n elif email['subject'].lower() == \"can't stop thinking about you\":\n balance += 20\n elif email['subject'].lower() == \"upcoming software update\":\n balance += 50\n\n if not email_found:\n print_slow(Fore.RED + \"\\nNo email found with that subject, please try again.\" + Style.RESET_ALL)\n\n\ndef connect():\n if has_item(\"EnigmaLink\"):\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Connecting to Enigma Corps network using EnigmaLink...\" + Style.RESET_ALL)\n time.sleep(0.5)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Establishing connection...\")\n time.sleep(1)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Linking EnigmaLink to remote server...\")\n time.sleep(2)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Decrypting server security protocols...\")\n time.sleep(3)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Bypassing firewall...\")\n time.sleep(2)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"Connection established!\")\n time.sleep(2)\n print_slow(\"\")\n print_slow(Fore.GREEN + \"You are now connected to Enigma Corps network.\")\n print_slow(\"\")\n\n # Network command loop\n while True:\n command = input(Fore.GREEN + \"> \" + Style.RESET_ALL)\n\n # Scan the network for systems and vulnerabilities\n if command.lower() == \"scan\":\n scan()\n # Hack into a system or vulnerability\n elif command.lower().startswith(\"hack \"):\n target = command[5:]\n hack(target)\n # Display connect help message\n elif command.lower() == \"help\":"},"next_line":{"kind":"string","value":" connect_help()"},"gold_snippet_index":{"kind":"number","value":4,"string":"4"},"created_at":{"kind":"string","value":"2023-11-06 09:52:13+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5276,"cells":{"repo_name":{"kind":"string","value":"ziqi-zhang/TAOISM"},"file_path":{"kind":"string","value":"python/test/test_conv.py"},"context":{"kind":"list like","value":[{"identifier":"register_layer","path":"python/common_net.py","snippet":"def register_layer(layer, name):\n layer.register_forward_hook(hooking_layer(name))\n layer.register_backward_hook(hooking_layer_backward(name))\n layer_names.append(name)"},{"identifier":"register_weight_layer","path":"python/common_net.py","snippet":"def register_weight_layer(layer, name):\n register_layer(layer, name)\n layer_weight[name] = layer.weight\n linear_layer_names.append(name)"},{"identifier":"get_layer_weight","path":"python/common_net.py","snippet":"def get_layer_weight(name):\n return layer_weight[name]"},{"identifier":"get_layer_input","path":"python/common_net.py","snippet":"def get_layer_input(name):\n return layer_input[name]"},{"identifier":"get_layer_weight_grad","path":"python/common_net.py","snippet":"def get_layer_weight_grad(name):\n return layer_weight[name].grad.data"},{"identifier":"get_layer_output","path":"python/common_net.py","snippet":"def get_layer_output(name):\n return layer_output[name]"},{"identifier":"get_layer_output_grad","path":"python/common_net.py","snippet":"def get_layer_output_grad(name):\n return layer_output_grad[name]"},{"identifier":"get_layer_input_grad","path":"python/common_net.py","snippet":"def get_layer_input_grad(name):\n return layer_input_grad[name]"},{"identifier":"GlobalTensor","path":"python/enclave_interfaces.py","snippet":"class GlobalTensor(object):\n cpu_tensor = {}\n gpu_tensors = {}\n encrypted_tensors = {}\n LinkedTags = {}\n InverseLinkedTags = {}\n IsInitEnclaveTensor = {}\n EnclaveInterface = None\n eid = None\n is_init_global_tensor = False\n\n @staticmethod\n def init():\n if GlobalTensor.is_init_global_tensor:\n return\n GlobalTensor.EnclaveInterface = EnclaveInterface()\n GlobalTensor.EnclaveInterface.init_enclave()\n GlobalTensor.is_init_global_tensor = True\n\n @staticmethod\n def destroy():\n GlobalTensor.EnclaveInterface.destroy_enclave()\n\n GlobalTensor.cpu_tensor = {}\n GlobalTensor.gpu_tensors = {}\n GlobalTensor.encrypted_tensors = {}\n GlobalTensor.LinkedTags = {}\n GlobalTensor.InverseLinkedTags = {}\n GlobalTensor.IsInitEnclaveTensor = {}\n GlobalTensor.EnclaveInterface = None\n GlobalTensor.eid = None\n GlobalTensor.is_init_global_tensor = False\n\n\n @staticmethod\n def get_eid():\n return GlobalTensor.EnclaveInterface.get_eid()\n\n @staticmethod\n def link_tags(tag1, tag2):\n if tag1 == tag2:\n return\n\n friends = []\n\n def add_friends(tag):\n nonlocal friends\n if tag in GlobalTensor.LinkedTags:\n its_leader_tag = GlobalTensor.LinkedTags[tag]\n if its_leader_tag in GlobalTensor.InverseLinkedTags:\n friends += GlobalTensor.InverseLinkedTags.pop(its_leader_tag)\n else:\n friends += [tag]\n\n add_friends(tag1)\n add_friends(tag2)\n leader_tag = min(friends)\n\n GlobalTensor.InverseLinkedTags[leader_tag] = friends\n for t in friends:\n if t in GlobalTensor.IsInitEnclaveTensor:\n raise ValueError(\"Tags must linked before tensor initialization\")\n GlobalTensor.LinkedTags[t] = leader_tag\n\n @staticmethod\n def get_remapped_tags(tag):\n return GlobalTensor.LinkedTags[tag] if tag in GlobalTensor.LinkedTags else tag\n\n @staticmethod\n def set_cpu(tag, tensor):\n GlobalTensor.cpu_tensor[tag] = tensor.to(torch.device(\"cpu\"))\n\n @staticmethod\n def set_gpu(tag, tensor):\n GlobalTensor.gpu_tensors[tag] = tensor\n\n @staticmethod\n def set_encrypted(tag, tensor):\n GlobalTensor.encrypted_tensors[tag] = tensor\n\n @staticmethod\n def get_cpu(tag):\n return GlobalTensor.cpu_tensor[tag]\n\n @staticmethod\n def get_gpu(tag):\n return GlobalTensor.gpu_tensors[tag]\n\n @staticmethod\n def get_encryption(tag):\n return GlobalTensor.encrypted_tensors[tag]\n\n @staticmethod\n def init_enclave_tensor(tag, size):\n size = list(size)\n if len(size) < 4:\n size = [1] * (4 - len(size)) + size\n remapped_tag = GlobalTensor.get_remapped_tags(tag)\n if remapped_tag in GlobalTensor.IsInitEnclaveTensor:\n return\n else:\n GlobalTensor.IsInitEnclaveTensor[remapped_tag] = True\n eid = GlobalTensor.get_eid()\n GlobalTensor.EnclaveInterface.lib.InitTensor(eid, remapped_tag, size[0], size[1], size[2], size[3])\n\n @staticmethod\n def init_encrypted_tensor(tag, shape):\n GlobalTensor.encrypted_tensors[GlobalTensor.get_remapped_tags(tag)] = \\\n GlobalTensor.EnclaveInterface.create_encrypt_torch(shape)"},{"identifier":"SecretBatchNorm2dLayer","path":"python/layers/batch_norm_2d.py","snippet":"class SecretBatchNorm2dLayer(SecretActivationLayer):\n # https://pytorch.org/docs/stable/nn.html#batchnorm2d\n\n BatchSize = None\n NumChannel = None\n ImgH = None\n ImgW = None\n WeightShape = None\n def __init__(\n self, sid, LayerName, EnclaveMode, link_prev=True, link_next=True,\n manually_register_prev=False, manually_register_next=False, merge_own_tensors=False\n ):\n \n super().__init__(\n sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next, merge_own_tensors\n )\n \n self.ForwardFuncName = \"BatchNorm2d\"\n self.BackwardFuncName = \"DerBatchNorm2d\"\n self.PlainFunc = torch.nn.BatchNorm2d\n self.IsAffine = True\n self.momentum = 0.1\n self.IsCumulative = (self.momentum is None)\n self.epsilon = 1e-5\n\n if EnclaveMode is ExecutionModeOptions.CPU or EnclaveMode is ExecutionModeOptions.GPU:\n self.ForwardFunc = torch.nn.BatchNorm2d\n # if self.is_enclave_mode:\n # self.StoreInEnclave = True\n # else:\n # self.ForwardFunc = torch.nn.BatchNorm2d\n # self.StoreInEnclave = False\n \n\n def init_shape(self):\n self.InputShape = self.PrevLayer.get_output_shape()\n self.OutputShape = self.InputShape\n self.BatchSize, self.NumChannel, self.ImgH, self.ImgW = self.InputShape\n self.WeightShape = [self.NumChannel]\n self.LearnableParamsList = [\n LearnableParamTuple(dw_name=\"DerWeight\", w_name=\"weight\", shape=self.WeightShape),\n LearnableParamTuple(dw_name=\"DerBias\", w_name=\"bias\", shape=self.WeightShape),\n ]\n \n\n # def init(self, start_enclave=True):\n \n # if self.sid == 2:\n # return\n # TensorLoader.init(self, start_enclave)\n\n # if self.is_enclave_mode:\n # self.PlainFunc = self.PlainFunc(self.InputShape[1])\n # self.PlainFunc.eval()\n # self.get_cpu(\"weight\").data.copy_(self.PlainFunc.weight.data)\n # self.get_cpu(\"bias\").data.copy_(self.PlainFunc.bias.data)\n # self.get_cpu(\"RunMean\").data.copy_(self.PlainFunc.running_mean.data)\n # # inject sqrt(running_var) instead of running_var for precision\n # self.get_cpu(\"RunVar\").data.copy_(self.PlainFunc.running_var.data)\n # self.transfer_cpu_to_enclave(\"weight\")\n # self.transfer_cpu_to_enclave(\"bias\")\n # self.transfer_cpu_to_enclave(\"RunMean\")\n # self.transfer_cpu_to_enclave(\"RunVar\")\n # self.batchnorm_init(\n # self.LayerName,\n # \"input\", \"output\", \"weight\", \"bias\",\n # \"DerInput\", \"DerOutput\", \"DerWeight\", \"DerBias\",\n # \"RunMean\", \"RunVar\", \"CurMean\", \"CurVar\",\n # \"mu\",\n # self.BatchSize, self.NumChannel, self.ImgH, self.ImgW,\n # int(self.IsAffine), int(self.IsCumulative), self.momentum, self.epsilon)\n # else:\n # self.ForwardFunc = self.ForwardFunc(self.InputShape[1])\n # self.PlainFunc = self.PlainFunc(self.InputShape[1])\n # self.PlainFunc.eval()\n # self.ForwardFunc.weight.data.copy_(self.PlainFunc.weight.data)\n # self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\n # self.ForwardFunc.running_mean.data.copy_(self.PlainFunc.running_mean.data)\n # self.ForwardFunc.running_var.data.copy_(self.PlainFunc.running_var.data)\n # self.set_cpu(\"weight\", list(self.ForwardFunc.parameters())[0].data)\n # self.set_cpu(\"bias\", list(self.ForwardFunc.parameters())[1].data)\n # self.set_cpu(\"RunMean\", self.ForwardFunc.running_mean.data)\n # self.set_cpu(\"RunVar\", self.ForwardFunc.running_var.data)\n # self.ForwardFunc.eval()\n\n def init(self, start_enclave=True):\n # if self.LayerName == \"Layer3.10.proxies.0.bn2\":\n # st()\n TensorLoader.init(self, start_enclave)\n\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\n self.PlainFunc = self.PlainFunc(self.InputShape[1])\n self.PlainFunc.eval()\n self.get_cpu(\"weight\").data.copy_(self.PlainFunc.weight.data)\n self.get_cpu(\"bias\").data.copy_(self.PlainFunc.bias.data)\n self.get_cpu(\"RunMean\").data.copy_(self.PlainFunc.running_mean.data)\n # inject sqrt(running_var) instead of running_var for precision\n self.get_cpu(\"RunVar\").data.copy_(self.PlainFunc.running_var.data)\n self.transfer_cpu_to_enclave(\"weight\")\n self.transfer_cpu_to_enclave(\"bias\")\n self.transfer_cpu_to_enclave(\"RunMean\")\n self.transfer_cpu_to_enclave(\"RunVar\")\n self.batchnorm_init(\n self.LayerName,\n \"input\", \"output\", \"weight\", \"bias\",\n # \"DerInput\", \"DerOutput\", \"DerWeight\", \"DerBias\",\n \"RunMean\", \"RunVar\", \"CurMean\", \"CurVar\",\n \"mu\",\n self.BatchSize, self.NumChannel, self.ImgH, self.ImgW,\n int(self.IsAffine), int(self.IsCumulative), self.momentum, self.epsilon)\n elif self.EnclaveMode is ExecutionModeOptions.CPU:\n self.ForwardFunc = self.ForwardFunc(self.InputShape[1])\n self.PlainFunc = self.PlainFunc(self.InputShape[1])\n self.PlainFunc.eval()\n self.ForwardFunc.weight.data.copy_(self.PlainFunc.weight.data)\n self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\n self.ForwardFunc.running_mean.data.copy_(self.PlainFunc.running_mean.data)\n self.ForwardFunc.running_var.data.copy_(self.PlainFunc.running_var.data)\n self.set_cpu(\"weight\", list(self.ForwardFunc.parameters())[0].data)\n self.set_cpu(\"bias\", list(self.ForwardFunc.parameters())[1].data)\n self.set_cpu(\"RunMean\", self.ForwardFunc.running_mean.data)\n self.set_cpu(\"RunVar\", self.ForwardFunc.running_var.data)\n self.ForwardFunc.eval()\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\n self.ForwardFunc = self.ForwardFunc(self.InputShape[1])\n self.PlainFunc = self.PlainFunc(self.InputShape[1])\n self.ForwardFunc.weight.data.copy_(self.PlainFunc.weight.data)\n self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\n self.ForwardFunc.running_mean.data.copy_(self.PlainFunc.running_mean.data)\n self.ForwardFunc.running_var.data.copy_(self.PlainFunc.running_var.data)\n self.set_gpu(\"weight\", list(self.ForwardFunc.parameters())[0].data)\n self.set_gpu(\"bias\", list(self.ForwardFunc.parameters())[1].data)\n self.set_gpu(\"RunMean\", self.ForwardFunc.running_mean.data)\n self.set_gpu(\"RunVar\", self.ForwardFunc.running_var.data)\n self.PlainFunc.eval()\n self.ForwardFunc.cuda().eval()\n\n # def inject_params(self, params):\n # if self.sid == -2:\n # raise ValueError(\"S2 has no learnable parameters for injection\")\n # self.get_cpu(\"weight\").copy_(params.weight.data)\n # self.get_cpu(\"bias\").copy_(params.bias.data)\n # self.get_cpu(\"RunMean\").copy_(params.running_mean.data)\n # # inject sqrt(running_var) instead of running_var for precision\n # self.get_cpu(\"RunVar\").copy_(params.running_var.data)\n # if self.is_enclave_mode:\n # self.transfer_cpu_to_enclave(\"weight\")\n # self.transfer_cpu_to_enclave(\"bias\")\n # self.transfer_cpu_to_enclave(\"RunMean\")\n # self.transfer_cpu_to_enclave(\"RunVar\")\n\n def inject_params(self, params):\n if self.sid == -2:\n raise ValueError(\"S2 has no learnable parameters for injection\")\n if self.EnclaveMode in [ExecutionModeOptions.CPU, ExecutionModeOptions.Enclave]: \n self.get_cpu(\"weight\").copy_(params.weight.data)\n self.get_cpu(\"bias\").copy_(params.bias.data)\n self.get_cpu(\"RunMean\").copy_(params.running_mean.data)\n self.get_cpu(\"RunVar\").copy_(params.running_var.data)\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\n self.transfer_cpu_to_enclave(\"weight\")\n self.transfer_cpu_to_enclave(\"bias\")\n self.transfer_cpu_to_enclave(\"RunMean\")\n self.transfer_cpu_to_enclave(\"RunVar\")\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\n self.get_gpu(\"weight\").copy_(params.weight.data)\n self.get_gpu(\"bias\").copy_(params.bias.data)\n self.get_gpu(\"RunMean\").copy_(params.running_mean.data)\n self.get_gpu(\"RunVar\").copy_(params.running_var.data)\n\n def reset_plain_bn(self):\n # module = torch.BatchNorm2d()\n self.get_cpu(\"weight\").copy_(torch.ones(self.InputShape[1]))\n self.get_cpu(\"bias\").copy_(torch.zeros(self.InputShape[1]))\n self.get_cpu(\"RunMean\").copy_(torch.zeros(self.InputShape[1]))\n self.get_cpu(\"RunVar\").copy_(torch.ones(self.InputShape[1]))\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\n self.transfer_cpu_to_enclave(\"weight\")\n self.transfer_cpu_to_enclave(\"bias\")\n self.transfer_cpu_to_enclave(\"RunMean\")\n self.transfer_cpu_to_enclave(\"RunVar\")\n\n\n def inject_to_plain(self, plain_layer: torch.nn.Module) -> None:\n raise NotImplementedError\n if self.sid == -2:\n raise ValueError(\"S2 has no learnable parameters for injection\")\n self.make_sure_cpu_is_latest(\"weight\")\n self.make_sure_cpu_is_latest(\"bias\")\n plain_layer.weight.data.copy_(self.get_cpu(\"weight\"))\n plain_layer.bias.data.copy_(self.get_cpu(\"bias\"))\n plain_layer.running_mean.data.copy_(self.get_cpu(\"RunMean\"))\n plain_layer.running_var.data.copy_(self.get_cpu(\"RunVar\"))\n\n def generate_tensor_name_list(self, force=False):\n if not force and self.tensor_name_list:\n return\n if self.sid == 2:\n self.tensor_name_list = {}\n return\n\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\n NeededTensorNames = [\n (\"input\", self.InputShape, None),\n # (\"DerInput\", self.InputShape, None),\n (\"output\", self.OutputShape, None),\n # (\"DerOutput\", self.OutputShape, None),\n (\"weight\", self.WeightShape, None),\n # (\"DerWeight\", self.WeightShape, None),\n (\"bias\", self.WeightShape, None),\n # (\"DerBias\", self.WeightShape, None),\n (\"RunMean\", self.WeightShape, None),\n (\"CurMean\", self.WeightShape, None),\n (\"RunVar\", self.WeightShape, None),\n (\"CurVar\", self.WeightShape, None),\n (\"mu\", self.InputShape, None),\n ]\n else:\n NeededTensorNames = [\n (\"output\", self.OutputShape, None),\n # (\"DerInput\", self.InputShape, None),\n (\"input\", self.InputShape, None),\n (\"weight\", self.WeightShape, None),\n # (\"DerWeight\", self.WeightShape, None),\n (\"bias\", self.WeightShape, None),\n # (\"DerBias\", self.WeightShape, None),\n # (\"DerOutput\", self.OutputShape, None)\n ]\n\n self.tensor_name_list = NeededTensorNames\n\n # def forward(self):\n # if self.sid == 2:\n # return\n # with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} Forward\", verbose_level=VerboseLevel.LAYER):\n # if self.is_enclave_mode:\n # self.forward_tensor_transfer()\n # self.batchnorm_forward(self.LayerName, int(False))\n # else:\n # self.forward_tensor_transfer()\n # self.requires_grad_on_cpu(\"input\")\n # self.ForwardFunc.bias.data.copy_(self.get_cpu(\"bias\"))\n # self.ForwardFunc.weight.data.copy_(self.get_cpu(\"weight\"))\n # self.ForwardFunc.running_mean.data.copy_(self.get_cpu(\"RunMean\"))\n # # running_var of PlainFunc is ^2 of that in the enclave\n # enclave_running_var = self.get_cpu(\"RunVar\")\n # self.ForwardFunc.running_var.data.copy_(enclave_running_var)\n # self.set_cpu(\"output\", self.ForwardFunc(self.get_cpu(\"input\")))\n\n def forward(self):\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} Forward\", verbose_level=VerboseLevel.LAYER):\n if self.EnclaveMode == ExecutionModeOptions.Enclave:\n # if self.LayerName == \"Layer2.0.downsample.bn\":\n # st()\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} Input Preprocess\", verbose_level=VerboseLevel.LAYER):\n self.forward_tensor_transfer()\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} batchnorm_forward\", verbose_level=VerboseLevel.LAYER):\n self.batchnorm_forward(self.LayerName, int(False))\n elif self.EnclaveMode == ExecutionModeOptions.CPU:\n self.forward_tensor_transfer()\n self.ForwardFunc.bias.data.copy_(self.get_cpu(\"bias\"))\n self.ForwardFunc.weight.data.copy_(self.get_cpu(\"weight\"))\n self.ForwardFunc.running_mean.data.copy_(self.get_cpu(\"RunMean\"))\n # running_var of PlainFunc is ^2 of that in the enclave\n enclave_running_var = self.get_cpu(\"RunVar\")\n self.ForwardFunc.running_var.data.copy_(enclave_running_var)\n self.set_cpu(\"output\", self.ForwardFunc(self.get_cpu(\"input\")))\n elif self.EnclaveMode == ExecutionModeOptions.GPU:\n self.forward_tensor_transfer()\n self.ForwardFunc.bias.data.copy_(self.get_gpu(\"bias\"))\n self.ForwardFunc.weight.data.copy_(self.get_gpu(\"weight\"))\n self.ForwardFunc.running_mean.data.copy_(self.get_gpu(\"RunMean\"))\n # running_var of PlainFunc is ^2 of that in the enclave\n enclave_running_var = self.get_gpu(\"RunVar\")\n self.ForwardFunc.running_var.data.copy_(enclave_running_var)\n # st()\n # print(self.get_gpu(\"input\")[0,0,0])\n self.set_gpu(\"output\", self.ForwardFunc(self.get_gpu(\"input\").type(SecretConfig.dtypeForCpuOp)))\n\n def backward(self):\n raise NotImplementedError\n if self.sid == 2:\n return\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} Backward\", verbose_level=VerboseLevel.LAYER):\n if self.is_enclave_mode:\n self.backward_tensor_transfer()\n self.batchnorm_backward(self.LayerName)\n else:\n self.backward_tensor_transfer()\n BackwardInput, BackwardWeight, BackwardBias = self.get_cpu(\"output\").grad_fn(self.get_cpu(\"DerOutput\"))\n self.set_cpu(\"DerInput\", BackwardInput.data)\n self.set_cpu(\"DerWeight\", BackwardWeight.data)\n self.set_cpu(\"DerBias\", BackwardBias.data)\n if list(self.get_cpu(\"DerWeight\").shape) != self.WeightShape:\n real_shape = self.get_cpu(\"DerWeight\").shape\n ideal_shape = self.WeightShape\n raise ValueError(\n f\"DerWeight is not of shape self.AffineShape: real: {real_shape}, ideal: {ideal_shape}\")\n if list(self.get_cpu(\"DerBias\").shape) != self.WeightShape:\n raise ValueError(\"DerBias is not of shape self.AffineShape\")\n\n def plain_forward(self, NeedBackward=False):\n if self.sid == 2:\n return\n if self.EnclaveMode in [ExecutionModeOptions.Enclave, ExecutionModeOptions.GPU]:\n self.make_sure_cpu_is_latest(\"input\")\n self.make_sure_cpu_is_latest(\"bias\")\n self.make_sure_cpu_is_latest(\"weight\")\n self.requires_grad_on_cpu(\"input\")\n self.PlainFunc.bias.data.copy_(self.get_cpu(\"bias\"))\n self.PlainFunc.weight.data.copy_(self.get_cpu(\"weight\"))\n self.PlainFunc.running_mean.data.copy_(self.get_cpu(\"RunMean\"))\n # self.PlainFunc.running_var.data.copy_(self.get_cpu(\"RunVar\"))\n # running_var of PlainFunc is ^2 of that in the enclave\n enclave_running_var = self.get_cpu(\"RunVar\")\n self.PlainFunc.running_var.data.copy_(enclave_running_var)\n else:\n self.make_sure_cpu_is_latest(\"input\")\n self.requires_grad_on_cpu(\"input\")\n\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} PlainForward\"):\n torch.set_num_threads(1)\n self.PlainForwardResult = self.PlainFunc(self.get_cpu(\"input\"))\n torch.set_num_threads(4)\n\n def plain_backward(self):\n if self.sid == 2:\n return\n self.make_sure_cpu_is_latest(\"DerOutput\")\n GradFunction = self.PlainForwardResult.grad_fn\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} PlainBackward\"):\n torch.set_num_threads(1)\n self.PlainBackwardResult = GradFunction(self.get_cpu(\"DerOutput\"))\n torch.set_num_threads(4)\n\n def show_plain_error(self):\n if self.sid == 2:\n return\n self.make_sure_cpu_is_latest(\"output\")\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\"output\"), get_relative=True)\n print(f\"S{self.sid}: {self.LayerName} Forward Error: {err}\")\n\n if self.PlainBackwardResult is None:\n return\n if self.is_enclave_mode:\n self.make_sure_cpu_is_latest(\"DerInput\")\n self.make_sure_cpu_is_latest(\"DerWeight\")\n self.make_sure_cpu_is_latest(\"DerBias\")\n else:\n self.make_sure_cpu_is_latest(\"DerInput\")\n BackwardInput, BackwardWeight, BackwardBias = self.PlainBackwardResult\n err_input = compare_expected_actual(BackwardInput, self.get_cpu(\"DerInput\"), show_where_err=False, get_relative=True)\n err_weight = compare_expected_actual(BackwardWeight, self.get_cpu(\"DerWeight\"), show_where_err=False,\n get_relative=True)\n err_bias = compare_expected_actual(BackwardBias, self.get_cpu(\"DerBias\"))\n print(f\"S{self.sid}: {self.LayerName} Backward Error input: {err_input}, weight {err_weight}, bias: {err_bias}\")\n\n def show_plain_error_forward(self):\n if self.sid == 2:\n return\n self.make_sure_cpu_is_latest(\"output\")\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\"output\"), get_relative=False, show_values=False)\n print(f\"S{self.sid}: {self.LayerName} Forward Error: {err}\")"},{"identifier":"SecretFlattenLayer","path":"python/layers/flatten.py","snippet":"class SecretFlattenLayer(SecretNonlinearLayer):\n batch_size = None\n n_features = None\n input_shape = None\n output_shape = None\n\n def __init__(\n self, sid, LayerName, EnclaveMode, link_prev=True, link_next=True,\n manually_register_prev=False, manually_register_next=False\n ):\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\n self.StoreInEnclave = False\n self.ForwardFuncName = \"Flatten\"\n self.BackwardFuncName = \"DerFlatten\"\n\n\n def init(self, start_enclave=True):\n super().init(start_enclave)\n self.ForwardFunc = lambda x: x.view(-1, self.n_features)\n self.PlainFunc = lambda x: x.view(-1, self.n_features)\n\n def init_shape(self):\n self.input_shape = self.PrevLayer.get_output_shape()\n if len(self.input_shape) != 4:\n return ValueError(\"The dimension of the tensor form prev. layer has to be 4D.\")\n\n self.batch_size = self.input_shape[0]\n self.n_features = self.input_shape[1] * self.input_shape[2] * self.input_shape[3]\n self.output_shape = [self.batch_size, self.n_features]\n\n def get_output_shape(self):\n return self.output_shape\n\n def generate_tensor_name_list(self, force=False):\n if not force and self.tensor_name_list:\n return\n if self.sid == 2:\n self.tensor_name_list = {}\n return\n\n NeededTensorNames = [(\"output\", self.output_shape, None),\n (\"input\", self.input_shape, None),\n (\"DerInput\", self.input_shape, None),\n (\"DerOutput\", self.output_shape, None)\n ]\n\n self.tensor_name_list = NeededTensorNames\n\n def forward(self):\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} Forward\", verbose_level=VerboseLevel.LAYER):\n if self.EnclaveMode == ExecutionModeOptions.Enclave:\n self.transfer_enclave_to_cpu(\"input\")\n self.set_cpu(\"output\", self.ForwardFunc(self.get_cpu(\"input\")))\n self.transfer_cpu_to_enclave(\"output\")\n elif self.EnclaveMode == ExecutionModeOptions.CPU:\n self.set_cpu(\"output\", self.ForwardFunc(self.get_cpu(\"input\")))\n elif self.EnclaveMode == ExecutionModeOptions.GPU:\n self.set_gpu(\"output\", self.ForwardFunc(self.get_gpu(\"input\")))\n\n # self.forward_tensor_transfer()\n # self.set_cpu(\"output\", self.ForwardFunc(self.get_cpu(\"input\")))\n\n def backward(self):\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} Backward\", verbose_level=VerboseLevel.LAYER):\n self.backward_tensor_transfer()\n self.set_cpu(\"DerInput\", self.get_cpu(\"DerOutput\").view(self.input_shape))\n\n def plain_forward(self, NeedBackward=False):\n self.requires_grad_on_cpu(\"input\")\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} PlainForward\"):\n self.PlainForwardResult = self.PlainFunc(self.get_cpu(\"input\"))\n\n def plain_backward(self):\n self.make_sure_cpu_is_latest(\"DerOutput\")\n GradFunction = self.PlainForwardResult.grad_fn\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} PlainBackward\"):\n self.PlainBackwardResult = GradFunction(self.get_cpu(\"DerOutput\"))\n\n def show_plain_error(self):\n if self.StoreInEnclave:\n self.transfer_enclave_to_cpu(\"output\")\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\"output\"))\n print(f\"S{self.sid}: {self.LayerName} Forward Error: {err}\")\n\n if self.PlainBackwardResult is None:\n return\n err = compare_expected_actual(self.PlainBackwardResult, self.get_cpu(\"DerInput\"), get_relative=True)\n print(f\"S{self.sid}: {self.LayerName} Backward Error {err}\")"},{"identifier":"SecretInputLayer","path":"python/layers/input.py","snippet":"class SecretInputLayer(SecretNonlinearLayer):\n shape = None\n\n def __init__(\n self, sid, LayerName, input_shape, EnclaveMode, link_prev=True, link_next=True, \n manually_register_prev=False, manually_register_next=False\n ):\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\n self.shape = input_shape\n\n def link_tensors(self):\n gt.link_tags(self.get_tag(\"input\", remap=False), self.get_tag(\"output\", remap=False))\n super().link_tensors()\n\n def init_shape(self):\n return\n\n def set_input(self, tensor):\n self.set_tensor_cpu_gpu_enclave(\"input\", tensor)\n\n def get_output_shape(self):\n return self.shape\n\n def forward(self):\n return\n\n def backward(self):\n return\n\n def plain_forward(self):\n return\n\n def plain_backward(self):\n return\n\n def show_plain_error(self):\n return\n\n def print_connection_info(self):\n print(f\"{self.LayerName:30} shape{self.shape} output {self.NextLayer.LayerName:30}\")"},{"identifier":"SecretMaxpool2dLayer","path":"python/layers/maxpool2d.py","snippet":"class SecretMaxpool2dLayer(SecretActivationLayer):\n def __init__(\n self, sid, LayerName, EnclaveMode, filter_hw, stride, padding, link_prev=True, link_next=True,\n manually_register_prev=False, manually_register_next=False\n ):\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\n self.ForwardFuncName = \"Maxpool2d\"\n self.BackwardFuncName = \"DerMaxpool2d\"\n self.filter_hw = filter_hw\n self.startmaxpool = False\n self.PlainFunc = torch.nn.MaxPool2d\n self.maxpoolpadding = padding\n self.stride = stride\n self.STORE_CHUNK_ELEM = 401408\n\n self.ForwardFunc = torch.nn.MaxPool2d\n\n if EnclaveMode == ExecutionModeOptions.Enclave :\n self.ForwardFunc = self.maxpoolfunc\n self.BackwardFunc = self.maxpoolbackfunc\n else:\n self.ForwardFunc = torch.nn.MaxPool2d\n\n def init_shape(self):\n self.InputShape = self.PrevLayer.get_output_shape()\n if len(self.InputShape) != 4:\n raise ValueError(\"Maxpooling2d apply only to 4D Tensor\")\n if self.InputShape[2] != self.InputShape[3]:\n raise ValueError(\"The input tensor has to be square images\")\n if self.InputShape[2] % self.stride != 0:\n raise ValueError(\"The input tensor needs padding for this filter size\")\n InputHw = self.InputShape[2]\n output_hw = InputHw // self.stride\n self.OutputShape = [self.InputShape[0], self.InputShape[1], output_hw, output_hw]\n self.HandleShape = self.InputShape\n # self.Shapefortranspose = [int(round(((self.InputShape[0] * self.InputShape[1] * self.InputShape[2] * self.InputShape[3])/262144)+1/2)), 262144, 1, 1]\n self.Shapefortranspose = [\n int(round(((self.InputShape[0] * self.InputShape[1] * self.InputShape[2] * self.InputShape[3])/self.STORE_CHUNK_ELEM)+1/2)), self.STORE_CHUNK_ELEM, 1, 1]\n\n\n def init(self, start_enclave=True):\n if self.EnclaveMode == ExecutionModeOptions.Enclave:\n self.PlainFunc = self.PlainFunc(self.filter_hw, self.stride, self.maxpoolpadding)\n TensorLoader.init(self, start_enclave)\n\n if self.startmaxpool is False:\n self.startmaxpool = True\n return self.maxpoolinit(self.LayerName, \"inputtrans\", \"outputtrans\")\n else:\n self.ForwardFunc = self.ForwardFunc(self.filter_hw, stride=self.stride, padding=self.maxpoolpadding)\n self.PlainFunc = self.PlainFunc(self.filter_hw, stride=self.stride, padding=self.maxpoolpadding)\n\n # TensorLoader.init(self, start_enclave)\n # self.ForwardFunc = self.ForwardFunc(self.filter_hw, stride=self.stride, padding=self.maxpoolpadding)\n # self.PlainFunc = self.PlainFunc(self.filter_hw, stride=self.stride, padding=self.maxpoolpadding)\n\n # TensorLoader.init(self, start_enclave)\n\n # def forward(self):\n # with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} Forward\", verbose_level=VerboseLevel.LAYER):\n # self.forward_tensor_transfer()\n # # self.requires_grad_on_cpu(\"input\")\n # if self.EnclaveMode == ExecutionModeOptions.Enclave:\n # self.set_gpu(\"output\", self.ForwardFunc(self.get_gpu(\"input\")))\n # st()\n\n # # if self.PrevLayer.EnclaveMode is not ExecutionModeOptions.Enclave:\n # # self.transfer_enclave_to_cpu(\"input\")\n # # if torch.sum(self.get_cpu(\"input\").abs()) == 0:\n # # raise RuntimeError(f\"{self.LayerName}: SGX input not load\")\n # # self.transfer_cpu_to_enclave(\"input\")\n # # self.transfer_enclave_to_cpu(\"input\")\n # # self.set_cpu(\"output\", self.ForwardFunc(self.get_cpu(\"input\")))\n # # self.transfer_cpu_to_enclave(\"output\")\n # elif self.EnclaveMode == ExecutionModeOptions.CPU:\n # if self.PrevLayer.EnclaveMode is not ExecutionModeOptions.CPU and torch.sum(self.get_cpu(\"input\").abs()) == 0:\n # raise RuntimeError(f\"{self.LayerName}: SGX input not load\")\n # self.set_cpu(\"output\", self.ForwardFunc(self.get_cpu(\"input\")))\n # elif self.EnclaveMode == ExecutionModeOptions.GPU:\n # if self.PrevLayer.EnclaveMode is not ExecutionModeOptions.GPU and torch.sum(self.get_gpu(\"input\").abs()) == 0:\n # raise RuntimeError(f\"{self.LayerName}: SGX input not load\")\n # self.set_gpu(\"output\", self.ForwardFunc(self.get_gpu(\"input\")))\n # else:\n # raise RuntimeError\n\n def maxpoolfunc(self, namein, nameout):\n # assume row_stride and col_stride are both None or both not None\n # assume row_pad and col_pad are both None or both not None\n # if self.LayerName == \"Layer3.0.proxies.2.maxpool\":\n # print(self.LayerName, \"Input: \", self.get_cpu(\"input\")[0,0,0,:10])\n output = self.maxpoolnew(self.LayerName, namein, nameout, self.InputShape, self.OutputShape[2], self.OutputShape[3],\n self.filter_hw, self.filter_hw, self.stride, self.stride, self.maxpoolpadding,\n self.maxpoolpadding)\n # if self.LayerName == \"Layer3.0.proxies.2.maxpool\":\n # self.transfer_enclave_to_cpu(\"output\")\n # print(self.LayerName, \"Output: \", self.get_cpu(\"output\")[0,0,0,:])\n # self.transfer_cpu_to_enclave(\"output\")\n return output\n\n def maxpoolbackfunc(self, nameout, namedout, namedin):\n return self.maxpoolback(self.LayerName, namedout, namedin, self.InputShape, self.OutputShape[2], self.OutputShape[3],\n self.filter_hw, self.filter_hw, self.row_stride, self.col_stride, self.maxpoolpadding,\n self.maxpoolpadding)"},{"identifier":"SecretOutputLayer","path":"python/layers/output.py","snippet":"class SecretOutputLayer(SecretNonlinearLayer):\n TargetShape = None\n loss = 0\n\n def __init__(\n self, sid, LayerName, EnclaveMode, inference=False, link_prev=True, link_next=True, \n manually_register_prev=False, manually_register_next=False\n ):\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\n self.ForwardFunc = torch.nn.CrossEntropyLoss()\n self.PlainFunc = torch.nn.CrossEntropyLoss()\n self.EnclaveMode = ExecutionModeOptions.CPU\n self.inference = inference\n\n\n def init_shape(self):\n self.InputShape = self.PrevLayer.get_output_shape()\n self.OutputShape = [1]\n self.TargetShape = [self.InputShape[0]] # number of Minibatch\n\n def init(self, start_enclave=True):\n TensorLoader.init(self, start_enclave)\n\n def generate_tensor_name_list(self, force=False):\n if not force and self.tensor_name_list:\n return\n if self.sid == 2:\n self.tensor_name_list = {}\n return\n\n NeededTensorNames = [\n (\"output\", self.OutputShape, None),\n (\"DerInput\", self.InputShape, None),\n (\"input\", self.InputShape, None),\n (\"target\", self.TargetShape, None),\n ]\n\n self.tensor_name_list = NeededTensorNames\n\n def load_target(self, tensor):\n self.set_tensor_with_name(\"target\", tensor)\n\n def get_loss(self):\n return self.loss\n \n def get_prediction(self):\n self.forward_tensor_transfer(\"input\")\n if torch.sum(self.get_cpu(\"input\").abs()) == 0:\n raise RuntimeError(\"SGX input not load\")\n return self.get_cpu(\"input\")\n\n def forward(self):\n if not self.inference:\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} Forward\", verbose_level=VerboseLevel.LAYER):\n self.forward_tensor_transfer()\n self.set_cpu(\"input\", self.get_cpu(\"input\").detach())\n self.requires_grad_on_cpu(\"input\")\n self.set_cpu(\"output\", self.ForwardFunc(self.get_cpu(\"input\"), self.get_cpu(\"target\")))\n loss = self.get_cpu(\"output\").item()\n self.loss = loss\n\n def backward(self):\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} Backward\", verbose_level=VerboseLevel.LAYER):\n self.backward_tensor_transfer(transfer_tensor=\"output\")\n self.get_cpu(\"output\").backward()\n self.set_cpu(\"DerInput\", self.get_cpu(\"input\").grad)\n\n def plain_forward(self):\n if not self.inference:\n self.make_sure_cpu_is_latest(\"input\")\n self.set_cpu(\"input\", self.get_cpu(\"input\").detach())\n self.requires_grad_on_cpu(\"input\")\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} PlainForward\"):\n self.PlainForwardResult = self.PlainFunc(self.get_cpu(\"input\"), self.get_cpu(\"target\"))\n\n def plain_backward(self):\n self.make_sure_cpu_is_latest(\"output\")\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} PlainBackward\"):\n self.PlainForwardResult.backward()\n self.set_cpu(\"DerInput\", self.get_cpu(\"input\").grad)\n\n def show_plain_error(self):\n self.make_sure_cpu_is_latest(\"output\")\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\"output\"))\n print(f\"S{self.sid}: {self.LayerName} Forward Error: {err}\")\n\n if self.PlainBackwardResult is None:\n return\n self.make_sure_cpu_is_latest(\"DerInput\")\n\n err = compare_expected_actual(self.PlainBackwardResult, self.get_cpu(\"DerInput\"))\n print(f\"S{self.sid}: {self.LayerName} Backward Error {err}\")\n\n def print_connection_info(self):\n print(f\"{self.LayerName:30} shape{self.InputShape}{' ':30} input {self.PrevLayer.LayerName:30}\")"},{"identifier":"SecretReLULayer","path":"python/layers/relu.py","snippet":"class SecretReLULayer(SecretActivationLayer):\n def __init__(\n self, sid, LayerName, EnclaveMode, link_prev=True, link_next=True,\n manually_register_prev=False, manually_register_next=False, merge_own_tensors=False\n ):\n super().__init__(\n sid, LayerName, EnclaveMode, link_prev, link_next,\n manually_register_prev, manually_register_next, merge_own_tensors\n )\n self.ForwardFuncName = \"ReLU\"\n self.BackwardFuncName = \"DerReLU\"\n self.PlainFunc = torch.nn.ReLU\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\n self.ForwardFunc = self.relufunc\n self.BackwardFunc = self.relubackfunc\n elif self.EnclaveMode is ExecutionModeOptions.CPU:\n self.ForwardFunc = torch.nn.ReLU\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\n self.ForwardFunc = torch.nn.ReLU\n\n # if self.is_enclave_mode:\n # self.ForwardFunc = self.relufunc\n # self.BackwardFunc = self.relubackfunc\n # self.StoreInEnclave = True\n # else:\n # self.ForwardFunc = torch.nn.ReLU\n # self.StoreInEnclave = False\n\n def init(self, start_enclave=True):\n super().init(start_enclave)\n self.PlainFunc = self.PlainFunc()\n # if not self.is_enclave_mode:\n if self.EnclaveMode is not ExecutionModeOptions.Enclave:\n self.ForwardFunc = self.ForwardFunc()\n\n def relufunc(self, namein, nameout):\n return self.relunew(namein, nameout, self.InputShape)\n\n def relubackfunc(self, nameout, namedout, namedin):\n return self.relubackward(nameout, namedout, namedin, self.InputShape)\n\n def show_plain_error_forward(self):\n if self.sid == 2:\n return\n self.make_sure_cpu_is_latest(\"output\")\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\"output\"), get_relative=False, show_values=False)\n print(f\"S{self.sid}: {self.LayerName} Forward Error: {err}\")"},{"identifier":"init_communicate","path":"python/sgx_net.py","snippet":"def init_communicate(rank, master_address, master_port, backend='gloo'):\n os.environ['MASTER_ADDR'] = master_address\n os.environ['MASTER_PORT'] = master_port\n dist.init_process_group(backend, rank=rank, world_size=SecretConfig.worldSize)"},{"identifier":"warming_up_cuda","path":"python/sgx_net.py","snippet":"def warming_up_cuda():\n device = torch.device(\"cuda:0\")\n # device = torch.device(\"cpu\")\n\n print(\"Execution device: \", device)\n print(\"PyTorch version: \", torch.__version__)\n print(\"CUDA version: \", torch.version.cuda)\n print(\"CUDA device:\", torch.cuda.get_device_name(0))\n\n batch_size, n_input_channel, n_output_channel, img_hw, filter_hw = 512, 512, 256, 4, 3\n x_shape = [batch_size, n_input_channel, img_hw, img_hw]\n w_shape = [n_output_channel, n_input_channel, filter_hw, filter_hw]\n with NamedTimerInstance(\"Warming up Cuda double\"):\n dummy_a = get_random_uniform(SecretConfig.PrimeLimit, x_shape).type(SecretConfig.dtypeForSave)\n dummy_b = get_random_uniform(SecretConfig.PrimeLimit, w_shape).type(SecretConfig.dtypeForSave)\n F.conv2d(dummy_a.cuda().type(SecretConfig.dtypeForCudaMm), dummy_b.cuda().type(SecretConfig.dtypeForCudaMm),\n padding=1)\n\n with NamedTimerInstance(\"Warming up Cuda dobule 2nd\"):\n F.conv2d(dummy_a.cuda().type(torch.double), dummy_b.cuda().type(torch.double),\n padding=1)\n\n with NamedTimerInstance(\"Warming up Cuda float\"):\n F.conv2d(dummy_a.cuda().type(torch.float), dummy_b.cuda().type(torch.float), padding=1)\n\n with NamedTimerInstance(\"Warming up Cuda float 2nd\"):\n F.conv2d(dummy_a.cuda().type(torch.float), dummy_b.cuda().type(torch.float), padding=1)\n\n batch_size, n_input_channel, n_output_channel, img_hw, filter_hw = 64, 64, 64, 8, 3\n x_shape = [batch_size, n_input_channel, img_hw, img_hw]\n w_shape = [n_output_channel, n_input_channel, filter_hw, filter_hw]\n with NamedTimerInstance(\"Warming up Cpu\"):\n dummy_a = get_random_uniform(SecretConfig.PrimeLimit, x_shape).type(SecretConfig.dtypeForSave)\n dummy_b = get_random_uniform(SecretConfig.PrimeLimit, w_shape).type(SecretConfig.dtypeForSave)\n F.conv2d(dummy_a.type(SecretConfig.dtypeForCpuOp), dummy_b.type(SecretConfig.dtypeForCpuOp),\n padding=1)\n\n with NamedTimerInstance(\"Warming up CppExtension\"):\n GlobalCppExtension.get_conv2d_cudnn()"},{"identifier":"SecretNeuralNetwork","path":"python/sgx_net.py","snippet":"class SecretNeuralNetwork(TensorLoader):\n nn_name = None\n layers = None\n\n def __init__(self, sid, nn_name):\n super().__init__()\n self.sid = sid\n self.init(start_enclave=False)\n self.nn_name = nn_name\n\n def set_layers(self, layers):\n self.layers = layers\n\n if not isinstance(self.layers[0], SecretInputLayer):\n raise ValueError(\"The first layer has to be input layer\")\n if not isinstance(self.layers[-1], SecretOutputLayer):\n raise ValueError(\"The last layer has to be output layer\")\n \n for i in range(len(self.layers) - 1):\n PrevLayer = self.layers[i]\n NextLayer = self.layers[i + 1]\n if not PrevLayer.manually_register_next:\n PrevLayer.register_next_layer(NextLayer)\n if not NextLayer.manually_register_prev:\n NextLayer.register_prev_layer(PrevLayer)\n\n \n for layer in self.layers:\n # print(f\"Init_shape/link layer {layer.LayerName}\")\n layer.set_eid(self.get_eid())\n layer.init_shape()\n # if layer.LayerName in [\"Layer1.0.weighted_add\", \"Layer1.0.proxies.0.bn\"]:\n # st()\n layer.link_tensors()\n # print(layer.LayerName)\n # layer.print_tensor_link_relation()\n # if layer.LayerName in [\"Layer1.0.weighted_add\", \"Layer1.0.proxies.0.bn\"]:\n # st()\n \n for idx, layer in enumerate(self.layers):\n # print(f\"Init layer {layer.LayerName}\")\n # if layer.LayerName == \"Layer1.0.main.relu2\":\n # st()\n layer.init(start_enclave=False)\n # if idx > 3:\n # print(layer.LayerName, self.layers[4].get_cpu(\"input\").shape, self.layers[4].PrevLayer.LayerName)\n\n def execute_for_each_layer(self, func, reverse=False):\n layers = self.layers[::-1] if reverse else self.layers\n for layer in layers:\n # print(f\"SID: {self.sid} {layer.LayerName}, {func}\")\n if self.sid == 2 and layer.IsDummyForS2:\n continue\n # print(\"Processing \", layer.LayerName)\n func(layer)\n \n # st()\n\n def classifier_output(self):\n with NamedTimerInstance(f\"S{self.sid}: {self.nn_name} classifier_output\"):\n self.forward()\n if self.sid == 2:\n return\n # layers: input_layer, ..., fc_layer, output_layer\n last_fc = self.layers[-2]\n last_fc.transfer_enclave_to_cpu(\"output\")\n outputs = last_fc.get_cpu(\"output\")\n _, predicted = torch.max(outputs.data, 1)\n return predicted\n\n def get_loss(self):\n return self.layers[-1].get_loss()\n\n def forward_with_time(self):\n def run_forward(layer):\n layer.forward()\n t0 = time()\n with NetworkNamedTimerInstance(f\"S{self.sid}: {self.nn_name} Forward\"):\n self.execute_for_each_layer(run_forward)\n t1 = time()\n # time in ms\n elapse_time = (t1 - t0) * (10 ** 3) \n return elapse_time\n\n def forward(self):\n def run_forward(layer):\n layer.forward()\n with NetworkNamedTimerInstance(f\"S{self.sid}: {self.nn_name} Forward\"):\n self.execute_for_each_layer(run_forward)\n\n def backward(self):\n def run_backward(layer):\n layer.backward()\n with NamedTimerInstance(f\"S{self.sid}: {self.nn_name} Backward\"):\n self.execute_for_each_layer(run_backward, reverse=True)\n\n def plain_forward(self):\n with NetworkNamedTimerInstance(f\"S{self.sid}: {self.nn_name} PlainForward\"):\n self.execute_for_each_layer(lambda x: x.plain_forward())\n\n def plain_backward(self):\n with NetworkNamedTimerInstance(f\"S{self.sid}: {self.nn_name} PlainBackward\"):\n self.execute_for_each_layer(lambda x: x.plain_backward(), reverse=True)\n\n def show_plain_error(self):\n self.execute_for_each_layer(lambda x: x.show_plain_error())"},{"identifier":"SgdOptimizer","path":"python/sgx_net.py","snippet":"class SgdOptimizer(TensorLoader):\n def __init__(self, sid):\n super().__init__()\n self.sid = sid\n self.learning_rate = 0.05\n self.momentum = 0.9\n self.weight_decay = 5e-4\n self.momentum_init_flags = defaultdict(lambda: False)\n self.ideal_momentum_buf = {}\n\n self.lr_gamma = 0.5\n self.lr_step = 30\n self.step_counter = 0\n\n self.layers = None\n\n def set_layers(self, layers):\n self.layers = layers\n\n def generate_tensor_name_list(self, force=False):\n # Run if forced or self.tensor_name_list is not generated\n if not force and self.tensor_name_list:\n return\n if self.sid == 2:\n return\n\n self.tensor_name_list = []\n for layer in self.layers:\n for (DerName, ParamName, shape) in layer.LearnableParamsList:\n self.tensor_name_list.append((ParamName + \"Momentum\", shape, None))\n\n def update_params(self, test_with_ideal=False):\n if self.sid == 2:\n return\n for layer in self.layers:\n self.update_params_in_layer(layer, test_with_ideal=test_with_ideal)\n\n def update_params_in_layer(self, layer, test_with_ideal=False):\n # ref: https://github.com/pytorch/pytorch/blob/master/torch/optim/sgd.py\n if layer.LearnableParamsList is None:\n return\n\n task_ids = []\n for (der_name, param_name, shape) in layer.LearnableParamsList:\n momentum_name = param_name + \"Momentum\"\n global_momentum_name = layer.name_modifier(momentum_name)\n\n if layer.StoreInEnclave:\n if test_with_ideal:\n ideal_p, ideal_momentum = self.ideal_update_params_with_name(layer, der_name, param_name, shape)\n first_momentum = not self.momentum_init_flags[global_momentum_name]\n if first_momentum:\n # print(\"FIRST MOMENTUM\")\n self.momentum_init_flags[global_momentum_name] = True\n layer.init_enclave_tensor(momentum_name, shape)\n task_id = layer.sgd_update(param_name=param_name, grad_name=der_name, momentum_name=momentum_name,\n lr=self.learning_rate, momentum=self.momentum,\n weight_decay=self.weight_decay,\n first_momentum=first_momentum, is_async=True)\n if test_with_ideal:\n while not self.get_task_status(task_id):\n pass\n layer.generate_cpu_tensor(momentum_name, shape)\n layer.transfer_enclave_to_cpu(momentum_name)\n layer.transfer_enclave_to_cpu(param_name)\n param_err = compare_expected_actual(ideal_p, layer.get_cpu(param_name), get_relative=True)\n print(f\"S{self.sid}: {layer.LayerName} Param Error: {param_err}\")\n momentum_err = compare_expected_actual(ideal_momentum, layer.get_cpu(momentum_name), get_relative=True)\n print(f\"S{self.sid}: {layer.LayerName} Momentum Error: {momentum_err}\")\n else:\n task_ids.append(task_id)\n else:\n DerCpu = layer.get_cpu(der_name)\n ParamsCpu = layer.get_cpu(param_name)\n\n if test_with_ideal:\n ideal_p, ideal_momentum = self.ideal_update_params_with_name(layer, der_name, param_name, shape)\n\n DerCpu.add_(self.weight_decay, ParamsCpu)\n\n if not self.momentum_init_flags[global_momentum_name]:\n self.momentum_init_flags[global_momentum_name] = True\n layer.generate_cpu_tensor(momentum_name, shape)\n layer.get_cpu(momentum_name).copy_(DerCpu)\n MomentumCpu = layer.get_cpu(momentum_name)\n else:\n MomentumCpu = layer.get_cpu(momentum_name)\n MomentumCpu.mul_(self.momentum).add_(1, DerCpu)\n\n ParamsCpu.add_(-self.learning_rate, MomentumCpu)\n\n if test_with_ideal:\n param_err = compare_expected_actual(ideal_p, layer.get_cpu(param_name), get_relative=True)\n print(f\"S{self.sid}: {layer.LayerName} Param Error: {param_err}\")\n momentum_err = compare_expected_actual(ideal_momentum, layer.get_cpu(momentum_name), get_relative=True)\n print(f\"S{self.sid}: {layer.LayerName} Momentum Error: {momentum_err}\")\n\n # Wait for all tasks to be finished\n for task_id in task_ids:\n while not self.get_task_status(task_id):\n pass\n\n def ideal_update_params_with_name(self, layer, der_name, param_name, shape):\n weight_decay = self.weight_decay\n momentum = self.momentum\n dampening = 0\n nesterov = False\n lr = self.learning_rate\n\n global_momentum_name = layer.name_modifier(param_name + 'Momentum')\n\n if layer.StoreInEnclave:\n layer.transfer_enclave_to_cpu(der_name)\n layer.transfer_enclave_to_cpu(param_name)\n d_p = torch.clone(layer.get_cpu(der_name)).detach()\n p = torch.clone(layer.get_cpu(param_name)).detach()\n\n if weight_decay != 0:\n d_p.add_(weight_decay, p)\n if global_momentum_name not in self.ideal_momentum_buf:\n buf = self.ideal_momentum_buf[global_momentum_name] = torch.clone(d_p).detach()\n else:\n buf = self.ideal_momentum_buf[global_momentum_name]\n buf.mul_(momentum).add_(1 - dampening, d_p)\n if nesterov:\n d_p = d_p.add(momentum, buf)\n else:\n d_p = buf\n p.add_(-lr, d_p)\n\n return p, buf"},{"identifier":"SGXLinearBase","path":"python/layers/sgx_linear_base.py","snippet":"class SGXLinearBase(SecretLayerBase):\n batch_size = None\n InputShape = None\n WeightShape = None\n OutputShape = None\n\n def __init__(\n self, sid, LayerName, EnclaveMode, batch_size, n_output_features, \n n_input_features=None, is_enclave_mode=False, link_prev=True, link_next=True,\n manually_register_prev=False, manually_register_next=False\n ):\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\n\n self.ForwardFuncName = \"SGXLinear\"\n self.BackwardFuncName = \"DerSGXLinear\"\n self.PlainFunc = torch.nn.Linear\n self.is_enclave_mode = is_enclave_mode\n self.n_output_features = n_output_features\n self.n_input_features = n_input_features\n self.batch_size = batch_size\n\n if EnclaveMode is ExecutionModeOptions.CPU or EnclaveMode is ExecutionModeOptions.GPU:\n self.ForwardFunc = torch.nn.Linear\n # if self.is_enclave_mode:\n # self.StoreInEnclave = True\n # else:\n # self.ForwardFunc = torch.nn.Linear\n # self.StoreInEnclave = False\n\n def init_shape(self):\n self.WeightShape = self.DerWeightShape = [self.n_output_features, self.n_input_features]\n self.BiasShape = self.DerBiasShape = [self.n_output_features]\n if self.n_input_features is None:\n self.InputShape = self.PrevLayer.get_output_shape()\n else:\n self.InputShape = self.DerInputShape = [self.batch_size, self.n_input_features]\n self.OutputShape = self.DerOutputShape = [self.batch_size, self.n_output_features]\n self.LearnableParamsList = [\n LearnableParamTuple(dw_name=\"DerWeight\", w_name=\"weight\", shape=self.WeightShape),\n LearnableParamTuple(dw_name=\"DerBias\", w_name=\"bias\", shape=self.WeightShape),\n ]\n\n def init(self, start_enclave=True):\n TensorLoader.init(self, start_enclave)\n \n if self.EnclaveMode is ExecutionModeOptions.Enclave:\n self.PlainFunc = self.PlainFunc(self.n_input_features, self.n_output_features)\n self.get_cpu(\"weight\").data.copy_(self.PlainFunc.weight.data)\n self.get_cpu(\"bias\").data.copy_(self.PlainFunc.bias.data)\n self.transfer_cpu_to_enclave(\"weight\")\n self.transfer_cpu_to_enclave(\"bias\")\n self.sgx_linear_init(\n self.LayerName,\n \"input\", \"output\", \"weight\", \"bias\",\n # \"DerInput\", \"DerOutput\", \"DerWeight\", \"DerBias\",\n self.batch_size, self.n_input_features, self.n_output_features)\n else:\n self.ForwardFunc = self.ForwardFunc(self.n_input_features, self.n_output_features)\n self.PlainFunc = self.PlainFunc(self.n_input_features, self.n_output_features)\n self.ForwardFunc.weight.data.copy_(self.PlainFunc.weight.data)\n self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\n if self.EnclaveMode is ExecutionModeOptions.CPU:\n self.set_cpu(\"weight\", list(self.ForwardFunc.parameters())[0].data)\n self.set_cpu(\"bias\", list(self.ForwardFunc.parameters())[1].data)\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\n self.set_gpu(\"weight\", list(self.ForwardFunc.parameters())[0].data)\n self.set_gpu(\"bias\", list(self.ForwardFunc.parameters())[1].data)\n self.ForwardFunc.cuda()\n # print(\"======== SGX linear init finish\")\n\n def link_tensors(self):\n super().link_tensors()\n\n def init_params(self):\n cpu_w = torch.zeros(self.w_shape)\n torch.nn.init.xavier_normal_(cpu_w, 1)\n self.set_tensor_cpu_enclave(\"weight\", cpu_w)\n cpu_b = torch.zeros(self.b_shape)\n torch.nn.init.constant_(cpu_b, 0)\n self.set_tensor_cpu_enclave(\"bias\", cpu_b)\n\n def get_output_shape(self):\n return self.OutputShape\n\n def inject_params(self, params):\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\n cpu_w = self.get_cpu(\"weight\")\n cpu_w.copy_(params.weight.data)\n self.transfer_cpu_to_enclave(\"weight\")\n cpu_b = self.get_cpu(\"bias\")\n cpu_b.copy_(params.bias.data)\n self.transfer_cpu_to_enclave(\"bias\")\n elif self.EnclaveMode is ExecutionModeOptions.CPU:\n cpu_w = self.get_cpu(\"weight\")\n cpu_w.copy_(params.weight.data)\n cpu_b = self.get_cpu(\"bias\")\n cpu_b.copy_(params.bias.data)\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\n cpu_w = self.get_gpu(\"weight\")\n cpu_w.copy_(params.weight.data)\n cpu_b = self.get_gpu(\"bias\")\n cpu_b.copy_(params.bias.data)\n\n def inject_to_plain(self, plain_layer: torch.nn.Module) -> None:\n self.make_sure_cpu_is_latest(\"weight\")\n plain_layer.weight.data.copy_(self.get_cpu(\"weight\"))\n self.make_sure_cpu_is_latest(\"bias\")\n plain_layer.bias.data.copy_(self.get_cpu(\"bias\"))\n\n def generate_tensor_name_list(self, force=False):\n if not force and self.tensor_name_list:\n return\n NeededTensorNames = [(\"output\", self.OutputShape, None),\n # (\"DerInput\", self.InputShape, None),\n (\"input\", self.InputShape, None),\n # (\"DerOutput\", self.OutputShape, None),\n (\"weight\", self.WeightShape, None),\n (\"bias\", self.BiasShape, None),\n ]\n\n self.tensor_name_list = NeededTensorNames\n\n def forward(self):\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} Forward\", verbose_level=VerboseLevel.LAYER):\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\n self.forward_tensor_transfer()\n self.sgx_linear_forward(self.LayerName)\n elif self.EnclaveMode == ExecutionModeOptions.CPU:\n self.forward_tensor_transfer()\n self.requires_grad_on_cpu(\"input\")\n self.ForwardFunc.weight.data.copy_(self.get_cpu(\"weight\"))\n self.ForwardFunc.bias.data.copy_(self.get_cpu(\"bias\"))\n self.set_cpu(\"output\", self.ForwardFunc(self.get_cpu(\"input\")))\n elif self.EnclaveMode == ExecutionModeOptions.GPU:\n self.forward_tensor_transfer()\n self.ForwardFunc.weight.data.copy_(self.get_gpu(\"weight\"))\n self.ForwardFunc.bias.data.copy_(self.get_gpu(\"bias\"))\n self.set_gpu(\"output\", self.ForwardFunc(self.get_gpu(\"input\").type(SecretConfig.dtypeForCpuOp)))\n\n def plain_forward(self, NeedBackward=False):\n if self.is_enclave_mode:\n self.make_sure_cpu_is_latest(\"input\")\n self.make_sure_cpu_is_latest(\"weight\")\n self.make_sure_cpu_is_latest(\"bias\")\n # self.requires_grad_on_cpu(\"input\")\n self.PlainFunc.weight.data.copy_(self.get_cpu(\"weight\"))\n self.PlainFunc.bias.data.copy_(self.get_cpu(\"bias\"))\n else:\n self.make_sure_cpu_is_latest(\"input\")\n self.requires_grad_on_cpu(\"input\")\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} PlainForward\"):\n # torch.set_num_threads(1)\n self.PlainForwardResult = self.PlainFunc(self.get_cpu(\"input\"))\n # torch.set_num_threads(4)\n\n def show_plain_error_forward(self):\n self.make_sure_cpu_is_latest(\"output\")\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\"output\"), get_relative=True)\n print(f\"S{self.sid}: {self.LayerName} Forward Error: {err}\")"},{"identifier":"SGXConvBase","path":"python/layers/sgx_conv_base.py","snippet":"class SGXConvBase(SecretLayerBase):\n batch_size = None\n pytorch_x_shape, sgx_x_shape = None, None\n pytorch_w_shape, sgx_w_shape = None, None\n bias_shape = None\n pytorch_y_shape, sgx_y_shape = None, None\n\n def __init__(\n self, sid, LayerName, EnclaveMode,\n n_output_channel, filter_hw, stride, padding, batch_size=None, n_input_channel=None,\n img_hw=None, bias=True,\n is_enclave_mode=False, link_prev=True, link_next=True, manually_register_prev=False, manually_register_next=False\n ):\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\n\n self.ForwardFuncName = \"SGXConv\"\n self.BackwardFuncName = \"DerSGXConv\"\n self.PlainFunc = torch.nn.Conv2d\n self.is_enclave_mode = is_enclave_mode\n self.batch_size = batch_size\n self.n_input_channel = n_input_channel\n self.n_output_channel = n_output_channel\n self.img_hw = img_hw\n self.filter_hw = filter_hw\n self.padding = padding\n self.stride = stride\n self.bias = bias\n\n if EnclaveMode is ExecutionModeOptions.CPU or EnclaveMode is ExecutionModeOptions.GPU:\n self.ForwardFunc = torch.nn.Conv2d\n\n # --------------\n # Add BIAS!!!!!\n # --------------\n\n def init_shape(self):\n if self.batch_size is None and self.PrevLayer is not None:\n self.pytorch_x_shape = self.PrevLayer.get_output_shape()\n self.batch_size, self.n_input_channel, self.img_hw, _ = self.pytorch_x_shape\n else:\n self.pytorch_x_shape = [self.batch_size, self.n_input_channel, self.img_hw, self.img_hw]\n # print(self.LayerName)\n # st()\n # BHWC\n self.sgx_x_shape = [self.pytorch_x_shape[0], self.pytorch_x_shape[2], self.pytorch_x_shape[3], self.pytorch_x_shape[1]]\n # pytorch weight is out * in * h * w\n self.pytorch_w_shape = [self.n_output_channel, self.n_input_channel, self.filter_hw, self.filter_hw]\n # w shape is in * w * h * out, the transpose of out * h * w * in\n self.sgx_w_shape = [self.n_output_channel, self.filter_hw, self.filter_hw, self.n_input_channel]\n # BCHW\n self.pytorch_y_shape = calc_conv2d_output_shape_stride(self.pytorch_x_shape, self.pytorch_w_shape, self.padding, self.stride)\n # BHWC\n self.sgx_y_shape = [self.pytorch_y_shape[0], self.pytorch_y_shape[2], self.pytorch_y_shape[3], self.pytorch_y_shape[1]]\n self.bias_shape = [self.n_output_channel]\n\n # print(\n # f\"Init_shape pytorch_input {self.pytorch_x_shape}, sgx_input {self.sgx_x_shape}, \"\n # f\"pytorch_output {self.pytorch_y_shape}, sgx_output {self.sgx_y_shape}, \"\n # f\"pytorch_weight {self.pytorch_w_shape}, sgx_weight {self.sgx_w_shape}, \"\n # f\"bias {self.bias_shape}\"\n # )\n\n self.LearnableParamsList = [\n LearnableParamTuple(dw_name=\"DerWeight\", w_name=\"weight\", shape=self.sgx_w_shape),\n LearnableParamTuple(dw_name=\"DerBias\", w_name=\"bias\", shape=self.bias_shape),\n ]\n\n def init(self, start_enclave=True):\n # print(f\"Weight shape {self.sgx_w_shape}\")\n TensorLoader.init(self, start_enclave)\n \n \n if self.EnclaveMode is ExecutionModeOptions.Enclave:\n self.PlainFunc = self.PlainFunc(\n self.n_input_channel, self.n_output_channel, self.filter_hw,\n self.stride, self.padding, bias=self.bias)\n weight_pytorch_form = self.PlainFunc.weight.data\n weight_tf_form = self.weight_pytorch2tf(weight_pytorch_form)\n self.get_cpu(\"weight\").data.copy_(weight_tf_form)\n self.transfer_cpu_to_enclave(\"weight\")\n # Bias\n if self.bias:\n bias_data = self.PlainFunc.bias.data\n else:\n bias_data = torch.zeros(self.bias_shape)\n self.get_cpu(\"bias\").data.copy_(bias_data)\n self.transfer_cpu_to_enclave(\"bias\")\n self.sgx_conv_init(\n self.LayerName,\n \"sgx_input\", \"sgx_output\", \"weight\", \"bias\",\n # \"sgx_DerInput\", \"sgx_DerOutput\", \"DerWeight\", \"DerBias\",\n # \"input\", \"output\", \"weight\", \n # \"DerInput\", \"DerOutput\", \"DerWeight\", \n self.batch_size, self.img_hw, self.img_hw, self.n_input_channel, \n self.pytorch_y_shape[2], self.pytorch_y_shape[3], self.n_output_channel, \n self.filter_hw, self.padding, self.stride)\n elif self.EnclaveMode in[ ExecutionModeOptions.CPU, ExecutionModeOptions.GPU]:\n self.ForwardFunc = self.ForwardFunc(\n self.n_input_channel, self.n_output_channel, self.filter_hw,\n self.stride, self.padding, bias=self.bias)\n self.PlainFunc = self.PlainFunc(\n self.n_input_channel, self.n_output_channel, self.filter_hw,\n self.stride, self.padding, bias=self.bias)\n self.ForwardFunc.weight.data.copy_(self.PlainFunc.weight.data)\n weight_pytorch_form = list(self.ForwardFunc.parameters())[0].data\n weight_tf_form = self.weight_pytorch2tf(weight_pytorch_form)\n if self.EnclaveMode is ExecutionModeOptions.CPU:\n self.set_cpu(\"weight\", weight_tf_form)\n if self.bias:\n self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\n bias_data = self.PlainFunc.bias.data\n self.set_cpu(\"bias\", bias_data)\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\n self.set_gpu(\"weight\", weight_tf_form)\n if self.bias:\n self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\n bias_data = self.PlainFunc.bias.data\n self.set_gpu(\"bias\", bias_data)\n self.ForwardFunc.cuda()\n\n\n def link_tensors(self):\n super().link_tensors()\n\n def init_params(self):\n cpu_w = torch.zeros(self.sgx_w_shape)\n torch.nn.init.xavier_normal_(cpu_w, 1)\n self.set_tensor_cpu_gpu_enclave(\"weight\", cpu_w)\n\n def get_output_shape(self):\n return self.pytorch_y_shape\n \n def weight_pytorch2tf(self, weight_pytorch_form):\n # weight_pytorch_form is out * in * h * w\n # out * (h * w) * in, \n # h and w dont transpose\n # weight_tf_form = weight_pytorch_form.permute(1,3,2,0).contiguous()\n weight_tf_form = weight_pytorch_form.permute(0,2,3,1).contiguous()\n return weight_tf_form\n\n def weight_tf2pytorch(self, weight_tf_form):\n # weight_tf_form is out * (h * w) * in, the transpose of out * (h * w) * in\n # out * in * h * w\n # h and w dont transpose\n # weight_pytorch_form = weight_tf_form.permute(3, 0, 2, 1).contiguous()\n weight_pytorch_form = weight_tf_form.permute(0,3,1,2).contiguous()\n return weight_pytorch_form\n\n def feature_pytorch2tf(self, tensor_pytorch_form):\n # tensor_pytorch_form is b * in * h * w\n # b * h * w * in\n tensor_tf_form = tensor_pytorch_form.permute(0, 2, 3, 1).contiguous()\n return tensor_tf_form\n \n def feature_tf2pytorch(self, tensor_tf_form):\n # tensor_tf_form is b * h * w * in\n # b * in * h * w\n tensor_pytorch_form = tensor_tf_form.permute(0, 3, 1, 2).contiguous()\n return tensor_pytorch_form\n\n def inject_params(self, params):\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\n cpu_w = self.get_cpu(\"weight\")\n weight_pytorch_form = params.weight.data\n weight_tf_form = self.weight_pytorch2tf(weight_pytorch_form)\n cpu_w.copy_(weight_tf_form)\n self.transfer_cpu_to_enclave(\"weight\")\n\n # bias\n assert (\n (self.bias and params.bias is not None) or\n (not self.bias and params.bias is None)\n )\n if self.bias:\n bias_data = params.bias.data\n else:\n bias_data = torch.zeros(self.n_output_channel)\n cpu_b = self.get_cpu(\"bias\")\n cpu_b.copy_(bias_data)\n self.transfer_cpu_to_enclave(\"bias\")\n elif self.EnclaveMode is ExecutionModeOptions.CPU:\n weight_pytorch_form = params.weight.data\n weight_tf_form = self.weight_pytorch2tf(weight_pytorch_form)\n self.get_cpu(\"weight\").copy_(weight_tf_form)\n # bias\n assert (\n (self.bias and params.bias is not None) or\n (not self.bias and params.bias is None)\n )\n if self.bias:\n self.get_cpu(\"bias\").copy_(params.bias.data)\n\n # Move weight to ForwardFunc\n weight_tf_form = self.get_cpu(\"weight\")\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\n self.ForwardFunc.weight.data.copy_(weight_pytorch_form)\n\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\n weight_pytorch_form = params.weight.data\n weight_tf_form = self.weight_pytorch2tf(weight_pytorch_form)\n self.get_gpu(\"weight\").copy_(weight_tf_form)\n # bias\n assert (\n (self.bias and params.bias is not None) or\n (not self.bias and params.bias is None)\n )\n if self.bias:\n self.get_gpu(\"bias\").copy_(params.bias.data)\n\n # Move weight to ForwardFunc\n weight_tf_form = self.get_gpu(\"weight\")\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\n self.ForwardFunc.weight.data.copy_(weight_pytorch_form)\n\n\n def inject_to_plain(self, plain_layer: torch.nn.Module) -> None:\n self.make_sure_cpu_is_latest(\"weight\")\n weight_tf_form = self.get_cpu(\"weight\")\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\n plain_layer.weight.data.copy_(weight_pytorch_form)\n\n assert (\n (self.bias and plain_layer.bias is not None) or\n (not self.bias and plain_layer.bias is None)\n )\n if self.bias:\n self.make_sure_cpu_is_latest(\"bias\")\n bias_data = self.get_cpu(\"bias\")\n plain_layer.weight.data.copy_(bias_data)\n\n def generate_tensor_name_list(self, force=False):\n if not force and self.tensor_name_list:\n return\n NeededTensorNames = [(\"output\", self.pytorch_y_shape, None), (\"sgx_output\", self.sgx_y_shape, None),\n (\"DerInput\", self.pytorch_x_shape, None), (\"sgx_DerInput\", self.sgx_x_shape, None),\n (\"input\", self.pytorch_x_shape, None), (\"sgx_input\", self.sgx_x_shape, None),\n (\"DerOutput\", self.pytorch_y_shape, None), (\"sgx_DerOutput\", self.sgx_y_shape, None),\n (\"weight\", self.sgx_w_shape, None),\n (\"bias\", self.bias_shape, None),\n ]\n self.tensor_name_list = NeededTensorNames\n\n\n def forward(self):\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} Forward\", verbose_level=VerboseLevel.LAYER):\n self.forward_tensor_transfer(\"input\")\n if self.EnclaveMode == ExecutionModeOptions.Enclave:\n \n # \"input\" is pytorch form\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} Input Preprocess\", verbose_level=VerboseLevel.LAYER):\n if self.PrevLayer.EnclaveMode is ExecutionModeOptions.Enclave:\n self.transfer_enclave_to_cpu(\"input\")\n input_pytorch_form = self.get_cpu(\"input\")\n \n if torch.sum(self.get_cpu(\"input\").abs()) == 0:\n print(self.LayerName)\n raise RuntimeError(\"SGX input not load\")\n input_tf_form = self.feature_pytorch2tf(input_pytorch_form)\n self.set_cpu(\"sgx_input\", input_tf_form)\n self.transfer_cpu_to_enclave(\"sgx_input\")\n # self.forward_tensor_transfer(\"sgx_input\")\n # print(self.get_cpu(\"sgx_input\").squeeze())\n \n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} sgx_conv_forward\", verbose_level=VerboseLevel.LAYER):\n # if self.LayerName == \"Layer2.0.downsample.conv\":\n # st()\n self.sgx_conv_forward(self.LayerName)\n \n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} Output Postprocess\", verbose_level=VerboseLevel.LAYER):\n self.make_sure_cpu_is_latest(\"sgx_output\")\n output_tf_form = self.get_cpu(\"sgx_output\")\n output_pytorch_form = self.feature_tf2pytorch(output_tf_form)\n self.set_cpu(\"output\", output_pytorch_form)\n self.transfer_cpu_to_enclave(\"output\")\n elif self.EnclaveMode == ExecutionModeOptions.CPU:\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} Input Preprocess\", verbose_level=VerboseLevel.LAYER):\n self.forward_tensor_transfer()\n # self.requires_grad_on_cpu(\"input\")\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} Weight Transfer\", verbose_level=VerboseLevel.LAYER):\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} get weight_tf_form\", verbose_level=VerboseLevel.LAYER):\n weight_tf_form = self.get_cpu(\"weight\")\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} weight_tf2pytorch\", verbose_level=VerboseLevel.LAYER):\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} copy data\", verbose_level=VerboseLevel.LAYER):\n self.ForwardFunc.weight.data.copy_(weight_pytorch_form)\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} GPU conv forward\", verbose_level=VerboseLevel.LAYER):\n self.set_cpu(\"output\", self.ForwardFunc(self.get_cpu(\"input\")))\n elif self.EnclaveMode == ExecutionModeOptions.GPU:\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} Input Preprocess\", verbose_level=VerboseLevel.LAYER):\n self.forward_tensor_transfer()\n # self.requires_grad_on_cpu(\"input\")\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} Weight Transfer\", verbose_level=VerboseLevel.LAYER):\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} get weight_tf_form\", verbose_level=VerboseLevel.LAYER):\n weight_tf_form = self.get_gpu(\"weight\")\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} weight_tf2pytorch\", verbose_level=VerboseLevel.LAYER):\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} copy data\", verbose_level=VerboseLevel.LAYER):\n self.ForwardFunc.weight.data.copy_(weight_pytorch_form)\n with NamedTimerInstance(f\" S{self.sid}: {self.LayerName} GPU conv forward\", verbose_level=VerboseLevel.LAYER):\n self.set_gpu(\"output\", self.ForwardFunc(self.get_gpu(\"input\").type(SecretConfig.dtypeForCpuOp)))\n\n\n def plain_forward(self, NeedBackward=False):\n if self.EnclaveMode == ExecutionModeOptions.Enclave:\n self.make_sure_cpu_is_latest(\"input\")\n self.make_sure_cpu_is_latest(\"weight\")\n if self.bias:\n self.make_sure_cpu_is_latest(\"bias\")\n # self.requires_grad_on_cpu(\"input\")\n weight_tf_form = self.get_cpu(\"weight\")\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\n self.PlainFunc.weight.data.copy_(weight_pytorch_form)\n if self.bias:\n bias_data = self.get_cpu(\"bias\")\n self.PlainFunc.bias.data.copy_(bias_data)\n elif self.EnclaveMode in [ExecutionModeOptions.CPU, ExecutionModeOptions.GPU]:\n self.make_sure_cpu_is_latest(\"input\")\n self.requires_grad_on_cpu(\"input\")\n with NamedTimerInstance(f\"S{self.sid}: {self.LayerName} PlainForward\"):\n # torch.set_num_threads(1)\n self.PlainForwardResult = self.PlainFunc(self.get_cpu(\"input\"))\n # torch.set_num_threads(4)\n\n def show_plain_error_forward(self):\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\"output\"), get_relative=True)\n print(f\"S{self.sid}: {self.LayerName} Forward Error: {err}\")\n\n def print_connection_info(self):\n print(f\"{self.LayerName:20} shape{self.pytorch_x_shape}{' ':20} mode{self.EnclaveMode}{' ':20} input {self.PrevLayer.LayerName:20} output {self.NextLayer.LayerName:20}\")"},{"identifier":"ExecutionModeOptions","path":"python/utils/basic_utils.py","snippet":"class ExecutionModeOptions(Enum):\n Enclave = 1\n CPU = 2\n GPU = 3"},{"identifier":"Logger","path":"python/utils/logger_utils.py","snippet":"class Logger(object):\n logfile_path = \"logfile.log\"\n\n def __init__(self):\n self.terminal = sys.stdout\n self.log = open(self.logfile_path, \"a\")\n\n def reset_logfile(self, path):\n self.logfile_path = path\n self.log = open(self.logfile_path, \"a\")\n\n def write(self, message):\n self.terminal.write(message)\n self.log.write(message)\n\n def flush(self):\n #this flush method is needed for python 3 compatibility.\n #this handles the flush command by doing nothing.\n #you might want to specify some extra behavior here.\n # pass\n self.terminal.flush()\n self.log.flush()"},{"identifier":"NamedTimerInstance","path":"python/utils/timer_utils.py","snippet":"class NamedTimerInstance(object):\n def __init__(self, name, verbose_level=VerboseLevel.EVERY):\n self.name = name\n self.verbose_level = verbose_level\n\n def __enter__(self):\n return NamedTimer.start(self.name, verbose_level=self.verbose_level)\n ...\n\n def __exit__(self, *args):\n NamedTimer.end(self.name)\n ..."},{"identifier":"VerboseLevel","path":"python/utils/timer_utils.py","snippet":"class VerboseLevel(IntEnum):\n EVERY = 1\n LAYER = 2\n RUN = 3\n EPOCH = 4"},{"identifier":"NamedTimer","path":"python/utils/timer_utils.py","snippet":"class NamedTimer(object):\n __instance = None\n\n @staticmethod\n def get_instance():\n if NamedTimer.__instance is None:\n NamedTimer()\n return NamedTimer.__instance\n\n def __init__(self):\n NamedTimer.__instance = self\n self.timers = {}\n self.verbose_level = VerboseLevel.EVERY\n\n @staticmethod\n def start_timer(name, **kwargs):\n NamedTimer.get_instance().timers[name] = Timer(name, **kwargs)\n return NamedTimer.get_instance().timers[name]\n\n @staticmethod\n def start(name, **kwargs):\n return NamedTimer.get_instance().start_timer(name, **kwargs)\n\n @staticmethod\n def end_timer(name, **kwargs):\n NamedTimer.get_instance().timers[name].end(**kwargs)\n\n @staticmethod\n def end(name, tmp_name=None):\n # print(NamedTimer.get_instance().timers[name].verbose_level, NamedTimer.get_instance().verbose_level)\n NamedTimer.get_instance().end_timer(name, tmp_name=tmp_name)\n\n @staticmethod\n def set_verbose_level(verbose_level):\n if not isinstance(verbose_level, VerboseLevel):\n raise ValueError(\"Please set an enum from VerboseLevel\")\n NamedTimer.get_instance().verbose_level = verbose_level"},{"identifier":"compare_expected_actual","path":"python/utils/torch_utils.py","snippet":"def compare_expected_actual(expected, actual, show_where_err=False, get_relative=False, verbose=False, show_values=False):\n def purify(x):\n # return torch.tensor(x)\n res = x\n # if not (isinstance(x, torch.Tensor) or isinstance(x, torch.Variable)):\n if not (isinstance(x, torch.Tensor) ):\n res = torch.tensor(x)\n # return x.detach().numpy()\n return res.type(torch.float).to(\"cpu\")\n expected = purify(expected)\n actual = purify(actual)\n\n if show_values:\n print(\"expected:\", expected[0, 0])\n print(\"actual:\", actual[0, 0])\n\n avg_abs_diff = torch.mean(torch.abs(expected - actual)).item()\n res = avg_abs_diff\n\n if show_where_err:\n show_indices = torch.abs(expected - actual) / torch.abs(expected) > 0.5\n # show_indices = (expected != actual)\n print(\"error indices: \", np.where(show_indices.cpu()))\n print(\"expected values:\", expected[show_indices])\n print(\"difference:\", (expected - actual)[show_indices])\n\n if get_relative:\n tmp_expected, tmp_actual = expected[expected != 0], actual[expected != 0]\n relative_diff = torch.abs(tmp_expected - tmp_actual) / torch.abs(tmp_expected)\n relative_avg_diff = torch.mean(torch.abs(tmp_actual - tmp_expected)) / torch.mean(torch.abs(tmp_expected))\n Error = namedtuple(\"Error\", (\"AvgAbsDiff\", \"RelAvgDiff\", \"AvgRelDiff\", \"StdRelDiff\"))\n res = Error(avg_abs_diff, relative_avg_diff.item(), torch.mean(relative_diff).item(), torch.std(relative_diff).item())\n\n if verbose:\n print(res)\n\n return res"}],"string":"[\n {\n \"identifier\": \"register_layer\",\n \"path\": \"python/common_net.py\",\n \"snippet\": \"def register_layer(layer, name):\\n layer.register_forward_hook(hooking_layer(name))\\n layer.register_backward_hook(hooking_layer_backward(name))\\n layer_names.append(name)\"\n },\n {\n \"identifier\": \"register_weight_layer\",\n \"path\": \"python/common_net.py\",\n \"snippet\": \"def register_weight_layer(layer, name):\\n register_layer(layer, name)\\n layer_weight[name] = layer.weight\\n linear_layer_names.append(name)\"\n },\n {\n \"identifier\": \"get_layer_weight\",\n \"path\": \"python/common_net.py\",\n \"snippet\": \"def get_layer_weight(name):\\n return layer_weight[name]\"\n },\n {\n \"identifier\": \"get_layer_input\",\n \"path\": \"python/common_net.py\",\n \"snippet\": \"def get_layer_input(name):\\n return layer_input[name]\"\n },\n {\n \"identifier\": \"get_layer_weight_grad\",\n \"path\": \"python/common_net.py\",\n \"snippet\": \"def get_layer_weight_grad(name):\\n return layer_weight[name].grad.data\"\n },\n {\n \"identifier\": \"get_layer_output\",\n \"path\": \"python/common_net.py\",\n \"snippet\": \"def get_layer_output(name):\\n return layer_output[name]\"\n },\n {\n \"identifier\": \"get_layer_output_grad\",\n \"path\": \"python/common_net.py\",\n \"snippet\": \"def get_layer_output_grad(name):\\n return layer_output_grad[name]\"\n },\n {\n \"identifier\": \"get_layer_input_grad\",\n \"path\": \"python/common_net.py\",\n \"snippet\": \"def get_layer_input_grad(name):\\n return layer_input_grad[name]\"\n },\n {\n \"identifier\": \"GlobalTensor\",\n \"path\": \"python/enclave_interfaces.py\",\n \"snippet\": \"class GlobalTensor(object):\\n cpu_tensor = {}\\n gpu_tensors = {}\\n encrypted_tensors = {}\\n LinkedTags = {}\\n InverseLinkedTags = {}\\n IsInitEnclaveTensor = {}\\n EnclaveInterface = None\\n eid = None\\n is_init_global_tensor = False\\n\\n @staticmethod\\n def init():\\n if GlobalTensor.is_init_global_tensor:\\n return\\n GlobalTensor.EnclaveInterface = EnclaveInterface()\\n GlobalTensor.EnclaveInterface.init_enclave()\\n GlobalTensor.is_init_global_tensor = True\\n\\n @staticmethod\\n def destroy():\\n GlobalTensor.EnclaveInterface.destroy_enclave()\\n\\n GlobalTensor.cpu_tensor = {}\\n GlobalTensor.gpu_tensors = {}\\n GlobalTensor.encrypted_tensors = {}\\n GlobalTensor.LinkedTags = {}\\n GlobalTensor.InverseLinkedTags = {}\\n GlobalTensor.IsInitEnclaveTensor = {}\\n GlobalTensor.EnclaveInterface = None\\n GlobalTensor.eid = None\\n GlobalTensor.is_init_global_tensor = False\\n\\n\\n @staticmethod\\n def get_eid():\\n return GlobalTensor.EnclaveInterface.get_eid()\\n\\n @staticmethod\\n def link_tags(tag1, tag2):\\n if tag1 == tag2:\\n return\\n\\n friends = []\\n\\n def add_friends(tag):\\n nonlocal friends\\n if tag in GlobalTensor.LinkedTags:\\n its_leader_tag = GlobalTensor.LinkedTags[tag]\\n if its_leader_tag in GlobalTensor.InverseLinkedTags:\\n friends += GlobalTensor.InverseLinkedTags.pop(its_leader_tag)\\n else:\\n friends += [tag]\\n\\n add_friends(tag1)\\n add_friends(tag2)\\n leader_tag = min(friends)\\n\\n GlobalTensor.InverseLinkedTags[leader_tag] = friends\\n for t in friends:\\n if t in GlobalTensor.IsInitEnclaveTensor:\\n raise ValueError(\\\"Tags must linked before tensor initialization\\\")\\n GlobalTensor.LinkedTags[t] = leader_tag\\n\\n @staticmethod\\n def get_remapped_tags(tag):\\n return GlobalTensor.LinkedTags[tag] if tag in GlobalTensor.LinkedTags else tag\\n\\n @staticmethod\\n def set_cpu(tag, tensor):\\n GlobalTensor.cpu_tensor[tag] = tensor.to(torch.device(\\\"cpu\\\"))\\n\\n @staticmethod\\n def set_gpu(tag, tensor):\\n GlobalTensor.gpu_tensors[tag] = tensor\\n\\n @staticmethod\\n def set_encrypted(tag, tensor):\\n GlobalTensor.encrypted_tensors[tag] = tensor\\n\\n @staticmethod\\n def get_cpu(tag):\\n return GlobalTensor.cpu_tensor[tag]\\n\\n @staticmethod\\n def get_gpu(tag):\\n return GlobalTensor.gpu_tensors[tag]\\n\\n @staticmethod\\n def get_encryption(tag):\\n return GlobalTensor.encrypted_tensors[tag]\\n\\n @staticmethod\\n def init_enclave_tensor(tag, size):\\n size = list(size)\\n if len(size) < 4:\\n size = [1] * (4 - len(size)) + size\\n remapped_tag = GlobalTensor.get_remapped_tags(tag)\\n if remapped_tag in GlobalTensor.IsInitEnclaveTensor:\\n return\\n else:\\n GlobalTensor.IsInitEnclaveTensor[remapped_tag] = True\\n eid = GlobalTensor.get_eid()\\n GlobalTensor.EnclaveInterface.lib.InitTensor(eid, remapped_tag, size[0], size[1], size[2], size[3])\\n\\n @staticmethod\\n def init_encrypted_tensor(tag, shape):\\n GlobalTensor.encrypted_tensors[GlobalTensor.get_remapped_tags(tag)] = \\\\\\n GlobalTensor.EnclaveInterface.create_encrypt_torch(shape)\"\n },\n {\n \"identifier\": \"SecretBatchNorm2dLayer\",\n \"path\": \"python/layers/batch_norm_2d.py\",\n \"snippet\": \"class SecretBatchNorm2dLayer(SecretActivationLayer):\\n # https://pytorch.org/docs/stable/nn.html#batchnorm2d\\n\\n BatchSize = None\\n NumChannel = None\\n ImgH = None\\n ImgW = None\\n WeightShape = None\\n def __init__(\\n self, sid, LayerName, EnclaveMode, link_prev=True, link_next=True,\\n manually_register_prev=False, manually_register_next=False, merge_own_tensors=False\\n ):\\n \\n super().__init__(\\n sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next, merge_own_tensors\\n )\\n \\n self.ForwardFuncName = \\\"BatchNorm2d\\\"\\n self.BackwardFuncName = \\\"DerBatchNorm2d\\\"\\n self.PlainFunc = torch.nn.BatchNorm2d\\n self.IsAffine = True\\n self.momentum = 0.1\\n self.IsCumulative = (self.momentum is None)\\n self.epsilon = 1e-5\\n\\n if EnclaveMode is ExecutionModeOptions.CPU or EnclaveMode is ExecutionModeOptions.GPU:\\n self.ForwardFunc = torch.nn.BatchNorm2d\\n # if self.is_enclave_mode:\\n # self.StoreInEnclave = True\\n # else:\\n # self.ForwardFunc = torch.nn.BatchNorm2d\\n # self.StoreInEnclave = False\\n \\n\\n def init_shape(self):\\n self.InputShape = self.PrevLayer.get_output_shape()\\n self.OutputShape = self.InputShape\\n self.BatchSize, self.NumChannel, self.ImgH, self.ImgW = self.InputShape\\n self.WeightShape = [self.NumChannel]\\n self.LearnableParamsList = [\\n LearnableParamTuple(dw_name=\\\"DerWeight\\\", w_name=\\\"weight\\\", shape=self.WeightShape),\\n LearnableParamTuple(dw_name=\\\"DerBias\\\", w_name=\\\"bias\\\", shape=self.WeightShape),\\n ]\\n \\n\\n # def init(self, start_enclave=True):\\n \\n # if self.sid == 2:\\n # return\\n # TensorLoader.init(self, start_enclave)\\n\\n # if self.is_enclave_mode:\\n # self.PlainFunc = self.PlainFunc(self.InputShape[1])\\n # self.PlainFunc.eval()\\n # self.get_cpu(\\\"weight\\\").data.copy_(self.PlainFunc.weight.data)\\n # self.get_cpu(\\\"bias\\\").data.copy_(self.PlainFunc.bias.data)\\n # self.get_cpu(\\\"RunMean\\\").data.copy_(self.PlainFunc.running_mean.data)\\n # # inject sqrt(running_var) instead of running_var for precision\\n # self.get_cpu(\\\"RunVar\\\").data.copy_(self.PlainFunc.running_var.data)\\n # self.transfer_cpu_to_enclave(\\\"weight\\\")\\n # self.transfer_cpu_to_enclave(\\\"bias\\\")\\n # self.transfer_cpu_to_enclave(\\\"RunMean\\\")\\n # self.transfer_cpu_to_enclave(\\\"RunVar\\\")\\n # self.batchnorm_init(\\n # self.LayerName,\\n # \\\"input\\\", \\\"output\\\", \\\"weight\\\", \\\"bias\\\",\\n # \\\"DerInput\\\", \\\"DerOutput\\\", \\\"DerWeight\\\", \\\"DerBias\\\",\\n # \\\"RunMean\\\", \\\"RunVar\\\", \\\"CurMean\\\", \\\"CurVar\\\",\\n # \\\"mu\\\",\\n # self.BatchSize, self.NumChannel, self.ImgH, self.ImgW,\\n # int(self.IsAffine), int(self.IsCumulative), self.momentum, self.epsilon)\\n # else:\\n # self.ForwardFunc = self.ForwardFunc(self.InputShape[1])\\n # self.PlainFunc = self.PlainFunc(self.InputShape[1])\\n # self.PlainFunc.eval()\\n # self.ForwardFunc.weight.data.copy_(self.PlainFunc.weight.data)\\n # self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\\n # self.ForwardFunc.running_mean.data.copy_(self.PlainFunc.running_mean.data)\\n # self.ForwardFunc.running_var.data.copy_(self.PlainFunc.running_var.data)\\n # self.set_cpu(\\\"weight\\\", list(self.ForwardFunc.parameters())[0].data)\\n # self.set_cpu(\\\"bias\\\", list(self.ForwardFunc.parameters())[1].data)\\n # self.set_cpu(\\\"RunMean\\\", self.ForwardFunc.running_mean.data)\\n # self.set_cpu(\\\"RunVar\\\", self.ForwardFunc.running_var.data)\\n # self.ForwardFunc.eval()\\n\\n def init(self, start_enclave=True):\\n # if self.LayerName == \\\"Layer3.10.proxies.0.bn2\\\":\\n # st()\\n TensorLoader.init(self, start_enclave)\\n\\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\\n self.PlainFunc = self.PlainFunc(self.InputShape[1])\\n self.PlainFunc.eval()\\n self.get_cpu(\\\"weight\\\").data.copy_(self.PlainFunc.weight.data)\\n self.get_cpu(\\\"bias\\\").data.copy_(self.PlainFunc.bias.data)\\n self.get_cpu(\\\"RunMean\\\").data.copy_(self.PlainFunc.running_mean.data)\\n # inject sqrt(running_var) instead of running_var for precision\\n self.get_cpu(\\\"RunVar\\\").data.copy_(self.PlainFunc.running_var.data)\\n self.transfer_cpu_to_enclave(\\\"weight\\\")\\n self.transfer_cpu_to_enclave(\\\"bias\\\")\\n self.transfer_cpu_to_enclave(\\\"RunMean\\\")\\n self.transfer_cpu_to_enclave(\\\"RunVar\\\")\\n self.batchnorm_init(\\n self.LayerName,\\n \\\"input\\\", \\\"output\\\", \\\"weight\\\", \\\"bias\\\",\\n # \\\"DerInput\\\", \\\"DerOutput\\\", \\\"DerWeight\\\", \\\"DerBias\\\",\\n \\\"RunMean\\\", \\\"RunVar\\\", \\\"CurMean\\\", \\\"CurVar\\\",\\n \\\"mu\\\",\\n self.BatchSize, self.NumChannel, self.ImgH, self.ImgW,\\n int(self.IsAffine), int(self.IsCumulative), self.momentum, self.epsilon)\\n elif self.EnclaveMode is ExecutionModeOptions.CPU:\\n self.ForwardFunc = self.ForwardFunc(self.InputShape[1])\\n self.PlainFunc = self.PlainFunc(self.InputShape[1])\\n self.PlainFunc.eval()\\n self.ForwardFunc.weight.data.copy_(self.PlainFunc.weight.data)\\n self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\\n self.ForwardFunc.running_mean.data.copy_(self.PlainFunc.running_mean.data)\\n self.ForwardFunc.running_var.data.copy_(self.PlainFunc.running_var.data)\\n self.set_cpu(\\\"weight\\\", list(self.ForwardFunc.parameters())[0].data)\\n self.set_cpu(\\\"bias\\\", list(self.ForwardFunc.parameters())[1].data)\\n self.set_cpu(\\\"RunMean\\\", self.ForwardFunc.running_mean.data)\\n self.set_cpu(\\\"RunVar\\\", self.ForwardFunc.running_var.data)\\n self.ForwardFunc.eval()\\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\\n self.ForwardFunc = self.ForwardFunc(self.InputShape[1])\\n self.PlainFunc = self.PlainFunc(self.InputShape[1])\\n self.ForwardFunc.weight.data.copy_(self.PlainFunc.weight.data)\\n self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\\n self.ForwardFunc.running_mean.data.copy_(self.PlainFunc.running_mean.data)\\n self.ForwardFunc.running_var.data.copy_(self.PlainFunc.running_var.data)\\n self.set_gpu(\\\"weight\\\", list(self.ForwardFunc.parameters())[0].data)\\n self.set_gpu(\\\"bias\\\", list(self.ForwardFunc.parameters())[1].data)\\n self.set_gpu(\\\"RunMean\\\", self.ForwardFunc.running_mean.data)\\n self.set_gpu(\\\"RunVar\\\", self.ForwardFunc.running_var.data)\\n self.PlainFunc.eval()\\n self.ForwardFunc.cuda().eval()\\n\\n # def inject_params(self, params):\\n # if self.sid == -2:\\n # raise ValueError(\\\"S2 has no learnable parameters for injection\\\")\\n # self.get_cpu(\\\"weight\\\").copy_(params.weight.data)\\n # self.get_cpu(\\\"bias\\\").copy_(params.bias.data)\\n # self.get_cpu(\\\"RunMean\\\").copy_(params.running_mean.data)\\n # # inject sqrt(running_var) instead of running_var for precision\\n # self.get_cpu(\\\"RunVar\\\").copy_(params.running_var.data)\\n # if self.is_enclave_mode:\\n # self.transfer_cpu_to_enclave(\\\"weight\\\")\\n # self.transfer_cpu_to_enclave(\\\"bias\\\")\\n # self.transfer_cpu_to_enclave(\\\"RunMean\\\")\\n # self.transfer_cpu_to_enclave(\\\"RunVar\\\")\\n\\n def inject_params(self, params):\\n if self.sid == -2:\\n raise ValueError(\\\"S2 has no learnable parameters for injection\\\")\\n if self.EnclaveMode in [ExecutionModeOptions.CPU, ExecutionModeOptions.Enclave]: \\n self.get_cpu(\\\"weight\\\").copy_(params.weight.data)\\n self.get_cpu(\\\"bias\\\").copy_(params.bias.data)\\n self.get_cpu(\\\"RunMean\\\").copy_(params.running_mean.data)\\n self.get_cpu(\\\"RunVar\\\").copy_(params.running_var.data)\\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\\n self.transfer_cpu_to_enclave(\\\"weight\\\")\\n self.transfer_cpu_to_enclave(\\\"bias\\\")\\n self.transfer_cpu_to_enclave(\\\"RunMean\\\")\\n self.transfer_cpu_to_enclave(\\\"RunVar\\\")\\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\\n self.get_gpu(\\\"weight\\\").copy_(params.weight.data)\\n self.get_gpu(\\\"bias\\\").copy_(params.bias.data)\\n self.get_gpu(\\\"RunMean\\\").copy_(params.running_mean.data)\\n self.get_gpu(\\\"RunVar\\\").copy_(params.running_var.data)\\n\\n def reset_plain_bn(self):\\n # module = torch.BatchNorm2d()\\n self.get_cpu(\\\"weight\\\").copy_(torch.ones(self.InputShape[1]))\\n self.get_cpu(\\\"bias\\\").copy_(torch.zeros(self.InputShape[1]))\\n self.get_cpu(\\\"RunMean\\\").copy_(torch.zeros(self.InputShape[1]))\\n self.get_cpu(\\\"RunVar\\\").copy_(torch.ones(self.InputShape[1]))\\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\\n self.transfer_cpu_to_enclave(\\\"weight\\\")\\n self.transfer_cpu_to_enclave(\\\"bias\\\")\\n self.transfer_cpu_to_enclave(\\\"RunMean\\\")\\n self.transfer_cpu_to_enclave(\\\"RunVar\\\")\\n\\n\\n def inject_to_plain(self, plain_layer: torch.nn.Module) -> None:\\n raise NotImplementedError\\n if self.sid == -2:\\n raise ValueError(\\\"S2 has no learnable parameters for injection\\\")\\n self.make_sure_cpu_is_latest(\\\"weight\\\")\\n self.make_sure_cpu_is_latest(\\\"bias\\\")\\n plain_layer.weight.data.copy_(self.get_cpu(\\\"weight\\\"))\\n plain_layer.bias.data.copy_(self.get_cpu(\\\"bias\\\"))\\n plain_layer.running_mean.data.copy_(self.get_cpu(\\\"RunMean\\\"))\\n plain_layer.running_var.data.copy_(self.get_cpu(\\\"RunVar\\\"))\\n\\n def generate_tensor_name_list(self, force=False):\\n if not force and self.tensor_name_list:\\n return\\n if self.sid == 2:\\n self.tensor_name_list = {}\\n return\\n\\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\\n NeededTensorNames = [\\n (\\\"input\\\", self.InputShape, None),\\n # (\\\"DerInput\\\", self.InputShape, None),\\n (\\\"output\\\", self.OutputShape, None),\\n # (\\\"DerOutput\\\", self.OutputShape, None),\\n (\\\"weight\\\", self.WeightShape, None),\\n # (\\\"DerWeight\\\", self.WeightShape, None),\\n (\\\"bias\\\", self.WeightShape, None),\\n # (\\\"DerBias\\\", self.WeightShape, None),\\n (\\\"RunMean\\\", self.WeightShape, None),\\n (\\\"CurMean\\\", self.WeightShape, None),\\n (\\\"RunVar\\\", self.WeightShape, None),\\n (\\\"CurVar\\\", self.WeightShape, None),\\n (\\\"mu\\\", self.InputShape, None),\\n ]\\n else:\\n NeededTensorNames = [\\n (\\\"output\\\", self.OutputShape, None),\\n # (\\\"DerInput\\\", self.InputShape, None),\\n (\\\"input\\\", self.InputShape, None),\\n (\\\"weight\\\", self.WeightShape, None),\\n # (\\\"DerWeight\\\", self.WeightShape, None),\\n (\\\"bias\\\", self.WeightShape, None),\\n # (\\\"DerBias\\\", self.WeightShape, None),\\n # (\\\"DerOutput\\\", self.OutputShape, None)\\n ]\\n\\n self.tensor_name_list = NeededTensorNames\\n\\n # def forward(self):\\n # if self.sid == 2:\\n # return\\n # with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} Forward\\\", verbose_level=VerboseLevel.LAYER):\\n # if self.is_enclave_mode:\\n # self.forward_tensor_transfer()\\n # self.batchnorm_forward(self.LayerName, int(False))\\n # else:\\n # self.forward_tensor_transfer()\\n # self.requires_grad_on_cpu(\\\"input\\\")\\n # self.ForwardFunc.bias.data.copy_(self.get_cpu(\\\"bias\\\"))\\n # self.ForwardFunc.weight.data.copy_(self.get_cpu(\\\"weight\\\"))\\n # self.ForwardFunc.running_mean.data.copy_(self.get_cpu(\\\"RunMean\\\"))\\n # # running_var of PlainFunc is ^2 of that in the enclave\\n # enclave_running_var = self.get_cpu(\\\"RunVar\\\")\\n # self.ForwardFunc.running_var.data.copy_(enclave_running_var)\\n # self.set_cpu(\\\"output\\\", self.ForwardFunc(self.get_cpu(\\\"input\\\")))\\n\\n def forward(self):\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} Forward\\\", verbose_level=VerboseLevel.LAYER):\\n if self.EnclaveMode == ExecutionModeOptions.Enclave:\\n # if self.LayerName == \\\"Layer2.0.downsample.bn\\\":\\n # st()\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} Input Preprocess\\\", verbose_level=VerboseLevel.LAYER):\\n self.forward_tensor_transfer()\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} batchnorm_forward\\\", verbose_level=VerboseLevel.LAYER):\\n self.batchnorm_forward(self.LayerName, int(False))\\n elif self.EnclaveMode == ExecutionModeOptions.CPU:\\n self.forward_tensor_transfer()\\n self.ForwardFunc.bias.data.copy_(self.get_cpu(\\\"bias\\\"))\\n self.ForwardFunc.weight.data.copy_(self.get_cpu(\\\"weight\\\"))\\n self.ForwardFunc.running_mean.data.copy_(self.get_cpu(\\\"RunMean\\\"))\\n # running_var of PlainFunc is ^2 of that in the enclave\\n enclave_running_var = self.get_cpu(\\\"RunVar\\\")\\n self.ForwardFunc.running_var.data.copy_(enclave_running_var)\\n self.set_cpu(\\\"output\\\", self.ForwardFunc(self.get_cpu(\\\"input\\\")))\\n elif self.EnclaveMode == ExecutionModeOptions.GPU:\\n self.forward_tensor_transfer()\\n self.ForwardFunc.bias.data.copy_(self.get_gpu(\\\"bias\\\"))\\n self.ForwardFunc.weight.data.copy_(self.get_gpu(\\\"weight\\\"))\\n self.ForwardFunc.running_mean.data.copy_(self.get_gpu(\\\"RunMean\\\"))\\n # running_var of PlainFunc is ^2 of that in the enclave\\n enclave_running_var = self.get_gpu(\\\"RunVar\\\")\\n self.ForwardFunc.running_var.data.copy_(enclave_running_var)\\n # st()\\n # print(self.get_gpu(\\\"input\\\")[0,0,0])\\n self.set_gpu(\\\"output\\\", self.ForwardFunc(self.get_gpu(\\\"input\\\").type(SecretConfig.dtypeForCpuOp)))\\n\\n def backward(self):\\n raise NotImplementedError\\n if self.sid == 2:\\n return\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} Backward\\\", verbose_level=VerboseLevel.LAYER):\\n if self.is_enclave_mode:\\n self.backward_tensor_transfer()\\n self.batchnorm_backward(self.LayerName)\\n else:\\n self.backward_tensor_transfer()\\n BackwardInput, BackwardWeight, BackwardBias = self.get_cpu(\\\"output\\\").grad_fn(self.get_cpu(\\\"DerOutput\\\"))\\n self.set_cpu(\\\"DerInput\\\", BackwardInput.data)\\n self.set_cpu(\\\"DerWeight\\\", BackwardWeight.data)\\n self.set_cpu(\\\"DerBias\\\", BackwardBias.data)\\n if list(self.get_cpu(\\\"DerWeight\\\").shape) != self.WeightShape:\\n real_shape = self.get_cpu(\\\"DerWeight\\\").shape\\n ideal_shape = self.WeightShape\\n raise ValueError(\\n f\\\"DerWeight is not of shape self.AffineShape: real: {real_shape}, ideal: {ideal_shape}\\\")\\n if list(self.get_cpu(\\\"DerBias\\\").shape) != self.WeightShape:\\n raise ValueError(\\\"DerBias is not of shape self.AffineShape\\\")\\n\\n def plain_forward(self, NeedBackward=False):\\n if self.sid == 2:\\n return\\n if self.EnclaveMode in [ExecutionModeOptions.Enclave, ExecutionModeOptions.GPU]:\\n self.make_sure_cpu_is_latest(\\\"input\\\")\\n self.make_sure_cpu_is_latest(\\\"bias\\\")\\n self.make_sure_cpu_is_latest(\\\"weight\\\")\\n self.requires_grad_on_cpu(\\\"input\\\")\\n self.PlainFunc.bias.data.copy_(self.get_cpu(\\\"bias\\\"))\\n self.PlainFunc.weight.data.copy_(self.get_cpu(\\\"weight\\\"))\\n self.PlainFunc.running_mean.data.copy_(self.get_cpu(\\\"RunMean\\\"))\\n # self.PlainFunc.running_var.data.copy_(self.get_cpu(\\\"RunVar\\\"))\\n # running_var of PlainFunc is ^2 of that in the enclave\\n enclave_running_var = self.get_cpu(\\\"RunVar\\\")\\n self.PlainFunc.running_var.data.copy_(enclave_running_var)\\n else:\\n self.make_sure_cpu_is_latest(\\\"input\\\")\\n self.requires_grad_on_cpu(\\\"input\\\")\\n\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} PlainForward\\\"):\\n torch.set_num_threads(1)\\n self.PlainForwardResult = self.PlainFunc(self.get_cpu(\\\"input\\\"))\\n torch.set_num_threads(4)\\n\\n def plain_backward(self):\\n if self.sid == 2:\\n return\\n self.make_sure_cpu_is_latest(\\\"DerOutput\\\")\\n GradFunction = self.PlainForwardResult.grad_fn\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} PlainBackward\\\"):\\n torch.set_num_threads(1)\\n self.PlainBackwardResult = GradFunction(self.get_cpu(\\\"DerOutput\\\"))\\n torch.set_num_threads(4)\\n\\n def show_plain_error(self):\\n if self.sid == 2:\\n return\\n self.make_sure_cpu_is_latest(\\\"output\\\")\\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\\\"output\\\"), get_relative=True)\\n print(f\\\"S{self.sid}: {self.LayerName} Forward Error: {err}\\\")\\n\\n if self.PlainBackwardResult is None:\\n return\\n if self.is_enclave_mode:\\n self.make_sure_cpu_is_latest(\\\"DerInput\\\")\\n self.make_sure_cpu_is_latest(\\\"DerWeight\\\")\\n self.make_sure_cpu_is_latest(\\\"DerBias\\\")\\n else:\\n self.make_sure_cpu_is_latest(\\\"DerInput\\\")\\n BackwardInput, BackwardWeight, BackwardBias = self.PlainBackwardResult\\n err_input = compare_expected_actual(BackwardInput, self.get_cpu(\\\"DerInput\\\"), show_where_err=False, get_relative=True)\\n err_weight = compare_expected_actual(BackwardWeight, self.get_cpu(\\\"DerWeight\\\"), show_where_err=False,\\n get_relative=True)\\n err_bias = compare_expected_actual(BackwardBias, self.get_cpu(\\\"DerBias\\\"))\\n print(f\\\"S{self.sid}: {self.LayerName} Backward Error input: {err_input}, weight {err_weight}, bias: {err_bias}\\\")\\n\\n def show_plain_error_forward(self):\\n if self.sid == 2:\\n return\\n self.make_sure_cpu_is_latest(\\\"output\\\")\\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\\\"output\\\"), get_relative=False, show_values=False)\\n print(f\\\"S{self.sid}: {self.LayerName} Forward Error: {err}\\\")\"\n },\n {\n \"identifier\": \"SecretFlattenLayer\",\n \"path\": \"python/layers/flatten.py\",\n \"snippet\": \"class SecretFlattenLayer(SecretNonlinearLayer):\\n batch_size = None\\n n_features = None\\n input_shape = None\\n output_shape = None\\n\\n def __init__(\\n self, sid, LayerName, EnclaveMode, link_prev=True, link_next=True,\\n manually_register_prev=False, manually_register_next=False\\n ):\\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\\n self.StoreInEnclave = False\\n self.ForwardFuncName = \\\"Flatten\\\"\\n self.BackwardFuncName = \\\"DerFlatten\\\"\\n\\n\\n def init(self, start_enclave=True):\\n super().init(start_enclave)\\n self.ForwardFunc = lambda x: x.view(-1, self.n_features)\\n self.PlainFunc = lambda x: x.view(-1, self.n_features)\\n\\n def init_shape(self):\\n self.input_shape = self.PrevLayer.get_output_shape()\\n if len(self.input_shape) != 4:\\n return ValueError(\\\"The dimension of the tensor form prev. layer has to be 4D.\\\")\\n\\n self.batch_size = self.input_shape[0]\\n self.n_features = self.input_shape[1] * self.input_shape[2] * self.input_shape[3]\\n self.output_shape = [self.batch_size, self.n_features]\\n\\n def get_output_shape(self):\\n return self.output_shape\\n\\n def generate_tensor_name_list(self, force=False):\\n if not force and self.tensor_name_list:\\n return\\n if self.sid == 2:\\n self.tensor_name_list = {}\\n return\\n\\n NeededTensorNames = [(\\\"output\\\", self.output_shape, None),\\n (\\\"input\\\", self.input_shape, None),\\n (\\\"DerInput\\\", self.input_shape, None),\\n (\\\"DerOutput\\\", self.output_shape, None)\\n ]\\n\\n self.tensor_name_list = NeededTensorNames\\n\\n def forward(self):\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} Forward\\\", verbose_level=VerboseLevel.LAYER):\\n if self.EnclaveMode == ExecutionModeOptions.Enclave:\\n self.transfer_enclave_to_cpu(\\\"input\\\")\\n self.set_cpu(\\\"output\\\", self.ForwardFunc(self.get_cpu(\\\"input\\\")))\\n self.transfer_cpu_to_enclave(\\\"output\\\")\\n elif self.EnclaveMode == ExecutionModeOptions.CPU:\\n self.set_cpu(\\\"output\\\", self.ForwardFunc(self.get_cpu(\\\"input\\\")))\\n elif self.EnclaveMode == ExecutionModeOptions.GPU:\\n self.set_gpu(\\\"output\\\", self.ForwardFunc(self.get_gpu(\\\"input\\\")))\\n\\n # self.forward_tensor_transfer()\\n # self.set_cpu(\\\"output\\\", self.ForwardFunc(self.get_cpu(\\\"input\\\")))\\n\\n def backward(self):\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} Backward\\\", verbose_level=VerboseLevel.LAYER):\\n self.backward_tensor_transfer()\\n self.set_cpu(\\\"DerInput\\\", self.get_cpu(\\\"DerOutput\\\").view(self.input_shape))\\n\\n def plain_forward(self, NeedBackward=False):\\n self.requires_grad_on_cpu(\\\"input\\\")\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} PlainForward\\\"):\\n self.PlainForwardResult = self.PlainFunc(self.get_cpu(\\\"input\\\"))\\n\\n def plain_backward(self):\\n self.make_sure_cpu_is_latest(\\\"DerOutput\\\")\\n GradFunction = self.PlainForwardResult.grad_fn\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} PlainBackward\\\"):\\n self.PlainBackwardResult = GradFunction(self.get_cpu(\\\"DerOutput\\\"))\\n\\n def show_plain_error(self):\\n if self.StoreInEnclave:\\n self.transfer_enclave_to_cpu(\\\"output\\\")\\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\\\"output\\\"))\\n print(f\\\"S{self.sid}: {self.LayerName} Forward Error: {err}\\\")\\n\\n if self.PlainBackwardResult is None:\\n return\\n err = compare_expected_actual(self.PlainBackwardResult, self.get_cpu(\\\"DerInput\\\"), get_relative=True)\\n print(f\\\"S{self.sid}: {self.LayerName} Backward Error {err}\\\")\"\n },\n {\n \"identifier\": \"SecretInputLayer\",\n \"path\": \"python/layers/input.py\",\n \"snippet\": \"class SecretInputLayer(SecretNonlinearLayer):\\n shape = None\\n\\n def __init__(\\n self, sid, LayerName, input_shape, EnclaveMode, link_prev=True, link_next=True, \\n manually_register_prev=False, manually_register_next=False\\n ):\\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\\n self.shape = input_shape\\n\\n def link_tensors(self):\\n gt.link_tags(self.get_tag(\\\"input\\\", remap=False), self.get_tag(\\\"output\\\", remap=False))\\n super().link_tensors()\\n\\n def init_shape(self):\\n return\\n\\n def set_input(self, tensor):\\n self.set_tensor_cpu_gpu_enclave(\\\"input\\\", tensor)\\n\\n def get_output_shape(self):\\n return self.shape\\n\\n def forward(self):\\n return\\n\\n def backward(self):\\n return\\n\\n def plain_forward(self):\\n return\\n\\n def plain_backward(self):\\n return\\n\\n def show_plain_error(self):\\n return\\n\\n def print_connection_info(self):\\n print(f\\\"{self.LayerName:30} shape{self.shape} output {self.NextLayer.LayerName:30}\\\")\"\n },\n {\n \"identifier\": \"SecretMaxpool2dLayer\",\n \"path\": \"python/layers/maxpool2d.py\",\n \"snippet\": \"class SecretMaxpool2dLayer(SecretActivationLayer):\\n def __init__(\\n self, sid, LayerName, EnclaveMode, filter_hw, stride, padding, link_prev=True, link_next=True,\\n manually_register_prev=False, manually_register_next=False\\n ):\\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\\n self.ForwardFuncName = \\\"Maxpool2d\\\"\\n self.BackwardFuncName = \\\"DerMaxpool2d\\\"\\n self.filter_hw = filter_hw\\n self.startmaxpool = False\\n self.PlainFunc = torch.nn.MaxPool2d\\n self.maxpoolpadding = padding\\n self.stride = stride\\n self.STORE_CHUNK_ELEM = 401408\\n\\n self.ForwardFunc = torch.nn.MaxPool2d\\n\\n if EnclaveMode == ExecutionModeOptions.Enclave :\\n self.ForwardFunc = self.maxpoolfunc\\n self.BackwardFunc = self.maxpoolbackfunc\\n else:\\n self.ForwardFunc = torch.nn.MaxPool2d\\n\\n def init_shape(self):\\n self.InputShape = self.PrevLayer.get_output_shape()\\n if len(self.InputShape) != 4:\\n raise ValueError(\\\"Maxpooling2d apply only to 4D Tensor\\\")\\n if self.InputShape[2] != self.InputShape[3]:\\n raise ValueError(\\\"The input tensor has to be square images\\\")\\n if self.InputShape[2] % self.stride != 0:\\n raise ValueError(\\\"The input tensor needs padding for this filter size\\\")\\n InputHw = self.InputShape[2]\\n output_hw = InputHw // self.stride\\n self.OutputShape = [self.InputShape[0], self.InputShape[1], output_hw, output_hw]\\n self.HandleShape = self.InputShape\\n # self.Shapefortranspose = [int(round(((self.InputShape[0] * self.InputShape[1] * self.InputShape[2] * self.InputShape[3])/262144)+1/2)), 262144, 1, 1]\\n self.Shapefortranspose = [\\n int(round(((self.InputShape[0] * self.InputShape[1] * self.InputShape[2] * self.InputShape[3])/self.STORE_CHUNK_ELEM)+1/2)), self.STORE_CHUNK_ELEM, 1, 1]\\n\\n\\n def init(self, start_enclave=True):\\n if self.EnclaveMode == ExecutionModeOptions.Enclave:\\n self.PlainFunc = self.PlainFunc(self.filter_hw, self.stride, self.maxpoolpadding)\\n TensorLoader.init(self, start_enclave)\\n\\n if self.startmaxpool is False:\\n self.startmaxpool = True\\n return self.maxpoolinit(self.LayerName, \\\"inputtrans\\\", \\\"outputtrans\\\")\\n else:\\n self.ForwardFunc = self.ForwardFunc(self.filter_hw, stride=self.stride, padding=self.maxpoolpadding)\\n self.PlainFunc = self.PlainFunc(self.filter_hw, stride=self.stride, padding=self.maxpoolpadding)\\n\\n # TensorLoader.init(self, start_enclave)\\n # self.ForwardFunc = self.ForwardFunc(self.filter_hw, stride=self.stride, padding=self.maxpoolpadding)\\n # self.PlainFunc = self.PlainFunc(self.filter_hw, stride=self.stride, padding=self.maxpoolpadding)\\n\\n # TensorLoader.init(self, start_enclave)\\n\\n # def forward(self):\\n # with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} Forward\\\", verbose_level=VerboseLevel.LAYER):\\n # self.forward_tensor_transfer()\\n # # self.requires_grad_on_cpu(\\\"input\\\")\\n # if self.EnclaveMode == ExecutionModeOptions.Enclave:\\n # self.set_gpu(\\\"output\\\", self.ForwardFunc(self.get_gpu(\\\"input\\\")))\\n # st()\\n\\n # # if self.PrevLayer.EnclaveMode is not ExecutionModeOptions.Enclave:\\n # # self.transfer_enclave_to_cpu(\\\"input\\\")\\n # # if torch.sum(self.get_cpu(\\\"input\\\").abs()) == 0:\\n # # raise RuntimeError(f\\\"{self.LayerName}: SGX input not load\\\")\\n # # self.transfer_cpu_to_enclave(\\\"input\\\")\\n # # self.transfer_enclave_to_cpu(\\\"input\\\")\\n # # self.set_cpu(\\\"output\\\", self.ForwardFunc(self.get_cpu(\\\"input\\\")))\\n # # self.transfer_cpu_to_enclave(\\\"output\\\")\\n # elif self.EnclaveMode == ExecutionModeOptions.CPU:\\n # if self.PrevLayer.EnclaveMode is not ExecutionModeOptions.CPU and torch.sum(self.get_cpu(\\\"input\\\").abs()) == 0:\\n # raise RuntimeError(f\\\"{self.LayerName}: SGX input not load\\\")\\n # self.set_cpu(\\\"output\\\", self.ForwardFunc(self.get_cpu(\\\"input\\\")))\\n # elif self.EnclaveMode == ExecutionModeOptions.GPU:\\n # if self.PrevLayer.EnclaveMode is not ExecutionModeOptions.GPU and torch.sum(self.get_gpu(\\\"input\\\").abs()) == 0:\\n # raise RuntimeError(f\\\"{self.LayerName}: SGX input not load\\\")\\n # self.set_gpu(\\\"output\\\", self.ForwardFunc(self.get_gpu(\\\"input\\\")))\\n # else:\\n # raise RuntimeError\\n\\n def maxpoolfunc(self, namein, nameout):\\n # assume row_stride and col_stride are both None or both not None\\n # assume row_pad and col_pad are both None or both not None\\n # if self.LayerName == \\\"Layer3.0.proxies.2.maxpool\\\":\\n # print(self.LayerName, \\\"Input: \\\", self.get_cpu(\\\"input\\\")[0,0,0,:10])\\n output = self.maxpoolnew(self.LayerName, namein, nameout, self.InputShape, self.OutputShape[2], self.OutputShape[3],\\n self.filter_hw, self.filter_hw, self.stride, self.stride, self.maxpoolpadding,\\n self.maxpoolpadding)\\n # if self.LayerName == \\\"Layer3.0.proxies.2.maxpool\\\":\\n # self.transfer_enclave_to_cpu(\\\"output\\\")\\n # print(self.LayerName, \\\"Output: \\\", self.get_cpu(\\\"output\\\")[0,0,0,:])\\n # self.transfer_cpu_to_enclave(\\\"output\\\")\\n return output\\n\\n def maxpoolbackfunc(self, nameout, namedout, namedin):\\n return self.maxpoolback(self.LayerName, namedout, namedin, self.InputShape, self.OutputShape[2], self.OutputShape[3],\\n self.filter_hw, self.filter_hw, self.row_stride, self.col_stride, self.maxpoolpadding,\\n self.maxpoolpadding)\"\n },\n {\n \"identifier\": \"SecretOutputLayer\",\n \"path\": \"python/layers/output.py\",\n \"snippet\": \"class SecretOutputLayer(SecretNonlinearLayer):\\n TargetShape = None\\n loss = 0\\n\\n def __init__(\\n self, sid, LayerName, EnclaveMode, inference=False, link_prev=True, link_next=True, \\n manually_register_prev=False, manually_register_next=False\\n ):\\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\\n self.ForwardFunc = torch.nn.CrossEntropyLoss()\\n self.PlainFunc = torch.nn.CrossEntropyLoss()\\n self.EnclaveMode = ExecutionModeOptions.CPU\\n self.inference = inference\\n\\n\\n def init_shape(self):\\n self.InputShape = self.PrevLayer.get_output_shape()\\n self.OutputShape = [1]\\n self.TargetShape = [self.InputShape[0]] # number of Minibatch\\n\\n def init(self, start_enclave=True):\\n TensorLoader.init(self, start_enclave)\\n\\n def generate_tensor_name_list(self, force=False):\\n if not force and self.tensor_name_list:\\n return\\n if self.sid == 2:\\n self.tensor_name_list = {}\\n return\\n\\n NeededTensorNames = [\\n (\\\"output\\\", self.OutputShape, None),\\n (\\\"DerInput\\\", self.InputShape, None),\\n (\\\"input\\\", self.InputShape, None),\\n (\\\"target\\\", self.TargetShape, None),\\n ]\\n\\n self.tensor_name_list = NeededTensorNames\\n\\n def load_target(self, tensor):\\n self.set_tensor_with_name(\\\"target\\\", tensor)\\n\\n def get_loss(self):\\n return self.loss\\n \\n def get_prediction(self):\\n self.forward_tensor_transfer(\\\"input\\\")\\n if torch.sum(self.get_cpu(\\\"input\\\").abs()) == 0:\\n raise RuntimeError(\\\"SGX input not load\\\")\\n return self.get_cpu(\\\"input\\\")\\n\\n def forward(self):\\n if not self.inference:\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} Forward\\\", verbose_level=VerboseLevel.LAYER):\\n self.forward_tensor_transfer()\\n self.set_cpu(\\\"input\\\", self.get_cpu(\\\"input\\\").detach())\\n self.requires_grad_on_cpu(\\\"input\\\")\\n self.set_cpu(\\\"output\\\", self.ForwardFunc(self.get_cpu(\\\"input\\\"), self.get_cpu(\\\"target\\\")))\\n loss = self.get_cpu(\\\"output\\\").item()\\n self.loss = loss\\n\\n def backward(self):\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} Backward\\\", verbose_level=VerboseLevel.LAYER):\\n self.backward_tensor_transfer(transfer_tensor=\\\"output\\\")\\n self.get_cpu(\\\"output\\\").backward()\\n self.set_cpu(\\\"DerInput\\\", self.get_cpu(\\\"input\\\").grad)\\n\\n def plain_forward(self):\\n if not self.inference:\\n self.make_sure_cpu_is_latest(\\\"input\\\")\\n self.set_cpu(\\\"input\\\", self.get_cpu(\\\"input\\\").detach())\\n self.requires_grad_on_cpu(\\\"input\\\")\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} PlainForward\\\"):\\n self.PlainForwardResult = self.PlainFunc(self.get_cpu(\\\"input\\\"), self.get_cpu(\\\"target\\\"))\\n\\n def plain_backward(self):\\n self.make_sure_cpu_is_latest(\\\"output\\\")\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} PlainBackward\\\"):\\n self.PlainForwardResult.backward()\\n self.set_cpu(\\\"DerInput\\\", self.get_cpu(\\\"input\\\").grad)\\n\\n def show_plain_error(self):\\n self.make_sure_cpu_is_latest(\\\"output\\\")\\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\\\"output\\\"))\\n print(f\\\"S{self.sid}: {self.LayerName} Forward Error: {err}\\\")\\n\\n if self.PlainBackwardResult is None:\\n return\\n self.make_sure_cpu_is_latest(\\\"DerInput\\\")\\n\\n err = compare_expected_actual(self.PlainBackwardResult, self.get_cpu(\\\"DerInput\\\"))\\n print(f\\\"S{self.sid}: {self.LayerName} Backward Error {err}\\\")\\n\\n def print_connection_info(self):\\n print(f\\\"{self.LayerName:30} shape{self.InputShape}{' ':30} input {self.PrevLayer.LayerName:30}\\\")\"\n },\n {\n \"identifier\": \"SecretReLULayer\",\n \"path\": \"python/layers/relu.py\",\n \"snippet\": \"class SecretReLULayer(SecretActivationLayer):\\n def __init__(\\n self, sid, LayerName, EnclaveMode, link_prev=True, link_next=True,\\n manually_register_prev=False, manually_register_next=False, merge_own_tensors=False\\n ):\\n super().__init__(\\n sid, LayerName, EnclaveMode, link_prev, link_next,\\n manually_register_prev, manually_register_next, merge_own_tensors\\n )\\n self.ForwardFuncName = \\\"ReLU\\\"\\n self.BackwardFuncName = \\\"DerReLU\\\"\\n self.PlainFunc = torch.nn.ReLU\\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\\n self.ForwardFunc = self.relufunc\\n self.BackwardFunc = self.relubackfunc\\n elif self.EnclaveMode is ExecutionModeOptions.CPU:\\n self.ForwardFunc = torch.nn.ReLU\\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\\n self.ForwardFunc = torch.nn.ReLU\\n\\n # if self.is_enclave_mode:\\n # self.ForwardFunc = self.relufunc\\n # self.BackwardFunc = self.relubackfunc\\n # self.StoreInEnclave = True\\n # else:\\n # self.ForwardFunc = torch.nn.ReLU\\n # self.StoreInEnclave = False\\n\\n def init(self, start_enclave=True):\\n super().init(start_enclave)\\n self.PlainFunc = self.PlainFunc()\\n # if not self.is_enclave_mode:\\n if self.EnclaveMode is not ExecutionModeOptions.Enclave:\\n self.ForwardFunc = self.ForwardFunc()\\n\\n def relufunc(self, namein, nameout):\\n return self.relunew(namein, nameout, self.InputShape)\\n\\n def relubackfunc(self, nameout, namedout, namedin):\\n return self.relubackward(nameout, namedout, namedin, self.InputShape)\\n\\n def show_plain_error_forward(self):\\n if self.sid == 2:\\n return\\n self.make_sure_cpu_is_latest(\\\"output\\\")\\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\\\"output\\\"), get_relative=False, show_values=False)\\n print(f\\\"S{self.sid}: {self.LayerName} Forward Error: {err}\\\")\"\n },\n {\n \"identifier\": \"init_communicate\",\n \"path\": \"python/sgx_net.py\",\n \"snippet\": \"def init_communicate(rank, master_address, master_port, backend='gloo'):\\n os.environ['MASTER_ADDR'] = master_address\\n os.environ['MASTER_PORT'] = master_port\\n dist.init_process_group(backend, rank=rank, world_size=SecretConfig.worldSize)\"\n },\n {\n \"identifier\": \"warming_up_cuda\",\n \"path\": \"python/sgx_net.py\",\n \"snippet\": \"def warming_up_cuda():\\n device = torch.device(\\\"cuda:0\\\")\\n # device = torch.device(\\\"cpu\\\")\\n\\n print(\\\"Execution device: \\\", device)\\n print(\\\"PyTorch version: \\\", torch.__version__)\\n print(\\\"CUDA version: \\\", torch.version.cuda)\\n print(\\\"CUDA device:\\\", torch.cuda.get_device_name(0))\\n\\n batch_size, n_input_channel, n_output_channel, img_hw, filter_hw = 512, 512, 256, 4, 3\\n x_shape = [batch_size, n_input_channel, img_hw, img_hw]\\n w_shape = [n_output_channel, n_input_channel, filter_hw, filter_hw]\\n with NamedTimerInstance(\\\"Warming up Cuda double\\\"):\\n dummy_a = get_random_uniform(SecretConfig.PrimeLimit, x_shape).type(SecretConfig.dtypeForSave)\\n dummy_b = get_random_uniform(SecretConfig.PrimeLimit, w_shape).type(SecretConfig.dtypeForSave)\\n F.conv2d(dummy_a.cuda().type(SecretConfig.dtypeForCudaMm), dummy_b.cuda().type(SecretConfig.dtypeForCudaMm),\\n padding=1)\\n\\n with NamedTimerInstance(\\\"Warming up Cuda dobule 2nd\\\"):\\n F.conv2d(dummy_a.cuda().type(torch.double), dummy_b.cuda().type(torch.double),\\n padding=1)\\n\\n with NamedTimerInstance(\\\"Warming up Cuda float\\\"):\\n F.conv2d(dummy_a.cuda().type(torch.float), dummy_b.cuda().type(torch.float), padding=1)\\n\\n with NamedTimerInstance(\\\"Warming up Cuda float 2nd\\\"):\\n F.conv2d(dummy_a.cuda().type(torch.float), dummy_b.cuda().type(torch.float), padding=1)\\n\\n batch_size, n_input_channel, n_output_channel, img_hw, filter_hw = 64, 64, 64, 8, 3\\n x_shape = [batch_size, n_input_channel, img_hw, img_hw]\\n w_shape = [n_output_channel, n_input_channel, filter_hw, filter_hw]\\n with NamedTimerInstance(\\\"Warming up Cpu\\\"):\\n dummy_a = get_random_uniform(SecretConfig.PrimeLimit, x_shape).type(SecretConfig.dtypeForSave)\\n dummy_b = get_random_uniform(SecretConfig.PrimeLimit, w_shape).type(SecretConfig.dtypeForSave)\\n F.conv2d(dummy_a.type(SecretConfig.dtypeForCpuOp), dummy_b.type(SecretConfig.dtypeForCpuOp),\\n padding=1)\\n\\n with NamedTimerInstance(\\\"Warming up CppExtension\\\"):\\n GlobalCppExtension.get_conv2d_cudnn()\"\n },\n {\n \"identifier\": \"SecretNeuralNetwork\",\n \"path\": \"python/sgx_net.py\",\n \"snippet\": \"class SecretNeuralNetwork(TensorLoader):\\n nn_name = None\\n layers = None\\n\\n def __init__(self, sid, nn_name):\\n super().__init__()\\n self.sid = sid\\n self.init(start_enclave=False)\\n self.nn_name = nn_name\\n\\n def set_layers(self, layers):\\n self.layers = layers\\n\\n if not isinstance(self.layers[0], SecretInputLayer):\\n raise ValueError(\\\"The first layer has to be input layer\\\")\\n if not isinstance(self.layers[-1], SecretOutputLayer):\\n raise ValueError(\\\"The last layer has to be output layer\\\")\\n \\n for i in range(len(self.layers) - 1):\\n PrevLayer = self.layers[i]\\n NextLayer = self.layers[i + 1]\\n if not PrevLayer.manually_register_next:\\n PrevLayer.register_next_layer(NextLayer)\\n if not NextLayer.manually_register_prev:\\n NextLayer.register_prev_layer(PrevLayer)\\n\\n \\n for layer in self.layers:\\n # print(f\\\"Init_shape/link layer {layer.LayerName}\\\")\\n layer.set_eid(self.get_eid())\\n layer.init_shape()\\n # if layer.LayerName in [\\\"Layer1.0.weighted_add\\\", \\\"Layer1.0.proxies.0.bn\\\"]:\\n # st()\\n layer.link_tensors()\\n # print(layer.LayerName)\\n # layer.print_tensor_link_relation()\\n # if layer.LayerName in [\\\"Layer1.0.weighted_add\\\", \\\"Layer1.0.proxies.0.bn\\\"]:\\n # st()\\n \\n for idx, layer in enumerate(self.layers):\\n # print(f\\\"Init layer {layer.LayerName}\\\")\\n # if layer.LayerName == \\\"Layer1.0.main.relu2\\\":\\n # st()\\n layer.init(start_enclave=False)\\n # if idx > 3:\\n # print(layer.LayerName, self.layers[4].get_cpu(\\\"input\\\").shape, self.layers[4].PrevLayer.LayerName)\\n\\n def execute_for_each_layer(self, func, reverse=False):\\n layers = self.layers[::-1] if reverse else self.layers\\n for layer in layers:\\n # print(f\\\"SID: {self.sid} {layer.LayerName}, {func}\\\")\\n if self.sid == 2 and layer.IsDummyForS2:\\n continue\\n # print(\\\"Processing \\\", layer.LayerName)\\n func(layer)\\n \\n # st()\\n\\n def classifier_output(self):\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.nn_name} classifier_output\\\"):\\n self.forward()\\n if self.sid == 2:\\n return\\n # layers: input_layer, ..., fc_layer, output_layer\\n last_fc = self.layers[-2]\\n last_fc.transfer_enclave_to_cpu(\\\"output\\\")\\n outputs = last_fc.get_cpu(\\\"output\\\")\\n _, predicted = torch.max(outputs.data, 1)\\n return predicted\\n\\n def get_loss(self):\\n return self.layers[-1].get_loss()\\n\\n def forward_with_time(self):\\n def run_forward(layer):\\n layer.forward()\\n t0 = time()\\n with NetworkNamedTimerInstance(f\\\"S{self.sid}: {self.nn_name} Forward\\\"):\\n self.execute_for_each_layer(run_forward)\\n t1 = time()\\n # time in ms\\n elapse_time = (t1 - t0) * (10 ** 3) \\n return elapse_time\\n\\n def forward(self):\\n def run_forward(layer):\\n layer.forward()\\n with NetworkNamedTimerInstance(f\\\"S{self.sid}: {self.nn_name} Forward\\\"):\\n self.execute_for_each_layer(run_forward)\\n\\n def backward(self):\\n def run_backward(layer):\\n layer.backward()\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.nn_name} Backward\\\"):\\n self.execute_for_each_layer(run_backward, reverse=True)\\n\\n def plain_forward(self):\\n with NetworkNamedTimerInstance(f\\\"S{self.sid}: {self.nn_name} PlainForward\\\"):\\n self.execute_for_each_layer(lambda x: x.plain_forward())\\n\\n def plain_backward(self):\\n with NetworkNamedTimerInstance(f\\\"S{self.sid}: {self.nn_name} PlainBackward\\\"):\\n self.execute_for_each_layer(lambda x: x.plain_backward(), reverse=True)\\n\\n def show_plain_error(self):\\n self.execute_for_each_layer(lambda x: x.show_plain_error())\"\n },\n {\n \"identifier\": \"SgdOptimizer\",\n \"path\": \"python/sgx_net.py\",\n \"snippet\": \"class SgdOptimizer(TensorLoader):\\n def __init__(self, sid):\\n super().__init__()\\n self.sid = sid\\n self.learning_rate = 0.05\\n self.momentum = 0.9\\n self.weight_decay = 5e-4\\n self.momentum_init_flags = defaultdict(lambda: False)\\n self.ideal_momentum_buf = {}\\n\\n self.lr_gamma = 0.5\\n self.lr_step = 30\\n self.step_counter = 0\\n\\n self.layers = None\\n\\n def set_layers(self, layers):\\n self.layers = layers\\n\\n def generate_tensor_name_list(self, force=False):\\n # Run if forced or self.tensor_name_list is not generated\\n if not force and self.tensor_name_list:\\n return\\n if self.sid == 2:\\n return\\n\\n self.tensor_name_list = []\\n for layer in self.layers:\\n for (DerName, ParamName, shape) in layer.LearnableParamsList:\\n self.tensor_name_list.append((ParamName + \\\"Momentum\\\", shape, None))\\n\\n def update_params(self, test_with_ideal=False):\\n if self.sid == 2:\\n return\\n for layer in self.layers:\\n self.update_params_in_layer(layer, test_with_ideal=test_with_ideal)\\n\\n def update_params_in_layer(self, layer, test_with_ideal=False):\\n # ref: https://github.com/pytorch/pytorch/blob/master/torch/optim/sgd.py\\n if layer.LearnableParamsList is None:\\n return\\n\\n task_ids = []\\n for (der_name, param_name, shape) in layer.LearnableParamsList:\\n momentum_name = param_name + \\\"Momentum\\\"\\n global_momentum_name = layer.name_modifier(momentum_name)\\n\\n if layer.StoreInEnclave:\\n if test_with_ideal:\\n ideal_p, ideal_momentum = self.ideal_update_params_with_name(layer, der_name, param_name, shape)\\n first_momentum = not self.momentum_init_flags[global_momentum_name]\\n if first_momentum:\\n # print(\\\"FIRST MOMENTUM\\\")\\n self.momentum_init_flags[global_momentum_name] = True\\n layer.init_enclave_tensor(momentum_name, shape)\\n task_id = layer.sgd_update(param_name=param_name, grad_name=der_name, momentum_name=momentum_name,\\n lr=self.learning_rate, momentum=self.momentum,\\n weight_decay=self.weight_decay,\\n first_momentum=first_momentum, is_async=True)\\n if test_with_ideal:\\n while not self.get_task_status(task_id):\\n pass\\n layer.generate_cpu_tensor(momentum_name, shape)\\n layer.transfer_enclave_to_cpu(momentum_name)\\n layer.transfer_enclave_to_cpu(param_name)\\n param_err = compare_expected_actual(ideal_p, layer.get_cpu(param_name), get_relative=True)\\n print(f\\\"S{self.sid}: {layer.LayerName} Param Error: {param_err}\\\")\\n momentum_err = compare_expected_actual(ideal_momentum, layer.get_cpu(momentum_name), get_relative=True)\\n print(f\\\"S{self.sid}: {layer.LayerName} Momentum Error: {momentum_err}\\\")\\n else:\\n task_ids.append(task_id)\\n else:\\n DerCpu = layer.get_cpu(der_name)\\n ParamsCpu = layer.get_cpu(param_name)\\n\\n if test_with_ideal:\\n ideal_p, ideal_momentum = self.ideal_update_params_with_name(layer, der_name, param_name, shape)\\n\\n DerCpu.add_(self.weight_decay, ParamsCpu)\\n\\n if not self.momentum_init_flags[global_momentum_name]:\\n self.momentum_init_flags[global_momentum_name] = True\\n layer.generate_cpu_tensor(momentum_name, shape)\\n layer.get_cpu(momentum_name).copy_(DerCpu)\\n MomentumCpu = layer.get_cpu(momentum_name)\\n else:\\n MomentumCpu = layer.get_cpu(momentum_name)\\n MomentumCpu.mul_(self.momentum).add_(1, DerCpu)\\n\\n ParamsCpu.add_(-self.learning_rate, MomentumCpu)\\n\\n if test_with_ideal:\\n param_err = compare_expected_actual(ideal_p, layer.get_cpu(param_name), get_relative=True)\\n print(f\\\"S{self.sid}: {layer.LayerName} Param Error: {param_err}\\\")\\n momentum_err = compare_expected_actual(ideal_momentum, layer.get_cpu(momentum_name), get_relative=True)\\n print(f\\\"S{self.sid}: {layer.LayerName} Momentum Error: {momentum_err}\\\")\\n\\n # Wait for all tasks to be finished\\n for task_id in task_ids:\\n while not self.get_task_status(task_id):\\n pass\\n\\n def ideal_update_params_with_name(self, layer, der_name, param_name, shape):\\n weight_decay = self.weight_decay\\n momentum = self.momentum\\n dampening = 0\\n nesterov = False\\n lr = self.learning_rate\\n\\n global_momentum_name = layer.name_modifier(param_name + 'Momentum')\\n\\n if layer.StoreInEnclave:\\n layer.transfer_enclave_to_cpu(der_name)\\n layer.transfer_enclave_to_cpu(param_name)\\n d_p = torch.clone(layer.get_cpu(der_name)).detach()\\n p = torch.clone(layer.get_cpu(param_name)).detach()\\n\\n if weight_decay != 0:\\n d_p.add_(weight_decay, p)\\n if global_momentum_name not in self.ideal_momentum_buf:\\n buf = self.ideal_momentum_buf[global_momentum_name] = torch.clone(d_p).detach()\\n else:\\n buf = self.ideal_momentum_buf[global_momentum_name]\\n buf.mul_(momentum).add_(1 - dampening, d_p)\\n if nesterov:\\n d_p = d_p.add(momentum, buf)\\n else:\\n d_p = buf\\n p.add_(-lr, d_p)\\n\\n return p, buf\"\n },\n {\n \"identifier\": \"SGXLinearBase\",\n \"path\": \"python/layers/sgx_linear_base.py\",\n \"snippet\": \"class SGXLinearBase(SecretLayerBase):\\n batch_size = None\\n InputShape = None\\n WeightShape = None\\n OutputShape = None\\n\\n def __init__(\\n self, sid, LayerName, EnclaveMode, batch_size, n_output_features, \\n n_input_features=None, is_enclave_mode=False, link_prev=True, link_next=True,\\n manually_register_prev=False, manually_register_next=False\\n ):\\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\\n\\n self.ForwardFuncName = \\\"SGXLinear\\\"\\n self.BackwardFuncName = \\\"DerSGXLinear\\\"\\n self.PlainFunc = torch.nn.Linear\\n self.is_enclave_mode = is_enclave_mode\\n self.n_output_features = n_output_features\\n self.n_input_features = n_input_features\\n self.batch_size = batch_size\\n\\n if EnclaveMode is ExecutionModeOptions.CPU or EnclaveMode is ExecutionModeOptions.GPU:\\n self.ForwardFunc = torch.nn.Linear\\n # if self.is_enclave_mode:\\n # self.StoreInEnclave = True\\n # else:\\n # self.ForwardFunc = torch.nn.Linear\\n # self.StoreInEnclave = False\\n\\n def init_shape(self):\\n self.WeightShape = self.DerWeightShape = [self.n_output_features, self.n_input_features]\\n self.BiasShape = self.DerBiasShape = [self.n_output_features]\\n if self.n_input_features is None:\\n self.InputShape = self.PrevLayer.get_output_shape()\\n else:\\n self.InputShape = self.DerInputShape = [self.batch_size, self.n_input_features]\\n self.OutputShape = self.DerOutputShape = [self.batch_size, self.n_output_features]\\n self.LearnableParamsList = [\\n LearnableParamTuple(dw_name=\\\"DerWeight\\\", w_name=\\\"weight\\\", shape=self.WeightShape),\\n LearnableParamTuple(dw_name=\\\"DerBias\\\", w_name=\\\"bias\\\", shape=self.WeightShape),\\n ]\\n\\n def init(self, start_enclave=True):\\n TensorLoader.init(self, start_enclave)\\n \\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\\n self.PlainFunc = self.PlainFunc(self.n_input_features, self.n_output_features)\\n self.get_cpu(\\\"weight\\\").data.copy_(self.PlainFunc.weight.data)\\n self.get_cpu(\\\"bias\\\").data.copy_(self.PlainFunc.bias.data)\\n self.transfer_cpu_to_enclave(\\\"weight\\\")\\n self.transfer_cpu_to_enclave(\\\"bias\\\")\\n self.sgx_linear_init(\\n self.LayerName,\\n \\\"input\\\", \\\"output\\\", \\\"weight\\\", \\\"bias\\\",\\n # \\\"DerInput\\\", \\\"DerOutput\\\", \\\"DerWeight\\\", \\\"DerBias\\\",\\n self.batch_size, self.n_input_features, self.n_output_features)\\n else:\\n self.ForwardFunc = self.ForwardFunc(self.n_input_features, self.n_output_features)\\n self.PlainFunc = self.PlainFunc(self.n_input_features, self.n_output_features)\\n self.ForwardFunc.weight.data.copy_(self.PlainFunc.weight.data)\\n self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\\n if self.EnclaveMode is ExecutionModeOptions.CPU:\\n self.set_cpu(\\\"weight\\\", list(self.ForwardFunc.parameters())[0].data)\\n self.set_cpu(\\\"bias\\\", list(self.ForwardFunc.parameters())[1].data)\\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\\n self.set_gpu(\\\"weight\\\", list(self.ForwardFunc.parameters())[0].data)\\n self.set_gpu(\\\"bias\\\", list(self.ForwardFunc.parameters())[1].data)\\n self.ForwardFunc.cuda()\\n # print(\\\"======== SGX linear init finish\\\")\\n\\n def link_tensors(self):\\n super().link_tensors()\\n\\n def init_params(self):\\n cpu_w = torch.zeros(self.w_shape)\\n torch.nn.init.xavier_normal_(cpu_w, 1)\\n self.set_tensor_cpu_enclave(\\\"weight\\\", cpu_w)\\n cpu_b = torch.zeros(self.b_shape)\\n torch.nn.init.constant_(cpu_b, 0)\\n self.set_tensor_cpu_enclave(\\\"bias\\\", cpu_b)\\n\\n def get_output_shape(self):\\n return self.OutputShape\\n\\n def inject_params(self, params):\\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\\n cpu_w = self.get_cpu(\\\"weight\\\")\\n cpu_w.copy_(params.weight.data)\\n self.transfer_cpu_to_enclave(\\\"weight\\\")\\n cpu_b = self.get_cpu(\\\"bias\\\")\\n cpu_b.copy_(params.bias.data)\\n self.transfer_cpu_to_enclave(\\\"bias\\\")\\n elif self.EnclaveMode is ExecutionModeOptions.CPU:\\n cpu_w = self.get_cpu(\\\"weight\\\")\\n cpu_w.copy_(params.weight.data)\\n cpu_b = self.get_cpu(\\\"bias\\\")\\n cpu_b.copy_(params.bias.data)\\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\\n cpu_w = self.get_gpu(\\\"weight\\\")\\n cpu_w.copy_(params.weight.data)\\n cpu_b = self.get_gpu(\\\"bias\\\")\\n cpu_b.copy_(params.bias.data)\\n\\n def inject_to_plain(self, plain_layer: torch.nn.Module) -> None:\\n self.make_sure_cpu_is_latest(\\\"weight\\\")\\n plain_layer.weight.data.copy_(self.get_cpu(\\\"weight\\\"))\\n self.make_sure_cpu_is_latest(\\\"bias\\\")\\n plain_layer.bias.data.copy_(self.get_cpu(\\\"bias\\\"))\\n\\n def generate_tensor_name_list(self, force=False):\\n if not force and self.tensor_name_list:\\n return\\n NeededTensorNames = [(\\\"output\\\", self.OutputShape, None),\\n # (\\\"DerInput\\\", self.InputShape, None),\\n (\\\"input\\\", self.InputShape, None),\\n # (\\\"DerOutput\\\", self.OutputShape, None),\\n (\\\"weight\\\", self.WeightShape, None),\\n (\\\"bias\\\", self.BiasShape, None),\\n ]\\n\\n self.tensor_name_list = NeededTensorNames\\n\\n def forward(self):\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} Forward\\\", verbose_level=VerboseLevel.LAYER):\\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\\n self.forward_tensor_transfer()\\n self.sgx_linear_forward(self.LayerName)\\n elif self.EnclaveMode == ExecutionModeOptions.CPU:\\n self.forward_tensor_transfer()\\n self.requires_grad_on_cpu(\\\"input\\\")\\n self.ForwardFunc.weight.data.copy_(self.get_cpu(\\\"weight\\\"))\\n self.ForwardFunc.bias.data.copy_(self.get_cpu(\\\"bias\\\"))\\n self.set_cpu(\\\"output\\\", self.ForwardFunc(self.get_cpu(\\\"input\\\")))\\n elif self.EnclaveMode == ExecutionModeOptions.GPU:\\n self.forward_tensor_transfer()\\n self.ForwardFunc.weight.data.copy_(self.get_gpu(\\\"weight\\\"))\\n self.ForwardFunc.bias.data.copy_(self.get_gpu(\\\"bias\\\"))\\n self.set_gpu(\\\"output\\\", self.ForwardFunc(self.get_gpu(\\\"input\\\").type(SecretConfig.dtypeForCpuOp)))\\n\\n def plain_forward(self, NeedBackward=False):\\n if self.is_enclave_mode:\\n self.make_sure_cpu_is_latest(\\\"input\\\")\\n self.make_sure_cpu_is_latest(\\\"weight\\\")\\n self.make_sure_cpu_is_latest(\\\"bias\\\")\\n # self.requires_grad_on_cpu(\\\"input\\\")\\n self.PlainFunc.weight.data.copy_(self.get_cpu(\\\"weight\\\"))\\n self.PlainFunc.bias.data.copy_(self.get_cpu(\\\"bias\\\"))\\n else:\\n self.make_sure_cpu_is_latest(\\\"input\\\")\\n self.requires_grad_on_cpu(\\\"input\\\")\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} PlainForward\\\"):\\n # torch.set_num_threads(1)\\n self.PlainForwardResult = self.PlainFunc(self.get_cpu(\\\"input\\\"))\\n # torch.set_num_threads(4)\\n\\n def show_plain_error_forward(self):\\n self.make_sure_cpu_is_latest(\\\"output\\\")\\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\\\"output\\\"), get_relative=True)\\n print(f\\\"S{self.sid}: {self.LayerName} Forward Error: {err}\\\")\"\n },\n {\n \"identifier\": \"SGXConvBase\",\n \"path\": \"python/layers/sgx_conv_base.py\",\n \"snippet\": \"class SGXConvBase(SecretLayerBase):\\n batch_size = None\\n pytorch_x_shape, sgx_x_shape = None, None\\n pytorch_w_shape, sgx_w_shape = None, None\\n bias_shape = None\\n pytorch_y_shape, sgx_y_shape = None, None\\n\\n def __init__(\\n self, sid, LayerName, EnclaveMode,\\n n_output_channel, filter_hw, stride, padding, batch_size=None, n_input_channel=None,\\n img_hw=None, bias=True,\\n is_enclave_mode=False, link_prev=True, link_next=True, manually_register_prev=False, manually_register_next=False\\n ):\\n super().__init__(sid, LayerName, EnclaveMode, link_prev, link_next, manually_register_prev, manually_register_next)\\n\\n self.ForwardFuncName = \\\"SGXConv\\\"\\n self.BackwardFuncName = \\\"DerSGXConv\\\"\\n self.PlainFunc = torch.nn.Conv2d\\n self.is_enclave_mode = is_enclave_mode\\n self.batch_size = batch_size\\n self.n_input_channel = n_input_channel\\n self.n_output_channel = n_output_channel\\n self.img_hw = img_hw\\n self.filter_hw = filter_hw\\n self.padding = padding\\n self.stride = stride\\n self.bias = bias\\n\\n if EnclaveMode is ExecutionModeOptions.CPU or EnclaveMode is ExecutionModeOptions.GPU:\\n self.ForwardFunc = torch.nn.Conv2d\\n\\n # --------------\\n # Add BIAS!!!!!\\n # --------------\\n\\n def init_shape(self):\\n if self.batch_size is None and self.PrevLayer is not None:\\n self.pytorch_x_shape = self.PrevLayer.get_output_shape()\\n self.batch_size, self.n_input_channel, self.img_hw, _ = self.pytorch_x_shape\\n else:\\n self.pytorch_x_shape = [self.batch_size, self.n_input_channel, self.img_hw, self.img_hw]\\n # print(self.LayerName)\\n # st()\\n # BHWC\\n self.sgx_x_shape = [self.pytorch_x_shape[0], self.pytorch_x_shape[2], self.pytorch_x_shape[3], self.pytorch_x_shape[1]]\\n # pytorch weight is out * in * h * w\\n self.pytorch_w_shape = [self.n_output_channel, self.n_input_channel, self.filter_hw, self.filter_hw]\\n # w shape is in * w * h * out, the transpose of out * h * w * in\\n self.sgx_w_shape = [self.n_output_channel, self.filter_hw, self.filter_hw, self.n_input_channel]\\n # BCHW\\n self.pytorch_y_shape = calc_conv2d_output_shape_stride(self.pytorch_x_shape, self.pytorch_w_shape, self.padding, self.stride)\\n # BHWC\\n self.sgx_y_shape = [self.pytorch_y_shape[0], self.pytorch_y_shape[2], self.pytorch_y_shape[3], self.pytorch_y_shape[1]]\\n self.bias_shape = [self.n_output_channel]\\n\\n # print(\\n # f\\\"Init_shape pytorch_input {self.pytorch_x_shape}, sgx_input {self.sgx_x_shape}, \\\"\\n # f\\\"pytorch_output {self.pytorch_y_shape}, sgx_output {self.sgx_y_shape}, \\\"\\n # f\\\"pytorch_weight {self.pytorch_w_shape}, sgx_weight {self.sgx_w_shape}, \\\"\\n # f\\\"bias {self.bias_shape}\\\"\\n # )\\n\\n self.LearnableParamsList = [\\n LearnableParamTuple(dw_name=\\\"DerWeight\\\", w_name=\\\"weight\\\", shape=self.sgx_w_shape),\\n LearnableParamTuple(dw_name=\\\"DerBias\\\", w_name=\\\"bias\\\", shape=self.bias_shape),\\n ]\\n\\n def init(self, start_enclave=True):\\n # print(f\\\"Weight shape {self.sgx_w_shape}\\\")\\n TensorLoader.init(self, start_enclave)\\n \\n \\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\\n self.PlainFunc = self.PlainFunc(\\n self.n_input_channel, self.n_output_channel, self.filter_hw,\\n self.stride, self.padding, bias=self.bias)\\n weight_pytorch_form = self.PlainFunc.weight.data\\n weight_tf_form = self.weight_pytorch2tf(weight_pytorch_form)\\n self.get_cpu(\\\"weight\\\").data.copy_(weight_tf_form)\\n self.transfer_cpu_to_enclave(\\\"weight\\\")\\n # Bias\\n if self.bias:\\n bias_data = self.PlainFunc.bias.data\\n else:\\n bias_data = torch.zeros(self.bias_shape)\\n self.get_cpu(\\\"bias\\\").data.copy_(bias_data)\\n self.transfer_cpu_to_enclave(\\\"bias\\\")\\n self.sgx_conv_init(\\n self.LayerName,\\n \\\"sgx_input\\\", \\\"sgx_output\\\", \\\"weight\\\", \\\"bias\\\",\\n # \\\"sgx_DerInput\\\", \\\"sgx_DerOutput\\\", \\\"DerWeight\\\", \\\"DerBias\\\",\\n # \\\"input\\\", \\\"output\\\", \\\"weight\\\", \\n # \\\"DerInput\\\", \\\"DerOutput\\\", \\\"DerWeight\\\", \\n self.batch_size, self.img_hw, self.img_hw, self.n_input_channel, \\n self.pytorch_y_shape[2], self.pytorch_y_shape[3], self.n_output_channel, \\n self.filter_hw, self.padding, self.stride)\\n elif self.EnclaveMode in[ ExecutionModeOptions.CPU, ExecutionModeOptions.GPU]:\\n self.ForwardFunc = self.ForwardFunc(\\n self.n_input_channel, self.n_output_channel, self.filter_hw,\\n self.stride, self.padding, bias=self.bias)\\n self.PlainFunc = self.PlainFunc(\\n self.n_input_channel, self.n_output_channel, self.filter_hw,\\n self.stride, self.padding, bias=self.bias)\\n self.ForwardFunc.weight.data.copy_(self.PlainFunc.weight.data)\\n weight_pytorch_form = list(self.ForwardFunc.parameters())[0].data\\n weight_tf_form = self.weight_pytorch2tf(weight_pytorch_form)\\n if self.EnclaveMode is ExecutionModeOptions.CPU:\\n self.set_cpu(\\\"weight\\\", weight_tf_form)\\n if self.bias:\\n self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\\n bias_data = self.PlainFunc.bias.data\\n self.set_cpu(\\\"bias\\\", bias_data)\\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\\n self.set_gpu(\\\"weight\\\", weight_tf_form)\\n if self.bias:\\n self.ForwardFunc.bias.data.copy_(self.PlainFunc.bias.data)\\n bias_data = self.PlainFunc.bias.data\\n self.set_gpu(\\\"bias\\\", bias_data)\\n self.ForwardFunc.cuda()\\n\\n\\n def link_tensors(self):\\n super().link_tensors()\\n\\n def init_params(self):\\n cpu_w = torch.zeros(self.sgx_w_shape)\\n torch.nn.init.xavier_normal_(cpu_w, 1)\\n self.set_tensor_cpu_gpu_enclave(\\\"weight\\\", cpu_w)\\n\\n def get_output_shape(self):\\n return self.pytorch_y_shape\\n \\n def weight_pytorch2tf(self, weight_pytorch_form):\\n # weight_pytorch_form is out * in * h * w\\n # out * (h * w) * in, \\n # h and w dont transpose\\n # weight_tf_form = weight_pytorch_form.permute(1,3,2,0).contiguous()\\n weight_tf_form = weight_pytorch_form.permute(0,2,3,1).contiguous()\\n return weight_tf_form\\n\\n def weight_tf2pytorch(self, weight_tf_form):\\n # weight_tf_form is out * (h * w) * in, the transpose of out * (h * w) * in\\n # out * in * h * w\\n # h and w dont transpose\\n # weight_pytorch_form = weight_tf_form.permute(3, 0, 2, 1).contiguous()\\n weight_pytorch_form = weight_tf_form.permute(0,3,1,2).contiguous()\\n return weight_pytorch_form\\n\\n def feature_pytorch2tf(self, tensor_pytorch_form):\\n # tensor_pytorch_form is b * in * h * w\\n # b * h * w * in\\n tensor_tf_form = tensor_pytorch_form.permute(0, 2, 3, 1).contiguous()\\n return tensor_tf_form\\n \\n def feature_tf2pytorch(self, tensor_tf_form):\\n # tensor_tf_form is b * h * w * in\\n # b * in * h * w\\n tensor_pytorch_form = tensor_tf_form.permute(0, 3, 1, 2).contiguous()\\n return tensor_pytorch_form\\n\\n def inject_params(self, params):\\n if self.EnclaveMode is ExecutionModeOptions.Enclave:\\n cpu_w = self.get_cpu(\\\"weight\\\")\\n weight_pytorch_form = params.weight.data\\n weight_tf_form = self.weight_pytorch2tf(weight_pytorch_form)\\n cpu_w.copy_(weight_tf_form)\\n self.transfer_cpu_to_enclave(\\\"weight\\\")\\n\\n # bias\\n assert (\\n (self.bias and params.bias is not None) or\\n (not self.bias and params.bias is None)\\n )\\n if self.bias:\\n bias_data = params.bias.data\\n else:\\n bias_data = torch.zeros(self.n_output_channel)\\n cpu_b = self.get_cpu(\\\"bias\\\")\\n cpu_b.copy_(bias_data)\\n self.transfer_cpu_to_enclave(\\\"bias\\\")\\n elif self.EnclaveMode is ExecutionModeOptions.CPU:\\n weight_pytorch_form = params.weight.data\\n weight_tf_form = self.weight_pytorch2tf(weight_pytorch_form)\\n self.get_cpu(\\\"weight\\\").copy_(weight_tf_form)\\n # bias\\n assert (\\n (self.bias and params.bias is not None) or\\n (not self.bias and params.bias is None)\\n )\\n if self.bias:\\n self.get_cpu(\\\"bias\\\").copy_(params.bias.data)\\n\\n # Move weight to ForwardFunc\\n weight_tf_form = self.get_cpu(\\\"weight\\\")\\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\\n self.ForwardFunc.weight.data.copy_(weight_pytorch_form)\\n\\n elif self.EnclaveMode is ExecutionModeOptions.GPU:\\n weight_pytorch_form = params.weight.data\\n weight_tf_form = self.weight_pytorch2tf(weight_pytorch_form)\\n self.get_gpu(\\\"weight\\\").copy_(weight_tf_form)\\n # bias\\n assert (\\n (self.bias and params.bias is not None) or\\n (not self.bias and params.bias is None)\\n )\\n if self.bias:\\n self.get_gpu(\\\"bias\\\").copy_(params.bias.data)\\n\\n # Move weight to ForwardFunc\\n weight_tf_form = self.get_gpu(\\\"weight\\\")\\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\\n self.ForwardFunc.weight.data.copy_(weight_pytorch_form)\\n\\n\\n def inject_to_plain(self, plain_layer: torch.nn.Module) -> None:\\n self.make_sure_cpu_is_latest(\\\"weight\\\")\\n weight_tf_form = self.get_cpu(\\\"weight\\\")\\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\\n plain_layer.weight.data.copy_(weight_pytorch_form)\\n\\n assert (\\n (self.bias and plain_layer.bias is not None) or\\n (not self.bias and plain_layer.bias is None)\\n )\\n if self.bias:\\n self.make_sure_cpu_is_latest(\\\"bias\\\")\\n bias_data = self.get_cpu(\\\"bias\\\")\\n plain_layer.weight.data.copy_(bias_data)\\n\\n def generate_tensor_name_list(self, force=False):\\n if not force and self.tensor_name_list:\\n return\\n NeededTensorNames = [(\\\"output\\\", self.pytorch_y_shape, None), (\\\"sgx_output\\\", self.sgx_y_shape, None),\\n (\\\"DerInput\\\", self.pytorch_x_shape, None), (\\\"sgx_DerInput\\\", self.sgx_x_shape, None),\\n (\\\"input\\\", self.pytorch_x_shape, None), (\\\"sgx_input\\\", self.sgx_x_shape, None),\\n (\\\"DerOutput\\\", self.pytorch_y_shape, None), (\\\"sgx_DerOutput\\\", self.sgx_y_shape, None),\\n (\\\"weight\\\", self.sgx_w_shape, None),\\n (\\\"bias\\\", self.bias_shape, None),\\n ]\\n self.tensor_name_list = NeededTensorNames\\n\\n\\n def forward(self):\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} Forward\\\", verbose_level=VerboseLevel.LAYER):\\n self.forward_tensor_transfer(\\\"input\\\")\\n if self.EnclaveMode == ExecutionModeOptions.Enclave:\\n \\n # \\\"input\\\" is pytorch form\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} Input Preprocess\\\", verbose_level=VerboseLevel.LAYER):\\n if self.PrevLayer.EnclaveMode is ExecutionModeOptions.Enclave:\\n self.transfer_enclave_to_cpu(\\\"input\\\")\\n input_pytorch_form = self.get_cpu(\\\"input\\\")\\n \\n if torch.sum(self.get_cpu(\\\"input\\\").abs()) == 0:\\n print(self.LayerName)\\n raise RuntimeError(\\\"SGX input not load\\\")\\n input_tf_form = self.feature_pytorch2tf(input_pytorch_form)\\n self.set_cpu(\\\"sgx_input\\\", input_tf_form)\\n self.transfer_cpu_to_enclave(\\\"sgx_input\\\")\\n # self.forward_tensor_transfer(\\\"sgx_input\\\")\\n # print(self.get_cpu(\\\"sgx_input\\\").squeeze())\\n \\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} sgx_conv_forward\\\", verbose_level=VerboseLevel.LAYER):\\n # if self.LayerName == \\\"Layer2.0.downsample.conv\\\":\\n # st()\\n self.sgx_conv_forward(self.LayerName)\\n \\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} Output Postprocess\\\", verbose_level=VerboseLevel.LAYER):\\n self.make_sure_cpu_is_latest(\\\"sgx_output\\\")\\n output_tf_form = self.get_cpu(\\\"sgx_output\\\")\\n output_pytorch_form = self.feature_tf2pytorch(output_tf_form)\\n self.set_cpu(\\\"output\\\", output_pytorch_form)\\n self.transfer_cpu_to_enclave(\\\"output\\\")\\n elif self.EnclaveMode == ExecutionModeOptions.CPU:\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} Input Preprocess\\\", verbose_level=VerboseLevel.LAYER):\\n self.forward_tensor_transfer()\\n # self.requires_grad_on_cpu(\\\"input\\\")\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} Weight Transfer\\\", verbose_level=VerboseLevel.LAYER):\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} get weight_tf_form\\\", verbose_level=VerboseLevel.LAYER):\\n weight_tf_form = self.get_cpu(\\\"weight\\\")\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} weight_tf2pytorch\\\", verbose_level=VerboseLevel.LAYER):\\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} copy data\\\", verbose_level=VerboseLevel.LAYER):\\n self.ForwardFunc.weight.data.copy_(weight_pytorch_form)\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} GPU conv forward\\\", verbose_level=VerboseLevel.LAYER):\\n self.set_cpu(\\\"output\\\", self.ForwardFunc(self.get_cpu(\\\"input\\\")))\\n elif self.EnclaveMode == ExecutionModeOptions.GPU:\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} Input Preprocess\\\", verbose_level=VerboseLevel.LAYER):\\n self.forward_tensor_transfer()\\n # self.requires_grad_on_cpu(\\\"input\\\")\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} Weight Transfer\\\", verbose_level=VerboseLevel.LAYER):\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} get weight_tf_form\\\", verbose_level=VerboseLevel.LAYER):\\n weight_tf_form = self.get_gpu(\\\"weight\\\")\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} weight_tf2pytorch\\\", verbose_level=VerboseLevel.LAYER):\\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} copy data\\\", verbose_level=VerboseLevel.LAYER):\\n self.ForwardFunc.weight.data.copy_(weight_pytorch_form)\\n with NamedTimerInstance(f\\\" S{self.sid}: {self.LayerName} GPU conv forward\\\", verbose_level=VerboseLevel.LAYER):\\n self.set_gpu(\\\"output\\\", self.ForwardFunc(self.get_gpu(\\\"input\\\").type(SecretConfig.dtypeForCpuOp)))\\n\\n\\n def plain_forward(self, NeedBackward=False):\\n if self.EnclaveMode == ExecutionModeOptions.Enclave:\\n self.make_sure_cpu_is_latest(\\\"input\\\")\\n self.make_sure_cpu_is_latest(\\\"weight\\\")\\n if self.bias:\\n self.make_sure_cpu_is_latest(\\\"bias\\\")\\n # self.requires_grad_on_cpu(\\\"input\\\")\\n weight_tf_form = self.get_cpu(\\\"weight\\\")\\n weight_pytorch_form = self.weight_tf2pytorch(weight_tf_form)\\n self.PlainFunc.weight.data.copy_(weight_pytorch_form)\\n if self.bias:\\n bias_data = self.get_cpu(\\\"bias\\\")\\n self.PlainFunc.bias.data.copy_(bias_data)\\n elif self.EnclaveMode in [ExecutionModeOptions.CPU, ExecutionModeOptions.GPU]:\\n self.make_sure_cpu_is_latest(\\\"input\\\")\\n self.requires_grad_on_cpu(\\\"input\\\")\\n with NamedTimerInstance(f\\\"S{self.sid}: {self.LayerName} PlainForward\\\"):\\n # torch.set_num_threads(1)\\n self.PlainForwardResult = self.PlainFunc(self.get_cpu(\\\"input\\\"))\\n # torch.set_num_threads(4)\\n\\n def show_plain_error_forward(self):\\n err = compare_expected_actual(self.PlainForwardResult, self.get_cpu(\\\"output\\\"), get_relative=True)\\n print(f\\\"S{self.sid}: {self.LayerName} Forward Error: {err}\\\")\\n\\n def print_connection_info(self):\\n print(f\\\"{self.LayerName:20} shape{self.pytorch_x_shape}{' ':20} mode{self.EnclaveMode}{' ':20} input {self.PrevLayer.LayerName:20} output {self.NextLayer.LayerName:20}\\\")\"\n },\n {\n \"identifier\": \"ExecutionModeOptions\",\n \"path\": \"python/utils/basic_utils.py\",\n \"snippet\": \"class ExecutionModeOptions(Enum):\\n Enclave = 1\\n CPU = 2\\n GPU = 3\"\n },\n {\n \"identifier\": \"Logger\",\n \"path\": \"python/utils/logger_utils.py\",\n \"snippet\": \"class Logger(object):\\n logfile_path = \\\"logfile.log\\\"\\n\\n def __init__(self):\\n self.terminal = sys.stdout\\n self.log = open(self.logfile_path, \\\"a\\\")\\n\\n def reset_logfile(self, path):\\n self.logfile_path = path\\n self.log = open(self.logfile_path, \\\"a\\\")\\n\\n def write(self, message):\\n self.terminal.write(message)\\n self.log.write(message)\\n\\n def flush(self):\\n #this flush method is needed for python 3 compatibility.\\n #this handles the flush command by doing nothing.\\n #you might want to specify some extra behavior here.\\n # pass\\n self.terminal.flush()\\n self.log.flush()\"\n },\n {\n \"identifier\": \"NamedTimerInstance\",\n \"path\": \"python/utils/timer_utils.py\",\n \"snippet\": \"class NamedTimerInstance(object):\\n def __init__(self, name, verbose_level=VerboseLevel.EVERY):\\n self.name = name\\n self.verbose_level = verbose_level\\n\\n def __enter__(self):\\n return NamedTimer.start(self.name, verbose_level=self.verbose_level)\\n ...\\n\\n def __exit__(self, *args):\\n NamedTimer.end(self.name)\\n ...\"\n },\n {\n \"identifier\": \"VerboseLevel\",\n \"path\": \"python/utils/timer_utils.py\",\n \"snippet\": \"class VerboseLevel(IntEnum):\\n EVERY = 1\\n LAYER = 2\\n RUN = 3\\n EPOCH = 4\"\n },\n {\n \"identifier\": \"NamedTimer\",\n \"path\": \"python/utils/timer_utils.py\",\n \"snippet\": \"class NamedTimer(object):\\n __instance = None\\n\\n @staticmethod\\n def get_instance():\\n if NamedTimer.__instance is None:\\n NamedTimer()\\n return NamedTimer.__instance\\n\\n def __init__(self):\\n NamedTimer.__instance = self\\n self.timers = {}\\n self.verbose_level = VerboseLevel.EVERY\\n\\n @staticmethod\\n def start_timer(name, **kwargs):\\n NamedTimer.get_instance().timers[name] = Timer(name, **kwargs)\\n return NamedTimer.get_instance().timers[name]\\n\\n @staticmethod\\n def start(name, **kwargs):\\n return NamedTimer.get_instance().start_timer(name, **kwargs)\\n\\n @staticmethod\\n def end_timer(name, **kwargs):\\n NamedTimer.get_instance().timers[name].end(**kwargs)\\n\\n @staticmethod\\n def end(name, tmp_name=None):\\n # print(NamedTimer.get_instance().timers[name].verbose_level, NamedTimer.get_instance().verbose_level)\\n NamedTimer.get_instance().end_timer(name, tmp_name=tmp_name)\\n\\n @staticmethod\\n def set_verbose_level(verbose_level):\\n if not isinstance(verbose_level, VerboseLevel):\\n raise ValueError(\\\"Please set an enum from VerboseLevel\\\")\\n NamedTimer.get_instance().verbose_level = verbose_level\"\n },\n {\n \"identifier\": \"compare_expected_actual\",\n \"path\": \"python/utils/torch_utils.py\",\n \"snippet\": \"def compare_expected_actual(expected, actual, show_where_err=False, get_relative=False, verbose=False, show_values=False):\\n def purify(x):\\n # return torch.tensor(x)\\n res = x\\n # if not (isinstance(x, torch.Tensor) or isinstance(x, torch.Variable)):\\n if not (isinstance(x, torch.Tensor) ):\\n res = torch.tensor(x)\\n # return x.detach().numpy()\\n return res.type(torch.float).to(\\\"cpu\\\")\\n expected = purify(expected)\\n actual = purify(actual)\\n\\n if show_values:\\n print(\\\"expected:\\\", expected[0, 0])\\n print(\\\"actual:\\\", actual[0, 0])\\n\\n avg_abs_diff = torch.mean(torch.abs(expected - actual)).item()\\n res = avg_abs_diff\\n\\n if show_where_err:\\n show_indices = torch.abs(expected - actual) / torch.abs(expected) > 0.5\\n # show_indices = (expected != actual)\\n print(\\\"error indices: \\\", np.where(show_indices.cpu()))\\n print(\\\"expected values:\\\", expected[show_indices])\\n print(\\\"difference:\\\", (expected - actual)[show_indices])\\n\\n if get_relative:\\n tmp_expected, tmp_actual = expected[expected != 0], actual[expected != 0]\\n relative_diff = torch.abs(tmp_expected - tmp_actual) / torch.abs(tmp_expected)\\n relative_avg_diff = torch.mean(torch.abs(tmp_actual - tmp_expected)) / torch.mean(torch.abs(tmp_expected))\\n Error = namedtuple(\\\"Error\\\", (\\\"AvgAbsDiff\\\", \\\"RelAvgDiff\\\", \\\"AvgRelDiff\\\", \\\"StdRelDiff\\\"))\\n res = Error(avg_abs_diff, relative_avg_diff.item(), torch.mean(relative_diff).item(), torch.std(relative_diff).item())\\n\\n if verbose:\\n print(res)\\n\\n return res\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport sys\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport sys\nimport pdb\nfrom pdb import set_trace as st\nfrom torch import optim, nn\nfrom python.common_net import register_layer, register_weight_layer, get_layer_weight, get_layer_input, \\\n get_layer_weight_grad, get_layer_output, get_layer_output_grad, get_layer_input_grad\nfrom python.enclave_interfaces import GlobalTensor\nfrom python.layers.batch_norm_2d import SecretBatchNorm2dLayer\nfrom python.layers.flatten import SecretFlattenLayer\nfrom python.layers.input import SecretInputLayer\nfrom python.layers.maxpool2d import SecretMaxpool2dLayer\nfrom python.layers.output import SecretOutputLayer\nfrom python.layers.relu import SecretReLULayer\nfrom python.sgx_net import init_communicate, warming_up_cuda, SecretNeuralNetwork, SgdOptimizer\nfrom python.layers.sgx_linear_base import SGXLinearBase\nfrom python.layers.sgx_conv_base import SGXConvBase\nfrom python.utils.basic_utils import ExecutionModeOptions\nfrom python.utils.logger_utils import Logger\nfrom python.quantize_net import NetQ\nfrom python.test_sgx_net import argparser_distributed, marshal_process, load_cifar10, seed_torch\nfrom python.utils.timer_utils import NamedTimerInstance, VerboseLevel, NamedTimer\nfrom python.utils.torch_utils import compare_expected_actual\nfrom pdb import set_trace as st"},"token_num":{"kind":"number","value":21043,"string":"21,043"},"cropped_code":{"kind":"string","value":"\n\n\ndevice_cuda = torch.device(\"cuda:0\")\ntorch.set_printoptions(precision=10)\ndef compare_layer_member(layer: SGXLinearBase, layer_name: str,\n extract_func , member_name: str, save_path=None) -> None:\n print(member_name)\n layer.make_sure_cpu_is_latest(member_name)\n compare_expected_actual(extract_func(layer_name), layer.get_cpu(member_name), get_relative=True, verbose=True)\n if save_path is not None:\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n print(\"Directory \", save_path, \" Created \")\n else:\n print(\"Directory \", save_path, \" already exists\")\n\n torch.save(extract_func(layer_name), os.path.join(save_path, member_name + \"_expected\"))\n torch.save(layer.get_cpu(member_name), os.path.join(save_path, member_name + \"_actual\"))\n\n\ndef compare_layer(layer: SGXLinearBase, layer_name: str, save_path=None) -> None:\n print(\"comparing with layer in expected NN :\", layer_name)\n"},"all_code":{"kind":"string","value":"\n\n\ndevice_cuda = torch.device(\"cuda:0\")\ntorch.set_printoptions(precision=10)\ndef compare_layer_member(layer: SGXLinearBase, layer_name: str,\n extract_func , member_name: str, save_path=None) -> None:\n print(member_name)\n layer.make_sure_cpu_is_latest(member_name)\n compare_expected_actual(extract_func(layer_name), layer.get_cpu(member_name), get_relative=True, verbose=True)\n if save_path is not None:\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n print(\"Directory \", save_path, \" Created \")\n else:\n print(\"Directory \", save_path, \" already exists\")\n\n torch.save(extract_func(layer_name), os.path.join(save_path, member_name + \"_expected\"))\n torch.save(layer.get_cpu(member_name), os.path.join(save_path, member_name + \"_actual\"))\n\n\ndef compare_layer(layer: SGXLinearBase, layer_name: str, save_path=None) -> None:\n print(\"comparing with layer in expected NN :\", layer_name)"},"next_line":{"kind":"string","value":" compare_name_function = [(\"input\", get_layer_input), (\"output\", get_layer_output),"},"gold_snippet_index":{"kind":"number","value":5,"string":"5"},"created_at":{"kind":"string","value":"2023-11-01 10:37:37+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5277,"cells":{"repo_name":{"kind":"string","value":"Codra-Ingenierie-Informatique/DataLab"},"file_path":{"kind":"string","value":"cdl/tests/scenarios/common.py"},"context":{"kind":"list like","value":[{"identifier":"_","path":"cdl/config.py","snippet":"CONF_VERSION = \"1.0.0\"\nAPP_NAME = \"DataLab\"\nMOD_NAME = \"cdl\"\nAPP_DESC = _(\"\"\"DataLab is a generic signal and image processing platform\"\"\")\nAPP_PATH = osp.dirname(__file__)\nDEBUG = os.environ.get(\"DEBUG\", \"\").lower() in (\"1\", \"true\")\nTEST_SEGFAULT_ERROR = len(os.environ.get(\"TEST_SEGFAULT_ERROR\", \"\")) > 0\nDATETIME_FORMAT = \"%d/%m/%Y - %H:%M:%S\"\nDATAPATH = configtools.get_module_data_path(MOD_NAME, \"data\")\nSHOTPATH = osp.join(\n configtools.get_module_data_path(MOD_NAME), os.pardir, \"doc\", \"images\", \"shots\"\n)\nOTHER_PLUGINS_PATHLIST = [configtools.get_module_data_path(MOD_NAME, \"plugins\")]\nIS_FROZEN = is_frozen(MOD_NAME)\nPLOTPY_DEFAULTS = {\n \"plot\": {\n # \"antialiasing\": False,\n # \"title/font/size\": 12,\n # \"title/font/bold\": False,\n # \"marker/curve/text/font/size\": 8,\n # \"marker/curve/text/font/family\": \"default\",\n # \"marker/curve/text/font/bold\": False,\n # \"marker/curve/text/font/italic\": False,\n \"marker/curve/text/textcolor\": \"black\",\n # \"marker/curve/text/background_color\": \"#ffffff\",\n # \"marker/curve/text/background_alpha\": 0.8,\n # \"marker/cross/text/font/family\": \"default\",\n # \"marker/cross/text/font/size\": 8,\n # \"marker/cross/text/font/bold\": False,\n # \"marker/cross/text/font/italic\": False,\n \"marker/cross/text/textcolor\": \"black\",\n # \"marker/cross/text/background_color\": \"#ffffff\",\n \"marker/cross/text/background_alpha\": 0.7,\n # \"marker/cross/line/style\": \"DashLine\",\n # \"marker/cross/line/color\": \"yellow\",\n # \"marker/cross/line/width\": 1,\n # \"marker/cursor/text/font/size\": 8,\n # \"marker/cursor/text/font/family\": \"default\",\n # \"marker/cursor/text/font/bold\": False,\n # \"marker/cursor/text/font/italic\": False,\n # \"marker/cursor/text/textcolor\": \"#ff9393\",\n # \"marker/cursor/text/background_color\": \"#ffffff\",\n # \"marker/cursor/text/background_alpha\": 0.8,\n \"shape/drag/symbol/marker\": \"NoSymbol\",\n \"shape/mask/symbol/size\": 5,\n \"shape/mask/sel_symbol/size\": 8,\n # -----------------------------------------------------------------------------\n # Annotated shape style for annotations:\n \"shape/annotation/line/style\": \"SolidLine\",\n \"shape/annotation/line/color\": \"#ffff00\",\n \"shape/annotation/line/width\": 1,\n \"shape/annotation/fill/style\": \"SolidPattern\",\n \"shape/annotation/fill/color\": MAIN_BG_COLOR,\n \"shape/annotation/fill/alpha\": 0.1,\n \"shape/annotation/symbol/marker\": \"Rect\",\n \"shape/annotation/symbol/size\": 3,\n \"shape/annotation/symbol/edgecolor\": \"#ffff00\",\n \"shape/annotation/symbol/facecolor\": \"#ffff00\",\n \"shape/annotation/symbol/alpha\": 1.0,\n \"shape/annotation/sel_line/style\": \"SolidLine\",\n \"shape/annotation/sel_line/color\": \"#00ff00\",\n \"shape/annotation/sel_line/width\": 1,\n \"shape/annotation/sel_fill/style\": \"SolidPattern\",\n \"shape/annotation/sel_fill/color\": MAIN_BG_COLOR,\n \"shape/annotation/sel_fill/alpha\": 0.1,\n \"shape/annotation/sel_symbol/marker\": \"Rect\",\n \"shape/annotation/sel_symbol/size\": 9,\n \"shape/annotation/sel_symbol/edgecolor\": \"#00aa00\",\n \"shape/annotation/sel_symbol/facecolor\": \"#00ff00\",\n \"shape/annotation/sel_symbol/alpha\": 0.7,\n # -----------------------------------------------------------------------------\n # Annotated shape style for result shapes / signals:\n \"shape/result/s/line/style\": \"SolidLine\",\n \"shape/result/s/line/color\": MAIN_FG_COLOR,\n \"shape/result/s/line/width\": 1,\n \"shape/result/s/fill/style\": \"SolidPattern\",\n \"shape/result/s/fill/color\": MAIN_BG_COLOR,\n \"shape/result/s/fill/alpha\": 0.1,\n \"shape/result/s/symbol/marker\": \"XCross\",\n \"shape/result/s/symbol/size\": 7,\n \"shape/result/s/symbol/edgecolor\": MAIN_FG_COLOR,\n \"shape/result/s/symbol/facecolor\": MAIN_FG_COLOR,\n \"shape/result/s/symbol/alpha\": 1.0,\n \"shape/result/s/sel_line/style\": \"SolidLine\",\n \"shape/result/s/sel_line/color\": \"#00ff00\",\n \"shape/result/s/sel_line/width\": 1,\n \"shape/result/s/sel_fill/style\": \"SolidPattern\",\n \"shape/result/s/sel_fill/color\": MAIN_BG_COLOR,\n \"shape/result/s/sel_fill/alpha\": 0.1,\n \"shape/result/s/sel_symbol/marker\": \"Rect\",\n \"shape/result/s/sel_symbol/size\": 9,\n \"shape/result/s/sel_symbol/edgecolor\": \"#00aa00\",\n \"shape/result/s/sel_symbol/facecolor\": \"#00ff00\",\n \"shape/result/s/sel_symbol/alpha\": 0.7,\n # -----------------------------------------------------------------------------\n # Annotated shape style for result shapes / images:\n \"shape/result/i/line/style\": \"SolidLine\",\n \"shape/result/i/line/color\": \"#ffff00\",\n \"shape/result/i/line/width\": 1,\n \"shape/result/i/fill/style\": \"SolidPattern\",\n \"shape/result/i/fill/color\": MAIN_BG_COLOR,\n \"shape/result/i/fill/alpha\": 0.1,\n \"shape/result/i/symbol/marker\": \"Rect\",\n \"shape/result/i/symbol/size\": 3,\n \"shape/result/i/symbol/edgecolor\": \"#ffff00\",\n \"shape/result/i/symbol/facecolor\": \"#ffff00\",\n \"shape/result/i/symbol/alpha\": 1.0,\n \"shape/result/i/sel_line/style\": \"SolidLine\",\n \"shape/result/i/sel_line/color\": \"#00ff00\",\n \"shape/result/i/sel_line/width\": 1,\n \"shape/result/i/sel_fill/style\": \"SolidPattern\",\n \"shape/result/i/sel_fill/color\": MAIN_BG_COLOR,\n \"shape/result/i/sel_fill/alpha\": 0.1,\n \"shape/result/i/sel_symbol/marker\": \"Rect\",\n \"shape/result/i/sel_symbol/size\": 9,\n \"shape/result/i/sel_symbol/edgecolor\": \"#00aa00\",\n \"shape/result/i/sel_symbol/facecolor\": \"#00ff00\",\n \"shape/result/i/sel_symbol/alpha\": 0.7,\n # -----------------------------------------------------------------------------\n },\n}\ndef is_frozen(module_name: str) -> bool:\ndef get_mod_source_dir() -> str | None:\n def get_def_dict(cls, category: str) -> dict:\n def set_def_dict(cls, category: str, def_dict: dict) -> None:\ndef get_old_log_fname(fname):\ndef initialize():\ndef reset():\nclass MainSection(conf.Section, metaclass=conf.SectionMeta):\nclass ConsoleSection(conf.Section, metaclass=conf.SectionMeta):\nclass IOSection(conf.Section, metaclass=conf.SectionMeta):\nclass ProcSection(conf.Section, metaclass=conf.SectionMeta):\nclass ViewSection(conf.Section, metaclass=conf.SectionMeta):\nclass Conf(conf.Configuration, metaclass=conf.ConfMeta):"},{"identifier":"CDLMainWindow","path":"cdl/core/gui/main.py","snippet":"class CDLMainWindow(QW.QMainWindow, AbstractCDLControl, metaclass=CDLMainWindowMeta):\n \"\"\"DataLab main window\n\n Args:\n console: enable internal console\n hide_on_close: True to hide window on close\n \"\"\"\n\n __instance = None\n\n SIG_READY = QC.Signal()\n SIG_SEND_OBJECT = QC.Signal(object)\n SIG_SEND_OBJECTLIST = QC.Signal(object)\n SIG_CLOSING = QC.Signal()\n\n @staticmethod\n def get_instance(console=None, hide_on_close=False):\n \"\"\"Return singleton instance\"\"\"\n if CDLMainWindow.__instance is None:\n return CDLMainWindow(console, hide_on_close)\n return CDLMainWindow.__instance\n\n def __init__(self, console=None, hide_on_close=False):\n \"\"\"Initialize main window\"\"\"\n CDLMainWindow.__instance = self\n super().__init__()\n win32_fix_title_bar_background(self)\n self.setObjectName(APP_NAME)\n self.setWindowIcon(get_icon(\"DataLab.svg\"))\n\n execenv.log(self, \"Starting initialization\")\n\n self.__restore_pos_and_size()\n\n self.ready_flag = True\n\n self.hide_on_close = hide_on_close\n self.__old_size = None\n self.__memory_warning = False\n self.memorystatus = None\n\n self.console = None\n self.macropanel: MacroPanel = None\n\n self.signal_toolbar: QW.QToolBar = None\n self.image_toolbar: QW.QToolBar = None\n self.signalpanel: SignalPanel = None\n self.imagepanel: ImagePanel = None\n self.tabwidget: QW.QTabWidget = None\n self.docks: dict[AbstractPanel, QW.QDockWidget] = None\n self.h5inputoutput = H5InputOutput(self)\n\n self.openh5_action: QW.QAction = None\n self.saveh5_action: QW.QAction = None\n self.browseh5_action: QW.QAction = None\n self.settings_action: QW.QAction = None\n self.quit_action: QW.QAction = None\n self.auto_refresh_action: QW.QAction = None\n self.showlabel_action: QW.QAction = None\n\n self.file_menu: QW.QMenu = None\n self.edit_menu: QW.QMenu = None\n self.operation_menu: QW.QMenu = None\n self.processing_menu: QW.QMenu = None\n self.computing_menu: QW.QMenu = None\n self.plugins_menu: QW.QMenu = None\n self.view_menu: QW.QMenu = None\n self.help_menu: QW.QMenu = None\n\n self.__is_modified = None\n self.set_modified(False)\n\n # Starting XML-RPC server thread\n self.remote_server = RemoteServer(self)\n if Conf.main.rpc_server_enabled.get():\n self.remote_server.SIG_SERVER_PORT.connect(self.xmlrpc_server_started)\n self.remote_server.start()\n\n # Setup actions and menus\n if console is None:\n console = Conf.console.console_enabled.get()\n self.setup(console)\n\n execenv.log(self, \"Initialization done\")\n\n # ------API related to XML-RPC remote control\n @staticmethod\n def xmlrpc_server_started(port):\n \"\"\"XML-RPC server has started, writing comm port in configuration file\"\"\"\n Conf.main.rpc_server_port.set(port)\n\n def __get_current_basedatapanel(self) -> BaseDataPanel:\n \"\"\"Return the current BaseDataPanel,\n or the signal panel if macro panel is active\n\n Returns:\n BaseDataPanel: current panel\n \"\"\"\n panel = self.tabwidget.currentWidget()\n if not isinstance(panel, base.BaseDataPanel):\n panel = self.signalpanel\n return panel\n\n def __get_specific_panel(self, panel: str | None) -> BaseDataPanel:\n \"\"\"Return a specific BaseDataPanel.\n\n Args:\n panel (str | None): panel name (valid values: \"signal\", \"image\").\n If None, current panel is used.\n\n Returns:\n BaseDataPanel: panel\n\n Raises:\n ValueError: if panel is unknown\n \"\"\"\n if not panel:\n return self.__get_current_basedatapanel()\n if panel == \"signal\":\n return self.signalpanel\n if panel == \"image\":\n return self.imagepanel\n raise ValueError(f\"Unknown panel: {panel}\")\n\n @remote_controlled\n def get_group_titles_with_object_infos(\n self,\n ) -> tuple[list[str], list[list[str]], list[list[str]]]:\n \"\"\"Return groups titles and lists of inner objects uuids and titles.\n\n Returns:\n Tuple: groups titles, lists of inner objects uuids and titles\n \"\"\"\n panel = self.__get_current_basedatapanel()\n return panel.objmodel.get_group_titles_with_object_infos()\n\n @remote_controlled\n def get_object_titles(self, panel: str | None = None) -> list[str]:\n \"\"\"Get object (signal/image) list for current panel.\n Objects are sorted by group number and object index in group.\n\n Args:\n panel (str | None): panel name (valid values: \"signal\", \"image\").\n If None, current panel is used.\n\n Returns:\n list[str]: list of object titles\n\n Raises:\n ValueError: if panel is unknown\n \"\"\"\n return self.__get_specific_panel(panel).objmodel.get_object_titles()\n\n @remote_controlled\n def get_object(\n self,\n nb_id_title: int | str | None = None,\n panel: str | None = None,\n ) -> SignalObj | ImageObj:\n \"\"\"Get object (signal/image) from index.\n\n Args:\n nb_id_title: Object number, or object id, or object title.\n Defaults to None (current object).\n panel: Panel name. Defaults to None (current panel).\n\n Returns:\n Object\n\n Raises:\n KeyError: if object not found\n TypeError: if index_id_title type is invalid\n \"\"\"\n panelw = self.__get_specific_panel(panel)\n if nb_id_title is None:\n return panelw.objview.get_current_object()\n if isinstance(nb_id_title, int):\n return panelw.objmodel.get_object_from_number(nb_id_title)\n if isinstance(nb_id_title, str):\n try:\n return panelw.objmodel[nb_id_title]\n except KeyError:\n try:\n return panelw.objmodel.get_object_from_title(nb_id_title)\n except KeyError as exc:\n raise KeyError(\n f\"Invalid object index, id or title: {nb_id_title}\"\n ) from exc\n raise TypeError(f\"Invalid index_id_title type: {type(nb_id_title)}\")\n\n @remote_controlled\n def get_object_uuids(self, panel: str | None = None) -> list[str]:\n \"\"\"Get object (signal/image) uuid list for current panel.\n Objects are sorted by group number and object index in group.\n\n Args:\n panel (str | None): panel name (valid values: \"signal\", \"image\").\n If None, current panel is used.\n\n Returns:\n list[str]: list of object uuids\n\n Raises:\n ValueError: if panel is unknown\n \"\"\"\n return self.__get_specific_panel(panel).objmodel.get_object_ids()\n\n @remote_controlled\n def get_sel_object_uuids(self, include_groups: bool = False) -> list[str]:\n \"\"\"Return selected objects uuids.\n\n Args:\n include_groups: If True, also return objects from selected groups.\n\n Returns:\n List of selected objects uuids.\n \"\"\"\n panel = self.__get_current_basedatapanel()\n return panel.objview.get_sel_object_uuids(include_groups)\n\n @remote_controlled\n def select_objects(\n self,\n selection: list[int | str],\n panel: str | None = None,\n ) -> None:\n \"\"\"Select objects in current panel.\n\n Args:\n selection: List of object numbers (1 to N) or uuids to select\n panel: panel name (valid values: \"signal\", \"image\").\n If None, current panel is used. Defaults to None.\n \"\"\"\n panel = self.__get_specific_panel(panel)\n panel.objview.select_objects(selection)\n\n @remote_controlled\n def select_groups(\n self, selection: list[int | str] | None = None, panel: str | None = None\n ) -> None:\n \"\"\"Select groups in current panel.\n\n Args:\n selection: List of group numbers (1 to N), or list of group uuids,\n or None to select all groups. Defaults to None.\n panel (str | None): panel name (valid values: \"signal\", \"image\").\n If None, current panel is used. Defaults to None.\n \"\"\"\n panel = self.__get_specific_panel(panel)\n panel.objview.select_groups(selection)\n\n @remote_controlled\n def delete_metadata(self, refresh_plot: bool = True) -> None:\n \"\"\"Delete metadata of selected objects\n\n Args:\n refresh_plot (bool | None): Refresh plot. Defaults to True.\n \"\"\"\n panel = self.__get_current_basedatapanel()\n panel.delete_metadata(refresh_plot)\n\n @remote_controlled\n def get_object_shapes(\n self,\n nb_id_title: int | str | None = None,\n panel: str | None = None,\n ) -> list:\n \"\"\"Get plot item shapes associated to object (signal/image).\n\n Args:\n nb_id_title: Object number, or object id, or object title.\n Defaults to None (current object).\n panel: Panel name. Defaults to None (current panel).\n\n Returns:\n List of plot item shapes\n \"\"\"\n obj = self.get_object(nb_id_title, panel)\n return list(obj.iterate_shape_items(editable=False))\n\n @remote_controlled\n def add_annotations_from_items(\n self, items: list, refresh_plot: bool = True, panel: str | None = None\n ) -> None:\n \"\"\"Add object annotations (annotation plot items).\n\n Args:\n items (list): annotation plot items\n refresh_plot (bool | None): refresh plot. Defaults to True.\n panel (str | None): panel name (valid values: \"signal\", \"image\").\n If None, current panel is used.\n \"\"\"\n panel = self.__get_specific_panel(panel)\n panel.add_annotations_from_items(items, refresh_plot)\n\n @remote_controlled\n def add_label_with_title(\n self, title: str | None = None, panel: str | None = None\n ) -> None:\n \"\"\"Add a label with object title on the associated plot\n\n Args:\n title (str | None): Label title. Defaults to None.\n If None, the title is the object title.\n panel (str | None): panel name (valid values: \"signal\", \"image\").\n If None, current panel is used.\n \"\"\"\n self.__get_specific_panel(panel).add_label_with_title(title)\n\n # ------Misc.\n @property\n def panels(self) -> tuple[AbstractPanel, ...]:\n \"\"\"Return the tuple of implemented panels (signal, image)\n\n Returns:\n tuple[SignalPanel, ImagePanel, MacroPanel]: tuple of panels\n \"\"\"\n return (self.signalpanel, self.imagepanel, self.macropanel)\n\n def __set_low_memory_state(self, state: bool) -> None:\n \"\"\"Set memory warning state\"\"\"\n self.__memory_warning = state\n\n def confirm_memory_state(self) -> bool: # pragma: no cover\n \"\"\"Check memory warning state and eventually show a warning dialog\n\n Returns:\n bool: True if memory state is ok\n \"\"\"\n if not env.execenv.unattended and self.__memory_warning:\n threshold = Conf.main.available_memory_threshold.get()\n answer = QW.QMessageBox.critical(\n self,\n _(\"Warning\"),\n _(\"Available memory is below %d MB.<br><br>Do you want to continue?\")\n % threshold,\n QW.QMessageBox.Yes | QW.QMessageBox.No,\n )\n return answer == QW.QMessageBox.Yes\n return True\n\n def check_stable_release(self) -> None: # pragma: no cover\n \"\"\"Check if this is a stable release\"\"\"\n if __version__.replace(\".\", \"\").isdigit():\n # This is a stable release\n return\n if \"b\" in __version__:\n # This is a beta release\n rel = _(\n \"This software is in the <b>beta stage</b> of its release cycle. \"\n \"The focus of beta testing is providing a feature complete \"\n \"software for users interested in trying new features before \"\n \"the final release. However, <u>beta software may not behave as \"\n \"expected and will probably have more bugs or performance issues \"\n \"than completed software</u>.\"\n )\n else:\n # This is an alpha release\n rel = _(\n \"This software is in the <b>alpha stage</b> of its release cycle. \"\n \"The focus of alpha testing is providing an incomplete software \"\n \"for early testing of specific features by users. \"\n \"Please note that <u>alpha software was not thoroughly tested</u> \"\n \"by the developer before it is released.\"\n )\n txtlist = [\n f\"<b>{APP_NAME}</b> v{__version__}:\",\n \"\",\n _(\"<i>This is not a stable release.</i>\"),\n \"\",\n rel,\n ]\n QW.QMessageBox.warning(self, APP_NAME, \"<br>\".join(txtlist), QW.QMessageBox.Ok)\n\n def __check_dependencies(self) -> None: # pragma: no cover\n \"\"\"Check dependencies\"\"\"\n if IS_FROZEN or execenv.unattended:\n # No need to check dependencies if DataLab has been frozen, or if\n # the user has chosen to ignore this check, or if we are in unattended mode\n # (i.e. running automated tests)\n\n if IS_FROZEN:\n QW.QMessageBox.information(\n self,\n _(\"Information\"),\n _(\n \"The dependency check feature is not relevant for the \"\n \"standalone version of DataLab.\"\n ),\n QW.QMessageBox.Ok,\n )\n return\n try:\n state = dephash.check_dependencies_hash(DATAPATH)\n bad_deps = [name for name in state if not state[name]]\n if not bad_deps:\n # Everything is OK\n QW.QMessageBox.information(\n self,\n _(\"Information\"),\n _(\n \"All critical dependencies of DataLab have been qualified \"\n \"on this operating system.\"\n ),\n QW.QMessageBox.Ok,\n )\n return\n except IOError:\n bad_deps = None\n txt0 = _(\"Non-compliant dependency:\")\n if bad_deps is None or len(bad_deps) > 1:\n txt0 = _(\"Non-compliant dependencies:\")\n if bad_deps is None:\n txtlist = [\n _(\"DataLab has not yet been qualified on your operating system.\"),\n ]\n else:\n txtlist = [\n \"<u>\" + txt0 + \"</u> \" + \", \".join(bad_deps),\n \"\",\n _(\n \"At least one dependency does not comply with DataLab \"\n \"qualification standard reference (wrong dependency version \"\n \"has been installed, or dependency source code has been \"\n \"modified, or the application has not yet been qualified \"\n \"on your operating system).\"\n ),\n ]\n txtlist += [\n \"\",\n _(\n \"This means that the application has not been officially qualified \"\n \"in this context and may not behave as expected.\"\n ),\n ]\n txt = \"<br>\".join(txtlist)\n QW.QMessageBox.warning(self, APP_NAME, txt, QW.QMessageBox.Ok)\n\n def check_for_previous_crash(self) -> None: # pragma: no cover\n \"\"\"Check for previous crash\"\"\"\n if execenv.unattended:\n self.__show_logviewer()\n elif Conf.main.faulthandler_log_available.get(\n False\n ) or Conf.main.traceback_log_available.get(False):\n txt = \"<br>\".join(\n [\n logviewer.get_log_prompt_message(),\n \"\",\n _(\"Do you want to see available log files?\"),\n ]\n )\n btns = QW.QMessageBox.StandardButton.Yes | QW.QMessageBox.StandardButton.No\n choice = QW.QMessageBox.warning(self, APP_NAME, txt, btns)\n if choice == QW.QMessageBox.StandardButton.Yes:\n self.__show_logviewer()\n\n def take_screenshot(self, name: str) -> None: # pragma: no cover\n \"\"\"Take main window screenshot\"\"\"\n self.memorystatus.set_demo_mode(True)\n qth.grab_save_window(self, f\"{name}\")\n self.memorystatus.set_demo_mode(False)\n\n def take_menu_screenshots(self) -> None: # pragma: no cover\n \"\"\"Take menu screenshots\"\"\"\n for panel in self.panels:\n if isinstance(panel, base.BaseDataPanel):\n self.tabwidget.setCurrentWidget(panel)\n for name in (\n \"file\",\n \"edit\",\n \"view\",\n \"operation\",\n \"processing\",\n \"computing\",\n \"help\",\n ):\n menu = getattr(self, f\"{name}_menu\")\n menu.popup(self.pos())\n qth.grab_save_window(menu, f\"{panel.objectName()}_{name}\")\n menu.close()\n\n # ------GUI setup\n def __restore_pos_and_size(self) -> None:\n \"\"\"Restore main window position and size from configuration\"\"\"\n pos = Conf.main.window_position.get(None)\n if pos is not None:\n posx, posy = pos\n self.move(QC.QPoint(posx, posy))\n size = Conf.main.window_size.get(None)\n if size is not None:\n width, height = size\n self.resize(QC.QSize(width, height))\n if pos is not None and size is not None:\n sgeo = self.screen().availableGeometry()\n out_inf = posx < -int(0.9 * width) or posy < -int(0.9 * height)\n out_sup = posx > int(0.9 * sgeo.width()) or posy > int(0.9 * sgeo.height())\n if len(QW.QApplication.screens()) == 1 and (out_inf or out_sup):\n # Main window is offscreen\n posx = min(max(posx, 0), sgeo.width() - width)\n posy = min(max(posy, 0), sgeo.height() - height)\n self.move(QC.QPoint(posx, posy))\n\n def __save_pos_and_size(self) -> None:\n \"\"\"Save main window position and size to configuration\"\"\"\n is_maximized = self.windowState() == QC.Qt.WindowMaximized\n Conf.main.window_maximized.set(is_maximized)\n if not is_maximized:\n size = self.size()\n Conf.main.window_size.set((size.width(), size.height()))\n pos = self.pos()\n Conf.main.window_position.set((pos.x(), pos.y()))\n\n def setup(self, console: bool = False) -> None:\n \"\"\"Setup main window\n\n Args:\n console: True to setup console\n \"\"\"\n self.__register_plugins()\n self.__configure_statusbar()\n self.__setup_global_actions()\n self.__add_signal_image_panels()\n self.__create_plugins_actions()\n self.__setup_central_widget()\n self.__add_menus()\n if console:\n self.__setup_console()\n self.__update_actions()\n self.__add_macro_panel()\n self.__configure_panels()\n\n def __register_plugins(self) -> None:\n \"\"\"Register plugins\"\"\"\n with qth.try_or_log_error(\"Discovering plugins\"):\n # Discovering plugins\n plugin_nb = len(discover_plugins())\n execenv.log(self, f\"{plugin_nb} plugin(s) found\")\n for plugin_class in PluginRegistry.get_plugin_classes():\n with qth.try_or_log_error(f\"Instantiating plugin {plugin_class.__name__}\"):\n # Instantiating plugin\n plugin: PluginBase = plugin_class()\n with qth.try_or_log_error(f\"Registering plugin {plugin.info.name}\"):\n # Registering plugin\n plugin.register(self)\n\n def __create_plugins_actions(self) -> None:\n \"\"\"Create plugins actions\"\"\"\n with self.signalpanel.acthandler.new_category(ActionCategory.PLUGINS):\n with self.imagepanel.acthandler.new_category(ActionCategory.PLUGINS):\n for plugin in PluginRegistry.get_plugins():\n with qth.try_or_log_error(f\"Create actions for {plugin.info.name}\"):\n plugin.create_actions()\n\n @staticmethod\n def __unregister_plugins() -> None:\n \"\"\"Unregister plugins\"\"\"\n while PluginRegistry.get_plugins():\n # Unregistering plugin\n plugin = PluginRegistry.get_plugins()[-1]\n with qth.try_or_log_error(f\"Unregistering plugin {plugin.info.name}\"):\n plugin.unregister()\n\n def __configure_statusbar(self) -> None:\n \"\"\"Configure status bar\"\"\"\n self.statusBar().showMessage(_(\"Welcome to %s!\") % APP_NAME, 5000)\n # Plugin status\n pluginstatus = status.PluginStatus()\n self.statusBar().addPermanentWidget(pluginstatus)\n # XML-RPC server status\n xmlrpcstatus = status.XMLRPCStatus()\n xmlrpcstatus.set_port(self.remote_server.port)\n self.statusBar().addPermanentWidget(xmlrpcstatus)\n # Memory status\n threshold = Conf.main.available_memory_threshold.get()\n self.memorystatus = status.MemoryStatus(threshold)\n self.memorystatus.SIG_MEMORY_ALARM.connect(self.__set_low_memory_state)\n self.statusBar().addPermanentWidget(self.memorystatus)\n\n def __setup_global_actions(self) -> None:\n \"\"\"Setup global actions\"\"\"\n self.openh5_action = create_action(\n self,\n _(\"Open HDF5 files...\"),\n icon=get_icon(\"fileopen_h5.svg\"),\n tip=_(\"Open one or several HDF5 files\"),\n triggered=lambda checked=False: self.open_h5_files(import_all=True),\n )\n self.saveh5_action = create_action(\n self,\n _(\"Save to HDF5 file...\"),\n icon=get_icon(\"filesave_h5.svg\"),\n tip=_(\"Save to HDF5 file\"),\n triggered=self.save_to_h5_file,\n )\n self.browseh5_action = create_action(\n self,\n _(\"Browse HDF5 file...\"),\n icon=get_icon(\"h5browser.svg\"),\n tip=_(\"Browse an HDF5 file\"),\n triggered=lambda checked=False: self.open_h5_files(import_all=None),\n )\n self.settings_action = create_action(\n self,\n _(\"Settings...\"),\n icon=get_icon(\"libre-gui-settings.svg\"),\n tip=_(\"Open settings dialog\"),\n triggered=self.__edit_settings,\n )\n main_toolbar = self.addToolBar(_(\"Main Toolbar\"))\n add_actions(\n main_toolbar,\n [\n self.openh5_action,\n self.saveh5_action,\n self.browseh5_action,\n None,\n self.settings_action,\n ],\n )\n # Quit action for \"File menu\" (added when populating menu on demand)\n if self.hide_on_close:\n quit_text = _(\"Hide window\")\n quit_tip = _(\"Hide DataLab window\")\n else:\n quit_text = _(\"Quit\")\n quit_tip = _(\"Quit application\")\n if sys.platform != \"darwin\":\n # On macOS, the \"Quit\" action is automatically added to the application menu\n self.quit_action = create_action(\n self,\n quit_text,\n shortcut=QG.QKeySequence(QG.QKeySequence.Quit),\n icon=get_icon(\"libre-gui-close.svg\"),\n tip=quit_tip,\n triggered=self.close,\n )\n # View menu actions\n self.auto_refresh_action = create_action(\n self,\n _(\"Auto-refresh\"),\n icon=get_icon(\"refresh-auto.svg\"),\n tip=_(\"Auto-refresh plot when object is modified, added or removed\"),\n toggled=self.toggle_auto_refresh,\n )\n self.showlabel_action = create_action(\n self,\n _(\"Show graphical object titles\"),\n icon=get_icon(\"show_titles.svg\"),\n tip=_(\"Show or hide ROI and other graphical object titles or subtitles\"),\n toggled=self.toggle_show_titles,\n )\n\n def __add_signal_panel(self) -> None:\n \"\"\"Setup signal toolbar, widgets and panel\"\"\"\n self.signal_toolbar = self.addToolBar(_(\"Signal Processing Toolbar\"))\n curvewidget = DockablePlotWidget(self, PlotType.CURVE)\n curveplot = curvewidget.get_plot()\n curveplot.add_item(make.legend(\"TR\"))\n self.signalpanel = signal.SignalPanel(\n self, curvewidget.plotwidget, self.signal_toolbar\n )\n self.signalpanel.SIG_STATUS_MESSAGE.connect(self.statusBar().showMessage)\n return curvewidget\n\n def __add_image_panel(self) -> None:\n \"\"\"Setup image toolbar, widgets and panel\"\"\"\n self.image_toolbar = self.addToolBar(_(\"Image Processing Toolbar\"))\n imagewidget = DockablePlotWidget(self, PlotType.IMAGE)\n self.imagepanel = image.ImagePanel(\n self, imagewidget.plotwidget, self.image_toolbar\n )\n # -----------------------------------------------------------------------------\n # # Before eventually disabling the \"peritem\" mode by default, wait for the\n # # plotpy bug to be fixed (peritem mode is not compatible with multiple image\n # # items):\n # for cspanel in (\n # self.imagepanel.plotwidget.get_xcs_panel(),\n # self.imagepanel.plotwidget.get_ycs_panel(),\n # ):\n # cspanel.peritem_ac.setChecked(False)\n # -----------------------------------------------------------------------------\n self.imagepanel.SIG_STATUS_MESSAGE.connect(self.statusBar().showMessage)\n return imagewidget\n\n def __add_signal_image_panels(self) -> None:\n \"\"\"Add signal and image panels\"\"\"\n self.tabwidget = QW.QTabWidget()\n cdock = self.__add_dockwidget(self.__add_signal_panel(), title=_(\"Curve panel\"))\n idock = self.__add_dockwidget(self.__add_image_panel(), title=_(\"Image panel\"))\n self.tabifyDockWidget(cdock, idock)\n self.docks = {self.signalpanel: cdock, self.imagepanel: idock}\n self.tabwidget.currentChanged.connect(self.__tab_index_changed)\n self.signalpanel.SIG_OBJECT_ADDED.connect(\n lambda: self.set_current_panel(\"signal\")\n )\n self.imagepanel.SIG_OBJECT_ADDED.connect(\n lambda: self.set_current_panel(\"image\")\n )\n for panel in (self.signalpanel, self.imagepanel):\n panel.setup_panel()\n\n def __setup_central_widget(self) -> None:\n \"\"\"Setup central widget (main panel)\"\"\"\n self.tabwidget.setMaximumWidth(500)\n self.tabwidget.addTab(self.signalpanel, get_icon(\"signal.svg\"), _(\"Signals\"))\n self.tabwidget.addTab(self.imagepanel, get_icon(\"image.svg\"), _(\"Images\"))\n self.setCentralWidget(self.tabwidget)\n\n @staticmethod\n def __get_local_doc_path() -> str | None:\n \"\"\"Return local documentation path, if it exists\"\"\"\n locale = QC.QLocale.system().name()\n for suffix in (\"_\" + locale[:2], \"_en\"):\n path = osp.join(DATAPATH, \"doc\", f\"{APP_NAME}{suffix}.pdf\")\n if osp.isfile(path):\n return path\n return None\n\n def __add_menus(self) -> None:\n \"\"\"Adding menus\"\"\"\n self.file_menu = self.menuBar().addMenu(_(\"File\"))\n configure_menu_about_to_show(self.file_menu, self.__update_file_menu)\n self.edit_menu = self.menuBar().addMenu(_(\"&Edit\"))\n self.operation_menu = self.menuBar().addMenu(_(\"Operations\"))\n self.processing_menu = self.menuBar().addMenu(_(\"Processing\"))\n self.computing_menu = self.menuBar().addMenu(_(\"Computing\"))\n self.plugins_menu = self.menuBar().addMenu(_(\"Plugins\"))\n self.view_menu = self.menuBar().addMenu(_(\"&View\"))\n configure_menu_about_to_show(self.view_menu, self.__update_view_menu)\n self.help_menu = self.menuBar().addMenu(\"?\")\n for menu in (\n self.edit_menu,\n self.operation_menu,\n self.processing_menu,\n self.computing_menu,\n self.plugins_menu,\n ):\n configure_menu_about_to_show(menu, self.__update_generic_menu)\n help_menu_actions = [\n create_action(\n self,\n _(\"Online documentation\"),\n icon=get_icon(\"libre-gui-help.svg\"),\n triggered=lambda: webbrowser.open(__docurl__),\n ),\n ]\n localdocpath = self.__get_local_doc_path()\n if localdocpath is not None:\n help_menu_actions += [\n create_action(\n self,\n _(\"PDF documentation\"),\n icon=get_icon(\"help_pdf.svg\"),\n triggered=lambda: webbrowser.open(localdocpath),\n ),\n ]\n help_menu_actions += [None]\n if TEST_SEGFAULT_ERROR:\n help_menu_actions += [\n create_action(\n self,\n _(\"Test segfault/Python error\"),\n triggered=self.test_segfault_error,\n )\n ]\n help_menu_actions += [\n create_action(\n self,\n _(\"Log files\") + \"...\",\n icon=get_icon(\"logs.svg\"),\n triggered=self.__show_logviewer,\n ),\n create_action(\n self,\n _(\"Installation and configuration\") + \"...\",\n icon=get_icon(\"libre-toolbox.svg\"),\n triggered=lambda: instconfviewer.exec_cdl_installconfig_dialog(self),\n ),\n None,\n create_action(\n self,\n _(\"Project home page\"),\n icon=get_icon(\"libre-gui-globe.svg\"),\n triggered=lambda: webbrowser.open(__homeurl__),\n ),\n create_action(\n self,\n _(\"Bug report or feature request\"),\n icon=get_icon(\"libre-gui-globe.svg\"),\n triggered=lambda: webbrowser.open(__supporturl__),\n ),\n create_action(\n self,\n _(\"Check critical dependencies...\"),\n triggered=self.__check_dependencies,\n ),\n create_action(\n self,\n _(\"About...\"),\n icon=get_icon(\"libre-gui-about.svg\"),\n triggered=self.__about,\n ),\n ]\n add_actions(self.help_menu, help_menu_actions)\n\n def __setup_console(self) -> None:\n \"\"\"Add an internal console\"\"\"\n ns = {\n \"cdl\": self,\n \"np\": np,\n \"sps\": sps,\n \"spi\": spi,\n \"os\": os,\n \"sys\": sys,\n \"osp\": osp,\n \"time\": time,\n }\n msg = (\n \"Welcome to DataLab console!\\n\"\n \"---------------------------\\n\"\n \"You can access the main window with the 'cdl' variable.\\n\"\n \"Example:\\n\"\n \" o = cdl.get_object() # returns currently selected object\\n\"\n \" o = cdl[1] # returns object number 1\\n\"\n \" o = cdl['My image'] # returns object which title is 'My image'\\n\"\n \" o.data # returns object data\\n\"\n \"Modules imported at startup: \"\n \"os, sys, os.path as osp, time, \"\n \"numpy as np, scipy.signal as sps, scipy.ndimage as spi\"\n )\n self.console = DockableConsole(self, namespace=ns, message=msg, debug=DEBUG)\n self.console.setMaximumBlockCount(Conf.console.max_line_count.get(5000))\n self.console.go_to_error.connect(go_to_error)\n console_dock = self.__add_dockwidget(self.console, _(\"Console\"))\n console_dock.hide()\n self.console.interpreter.widget_proxy.sig_new_prompt.connect(\n lambda txt: self.repopulate_panel_trees()\n )\n\n def __add_macro_panel(self) -> None:\n \"\"\"Add macro panel\"\"\"\n self.macropanel = macro.MacroPanel()\n mdock = self.__add_dockwidget(self.macropanel, _(\"Macro manager\"))\n self.docks[self.macropanel] = mdock\n self.tabifyDockWidget(self.docks[self.imagepanel], mdock)\n self.docks[self.signalpanel].raise_()\n\n def __configure_panels(self) -> None:\n \"\"\"Configure panels\"\"\"\n # Connectings signals\n for panel in self.panels:\n panel.SIG_OBJECT_ADDED.connect(self.set_modified)\n panel.SIG_OBJECT_REMOVED.connect(self.set_modified)\n self.macropanel.SIG_OBJECT_MODIFIED.connect(self.set_modified)\n # Initializing common panel actions\n self.auto_refresh_action.setChecked(Conf.view.auto_refresh.get(True))\n self.showlabel_action.setChecked(Conf.view.show_label.get(False))\n # Restoring current tab from last session\n tab_idx = Conf.main.current_tab.get(None)\n if tab_idx is not None:\n self.tabwidget.setCurrentIndex(tab_idx)\n # Set focus on current panel, so that keyboard shortcuts work (Fixes #10)\n self.tabwidget.currentWidget().setFocus()\n\n def set_process_isolation_enabled(self, state: bool) -> None:\n \"\"\"Enable/disable process isolation\n\n Args:\n state (bool): True to enable process isolation\n \"\"\"\n for processor in (self.imagepanel.processor, self.signalpanel.processor):\n processor.set_process_isolation_enabled(state)\n\n # ------Remote control\n @remote_controlled\n def get_current_panel(self) -> str:\n \"\"\"Return current panel name\n\n Returns:\n str: panel name (valid values: \"signal\", \"image\", \"macro\")\n \"\"\"\n panel = self.tabwidget.currentWidget()\n dock = self.docks[panel]\n if panel is self.signalpanel and dock.isVisible():\n return \"signal\"\n if panel is self.imagepanel and dock.isVisible():\n return \"image\"\n return \"macro\"\n\n @remote_controlled\n def set_current_panel(self, panel: str) -> None:\n \"\"\"Switch to panel.\n\n Args:\n panel (str): panel name (valid values: \"signal\", \"image\", \"macro\")\n\n Raises:\n ValueError: unknown panel\n \"\"\"\n if self.get_current_panel() == panel:\n if panel in (\"signal\", \"image\"):\n # Force tab index changed event to be sure that the dock associated\n # to the current panel is raised\n self.__tab_index_changed(self.tabwidget.currentIndex())\n return\n if panel == \"signal\":\n self.tabwidget.setCurrentWidget(self.signalpanel)\n elif panel == \"image\":\n self.tabwidget.setCurrentWidget(self.imagepanel)\n elif panel == \"macro\":\n self.docks[self.macropanel].raise_()\n else:\n raise ValueError(f\"Unknown panel {panel}\")\n\n @remote_controlled\n def calc(self, name: str, param: gds.DataSet | None = None) -> None:\n \"\"\"Call compute function `name` in current panel's processor\n\n Args:\n name (str): function name\n param (guidata.dataset.DataSet): optional parameters\n (default: None)\n\n Raises:\n ValueError: unknown function\n \"\"\"\n panel = self.tabwidget.currentWidget()\n if isinstance(panel, base.BaseDataPanel):\n for funcname in (name, f\"compute_{name}\"):\n func = getattr(panel.processor, funcname, None)\n if func is not None:\n break\n else:\n raise ValueError(f\"Unknown function {funcname}\")\n if param is None:\n func()\n else:\n func(param)\n\n # ------GUI refresh\n def has_objects(self) -> bool:\n \"\"\"Return True if sig/ima panels have any object\"\"\"\n return sum(len(panel) for panel in self.panels) > 0\n\n def set_modified(self, state: bool = True) -> None:\n \"\"\"Set mainwindow modified state\"\"\"\n state = state and self.has_objects()\n self.__is_modified = state\n self.setWindowTitle(APP_NAME + (\"*\" if state else \"\"))\n\n def __add_dockwidget(self, child, title: str) -> QW.QDockWidget:\n \"\"\"Add QDockWidget and toggleViewAction\"\"\"\n dockwidget, location = child.create_dockwidget(title)\n self.addDockWidget(location, dockwidget)\n return dockwidget\n\n def repopulate_panel_trees(self) -> None:\n \"\"\"Repopulate all panel trees\"\"\"\n for panel in self.panels:\n if isinstance(panel, base.BaseDataPanel):\n panel.objview.populate_tree()\n\n def __update_actions(self) -> None:\n \"\"\"Update selection dependent actions\"\"\"\n is_signal = self.tabwidget.currentWidget() is self.signalpanel\n panel = self.signalpanel if is_signal else self.imagepanel\n panel.selection_changed()\n self.signal_toolbar.setVisible(is_signal)\n self.image_toolbar.setVisible(not is_signal)\n if self.plugins_menu is not None:\n plugin_actions = panel.get_category_actions(ActionCategory.PLUGINS)\n self.plugins_menu.setEnabled(len(plugin_actions) > 0)\n\n def __tab_index_changed(self, index: int) -> None:\n \"\"\"Switch from signal to image mode, or vice-versa\"\"\"\n dock = self.docks[self.tabwidget.widget(index)]\n dock.raise_()\n self.__update_actions()\n\n def __update_generic_menu(self, menu: QW.QMenu | None = None) -> None:\n \"\"\"Update menu before showing up -- Generic method\"\"\"\n if menu is None:\n menu = self.sender()\n menu.clear()\n panel = self.tabwidget.currentWidget()\n category = {\n self.file_menu: ActionCategory.FILE,\n self.edit_menu: ActionCategory.EDIT,\n self.view_menu: ActionCategory.VIEW,\n self.operation_menu: ActionCategory.OPERATION,\n self.processing_menu: ActionCategory.PROCESSING,\n self.computing_menu: ActionCategory.COMPUTING,\n self.plugins_menu: ActionCategory.PLUGINS,\n }[menu]\n actions = panel.get_category_actions(category)\n add_actions(menu, actions)\n\n def __update_file_menu(self) -> None:\n \"\"\"Update file menu before showing up\"\"\"\n self.saveh5_action.setEnabled(self.has_objects())\n self.__update_generic_menu(self.file_menu)\n add_actions(\n self.file_menu,\n [\n None,\n self.openh5_action,\n self.saveh5_action,\n self.browseh5_action,\n None,\n self.settings_action,\n ],\n )\n if self.quit_action is not None:\n add_actions(self.file_menu, [None, self.quit_action])\n\n def __update_view_menu(self) -> None:\n \"\"\"Update view menu before showing up\"\"\"\n self.__update_generic_menu(self.view_menu)\n add_actions(self.view_menu, [None] + self.createPopupMenu().actions())\n\n @remote_controlled\n def toggle_show_titles(self, state: bool) -> None:\n \"\"\"Toggle show annotations option\n\n Args:\n state: state\n \"\"\"\n Conf.view.show_label.set(state)\n for datapanel in (self.signalpanel, self.imagepanel):\n for obj in datapanel.objmodel:\n obj.set_metadata_option(\"showlabel\", state)\n datapanel.SIG_REFRESH_PLOT.emit(\"selected\", True)\n\n @remote_controlled\n def toggle_auto_refresh(self, state: bool) -> None:\n \"\"\"Toggle auto refresh option\n\n Args:\n state: state\n \"\"\"\n Conf.view.auto_refresh.set(state)\n for datapanel in (self.signalpanel, self.imagepanel):\n datapanel.plothandler.set_auto_refresh(state)\n\n # ------Common features\n @remote_controlled\n def reset_all(self) -> None:\n \"\"\"Reset all application data\"\"\"\n for panel in self.panels:\n if panel is not None:\n panel.remove_all_objects()\n\n @staticmethod\n def __check_h5file(filename: str, operation: str) -> str:\n \"\"\"Check HDF5 filename\"\"\"\n filename = osp.abspath(osp.normpath(filename))\n bname = osp.basename(filename)\n if operation == \"load\" and not osp.isfile(filename):\n raise IOError(f'File not found \"{bname}\"')\n if not filename.endswith(\".h5\"):\n raise IOError(f'Invalid HDF5 file \"{bname}\"')\n Conf.main.base_dir.set(filename)\n return filename\n\n @remote_controlled\n def save_to_h5_file(self, filename=None) -> None:\n \"\"\"Save to a DataLab HDF5 file\n\n Args:\n filename (str): HDF5 filename. If None, a file dialog is opened.\n\n Raises:\n IOError: if filename is invalid or file cannot be saved.\n \"\"\"\n if filename is None:\n basedir = Conf.main.base_dir.get()\n with qth.save_restore_stds():\n filename, _fl = getsavefilename(self, _(\"Save\"), basedir, \"HDF5 (*.h5)\")\n if not filename:\n return\n with qth.qt_try_loadsave_file(self, filename, \"save\"):\n filename = self.__check_h5file(filename, \"save\")\n self.h5inputoutput.save_file(filename)\n self.set_modified(False)\n\n @remote_controlled\n def open_h5_files(\n self,\n h5files: list[str] | None = None,\n import_all: bool | None = None,\n reset_all: bool | None = None,\n ) -> None:\n \"\"\"Open a DataLab HDF5 file or import from any other HDF5 file.\n\n Args:\n h5files: HDF5 filenames (optionally with dataset name, separated by \":\")\n import_all (bool): Import all datasets from HDF5 files\n reset_all (bool): Reset all application data before importing\n\n Returns:\n None\n \"\"\"\n if not self.confirm_memory_state():\n return\n if reset_all is None:\n reset_all = False\n if self.has_objects():\n answer = QW.QMessageBox.question(\n self,\n _(\"Warning\"),\n _(\n \"Do you want to remove all signals and images \"\n \"before importing data from HDF5 files?\"\n ),\n QW.QMessageBox.Yes | QW.QMessageBox.No,\n )\n if answer == QW.QMessageBox.Yes:\n reset_all = True\n if h5files is None:\n basedir = Conf.main.base_dir.get()\n with qth.save_restore_stds():\n h5files, _fl = getopenfilenames(self, _(\"Open\"), basedir, \"HDF5 (*.h5)\")\n for fname_with_dset in h5files:\n if \",\" in fname_with_dset:\n filename, dsetname = fname_with_dset.split(\",\")\n else:\n filename, dsetname = fname_with_dset, None\n if import_all is None and dsetname is None:\n self.import_h5_file(filename, reset_all)\n else:\n with qth.qt_try_loadsave_file(self, filename, \"load\"):\n filename = self.__check_h5file(filename, \"load\")\n if dsetname is None:\n self.h5inputoutput.open_file(filename, import_all, reset_all)\n else:\n self.h5inputoutput.import_dataset_from_file(filename, dsetname)\n reset_all = False\n\n @remote_controlled\n def import_h5_file(self, filename: str, reset_all: bool | None = None) -> None:\n \"\"\"Import HDF5 file into DataLab\n\n Args:\n filename (str): HDF5 filename (optionally with dataset name,\n separated by \":\")\n reset_all (bool): Delete all DataLab signals/images before importing data\n\n Returns:\n None\n \"\"\"\n with qth.qt_try_loadsave_file(self, filename, \"load\"):\n filename = self.__check_h5file(filename, \"load\")\n self.h5inputoutput.import_file(filename, False, reset_all)\n\n # This method is intentionally *not* remote controlled\n # (see TODO regarding RemoteClient.add_object method)\n # @remote_controlled\n def add_object(self, obj: SignalObj | ImageObj) -> None:\n \"\"\"Add object - signal or image\n\n Args:\n obj (SignalObj or ImageObj): object to add (signal or image)\n \"\"\"\n if self.confirm_memory_state():\n if isinstance(obj, SignalObj):\n self.signalpanel.add_object(obj)\n elif isinstance(obj, ImageObj):\n self.imagepanel.add_object(obj)\n else:\n raise TypeError(f\"Unsupported object type {type(obj)}\")\n\n @remote_controlled\n def open_object(self, filename: str) -> None:\n \"\"\"Open object from file in current panel (signal/image)\n\n Args:\n filename (str): HDF5 filename\n\n Returns:\n None\n \"\"\"\n panel = self.tabwidget.currentWidget()\n panel.open_object(filename)\n\n # ------Other methods related to AbstractCDLControl interface\n def get_version(self) -> str:\n \"\"\"Return DataLab version.\n\n Returns:\n str: DataLab version\n \"\"\"\n return __version__\n\n def close_application(self) -> None: # Implementing AbstractCDLControl interface\n \"\"\"Close DataLab application\"\"\"\n self.close()\n\n def raise_window(self) -> None: # Implementing AbstractCDLControl interface\n \"\"\"Raise DataLab window\"\"\"\n bring_to_front(self)\n\n def add_signal(\n self,\n title: str,\n xdata: np.ndarray,\n ydata: np.ndarray,\n xunit: str | None = None,\n yunit: str | None = None,\n xlabel: str | None = None,\n ylabel: str | None = None,\n ) -> bool: # pylint: disable=too-many-arguments\n \"\"\"Add signal data to DataLab.\n\n Args:\n title (str): Signal title\n xdata (numpy.ndarray): X data\n ydata (numpy.ndarray): Y data\n xunit (str | None): X unit. Defaults to None.\n yunit (str | None): Y unit. Defaults to None.\n xlabel (str | None): X label. Defaults to None.\n ylabel (str | None): Y label. Defaults to None.\n\n Returns:\n bool: True if signal was added successfully, False otherwise\n\n Raises:\n ValueError: Invalid xdata dtype\n ValueError: Invalid ydata dtype\n \"\"\"\n obj = create_signal(\n title,\n xdata,\n ydata,\n units=(xunit, yunit),\n labels=(xlabel, ylabel),\n )\n self.add_object(obj)\n return True\n\n def add_image(\n self,\n title: str,\n data: np.ndarray,\n xunit: str | None = None,\n yunit: str | None = None,\n zunit: str | None = None,\n xlabel: str | None = None,\n ylabel: str | None = None,\n zlabel: str | None = None,\n ) -> bool: # pylint: disable=too-many-arguments\n \"\"\"Add image data to DataLab.\n\n Args:\n title (str): Image title\n data (numpy.ndarray): Image data\n xunit (str | None): X unit. Defaults to None.\n yunit (str | None): Y unit. Defaults to None.\n zunit (str | None): Z unit. Defaults to None.\n xlabel (str | None): X label. Defaults to None.\n ylabel (str | None): Y label. Defaults to None.\n zlabel (str | None): Z label. Defaults to None.\n\n Returns:\n bool: True if image was added successfully, False otherwise\n\n Raises:\n ValueError: Invalid data dtype\n \"\"\"\n obj = create_image(\n title,\n data,\n units=(xunit, yunit, zunit),\n labels=(xlabel, ylabel, zlabel),\n )\n self.add_object(obj)\n return True\n\n # ------?\n def __about(self) -> None: # pragma: no cover\n \"\"\"About dialog box\"\"\"\n self.check_stable_release()\n if self.remote_server.port is None:\n xrpcstate = '<font color=\"red\">' + _(\"not started\") + \"</font>\"\n else:\n xrpcstate = _(\"started (port %s)\") % self.remote_server.port\n xrpcstate = f\"<font color='green'>{xrpcstate}</font>\"\n if Conf.main.process_isolation_enabled.get():\n pistate = \"<font color='green'>\" + _(\"enabled\") + \"</font>\"\n else:\n pistate = \"<font color='red'>\" + _(\"disabled\") + \"</font>\"\n adv_conf = \"<br>\".join(\n [\n \"<i>\" + _(\"Advanced configuration:\") + \"</i>\",\n \"• \" + _(\"XML-RPC server:\") + \" \" + xrpcstate,\n \"• \" + _(\"Process isolation:\") + \" \" + pistate,\n ]\n )\n pinfos = PluginRegistry.get_plugin_infos()\n created_by = _(\"Created by\")\n dev_by = _(\"Developed and maintained by %s open-source project team\") % APP_NAME\n copyrght = \"2023 Codra\"\n QW.QMessageBox.about(\n self,\n _(\"About\") + \" \" + APP_NAME,\n f\"\"\"<b>{APP_NAME}</b> v{__version__}<br>{APP_DESC}\n <p>{created_by} Pierre Raybaut<br>{dev_by}<br>Copyright © {copyrght}\n <p>{adv_conf}<br><br>{pinfos}\"\"\",\n )\n\n def __edit_settings(self) -> None:\n \"\"\"Edit settings\"\"\"\n changed_options = edit_settings(self)\n for option in changed_options:\n if option == \"plot_toolbar_position\":\n for dock in self.docks.values():\n widget = dock.widget()\n if isinstance(widget, DockablePlotWidget):\n widget.update_toolbar_position()\n if option == \"ima_defaults\" and len(self.imagepanel) > 0:\n answer = QW.QMessageBox.question(\n self,\n _(\"Visualization settings\"),\n _(\n \"Default visualization settings have changed.<br><br>\"\n \"Do you want to update all active %s objects?\"\n )\n % _(\"image\"),\n QW.QMessageBox.Yes | QW.QMessageBox.No,\n )\n if answer == QW.QMessageBox.Yes:\n self.imagepanel.update_metadata_view_settings()\n\n def __show_logviewer(self) -> None:\n \"\"\"Show error logs\"\"\"\n logviewer.exec_cdl_logviewer_dialog(self)\n\n @staticmethod\n def test_segfault_error() -> None:\n \"\"\"Generate errors (both fault and traceback)\"\"\"\n import ctypes # pylint: disable=import-outside-toplevel\n\n ctypes.string_at(0)\n raise RuntimeError(\"!!! Testing RuntimeError !!!\")\n\n def show(self) -> None:\n \"\"\"Reimplement QMainWindow method\"\"\"\n super().show()\n if self.__old_size is not None:\n self.resize(self.__old_size)\n\n # ------Close window\n def close_properly(self) -> bool:\n \"\"\"Close properly\n\n Returns:\n bool: True if closed properly, False otherwise\n \"\"\"\n if not env.execenv.unattended and self.__is_modified:\n answer = QW.QMessageBox.warning(\n self,\n _(\"Quit\"),\n _(\n \"Do you want to save all signals and images \"\n \"to an HDF5 file before quitting DataLab?\"\n ),\n QW.QMessageBox.Yes | QW.QMessageBox.No | QW.QMessageBox.Cancel,\n )\n if answer == QW.QMessageBox.Yes:\n self.save_to_h5_file()\n if self.__is_modified:\n return False\n elif answer == QW.QMessageBox.Cancel:\n return False\n for panel in self.panels:\n if panel is not None:\n panel.close()\n if self.console is not None:\n try:\n self.console.close()\n except RuntimeError:\n # TODO: [P3] Investigate further why the following error occurs when\n # restarting the mainwindow (this is *not* a production case):\n # \"RuntimeError: wrapped C/C++ object of type DockableConsole\n # has been deleted\".\n # Another solution to avoid this error would be to really restart\n # the application (run each unit test in a separate process), but\n # it would represent too much effort for an error occuring in test\n # configurations only.\n pass\n self.reset_all()\n self.__save_pos_and_size()\n self.__unregister_plugins()\n\n # Saving current tab for next session\n Conf.main.current_tab.set(self.tabwidget.currentIndex())\n\n execenv.log(self, \"closed properly\")\n return True\n\n def closeEvent(self, event: QG.QCloseEvent) -> None:\n \"\"\"Reimplement QMainWindow method\"\"\"\n if self.hide_on_close:\n self.__old_size = self.size()\n self.hide()\n else:\n if self.close_properly():\n self.SIG_CLOSING.emit()\n event.accept()\n else:\n event.ignore()"},{"identifier":"ImagePanel","path":"cdl/core/gui/panel/image.py","snippet":"class ImagePanel(BaseDataPanel):\n \"\"\"Object handling the item list, the selected item properties and plot,\n specialized for Image objects\"\"\"\n\n PANEL_STR = _(\"Image panel\")\n PARAMCLASS = ImageObj\n DIALOGSIZE = (800, 800)\n ANNOTATION_TOOLS = (\n AnnotatedCircleTool,\n AnnotatedSegmentTool,\n AnnotatedRectangleTool,\n AnnotatedPointTool,\n AnnotatedEllipseTool,\n LabelTool,\n )\n IO_REGISTRY = ImageIORegistry\n H5_PREFIX = \"DataLab_Ima\"\n ROIDIALOGOPTIONS = {\"show_itemlist\": True, \"show_contrast\": False}\n ROIDIALOGCLASS = roieditor.ImageROIEditor\n\n # pylint: disable=duplicate-code\n\n def __init__(self, parent: QW.QWidget, plotwidget: PlotWidget, toolbar) -> None:\n super().__init__(parent, plotwidget, toolbar)\n self.plothandler = ImagePlotHandler(self, plotwidget)\n self.processor = ImageProcessor(self, plotwidget)\n self.acthandler = ImageActionHandler(self, toolbar)\n\n # ------Refreshing GUI--------------------------------------------------------------\n def properties_changed(self) -> None:\n \"\"\"The properties 'Apply' button was clicked: updating signal\"\"\"\n obj = self.objview.get_current_object()\n if obj is not None:\n obj.invalidate_maskdata_cache()\n super().properties_changed()\n\n # ------Creating, adding, removing objects------------------------------------------\n def get_newparam_from_current(\n self, newparam: NewImageParam | None = None\n ) -> NewImageParam | None:\n \"\"\"Get new object parameters from the current object.\n\n Args:\n newparam (guidata.dataset.DataSet): new object parameters.\n If None, create a new one.\n\n Returns:\n New object parameters\n \"\"\"\n curobj: ImageObj = self.objview.get_current_object()\n newparam = new_image_param() if newparam is None else newparam\n if curobj is not None:\n newparam.width, newparam.height = curobj.size\n newparam.dtype = ImageDatatypes.from_dtype(curobj.data.dtype)\n return newparam\n\n def new_object(\n self,\n newparam: NewImageParam | None = None,\n addparam: gds.DataSet | None = None,\n edit: bool = True,\n add_to_panel: bool = True,\n ) -> ImageObj | None:\n \"\"\"Create a new object (image).\n\n Args:\n newparam (Daguidata.dataset.datatypes.DataSettaSet): new object parameters\n addparam (guidata.dataset.DataSet): additional parameters\n edit (bool): Open a dialog box to edit parameters (default: True)\n add_to_panel (bool): Add the object to the panel (default: True)\n\n Returns:\n New object\n \"\"\"\n if not self.mainwindow.confirm_memory_state():\n return None\n newparam = self.get_newparam_from_current(newparam)\n image = create_image_from_param(\n newparam, addparam=addparam, edit=edit, parent=self\n )\n if image is None:\n return None\n if add_to_panel:\n self.add_object(image)\n return image\n\n def delete_metadata(self, refresh_plot: bool = True) -> None:\n \"\"\"Delete metadata of selected objects\n\n Args:\n refresh_plot (bool | None): Refresh plot. Defaults to True.\n \"\"\"\n for obj in self.objview.get_sel_objects(include_groups=True):\n obj.invalidate_maskdata_cache()\n super().delete_metadata(refresh_plot)\n\n def toggle_show_contrast(self, state: bool) -> None:\n \"\"\"Toggle show contrast option\"\"\"\n Conf.view.show_contrast.set(state)\n self.SIG_REFRESH_PLOT.emit(\"selected\", True)"},{"identifier":"SignalPanel","path":"cdl/core/gui/panel/signal.py","snippet":"class SignalPanel(BaseDataPanel):\n \"\"\"Object handling the item list, the selected item properties and plot,\n specialized for Signal objects\"\"\"\n\n PANEL_STR = _(\"Signal panel\")\n PARAMCLASS = SignalObj\n ANNOTATION_TOOLS = (\n LabelTool,\n VCursorTool,\n HCursorTool,\n XCursorTool,\n SegmentTool,\n RectangleTool,\n HRangeTool,\n )\n IO_REGISTRY = SignalIORegistry\n H5_PREFIX = \"DataLab_Sig\"\n ROIDIALOGCLASS = roieditor.SignalROIEditor\n\n # pylint: disable=duplicate-code\n\n def __init__(self, parent: QW.QWidget, plotwidget: PlotWidget, toolbar) -> None:\n super().__init__(parent, plotwidget, toolbar)\n self.plothandler = SignalPlotHandler(self, plotwidget)\n self.processor = SignalProcessor(self, plotwidget)\n self.acthandler = SignalActionHandler(self, toolbar)\n\n # ------Creating, adding, removing objects------------------------------------------\n def get_newparam_from_current(\n self, newparam: NewSignalParam | None = None\n ) -> NewSignalParam | None:\n \"\"\"Get new object parameters from the current object.\n\n Args:\n newparam (guidata.dataset.DataSet): new object parameters.\n If None, create a new one.\n\n Returns:\n New object parameters\n \"\"\"\n curobj: SignalObj = self.objview.get_current_object()\n newparam = new_signal_param() if newparam is None else newparam\n if curobj is not None:\n newparam.size = len(curobj.data)\n newparam.xmin = curobj.x.min()\n newparam.xmax = curobj.x.max()\n return newparam\n\n def new_object(\n self,\n newparam: NewSignalParam | None = None,\n addparam: gds.DataSet | None = None,\n edit: bool = True,\n add_to_panel: bool = True,\n ) -> SignalObj | None:\n \"\"\"Create a new object (signal).\n\n Args:\n newparam (guidata.dataset.DataSet): new object parameters\n addparam (guidata.dataset.DataSet): additional parameters\n edit (bool): Open a dialog box to edit parameters (default: True)\n add_to_panel (bool): Add the new object to the panel (default: True)\n\n Returns:\n New object\n \"\"\"\n if not self.mainwindow.confirm_memory_state():\n return None\n newparam = self.get_newparam_from_current(newparam)\n signal = create_signal_from_param(\n newparam, addparam=addparam, edit=edit, parent=self\n )\n if signal is None:\n return None\n if add_to_panel:\n self.add_object(signal)\n return signal\n\n # ------Plotting--------------------------------------------------------------------\n def toggle_anti_aliasing(self, state: bool) -> None:\n \"\"\"Toggle anti-aliasing on/off\n\n Args:\n state: state of the anti-aliasing\n \"\"\"\n self.plothandler.toggle_anti_aliasing(state)"},{"identifier":"create_paracetamol_signal","path":"cdl/tests/data.py","snippet":"def create_paracetamol_signal(\n size: int | None = None, title: str | None = None\n) -> cdl.obj.SignalObj:\n \"\"\"Create test signal (Paracetamol molecule spectrum)\n\n Args:\n size (int | None): Size of the data. Defaults to None.\n title (str | None): Title of the signal. Defaults to None.\n\n Returns:\n SignalObj: Signal object\n \"\"\"\n obj = cdl.obj.read_signal(get_test_fnames(\"paracetamol.txt\")[0])\n if title is not None:\n obj.title = title\n if size is not None:\n x0, y0 = obj.xydata\n x1 = np.linspace(x0[0], x0[-1], size)\n y1 = np.interp(x1, x0, y0)\n obj.set_xydata(x1, y1)\n return obj"},{"identifier":"create_peak2d_image","path":"cdl/tests/data.py","snippet":"def create_peak2d_image(\n p: cdl.obj.NewImageParam | None = None,\n) -> cdl.obj.ImageObj:\n \"\"\"Creating 2D peak image\n\n Args:\n p (cdl.obj.NewImageParam | None): Image parameters. Defaults to None.\n\n Returns:\n cdl.obj.ImageObj: Image object\n \"\"\"\n p = __set_default_size_dtype(p)\n p.title = \"Test image (2D peaks)\" if p.title is None else p.title\n obj = cdl.obj.create_image_from_param(p)\n param = PeakDataParam()\n if p.height is not None and p.width is not None:\n param.size = max(p.height, p.width)\n obj.data = get_peak2d_data(param)\n return obj"},{"identifier":"create_sincos_image","path":"cdl/tests/data.py","snippet":"def create_sincos_image(\n p: cdl.obj.NewImageParam | None = None,\n) -> cdl.obj.ImageObj:\n \"\"\"Creating test image (sin(x)+cos(y))\n\n Args:\n p (cdl.obj.NewImageParam | None): Image parameters. Defaults to None.\n\n Returns:\n cdl.obj.ImageObj: Image object\n \"\"\"\n p = __set_default_size_dtype(p)\n p.title = \"Test image (sin(x)+cos(y))\" if p.title is None else p.title\n dtype = p.dtype.value\n x, y = np.meshgrid(np.linspace(0, 10, p.width), np.linspace(0, 10, p.height))\n raw_data = 0.5 * (np.sin(x) + np.cos(y)) + 0.5\n dmin = np.iinfo(dtype).min * 0.95\n dmax = np.iinfo(dtype).max * 0.95\n obj = cdl.obj.create_image_from_param(p)\n obj.data = np.array(raw_data * (dmax - dmin) + dmin, dtype=dtype)\n return obj"},{"identifier":"iterate_image_creation","path":"cdl/tests/features/common/newobject_unit.py","snippet":"def iterate_image_creation(\n data_size: int = 500, non_zero: bool = False, verbose: bool = True\n) -> Generator[ImageObj, None, None]:\n \"\"\"Iterate over all possible images created from parameters\"\"\"\n if verbose:\n execenv.print(\n f\" Iterating over image types (size={data_size}, non_zero={non_zero}):\"\n )\n for itype in ImageTypes:\n if non_zero and itype in (ImageTypes.EMPTY, ImageTypes.ZEROS):\n continue\n if verbose:\n execenv.print(f\" {itype.value}\")\n for dtype in ImageDatatypes:\n if verbose:\n execenv.print(f\" {dtype.value}\")\n newparam = new_image_param(\n itype=itype, dtype=dtype, width=data_size, height=data_size\n )\n if itype == ImageTypes.GAUSS:\n addparam = Gauss2DParam()\n addparam.x0 = addparam.y0 = 3\n addparam.sigma = 5\n elif itype == ImageTypes.UNIFORMRANDOM:\n addparam = UniformRandomParam()\n addparam.set_from_datatype(dtype.value)\n elif itype == ImageTypes.NORMALRANDOM:\n addparam = NormalRandomParam()\n addparam.set_from_datatype(dtype.value)\n else:\n addparam = None\n image = create_image_from_param(newparam, addparam=addparam)\n if itype == ImageTypes.ZEROS:\n assert (image.data == 0).all()\n yield image"},{"identifier":"iterate_signal_creation","path":"cdl/tests/features/common/newobject_unit.py","snippet":"def iterate_signal_creation(\n data_size: int = 500, non_zero: bool = False, verbose: bool = True\n) -> Generator[SignalObj, None, None]:\n \"\"\"Iterate over all possible signals created from parameters\"\"\"\n if verbose:\n execenv.print(\n f\" Iterating over signal types (size={data_size}, non_zero={non_zero}):\"\n )\n for stype in SignalTypes:\n if non_zero and stype in (SignalTypes.ZEROS,):\n continue\n if verbose:\n execenv.print(f\" {stype.value}\")\n newparam = new_signal_param(stype=stype, size=data_size)\n if stype == SignalTypes.UNIFORMRANDOM:\n addparam = UniformRandomParam()\n elif stype == SignalTypes.NORMALRANDOM:\n addparam = NormalRandomParam()\n else:\n addparam = None\n signal = create_signal_from_param(newparam, addparam=addparam)\n if stype == SignalTypes.ZEROS:\n assert (signal.y == 0).all()\n yield signal"},{"identifier":"fitdialog","path":"cdl/widgets/fitdialog.py","snippet":"def guifit(\n x,\n y,\n fitfunc,\n fitparams,\n fitargs=None,\n fitkwargs=None,\n wintitle=None,\n title=None,\n xlabel=None,\n ylabel=None,\n param_cols=1,\n auto_fit=True,\n winsize=None,\n winpos=None,\n parent=None,\n name=None,\n):\ndef polynomialfit(x, y, degree, parent=None, name=None):\n def fitfunc(x, params):\ndef gaussianfit(x, y, parent=None, name=None):\n def fitfunc(x, params):\ndef lorentzianfit(x, y, parent=None, name=None):\n def fitfunc(x, params):\ndef voigtfit(x, y, parent=None, name=None):\n def fitfunc(x, params):\ndef multigaussian(x, *values, **kwargs):\ndef multigaussianfit(x, y, peak_indexes, parent=None, name=None):\n def fitfunc(xi, params):"}],"string":"[\n {\n \"identifier\": \"_\",\n \"path\": \"cdl/config.py\",\n \"snippet\": \"CONF_VERSION = \\\"1.0.0\\\"\\nAPP_NAME = \\\"DataLab\\\"\\nMOD_NAME = \\\"cdl\\\"\\nAPP_DESC = _(\\\"\\\"\\\"DataLab is a generic signal and image processing platform\\\"\\\"\\\")\\nAPP_PATH = osp.dirname(__file__)\\nDEBUG = os.environ.get(\\\"DEBUG\\\", \\\"\\\").lower() in (\\\"1\\\", \\\"true\\\")\\nTEST_SEGFAULT_ERROR = len(os.environ.get(\\\"TEST_SEGFAULT_ERROR\\\", \\\"\\\")) > 0\\nDATETIME_FORMAT = \\\"%d/%m/%Y - %H:%M:%S\\\"\\nDATAPATH = configtools.get_module_data_path(MOD_NAME, \\\"data\\\")\\nSHOTPATH = osp.join(\\n configtools.get_module_data_path(MOD_NAME), os.pardir, \\\"doc\\\", \\\"images\\\", \\\"shots\\\"\\n)\\nOTHER_PLUGINS_PATHLIST = [configtools.get_module_data_path(MOD_NAME, \\\"plugins\\\")]\\nIS_FROZEN = is_frozen(MOD_NAME)\\nPLOTPY_DEFAULTS = {\\n \\\"plot\\\": {\\n # \\\"antialiasing\\\": False,\\n # \\\"title/font/size\\\": 12,\\n # \\\"title/font/bold\\\": False,\\n # \\\"marker/curve/text/font/size\\\": 8,\\n # \\\"marker/curve/text/font/family\\\": \\\"default\\\",\\n # \\\"marker/curve/text/font/bold\\\": False,\\n # \\\"marker/curve/text/font/italic\\\": False,\\n \\\"marker/curve/text/textcolor\\\": \\\"black\\\",\\n # \\\"marker/curve/text/background_color\\\": \\\"#ffffff\\\",\\n # \\\"marker/curve/text/background_alpha\\\": 0.8,\\n # \\\"marker/cross/text/font/family\\\": \\\"default\\\",\\n # \\\"marker/cross/text/font/size\\\": 8,\\n # \\\"marker/cross/text/font/bold\\\": False,\\n # \\\"marker/cross/text/font/italic\\\": False,\\n \\\"marker/cross/text/textcolor\\\": \\\"black\\\",\\n # \\\"marker/cross/text/background_color\\\": \\\"#ffffff\\\",\\n \\\"marker/cross/text/background_alpha\\\": 0.7,\\n # \\\"marker/cross/line/style\\\": \\\"DashLine\\\",\\n # \\\"marker/cross/line/color\\\": \\\"yellow\\\",\\n # \\\"marker/cross/line/width\\\": 1,\\n # \\\"marker/cursor/text/font/size\\\": 8,\\n # \\\"marker/cursor/text/font/family\\\": \\\"default\\\",\\n # \\\"marker/cursor/text/font/bold\\\": False,\\n # \\\"marker/cursor/text/font/italic\\\": False,\\n # \\\"marker/cursor/text/textcolor\\\": \\\"#ff9393\\\",\\n # \\\"marker/cursor/text/background_color\\\": \\\"#ffffff\\\",\\n # \\\"marker/cursor/text/background_alpha\\\": 0.8,\\n \\\"shape/drag/symbol/marker\\\": \\\"NoSymbol\\\",\\n \\\"shape/mask/symbol/size\\\": 5,\\n \\\"shape/mask/sel_symbol/size\\\": 8,\\n # -----------------------------------------------------------------------------\\n # Annotated shape style for annotations:\\n \\\"shape/annotation/line/style\\\": \\\"SolidLine\\\",\\n \\\"shape/annotation/line/color\\\": \\\"#ffff00\\\",\\n \\\"shape/annotation/line/width\\\": 1,\\n \\\"shape/annotation/fill/style\\\": \\\"SolidPattern\\\",\\n \\\"shape/annotation/fill/color\\\": MAIN_BG_COLOR,\\n \\\"shape/annotation/fill/alpha\\\": 0.1,\\n \\\"shape/annotation/symbol/marker\\\": \\\"Rect\\\",\\n \\\"shape/annotation/symbol/size\\\": 3,\\n \\\"shape/annotation/symbol/edgecolor\\\": \\\"#ffff00\\\",\\n \\\"shape/annotation/symbol/facecolor\\\": \\\"#ffff00\\\",\\n \\\"shape/annotation/symbol/alpha\\\": 1.0,\\n \\\"shape/annotation/sel_line/style\\\": \\\"SolidLine\\\",\\n \\\"shape/annotation/sel_line/color\\\": \\\"#00ff00\\\",\\n \\\"shape/annotation/sel_line/width\\\": 1,\\n \\\"shape/annotation/sel_fill/style\\\": \\\"SolidPattern\\\",\\n \\\"shape/annotation/sel_fill/color\\\": MAIN_BG_COLOR,\\n \\\"shape/annotation/sel_fill/alpha\\\": 0.1,\\n \\\"shape/annotation/sel_symbol/marker\\\": \\\"Rect\\\",\\n \\\"shape/annotation/sel_symbol/size\\\": 9,\\n \\\"shape/annotation/sel_symbol/edgecolor\\\": \\\"#00aa00\\\",\\n \\\"shape/annotation/sel_symbol/facecolor\\\": \\\"#00ff00\\\",\\n \\\"shape/annotation/sel_symbol/alpha\\\": 0.7,\\n # -----------------------------------------------------------------------------\\n # Annotated shape style for result shapes / signals:\\n \\\"shape/result/s/line/style\\\": \\\"SolidLine\\\",\\n \\\"shape/result/s/line/color\\\": MAIN_FG_COLOR,\\n \\\"shape/result/s/line/width\\\": 1,\\n \\\"shape/result/s/fill/style\\\": \\\"SolidPattern\\\",\\n \\\"shape/result/s/fill/color\\\": MAIN_BG_COLOR,\\n \\\"shape/result/s/fill/alpha\\\": 0.1,\\n \\\"shape/result/s/symbol/marker\\\": \\\"XCross\\\",\\n \\\"shape/result/s/symbol/size\\\": 7,\\n \\\"shape/result/s/symbol/edgecolor\\\": MAIN_FG_COLOR,\\n \\\"shape/result/s/symbol/facecolor\\\": MAIN_FG_COLOR,\\n \\\"shape/result/s/symbol/alpha\\\": 1.0,\\n \\\"shape/result/s/sel_line/style\\\": \\\"SolidLine\\\",\\n \\\"shape/result/s/sel_line/color\\\": \\\"#00ff00\\\",\\n \\\"shape/result/s/sel_line/width\\\": 1,\\n \\\"shape/result/s/sel_fill/style\\\": \\\"SolidPattern\\\",\\n \\\"shape/result/s/sel_fill/color\\\": MAIN_BG_COLOR,\\n \\\"shape/result/s/sel_fill/alpha\\\": 0.1,\\n \\\"shape/result/s/sel_symbol/marker\\\": \\\"Rect\\\",\\n \\\"shape/result/s/sel_symbol/size\\\": 9,\\n \\\"shape/result/s/sel_symbol/edgecolor\\\": \\\"#00aa00\\\",\\n \\\"shape/result/s/sel_symbol/facecolor\\\": \\\"#00ff00\\\",\\n \\\"shape/result/s/sel_symbol/alpha\\\": 0.7,\\n # -----------------------------------------------------------------------------\\n # Annotated shape style for result shapes / images:\\n \\\"shape/result/i/line/style\\\": \\\"SolidLine\\\",\\n \\\"shape/result/i/line/color\\\": \\\"#ffff00\\\",\\n \\\"shape/result/i/line/width\\\": 1,\\n \\\"shape/result/i/fill/style\\\": \\\"SolidPattern\\\",\\n \\\"shape/result/i/fill/color\\\": MAIN_BG_COLOR,\\n \\\"shape/result/i/fill/alpha\\\": 0.1,\\n \\\"shape/result/i/symbol/marker\\\": \\\"Rect\\\",\\n \\\"shape/result/i/symbol/size\\\": 3,\\n \\\"shape/result/i/symbol/edgecolor\\\": \\\"#ffff00\\\",\\n \\\"shape/result/i/symbol/facecolor\\\": \\\"#ffff00\\\",\\n \\\"shape/result/i/symbol/alpha\\\": 1.0,\\n \\\"shape/result/i/sel_line/style\\\": \\\"SolidLine\\\",\\n \\\"shape/result/i/sel_line/color\\\": \\\"#00ff00\\\",\\n \\\"shape/result/i/sel_line/width\\\": 1,\\n \\\"shape/result/i/sel_fill/style\\\": \\\"SolidPattern\\\",\\n \\\"shape/result/i/sel_fill/color\\\": MAIN_BG_COLOR,\\n \\\"shape/result/i/sel_fill/alpha\\\": 0.1,\\n \\\"shape/result/i/sel_symbol/marker\\\": \\\"Rect\\\",\\n \\\"shape/result/i/sel_symbol/size\\\": 9,\\n \\\"shape/result/i/sel_symbol/edgecolor\\\": \\\"#00aa00\\\",\\n \\\"shape/result/i/sel_symbol/facecolor\\\": \\\"#00ff00\\\",\\n \\\"shape/result/i/sel_symbol/alpha\\\": 0.7,\\n # -----------------------------------------------------------------------------\\n },\\n}\\ndef is_frozen(module_name: str) -> bool:\\ndef get_mod_source_dir() -> str | None:\\n def get_def_dict(cls, category: str) -> dict:\\n def set_def_dict(cls, category: str, def_dict: dict) -> None:\\ndef get_old_log_fname(fname):\\ndef initialize():\\ndef reset():\\nclass MainSection(conf.Section, metaclass=conf.SectionMeta):\\nclass ConsoleSection(conf.Section, metaclass=conf.SectionMeta):\\nclass IOSection(conf.Section, metaclass=conf.SectionMeta):\\nclass ProcSection(conf.Section, metaclass=conf.SectionMeta):\\nclass ViewSection(conf.Section, metaclass=conf.SectionMeta):\\nclass Conf(conf.Configuration, metaclass=conf.ConfMeta):\"\n },\n {\n \"identifier\": \"CDLMainWindow\",\n \"path\": \"cdl/core/gui/main.py\",\n \"snippet\": \"class CDLMainWindow(QW.QMainWindow, AbstractCDLControl, metaclass=CDLMainWindowMeta):\\n \\\"\\\"\\\"DataLab main window\\n\\n Args:\\n console: enable internal console\\n hide_on_close: True to hide window on close\\n \\\"\\\"\\\"\\n\\n __instance = None\\n\\n SIG_READY = QC.Signal()\\n SIG_SEND_OBJECT = QC.Signal(object)\\n SIG_SEND_OBJECTLIST = QC.Signal(object)\\n SIG_CLOSING = QC.Signal()\\n\\n @staticmethod\\n def get_instance(console=None, hide_on_close=False):\\n \\\"\\\"\\\"Return singleton instance\\\"\\\"\\\"\\n if CDLMainWindow.__instance is None:\\n return CDLMainWindow(console, hide_on_close)\\n return CDLMainWindow.__instance\\n\\n def __init__(self, console=None, hide_on_close=False):\\n \\\"\\\"\\\"Initialize main window\\\"\\\"\\\"\\n CDLMainWindow.__instance = self\\n super().__init__()\\n win32_fix_title_bar_background(self)\\n self.setObjectName(APP_NAME)\\n self.setWindowIcon(get_icon(\\\"DataLab.svg\\\"))\\n\\n execenv.log(self, \\\"Starting initialization\\\")\\n\\n self.__restore_pos_and_size()\\n\\n self.ready_flag = True\\n\\n self.hide_on_close = hide_on_close\\n self.__old_size = None\\n self.__memory_warning = False\\n self.memorystatus = None\\n\\n self.console = None\\n self.macropanel: MacroPanel = None\\n\\n self.signal_toolbar: QW.QToolBar = None\\n self.image_toolbar: QW.QToolBar = None\\n self.signalpanel: SignalPanel = None\\n self.imagepanel: ImagePanel = None\\n self.tabwidget: QW.QTabWidget = None\\n self.docks: dict[AbstractPanel, QW.QDockWidget] = None\\n self.h5inputoutput = H5InputOutput(self)\\n\\n self.openh5_action: QW.QAction = None\\n self.saveh5_action: QW.QAction = None\\n self.browseh5_action: QW.QAction = None\\n self.settings_action: QW.QAction = None\\n self.quit_action: QW.QAction = None\\n self.auto_refresh_action: QW.QAction = None\\n self.showlabel_action: QW.QAction = None\\n\\n self.file_menu: QW.QMenu = None\\n self.edit_menu: QW.QMenu = None\\n self.operation_menu: QW.QMenu = None\\n self.processing_menu: QW.QMenu = None\\n self.computing_menu: QW.QMenu = None\\n self.plugins_menu: QW.QMenu = None\\n self.view_menu: QW.QMenu = None\\n self.help_menu: QW.QMenu = None\\n\\n self.__is_modified = None\\n self.set_modified(False)\\n\\n # Starting XML-RPC server thread\\n self.remote_server = RemoteServer(self)\\n if Conf.main.rpc_server_enabled.get():\\n self.remote_server.SIG_SERVER_PORT.connect(self.xmlrpc_server_started)\\n self.remote_server.start()\\n\\n # Setup actions and menus\\n if console is None:\\n console = Conf.console.console_enabled.get()\\n self.setup(console)\\n\\n execenv.log(self, \\\"Initialization done\\\")\\n\\n # ------API related to XML-RPC remote control\\n @staticmethod\\n def xmlrpc_server_started(port):\\n \\\"\\\"\\\"XML-RPC server has started, writing comm port in configuration file\\\"\\\"\\\"\\n Conf.main.rpc_server_port.set(port)\\n\\n def __get_current_basedatapanel(self) -> BaseDataPanel:\\n \\\"\\\"\\\"Return the current BaseDataPanel,\\n or the signal panel if macro panel is active\\n\\n Returns:\\n BaseDataPanel: current panel\\n \\\"\\\"\\\"\\n panel = self.tabwidget.currentWidget()\\n if not isinstance(panel, base.BaseDataPanel):\\n panel = self.signalpanel\\n return panel\\n\\n def __get_specific_panel(self, panel: str | None) -> BaseDataPanel:\\n \\\"\\\"\\\"Return a specific BaseDataPanel.\\n\\n Args:\\n panel (str | None): panel name (valid values: \\\"signal\\\", \\\"image\\\").\\n If None, current panel is used.\\n\\n Returns:\\n BaseDataPanel: panel\\n\\n Raises:\\n ValueError: if panel is unknown\\n \\\"\\\"\\\"\\n if not panel:\\n return self.__get_current_basedatapanel()\\n if panel == \\\"signal\\\":\\n return self.signalpanel\\n if panel == \\\"image\\\":\\n return self.imagepanel\\n raise ValueError(f\\\"Unknown panel: {panel}\\\")\\n\\n @remote_controlled\\n def get_group_titles_with_object_infos(\\n self,\\n ) -> tuple[list[str], list[list[str]], list[list[str]]]:\\n \\\"\\\"\\\"Return groups titles and lists of inner objects uuids and titles.\\n\\n Returns:\\n Tuple: groups titles, lists of inner objects uuids and titles\\n \\\"\\\"\\\"\\n panel = self.__get_current_basedatapanel()\\n return panel.objmodel.get_group_titles_with_object_infos()\\n\\n @remote_controlled\\n def get_object_titles(self, panel: str | None = None) -> list[str]:\\n \\\"\\\"\\\"Get object (signal/image) list for current panel.\\n Objects are sorted by group number and object index in group.\\n\\n Args:\\n panel (str | None): panel name (valid values: \\\"signal\\\", \\\"image\\\").\\n If None, current panel is used.\\n\\n Returns:\\n list[str]: list of object titles\\n\\n Raises:\\n ValueError: if panel is unknown\\n \\\"\\\"\\\"\\n return self.__get_specific_panel(panel).objmodel.get_object_titles()\\n\\n @remote_controlled\\n def get_object(\\n self,\\n nb_id_title: int | str | None = None,\\n panel: str | None = None,\\n ) -> SignalObj | ImageObj:\\n \\\"\\\"\\\"Get object (signal/image) from index.\\n\\n Args:\\n nb_id_title: Object number, or object id, or object title.\\n Defaults to None (current object).\\n panel: Panel name. Defaults to None (current panel).\\n\\n Returns:\\n Object\\n\\n Raises:\\n KeyError: if object not found\\n TypeError: if index_id_title type is invalid\\n \\\"\\\"\\\"\\n panelw = self.__get_specific_panel(panel)\\n if nb_id_title is None:\\n return panelw.objview.get_current_object()\\n if isinstance(nb_id_title, int):\\n return panelw.objmodel.get_object_from_number(nb_id_title)\\n if isinstance(nb_id_title, str):\\n try:\\n return panelw.objmodel[nb_id_title]\\n except KeyError:\\n try:\\n return panelw.objmodel.get_object_from_title(nb_id_title)\\n except KeyError as exc:\\n raise KeyError(\\n f\\\"Invalid object index, id or title: {nb_id_title}\\\"\\n ) from exc\\n raise TypeError(f\\\"Invalid index_id_title type: {type(nb_id_title)}\\\")\\n\\n @remote_controlled\\n def get_object_uuids(self, panel: str | None = None) -> list[str]:\\n \\\"\\\"\\\"Get object (signal/image) uuid list for current panel.\\n Objects are sorted by group number and object index in group.\\n\\n Args:\\n panel (str | None): panel name (valid values: \\\"signal\\\", \\\"image\\\").\\n If None, current panel is used.\\n\\n Returns:\\n list[str]: list of object uuids\\n\\n Raises:\\n ValueError: if panel is unknown\\n \\\"\\\"\\\"\\n return self.__get_specific_panel(panel).objmodel.get_object_ids()\\n\\n @remote_controlled\\n def get_sel_object_uuids(self, include_groups: bool = False) -> list[str]:\\n \\\"\\\"\\\"Return selected objects uuids.\\n\\n Args:\\n include_groups: If True, also return objects from selected groups.\\n\\n Returns:\\n List of selected objects uuids.\\n \\\"\\\"\\\"\\n panel = self.__get_current_basedatapanel()\\n return panel.objview.get_sel_object_uuids(include_groups)\\n\\n @remote_controlled\\n def select_objects(\\n self,\\n selection: list[int | str],\\n panel: str | None = None,\\n ) -> None:\\n \\\"\\\"\\\"Select objects in current panel.\\n\\n Args:\\n selection: List of object numbers (1 to N) or uuids to select\\n panel: panel name (valid values: \\\"signal\\\", \\\"image\\\").\\n If None, current panel is used. Defaults to None.\\n \\\"\\\"\\\"\\n panel = self.__get_specific_panel(panel)\\n panel.objview.select_objects(selection)\\n\\n @remote_controlled\\n def select_groups(\\n self, selection: list[int | str] | None = None, panel: str | None = None\\n ) -> None:\\n \\\"\\\"\\\"Select groups in current panel.\\n\\n Args:\\n selection: List of group numbers (1 to N), or list of group uuids,\\n or None to select all groups. Defaults to None.\\n panel (str | None): panel name (valid values: \\\"signal\\\", \\\"image\\\").\\n If None, current panel is used. Defaults to None.\\n \\\"\\\"\\\"\\n panel = self.__get_specific_panel(panel)\\n panel.objview.select_groups(selection)\\n\\n @remote_controlled\\n def delete_metadata(self, refresh_plot: bool = True) -> None:\\n \\\"\\\"\\\"Delete metadata of selected objects\\n\\n Args:\\n refresh_plot (bool | None): Refresh plot. Defaults to True.\\n \\\"\\\"\\\"\\n panel = self.__get_current_basedatapanel()\\n panel.delete_metadata(refresh_plot)\\n\\n @remote_controlled\\n def get_object_shapes(\\n self,\\n nb_id_title: int | str | None = None,\\n panel: str | None = None,\\n ) -> list:\\n \\\"\\\"\\\"Get plot item shapes associated to object (signal/image).\\n\\n Args:\\n nb_id_title: Object number, or object id, or object title.\\n Defaults to None (current object).\\n panel: Panel name. Defaults to None (current panel).\\n\\n Returns:\\n List of plot item shapes\\n \\\"\\\"\\\"\\n obj = self.get_object(nb_id_title, panel)\\n return list(obj.iterate_shape_items(editable=False))\\n\\n @remote_controlled\\n def add_annotations_from_items(\\n self, items: list, refresh_plot: bool = True, panel: str | None = None\\n ) -> None:\\n \\\"\\\"\\\"Add object annotations (annotation plot items).\\n\\n Args:\\n items (list): annotation plot items\\n refresh_plot (bool | None): refresh plot. Defaults to True.\\n panel (str | None): panel name (valid values: \\\"signal\\\", \\\"image\\\").\\n If None, current panel is used.\\n \\\"\\\"\\\"\\n panel = self.__get_specific_panel(panel)\\n panel.add_annotations_from_items(items, refresh_plot)\\n\\n @remote_controlled\\n def add_label_with_title(\\n self, title: str | None = None, panel: str | None = None\\n ) -> None:\\n \\\"\\\"\\\"Add a label with object title on the associated plot\\n\\n Args:\\n title (str | None): Label title. Defaults to None.\\n If None, the title is the object title.\\n panel (str | None): panel name (valid values: \\\"signal\\\", \\\"image\\\").\\n If None, current panel is used.\\n \\\"\\\"\\\"\\n self.__get_specific_panel(panel).add_label_with_title(title)\\n\\n # ------Misc.\\n @property\\n def panels(self) -> tuple[AbstractPanel, ...]:\\n \\\"\\\"\\\"Return the tuple of implemented panels (signal, image)\\n\\n Returns:\\n tuple[SignalPanel, ImagePanel, MacroPanel]: tuple of panels\\n \\\"\\\"\\\"\\n return (self.signalpanel, self.imagepanel, self.macropanel)\\n\\n def __set_low_memory_state(self, state: bool) -> None:\\n \\\"\\\"\\\"Set memory warning state\\\"\\\"\\\"\\n self.__memory_warning = state\\n\\n def confirm_memory_state(self) -> bool: # pragma: no cover\\n \\\"\\\"\\\"Check memory warning state and eventually show a warning dialog\\n\\n Returns:\\n bool: True if memory state is ok\\n \\\"\\\"\\\"\\n if not env.execenv.unattended and self.__memory_warning:\\n threshold = Conf.main.available_memory_threshold.get()\\n answer = QW.QMessageBox.critical(\\n self,\\n _(\\\"Warning\\\"),\\n _(\\\"Available memory is below %d MB.<br><br>Do you want to continue?\\\")\\n % threshold,\\n QW.QMessageBox.Yes | QW.QMessageBox.No,\\n )\\n return answer == QW.QMessageBox.Yes\\n return True\\n\\n def check_stable_release(self) -> None: # pragma: no cover\\n \\\"\\\"\\\"Check if this is a stable release\\\"\\\"\\\"\\n if __version__.replace(\\\".\\\", \\\"\\\").isdigit():\\n # This is a stable release\\n return\\n if \\\"b\\\" in __version__:\\n # This is a beta release\\n rel = _(\\n \\\"This software is in the <b>beta stage</b> of its release cycle. \\\"\\n \\\"The focus of beta testing is providing a feature complete \\\"\\n \\\"software for users interested in trying new features before \\\"\\n \\\"the final release. However, <u>beta software may not behave as \\\"\\n \\\"expected and will probably have more bugs or performance issues \\\"\\n \\\"than completed software</u>.\\\"\\n )\\n else:\\n # This is an alpha release\\n rel = _(\\n \\\"This software is in the <b>alpha stage</b> of its release cycle. \\\"\\n \\\"The focus of alpha testing is providing an incomplete software \\\"\\n \\\"for early testing of specific features by users. \\\"\\n \\\"Please note that <u>alpha software was not thoroughly tested</u> \\\"\\n \\\"by the developer before it is released.\\\"\\n )\\n txtlist = [\\n f\\\"<b>{APP_NAME}</b> v{__version__}:\\\",\\n \\\"\\\",\\n _(\\\"<i>This is not a stable release.</i>\\\"),\\n \\\"\\\",\\n rel,\\n ]\\n QW.QMessageBox.warning(self, APP_NAME, \\\"<br>\\\".join(txtlist), QW.QMessageBox.Ok)\\n\\n def __check_dependencies(self) -> None: # pragma: no cover\\n \\\"\\\"\\\"Check dependencies\\\"\\\"\\\"\\n if IS_FROZEN or execenv.unattended:\\n # No need to check dependencies if DataLab has been frozen, or if\\n # the user has chosen to ignore this check, or if we are in unattended mode\\n # (i.e. running automated tests)\\n\\n if IS_FROZEN:\\n QW.QMessageBox.information(\\n self,\\n _(\\\"Information\\\"),\\n _(\\n \\\"The dependency check feature is not relevant for the \\\"\\n \\\"standalone version of DataLab.\\\"\\n ),\\n QW.QMessageBox.Ok,\\n )\\n return\\n try:\\n state = dephash.check_dependencies_hash(DATAPATH)\\n bad_deps = [name for name in state if not state[name]]\\n if not bad_deps:\\n # Everything is OK\\n QW.QMessageBox.information(\\n self,\\n _(\\\"Information\\\"),\\n _(\\n \\\"All critical dependencies of DataLab have been qualified \\\"\\n \\\"on this operating system.\\\"\\n ),\\n QW.QMessageBox.Ok,\\n )\\n return\\n except IOError:\\n bad_deps = None\\n txt0 = _(\\\"Non-compliant dependency:\\\")\\n if bad_deps is None or len(bad_deps) > 1:\\n txt0 = _(\\\"Non-compliant dependencies:\\\")\\n if bad_deps is None:\\n txtlist = [\\n _(\\\"DataLab has not yet been qualified on your operating system.\\\"),\\n ]\\n else:\\n txtlist = [\\n \\\"<u>\\\" + txt0 + \\\"</u> \\\" + \\\", \\\".join(bad_deps),\\n \\\"\\\",\\n _(\\n \\\"At least one dependency does not comply with DataLab \\\"\\n \\\"qualification standard reference (wrong dependency version \\\"\\n \\\"has been installed, or dependency source code has been \\\"\\n \\\"modified, or the application has not yet been qualified \\\"\\n \\\"on your operating system).\\\"\\n ),\\n ]\\n txtlist += [\\n \\\"\\\",\\n _(\\n \\\"This means that the application has not been officially qualified \\\"\\n \\\"in this context and may not behave as expected.\\\"\\n ),\\n ]\\n txt = \\\"<br>\\\".join(txtlist)\\n QW.QMessageBox.warning(self, APP_NAME, txt, QW.QMessageBox.Ok)\\n\\n def check_for_previous_crash(self) -> None: # pragma: no cover\\n \\\"\\\"\\\"Check for previous crash\\\"\\\"\\\"\\n if execenv.unattended:\\n self.__show_logviewer()\\n elif Conf.main.faulthandler_log_available.get(\\n False\\n ) or Conf.main.traceback_log_available.get(False):\\n txt = \\\"<br>\\\".join(\\n [\\n logviewer.get_log_prompt_message(),\\n \\\"\\\",\\n _(\\\"Do you want to see available log files?\\\"),\\n ]\\n )\\n btns = QW.QMessageBox.StandardButton.Yes | QW.QMessageBox.StandardButton.No\\n choice = QW.QMessageBox.warning(self, APP_NAME, txt, btns)\\n if choice == QW.QMessageBox.StandardButton.Yes:\\n self.__show_logviewer()\\n\\n def take_screenshot(self, name: str) -> None: # pragma: no cover\\n \\\"\\\"\\\"Take main window screenshot\\\"\\\"\\\"\\n self.memorystatus.set_demo_mode(True)\\n qth.grab_save_window(self, f\\\"{name}\\\")\\n self.memorystatus.set_demo_mode(False)\\n\\n def take_menu_screenshots(self) -> None: # pragma: no cover\\n \\\"\\\"\\\"Take menu screenshots\\\"\\\"\\\"\\n for panel in self.panels:\\n if isinstance(panel, base.BaseDataPanel):\\n self.tabwidget.setCurrentWidget(panel)\\n for name in (\\n \\\"file\\\",\\n \\\"edit\\\",\\n \\\"view\\\",\\n \\\"operation\\\",\\n \\\"processing\\\",\\n \\\"computing\\\",\\n \\\"help\\\",\\n ):\\n menu = getattr(self, f\\\"{name}_menu\\\")\\n menu.popup(self.pos())\\n qth.grab_save_window(menu, f\\\"{panel.objectName()}_{name}\\\")\\n menu.close()\\n\\n # ------GUI setup\\n def __restore_pos_and_size(self) -> None:\\n \\\"\\\"\\\"Restore main window position and size from configuration\\\"\\\"\\\"\\n pos = Conf.main.window_position.get(None)\\n if pos is not None:\\n posx, posy = pos\\n self.move(QC.QPoint(posx, posy))\\n size = Conf.main.window_size.get(None)\\n if size is not None:\\n width, height = size\\n self.resize(QC.QSize(width, height))\\n if pos is not None and size is not None:\\n sgeo = self.screen().availableGeometry()\\n out_inf = posx < -int(0.9 * width) or posy < -int(0.9 * height)\\n out_sup = posx > int(0.9 * sgeo.width()) or posy > int(0.9 * sgeo.height())\\n if len(QW.QApplication.screens()) == 1 and (out_inf or out_sup):\\n # Main window is offscreen\\n posx = min(max(posx, 0), sgeo.width() - width)\\n posy = min(max(posy, 0), sgeo.height() - height)\\n self.move(QC.QPoint(posx, posy))\\n\\n def __save_pos_and_size(self) -> None:\\n \\\"\\\"\\\"Save main window position and size to configuration\\\"\\\"\\\"\\n is_maximized = self.windowState() == QC.Qt.WindowMaximized\\n Conf.main.window_maximized.set(is_maximized)\\n if not is_maximized:\\n size = self.size()\\n Conf.main.window_size.set((size.width(), size.height()))\\n pos = self.pos()\\n Conf.main.window_position.set((pos.x(), pos.y()))\\n\\n def setup(self, console: bool = False) -> None:\\n \\\"\\\"\\\"Setup main window\\n\\n Args:\\n console: True to setup console\\n \\\"\\\"\\\"\\n self.__register_plugins()\\n self.__configure_statusbar()\\n self.__setup_global_actions()\\n self.__add_signal_image_panels()\\n self.__create_plugins_actions()\\n self.__setup_central_widget()\\n self.__add_menus()\\n if console:\\n self.__setup_console()\\n self.__update_actions()\\n self.__add_macro_panel()\\n self.__configure_panels()\\n\\n def __register_plugins(self) -> None:\\n \\\"\\\"\\\"Register plugins\\\"\\\"\\\"\\n with qth.try_or_log_error(\\\"Discovering plugins\\\"):\\n # Discovering plugins\\n plugin_nb = len(discover_plugins())\\n execenv.log(self, f\\\"{plugin_nb} plugin(s) found\\\")\\n for plugin_class in PluginRegistry.get_plugin_classes():\\n with qth.try_or_log_error(f\\\"Instantiating plugin {plugin_class.__name__}\\\"):\\n # Instantiating plugin\\n plugin: PluginBase = plugin_class()\\n with qth.try_or_log_error(f\\\"Registering plugin {plugin.info.name}\\\"):\\n # Registering plugin\\n plugin.register(self)\\n\\n def __create_plugins_actions(self) -> None:\\n \\\"\\\"\\\"Create plugins actions\\\"\\\"\\\"\\n with self.signalpanel.acthandler.new_category(ActionCategory.PLUGINS):\\n with self.imagepanel.acthandler.new_category(ActionCategory.PLUGINS):\\n for plugin in PluginRegistry.get_plugins():\\n with qth.try_or_log_error(f\\\"Create actions for {plugin.info.name}\\\"):\\n plugin.create_actions()\\n\\n @staticmethod\\n def __unregister_plugins() -> None:\\n \\\"\\\"\\\"Unregister plugins\\\"\\\"\\\"\\n while PluginRegistry.get_plugins():\\n # Unregistering plugin\\n plugin = PluginRegistry.get_plugins()[-1]\\n with qth.try_or_log_error(f\\\"Unregistering plugin {plugin.info.name}\\\"):\\n plugin.unregister()\\n\\n def __configure_statusbar(self) -> None:\\n \\\"\\\"\\\"Configure status bar\\\"\\\"\\\"\\n self.statusBar().showMessage(_(\\\"Welcome to %s!\\\") % APP_NAME, 5000)\\n # Plugin status\\n pluginstatus = status.PluginStatus()\\n self.statusBar().addPermanentWidget(pluginstatus)\\n # XML-RPC server status\\n xmlrpcstatus = status.XMLRPCStatus()\\n xmlrpcstatus.set_port(self.remote_server.port)\\n self.statusBar().addPermanentWidget(xmlrpcstatus)\\n # Memory status\\n threshold = Conf.main.available_memory_threshold.get()\\n self.memorystatus = status.MemoryStatus(threshold)\\n self.memorystatus.SIG_MEMORY_ALARM.connect(self.__set_low_memory_state)\\n self.statusBar().addPermanentWidget(self.memorystatus)\\n\\n def __setup_global_actions(self) -> None:\\n \\\"\\\"\\\"Setup global actions\\\"\\\"\\\"\\n self.openh5_action = create_action(\\n self,\\n _(\\\"Open HDF5 files...\\\"),\\n icon=get_icon(\\\"fileopen_h5.svg\\\"),\\n tip=_(\\\"Open one or several HDF5 files\\\"),\\n triggered=lambda checked=False: self.open_h5_files(import_all=True),\\n )\\n self.saveh5_action = create_action(\\n self,\\n _(\\\"Save to HDF5 file...\\\"),\\n icon=get_icon(\\\"filesave_h5.svg\\\"),\\n tip=_(\\\"Save to HDF5 file\\\"),\\n triggered=self.save_to_h5_file,\\n )\\n self.browseh5_action = create_action(\\n self,\\n _(\\\"Browse HDF5 file...\\\"),\\n icon=get_icon(\\\"h5browser.svg\\\"),\\n tip=_(\\\"Browse an HDF5 file\\\"),\\n triggered=lambda checked=False: self.open_h5_files(import_all=None),\\n )\\n self.settings_action = create_action(\\n self,\\n _(\\\"Settings...\\\"),\\n icon=get_icon(\\\"libre-gui-settings.svg\\\"),\\n tip=_(\\\"Open settings dialog\\\"),\\n triggered=self.__edit_settings,\\n )\\n main_toolbar = self.addToolBar(_(\\\"Main Toolbar\\\"))\\n add_actions(\\n main_toolbar,\\n [\\n self.openh5_action,\\n self.saveh5_action,\\n self.browseh5_action,\\n None,\\n self.settings_action,\\n ],\\n )\\n # Quit action for \\\"File menu\\\" (added when populating menu on demand)\\n if self.hide_on_close:\\n quit_text = _(\\\"Hide window\\\")\\n quit_tip = _(\\\"Hide DataLab window\\\")\\n else:\\n quit_text = _(\\\"Quit\\\")\\n quit_tip = _(\\\"Quit application\\\")\\n if sys.platform != \\\"darwin\\\":\\n # On macOS, the \\\"Quit\\\" action is automatically added to the application menu\\n self.quit_action = create_action(\\n self,\\n quit_text,\\n shortcut=QG.QKeySequence(QG.QKeySequence.Quit),\\n icon=get_icon(\\\"libre-gui-close.svg\\\"),\\n tip=quit_tip,\\n triggered=self.close,\\n )\\n # View menu actions\\n self.auto_refresh_action = create_action(\\n self,\\n _(\\\"Auto-refresh\\\"),\\n icon=get_icon(\\\"refresh-auto.svg\\\"),\\n tip=_(\\\"Auto-refresh plot when object is modified, added or removed\\\"),\\n toggled=self.toggle_auto_refresh,\\n )\\n self.showlabel_action = create_action(\\n self,\\n _(\\\"Show graphical object titles\\\"),\\n icon=get_icon(\\\"show_titles.svg\\\"),\\n tip=_(\\\"Show or hide ROI and other graphical object titles or subtitles\\\"),\\n toggled=self.toggle_show_titles,\\n )\\n\\n def __add_signal_panel(self) -> None:\\n \\\"\\\"\\\"Setup signal toolbar, widgets and panel\\\"\\\"\\\"\\n self.signal_toolbar = self.addToolBar(_(\\\"Signal Processing Toolbar\\\"))\\n curvewidget = DockablePlotWidget(self, PlotType.CURVE)\\n curveplot = curvewidget.get_plot()\\n curveplot.add_item(make.legend(\\\"TR\\\"))\\n self.signalpanel = signal.SignalPanel(\\n self, curvewidget.plotwidget, self.signal_toolbar\\n )\\n self.signalpanel.SIG_STATUS_MESSAGE.connect(self.statusBar().showMessage)\\n return curvewidget\\n\\n def __add_image_panel(self) -> None:\\n \\\"\\\"\\\"Setup image toolbar, widgets and panel\\\"\\\"\\\"\\n self.image_toolbar = self.addToolBar(_(\\\"Image Processing Toolbar\\\"))\\n imagewidget = DockablePlotWidget(self, PlotType.IMAGE)\\n self.imagepanel = image.ImagePanel(\\n self, imagewidget.plotwidget, self.image_toolbar\\n )\\n # -----------------------------------------------------------------------------\\n # # Before eventually disabling the \\\"peritem\\\" mode by default, wait for the\\n # # plotpy bug to be fixed (peritem mode is not compatible with multiple image\\n # # items):\\n # for cspanel in (\\n # self.imagepanel.plotwidget.get_xcs_panel(),\\n # self.imagepanel.plotwidget.get_ycs_panel(),\\n # ):\\n # cspanel.peritem_ac.setChecked(False)\\n # -----------------------------------------------------------------------------\\n self.imagepanel.SIG_STATUS_MESSAGE.connect(self.statusBar().showMessage)\\n return imagewidget\\n\\n def __add_signal_image_panels(self) -> None:\\n \\\"\\\"\\\"Add signal and image panels\\\"\\\"\\\"\\n self.tabwidget = QW.QTabWidget()\\n cdock = self.__add_dockwidget(self.__add_signal_panel(), title=_(\\\"Curve panel\\\"))\\n idock = self.__add_dockwidget(self.__add_image_panel(), title=_(\\\"Image panel\\\"))\\n self.tabifyDockWidget(cdock, idock)\\n self.docks = {self.signalpanel: cdock, self.imagepanel: idock}\\n self.tabwidget.currentChanged.connect(self.__tab_index_changed)\\n self.signalpanel.SIG_OBJECT_ADDED.connect(\\n lambda: self.set_current_panel(\\\"signal\\\")\\n )\\n self.imagepanel.SIG_OBJECT_ADDED.connect(\\n lambda: self.set_current_panel(\\\"image\\\")\\n )\\n for panel in (self.signalpanel, self.imagepanel):\\n panel.setup_panel()\\n\\n def __setup_central_widget(self) -> None:\\n \\\"\\\"\\\"Setup central widget (main panel)\\\"\\\"\\\"\\n self.tabwidget.setMaximumWidth(500)\\n self.tabwidget.addTab(self.signalpanel, get_icon(\\\"signal.svg\\\"), _(\\\"Signals\\\"))\\n self.tabwidget.addTab(self.imagepanel, get_icon(\\\"image.svg\\\"), _(\\\"Images\\\"))\\n self.setCentralWidget(self.tabwidget)\\n\\n @staticmethod\\n def __get_local_doc_path() -> str | None:\\n \\\"\\\"\\\"Return local documentation path, if it exists\\\"\\\"\\\"\\n locale = QC.QLocale.system().name()\\n for suffix in (\\\"_\\\" + locale[:2], \\\"_en\\\"):\\n path = osp.join(DATAPATH, \\\"doc\\\", f\\\"{APP_NAME}{suffix}.pdf\\\")\\n if osp.isfile(path):\\n return path\\n return None\\n\\n def __add_menus(self) -> None:\\n \\\"\\\"\\\"Adding menus\\\"\\\"\\\"\\n self.file_menu = self.menuBar().addMenu(_(\\\"File\\\"))\\n configure_menu_about_to_show(self.file_menu, self.__update_file_menu)\\n self.edit_menu = self.menuBar().addMenu(_(\\\"&Edit\\\"))\\n self.operation_menu = self.menuBar().addMenu(_(\\\"Operations\\\"))\\n self.processing_menu = self.menuBar().addMenu(_(\\\"Processing\\\"))\\n self.computing_menu = self.menuBar().addMenu(_(\\\"Computing\\\"))\\n self.plugins_menu = self.menuBar().addMenu(_(\\\"Plugins\\\"))\\n self.view_menu = self.menuBar().addMenu(_(\\\"&View\\\"))\\n configure_menu_about_to_show(self.view_menu, self.__update_view_menu)\\n self.help_menu = self.menuBar().addMenu(\\\"?\\\")\\n for menu in (\\n self.edit_menu,\\n self.operation_menu,\\n self.processing_menu,\\n self.computing_menu,\\n self.plugins_menu,\\n ):\\n configure_menu_about_to_show(menu, self.__update_generic_menu)\\n help_menu_actions = [\\n create_action(\\n self,\\n _(\\\"Online documentation\\\"),\\n icon=get_icon(\\\"libre-gui-help.svg\\\"),\\n triggered=lambda: webbrowser.open(__docurl__),\\n ),\\n ]\\n localdocpath = self.__get_local_doc_path()\\n if localdocpath is not None:\\n help_menu_actions += [\\n create_action(\\n self,\\n _(\\\"PDF documentation\\\"),\\n icon=get_icon(\\\"help_pdf.svg\\\"),\\n triggered=lambda: webbrowser.open(localdocpath),\\n ),\\n ]\\n help_menu_actions += [None]\\n if TEST_SEGFAULT_ERROR:\\n help_menu_actions += [\\n create_action(\\n self,\\n _(\\\"Test segfault/Python error\\\"),\\n triggered=self.test_segfault_error,\\n )\\n ]\\n help_menu_actions += [\\n create_action(\\n self,\\n _(\\\"Log files\\\") + \\\"...\\\",\\n icon=get_icon(\\\"logs.svg\\\"),\\n triggered=self.__show_logviewer,\\n ),\\n create_action(\\n self,\\n _(\\\"Installation and configuration\\\") + \\\"...\\\",\\n icon=get_icon(\\\"libre-toolbox.svg\\\"),\\n triggered=lambda: instconfviewer.exec_cdl_installconfig_dialog(self),\\n ),\\n None,\\n create_action(\\n self,\\n _(\\\"Project home page\\\"),\\n icon=get_icon(\\\"libre-gui-globe.svg\\\"),\\n triggered=lambda: webbrowser.open(__homeurl__),\\n ),\\n create_action(\\n self,\\n _(\\\"Bug report or feature request\\\"),\\n icon=get_icon(\\\"libre-gui-globe.svg\\\"),\\n triggered=lambda: webbrowser.open(__supporturl__),\\n ),\\n create_action(\\n self,\\n _(\\\"Check critical dependencies...\\\"),\\n triggered=self.__check_dependencies,\\n ),\\n create_action(\\n self,\\n _(\\\"About...\\\"),\\n icon=get_icon(\\\"libre-gui-about.svg\\\"),\\n triggered=self.__about,\\n ),\\n ]\\n add_actions(self.help_menu, help_menu_actions)\\n\\n def __setup_console(self) -> None:\\n \\\"\\\"\\\"Add an internal console\\\"\\\"\\\"\\n ns = {\\n \\\"cdl\\\": self,\\n \\\"np\\\": np,\\n \\\"sps\\\": sps,\\n \\\"spi\\\": spi,\\n \\\"os\\\": os,\\n \\\"sys\\\": sys,\\n \\\"osp\\\": osp,\\n \\\"time\\\": time,\\n }\\n msg = (\\n \\\"Welcome to DataLab console!\\\\n\\\"\\n \\\"---------------------------\\\\n\\\"\\n \\\"You can access the main window with the 'cdl' variable.\\\\n\\\"\\n \\\"Example:\\\\n\\\"\\n \\\" o = cdl.get_object() # returns currently selected object\\\\n\\\"\\n \\\" o = cdl[1] # returns object number 1\\\\n\\\"\\n \\\" o = cdl['My image'] # returns object which title is 'My image'\\\\n\\\"\\n \\\" o.data # returns object data\\\\n\\\"\\n \\\"Modules imported at startup: \\\"\\n \\\"os, sys, os.path as osp, time, \\\"\\n \\\"numpy as np, scipy.signal as sps, scipy.ndimage as spi\\\"\\n )\\n self.console = DockableConsole(self, namespace=ns, message=msg, debug=DEBUG)\\n self.console.setMaximumBlockCount(Conf.console.max_line_count.get(5000))\\n self.console.go_to_error.connect(go_to_error)\\n console_dock = self.__add_dockwidget(self.console, _(\\\"Console\\\"))\\n console_dock.hide()\\n self.console.interpreter.widget_proxy.sig_new_prompt.connect(\\n lambda txt: self.repopulate_panel_trees()\\n )\\n\\n def __add_macro_panel(self) -> None:\\n \\\"\\\"\\\"Add macro panel\\\"\\\"\\\"\\n self.macropanel = macro.MacroPanel()\\n mdock = self.__add_dockwidget(self.macropanel, _(\\\"Macro manager\\\"))\\n self.docks[self.macropanel] = mdock\\n self.tabifyDockWidget(self.docks[self.imagepanel], mdock)\\n self.docks[self.signalpanel].raise_()\\n\\n def __configure_panels(self) -> None:\\n \\\"\\\"\\\"Configure panels\\\"\\\"\\\"\\n # Connectings signals\\n for panel in self.panels:\\n panel.SIG_OBJECT_ADDED.connect(self.set_modified)\\n panel.SIG_OBJECT_REMOVED.connect(self.set_modified)\\n self.macropanel.SIG_OBJECT_MODIFIED.connect(self.set_modified)\\n # Initializing common panel actions\\n self.auto_refresh_action.setChecked(Conf.view.auto_refresh.get(True))\\n self.showlabel_action.setChecked(Conf.view.show_label.get(False))\\n # Restoring current tab from last session\\n tab_idx = Conf.main.current_tab.get(None)\\n if tab_idx is not None:\\n self.tabwidget.setCurrentIndex(tab_idx)\\n # Set focus on current panel, so that keyboard shortcuts work (Fixes #10)\\n self.tabwidget.currentWidget().setFocus()\\n\\n def set_process_isolation_enabled(self, state: bool) -> None:\\n \\\"\\\"\\\"Enable/disable process isolation\\n\\n Args:\\n state (bool): True to enable process isolation\\n \\\"\\\"\\\"\\n for processor in (self.imagepanel.processor, self.signalpanel.processor):\\n processor.set_process_isolation_enabled(state)\\n\\n # ------Remote control\\n @remote_controlled\\n def get_current_panel(self) -> str:\\n \\\"\\\"\\\"Return current panel name\\n\\n Returns:\\n str: panel name (valid values: \\\"signal\\\", \\\"image\\\", \\\"macro\\\")\\n \\\"\\\"\\\"\\n panel = self.tabwidget.currentWidget()\\n dock = self.docks[panel]\\n if panel is self.signalpanel and dock.isVisible():\\n return \\\"signal\\\"\\n if panel is self.imagepanel and dock.isVisible():\\n return \\\"image\\\"\\n return \\\"macro\\\"\\n\\n @remote_controlled\\n def set_current_panel(self, panel: str) -> None:\\n \\\"\\\"\\\"Switch to panel.\\n\\n Args:\\n panel (str): panel name (valid values: \\\"signal\\\", \\\"image\\\", \\\"macro\\\")\\n\\n Raises:\\n ValueError: unknown panel\\n \\\"\\\"\\\"\\n if self.get_current_panel() == panel:\\n if panel in (\\\"signal\\\", \\\"image\\\"):\\n # Force tab index changed event to be sure that the dock associated\\n # to the current panel is raised\\n self.__tab_index_changed(self.tabwidget.currentIndex())\\n return\\n if panel == \\\"signal\\\":\\n self.tabwidget.setCurrentWidget(self.signalpanel)\\n elif panel == \\\"image\\\":\\n self.tabwidget.setCurrentWidget(self.imagepanel)\\n elif panel == \\\"macro\\\":\\n self.docks[self.macropanel].raise_()\\n else:\\n raise ValueError(f\\\"Unknown panel {panel}\\\")\\n\\n @remote_controlled\\n def calc(self, name: str, param: gds.DataSet | None = None) -> None:\\n \\\"\\\"\\\"Call compute function `name` in current panel's processor\\n\\n Args:\\n name (str): function name\\n param (guidata.dataset.DataSet): optional parameters\\n (default: None)\\n\\n Raises:\\n ValueError: unknown function\\n \\\"\\\"\\\"\\n panel = self.tabwidget.currentWidget()\\n if isinstance(panel, base.BaseDataPanel):\\n for funcname in (name, f\\\"compute_{name}\\\"):\\n func = getattr(panel.processor, funcname, None)\\n if func is not None:\\n break\\n else:\\n raise ValueError(f\\\"Unknown function {funcname}\\\")\\n if param is None:\\n func()\\n else:\\n func(param)\\n\\n # ------GUI refresh\\n def has_objects(self) -> bool:\\n \\\"\\\"\\\"Return True if sig/ima panels have any object\\\"\\\"\\\"\\n return sum(len(panel) for panel in self.panels) > 0\\n\\n def set_modified(self, state: bool = True) -> None:\\n \\\"\\\"\\\"Set mainwindow modified state\\\"\\\"\\\"\\n state = state and self.has_objects()\\n self.__is_modified = state\\n self.setWindowTitle(APP_NAME + (\\\"*\\\" if state else \\\"\\\"))\\n\\n def __add_dockwidget(self, child, title: str) -> QW.QDockWidget:\\n \\\"\\\"\\\"Add QDockWidget and toggleViewAction\\\"\\\"\\\"\\n dockwidget, location = child.create_dockwidget(title)\\n self.addDockWidget(location, dockwidget)\\n return dockwidget\\n\\n def repopulate_panel_trees(self) -> None:\\n \\\"\\\"\\\"Repopulate all panel trees\\\"\\\"\\\"\\n for panel in self.panels:\\n if isinstance(panel, base.BaseDataPanel):\\n panel.objview.populate_tree()\\n\\n def __update_actions(self) -> None:\\n \\\"\\\"\\\"Update selection dependent actions\\\"\\\"\\\"\\n is_signal = self.tabwidget.currentWidget() is self.signalpanel\\n panel = self.signalpanel if is_signal else self.imagepanel\\n panel.selection_changed()\\n self.signal_toolbar.setVisible(is_signal)\\n self.image_toolbar.setVisible(not is_signal)\\n if self.plugins_menu is not None:\\n plugin_actions = panel.get_category_actions(ActionCategory.PLUGINS)\\n self.plugins_menu.setEnabled(len(plugin_actions) > 0)\\n\\n def __tab_index_changed(self, index: int) -> None:\\n \\\"\\\"\\\"Switch from signal to image mode, or vice-versa\\\"\\\"\\\"\\n dock = self.docks[self.tabwidget.widget(index)]\\n dock.raise_()\\n self.__update_actions()\\n\\n def __update_generic_menu(self, menu: QW.QMenu | None = None) -> None:\\n \\\"\\\"\\\"Update menu before showing up -- Generic method\\\"\\\"\\\"\\n if menu is None:\\n menu = self.sender()\\n menu.clear()\\n panel = self.tabwidget.currentWidget()\\n category = {\\n self.file_menu: ActionCategory.FILE,\\n self.edit_menu: ActionCategory.EDIT,\\n self.view_menu: ActionCategory.VIEW,\\n self.operation_menu: ActionCategory.OPERATION,\\n self.processing_menu: ActionCategory.PROCESSING,\\n self.computing_menu: ActionCategory.COMPUTING,\\n self.plugins_menu: ActionCategory.PLUGINS,\\n }[menu]\\n actions = panel.get_category_actions(category)\\n add_actions(menu, actions)\\n\\n def __update_file_menu(self) -> None:\\n \\\"\\\"\\\"Update file menu before showing up\\\"\\\"\\\"\\n self.saveh5_action.setEnabled(self.has_objects())\\n self.__update_generic_menu(self.file_menu)\\n add_actions(\\n self.file_menu,\\n [\\n None,\\n self.openh5_action,\\n self.saveh5_action,\\n self.browseh5_action,\\n None,\\n self.settings_action,\\n ],\\n )\\n if self.quit_action is not None:\\n add_actions(self.file_menu, [None, self.quit_action])\\n\\n def __update_view_menu(self) -> None:\\n \\\"\\\"\\\"Update view menu before showing up\\\"\\\"\\\"\\n self.__update_generic_menu(self.view_menu)\\n add_actions(self.view_menu, [None] + self.createPopupMenu().actions())\\n\\n @remote_controlled\\n def toggle_show_titles(self, state: bool) -> None:\\n \\\"\\\"\\\"Toggle show annotations option\\n\\n Args:\\n state: state\\n \\\"\\\"\\\"\\n Conf.view.show_label.set(state)\\n for datapanel in (self.signalpanel, self.imagepanel):\\n for obj in datapanel.objmodel:\\n obj.set_metadata_option(\\\"showlabel\\\", state)\\n datapanel.SIG_REFRESH_PLOT.emit(\\\"selected\\\", True)\\n\\n @remote_controlled\\n def toggle_auto_refresh(self, state: bool) -> None:\\n \\\"\\\"\\\"Toggle auto refresh option\\n\\n Args:\\n state: state\\n \\\"\\\"\\\"\\n Conf.view.auto_refresh.set(state)\\n for datapanel in (self.signalpanel, self.imagepanel):\\n datapanel.plothandler.set_auto_refresh(state)\\n\\n # ------Common features\\n @remote_controlled\\n def reset_all(self) -> None:\\n \\\"\\\"\\\"Reset all application data\\\"\\\"\\\"\\n for panel in self.panels:\\n if panel is not None:\\n panel.remove_all_objects()\\n\\n @staticmethod\\n def __check_h5file(filename: str, operation: str) -> str:\\n \\\"\\\"\\\"Check HDF5 filename\\\"\\\"\\\"\\n filename = osp.abspath(osp.normpath(filename))\\n bname = osp.basename(filename)\\n if operation == \\\"load\\\" and not osp.isfile(filename):\\n raise IOError(f'File not found \\\"{bname}\\\"')\\n if not filename.endswith(\\\".h5\\\"):\\n raise IOError(f'Invalid HDF5 file \\\"{bname}\\\"')\\n Conf.main.base_dir.set(filename)\\n return filename\\n\\n @remote_controlled\\n def save_to_h5_file(self, filename=None) -> None:\\n \\\"\\\"\\\"Save to a DataLab HDF5 file\\n\\n Args:\\n filename (str): HDF5 filename. If None, a file dialog is opened.\\n\\n Raises:\\n IOError: if filename is invalid or file cannot be saved.\\n \\\"\\\"\\\"\\n if filename is None:\\n basedir = Conf.main.base_dir.get()\\n with qth.save_restore_stds():\\n filename, _fl = getsavefilename(self, _(\\\"Save\\\"), basedir, \\\"HDF5 (*.h5)\\\")\\n if not filename:\\n return\\n with qth.qt_try_loadsave_file(self, filename, \\\"save\\\"):\\n filename = self.__check_h5file(filename, \\\"save\\\")\\n self.h5inputoutput.save_file(filename)\\n self.set_modified(False)\\n\\n @remote_controlled\\n def open_h5_files(\\n self,\\n h5files: list[str] | None = None,\\n import_all: bool | None = None,\\n reset_all: bool | None = None,\\n ) -> None:\\n \\\"\\\"\\\"Open a DataLab HDF5 file or import from any other HDF5 file.\\n\\n Args:\\n h5files: HDF5 filenames (optionally with dataset name, separated by \\\":\\\")\\n import_all (bool): Import all datasets from HDF5 files\\n reset_all (bool): Reset all application data before importing\\n\\n Returns:\\n None\\n \\\"\\\"\\\"\\n if not self.confirm_memory_state():\\n return\\n if reset_all is None:\\n reset_all = False\\n if self.has_objects():\\n answer = QW.QMessageBox.question(\\n self,\\n _(\\\"Warning\\\"),\\n _(\\n \\\"Do you want to remove all signals and images \\\"\\n \\\"before importing data from HDF5 files?\\\"\\n ),\\n QW.QMessageBox.Yes | QW.QMessageBox.No,\\n )\\n if answer == QW.QMessageBox.Yes:\\n reset_all = True\\n if h5files is None:\\n basedir = Conf.main.base_dir.get()\\n with qth.save_restore_stds():\\n h5files, _fl = getopenfilenames(self, _(\\\"Open\\\"), basedir, \\\"HDF5 (*.h5)\\\")\\n for fname_with_dset in h5files:\\n if \\\",\\\" in fname_with_dset:\\n filename, dsetname = fname_with_dset.split(\\\",\\\")\\n else:\\n filename, dsetname = fname_with_dset, None\\n if import_all is None and dsetname is None:\\n self.import_h5_file(filename, reset_all)\\n else:\\n with qth.qt_try_loadsave_file(self, filename, \\\"load\\\"):\\n filename = self.__check_h5file(filename, \\\"load\\\")\\n if dsetname is None:\\n self.h5inputoutput.open_file(filename, import_all, reset_all)\\n else:\\n self.h5inputoutput.import_dataset_from_file(filename, dsetname)\\n reset_all = False\\n\\n @remote_controlled\\n def import_h5_file(self, filename: str, reset_all: bool | None = None) -> None:\\n \\\"\\\"\\\"Import HDF5 file into DataLab\\n\\n Args:\\n filename (str): HDF5 filename (optionally with dataset name,\\n separated by \\\":\\\")\\n reset_all (bool): Delete all DataLab signals/images before importing data\\n\\n Returns:\\n None\\n \\\"\\\"\\\"\\n with qth.qt_try_loadsave_file(self, filename, \\\"load\\\"):\\n filename = self.__check_h5file(filename, \\\"load\\\")\\n self.h5inputoutput.import_file(filename, False, reset_all)\\n\\n # This method is intentionally *not* remote controlled\\n # (see TODO regarding RemoteClient.add_object method)\\n # @remote_controlled\\n def add_object(self, obj: SignalObj | ImageObj) -> None:\\n \\\"\\\"\\\"Add object - signal or image\\n\\n Args:\\n obj (SignalObj or ImageObj): object to add (signal or image)\\n \\\"\\\"\\\"\\n if self.confirm_memory_state():\\n if isinstance(obj, SignalObj):\\n self.signalpanel.add_object(obj)\\n elif isinstance(obj, ImageObj):\\n self.imagepanel.add_object(obj)\\n else:\\n raise TypeError(f\\\"Unsupported object type {type(obj)}\\\")\\n\\n @remote_controlled\\n def open_object(self, filename: str) -> None:\\n \\\"\\\"\\\"Open object from file in current panel (signal/image)\\n\\n Args:\\n filename (str): HDF5 filename\\n\\n Returns:\\n None\\n \\\"\\\"\\\"\\n panel = self.tabwidget.currentWidget()\\n panel.open_object(filename)\\n\\n # ------Other methods related to AbstractCDLControl interface\\n def get_version(self) -> str:\\n \\\"\\\"\\\"Return DataLab version.\\n\\n Returns:\\n str: DataLab version\\n \\\"\\\"\\\"\\n return __version__\\n\\n def close_application(self) -> None: # Implementing AbstractCDLControl interface\\n \\\"\\\"\\\"Close DataLab application\\\"\\\"\\\"\\n self.close()\\n\\n def raise_window(self) -> None: # Implementing AbstractCDLControl interface\\n \\\"\\\"\\\"Raise DataLab window\\\"\\\"\\\"\\n bring_to_front(self)\\n\\n def add_signal(\\n self,\\n title: str,\\n xdata: np.ndarray,\\n ydata: np.ndarray,\\n xunit: str | None = None,\\n yunit: str | None = None,\\n xlabel: str | None = None,\\n ylabel: str | None = None,\\n ) -> bool: # pylint: disable=too-many-arguments\\n \\\"\\\"\\\"Add signal data to DataLab.\\n\\n Args:\\n title (str): Signal title\\n xdata (numpy.ndarray): X data\\n ydata (numpy.ndarray): Y data\\n xunit (str | None): X unit. Defaults to None.\\n yunit (str | None): Y unit. Defaults to None.\\n xlabel (str | None): X label. Defaults to None.\\n ylabel (str | None): Y label. Defaults to None.\\n\\n Returns:\\n bool: True if signal was added successfully, False otherwise\\n\\n Raises:\\n ValueError: Invalid xdata dtype\\n ValueError: Invalid ydata dtype\\n \\\"\\\"\\\"\\n obj = create_signal(\\n title,\\n xdata,\\n ydata,\\n units=(xunit, yunit),\\n labels=(xlabel, ylabel),\\n )\\n self.add_object(obj)\\n return True\\n\\n def add_image(\\n self,\\n title: str,\\n data: np.ndarray,\\n xunit: str | None = None,\\n yunit: str | None = None,\\n zunit: str | None = None,\\n xlabel: str | None = None,\\n ylabel: str | None = None,\\n zlabel: str | None = None,\\n ) -> bool: # pylint: disable=too-many-arguments\\n \\\"\\\"\\\"Add image data to DataLab.\\n\\n Args:\\n title (str): Image title\\n data (numpy.ndarray): Image data\\n xunit (str | None): X unit. Defaults to None.\\n yunit (str | None): Y unit. Defaults to None.\\n zunit (str | None): Z unit. Defaults to None.\\n xlabel (str | None): X label. Defaults to None.\\n ylabel (str | None): Y label. Defaults to None.\\n zlabel (str | None): Z label. Defaults to None.\\n\\n Returns:\\n bool: True if image was added successfully, False otherwise\\n\\n Raises:\\n ValueError: Invalid data dtype\\n \\\"\\\"\\\"\\n obj = create_image(\\n title,\\n data,\\n units=(xunit, yunit, zunit),\\n labels=(xlabel, ylabel, zlabel),\\n )\\n self.add_object(obj)\\n return True\\n\\n # ------?\\n def __about(self) -> None: # pragma: no cover\\n \\\"\\\"\\\"About dialog box\\\"\\\"\\\"\\n self.check_stable_release()\\n if self.remote_server.port is None:\\n xrpcstate = '<font color=\\\"red\\\">' + _(\\\"not started\\\") + \\\"</font>\\\"\\n else:\\n xrpcstate = _(\\\"started (port %s)\\\") % self.remote_server.port\\n xrpcstate = f\\\"<font color='green'>{xrpcstate}</font>\\\"\\n if Conf.main.process_isolation_enabled.get():\\n pistate = \\\"<font color='green'>\\\" + _(\\\"enabled\\\") + \\\"</font>\\\"\\n else:\\n pistate = \\\"<font color='red'>\\\" + _(\\\"disabled\\\") + \\\"</font>\\\"\\n adv_conf = \\\"<br>\\\".join(\\n [\\n \\\"<i>\\\" + _(\\\"Advanced configuration:\\\") + \\\"</i>\\\",\\n \\\"• \\\" + _(\\\"XML-RPC server:\\\") + \\\" \\\" + xrpcstate,\\n \\\"• \\\" + _(\\\"Process isolation:\\\") + \\\" \\\" + pistate,\\n ]\\n )\\n pinfos = PluginRegistry.get_plugin_infos()\\n created_by = _(\\\"Created by\\\")\\n dev_by = _(\\\"Developed and maintained by %s open-source project team\\\") % APP_NAME\\n copyrght = \\\"2023 Codra\\\"\\n QW.QMessageBox.about(\\n self,\\n _(\\\"About\\\") + \\\" \\\" + APP_NAME,\\n f\\\"\\\"\\\"<b>{APP_NAME}</b> v{__version__}<br>{APP_DESC}\\n <p>{created_by} Pierre Raybaut<br>{dev_by}<br>Copyright © {copyrght}\\n <p>{adv_conf}<br><br>{pinfos}\\\"\\\"\\\",\\n )\\n\\n def __edit_settings(self) -> None:\\n \\\"\\\"\\\"Edit settings\\\"\\\"\\\"\\n changed_options = edit_settings(self)\\n for option in changed_options:\\n if option == \\\"plot_toolbar_position\\\":\\n for dock in self.docks.values():\\n widget = dock.widget()\\n if isinstance(widget, DockablePlotWidget):\\n widget.update_toolbar_position()\\n if option == \\\"ima_defaults\\\" and len(self.imagepanel) > 0:\\n answer = QW.QMessageBox.question(\\n self,\\n _(\\\"Visualization settings\\\"),\\n _(\\n \\\"Default visualization settings have changed.<br><br>\\\"\\n \\\"Do you want to update all active %s objects?\\\"\\n )\\n % _(\\\"image\\\"),\\n QW.QMessageBox.Yes | QW.QMessageBox.No,\\n )\\n if answer == QW.QMessageBox.Yes:\\n self.imagepanel.update_metadata_view_settings()\\n\\n def __show_logviewer(self) -> None:\\n \\\"\\\"\\\"Show error logs\\\"\\\"\\\"\\n logviewer.exec_cdl_logviewer_dialog(self)\\n\\n @staticmethod\\n def test_segfault_error() -> None:\\n \\\"\\\"\\\"Generate errors (both fault and traceback)\\\"\\\"\\\"\\n import ctypes # pylint: disable=import-outside-toplevel\\n\\n ctypes.string_at(0)\\n raise RuntimeError(\\\"!!! Testing RuntimeError !!!\\\")\\n\\n def show(self) -> None:\\n \\\"\\\"\\\"Reimplement QMainWindow method\\\"\\\"\\\"\\n super().show()\\n if self.__old_size is not None:\\n self.resize(self.__old_size)\\n\\n # ------Close window\\n def close_properly(self) -> bool:\\n \\\"\\\"\\\"Close properly\\n\\n Returns:\\n bool: True if closed properly, False otherwise\\n \\\"\\\"\\\"\\n if not env.execenv.unattended and self.__is_modified:\\n answer = QW.QMessageBox.warning(\\n self,\\n _(\\\"Quit\\\"),\\n _(\\n \\\"Do you want to save all signals and images \\\"\\n \\\"to an HDF5 file before quitting DataLab?\\\"\\n ),\\n QW.QMessageBox.Yes | QW.QMessageBox.No | QW.QMessageBox.Cancel,\\n )\\n if answer == QW.QMessageBox.Yes:\\n self.save_to_h5_file()\\n if self.__is_modified:\\n return False\\n elif answer == QW.QMessageBox.Cancel:\\n return False\\n for panel in self.panels:\\n if panel is not None:\\n panel.close()\\n if self.console is not None:\\n try:\\n self.console.close()\\n except RuntimeError:\\n # TODO: [P3] Investigate further why the following error occurs when\\n # restarting the mainwindow (this is *not* a production case):\\n # \\\"RuntimeError: wrapped C/C++ object of type DockableConsole\\n # has been deleted\\\".\\n # Another solution to avoid this error would be to really restart\\n # the application (run each unit test in a separate process), but\\n # it would represent too much effort for an error occuring in test\\n # configurations only.\\n pass\\n self.reset_all()\\n self.__save_pos_and_size()\\n self.__unregister_plugins()\\n\\n # Saving current tab for next session\\n Conf.main.current_tab.set(self.tabwidget.currentIndex())\\n\\n execenv.log(self, \\\"closed properly\\\")\\n return True\\n\\n def closeEvent(self, event: QG.QCloseEvent) -> None:\\n \\\"\\\"\\\"Reimplement QMainWindow method\\\"\\\"\\\"\\n if self.hide_on_close:\\n self.__old_size = self.size()\\n self.hide()\\n else:\\n if self.close_properly():\\n self.SIG_CLOSING.emit()\\n event.accept()\\n else:\\n event.ignore()\"\n },\n {\n \"identifier\": \"ImagePanel\",\n \"path\": \"cdl/core/gui/panel/image.py\",\n \"snippet\": \"class ImagePanel(BaseDataPanel):\\n \\\"\\\"\\\"Object handling the item list, the selected item properties and plot,\\n specialized for Image objects\\\"\\\"\\\"\\n\\n PANEL_STR = _(\\\"Image panel\\\")\\n PARAMCLASS = ImageObj\\n DIALOGSIZE = (800, 800)\\n ANNOTATION_TOOLS = (\\n AnnotatedCircleTool,\\n AnnotatedSegmentTool,\\n AnnotatedRectangleTool,\\n AnnotatedPointTool,\\n AnnotatedEllipseTool,\\n LabelTool,\\n )\\n IO_REGISTRY = ImageIORegistry\\n H5_PREFIX = \\\"DataLab_Ima\\\"\\n ROIDIALOGOPTIONS = {\\\"show_itemlist\\\": True, \\\"show_contrast\\\": False}\\n ROIDIALOGCLASS = roieditor.ImageROIEditor\\n\\n # pylint: disable=duplicate-code\\n\\n def __init__(self, parent: QW.QWidget, plotwidget: PlotWidget, toolbar) -> None:\\n super().__init__(parent, plotwidget, toolbar)\\n self.plothandler = ImagePlotHandler(self, plotwidget)\\n self.processor = ImageProcessor(self, plotwidget)\\n self.acthandler = ImageActionHandler(self, toolbar)\\n\\n # ------Refreshing GUI--------------------------------------------------------------\\n def properties_changed(self) -> None:\\n \\\"\\\"\\\"The properties 'Apply' button was clicked: updating signal\\\"\\\"\\\"\\n obj = self.objview.get_current_object()\\n if obj is not None:\\n obj.invalidate_maskdata_cache()\\n super().properties_changed()\\n\\n # ------Creating, adding, removing objects------------------------------------------\\n def get_newparam_from_current(\\n self, newparam: NewImageParam | None = None\\n ) -> NewImageParam | None:\\n \\\"\\\"\\\"Get new object parameters from the current object.\\n\\n Args:\\n newparam (guidata.dataset.DataSet): new object parameters.\\n If None, create a new one.\\n\\n Returns:\\n New object parameters\\n \\\"\\\"\\\"\\n curobj: ImageObj = self.objview.get_current_object()\\n newparam = new_image_param() if newparam is None else newparam\\n if curobj is not None:\\n newparam.width, newparam.height = curobj.size\\n newparam.dtype = ImageDatatypes.from_dtype(curobj.data.dtype)\\n return newparam\\n\\n def new_object(\\n self,\\n newparam: NewImageParam | None = None,\\n addparam: gds.DataSet | None = None,\\n edit: bool = True,\\n add_to_panel: bool = True,\\n ) -> ImageObj | None:\\n \\\"\\\"\\\"Create a new object (image).\\n\\n Args:\\n newparam (Daguidata.dataset.datatypes.DataSettaSet): new object parameters\\n addparam (guidata.dataset.DataSet): additional parameters\\n edit (bool): Open a dialog box to edit parameters (default: True)\\n add_to_panel (bool): Add the object to the panel (default: True)\\n\\n Returns:\\n New object\\n \\\"\\\"\\\"\\n if not self.mainwindow.confirm_memory_state():\\n return None\\n newparam = self.get_newparam_from_current(newparam)\\n image = create_image_from_param(\\n newparam, addparam=addparam, edit=edit, parent=self\\n )\\n if image is None:\\n return None\\n if add_to_panel:\\n self.add_object(image)\\n return image\\n\\n def delete_metadata(self, refresh_plot: bool = True) -> None:\\n \\\"\\\"\\\"Delete metadata of selected objects\\n\\n Args:\\n refresh_plot (bool | None): Refresh plot. Defaults to True.\\n \\\"\\\"\\\"\\n for obj in self.objview.get_sel_objects(include_groups=True):\\n obj.invalidate_maskdata_cache()\\n super().delete_metadata(refresh_plot)\\n\\n def toggle_show_contrast(self, state: bool) -> None:\\n \\\"\\\"\\\"Toggle show contrast option\\\"\\\"\\\"\\n Conf.view.show_contrast.set(state)\\n self.SIG_REFRESH_PLOT.emit(\\\"selected\\\", True)\"\n },\n {\n \"identifier\": \"SignalPanel\",\n \"path\": \"cdl/core/gui/panel/signal.py\",\n \"snippet\": \"class SignalPanel(BaseDataPanel):\\n \\\"\\\"\\\"Object handling the item list, the selected item properties and plot,\\n specialized for Signal objects\\\"\\\"\\\"\\n\\n PANEL_STR = _(\\\"Signal panel\\\")\\n PARAMCLASS = SignalObj\\n ANNOTATION_TOOLS = (\\n LabelTool,\\n VCursorTool,\\n HCursorTool,\\n XCursorTool,\\n SegmentTool,\\n RectangleTool,\\n HRangeTool,\\n )\\n IO_REGISTRY = SignalIORegistry\\n H5_PREFIX = \\\"DataLab_Sig\\\"\\n ROIDIALOGCLASS = roieditor.SignalROIEditor\\n\\n # pylint: disable=duplicate-code\\n\\n def __init__(self, parent: QW.QWidget, plotwidget: PlotWidget, toolbar) -> None:\\n super().__init__(parent, plotwidget, toolbar)\\n self.plothandler = SignalPlotHandler(self, plotwidget)\\n self.processor = SignalProcessor(self, plotwidget)\\n self.acthandler = SignalActionHandler(self, toolbar)\\n\\n # ------Creating, adding, removing objects------------------------------------------\\n def get_newparam_from_current(\\n self, newparam: NewSignalParam | None = None\\n ) -> NewSignalParam | None:\\n \\\"\\\"\\\"Get new object parameters from the current object.\\n\\n Args:\\n newparam (guidata.dataset.DataSet): new object parameters.\\n If None, create a new one.\\n\\n Returns:\\n New object parameters\\n \\\"\\\"\\\"\\n curobj: SignalObj = self.objview.get_current_object()\\n newparam = new_signal_param() if newparam is None else newparam\\n if curobj is not None:\\n newparam.size = len(curobj.data)\\n newparam.xmin = curobj.x.min()\\n newparam.xmax = curobj.x.max()\\n return newparam\\n\\n def new_object(\\n self,\\n newparam: NewSignalParam | None = None,\\n addparam: gds.DataSet | None = None,\\n edit: bool = True,\\n add_to_panel: bool = True,\\n ) -> SignalObj | None:\\n \\\"\\\"\\\"Create a new object (signal).\\n\\n Args:\\n newparam (guidata.dataset.DataSet): new object parameters\\n addparam (guidata.dataset.DataSet): additional parameters\\n edit (bool): Open a dialog box to edit parameters (default: True)\\n add_to_panel (bool): Add the new object to the panel (default: True)\\n\\n Returns:\\n New object\\n \\\"\\\"\\\"\\n if not self.mainwindow.confirm_memory_state():\\n return None\\n newparam = self.get_newparam_from_current(newparam)\\n signal = create_signal_from_param(\\n newparam, addparam=addparam, edit=edit, parent=self\\n )\\n if signal is None:\\n return None\\n if add_to_panel:\\n self.add_object(signal)\\n return signal\\n\\n # ------Plotting--------------------------------------------------------------------\\n def toggle_anti_aliasing(self, state: bool) -> None:\\n \\\"\\\"\\\"Toggle anti-aliasing on/off\\n\\n Args:\\n state: state of the anti-aliasing\\n \\\"\\\"\\\"\\n self.plothandler.toggle_anti_aliasing(state)\"\n },\n {\n \"identifier\": \"create_paracetamol_signal\",\n \"path\": \"cdl/tests/data.py\",\n \"snippet\": \"def create_paracetamol_signal(\\n size: int | None = None, title: str | None = None\\n) -> cdl.obj.SignalObj:\\n \\\"\\\"\\\"Create test signal (Paracetamol molecule spectrum)\\n\\n Args:\\n size (int | None): Size of the data. Defaults to None.\\n title (str | None): Title of the signal. Defaults to None.\\n\\n Returns:\\n SignalObj: Signal object\\n \\\"\\\"\\\"\\n obj = cdl.obj.read_signal(get_test_fnames(\\\"paracetamol.txt\\\")[0])\\n if title is not None:\\n obj.title = title\\n if size is not None:\\n x0, y0 = obj.xydata\\n x1 = np.linspace(x0[0], x0[-1], size)\\n y1 = np.interp(x1, x0, y0)\\n obj.set_xydata(x1, y1)\\n return obj\"\n },\n {\n \"identifier\": \"create_peak2d_image\",\n \"path\": \"cdl/tests/data.py\",\n \"snippet\": \"def create_peak2d_image(\\n p: cdl.obj.NewImageParam | None = None,\\n) -> cdl.obj.ImageObj:\\n \\\"\\\"\\\"Creating 2D peak image\\n\\n Args:\\n p (cdl.obj.NewImageParam | None): Image parameters. Defaults to None.\\n\\n Returns:\\n cdl.obj.ImageObj: Image object\\n \\\"\\\"\\\"\\n p = __set_default_size_dtype(p)\\n p.title = \\\"Test image (2D peaks)\\\" if p.title is None else p.title\\n obj = cdl.obj.create_image_from_param(p)\\n param = PeakDataParam()\\n if p.height is not None and p.width is not None:\\n param.size = max(p.height, p.width)\\n obj.data = get_peak2d_data(param)\\n return obj\"\n },\n {\n \"identifier\": \"create_sincos_image\",\n \"path\": \"cdl/tests/data.py\",\n \"snippet\": \"def create_sincos_image(\\n p: cdl.obj.NewImageParam | None = None,\\n) -> cdl.obj.ImageObj:\\n \\\"\\\"\\\"Creating test image (sin(x)+cos(y))\\n\\n Args:\\n p (cdl.obj.NewImageParam | None): Image parameters. Defaults to None.\\n\\n Returns:\\n cdl.obj.ImageObj: Image object\\n \\\"\\\"\\\"\\n p = __set_default_size_dtype(p)\\n p.title = \\\"Test image (sin(x)+cos(y))\\\" if p.title is None else p.title\\n dtype = p.dtype.value\\n x, y = np.meshgrid(np.linspace(0, 10, p.width), np.linspace(0, 10, p.height))\\n raw_data = 0.5 * (np.sin(x) + np.cos(y)) + 0.5\\n dmin = np.iinfo(dtype).min * 0.95\\n dmax = np.iinfo(dtype).max * 0.95\\n obj = cdl.obj.create_image_from_param(p)\\n obj.data = np.array(raw_data * (dmax - dmin) + dmin, dtype=dtype)\\n return obj\"\n },\n {\n \"identifier\": \"iterate_image_creation\",\n \"path\": \"cdl/tests/features/common/newobject_unit.py\",\n \"snippet\": \"def iterate_image_creation(\\n data_size: int = 500, non_zero: bool = False, verbose: bool = True\\n) -> Generator[ImageObj, None, None]:\\n \\\"\\\"\\\"Iterate over all possible images created from parameters\\\"\\\"\\\"\\n if verbose:\\n execenv.print(\\n f\\\" Iterating over image types (size={data_size}, non_zero={non_zero}):\\\"\\n )\\n for itype in ImageTypes:\\n if non_zero and itype in (ImageTypes.EMPTY, ImageTypes.ZEROS):\\n continue\\n if verbose:\\n execenv.print(f\\\" {itype.value}\\\")\\n for dtype in ImageDatatypes:\\n if verbose:\\n execenv.print(f\\\" {dtype.value}\\\")\\n newparam = new_image_param(\\n itype=itype, dtype=dtype, width=data_size, height=data_size\\n )\\n if itype == ImageTypes.GAUSS:\\n addparam = Gauss2DParam()\\n addparam.x0 = addparam.y0 = 3\\n addparam.sigma = 5\\n elif itype == ImageTypes.UNIFORMRANDOM:\\n addparam = UniformRandomParam()\\n addparam.set_from_datatype(dtype.value)\\n elif itype == ImageTypes.NORMALRANDOM:\\n addparam = NormalRandomParam()\\n addparam.set_from_datatype(dtype.value)\\n else:\\n addparam = None\\n image = create_image_from_param(newparam, addparam=addparam)\\n if itype == ImageTypes.ZEROS:\\n assert (image.data == 0).all()\\n yield image\"\n },\n {\n \"identifier\": \"iterate_signal_creation\",\n \"path\": \"cdl/tests/features/common/newobject_unit.py\",\n \"snippet\": \"def iterate_signal_creation(\\n data_size: int = 500, non_zero: bool = False, verbose: bool = True\\n) -> Generator[SignalObj, None, None]:\\n \\\"\\\"\\\"Iterate over all possible signals created from parameters\\\"\\\"\\\"\\n if verbose:\\n execenv.print(\\n f\\\" Iterating over signal types (size={data_size}, non_zero={non_zero}):\\\"\\n )\\n for stype in SignalTypes:\\n if non_zero and stype in (SignalTypes.ZEROS,):\\n continue\\n if verbose:\\n execenv.print(f\\\" {stype.value}\\\")\\n newparam = new_signal_param(stype=stype, size=data_size)\\n if stype == SignalTypes.UNIFORMRANDOM:\\n addparam = UniformRandomParam()\\n elif stype == SignalTypes.NORMALRANDOM:\\n addparam = NormalRandomParam()\\n else:\\n addparam = None\\n signal = create_signal_from_param(newparam, addparam=addparam)\\n if stype == SignalTypes.ZEROS:\\n assert (signal.y == 0).all()\\n yield signal\"\n },\n {\n \"identifier\": \"fitdialog\",\n \"path\": \"cdl/widgets/fitdialog.py\",\n \"snippet\": \"def guifit(\\n x,\\n y,\\n fitfunc,\\n fitparams,\\n fitargs=None,\\n fitkwargs=None,\\n wintitle=None,\\n title=None,\\n xlabel=None,\\n ylabel=None,\\n param_cols=1,\\n auto_fit=True,\\n winsize=None,\\n winpos=None,\\n parent=None,\\n name=None,\\n):\\ndef polynomialfit(x, y, degree, parent=None, name=None):\\n def fitfunc(x, params):\\ndef gaussianfit(x, y, parent=None, name=None):\\n def fitfunc(x, params):\\ndef lorentzianfit(x, y, parent=None, name=None):\\n def fitfunc(x, params):\\ndef voigtfit(x, y, parent=None, name=None):\\n def fitfunc(x, params):\\ndef multigaussian(x, *values, **kwargs):\\ndef multigaussianfit(x, y, peak_indexes, parent=None, name=None):\\n def fitfunc(xi, params):\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import numpy as np\nimport cdl.obj as dlo\nimport cdl.param as dlp\nfrom cdl.config import _\nfrom cdl.core.gui.main import CDLMainWindow\nfrom cdl.core.gui.panel.image import ImagePanel\nfrom cdl.core.gui.panel.signal import SignalPanel\nfrom cdl.tests.data import (\n create_paracetamol_signal,\n create_peak2d_image,\n create_sincos_image,\n)\nfrom cdl.tests.features.common.newobject_unit import (\n iterate_image_creation,\n iterate_signal_creation,\n)\nfrom cdl.widgets import fitdialog"},"token_num":{"kind":"number","value":19718,"string":"19,718"},"cropped_code":{"kind":"string","value":" panel.add_label_with_title()\n\n __compute_11_operations(panel, 2)\n\n\ndef run_signal_computations(\n win: CDLMainWindow, data_size: int = 500, all_types: bool = True\n) -> None:\n \"\"\"Testing signal features\"\"\"\n panel = win.signalpanel\n win.set_current_panel(\"signal\")\n\n if all_types:\n for signal in iterate_signal_creation(data_size, non_zero=True):\n panel.add_object(create_paracetamol_signal(data_size))\n panel.add_object(signal)\n compute_common_operations(panel)\n panel.remove_all_objects()\n\n sig1 = create_paracetamol_signal(data_size)\n win.add_object(sig1)\n\n # Add new signal based on s0\n panel.objview.set_current_object(sig1)\n newparam = dlo.new_signal_param(\n _(\"Random function\"), stype=dlo.SignalTypes.UNIFORMRANDOM\n )\n addparam = dlo.UniformRandomParam.create(vmin=0, vmax=sig1.y.max() * 0.2)\n panel.new_object(newparam, addparam=addparam, edit=False)\n\n compute_common_operations(panel)\n\n win.add_object(create_paracetamol_signal(data_size))\n\n param = dlp.NormalizeYParam()\n for _name, method in param.methods:\n param.method = method\n panel.processor.compute_normalize(param)\n\n param = dlp.XYCalibrateParam.create(a=1.2, b=0.1)\n panel.processor.compute_calibration(param)\n\n panel.processor.compute_derivative()\n panel.processor.compute_integral()\n\n param = dlp.PeakDetectionParam()\n panel.processor.compute_peak_detection(param)\n\n panel.processor.compute_multigaussianfit()\n\n panel.objview.select_objects([-3])\n sig = panel.objview.get_sel_objects()[0]\n i1 = data_size // 10\n i2 = len(sig.y) - i1\n panel.processor.compute_roi_extraction(dlp.ROIDataParam.create([[i1, i2]]))\n\n param = dlp.PolynomialFitParam()\n panel.processor.compute_polyfit(param)\n\n panel.processor.compute_fit(_(\"Gaussian fit\"), fitdialog.gaussianfit)\n panel.processor.compute_fit(_(\"Lorentzian fit\"), fitdialog.lorentzianfit)\n panel.processor.compute_fit(_(\"Voigt fit\"), fitdialog.voigtfit)\n\n newparam = dlo.new_signal_param(_(\"Gaussian\"), stype=dlo.SignalTypes.GAUSS)\n sig = dlo.create_signal_from_param(\n newparam, dlo.GaussLorentzVoigtParam(), edit=False\n )\n panel.add_object(sig)\n\n param = dlp.FWHMParam()\n for fittype, _name in param.fittypes:\n param.fittype = fittype\n panel.processor.compute_fwhm(param)\n panel.processor.compute_fw1e2()\n\n # Create a new signal which X values are a subset of sig1\n x = np.linspace(sig1.x.min(), sig1.x.max(), data_size // 2)[: data_size // 4]\n y = x * 0.0\n sig2 = dlo.create_signal(\"X values for interpolation\", x, y)\n panel.add_object(sig2)\n\n # Test interpolation\n for method_choice_tuple in dlp.InterpolationParam._methods:\n method = method_choice_tuple[0]\n for fill_value in (None, 0.0):\n panel.objview.set_current_object(sig1)\n param = dlp.InterpolationParam.create(method=method, fill_value=fill_value)\n panel.processor.compute_interpolation(sig2, param)\n\n # Test resampling\n xmin, xmax = x[0], x[-1]\n for mode, dx, nbpts in ((\"dx\", 0.1, 10), (\"nbpts\", 0.0, 100)):\n panel.objview.set_current_object(sig1)\n param = dlp.ResamplingParam.create(\n xmin=xmin, xmax=xmax, mode=mode, dx=dx, nbpts=nbpts\n )\n panel.processor.compute_resampling(param)\n\n # Test convolution\n panel.objview.set_current_object(sig1)\n panel.processor.compute_derivative()\n panel.processor.compute_convolution(sig1)\n\n # Test detrending\n panel.objview.set_current_object(sig1)\n for method_choice_tuple in dlp.DetrendingParam._methods:\n param = dlp.DetrendingParam.create(method=method_choice_tuple[0])\n panel.processor.compute_detrending(param)\n\n\ndef run_image_computations(\n win: CDLMainWindow, data_size: int = 150, all_types: bool = True\n) -> None:\n \"\"\"Testing signal features\"\"\"\n win.set_current_panel(\"image\")\n panel = win.imagepanel\n\n newparam = dlo.new_image_param(height=data_size, width=data_size)\n\n if all_types:\n"},"all_code":{"kind":"string","value":"# -*- coding: utf-8 -*-\n#\n# Licensed under the terms of the BSD 3-Clause\n# (see cdl/LICENSE for details)\n\n\"\"\"\nScenarios common functions\n\"\"\"\n\n# pylint: disable=invalid-name # Allows short reference names like x, y, ...\n# guitest: skip\n\nfrom __future__ import annotations\n\n\n\n\ndef __compute_11_operations(panel: SignalPanel | ImagePanel, number: int) -> None:\n \"\"\"Test compute_11 type operations on a signal or image\n\n Requires that one signal or image has been added at index.\"\"\"\n assert len(panel) >= number - 1\n panel.objview.select_objects((number,))\n panel.processor.compute_gaussian_filter(dlp.GaussianParam())\n panel.processor.compute_moving_average(dlp.MovingAverageParam())\n panel.processor.compute_moving_median(dlp.MovingMedianParam())\n panel.processor.compute_wiener()\n panel.processor.compute_fft()\n panel.processor.compute_ifft()\n panel.processor.compute_abs()\n panel.remove_object()\n panel.processor.compute_re()\n panel.remove_object()\n panel.processor.compute_im()\n panel.remove_object()\n panel.processor.compute_astype(dlp.DataTypeIParam.create(dtype=\"float64\"))\n panel.processor.compute_log10()\n panel.processor.compute_swap_axes()\n panel.processor.compute_swap_axes()\n\n\ndef compute_common_operations(panel: SignalPanel | ImagePanel) -> None:\n \"\"\"Test operations common to signal/image\n\n Requires that two (and only two) signals/images are created/added to panel\n\n First signal/image is supposed to be always the same (reference)\n Second signal/image is the tested object\n \"\"\"\n assert len(panel) == 2\n\n panel.objview.select_objects((2,))\n panel.processor.compute_difference(panel[1]) # difference with obj #1\n panel.remove_object()\n panel.objview.select_objects((2,))\n panel.processor.compute_quadratic_difference() # quadratic difference with itself\n panel.delete_metadata()\n panel.objview.select_objects((3,))\n panel.remove_object()\n\n panel.objview.select_objects((1, 2))\n panel.processor.compute_sum()\n panel.objview.select_objects((1, 2))\n panel.processor.compute_sum()\n panel.objview.select_objects((1, 2))\n panel.processor.compute_product()\n\n obj = panel.objmodel.get_groups()[0][-1]\n param = dlp.ThresholdParam()\n param.value = (obj.data.max() - obj.data.min()) * 0.2 + obj.data.min()\n panel.processor.compute_threshold(param)\n param = dlp.ClipParam() # Clipping before division...\n param.value = (obj.data.max() - obj.data.min()) * 0.8 + obj.data.min()\n panel.processor.compute_clip(param)\n\n panel.objview.select_objects((3, 7))\n panel.processor.compute_division()\n panel.objview.select_objects((1, 2, 3))\n panel.processor.compute_average()\n\n panel.add_label_with_title()\n\n __compute_11_operations(panel, 2)\n\n\ndef run_signal_computations(\n win: CDLMainWindow, data_size: int = 500, all_types: bool = True\n) -> None:\n \"\"\"Testing signal features\"\"\"\n panel = win.signalpanel\n win.set_current_panel(\"signal\")\n\n if all_types:\n for signal in iterate_signal_creation(data_size, non_zero=True):\n panel.add_object(create_paracetamol_signal(data_size))\n panel.add_object(signal)\n compute_common_operations(panel)\n panel.remove_all_objects()\n\n sig1 = create_paracetamol_signal(data_size)\n win.add_object(sig1)\n\n # Add new signal based on s0\n panel.objview.set_current_object(sig1)\n newparam = dlo.new_signal_param(\n _(\"Random function\"), stype=dlo.SignalTypes.UNIFORMRANDOM\n )\n addparam = dlo.UniformRandomParam.create(vmin=0, vmax=sig1.y.max() * 0.2)\n panel.new_object(newparam, addparam=addparam, edit=False)\n\n compute_common_operations(panel)\n\n win.add_object(create_paracetamol_signal(data_size))\n\n param = dlp.NormalizeYParam()\n for _name, method in param.methods:\n param.method = method\n panel.processor.compute_normalize(param)\n\n param = dlp.XYCalibrateParam.create(a=1.2, b=0.1)\n panel.processor.compute_calibration(param)\n\n panel.processor.compute_derivative()\n panel.processor.compute_integral()\n\n param = dlp.PeakDetectionParam()\n panel.processor.compute_peak_detection(param)\n\n panel.processor.compute_multigaussianfit()\n\n panel.objview.select_objects([-3])\n sig = panel.objview.get_sel_objects()[0]\n i1 = data_size // 10\n i2 = len(sig.y) - i1\n panel.processor.compute_roi_extraction(dlp.ROIDataParam.create([[i1, i2]]))\n\n param = dlp.PolynomialFitParam()\n panel.processor.compute_polyfit(param)\n\n panel.processor.compute_fit(_(\"Gaussian fit\"), fitdialog.gaussianfit)\n panel.processor.compute_fit(_(\"Lorentzian fit\"), fitdialog.lorentzianfit)\n panel.processor.compute_fit(_(\"Voigt fit\"), fitdialog.voigtfit)\n\n newparam = dlo.new_signal_param(_(\"Gaussian\"), stype=dlo.SignalTypes.GAUSS)\n sig = dlo.create_signal_from_param(\n newparam, dlo.GaussLorentzVoigtParam(), edit=False\n )\n panel.add_object(sig)\n\n param = dlp.FWHMParam()\n for fittype, _name in param.fittypes:\n param.fittype = fittype\n panel.processor.compute_fwhm(param)\n panel.processor.compute_fw1e2()\n\n # Create a new signal which X values are a subset of sig1\n x = np.linspace(sig1.x.min(), sig1.x.max(), data_size // 2)[: data_size // 4]\n y = x * 0.0\n sig2 = dlo.create_signal(\"X values for interpolation\", x, y)\n panel.add_object(sig2)\n\n # Test interpolation\n for method_choice_tuple in dlp.InterpolationParam._methods:\n method = method_choice_tuple[0]\n for fill_value in (None, 0.0):\n panel.objview.set_current_object(sig1)\n param = dlp.InterpolationParam.create(method=method, fill_value=fill_value)\n panel.processor.compute_interpolation(sig2, param)\n\n # Test resampling\n xmin, xmax = x[0], x[-1]\n for mode, dx, nbpts in ((\"dx\", 0.1, 10), (\"nbpts\", 0.0, 100)):\n panel.objview.set_current_object(sig1)\n param = dlp.ResamplingParam.create(\n xmin=xmin, xmax=xmax, mode=mode, dx=dx, nbpts=nbpts\n )\n panel.processor.compute_resampling(param)\n\n # Test convolution\n panel.objview.set_current_object(sig1)\n panel.processor.compute_derivative()\n panel.processor.compute_convolution(sig1)\n\n # Test detrending\n panel.objview.set_current_object(sig1)\n for method_choice_tuple in dlp.DetrendingParam._methods:\n param = dlp.DetrendingParam.create(method=method_choice_tuple[0])\n panel.processor.compute_detrending(param)\n\n\ndef run_image_computations(\n win: CDLMainWindow, data_size: int = 150, all_types: bool = True\n) -> None:\n \"\"\"Testing signal features\"\"\"\n win.set_current_panel(\"image\")\n panel = win.imagepanel\n\n newparam = dlo.new_image_param(height=data_size, width=data_size)\n\n if all_types:"},"next_line":{"kind":"string","value":" for image in iterate_image_creation(data_size, non_zero=True):"},"gold_snippet_index":{"kind":"number","value":7,"string":"7"},"created_at":{"kind":"string","value":"2023-11-09 16:56:03+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5278,"cells":{"repo_name":{"kind":"string","value":"ingra14m/Tensor4D-DNeRF"},"file_path":{"kind":"string","value":"exp_runner.py"},"context":{"kind":"list like","value":[{"identifier":"Dataset","path":"models/dataset.py","snippet":"class Dataset:\n def __init__(self, conf):\n super(Dataset, self).__init__()\n print('Load data: Begin')\n self.device = torch.device('cuda')\n self.conf = conf\n\n self.data_dir = conf.get_string('data_dir')\n self.render_cameras_name = conf.get_string('render_cameras_name')\n self.object_cameras_name = conf.get_string('object_cameras_name')\n\n self.camera_outside_sphere = conf.get_bool('camera_outside_sphere', default=True)\n self.scale_mat_scale = conf.get_float('scale_mat_scale', default=1.1)\n self.near = conf.get_float('near', default=-1)\n self.far = conf.get_float('far', default=-1)\n self.n_frames = conf.get_int('n_frames', default=128)\n\n camera_dict = np.load(os.path.join(self.data_dir, self.render_cameras_name))\n self.camera_dict = camera_dict\n self.images_lis = sorted(glob(os.path.join(self.data_dir, 'image/*.png')))\n self.n_images = len(self.images_lis)\n self.images_np = np.stack([cv.imread(im_name) for im_name in self.images_lis]) / 256.0\n self.masks_lis = sorted(glob(os.path.join(self.data_dir, 'mask/*.png')))\n self.masks_np = np.stack([cv.imread(im_name) for im_name in self.masks_lis]) / 256.0\n\n # world_mat is a projection matrix from world to image\n self.world_mats_np = [camera_dict['world_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images)]\n self.fid_list = [torch.LongTensor(np.array([camera_dict['fid_%d' % idx]])) for idx in range(self.n_images)]\n self.scale_mats_np = []\n\n # scale_mat: used for coordinate normalization, we assume the scene to render is inside a unit sphere at origin.\n self.scale_mats_np = [camera_dict['scale_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images)]\n\n self.intrinsics_all = []\n self.pose_all = []\n self.proj_all = []\n\n for scale_mat, world_mat in zip(self.scale_mats_np, self.world_mats_np):\n P = world_mat @ scale_mat\n P = P[:3, :4]\n intrinsics, pose = load_K_Rt_from_P(None, P)\n self.intrinsics_all.append(torch.from_numpy(intrinsics).float())\n self.pose_all.append(torch.from_numpy(pose).float())\n self.proj_all.append(torch.from_numpy(P).float())\n\n self.images = torch.from_numpy(self.images_np.astype(np.float32)).cpu() # [n_images, H, W, 3]\n self.masks = torch.from_numpy(self.masks_np.astype(np.float32)).cpu() # [n_images, H, W, 3]\n self.errors = self.masks[:, :, :, :1].clone()\n self.errors = F.interpolate(self.errors.permute(0, 3, 1, 2), (self.images.shape[1] // 8, self.images.shape[2] // 8), mode='bilinear')\n self.errors = F.max_pool2d(self.errors, 7, stride=1, padding=3)\n self.errors = self.errors.permute(0, 2, 3, 1)\n self.radius = torch.zeros(self.masks.shape[0], self.masks.shape[2], self.masks.shape[1], 1) # [n_images, W, H, 3]\n \n self.intrinsics_all = torch.stack(self.intrinsics_all).to(self.device) # [n_images, 4, 4]\n self.intrinsics_all_inv = torch.inverse(self.intrinsics_all) # [n_images, 4, 4]\n self.focal = self.intrinsics_all[0][0, 0]\n self.pose_all = torch.stack(self.pose_all).to(self.device) # [n_images, 4, 4]\n self.proj_all = torch.stack(self.proj_all).to(self.device)\n self.H, self.W = self.images.shape[1], self.images.shape[2]\n self.image_pixels = self.H * self.W\n self.fid_all = torch.stack(self.fid_list).to(self.device)\n self.time_emb_list = (self.fid_all / self.n_frames * 2) - 0.95\n\n object_bbox_min = np.array([-1.01, -1.01, -1.01, 1.0])\n object_bbox_max = np.array([ 1.01, 1.01, 1.01, 1.0])\n # Object scale mat: region of interest to **extract mesh**\n object_scale_mat = np.load(os.path.join(self.data_dir, self.object_cameras_name))['scale_mat_0']\n object_bbox_min = np.linalg.inv(self.scale_mats_np[0]) @ object_scale_mat @ object_bbox_min[:, None]\n object_bbox_max = np.linalg.inv(self.scale_mats_np[0]) @ object_scale_mat @ object_bbox_max[:, None]\n self.object_bbox_min = object_bbox_min[:3, 0]\n self.object_bbox_max = object_bbox_max[:3, 0]\n self.process_radius()\n\n print('Load data: End')\n\n def process_radius(self):\n for img_idx in tqdm(range(self.images.shape[0])):\n tx = torch.linspace(0, self.W - 1, self.W, device=self.device)\n ty = torch.linspace(0, self.H - 1, self.H, device=self.device)\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\n rays_v = torch.matmul(self.intrinsics_all_inv[img_idx, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3\n rays_v = torch.matmul(self.pose_all[img_idx, None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\n # Cut the distance in half, and then round it out so that it's\n # halfway between inscribed by / circumscribed about the pixel.\n radii = dx[..., None] * 2 / np.sqrt(12)\n self.radius[img_idx] = radii.detach().cpu() # W H 3\n\n def gen_rays_at(self, img_idx, resolution_level=1):\n \"\"\"\n Generate rays at world space from one camera.\n \"\"\"\n l = resolution_level\n tx = torch.linspace(0, self.W - 1, self.W // l, device=self.device)\n ty = torch.linspace(0, self.H - 1, self.H // l, device=self.device)\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\n rays_v = torch.matmul(self.intrinsics_all_inv[img_idx, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3\n rays_v = torch.matmul(self.pose_all[img_idx, None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\n rays_r = dx[..., None] * 2 / np.sqrt(12)\n rays_o = self.pose_all[img_idx, None, None, :3, 3].expand(rays_v.shape) # W, H, 3\n rays_v = rays_v / torch.linalg.norm(rays_v, ord=2, dim=-1, keepdim=True) # W, H, 3\n return rays_o.transpose(0, 1), rays_v.transpose(0, 1), rays_r.transpose(0, 1)\n\n def gen_random_rays_at(self, img_idx, batch_size):\n \"\"\"\n Generate random rays at world space from one camera.\n \"\"\"\n error = self.errors[img_idx].reshape(-1).numpy()\n max_error = np.max(error) + 1e-8\n error = error / max_error\n error[error < 0.1] = 0.1\n error = error / np.sum(error)\n index = np.arange(0, self.W*self.H // 64)\n select_index = np.random.choice(index, size=[batch_size], p=error)\n pixels_y = torch.LongTensor(select_index // (self.W // 8)) * 8\n pixels_y += torch.randint_like(pixels_y, 8)\n pixels_x = torch.LongTensor(select_index % (self.W // 8)) * 8\n pixels_x += torch.randint_like(pixels_x, 8)\n\n color = self.images[img_idx][(pixels_y, pixels_x)] # batch_size, 3\n mask = self.masks[img_idx][(pixels_y, pixels_x)] # batch_size, 3\n rays_r = self.radius[img_idx][(pixels_x, pixels_y)] # batch_size, 1\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1).float().to(self.device) # batch_size, 3\n p = torch.matmul(self.intrinsics_all_inv[img_idx, None, :3, :3], p[:, :, None]).squeeze() # batch_size, 3\n rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True) # batch_size, 3\n rays_v = torch.matmul(self.pose_all[img_idx, None, :3, :3], rays_v[:, :, None]).squeeze() # batch_size, 3\n rays_o = self.pose_all[img_idx, None, :3, 3].expand(rays_v.shape) # batch_size, 3\n return torch.cat([rays_o.cpu(), rays_v.cpu(), color.cpu(), mask[:, :1].cpu(), rays_r.cpu()], dim=-1).cuda(), pixels_y.cpu(), pixels_x.cpu() # batch_size, 10\n\n def gen_rays_between(self, idx_0, idx_1, ratio, resolution_level=1):\n \"\"\"\n Interpolate pose between two cameras.\n \"\"\"\n l = resolution_level\n tx = torch.linspace(0, self.W - 1, self.W // l, device=self.device)\n ty = torch.linspace(0, self.H - 1, self.H // l, device=self.device)\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\n rays_v = torch.matmul(self.intrinsics_all_inv[0, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3\n trans = self.pose_all[idx_0, :3, 3] * (1.0 - ratio) + self.pose_all[idx_1, :3, 3] * ratio\n pose_0 = self.pose_all[idx_0].detach().cpu().numpy()\n pose_1 = self.pose_all[idx_1].detach().cpu().numpy()\n pose_0 = np.linalg.inv(pose_0)\n pose_1 = np.linalg.inv(pose_1)\n rot_0 = pose_0[:3, :3]\n rot_1 = pose_1[:3, :3]\n rots = Rot.from_matrix(np.stack([rot_0, rot_1]))\n key_times = [0, 1]\n slerp = Slerp(key_times, rots)\n rot = slerp(ratio)\n pose = np.diag([1.0, 1.0, 1.0, 1.0])\n pose = pose.astype(np.float32)\n pose[:3, :3] = rot.as_matrix()\n pose[:3, 3] = ((1.0 - ratio) * pose_0 + ratio * pose_1)[:3, 3]\n pose = np.linalg.inv(pose)\n rot = torch.from_numpy(pose[:3, :3]).cuda()\n trans = torch.from_numpy(pose[:3, 3]).cuda()\n rays_v = torch.matmul(rot[None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\n rays_r = dx[..., None] * 2 / np.sqrt(12)\n rays_v = rays_v / torch.linalg.norm(rays_v, ord=2, dim=-1, keepdim=True) # W, H, 3\n rays_o = trans[None, None, :3].expand(rays_v.shape) # W, H, 3\n return rays_o.transpose(0, 1), rays_v.transpose(0, 1), rays_r.transpose(0, 1)\n\n def near_far_from_sphere(self, rays_o, rays_d):\n if self.near > 0:\n return self.near, self.far\n a = torch.sum(rays_d**2, dim=-1, keepdim=True)\n b = 2.0 * torch.sum(rays_o * rays_d, dim=-1, keepdim=True)\n mid = 0.5 * (-b) / a\n near = mid - 1.0\n far = mid + 1.0\n return near, far\n\n def image_at(self, idx, resolution_level):\n img = cv.imread(self.images_lis[idx])\n return (cv.resize(img, (self.W // resolution_level, self.H // resolution_level))).clip(0, 255)"},{"identifier":"BlenderDataset","path":"models/dataset.py","snippet":"class BlenderDataset:\n def __init__(self, conf):\n super(BlenderDataset, self).__init__()\n print('Load data: Begin')\n self.device = torch.device('cuda')\n self.conf = conf\n\n self.near = conf.get_float('near', default=-1)\n self.far = conf.get_float('far', default=-1)\n self.n_frames = conf.get_int('n_frames', default=128)\n\n self.data_dir = conf.get_string('data_dir')\n splits = ['train']\n metas = {}\n for s in splits:\n with open(os.path.join(self.data_dir, 'transforms_{}.json'.format(s)), 'r') as fp:\n metas[s] = json.load(fp)\n self.images_lis = sorted(glob(os.path.join(self.data_dir, 'train/*.png')), key=lambda x: int(x.split('.')[0].split('_')[-1]))\n # if self.data_dir.split('/')[-2] == 'lego':\n # # self.images_lis = self.images_lis[1:]\n # self.images_lis.append('/data00/yzy/Git_Project/data/dynamic/D-NeRF/lego/val/r_0.png')\n all_imgs = []\n all_poses = []\n all_masks = []\n all_times = []\n counts = [0]\n for s in splits:\n meta = metas[s]\n\n imgs = []\n poses = []\n times = []\n\n for t, frame in enumerate(meta['frames']):\n fname = os.path.join(self.data_dir, frame['file_path'] + '.png')\n image = cv.imread(fname, cv.IMREAD_UNCHANGED)\n imgs.append(image)\n pose = np.array(frame['transform_matrix'])\n time = np.array([frame['time']])\n\n a = pose[:, 0:1]\n b = pose[:, 1:2]\n c = pose[:, 2:3]\n d = pose[:, 3:].copy()\n d[:3, :] *= 0.8\n\n pose = np.concatenate([a, -b, -c, d], 1)\n\n poses.append(pose)\n times.append(time)\n\n imgs = (np.array(imgs) / 255.).astype(np.float32) # keep all 4 channels (RGBA)\n poses = np.array(poses).astype(np.float32)\n times = np.array(times).astype(np.float32)\n masks = (imgs[..., 3:] > 0).astype(np.float32)\n imgs = imgs[..., :3]\n counts.append(counts[-1] + imgs.shape[0])\n all_imgs.append(imgs)\n all_poses.append(poses)\n all_masks.append(masks)\n all_times.append(times)\n\n self.images = torch.from_numpy(np.concatenate(all_imgs, 0)).cpu()\n self.masks = torch.from_numpy(np.concatenate(all_masks, 0)).cpu()\n self.radius = torch.zeros(self.masks.shape[0], self.masks.shape[2], self.masks.shape[1], 1) # no use\n self.errors = self.masks[:, :, :, :1].clone()\n self.errors = F.interpolate(self.errors.permute(0, 3, 1, 2),\n (self.images.shape[1] // 8, self.images.shape[2] // 8), mode='bilinear')\n self.errors = F.max_pool2d(self.errors, 7, stride=1, padding=3)\n self.errors = self.errors.permute(0, 2, 3, 1)\n self.n_images = self.images.shape[0]\n\n self.fid_list = [torch.LongTensor(np.array([idx])) for idx in range(self.n_images)]\n # if self.data_dir.split('/')[-2] == 'lego':\n # self.fid_list[-1] = torch.LongTensor(np.array([0]))\n self.pose_all = torch.from_numpy(np.concatenate(all_poses, 0)).to(self.device)\n self.fid_all = torch.stack(self.fid_list).to(self.device)\n self.time_emb_list = torch.from_numpy(np.concatenate(all_times, 0)).to(self.device)\n\n self.H, self.W = self.images[0].shape[:2]\n self.image_pixels = self.H * self.W\n\n camera_angle_x = float(meta['camera_angle_x'])\n self.focal = .5 * self.W / np.tan(.5 * camera_angle_x)\n intrinsics = torch.Tensor(\n [[self.focal, 0, 0.5 * self.W, 0],\n [0, self.focal, 0.5 * self.H, 0],\n [0, 0, 1, 0],\n [0, 0, 0, 1]]).to(self.device)\n self.intrinsics_all = intrinsics.expand(self.n_images, -1, -1)\n self.intrinsics_all_inv = torch.inverse(self.intrinsics_all) # [n_images, 4, 4]\n self.object_bbox_min = np.array([-1.01, -1.01, -1.01]) # hard code bbox\n self.object_bbox_max = np.array([1.01, 1.01, 1.01])\n self.process_radius()\n\n print('Load data: End')\n\n def process_radius(self):\n for img_idx in tqdm(range(self.images.shape[0])):\n tx = torch.linspace(0, self.W - 1, self.W, device=self.device)\n ty = torch.linspace(0, self.H - 1, self.H, device=self.device)\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\n rays_v = torch.matmul(self.intrinsics_all_inv[img_idx, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3\n rays_v = torch.matmul(self.pose_all[img_idx, None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\n # Cut the distance in half, and then round it out so that it's\n # halfway between inscribed by / circumscribed about the pixel.\n radii = dx[..., None] * 2 / np.sqrt(12)\n self.radius[img_idx] = radii.detach().cpu() # W H 3\n\n def gen_rays_at(self, img_idx, resolution_level=1):\n \"\"\"\n Generate rays at world space from one camera.\n \"\"\"\n l = resolution_level\n tx = torch.linspace(0, self.W - 1, self.W // l, device=self.device)\n ty = torch.linspace(0, self.H - 1, self.H // l, device=self.device)\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\n rays_v = torch.matmul(self.intrinsics_all_inv[img_idx, None, None, :3, :3],\n p[:, :, :, None]).squeeze() # W, H, 3\n rays_v = torch.matmul(self.pose_all[img_idx, None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\n rays_r = dx[..., None] * 2 / np.sqrt(12)\n rays_o = self.pose_all[img_idx, None, None, :3, 3].expand(rays_v.shape) # W, H, 3\n rays_v = rays_v / torch.linalg.norm(rays_v, ord=2, dim=-1, keepdim=True) # W, H, 3\n return rays_o.transpose(0, 1), rays_v.transpose(0, 1), rays_r.transpose(0, 1)\n\n def gen_random_rays_at(self, img_idx, batch_size):\n \"\"\"\n Generate random rays at world space from one camera.\n \"\"\"\n error = self.errors[img_idx].reshape(-1).numpy()\n max_error = np.max(error) + 1e-8\n error = error / max_error\n error[error < 0.1] = 0.1\n error = error / np.sum(error)\n index = np.arange(0, self.W * self.H // 64)\n select_index = np.random.choice(index, size=[batch_size], p=error)\n pixels_y = torch.LongTensor(select_index // (self.W // 8)) * 8\n pixels_y += torch.randint_like(pixels_y, 8)\n pixels_x = torch.LongTensor(select_index % (self.W // 8)) * 8\n pixels_x += torch.randint_like(pixels_x, 8)\n\n color = self.images[img_idx][(pixels_y, pixels_x)] # batch_size, 3\n mask = self.masks[img_idx][(pixels_y, pixels_x)] # batch_size, 3\n rays_r = self.radius[img_idx][(pixels_x, pixels_y)] # batch_size, 1\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1).float().to(\n self.device) # batch_size, 3\n p = torch.matmul(self.intrinsics_all_inv[img_idx, None, :3, :3], p[:, :, None]).squeeze() # batch_size, 3\n rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True) # batch_size, 3\n rays_v = torch.matmul(self.pose_all[img_idx, None, :3, :3], rays_v[:, :, None]).squeeze() # batch_size, 3\n rays_o = self.pose_all[img_idx, None, :3, 3].expand(rays_v.shape) # batch_size, 3\n return torch.cat([rays_o.cpu(), rays_v.cpu(), color.cpu(), mask[:, :1].cpu(), rays_r.cpu()],\n dim=-1).cuda(), pixels_y.cpu(), pixels_x.cpu() # batch_size, 10\n\n def gen_rays_between(self, idx_0, idx_1, ratio, resolution_level=1):\n \"\"\"\n Interpolate pose between two cameras.\n \"\"\"\n l = resolution_level\n tx = torch.linspace(0, self.W - 1, self.W // l, device=self.device)\n ty = torch.linspace(0, self.H - 1, self.H // l, device=self.device)\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\n rays_v = torch.matmul(self.intrinsics_all_inv[0, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3\n trans = self.pose_all[idx_0, :3, 3] * (1.0 - ratio) + self.pose_all[idx_1, :3, 3] * ratio\n pose_0 = self.pose_all[idx_0].detach().cpu().numpy()\n pose_1 = self.pose_all[idx_1].detach().cpu().numpy()\n pose_0 = np.linalg.inv(pose_0)\n pose_1 = np.linalg.inv(pose_1)\n rot_0 = pose_0[:3, :3]\n rot_1 = pose_1[:3, :3]\n rots = Rot.from_matrix(np.stack([rot_0, rot_1]))\n key_times = [0, 1]\n slerp = Slerp(key_times, rots)\n rot = slerp(ratio)\n pose = np.diag([1.0, 1.0, 1.0, 1.0])\n pose = pose.astype(np.float32)\n pose[:3, :3] = rot.as_matrix()\n pose[:3, 3] = ((1.0 - ratio) * pose_0 + ratio * pose_1)[:3, 3]\n pose = np.linalg.inv(pose)\n rot = torch.from_numpy(pose[:3, :3]).cuda()\n trans = torch.from_numpy(pose[:3, 3]).cuda()\n rays_v = torch.matmul(rot[None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\n rays_r = dx[..., None] * 2 / np.sqrt(12)\n rays_v = rays_v / torch.linalg.norm(rays_v, ord=2, dim=-1, keepdim=True) # W, H, 3\n rays_o = trans[None, None, :3].expand(rays_v.shape) # W, H, 3\n return rays_o.transpose(0, 1), rays_v.transpose(0, 1), rays_r.transpose(0, 1)\n\n def near_far_from_sphere(self, rays_o, rays_d):\n if self.near > 0:\n return self.near, self.far\n a = torch.sum(rays_d ** 2, dim=-1, keepdim=True)\n b = 2.0 * torch.sum(rays_o * rays_d, dim=-1, keepdim=True)\n mid = 0.5 * (-b) / a\n near = mid - 1.0\n far = mid + 1.0\n return near, far\n\n def image_at(self, idx, resolution_level):\n img = cv.imread(self.images_lis[idx])\n return (cv.resize(img, (self.W // resolution_level, self.H // resolution_level))).clip(0, 255)"},{"identifier":"RenderingNetwork","path":"models/fields.py","snippet":"class RenderingNetwork(nn.Module):\n def __init__(self,\n d_feature,\n mode,\n d_in,\n d_out,\n d_hidden,\n n_layers,\n weight_norm=True,\n multires_view=0,\n squeeze_out=True):\n super().__init__()\n\n self.mode = mode\n self.squeeze_out = squeeze_out\n dims = [d_in + d_feature] + [d_hidden for _ in range(n_layers)] + [d_out]\n\n self.embedview_fn = None\n if multires_view > 0:\n embedview_fn, input_ch = get_embedder(multires_view)\n self.embedview_fn = embedview_fn\n dims[0] += (input_ch - 3)\n\n self.num_layers = len(dims)\n\n for l in range(0, self.num_layers - 1):\n out_dim = dims[l + 1]\n lin = nn.Linear(dims[l], out_dim)\n if weight_norm:\n lin = nn.utils.weight_norm(lin)\n\n setattr(self, \"lin\" + str(l), lin)\n\n self.relu = nn.ReLU()\n\n self.mask = -torch.ones((1, 1, 256, 256, 256)).float().cuda()\n \n\n def forward(self, points, normals, view_dirs, feature_vectors):\n if self.embedview_fn is not None:\n view_dirs = self.embedview_fn(view_dirs)\n\n rendering_input = NoOptionError\n\n if self.mode == 'idr':\n rendering_input = torch.cat([points, view_dirs, normals, feature_vectors], dim=-1)\n elif self.mode == 'no_view_dir':\n rendering_input = torch.cat([points, normals, feature_vectors], dim=-1)\n elif self.mode == 'no_normal':\n rendering_input = torch.cat([points, view_dirs, feature_vectors], dim=-1)\n\n x = rendering_input\n for l in range(0, self.num_layers - 1):\n lin = getattr(self, \"lin\" + str(l))\n\n x = lin(x)\n\n if l < self.num_layers - 2:\n x = self.relu(x)\n\n if self.squeeze_out:\n x = torch.sigmoid(x)\n return x"},{"identifier":"FieldNetwork","path":"models/fields.py","snippet":"class FieldNetwork(nn.Module):\n def __init__(self,\n d_in,\n d_out,\n d_hidden,\n d_t4d,\n min_emb,\n max_emb,\n n_layers,\n t_emb=-1,\n skip_in=(4,),\n bias=0.5,\n geometric_init=True,\n weight_norm=True):\n super(FieldNetwork, self).__init__()\n\n dims = [d_in] + [d_hidden for _ in range(n_layers)] + [d_out]\n dims[0] = d_in + (max_emb - min_emb)*3*2\n\n self.num_layers = len(dims)\n self.skip_in = skip_in\n self.min_emb = min_emb\n self.max_emb = max_emb\n self.t_emb = t_emb\n\n if t_emb > 0:\n embed_fn, time_input_ch = get_embedder(t_emb, input_dims=1)\n self.embed_fn = embed_fn\n dims[0] += time_input_ch\n\n for l in range(0, self.num_layers - 1):\n if l in self.skip_in:\n in_dim = dims[l] + dims[0] + d_t4d\n else:\n in_dim = dims[l]\n out_dim = dims[l+1]\n\n lin = nn.Linear(in_dim, out_dim)\n \n if geometric_init:\n if l == self.num_layers - 2:\n torch.nn.init.normal_(lin.weight, mean=np.sqrt(np.pi) / np.sqrt(dims[l]), std=0.0001)\n torch.nn.init.constant_(lin.bias, -bias)\n elif l == 0:\n torch.nn.init.constant_(lin.bias, 0.0)\n torch.nn.init.constant_(lin.weight[:, 3:], 0.0)\n torch.nn.init.normal_(lin.weight[:, :3], 0.0, np.sqrt(2) / np.sqrt(out_dim))\n elif l in self.skip_in:\n torch.nn.init.constant_(lin.bias, 0.0)\n torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim))\n torch.nn.init.constant_(lin.weight[:, -(dims[0] + d_t4d):], 0.0)\n else:\n torch.nn.init.constant_(lin.bias, 0.0)\n torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim))\n\n if weight_norm:\n lin = nn.utils.weight_norm(lin)\n\n setattr(self, \"lin\" + str(l), lin)\n\n self.activation = nn.Softplus(beta=100)\n\n def set_tensor4d(self, tensor4d):\n self.tensor4d = tensor4d\n\n def forward(self, mean, cov, fid, time_emb, reg_l2=False):\n cones_embedding = integrated_pos_enc((mean[:, None, :], cov[:, None, :]), self.min_emb, self.max_emb, diagonal=True).reshape(mean.shape[0], -1)\n inputs = mean\n tri_feat = self.tensor4d(inputs, fid, torch.mean(time_emb))\n\n if reg_l2:\n d_vec = F.normalize(torch.randn_like(inputs), dim=-1) * 1e-3\n d_tri_feat = self.tensor4d(inputs + d_vec, fid, torch.mean(time_emb))\n pred_reg_l2 = (d_tri_feat - tri_feat)**2\n \n xyz = inputs\n if self.t_emb > 0:\n time_input = self.embed_fn(time_emb)\n x = torch.cat([xyz, cones_embedding, time_input], 1)\n else:\n x = torch.cat([xyz, cones_embedding], 1)\n\n for l in range(0, self.num_layers - 1):\n lin = getattr(self, \"lin\" + str(l))\n \n if l in self.skip_in:\n if self.t_emb > 0:\n x = torch.cat([x, tri_feat, xyz, cones_embedding, time_input], 1) / np.sqrt(2)\n else:\n x = torch.cat([x, tri_feat, xyz, cones_embedding], 1) / np.sqrt(2)\n x = lin(x)\n\n if l < self.num_layers - 2:\n x = self.activation(x)\n if reg_l2:\n return x, pred_reg_l2\n return x"},{"identifier":"SingleVarianceNetwork","path":"models/fields.py","snippet":"class SingleVarianceNetwork(nn.Module):\n def __init__(self, init_val):\n super(SingleVarianceNetwork, self).__init__()\n init_tensor = torch.zeros(120)\n init_tensor[:] = init_val\n self.register_parameter('variance', nn.Parameter(init_tensor))\n\n def forward(self, x):\n return torch.ones([len(x), 1], device=x.device) * torch.exp(self.variance[0] * 10.0)"},{"identifier":"Tensor4D","path":"models/tensor4d.py","snippet":"class Tensor4D(nn.Module):\n def __init__(self, feature_type, lr_resolution, hr_resolution, image_guide=False, image_guide_interval=2, image_guide_base=16) -> None:\n super(Tensor4D, self).__init__()\n \n self.data_dims = 0\n self.feature_type = feature_type\n if feature_type == '3d':\n self.feature_plane = SpacePlane(lr_resolution, hr_resolution)\n self.data_dims = self.feature_plane.dims\n elif feature_type == '4d':\n self.feature_plane = TimeSpacePlane(lr_resolution, hr_resolution)\n self.data_dims = self.feature_plane.dims\n\n self.img_dims = 0\n self.image_guide = image_guide\n if image_guide:\n self.conv_net = ConvNet(image_guide_base)\n self.img_dims = image_guide_base*8*2\n self.ig_interval = image_guide_interval\n\n if feature_type == '4d':\n self.compress_network = CompressNetwork(self.data_dims, self.data_dims // 3)\n self.compress_list = [self.compress_network.compress1, self.compress_network.compress2, self.compress_network.compress3]\n\n self.dims = self.data_dims + self.img_dims\n self.matMode = torch.BoolTensor([[0, 1, 1], [1, 0, 1], [1, 1, 0]]).cuda()\n self.vecMode = torch.BoolTensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]]).cuda()\n \n def get_data_parameters(self):\n return list(self.feature_plane.parameters())\n \n def get_network_parameters(self):\n params = []\n if self.feature_type == '4d':\n params += list(self.compress_network.parameters())\n if self.image_guide:\n params += list(self.conv_net.parameters())\n return params\n\n def set_images(self, image, proj):\n step = self.ig_interval\n select_proj = torch.cat([proj[i*step:i*step+1] for i in range(proj.shape[0] // step)], dim=0)\n self.proj = select_proj\n self.img_shape = image.shape\n select_image = torch.cat([image[i*step:i*step+1] for i in range(image.shape[0] // step)], dim=0)\n self.image_feature, self.image_feature_hr = self.conv_net(F.interpolate(select_image.permute(0, 3, 1, 2), size=(1024, 1024)))\n\n def forward(self, xyz_sampled_ori, fid, time_emb):\n sigma_feature_list = [] \n\n if self.image_guide:\n proj_pts = ((self.proj[:, :3, :3] @ xyz_sampled_ori.T.unsqueeze(0)) + self.proj[:, :3, 3:]).transpose(1, 2)\n proj_xy = proj_pts[:, :, :2] / (proj_pts[:, :, 2:] + 1e-6)\n B, H, W, C = self.img_shape\n proj_xy[:, :, 0] = (proj_xy[:, :, 0] - W / 2) / (W / 2)\n proj_xy[:, :, 1] = (proj_xy[:, :, 1] - H / 2) / (H / 2)\n N = self.image_feature.shape[0]\n img_feature = grid_sample(self.image_feature, proj_xy.reshape(N, -1, 1, 2)).reshape(N, -1, xyz_sampled_ori.shape[0])\n img_feature_cost = torch.sqrt(torch.sum((img_feature - torch.sum(img_feature, dim=0).unsqueeze(0) / N)**2, dim=0) / N + 1e-8)\n img_feature_max = torch.mean(img_feature, dim=0) + torch.max(img_feature, dim=0)[0]\n image_feature_hr = grid_sample(self.image_feature_hr, proj_xy.reshape(N, -1, 1, 2)).reshape(N, -1, xyz_sampled_ori.shape[0])\n image_feature_hr_cost = torch.sqrt(torch.sum((image_feature_hr - torch.sum(image_feature_hr, dim=0).unsqueeze(0) / N)**2, dim=0) / N + 1e-8)\n image_feature_hr_max = torch.mean(image_feature_hr, dim=0) + torch.max(image_feature_hr, dim=0)[0]\n sigma_feature_list = [img_feature_cost, img_feature_max, image_feature_hr_cost, image_feature_hr_max]\n \n xyz_sampled = xyz_sampled_ori\n scale = 1.0\n matMode = self.matMode\n coordinate_plane = torch.stack((xyz_sampled[..., matMode[0]] * scale, xyz_sampled[..., matMode[1]] * scale, xyz_sampled[..., matMode[2]] * scale)).view(3, -1, 1, 2)\n\n for idx_plane in range(3):\n sample_points = coordinate_plane[[idx_plane]]\n plane_coef_point = self.feature_plane.sample(sample_points, idx_plane, time_emb).view(-1, *xyz_sampled.shape[:1])\n if self.feature_type == '4d':\n plane_coef_point = self.compress_list[idx_plane](plane_coef_point.T).T\n sigma_feature_list.append(plane_coef_point)\n \n sigma_feature_list = torch.cat(sigma_feature_list, dim=0)\n # print(sigma_feature_list.shape)\n return sigma_feature_list.T"},{"identifier":"NeuSRenderer","path":"models/renderer.py","snippet":"class NeuSRenderer:\n def __init__(self,\n sdf_network,\n deviation_network,\n color_network,\n mask3d,\n n_samples,\n n_importance,\n n_outside,\n up_sample_steps,\n perturb,\n reg_l2=False,\n mip_render=False,\n flow_network=None):\n \n self.sdf_network = sdf_network\n self.deviation_network = deviation_network\n self.color_network = color_network\n self.mask3d = mask3d\n self.n_samples = n_samples\n self.n_importance = n_importance\n self.n_outside = n_outside\n self.up_sample_steps = up_sample_steps\n self.perturb = perturb\n self.reg_l2 = reg_l2\n self.flow_network = flow_network\n self.mip_render = mip_render\n\n def mask_query_geometry(self, mean, cov, only_sdf=False):\n fid = self.fid\n time_emb = self.time_emb\n time_input = time_emb.expand(mean[:, :1].shape)\n space_time_input = torch.cat([mean, time_input], dim=-1)\n if not only_sdf:\n space_time_input.requires_grad_(True)\n inputs = space_time_input[:, :3]\n time_emb = space_time_input[:, 3:]\n N, _ = inputs.shape\n grads = torch.zeros((N, 4), device=inputs.device)\n sdf_nn = torch.zeros((N, 257), device=inputs.device)\n\n reg_l2 = torch.zeros((N, self.sdf_network.tensor4d.dims), device=inputs.device)\n grads[:, 0] = 1\n sdf_nn[:, 0] = -10\n\n mask = self.mask3d.valid_input(inputs, fid)\n if torch.sum(mask) == 0:\n results = {\n 'sdf_nn': sdf_nn,\n 'grads': grads[:, :3],\n 'time_grads': grads[:, 3:],\n 'pts_mask': mask,\n 'reg_l2': reg_l2\n }\n return results\n mask_mean = inputs[mask, :]\n mask_time_emb = time_emb[mask, :]\n mask_cov = cov[mask, :]\n \n if self.flow_network is not None:\n mask_cov = torch.zeros_like(mask_mean) # flow mode, disable mip_render\n if fid != 0:\n pred_flow = self.flow_network(mask_mean, mask_cov, fid, mask_time_emb, reg_l2=False)\n mask_mean = mask_mean + pred_flow\n elif not self.mip_render:\n mask_cov = torch.zeros_like(mask_mean)\n\n if (not only_sdf) and self.reg_l2:\n pred_sdf_nn, pred_reg_l2 = self.sdf_network(mask_mean, mask_cov, fid, mask_time_emb, reg_l2=True)\n reg_l2[mask] = pred_reg_l2\n else:\n pred_sdf_nn = self.sdf_network(mask_mean, mask_cov, fid, mask_time_emb, reg_l2=False)\n\n if not only_sdf:\n pred_sdf = pred_sdf_nn[:, :1]\n d_output = torch.ones_like(pred_sdf, requires_grad=False, device=pred_sdf.device)\n gradients = torch.autograd.grad(\n outputs=pred_sdf,\n inputs=space_time_input,\n grad_outputs=d_output,\n create_graph=True,\n retain_graph=True,\n only_inputs=True)[0]\n grads[mask] = gradients.reshape(-1, 4)[mask]\n \n sdf_nn[mask] = pred_sdf_nn\n results = {\n 'sdf_nn': sdf_nn,\n 'grads': grads[:, :3],\n 'time_grads': grads[:, 3:],\n 'pts_mask': mask,\n 'reg_l2': reg_l2\n }\n return results\n\n def mask_query_color(self, pts, mask, normals, view_dirs, features):\n N, _ = pts.shape\n out = torch.zeros((N, 3), device=pts.device)\n if torch.sum(mask) > 0:\n x = self.color_network(pts[mask], normals[mask], view_dirs[mask], features[mask])\n out[mask] = x\n return out\n else:\n return torch.zeros((N, 3), device=pts.device)\n\n def up_sample(self, rays_o, rays_d, z_vals, sdf, n_importance, inv_s, pts_mask):\n \"\"\"\n Up sampling give a fixed inv_s\n \"\"\"\n batch_size, n_samples = z_vals.shape\n pts = rays_o[:, None, :] + rays_d[:, None, :] * z_vals[..., :, None] # n_rays, n_samples, 3\n radius = torch.linalg.norm(pts, ord=2, dim=-1, keepdim=False)\n inside_sphere = (radius[:, :-1] < 1.0) | (radius[:, 1:] < 1.0)\n sdf = sdf.reshape(batch_size, n_samples)\n prev_sdf, next_sdf = sdf[:, :-1], sdf[:, 1:]\n prev_mask, next_mask = pts_mask[:, :-1], pts_mask[:, 1:]\n mid_mask = torch.logical_and(prev_mask, next_mask)\n prev_z_vals, next_z_vals = z_vals[:, :-1], z_vals[:, 1:]\n mid_sdf = (prev_sdf + next_sdf) * 0.5\n cos_val = (next_sdf - prev_sdf) / (next_z_vals - prev_z_vals + 1e-5)\n\n # ----------------------------------------------------------------------------------------------------------\n # Use min value of [ cos, prev_cos ]\n # Though it makes the sampling (not rendering) a little bit biased, this strategy can make the sampling more\n # robust when meeting situations like below:\n #\n # SDF\n # ^\n # |\\ -----x----...\n # | \\ /\n # | x x\n # |---\\----/-------------> 0 level\n # | \\ /\n # | \\/\n # |\n # ----------------------------------------------------------------------------------------------------------\n prev_cos_val = torch.cat([torch.zeros([batch_size, 1], device=sdf.device), cos_val[:, :-1]], dim=-1)\n cos_val = torch.stack([prev_cos_val, cos_val], dim=-1)\n cos_val, _ = torch.min(cos_val, dim=-1, keepdim=False)\n cos_val = cos_val.clip(-1e3, 0.0) * inside_sphere\n\n dist = (next_z_vals - prev_z_vals)\n prev_esti_sdf = mid_sdf - cos_val * dist * 0.5\n next_esti_sdf = mid_sdf + cos_val * dist * 0.5\n prev_cdf = torch.sigmoid(prev_esti_sdf * inv_s)\n next_cdf = torch.sigmoid(next_esti_sdf * inv_s)\n\n alpha = (prev_cdf - next_cdf + 1e-5) / (prev_cdf + 1e-5)\n alpha[~mid_mask] = 0\n alpha = alpha.clamp(0.0, 1.0)\n \n alpha = torch.cat([alpha, torch.zeros([batch_size, 1], device=alpha.device)], dim=-1)\n weights = alpha * torch.cumprod(\n torch.cat([torch.ones([batch_size, 1], device=alpha.device), 1. - alpha + 1e-7], -1), -1)[:, :-1]\n\n z_samples = sample_pdf(z_vals, weights, n_importance, det=True).detach()\n return z_samples\n\n def cat_z_vals(self, rays_o, rays_d, z_vals, new_z_vals, sdf, pts_mask, last=False):\n batch_size, n_samples = z_vals.shape\n _, n_importance = new_z_vals.shape\n pts = rays_o[:, None, :] + rays_d[:, None, :] * new_z_vals[..., :, None]\n z_vals = torch.cat([z_vals, new_z_vals], dim=-1)\n z_vals, index = torch.sort(z_vals, dim=-1)\n if not last:\n new_sdf, new_pts_mask = self.sdf_network.sdf(pts.reshape(-1, 3), rt_mask=True)\n new_sdf = new_sdf.reshape(batch_size, n_importance)\n new_pts_mask = new_pts_mask.reshape(batch_size, n_importance)\n sdf = torch.cat([sdf, new_sdf], dim=-1)\n pts_mask = torch.cat([pts_mask, new_pts_mask], dim=-1)\n xx = torch.arange(batch_size)[:, None].expand(batch_size, n_samples + n_importance).reshape(-1)\n index = index.reshape(-1)\n sdf = sdf[(xx, index)].reshape(batch_size, n_samples + n_importance)\n pts_mask = pts_mask[(xx, index)].reshape(batch_size, n_samples + n_importance)\n\n return z_vals, sdf, pts_mask\n\n def render_core(self,\n rays_o,\n rays_d,\n rays_r,\n z_vals,\n sample_dist,\n background_alpha=None,\n background_sampled_color=None,\n background_rgb=None,\n cos_anneal_ratio=0.0):\n batch_size, n_samples = z_vals[:, :-1].shape\n\n # Section length\n dists = z_vals[..., 1:] - z_vals[..., :-1]\n cat_dists = torch.cat([dists, torch.Tensor([sample_dist]).to(dists.device).expand(dists[..., :1].shape)], -1)\n mid_z_vals = z_vals + cat_dists * 0.5\n\n cones = cast_rays(z_vals, rays_o, rays_d, rays_r, 'cone', diagonal=True)\n dirs = rays_d[:, None, :].expand(cones[0].shape)\n dirs = dirs.reshape(-1, 3)\n\n results = self.mask_query_geometry(cones[0].reshape(-1, 3), cones[1].reshape(-1, 3))\n sdf_nn_output, gradients, t_grads, pts_mask = results['sdf_nn'], results['grads'], results['time_grads'], results['pts_mask']\n sdf = sdf_nn_output[:, :1]\n feature_vector = sdf_nn_output[:, 1:]\n\n gradients = gradients.squeeze()\n sampled_color = self.mask_query_color(cones[0].reshape(-1, 3), pts_mask, gradients, dirs, feature_vector).reshape(batch_size, n_samples, 3)\n \n inv_s = self.deviation_network(torch.zeros([1, 3], device=sdf.device))[:, :1].clip(1e-6, 1e6) # Single parameter\n inv_s = inv_s.expand(batch_size * n_samples, 1)\n\n true_cos = (dirs * gradients).sum(-1, keepdim=True)\n\n # \"cos_anneal_ratio\" grows from 0 to 1 in the beginning training iterations. The anneal strategy below makes\n # the cos value \"not dead\" at the beginning training iterations, for better convergence.\n iter_cos = -(F.relu(-true_cos * 0.5 + 0.5) * (1.0 - cos_anneal_ratio) +\n F.relu(-true_cos) * cos_anneal_ratio) # always non-positive\n\n # Estimate signed distances at section points\n estimated_next_sdf = sdf + iter_cos * dists.reshape(-1, 1) * 0.5\n estimated_prev_sdf = sdf - iter_cos * dists.reshape(-1, 1) * 0.5\n\n prev_cdf = torch.sigmoid(estimated_prev_sdf * inv_s)\n next_cdf = torch.sigmoid(estimated_next_sdf * inv_s)\n\n p = prev_cdf - next_cdf\n c = prev_cdf\n\n alpha = ((p + 1e-5) / (c + 1e-5))\n \n alpha[~pts_mask] = 0\n alpha = alpha.reshape(batch_size, n_samples).clip(0.0, 1.0)\n \n weights = alpha * torch.cumprod(torch.cat([torch.ones([batch_size, 1], device=alpha.device), 1. - alpha + 1e-7], -1), -1)[:, :-1]\n weights_sum = weights.sum(dim=-1, keepdim=True)\n \n color = (sampled_color * weights[:, :, None]).sum(dim=1)\n if background_rgb is not None: # Fixed background, usually black\n color = color + background_rgb * (1.0 - weights_sum)\n\n # Eikonal loss\n gradient_error = torch.mean((torch.linalg.norm(gradients.reshape(batch_size, n_samples, 3), ord=2,\n dim=-1) - 1.0) ** 2)\n time_grad_error = torch.mean(t_grads**2)\n return {\n 'color': color,\n 'sdf': sdf,\n 'pts_mask': pts_mask,\n 'dists': dists,\n 'gradients': gradients.reshape(batch_size, n_samples, 3),\n 's_val': 1.0 / inv_s,\n 'mid_z_vals': mid_z_vals,\n 'weights': weights,\n 'gradient_error': gradient_error,\n 'time_grad_error': time_grad_error,\n 'reg_l2': results['reg_l2'].reshape(batch_size, n_samples, -1),\n }\n\n def render(self, rays_o, rays_d, rays_r, near, far, fid, time_emb, perturb_overwrite=-1, background_rgb=None, cos_anneal_ratio=0.0):\n self.fid = fid\n self.time_emb = time_emb\n self.mask3d.set_fid(fid)\n\n batch_size = len(rays_o)\n sample_dist = 2.0 / self.n_samples # Assuming the region of interest is a unit sphere\n z_vals = torch.linspace(0.0, 1.0, self.n_samples, device=rays_o.device)\n z_vals = near + (far - near) * z_vals[None, :]\n\n z_vals_outside = None\n \n n_samples = self.n_samples\n perturb = self.perturb\n\n if perturb_overwrite >= 0:\n perturb = perturb_overwrite\n if perturb > 0:\n t_rand = (torch.rand([batch_size, 1], device=z_vals.device) - 0.5)\n z_vals = z_vals + t_rand * 2.0 / self.n_samples\n\n background_alpha = None\n background_sampled_color = None\n\n # Up sample\n if self.n_importance > 0:\n with torch.no_grad():\n cast_z_vals = torch.cat([z_vals, z_vals[:, -1:]], dim=1)\n cones = cast_rays(cast_z_vals, rays_o, rays_d, rays_r, 'cone', diagonal=True)\n results = self.mask_query_geometry(cones[0].reshape(-1, 3), cones[1].reshape(-1, 3), only_sdf=True)\n sdf, pts_mask = results['sdf_nn'][:, :1], results['pts_mask']\n # sdf, pts_mask = self.sdf_network.sdf(pts.reshape(-1, 3), rt_mask=True)\n sdf = sdf.reshape(batch_size, self.n_samples)\n pts_mask = pts_mask.reshape(batch_size, self.n_samples)\n for i in range(self.up_sample_steps):\n new_z_vals = self.up_sample(rays_o,\n rays_d,\n z_vals,\n sdf,\n self.n_importance // self.up_sample_steps + 1,\n 64 * 2**i, pts_mask)\n z_vals, sdf, pts_mask = self.cat_z_vals(rays_o,\n rays_d,\n z_vals,\n new_z_vals,\n sdf, pts_mask,\n last=(i + 1 == self.up_sample_steps))\n\n n_samples = self.n_samples + self.n_importance\n\n background_alpha = None\n background_sampled_color = None\n sample_dist = 1e-2\n\n # Render core\n ret_fine = self.render_core(rays_o,\n rays_d,\n rays_r,\n z_vals,\n sample_dist,\n background_rgb=background_rgb,\n background_alpha=background_alpha,\n background_sampled_color=background_sampled_color,\n cos_anneal_ratio=cos_anneal_ratio)\n\n\n return {\n 'color_fine': ret_fine['color'],\n 's_val': ret_fine['s_val'].reshape(batch_size, n_samples).mean(dim=-1, keepdim=True),\n 'mid_z_vals': ret_fine['mid_z_vals'],\n 'weights': ret_fine['weights'],\n 'weight_sum': ret_fine['weights'].sum(dim=-1, keepdim=True),\n 'weight_max': torch.max(ret_fine['weights'], dim=-1, keepdim=True)[0],\n 'gradients': ret_fine['gradients'],\n 'gradient_error': ret_fine['gradient_error'],\n 'time_grad_error': ret_fine['time_grad_error'],\n 'reg_l2': ret_fine['reg_l2']\n }"},{"identifier":"Mask3D","path":"models/mask.py","snippet":"class Mask3D:\n def __init__(self, mask_type, num_frames=None, mask_reso=None, device=None):\n self.mask_type = mask_type # 'bounding or visualhull'\n if mask_type == 'visualhull':\n self.R = mask_reso\n self.mask = torch.ones([num_frames, self.R, self.R, self.R]).float()\n self.device = device\n self.current_fid = -1\n self.current_mask = None\n\n def set_fid(self, fid):\n if fid != self.current_fid:\n self.current_fid = fid\n if self.mask_type == 'visualhull':\n self.current_mask = self.mask[fid.cpu()].to(self.device)\n \n def valid_input(self, pts, fid):\n with torch.no_grad():\n pts = pts.reshape(1, -1, 1, 1, 3)\n pts_max = torch.max(pts, dim=-1)[0]\n pts_min = torch.min(pts, dim=-1)[0]\n mask_max = (pts_max > 1).reshape(-1)\n mask_min = (pts_min < -1).reshape(-1)\n if self.mask_type == 'visualhull':\n R = self.R\n sigma = F.grid_sample(self.current_mask.view(1, 1, R, R, R), pts, mode='bilinear', padding_mode='border').reshape(-1)\n calc_mask = sigma < 0.05\n else:\n calc_mask = torch.ones_like(mask_max)\n calc_mask[mask_max] = 0\n calc_mask[mask_min] = 0\n return calc_mask\n\n def visualhull(self, pts_ori, projs, masks, g_nums):\n cam_nums = projs.shape[0]\n interval = 1\n pts_mask = torch.zeros(pts_ori.shape[0], g_nums)\n out_mask = torch.zeros(pts_ori.shape[0])\n N, H, W, C = masks.shape\n for gp in range(cam_nums // (g_nums*interval)):\n for j in range(g_nums):\n i = j + gp*(g_nums*interval)\n mask = masks[i, :, :, :1].permute(2, 0, 1).unsqueeze(0).clone()\n mask = torch.max_pool2d(mask, 7, 1, 3, 1)\n pts = torch.cat([pts_ori, torch.ones_like(pts_ori[:, :1])], dim=-1)\n pts = projs[i] @ pts.T\n pts = pts[:2] / pts[2:]\n pts[0] = pts[0] / W * 2 - 1\n pts[1] = pts[1] / H * 2 - 1\n pts = pts.T.reshape(1, -1, 1, 2)\n \n sample_mask = torch.nn.functional.grid_sample(mask, pts, mode='bilinear', padding_mode='zeros').reshape(-1)\n pts_mask[:, j] = sample_mask\n pts_mask_sum = torch.min(pts_mask, dim=1)[0]\n valid = pts_mask_sum > 0.1\n out_mask[valid] = -1\n if gp == 0:\n out_mask[~valid] = 1\n return out_mask\n\n def compute_image_mask(self, projs, masks, g_nums):\n N = 64\n R = self.R\n X = torch.linspace(-1, 1, R).split(N)\n Y = torch.linspace(-1, 1, R).split(N)\n Z = torch.linspace(-1, 1, R).split(N)\n cam_nums = projs.shape[0]\n \n self.mask = self.mask.to(self.device)\n for gp in tqdm(range(cam_nums // g_nums)):\n # for gp in range(1):\n with torch.no_grad():\n for xi, xs in enumerate(X):\n for yi, ys in enumerate(Y):\n for zi, zs in enumerate(Z):\n xx, yy, zz = torch.meshgrid(xs, ys, zs)\n pts = torch.cat([zz.reshape(-1, 1), yy.reshape(-1, 1), xx.reshape(-1, 1)], dim=-1).to(self.device)\n val = self.visualhull(pts, projs[gp*g_nums:gp*g_nums+g_nums].to(self.device), masks[gp*g_nums:gp*g_nums+g_nums].to(self.device), g_nums).reshape(len(xs), len(ys), len(zs))\n self.mask[gp, xi * N: xi * N + len(xs),yi * N: yi * N + len(ys), zi * N: zi * N + len(zs)] = val\n self.mask = self.mask.unsqueeze(1)\n self.mask = -torch.max_pool3d(-self.mask, 7, 1, 3)\n self.mask[self.mask > -0.5] = 1\n self.mask = self.mask.detach().cpu()\n \n def compute_mask(self, fid, query_func, inv_s):\n N = 64\n R = 128\n X = torch.linspace(-1, 1, R).split(N)\n Y = torch.linspace(-1, 1, R).split(N)\n Z = torch.linspace(-1, 1, R).split(N)\n from .renderer import sigma_f\n mask = self.mask[fid].reshape(R, R, R).clone()\n self.triplane[0].flow(fid)\n with torch.no_grad():\n for xi, xs in enumerate(X):\n for yi, ys in enumerate(Y):\n for zi, zs in enumerate(Z):\n xx, yy, zz = torch.meshgrid(xs, ys, zs)\n pts = torch.cat([zz.reshape(-1, 1), yy.reshape(-1, 1), xx.reshape(-1, 1)], dim=-1)\n val = sigma_f(query_func(pts), inv_s).reshape(len(xs), len(ys), len(zs))\n mask[xi * N: xi * N + len(xs),yi * N: yi * N + len(ys), zi * N: zi * N + len(zs)] = val\n valid = mask > 0.02\n mask[valid] = 1\n mask[~valid] = -1\n mask = -torch.max_pool3d(mask.reshape(1, 1, 128, 128, 128), 7, 1, 3)\n self.mask[fid][mask[0] > -0.5] = 1"}],"string":"[\n {\n \"identifier\": \"Dataset\",\n \"path\": \"models/dataset.py\",\n \"snippet\": \"class Dataset:\\n def __init__(self, conf):\\n super(Dataset, self).__init__()\\n print('Load data: Begin')\\n self.device = torch.device('cuda')\\n self.conf = conf\\n\\n self.data_dir = conf.get_string('data_dir')\\n self.render_cameras_name = conf.get_string('render_cameras_name')\\n self.object_cameras_name = conf.get_string('object_cameras_name')\\n\\n self.camera_outside_sphere = conf.get_bool('camera_outside_sphere', default=True)\\n self.scale_mat_scale = conf.get_float('scale_mat_scale', default=1.1)\\n self.near = conf.get_float('near', default=-1)\\n self.far = conf.get_float('far', default=-1)\\n self.n_frames = conf.get_int('n_frames', default=128)\\n\\n camera_dict = np.load(os.path.join(self.data_dir, self.render_cameras_name))\\n self.camera_dict = camera_dict\\n self.images_lis = sorted(glob(os.path.join(self.data_dir, 'image/*.png')))\\n self.n_images = len(self.images_lis)\\n self.images_np = np.stack([cv.imread(im_name) for im_name in self.images_lis]) / 256.0\\n self.masks_lis = sorted(glob(os.path.join(self.data_dir, 'mask/*.png')))\\n self.masks_np = np.stack([cv.imread(im_name) for im_name in self.masks_lis]) / 256.0\\n\\n # world_mat is a projection matrix from world to image\\n self.world_mats_np = [camera_dict['world_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images)]\\n self.fid_list = [torch.LongTensor(np.array([camera_dict['fid_%d' % idx]])) for idx in range(self.n_images)]\\n self.scale_mats_np = []\\n\\n # scale_mat: used for coordinate normalization, we assume the scene to render is inside a unit sphere at origin.\\n self.scale_mats_np = [camera_dict['scale_mat_%d' % idx].astype(np.float32) for idx in range(self.n_images)]\\n\\n self.intrinsics_all = []\\n self.pose_all = []\\n self.proj_all = []\\n\\n for scale_mat, world_mat in zip(self.scale_mats_np, self.world_mats_np):\\n P = world_mat @ scale_mat\\n P = P[:3, :4]\\n intrinsics, pose = load_K_Rt_from_P(None, P)\\n self.intrinsics_all.append(torch.from_numpy(intrinsics).float())\\n self.pose_all.append(torch.from_numpy(pose).float())\\n self.proj_all.append(torch.from_numpy(P).float())\\n\\n self.images = torch.from_numpy(self.images_np.astype(np.float32)).cpu() # [n_images, H, W, 3]\\n self.masks = torch.from_numpy(self.masks_np.astype(np.float32)).cpu() # [n_images, H, W, 3]\\n self.errors = self.masks[:, :, :, :1].clone()\\n self.errors = F.interpolate(self.errors.permute(0, 3, 1, 2), (self.images.shape[1] // 8, self.images.shape[2] // 8), mode='bilinear')\\n self.errors = F.max_pool2d(self.errors, 7, stride=1, padding=3)\\n self.errors = self.errors.permute(0, 2, 3, 1)\\n self.radius = torch.zeros(self.masks.shape[0], self.masks.shape[2], self.masks.shape[1], 1) # [n_images, W, H, 3]\\n \\n self.intrinsics_all = torch.stack(self.intrinsics_all).to(self.device) # [n_images, 4, 4]\\n self.intrinsics_all_inv = torch.inverse(self.intrinsics_all) # [n_images, 4, 4]\\n self.focal = self.intrinsics_all[0][0, 0]\\n self.pose_all = torch.stack(self.pose_all).to(self.device) # [n_images, 4, 4]\\n self.proj_all = torch.stack(self.proj_all).to(self.device)\\n self.H, self.W = self.images.shape[1], self.images.shape[2]\\n self.image_pixels = self.H * self.W\\n self.fid_all = torch.stack(self.fid_list).to(self.device)\\n self.time_emb_list = (self.fid_all / self.n_frames * 2) - 0.95\\n\\n object_bbox_min = np.array([-1.01, -1.01, -1.01, 1.0])\\n object_bbox_max = np.array([ 1.01, 1.01, 1.01, 1.0])\\n # Object scale mat: region of interest to **extract mesh**\\n object_scale_mat = np.load(os.path.join(self.data_dir, self.object_cameras_name))['scale_mat_0']\\n object_bbox_min = np.linalg.inv(self.scale_mats_np[0]) @ object_scale_mat @ object_bbox_min[:, None]\\n object_bbox_max = np.linalg.inv(self.scale_mats_np[0]) @ object_scale_mat @ object_bbox_max[:, None]\\n self.object_bbox_min = object_bbox_min[:3, 0]\\n self.object_bbox_max = object_bbox_max[:3, 0]\\n self.process_radius()\\n\\n print('Load data: End')\\n\\n def process_radius(self):\\n for img_idx in tqdm(range(self.images.shape[0])):\\n tx = torch.linspace(0, self.W - 1, self.W, device=self.device)\\n ty = torch.linspace(0, self.H - 1, self.H, device=self.device)\\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\\n rays_v = torch.matmul(self.intrinsics_all_inv[img_idx, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3\\n rays_v = torch.matmul(self.pose_all[img_idx, None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\\n # Cut the distance in half, and then round it out so that it's\\n # halfway between inscribed by / circumscribed about the pixel.\\n radii = dx[..., None] * 2 / np.sqrt(12)\\n self.radius[img_idx] = radii.detach().cpu() # W H 3\\n\\n def gen_rays_at(self, img_idx, resolution_level=1):\\n \\\"\\\"\\\"\\n Generate rays at world space from one camera.\\n \\\"\\\"\\\"\\n l = resolution_level\\n tx = torch.linspace(0, self.W - 1, self.W // l, device=self.device)\\n ty = torch.linspace(0, self.H - 1, self.H // l, device=self.device)\\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\\n rays_v = torch.matmul(self.intrinsics_all_inv[img_idx, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3\\n rays_v = torch.matmul(self.pose_all[img_idx, None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\\n rays_r = dx[..., None] * 2 / np.sqrt(12)\\n rays_o = self.pose_all[img_idx, None, None, :3, 3].expand(rays_v.shape) # W, H, 3\\n rays_v = rays_v / torch.linalg.norm(rays_v, ord=2, dim=-1, keepdim=True) # W, H, 3\\n return rays_o.transpose(0, 1), rays_v.transpose(0, 1), rays_r.transpose(0, 1)\\n\\n def gen_random_rays_at(self, img_idx, batch_size):\\n \\\"\\\"\\\"\\n Generate random rays at world space from one camera.\\n \\\"\\\"\\\"\\n error = self.errors[img_idx].reshape(-1).numpy()\\n max_error = np.max(error) + 1e-8\\n error = error / max_error\\n error[error < 0.1] = 0.1\\n error = error / np.sum(error)\\n index = np.arange(0, self.W*self.H // 64)\\n select_index = np.random.choice(index, size=[batch_size], p=error)\\n pixels_y = torch.LongTensor(select_index // (self.W // 8)) * 8\\n pixels_y += torch.randint_like(pixels_y, 8)\\n pixels_x = torch.LongTensor(select_index % (self.W // 8)) * 8\\n pixels_x += torch.randint_like(pixels_x, 8)\\n\\n color = self.images[img_idx][(pixels_y, pixels_x)] # batch_size, 3\\n mask = self.masks[img_idx][(pixels_y, pixels_x)] # batch_size, 3\\n rays_r = self.radius[img_idx][(pixels_x, pixels_y)] # batch_size, 1\\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1).float().to(self.device) # batch_size, 3\\n p = torch.matmul(self.intrinsics_all_inv[img_idx, None, :3, :3], p[:, :, None]).squeeze() # batch_size, 3\\n rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True) # batch_size, 3\\n rays_v = torch.matmul(self.pose_all[img_idx, None, :3, :3], rays_v[:, :, None]).squeeze() # batch_size, 3\\n rays_o = self.pose_all[img_idx, None, :3, 3].expand(rays_v.shape) # batch_size, 3\\n return torch.cat([rays_o.cpu(), rays_v.cpu(), color.cpu(), mask[:, :1].cpu(), rays_r.cpu()], dim=-1).cuda(), pixels_y.cpu(), pixels_x.cpu() # batch_size, 10\\n\\n def gen_rays_between(self, idx_0, idx_1, ratio, resolution_level=1):\\n \\\"\\\"\\\"\\n Interpolate pose between two cameras.\\n \\\"\\\"\\\"\\n l = resolution_level\\n tx = torch.linspace(0, self.W - 1, self.W // l, device=self.device)\\n ty = torch.linspace(0, self.H - 1, self.H // l, device=self.device)\\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\\n rays_v = torch.matmul(self.intrinsics_all_inv[0, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3\\n trans = self.pose_all[idx_0, :3, 3] * (1.0 - ratio) + self.pose_all[idx_1, :3, 3] * ratio\\n pose_0 = self.pose_all[idx_0].detach().cpu().numpy()\\n pose_1 = self.pose_all[idx_1].detach().cpu().numpy()\\n pose_0 = np.linalg.inv(pose_0)\\n pose_1 = np.linalg.inv(pose_1)\\n rot_0 = pose_0[:3, :3]\\n rot_1 = pose_1[:3, :3]\\n rots = Rot.from_matrix(np.stack([rot_0, rot_1]))\\n key_times = [0, 1]\\n slerp = Slerp(key_times, rots)\\n rot = slerp(ratio)\\n pose = np.diag([1.0, 1.0, 1.0, 1.0])\\n pose = pose.astype(np.float32)\\n pose[:3, :3] = rot.as_matrix()\\n pose[:3, 3] = ((1.0 - ratio) * pose_0 + ratio * pose_1)[:3, 3]\\n pose = np.linalg.inv(pose)\\n rot = torch.from_numpy(pose[:3, :3]).cuda()\\n trans = torch.from_numpy(pose[:3, 3]).cuda()\\n rays_v = torch.matmul(rot[None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\\n rays_r = dx[..., None] * 2 / np.sqrt(12)\\n rays_v = rays_v / torch.linalg.norm(rays_v, ord=2, dim=-1, keepdim=True) # W, H, 3\\n rays_o = trans[None, None, :3].expand(rays_v.shape) # W, H, 3\\n return rays_o.transpose(0, 1), rays_v.transpose(0, 1), rays_r.transpose(0, 1)\\n\\n def near_far_from_sphere(self, rays_o, rays_d):\\n if self.near > 0:\\n return self.near, self.far\\n a = torch.sum(rays_d**2, dim=-1, keepdim=True)\\n b = 2.0 * torch.sum(rays_o * rays_d, dim=-1, keepdim=True)\\n mid = 0.5 * (-b) / a\\n near = mid - 1.0\\n far = mid + 1.0\\n return near, far\\n\\n def image_at(self, idx, resolution_level):\\n img = cv.imread(self.images_lis[idx])\\n return (cv.resize(img, (self.W // resolution_level, self.H // resolution_level))).clip(0, 255)\"\n },\n {\n \"identifier\": \"BlenderDataset\",\n \"path\": \"models/dataset.py\",\n \"snippet\": \"class BlenderDataset:\\n def __init__(self, conf):\\n super(BlenderDataset, self).__init__()\\n print('Load data: Begin')\\n self.device = torch.device('cuda')\\n self.conf = conf\\n\\n self.near = conf.get_float('near', default=-1)\\n self.far = conf.get_float('far', default=-1)\\n self.n_frames = conf.get_int('n_frames', default=128)\\n\\n self.data_dir = conf.get_string('data_dir')\\n splits = ['train']\\n metas = {}\\n for s in splits:\\n with open(os.path.join(self.data_dir, 'transforms_{}.json'.format(s)), 'r') as fp:\\n metas[s] = json.load(fp)\\n self.images_lis = sorted(glob(os.path.join(self.data_dir, 'train/*.png')), key=lambda x: int(x.split('.')[0].split('_')[-1]))\\n # if self.data_dir.split('/')[-2] == 'lego':\\n # # self.images_lis = self.images_lis[1:]\\n # self.images_lis.append('/data00/yzy/Git_Project/data/dynamic/D-NeRF/lego/val/r_0.png')\\n all_imgs = []\\n all_poses = []\\n all_masks = []\\n all_times = []\\n counts = [0]\\n for s in splits:\\n meta = metas[s]\\n\\n imgs = []\\n poses = []\\n times = []\\n\\n for t, frame in enumerate(meta['frames']):\\n fname = os.path.join(self.data_dir, frame['file_path'] + '.png')\\n image = cv.imread(fname, cv.IMREAD_UNCHANGED)\\n imgs.append(image)\\n pose = np.array(frame['transform_matrix'])\\n time = np.array([frame['time']])\\n\\n a = pose[:, 0:1]\\n b = pose[:, 1:2]\\n c = pose[:, 2:3]\\n d = pose[:, 3:].copy()\\n d[:3, :] *= 0.8\\n\\n pose = np.concatenate([a, -b, -c, d], 1)\\n\\n poses.append(pose)\\n times.append(time)\\n\\n imgs = (np.array(imgs) / 255.).astype(np.float32) # keep all 4 channels (RGBA)\\n poses = np.array(poses).astype(np.float32)\\n times = np.array(times).astype(np.float32)\\n masks = (imgs[..., 3:] > 0).astype(np.float32)\\n imgs = imgs[..., :3]\\n counts.append(counts[-1] + imgs.shape[0])\\n all_imgs.append(imgs)\\n all_poses.append(poses)\\n all_masks.append(masks)\\n all_times.append(times)\\n\\n self.images = torch.from_numpy(np.concatenate(all_imgs, 0)).cpu()\\n self.masks = torch.from_numpy(np.concatenate(all_masks, 0)).cpu()\\n self.radius = torch.zeros(self.masks.shape[0], self.masks.shape[2], self.masks.shape[1], 1) # no use\\n self.errors = self.masks[:, :, :, :1].clone()\\n self.errors = F.interpolate(self.errors.permute(0, 3, 1, 2),\\n (self.images.shape[1] // 8, self.images.shape[2] // 8), mode='bilinear')\\n self.errors = F.max_pool2d(self.errors, 7, stride=1, padding=3)\\n self.errors = self.errors.permute(0, 2, 3, 1)\\n self.n_images = self.images.shape[0]\\n\\n self.fid_list = [torch.LongTensor(np.array([idx])) for idx in range(self.n_images)]\\n # if self.data_dir.split('/')[-2] == 'lego':\\n # self.fid_list[-1] = torch.LongTensor(np.array([0]))\\n self.pose_all = torch.from_numpy(np.concatenate(all_poses, 0)).to(self.device)\\n self.fid_all = torch.stack(self.fid_list).to(self.device)\\n self.time_emb_list = torch.from_numpy(np.concatenate(all_times, 0)).to(self.device)\\n\\n self.H, self.W = self.images[0].shape[:2]\\n self.image_pixels = self.H * self.W\\n\\n camera_angle_x = float(meta['camera_angle_x'])\\n self.focal = .5 * self.W / np.tan(.5 * camera_angle_x)\\n intrinsics = torch.Tensor(\\n [[self.focal, 0, 0.5 * self.W, 0],\\n [0, self.focal, 0.5 * self.H, 0],\\n [0, 0, 1, 0],\\n [0, 0, 0, 1]]).to(self.device)\\n self.intrinsics_all = intrinsics.expand(self.n_images, -1, -1)\\n self.intrinsics_all_inv = torch.inverse(self.intrinsics_all) # [n_images, 4, 4]\\n self.object_bbox_min = np.array([-1.01, -1.01, -1.01]) # hard code bbox\\n self.object_bbox_max = np.array([1.01, 1.01, 1.01])\\n self.process_radius()\\n\\n print('Load data: End')\\n\\n def process_radius(self):\\n for img_idx in tqdm(range(self.images.shape[0])):\\n tx = torch.linspace(0, self.W - 1, self.W, device=self.device)\\n ty = torch.linspace(0, self.H - 1, self.H, device=self.device)\\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\\n rays_v = torch.matmul(self.intrinsics_all_inv[img_idx, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3\\n rays_v = torch.matmul(self.pose_all[img_idx, None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\\n # Cut the distance in half, and then round it out so that it's\\n # halfway between inscribed by / circumscribed about the pixel.\\n radii = dx[..., None] * 2 / np.sqrt(12)\\n self.radius[img_idx] = radii.detach().cpu() # W H 3\\n\\n def gen_rays_at(self, img_idx, resolution_level=1):\\n \\\"\\\"\\\"\\n Generate rays at world space from one camera.\\n \\\"\\\"\\\"\\n l = resolution_level\\n tx = torch.linspace(0, self.W - 1, self.W // l, device=self.device)\\n ty = torch.linspace(0, self.H - 1, self.H // l, device=self.device)\\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\\n rays_v = torch.matmul(self.intrinsics_all_inv[img_idx, None, None, :3, :3],\\n p[:, :, :, None]).squeeze() # W, H, 3\\n rays_v = torch.matmul(self.pose_all[img_idx, None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\\n rays_r = dx[..., None] * 2 / np.sqrt(12)\\n rays_o = self.pose_all[img_idx, None, None, :3, 3].expand(rays_v.shape) # W, H, 3\\n rays_v = rays_v / torch.linalg.norm(rays_v, ord=2, dim=-1, keepdim=True) # W, H, 3\\n return rays_o.transpose(0, 1), rays_v.transpose(0, 1), rays_r.transpose(0, 1)\\n\\n def gen_random_rays_at(self, img_idx, batch_size):\\n \\\"\\\"\\\"\\n Generate random rays at world space from one camera.\\n \\\"\\\"\\\"\\n error = self.errors[img_idx].reshape(-1).numpy()\\n max_error = np.max(error) + 1e-8\\n error = error / max_error\\n error[error < 0.1] = 0.1\\n error = error / np.sum(error)\\n index = np.arange(0, self.W * self.H // 64)\\n select_index = np.random.choice(index, size=[batch_size], p=error)\\n pixels_y = torch.LongTensor(select_index // (self.W // 8)) * 8\\n pixels_y += torch.randint_like(pixels_y, 8)\\n pixels_x = torch.LongTensor(select_index % (self.W // 8)) * 8\\n pixels_x += torch.randint_like(pixels_x, 8)\\n\\n color = self.images[img_idx][(pixels_y, pixels_x)] # batch_size, 3\\n mask = self.masks[img_idx][(pixels_y, pixels_x)] # batch_size, 3\\n rays_r = self.radius[img_idx][(pixels_x, pixels_y)] # batch_size, 1\\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1).float().to(\\n self.device) # batch_size, 3\\n p = torch.matmul(self.intrinsics_all_inv[img_idx, None, :3, :3], p[:, :, None]).squeeze() # batch_size, 3\\n rays_v = p / torch.linalg.norm(p, ord=2, dim=-1, keepdim=True) # batch_size, 3\\n rays_v = torch.matmul(self.pose_all[img_idx, None, :3, :3], rays_v[:, :, None]).squeeze() # batch_size, 3\\n rays_o = self.pose_all[img_idx, None, :3, 3].expand(rays_v.shape) # batch_size, 3\\n return torch.cat([rays_o.cpu(), rays_v.cpu(), color.cpu(), mask[:, :1].cpu(), rays_r.cpu()],\\n dim=-1).cuda(), pixels_y.cpu(), pixels_x.cpu() # batch_size, 10\\n\\n def gen_rays_between(self, idx_0, idx_1, ratio, resolution_level=1):\\n \\\"\\\"\\\"\\n Interpolate pose between two cameras.\\n \\\"\\\"\\\"\\n l = resolution_level\\n tx = torch.linspace(0, self.W - 1, self.W // l, device=self.device)\\n ty = torch.linspace(0, self.H - 1, self.H // l, device=self.device)\\n pixels_x, pixels_y = torch.meshgrid(tx, ty)\\n p = torch.stack([pixels_x, pixels_y, torch.ones_like(pixels_y)], dim=-1) # W, H, 3\\n rays_v = torch.matmul(self.intrinsics_all_inv[0, None, None, :3, :3], p[:, :, :, None]).squeeze() # W, H, 3\\n trans = self.pose_all[idx_0, :3, 3] * (1.0 - ratio) + self.pose_all[idx_1, :3, 3] * ratio\\n pose_0 = self.pose_all[idx_0].detach().cpu().numpy()\\n pose_1 = self.pose_all[idx_1].detach().cpu().numpy()\\n pose_0 = np.linalg.inv(pose_0)\\n pose_1 = np.linalg.inv(pose_1)\\n rot_0 = pose_0[:3, :3]\\n rot_1 = pose_1[:3, :3]\\n rots = Rot.from_matrix(np.stack([rot_0, rot_1]))\\n key_times = [0, 1]\\n slerp = Slerp(key_times, rots)\\n rot = slerp(ratio)\\n pose = np.diag([1.0, 1.0, 1.0, 1.0])\\n pose = pose.astype(np.float32)\\n pose[:3, :3] = rot.as_matrix()\\n pose[:3, 3] = ((1.0 - ratio) * pose_0 + ratio * pose_1)[:3, 3]\\n pose = np.linalg.inv(pose)\\n rot = torch.from_numpy(pose[:3, :3]).cuda()\\n trans = torch.from_numpy(pose[:3, 3]).cuda()\\n rays_v = torch.matmul(rot[None, None, :3, :3], rays_v[:, :, :, None]).squeeze() # W, H, 3\\n dx = torch.sqrt(torch.sum((rays_v[:-1, :, :] - rays_v[1:, :, :]) ** 2, dim=-1))\\n dx = torch.cat([dx, dx[-2:-1, :]], dim=0)\\n rays_r = dx[..., None] * 2 / np.sqrt(12)\\n rays_v = rays_v / torch.linalg.norm(rays_v, ord=2, dim=-1, keepdim=True) # W, H, 3\\n rays_o = trans[None, None, :3].expand(rays_v.shape) # W, H, 3\\n return rays_o.transpose(0, 1), rays_v.transpose(0, 1), rays_r.transpose(0, 1)\\n\\n def near_far_from_sphere(self, rays_o, rays_d):\\n if self.near > 0:\\n return self.near, self.far\\n a = torch.sum(rays_d ** 2, dim=-1, keepdim=True)\\n b = 2.0 * torch.sum(rays_o * rays_d, dim=-1, keepdim=True)\\n mid = 0.5 * (-b) / a\\n near = mid - 1.0\\n far = mid + 1.0\\n return near, far\\n\\n def image_at(self, idx, resolution_level):\\n img = cv.imread(self.images_lis[idx])\\n return (cv.resize(img, (self.W // resolution_level, self.H // resolution_level))).clip(0, 255)\"\n },\n {\n \"identifier\": \"RenderingNetwork\",\n \"path\": \"models/fields.py\",\n \"snippet\": \"class RenderingNetwork(nn.Module):\\n def __init__(self,\\n d_feature,\\n mode,\\n d_in,\\n d_out,\\n d_hidden,\\n n_layers,\\n weight_norm=True,\\n multires_view=0,\\n squeeze_out=True):\\n super().__init__()\\n\\n self.mode = mode\\n self.squeeze_out = squeeze_out\\n dims = [d_in + d_feature] + [d_hidden for _ in range(n_layers)] + [d_out]\\n\\n self.embedview_fn = None\\n if multires_view > 0:\\n embedview_fn, input_ch = get_embedder(multires_view)\\n self.embedview_fn = embedview_fn\\n dims[0] += (input_ch - 3)\\n\\n self.num_layers = len(dims)\\n\\n for l in range(0, self.num_layers - 1):\\n out_dim = dims[l + 1]\\n lin = nn.Linear(dims[l], out_dim)\\n if weight_norm:\\n lin = nn.utils.weight_norm(lin)\\n\\n setattr(self, \\\"lin\\\" + str(l), lin)\\n\\n self.relu = nn.ReLU()\\n\\n self.mask = -torch.ones((1, 1, 256, 256, 256)).float().cuda()\\n \\n\\n def forward(self, points, normals, view_dirs, feature_vectors):\\n if self.embedview_fn is not None:\\n view_dirs = self.embedview_fn(view_dirs)\\n\\n rendering_input = NoOptionError\\n\\n if self.mode == 'idr':\\n rendering_input = torch.cat([points, view_dirs, normals, feature_vectors], dim=-1)\\n elif self.mode == 'no_view_dir':\\n rendering_input = torch.cat([points, normals, feature_vectors], dim=-1)\\n elif self.mode == 'no_normal':\\n rendering_input = torch.cat([points, view_dirs, feature_vectors], dim=-1)\\n\\n x = rendering_input\\n for l in range(0, self.num_layers - 1):\\n lin = getattr(self, \\\"lin\\\" + str(l))\\n\\n x = lin(x)\\n\\n if l < self.num_layers - 2:\\n x = self.relu(x)\\n\\n if self.squeeze_out:\\n x = torch.sigmoid(x)\\n return x\"\n },\n {\n \"identifier\": \"FieldNetwork\",\n \"path\": \"models/fields.py\",\n \"snippet\": \"class FieldNetwork(nn.Module):\\n def __init__(self,\\n d_in,\\n d_out,\\n d_hidden,\\n d_t4d,\\n min_emb,\\n max_emb,\\n n_layers,\\n t_emb=-1,\\n skip_in=(4,),\\n bias=0.5,\\n geometric_init=True,\\n weight_norm=True):\\n super(FieldNetwork, self).__init__()\\n\\n dims = [d_in] + [d_hidden for _ in range(n_layers)] + [d_out]\\n dims[0] = d_in + (max_emb - min_emb)*3*2\\n\\n self.num_layers = len(dims)\\n self.skip_in = skip_in\\n self.min_emb = min_emb\\n self.max_emb = max_emb\\n self.t_emb = t_emb\\n\\n if t_emb > 0:\\n embed_fn, time_input_ch = get_embedder(t_emb, input_dims=1)\\n self.embed_fn = embed_fn\\n dims[0] += time_input_ch\\n\\n for l in range(0, self.num_layers - 1):\\n if l in self.skip_in:\\n in_dim = dims[l] + dims[0] + d_t4d\\n else:\\n in_dim = dims[l]\\n out_dim = dims[l+1]\\n\\n lin = nn.Linear(in_dim, out_dim)\\n \\n if geometric_init:\\n if l == self.num_layers - 2:\\n torch.nn.init.normal_(lin.weight, mean=np.sqrt(np.pi) / np.sqrt(dims[l]), std=0.0001)\\n torch.nn.init.constant_(lin.bias, -bias)\\n elif l == 0:\\n torch.nn.init.constant_(lin.bias, 0.0)\\n torch.nn.init.constant_(lin.weight[:, 3:], 0.0)\\n torch.nn.init.normal_(lin.weight[:, :3], 0.0, np.sqrt(2) / np.sqrt(out_dim))\\n elif l in self.skip_in:\\n torch.nn.init.constant_(lin.bias, 0.0)\\n torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim))\\n torch.nn.init.constant_(lin.weight[:, -(dims[0] + d_t4d):], 0.0)\\n else:\\n torch.nn.init.constant_(lin.bias, 0.0)\\n torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim))\\n\\n if weight_norm:\\n lin = nn.utils.weight_norm(lin)\\n\\n setattr(self, \\\"lin\\\" + str(l), lin)\\n\\n self.activation = nn.Softplus(beta=100)\\n\\n def set_tensor4d(self, tensor4d):\\n self.tensor4d = tensor4d\\n\\n def forward(self, mean, cov, fid, time_emb, reg_l2=False):\\n cones_embedding = integrated_pos_enc((mean[:, None, :], cov[:, None, :]), self.min_emb, self.max_emb, diagonal=True).reshape(mean.shape[0], -1)\\n inputs = mean\\n tri_feat = self.tensor4d(inputs, fid, torch.mean(time_emb))\\n\\n if reg_l2:\\n d_vec = F.normalize(torch.randn_like(inputs), dim=-1) * 1e-3\\n d_tri_feat = self.tensor4d(inputs + d_vec, fid, torch.mean(time_emb))\\n pred_reg_l2 = (d_tri_feat - tri_feat)**2\\n \\n xyz = inputs\\n if self.t_emb > 0:\\n time_input = self.embed_fn(time_emb)\\n x = torch.cat([xyz, cones_embedding, time_input], 1)\\n else:\\n x = torch.cat([xyz, cones_embedding], 1)\\n\\n for l in range(0, self.num_layers - 1):\\n lin = getattr(self, \\\"lin\\\" + str(l))\\n \\n if l in self.skip_in:\\n if self.t_emb > 0:\\n x = torch.cat([x, tri_feat, xyz, cones_embedding, time_input], 1) / np.sqrt(2)\\n else:\\n x = torch.cat([x, tri_feat, xyz, cones_embedding], 1) / np.sqrt(2)\\n x = lin(x)\\n\\n if l < self.num_layers - 2:\\n x = self.activation(x)\\n if reg_l2:\\n return x, pred_reg_l2\\n return x\"\n },\n {\n \"identifier\": \"SingleVarianceNetwork\",\n \"path\": \"models/fields.py\",\n \"snippet\": \"class SingleVarianceNetwork(nn.Module):\\n def __init__(self, init_val):\\n super(SingleVarianceNetwork, self).__init__()\\n init_tensor = torch.zeros(120)\\n init_tensor[:] = init_val\\n self.register_parameter('variance', nn.Parameter(init_tensor))\\n\\n def forward(self, x):\\n return torch.ones([len(x), 1], device=x.device) * torch.exp(self.variance[0] * 10.0)\"\n },\n {\n \"identifier\": \"Tensor4D\",\n \"path\": \"models/tensor4d.py\",\n \"snippet\": \"class Tensor4D(nn.Module):\\n def __init__(self, feature_type, lr_resolution, hr_resolution, image_guide=False, image_guide_interval=2, image_guide_base=16) -> None:\\n super(Tensor4D, self).__init__()\\n \\n self.data_dims = 0\\n self.feature_type = feature_type\\n if feature_type == '3d':\\n self.feature_plane = SpacePlane(lr_resolution, hr_resolution)\\n self.data_dims = self.feature_plane.dims\\n elif feature_type == '4d':\\n self.feature_plane = TimeSpacePlane(lr_resolution, hr_resolution)\\n self.data_dims = self.feature_plane.dims\\n\\n self.img_dims = 0\\n self.image_guide = image_guide\\n if image_guide:\\n self.conv_net = ConvNet(image_guide_base)\\n self.img_dims = image_guide_base*8*2\\n self.ig_interval = image_guide_interval\\n\\n if feature_type == '4d':\\n self.compress_network = CompressNetwork(self.data_dims, self.data_dims // 3)\\n self.compress_list = [self.compress_network.compress1, self.compress_network.compress2, self.compress_network.compress3]\\n\\n self.dims = self.data_dims + self.img_dims\\n self.matMode = torch.BoolTensor([[0, 1, 1], [1, 0, 1], [1, 1, 0]]).cuda()\\n self.vecMode = torch.BoolTensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]]).cuda()\\n \\n def get_data_parameters(self):\\n return list(self.feature_plane.parameters())\\n \\n def get_network_parameters(self):\\n params = []\\n if self.feature_type == '4d':\\n params += list(self.compress_network.parameters())\\n if self.image_guide:\\n params += list(self.conv_net.parameters())\\n return params\\n\\n def set_images(self, image, proj):\\n step = self.ig_interval\\n select_proj = torch.cat([proj[i*step:i*step+1] for i in range(proj.shape[0] // step)], dim=0)\\n self.proj = select_proj\\n self.img_shape = image.shape\\n select_image = torch.cat([image[i*step:i*step+1] for i in range(image.shape[0] // step)], dim=0)\\n self.image_feature, self.image_feature_hr = self.conv_net(F.interpolate(select_image.permute(0, 3, 1, 2), size=(1024, 1024)))\\n\\n def forward(self, xyz_sampled_ori, fid, time_emb):\\n sigma_feature_list = [] \\n\\n if self.image_guide:\\n proj_pts = ((self.proj[:, :3, :3] @ xyz_sampled_ori.T.unsqueeze(0)) + self.proj[:, :3, 3:]).transpose(1, 2)\\n proj_xy = proj_pts[:, :, :2] / (proj_pts[:, :, 2:] + 1e-6)\\n B, H, W, C = self.img_shape\\n proj_xy[:, :, 0] = (proj_xy[:, :, 0] - W / 2) / (W / 2)\\n proj_xy[:, :, 1] = (proj_xy[:, :, 1] - H / 2) / (H / 2)\\n N = self.image_feature.shape[0]\\n img_feature = grid_sample(self.image_feature, proj_xy.reshape(N, -1, 1, 2)).reshape(N, -1, xyz_sampled_ori.shape[0])\\n img_feature_cost = torch.sqrt(torch.sum((img_feature - torch.sum(img_feature, dim=0).unsqueeze(0) / N)**2, dim=0) / N + 1e-8)\\n img_feature_max = torch.mean(img_feature, dim=0) + torch.max(img_feature, dim=0)[0]\\n image_feature_hr = grid_sample(self.image_feature_hr, proj_xy.reshape(N, -1, 1, 2)).reshape(N, -1, xyz_sampled_ori.shape[0])\\n image_feature_hr_cost = torch.sqrt(torch.sum((image_feature_hr - torch.sum(image_feature_hr, dim=0).unsqueeze(0) / N)**2, dim=0) / N + 1e-8)\\n image_feature_hr_max = torch.mean(image_feature_hr, dim=0) + torch.max(image_feature_hr, dim=0)[0]\\n sigma_feature_list = [img_feature_cost, img_feature_max, image_feature_hr_cost, image_feature_hr_max]\\n \\n xyz_sampled = xyz_sampled_ori\\n scale = 1.0\\n matMode = self.matMode\\n coordinate_plane = torch.stack((xyz_sampled[..., matMode[0]] * scale, xyz_sampled[..., matMode[1]] * scale, xyz_sampled[..., matMode[2]] * scale)).view(3, -1, 1, 2)\\n\\n for idx_plane in range(3):\\n sample_points = coordinate_plane[[idx_plane]]\\n plane_coef_point = self.feature_plane.sample(sample_points, idx_plane, time_emb).view(-1, *xyz_sampled.shape[:1])\\n if self.feature_type == '4d':\\n plane_coef_point = self.compress_list[idx_plane](plane_coef_point.T).T\\n sigma_feature_list.append(plane_coef_point)\\n \\n sigma_feature_list = torch.cat(sigma_feature_list, dim=0)\\n # print(sigma_feature_list.shape)\\n return sigma_feature_list.T\"\n },\n {\n \"identifier\": \"NeuSRenderer\",\n \"path\": \"models/renderer.py\",\n \"snippet\": \"class NeuSRenderer:\\n def __init__(self,\\n sdf_network,\\n deviation_network,\\n color_network,\\n mask3d,\\n n_samples,\\n n_importance,\\n n_outside,\\n up_sample_steps,\\n perturb,\\n reg_l2=False,\\n mip_render=False,\\n flow_network=None):\\n \\n self.sdf_network = sdf_network\\n self.deviation_network = deviation_network\\n self.color_network = color_network\\n self.mask3d = mask3d\\n self.n_samples = n_samples\\n self.n_importance = n_importance\\n self.n_outside = n_outside\\n self.up_sample_steps = up_sample_steps\\n self.perturb = perturb\\n self.reg_l2 = reg_l2\\n self.flow_network = flow_network\\n self.mip_render = mip_render\\n\\n def mask_query_geometry(self, mean, cov, only_sdf=False):\\n fid = self.fid\\n time_emb = self.time_emb\\n time_input = time_emb.expand(mean[:, :1].shape)\\n space_time_input = torch.cat([mean, time_input], dim=-1)\\n if not only_sdf:\\n space_time_input.requires_grad_(True)\\n inputs = space_time_input[:, :3]\\n time_emb = space_time_input[:, 3:]\\n N, _ = inputs.shape\\n grads = torch.zeros((N, 4), device=inputs.device)\\n sdf_nn = torch.zeros((N, 257), device=inputs.device)\\n\\n reg_l2 = torch.zeros((N, self.sdf_network.tensor4d.dims), device=inputs.device)\\n grads[:, 0] = 1\\n sdf_nn[:, 0] = -10\\n\\n mask = self.mask3d.valid_input(inputs, fid)\\n if torch.sum(mask) == 0:\\n results = {\\n 'sdf_nn': sdf_nn,\\n 'grads': grads[:, :3],\\n 'time_grads': grads[:, 3:],\\n 'pts_mask': mask,\\n 'reg_l2': reg_l2\\n }\\n return results\\n mask_mean = inputs[mask, :]\\n mask_time_emb = time_emb[mask, :]\\n mask_cov = cov[mask, :]\\n \\n if self.flow_network is not None:\\n mask_cov = torch.zeros_like(mask_mean) # flow mode, disable mip_render\\n if fid != 0:\\n pred_flow = self.flow_network(mask_mean, mask_cov, fid, mask_time_emb, reg_l2=False)\\n mask_mean = mask_mean + pred_flow\\n elif not self.mip_render:\\n mask_cov = torch.zeros_like(mask_mean)\\n\\n if (not only_sdf) and self.reg_l2:\\n pred_sdf_nn, pred_reg_l2 = self.sdf_network(mask_mean, mask_cov, fid, mask_time_emb, reg_l2=True)\\n reg_l2[mask] = pred_reg_l2\\n else:\\n pred_sdf_nn = self.sdf_network(mask_mean, mask_cov, fid, mask_time_emb, reg_l2=False)\\n\\n if not only_sdf:\\n pred_sdf = pred_sdf_nn[:, :1]\\n d_output = torch.ones_like(pred_sdf, requires_grad=False, device=pred_sdf.device)\\n gradients = torch.autograd.grad(\\n outputs=pred_sdf,\\n inputs=space_time_input,\\n grad_outputs=d_output,\\n create_graph=True,\\n retain_graph=True,\\n only_inputs=True)[0]\\n grads[mask] = gradients.reshape(-1, 4)[mask]\\n \\n sdf_nn[mask] = pred_sdf_nn\\n results = {\\n 'sdf_nn': sdf_nn,\\n 'grads': grads[:, :3],\\n 'time_grads': grads[:, 3:],\\n 'pts_mask': mask,\\n 'reg_l2': reg_l2\\n }\\n return results\\n\\n def mask_query_color(self, pts, mask, normals, view_dirs, features):\\n N, _ = pts.shape\\n out = torch.zeros((N, 3), device=pts.device)\\n if torch.sum(mask) > 0:\\n x = self.color_network(pts[mask], normals[mask], view_dirs[mask], features[mask])\\n out[mask] = x\\n return out\\n else:\\n return torch.zeros((N, 3), device=pts.device)\\n\\n def up_sample(self, rays_o, rays_d, z_vals, sdf, n_importance, inv_s, pts_mask):\\n \\\"\\\"\\\"\\n Up sampling give a fixed inv_s\\n \\\"\\\"\\\"\\n batch_size, n_samples = z_vals.shape\\n pts = rays_o[:, None, :] + rays_d[:, None, :] * z_vals[..., :, None] # n_rays, n_samples, 3\\n radius = torch.linalg.norm(pts, ord=2, dim=-1, keepdim=False)\\n inside_sphere = (radius[:, :-1] < 1.0) | (radius[:, 1:] < 1.0)\\n sdf = sdf.reshape(batch_size, n_samples)\\n prev_sdf, next_sdf = sdf[:, :-1], sdf[:, 1:]\\n prev_mask, next_mask = pts_mask[:, :-1], pts_mask[:, 1:]\\n mid_mask = torch.logical_and(prev_mask, next_mask)\\n prev_z_vals, next_z_vals = z_vals[:, :-1], z_vals[:, 1:]\\n mid_sdf = (prev_sdf + next_sdf) * 0.5\\n cos_val = (next_sdf - prev_sdf) / (next_z_vals - prev_z_vals + 1e-5)\\n\\n # ----------------------------------------------------------------------------------------------------------\\n # Use min value of [ cos, prev_cos ]\\n # Though it makes the sampling (not rendering) a little bit biased, this strategy can make the sampling more\\n # robust when meeting situations like below:\\n #\\n # SDF\\n # ^\\n # |\\\\ -----x----...\\n # | \\\\ /\\n # | x x\\n # |---\\\\----/-------------> 0 level\\n # | \\\\ /\\n # | \\\\/\\n # |\\n # ----------------------------------------------------------------------------------------------------------\\n prev_cos_val = torch.cat([torch.zeros([batch_size, 1], device=sdf.device), cos_val[:, :-1]], dim=-1)\\n cos_val = torch.stack([prev_cos_val, cos_val], dim=-1)\\n cos_val, _ = torch.min(cos_val, dim=-1, keepdim=False)\\n cos_val = cos_val.clip(-1e3, 0.0) * inside_sphere\\n\\n dist = (next_z_vals - prev_z_vals)\\n prev_esti_sdf = mid_sdf - cos_val * dist * 0.5\\n next_esti_sdf = mid_sdf + cos_val * dist * 0.5\\n prev_cdf = torch.sigmoid(prev_esti_sdf * inv_s)\\n next_cdf = torch.sigmoid(next_esti_sdf * inv_s)\\n\\n alpha = (prev_cdf - next_cdf + 1e-5) / (prev_cdf + 1e-5)\\n alpha[~mid_mask] = 0\\n alpha = alpha.clamp(0.0, 1.0)\\n \\n alpha = torch.cat([alpha, torch.zeros([batch_size, 1], device=alpha.device)], dim=-1)\\n weights = alpha * torch.cumprod(\\n torch.cat([torch.ones([batch_size, 1], device=alpha.device), 1. - alpha + 1e-7], -1), -1)[:, :-1]\\n\\n z_samples = sample_pdf(z_vals, weights, n_importance, det=True).detach()\\n return z_samples\\n\\n def cat_z_vals(self, rays_o, rays_d, z_vals, new_z_vals, sdf, pts_mask, last=False):\\n batch_size, n_samples = z_vals.shape\\n _, n_importance = new_z_vals.shape\\n pts = rays_o[:, None, :] + rays_d[:, None, :] * new_z_vals[..., :, None]\\n z_vals = torch.cat([z_vals, new_z_vals], dim=-1)\\n z_vals, index = torch.sort(z_vals, dim=-1)\\n if not last:\\n new_sdf, new_pts_mask = self.sdf_network.sdf(pts.reshape(-1, 3), rt_mask=True)\\n new_sdf = new_sdf.reshape(batch_size, n_importance)\\n new_pts_mask = new_pts_mask.reshape(batch_size, n_importance)\\n sdf = torch.cat([sdf, new_sdf], dim=-1)\\n pts_mask = torch.cat([pts_mask, new_pts_mask], dim=-1)\\n xx = torch.arange(batch_size)[:, None].expand(batch_size, n_samples + n_importance).reshape(-1)\\n index = index.reshape(-1)\\n sdf = sdf[(xx, index)].reshape(batch_size, n_samples + n_importance)\\n pts_mask = pts_mask[(xx, index)].reshape(batch_size, n_samples + n_importance)\\n\\n return z_vals, sdf, pts_mask\\n\\n def render_core(self,\\n rays_o,\\n rays_d,\\n rays_r,\\n z_vals,\\n sample_dist,\\n background_alpha=None,\\n background_sampled_color=None,\\n background_rgb=None,\\n cos_anneal_ratio=0.0):\\n batch_size, n_samples = z_vals[:, :-1].shape\\n\\n # Section length\\n dists = z_vals[..., 1:] - z_vals[..., :-1]\\n cat_dists = torch.cat([dists, torch.Tensor([sample_dist]).to(dists.device).expand(dists[..., :1].shape)], -1)\\n mid_z_vals = z_vals + cat_dists * 0.5\\n\\n cones = cast_rays(z_vals, rays_o, rays_d, rays_r, 'cone', diagonal=True)\\n dirs = rays_d[:, None, :].expand(cones[0].shape)\\n dirs = dirs.reshape(-1, 3)\\n\\n results = self.mask_query_geometry(cones[0].reshape(-1, 3), cones[1].reshape(-1, 3))\\n sdf_nn_output, gradients, t_grads, pts_mask = results['sdf_nn'], results['grads'], results['time_grads'], results['pts_mask']\\n sdf = sdf_nn_output[:, :1]\\n feature_vector = sdf_nn_output[:, 1:]\\n\\n gradients = gradients.squeeze()\\n sampled_color = self.mask_query_color(cones[0].reshape(-1, 3), pts_mask, gradients, dirs, feature_vector).reshape(batch_size, n_samples, 3)\\n \\n inv_s = self.deviation_network(torch.zeros([1, 3], device=sdf.device))[:, :1].clip(1e-6, 1e6) # Single parameter\\n inv_s = inv_s.expand(batch_size * n_samples, 1)\\n\\n true_cos = (dirs * gradients).sum(-1, keepdim=True)\\n\\n # \\\"cos_anneal_ratio\\\" grows from 0 to 1 in the beginning training iterations. The anneal strategy below makes\\n # the cos value \\\"not dead\\\" at the beginning training iterations, for better convergence.\\n iter_cos = -(F.relu(-true_cos * 0.5 + 0.5) * (1.0 - cos_anneal_ratio) +\\n F.relu(-true_cos) * cos_anneal_ratio) # always non-positive\\n\\n # Estimate signed distances at section points\\n estimated_next_sdf = sdf + iter_cos * dists.reshape(-1, 1) * 0.5\\n estimated_prev_sdf = sdf - iter_cos * dists.reshape(-1, 1) * 0.5\\n\\n prev_cdf = torch.sigmoid(estimated_prev_sdf * inv_s)\\n next_cdf = torch.sigmoid(estimated_next_sdf * inv_s)\\n\\n p = prev_cdf - next_cdf\\n c = prev_cdf\\n\\n alpha = ((p + 1e-5) / (c + 1e-5))\\n \\n alpha[~pts_mask] = 0\\n alpha = alpha.reshape(batch_size, n_samples).clip(0.0, 1.0)\\n \\n weights = alpha * torch.cumprod(torch.cat([torch.ones([batch_size, 1], device=alpha.device), 1. - alpha + 1e-7], -1), -1)[:, :-1]\\n weights_sum = weights.sum(dim=-1, keepdim=True)\\n \\n color = (sampled_color * weights[:, :, None]).sum(dim=1)\\n if background_rgb is not None: # Fixed background, usually black\\n color = color + background_rgb * (1.0 - weights_sum)\\n\\n # Eikonal loss\\n gradient_error = torch.mean((torch.linalg.norm(gradients.reshape(batch_size, n_samples, 3), ord=2,\\n dim=-1) - 1.0) ** 2)\\n time_grad_error = torch.mean(t_grads**2)\\n return {\\n 'color': color,\\n 'sdf': sdf,\\n 'pts_mask': pts_mask,\\n 'dists': dists,\\n 'gradients': gradients.reshape(batch_size, n_samples, 3),\\n 's_val': 1.0 / inv_s,\\n 'mid_z_vals': mid_z_vals,\\n 'weights': weights,\\n 'gradient_error': gradient_error,\\n 'time_grad_error': time_grad_error,\\n 'reg_l2': results['reg_l2'].reshape(batch_size, n_samples, -1),\\n }\\n\\n def render(self, rays_o, rays_d, rays_r, near, far, fid, time_emb, perturb_overwrite=-1, background_rgb=None, cos_anneal_ratio=0.0):\\n self.fid = fid\\n self.time_emb = time_emb\\n self.mask3d.set_fid(fid)\\n\\n batch_size = len(rays_o)\\n sample_dist = 2.0 / self.n_samples # Assuming the region of interest is a unit sphere\\n z_vals = torch.linspace(0.0, 1.0, self.n_samples, device=rays_o.device)\\n z_vals = near + (far - near) * z_vals[None, :]\\n\\n z_vals_outside = None\\n \\n n_samples = self.n_samples\\n perturb = self.perturb\\n\\n if perturb_overwrite >= 0:\\n perturb = perturb_overwrite\\n if perturb > 0:\\n t_rand = (torch.rand([batch_size, 1], device=z_vals.device) - 0.5)\\n z_vals = z_vals + t_rand * 2.0 / self.n_samples\\n\\n background_alpha = None\\n background_sampled_color = None\\n\\n # Up sample\\n if self.n_importance > 0:\\n with torch.no_grad():\\n cast_z_vals = torch.cat([z_vals, z_vals[:, -1:]], dim=1)\\n cones = cast_rays(cast_z_vals, rays_o, rays_d, rays_r, 'cone', diagonal=True)\\n results = self.mask_query_geometry(cones[0].reshape(-1, 3), cones[1].reshape(-1, 3), only_sdf=True)\\n sdf, pts_mask = results['sdf_nn'][:, :1], results['pts_mask']\\n # sdf, pts_mask = self.sdf_network.sdf(pts.reshape(-1, 3), rt_mask=True)\\n sdf = sdf.reshape(batch_size, self.n_samples)\\n pts_mask = pts_mask.reshape(batch_size, self.n_samples)\\n for i in range(self.up_sample_steps):\\n new_z_vals = self.up_sample(rays_o,\\n rays_d,\\n z_vals,\\n sdf,\\n self.n_importance // self.up_sample_steps + 1,\\n 64 * 2**i, pts_mask)\\n z_vals, sdf, pts_mask = self.cat_z_vals(rays_o,\\n rays_d,\\n z_vals,\\n new_z_vals,\\n sdf, pts_mask,\\n last=(i + 1 == self.up_sample_steps))\\n\\n n_samples = self.n_samples + self.n_importance\\n\\n background_alpha = None\\n background_sampled_color = None\\n sample_dist = 1e-2\\n\\n # Render core\\n ret_fine = self.render_core(rays_o,\\n rays_d,\\n rays_r,\\n z_vals,\\n sample_dist,\\n background_rgb=background_rgb,\\n background_alpha=background_alpha,\\n background_sampled_color=background_sampled_color,\\n cos_anneal_ratio=cos_anneal_ratio)\\n\\n\\n return {\\n 'color_fine': ret_fine['color'],\\n 's_val': ret_fine['s_val'].reshape(batch_size, n_samples).mean(dim=-1, keepdim=True),\\n 'mid_z_vals': ret_fine['mid_z_vals'],\\n 'weights': ret_fine['weights'],\\n 'weight_sum': ret_fine['weights'].sum(dim=-1, keepdim=True),\\n 'weight_max': torch.max(ret_fine['weights'], dim=-1, keepdim=True)[0],\\n 'gradients': ret_fine['gradients'],\\n 'gradient_error': ret_fine['gradient_error'],\\n 'time_grad_error': ret_fine['time_grad_error'],\\n 'reg_l2': ret_fine['reg_l2']\\n }\"\n },\n {\n \"identifier\": \"Mask3D\",\n \"path\": \"models/mask.py\",\n \"snippet\": \"class Mask3D:\\n def __init__(self, mask_type, num_frames=None, mask_reso=None, device=None):\\n self.mask_type = mask_type # 'bounding or visualhull'\\n if mask_type == 'visualhull':\\n self.R = mask_reso\\n self.mask = torch.ones([num_frames, self.R, self.R, self.R]).float()\\n self.device = device\\n self.current_fid = -1\\n self.current_mask = None\\n\\n def set_fid(self, fid):\\n if fid != self.current_fid:\\n self.current_fid = fid\\n if self.mask_type == 'visualhull':\\n self.current_mask = self.mask[fid.cpu()].to(self.device)\\n \\n def valid_input(self, pts, fid):\\n with torch.no_grad():\\n pts = pts.reshape(1, -1, 1, 1, 3)\\n pts_max = torch.max(pts, dim=-1)[0]\\n pts_min = torch.min(pts, dim=-1)[0]\\n mask_max = (pts_max > 1).reshape(-1)\\n mask_min = (pts_min < -1).reshape(-1)\\n if self.mask_type == 'visualhull':\\n R = self.R\\n sigma = F.grid_sample(self.current_mask.view(1, 1, R, R, R), pts, mode='bilinear', padding_mode='border').reshape(-1)\\n calc_mask = sigma < 0.05\\n else:\\n calc_mask = torch.ones_like(mask_max)\\n calc_mask[mask_max] = 0\\n calc_mask[mask_min] = 0\\n return calc_mask\\n\\n def visualhull(self, pts_ori, projs, masks, g_nums):\\n cam_nums = projs.shape[0]\\n interval = 1\\n pts_mask = torch.zeros(pts_ori.shape[0], g_nums)\\n out_mask = torch.zeros(pts_ori.shape[0])\\n N, H, W, C = masks.shape\\n for gp in range(cam_nums // (g_nums*interval)):\\n for j in range(g_nums):\\n i = j + gp*(g_nums*interval)\\n mask = masks[i, :, :, :1].permute(2, 0, 1).unsqueeze(0).clone()\\n mask = torch.max_pool2d(mask, 7, 1, 3, 1)\\n pts = torch.cat([pts_ori, torch.ones_like(pts_ori[:, :1])], dim=-1)\\n pts = projs[i] @ pts.T\\n pts = pts[:2] / pts[2:]\\n pts[0] = pts[0] / W * 2 - 1\\n pts[1] = pts[1] / H * 2 - 1\\n pts = pts.T.reshape(1, -1, 1, 2)\\n \\n sample_mask = torch.nn.functional.grid_sample(mask, pts, mode='bilinear', padding_mode='zeros').reshape(-1)\\n pts_mask[:, j] = sample_mask\\n pts_mask_sum = torch.min(pts_mask, dim=1)[0]\\n valid = pts_mask_sum > 0.1\\n out_mask[valid] = -1\\n if gp == 0:\\n out_mask[~valid] = 1\\n return out_mask\\n\\n def compute_image_mask(self, projs, masks, g_nums):\\n N = 64\\n R = self.R\\n X = torch.linspace(-1, 1, R).split(N)\\n Y = torch.linspace(-1, 1, R).split(N)\\n Z = torch.linspace(-1, 1, R).split(N)\\n cam_nums = projs.shape[0]\\n \\n self.mask = self.mask.to(self.device)\\n for gp in tqdm(range(cam_nums // g_nums)):\\n # for gp in range(1):\\n with torch.no_grad():\\n for xi, xs in enumerate(X):\\n for yi, ys in enumerate(Y):\\n for zi, zs in enumerate(Z):\\n xx, yy, zz = torch.meshgrid(xs, ys, zs)\\n pts = torch.cat([zz.reshape(-1, 1), yy.reshape(-1, 1), xx.reshape(-1, 1)], dim=-1).to(self.device)\\n val = self.visualhull(pts, projs[gp*g_nums:gp*g_nums+g_nums].to(self.device), masks[gp*g_nums:gp*g_nums+g_nums].to(self.device), g_nums).reshape(len(xs), len(ys), len(zs))\\n self.mask[gp, xi * N: xi * N + len(xs),yi * N: yi * N + len(ys), zi * N: zi * N + len(zs)] = val\\n self.mask = self.mask.unsqueeze(1)\\n self.mask = -torch.max_pool3d(-self.mask, 7, 1, 3)\\n self.mask[self.mask > -0.5] = 1\\n self.mask = self.mask.detach().cpu()\\n \\n def compute_mask(self, fid, query_func, inv_s):\\n N = 64\\n R = 128\\n X = torch.linspace(-1, 1, R).split(N)\\n Y = torch.linspace(-1, 1, R).split(N)\\n Z = torch.linspace(-1, 1, R).split(N)\\n from .renderer import sigma_f\\n mask = self.mask[fid].reshape(R, R, R).clone()\\n self.triplane[0].flow(fid)\\n with torch.no_grad():\\n for xi, xs in enumerate(X):\\n for yi, ys in enumerate(Y):\\n for zi, zs in enumerate(Z):\\n xx, yy, zz = torch.meshgrid(xs, ys, zs)\\n pts = torch.cat([zz.reshape(-1, 1), yy.reshape(-1, 1), xx.reshape(-1, 1)], dim=-1)\\n val = sigma_f(query_func(pts), inv_s).reshape(len(xs), len(ys), len(zs))\\n mask[xi * N: xi * N + len(xs),yi * N: yi * N + len(ys), zi * N: zi * N + len(zs)] = val\\n valid = mask > 0.02\\n mask[valid] = 1\\n mask[~valid] = -1\\n mask = -torch.max_pool3d(mask.reshape(1, 1, 128, 128, 128), 7, 1, 3)\\n self.mask[fid][mask[0] > -0.5] = 1\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport time\nimport logging\nimport argparse\nimport numpy as np\nimport cv2 as cv\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.tensorboard import SummaryWriter\nfrom shutil import copyfile\nfrom tqdm import tqdm\nfrom pyhocon import ConfigFactory\nfrom models.dataset import Dataset, BlenderDataset\nfrom models.fields import RenderingNetwork, FieldNetwork, SingleVarianceNetwork\nfrom models.tensor4d import Tensor4D\nfrom models.renderer import NeuSRenderer\nfrom models.mask import Mask3D\nfrom metrics import *"},"token_num":{"kind":"number","value":15678,"string":"15,678"},"cropped_code":{"kind":"string","value":"os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'\n\nclass Runner:\n def __init__(self, conf_path, mode='train', case='CASE_NAME', is_continue=False):\n self.device = torch.device('cuda')\n\n # Configuration\n self.conf_path = conf_path\n f = open(self.conf_path)\n conf_text = f.read()\n conf_text = conf_text.replace('CASE_NAME', case)\n f.close()\n\n self.conf = ConfigFactory.parse_string(conf_text)\n self.conf['dataset.data_dir'] = self.conf['dataset.data_dir'].replace('CASE_NAME', case)\n self.base_exp_dir = self.conf['general.base_exp_dir']\n os.makedirs(self.base_exp_dir, exist_ok=True)\n self.is_blender = self.conf['dataset'].get_bool('is_blender', default=False)\n"},"all_code":{"kind":"string","value":"os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'\n\nclass Runner:\n def __init__(self, conf_path, mode='train', case='CASE_NAME', is_continue=False):\n self.device = torch.device('cuda')\n\n # Configuration\n self.conf_path = conf_path\n f = open(self.conf_path)\n conf_text = f.read()\n conf_text = conf_text.replace('CASE_NAME', case)\n f.close()\n\n self.conf = ConfigFactory.parse_string(conf_text)\n self.conf['dataset.data_dir'] = self.conf['dataset.data_dir'].replace('CASE_NAME', case)\n self.base_exp_dir = self.conf['general.base_exp_dir']\n os.makedirs(self.base_exp_dir, exist_ok=True)\n self.is_blender = self.conf['dataset'].get_bool('is_blender', default=False)"},"next_line":{"kind":"string","value":" self.dataset = BlenderDataset(self.conf['dataset']) if self.is_blender else Dataset(self.conf['dataset'])"},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-11-07 10:16:33+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5279,"cells":{"repo_name":{"kind":"string","value":"Giftify-Bot/Giftify-Bot"},"file_path":{"kind":"string","value":"bot.py"},"context":{"kind":"list like","value":[{"identifier":"GuildConfig","path":"models/giveaway_settings.py","snippet":"class GuildConfig:\n \"\"\"Represents the configuration settings for a guild.\n\n Parameters\n ----------\n guild: discord.Guild\n The guild associated with the configuration.\n logging: Optional[discord.TextChannel]\n The logging text channel for the guild.\n ping: Optional[discord.Role]\n The role to ping for notifications.\n reaction: str\n The reaction used for giveaways.\n participants_reaction,: str\n The reaction used for giveaways participants button.\n required_roles: List[discord.Role]\n The default roles required to join giveaway.\n blacklisted_roles: List[discord.Role]\n The default roles blacklisted from joining a giveaway.\n bypass_roles: List[discord.Role]\n The roles that bypass_roles certain restrictions.\n multiplier_roles: Dict[discord.Role, int]\n The multiplier_roles points assigned to each role.\n managers: List[discord.Role]\n The roles with manager permissions.\n dm_winner: bool\n Whether to send a direct message to the winner.\n dm_host: bool\n Whether to send a direct message to the host.\n channel_settings: List[ChannelConfig]\n The settings for each channel.\n color: discord.Colour\n The color used for messages.\n button_style: discord.ButtonStyle\n The style of the button.\n end_message: str\n The message sent when a giveaway ends.\n reroll_message: str\n The message sent when a giveaway rerolls.\n dm_message: str\n The direct message sent to winner.\n dm_host_message: str\n The direct message sent to host.\n gw_header: str\n The header for the giveaway message.\n gw_end_header: str\n The header for the giveaway end.\n \"\"\"\n\n __slots__: Tuple[str, ...] = (\n \"guild\",\n \"logging\",\n \"ping\",\n \"reaction\",\n \"participants_reaction\",\n \"required_roles\",\n \"blacklisted_roles\",\n \"bypass_roles\",\n \"multiplier_roles\",\n \"managers\",\n \"dm_winner\",\n \"dm_host\",\n \"channel_settings\",\n \"color\",\n \"button_style\",\n \"end_message\",\n \"reroll_message\",\n \"dm_message\",\n \"dm_host_message\",\n \"gw_header\",\n \"gw_end_header\",\n )\n\n def __init__(\n self,\n guild: discord.Guild,\n *,\n logging: Optional[discord.TextChannel],\n ping: Optional[discord.Role],\n reaction: str,\n participants_reaction: str,\n required_roles: List[discord.Role],\n blacklisted_roles: List[discord.Role],\n bypass_roles: List[discord.Role],\n multiplier_roles: Dict[discord.Role, int],\n managers: List[discord.Role],\n dm_winner: bool,\n dm_host: bool,\n channel_settings: List[ChannelConfig],\n color: discord.Colour,\n button_style: discord.ButtonStyle,\n end_message: str,\n reroll_message: str,\n dm_message: str,\n dm_host_message: str,\n gw_header: str,\n gw_end_header: str,\n ):\n self.guild = guild\n self.logging = logging\n self.ping = ping\n self.reaction = reaction\n self.participants_reaction = participants_reaction\n self.required_roles = required_roles\n self.blacklisted_roles = blacklisted_roles\n self.bypass_roles = bypass_roles\n self.multiplier_roles = multiplier_roles\n self.managers = managers\n self.dm_winner = dm_winner\n self.dm_host = dm_host\n self.channel_settings = channel_settings\n self.color = color\n self.button_style = button_style\n self.end_message = end_message\n self.reroll_message = reroll_message\n self.dm_host_message = dm_host_message\n self.dm_message = dm_message\n self.gw_header = gw_header\n self.gw_end_header = gw_end_header\n\n def __repr__(self):\n return f\"<GuildConfig guild={self.guild!r}>\"\n\n @staticmethod\n async def _create_config(guild_id: int, pool: asyncpg.Pool) -> asyncpg.Record:\n return await pool.fetchrow(\n \"INSERT INTO configs (guild) VALUES ($1) RETURNING *\",\n guild_id,\n )\n\n @classmethod\n def _from_data(\n cls,\n guild: discord.Guild,\n data: asyncpg.Record,\n channel_data: List[asyncpg.Record],\n ) -> \"GuildConfig\":\n data = dict(data)\n data[\"color\"] = discord.Colour(data[\"color\"])\n\n data[\"logging\"] = guild.get_channel(data[\"logging\"])\n data[\"ping\"] = guild.get_role(data[\"ping\"])\n data[\"required_roles\"] = [\n guild.get_role(role) for role in data[\"required_roles\"] if role is not None\n ]\n data[\"blacklisted_roles\"] = [\n guild.get_role(role)\n for role in data[\"blacklisted_roles\"]\n if role is not None\n ]\n data[\"bypass_roles\"] = [\n guild.get_role(role) for role in data[\"bypass_roles\"] if role is None\n ]\n data[\"multiplier_roles\"] = {\n guild.get_role(role): multiplier\n for role, multiplier in data[\"multiplier_roles\"].items()\n if role is not None and multiplier > 1\n }\n data[\"managers\"] = [\n guild.get_role(role) for role in data[\"managers\"] if role is not None\n ]\n\n data[\"button_style\"] = discord.utils.get(\n discord.ButtonStyle, value=data[\"button_style\"]\n )\n\n data[\"channel_settings\"] = [\n channel_setting\n for record in channel_data\n if (channel_setting := ChannelConfig.from_data(guild, record))\n ]\n\n data.pop(\"guild\") # We do not need this.\n\n return cls(guild, **data)\n\n def to_dict(self) -> GuildConfigData:\n \"\"\"Converts this GuildConfig object into a dict.\"\"\"\n\n data = GuildConfigData(\n guild=self.guild.id,\n reaction=self.reaction,\n participants_reaction=self.participants_reaction,\n required_roles=[\n role.id for role in self.required_roles if role is not None\n ],\n blacklisted_roles=[\n role.id for role in self.blacklisted_roles if role is not None\n ],\n bypass_roles=[role.id for role in self.bypass_roles if role is not None],\n multiplier_roles={\n role.id: multiplier_roles\n for role, multiplier_roles in self.multiplier_roles.items()\n if role is not None\n },\n managers=[role.id for role in self.managers if role is not None],\n dm_winner=self.dm_winner,\n dm_host=self.dm_host,\n color=int(self.color),\n button_style=self.button_style.value,\n end_message=self.end_message,\n reroll_message=self.reroll_message,\n dm_message=self.dm_message,\n dm_host_message=self.dm_host_message,\n gw_header=self.gw_header,\n gw_end_header=self.gw_end_header,\n ) # type: ignore\n if self.logging:\n data[\"logging\"] = self.logging.id\n if self.ping:\n data[\"ping\"] = self.ping.id\n return data\n\n @classmethod\n async def fetch(cls, guild: discord.Guild, pool: asyncpg.Pool) -> \"GuildConfig\":\n \"\"\"Create a GuildConfig instance from data retrieved from a database.\n\n Parameters\n ----------\n guild: discord.Guild\n The discord guild.\n pool: asyncpg.Pool\n The database connection pool.\n\n Returns\n -------\n GuildConfig\n An instance of GuildConfig populated with the retrieved data.\n \"\"\"\n\n data = await pool.fetchrow(\"SELECT * FROM configs WHERE guild = $1\", guild.id)\n channel_data: List[asyncpg.Record] = await pool.fetch(\n \"SELECT * FROM channel_configs WHERE guild = $1\", guild.id\n )\n\n if not data:\n data: asyncpg.Record = await cls._create_config(guild.id, pool)\n\n return cls._from_data(guild, data, channel_data)\n\n async def update(\n self, column: str, value: Any, pool: asyncpg.Pool\n ) -> \"GuildConfig\":\n \"\"\"Update the specified column with the provided value in the database.\n\n Parameters\n ----------\n column: str\n The column to be updated.\n value: Any\n The new value for the column.\n pool: asyncpg.Pool\n The database connection pool.\n\n Raises\n ------\n ValueError\n If the provided column is not a valid column name in `self.__slots__`.\n\n Returns\n -------\n GuildConfig\n The updated `GuildConfig` instance.\n \"\"\"\n if column not in self.__slots__:\n raise ValueError(f\"Invalid column: {column}\")\n\n setattr(self, column, value)\n\n data = self.to_dict()\n\n columns = \", \".join(data.keys())\n placeholders = \", \".join([f\"${i+1}\" for i in range(len(data))])\n update_clause = \", \".join(\n [f\"{key} = EXCLUDED.{key}\" for key in data.keys() if key != \"guild\"]\n )\n\n query = f\"\"\"\n INSERT INTO configs ({columns}) \n VALUES ({placeholders})\n ON CONFLICT (guild) DO \n UPDATE SET {update_clause}\n \"\"\"\n\n values = list(data.values())\n await pool.execute(query, *values)\n return self\n\n @overload\n async def get_channel_config(\n self,\n channel: Union[discord.TextChannel, discord.CategoryChannel],\n create_if_not_exists: bool = True,\n pool: Optional[asyncpg.Pool] = None,\n ) -> ChannelConfig:\n ...\n\n @overload\n async def get_channel_config(\n self,\n channel: Union[discord.TextChannel, discord.CategoryChannel],\n create_if_not_exists: bool = False,\n pool: Optional[asyncpg.Pool] = None,\n ) -> Optional[ChannelConfig]:\n ...\n\n async def get_channel_config(\n self,\n channel: Union[discord.TextChannel, discord.CategoryChannel],\n create_if_not_exists: bool = True,\n pool: Optional[asyncpg.Pool] = None,\n ) -> Optional[ChannelConfig]:\n \"\"\"\n Retrieves the configuration for a specific channel.\n\n Parameters\n ----------\n channel: Union[discord.TextChannel, discord.CategoryChannel]\n The channel for which to retrieve the configuration.\n create_if_not_exists: Optional[bool]\n Whether to create a new configuration if it doesn't exist. Default is True.\n pool: Optional[asyncpg.Pool]\n The connection pool for interacting with the database.\n\n Returns\n -------\n Optional[ChannelConfig]\n The ChannelConfig object if it exists, or None if it doesn't exist and create_if_not_exists is set to False.\n\n Raises\n ------\n MaxChannelConfigCreationError\n If create_if_not_exists is True and the maximum number of channel configurations has already been reached.\n \"\"\"\n\n config = discord.utils.get(self.channel_settings, channel=channel)\n if config is not None:\n return config\n\n if create_if_not_exists:\n if len(self.channel_settings) >= 25:\n raise MaxChannelConfigCreationError()\n else:\n if pool:\n config = await ChannelConfig.create(channel.guild, channel, pool)\n self.channel_settings.append(config)\n return config\n\n return None"},{"identifier":"Giveaway","path":"models/giveaways.py","snippet":"class Giveaway:\n \"\"\"\n Represents a giveaway object.\n\n Attributes\n ----------\n bot: Giftify\n The bot instance to handle the giveaway.\n guild_id: int\n The ID of the guild (server) where the giveaway is hosted.\n channel_id: int\n The ID of the channel where the giveaway is hosted.\n message_id: int\n The ID of the giveaway message.\n extra_message_id: int\n The ID of the extra message with giveaway.\n host_id: int\n The ID of the user hosting the giveaway.\n donor_id: int\n The ID of the user donating for the giveaway.\n prize: int\n The prize of the giveaway.\n winner_count: int\n The number of winners for the giveaway.\n winners: List[int]\n The winners of the giveaway.\n participants: List[int]\n The IDs participants for the giveaway.\n ended: bool\n Indicates whether the giveaway has ended.\n ends: datetime.datetime\n The timestamp when the giveaway will be ended.\n required_roles: List[int]\n The list of role IDs required to participate in the giveaway.\n blacklisted_roles: List[int]\n The list of role IDs excluded from participating in the giveaway.\n bypass_roles: List[int]\n The list of user IDs exempted from giveaway restrictions.\n multiplier_roles: Optional[dict]\n A dictionary containing multiplier_roles criteria for the giveaway.\n messages: Optional[dict]\n A dictionary containing message-based criteria for the giveaway.\n messages_required: Optional[int]\n The number of messages required to participate in the giveaway.\n allowed_message_channels: Optional[List[int]]\n The ID of the channels where the message count is tracked.\n amari: Optional[int]\n The required Amari XP to participate in the giveaway.\n weekly_amari: Optional[int]\n The required weekly Amari XP to participate in the giveaway.\n \"\"\"\n\n __slots__ = (\n \"bot\",\n \"guild_id\",\n \"channel_id\",\n \"message_id\",\n \"extra_message_id\",\n \"prize\",\n \"host_id\",\n \"donor_id\",\n \"winner_count\",\n \"winners\",\n \"participants\",\n \"ended\",\n \"ends\",\n \"required_roles\",\n \"blacklisted_roles\",\n \"bypass_roles\",\n \"multiplier_roles\",\n \"messages\",\n \"messages_required\",\n \"allowed_message_channels\",\n \"amari\",\n \"weekly_amari\",\n )\n\n def __init__(self, *, bot: Giftify, record: asyncpg.Record):\n self.bot = bot\n self.guild_id: int = record[\"guild\"]\n self.channel_id: int = record[\"channel\"]\n self.message_id: int = record[\"message\"]\n self.extra_message_id: int = record[\"extra_message\"]\n self.prize: str = record[\"prize\"]\n self.host_id: int = record[\"host\"]\n self.donor_id: Optional[int] = record[\"donor\"]\n self.winner_count: int = record[\"winner_count\"]\n self.winners: List[int] = record[\"winners\"]\n self.participants: List[int] = record[\"participants\"]\n self.ended: bool = record[\"ended\"]\n self.ends: datetime.datetime = record[\"ends\"]\n self.required_roles: List[int] = record[\"required_roles\"] or []\n self.blacklisted_roles: List[int] = record[\"blacklisted_roles\"] or []\n self.bypass_roles: List[int] = record[\"bypass_roles\"] or []\n self.multiplier_roles: Dict[int, int] = {\n int(role): entries\n for role, entries in record[\"multiplier_roles\"].items()\n if entries > 1\n }\n self.messages: Dict[int, int] = {\n int(member): messages for member, messages in record[\"messages\"].items()\n }\n self.messages_required: Optional[int] = record[\"messages_required\"]\n self.allowed_message_channels: Optional[List[int]] = record[\"messages_channel\"]\n self.amari: Optional[int] = record[\"amari\"]\n self.weekly_amari: Optional[int] = record[\"weekly_amari\"]\n\n def __eq__(self, other: \"Giveaway\") -> bool:\n try:\n return (\n self.guild_id == other.guild_id\n and self.channel_id == other.channel_id\n and self.message_id == other.message_id\n )\n except AttributeError:\n return False\n\n def __hash__(self) -> int:\n return hash((self.guild_id, self.channel_id, self.message_id))\n\n def __repr__(self) -> str:\n return f\"<Giveaway guild_id={self.guild_id} channel_id={self.channel_id} message_id={self.message_id}>\"\n\n @property\n def jump_to_giveaway(self) -> discord.ui.View:\n url = f\"https://discord.com/channels/{self.guild_id}/{self.channel_id}/{self.message_id}\"\n view = BaseView(timeout=None)\n button = discord.ui.Button(label=\"Jump To Giveaway\", url=url)\n view.add_item(button)\n return view\n\n @staticmethod\n def create_embed(\n interaction: Interaction,\n config: GuildConfig,\n duration: datetime.datetime,\n winners: int,\n prize: str,\n required_roles: Optional[List[discord.Role]] = None,\n blacklisted_roles: Optional[List[discord.Role]] = None,\n bypass_roles: Optional[List[discord.Role]] = None,\n multiplier_roles: Optional[Dict[discord.Role, int]] = None,\n messages_required: Optional[int] = None,\n allowed_message_channels: Optional[List[discord.TextChannel]] = None,\n amari: Optional[int] = None,\n weekly_amari: Optional[int] = None,\n donor: Optional[discord.Member] = None,\n ) -> discord.Embed:\n assert interaction.guild is not None\n\n description = f\"Click the {config.reaction} button to join the giveaway!\\n\"\n description += f\"Hosted By: {interaction.user.mention}\\n\"\n\n if donor:\n description += f\"Donor: {donor.mention}\\n\"\n\n description += f\"Ends: {discord.utils.format_dt(duration, style='R')} ({discord.utils.format_dt(duration, style='f')})\\n\"\n\n embed = discord.Embed(\n title=prize,\n description=description,\n colour=config.color,\n timestamp=duration,\n )\n embed.set_footer(\n text=f\"{winners} winner(s) • Ends\",\n icon_url=interaction.guild.icon or interaction.client.user.display_avatar,\n )\n requirements = \"\"\n if required_roles:\n requirements += f\"Required Roles: {', '.join(role.mention for role in required_roles if role is not None)}\\n\"\n if bypass_roles:\n requirements += f\"Bypass Roles: {', '.join(role.mention for role in bypass_roles if role is not None)}\\n\"\n\n if blacklisted_roles:\n requirements += f\"Blacklisted Roles: {', '.join(role.mention for role in blacklisted_roles if role is not None)}\\n\"\n if messages_required:\n requirements += (\n f\"Messages Required: **{messages_required}** message(s) (5s cooldown)\\n\"\n )\n if allowed_message_channels:\n requirements += f\"Allowed Channels: {', '.join(f'<#{c.id}>' for c in allowed_message_channels)}\\n\"\n\n if amari:\n requirements += f\"Amari Level: {amari}\\n\"\n if weekly_amari:\n requirements += f\"Weekly Amari: {weekly_amari} XP Points\\n\"\n\n if requirements:\n embed.add_field(name=\"Requirements\", value=requirements, inline=False)\n\n if multiplier_roles:\n multiplier_roles_mention = \"\\n\".join(\n [\n f\"- {entry}x ・ {role.mention}\"\n for role, entry in multiplier_roles.items()\n if role is not None\n ]\n )\n embed.add_field(\n name=\"Bonus Entries\", value=multiplier_roles_mention, inline=False\n )\n\n return embed\n\n @classmethod\n async def start(\n cls,\n interaction: Interaction,\n duration: datetime.datetime,\n winners: int,\n prize: str,\n config: GuildConfig,\n channel_config: Optional[ChannelConfig],\n required_roles: Optional[List[discord.Role]] = None,\n blacklisted_roles: Optional[List[discord.Role]] = None,\n bypass_roles: Optional[List[discord.Role]] = None,\n multiplier_roles: Optional[Dict[discord.Role, int]] = None,\n messages_required: Optional[int] = None,\n allowed_message_channels: Optional[List[discord.TextChannel]] = None,\n amari: Optional[int] = None,\n weekly_amari: Optional[int] = None,\n image: Optional[discord.Attachment] = None,\n donor: Optional[discord.Member] = None,\n ping: bool = False,\n message: Optional[str] = None,\n ):\n assert isinstance(interaction.channel, discord.TextChannel)\n assert interaction.guild is not None\n\n embed = cls.create_embed(\n interaction=interaction,\n config=config,\n duration=duration,\n winners=winners,\n prize=prize,\n required_roles=required_roles,\n blacklisted_roles=blacklisted_roles,\n bypass_roles=bypass_roles,\n multiplier_roles=multiplier_roles,\n messages_required=messages_required,\n allowed_message_channels=allowed_message_channels,\n amari=amari,\n weekly_amari=weekly_amari,\n donor=donor,\n )\n view = GiveawayView(\n config.reaction, config.participants_reaction, config.button_style\n )\n giveaway_message = await interaction.channel.send(\n config.gw_header, embed=embed, view=view\n )\n\n message_embed = discord.Embed(\n title=f\"{GIFT_EMOJI} Giveaway\",\n description=f\"**Message・** {message}\" if message else None,\n color=config.color,\n )\n\n if image:\n message_embed.set_image(url=image)\n\n extra_message = None\n\n if ping or image:\n ping_role = (\n channel_config.ping\n if channel_config and channel_config.ping\n else config.ping\n )\n extra_message = await interaction.channel.send(\n ping_role.mention if ping_role else \"\",\n embed=message_embed if message or image else None, # type: ignore\n allowed_mentions=discord.AllowedMentions(roles=True),\n )\n\n if extra_message is None and message is not None:\n extra_message = await interaction.channel.send(embed=message_embed)\n\n await interaction.client.timer_cog.create_timer(\n message_id=giveaway_message.id,\n channel_id=interaction.channel.id,\n guild_id=interaction.guild.id,\n author_id=interaction.user.id,\n title=\"Giveaway\",\n event=\"giveaway\",\n expires=duration,\n pool=interaction.client.pool,\n )\n\n return await cls.create_entry(\n bot=interaction.client,\n guild_id=interaction.guild.id,\n channel_id=interaction.channel.id,\n message_id=giveaway_message.id,\n prize=prize,\n host_id=interaction.user.id,\n donor_id=donor.id if donor else None,\n winner_count=winners,\n ends=duration,\n required_roles=[role.id for role in required_roles if role is not None]\n if required_roles\n else [],\n blacklisted_roles=[\n role.id for role in blacklisted_roles if role is not None\n ]\n if blacklisted_roles\n else [],\n bypass_roles=[role.id for role in bypass_roles if role is not None]\n if bypass_roles\n else [],\n multiplier_roles={\n role.id: entries\n for role, entries in multiplier_roles.items()\n if role is not None\n }\n if multiplier_roles\n else {},\n messages={},\n messages_required=messages_required,\n allowed_message_channels=[c.id for c in allowed_message_channels]\n if allowed_message_channels\n else [],\n extra_message_id=extra_message.id if extra_message else None,\n amari=amari,\n weekly_amari=weekly_amari,\n )\n\n @classmethod\n async def create_entry(\n cls,\n bot: Giftify,\n guild_id: int,\n channel_id: int,\n message_id: int,\n prize: str,\n host_id: int,\n winner_count: int,\n ends: datetime.datetime,\n required_roles: List[int],\n blacklisted_roles: List[int],\n bypass_roles: List[int],\n donor_id: Optional[int],\n multiplier_roles: Optional[dict],\n messages: Optional[dict],\n messages_required: Optional[int],\n allowed_message_channels: Optional[List[int]],\n extra_message_id: Optional[int],\n amari: Optional[int],\n weekly_amari: Optional[int],\n ) -> \"Giveaway\":\n \"\"\"\n Create a new Giveaway object and insert it into the database.\n\n Parameters\n ----------\n bot: Giftify\n The bot instance.\n guild_id: int\n The ID of the guild (server) where the giveaway is hosted.\n channel_id: int\n The ID of the channel where the giveaway is hosted.\n message_id: int\n The ID of the message having the giveaway view.\n prize: str\n The prize of the giveaway.\n host_id: int\n The ID of the user hosting the giveaway.\n donor_id: int\n The ID of the donor of the giveaway.\n winner_count: int\n The number of winners for the giveaway.\n ends: datetime.datetime\n The time when the giveaway ends.\n required_roles: List[int]\n The list of role IDs required to participate in the giveaway.\n blacklisted_roles: List[int]\n The list of role IDs excluded from participating in the giveaway.\n bypass_roles: List[int]\n The list of user IDs exempted from giveaway restrictions.\n multiplier_roles: Optional[dict]\n A dictionary containing multiplier_roles criteria for the giveaway.\n messages: Optional[dict]\n A dictionary containing message-based criteria for the giveaway.\n messages_required: Optional[int]\n The number of messages required to participate in the giveaway.\n allowed_message_channels: Optional[int]\n The ID of the channel where the message count is tracked.\n amari: Optional[int]\n The required Amari XP to participate in the giveaway.\n weekly_amari: Optional[int]\n The required weekly Amari XP to participate in the giveaway.\n\n Returns\n -------\n Giveaway\n The created Giveaway object.\n \"\"\"\n record = await bot.pool.fetchrow(\n \"INSERT INTO giveaways (guild, channel, message, extra_message, host, donor, prize, winner_count, ends, required_roles, blacklisted_roles, bypass_roles, multiplier_roles, messages, messages_required, messages_channel, amari, weekly_amari) \"\n \"VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13, $14, $15, $16, $17, $18) \"\n \"RETURNING *\",\n guild_id,\n channel_id,\n message_id,\n extra_message_id,\n host_id,\n donor_id,\n prize,\n winner_count,\n ends,\n required_roles,\n blacklisted_roles,\n bypass_roles,\n multiplier_roles,\n messages,\n messages_required,\n allowed_message_channels,\n amari,\n weekly_amari,\n )\n return cls(bot=bot, record=record)\n\n async def check_requirements(self, member: discord.Member) -> None:\n missing_roles = [\n role.mention\n for role_id in self.required_roles\n if (role := member.guild.get_role(role_id)) and role not in member.roles\n ]\n if missing_roles:\n raise GiveawayError(\n f\"You cannot join this giveaway as you are missing the following required roles: {', '.join(missing_roles)}\"\n )\n\n blacklisted_roles = [\n role.mention\n for role_id in self.blacklisted_roles\n if (role := member.guild.get_role(role_id)) and role in member.roles\n ]\n if blacklisted_roles:\n raise GiveawayError(\n f\"You cannot join this giveaway as you have the following blacklisted roles: {', '.join(blacklisted_roles)}\"\n )\n\n if self.amari:\n if (user_level := await self.bot.fetch_level(member)) < self.amari:\n raise GiveawayError(\n f\"Your amari level is less than the required level, you need `{self.amari - user_level}` more level(s) to join the giveaway.\"\n )\n\n if self.weekly_amari:\n if (\n weekly_exp := await self.bot.fetch_weekly_experience(member)\n ) < self.weekly_amari:\n raise GiveawayError(\n f\"Your weekly amari experience is less than the required weekly amari experience, you need `{self.weekly_amari - weekly_exp}` more experience point(s) to join the giveaway.\"\n )\n\n if self.messages_required and self.messages_required > 0:\n if (\n user_messages := self.messages.get(member.id, 0)\n ) < self.messages_required:\n raise GiveawayError(\n f\"You have sent less messages than the required messages, you need to send `{self.messages_required - user_messages}` more messages to join the giveaway.\"\n )\n\n def can_bypass(self, member: discord.Member) -> bool:\n return any(\n member.guild.get_role(role_id) in member.roles\n for role_id in self.bypass_roles\n )\n\n def get_multiplier_entries(self, member: discord.Member) -> int:\n entries = 0\n for role_id, multiplier_roles_entries in self.multiplier_roles.items():\n if member.get_role(int(role_id)):\n entries += multiplier_roles_entries\n\n return entries or 1\n\n async def join(self, member: discord.Member) -> int:\n try:\n await self.check_requirements(member)\n except GiveawayError as error:\n if not self.can_bypass(member):\n raise error\n\n if member.id in self.participants:\n raise GiveawayError(\"You have already joined the giveaway.\")\n\n number_of_entries = self.get_multiplier_entries(member)\n entries = [member.id] * number_of_entries\n\n self.participants += entries\n\n query = \"\"\"UPDATE giveaways SET participants = $1 \n WHERE guild = $2 AND channel = $3 AND message = $4\"\"\"\n\n await self.bot.pool.execute(\n query, self.participants, self.guild_id, self.channel_id, self.message_id\n )\n\n return len(set(self.participants))\n\n async def leave(self, member: discord.Member) -> int:\n if member.id not in self.participants:\n raise GiveawayError(\"You are not a participant of this giveaway.\")\n\n self.participants = [\n participant for participant in self.participants if participant != member.id\n ]\n\n query = \"\"\"UPDATE giveaways SET participants = $1 \n WHERE guild = $2 AND channel = $3 AND message = $4\"\"\"\n\n await self.bot.pool.execute(\n query, self.participants, self.guild_id, self.channel_id, self.message_id\n )\n\n return len(set(self.participants))\n\n async def _end(self):\n await self.bot.pool.execute(\n \"UPDATE giveaways SET ended = $1, winners = $2 WHERE guild = $3 AND channel = $4 AND message = $5\",\n True,\n self.winners,\n self.guild_id,\n self.channel_id,\n self.message_id,\n )\n\n async def end(self):\n guild = self.bot.get_guild(self.guild_id)\n if not guild:\n return await self._end()\n\n config = await self.bot.fetch_config(guild)\n winners = await self.pick_winners(self.winner_count, guild)\n self.winners = [winner.id for winner in winners]\n\n await self._end()\n\n if config.dm_host:\n await self.dm_host(guild, winners, config.dm_host_message)\n\n if config.dm_winner:\n await self.dm_winners(config.dm_message, winners)\n\n channel = guild.get_channel(self.channel_id)\n if not channel or not isinstance(channel, discord.TextChannel):\n return\n\n gw_message = channel.get_partial_message(self.message_id)\n message = (\n safe_format(\n config.end_message,\n winners=\", \".join(winner.mention for winner in winners),\n prize=bold(self.prize),\n )\n if winners\n else f\"Could not pick any winners for the giveaway of {bold(self.prize)}!\"\n )\n embed = self.get_end_embed(guild, config)\n\n view = GiveawayView(\n config.reaction,\n config.participants_reaction,\n config.button_style,\n participant_count=len(set(self.participants)),\n disabled=True,\n )\n\n with contextlib.suppress(discord.HTTPException):\n await gw_message.edit(content=config.gw_end_header, embed=embed, view=view)\n await gw_message.reply(message, view=self.jump_to_giveaway)\n\n async def reroll(self, winner_count: int):\n guild = self.bot.get_guild(self.guild_id)\n if not guild:\n return\n\n config = await self.bot.fetch_config(guild)\n winners = await self.pick_winners(winner_count, guild)\n self.winners = [winner.id for winner in winners]\n\n await self._end()\n\n if config.dm_winner:\n await self.dm_winners(config.dm_message, winners)\n\n channel = guild.get_channel(self.channel_id)\n if not channel or not isinstance(channel, discord.TextChannel):\n return\n\n gw_message = channel.get_partial_message(self.message_id)\n message = (\n safe_format(\n config.reroll_message,\n winners=\", \".join(winner.mention for winner in winners),\n prize=bold(self.prize),\n )\n if winners\n else f\"Could not pick any winners for the giveaway of {bold(self.prize)}!\"\n )\n embed = self.get_end_embed(guild, config)\n\n view = GiveawayView(\n config.reaction,\n config.participants_reaction,\n config.button_style,\n participant_count=len(set(self.participants)),\n disabled=True,\n )\n\n with contextlib.suppress(discord.HTTPException):\n await gw_message.edit(content=config.gw_end_header, embed=embed, view=view)\n await gw_message.reply(message, view=self.jump_to_giveaway)\n\n async def cancel(self):\n await self.bot.pool.execute(\n \"\"\"DELETE FROM giveaways WHERE guild = $1 AND channel = $2 AND message = $3\"\"\",\n self.guild_id,\n self.channel_id,\n self.message_id,\n )\n if self.extra_message_id is not None:\n channel = self.bot.get_channel(self.channel_id)\n if channel is not None:\n await channel.get_partial_message(self.extra_message_id).delete() # type: ignore\n\n async def dm_host(\n self, guild: discord.Guild, winners: List[discord.Member], message: str\n ) -> None:\n host = await self.bot.get_or_fetch_member(guild, self.host_id)\n if not host:\n return\n\n description = safe_format(\n message,\n winners=\", \".join(winner.mention for winner in winners)\n if winners\n else \"No Winners\",\n prize=bold(self.prize),\n )\n\n embed = discord.Embed(\n title=f\"Your giveaway for {self.prize} has ended!\"[:256],\n description=description,\n colour=self.bot.colour,\n )\n view = self.jump_to_giveaway\n\n with contextlib.suppress(discord.HTTPException):\n await host.send(embed=embed, view=view)\n\n async def dm_winners(self, message: str, winners: List[discord.Member]) -> None:\n for winner in winners:\n description = safe_format(\n message, winner=winner.mention, prize=bold(self.prize)\n )\n\n embed = discord.Embed(\n title=\"You won!\",\n description=description,\n colour=self.bot.colour,\n )\n view = self.jump_to_giveaway\n\n with contextlib.suppress(discord.HTTPException):\n await winner.send(embed=embed, view=view)\n\n async def pick_winners(\n self, count: int, guild: discord.Guild\n ) -> List[discord.Member]:\n winners = []\n\n participants = self.participants.copy()\n\n while count > 0 and participants:\n member_id = random.choice(participants)\n member = await self.bot.get_or_fetch_member(guild, member_id)\n if member is not None and member not in winners:\n try:\n await self.check_requirements(member)\n except GiveawayError:\n pass\n else:\n winners.append(member)\n count -= 1\n\n participants.remove(member_id)\n\n return winners\n\n def get_end_embed(self, guild: discord.Guild, config: GuildConfig) -> discord.Embed:\n description = (\n f\"This giveaway has ended!\\n\"\n f\"Hosted By: <@!{self.host_id}>\\n\"\n f\"Winners: {', '.join(f'<@!{winner_id}>' for winner_id in self.winners) if self.winners else 'No Winners'}\\n\"\n f\"Ended: {discord.utils.format_dt(datetime.datetime.now(datetime.timezone.utc), style='R')} ({discord.utils.format_dt(datetime.datetime.now(datetime.timezone.utc), style='f')})\\n\"\n )\n if self.donor_id:\n description += f\"Donor: <@!{self.donor_id}>\\n\"\n embed = discord.Embed(\n title=self.prize,\n description=description,\n colour=config.color,\n timestamp=self.ends,\n )\n embed.set_footer(\n text=f\"{self.winner_count} winner(s) • Ended\",\n icon_url=guild.icon or self.bot.user.display_avatar,\n )\n\n requirements = \"\"\n if self.required_roles:\n requirements += f\"Required Roles: {', '.join(f'<@&{role_id}>' for role_id in self.required_roles)}\\n\"\n if self.bypass_roles:\n requirements += f\"Bypass Roles: {', '.join(f'<@&{role_id}>' for role_id in self.bypass_roles)}\\n\"\n if self.blacklisted_roles:\n requirements += f\"Blacklisted Roles: {', '.join(f'<@&{role_id}>' for role_id in self.blacklisted_roles)}\\n\"\n if self.messages_required:\n requirements += f\"Messages Required: **{self.messages_required}** message(s) (5s cooldown)\\n\"\n if self.allowed_message_channels:\n requirements += f\"Allowed Channels: {', '.join(f'<#{cid}>' for cid in self.allowed_message_channels)}\\n\"\n if self.amari:\n requirements += f\"Amari Level: {self.amari}\\n\"\n if self.weekly_amari:\n requirements += f\"Weekly Amari: {self.weekly_amari} XP Points\\n\"\n\n if requirements:\n embed.add_field(name=\"Requirements\", value=requirements, inline=False)\n\n if self.multiplier_roles:\n multiplier_roles = \"\\n\".join(\n [\n f\"- {multiplier_entries}x ・ <@&{multiplier_role}>\"\n for multiplier_role, multiplier_entries in self.multiplier_roles.items()\n ]\n )\n embed.add_field(name=\"Bonus Entries\", value=multiplier_roles, inline=False)\n\n return embed"},{"identifier":"Raffle","path":"models/raffles.py","snippet":"class Raffle:\n \"\"\"\n Represents a raffle object.\n\n Attributes\n ----------\n pool: asyncpg.Pool\n The PostgreSQL connection pool instance.\n guild: discord.Guild\n The guild (server) where the raffle is hosted.\n name: str\n The name of the raffle.\n winner: Optional[discord.Member]\n The member instance of the winner, or None if the raffle hasn't ended yet.\n deputy_roles: List[discord.Role]\n A list of roles associated with the raffle.\n deputy_members: List[discord.Member]\n A list of members associated with the raffle.\n tickets: Dict[discord.Member, int]\n A mapping of members to the number of tickets they have.\n \"\"\"\n\n def __init__(\n self,\n pool: asyncpg.Pool,\n *,\n guild: discord.Guild,\n name: str,\n winner: Optional[discord.Member],\n deputy_roles: List[discord.Role],\n deputy_members: List[discord.Member],\n tickets: Dict[discord.Member, int],\n ):\n self.pool = pool\n\n self.guild = guild\n self.name = name\n self.winner = winner\n self.deputy_roles = deputy_roles\n self.deputy_members = deputy_members\n self.tickets = tickets\n\n def __str__(self):\n return self.name\n\n def __repr__(self) -> str:\n return f\"<Raffle name={self.name} guild={self.guild} winner={self.winner}>\"\n\n def __hash__(self) -> int:\n return hash((self.name, self.guild))\n\n def __eq__(self, other: Raffle) -> bool:\n return self.name == other.name and self.guild == other.guild\n\n @classmethod\n async def from_record(cls, bot: Giftify, *, record: asyncpg.Record) -> Raffle:\n name = record[\"name\"]\n guild = bot.get_guild(record[\"guild\"])\n if guild is None:\n raise RaffleError(\"The guild having the raffle was not found.\")\n\n winner_id = record[\"winner\"]\n winner: Optional[discord.Member] = (\n (await bot.get_or_fetch_member(guild, winner_id) or FakeMember(winner_id))\n if winner_id\n else None\n ) # type: ignore\n\n deputy_roles = [guild.get_role(role_id) for role_id in record[\"deputy_roles\"]]\n deputy_members = [\n await bot.get_or_fetch_member(guild, member_id)\n for member_id in record[\"deputy_members\"]\n ]\n\n tickets = {\n await bot.get_or_fetch_member(guild, int(member_id)): num_tickets\n for member_id, num_tickets in record[\"tickets\"].items()\n }\n\n return cls(\n bot.pool,\n guild=guild,\n name=name,\n winner=winner,\n deputy_roles=filter_none(deputy_roles),\n deputy_members=filter_none(deputy_members),\n tickets=filter_none(tickets),\n )\n\n async def roll(self) -> discord.Member:\n \"\"\"\n End the raffle and set the winner.\n \"\"\"\n members = list(self.tickets.keys())\n weights = list(self.tickets.values())\n\n self.winner = random.choices(members, weights, k=1)[0]\n\n await self.save()\n\n return self.winner\n\n async def add_deputy(self, obj: Union[discord.Member, discord.Role]) -> None:\n \"\"\"\n Add a deputy to the raffle.\n\n Parameters\n ----------\n obj: Union[discord.Member, discord.Role]\n The instance of deputy member or role to be added.\n \"\"\"\n if isinstance(obj, discord.Member):\n if len(self.deputy_members) >= 25:\n raise RaffleError(\"You cannot add more than 25 deputy members.\")\n self.deputy_members.append(obj)\n elif isinstance(obj, discord.Role):\n if len(self.deputy_roles) >= 10:\n raise RaffleError(\"You cannot add more than 10 deputy roles.\")\n self.deputy_roles.append(obj)\n else:\n raise RaffleError(\"Invalid obj type.\")\n\n await self.save()\n\n async def remove_deputy(self, obj: Union[discord.Member, discord.Role]) -> None:\n \"\"\"\n Remove a deputy from the raffle.\n\n Parameters\n ----------\n obj: Union[discord.Member, discord.Role]\n The instance of deputy member or role to be removed.\n \"\"\"\n if isinstance(obj, discord.Member):\n if obj not in self.deputy_members:\n raise RaffleError(\"That member is not a deputy.\")\n self.deputy_members.remove(obj)\n elif isinstance(obj, discord.Role):\n if obj not in self.deputy_roles:\n raise RaffleError(\"That role is not a deputy.\")\n self.deputy_roles.remove(obj)\n else:\n raise RaffleError(\"Invalid obj type.\")\n\n await self.save()\n\n async def add_tickets(self, member: discord.Member, num_tickets: int) -> None:\n \"\"\"\n Add tickets to a member.\n\n Parameters\n ----------\n member: discord.Member\n The instance of the member.\n num_tickets: int\n The number of tickets to add.\n \"\"\"\n if member in self.tickets:\n self.tickets[member] += num_tickets\n else:\n self.tickets[member] = num_tickets\n\n await self.save()\n\n async def remove_tickets(self, member: discord.Member, num_tickets: int) -> None:\n \"\"\"\n Remove tickets from a member.\n\n Parameters\n ----------\n member: discord.Member\n The instance of the member.\n num_tickets: int\n The number of tickets to remove.\n \"\"\"\n if member in self.tickets:\n self.tickets[member] -= num_tickets\n if self.tickets[member] <= 0:\n del self.tickets[member]\n\n await self.save()\n else:\n raise RaffleError(\n f\"That member does not have any tickets in {self.name} raffle.\"\n )\n\n async def save(self) -> None:\n \"\"\"\n Update raffle attributes in the database.\n \"\"\"\n query = \"\"\"\n INSERT INTO raffles (guild, name, winner, deputy_roles, deputy_members, tickets)\n VALUES ($1, $2, $3, $4, $5, $6)\n ON CONFLICT (guild, name)\n DO UPDATE SET winner = EXCLUDED.winner, deputy_roles = EXCLUDED.deputy_roles,\n deputy_members = EXCLUDED.deputy_members, tickets = EXCLUDED.tickets;\n \"\"\"\n await self.pool.execute(\n query,\n self.guild.id,\n self.name,\n self.winner.id if self.winner else None,\n [role.id for role in self.deputy_roles],\n [member.id for member in self.deputy_members],\n {\n str(member.id): num_tickets\n for member, num_tickets in self.tickets.items()\n },\n )\n\n async def delete(self):\n \"\"\"\n Delete the raffle from the database.\n \"\"\"\n query = \"\"\"DELETE FROM raffles WHERE guild = $1 AND name = $2\"\"\"\n await self.pool.execute(query, self.guild.id, self.name)"},{"identifier":"ERROR_EMOJI","path":"utils/constants.py","snippet":"ERROR_EMOJI = \"<:GiftifyError:1117842868057423914>\""},{"identifier":"SUCCESS_EMOJI","path":"utils/constants.py","snippet":"SUCCESS_EMOJI = \"<:GiftifySuccess:1100674526318166048>\""},{"identifier":"WARN_EMOJI","path":"utils/constants.py","snippet":"WARN_EMOJI = \"<:GiftifyWarn:1098498926564356106>\""},{"identifier":"db_init","path":"utils/db.py","snippet":"async def db_init(connection: asyncpg.Connection) -> None:\n await connection.set_type_codec(\n \"jsonb\", schema=\"pg_catalog\", encoder=_encode_jsonb, decoder=_decode_jsonb\n )"},{"identifier":"CommandTree","path":"utils/tree.py","snippet":"class CommandTree(app_commands.CommandTree):\r\n client: \"Giftify\"\r\n\r\n async def on_error(\r\n self,\r\n interaction: Interaction,\r\n error: app_commands.AppCommandError,\r\n ) -> None:\r\n view = discord.ui.View()\r\n\r\n button = discord.ui.Button(label=\"Support\", url=\"https://discord.gg/GQSGChbEKz\")\r\n\r\n view.add_item(button)\r\n\r\n if not interaction.response.is_done():\r\n await interaction.response.defer(thinking=True, ephemeral=True)\r\n\r\n embed = discord.Embed(\r\n title=\"An error was raised while executing this command!\",\r\n color=discord.Colour.red(),\r\n )\r\n\r\n if isinstance(error, app_commands.CommandInvokeError):\r\n if isinstance(error, MaxChannelConfigCreationError):\r\n embed.description = (\r\n f\"{WARN_EMOJI} You cannot setup configuration for more than 25 channels, please try removing some.\"\r\n )\r\n elif isinstance(error, discord.HTTPException):\r\n embed.description = f\"{WARN_EMOJI} Unknown HTTP error occured!\"\r\n else:\r\n embed.description = (\r\n f\"{WARN_EMOJI} An unknown error occurred , my developers have been notified about this error.\"\r\n )\r\n self.client.log_handler.log.exception(\"Exception occurred in the CommandTree:\\n\", exc_info=error)\r\n sentry_sdk.capture_exception(error)\r\n elif isinstance(error, app_commands.TransformerError):\r\n if isinstance(error, TransformerError):\r\n embed.description = f\"{WARN_EMOJI} {error.message}\"\r\n else:\r\n embed.description = f\"{WARN_EMOJI} {str(error)}\"\r\n\r\n elif isinstance(error, app_commands.MissingPermissions):\r\n missing = [perm.replace(\"_\", \" \").replace(\"guild\", \"server\").title() for perm in error.missing_permissions]\r\n\r\n format = \"\\n> \".join(missing)\r\n\r\n embed.description = (\r\n f\"{WARN_EMOJI} You are missing follwing permission(s) to run this command: \\n\\n> {format}\"\r\n )\r\n\r\n elif isinstance(error, app_commands.BotMissingPermissions):\r\n missing = [perm.replace(\"_\", \" \").replace(\"guild\", \"server\").title() for perm in error.missing_permissions]\r\n\r\n format = \"\\n> \".join(missing)\r\n\r\n embed.description = f\"{WARN_EMOJI} I am missing follwing permission(s) to run this command: \\n\\n > {format}\"\r\n\r\n elif isinstance(error, app_commands.CommandOnCooldown):\r\n cooldown = int(error.cooldown.per)\r\n retry_after = int(error.retry_after)\r\n embed.description = f\"{WARN_EMOJI} The cooldown for this command is **{cooldown}s**. Try running the command again after **{retry_after}s**.\"\r\n\r\n elif isinstance(error, app_commands.CommandNotFound):\r\n embed.description = f'{WARN_EMOJI} The command \"{error.name}\" was not found.'\r\n elif isinstance(error, DonationError):\r\n embed.description = f\"{WARN_EMOJI} {str(error)}\"\r\n elif isinstance(error, app_commands.CheckFailure):\r\n if isinstance(error, (DonationCategoryError, DonationPermissionsError)):\r\n embed.description = f\"{WARN_EMOJI} {str(error.message)}\"\r\n else:\r\n return\r\n else:\r\n embed.description = (\r\n f\"{WARN_EMOJI} An unknown error occured, my developers have been notified about this errors.\"\r\n )\r\n await interaction.followup.send(embed=embed, ephemeral=True)\r\n sentry_sdk.capture_exception(error)\r\n return self.client.log_handler.log.exception(\"Exception occurred in the CommandTree:\\n\", exc_info=error)\r\n\r\n return await interaction.followup.send(embed=embed, ephemeral=True)\r"},{"identifier":"ConfirmationView","path":"utils/view.py","snippet":"class ConfirmationView(BaseView):\r\n def __init__(\r\n self,\r\n *,\r\n timeout: float,\r\n interaction: Interaction,\r\n success_message: str,\r\n cancel_message: str,\r\n ) -> None:\r\n super().__init__(timeout=timeout)\r\n self.interaction = interaction\r\n self.success_message = success_message\r\n self.cancel_message = cancel_message\r\n self.value: Optional[bool] = None\r\n\r\n @property\r\n def success_embed(self) -> discord.Embed:\r\n return discord.Embed(\r\n description=f\"{SUCCESS_EMOJI} {self.success_message}\",\r\n colour=discord.Colour.green(),\r\n )\r\n\r\n @property\r\n def cancel_embed(self) -> discord.Embed:\r\n return discord.Embed(\r\n description=f\"{SUCCESS_EMOJI} {self.cancel_message}\",\r\n colour=discord.Colour.green(),\r\n )\r\n\r\n async def interaction_check(self, interaction: Interaction) -> bool:\r\n if interaction.user and interaction.user.id == self.interaction.user.id:\r\n return True\r\n else:\r\n await interaction.response.send_message(\r\n \"This confirmation dialog is not for you.\", ephemeral=True\r\n )\r\n return False\r\n\r\n async def on_timeout(self) -> None:\r\n with contextlib.suppress(discord.HTTPException):\r\n for item in self.children:\r\n item.disabled = True\r\n await self.interaction.edit_original_response(view=self)\r\n\r\n @discord.ui.button(label=\"Confirm\", style=discord.ButtonStyle.green)\r\n async def confirm(self, interaction: Interaction, button: discord.ui.Button):\r\n self.value = True\r\n await interaction.response.edit_message(embed=self.success_embed, view=None)\r\n self.stop()\r\n\r\n @discord.ui.button(label=\"Cancel\", style=discord.ButtonStyle.red)\r\n async def cancel(self, interaction: Interaction, button: discord.ui.Button):\r\n self.value = False\r\n await interaction.response.edit_message(embed=self.cancel_embed, view=None)\r\n\r\n self.stop()\r"}],"string":"[\n {\n \"identifier\": \"GuildConfig\",\n \"path\": \"models/giveaway_settings.py\",\n \"snippet\": \"class GuildConfig:\\n \\\"\\\"\\\"Represents the configuration settings for a guild.\\n\\n Parameters\\n ----------\\n guild: discord.Guild\\n The guild associated with the configuration.\\n logging: Optional[discord.TextChannel]\\n The logging text channel for the guild.\\n ping: Optional[discord.Role]\\n The role to ping for notifications.\\n reaction: str\\n The reaction used for giveaways.\\n participants_reaction,: str\\n The reaction used for giveaways participants button.\\n required_roles: List[discord.Role]\\n The default roles required to join giveaway.\\n blacklisted_roles: List[discord.Role]\\n The default roles blacklisted from joining a giveaway.\\n bypass_roles: List[discord.Role]\\n The roles that bypass_roles certain restrictions.\\n multiplier_roles: Dict[discord.Role, int]\\n The multiplier_roles points assigned to each role.\\n managers: List[discord.Role]\\n The roles with manager permissions.\\n dm_winner: bool\\n Whether to send a direct message to the winner.\\n dm_host: bool\\n Whether to send a direct message to the host.\\n channel_settings: List[ChannelConfig]\\n The settings for each channel.\\n color: discord.Colour\\n The color used for messages.\\n button_style: discord.ButtonStyle\\n The style of the button.\\n end_message: str\\n The message sent when a giveaway ends.\\n reroll_message: str\\n The message sent when a giveaway rerolls.\\n dm_message: str\\n The direct message sent to winner.\\n dm_host_message: str\\n The direct message sent to host.\\n gw_header: str\\n The header for the giveaway message.\\n gw_end_header: str\\n The header for the giveaway end.\\n \\\"\\\"\\\"\\n\\n __slots__: Tuple[str, ...] = (\\n \\\"guild\\\",\\n \\\"logging\\\",\\n \\\"ping\\\",\\n \\\"reaction\\\",\\n \\\"participants_reaction\\\",\\n \\\"required_roles\\\",\\n \\\"blacklisted_roles\\\",\\n \\\"bypass_roles\\\",\\n \\\"multiplier_roles\\\",\\n \\\"managers\\\",\\n \\\"dm_winner\\\",\\n \\\"dm_host\\\",\\n \\\"channel_settings\\\",\\n \\\"color\\\",\\n \\\"button_style\\\",\\n \\\"end_message\\\",\\n \\\"reroll_message\\\",\\n \\\"dm_message\\\",\\n \\\"dm_host_message\\\",\\n \\\"gw_header\\\",\\n \\\"gw_end_header\\\",\\n )\\n\\n def __init__(\\n self,\\n guild: discord.Guild,\\n *,\\n logging: Optional[discord.TextChannel],\\n ping: Optional[discord.Role],\\n reaction: str,\\n participants_reaction: str,\\n required_roles: List[discord.Role],\\n blacklisted_roles: List[discord.Role],\\n bypass_roles: List[discord.Role],\\n multiplier_roles: Dict[discord.Role, int],\\n managers: List[discord.Role],\\n dm_winner: bool,\\n dm_host: bool,\\n channel_settings: List[ChannelConfig],\\n color: discord.Colour,\\n button_style: discord.ButtonStyle,\\n end_message: str,\\n reroll_message: str,\\n dm_message: str,\\n dm_host_message: str,\\n gw_header: str,\\n gw_end_header: str,\\n ):\\n self.guild = guild\\n self.logging = logging\\n self.ping = ping\\n self.reaction = reaction\\n self.participants_reaction = participants_reaction\\n self.required_roles = required_roles\\n self.blacklisted_roles = blacklisted_roles\\n self.bypass_roles = bypass_roles\\n self.multiplier_roles = multiplier_roles\\n self.managers = managers\\n self.dm_winner = dm_winner\\n self.dm_host = dm_host\\n self.channel_settings = channel_settings\\n self.color = color\\n self.button_style = button_style\\n self.end_message = end_message\\n self.reroll_message = reroll_message\\n self.dm_host_message = dm_host_message\\n self.dm_message = dm_message\\n self.gw_header = gw_header\\n self.gw_end_header = gw_end_header\\n\\n def __repr__(self):\\n return f\\\"<GuildConfig guild={self.guild!r}>\\\"\\n\\n @staticmethod\\n async def _create_config(guild_id: int, pool: asyncpg.Pool) -> asyncpg.Record:\\n return await pool.fetchrow(\\n \\\"INSERT INTO configs (guild) VALUES ($1) RETURNING *\\\",\\n guild_id,\\n )\\n\\n @classmethod\\n def _from_data(\\n cls,\\n guild: discord.Guild,\\n data: asyncpg.Record,\\n channel_data: List[asyncpg.Record],\\n ) -> \\\"GuildConfig\\\":\\n data = dict(data)\\n data[\\\"color\\\"] = discord.Colour(data[\\\"color\\\"])\\n\\n data[\\\"logging\\\"] = guild.get_channel(data[\\\"logging\\\"])\\n data[\\\"ping\\\"] = guild.get_role(data[\\\"ping\\\"])\\n data[\\\"required_roles\\\"] = [\\n guild.get_role(role) for role in data[\\\"required_roles\\\"] if role is not None\\n ]\\n data[\\\"blacklisted_roles\\\"] = [\\n guild.get_role(role)\\n for role in data[\\\"blacklisted_roles\\\"]\\n if role is not None\\n ]\\n data[\\\"bypass_roles\\\"] = [\\n guild.get_role(role) for role in data[\\\"bypass_roles\\\"] if role is None\\n ]\\n data[\\\"multiplier_roles\\\"] = {\\n guild.get_role(role): multiplier\\n for role, multiplier in data[\\\"multiplier_roles\\\"].items()\\n if role is not None and multiplier > 1\\n }\\n data[\\\"managers\\\"] = [\\n guild.get_role(role) for role in data[\\\"managers\\\"] if role is not None\\n ]\\n\\n data[\\\"button_style\\\"] = discord.utils.get(\\n discord.ButtonStyle, value=data[\\\"button_style\\\"]\\n )\\n\\n data[\\\"channel_settings\\\"] = [\\n channel_setting\\n for record in channel_data\\n if (channel_setting := ChannelConfig.from_data(guild, record))\\n ]\\n\\n data.pop(\\\"guild\\\") # We do not need this.\\n\\n return cls(guild, **data)\\n\\n def to_dict(self) -> GuildConfigData:\\n \\\"\\\"\\\"Converts this GuildConfig object into a dict.\\\"\\\"\\\"\\n\\n data = GuildConfigData(\\n guild=self.guild.id,\\n reaction=self.reaction,\\n participants_reaction=self.participants_reaction,\\n required_roles=[\\n role.id for role in self.required_roles if role is not None\\n ],\\n blacklisted_roles=[\\n role.id for role in self.blacklisted_roles if role is not None\\n ],\\n bypass_roles=[role.id for role in self.bypass_roles if role is not None],\\n multiplier_roles={\\n role.id: multiplier_roles\\n for role, multiplier_roles in self.multiplier_roles.items()\\n if role is not None\\n },\\n managers=[role.id for role in self.managers if role is not None],\\n dm_winner=self.dm_winner,\\n dm_host=self.dm_host,\\n color=int(self.color),\\n button_style=self.button_style.value,\\n end_message=self.end_message,\\n reroll_message=self.reroll_message,\\n dm_message=self.dm_message,\\n dm_host_message=self.dm_host_message,\\n gw_header=self.gw_header,\\n gw_end_header=self.gw_end_header,\\n ) # type: ignore\\n if self.logging:\\n data[\\\"logging\\\"] = self.logging.id\\n if self.ping:\\n data[\\\"ping\\\"] = self.ping.id\\n return data\\n\\n @classmethod\\n async def fetch(cls, guild: discord.Guild, pool: asyncpg.Pool) -> \\\"GuildConfig\\\":\\n \\\"\\\"\\\"Create a GuildConfig instance from data retrieved from a database.\\n\\n Parameters\\n ----------\\n guild: discord.Guild\\n The discord guild.\\n pool: asyncpg.Pool\\n The database connection pool.\\n\\n Returns\\n -------\\n GuildConfig\\n An instance of GuildConfig populated with the retrieved data.\\n \\\"\\\"\\\"\\n\\n data = await pool.fetchrow(\\\"SELECT * FROM configs WHERE guild = $1\\\", guild.id)\\n channel_data: List[asyncpg.Record] = await pool.fetch(\\n \\\"SELECT * FROM channel_configs WHERE guild = $1\\\", guild.id\\n )\\n\\n if not data:\\n data: asyncpg.Record = await cls._create_config(guild.id, pool)\\n\\n return cls._from_data(guild, data, channel_data)\\n\\n async def update(\\n self, column: str, value: Any, pool: asyncpg.Pool\\n ) -> \\\"GuildConfig\\\":\\n \\\"\\\"\\\"Update the specified column with the provided value in the database.\\n\\n Parameters\\n ----------\\n column: str\\n The column to be updated.\\n value: Any\\n The new value for the column.\\n pool: asyncpg.Pool\\n The database connection pool.\\n\\n Raises\\n ------\\n ValueError\\n If the provided column is not a valid column name in `self.__slots__`.\\n\\n Returns\\n -------\\n GuildConfig\\n The updated `GuildConfig` instance.\\n \\\"\\\"\\\"\\n if column not in self.__slots__:\\n raise ValueError(f\\\"Invalid column: {column}\\\")\\n\\n setattr(self, column, value)\\n\\n data = self.to_dict()\\n\\n columns = \\\", \\\".join(data.keys())\\n placeholders = \\\", \\\".join([f\\\"${i+1}\\\" for i in range(len(data))])\\n update_clause = \\\", \\\".join(\\n [f\\\"{key} = EXCLUDED.{key}\\\" for key in data.keys() if key != \\\"guild\\\"]\\n )\\n\\n query = f\\\"\\\"\\\"\\n INSERT INTO configs ({columns}) \\n VALUES ({placeholders})\\n ON CONFLICT (guild) DO \\n UPDATE SET {update_clause}\\n \\\"\\\"\\\"\\n\\n values = list(data.values())\\n await pool.execute(query, *values)\\n return self\\n\\n @overload\\n async def get_channel_config(\\n self,\\n channel: Union[discord.TextChannel, discord.CategoryChannel],\\n create_if_not_exists: bool = True,\\n pool: Optional[asyncpg.Pool] = None,\\n ) -> ChannelConfig:\\n ...\\n\\n @overload\\n async def get_channel_config(\\n self,\\n channel: Union[discord.TextChannel, discord.CategoryChannel],\\n create_if_not_exists: bool = False,\\n pool: Optional[asyncpg.Pool] = None,\\n ) -> Optional[ChannelConfig]:\\n ...\\n\\n async def get_channel_config(\\n self,\\n channel: Union[discord.TextChannel, discord.CategoryChannel],\\n create_if_not_exists: bool = True,\\n pool: Optional[asyncpg.Pool] = None,\\n ) -> Optional[ChannelConfig]:\\n \\\"\\\"\\\"\\n Retrieves the configuration for a specific channel.\\n\\n Parameters\\n ----------\\n channel: Union[discord.TextChannel, discord.CategoryChannel]\\n The channel for which to retrieve the configuration.\\n create_if_not_exists: Optional[bool]\\n Whether to create a new configuration if it doesn't exist. Default is True.\\n pool: Optional[asyncpg.Pool]\\n The connection pool for interacting with the database.\\n\\n Returns\\n -------\\n Optional[ChannelConfig]\\n The ChannelConfig object if it exists, or None if it doesn't exist and create_if_not_exists is set to False.\\n\\n Raises\\n ------\\n MaxChannelConfigCreationError\\n If create_if_not_exists is True and the maximum number of channel configurations has already been reached.\\n \\\"\\\"\\\"\\n\\n config = discord.utils.get(self.channel_settings, channel=channel)\\n if config is not None:\\n return config\\n\\n if create_if_not_exists:\\n if len(self.channel_settings) >= 25:\\n raise MaxChannelConfigCreationError()\\n else:\\n if pool:\\n config = await ChannelConfig.create(channel.guild, channel, pool)\\n self.channel_settings.append(config)\\n return config\\n\\n return None\"\n },\n {\n \"identifier\": \"Giveaway\",\n \"path\": \"models/giveaways.py\",\n \"snippet\": \"class Giveaway:\\n \\\"\\\"\\\"\\n Represents a giveaway object.\\n\\n Attributes\\n ----------\\n bot: Giftify\\n The bot instance to handle the giveaway.\\n guild_id: int\\n The ID of the guild (server) where the giveaway is hosted.\\n channel_id: int\\n The ID of the channel where the giveaway is hosted.\\n message_id: int\\n The ID of the giveaway message.\\n extra_message_id: int\\n The ID of the extra message with giveaway.\\n host_id: int\\n The ID of the user hosting the giveaway.\\n donor_id: int\\n The ID of the user donating for the giveaway.\\n prize: int\\n The prize of the giveaway.\\n winner_count: int\\n The number of winners for the giveaway.\\n winners: List[int]\\n The winners of the giveaway.\\n participants: List[int]\\n The IDs participants for the giveaway.\\n ended: bool\\n Indicates whether the giveaway has ended.\\n ends: datetime.datetime\\n The timestamp when the giveaway will be ended.\\n required_roles: List[int]\\n The list of role IDs required to participate in the giveaway.\\n blacklisted_roles: List[int]\\n The list of role IDs excluded from participating in the giveaway.\\n bypass_roles: List[int]\\n The list of user IDs exempted from giveaway restrictions.\\n multiplier_roles: Optional[dict]\\n A dictionary containing multiplier_roles criteria for the giveaway.\\n messages: Optional[dict]\\n A dictionary containing message-based criteria for the giveaway.\\n messages_required: Optional[int]\\n The number of messages required to participate in the giveaway.\\n allowed_message_channels: Optional[List[int]]\\n The ID of the channels where the message count is tracked.\\n amari: Optional[int]\\n The required Amari XP to participate in the giveaway.\\n weekly_amari: Optional[int]\\n The required weekly Amari XP to participate in the giveaway.\\n \\\"\\\"\\\"\\n\\n __slots__ = (\\n \\\"bot\\\",\\n \\\"guild_id\\\",\\n \\\"channel_id\\\",\\n \\\"message_id\\\",\\n \\\"extra_message_id\\\",\\n \\\"prize\\\",\\n \\\"host_id\\\",\\n \\\"donor_id\\\",\\n \\\"winner_count\\\",\\n \\\"winners\\\",\\n \\\"participants\\\",\\n \\\"ended\\\",\\n \\\"ends\\\",\\n \\\"required_roles\\\",\\n \\\"blacklisted_roles\\\",\\n \\\"bypass_roles\\\",\\n \\\"multiplier_roles\\\",\\n \\\"messages\\\",\\n \\\"messages_required\\\",\\n \\\"allowed_message_channels\\\",\\n \\\"amari\\\",\\n \\\"weekly_amari\\\",\\n )\\n\\n def __init__(self, *, bot: Giftify, record: asyncpg.Record):\\n self.bot = bot\\n self.guild_id: int = record[\\\"guild\\\"]\\n self.channel_id: int = record[\\\"channel\\\"]\\n self.message_id: int = record[\\\"message\\\"]\\n self.extra_message_id: int = record[\\\"extra_message\\\"]\\n self.prize: str = record[\\\"prize\\\"]\\n self.host_id: int = record[\\\"host\\\"]\\n self.donor_id: Optional[int] = record[\\\"donor\\\"]\\n self.winner_count: int = record[\\\"winner_count\\\"]\\n self.winners: List[int] = record[\\\"winners\\\"]\\n self.participants: List[int] = record[\\\"participants\\\"]\\n self.ended: bool = record[\\\"ended\\\"]\\n self.ends: datetime.datetime = record[\\\"ends\\\"]\\n self.required_roles: List[int] = record[\\\"required_roles\\\"] or []\\n self.blacklisted_roles: List[int] = record[\\\"blacklisted_roles\\\"] or []\\n self.bypass_roles: List[int] = record[\\\"bypass_roles\\\"] or []\\n self.multiplier_roles: Dict[int, int] = {\\n int(role): entries\\n for role, entries in record[\\\"multiplier_roles\\\"].items()\\n if entries > 1\\n }\\n self.messages: Dict[int, int] = {\\n int(member): messages for member, messages in record[\\\"messages\\\"].items()\\n }\\n self.messages_required: Optional[int] = record[\\\"messages_required\\\"]\\n self.allowed_message_channels: Optional[List[int]] = record[\\\"messages_channel\\\"]\\n self.amari: Optional[int] = record[\\\"amari\\\"]\\n self.weekly_amari: Optional[int] = record[\\\"weekly_amari\\\"]\\n\\n def __eq__(self, other: \\\"Giveaway\\\") -> bool:\\n try:\\n return (\\n self.guild_id == other.guild_id\\n and self.channel_id == other.channel_id\\n and self.message_id == other.message_id\\n )\\n except AttributeError:\\n return False\\n\\n def __hash__(self) -> int:\\n return hash((self.guild_id, self.channel_id, self.message_id))\\n\\n def __repr__(self) -> str:\\n return f\\\"<Giveaway guild_id={self.guild_id} channel_id={self.channel_id} message_id={self.message_id}>\\\"\\n\\n @property\\n def jump_to_giveaway(self) -> discord.ui.View:\\n url = f\\\"https://discord.com/channels/{self.guild_id}/{self.channel_id}/{self.message_id}\\\"\\n view = BaseView(timeout=None)\\n button = discord.ui.Button(label=\\\"Jump To Giveaway\\\", url=url)\\n view.add_item(button)\\n return view\\n\\n @staticmethod\\n def create_embed(\\n interaction: Interaction,\\n config: GuildConfig,\\n duration: datetime.datetime,\\n winners: int,\\n prize: str,\\n required_roles: Optional[List[discord.Role]] = None,\\n blacklisted_roles: Optional[List[discord.Role]] = None,\\n bypass_roles: Optional[List[discord.Role]] = None,\\n multiplier_roles: Optional[Dict[discord.Role, int]] = None,\\n messages_required: Optional[int] = None,\\n allowed_message_channels: Optional[List[discord.TextChannel]] = None,\\n amari: Optional[int] = None,\\n weekly_amari: Optional[int] = None,\\n donor: Optional[discord.Member] = None,\\n ) -> discord.Embed:\\n assert interaction.guild is not None\\n\\n description = f\\\"Click the {config.reaction} button to join the giveaway!\\\\n\\\"\\n description += f\\\"Hosted By: {interaction.user.mention}\\\\n\\\"\\n\\n if donor:\\n description += f\\\"Donor: {donor.mention}\\\\n\\\"\\n\\n description += f\\\"Ends: {discord.utils.format_dt(duration, style='R')} ({discord.utils.format_dt(duration, style='f')})\\\\n\\\"\\n\\n embed = discord.Embed(\\n title=prize,\\n description=description,\\n colour=config.color,\\n timestamp=duration,\\n )\\n embed.set_footer(\\n text=f\\\"{winners} winner(s) • Ends\\\",\\n icon_url=interaction.guild.icon or interaction.client.user.display_avatar,\\n )\\n requirements = \\\"\\\"\\n if required_roles:\\n requirements += f\\\"Required Roles: {', '.join(role.mention for role in required_roles if role is not None)}\\\\n\\\"\\n if bypass_roles:\\n requirements += f\\\"Bypass Roles: {', '.join(role.mention for role in bypass_roles if role is not None)}\\\\n\\\"\\n\\n if blacklisted_roles:\\n requirements += f\\\"Blacklisted Roles: {', '.join(role.mention for role in blacklisted_roles if role is not None)}\\\\n\\\"\\n if messages_required:\\n requirements += (\\n f\\\"Messages Required: **{messages_required}** message(s) (5s cooldown)\\\\n\\\"\\n )\\n if allowed_message_channels:\\n requirements += f\\\"Allowed Channels: {', '.join(f'<#{c.id}>' for c in allowed_message_channels)}\\\\n\\\"\\n\\n if amari:\\n requirements += f\\\"Amari Level: {amari}\\\\n\\\"\\n if weekly_amari:\\n requirements += f\\\"Weekly Amari: {weekly_amari} XP Points\\\\n\\\"\\n\\n if requirements:\\n embed.add_field(name=\\\"Requirements\\\", value=requirements, inline=False)\\n\\n if multiplier_roles:\\n multiplier_roles_mention = \\\"\\\\n\\\".join(\\n [\\n f\\\"- {entry}x ・ {role.mention}\\\"\\n for role, entry in multiplier_roles.items()\\n if role is not None\\n ]\\n )\\n embed.add_field(\\n name=\\\"Bonus Entries\\\", value=multiplier_roles_mention, inline=False\\n )\\n\\n return embed\\n\\n @classmethod\\n async def start(\\n cls,\\n interaction: Interaction,\\n duration: datetime.datetime,\\n winners: int,\\n prize: str,\\n config: GuildConfig,\\n channel_config: Optional[ChannelConfig],\\n required_roles: Optional[List[discord.Role]] = None,\\n blacklisted_roles: Optional[List[discord.Role]] = None,\\n bypass_roles: Optional[List[discord.Role]] = None,\\n multiplier_roles: Optional[Dict[discord.Role, int]] = None,\\n messages_required: Optional[int] = None,\\n allowed_message_channels: Optional[List[discord.TextChannel]] = None,\\n amari: Optional[int] = None,\\n weekly_amari: Optional[int] = None,\\n image: Optional[discord.Attachment] = None,\\n donor: Optional[discord.Member] = None,\\n ping: bool = False,\\n message: Optional[str] = None,\\n ):\\n assert isinstance(interaction.channel, discord.TextChannel)\\n assert interaction.guild is not None\\n\\n embed = cls.create_embed(\\n interaction=interaction,\\n config=config,\\n duration=duration,\\n winners=winners,\\n prize=prize,\\n required_roles=required_roles,\\n blacklisted_roles=blacklisted_roles,\\n bypass_roles=bypass_roles,\\n multiplier_roles=multiplier_roles,\\n messages_required=messages_required,\\n allowed_message_channels=allowed_message_channels,\\n amari=amari,\\n weekly_amari=weekly_amari,\\n donor=donor,\\n )\\n view = GiveawayView(\\n config.reaction, config.participants_reaction, config.button_style\\n )\\n giveaway_message = await interaction.channel.send(\\n config.gw_header, embed=embed, view=view\\n )\\n\\n message_embed = discord.Embed(\\n title=f\\\"{GIFT_EMOJI} Giveaway\\\",\\n description=f\\\"**Message・** {message}\\\" if message else None,\\n color=config.color,\\n )\\n\\n if image:\\n message_embed.set_image(url=image)\\n\\n extra_message = None\\n\\n if ping or image:\\n ping_role = (\\n channel_config.ping\\n if channel_config and channel_config.ping\\n else config.ping\\n )\\n extra_message = await interaction.channel.send(\\n ping_role.mention if ping_role else \\\"\\\",\\n embed=message_embed if message or image else None, # type: ignore\\n allowed_mentions=discord.AllowedMentions(roles=True),\\n )\\n\\n if extra_message is None and message is not None:\\n extra_message = await interaction.channel.send(embed=message_embed)\\n\\n await interaction.client.timer_cog.create_timer(\\n message_id=giveaway_message.id,\\n channel_id=interaction.channel.id,\\n guild_id=interaction.guild.id,\\n author_id=interaction.user.id,\\n title=\\\"Giveaway\\\",\\n event=\\\"giveaway\\\",\\n expires=duration,\\n pool=interaction.client.pool,\\n )\\n\\n return await cls.create_entry(\\n bot=interaction.client,\\n guild_id=interaction.guild.id,\\n channel_id=interaction.channel.id,\\n message_id=giveaway_message.id,\\n prize=prize,\\n host_id=interaction.user.id,\\n donor_id=donor.id if donor else None,\\n winner_count=winners,\\n ends=duration,\\n required_roles=[role.id for role in required_roles if role is not None]\\n if required_roles\\n else [],\\n blacklisted_roles=[\\n role.id for role in blacklisted_roles if role is not None\\n ]\\n if blacklisted_roles\\n else [],\\n bypass_roles=[role.id for role in bypass_roles if role is not None]\\n if bypass_roles\\n else [],\\n multiplier_roles={\\n role.id: entries\\n for role, entries in multiplier_roles.items()\\n if role is not None\\n }\\n if multiplier_roles\\n else {},\\n messages={},\\n messages_required=messages_required,\\n allowed_message_channels=[c.id for c in allowed_message_channels]\\n if allowed_message_channels\\n else [],\\n extra_message_id=extra_message.id if extra_message else None,\\n amari=amari,\\n weekly_amari=weekly_amari,\\n )\\n\\n @classmethod\\n async def create_entry(\\n cls,\\n bot: Giftify,\\n guild_id: int,\\n channel_id: int,\\n message_id: int,\\n prize: str,\\n host_id: int,\\n winner_count: int,\\n ends: datetime.datetime,\\n required_roles: List[int],\\n blacklisted_roles: List[int],\\n bypass_roles: List[int],\\n donor_id: Optional[int],\\n multiplier_roles: Optional[dict],\\n messages: Optional[dict],\\n messages_required: Optional[int],\\n allowed_message_channels: Optional[List[int]],\\n extra_message_id: Optional[int],\\n amari: Optional[int],\\n weekly_amari: Optional[int],\\n ) -> \\\"Giveaway\\\":\\n \\\"\\\"\\\"\\n Create a new Giveaway object and insert it into the database.\\n\\n Parameters\\n ----------\\n bot: Giftify\\n The bot instance.\\n guild_id: int\\n The ID of the guild (server) where the giveaway is hosted.\\n channel_id: int\\n The ID of the channel where the giveaway is hosted.\\n message_id: int\\n The ID of the message having the giveaway view.\\n prize: str\\n The prize of the giveaway.\\n host_id: int\\n The ID of the user hosting the giveaway.\\n donor_id: int\\n The ID of the donor of the giveaway.\\n winner_count: int\\n The number of winners for the giveaway.\\n ends: datetime.datetime\\n The time when the giveaway ends.\\n required_roles: List[int]\\n The list of role IDs required to participate in the giveaway.\\n blacklisted_roles: List[int]\\n The list of role IDs excluded from participating in the giveaway.\\n bypass_roles: List[int]\\n The list of user IDs exempted from giveaway restrictions.\\n multiplier_roles: Optional[dict]\\n A dictionary containing multiplier_roles criteria for the giveaway.\\n messages: Optional[dict]\\n A dictionary containing message-based criteria for the giveaway.\\n messages_required: Optional[int]\\n The number of messages required to participate in the giveaway.\\n allowed_message_channels: Optional[int]\\n The ID of the channel where the message count is tracked.\\n amari: Optional[int]\\n The required Amari XP to participate in the giveaway.\\n weekly_amari: Optional[int]\\n The required weekly Amari XP to participate in the giveaway.\\n\\n Returns\\n -------\\n Giveaway\\n The created Giveaway object.\\n \\\"\\\"\\\"\\n record = await bot.pool.fetchrow(\\n \\\"INSERT INTO giveaways (guild, channel, message, extra_message, host, donor, prize, winner_count, ends, required_roles, blacklisted_roles, bypass_roles, multiplier_roles, messages, messages_required, messages_channel, amari, weekly_amari) \\\"\\n \\\"VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13, $14, $15, $16, $17, $18) \\\"\\n \\\"RETURNING *\\\",\\n guild_id,\\n channel_id,\\n message_id,\\n extra_message_id,\\n host_id,\\n donor_id,\\n prize,\\n winner_count,\\n ends,\\n required_roles,\\n blacklisted_roles,\\n bypass_roles,\\n multiplier_roles,\\n messages,\\n messages_required,\\n allowed_message_channels,\\n amari,\\n weekly_amari,\\n )\\n return cls(bot=bot, record=record)\\n\\n async def check_requirements(self, member: discord.Member) -> None:\\n missing_roles = [\\n role.mention\\n for role_id in self.required_roles\\n if (role := member.guild.get_role(role_id)) and role not in member.roles\\n ]\\n if missing_roles:\\n raise GiveawayError(\\n f\\\"You cannot join this giveaway as you are missing the following required roles: {', '.join(missing_roles)}\\\"\\n )\\n\\n blacklisted_roles = [\\n role.mention\\n for role_id in self.blacklisted_roles\\n if (role := member.guild.get_role(role_id)) and role in member.roles\\n ]\\n if blacklisted_roles:\\n raise GiveawayError(\\n f\\\"You cannot join this giveaway as you have the following blacklisted roles: {', '.join(blacklisted_roles)}\\\"\\n )\\n\\n if self.amari:\\n if (user_level := await self.bot.fetch_level(member)) < self.amari:\\n raise GiveawayError(\\n f\\\"Your amari level is less than the required level, you need `{self.amari - user_level}` more level(s) to join the giveaway.\\\"\\n )\\n\\n if self.weekly_amari:\\n if (\\n weekly_exp := await self.bot.fetch_weekly_experience(member)\\n ) < self.weekly_amari:\\n raise GiveawayError(\\n f\\\"Your weekly amari experience is less than the required weekly amari experience, you need `{self.weekly_amari - weekly_exp}` more experience point(s) to join the giveaway.\\\"\\n )\\n\\n if self.messages_required and self.messages_required > 0:\\n if (\\n user_messages := self.messages.get(member.id, 0)\\n ) < self.messages_required:\\n raise GiveawayError(\\n f\\\"You have sent less messages than the required messages, you need to send `{self.messages_required - user_messages}` more messages to join the giveaway.\\\"\\n )\\n\\n def can_bypass(self, member: discord.Member) -> bool:\\n return any(\\n member.guild.get_role(role_id) in member.roles\\n for role_id in self.bypass_roles\\n )\\n\\n def get_multiplier_entries(self, member: discord.Member) -> int:\\n entries = 0\\n for role_id, multiplier_roles_entries in self.multiplier_roles.items():\\n if member.get_role(int(role_id)):\\n entries += multiplier_roles_entries\\n\\n return entries or 1\\n\\n async def join(self, member: discord.Member) -> int:\\n try:\\n await self.check_requirements(member)\\n except GiveawayError as error:\\n if not self.can_bypass(member):\\n raise error\\n\\n if member.id in self.participants:\\n raise GiveawayError(\\\"You have already joined the giveaway.\\\")\\n\\n number_of_entries = self.get_multiplier_entries(member)\\n entries = [member.id] * number_of_entries\\n\\n self.participants += entries\\n\\n query = \\\"\\\"\\\"UPDATE giveaways SET participants = $1 \\n WHERE guild = $2 AND channel = $3 AND message = $4\\\"\\\"\\\"\\n\\n await self.bot.pool.execute(\\n query, self.participants, self.guild_id, self.channel_id, self.message_id\\n )\\n\\n return len(set(self.participants))\\n\\n async def leave(self, member: discord.Member) -> int:\\n if member.id not in self.participants:\\n raise GiveawayError(\\\"You are not a participant of this giveaway.\\\")\\n\\n self.participants = [\\n participant for participant in self.participants if participant != member.id\\n ]\\n\\n query = \\\"\\\"\\\"UPDATE giveaways SET participants = $1 \\n WHERE guild = $2 AND channel = $3 AND message = $4\\\"\\\"\\\"\\n\\n await self.bot.pool.execute(\\n query, self.participants, self.guild_id, self.channel_id, self.message_id\\n )\\n\\n return len(set(self.participants))\\n\\n async def _end(self):\\n await self.bot.pool.execute(\\n \\\"UPDATE giveaways SET ended = $1, winners = $2 WHERE guild = $3 AND channel = $4 AND message = $5\\\",\\n True,\\n self.winners,\\n self.guild_id,\\n self.channel_id,\\n self.message_id,\\n )\\n\\n async def end(self):\\n guild = self.bot.get_guild(self.guild_id)\\n if not guild:\\n return await self._end()\\n\\n config = await self.bot.fetch_config(guild)\\n winners = await self.pick_winners(self.winner_count, guild)\\n self.winners = [winner.id for winner in winners]\\n\\n await self._end()\\n\\n if config.dm_host:\\n await self.dm_host(guild, winners, config.dm_host_message)\\n\\n if config.dm_winner:\\n await self.dm_winners(config.dm_message, winners)\\n\\n channel = guild.get_channel(self.channel_id)\\n if not channel or not isinstance(channel, discord.TextChannel):\\n return\\n\\n gw_message = channel.get_partial_message(self.message_id)\\n message = (\\n safe_format(\\n config.end_message,\\n winners=\\\", \\\".join(winner.mention for winner in winners),\\n prize=bold(self.prize),\\n )\\n if winners\\n else f\\\"Could not pick any winners for the giveaway of {bold(self.prize)}!\\\"\\n )\\n embed = self.get_end_embed(guild, config)\\n\\n view = GiveawayView(\\n config.reaction,\\n config.participants_reaction,\\n config.button_style,\\n participant_count=len(set(self.participants)),\\n disabled=True,\\n )\\n\\n with contextlib.suppress(discord.HTTPException):\\n await gw_message.edit(content=config.gw_end_header, embed=embed, view=view)\\n await gw_message.reply(message, view=self.jump_to_giveaway)\\n\\n async def reroll(self, winner_count: int):\\n guild = self.bot.get_guild(self.guild_id)\\n if not guild:\\n return\\n\\n config = await self.bot.fetch_config(guild)\\n winners = await self.pick_winners(winner_count, guild)\\n self.winners = [winner.id for winner in winners]\\n\\n await self._end()\\n\\n if config.dm_winner:\\n await self.dm_winners(config.dm_message, winners)\\n\\n channel = guild.get_channel(self.channel_id)\\n if not channel or not isinstance(channel, discord.TextChannel):\\n return\\n\\n gw_message = channel.get_partial_message(self.message_id)\\n message = (\\n safe_format(\\n config.reroll_message,\\n winners=\\\", \\\".join(winner.mention for winner in winners),\\n prize=bold(self.prize),\\n )\\n if winners\\n else f\\\"Could not pick any winners for the giveaway of {bold(self.prize)}!\\\"\\n )\\n embed = self.get_end_embed(guild, config)\\n\\n view = GiveawayView(\\n config.reaction,\\n config.participants_reaction,\\n config.button_style,\\n participant_count=len(set(self.participants)),\\n disabled=True,\\n )\\n\\n with contextlib.suppress(discord.HTTPException):\\n await gw_message.edit(content=config.gw_end_header, embed=embed, view=view)\\n await gw_message.reply(message, view=self.jump_to_giveaway)\\n\\n async def cancel(self):\\n await self.bot.pool.execute(\\n \\\"\\\"\\\"DELETE FROM giveaways WHERE guild = $1 AND channel = $2 AND message = $3\\\"\\\"\\\",\\n self.guild_id,\\n self.channel_id,\\n self.message_id,\\n )\\n if self.extra_message_id is not None:\\n channel = self.bot.get_channel(self.channel_id)\\n if channel is not None:\\n await channel.get_partial_message(self.extra_message_id).delete() # type: ignore\\n\\n async def dm_host(\\n self, guild: discord.Guild, winners: List[discord.Member], message: str\\n ) -> None:\\n host = await self.bot.get_or_fetch_member(guild, self.host_id)\\n if not host:\\n return\\n\\n description = safe_format(\\n message,\\n winners=\\\", \\\".join(winner.mention for winner in winners)\\n if winners\\n else \\\"No Winners\\\",\\n prize=bold(self.prize),\\n )\\n\\n embed = discord.Embed(\\n title=f\\\"Your giveaway for {self.prize} has ended!\\\"[:256],\\n description=description,\\n colour=self.bot.colour,\\n )\\n view = self.jump_to_giveaway\\n\\n with contextlib.suppress(discord.HTTPException):\\n await host.send(embed=embed, view=view)\\n\\n async def dm_winners(self, message: str, winners: List[discord.Member]) -> None:\\n for winner in winners:\\n description = safe_format(\\n message, winner=winner.mention, prize=bold(self.prize)\\n )\\n\\n embed = discord.Embed(\\n title=\\\"You won!\\\",\\n description=description,\\n colour=self.bot.colour,\\n )\\n view = self.jump_to_giveaway\\n\\n with contextlib.suppress(discord.HTTPException):\\n await winner.send(embed=embed, view=view)\\n\\n async def pick_winners(\\n self, count: int, guild: discord.Guild\\n ) -> List[discord.Member]:\\n winners = []\\n\\n participants = self.participants.copy()\\n\\n while count > 0 and participants:\\n member_id = random.choice(participants)\\n member = await self.bot.get_or_fetch_member(guild, member_id)\\n if member is not None and member not in winners:\\n try:\\n await self.check_requirements(member)\\n except GiveawayError:\\n pass\\n else:\\n winners.append(member)\\n count -= 1\\n\\n participants.remove(member_id)\\n\\n return winners\\n\\n def get_end_embed(self, guild: discord.Guild, config: GuildConfig) -> discord.Embed:\\n description = (\\n f\\\"This giveaway has ended!\\\\n\\\"\\n f\\\"Hosted By: <@!{self.host_id}>\\\\n\\\"\\n f\\\"Winners: {', '.join(f'<@!{winner_id}>' for winner_id in self.winners) if self.winners else 'No Winners'}\\\\n\\\"\\n f\\\"Ended: {discord.utils.format_dt(datetime.datetime.now(datetime.timezone.utc), style='R')} ({discord.utils.format_dt(datetime.datetime.now(datetime.timezone.utc), style='f')})\\\\n\\\"\\n )\\n if self.donor_id:\\n description += f\\\"Donor: <@!{self.donor_id}>\\\\n\\\"\\n embed = discord.Embed(\\n title=self.prize,\\n description=description,\\n colour=config.color,\\n timestamp=self.ends,\\n )\\n embed.set_footer(\\n text=f\\\"{self.winner_count} winner(s) • Ended\\\",\\n icon_url=guild.icon or self.bot.user.display_avatar,\\n )\\n\\n requirements = \\\"\\\"\\n if self.required_roles:\\n requirements += f\\\"Required Roles: {', '.join(f'<@&{role_id}>' for role_id in self.required_roles)}\\\\n\\\"\\n if self.bypass_roles:\\n requirements += f\\\"Bypass Roles: {', '.join(f'<@&{role_id}>' for role_id in self.bypass_roles)}\\\\n\\\"\\n if self.blacklisted_roles:\\n requirements += f\\\"Blacklisted Roles: {', '.join(f'<@&{role_id}>' for role_id in self.blacklisted_roles)}\\\\n\\\"\\n if self.messages_required:\\n requirements += f\\\"Messages Required: **{self.messages_required}** message(s) (5s cooldown)\\\\n\\\"\\n if self.allowed_message_channels:\\n requirements += f\\\"Allowed Channels: {', '.join(f'<#{cid}>' for cid in self.allowed_message_channels)}\\\\n\\\"\\n if self.amari:\\n requirements += f\\\"Amari Level: {self.amari}\\\\n\\\"\\n if self.weekly_amari:\\n requirements += f\\\"Weekly Amari: {self.weekly_amari} XP Points\\\\n\\\"\\n\\n if requirements:\\n embed.add_field(name=\\\"Requirements\\\", value=requirements, inline=False)\\n\\n if self.multiplier_roles:\\n multiplier_roles = \\\"\\\\n\\\".join(\\n [\\n f\\\"- {multiplier_entries}x ・ <@&{multiplier_role}>\\\"\\n for multiplier_role, multiplier_entries in self.multiplier_roles.items()\\n ]\\n )\\n embed.add_field(name=\\\"Bonus Entries\\\", value=multiplier_roles, inline=False)\\n\\n return embed\"\n },\n {\n \"identifier\": \"Raffle\",\n \"path\": \"models/raffles.py\",\n \"snippet\": \"class Raffle:\\n \\\"\\\"\\\"\\n Represents a raffle object.\\n\\n Attributes\\n ----------\\n pool: asyncpg.Pool\\n The PostgreSQL connection pool instance.\\n guild: discord.Guild\\n The guild (server) where the raffle is hosted.\\n name: str\\n The name of the raffle.\\n winner: Optional[discord.Member]\\n The member instance of the winner, or None if the raffle hasn't ended yet.\\n deputy_roles: List[discord.Role]\\n A list of roles associated with the raffle.\\n deputy_members: List[discord.Member]\\n A list of members associated with the raffle.\\n tickets: Dict[discord.Member, int]\\n A mapping of members to the number of tickets they have.\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n pool: asyncpg.Pool,\\n *,\\n guild: discord.Guild,\\n name: str,\\n winner: Optional[discord.Member],\\n deputy_roles: List[discord.Role],\\n deputy_members: List[discord.Member],\\n tickets: Dict[discord.Member, int],\\n ):\\n self.pool = pool\\n\\n self.guild = guild\\n self.name = name\\n self.winner = winner\\n self.deputy_roles = deputy_roles\\n self.deputy_members = deputy_members\\n self.tickets = tickets\\n\\n def __str__(self):\\n return self.name\\n\\n def __repr__(self) -> str:\\n return f\\\"<Raffle name={self.name} guild={self.guild} winner={self.winner}>\\\"\\n\\n def __hash__(self) -> int:\\n return hash((self.name, self.guild))\\n\\n def __eq__(self, other: Raffle) -> bool:\\n return self.name == other.name and self.guild == other.guild\\n\\n @classmethod\\n async def from_record(cls, bot: Giftify, *, record: asyncpg.Record) -> Raffle:\\n name = record[\\\"name\\\"]\\n guild = bot.get_guild(record[\\\"guild\\\"])\\n if guild is None:\\n raise RaffleError(\\\"The guild having the raffle was not found.\\\")\\n\\n winner_id = record[\\\"winner\\\"]\\n winner: Optional[discord.Member] = (\\n (await bot.get_or_fetch_member(guild, winner_id) or FakeMember(winner_id))\\n if winner_id\\n else None\\n ) # type: ignore\\n\\n deputy_roles = [guild.get_role(role_id) for role_id in record[\\\"deputy_roles\\\"]]\\n deputy_members = [\\n await bot.get_or_fetch_member(guild, member_id)\\n for member_id in record[\\\"deputy_members\\\"]\\n ]\\n\\n tickets = {\\n await bot.get_or_fetch_member(guild, int(member_id)): num_tickets\\n for member_id, num_tickets in record[\\\"tickets\\\"].items()\\n }\\n\\n return cls(\\n bot.pool,\\n guild=guild,\\n name=name,\\n winner=winner,\\n deputy_roles=filter_none(deputy_roles),\\n deputy_members=filter_none(deputy_members),\\n tickets=filter_none(tickets),\\n )\\n\\n async def roll(self) -> discord.Member:\\n \\\"\\\"\\\"\\n End the raffle and set the winner.\\n \\\"\\\"\\\"\\n members = list(self.tickets.keys())\\n weights = list(self.tickets.values())\\n\\n self.winner = random.choices(members, weights, k=1)[0]\\n\\n await self.save()\\n\\n return self.winner\\n\\n async def add_deputy(self, obj: Union[discord.Member, discord.Role]) -> None:\\n \\\"\\\"\\\"\\n Add a deputy to the raffle.\\n\\n Parameters\\n ----------\\n obj: Union[discord.Member, discord.Role]\\n The instance of deputy member or role to be added.\\n \\\"\\\"\\\"\\n if isinstance(obj, discord.Member):\\n if len(self.deputy_members) >= 25:\\n raise RaffleError(\\\"You cannot add more than 25 deputy members.\\\")\\n self.deputy_members.append(obj)\\n elif isinstance(obj, discord.Role):\\n if len(self.deputy_roles) >= 10:\\n raise RaffleError(\\\"You cannot add more than 10 deputy roles.\\\")\\n self.deputy_roles.append(obj)\\n else:\\n raise RaffleError(\\\"Invalid obj type.\\\")\\n\\n await self.save()\\n\\n async def remove_deputy(self, obj: Union[discord.Member, discord.Role]) -> None:\\n \\\"\\\"\\\"\\n Remove a deputy from the raffle.\\n\\n Parameters\\n ----------\\n obj: Union[discord.Member, discord.Role]\\n The instance of deputy member or role to be removed.\\n \\\"\\\"\\\"\\n if isinstance(obj, discord.Member):\\n if obj not in self.deputy_members:\\n raise RaffleError(\\\"That member is not a deputy.\\\")\\n self.deputy_members.remove(obj)\\n elif isinstance(obj, discord.Role):\\n if obj not in self.deputy_roles:\\n raise RaffleError(\\\"That role is not a deputy.\\\")\\n self.deputy_roles.remove(obj)\\n else:\\n raise RaffleError(\\\"Invalid obj type.\\\")\\n\\n await self.save()\\n\\n async def add_tickets(self, member: discord.Member, num_tickets: int) -> None:\\n \\\"\\\"\\\"\\n Add tickets to a member.\\n\\n Parameters\\n ----------\\n member: discord.Member\\n The instance of the member.\\n num_tickets: int\\n The number of tickets to add.\\n \\\"\\\"\\\"\\n if member in self.tickets:\\n self.tickets[member] += num_tickets\\n else:\\n self.tickets[member] = num_tickets\\n\\n await self.save()\\n\\n async def remove_tickets(self, member: discord.Member, num_tickets: int) -> None:\\n \\\"\\\"\\\"\\n Remove tickets from a member.\\n\\n Parameters\\n ----------\\n member: discord.Member\\n The instance of the member.\\n num_tickets: int\\n The number of tickets to remove.\\n \\\"\\\"\\\"\\n if member in self.tickets:\\n self.tickets[member] -= num_tickets\\n if self.tickets[member] <= 0:\\n del self.tickets[member]\\n\\n await self.save()\\n else:\\n raise RaffleError(\\n f\\\"That member does not have any tickets in {self.name} raffle.\\\"\\n )\\n\\n async def save(self) -> None:\\n \\\"\\\"\\\"\\n Update raffle attributes in the database.\\n \\\"\\\"\\\"\\n query = \\\"\\\"\\\"\\n INSERT INTO raffles (guild, name, winner, deputy_roles, deputy_members, tickets)\\n VALUES ($1, $2, $3, $4, $5, $6)\\n ON CONFLICT (guild, name)\\n DO UPDATE SET winner = EXCLUDED.winner, deputy_roles = EXCLUDED.deputy_roles,\\n deputy_members = EXCLUDED.deputy_members, tickets = EXCLUDED.tickets;\\n \\\"\\\"\\\"\\n await self.pool.execute(\\n query,\\n self.guild.id,\\n self.name,\\n self.winner.id if self.winner else None,\\n [role.id for role in self.deputy_roles],\\n [member.id for member in self.deputy_members],\\n {\\n str(member.id): num_tickets\\n for member, num_tickets in self.tickets.items()\\n },\\n )\\n\\n async def delete(self):\\n \\\"\\\"\\\"\\n Delete the raffle from the database.\\n \\\"\\\"\\\"\\n query = \\\"\\\"\\\"DELETE FROM raffles WHERE guild = $1 AND name = $2\\\"\\\"\\\"\\n await self.pool.execute(query, self.guild.id, self.name)\"\n },\n {\n \"identifier\": \"ERROR_EMOJI\",\n \"path\": \"utils/constants.py\",\n \"snippet\": \"ERROR_EMOJI = \\\"<:GiftifyError:1117842868057423914>\\\"\"\n },\n {\n \"identifier\": \"SUCCESS_EMOJI\",\n \"path\": \"utils/constants.py\",\n \"snippet\": \"SUCCESS_EMOJI = \\\"<:GiftifySuccess:1100674526318166048>\\\"\"\n },\n {\n \"identifier\": \"WARN_EMOJI\",\n \"path\": \"utils/constants.py\",\n \"snippet\": \"WARN_EMOJI = \\\"<:GiftifyWarn:1098498926564356106>\\\"\"\n },\n {\n \"identifier\": \"db_init\",\n \"path\": \"utils/db.py\",\n \"snippet\": \"async def db_init(connection: asyncpg.Connection) -> None:\\n await connection.set_type_codec(\\n \\\"jsonb\\\", schema=\\\"pg_catalog\\\", encoder=_encode_jsonb, decoder=_decode_jsonb\\n )\"\n },\n {\n \"identifier\": \"CommandTree\",\n \"path\": \"utils/tree.py\",\n \"snippet\": \"class CommandTree(app_commands.CommandTree):\\r\\n client: \\\"Giftify\\\"\\r\\n\\r\\n async def on_error(\\r\\n self,\\r\\n interaction: Interaction,\\r\\n error: app_commands.AppCommandError,\\r\\n ) -> None:\\r\\n view = discord.ui.View()\\r\\n\\r\\n button = discord.ui.Button(label=\\\"Support\\\", url=\\\"https://discord.gg/GQSGChbEKz\\\")\\r\\n\\r\\n view.add_item(button)\\r\\n\\r\\n if not interaction.response.is_done():\\r\\n await interaction.response.defer(thinking=True, ephemeral=True)\\r\\n\\r\\n embed = discord.Embed(\\r\\n title=\\\"An error was raised while executing this command!\\\",\\r\\n color=discord.Colour.red(),\\r\\n )\\r\\n\\r\\n if isinstance(error, app_commands.CommandInvokeError):\\r\\n if isinstance(error, MaxChannelConfigCreationError):\\r\\n embed.description = (\\r\\n f\\\"{WARN_EMOJI} You cannot setup configuration for more than 25 channels, please try removing some.\\\"\\r\\n )\\r\\n elif isinstance(error, discord.HTTPException):\\r\\n embed.description = f\\\"{WARN_EMOJI} Unknown HTTP error occured!\\\"\\r\\n else:\\r\\n embed.description = (\\r\\n f\\\"{WARN_EMOJI} An unknown error occurred , my developers have been notified about this error.\\\"\\r\\n )\\r\\n self.client.log_handler.log.exception(\\\"Exception occurred in the CommandTree:\\\\n\\\", exc_info=error)\\r\\n sentry_sdk.capture_exception(error)\\r\\n elif isinstance(error, app_commands.TransformerError):\\r\\n if isinstance(error, TransformerError):\\r\\n embed.description = f\\\"{WARN_EMOJI} {error.message}\\\"\\r\\n else:\\r\\n embed.description = f\\\"{WARN_EMOJI} {str(error)}\\\"\\r\\n\\r\\n elif isinstance(error, app_commands.MissingPermissions):\\r\\n missing = [perm.replace(\\\"_\\\", \\\" \\\").replace(\\\"guild\\\", \\\"server\\\").title() for perm in error.missing_permissions]\\r\\n\\r\\n format = \\\"\\\\n> \\\".join(missing)\\r\\n\\r\\n embed.description = (\\r\\n f\\\"{WARN_EMOJI} You are missing follwing permission(s) to run this command: \\\\n\\\\n> {format}\\\"\\r\\n )\\r\\n\\r\\n elif isinstance(error, app_commands.BotMissingPermissions):\\r\\n missing = [perm.replace(\\\"_\\\", \\\" \\\").replace(\\\"guild\\\", \\\"server\\\").title() for perm in error.missing_permissions]\\r\\n\\r\\n format = \\\"\\\\n> \\\".join(missing)\\r\\n\\r\\n embed.description = f\\\"{WARN_EMOJI} I am missing follwing permission(s) to run this command: \\\\n\\\\n > {format}\\\"\\r\\n\\r\\n elif isinstance(error, app_commands.CommandOnCooldown):\\r\\n cooldown = int(error.cooldown.per)\\r\\n retry_after = int(error.retry_after)\\r\\n embed.description = f\\\"{WARN_EMOJI} The cooldown for this command is **{cooldown}s**. Try running the command again after **{retry_after}s**.\\\"\\r\\n\\r\\n elif isinstance(error, app_commands.CommandNotFound):\\r\\n embed.description = f'{WARN_EMOJI} The command \\\"{error.name}\\\" was not found.'\\r\\n elif isinstance(error, DonationError):\\r\\n embed.description = f\\\"{WARN_EMOJI} {str(error)}\\\"\\r\\n elif isinstance(error, app_commands.CheckFailure):\\r\\n if isinstance(error, (DonationCategoryError, DonationPermissionsError)):\\r\\n embed.description = f\\\"{WARN_EMOJI} {str(error.message)}\\\"\\r\\n else:\\r\\n return\\r\\n else:\\r\\n embed.description = (\\r\\n f\\\"{WARN_EMOJI} An unknown error occured, my developers have been notified about this errors.\\\"\\r\\n )\\r\\n await interaction.followup.send(embed=embed, ephemeral=True)\\r\\n sentry_sdk.capture_exception(error)\\r\\n return self.client.log_handler.log.exception(\\\"Exception occurred in the CommandTree:\\\\n\\\", exc_info=error)\\r\\n\\r\\n return await interaction.followup.send(embed=embed, ephemeral=True)\\r\"\n },\n {\n \"identifier\": \"ConfirmationView\",\n \"path\": \"utils/view.py\",\n \"snippet\": \"class ConfirmationView(BaseView):\\r\\n def __init__(\\r\\n self,\\r\\n *,\\r\\n timeout: float,\\r\\n interaction: Interaction,\\r\\n success_message: str,\\r\\n cancel_message: str,\\r\\n ) -> None:\\r\\n super().__init__(timeout=timeout)\\r\\n self.interaction = interaction\\r\\n self.success_message = success_message\\r\\n self.cancel_message = cancel_message\\r\\n self.value: Optional[bool] = None\\r\\n\\r\\n @property\\r\\n def success_embed(self) -> discord.Embed:\\r\\n return discord.Embed(\\r\\n description=f\\\"{SUCCESS_EMOJI} {self.success_message}\\\",\\r\\n colour=discord.Colour.green(),\\r\\n )\\r\\n\\r\\n @property\\r\\n def cancel_embed(self) -> discord.Embed:\\r\\n return discord.Embed(\\r\\n description=f\\\"{SUCCESS_EMOJI} {self.cancel_message}\\\",\\r\\n colour=discord.Colour.green(),\\r\\n )\\r\\n\\r\\n async def interaction_check(self, interaction: Interaction) -> bool:\\r\\n if interaction.user and interaction.user.id == self.interaction.user.id:\\r\\n return True\\r\\n else:\\r\\n await interaction.response.send_message(\\r\\n \\\"This confirmation dialog is not for you.\\\", ephemeral=True\\r\\n )\\r\\n return False\\r\\n\\r\\n async def on_timeout(self) -> None:\\r\\n with contextlib.suppress(discord.HTTPException):\\r\\n for item in self.children:\\r\\n item.disabled = True\\r\\n await self.interaction.edit_original_response(view=self)\\r\\n\\r\\n @discord.ui.button(label=\\\"Confirm\\\", style=discord.ButtonStyle.green)\\r\\n async def confirm(self, interaction: Interaction, button: discord.ui.Button):\\r\\n self.value = True\\r\\n await interaction.response.edit_message(embed=self.success_embed, view=None)\\r\\n self.stop()\\r\\n\\r\\n @discord.ui.button(label=\\\"Cancel\\\", style=discord.ButtonStyle.red)\\r\\n async def cancel(self, interaction: Interaction, button: discord.ui.Button):\\r\\n self.value = False\\r\\n await interaction.response.edit_message(embed=self.cancel_embed, view=None)\\r\\n\\r\\n self.stop()\\r\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import asyncio\r\nimport datetime\r\nimport logging\r\nimport os\r\nimport pathlib\r\nimport sys\r\nimport traceback\r\nimport aiohttp\r\nimport asyncpg\r\nimport discord\r\nimport dotenv\r\nimport jishaku\r\nimport sentry_sdk\r\n import uvloop\r\nfrom logging.handlers import RotatingFileHandler\r\nfrom typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple\r\nfrom amari import AmariClient\r\nfrom discord.ext import commands\r\nfrom discord.utils import MISSING\r\nfrom discord.utils import _ColourFormatter as ColourFormatter\r\nfrom expiringdict import ExpiringDict\r\nfrom sentry_sdk.integrations.logging import LoggingIntegration\r\nfrom models.giveaway_settings import GuildConfig\r\nfrom models.giveaways import Giveaway\r\nfrom models.raffles import Raffle\r\nfrom utils.constants import ERROR_EMOJI, SUCCESS_EMOJI, WARN_EMOJI\r\nfrom utils.db import db_init\r\nfrom utils.tree import CommandTree\r\nfrom utils.view import ConfirmationView\r\n from cogs.timer_manager import TimerManager\r\n from models.donation_settings import GuildDonationConfig\r"},"token_num":{"kind":"number","value":14886,"string":"14,886"},"cropped_code":{"kind":"string","value":"\r\n async def fetch_level(self, member: discord.Member, /) -> int:\r\n \"\"\"Fetches user level from Amari Bot API.\r\n\r\n Parameters\r\n -----------\r\n member: discord.Member\r\n The member whose level is to be fetched.\r\n\r\n Returns\r\n ---------\r\n int\r\n The retrieved level.\r\n \"\"\"\r\n try:\r\n user = await self.amari_client.fetch_user(member.guild.id, member.id)\r\n except Exception:\r\n return 0\r\n else:\r\n return user.level or 0\r\n\r\n async def fetch_weekly_experience(self, member: discord.Member, /) -> int:\r\n \"\"\"Fetches user's weekly experience from Amari Bot API.\r\n\r\n Parameters\r\n -----------\r\n member: discord.Member\r\n The member whose weekly experience is to be fetched.\r\n\r\n Returns\r\n ---------\r\n int\r\n The retrieved weekly experience.\r\n \"\"\"\r\n try:\r\n user = await self.amari_client.fetch_user(member.guild.id, member.id)\r\n except Exception:\r\n return 0\r\n else:\r\n return user.weeklyexp or 0\r\n\r\n async def prompt(\r\n self,\r\n message: str,\r\n *,\r\n interaction: discord.Interaction[Giftify],\r\n success_message: str,\r\n cancel_message: str,\r\n timeout: float = 60.0,\r\n ) -> Optional[bool]:\r\n \"\"\"An interactive reaction confirmation dialog.\r\n\r\n Parameters\r\n -----------\r\n message: str\r\n The message to show along with the prompt.\r\n timeout: float\r\n How long to wait before returning.\r\n interaction: Interaction\r\n The interaction object to handle the confirmation dialog.\r\n success_message: str\r\n The message to show when the user clicks Confirm.\r\n cancel_message: str\r\n The message to show when the user clicks Cancel.\r\n\r\n Returns\r\n --------\r\n Optional[bool]\r\n ``True`` if explicit confirm,\r\n ``False`` if explicit deny,\r\n ``None`` if deny due to timeout\r\n \"\"\"\r\n\r\n view = ConfirmationView(\r\n timeout=timeout,\r\n interaction=interaction,\r\n success_message=success_message,\r\n cancel_message=cancel_message,\r\n )\r\n view.message = await self.send(interaction, message, view=view, reason=\"warn\")\r\n await view.wait()\r\n return view.value\r\n\r\n\r\nclass Giftify(GiftifyHelper, commands.AutoShardedBot):\r\n user: discord.ClientUser\r\n\r\n colour: int = 0xCB3045\r\n __version_info__ = \"1.1.4\"\r\n\r\n def __init__(\r\n self,\r\n *,\r\n log_handler: LogHandler,\r\n pool: asyncpg.Pool,\r\n session: aiohttp.ClientSession,\r\n amari_client: AmariClient,\r\n ) -> None:\r\n self._log_handler = log_handler\r\n self._pool = pool\r\n self._session = session\r\n self._amari_client = amari_client\r\n\r\n intents = discord.Intents(messages=True, emojis=True, guilds=True)\r\n allowed_mentions = discord.AllowedMentions(everyone=False, roles=False, users=True, replied_user=False)\r\n member_cache_flags = discord.MemberCacheFlags.from_intents(intents=intents)\r\n\r\n sentry_sdk.init(\r\n dsn=os.environ[\"SENTRY_DSN\"],\r\n integrations=[\r\n LoggingIntegration(\r\n level=logging.INFO,\r\n event_level=logging.ERROR,\r\n )\r\n ],\r\n traces_sample_rate=1.0,\r\n )\r\n\r\n super().__init__(\r\n command_prefix=commands.when_mentioned,\r\n"},"all_code":{"kind":"string","value":"from __future__ import annotations\r\n\r\n\r\n\r\n\r\nif TYPE_CHECKING:\r\n\r\ndotenv.load_dotenv()\r\n\r\ntry:\r\nexcept ImportError: # Windows\r\n pass\r\nelse:\r\n uvloop.install()\r\n\r\n\r\njishaku.Flags.HIDE = True\r\njishaku.Flags.RETAIN = True\r\njishaku.Flags.NO_UNDERSCORE = True\r\njishaku.Flags.NO_DM_TRACEBACK = True\r\n\r\nOWNER_IDS = (747403406154399765,)\r\n\r\nEXTENSIONS: Tuple[str, ...] = (\r\n \"meta\",\r\n \"settings\",\r\n \"timers\",\r\n \"giveaways\",\r\n \"donations\",\r\n \"raffles\",\r\n \"logger\",\r\n \"webserver\",\r\n)\r\n\r\n\r\nclass RemoveNoise(logging.Filter):\r\n def __init__(self) -> None:\r\n super().__init__(name=\"discord.state\")\r\n\r\n def filter(self, record) -> bool:\r\n if record.levelname == \"WARNING\" and \"referencing an unknown\" in record.msg:\r\n return False\r\n return True\r\n\r\n\r\nclass LogHandler:\r\n def __init__(self, stream: bool = True) -> None:\r\n self.log: logging.Logger = logging.getLogger()\r\n self.max_bytes: int = 32 * 1024 * 1024\r\n self.logging_path = pathlib.Path(\"./logs/\")\r\n self.logging_path.mkdir(exist_ok=True)\r\n self.stream = stream\r\n\r\n async def __aenter__(self) -> \"LogHandler\":\r\n return self.__enter__()\r\n\r\n def __enter__(self: \"LogHandler\") -> \"LogHandler\":\r\n logging.getLogger(\"discord\").setLevel(logging.INFO)\r\n logging.getLogger(\"discord.http\").setLevel(logging.INFO)\r\n logging.getLogger(\"discord.state\").addFilter(RemoveNoise())\r\n\r\n self.log.setLevel(logging.INFO)\r\n handler = RotatingFileHandler(\r\n filename=self.logging_path / \"Giftify.log\",\r\n encoding=\"utf-8\",\r\n mode=\"w\",\r\n maxBytes=self.max_bytes,\r\n backupCount=5,\r\n )\r\n dt_fmt = \"%Y-%m-%d %H:%M:%S\"\r\n fmt = logging.Formatter(\"[{asctime}] [{levelname:<7}] {name}: {message}\", dt_fmt, style=\"{\")\r\n handler.setFormatter(fmt)\r\n self.log.addHandler(handler)\r\n\r\n if self.stream:\r\n stream_handler = logging.StreamHandler()\r\n stream_handler.setFormatter(ColourFormatter())\r\n self.log.addHandler(stream_handler)\r\n\r\n return self\r\n\r\n async def __aexit__(self, *args: Any) -> None:\r\n return self.__exit__(*args)\r\n\r\n def __exit__(self, *args: Any) -> None:\r\n handlers = self.log.handlers[:]\r\n for handler in handlers:\r\n handler.close()\r\n self.log.removeHandler(handler)\r\n\r\n\r\nclass GiftifyHelper:\r\n configs: List[GuildConfig] = []\r\n donation_configs: List[GuildDonationConfig] = []\r\n cached_giveaways: List[\"Giveaway\"] = []\r\n webhook_cache: Dict[discord.TextChannel, discord.Webhook] = {}\r\n raffles_cache: Dict[discord.Guild, List[Raffle]] = ExpiringDict(max_len=100, max_age_seconds=300)\r\n\r\n pool: asyncpg.Pool\r\n user: discord.ClientUser\r\n amari_client: AmariClient\r\n\r\n \"\"\"A helper class for Giftify's operations.\r\n\r\n This class provides methods to send interaction messages with embeds,\r\n fetch webhooks for a channel, and retrieve or fetch guild configuration.\r\n \"\"\"\r\n\r\n async def send(\r\n self,\r\n interaction: discord.Interaction,\r\n message: str,\r\n reason: str = \"success\",\r\n ephemeral: bool = True,\r\n view: discord.ui.View = MISSING,\r\n ) -> None:\r\n \"\"\"Sends an interaction message with embed.\r\n\r\n Parameters\r\n -----------\r\n interaction: discord.Interaction\r\n The interaction to respond to.\r\n message: str\r\n The response message to send.\r\n reason: str\r\n The reason to send the message, can be \"warn\", \"error\" or \"success\".\r\n ephemeral: bool\r\n If the response should be sent ephemerally.\r\n \"\"\"\r\n emoji = WARN_EMOJI if reason == \"warn\" else ERROR_EMOJI if reason == \"error\" else SUCCESS_EMOJI\r\n colour = (\r\n discord.Colour.orange()\r\n if reason == \"warn\"\r\n else discord.Colour.red()\r\n if reason == \"error\"\r\n else discord.Colour.green()\r\n )\r\n embed = discord.Embed(description=f\"> {emoji} {message}\", colour=colour)\r\n\r\n if interaction.response.is_done():\r\n await interaction.followup.send(embed=embed, view=view, ephemeral=ephemeral)\r\n else:\r\n await interaction.response.send_message(embed=embed, view=view, ephemeral=ephemeral)\r\n\r\n async def _get_webhook(self, channel: discord.TextChannel, force_create: bool = False) -> discord.Webhook:\r\n if not force_create and (webhook := self.webhook_cache.get(channel)):\r\n return webhook\r\n\r\n webhook_list = await channel.webhooks()\r\n if webhook_list:\r\n for hook in webhook_list:\r\n if hook.token:\r\n if hook.user and hook.user.id == self.user.id:\r\n self.webhook_cache[channel] = hook\r\n return hook\r\n\r\n # If no suitable webhook is found, create a new one\r\n hook = await channel.create_webhook(name=\"Giftify Logging\", avatar=await channel.guild.me.display_avatar.read())\r\n self.webhook_cache[channel] = hook\r\n return hook\r\n\r\n async def send_to_webhook(self, channel: discord.TextChannel, embed: discord.Embed):\r\n \"\"\"Sends an embed to a webhook associated with the provided channel.\r\n\r\n Parameters\r\n -----------\r\n channel: discord.TextChannel\r\n The channel to send message to.\r\n\r\n \"\"\"\r\n try:\r\n webhook = await self._get_webhook(channel)\r\n await webhook.send(embed=embed, username=\"Giftify Logging\", avatar_url=self.user.display_avatar)\r\n except discord.NotFound:\r\n new_webhook = await self._get_webhook(channel, force_create=True)\r\n await new_webhook.send(embed=embed, username=\"Giftify Logging\", avatar_url=self.user.display_avatar)\r\n\r\n except discord.HTTPException:\r\n return\r\n\r\n async def fetch_config(self, guild: discord.Guild) -> GuildConfig:\r\n \"\"\"Looks up a guild config in cache or fetches if not found.\r\n\r\n Parameters\r\n -----------\r\n guild: discord.Guild\r\n The guild to look for.\r\n\r\n Returns\r\n ---------\r\n GuildConfig\r\n The retrieved guild config object.\r\n \"\"\"\r\n config = discord.utils.get(self.configs, guild=guild)\r\n if not config:\r\n config = await GuildConfig.fetch(guild, self.pool)\r\n self.configs.append(config)\r\n\r\n return config\r\n\r\n def get_donation_config(self, guild: discord.Guild, category: str) -> Optional[GuildDonationConfig]:\r\n \"\"\"Finds the donation config of a guild for some category.\r\n\r\n Parameters\r\n -----------\r\n guild: Guild\r\n The guild to which the category belongs.\r\n category: str\r\n The name of the category.\r\n\r\n Returns\r\n --------\r\n Optional[GuildDonationConfig]\r\n The fetched donation config.\r\n \"\"\"\r\n for config in self.donation_configs:\r\n if config.guild == guild and config.category == category:\r\n return config\r\n\r\n def get_guild_donation_categories(self, guild: discord.Guild) -> List[str]:\r\n \"\"\"Finds the donation categories of a guild.\r\n\r\n Parameters\r\n -----------\r\n guild: Guild\r\n The guild for which categories will be fetched.\r\n\r\n Returns\r\n --------\r\n List[str]\r\n The of names of donation categories.\r\n \"\"\"\r\n return [config.category for config in self.donation_configs if config.guild == guild]\r\n\r\n async def fetch_raffle(self, guild: discord.Guild, name: str) -> Optional[Raffle]:\r\n \"\"\"Finds a raffle in some guild.\r\n\r\n Parameters\r\n -----------\r\n guild: Guild\r\n The guild to which the raffle belongs.\r\n name: str\r\n The name of the raffle.\r\n\r\n Returns\r\n --------\r\n Optional[Raffle]\r\n The fetched raffle.\r\n \"\"\"\r\n record = await self.pool.fetchrow(\"SELECT * FROM raffles WHERE guild = $1 AND name = $2\", guild.id, name)\r\n if record is not None:\r\n return await Raffle.from_record(self, record=record) # type: ignore\r\n\r\n async def fetch_raffles(self, guild: discord.Guild, use_cache: bool = True) -> List[Raffle]:\r\n \"\"\"Fetch all the raffles in some guild\r\n\r\n Parameters\r\n -----------\r\n guild: Guild\r\n The guild for which raffles will be fetched.\r\n use_cache: bool\r\n Indicates wheter the bot should fetch the raffles from database or use internal cache.\r\n\r\n Returns\r\n --------\r\n List[Raffle]\r\n The of list of fetched raffles.\r\n \"\"\"\r\n if guild in self.raffles_cache and use_cache:\r\n return self.raffles_cache[guild]\r\n\r\n records = await self.pool.fetch(\"SELECT * FROM raffles WHERE guild = $1\", guild.id)\r\n raffles = [await Raffle.from_record(self, record=record) for record in records] # type: ignore\r\n self.raffles_cache[guild] = raffles\r\n\r\n return raffles\r\n\r\n async def fetch_giveaway(self, *, guild_id: int, channel_id: int, message_id: int) -> Optional[Giveaway]:\r\n \"\"\"Looks up a for a giveaway object in database.\r\n\r\n Parameters\r\n -----------\r\n message_id: int\r\n The ID of the giveaway message.\r\n\r\n Returns\r\n --------\r\n Optional[Giveaway]\r\n The retrieved giveaway object.\r\n \"\"\"\r\n giveaway = discord.utils.get(\r\n self.cached_giveaways,\r\n guild_id=guild_id,\r\n channel_id=channel_id,\r\n message_id=message_id,\r\n )\r\n if giveaway is not None:\r\n return giveaway\r\n record = await self.pool.fetchrow(\r\n \"SELECT * FROM giveaways WHERE guild = $1 AND channel = $2 AND message = $3\",\r\n guild_id,\r\n channel_id,\r\n message_id,\r\n )\r\n if record is not None:\r\n giveaway = Giveaway(bot=self, record=record) # type: ignore\r\n if giveaway.messages:\r\n self.cached_giveaways.append(giveaway)\r\n\r\n return giveaway\r\n\r\n async def running_giveaways(self, *, guild_id: Optional[int] = None, sort_by_ends: bool = True) -> List[Giveaway]:\r\n \"\"\"Looks up a list of active giveaways in the database.\r\n\r\n Parameters\r\n -----------\r\n guild_id: Optional[int]\r\n The ID of the guild. If provided, fetches giveaways only for that guild.\r\n sort_by_ends: bool\r\n If True, the results will be sorted by the 'ends' column in ascending order.\r\n\r\n Returns\r\n --------\r\n List[Giveaway]\r\n The list of fetched active giveaways.\r\n \"\"\"\r\n query = \"SELECT * FROM giveaways WHERE ended = FALSE\"\r\n if guild_id is not None:\r\n query += \" AND guild = $1\"\r\n if sort_by_ends:\r\n query += \" ORDER BY ends ASC\"\r\n\r\n if guild_id is not None:\r\n records = await self.pool.fetch(query, guild_id)\r\n else:\r\n records = await self.pool.fetch(query)\r\n\r\n return [Giveaway(bot=self, record=record) for record in records] # type: ignore\r\n\r\n async def fetch_level(self, member: discord.Member, /) -> int:\r\n \"\"\"Fetches user level from Amari Bot API.\r\n\r\n Parameters\r\n -----------\r\n member: discord.Member\r\n The member whose level is to be fetched.\r\n\r\n Returns\r\n ---------\r\n int\r\n The retrieved level.\r\n \"\"\"\r\n try:\r\n user = await self.amari_client.fetch_user(member.guild.id, member.id)\r\n except Exception:\r\n return 0\r\n else:\r\n return user.level or 0\r\n\r\n async def fetch_weekly_experience(self, member: discord.Member, /) -> int:\r\n \"\"\"Fetches user's weekly experience from Amari Bot API.\r\n\r\n Parameters\r\n -----------\r\n member: discord.Member\r\n The member whose weekly experience is to be fetched.\r\n\r\n Returns\r\n ---------\r\n int\r\n The retrieved weekly experience.\r\n \"\"\"\r\n try:\r\n user = await self.amari_client.fetch_user(member.guild.id, member.id)\r\n except Exception:\r\n return 0\r\n else:\r\n return user.weeklyexp or 0\r\n\r\n async def prompt(\r\n self,\r\n message: str,\r\n *,\r\n interaction: discord.Interaction[Giftify],\r\n success_message: str,\r\n cancel_message: str,\r\n timeout: float = 60.0,\r\n ) -> Optional[bool]:\r\n \"\"\"An interactive reaction confirmation dialog.\r\n\r\n Parameters\r\n -----------\r\n message: str\r\n The message to show along with the prompt.\r\n timeout: float\r\n How long to wait before returning.\r\n interaction: Interaction\r\n The interaction object to handle the confirmation dialog.\r\n success_message: str\r\n The message to show when the user clicks Confirm.\r\n cancel_message: str\r\n The message to show when the user clicks Cancel.\r\n\r\n Returns\r\n --------\r\n Optional[bool]\r\n ``True`` if explicit confirm,\r\n ``False`` if explicit deny,\r\n ``None`` if deny due to timeout\r\n \"\"\"\r\n\r\n view = ConfirmationView(\r\n timeout=timeout,\r\n interaction=interaction,\r\n success_message=success_message,\r\n cancel_message=cancel_message,\r\n )\r\n view.message = await self.send(interaction, message, view=view, reason=\"warn\")\r\n await view.wait()\r\n return view.value\r\n\r\n\r\nclass Giftify(GiftifyHelper, commands.AutoShardedBot):\r\n user: discord.ClientUser\r\n\r\n colour: int = 0xCB3045\r\n __version_info__ = \"1.1.4\"\r\n\r\n def __init__(\r\n self,\r\n *,\r\n log_handler: LogHandler,\r\n pool: asyncpg.Pool,\r\n session: aiohttp.ClientSession,\r\n amari_client: AmariClient,\r\n ) -> None:\r\n self._log_handler = log_handler\r\n self._pool = pool\r\n self._session = session\r\n self._amari_client = amari_client\r\n\r\n intents = discord.Intents(messages=True, emojis=True, guilds=True)\r\n allowed_mentions = discord.AllowedMentions(everyone=False, roles=False, users=True, replied_user=False)\r\n member_cache_flags = discord.MemberCacheFlags.from_intents(intents=intents)\r\n\r\n sentry_sdk.init(\r\n dsn=os.environ[\"SENTRY_DSN\"],\r\n integrations=[\r\n LoggingIntegration(\r\n level=logging.INFO,\r\n event_level=logging.ERROR,\r\n )\r\n ],\r\n traces_sample_rate=1.0,\r\n )\r\n\r\n super().__init__(\r\n command_prefix=commands.when_mentioned,\r"},"next_line":{"kind":"string","value":" tree_cls=CommandTree,\r"},"gold_snippet_index":{"kind":"number","value":7,"string":"7"},"created_at":{"kind":"string","value":"2023-11-09 15:00:15+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5280,"cells":{"repo_name":{"kind":"string","value":"Kushalhk/AutoFilter"},"file_path":{"kind":"string","value":"plugins/p_ttishow.py"},"context":{"kind":"list like","value":[{"identifier":"ADMINS","path":"info.py","snippet":"ADMINS = [int(admin) if id_pattern.search(admin) else admin for admin in environ.get('ADMINS', '').split()]"},{"identifier":"LOG_CHANNEL","path":"info.py","snippet":"LOG_CHANNEL = int(environ.get('LOG_CHANNEL', ''))"},{"identifier":"SUPPORT_CHAT","path":"info.py","snippet":"SUPPORT_CHAT = environ.get('SUPPORT_CHAT', '')"},{"identifier":"MELCOW_NEW_USERS","path":"info.py","snippet":"MELCOW_NEW_USERS = is_enabled((environ.get('MELCOW_NEW_USERS', \"True\")), True)"},{"identifier":"MELCOW_VID","path":"info.py","snippet":"MELCOW_VID = environ.get(\"MELCOW_VID\", \"https://te.legra.ph/file/6f55d902f9bf2d0afd4bb.mp4\")"},{"identifier":"CHNL_LNK","path":"info.py","snippet":"CHNL_LNK = environ.get('CHNL_LNK', 'https://t.me/TG_LINKS_CHANNEL')"},{"identifier":"GRP_LNK","path":"info.py","snippet":"GRP_LNK = environ.get('GRP_LNK', 'https://t.me/TG_SUPPORT_GROUP')"},{"identifier":"db","path":"database/users_chats_db.py","snippet":"class Database:\n def __init__(self, uri, database_name):\n def new_user(self, id, name):\n def new_group(self, id, title):\n async def add_user(self, id, name):\n async def is_user_exist(self, id):\n async def total_users_count(self):\n async def remove_ban(self, id):\n async def ban_user(self, user_id, ban_reason=\"No Reason\"):\n async def get_ban_status(self, id):\n async def get_all_users(self):\n async def delete_user(self, user_id):\n async def get_banned(self):\n async def add_chat(self, chat, title):\n async def get_chat(self, chat):\n async def re_enable_chat(self, id):\n async def update_settings(self, id, settings):\n async def get_settings(self, id):\n async def disable_chat(self, chat, reason=\"No Reason\"):\n async def total_chat_count(self):\n async def get_all_chats(self):\n async def get_db_size(self):"},{"identifier":"Media","path":"database/ia_filterdb.py","snippet":"class Media(Document):\n file_id = fields.StrField(attribute='_id')\n file_ref = fields.StrField(allow_none=True)\n file_name = fields.StrField(required=True)\n file_size = fields.IntField(required=True)\n file_type = fields.StrField(allow_none=True)\n mime_type = fields.StrField(allow_none=True)\n caption = fields.StrField(allow_none=True)\n\n class Meta:\n indexes = ('$file_name', )\n collection_name = COLLECTION_NAME"},{"identifier":"get_size","path":"utils.py","snippet":"def get_size(size):\n \"\"\"Get size in readable format\"\"\"\n\n units = [\"Bytes\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\"]\n size = float(size)\n i = 0\n while size >= 1024.0 and i < len(units):\n i += 1\n size /= 1024.0\n return \"%.2f %s\" % (size, units[i])"},{"identifier":"temp","path":"utils.py","snippet":"class temp(object):\n BANNED_USERS = []\n BANNED_CHATS = []\n ME = None\n CURRENT=int(os.environ.get(\"SKIP\", 2))\n CANCEL = False\n MELCOW = {}\n U_NAME = None\n B_NAME = None\n GETALL = {}\n SHORT = {}\n SETTINGS = {}"},{"identifier":"get_settings","path":"utils.py","snippet":"async def get_settings(group_id):\n settings = temp.SETTINGS.get(group_id)\n if not settings:\n settings = await db.get_settings(group_id)\n temp.SETTINGS[group_id] = settings\n return settings"},{"identifier":"script","path":"Script.py","snippet":"class script(object):\r\n START_TXT = \"\"\"<b>Hᴇʟʟᴏ 👋 {}</b>\r\n\r\n<b>Mʏ Nᴀᴍᴇ Is <a href=\"https://t.me/{}\">{}</a>, I Cᴀɴ Pʀᴏᴠɪᴅᴇ Mᴏᴠɪᴇs, Sᴇʀɪᴇs, Aɴɪᴍᴀᴛɪᴏɴ, Cᴀʀᴛᴏᴏɴ, Aɴɪᴍᴇ, K-Dʀᴀᴍᴀ & Mᴀɴʏ Mᴏʀᴇ ☺ Jᴜsᴛ Aᴅᴅ Mᴇ Tᴏ Yᴏᴜʀ Gʀᴏᴜᴘ As Aᴅᴍɪɴ EɴJᴏʏ 😍</b>\"\"\"\r\n\r\n HELP_TXT = \"\"\"<b>Hᴇʀᴇ Is Tʜᴇ Hᴇʟᴘ Fᴏʀ Mʏ Cᴏᴍᴍᴀɴᴅs.</b>\"\"\"\r\n \r\n ABOUT_TXT = \"\"\"\r\n<b>‣ ᴍʏ ɴᴀᴍᴇ : <a href=\"https://t.me/{}\">ʙᴏᴛ</a>\r\n‣ ᴄʀᴇᴀᴛᴏʀ : <a href=\"https://t.me/KUSHALHK\">𝐊𝐔𝐒𝐇𝐀𝐋</a>\r\n‣ ʟɪʙʀᴀʀʏ : <a href=\"https://pyrogram.org/\">ᴘʏʀᴏɢʀᴀᴍ</a>\r\n‣ ʟᴀɴɢᴜᴀɢᴇ : <a href=\"https://www.python.org/\">ᴘʏᴛʜᴏɴ</a>\r\n‣ ᴅᴀᴛᴀʙᴀꜱᴇ : <a href=\"https://www.mongodb.com/\">ᴍᴏɴɢᴏ ᴅʙ</a>\r\n‣ ʜᴏꜱᴛᴇᴅ ᴏɴ : <a href=\"https://render.com/\">Render</a>\r\n‣ ʙᴜɪʟᴅ ꜱᴛᴀᴛᴜꜱ : ᴠ.𝟹.𝟶 [ꜱᴛᴀʙʟᴇ]</b>\"\"\"\r\n \r\n DISCLAIMER_TXT = \"\"\"<b>ᴛʜɪꜱ ɪꜱ ᴀɴ ᴏᴘᴇɴ ꜱᴏᴜʀᴄᴇ ᴘʀᴏᴊᴇᴄᴛ.\r\n\r\nᴀʟʟ ᴛʜᴇ ꜰɪʟᴇꜱ ɪɴ ᴛʜɪꜱ ʙᴏᴛ ᴀʀᴇ ꜰʀᴇᴇʟʏ ᴀᴠᴀɪʟᴀʙʟᴇ ᴏɴ ᴛʜᴇ ɪɴᴛᴇʀɴᴇᴛ ᴏʀ ᴘᴏꜱᴛᴇᴅ ʙʏ ꜱᴏᴍᴇʙᴏᴅʏ ᴇʟꜱᴇ. ᴊᴜꜱᴛ ꜰᴏʀ ᴇᴀꜱʏ ꜱᴇᴀʀᴄʜɪɴɢ ᴛʜɪꜱ ʙᴏᴛ ɪꜱ ɪɴᴅᴇxɪɴɢ ꜰɪʟᴇꜱ ᴡʜɪᴄʜ ᴀʀᴇ ᴀʟʀᴇᴀᴅʏ ᴜᴘʟᴏᴀᴅᴇᴅ ᴏɴ ᴛᴇʟᴇɢʀᴀᴍ. ᴡᴇ ʀᴇꜱᴘᴇᴄᴛ ᴀʟʟ ᴛʜᴇ ᴄᴏᴘʏʀɪɢʜᴛ ʟᴀᴡꜱ ᴀɴᴅ ᴡᴏʀᴋꜱ ɪɴ ᴄᴏᴍᴘʟɪᴀɴᴄᴇ ᴡɪᴛʜ ᴅᴍᴄᴀ ᴀɴᴅ ᴇᴜᴄᴅ. ɪꜰ ᴀɴʏᴛʜɪɴɢ ɪꜱ ᴀɢᴀɪɴꜱᴛ ʟᴀᴡ ᴘʟᴇᴀꜱᴇ ᴄᴏɴᴛᴀᴄᴛ ᴍᴇ ꜱᴏ ᴛʜᴀᴛ ɪᴛ ᴄᴀɴ ʙᴇ ʀᴇᴍᴏᴠᴇᴅ ᴀꜱᴀᴘ. ɪᴛ ɪꜱ ꜰᴏʀʙɪᴅᴅᴇɴ ᴛᴏ ᴅᴏᴡɴʟᴏᴀᴅ, ꜱᴛʀᴇᴀᴍ, ʀᴇᴘʀᴏᴅᴜᴄᴇ, ꜱʜᴀʀᴇ ᴏʀ ᴄᴏɴꜱᴜᴍᴇ ᴄᴏɴᴛᴇɴᴛ ᴡɪᴛʜᴏᴜᴛ ᴇxᴘʟɪᴄɪᴛ ᴘᴇʀᴍɪꜱꜱɪᴏɴ ꜰʀᴏᴍ ᴛʜᴇ ᴄᴏɴᴛᴇɴᴛ ᴡɪᴛʜᴏᴜᴛ ᴇxᴘʟɪᴄɪᴛ ᴘᴇʀᴍɪꜱꜱɪᴏɴ ꜰʀᴏᴍ ᴛʜᴇ ᴄᴏɴᴛᴇɴᴛ ᴄʀᴇᴀᴛᴏʀ ᴏʀ ʟᴇɢᴀʟ ᴄᴏᴘʏʀɪɢʜᴛ ʜᴏʟᴅᴇʀ. ɪꜰ ʏᴏᴜ ʙᴇʟɪᴇᴠᴇ ᴛʜɪꜱ ʙᴏᴛ ɪꜱ ᴠɪᴏʟᴀᴛɪɴɢ ʏᴏᴜʀ ɪɴᴛᴇʟʟᴇᴄᴛᴜᴀʟ ᴘʀᴏᴘᴇʀᴛʏ, ᴄᴏɴᴛᴀᴄᴛ ᴛʜᴇ ʀᴇꜱᴘᴇᴄᴛɪᴠᴇ ᴄʜᴀɴɴᴇʟꜱ ꜰᴏʀ ʀᴇᴍᴏᴠᴀʟ. ᴛʜᴇ ʙᴏᴛ ᴅᴏᴇꜱ ɴᴏᴛ ᴏᴡɴ ᴀɴʏ ᴏꜰ ᴛʜᴇꜱᴇ ᴄᴏɴᴛᴇɴᴛꜱ, ɪᴛ ᴏɴʟʏ ɪɴᴅᴇx ᴛʜᴇ ꜰɪʟᴇꜱ ꜰʀᴏᴍ ᴛᴇʟᴇɢʀᴀᴍ.\r\n\r\nᴍᴀɪɴᴛᴀɪɴᴇᴅ ʙʏ : <a href=\"https://t.me/KUSHALHK\">𝐊𝐔𝐒𝐇𝐀𝐋</a></b>\"\"\"\r\n\r\n SOURCE_TXT = \"\"\"\r\n<b>Hᴇʏ, Tʜɪs ɪs ᴀ Oᴘᴇɴ Sᴏᴜʀᴄᴇ Pʀᴏᴊᴇᴄᴛ.\r\n\r\nTʜɪs Bᴏᴛ ʜᴀs Lᴀᴛᴇsᴛ ᴀɴᴅ Aᴅᴠᴀɴᴄᴇᴅ Fᴇᴀᴛᴜʀᴇs⚡️\r\n\r\nFork our repository and give star ⭐- <a href='https://github.com/Kushalhk/AutoFilter'>📥 ᴄʟɪᴄᴋ ʜᴇʀᴇ 📥</a></b>\r\n\"\"\"\r\n \r\n KUSHAL_TXT = \"\"\" \r\n<b>🔥 ᴘʀᴇᴍɪᴜᴍ ғᴇᴀᴛᴜʀᴇs 🔥\r\n\r\n➻ ɴᴏ ɴᴇᴇᴅ ᴛᴏ ᴠᴇʀɪғʏ\r\n➻ ᴅɪʀᴇᴄᴛ ғɪʟᴇs\r\n➻ ᴀᴅ-ғʀᴇᴇ ᴇxᴘᴇʀɪᴇɴᴄᴇ\r\n➻ ʜɪɢʜ-sᴘᴇᴇᴅ ᴅᴏᴡɴʟᴏᴀᴅ ʟɪɴᴋ\r\n➻ ᴜɴʟɪᴍɪᴛᴇᴅ ᴍᴏᴠɪᴇs ᴀɴᴅ sᴇʀɪᴇs\r\n➻ ғᴜʟʟ ᴀᴅᴍɪɴ sᴜᴘᴘᴏʀᴛ \r\n➻ ʀᴇǫᴜᴇsᴛ ᴡɪʟʟ ʙᴇ ᴄᴏᴍᴘʟᴇᴛᴇᴅ ɪɴ 𝟷ʜ ɪғ ᴀᴠᴀɪʟᴀʙʟᴇ\r\n\r\n‼️ ᴄʟɪᴄᴋ ᴏɴ ʙᴇʟᴏᴡ ʙᴜᴛᴛᴏɴ ᴛᴏ ᴄʜᴇᴄᴋ ᴀʟʟ ᴀᴠᴀɪʟᴀʙʟᴇ ᴘʀᴇᴍɪᴜᴍ ᴘʟᴀɴs ᴀɴᴅ ɪᴛ's ᴘʀɪᴄᴇs.</b>\"\"\"\r\n\r\n \r\n SETTINGS_TXT = \"\"\"\r\nHᴇʟᴘ : <b>Sᴇᴛᴛɪɴɢꜱ</b>\r\n \r\n◈ sᴇᴛᴛɪɴɢs ɪs ᴍᴏsᴛ ɪᴍᴘᴏʀᴛᴀɴᴛ ғᴇᴀᴛᴜʀᴇ ɪɴ ᴛʜɪs ʙᴏᴛ.\r\n◈ ʏᴏᴜ ᴄᴀɴ ᴇᴀsɪʟʏ ᴄᴜsᴛᴏᴍɪᴢᴇ ᴛʜɪs ʙᴏᴛ ғᴏʀ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\r\n\r\n<b>Nᴏᴛᴇ :</b>\r\n1. ᴏɴʟʏ ɢʀᴏᴜᴘ ᴀᴅᴍɪɴ ᴄᴀɴ ᴜsᴇ ᴛʜɪs ᴄᴏᴍᴍᴀɴᴅ ᴀɴᴅ ᴄʜᴀɴɢᴇ sᴇᴛᴛɪɴɢs.\r\n2. ɪᴛ ᴡᴏʀᴋs ᴏɴʟʏ ᴡʜᴇɴ ʙᴏᴛ ᴀʟʀᴇᴀᴅʏ ᴄᴏɴɴᴇᴄᴛᴇᴅ ᴛᴏ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\r\n\r\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\r\n• /connect - ᴄᴏɴɴᴇᴄᴛ ʏᴏᴜʀ ɢʀᴏᴜᴘ ᴛᴏ ʙᴏᴛ\r\n• /settings - ᴄʜᴀɴɢᴇ sᴇᴛᴛɪɴɢs ᴀs ʏᴏᴜʀ ᴡɪsʜ \"\"\"\r\n\r\n TELEGRAPH_TXT = \"\"\" Hᴇʟᴘ : <b>Tᴇʟᴇɢʀᴀᴘʜ</b>\r\n\r\n<b>Nᴏᴛᴇ</b>: ᴛʜɪꜱ ᴄᴏᴍᴍᴀɴᴅ ɪꜱ ᴀᴠᴀɪʟᴀʙʟᴇ ɪɴ ɢʀᴏᴜᴘꜱ ᴀɴᴅ ᴘᴍꜱ. ᴀʟꜱᴏ ᴄᴀɴ ʙᴇ ᴜꜱᴇ ʙʏ ᴇᴠᴇʀʏᴏɴᴇ.\r\n\r\n<b>Cᴏᴍᴍᴀɴᴅs & Usᴀɢᴇ :</b>\r\n• /telegraph - sᴇɴᴅ ᴍᴇ ᴘɪᴄᴛᴜʀᴇ ᴏʀ ᴠɪᴅᴇᴏ ᴜɴᴅᴇʀ 𝟻ᴍʙ\"\"\"\r\n\r\n FONT_TXT = \"\"\"Hᴇʟᴘ : <b>Fᴏɴᴛ</b>\r\n\r\n<b>Nᴏᴛᴇ</b>: ʏᴏᴜ ᴄᴀɴ ᴜꜱᴇ ᴛʜɪꜱ ᴍᴏᴅᴇ ᴛᴏ ᴄʜᴀɴɢᴇ ʏᴏᴜʀ ꜰᴏɴᴛꜱ ꜱᴛʏʟᴇ, ᴊᴜꜱᴛ ꜱᴇɴᴅ ᴍᴇ ʟɪᴋᴇ ᴛʜɪꜱ ꜰᴏʀᴍᴀᴛ. \r\n\r\n<code>/font TG_LINKS_CHANNEL</code>\"\"\"\r\n\r\n MANUELFILTER_TXT = \"\"\"Hᴇʟᴘ : <b>Fɪʟᴛᴇʀꜱ</b>\r\n \r\n◈ ꜰɪʟᴛᴇʀ ɪꜱ ᴀ ꜰᴇᴀᴛᴜʀᴇ ᴡᴇʀᴇ ᴜꜱᴇʀꜱ ᴄᴀɴ ꜱᴇᴛ ᴀᴜᴛᴏᴍᴀᴛᴇᴅ ʀᴇᴘʟɪᴇꜱ ꜰᴏʀ ᴀ ᴘᴀʀᴛɪᴄᴜʟᴀʀ ᴋᴇʏᴡᴏʀᴅ ᴀɴᴅ ɪ ᴡɪʟʟ ʀᴇꜱᴘᴏɴᴅ ᴡʜᴇɴᴇᴠᴇʀ ᴀ ᴋᴇʏᴡᴏʀᴅ ɪꜱ ꜰᴏᴜɴᴅ ɪɴ ᴛʜᴇ ᴍᴇꜱꜱᴀɢᴇ.\r\n\r\n<b>Nᴏᴛᴇ :</b>\r\n1. ᴛʜɪꜱ ʙᴏᴛ ꜱʜᴏᴜʟᴅ ʜᴀᴠᴇ ᴀᴅᴍɪɴ ᴘʀɪᴠɪʟᴇɢᴇ.\r\n2. ᴏɴʟʏ ᴀᴅᴍɪɴꜱ ᴄᴀɴ ᴀᴅᴅ ꜰɪʟᴛᴇʀꜱ ɪɴ ᴀ ᴄʜᴀᴛ.\r\n3. ᴀʟᴇʀᴛ ʙᴜᴛᴛᴏɴꜱ ʜᴀᴠᴇ ᴀ ʟɪᴍɪᴛ ᴏꜰ 64 ᴄʜᴀʀᴀᴄᴛᴇʀꜱ.\r\n\r\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\r\n• /filter - ᴀᴅᴅ ᴀ ꜰɪʟᴛᴇʀ ɪɴ ᴀ ᴄʜᴀᴛ\r\n• /filters - ʟɪꜱᴛ ᴀʟʟ ᴛʜᴇ ꜰɪʟᴛᴇʀꜱ ᴏꜰ ᴀ ᴄʜᴀᴛ\r\n• /del - ᴅᴇʟᴇᴛᴇ ᴀ ꜱᴘᴇᴄɪꜰɪᴄ ꜰɪʟᴛᴇʀ ɪɴ ᴀ ᴄʜᴀᴛ\r\n• /delall - ᴅᴇʟᴇᴛᴇ ᴛʜᴇ ᴡʜᴏʟᴇ ꜰɪʟᴛᴇʀꜱ ɪɴ ᴀ ᴄʜᴀᴛ (ᴄʜᴀᴛ ᴏᴡɴᴇʀ ᴏɴʟʏ)\"\"\"\r\n\r\n BUTTON_TXT = \"\"\"Hᴇʟᴘ : <b>Bᴜᴛᴛᴏɴꜱ</b>\r\n \r\n◈ ᴛʜɪꜱ ʙᴏᴛ ꜱᴜᴘᴘᴏʀᴛꜱ ʙᴏᴛʜ ᴜʀʟ ᴀɴᴅ ᴀʟᴇʀᴛ ɪɴʟɪɴᴇ ʙᴜᴛᴛᴏɴꜱ.\r\n\r\n<b>Nᴏᴛᴇ :</b>\r\n𝟷. ᴛᴇʟᴇɢʀᴀᴍ ᴡɪʟʟ ɴᴏᴛ ᴀʟʟᴏᴡꜱ ʏᴏᴜ ᴛᴏ ꜱᴇɴᴅ ʙᴜᴛᴛᴏɴꜱ ᴡɪᴛʜᴏᴜᴛ ᴀɴʏ ᴄᴏɴᴛᴇɴᴛ, ꜱᴏ ᴄᴏɴᴛᴇɴᴛ ɪꜱ ᴍᴀɴᴅᴀᴛᴏʀʏ.\r\n𝟸. ᴛʜɪꜱ ʙᴏᴛ ꜱᴜᴘᴘᴏʀᴛꜱ ʙᴜᴛᴛᴏɴꜱ ᴡɪᴛʜ ᴀɴʏ ᴛᴇʟᴇɢʀᴀᴍ ᴍᴇᴅɪᴀ ᴛʏᴘᴇ.\r\n𝟹. ʙᴜᴛᴛᴏɴꜱ ꜱʜᴏᴜʟᴅ ʙᴇ ᴘʀᴏᴘᴇʀʟʏ ᴘᴀʀꜱᴇᴅ ᴀꜱ ᴍᴀʀᴋᴅᴏᴡɴ ꜰᴏʀᴍᴀᴛ\r\n\r\nᴜʀʟ ʙᴜᴛᴛᴏɴꜱ :\r\n<code>[Button Text](buttonurl:https://t.me/TG_LINKS_CHANNEL)</code>\r\n\r\nᴀʟᴇʀᴛ ʙᴜᴛᴛᴏɴꜱ :\r\n<code>[Button Text](buttonalert:ᴛʜɪꜱ ɪꜱ ᴀɴ ᴀʟᴇʀᴛ ᴍᴇꜱꜱᴀɢᴇ)</code>\"\"\"\r\n\r\n AUTOFILTER_TXT = \"\"\"Hᴇʟᴘ : <b>Aᴜᴛᴏ Fɪʟᴛᴇʀ</b>\r\n \r\n<b>Nᴏᴛᴇ :</b> Fɪʟᴇ Iɴᴅᴇx\r\n𝟷. ᴍᴀᴋᴇ ᴍᴇ ᴛʜᴇ ᴀᴅᴍɪɴ ᴏꜰ ʏᴏᴜʀ ᴄʜᴀɴɴᴇʟ ɪꜰ ɪᴛ'ꜱ ᴘʀɪᴠᴀᴛᴇ.\r\n𝟸. ᴍᴀᴋᴇ ꜱᴜʀᴇ ᴛʜᴀᴛ ʏᴏᴜʀ ᴄʜᴀɴɴᴇʟ ᴅᴏᴇꜱ ɴᴏᴛ ᴄᴏɴᴛᴀɪɴꜱ ᴄᴀᴍʀɪᴘꜱ, ᴘᴏʀɴ ᴀɴᴅ ꜰᴀᴋᴇ ꜰɪʟᴇꜱ.\r\n𝟹. ꜰᴏʀᴡᴀʀᴅ ᴛʜᴇ ʟᴀꜱᴛ ᴍᴇꜱꜱᴀɢᴇ ᴛᴏ ᴍᴇ ᴡɪᴛʜ ǫᴜᴏᴛᴇꜱ. ɪ'ʟʟ ᴀᴅᴅ ᴀʟʟ ᴛʜᴇ ꜰɪʟᴇꜱ ɪɴ ᴛʜᴀᴛ ᴄʜᴀɴɴᴇʟ ᴛᴏ ᴍʏ ᴅʙ.\r\n\r\n<b>Nᴏᴛᴇ :</b> Aᴜᴛᴏ Fɪʟᴛᴇʀ\r\n𝟷. Aᴅᴅ ᴛʜᴇ ʙᴏᴛ ᴀs ᴀᴅᴍɪɴ ᴏɴ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\r\n𝟸. Usᴇ /connect ᴀɴᴅ ᴄᴏɴɴᴇᴄᴛ ʏᴏᴜʀ ɢʀᴏᴜᴘ ᴛᴏ ᴛʜᴇ ʙᴏᴛ.\r\n𝟹. Usᴇ /settings ᴏɴ ʙᴏᴛ's ᴘᴍ ᴀɴᴅ ᴛᴜʀɴ ᴏɴ AᴜᴛᴏFɪʟᴛᴇʀ ᴏɴ ᴛʜᴇ sᴇᴛᴛɪɴɢs ᴍᴇɴᴜ.\"\"\"\r\n\r\n \r\n RULE_TXT = \"\"\"♦ 𝗚𝗿𝗼𝘂𝗽 𝗥𝘂𝗹𝗲𝘀 ♦\r\n\r\n◈ <b>Sᴇᴀʀᴄʜ Mᴏᴠɪᴇ Wɪᴛʜ Cᴏʀʀᴇᴄᴛ Sᴘᴇʟʟɪɴɢ:</b>\r\n• ᴀᴠᴀᴛᴀʀ 𝟸𝟶𝟶𝟿 ✅\r\n• ᴀᴠᴀᴛᴀʀ ʜɪɴᴅɪ ✅\r\n• ᴀᴠᴀᴛᴀʀ ᴍᴏᴠɪᴇ ❌\r\n• ᴀᴠᴀᴛᴀʀ ʜɪɴᴅɪ ᴅᴜʙʙᴇᴅ..❌\r\n\r\n◈ <b>Sᴇᴀʀᴄʜ Wᴇʙ Sᴇʀɪᴇs Iɴ ᴛʜɪs Fᴏʀᴍᴀᴛ:</b>\r\n• ᴠɪᴋɪɴɢs S𝟶𝟷 ✅\r\n• ᴠɪᴋɪɴɢs S𝟶𝟷E𝟶𝟷 ✅\r\n• ᴠɪᴋɪɴɢs S𝟶𝟷 ʜɪɴᴅɪ ✅\r\n• ᴠɪᴋɪɴɢs S𝟶𝟷 ʜɪɴᴅɪ ᴅᴜʙʙ... ❌\r\n• ᴠɪᴋɪɴɢs sᴇᴀsᴏɴ 𝟷 ❌\r\n• ᴠɪᴋɪɴɢs ᴡᴇʙ sᴇʀɪᴇs ❌\r\n\r\n<b>➙ ᴅᴏɴ'ᴛ ᴅᴏ ᴀɴʏ ꜱᴇʟꜰ ᴘʀᴏᴍᴏᴛɪᴏɴ. \r\n➙ ᴅᴏɴ'ᴛ ꜱᴇɴᴅ ᴀɴʏ ᴋɪɴᴅ ᴏꜰ ᴘʜᴏᴛᴏ, ᴠɪᴅᴇᴏ, ᴅᴏᴄᴜᴍᴇɴᴛꜱ, ᴜʀʟ, ᴇᴛᴄ...\r\n➙ ᴅᴏɴ'ᴛ ʀᴇǫᴜᴇꜱᴛ ᴀɴʏ ᴛʜɪɴɢꜱ ᴏᴛʜᴇʀ ᴛʜᴀɴ ᴍᴏᴠɪᴇꜱ, ꜱᴇʀɪᴇꜱ, ᴀɴɪᴍᴀᴛɪᴏɴ, ᴄᴀʀᴛᴏᴏɴ, ᴀɴɪᴍᴇ, ᴋ-ᴅʀᴀᴍᴀ ᴍᴀɴʏ ᴍᴏʀᴇ.</b>\r\n\r\n🔰 <b>Nᴏᴛᴇ :</b> ᴀʟʟ ᴍᴇꜱꜱᴀɢᴇꜱ ᴡɪʟʟ ʙᴇ ᴀᴜᴛᴏ-ᴅᴇʟᴇᴛᴇᴅ ᴀꜰᴛᴇʀ 𝟷𝟶 ᴍɪɴᴜᴛᴇꜱ ᴛᴏ ᴀᴠᴏɪᴅ ᴄᴏᴘʏʀɪɢʜᴛ ɪꜱꜱᴜᴇꜱ.\"\"\"\r\n\r\n CONNECTION_TXT = \"\"\"Hᴇʟᴘ : <b>Cᴏɴɴᴇᴄᴛɪᴏɴꜱ</b>\r\n \r\n◈ ᴜꜱᴇᴅ ᴛᴏ ᴄᴏɴɴᴇᴄᴛ ʙᴏᴛ ᴛᴏ ᴘᴍ ꜰᴏʀ ᴍᴀɴᴀɢɪɴɢ ꜰɪʟᴛᴇʀꜱ \r\n◈ ɪᴛ ʜᴇʟᴘꜱ ᴛᴏ ᴀᴠᴏɪᴅ ꜱᴘᴀᴍᴍɪɴɢ ɪɴ ɢʀᴏᴜᴘꜱ.\r\n\r\n<b>Nᴏᴛᴇ :</b>\r\n1. ᴏɴʟʏ ᴀᴅᴍɪɴꜱ ᴄᴀɴ ᴀᴅᴅ ᴀ ᴄᴏɴɴᴇᴄᴛɪᴏɴ.\r\n2. ꜱᴇɴᴅ /ᴄᴏɴɴᴇᴄᴛ ꜰᴏʀ ᴄᴏɴɴᴇᴄᴛɪɴɢ ᴍᴇ ᴛᴏ ʏᴏᴜʀ ᴘᴍ\r\n\r\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\r\n• /connect - ᴄᴏɴɴᴇᴄᴛ ᴀ ᴘᴀʀᴛɪᴄᴜʟᴀʀ ᴄʜᴀᴛ ᴛᴏ ʏᴏᴜʀ ᴘᴍ\r\n• /disconnect - ᴅɪꜱᴄᴏɴɴᴇᴄᴛ ꜰʀᴏᴍ ᴀ ᴄʜᴀᴛ\r\n• /connections - ʟɪꜱᴛ ᴀʟʟ ʏᴏᴜʀ ᴄᴏɴɴᴇᴄᴛɪᴏɴꜱ\"\"\"\r\n\r\n EXTRAMOD_TXT = \"\"\"Hᴇʟᴘ : <b>Exᴛʀᴀ Mᴏᴅᴜʟᴇs</b>\r\n \r\n<b>Nᴏᴛᴇ :</b>\r\nᴛʜᴇꜱᴇ ᴀʀᴇ ᴛʜᴇ ᴇxᴛʀᴀ ꜰᴇᴀᴛᴜʀᴇꜱ ᴏꜰ ᴛʜɪꜱ ʙᴏᴛ\r\n\r\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\r\n• /id - ɢᴇᴛ ɪᴅ ᴏꜰ ᴀ ꜱᴘᴇᴄɪꜰɪᴇᴅ ᴜꜱᴇʀ.\r\n• /info - ɢᴇᴛ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ᴀʙᴏᴜᴛ ᴀ ᴜꜱᴇʀ.\r\n• /imdb - ɢᴇᴛ ᴛʜᴇ ꜰɪʟᴍ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ꜰʀᴏᴍ ɪᴍᴅʙ ꜱᴏᴜʀᴄᴇ.\r\n• /search - ɢᴇᴛ ᴛʜᴇ ꜰɪʟᴍ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ꜰʀᴏᴍ ᴠᴀʀɪᴏᴜꜱ ꜱᴏᴜʀᴄᴇꜱ.\"\"\"\r\n\r\n ADMIN_TXT = \"\"\"<b>Nᴏᴛᴇ :</b> Tʜɪs Mᴏᴅᴜʟᴇ Oɴʟʏ Wᴏʀᴋs Fᴏʀ Mʏ Aᴅᴍɪɴs.\r\n\r\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\r\n• /logs - ᴛᴏ ɢᴇᴛ ᴛʜᴇ ʀᴇᴄᴇɴᴛ ᴇʀʀᴏʀꜱ\r\n• /stats - ᴛᴏ ɢᴇᴛ ꜱᴛᴀᴛᴜꜱ ᴏꜰ ꜰɪʟᴇꜱ ɪɴ ᴅʙ. <b>[Tʜɪs Cᴏᴍᴍᴀɴᴅ Cᴀɴ Bᴇ Usᴇᴅ Bʏ Aɴʏᴏɴᴇ]</b>\r\n• /delete - ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀ ꜱᴘᴇᴄɪꜰɪᴄ ꜰɪʟᴇ ꜰʀᴏᴍ ᴅʙ.\r\n• /users - ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴍʏ ᴜꜱᴇʀꜱ ᴀɴᴅ ɪᴅꜱ.\r\n• /chats - ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴍʏ ᴄʜᴀᴛꜱ ᴀɴᴅ ɪᴅꜱ\r\n• /leave - ᴛᴏ ʟᴇᴀᴠᴇ ꜰʀᴏᴍ ᴀ ᴄʜᴀᴛ.\r\n• /disable - ᴛᴏ ᴅɪꜱᴀʙʟᴇ ᴀ ᴄʜᴀᴛ.\r\n• /ban - ᴛᴏ ʙᴀɴ ᴀ ᴜꜱᴇʀ.\r\n• /unban - ᴛᴏ ᴜɴʙᴀɴ ᴀ ᴜꜱᴇʀ.\r\n• /channel - ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴛᴏᴛᴀʟ ᴄᴏɴɴᴇᴄᴛᴇᴅ ᴄʜᴀɴɴᴇʟꜱ. \r\n• /broadcast - ᴛᴏ ʙʀᴏᴀᴅᴄᴀꜱᴛ ᴀ ᴍᴇꜱꜱᴀɢᴇ ᴛᴏ ᴀʟʟ ᴜꜱᴇʀꜱ. \r\n• /grp_broadcast - Tᴏ ʙʀᴏᴀᴅᴄᴀsᴛ ᴀ ᴍᴇssᴀɢᴇ ᴛᴏ ᴀʟʟ ᴄᴏɴɴᴇᴄᴛᴇᴅ ɢʀᴏᴜᴘs.\r\n• /gfilter - ᴛᴏ ᴀᴅᴅ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs. \r\n• /gfilters - ᴛᴏ ᴠɪᴇᴡ ʟɪsᴛ ᴏғ ᴀʟʟ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs. \r\n• /delg - ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀ sᴘᴇᴄɪғɪᴄ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ. \r\n• /request - ᴛᴏ sᴇɴᴅ ᴀ ᴍᴏᴠɪᴇ/sᴇʀɪᴇs ʀᴇᴏ̨ᴜᴇsᴛ ᴛᴏ ʙᴏᴛ ᴀᴅᴍɪɴs. ᴏɴʟʏ ᴡᴏʀᴋs ᴏɴ sᴜᴘᴘᴏʀᴛ ɢʀᴏᴜᴘ. <b>[Tʜɪs Cᴏᴍᴍᴀɴᴅ Cᴀɴ Bᴇ Usᴇᴅ Bʏ Aɴʏᴏɴᴇ]</b>\r\n• /delallg - ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀʟʟ ɢғɪʟᴛᴇʀs ғʀᴏᴍ ᴛʜᴇ ʙᴏᴛ's ᴅᴀᴛᴀʙᴀsᴇ.\r\n• /deletefiles - ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴄᴀᴍʀɪᴘ ᴀɴᴅ ᴘʀᴇ-ᴅᴠᴅ ғɪʟᴇs ғʀᴏᴍ ᴛʜᴇ ʙᴏᴛ's ᴅᴀᴛᴀʙᴀsᴇ.\"\"\"\r\n\r\n STICKER_TXT = \"\"\"<b>yᴏᴜ ᴄᴀɴ ᴜꜱᴇ ᴛʜɪꜱ ᴍᴏᴅᴜʟᴇ ᴛᴏ ꜰɪɴᴅᴀɴy ꜱᴛɪᴄᴋᴇʀꜱ ɪᴅ.\r\n• ᴜꜱᴀɢᴇ :ᴛᴏ ɢᴇᴛ ꜱᴛɪᴄᴋᴇʀ\r\n \r\n⭕ ʜᴏᴡ ᴛᴏ ᴜꜱᴇ\r\n◉ Reply To Any Sticker [/stickerid]\r\n\r\n/𝐬𝐭𝐢𝐜𝐤𝐞𝐫𝐢𝐝 𝐬𝐭𝐢𝐜𝐤𝐞𝐫 𝐢𝐝\r\n\r\n</b>\"\"\"\r\n \r\n STATUS_TXT = \"\"\"<b>⍟─────[ <b>Bᴏᴛ Sᴛᴀᴛᴜs</b> ]─────⍟\r\n \r\n★ ᴛᴏᴛᴀʟ ꜰɪʟᴇꜱ : <code>{}</code>\r\n★ ᴛᴏᴛᴀʟ ᴜꜱᴇʀꜱ : <code>{}</code>\r\n★ ᴛᴏᴛᴀʟ ɢʀᴏᴜᴘꜱ : <code>{}</code>\r\n★ ᴜꜱᴇᴅ ꜱᴛᴏʀᴀɢᴇ: <code>{}</code>\r\n★ ꜰʀᴇᴇ ꜱᴛᴏʀᴀɢᴇ : <code>{}</code>\r\n\r\n•❅──────✧❅✦❅✧──────❅•</b>\"\"\"\r\n\r\n\r\n LOG_TEXT_G = \"\"\"<b>#NewGroup\r\nGʀᴏᴜᴘ = {}(<code>{}</code>)\r\nTᴏᴛᴀʟ Mᴇᴍʙᴇʀs = <code>{}</code>\r\nAᴅᴅᴇᴅ Bʏ - {}</b>\"\"\"\r\n\r\n LOG_TEXT_P = \"\"\"<b>#NewUser\r\nID - <code>{}</code>\r\nNᴀᴍᴇ - {}</b>\"\"\"\r\n\r\n ALRT_TXT = \"\"\"<b>ʜᴇʟʟᴏ {},\r\nᴛʜɪꜱ ɪꜱ ɴᴏᴛ ʏᴏᴜʀ ᴍᴏᴠɪᴇ ʀᴇQᴜᴇꜱᴛ,\r\nʀᴇǫᴜᴇꜱᴛ ʏᴏᴜʀ'ꜱ...</b>\"\"\"\r\n\r\n OLD_ALRT_TXT = \"\"\"<b>ʜᴇʏ {},\r\nʏᴏᴜ ᴀʀᴇ ᴜꜱɪɴɢ ᴏɴᴇ ᴏꜰ ᴍʏ ᴏʟᴅ ᴍᴇꜱꜱᴀɢᴇꜱ, \r\nᴘʟᴇᴀꜱᴇ ꜱᴇɴᴅ ᴛʜᴇ ʀᴇǫᴜᴇꜱᴛ ᴀɢᴀɪɴ.</b>\"\"\"\r\n\r\n CUDNT_FND = \"\"\"<b>ɪ ᴄᴏᴜʟᴅɴ'ᴛ ꜰɪɴᴅ ᴀɴʏᴛʜɪɴɢ ʀᴇʟᴀᴛᴇᴅ ᴛᴏ {}\r\nᴅɪᴅ ʏᴏᴜ ᴍᴇᴀɴ ᴀɴʏ ᴏɴᴇ ᴏꜰ ᴛʜᴇꜱᴇ?</b>\"\"\"\r\n\r\n I_CUDNT = \"\"\"<b>sᴏʀʀʏ ɴᴏ ꜰɪʟᴇs ᴡᴇʀᴇ ꜰᴏᴜɴᴅ ꜰᴏʀ ʏᴏᴜʀ ʀᴇǫᴜᴇꜱᴛ {} 😕\r\n\r\nMᴏᴠɪᴇs Nᴏᴛ Aᴠᴀɪʟᴀʙʟᴇ Rᴇᴀsᴏɴ:\r\n𝟷. ᴏ.ᴛ.ᴛ ᴏʀ ᴅᴠᴅ ɴᴏᴛ ʀᴇʟᴇᴀsᴇᴅ\r\n𝟸. ᴛʏᴘᴇ ɴᴀᴍᴇ ᴡɪᴛʜ ʏᴇᴀʀ\r\n𝟹. ᴍᴏᴠɪᴇ ɪs ɴᴏᴛ ᴀᴠᴀɪʟᴀʙʟᴇ ɪɴ ᴛʜᴇ ᴅᴀᴛᴀʙᴀsᴇ ʀᴇᴘᴏʀᴛ ᴛᴏ ᴀᴅᴍɪɴs @TG_Bots_Supporter</b>\"\"\"\r\n\r\n I_CUD_NT = \"\"\"<b>ɪ ᴄᴏᴜʟᴅɴ'ᴛ ꜰɪɴᴅ ᴀɴʏ ᴍᴏᴠɪᴇ ʀᴇʟᴀᴛᴇᴅ ᴛᴏ {}.\r\nᴘʟᴇᴀꜱᴇ ᴄʜᴇᴄᴋ ᴛʜᴇ ꜱᴘᴇʟʟɪɴɢ ᴏɴ ɢᴏᴏɢʟᴇ ᴏʀ ɪᴍᴅʙ...</b>\"\"\"\r\n\r\n MVE_NT_FND = \"\"\"<b>ᴍᴏᴠɪᴇ ɴᴏᴛ ꜰᴏᴜɴᴅ ɪɴ ᴅᴀᴛᴀʙᴀꜱᴇ...</b>\"\"\"\r\n\r\n TOP_ALRT_MSG = \"\"\"<b>Cʜᴇᴄᴋɪɴɢ Fᴏʀ Mᴏᴠɪᴇ Iɴ Dᴀᴛᴀʙᴀsᴇ...</b>\"\"\"\r\n\r\n MELCOW_ENG = \"\"\"<b>Hᴇʟʟᴏ {} 😍, Aɴᴅ Wᴇʟᴄᴏᴍᴇ Tᴏ {} Gʀᴏᴜᴘ ❤️\r\n\r\n➻ ʜᴇʀᴇ ʏᴏᴜ ᴄᴀɴ ꜱᴇᴀʀᴄʜ ʏᴏᴜʀ ꜰᴀᴠᴏᴜʀɪᴛᴇ ᴍᴏᴠɪᴇꜱ ᴏʀ ꜱᴇʀɪᴇꜱ ʙʏ ᴊᴜꜱᴛ ᴛʏᴘɪɴɢ ɪᴛ'ꜱ ɴᴀᴍᴇ. \r\n\r\n⚠️ ɪꜰ ʏᴏᴜ ᴀʀᴇ ʜᴀᴠɪɴɢ ᴀɴʏ ᴘʀᴏʙʟᴇᴍ ʀᴇɢᴀʀᴅɪɴɢ ᴅᴏᴡɴʟᴏᴀᴅɪɴɢ ᴏʀ ꜱᴏᴍᴇᴛʜɪɴɢ ᴇʟꜱᴇ ᴛʜᴇɴ ᴍᴇꜱꜱᴀɢᴇ ʜᴇʀᴇ 👇</b>\"\"\"\r\n \r\n REQINFO = \"\"\"\r\n⚠ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ⚠\r\n\r\nᴀꜰᴛᴇʀ 5 ᴍɪɴᴜᴛᴇꜱ ᴛʜɪꜱ ᴍᴇꜱꜱᴀɢᴇ ᴡɪʟʟ ʙᴇ ᴀᴜᴛᴏᴍᴀᴛɪᴄᴀʟʟʏ ᴅᴇʟᴇᴛᴇᴅ\r\n\r\nɪꜰ ʏᴏᴜ ᴅᴏ ɴᴏᴛ ꜱᴇᴇ ᴛʜᴇ ʀᴇǫᴜᴇsᴛᴇᴅ ᴍᴏᴠɪᴇ / sᴇʀɪᴇs ꜰɪʟᴇ, ʟᴏᴏᴋ ᴀᴛ ᴛʜᴇ ɴᴇxᴛ ᴘᴀɢᴇ\"\"\"\r\n\r\n \r\n\r\n SINFO = \"\"\"\r\n⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯\r\nꜱᴇʀɪᴇꜱ ʀᴇǫᴜᴇꜱᴛ ꜰᴏʀᴍᴀᴛ\r\n⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯\r\n\r\nɢᴏ ᴛᴏ ɢᴏᴏɢʟᴇ ➠ ᴛʏᴘᴇ ꜱᴇʀɪᴇꜱ ɴᴀᴍᴇ ➠ ᴄᴏᴘʏ ᴄᴏʀʀᴇᴄᴛ ɴᴀᴍᴇ ➠ ᴘᴀꜱᴛᴇ ᴛʜɪꜱ ɢʀᴏᴜᴘ\r\n\r\nᴇxᴀᴍᴘʟᴇ : Loki S01E01\r\n\r\n🚯 ᴅᴏɴᴛ ᴜꜱᴇ ➠ ':(!,./)\"\"\"\r\n\r\n NORSLTS = \"\"\"\r\n★ #𝗡𝗼𝗥𝗲𝘀𝘂𝗹𝘁𝘀 ★\r\n\r\n𝗜𝗗 <b>: {}</b>\r\n\r\n𝗡𝗮𝗺𝗲 <b>: {}</b>\r\n\r\n𝗠𝗲𝘀𝘀𝗮𝗴𝗲 <b>: {}</b>🥲\"\"\"\r\n\r\n CAPTION = \"\"\" \r\n🗂 𝗙𝗶𝗹𝗲: <b><font class=smcp>{file_name}</font></b>\r\n📀 𝗦𝗶𝘇𝗲: <b><font class=smcp>{file_size}</font></b>\r\n\r\n<b>🔰 Cʀᴇᴀᴛᴏʀ : <a href=\"https://t.me/KUSHALHK\">𝐊𝐔𝐒𝐇𝐀𝐋</a>\r\n🔰 Cʜᴀɴɴᴇʟ : <a href=\"https://t.me/TG_LINKS_CHANNEL\">𝐌𝐎𝐕𝐈𝐄𝐒 𝐂𝐇𝐀𝐍𝐍𝐄𝐋</a>\r\n🔰 Gʀᴏᴜᴘ : <a href=\"https://t.me/movies_hub_official1\">𝐌𝐎𝐕𝐈𝐄 𝐑𝐄𝐐𝐔𝐄𝐒𝐓 𝐆𝐑𝐎𝐔𝐏</a></b>\"\"\"\r\n \r\n IMDB_TEMPLATE_TXT = \"\"\"\r\n<b>Query: {query}\r\nIMDb Data:\r\n\r\n🧿 𝐓𝐈𝐓𝐋𝐄: <a href={url}>{title}</a>\r\n🎭 𝐆𝐄𝐍𝐑𝐄𝐒: {genres}\r\n📆 𝐘𝐄𝐀𝐑: <a href={url}/releaseinfo>{year}</a>\r\n🌟 𝐑𝐀𝐓𝐈𝐍𝐆: <a href={url}/ratings>{rating}</a> / 10 (Based on {votes} user ratings)</b>\r\n☀️ 𝐋𝐀𝐍𝐆𝐔𝐀𝐆𝐄 : <code>{languages}</code></a>\r\n📀 𝐑𝐔𝐍𝐓𝐈𝐌𝐄: {runtime} Minutes</a>\r\n\r\n<b>👨💼 Requested by : {message.from_user.mention}</b>\"\"\"\r\n\r\n \r\n ALL_FILTERS = \"\"\"\r\n<b>Hᴇʏ {}, Tʜᴇsᴇ ᴀʀᴇ ᴍʏ ᴛʜʀᴇᴇ ᴛʏᴘᴇs ᴏғ ғɪʟᴛᴇʀs.</b>\"\"\"\r\n \r\n GFILTER_TXT = \"\"\"Hᴇʟᴘ : <b>Gʟᴏʙᴀʟ Fɪʟᴛᴇʀs</b>\r\n \r\n◈ Gʟᴏʙᴀʟ Fɪʟᴛᴇʀs ᴀʀᴇ ᴛʜᴇ ғɪʟᴛᴇʀs sᴇᴛ ʙʏ ʙᴏᴛ ᴀᴅᴍɪɴs ᴡʜɪᴄʜ ᴡɪʟʟ ᴡᴏʀᴋ ᴏɴ ᴀʟʟ ɢʀᴏᴜᴘs.\r\n \r\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\r\n• /gfilter - Tᴏ ᴄʀᴇᴀᴛᴇ ᴀ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ.\r\n• /gfilters - Tᴏ ᴠɪᴇᴡ ᴀʟʟ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs.\r\n• /delg - Tᴏ ᴅᴇʟᴇᴛᴇ ᴀ ᴘᴀʀᴛɪᴄᴜʟᴀʀ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ.\r\n• /delallg - ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀʟʟ ɢʟᴏʙᴀʟ ꜰɪʟᴛᴇʀꜱ.\"\"\"\r\n \r\n FILE_STORE_TXT = \"\"\"Hᴇʟᴘ : <b>Fɪʟᴇ Sᴛᴏʀᴇ</b>\r\n \r\n◈ Fɪʟᴇ sᴛᴏʀᴇ ɪs ᴛʜᴇ ғᴇᴀᴛᴜʀᴇ ᴡʜɪᴄʜ ᴡɪʟʟ ᴄʀᴇᴀᴛᴇ ᴀ sʜᴀʀᴇᴀʙʟᴇ ʟɪɴᴋ ᴏғ ᴀ sɪɴɢʟᴇ ᴏʀ ᴍᴜʟᴛɪᴘʟᴇ ғɪʟᴇs.\r\n\r\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\r\n• /batch - ᴛᴏ ᴄʀᴇᴀᴛᴇ ᴀ ʙᴀᴛᴄʜ ʟɪɴᴋ ᴏғ ᴍᴜʟᴛɪᴘʟᴇ ғɪʟᴇs.\r\n• /link - ᴛᴏ ᴄʀᴇᴀᴛᴇ ᴀ sɪɴɢʟᴇ ғɪʟᴇ sᴛᴏʀᴇ ʟɪɴᴋ.\r\n• /pbatch - ᴊᴜsᴛ ʟɪᴋᴇ <code>/batch</code>, ʙᴜᴛ ᴛʜᴇ ғɪʟᴇs ᴡɪʟʟ ʙᴇ sᴇɴᴅ ᴡɪᴛʜ ғᴏʀᴡᴀʀᴅ ʀᴇsᴛʀɪᴄᴛɪᴏɴs.\r\n• /plink - ᴊᴜsᴛ ʟɪᴋᴇ <code>/link</code>, ʙᴜᴛ ᴛʜᴇ ғɪʟᴇ ᴡɪʟʟ ʙᴇ sᴇɴᴅ ᴡɪᴛʜ ғᴏʀᴡᴀʀᴅ ʀᴇsᴛʀɪᴄᴛɪᴏɴ.\"\"\"\r\n\r\n CHECK_TXT = \"\"\"\r\n<b>🔥 ᴄʜᴏᴏsᴇ ʏᴏᴜʀ sᴜɪᴛᴀʙʟᴇ ᴘʟᴀɴ ᴀɴᴅ ᴘᴀʏ ʏᴏᴜʀ ᴘʀᴇᴍɪᴜᴍ ғᴇᴇs ᴜsɪɴɢ ᴀɴʏ ᴜᴘɪ ᴀᴘᴘ. \r\n\r\nᴘʟᴀɴ ᴀ : 𝟷 ᴡᴇᴇᴋ / ₹𝟷𝟻\r\nᴘʟᴀɴ ʙ : 𝟷 ᴍᴏɴᴛʜ / ₹𝟹𝟿\r\nᴘʟᴀɴ ᴄ : 𝟷 ʏᴇᴀʀ / ₹𝟹𝟼𝟶\r\n\r\n➻ ᴜᴘɪ ɪᴅ : harikushal234@paytm\r\n\r\n‼️ ᴍᴜsᴛ sᴇɴᴅ sᴄʀᴇᴇɴsʜᴏᴛ ᴀғᴛᴇʀ ᴘᴀʏᴍᴇɴᴛ ᴀɴᴅ ɢɪᴠᴇ ᴍᴇ sᴏᴍᴇ ᴛɪᴍᴇ ᴛᴏ ᴀᴅᴅ ʏᴏᴜ ɪɴ ᴛʜᴇ ᴘʀᴇᴍɪᴜᴍ ʟɪsᴛ.</b>\"\"\"\r\n\r\n PLAN1_TXT = \"\"\"\r\n<b>🔥 ᴘᴀʏ ʏᴏᴜʀ ᴘʀᴇᴍɪᴜᴍ ᴘʟᴀɴ ғᴇᴇs ₹𝟷𝟻 ғᴏʀ 𝟷 ᴡᴇᴇᴋ ᴘʀᴇᴍɪᴜᴍ ᴀᴄᴄᴇss ᴡɪᴛʜ ᴀᴅ-ғʀᴇᴇ ᴇxᴘᴇʀɪᴇɴᴄᴇ ᴀɴᴅ ᴍᴀɴʏ ᴍᴏʀᴇ. \r\n\r\n➻ ᴜᴘɪ ɪᴅ : harikushal234@paytm\r\n\r\n‼️ ᴍᴜsᴛ sᴇɴᴅ sᴄʀᴇᴇɴsʜᴏᴛ ᴀғᴛᴇʀ ᴘᴀʏᴍᴇɴᴛ ᴀɴᴅ ɢɪᴠᴇ ᴍᴇ sᴏᴍᴇ ᴛɪᴍᴇ ᴛᴏ ᴀᴅᴅ ʏᴏᴜ ɪɴ ᴛʜᴇ ᴘʀᴇᴍɪᴜᴍ ʟɪsᴛ.</b>\"\"\"\r\n\r\n PLAN2_TXT = \"\"\"\r\n<b>🔥 ᴘᴀʏ ʏᴏᴜʀ ᴘʀᴇᴍɪᴜᴍ ᴘʟᴀɴ ғᴇᴇs ₹𝟹𝟿 ғᴏʀ 𝟷 ᴍᴏɴᴛʜ ᴘʀᴇᴍɪᴜᴍ ᴀᴄᴄᴇss ᴡɪᴛʜ ᴀᴅ-ғʀᴇᴇ ᴇxᴘᴇʀɪᴇɴᴄᴇ ᴀɴᴅ ᴍᴀɴʏ ᴍᴏʀᴇ. \r\n\r\n➻ ᴜᴘɪ ɪᴅ : harikushal234@paytm\r\n\r\n‼️ ᴍᴜsᴛ sᴇɴᴅ sᴄʀᴇᴇɴsʜᴏᴛ ᴀғᴛᴇʀ ᴘᴀʏᴍᴇɴᴛ ᴀɴᴅ ɢɪᴠᴇ ᴍᴇ sᴏᴍᴇ ᴛɪᴍᴇ ᴛᴏ ᴀᴅᴅ ʏᴏᴜ ɪɴ ᴛʜᴇ ᴘʀᴇᴍɪᴜᴍ ʟɪsᴛ.</b>\"\"\"\r\n\r\n PLAN3_TXT = \"\"\"\r\n<b>🔥 ᴘᴀʏ ʏᴏᴜʀ ᴘʀᴇᴍɪᴜᴍ ᴘʟᴀɴ ғᴇᴇs ₹𝟹𝟼𝟶 ғᴏʀ 𝟷 ʏᴇᴀʀ ᴘʀᴇᴍɪᴜᴍ ᴀᴄᴄᴇss ᴡɪᴛʜ ᴀᴅ-ғʀᴇᴇ ᴇxᴘᴇʀɪᴇɴᴄᴇ ᴀɴᴅ ᴍᴀɴʏ ᴍᴏʀᴇ. \r\n\r\n➻ ᴜᴘɪ ɪᴅ : harikushal234@paytm\r\n\r\n‼️ ᴍᴜsᴛ sᴇɴᴅ sᴄʀᴇᴇɴsʜᴏᴛ ᴀғᴛᴇʀ ᴘᴀʏᴍᴇɴᴛ ᴀɴᴅ ɢɪᴠᴇ ᴍᴇ sᴏᴍᴇ ᴛɪᴍᴇ ᴛᴏ ᴀᴅᴅ ʏᴏᴜ ɪɴ ᴛʜᴇ ᴘʀᴇᴍɪᴜᴍ ʟɪsᴛ.</b>\"\"\"\r\n\r\n RESTART_TXT = \"\"\"\r\n<b>Bᴏᴛ Rᴇsᴛᴀʀᴛᴇᴅ !\r\n\r\n📅 Dᴀᴛᴇ : <code>{}</code>\r\n⏰ Tɪᴍᴇ : <code>{}</code>\r\n🌐 Tɪᴍᴇᴢᴏɴᴇ : <code>Asia/Kolkata</code>\r\n🛠️ Bᴜɪʟᴅ Sᴛᴀᴛᴜs: <code>ᴠ𝟹.𝟶 [ Sᴛᴀʙʟᴇ ]</code></b>\"\"\"\r\n\r\n LOGO = \"\"\"\r\n ____ ___ ____ __ ____ ____ \r\n(_ _)/ __) ( _ \\ / \\(_ _)(__ )\r\n )( ( (_ \\ ) _ (( O ) )( / _/ \r\n (__) \\___/ (____/ \\__/ (__) (____)\"\"\"\r"}],"string":"[\n {\n \"identifier\": \"ADMINS\",\n \"path\": \"info.py\",\n \"snippet\": \"ADMINS = [int(admin) if id_pattern.search(admin) else admin for admin in environ.get('ADMINS', '').split()]\"\n },\n {\n \"identifier\": \"LOG_CHANNEL\",\n \"path\": \"info.py\",\n \"snippet\": \"LOG_CHANNEL = int(environ.get('LOG_CHANNEL', ''))\"\n },\n {\n \"identifier\": \"SUPPORT_CHAT\",\n \"path\": \"info.py\",\n \"snippet\": \"SUPPORT_CHAT = environ.get('SUPPORT_CHAT', '')\"\n },\n {\n \"identifier\": \"MELCOW_NEW_USERS\",\n \"path\": \"info.py\",\n \"snippet\": \"MELCOW_NEW_USERS = is_enabled((environ.get('MELCOW_NEW_USERS', \\\"True\\\")), True)\"\n },\n {\n \"identifier\": \"MELCOW_VID\",\n \"path\": \"info.py\",\n \"snippet\": \"MELCOW_VID = environ.get(\\\"MELCOW_VID\\\", \\\"https://te.legra.ph/file/6f55d902f9bf2d0afd4bb.mp4\\\")\"\n },\n {\n \"identifier\": \"CHNL_LNK\",\n \"path\": \"info.py\",\n \"snippet\": \"CHNL_LNK = environ.get('CHNL_LNK', 'https://t.me/TG_LINKS_CHANNEL')\"\n },\n {\n \"identifier\": \"GRP_LNK\",\n \"path\": \"info.py\",\n \"snippet\": \"GRP_LNK = environ.get('GRP_LNK', 'https://t.me/TG_SUPPORT_GROUP')\"\n },\n {\n \"identifier\": \"db\",\n \"path\": \"database/users_chats_db.py\",\n \"snippet\": \"class Database:\\n def __init__(self, uri, database_name):\\n def new_user(self, id, name):\\n def new_group(self, id, title):\\n async def add_user(self, id, name):\\n async def is_user_exist(self, id):\\n async def total_users_count(self):\\n async def remove_ban(self, id):\\n async def ban_user(self, user_id, ban_reason=\\\"No Reason\\\"):\\n async def get_ban_status(self, id):\\n async def get_all_users(self):\\n async def delete_user(self, user_id):\\n async def get_banned(self):\\n async def add_chat(self, chat, title):\\n async def get_chat(self, chat):\\n async def re_enable_chat(self, id):\\n async def update_settings(self, id, settings):\\n async def get_settings(self, id):\\n async def disable_chat(self, chat, reason=\\\"No Reason\\\"):\\n async def total_chat_count(self):\\n async def get_all_chats(self):\\n async def get_db_size(self):\"\n },\n {\n \"identifier\": \"Media\",\n \"path\": \"database/ia_filterdb.py\",\n \"snippet\": \"class Media(Document):\\n file_id = fields.StrField(attribute='_id')\\n file_ref = fields.StrField(allow_none=True)\\n file_name = fields.StrField(required=True)\\n file_size = fields.IntField(required=True)\\n file_type = fields.StrField(allow_none=True)\\n mime_type = fields.StrField(allow_none=True)\\n caption = fields.StrField(allow_none=True)\\n\\n class Meta:\\n indexes = ('$file_name', )\\n collection_name = COLLECTION_NAME\"\n },\n {\n \"identifier\": \"get_size\",\n \"path\": \"utils.py\",\n \"snippet\": \"def get_size(size):\\n \\\"\\\"\\\"Get size in readable format\\\"\\\"\\\"\\n\\n units = [\\\"Bytes\\\", \\\"KB\\\", \\\"MB\\\", \\\"GB\\\", \\\"TB\\\", \\\"PB\\\", \\\"EB\\\"]\\n size = float(size)\\n i = 0\\n while size >= 1024.0 and i < len(units):\\n i += 1\\n size /= 1024.0\\n return \\\"%.2f %s\\\" % (size, units[i])\"\n },\n {\n \"identifier\": \"temp\",\n \"path\": \"utils.py\",\n \"snippet\": \"class temp(object):\\n BANNED_USERS = []\\n BANNED_CHATS = []\\n ME = None\\n CURRENT=int(os.environ.get(\\\"SKIP\\\", 2))\\n CANCEL = False\\n MELCOW = {}\\n U_NAME = None\\n B_NAME = None\\n GETALL = {}\\n SHORT = {}\\n SETTINGS = {}\"\n },\n {\n \"identifier\": \"get_settings\",\n \"path\": \"utils.py\",\n \"snippet\": \"async def get_settings(group_id):\\n settings = temp.SETTINGS.get(group_id)\\n if not settings:\\n settings = await db.get_settings(group_id)\\n temp.SETTINGS[group_id] = settings\\n return settings\"\n },\n {\n \"identifier\": \"script\",\n \"path\": \"Script.py\",\n \"snippet\": \"class script(object):\\r\\n START_TXT = \\\"\\\"\\\"<b>Hᴇʟʟᴏ 👋 {}</b>\\r\\n\\r\\n<b>Mʏ Nᴀᴍᴇ Is <a href=\\\"https://t.me/{}\\\">{}</a>, I Cᴀɴ Pʀᴏᴠɪᴅᴇ Mᴏᴠɪᴇs, Sᴇʀɪᴇs, Aɴɪᴍᴀᴛɪᴏɴ, Cᴀʀᴛᴏᴏɴ, Aɴɪᴍᴇ, K-Dʀᴀᴍᴀ & Mᴀɴʏ Mᴏʀᴇ ☺ Jᴜsᴛ Aᴅᴅ Mᴇ Tᴏ Yᴏᴜʀ Gʀᴏᴜᴘ As Aᴅᴍɪɴ EɴJᴏʏ 😍</b>\\\"\\\"\\\"\\r\\n\\r\\n HELP_TXT = \\\"\\\"\\\"<b>Hᴇʀᴇ Is Tʜᴇ Hᴇʟᴘ Fᴏʀ Mʏ Cᴏᴍᴍᴀɴᴅs.</b>\\\"\\\"\\\"\\r\\n \\r\\n ABOUT_TXT = \\\"\\\"\\\"\\r\\n<b>‣ ᴍʏ ɴᴀᴍᴇ : <a href=\\\"https://t.me/{}\\\">ʙᴏᴛ</a>\\r\\n‣ ᴄʀᴇᴀᴛᴏʀ : <a href=\\\"https://t.me/KUSHALHK\\\">𝐊𝐔𝐒𝐇𝐀𝐋</a>\\r\\n‣ ʟɪʙʀᴀʀʏ : <a href=\\\"https://pyrogram.org/\\\">ᴘʏʀᴏɢʀᴀᴍ</a>\\r\\n‣ ʟᴀɴɢᴜᴀɢᴇ : <a href=\\\"https://www.python.org/\\\">ᴘʏᴛʜᴏɴ</a>\\r\\n‣ ᴅᴀᴛᴀʙᴀꜱᴇ : <a href=\\\"https://www.mongodb.com/\\\">ᴍᴏɴɢᴏ ᴅʙ</a>\\r\\n‣ ʜᴏꜱᴛᴇᴅ ᴏɴ : <a href=\\\"https://render.com/\\\">Render</a>\\r\\n‣ ʙᴜɪʟᴅ ꜱᴛᴀᴛᴜꜱ : ᴠ.𝟹.𝟶 [ꜱᴛᴀʙʟᴇ]</b>\\\"\\\"\\\"\\r\\n \\r\\n DISCLAIMER_TXT = \\\"\\\"\\\"<b>ᴛʜɪꜱ ɪꜱ ᴀɴ ᴏᴘᴇɴ ꜱᴏᴜʀᴄᴇ ᴘʀᴏᴊᴇᴄᴛ.\\r\\n\\r\\nᴀʟʟ ᴛʜᴇ ꜰɪʟᴇꜱ ɪɴ ᴛʜɪꜱ ʙᴏᴛ ᴀʀᴇ ꜰʀᴇᴇʟʏ ᴀᴠᴀɪʟᴀʙʟᴇ ᴏɴ ᴛʜᴇ ɪɴᴛᴇʀɴᴇᴛ ᴏʀ ᴘᴏꜱᴛᴇᴅ ʙʏ ꜱᴏᴍᴇʙᴏᴅʏ ᴇʟꜱᴇ. ᴊᴜꜱᴛ ꜰᴏʀ ᴇᴀꜱʏ ꜱᴇᴀʀᴄʜɪɴɢ ᴛʜɪꜱ ʙᴏᴛ ɪꜱ ɪɴᴅᴇxɪɴɢ ꜰɪʟᴇꜱ ᴡʜɪᴄʜ ᴀʀᴇ ᴀʟʀᴇᴀᴅʏ ᴜᴘʟᴏᴀᴅᴇᴅ ᴏɴ ᴛᴇʟᴇɢʀᴀᴍ. ᴡᴇ ʀᴇꜱᴘᴇᴄᴛ ᴀʟʟ ᴛʜᴇ ᴄᴏᴘʏʀɪɢʜᴛ ʟᴀᴡꜱ ᴀɴᴅ ᴡᴏʀᴋꜱ ɪɴ ᴄᴏᴍᴘʟɪᴀɴᴄᴇ ᴡɪᴛʜ ᴅᴍᴄᴀ ᴀɴᴅ ᴇᴜᴄᴅ. ɪꜰ ᴀɴʏᴛʜɪɴɢ ɪꜱ ᴀɢᴀɪɴꜱᴛ ʟᴀᴡ ᴘʟᴇᴀꜱᴇ ᴄᴏɴᴛᴀᴄᴛ ᴍᴇ ꜱᴏ ᴛʜᴀᴛ ɪᴛ ᴄᴀɴ ʙᴇ ʀᴇᴍᴏᴠᴇᴅ ᴀꜱᴀᴘ. ɪᴛ ɪꜱ ꜰᴏʀʙɪᴅᴅᴇɴ ᴛᴏ ᴅᴏᴡɴʟᴏᴀᴅ, ꜱᴛʀᴇᴀᴍ, ʀᴇᴘʀᴏᴅᴜᴄᴇ, ꜱʜᴀʀᴇ ᴏʀ ᴄᴏɴꜱᴜᴍᴇ ᴄᴏɴᴛᴇɴᴛ ᴡɪᴛʜᴏᴜᴛ ᴇxᴘʟɪᴄɪᴛ ᴘᴇʀᴍɪꜱꜱɪᴏɴ ꜰʀᴏᴍ ᴛʜᴇ ᴄᴏɴᴛᴇɴᴛ ᴡɪᴛʜᴏᴜᴛ ᴇxᴘʟɪᴄɪᴛ ᴘᴇʀᴍɪꜱꜱɪᴏɴ ꜰʀᴏᴍ ᴛʜᴇ ᴄᴏɴᴛᴇɴᴛ ᴄʀᴇᴀᴛᴏʀ ᴏʀ ʟᴇɢᴀʟ ᴄᴏᴘʏʀɪɢʜᴛ ʜᴏʟᴅᴇʀ. ɪꜰ ʏᴏᴜ ʙᴇʟɪᴇᴠᴇ ᴛʜɪꜱ ʙᴏᴛ ɪꜱ ᴠɪᴏʟᴀᴛɪɴɢ ʏᴏᴜʀ ɪɴᴛᴇʟʟᴇᴄᴛᴜᴀʟ ᴘʀᴏᴘᴇʀᴛʏ, ᴄᴏɴᴛᴀᴄᴛ ᴛʜᴇ ʀᴇꜱᴘᴇᴄᴛɪᴠᴇ ᴄʜᴀɴɴᴇʟꜱ ꜰᴏʀ ʀᴇᴍᴏᴠᴀʟ. ᴛʜᴇ ʙᴏᴛ ᴅᴏᴇꜱ ɴᴏᴛ ᴏᴡɴ ᴀɴʏ ᴏꜰ ᴛʜᴇꜱᴇ ᴄᴏɴᴛᴇɴᴛꜱ, ɪᴛ ᴏɴʟʏ ɪɴᴅᴇx ᴛʜᴇ ꜰɪʟᴇꜱ ꜰʀᴏᴍ ᴛᴇʟᴇɢʀᴀᴍ.\\r\\n\\r\\nᴍᴀɪɴᴛᴀɪɴᴇᴅ ʙʏ : <a href=\\\"https://t.me/KUSHALHK\\\">𝐊𝐔𝐒𝐇𝐀𝐋</a></b>\\\"\\\"\\\"\\r\\n\\r\\n SOURCE_TXT = \\\"\\\"\\\"\\r\\n<b>Hᴇʏ, Tʜɪs ɪs ᴀ Oᴘᴇɴ Sᴏᴜʀᴄᴇ Pʀᴏᴊᴇᴄᴛ.\\r\\n\\r\\nTʜɪs Bᴏᴛ ʜᴀs Lᴀᴛᴇsᴛ ᴀɴᴅ Aᴅᴠᴀɴᴄᴇᴅ Fᴇᴀᴛᴜʀᴇs⚡️\\r\\n\\r\\nFork our repository and give star ⭐- <a href='https://github.com/Kushalhk/AutoFilter'>📥 ᴄʟɪᴄᴋ ʜᴇʀᴇ 📥</a></b>\\r\\n\\\"\\\"\\\"\\r\\n \\r\\n KUSHAL_TXT = \\\"\\\"\\\" \\r\\n<b>🔥 ᴘʀᴇᴍɪᴜᴍ ғᴇᴀᴛᴜʀᴇs 🔥\\r\\n\\r\\n➻ ɴᴏ ɴᴇᴇᴅ ᴛᴏ ᴠᴇʀɪғʏ\\r\\n➻ ᴅɪʀᴇᴄᴛ ғɪʟᴇs\\r\\n➻ ᴀᴅ-ғʀᴇᴇ ᴇxᴘᴇʀɪᴇɴᴄᴇ\\r\\n➻ ʜɪɢʜ-sᴘᴇᴇᴅ ᴅᴏᴡɴʟᴏᴀᴅ ʟɪɴᴋ\\r\\n➻ ᴜɴʟɪᴍɪᴛᴇᴅ ᴍᴏᴠɪᴇs ᴀɴᴅ sᴇʀɪᴇs\\r\\n➻ ғᴜʟʟ ᴀᴅᴍɪɴ sᴜᴘᴘᴏʀᴛ \\r\\n➻ ʀᴇǫᴜᴇsᴛ ᴡɪʟʟ ʙᴇ ᴄᴏᴍᴘʟᴇᴛᴇᴅ ɪɴ 𝟷ʜ ɪғ ᴀᴠᴀɪʟᴀʙʟᴇ\\r\\n\\r\\n‼️ ᴄʟɪᴄᴋ ᴏɴ ʙᴇʟᴏᴡ ʙᴜᴛᴛᴏɴ ᴛᴏ ᴄʜᴇᴄᴋ ᴀʟʟ ᴀᴠᴀɪʟᴀʙʟᴇ ᴘʀᴇᴍɪᴜᴍ ᴘʟᴀɴs ᴀɴᴅ ɪᴛ's ᴘʀɪᴄᴇs.</b>\\\"\\\"\\\"\\r\\n\\r\\n \\r\\n SETTINGS_TXT = \\\"\\\"\\\"\\r\\nHᴇʟᴘ : <b>Sᴇᴛᴛɪɴɢꜱ</b>\\r\\n \\r\\n◈ sᴇᴛᴛɪɴɢs ɪs ᴍᴏsᴛ ɪᴍᴘᴏʀᴛᴀɴᴛ ғᴇᴀᴛᴜʀᴇ ɪɴ ᴛʜɪs ʙᴏᴛ.\\r\\n◈ ʏᴏᴜ ᴄᴀɴ ᴇᴀsɪʟʏ ᴄᴜsᴛᴏᴍɪᴢᴇ ᴛʜɪs ʙᴏᴛ ғᴏʀ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\\r\\n\\r\\n<b>Nᴏᴛᴇ :</b>\\r\\n1. ᴏɴʟʏ ɢʀᴏᴜᴘ ᴀᴅᴍɪɴ ᴄᴀɴ ᴜsᴇ ᴛʜɪs ᴄᴏᴍᴍᴀɴᴅ ᴀɴᴅ ᴄʜᴀɴɢᴇ sᴇᴛᴛɪɴɢs.\\r\\n2. ɪᴛ ᴡᴏʀᴋs ᴏɴʟʏ ᴡʜᴇɴ ʙᴏᴛ ᴀʟʀᴇᴀᴅʏ ᴄᴏɴɴᴇᴄᴛᴇᴅ ᴛᴏ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\\r\\n\\r\\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\\r\\n• /connect - ᴄᴏɴɴᴇᴄᴛ ʏᴏᴜʀ ɢʀᴏᴜᴘ ᴛᴏ ʙᴏᴛ\\r\\n• /settings - ᴄʜᴀɴɢᴇ sᴇᴛᴛɪɴɢs ᴀs ʏᴏᴜʀ ᴡɪsʜ \\\"\\\"\\\"\\r\\n\\r\\n TELEGRAPH_TXT = \\\"\\\"\\\" Hᴇʟᴘ : <b>Tᴇʟᴇɢʀᴀᴘʜ</b>\\r\\n\\r\\n<b>Nᴏᴛᴇ</b>: ᴛʜɪꜱ ᴄᴏᴍᴍᴀɴᴅ ɪꜱ ᴀᴠᴀɪʟᴀʙʟᴇ ɪɴ ɢʀᴏᴜᴘꜱ ᴀɴᴅ ᴘᴍꜱ. ᴀʟꜱᴏ ᴄᴀɴ ʙᴇ ᴜꜱᴇ ʙʏ ᴇᴠᴇʀʏᴏɴᴇ.\\r\\n\\r\\n<b>Cᴏᴍᴍᴀɴᴅs & Usᴀɢᴇ :</b>\\r\\n• /telegraph - sᴇɴᴅ ᴍᴇ ᴘɪᴄᴛᴜʀᴇ ᴏʀ ᴠɪᴅᴇᴏ ᴜɴᴅᴇʀ 𝟻ᴍʙ\\\"\\\"\\\"\\r\\n\\r\\n FONT_TXT = \\\"\\\"\\\"Hᴇʟᴘ : <b>Fᴏɴᴛ</b>\\r\\n\\r\\n<b>Nᴏᴛᴇ</b>: ʏᴏᴜ ᴄᴀɴ ᴜꜱᴇ ᴛʜɪꜱ ᴍᴏᴅᴇ ᴛᴏ ᴄʜᴀɴɢᴇ ʏᴏᴜʀ ꜰᴏɴᴛꜱ ꜱᴛʏʟᴇ, ᴊᴜꜱᴛ ꜱᴇɴᴅ ᴍᴇ ʟɪᴋᴇ ᴛʜɪꜱ ꜰᴏʀᴍᴀᴛ. \\r\\n\\r\\n<code>/font TG_LINKS_CHANNEL</code>\\\"\\\"\\\"\\r\\n\\r\\n MANUELFILTER_TXT = \\\"\\\"\\\"Hᴇʟᴘ : <b>Fɪʟᴛᴇʀꜱ</b>\\r\\n \\r\\n◈ ꜰɪʟᴛᴇʀ ɪꜱ ᴀ ꜰᴇᴀᴛᴜʀᴇ ᴡᴇʀᴇ ᴜꜱᴇʀꜱ ᴄᴀɴ ꜱᴇᴛ ᴀᴜᴛᴏᴍᴀᴛᴇᴅ ʀᴇᴘʟɪᴇꜱ ꜰᴏʀ ᴀ ᴘᴀʀᴛɪᴄᴜʟᴀʀ ᴋᴇʏᴡᴏʀᴅ ᴀɴᴅ ɪ ᴡɪʟʟ ʀᴇꜱᴘᴏɴᴅ ᴡʜᴇɴᴇᴠᴇʀ ᴀ ᴋᴇʏᴡᴏʀᴅ ɪꜱ ꜰᴏᴜɴᴅ ɪɴ ᴛʜᴇ ᴍᴇꜱꜱᴀɢᴇ.\\r\\n\\r\\n<b>Nᴏᴛᴇ :</b>\\r\\n1. ᴛʜɪꜱ ʙᴏᴛ ꜱʜᴏᴜʟᴅ ʜᴀᴠᴇ ᴀᴅᴍɪɴ ᴘʀɪᴠɪʟᴇɢᴇ.\\r\\n2. ᴏɴʟʏ ᴀᴅᴍɪɴꜱ ᴄᴀɴ ᴀᴅᴅ ꜰɪʟᴛᴇʀꜱ ɪɴ ᴀ ᴄʜᴀᴛ.\\r\\n3. ᴀʟᴇʀᴛ ʙᴜᴛᴛᴏɴꜱ ʜᴀᴠᴇ ᴀ ʟɪᴍɪᴛ ᴏꜰ 64 ᴄʜᴀʀᴀᴄᴛᴇʀꜱ.\\r\\n\\r\\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\\r\\n• /filter - ᴀᴅᴅ ᴀ ꜰɪʟᴛᴇʀ ɪɴ ᴀ ᴄʜᴀᴛ\\r\\n• /filters - ʟɪꜱᴛ ᴀʟʟ ᴛʜᴇ ꜰɪʟᴛᴇʀꜱ ᴏꜰ ᴀ ᴄʜᴀᴛ\\r\\n• /del - ᴅᴇʟᴇᴛᴇ ᴀ ꜱᴘᴇᴄɪꜰɪᴄ ꜰɪʟᴛᴇʀ ɪɴ ᴀ ᴄʜᴀᴛ\\r\\n• /delall - ᴅᴇʟᴇᴛᴇ ᴛʜᴇ ᴡʜᴏʟᴇ ꜰɪʟᴛᴇʀꜱ ɪɴ ᴀ ᴄʜᴀᴛ (ᴄʜᴀᴛ ᴏᴡɴᴇʀ ᴏɴʟʏ)\\\"\\\"\\\"\\r\\n\\r\\n BUTTON_TXT = \\\"\\\"\\\"Hᴇʟᴘ : <b>Bᴜᴛᴛᴏɴꜱ</b>\\r\\n \\r\\n◈ ᴛʜɪꜱ ʙᴏᴛ ꜱᴜᴘᴘᴏʀᴛꜱ ʙᴏᴛʜ ᴜʀʟ ᴀɴᴅ ᴀʟᴇʀᴛ ɪɴʟɪɴᴇ ʙᴜᴛᴛᴏɴꜱ.\\r\\n\\r\\n<b>Nᴏᴛᴇ :</b>\\r\\n𝟷. ᴛᴇʟᴇɢʀᴀᴍ ᴡɪʟʟ ɴᴏᴛ ᴀʟʟᴏᴡꜱ ʏᴏᴜ ᴛᴏ ꜱᴇɴᴅ ʙᴜᴛᴛᴏɴꜱ ᴡɪᴛʜᴏᴜᴛ ᴀɴʏ ᴄᴏɴᴛᴇɴᴛ, ꜱᴏ ᴄᴏɴᴛᴇɴᴛ ɪꜱ ᴍᴀɴᴅᴀᴛᴏʀʏ.\\r\\n𝟸. ᴛʜɪꜱ ʙᴏᴛ ꜱᴜᴘᴘᴏʀᴛꜱ ʙᴜᴛᴛᴏɴꜱ ᴡɪᴛʜ ᴀɴʏ ᴛᴇʟᴇɢʀᴀᴍ ᴍᴇᴅɪᴀ ᴛʏᴘᴇ.\\r\\n𝟹. ʙᴜᴛᴛᴏɴꜱ ꜱʜᴏᴜʟᴅ ʙᴇ ᴘʀᴏᴘᴇʀʟʏ ᴘᴀʀꜱᴇᴅ ᴀꜱ ᴍᴀʀᴋᴅᴏᴡɴ ꜰᴏʀᴍᴀᴛ\\r\\n\\r\\nᴜʀʟ ʙᴜᴛᴛᴏɴꜱ :\\r\\n<code>[Button Text](buttonurl:https://t.me/TG_LINKS_CHANNEL)</code>\\r\\n\\r\\nᴀʟᴇʀᴛ ʙᴜᴛᴛᴏɴꜱ :\\r\\n<code>[Button Text](buttonalert:ᴛʜɪꜱ ɪꜱ ᴀɴ ᴀʟᴇʀᴛ ᴍᴇꜱꜱᴀɢᴇ)</code>\\\"\\\"\\\"\\r\\n\\r\\n AUTOFILTER_TXT = \\\"\\\"\\\"Hᴇʟᴘ : <b>Aᴜᴛᴏ Fɪʟᴛᴇʀ</b>\\r\\n \\r\\n<b>Nᴏᴛᴇ :</b> Fɪʟᴇ Iɴᴅᴇx\\r\\n𝟷. ᴍᴀᴋᴇ ᴍᴇ ᴛʜᴇ ᴀᴅᴍɪɴ ᴏꜰ ʏᴏᴜʀ ᴄʜᴀɴɴᴇʟ ɪꜰ ɪᴛ'ꜱ ᴘʀɪᴠᴀᴛᴇ.\\r\\n𝟸. ᴍᴀᴋᴇ ꜱᴜʀᴇ ᴛʜᴀᴛ ʏᴏᴜʀ ᴄʜᴀɴɴᴇʟ ᴅᴏᴇꜱ ɴᴏᴛ ᴄᴏɴᴛᴀɪɴꜱ ᴄᴀᴍʀɪᴘꜱ, ᴘᴏʀɴ ᴀɴᴅ ꜰᴀᴋᴇ ꜰɪʟᴇꜱ.\\r\\n𝟹. ꜰᴏʀᴡᴀʀᴅ ᴛʜᴇ ʟᴀꜱᴛ ᴍᴇꜱꜱᴀɢᴇ ᴛᴏ ᴍᴇ ᴡɪᴛʜ ǫᴜᴏᴛᴇꜱ. ɪ'ʟʟ ᴀᴅᴅ ᴀʟʟ ᴛʜᴇ ꜰɪʟᴇꜱ ɪɴ ᴛʜᴀᴛ ᴄʜᴀɴɴᴇʟ ᴛᴏ ᴍʏ ᴅʙ.\\r\\n\\r\\n<b>Nᴏᴛᴇ :</b> Aᴜᴛᴏ Fɪʟᴛᴇʀ\\r\\n𝟷. Aᴅᴅ ᴛʜᴇ ʙᴏᴛ ᴀs ᴀᴅᴍɪɴ ᴏɴ ʏᴏᴜʀ ɢʀᴏᴜᴘ.\\r\\n𝟸. Usᴇ /connect ᴀɴᴅ ᴄᴏɴɴᴇᴄᴛ ʏᴏᴜʀ ɢʀᴏᴜᴘ ᴛᴏ ᴛʜᴇ ʙᴏᴛ.\\r\\n𝟹. Usᴇ /settings ᴏɴ ʙᴏᴛ's ᴘᴍ ᴀɴᴅ ᴛᴜʀɴ ᴏɴ AᴜᴛᴏFɪʟᴛᴇʀ ᴏɴ ᴛʜᴇ sᴇᴛᴛɪɴɢs ᴍᴇɴᴜ.\\\"\\\"\\\"\\r\\n\\r\\n \\r\\n RULE_TXT = \\\"\\\"\\\"♦ 𝗚𝗿𝗼𝘂𝗽 𝗥𝘂𝗹𝗲𝘀 ♦\\r\\n\\r\\n◈ <b>Sᴇᴀʀᴄʜ Mᴏᴠɪᴇ Wɪᴛʜ Cᴏʀʀᴇᴄᴛ Sᴘᴇʟʟɪɴɢ:</b>\\r\\n• ᴀᴠᴀᴛᴀʀ 𝟸𝟶𝟶𝟿 ✅\\r\\n• ᴀᴠᴀᴛᴀʀ ʜɪɴᴅɪ ✅\\r\\n• ᴀᴠᴀᴛᴀʀ ᴍᴏᴠɪᴇ ❌\\r\\n• ᴀᴠᴀᴛᴀʀ ʜɪɴᴅɪ ᴅᴜʙʙᴇᴅ..❌\\r\\n\\r\\n◈ <b>Sᴇᴀʀᴄʜ Wᴇʙ Sᴇʀɪᴇs Iɴ ᴛʜɪs Fᴏʀᴍᴀᴛ:</b>\\r\\n• ᴠɪᴋɪɴɢs S𝟶𝟷 ✅\\r\\n• ᴠɪᴋɪɴɢs S𝟶𝟷E𝟶𝟷 ✅\\r\\n• ᴠɪᴋɪɴɢs S𝟶𝟷 ʜɪɴᴅɪ ✅\\r\\n• ᴠɪᴋɪɴɢs S𝟶𝟷 ʜɪɴᴅɪ ᴅᴜʙʙ... ❌\\r\\n• ᴠɪᴋɪɴɢs sᴇᴀsᴏɴ 𝟷 ❌\\r\\n• ᴠɪᴋɪɴɢs ᴡᴇʙ sᴇʀɪᴇs ❌\\r\\n\\r\\n<b>➙ ᴅᴏɴ'ᴛ ᴅᴏ ᴀɴʏ ꜱᴇʟꜰ ᴘʀᴏᴍᴏᴛɪᴏɴ. \\r\\n➙ ᴅᴏɴ'ᴛ ꜱᴇɴᴅ ᴀɴʏ ᴋɪɴᴅ ᴏꜰ ᴘʜᴏᴛᴏ, ᴠɪᴅᴇᴏ, ᴅᴏᴄᴜᴍᴇɴᴛꜱ, ᴜʀʟ, ᴇᴛᴄ...\\r\\n➙ ᴅᴏɴ'ᴛ ʀᴇǫᴜᴇꜱᴛ ᴀɴʏ ᴛʜɪɴɢꜱ ᴏᴛʜᴇʀ ᴛʜᴀɴ ᴍᴏᴠɪᴇꜱ, ꜱᴇʀɪᴇꜱ, ᴀɴɪᴍᴀᴛɪᴏɴ, ᴄᴀʀᴛᴏᴏɴ, ᴀɴɪᴍᴇ, ᴋ-ᴅʀᴀᴍᴀ ᴍᴀɴʏ ᴍᴏʀᴇ.</b>\\r\\n\\r\\n🔰 <b>Nᴏᴛᴇ :</b> ᴀʟʟ ᴍᴇꜱꜱᴀɢᴇꜱ ᴡɪʟʟ ʙᴇ ᴀᴜᴛᴏ-ᴅᴇʟᴇᴛᴇᴅ ᴀꜰᴛᴇʀ 𝟷𝟶 ᴍɪɴᴜᴛᴇꜱ ᴛᴏ ᴀᴠᴏɪᴅ ᴄᴏᴘʏʀɪɢʜᴛ ɪꜱꜱᴜᴇꜱ.\\\"\\\"\\\"\\r\\n\\r\\n CONNECTION_TXT = \\\"\\\"\\\"Hᴇʟᴘ : <b>Cᴏɴɴᴇᴄᴛɪᴏɴꜱ</b>\\r\\n \\r\\n◈ ᴜꜱᴇᴅ ᴛᴏ ᴄᴏɴɴᴇᴄᴛ ʙᴏᴛ ᴛᴏ ᴘᴍ ꜰᴏʀ ᴍᴀɴᴀɢɪɴɢ ꜰɪʟᴛᴇʀꜱ \\r\\n◈ ɪᴛ ʜᴇʟᴘꜱ ᴛᴏ ᴀᴠᴏɪᴅ ꜱᴘᴀᴍᴍɪɴɢ ɪɴ ɢʀᴏᴜᴘꜱ.\\r\\n\\r\\n<b>Nᴏᴛᴇ :</b>\\r\\n1. ᴏɴʟʏ ᴀᴅᴍɪɴꜱ ᴄᴀɴ ᴀᴅᴅ ᴀ ᴄᴏɴɴᴇᴄᴛɪᴏɴ.\\r\\n2. ꜱᴇɴᴅ /ᴄᴏɴɴᴇᴄᴛ ꜰᴏʀ ᴄᴏɴɴᴇᴄᴛɪɴɢ ᴍᴇ ᴛᴏ ʏᴏᴜʀ ᴘᴍ\\r\\n\\r\\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\\r\\n• /connect - ᴄᴏɴɴᴇᴄᴛ ᴀ ᴘᴀʀᴛɪᴄᴜʟᴀʀ ᴄʜᴀᴛ ᴛᴏ ʏᴏᴜʀ ᴘᴍ\\r\\n• /disconnect - ᴅɪꜱᴄᴏɴɴᴇᴄᴛ ꜰʀᴏᴍ ᴀ ᴄʜᴀᴛ\\r\\n• /connections - ʟɪꜱᴛ ᴀʟʟ ʏᴏᴜʀ ᴄᴏɴɴᴇᴄᴛɪᴏɴꜱ\\\"\\\"\\\"\\r\\n\\r\\n EXTRAMOD_TXT = \\\"\\\"\\\"Hᴇʟᴘ : <b>Exᴛʀᴀ Mᴏᴅᴜʟᴇs</b>\\r\\n \\r\\n<b>Nᴏᴛᴇ :</b>\\r\\nᴛʜᴇꜱᴇ ᴀʀᴇ ᴛʜᴇ ᴇxᴛʀᴀ ꜰᴇᴀᴛᴜʀᴇꜱ ᴏꜰ ᴛʜɪꜱ ʙᴏᴛ\\r\\n\\r\\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\\r\\n• /id - ɢᴇᴛ ɪᴅ ᴏꜰ ᴀ ꜱᴘᴇᴄɪꜰɪᴇᴅ ᴜꜱᴇʀ.\\r\\n• /info - ɢᴇᴛ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ᴀʙᴏᴜᴛ ᴀ ᴜꜱᴇʀ.\\r\\n• /imdb - ɢᴇᴛ ᴛʜᴇ ꜰɪʟᴍ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ꜰʀᴏᴍ ɪᴍᴅʙ ꜱᴏᴜʀᴄᴇ.\\r\\n• /search - ɢᴇᴛ ᴛʜᴇ ꜰɪʟᴍ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ꜰʀᴏᴍ ᴠᴀʀɪᴏᴜꜱ ꜱᴏᴜʀᴄᴇꜱ.\\\"\\\"\\\"\\r\\n\\r\\n ADMIN_TXT = \\\"\\\"\\\"<b>Nᴏᴛᴇ :</b> Tʜɪs Mᴏᴅᴜʟᴇ Oɴʟʏ Wᴏʀᴋs Fᴏʀ Mʏ Aᴅᴍɪɴs.\\r\\n\\r\\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\\r\\n• /logs - ᴛᴏ ɢᴇᴛ ᴛʜᴇ ʀᴇᴄᴇɴᴛ ᴇʀʀᴏʀꜱ\\r\\n• /stats - ᴛᴏ ɢᴇᴛ ꜱᴛᴀᴛᴜꜱ ᴏꜰ ꜰɪʟᴇꜱ ɪɴ ᴅʙ. <b>[Tʜɪs Cᴏᴍᴍᴀɴᴅ Cᴀɴ Bᴇ Usᴇᴅ Bʏ Aɴʏᴏɴᴇ]</b>\\r\\n• /delete - ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀ ꜱᴘᴇᴄɪꜰɪᴄ ꜰɪʟᴇ ꜰʀᴏᴍ ᴅʙ.\\r\\n• /users - ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴍʏ ᴜꜱᴇʀꜱ ᴀɴᴅ ɪᴅꜱ.\\r\\n• /chats - ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴍʏ ᴄʜᴀᴛꜱ ᴀɴᴅ ɪᴅꜱ\\r\\n• /leave - ᴛᴏ ʟᴇᴀᴠᴇ ꜰʀᴏᴍ ᴀ ᴄʜᴀᴛ.\\r\\n• /disable - ᴛᴏ ᴅɪꜱᴀʙʟᴇ ᴀ ᴄʜᴀᴛ.\\r\\n• /ban - ᴛᴏ ʙᴀɴ ᴀ ᴜꜱᴇʀ.\\r\\n• /unban - ᴛᴏ ᴜɴʙᴀɴ ᴀ ᴜꜱᴇʀ.\\r\\n• /channel - ᴛᴏ ɢᴇᴛ ʟɪꜱᴛ ᴏꜰ ᴛᴏᴛᴀʟ ᴄᴏɴɴᴇᴄᴛᴇᴅ ᴄʜᴀɴɴᴇʟꜱ. \\r\\n• /broadcast - ᴛᴏ ʙʀᴏᴀᴅᴄᴀꜱᴛ ᴀ ᴍᴇꜱꜱᴀɢᴇ ᴛᴏ ᴀʟʟ ᴜꜱᴇʀꜱ. \\r\\n• /grp_broadcast - Tᴏ ʙʀᴏᴀᴅᴄᴀsᴛ ᴀ ᴍᴇssᴀɢᴇ ᴛᴏ ᴀʟʟ ᴄᴏɴɴᴇᴄᴛᴇᴅ ɢʀᴏᴜᴘs.\\r\\n• /gfilter - ᴛᴏ ᴀᴅᴅ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs. \\r\\n• /gfilters - ᴛᴏ ᴠɪᴇᴡ ʟɪsᴛ ᴏғ ᴀʟʟ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs. \\r\\n• /delg - ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀ sᴘᴇᴄɪғɪᴄ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ. \\r\\n• /request - ᴛᴏ sᴇɴᴅ ᴀ ᴍᴏᴠɪᴇ/sᴇʀɪᴇs ʀᴇᴏ̨ᴜᴇsᴛ ᴛᴏ ʙᴏᴛ ᴀᴅᴍɪɴs. ᴏɴʟʏ ᴡᴏʀᴋs ᴏɴ sᴜᴘᴘᴏʀᴛ ɢʀᴏᴜᴘ. <b>[Tʜɪs Cᴏᴍᴍᴀɴᴅ Cᴀɴ Bᴇ Usᴇᴅ Bʏ Aɴʏᴏɴᴇ]</b>\\r\\n• /delallg - ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀʟʟ ɢғɪʟᴛᴇʀs ғʀᴏᴍ ᴛʜᴇ ʙᴏᴛ's ᴅᴀᴛᴀʙᴀsᴇ.\\r\\n• /deletefiles - ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴄᴀᴍʀɪᴘ ᴀɴᴅ ᴘʀᴇ-ᴅᴠᴅ ғɪʟᴇs ғʀᴏᴍ ᴛʜᴇ ʙᴏᴛ's ᴅᴀᴛᴀʙᴀsᴇ.\\\"\\\"\\\"\\r\\n\\r\\n STICKER_TXT = \\\"\\\"\\\"<b>yᴏᴜ ᴄᴀɴ ᴜꜱᴇ ᴛʜɪꜱ ᴍᴏᴅᴜʟᴇ ᴛᴏ ꜰɪɴᴅᴀɴy ꜱᴛɪᴄᴋᴇʀꜱ ɪᴅ.\\r\\n• ᴜꜱᴀɢᴇ :ᴛᴏ ɢᴇᴛ ꜱᴛɪᴄᴋᴇʀ\\r\\n \\r\\n⭕ ʜᴏᴡ ᴛᴏ ᴜꜱᴇ\\r\\n◉ Reply To Any Sticker [/stickerid]\\r\\n\\r\\n/𝐬𝐭𝐢𝐜𝐤𝐞𝐫𝐢𝐝 𝐬𝐭𝐢𝐜𝐤𝐞𝐫 𝐢𝐝\\r\\n\\r\\n</b>\\\"\\\"\\\"\\r\\n \\r\\n STATUS_TXT = \\\"\\\"\\\"<b>⍟─────[ <b>Bᴏᴛ Sᴛᴀᴛᴜs</b> ]─────⍟\\r\\n \\r\\n★ ᴛᴏᴛᴀʟ ꜰɪʟᴇꜱ : <code>{}</code>\\r\\n★ ᴛᴏᴛᴀʟ ᴜꜱᴇʀꜱ : <code>{}</code>\\r\\n★ ᴛᴏᴛᴀʟ ɢʀᴏᴜᴘꜱ : <code>{}</code>\\r\\n★ ᴜꜱᴇᴅ ꜱᴛᴏʀᴀɢᴇ: <code>{}</code>\\r\\n★ ꜰʀᴇᴇ ꜱᴛᴏʀᴀɢᴇ : <code>{}</code>\\r\\n\\r\\n•❅──────✧❅✦❅✧──────❅•</b>\\\"\\\"\\\"\\r\\n\\r\\n\\r\\n LOG_TEXT_G = \\\"\\\"\\\"<b>#NewGroup\\r\\nGʀᴏᴜᴘ = {}(<code>{}</code>)\\r\\nTᴏᴛᴀʟ Mᴇᴍʙᴇʀs = <code>{}</code>\\r\\nAᴅᴅᴇᴅ Bʏ - {}</b>\\\"\\\"\\\"\\r\\n\\r\\n LOG_TEXT_P = \\\"\\\"\\\"<b>#NewUser\\r\\nID - <code>{}</code>\\r\\nNᴀᴍᴇ - {}</b>\\\"\\\"\\\"\\r\\n\\r\\n ALRT_TXT = \\\"\\\"\\\"<b>ʜᴇʟʟᴏ {},\\r\\nᴛʜɪꜱ ɪꜱ ɴᴏᴛ ʏᴏᴜʀ ᴍᴏᴠɪᴇ ʀᴇQᴜᴇꜱᴛ,\\r\\nʀᴇǫᴜᴇꜱᴛ ʏᴏᴜʀ'ꜱ...</b>\\\"\\\"\\\"\\r\\n\\r\\n OLD_ALRT_TXT = \\\"\\\"\\\"<b>ʜᴇʏ {},\\r\\nʏᴏᴜ ᴀʀᴇ ᴜꜱɪɴɢ ᴏɴᴇ ᴏꜰ ᴍʏ ᴏʟᴅ ᴍᴇꜱꜱᴀɢᴇꜱ, \\r\\nᴘʟᴇᴀꜱᴇ ꜱᴇɴᴅ ᴛʜᴇ ʀᴇǫᴜᴇꜱᴛ ᴀɢᴀɪɴ.</b>\\\"\\\"\\\"\\r\\n\\r\\n CUDNT_FND = \\\"\\\"\\\"<b>ɪ ᴄᴏᴜʟᴅɴ'ᴛ ꜰɪɴᴅ ᴀɴʏᴛʜɪɴɢ ʀᴇʟᴀᴛᴇᴅ ᴛᴏ {}\\r\\nᴅɪᴅ ʏᴏᴜ ᴍᴇᴀɴ ᴀɴʏ ᴏɴᴇ ᴏꜰ ᴛʜᴇꜱᴇ?</b>\\\"\\\"\\\"\\r\\n\\r\\n I_CUDNT = \\\"\\\"\\\"<b>sᴏʀʀʏ ɴᴏ ꜰɪʟᴇs ᴡᴇʀᴇ ꜰᴏᴜɴᴅ ꜰᴏʀ ʏᴏᴜʀ ʀᴇǫᴜᴇꜱᴛ {} 😕\\r\\n\\r\\nMᴏᴠɪᴇs Nᴏᴛ Aᴠᴀɪʟᴀʙʟᴇ Rᴇᴀsᴏɴ:\\r\\n𝟷. ᴏ.ᴛ.ᴛ ᴏʀ ᴅᴠᴅ ɴᴏᴛ ʀᴇʟᴇᴀsᴇᴅ\\r\\n𝟸. ᴛʏᴘᴇ ɴᴀᴍᴇ ᴡɪᴛʜ ʏᴇᴀʀ\\r\\n𝟹. ᴍᴏᴠɪᴇ ɪs ɴᴏᴛ ᴀᴠᴀɪʟᴀʙʟᴇ ɪɴ ᴛʜᴇ ᴅᴀᴛᴀʙᴀsᴇ ʀᴇᴘᴏʀᴛ ᴛᴏ ᴀᴅᴍɪɴs @TG_Bots_Supporter</b>\\\"\\\"\\\"\\r\\n\\r\\n I_CUD_NT = \\\"\\\"\\\"<b>ɪ ᴄᴏᴜʟᴅɴ'ᴛ ꜰɪɴᴅ ᴀɴʏ ᴍᴏᴠɪᴇ ʀᴇʟᴀᴛᴇᴅ ᴛᴏ {}.\\r\\nᴘʟᴇᴀꜱᴇ ᴄʜᴇᴄᴋ ᴛʜᴇ ꜱᴘᴇʟʟɪɴɢ ᴏɴ ɢᴏᴏɢʟᴇ ᴏʀ ɪᴍᴅʙ...</b>\\\"\\\"\\\"\\r\\n\\r\\n MVE_NT_FND = \\\"\\\"\\\"<b>ᴍᴏᴠɪᴇ ɴᴏᴛ ꜰᴏᴜɴᴅ ɪɴ ᴅᴀᴛᴀʙᴀꜱᴇ...</b>\\\"\\\"\\\"\\r\\n\\r\\n TOP_ALRT_MSG = \\\"\\\"\\\"<b>Cʜᴇᴄᴋɪɴɢ Fᴏʀ Mᴏᴠɪᴇ Iɴ Dᴀᴛᴀʙᴀsᴇ...</b>\\\"\\\"\\\"\\r\\n\\r\\n MELCOW_ENG = \\\"\\\"\\\"<b>Hᴇʟʟᴏ {} 😍, Aɴᴅ Wᴇʟᴄᴏᴍᴇ Tᴏ {} Gʀᴏᴜᴘ ❤️\\r\\n\\r\\n➻ ʜᴇʀᴇ ʏᴏᴜ ᴄᴀɴ ꜱᴇᴀʀᴄʜ ʏᴏᴜʀ ꜰᴀᴠᴏᴜʀɪᴛᴇ ᴍᴏᴠɪᴇꜱ ᴏʀ ꜱᴇʀɪᴇꜱ ʙʏ ᴊᴜꜱᴛ ᴛʏᴘɪɴɢ ɪᴛ'ꜱ ɴᴀᴍᴇ. \\r\\n\\r\\n⚠️ ɪꜰ ʏᴏᴜ ᴀʀᴇ ʜᴀᴠɪɴɢ ᴀɴʏ ᴘʀᴏʙʟᴇᴍ ʀᴇɢᴀʀᴅɪɴɢ ᴅᴏᴡɴʟᴏᴀᴅɪɴɢ ᴏʀ ꜱᴏᴍᴇᴛʜɪɴɢ ᴇʟꜱᴇ ᴛʜᴇɴ ᴍᴇꜱꜱᴀɢᴇ ʜᴇʀᴇ 👇</b>\\\"\\\"\\\"\\r\\n \\r\\n REQINFO = \\\"\\\"\\\"\\r\\n⚠ ɪɴꜰᴏʀᴍᴀᴛɪᴏɴ ⚠\\r\\n\\r\\nᴀꜰᴛᴇʀ 5 ᴍɪɴᴜᴛᴇꜱ ᴛʜɪꜱ ᴍᴇꜱꜱᴀɢᴇ ᴡɪʟʟ ʙᴇ ᴀᴜᴛᴏᴍᴀᴛɪᴄᴀʟʟʏ ᴅᴇʟᴇᴛᴇᴅ\\r\\n\\r\\nɪꜰ ʏᴏᴜ ᴅᴏ ɴᴏᴛ ꜱᴇᴇ ᴛʜᴇ ʀᴇǫᴜᴇsᴛᴇᴅ ᴍᴏᴠɪᴇ / sᴇʀɪᴇs ꜰɪʟᴇ, ʟᴏᴏᴋ ᴀᴛ ᴛʜᴇ ɴᴇxᴛ ᴘᴀɢᴇ\\\"\\\"\\\"\\r\\n\\r\\n \\r\\n\\r\\n SINFO = \\\"\\\"\\\"\\r\\n⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯\\r\\nꜱᴇʀɪᴇꜱ ʀᴇǫᴜᴇꜱᴛ ꜰᴏʀᴍᴀᴛ\\r\\n⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯\\r\\n\\r\\nɢᴏ ᴛᴏ ɢᴏᴏɢʟᴇ ➠ ᴛʏᴘᴇ ꜱᴇʀɪᴇꜱ ɴᴀᴍᴇ ➠ ᴄᴏᴘʏ ᴄᴏʀʀᴇᴄᴛ ɴᴀᴍᴇ ➠ ᴘᴀꜱᴛᴇ ᴛʜɪꜱ ɢʀᴏᴜᴘ\\r\\n\\r\\nᴇxᴀᴍᴘʟᴇ : Loki S01E01\\r\\n\\r\\n🚯 ᴅᴏɴᴛ ᴜꜱᴇ ➠ ':(!,./)\\\"\\\"\\\"\\r\\n\\r\\n NORSLTS = \\\"\\\"\\\"\\r\\n★ #𝗡𝗼𝗥𝗲𝘀𝘂𝗹𝘁𝘀 ★\\r\\n\\r\\n𝗜𝗗 <b>: {}</b>\\r\\n\\r\\n𝗡𝗮𝗺𝗲 <b>: {}</b>\\r\\n\\r\\n𝗠𝗲𝘀𝘀𝗮𝗴𝗲 <b>: {}</b>🥲\\\"\\\"\\\"\\r\\n\\r\\n CAPTION = \\\"\\\"\\\" \\r\\n🗂 𝗙𝗶𝗹𝗲: <b><font class=smcp>{file_name}</font></b>\\r\\n📀 𝗦𝗶𝘇𝗲: <b><font class=smcp>{file_size}</font></b>\\r\\n\\r\\n<b>🔰 Cʀᴇᴀᴛᴏʀ : <a href=\\\"https://t.me/KUSHALHK\\\">𝐊𝐔𝐒𝐇𝐀𝐋</a>\\r\\n🔰 Cʜᴀɴɴᴇʟ : <a href=\\\"https://t.me/TG_LINKS_CHANNEL\\\">𝐌𝐎𝐕𝐈𝐄𝐒 𝐂𝐇𝐀𝐍𝐍𝐄𝐋</a>\\r\\n🔰 Gʀᴏᴜᴘ : <a href=\\\"https://t.me/movies_hub_official1\\\">𝐌𝐎𝐕𝐈𝐄 𝐑𝐄𝐐𝐔𝐄𝐒𝐓 𝐆𝐑𝐎𝐔𝐏</a></b>\\\"\\\"\\\"\\r\\n \\r\\n IMDB_TEMPLATE_TXT = \\\"\\\"\\\"\\r\\n<b>Query: {query}\\r\\nIMDb Data:\\r\\n\\r\\n🧿 𝐓𝐈𝐓𝐋𝐄: <a href={url}>{title}</a>\\r\\n🎭 𝐆𝐄𝐍𝐑𝐄𝐒: {genres}\\r\\n📆 𝐘𝐄𝐀𝐑: <a href={url}/releaseinfo>{year}</a>\\r\\n🌟 𝐑𝐀𝐓𝐈𝐍𝐆: <a href={url}/ratings>{rating}</a> / 10 (Based on {votes} user ratings)</b>\\r\\n☀️ 𝐋𝐀𝐍𝐆𝐔𝐀𝐆𝐄 : <code>{languages}</code></a>\\r\\n📀 𝐑𝐔𝐍𝐓𝐈𝐌𝐄: {runtime} Minutes</a>\\r\\n\\r\\n<b>👨💼 Requested by : {message.from_user.mention}</b>\\\"\\\"\\\"\\r\\n\\r\\n \\r\\n ALL_FILTERS = \\\"\\\"\\\"\\r\\n<b>Hᴇʏ {}, Tʜᴇsᴇ ᴀʀᴇ ᴍʏ ᴛʜʀᴇᴇ ᴛʏᴘᴇs ᴏғ ғɪʟᴛᴇʀs.</b>\\\"\\\"\\\"\\r\\n \\r\\n GFILTER_TXT = \\\"\\\"\\\"Hᴇʟᴘ : <b>Gʟᴏʙᴀʟ Fɪʟᴛᴇʀs</b>\\r\\n \\r\\n◈ Gʟᴏʙᴀʟ Fɪʟᴛᴇʀs ᴀʀᴇ ᴛʜᴇ ғɪʟᴛᴇʀs sᴇᴛ ʙʏ ʙᴏᴛ ᴀᴅᴍɪɴs ᴡʜɪᴄʜ ᴡɪʟʟ ᴡᴏʀᴋ ᴏɴ ᴀʟʟ ɢʀᴏᴜᴘs.\\r\\n \\r\\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\\r\\n• /gfilter - Tᴏ ᴄʀᴇᴀᴛᴇ ᴀ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ.\\r\\n• /gfilters - Tᴏ ᴠɪᴇᴡ ᴀʟʟ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀs.\\r\\n• /delg - Tᴏ ᴅᴇʟᴇᴛᴇ ᴀ ᴘᴀʀᴛɪᴄᴜʟᴀʀ ɢʟᴏʙᴀʟ ғɪʟᴛᴇʀ.\\r\\n• /delallg - ᴛᴏ ᴅᴇʟᴇᴛᴇ ᴀʟʟ ɢʟᴏʙᴀʟ ꜰɪʟᴛᴇʀꜱ.\\\"\\\"\\\"\\r\\n \\r\\n FILE_STORE_TXT = \\\"\\\"\\\"Hᴇʟᴘ : <b>Fɪʟᴇ Sᴛᴏʀᴇ</b>\\r\\n \\r\\n◈ Fɪʟᴇ sᴛᴏʀᴇ ɪs ᴛʜᴇ ғᴇᴀᴛᴜʀᴇ ᴡʜɪᴄʜ ᴡɪʟʟ ᴄʀᴇᴀᴛᴇ ᴀ sʜᴀʀᴇᴀʙʟᴇ ʟɪɴᴋ ᴏғ ᴀ sɪɴɢʟᴇ ᴏʀ ᴍᴜʟᴛɪᴘʟᴇ ғɪʟᴇs.\\r\\n\\r\\n<b>Cᴏᴍᴍᴀɴᴅs Aɴᴅ Usᴀɢᴇ :</b>\\r\\n• /batch - ᴛᴏ ᴄʀᴇᴀᴛᴇ ᴀ ʙᴀᴛᴄʜ ʟɪɴᴋ ᴏғ ᴍᴜʟᴛɪᴘʟᴇ ғɪʟᴇs.\\r\\n• /link - ᴛᴏ ᴄʀᴇᴀᴛᴇ ᴀ sɪɴɢʟᴇ ғɪʟᴇ sᴛᴏʀᴇ ʟɪɴᴋ.\\r\\n• /pbatch - ᴊᴜsᴛ ʟɪᴋᴇ <code>/batch</code>, ʙᴜᴛ ᴛʜᴇ ғɪʟᴇs ᴡɪʟʟ ʙᴇ sᴇɴᴅ ᴡɪᴛʜ ғᴏʀᴡᴀʀᴅ ʀᴇsᴛʀɪᴄᴛɪᴏɴs.\\r\\n• /plink - ᴊᴜsᴛ ʟɪᴋᴇ <code>/link</code>, ʙᴜᴛ ᴛʜᴇ ғɪʟᴇ ᴡɪʟʟ ʙᴇ sᴇɴᴅ ᴡɪᴛʜ ғᴏʀᴡᴀʀᴅ ʀᴇsᴛʀɪᴄᴛɪᴏɴ.\\\"\\\"\\\"\\r\\n\\r\\n CHECK_TXT = \\\"\\\"\\\"\\r\\n<b>🔥 ᴄʜᴏᴏsᴇ ʏᴏᴜʀ sᴜɪᴛᴀʙʟᴇ ᴘʟᴀɴ ᴀɴᴅ ᴘᴀʏ ʏᴏᴜʀ ᴘʀᴇᴍɪᴜᴍ ғᴇᴇs ᴜsɪɴɢ ᴀɴʏ ᴜᴘɪ ᴀᴘᴘ. \\r\\n\\r\\nᴘʟᴀɴ ᴀ : 𝟷 ᴡᴇᴇᴋ / ₹𝟷𝟻\\r\\nᴘʟᴀɴ ʙ : 𝟷 ᴍᴏɴᴛʜ / ₹𝟹𝟿\\r\\nᴘʟᴀɴ ᴄ : 𝟷 ʏᴇᴀʀ / ₹𝟹𝟼𝟶\\r\\n\\r\\n➻ ᴜᴘɪ ɪᴅ : harikushal234@paytm\\r\\n\\r\\n‼️ ᴍᴜsᴛ sᴇɴᴅ sᴄʀᴇᴇɴsʜᴏᴛ ᴀғᴛᴇʀ ᴘᴀʏᴍᴇɴᴛ ᴀɴᴅ ɢɪᴠᴇ ᴍᴇ sᴏᴍᴇ ᴛɪᴍᴇ ᴛᴏ ᴀᴅᴅ ʏᴏᴜ ɪɴ ᴛʜᴇ ᴘʀᴇᴍɪᴜᴍ ʟɪsᴛ.</b>\\\"\\\"\\\"\\r\\n\\r\\n PLAN1_TXT = \\\"\\\"\\\"\\r\\n<b>🔥 ᴘᴀʏ ʏᴏᴜʀ ᴘʀᴇᴍɪᴜᴍ ᴘʟᴀɴ ғᴇᴇs ₹𝟷𝟻 ғᴏʀ 𝟷 ᴡᴇᴇᴋ ᴘʀᴇᴍɪᴜᴍ ᴀᴄᴄᴇss ᴡɪᴛʜ ᴀᴅ-ғʀᴇᴇ ᴇxᴘᴇʀɪᴇɴᴄᴇ ᴀɴᴅ ᴍᴀɴʏ ᴍᴏʀᴇ. \\r\\n\\r\\n➻ ᴜᴘɪ ɪᴅ : harikushal234@paytm\\r\\n\\r\\n‼️ ᴍᴜsᴛ sᴇɴᴅ sᴄʀᴇᴇɴsʜᴏᴛ ᴀғᴛᴇʀ ᴘᴀʏᴍᴇɴᴛ ᴀɴᴅ ɢɪᴠᴇ ᴍᴇ sᴏᴍᴇ ᴛɪᴍᴇ ᴛᴏ ᴀᴅᴅ ʏᴏᴜ ɪɴ ᴛʜᴇ ᴘʀᴇᴍɪᴜᴍ ʟɪsᴛ.</b>\\\"\\\"\\\"\\r\\n\\r\\n PLAN2_TXT = \\\"\\\"\\\"\\r\\n<b>🔥 ᴘᴀʏ ʏᴏᴜʀ ᴘʀᴇᴍɪᴜᴍ ᴘʟᴀɴ ғᴇᴇs ₹𝟹𝟿 ғᴏʀ 𝟷 ᴍᴏɴᴛʜ ᴘʀᴇᴍɪᴜᴍ ᴀᴄᴄᴇss ᴡɪᴛʜ ᴀᴅ-ғʀᴇᴇ ᴇxᴘᴇʀɪᴇɴᴄᴇ ᴀɴᴅ ᴍᴀɴʏ ᴍᴏʀᴇ. \\r\\n\\r\\n➻ ᴜᴘɪ ɪᴅ : harikushal234@paytm\\r\\n\\r\\n‼️ ᴍᴜsᴛ sᴇɴᴅ sᴄʀᴇᴇɴsʜᴏᴛ ᴀғᴛᴇʀ ᴘᴀʏᴍᴇɴᴛ ᴀɴᴅ ɢɪᴠᴇ ᴍᴇ sᴏᴍᴇ ᴛɪᴍᴇ ᴛᴏ ᴀᴅᴅ ʏᴏᴜ ɪɴ ᴛʜᴇ ᴘʀᴇᴍɪᴜᴍ ʟɪsᴛ.</b>\\\"\\\"\\\"\\r\\n\\r\\n PLAN3_TXT = \\\"\\\"\\\"\\r\\n<b>🔥 ᴘᴀʏ ʏᴏᴜʀ ᴘʀᴇᴍɪᴜᴍ ᴘʟᴀɴ ғᴇᴇs ₹𝟹𝟼𝟶 ғᴏʀ 𝟷 ʏᴇᴀʀ ᴘʀᴇᴍɪᴜᴍ ᴀᴄᴄᴇss ᴡɪᴛʜ ᴀᴅ-ғʀᴇᴇ ᴇxᴘᴇʀɪᴇɴᴄᴇ ᴀɴᴅ ᴍᴀɴʏ ᴍᴏʀᴇ. \\r\\n\\r\\n➻ ᴜᴘɪ ɪᴅ : harikushal234@paytm\\r\\n\\r\\n‼️ ᴍᴜsᴛ sᴇɴᴅ sᴄʀᴇᴇɴsʜᴏᴛ ᴀғᴛᴇʀ ᴘᴀʏᴍᴇɴᴛ ᴀɴᴅ ɢɪᴠᴇ ᴍᴇ sᴏᴍᴇ ᴛɪᴍᴇ ᴛᴏ ᴀᴅᴅ ʏᴏᴜ ɪɴ ᴛʜᴇ ᴘʀᴇᴍɪᴜᴍ ʟɪsᴛ.</b>\\\"\\\"\\\"\\r\\n\\r\\n RESTART_TXT = \\\"\\\"\\\"\\r\\n<b>Bᴏᴛ Rᴇsᴛᴀʀᴛᴇᴅ !\\r\\n\\r\\n📅 Dᴀᴛᴇ : <code>{}</code>\\r\\n⏰ Tɪᴍᴇ : <code>{}</code>\\r\\n🌐 Tɪᴍᴇᴢᴏɴᴇ : <code>Asia/Kolkata</code>\\r\\n🛠️ Bᴜɪʟᴅ Sᴛᴀᴛᴜs: <code>ᴠ𝟹.𝟶 [ Sᴛᴀʙʟᴇ ]</code></b>\\\"\\\"\\\"\\r\\n\\r\\n LOGO = \\\"\\\"\\\"\\r\\n ____ ___ ____ __ ____ ____ \\r\\n(_ _)/ __) ( _ \\\\ / \\\\(_ _)(__ )\\r\\n )( ( (_ \\\\ ) _ (( O ) )( / _/ \\r\\n (__) \\\\___/ (____/ \\\\__/ (__) (____)\\\"\\\"\\\"\\r\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from pyrogram import Client, filters, enums\nfrom pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup, CallbackQuery\nfrom pyrogram.errors.exceptions.bad_request_400 import MessageTooLong, PeerIdInvalid\nfrom info import ADMINS, LOG_CHANNEL, SUPPORT_CHAT, MELCOW_NEW_USERS, MELCOW_VID, CHNL_LNK, GRP_LNK\nfrom database.users_chats_db import db\nfrom database.ia_filterdb import Media\nfrom utils import get_size, temp, get_settings\nfrom Script import script\nfrom pyrogram.errors import ChatAdminRequired\nimport asyncio "},"token_num":{"kind":"number","value":20001,"string":"20,001"},"cropped_code":{"kind":"string","value":"\n\"\"\"-----------------------------------------https://t.me/TG_LINKS_CHANNEL--------------------------------------\"\"\"\n\n@Client.on_message(filters.new_chat_members & filters.group)\nasync def save_group(bot, message):\n r_j_check = [u.id for u in message.new_chat_members]\n if temp.ME in r_j_check:\n if not await db.get_chat(message.chat.id):\n total=await bot.get_chat_members_count(message.chat.id)\n r_j = message.from_user.mention if message.from_user else \"Anonymous\" \n await bot.send_message(LOG_CHANNEL, script.LOG_TEXT_G.format(message.chat.title, message.chat.id, total, r_j)) \n await db.add_chat(message.chat.id, message.chat.title)\n if message.chat.id in temp.BANNED_CHATS:\n # Inspired from a boat of a banana tree\n buttons = [[\n InlineKeyboardButton('Support', url=f'https://t.me/{SUPPORT_CHAT}')\n ]]\n reply_markup=InlineKeyboardMarkup(buttons)\n k = await message.reply(\n text='<b>CHAT NOT ALLOWED 🐞\\n\\nMy admins has restricted me from working here ! If you want to know more about it contact support..</b>',\n reply_markup=reply_markup,\n )\n\n try:\n await k.pin()\n except:\n pass\n await bot.leave_chat(message.chat.id)\n return\n buttons = [[\n InlineKeyboardButton('🔸 ᴍᴇꜱꜱᴀɢᴇ ʜᴇʀᴇ 🔹', url=\"https://t.me/TG_Bots_Supporter\")\n ],[\n InlineKeyboardButton('ᴄʜᴀɴɴᴇʟ', url=CHNL_LNK),\n InlineKeyboardButton('ɢʀᴏᴜᴘ', url=GRP_LNK)\n ]]\n reply_markup=InlineKeyboardMarkup(buttons)\n await message.reply_text(\n text=f\"<b>ᴛʜᴀɴᴋ ʏᴏᴜ ꜰᴏʀ ᴀᴅᴅɪɴɢ ᴍᴇ ɪɴ {message.chat.title} ❣️\\n\\nᴅᴏɴ'ᴛ ꜰᴏʀɢᴇᴛ ᴛᴏ ᴍᴀᴋᴇ ᴍᴇ ᴀᴅᴍɪɴ. ɪꜰ ʏᴏᴜ ʜᴀᴠᴇ ᴀɴʏ ǫᴜᴇꜱᴛɪᴏɴꜱ & ᴅᴏᴜʙᴛꜱ ᴀʙᴏᴜᴛ ᴜꜱɪɴɢ ᴍᴇ ᴄᴏɴᴛᴀᴄᴛ ꜰʀᴏᴍ ᴀᴅᴍɪɴ & ᴍᴇꜱꜱᴀɢᴇ ʜᴇʀᴇ 👇</b>\",\n reply_markup=reply_markup)\n else:\n"},"all_code":{"kind":"string","value":"\n\"\"\"-----------------------------------------https://t.me/TG_LINKS_CHANNEL--------------------------------------\"\"\"\n\n@Client.on_message(filters.new_chat_members & filters.group)\nasync def save_group(bot, message):\n r_j_check = [u.id for u in message.new_chat_members]\n if temp.ME in r_j_check:\n if not await db.get_chat(message.chat.id):\n total=await bot.get_chat_members_count(message.chat.id)\n r_j = message.from_user.mention if message.from_user else \"Anonymous\" \n await bot.send_message(LOG_CHANNEL, script.LOG_TEXT_G.format(message.chat.title, message.chat.id, total, r_j)) \n await db.add_chat(message.chat.id, message.chat.title)\n if message.chat.id in temp.BANNED_CHATS:\n # Inspired from a boat of a banana tree\n buttons = [[\n InlineKeyboardButton('Support', url=f'https://t.me/{SUPPORT_CHAT}')\n ]]\n reply_markup=InlineKeyboardMarkup(buttons)\n k = await message.reply(\n text='<b>CHAT NOT ALLOWED 🐞\\n\\nMy admins has restricted me from working here ! If you want to know more about it contact support..</b>',\n reply_markup=reply_markup,\n )\n\n try:\n await k.pin()\n except:\n pass\n await bot.leave_chat(message.chat.id)\n return\n buttons = [[\n InlineKeyboardButton('🔸 ᴍᴇꜱꜱᴀɢᴇ ʜᴇʀᴇ 🔹', url=\"https://t.me/TG_Bots_Supporter\")\n ],[\n InlineKeyboardButton('ᴄʜᴀɴɴᴇʟ', url=CHNL_LNK),\n InlineKeyboardButton('ɢʀᴏᴜᴘ', url=GRP_LNK)\n ]]\n reply_markup=InlineKeyboardMarkup(buttons)\n await message.reply_text(\n text=f\"<b>ᴛʜᴀɴᴋ ʏᴏᴜ ꜰᴏʀ ᴀᴅᴅɪɴɢ ᴍᴇ ɪɴ {message.chat.title} ❣️\\n\\nᴅᴏɴ'ᴛ ꜰᴏʀɢᴇᴛ ᴛᴏ ᴍᴀᴋᴇ ᴍᴇ ᴀᴅᴍɪɴ. ɪꜰ ʏᴏᴜ ʜᴀᴠᴇ ᴀɴʏ ǫᴜᴇꜱᴛɪᴏɴꜱ & ᴅᴏᴜʙᴛꜱ ᴀʙᴏᴜᴛ ᴜꜱɪɴɢ ᴍᴇ ᴄᴏɴᴛᴀᴄᴛ ꜰʀᴏᴍ ᴀᴅᴍɪɴ & ᴍᴇꜱꜱᴀɢᴇ ʜᴇʀᴇ 👇</b>\",\n reply_markup=reply_markup)\n else:"},"next_line":{"kind":"string","value":" settings = await get_settings(message.chat.id)"},"gold_snippet_index":{"kind":"number","value":11,"string":"11"},"created_at":{"kind":"string","value":"2023-11-03 12:21:26+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5281,"cells":{"repo_name":{"kind":"string","value":"apple/ml-reed"},"file_path":{"kind":"string","value":"reed/algorithms/pebble.py"},"context":{"kind":"list like","value":[{"identifier":"utils","path":"BPref/utils.py","snippet":"def make_env(cfg):\ndef ppo_make_env(env_id, seed):\ndef tie_weights(src, trg):\ndef make_metaworld_env(cfg):\ndef ppo_make_metaworld_env(env_id, seed):\n def __init__(self, *models):\n def __enter__(self):\n def __exit__(self, *args):\n def __init__(self, *models):\n def __enter__(self):\n def __exit__(self, *args):\ndef soft_update_params(net, target_net, tau):\ndef set_seed_everywhere(seed):\ndef make_dir(*path_parts):\ndef weight_init(m):\n def __init__(self,\n input_dim,\n hidden_dim,\n output_dim,\n hidden_depth,\n output_mod=None):\n def forward(self, x):\n def __init__(self, cache_size=1):\n def atanh(x):\n def __eq__(self, other):\n def _call(self, x):\n def _inverse(self, y):\n def log_abs_det_jacobian(self, x, y):\n def __init__(self, loc, scale):\n def mean(self):\n def __init__(self, epsilon=1e-4, shape=(), device=None):\n def update(self, x):\n def update_from_moments(self, batch_mean, batch_var, batch_count):\n def std(self):\ndef update_mean_var_count_from_moments(\n mean, var, count, batch_mean, batch_var, batch_count\n):\ndef mlp(input_dim, hidden_dim, output_dim, hidden_depth, output_mod=None):\ndef to_np(t):\nclass eval_mode(object):\nclass train_mode(object):\nclass MLP(nn.Module):\nclass TanhTransform(pyd.transforms.Transform):\nclass SquashedNormal(pyd.transformed_distribution.TransformedDistribution):\nclass TorchRunningMeanStd:\n M2 = m_a + m_b + torch.pow(delta, 2) * count * batch_count / tot_count"},{"identifier":"Logger","path":"BPref/logger.py","snippet":"class Logger(object):\n def __init__(self,\n log_dir,\n save_tb=False,\n log_frequency=10000,\n agent='sac'):\n self._log_dir = log_dir\n self._log_frequency = log_frequency\n if save_tb:\n tb_dir = os.path.join(log_dir, 'tb')\n if os.path.exists(tb_dir):\n try:\n shutil.rmtree(tb_dir)\n except:\n print(\"logger.py warning: Unable to remove tb directory\")\n pass\n self._sw = SummaryWriter(tb_dir)\n else:\n self._sw = None\n # each agent has specific output format for training\n assert agent in AGENT_TRAIN_FORMAT\n train_format = COMMON_TRAIN_FORMAT + AGENT_TRAIN_FORMAT[agent]\n self._train_mg = MetersGroup(os.path.join(log_dir, 'train'),\n formating=train_format)\n self._eval_mg = MetersGroup(os.path.join(log_dir, 'eval'),\n formating=COMMON_EVAL_FORMAT)\n\n def _should_log(self, step, log_frequency):\n log_frequency = log_frequency or self._log_frequency\n return step % log_frequency == 0\n\n def _try_sw_log(self, key, value, step):\n if self._sw is not None:\n self._sw.add_scalar(key, value, step)\n\n def _try_sw_log_video(self, key, frames, step):\n if self._sw is not None:\n frames = torch.from_numpy(np.array(frames))\n frames = frames.unsqueeze(0)\n self._sw.add_video(key, frames, step, fps=30)\n\n def _try_sw_log_histogram(self, key, histogram, step):\n if self._sw is not None:\n self._sw.add_histogram(key, histogram, step)\n\n def log(self, key, value, step, n=1, log_frequency=1):\n if not self._should_log(step, log_frequency):\n return\n assert key.startswith('train') or key.startswith('eval')\n if type(value) == torch.Tensor:\n value = value.item()\n self._try_sw_log(key, value / n, step)\n mg = self._train_mg if key.startswith('train') else self._eval_mg\n mg.log(key, value, n)\n\n def log_param(self, key, param, step, log_frequency=None):\n if not self._should_log(step, log_frequency):\n return\n self.log_histogram(key + '_w', param.weight.data, step)\n if hasattr(param.weight, 'grad') and param.weight.grad is not None:\n self.log_histogram(key + '_w_g', param.weight.grad.data, step)\n if hasattr(param, 'bias') and hasattr(param.bias, 'data'):\n self.log_histogram(key + '_b', param.bias.data, step)\n if hasattr(param.bias, 'grad') and param.bias.grad is not None:\n self.log_histogram(key + '_b_g', param.bias.grad.data, step)\n\n def log_video(self, key, frames, step, log_frequency=None):\n if not self._should_log(step, log_frequency):\n return\n assert key.startswith('train') or key.startswith('eval')\n self._try_sw_log_video(key, frames, step)\n\n def log_histogram(self, key, histogram, step, log_frequency=None):\n if not self._should_log(step, log_frequency):\n return\n assert key.startswith('train') or key.startswith('eval')\n self._try_sw_log_histogram(key, histogram, step)\n\n def dump(self, step, save=True, ty=None):\n if ty is None:\n self._train_mg.dump(step, 'train', save)\n self._eval_mg.dump(step, 'eval', save)\n elif ty == 'eval':\n self._eval_mg.dump(step, 'eval', save)\n elif ty == 'train':\n self._train_mg.dump(step, 'train', save)\n else:\n raise f'invalid log type: {ty}'"},{"identifier":"TrajectoryReplayBuffer","path":"BPref/replay_buffer.py","snippet":"class TrajectoryReplayBuffer:\n \"\"\"\n Buffer to store trajectories of environment transitions. Unlike ReplayBuffer, which stores all transitions in a\n flat manner, transitions are sorted by trajectory. Each trajectory corresponds to an episode.\n \"\"\"\n _RELABEL_BATCH_SIZE = 256\n\n def __init__(self, capacity: int, device: torch.device, window: int = 1, num_envs: t.Optional[int] = None,\n image_observations: t.Optional[t.Union[int, np.ndarray]] = None):\n \"\"\"\n Args:\n capacity: the number of trajectories to hold in memory\n device: the device sampled transitions should be put on\n window: no idea - part of the original code and is used in add_batch(...) which has not yet been refactored\n num_envs: the number of environment instances used to train the policy. Only needs to be specified when the\n number is >1. Some algorithms train on multiple instances of an environment at once, e.g. PPO.\n Not currently used, but not yet removed because we have not tested with an algorithm that needs\n multiple environment instances.\n image_observations: (default = false) whether to collect image observations in addition to state\n observations. This is helpful to use when the policy is trained on the state, but you\n want to visualize the trajectories or the reward model is trained on images.\n\n \"\"\"\n self.capacity = capacity\n self.device = device\n\n self.observations: t.Optional[np.ndarray] = None\n self.actions: t.Optional[np.ndarray] = None\n self.rewards: t.Optional[np.ndarray] = None\n self.not_dones: t.Optional[np.ndarray] = None\n self.not_dones_no_max: t.Optional[np.ndarray] = None\n self.trajectory_lengths: t.List = []\n self.window = window\n self.env_rewards: t.Optional[np.ndarray] = None\n self.image_observations: t.Optional[np.ndarray] = None\n # track whether to collect image observations - when not None, specifies the dimensions of the images\n self._collect_image_observations = image_observations\n\n # track the trajectories as a list of Trajectory\n self.trajectories: t.List[Trajectory] = []\n\n self.idx = 0\n self.last_save = 0\n self.full = False\n\n def __len__(self):\n return np.sum(self.trajectory_lengths) - len(self.trajectory_lengths)\n\n def __getitem__(self, flat_indx: t.Union[int, t.Tuple[int, int], t.List[int]]) -> TRANSITION:\n \"\"\"\n Get the transition at the given index\n\n Args:\n flat_indx: the index assuming transitions are stored flat instead of nested in trajectories\n - when an integer is specified, a single transition is retrieved\n - when a tuple of integers is given, a slice is retrieved as if the transitions are stored flat\n\n Returns:\n current observation\n action\n reward\n next observation\n whether the episode ended\n whether the episode ended without reaching max steps\n image version of current observation (optional)\n \"\"\"\n if isinstance(flat_indx, int) or isinstance(flat_indx, np.int64):\n traj_indx, trans_indx = self._flat_indx_to_trajectory_index(flat_indx)\n # check we are grabbing from a trajectory currently being accumulated\n # When the done signal is given, the current trajectory being accumulated is converted to a trajectory,\n # is added to the list of trajectories, and the values used to accumulate the next trajectory are set to\n # done. The next trajectory is not started until the call to add(...) after the done signal is received.\n # Therefore, we need to check whether the trajectory to pull from is actually the last completed trajectory\n # prior to starting a new trajectory. This is why we compare the length of the lists containing trajectory\n # lengths and the list containing the trajectories.\n if (traj_indx == len(self.trajectory_lengths) - 1\n and len(self.trajectory_lengths) > len(self.trajectories)):\n # we need to grab from the trajectory currently being populated\n return (self.observations[trans_indx].astype(np.float32), self.actions[trans_indx].astype(np.float32),\n self.rewards[trans_indx].astype(np.float32), self.observations[trans_indx + 1].astype(np.float32),\n self.not_dones[trans_indx].astype(np.float32),\n self.not_dones_no_max[trans_indx].astype(np.float32),\n (self.env_rewards[trans_indx].astype(np.float32)\n if self.env_rewards is not None\n else None),\n ((self.image_observations[trans_indx].astype(np.float32))\n if self.image_observations is not None\n else None),\n ((self.image_observations[trans_indx+1].astype(np.float32))\n if self.image_observations is not None\n else None))\n else:\n # grab from a previously completed trajectory\n transition: Transition = self.trajectories[traj_indx][trans_indx]\n return (transition.observation.astype(np.float32), transition.action.astype(np.float32),\n transition.reward.astype(np.float32), transition.next_observation.astype(np.float32),\n transition.not_done.astype(np.float32), transition.not_done_no_max.astype(np.float32),\n transition.env_reward.astype(np.float32),\n (transition.image_observation.astype(np.float32)\n if transition.image_observation is not None\n else None),\n (transition.next_image_observation.astype(np.float32)\n if transition.next_image_observation is not None\n else None))\n elif isinstance(flat_indx, t.List):\n observations = []\n actions = []\n rewards = []\n next_observations = []\n not_dones = []\n not_dones_no_max = []\n env_rewards = []\n image_observations = []\n next_image_observations = []\n for indx in flat_indx:\n observation, action, reward, next_observation, not_done, not_done_no_max, env_reward, image_observation, next_image_observation = self[indx]\n observations.append(observation)\n actions.append(action)\n rewards.append(reward)\n next_observations.append(next_observation)\n not_dones.append(not_done)\n not_dones_no_max.append(not_done_no_max)\n if env_reward is not None:\n env_rewards.append(env_reward)\n if image_observation is not None:\n image_observations.append(image_observation)\n if next_image_observation is not None:\n next_image_observations.append(next_image_observation)\n return (np.asarray(observations, dtype=np.float32), np.asarray(actions, dtype=np.float32),\n np.asarray(rewards, dtype=np.float32), np.asarray(next_observations, dtype=np.float32),\n np.asarray(not_dones, dtype=np.float32), np.asarray(not_dones_no_max, dtype=np.float32),\n (np.asarray(env_rewards, dtype=np.float32) if len(env_rewards) > 0 else None),\n (np.asarray(image_observations, dtype=np.float32) if self._collect_image_observations else None),\n (np.asarray(next_image_observations, dtype=np.float32) if self._collect_image_observations else None))\n else:\n # get the locations of the start and end transitions\n start_traj_indx, start_trans_indx = self._flat_indx_to_trajectory_index(flat_indx[0])\n end_traj_indx, end_trans_indx = self._flat_indx_to_trajectory_index(flat_indx[1])\n # check that we are not spanning trajectories\n if start_traj_indx == end_traj_indx:\n # grab the sub-trajectory\n sub_trajectory = self.trajectories[start_traj_indx][tuple((start_trans_indx, end_trans_indx))]\n else:\n # grab what remains of the trajectory\n end_trans_indx = len(self.trajectories[start_traj_indx]) - 1\n sub_trajectory = self.trajectories[start_traj_indx][tuple((start_trans_indx, end_trans_indx))]\n return (sub_trajectory.initial_observations,\n sub_trajectory.actions,\n sub_trajectory.rewards,\n sub_trajectory.next_observations,\n sub_trajectory.not_dones,\n sub_trajectory.not_dones_no_max,\n sub_trajectory.env_rewards,\n (sub_trajectory.initial_image_observations\n if sub_trajectory.initial_image_observations is not None\n else None),\n (sub_trajectory.next_image_observations\n if sub_trajectory.next_image_observations is not None\n else None))\n\n @property\n def trajectory_count(self) -> int:\n \"\"\"\n The number of trajectories in the buffer\n \"\"\"\n return len(self.trajectories)\n\n @property\n def all_not_dones(self) -> np.ndarray:\n \"\"\"\n Rewards from the state-action pairs from all trajectories and all transitions, where the action was taken in the state\n \"\"\"\n return np.concatenate([np.expand_dims(traj.not_dones, axis=0) for traj in self.trajectories], axis=0)\n\n @property\n def all_rewards(self) -> np.ndarray:\n \"\"\"\n Rewards from the state-action pairs from all trajectories and all transitions, where the action was taken in the state\n \"\"\"\n return np.concatenate([np.expand_dims(traj.rewards, axis=0) for traj in self.trajectories], axis=0)\n\n @property\n def all_environment_rewards(self) -> np.ndarray:\n \"\"\"\n Environment rewards from all trajectories and all transitions\n \"\"\"\n return np.concatenate([np.expand_dims(traj.rewards, axis=0) for traj in self.trajectories], axis=0)\n\n @property\n def all_initial_image_observations(self) -> np.ndarray:\n \"\"\"\n Image observations from the state-action pairs from all trajectories and all transitions, where the action was taken in the state\n \"\"\"\n return np.concatenate([np.expand_dims(traj.initial_image_observations, axis=0)\n for traj in self.trajectories],\n axis=0)\n\n @property\n def all_next_image_observations(self) -> np.ndarray:\n \"\"\"\n Image observations from the state-action pairs from all trajectories and all transitions,\n\n The result of a transition\n \"\"\"\n return np.concatenate([np.expand_dims(traj.next_image_observations, axis=0)\n for traj in self.trajectories],\n axis=0)\n\n @property\n def all_initial_observations(self) -> np.ndarray:\n \"\"\"\n observations from the state-action pairs from all trajectories and all transitions, where the action was taken in the state\n \"\"\"\n return np.concatenate([np.expand_dims(traj.initial_observations, axis=0) for traj in self.trajectories], axis=0)\n\n @property\n def all_next_observations(self) -> np.ndarray:\n \"\"\"\n Observations from the state-action pairs from all trajectories and all transitions\n\n The result of a transition\n \"\"\"\n return np.concatenate([np.expand_dims(traj.next_observations, axis=0) for traj in self.trajectories], axis=0)\n\n @property\n def all_actions(self) -> np.ndarray:\n \"\"\"\n Actions from the state-action pairs from all trajectories and all transitions\n \"\"\"\n return np.concatenate([np.expand_dims(traj.actions, axis=0) for traj in self.trajectories], axis=0)\n\n def _flat_indx_to_trajectory_index(self, flat_indx: int) -> t.Tuple[int, int]:\n \"\"\"\n Converts an index that assumes the transitions are flat to a trajectory and transition (w/in trajectory) index\n\n Args:\n flat_indx: the index assuming transitions are stored flat\n\n Returns:\n the index of the trajectory containing the transition\n the index of the transition within the trajectory\n \"\"\"\n # need to figure out which transition indices are stored in which trajectories\n transition_cumulative_sum = np.cumsum(self.trajectory_lengths)\n # the trajectory containing the transition is at the first index where the cumulative sum of transitions is\n # less than the transition index\n target_trajectory_indx = int(np.argmax(flat_indx < transition_cumulative_sum))\n # get the transition's index within the trajectory as the different between the flat index and the cumulative\n # sum at the previous trajectory - tells us how far into the target trajectory the transition is\n if target_trajectory_indx == 0:\n transition_trajectory_indx = flat_indx\n else:\n transition_trajectory_indx = flat_indx - transition_cumulative_sum[target_trajectory_indx - 1]\n return target_trajectory_indx, transition_trajectory_indx\n\n def _add_transition(self, observation: np.ndarray, action: np.ndarray, reward: float, done: t.Union[float, bool],\n done_no_max: t.Union[float, bool],\n env_reward: t.Optional[float] = None, image_observations: t.Optional[np.ndarray] = None):\n \"\"\"\n Track the transition and update the length of the trajectory currently being accumulated\n\n Args:\n observation: the current observation\n action: the action taken in the current state\n reward: the reward associated with the last state-action pait\n done: whether the last action completed an episode\n done_no_max: whether the last action completed an episode without reaching the maximum allowed steps\n env_reward: (optional) the reward given by the environment - stored and used to train the preference-learned\n reward model when learning from synthetic feedback\n image_observations: (optional) image-based observation -> should not be given is observations is also an image. This\n should be used when you want to accumulate images separately from policy training.\n \"\"\"\n self.observations = np.concatenate([self.observations, np.expand_dims(observation, axis=0)], axis=0)\n self.actions = np.concatenate([self.actions, np.expand_dims(action, axis=0)], axis=0)\n self.rewards = np.concatenate([self.rewards, np.asarray(reward).reshape(1, 1)], axis=0)\n if type(done) is float:\n self.not_dones = np.concatenate([self.not_dones,\n np.asarray(not done, dtype=np.float32).reshape(1, 1)], axis=0)\n self.not_dones_no_max = np.concatenate([self.not_dones_no_max,\n np.asarray(not done_no_max, dtype=np.float32).reshape(1, 1)],\n axis=0)\n else:\n self.not_dones = np.concatenate([self.not_dones,\n np.asarray(~done, dtype=np.float32).reshape(1, 1)], axis=0)\n self.not_dones_no_max = np.concatenate([self.not_dones_no_max,\n np.asarray(~done_no_max, dtype=np.float32).reshape(1, 1)],\n axis=0)\n\n self.trajectory_lengths[-1] += 1\n if env_reward is not None:\n self.env_rewards = np.concatenate([self.env_rewards,\n np.asarray(env_reward, dtype=np.float32).reshape(1, 1)], axis=0)\n\n if image_observations is not None and self._collect_image_observations:\n self.image_observations = np.concatenate([self.image_observations, np.expand_dims(image_observations, axis=0)], axis=0)\n\n def _start_trajectory(self, observation: np.ndarray,\n action: np.ndarray,\n reward: float,\n done: t.Union[float, bool],\n done_no_max: t.Union[float, bool],\n env_reward: t.Optional[float] = None,\n image_observations: t.Optional[np.ndarray] = None):\n \"\"\"\n Start a new trajectory and track the transition\n\n Args:\n observation: the current observation\n action: the action taken in the current state\n reward: the reward associated with the last state-action pait\n done: whether the last action completed an episode\n done_no_max: whether the last action completed an episode without reaching the maximum allowed steps\n env_reward: (optional) the reward given by the environment - stored and used to train the preference-learned\n reward model when learning from synthetic feedback\n image_observations: (optional) image-based observation -> should not be given is observations is also an image. This\n should be used when you want to accumulate images separately from policy training.\n \"\"\"\n self.observations = np.expand_dims(observation, axis=0).astype(dtype=np.float32)\n self.actions = np.expand_dims(action, axis=0).astype(dtype=np.float32)\n self.rewards = np.asarray(reward, dtype=np.float32).reshape(1, 1)\n if type(done) is float:\n self.not_dones = np.asarray(not done, dtype=np.float32).reshape(1, 1)\n self.not_dones_no_max = np.asarray(not done_no_max, dtype=np.float32).reshape(1, 1)\n else:\n self.not_dones = np.asarray(~done, dtype=np.float32).reshape(1, 1)\n self.not_dones_no_max = np.asarray(~done_no_max, dtype=np.float32).reshape(1, 1)\n\n self.trajectory_lengths.append(1)\n\n if env_reward is not None:\n self.env_rewards = np.asarray(env_reward, dtype=np.float32).reshape(1, 1)\n\n if image_observations is not None and self._collect_image_observations:\n self.image_observations = np.expand_dims(image_observations, axis=0).astype(dtype=np.float32)\n\n def add(self, observation, action, reward, next_observation, done, done_no_max,\n env_reward: t.Optional[float] = None, image_observation: t.Optional[np.ndarray] = None,\n image_next_observation: t.Optional[np.ndarray] = None):\n \"\"\"\n Args:\n observation: the current observation\n action: the action taken in the current state\n reward: the reward associated with the last state-action pait\n next_observation: only used when an episode is completed to ensure the last observation is captured\n done: whether the last action completed an episode\n done_no_max: whether the last action completed an episode without reaching the maximum allowed steps\n env_reward: (optional) the reward given by the environment - stored and used to train the preference-learned\n reward model when learning from synthetic feedback\n image_observation: (optional) image-based observation -> should not be given is observations is also an image. This\n should be used when you want to accumulate images separately from policy training.\n image_next_observation: (optional) the image-based next observation -> should not be given when next_observation is also\n and image. This should be used when you want to accumulate the images separately from the\n trained policy.\n \"\"\"\n if self.observations is None:\n self._start_trajectory(observation, action, reward, done, done_no_max, env_reward, image_observation)\n elif done:\n self._add_transition(observation, action, reward, done, done_no_max, env_reward, image_observation)\n # the episode has ended, so we need to track the next observation\n self.observations = np.concatenate([self.observations, np.expand_dims(next_observation, axis=0)], axis=0)\n if image_next_observation is not None:\n self.image_observations = np.concatenate([self.image_observations,\n np.expand_dims(image_next_observation, axis=0)], axis=0)\n # create the trajectory\n self.trajectories.append(Trajectory(self.observations.astype(dtype=np.float32),\n (self.image_observations.astype(dtype=np.float32)\n if self.image_observations is not None\n else None),\n actions=self.actions.astype(dtype=np.float32),\n rewards=self.rewards.astype(dtype=np.float32),\n not_dones=self.not_dones.astype(dtype=np.float32),\n not_dones_no_max=self.not_dones_no_max.astype(dtype=np.float32),\n env_rewards=self.env_rewards.astype(dtype=np.float32)))\n # check if the inclusion of the just completed trajectory puts the buffer at capacity\n # if it does, remove the first trajectory as this is a FIFO buffer\n if np.sum(self.trajectory_lengths) >= self.capacity:\n self.trajectories = self.trajectories[1:]\n self.trajectory_lengths = self.trajectory_lengths[1:]\n self.observations = None\n self.actions = None\n self.rewards = None\n self.not_dones = None\n self.not_dones_no_max = None\n self.env_rewards = None\n self.image_observations = None\n else:\n self._add_transition(observation, action, reward, done, done_no_max, env_reward, image_observation)\n\n self.idx = (self.idx + 1) % self.capacity\n self.full = self.full or self.idx == 0\n\n def relabel_with_predictor(self, predictor, state_action_formatter: PreProcessInference):\n \"\"\"\n Relabel the rewards stored in the replay buffer using the given predictor\n\n Args:\n predictor: network that will consume state-action pairs and assign a reward\n state_action_formatter: formats the states and actions for consumption by the reward model\n \"\"\"\n print(\"Relabelling the replay buffer with the updated reward model.\")\n for trajectory in self.trajectories:\n # the number of batches to run through the model\n total_iter = int(len(trajectory) / self._RELABEL_BATCH_SIZE)\n # handle the case where we have more transitions than is evenly divisible by the batch size\n if len(trajectory) > self._RELABEL_BATCH_SIZE * total_iter:\n total_iter += 1\n # collect and process each batch to be passed through predictor\n for index in range(total_iter):\n start_indx = index * self._RELABEL_BATCH_SIZE\n # make sure we don't have an end index that is after the end of the trajectory\n end_indx = min((index + 1) * self._RELABEL_BATCH_SIZE, len(trajectory))\n\n # pull out the actions from the transitions that will be relabelled\n actions = trajectory.actions[start_indx:end_indx]\n # we need to handle the case where the reward model operates off of images\n if predictor.image_observations:\n observations = trajectory.all_image_observations[start_indx:end_indx]\n else:\n observations = trajectory.all_observations[start_indx:end_indx]\n formatted_state_action = state_action_formatter.format_state_action(observations, actions, batch_sa=True)\n pred_reward = predictor.r_hat_batch(formatted_state_action)\n # update the rewards assigned to the transitions\n trajectory.rewards[start_indx:end_indx] = pred_reward\n\n def sample(self, batch_size: int):\n indxs = list(np.random.randint(0, np.sum(self.trajectory_lengths) - 1, size=batch_size))\n observations, actions, rewards, next_observations, not_dones, not_dones_no_max, env_rewards, image_observations, next_image_observations = self[indxs]\n observations = torch.as_tensor(observations, device=self.device).float()\n actions = torch.as_tensor(actions, device=self.device)\n rewards = torch.as_tensor(rewards, device=self.device)\n next_observations = torch.as_tensor(next_observations, device=self.device).float()\n not_dones = torch.as_tensor(not_dones, device=self.device)\n not_dones_no_max = torch.as_tensor(not_dones_no_max, device=self.device)\n env_rewards = torch.as_tensor(env_rewards, device=self.device)\n image_observations = (torch.as_tensor(image_observations, device=self.device).float() if self._collect_image_observations else None)\n next_image_observations = (torch.as_tensor(next_image_observations, device=self.device).float() if self._collect_image_observations else None)\n return observations, actions, rewards, next_observations, not_dones, not_dones_no_max, env_rewards, image_observations, next_image_observations\n\n def sample_state_ent(self, batch_size: int):\n observations, actions, rewards, next_observations, not_dones, not_dones_no_max, _, _, _ = self.sample(batch_size)\n full_observation = torch.as_tensor(np.concatenate([traj.all_observations for traj in self.trajectories], axis=0),\n device=self.device)\n return observations, full_observation, actions, rewards, next_observations, not_dones, not_dones_no_max\n\n def save(self, out_directory: Path, env_id: str, step: int):\n \"\"\"\n Save the replay buffer to disk as a npz archive\n Args:\n out_directory: location where replay buffer will be saved\n env_id: the environment within which the data was generated\n step: the number of policy training steps taken to produce this dataset\n \"\"\"\n # create the ZipFile object\n zip_obj = ZipFile(out_directory / f\"{env_id}_replay_buffer_{step}.zip\", \"w\")\n\n # write each trajectory file to disk and to the zip archive\n for traj_id, trajectory in enumerate(self.trajectories):\n trajectory.save(out_directory / f\"{traj_id}.npz\")\n zip_obj.write(out_directory / f\"{traj_id}.npz\")\n # close the Zip File\n zip_obj.close()\n\n @staticmethod\n def from_directory(directory_path: Path,\n device: torch.device = 'cuda') -> \"TrajectoryReplayBuffer\":\n \"\"\"\n Create a TrajectoryReplay buffer from a directory of npz archive trajectories\n\n Args:\n directory_path: the location of the npz_archive on disk\n device: the device sampled transitions should be pushed to\n Returns:\n populated trajectory replay buffer\n \"\"\"\n # accumulate the trajectories\n trajectories = []\n trajectory_lengths = []\n # determine how many transitions are in the replay buffer\n capacity = 0\n # load each trajectory from disk\n for traj_filename in directory_path.iterdir():\n # we only load data from npz archives, so we need to skip anything else\n if not traj_filename.suffix == \".npz\": continue\n # load the trajectory from disk\n traj = Trajectory.from_npz(traj_filename)\n # track the trajectory\n trajectories.append(traj)\n # track the trajectory's length\n trajectory_lengths.append(len(traj))\n # track the trajectory's length\n capacity += len(traj)\n # create the buffer\n _buffer = TrajectoryReplayBuffer(capacity=capacity, device=device)\n # add the trajectories to the buffer\n _buffer.trajectories = trajectories\n _buffer.trajectory_lengths = trajectory_lengths\n\n return _buffer"},{"identifier":"StateActionRewardModel","path":"reed/models/reward_model.py","snippet":"class StateActionRewardModel:\n \"\"\"\n Reward model that operates over state action pairs\n \"\"\"\n def __init__(self,\n in_dim: t.Union[int, t.List[int]],\n ensemble_size: int = 3,\n hidden_dim: int = 256,\n hidden_layers: int = 3,\n final_activation: str = 'tanh',\n lr: float = 3e-4,\n optimizer: str = \"adam\",\n reward_train_batch: int = 128,\n size_segment: int = 1,\n device: torch.device = \"cuda\",\n multi_gpu: bool = False,\n image_observations: bool = False,\n image_encoder_architecture: str = \"pixl2r\",\n image_hidden_num_channels: int = 32,\n grayscale_images: bool = True):\n # the device the model will be put on\n self.device = device\n # whether data parallelism should be used during model training\n self.multi_gpu = multi_gpu\n # reward model configuration\n self.in_dim = in_dim\n self.hidden_dim = hidden_dim\n self.hidden_layers = hidden_layers\n self.ensemble_size = ensemble_size\n self.lr = lr\n self.optimizer_type = optimizer\n self.ensemble = []\n self.paramlst = []\n self.optimizer = None\n self.model = None\n self.final_activation = final_activation\n self.size_segment = size_segment\n\n self.image_observations = image_observations\n self.image_encoder_architecture = image_encoder_architecture\n self.image_hidden_num_channels = image_hidden_num_channels\n self.grayscale_images = grayscale_images\n\n # construct the reward ensemble\n self.construct_ensemble()\n\n # parameters used to train the reward model on the preference labelled trajectories\n self.train_batch_size = reward_train_batch\n self.CEloss = nn.CrossEntropyLoss()\n\n def eval(self):\n \"\"\"Set each reward model in the ensemble to evaluation mode\"\"\"\n self.ensemble = [net.eval() for net in self.ensemble]\n\n def train(self):\n \"\"\"Set each reward model in the ensemble to train mode\"\"\"\n self.ensemble = [net.train() for net in self.ensemble]\n\n def softXEnt_loss(self, predicted: torch.Tensor, target: torch.Tensor):\n logprobs = F.log_softmax(predicted, dim=1)\n return -(target * logprobs).sum() / predicted.shape[0]\n\n def construct_ensemble(self):\n for _ in range(self.ensemble_size):\n if self.image_observations:\n model = ImageStateActionNetwork(self.in_dim,\n out_size=1,\n hidden_dim=self.hidden_dim,\n hidden_depth=self.hidden_layers,\n final_activation=self.final_activation,\n image_encoder_architecture=self.image_encoder_architecture,\n image_hidden_num_channels=self.image_hidden_num_channels).float()\n else:\n model = StateActionNetwork(self.in_dim,\n out_size=1,\n hidden_dim=self.hidden_dim,\n hidden_depth=self.hidden_layers,\n final_activation=self.final_activation).float()\n print(model)\n # check if the model will be run with Data Parallelism\n if self.multi_gpu:\n print(f\"There are {torch.cuda.device_count()} GPU devices, so the reward ensemble WILL be trained \"\n f\"using nn.DataParallel\")\n self.ensemble.append(nn.DataParallel(model).to(self.device))\n else:\n print(f\"There are {torch.cuda.device_count()} GPU devices, so the reward ensemble will NOT be trained \"\n f\"using nn.DataParallel\")\n self.ensemble.append(model.to(self.device))\n # track all model parameters\n self.paramlst.extend(model.parameters())\n # create a single optimizer applied to all ensemble members\n if self.optimizer_type == \"adam\":\n self.optimizer = torch.optim.Adam(self.paramlst, lr=self.lr)\n elif self.optimizer_type == \"sgd\":\n self.optimizer = torch.optim.SGD(self.paramlst, lr=self.lr)\n else:\n raise NotImplementedError(f\"{self.optimizer_type} is not implemented as a reward optimizer and must be \"\n f\"one of 'adam' or 'sgd'.\")\n\n def format_state(self, obs: np.ndarray, batch_states: bool = False, by_trajectory: bool = False):\n \"\"\"\n Args:\n obs: the state observations\n batch_states: whether a batch of state is to be processed\n by_trajectory: whether the batch of states is structured by trajectory -> should only be\n True when batch_sa=True\n Returns:\n the state-action pairs as a single array\n \"\"\"\n if self.image_observations:\n # check if the images needs to be converted to grayscale\n if self.grayscale_images:\n obs = _to_grayscale(obs, batch_states=batch_states)\n if batch_states:\n # permute the input so that the channels are in the first dimension\n if by_trajectory:\n obs = np.transpose(obs, (0, 1, 4, 2, 3))\n else:\n print(obs.shape)\n obs = np.transpose(obs, (0, 3, 1, 2))\n return obs\n else:\n # permute the input so that the channels are in the first dimension\n obs = np.transpose(obs, (2, 0, 1))\n # add a dimension along the front for concatenation into the buffer\n return obs.reshape(1, *obs.shape)\n else:\n return obs.reshape(1, obs.shape[1:]) if batch_states else obs.reshape(1, obs.shape[0])\n\n def format_state_action(self, obs: np.ndarray, act: np.ndarray,\n batch_sa: bool = False, by_trajectory: bool = False) -> np.ndarray:\n \"\"\"\n Args:\n obs: the state observations\n act: the actions associated with each state observation\n batch_sa: whether a batch of state-action pairs is to be processed\n by_trajectory: whether the batch of state-action pairs is structured by trajectory -> should only be\n True when batch_sa=True\n Returns:\n the state-action pairs as a single array\n \"\"\"\n if self.image_observations:\n # check if the images needs to be converted to grayscale\n if self.grayscale_images:\n obs = _to_grayscale(obs, batch_states=batch_sa)\n if batch_sa:\n obs_dim = obs.shape[1:]\n # we concatenate the actions along channel dimension of the image\n if by_trajectory:\n repeated_actions = np.tile(act.reshape((act.shape[0], act.shape[1], 1, 1, act.shape[-1])),\n (1, 1, obs_dim[0], obs_dim[1], 1))\n else:\n repeated_actions = np.tile(act.reshape((act.shape[0], 1, 1, act.shape[-1])),\n (1, obs_dim[0], obs_dim[1], 1))\n # now concatenate the two\n sa_t = np.concatenate((obs, repeated_actions), axis=-1)\n # permute the input so that the channels are in the first dimension\n if by_trajectory:\n sa_t = np.transpose(sa_t, (0, 1, 4, 2, 3))\n else:\n sa_t = np.transpose(sa_t, (0, 3, 1, 2))\n return sa_t\n else:\n obs_dim = obs.shape\n # we concatenate the actions along channel dimension of the image\n repeated_actions = np.tile(act.reshape((1, 1, -1)), (obs_dim[0], obs_dim[1], 1))\n # now concatenate the two\n sa_t = np.concatenate((obs, repeated_actions), axis=-1)\n # permute the input so that the channels are in the first dimension\n sa_t = np.transpose(sa_t, (2, 0, 1))\n # add a dimension along the front for concatenation into the buffer\n return sa_t.reshape(1, *self.in_dim)\n else:\n sa_t = np.concatenate([obs, act], axis=-1)\n if batch_sa:\n return sa_t\n else:\n return sa_t.reshape(1, -1)\n\n def p_hat_member(self, x_1: np.ndarray, x_2: np.ndarray, member: int = -1):\n # softmaxing to get the probabilities according to eqn 1\n with torch.no_grad():\n # if we are using image observations, we need to collapse along the batch and time dimensions to push\n # a forward pass through the network\n # to compute the probabilities when then need to re-construct the batch and time dimensions\n if self.image_observations:\n # we need to compute the probabilities in batches to avoid out of memory issues\n # we use the train batch size as it should be an amount safe to put on the GPU's memory without causing\n # issues\n mb_size = self.train_batch_size\n start_indx = 0\n r_hat1 = None\n r_hat2 = None\n while start_indx < x_1.shape[0]:\n # check if there is a mb_size worth of trajectories to still be processed\n if start_indx + mb_size <= x_1.shape[0]:\n mb_x_1 = x_1[start_indx:start_indx + mb_size].reshape((-1, *x_1.shape[2:]))\n mb_x_2 = x_1[start_indx:start_indx + mb_size].reshape((-1, *x_1.shape[2:]))\n else:\n # process the leftover trajectories in a batch smaller than mb_size\n mb_x_1 = x_1[start_indx:].reshape((-1, *x_1.shape[2:]))\n mb_x_2 = x_2[start_indx:].reshape((-1, *x_2.shape[2:]))\n # process the leftover trajectories in a batch smaller than mb_size\n mb_rhat1 = self.r_hat_member(torch.from_numpy(mb_x_1).float().to(self.device),\n member=member).detach().cpu().reshape((mb_size, x_1.shape[1], 1))\n mb_rhat2 = self.r_hat_member(torch.from_numpy(mb_x_2).float().to(self.device),\n member=member).detach().cpu().reshape((mb_size, x_2.shape[1], 1))\n start_indx += mb_size\n\n # accumulate the rhats\n if r_hat1 is None:\n r_hat1 = mb_rhat1\n r_hat2 = mb_rhat2\n else:\n r_hat1 = torch.concat((r_hat1, mb_rhat1), dim=0)\n r_hat2 = torch.concat((r_hat2, mb_rhat2))\n\n else:\n r_hat1 = self.r_hat_member(x_1, member=member).cpu()\n r_hat2 = self.r_hat_member(x_2, member=member).cpu()\n r_hat1 = r_hat1.sum(axis=1)\n r_hat2 = r_hat2.sum(axis=1)\n r_hat = torch.cat([r_hat1, r_hat2], axis=-1)\n # taking 0 index for probability x_1 > x_2\n return F.softmax(r_hat, dim=-1)[:, 0]\n\n def p_hat_entropy(self, x_1: np.ndarray, x_2: np.ndarray, member: int = -1):\n # softmaxing to get the probabilities according to eqn 1\n with torch.no_grad():\n r_hat1 = self.r_hat_member(x_1, member=member)\n r_hat2 = self.r_hat_member(x_2, member=member)\n r_hat1 = r_hat1.sum(axis=1)\n r_hat2 = r_hat2.sum(axis=1)\n r_hat = torch.cat([r_hat1, r_hat2], axis=-1)\n\n ent = F.softmax(r_hat, dim=-1) * F.log_softmax(r_hat, dim=-1)\n ent = ent.sum(axis=-1).abs()\n return ent\n\n def r_hat_member(self, x: torch.Tensor, member: int = -1) -> torch.Tensor:\n # the network parameterizes r hat in eqn 1 from the paper\n # return self.ensemble[member](torch.from_numpy(x).float().to(device))\n return self.ensemble[member](x)\n\n def r_hat(self, x: np.ndarray):\n # they say they average the rewards from each member of the ensemble, but I think this only makes sense if the\n # rewards are already normalized and I don't understand how the normalization should be happening right now :(\n r_hats = []\n for member in range(self.ensemble_size):\n r_hats.append(self.r_hat_member(torch.from_numpy(x).float().to(self.device), member=member).detach().cpu().numpy())\n r_hats = np.array(r_hats)\n return np.mean(r_hats)\n\n def r_hat_batch(self, x: np.ndarray):\n # they say they average the rewards from each member of the ensemble, but I think this only makes sense if the rewards are already normalized\n # but I don't understand how the normalization should be happening right now :(\n r_hats = []\n for member in range(self.ensemble_size):\n r_hats.append(self.r_hat_member(torch.from_numpy(x).float().to(self.device), member=member).detach().cpu().numpy())\n r_hats = np.array(r_hats)\n return np.mean(r_hats, axis=0)\n\n def save(self, model_dir: str, env_id: str, step: int):\n \"\"\"\n Save the reward ensemble to disk\n\n Args:\n model_dir: path where the ensemble is to be saved\n env_id: the environment on which the ensemble has been trained\n step: the number of policy training steps\n \"\"\"\n for member in range(self.ensemble_size):\n torch.save(\n self.ensemble[member].state_dict(), f'{model_dir}/{env_id}_reward_model_{step}_{member}.pt'\n )\n\n def train_reward(self,\n preference_data_loader: PreferenceTripletEnsembleDataLoader,\n num_epoch: int):\n \"\"\"\n Train the reward model on the given preference dataset.\n\n Args:\n preference_data_loader: loads batches of preference triplets. Separated handles different preference\n dataset permutations for each member of the reward's ensemble.\n num_epoch: the number of training epochs to execute\n \"\"\"\n # track the accuracy and loss by ensemble member per epoch\n ensemble_accuracies = np.zeros((num_epoch, self.ensemble_size))\n ensemble_losses = np.zeros((num_epoch, self.ensemble_size))\n\n # train the reward model for the specified number of epochs\n for epoch in range(num_epoch):\n if epoch % 10 == 0:\n print(f\"Running preference training epoch {epoch} of {num_epoch}\")\n epoch_ensemble_losses = np.zeros(self.ensemble_size)\n epoch_ensemble_acc = np.zeros(self.ensemble_size)\n # train on each batch\n for batch_indx, batch in enumerate(preference_data_loader):\n # confirm there is either a single batch to be shared by all networks in the reward ensemble or\n # a batch per network in the ensemble\n assert len(batch) == 1 or len(batch) == self.ensemble_size\n # we need to zero out the gradients before we begin to process this batch\n self.optimizer.zero_grad()\n # we will need to accumulate the loss across the ensemble members\n batch_loss = 0.0\n for member_indx, preference_triplet_batch in enumerate(batch):\n # the predicted reward per transition in each trajectory\n # check if we need to collapse the batch and time dimensions into one and then reconstruct the two\n if self.image_observations:\n # get the rewards for each transition in the trajectories one\n traj_one_shape = preference_triplet_batch.trajectories_one.shape\n formatted_trajectories_one = preference_triplet_batch.trajectories_one.reshape(\n (-1, *traj_one_shape[2:]))\n r_hat1 = self.r_hat_member(formatted_trajectories_one,\n member=member_indx).reshape((traj_one_shape[0],\n traj_one_shape[1], 1))\n # get the rewards for each transition in the trajectories two\n traj_two_shape = preference_triplet_batch.trajectories_two.shape\n formatted_trajectories_two = preference_triplet_batch.trajectories_two.reshape(\n (-1, *traj_two_shape[2:]))\n r_hat2 = self.r_hat_member(formatted_trajectories_two,\n member=member_indx).reshape((traj_two_shape[0],\n traj_two_shape[1], 1))\n else:\n r_hat1 = self.r_hat_member(preference_triplet_batch.trajectories_one,\n member=member_indx)\n r_hat2 = self.r_hat_member(preference_triplet_batch.trajectories_two,\n member=member_indx)\n # compute the return per trajectory\n r_hat1 = r_hat1.sum(axis=1)\n r_hat2 = r_hat2.sum(axis=1)\n\n r_hat = torch.cat([r_hat1, r_hat2], dim=-1)\n\n # compute the ensemble member's loss\n curr_loss = self.CEloss(r_hat, preference_triplet_batch.preference_labels.squeeze())\n # add the loss from the ensemble member to the batch loss\n batch_loss += curr_loss\n # track the loss for this ensemble member\n epoch_ensemble_losses[member_indx] += curr_loss.item()\n\n # compute the accuracy of the ensemble member's predictions\n _, predicted = torch.max(r_hat.data, 1)\n correct = (predicted == preference_triplet_batch.preference_labels.squeeze()).sum().item()\n epoch_ensemble_acc[member_indx] += correct\n # compute the gradients\n batch_loss.backward()\n # apply the gradients to the model\n self.optimizer.step()\n # compute the ensemble accuracy for this epoch\n ensemble_accuracies[epoch] = epoch_ensemble_acc / preference_data_loader.dataset_length()\n # compute the mean ensemble loss for this epoch\n ensemble_losses[epoch] = epoch_ensemble_losses / preference_data_loader.dataset_length()\n\n if epoch % 10 == 0:\n print(f\"Epoch {epoch} mean accuracy = {np.mean(ensemble_accuracies[:epoch + 1]):.2f}\")\n\n # check the current mean accuracy, if it is greater than 0.97 then terminate training\n if np.mean(ensemble_accuracies[epoch]) >= 0.97:\n print(f\"Epoch accuracy {np.mean(ensemble_accuracies[epoch]):.2f} \"\n f\"after {epoch} epochs triggered early stopping.\")\n return ensemble_accuracies[:epoch + 1], ensemble_losses[:epoch + 1]\n\n print(f\"Epoch {num_epoch} mean accuracy = {np.mean(ensemble_accuracies):.2f}\")\n\n return ensemble_accuracies, ensemble_losses"},{"identifier":"PreferenceDataset","path":"reed/data/preference_dataset.py","snippet":"class PreferenceDataset:\n def __init__(self, observation_dim: t.Union[t.Tuple, int], action_dim: t.Union[t.Tuple, int], capacity: int,\n size_segment: int, out_path: Path, image_observations: bool, grayscale_images: bool,\n collect_image_pref_dataset: bool, state_action_formatter: PreProcessInference,\n teacher_beta: float = -1, teacher_gamma: float = 1,\n teacher_eps_mistake: float = 0, teacher_eps_skip: float = 0, teacher_eps_equal: float = 0):\n \"\"\"\n Args:\n observation_dim: the dimensionality of the observations\n action_dim: the dimensionality of the actions\n capacity: the maximum number of trajectory pairs to include in the action_dimtaset\n size_segment: the length of the trajectory segments\n out_path: the location where the preference action_dimtaset will be written to disk during training\n image_observations: whether the observations given to the reward model are images\n grayscale_images: whether the image observations should be converted to grayscale instead of color\n collect_image_pref_dataset: whether to collect the image preference dataset separate from the observations.\n Should NOT be set to true if the observations are images.\n state_action_formatter: function that maps states and actions to a single input\n teacher_beta\n teacher_gamma: used to determine how much influence each reward has on the preference label based on\n order within the trajectory. Used to compute the return\n teacher_eps_mistake: the frequency with which the teacher assigns an incorrect label\n teacher_eps_skip: the frequency with which the teacher does not assign a label\n teacher_eps_equal: the maximum difference between trajectory returns for the two trajectories to be labelled\n as equally preferred\n \"\"\"\n self.observation_dim = observation_dim\n self.action_dim = action_dim\n self.capacity = capacity\n self.size_segment = size_segment\n self.out_path = out_path\n self.image_observations = image_observations\n self.grayscale_images = grayscale_images\n # whether to collect the preference dataset as images\n # only needs to be set to True if we are not learning the reward function from images\n # if we are learning the reward function from images then we have an image dataset\n self.collect_image_pref_dataset = collect_image_pref_dataset\n\n # formats the state-action pairs into a single input to the reward model\n self.state_action_formatter = state_action_formatter\n\n # track where each preference triplet is written to disk\n self._preference_triplet_tracker: t.List[Path] = []\n\n self.buffer_index = 0\n self.buffer_full = False\n\n # create the preference labeller\n self._preference_labeller = _PreferenceLabeller(teacher_beta=teacher_beta, teacher_gamma=teacher_gamma,\n teacher_eps_mistake=teacher_eps_mistake,\n teacher_eps_skip=teacher_eps_skip,\n teacher_eps_equal=teacher_eps_equal)\n\n # make sure the outpath where the trajectories will be written exist\n self.out_path.mkdir(parents=True, exist_ok=True)\n\n def __len__(self):\n return len(self._preference_triplet_tracker)\n\n def __getitem__(self, item: int) -> PREFERENCE_TRIPLET:\n \"\"\"\n Load and return the preference triplet at the specified index in the buffer\n\n Args:\n item: index of the triplet in the buffer\n Returns:\n trajectory one\n trajectory two\n preference label\n \"\"\"\n # get the location of the specified preference triplet and load it into memory\n npz_archive = np.load(self._preference_triplet_tracker[item].as_posix())\n\n # grab the trajectories and preference labels\n trajectory_one = npz_archive[\"trajectory_one\"]\n trajectory_two = npz_archive[\"trajectory_two\"]\n preference_label = npz_archive[\"preference_label\"]\n\n return trajectory_one, trajectory_two, preference_label\n\n def get_batch(self, indices: t.List[int]) -> PREFERENCE_TRIPLET_BATCH:\n \"\"\"\n Load and return the batch of preference triplets at the given indices in the buffer\n\n Args:\n indices: the buffer indices of the preference triplets to load into memory\n Returns:\n batch of trajectories one\n batch of trajectories two\n batch of preference labels\n \"\"\"\n # accumulate the trajectory pairs and preference labels\n trajectories_one = []\n trajectories_two = []\n preference_labels = []\n # grab each preference triplet\n for index in indices:\n trajectory_one, trajectory_two, preference_label = self[index]\n trajectories_one.append(np.expand_dims(trajectory_one, axis=0))\n trajectories_two.append(np.expand_dims(trajectory_two, axis=0))\n preference_labels.append(preference_label)\n\n return (np.concatenate(trajectories_one, axis=0), np.concatenate(trajectories_two, axis=0),\n np.concatenate(preference_labels, axis=0))\n\n def _sample_trajectory_segments_uniform(self,\n experience_buffer: TrajectoryReplayBuffer,\n trajectory_count: int,\n mini_batch_size: int) -> t.Tuple[np.ndarray, np.ndarray, t.Optional[np.ndarray]]:\n \"\"\"\n Uniformly sample trajectories and then uniformly sample a segment of the trajectory.\n\n Format and track the state-action pairs from each trajectory segment\n Format and track rewards from each trajectory segment\n\n Combine the formatted state-action pairs and the rewards across trajectory segments\n\n Args:\n experience_buffer: the replay buffer from which trajectory pairs will be drawn\n trajectory_count: the number of trajectories to be sampled from\n mini_batch_size: the number of trajectories to sample\n\n Returns:\n the formatted state-action pairs from random trajectory segments from trajectories\n the rewards from each random trajectory segment\n (optionally) the image observations from each random trajectory segment - only returned when the flag to\n collect image observations in the preference dataset is true and image observations are not\n used to train the reward model\n \"\"\"\n # select the trajectories to be included in this batch of trajectory segments\n trajectory_indices = np.random.choice(trajectory_count, size=mini_batch_size, replace=True)\n\n # accumulate the formatted state-action pairs and rewards from each trajectory segment\n state_action_pairs = []\n rewards = []\n # optionally accumulate image observations\n image_observations = ([] if self.collect_image_pref_dataset and not self.image_observations else None)\n # extract each trajectory and randomly sample a segment\n for traj_index in trajectory_indices:\n # grab the trajectory\n trajectory = experience_buffer.trajectories[traj_index]\n # select a random segment from the trajectory\n traj_segment = trajectory.random_segment(length=self.size_segment)\n # track the rewards associated with the random segment\n rewards.append(np.expand_dims(traj_segment.env_rewards, axis=0))\n # format the state and action based on whether image observations are being used\n if self.image_observations:\n formatted_pair = self.state_action_formatter.format_state_action(\n traj_segment.initial_image_observations,\n traj_segment.actions,\n batch_sa=True)\n else:\n formatted_pair = self.state_action_formatter.format_state_action(\n traj_segment.initial_observations,\n traj_segment.actions,\n batch_sa=True)\n if self.collect_image_pref_dataset:\n image_observations.append(np.expand_dims(traj_segment.initial_image_observations, axis=0))\n # add a dimension in the front so we can concatenate later and the track\n state_action_pairs.append(np.expand_dims(formatted_pair, axis=0))\n return (np.concatenate(state_action_pairs, axis=0),\n np.concatenate(rewards, axis=0),\n (np.concatenate(image_observations, axis=0) if image_observations is not None else None))\n\n @staticmethod\n def get_rank_probability(trajectories_one: np.ndarray, trajectories_two: np.ndarray,\n reward_model: torch.nn.Module):\n \"\"\"\n Compute the preference-prediction disagreement between the ensemble members for each trajectory pair\n\n Args:\n trajectories_one: the trajectories one to be evaluated for ensemble disagreement\n trajectories_two: the trajectories two to be evaluated for ensemble disagreement\n reward_model: the ensemble of networks that will be used to compute disagreement\n \"\"\"\n\n # get probability x_1 > x_2\n probs = []\n for member in range(len(reward_model.ensemble)):\n probs.append(reward_model.p_hat_member(trajectories_one,\n trajectories_two,\n member=member).cpu().numpy())\n probs = np.array(probs)\n\n return np.mean(probs, axis=0), np.std(probs, axis=0)\n\n def get_queries(self, experience_buffer: TrajectoryReplayBuffer, mb_size=20):\n len_traj, max_len = experience_buffer.trajectory_lengths[0], experience_buffer.trajectory_count\n\n # if len(self.experience_buffer.trajectory_lengths[0][-1]) < len_traj:\n # check that the last trajectory contains at least as many transitions as the target segment length\n # we check the last trajectory, because it may be incomplete\n # this is a carry over from the original code. The authors had an assumption that all \"completed\" trajectories\n # will be at least as long as the target segment length\n if experience_buffer.trajectory_lengths[-1] < self.size_segment:\n max_len = max_len - 1\n\n # grab each trajectory, select a random segment from each, format the state-action pairs, and concatenate\n # along the batch dimension\n state_action_pair_traj_one, r_t_1, images_traj_one = self._sample_trajectory_segments_uniform(\n experience_buffer=experience_buffer,\n trajectory_count=max_len,\n mini_batch_size=mb_size)\n state_action_pair_traj_two, r_t_2, images_traj_two = self._sample_trajectory_segments_uniform(\n experience_buffer=experience_buffer,\n trajectory_count=max_len,\n mini_batch_size=mb_size)\n # confirm the image-specific variables are only populated when they should be\n if not self.collect_image_pref_dataset and self.image_observations:\n assert images_traj_one is None and images_traj_two is None\n return state_action_pair_traj_one, state_action_pair_traj_two, r_t_1, r_t_2, images_traj_one, images_traj_two\n\n def put_queries(self, state_action_pair_traj_one: np.ndarray, state_action_pair_traj_two: np.ndarray,\n preference_labels: np.ndarray,\n images_traj_one: t.Optional[np.ndarray] = None, images_traj_two: t.Optional[np.ndarray] = None):\n \"\"\"\n Args:\n state_action_pair_traj_one: the state-action pairs that make up the trajectories one in the queries\n state_action_pair_traj_two: the state-action pairs that make up the trajectories two in the queries\n preference_labels: the preference labels for each pair of trajectories\n images_traj_one: the images for trajectories one\n images_traj_two: the images for trajectories two\n \"\"\"\n # get the number of triplets to be stored\n total_sample = state_action_pair_traj_one.shape[0]\n # write each preference_triplet to disk\n for batch_indx in range(total_sample):\n # get the index of the triplet in the \"buffer\"\n preference_triplet_index = self.buffer_index + batch_indx\n # check if we need to wrap the buffer\n if preference_triplet_index >= self.capacity:\n preference_triplet_index -= self.capacity\n elif not self.buffer_full:\n # this is a previously unseen preference triplet buffer index, so we need to track the triplet location\n self._preference_triplet_tracker.append(self.out_path / f\"preference_triplet_{preference_triplet_index}.npz\")\n # save the preference triplet\n np.savez((self.out_path / f\"preference_triplet_{preference_triplet_index}.npz\").as_posix(),\n trajectory_one=state_action_pair_traj_one[batch_indx],\n trajectory_two=state_action_pair_traj_two[batch_indx],\n preference_label=preference_labels[batch_indx],\n image_trajectory_one=(\n None if images_traj_one is None else images_traj_one[batch_indx]),\n image_trajectory_two=(\n None if images_traj_two is None else images_traj_two[batch_indx]))\n # set the new buffer index\n next_index = self.buffer_index + total_sample\n # check if the buffer has wrapped\n if next_index >= self.capacity:\n self.buffer_full = True\n # wrap the buffer index\n self.buffer_index = next_index - self.capacity\n else:\n self.buffer_index = next_index\n\n def uniform_sampling(self, experience_buffer: TrajectoryReplayBuffer, mb_size: int) -> int:\n \"\"\"\n Grow the preference dataset with preference triplets uniformly sampled from the experience buffer\n\n Args:\n experience_buffer: the replay buffer from which to sample trajectory pairs\n mb_size: target number of preference triplets to add to the preference dataset. Fewer than the target may\n be added depending on the whether labeller skips labelling some trajectories.\n Returns:\n number of preference triplets added to the dataset\n \"\"\"\n # get queries\n sa_t_1, sa_t_2, r_t_1, r_t_2, img_sa_t_1, img_sa_t_2 = self.get_queries(experience_buffer=experience_buffer,\n mb_size=mb_size)\n\n # get labels\n sa_t_1, sa_t_2, r_t_1, r_t_2, labels = self._preference_labeller.get_label(sa_t_1, sa_t_2, r_t_1, r_t_2)\n if len(labels) > 0:\n self.put_queries(sa_t_1, sa_t_2, labels, img_sa_t_1, img_sa_t_2)\n\n return len(labels)\n\n # TODO: refactor to break the circular import that would need to happen in order to specify that reward_model here\n # should be BPref.reward_model.RewardModel\n def disagreement_sampling(self, experience_buffer: TrajectoryReplayBuffer, mb_size: int, large_batch: int,\n reward_model: torch.nn.Module) -> int:\n \"\"\"\n Grow the preference dataset with preference triplets from the experience buffer that the reward ensemble\n disagrees about\n\n Args:\n experience_buffer: the replay buffer from which to sample trajectory pairs\n mb_size: target number of preference triplets to add to the preference dataset. Fewer than the target may\n be added depending on the whether labeller skips labelling some trajectories.\n large_batch: scales up the number of triplets to add to the preference dataset to uniformly select a large\n number of trajectory pairs, which are then pruned based on which ones the reward ensemble\n has the most disagreement over\n reward_model: the ensemble of reward networks that will be used to assess disagreement.\n Should be BPref.reward_model.RewardModel, but cannot import and reference from here right now\n as it would lead to circular imports\n Returns:\n number of preference triplets added to the dataset\n \"\"\"\n # get queries\n sa_t_1, sa_t_2, r_t_1, r_t_2, img_sa_t_1, img_sa_t_2 = self.get_queries(\n experience_buffer=experience_buffer, mb_size=mb_size * large_batch)\n\n # get final queries based on ensemble member disagreement\n _, disagree = self.get_rank_probability(sa_t_1, sa_t_2, reward_model=reward_model)\n top_k_index = (-disagree).argsort()[:mb_size]\n r_t_1, sa_t_1 = r_t_1[top_k_index], sa_t_1[top_k_index]\n r_t_2, sa_t_2 = r_t_2[top_k_index], sa_t_2[top_k_index]\n if img_sa_t_1 is not None:\n img_sa_t_1 = img_sa_t_1[top_k_index]\n img_sa_t_2 = img_sa_t_2[top_k_index]\n\n # get labels\n sa_t_1, sa_t_2, r_t_1, r_t_2, labels = self._preference_labeller.get_label(\n sa_t_1, sa_t_2, r_t_1, r_t_2)\n if len(labels) > 0:\n self.put_queries(sa_t_1, sa_t_2, labels, img_sa_t_1, img_sa_t_2)\n\n return len(labels)\n\n def set_teacher_thres_skip(self, new_margin):\n self._preference_labeller.teacher_thres_skip = new_margin * self._preference_labeller.teacher_eps_skip\n\n def set_teacher_thres_equal(self, new_margin):\n self._preference_labeller.teacher_eps_equal = new_margin * self._preference_labeller.teacher_eps_equal\n\n def save(self, dataset_dir: Path, env_id: str, step: int):\n \"\"\"\n Saves the preference dataset as a zip archive and the labeller configuration as a yaml to the specified location\n\n Args:\n dataset_dir: path where the dataset is to be saved\n env_id: the environment/task within which the data was generated\n step: the number of policy training steps taken to produce this dataset\n \"\"\"\n # create the ZipFile object\n zip_obj = ZipFile(dataset_dir / f\"{env_id}_preference_dataset_{step}.zip\", \"w\")\n # the configuration for the online preference dataset\n config = {\"teacher_params\": {\"teacher_beta\": self._preference_labeller.teacher_beta,\n \"teacher_gamma\": self._preference_labeller.teacher_gamma,\n \"teacher_eps_mistake\": self._preference_labeller.teacher_eps_mistake,\n \"teacher_eps_equal\": self._preference_labeller.teacher_eps_equal,\n \"teacher_eps_skip\": self._preference_labeller.teacher_eps_skip,\n \"teacher_thres_skip\": self._preference_labeller.teacher_thres_skip,\n \"teacher_thres_equal\": self._preference_labeller.teacher_thres_equal,\n \"label_margin\": self._preference_labeller.label_margin,\n \"label_target\": self._preference_labeller.label_target}}\n with open((dataset_dir / f\"preference_dataset_config.yaml\").as_posix(), \"w+\") as f:\n yaml.dump(config, f)\n # write the labeller config to the preference dataset's zip archive\n zip_obj.write(dataset_dir / f\"preference_dataset_config.yaml\")\n\n # add each preference triplet to the zip archive\n for pref_triplet_path in self._preference_triplet_tracker:\n zip_obj.write(pref_triplet_path)\n # move the file from it temp location to the artifact directory\n file_dest_path = dataset_dir / pref_triplet_path.name\n shutil.move(pref_triplet_path, file_dest_path)\n # close the Zip File\n zip_obj.close()"},{"identifier":"PreferenceTripletEnsembleDataLoader","path":"reed/data/preference_data_loader.py","snippet":"class PreferenceTripletEnsembleDataLoader:\n \"\"\"\n Handles loading and generating batches of preference triplets.\n\n The special logic needed is to handle different batch orderings for different networks in the reward ensemble\n \"\"\"\n def __init__(self, dataset: PreferenceDataset, ensemble_size: int,\n batch_size: int = 64, num_workers: int = 0, shuffle: bool = True, device: torch.device = \"cuda\"):\n \"\"\"\n Args:\n\n \"\"\"\n # create a data loader per ensemble network\n self.loader_ensemble = [DataLoader(dataset=dataset,\n batch_size=batch_size,\n shuffle=shuffle,\n num_workers=num_workers)\n for _ in range(ensemble_size)]\n\n self.device = device\n\n def _format_batch(self, batch: UNFORMATTED_PREFERENCE_TRIPLET_BATCH) -> FORMATTED_PREFERENCE_TRIPLET_BATCH:\n \"\"\"\n Format the preference batch so that the tensors are longs and on the correct device\n \"\"\"\n return [PreferenceTripletBatch(trajectories_one=member[0].float().to(self.device),\n trajectories_two=member[1].float().to(self.device),\n preference_labels=member[2].long().to(self.device))\n for member in batch]\n\n def dataset_length(self) -> int:\n return len(self.loader_ensemble[0].dataset)\n\n def __iter__(self) -> FORMATTED_PREFERENCE_TRIPLET_BATCH:\n \"\"\"\n Iterate through the preference triplet data loaders and return the batch per ensemble member\n\n Returns:\n list of PreferenceTripletBatch\n \"\"\"\n # set up each loader as an iterator\n iter_loader_ensemble = [iter(loader) for loader in self.loader_ensemble]\n # for each data loader grab the next batch until there are no more batches to grab\n while True:\n # check if there is a next batch to return\n try:\n yield self._format_batch([next(dataloader_iterator) for dataloader_iterator in iter_loader_ensemble])\n except StopIteration:\n break"},{"identifier":"PreProcessInference","path":"reed/data/preprocess_images.py","snippet":"class PreProcessInference:\n \"\"\"\n Preprocess the data for inference by the reward, SSC, and SFC models\n \"\"\"\n def __init__(self,\n image_observations: bool = False,\n grayscale_images: bool = True,\n normalize_images: bool = True,\n environment_id: str = \"dmc\"):\n \"\"\"\n Args:\n image_observations: whether the observations are images\n grayscale_images: whether images observations should be in grayscale\n normalize_images: whether the image observations should be normalized\n environment_id: the environment from which the data is coming\n \"\"\"\n self.image_observations = image_observations\n self.grayscale_images = grayscale_images\n self.normalize_images = normalize_images\n self.environment_id = environment_id\n\n @staticmethod\n def _channel_first_to_last(observation: np.ndarray,\n batch_states: bool = False,\n by_trajectory: bool = False) -> np.ndarray:\n \"\"\"\n Move the channel from the first dimension to the last dimension\n \"\"\"\n if batch_states and by_trajectory:\n return np.transpose(observation, (0, 1, 3, 4, 2))\n elif batch_states:\n return np.transpose(observation, (0, 2, 3, 1))\n else:\n return np.transpose(observation, (1, 2, 0))\n\n @staticmethod\n def _channel_last_to_first(observation: np.ndarray, batch_states: bool = False,\n by_trajectory: bool = False) -> np.ndarray:\n \"\"\"\n Move the channel from the last dimension to the first dimension\n Args:\n observation: the state observations\n batch_states: whether a batch of state is to be processed\n by_trajectory: whether the batch of states is structured by trajectory -> should only be\n True when batch_sa=True\n Returns:\n the image with the channel dimension moved from first to last\n \"\"\"\n # permute the input so that the channels are in the first dimension of the images\n if batch_states and by_trajectory:\n return np.transpose(observation, (0, 1, 4, 2, 3))\n elif batch_states:\n return np.transpose(observation, (0, 3, 1, 2))\n else:\n # permute the input so that the channels are in the first dimension\n obs = np.transpose(observation, (2, 0, 1))\n # add a dimension along the front for concatenation into the buffer\n return np.expand_dims(obs, axis=0)\n\n def format_state(self, obs: np.ndarray, batch_states: bool = False,\n by_trajectory: bool = False, channel_first: bool = False) -> np.ndarray:\n \"\"\"\n Args:\n obs: the state observations\n batch_states: whether a batch of state is to be processed\n by_trajectory: whether the batch of states is structured by trajectory -> should only be\n True when batch_sa=True\n channel_first: whether the channel dimension is first when the observations are images.\n Returns:\n the state-action pairs as a single array\n \"\"\"\n if self.image_observations:\n if channel_first:\n # move the channel dimension from first to last to avoid a bunch of logic in our formatting methods\n # that handles variable locations for the channel dimension\n obs = self._channel_first_to_last(observation=obs,\n batch_states=batch_states,\n by_trajectory=by_trajectory)\n if self.grayscale_images:\n obs = _to_grayscale(observation=obs)\n if self.normalize_images:\n # TODO: add normalization based on pixel mean and standard deviation instead of scaling 0 to 1\n obs = np.divide(obs, 255.)\n # move the channel dimension from first to last\n return self._channel_last_to_first(observation=obs, batch_states=batch_states, by_trajectory=by_trajectory)\n\n else:\n return obs.reshape(1, obs.shape[1:]) if batch_states else obs.reshape(1, obs.shape[0])\n\n def format_state_action(self, obs: np.ndarray, act: np.ndarray,\n batch_sa: bool = False, by_trajectory: bool = False,\n channel_first: bool = False) -> np.ndarray:\n \"\"\"\n Args:\n obs: the state observations\n act: the actions associated with each state observation\n batch_sa: whether a batch of state-action pairs is to be processed\n by_trajectory: whether the batch of state-action pairs is structured by trajectory -> should only be\n True when batch_sa=True\n channel_first: whether the channel dimension is first when the observations are images.\n Returns:\n the state-action pairs as a single array\n \"\"\"\n if self.image_observations:\n if channel_first:\n # move the channel dimension from first to last to avoid a bunch of logic in our formatting methods\n # that handles variable locations for the channel dimension\n obs = self._channel_first_to_last(observation=obs,\n batch_states=batch_sa,\n by_trajectory=by_trajectory)\n if self.grayscale_images:\n obs = _to_grayscale(observation=obs)\n if self.normalize_images:\n # TODO: add normalization based on pixel mean and standard deviation instead of scaling 0 to 1\n obs = np.divide(obs, 255.)\n\n # get the dimensions of the image\n obs_dim = obs.shape[-3:]\n assert len(obs_dim) == 3\n # add the actions to the image channels and permute the input so that the channels are in the first\n # dimension of the images\n if batch_sa and by_trajectory:\n repeated_actions = np.tile(act.reshape((act.shape[0], act.shape[1], 1, 1, act.shape[-1])),\n (1, 1, obs_dim[0], obs_dim[1], 1))\n elif batch_sa:\n repeated_actions = np.tile(act.reshape((act.shape[0], 1, 1, act.shape[-1])),\n (1, obs_dim[0], obs_dim[1], 1))\n else:\n repeated_actions = np.tile(act.reshape((1, 1, -1)), (obs_dim[0], obs_dim[1], 1))\n sa_t = np.concatenate((obs, repeated_actions), axis=-1)\n return self._channel_last_to_first(sa_t, batch_states=batch_sa, by_trajectory=by_trajectory)\n else:\n sa_t = np.concatenate([obs, act], axis=-1)\n if batch_sa:\n return sa_t\n else:\n return sa_t.reshape(1, -1)"}],"string":"[\n {\n \"identifier\": \"utils\",\n \"path\": \"BPref/utils.py\",\n \"snippet\": \"def make_env(cfg):\\ndef ppo_make_env(env_id, seed):\\ndef tie_weights(src, trg):\\ndef make_metaworld_env(cfg):\\ndef ppo_make_metaworld_env(env_id, seed):\\n def __init__(self, *models):\\n def __enter__(self):\\n def __exit__(self, *args):\\n def __init__(self, *models):\\n def __enter__(self):\\n def __exit__(self, *args):\\ndef soft_update_params(net, target_net, tau):\\ndef set_seed_everywhere(seed):\\ndef make_dir(*path_parts):\\ndef weight_init(m):\\n def __init__(self,\\n input_dim,\\n hidden_dim,\\n output_dim,\\n hidden_depth,\\n output_mod=None):\\n def forward(self, x):\\n def __init__(self, cache_size=1):\\n def atanh(x):\\n def __eq__(self, other):\\n def _call(self, x):\\n def _inverse(self, y):\\n def log_abs_det_jacobian(self, x, y):\\n def __init__(self, loc, scale):\\n def mean(self):\\n def __init__(self, epsilon=1e-4, shape=(), device=None):\\n def update(self, x):\\n def update_from_moments(self, batch_mean, batch_var, batch_count):\\n def std(self):\\ndef update_mean_var_count_from_moments(\\n mean, var, count, batch_mean, batch_var, batch_count\\n):\\ndef mlp(input_dim, hidden_dim, output_dim, hidden_depth, output_mod=None):\\ndef to_np(t):\\nclass eval_mode(object):\\nclass train_mode(object):\\nclass MLP(nn.Module):\\nclass TanhTransform(pyd.transforms.Transform):\\nclass SquashedNormal(pyd.transformed_distribution.TransformedDistribution):\\nclass TorchRunningMeanStd:\\n M2 = m_a + m_b + torch.pow(delta, 2) * count * batch_count / tot_count\"\n },\n {\n \"identifier\": \"Logger\",\n \"path\": \"BPref/logger.py\",\n \"snippet\": \"class Logger(object):\\n def __init__(self,\\n log_dir,\\n save_tb=False,\\n log_frequency=10000,\\n agent='sac'):\\n self._log_dir = log_dir\\n self._log_frequency = log_frequency\\n if save_tb:\\n tb_dir = os.path.join(log_dir, 'tb')\\n if os.path.exists(tb_dir):\\n try:\\n shutil.rmtree(tb_dir)\\n except:\\n print(\\\"logger.py warning: Unable to remove tb directory\\\")\\n pass\\n self._sw = SummaryWriter(tb_dir)\\n else:\\n self._sw = None\\n # each agent has specific output format for training\\n assert agent in AGENT_TRAIN_FORMAT\\n train_format = COMMON_TRAIN_FORMAT + AGENT_TRAIN_FORMAT[agent]\\n self._train_mg = MetersGroup(os.path.join(log_dir, 'train'),\\n formating=train_format)\\n self._eval_mg = MetersGroup(os.path.join(log_dir, 'eval'),\\n formating=COMMON_EVAL_FORMAT)\\n\\n def _should_log(self, step, log_frequency):\\n log_frequency = log_frequency or self._log_frequency\\n return step % log_frequency == 0\\n\\n def _try_sw_log(self, key, value, step):\\n if self._sw is not None:\\n self._sw.add_scalar(key, value, step)\\n\\n def _try_sw_log_video(self, key, frames, step):\\n if self._sw is not None:\\n frames = torch.from_numpy(np.array(frames))\\n frames = frames.unsqueeze(0)\\n self._sw.add_video(key, frames, step, fps=30)\\n\\n def _try_sw_log_histogram(self, key, histogram, step):\\n if self._sw is not None:\\n self._sw.add_histogram(key, histogram, step)\\n\\n def log(self, key, value, step, n=1, log_frequency=1):\\n if not self._should_log(step, log_frequency):\\n return\\n assert key.startswith('train') or key.startswith('eval')\\n if type(value) == torch.Tensor:\\n value = value.item()\\n self._try_sw_log(key, value / n, step)\\n mg = self._train_mg if key.startswith('train') else self._eval_mg\\n mg.log(key, value, n)\\n\\n def log_param(self, key, param, step, log_frequency=None):\\n if not self._should_log(step, log_frequency):\\n return\\n self.log_histogram(key + '_w', param.weight.data, step)\\n if hasattr(param.weight, 'grad') and param.weight.grad is not None:\\n self.log_histogram(key + '_w_g', param.weight.grad.data, step)\\n if hasattr(param, 'bias') and hasattr(param.bias, 'data'):\\n self.log_histogram(key + '_b', param.bias.data, step)\\n if hasattr(param.bias, 'grad') and param.bias.grad is not None:\\n self.log_histogram(key + '_b_g', param.bias.grad.data, step)\\n\\n def log_video(self, key, frames, step, log_frequency=None):\\n if not self._should_log(step, log_frequency):\\n return\\n assert key.startswith('train') or key.startswith('eval')\\n self._try_sw_log_video(key, frames, step)\\n\\n def log_histogram(self, key, histogram, step, log_frequency=None):\\n if not self._should_log(step, log_frequency):\\n return\\n assert key.startswith('train') or key.startswith('eval')\\n self._try_sw_log_histogram(key, histogram, step)\\n\\n def dump(self, step, save=True, ty=None):\\n if ty is None:\\n self._train_mg.dump(step, 'train', save)\\n self._eval_mg.dump(step, 'eval', save)\\n elif ty == 'eval':\\n self._eval_mg.dump(step, 'eval', save)\\n elif ty == 'train':\\n self._train_mg.dump(step, 'train', save)\\n else:\\n raise f'invalid log type: {ty}'\"\n },\n {\n \"identifier\": \"TrajectoryReplayBuffer\",\n \"path\": \"BPref/replay_buffer.py\",\n \"snippet\": \"class TrajectoryReplayBuffer:\\n \\\"\\\"\\\"\\n Buffer to store trajectories of environment transitions. Unlike ReplayBuffer, which stores all transitions in a\\n flat manner, transitions are sorted by trajectory. Each trajectory corresponds to an episode.\\n \\\"\\\"\\\"\\n _RELABEL_BATCH_SIZE = 256\\n\\n def __init__(self, capacity: int, device: torch.device, window: int = 1, num_envs: t.Optional[int] = None,\\n image_observations: t.Optional[t.Union[int, np.ndarray]] = None):\\n \\\"\\\"\\\"\\n Args:\\n capacity: the number of trajectories to hold in memory\\n device: the device sampled transitions should be put on\\n window: no idea - part of the original code and is used in add_batch(...) which has not yet been refactored\\n num_envs: the number of environment instances used to train the policy. Only needs to be specified when the\\n number is >1. Some algorithms train on multiple instances of an environment at once, e.g. PPO.\\n Not currently used, but not yet removed because we have not tested with an algorithm that needs\\n multiple environment instances.\\n image_observations: (default = false) whether to collect image observations in addition to state\\n observations. This is helpful to use when the policy is trained on the state, but you\\n want to visualize the trajectories or the reward model is trained on images.\\n\\n \\\"\\\"\\\"\\n self.capacity = capacity\\n self.device = device\\n\\n self.observations: t.Optional[np.ndarray] = None\\n self.actions: t.Optional[np.ndarray] = None\\n self.rewards: t.Optional[np.ndarray] = None\\n self.not_dones: t.Optional[np.ndarray] = None\\n self.not_dones_no_max: t.Optional[np.ndarray] = None\\n self.trajectory_lengths: t.List = []\\n self.window = window\\n self.env_rewards: t.Optional[np.ndarray] = None\\n self.image_observations: t.Optional[np.ndarray] = None\\n # track whether to collect image observations - when not None, specifies the dimensions of the images\\n self._collect_image_observations = image_observations\\n\\n # track the trajectories as a list of Trajectory\\n self.trajectories: t.List[Trajectory] = []\\n\\n self.idx = 0\\n self.last_save = 0\\n self.full = False\\n\\n def __len__(self):\\n return np.sum(self.trajectory_lengths) - len(self.trajectory_lengths)\\n\\n def __getitem__(self, flat_indx: t.Union[int, t.Tuple[int, int], t.List[int]]) -> TRANSITION:\\n \\\"\\\"\\\"\\n Get the transition at the given index\\n\\n Args:\\n flat_indx: the index assuming transitions are stored flat instead of nested in trajectories\\n - when an integer is specified, a single transition is retrieved\\n - when a tuple of integers is given, a slice is retrieved as if the transitions are stored flat\\n\\n Returns:\\n current observation\\n action\\n reward\\n next observation\\n whether the episode ended\\n whether the episode ended without reaching max steps\\n image version of current observation (optional)\\n \\\"\\\"\\\"\\n if isinstance(flat_indx, int) or isinstance(flat_indx, np.int64):\\n traj_indx, trans_indx = self._flat_indx_to_trajectory_index(flat_indx)\\n # check we are grabbing from a trajectory currently being accumulated\\n # When the done signal is given, the current trajectory being accumulated is converted to a trajectory,\\n # is added to the list of trajectories, and the values used to accumulate the next trajectory are set to\\n # done. The next trajectory is not started until the call to add(...) after the done signal is received.\\n # Therefore, we need to check whether the trajectory to pull from is actually the last completed trajectory\\n # prior to starting a new trajectory. This is why we compare the length of the lists containing trajectory\\n # lengths and the list containing the trajectories.\\n if (traj_indx == len(self.trajectory_lengths) - 1\\n and len(self.trajectory_lengths) > len(self.trajectories)):\\n # we need to grab from the trajectory currently being populated\\n return (self.observations[trans_indx].astype(np.float32), self.actions[trans_indx].astype(np.float32),\\n self.rewards[trans_indx].astype(np.float32), self.observations[trans_indx + 1].astype(np.float32),\\n self.not_dones[trans_indx].astype(np.float32),\\n self.not_dones_no_max[trans_indx].astype(np.float32),\\n (self.env_rewards[trans_indx].astype(np.float32)\\n if self.env_rewards is not None\\n else None),\\n ((self.image_observations[trans_indx].astype(np.float32))\\n if self.image_observations is not None\\n else None),\\n ((self.image_observations[trans_indx+1].astype(np.float32))\\n if self.image_observations is not None\\n else None))\\n else:\\n # grab from a previously completed trajectory\\n transition: Transition = self.trajectories[traj_indx][trans_indx]\\n return (transition.observation.astype(np.float32), transition.action.astype(np.float32),\\n transition.reward.astype(np.float32), transition.next_observation.astype(np.float32),\\n transition.not_done.astype(np.float32), transition.not_done_no_max.astype(np.float32),\\n transition.env_reward.astype(np.float32),\\n (transition.image_observation.astype(np.float32)\\n if transition.image_observation is not None\\n else None),\\n (transition.next_image_observation.astype(np.float32)\\n if transition.next_image_observation is not None\\n else None))\\n elif isinstance(flat_indx, t.List):\\n observations = []\\n actions = []\\n rewards = []\\n next_observations = []\\n not_dones = []\\n not_dones_no_max = []\\n env_rewards = []\\n image_observations = []\\n next_image_observations = []\\n for indx in flat_indx:\\n observation, action, reward, next_observation, not_done, not_done_no_max, env_reward, image_observation, next_image_observation = self[indx]\\n observations.append(observation)\\n actions.append(action)\\n rewards.append(reward)\\n next_observations.append(next_observation)\\n not_dones.append(not_done)\\n not_dones_no_max.append(not_done_no_max)\\n if env_reward is not None:\\n env_rewards.append(env_reward)\\n if image_observation is not None:\\n image_observations.append(image_observation)\\n if next_image_observation is not None:\\n next_image_observations.append(next_image_observation)\\n return (np.asarray(observations, dtype=np.float32), np.asarray(actions, dtype=np.float32),\\n np.asarray(rewards, dtype=np.float32), np.asarray(next_observations, dtype=np.float32),\\n np.asarray(not_dones, dtype=np.float32), np.asarray(not_dones_no_max, dtype=np.float32),\\n (np.asarray(env_rewards, dtype=np.float32) if len(env_rewards) > 0 else None),\\n (np.asarray(image_observations, dtype=np.float32) if self._collect_image_observations else None),\\n (np.asarray(next_image_observations, dtype=np.float32) if self._collect_image_observations else None))\\n else:\\n # get the locations of the start and end transitions\\n start_traj_indx, start_trans_indx = self._flat_indx_to_trajectory_index(flat_indx[0])\\n end_traj_indx, end_trans_indx = self._flat_indx_to_trajectory_index(flat_indx[1])\\n # check that we are not spanning trajectories\\n if start_traj_indx == end_traj_indx:\\n # grab the sub-trajectory\\n sub_trajectory = self.trajectories[start_traj_indx][tuple((start_trans_indx, end_trans_indx))]\\n else:\\n # grab what remains of the trajectory\\n end_trans_indx = len(self.trajectories[start_traj_indx]) - 1\\n sub_trajectory = self.trajectories[start_traj_indx][tuple((start_trans_indx, end_trans_indx))]\\n return (sub_trajectory.initial_observations,\\n sub_trajectory.actions,\\n sub_trajectory.rewards,\\n sub_trajectory.next_observations,\\n sub_trajectory.not_dones,\\n sub_trajectory.not_dones_no_max,\\n sub_trajectory.env_rewards,\\n (sub_trajectory.initial_image_observations\\n if sub_trajectory.initial_image_observations is not None\\n else None),\\n (sub_trajectory.next_image_observations\\n if sub_trajectory.next_image_observations is not None\\n else None))\\n\\n @property\\n def trajectory_count(self) -> int:\\n \\\"\\\"\\\"\\n The number of trajectories in the buffer\\n \\\"\\\"\\\"\\n return len(self.trajectories)\\n\\n @property\\n def all_not_dones(self) -> np.ndarray:\\n \\\"\\\"\\\"\\n Rewards from the state-action pairs from all trajectories and all transitions, where the action was taken in the state\\n \\\"\\\"\\\"\\n return np.concatenate([np.expand_dims(traj.not_dones, axis=0) for traj in self.trajectories], axis=0)\\n\\n @property\\n def all_rewards(self) -> np.ndarray:\\n \\\"\\\"\\\"\\n Rewards from the state-action pairs from all trajectories and all transitions, where the action was taken in the state\\n \\\"\\\"\\\"\\n return np.concatenate([np.expand_dims(traj.rewards, axis=0) for traj in self.trajectories], axis=0)\\n\\n @property\\n def all_environment_rewards(self) -> np.ndarray:\\n \\\"\\\"\\\"\\n Environment rewards from all trajectories and all transitions\\n \\\"\\\"\\\"\\n return np.concatenate([np.expand_dims(traj.rewards, axis=0) for traj in self.trajectories], axis=0)\\n\\n @property\\n def all_initial_image_observations(self) -> np.ndarray:\\n \\\"\\\"\\\"\\n Image observations from the state-action pairs from all trajectories and all transitions, where the action was taken in the state\\n \\\"\\\"\\\"\\n return np.concatenate([np.expand_dims(traj.initial_image_observations, axis=0)\\n for traj in self.trajectories],\\n axis=0)\\n\\n @property\\n def all_next_image_observations(self) -> np.ndarray:\\n \\\"\\\"\\\"\\n Image observations from the state-action pairs from all trajectories and all transitions,\\n\\n The result of a transition\\n \\\"\\\"\\\"\\n return np.concatenate([np.expand_dims(traj.next_image_observations, axis=0)\\n for traj in self.trajectories],\\n axis=0)\\n\\n @property\\n def all_initial_observations(self) -> np.ndarray:\\n \\\"\\\"\\\"\\n observations from the state-action pairs from all trajectories and all transitions, where the action was taken in the state\\n \\\"\\\"\\\"\\n return np.concatenate([np.expand_dims(traj.initial_observations, axis=0) for traj in self.trajectories], axis=0)\\n\\n @property\\n def all_next_observations(self) -> np.ndarray:\\n \\\"\\\"\\\"\\n Observations from the state-action pairs from all trajectories and all transitions\\n\\n The result of a transition\\n \\\"\\\"\\\"\\n return np.concatenate([np.expand_dims(traj.next_observations, axis=0) for traj in self.trajectories], axis=0)\\n\\n @property\\n def all_actions(self) -> np.ndarray:\\n \\\"\\\"\\\"\\n Actions from the state-action pairs from all trajectories and all transitions\\n \\\"\\\"\\\"\\n return np.concatenate([np.expand_dims(traj.actions, axis=0) for traj in self.trajectories], axis=0)\\n\\n def _flat_indx_to_trajectory_index(self, flat_indx: int) -> t.Tuple[int, int]:\\n \\\"\\\"\\\"\\n Converts an index that assumes the transitions are flat to a trajectory and transition (w/in trajectory) index\\n\\n Args:\\n flat_indx: the index assuming transitions are stored flat\\n\\n Returns:\\n the index of the trajectory containing the transition\\n the index of the transition within the trajectory\\n \\\"\\\"\\\"\\n # need to figure out which transition indices are stored in which trajectories\\n transition_cumulative_sum = np.cumsum(self.trajectory_lengths)\\n # the trajectory containing the transition is at the first index where the cumulative sum of transitions is\\n # less than the transition index\\n target_trajectory_indx = int(np.argmax(flat_indx < transition_cumulative_sum))\\n # get the transition's index within the trajectory as the different between the flat index and the cumulative\\n # sum at the previous trajectory - tells us how far into the target trajectory the transition is\\n if target_trajectory_indx == 0:\\n transition_trajectory_indx = flat_indx\\n else:\\n transition_trajectory_indx = flat_indx - transition_cumulative_sum[target_trajectory_indx - 1]\\n return target_trajectory_indx, transition_trajectory_indx\\n\\n def _add_transition(self, observation: np.ndarray, action: np.ndarray, reward: float, done: t.Union[float, bool],\\n done_no_max: t.Union[float, bool],\\n env_reward: t.Optional[float] = None, image_observations: t.Optional[np.ndarray] = None):\\n \\\"\\\"\\\"\\n Track the transition and update the length of the trajectory currently being accumulated\\n\\n Args:\\n observation: the current observation\\n action: the action taken in the current state\\n reward: the reward associated with the last state-action pait\\n done: whether the last action completed an episode\\n done_no_max: whether the last action completed an episode without reaching the maximum allowed steps\\n env_reward: (optional) the reward given by the environment - stored and used to train the preference-learned\\n reward model when learning from synthetic feedback\\n image_observations: (optional) image-based observation -> should not be given is observations is also an image. This\\n should be used when you want to accumulate images separately from policy training.\\n \\\"\\\"\\\"\\n self.observations = np.concatenate([self.observations, np.expand_dims(observation, axis=0)], axis=0)\\n self.actions = np.concatenate([self.actions, np.expand_dims(action, axis=0)], axis=0)\\n self.rewards = np.concatenate([self.rewards, np.asarray(reward).reshape(1, 1)], axis=0)\\n if type(done) is float:\\n self.not_dones = np.concatenate([self.not_dones,\\n np.asarray(not done, dtype=np.float32).reshape(1, 1)], axis=0)\\n self.not_dones_no_max = np.concatenate([self.not_dones_no_max,\\n np.asarray(not done_no_max, dtype=np.float32).reshape(1, 1)],\\n axis=0)\\n else:\\n self.not_dones = np.concatenate([self.not_dones,\\n np.asarray(~done, dtype=np.float32).reshape(1, 1)], axis=0)\\n self.not_dones_no_max = np.concatenate([self.not_dones_no_max,\\n np.asarray(~done_no_max, dtype=np.float32).reshape(1, 1)],\\n axis=0)\\n\\n self.trajectory_lengths[-1] += 1\\n if env_reward is not None:\\n self.env_rewards = np.concatenate([self.env_rewards,\\n np.asarray(env_reward, dtype=np.float32).reshape(1, 1)], axis=0)\\n\\n if image_observations is not None and self._collect_image_observations:\\n self.image_observations = np.concatenate([self.image_observations, np.expand_dims(image_observations, axis=0)], axis=0)\\n\\n def _start_trajectory(self, observation: np.ndarray,\\n action: np.ndarray,\\n reward: float,\\n done: t.Union[float, bool],\\n done_no_max: t.Union[float, bool],\\n env_reward: t.Optional[float] = None,\\n image_observations: t.Optional[np.ndarray] = None):\\n \\\"\\\"\\\"\\n Start a new trajectory and track the transition\\n\\n Args:\\n observation: the current observation\\n action: the action taken in the current state\\n reward: the reward associated with the last state-action pait\\n done: whether the last action completed an episode\\n done_no_max: whether the last action completed an episode without reaching the maximum allowed steps\\n env_reward: (optional) the reward given by the environment - stored and used to train the preference-learned\\n reward model when learning from synthetic feedback\\n image_observations: (optional) image-based observation -> should not be given is observations is also an image. This\\n should be used when you want to accumulate images separately from policy training.\\n \\\"\\\"\\\"\\n self.observations = np.expand_dims(observation, axis=0).astype(dtype=np.float32)\\n self.actions = np.expand_dims(action, axis=0).astype(dtype=np.float32)\\n self.rewards = np.asarray(reward, dtype=np.float32).reshape(1, 1)\\n if type(done) is float:\\n self.not_dones = np.asarray(not done, dtype=np.float32).reshape(1, 1)\\n self.not_dones_no_max = np.asarray(not done_no_max, dtype=np.float32).reshape(1, 1)\\n else:\\n self.not_dones = np.asarray(~done, dtype=np.float32).reshape(1, 1)\\n self.not_dones_no_max = np.asarray(~done_no_max, dtype=np.float32).reshape(1, 1)\\n\\n self.trajectory_lengths.append(1)\\n\\n if env_reward is not None:\\n self.env_rewards = np.asarray(env_reward, dtype=np.float32).reshape(1, 1)\\n\\n if image_observations is not None and self._collect_image_observations:\\n self.image_observations = np.expand_dims(image_observations, axis=0).astype(dtype=np.float32)\\n\\n def add(self, observation, action, reward, next_observation, done, done_no_max,\\n env_reward: t.Optional[float] = None, image_observation: t.Optional[np.ndarray] = None,\\n image_next_observation: t.Optional[np.ndarray] = None):\\n \\\"\\\"\\\"\\n Args:\\n observation: the current observation\\n action: the action taken in the current state\\n reward: the reward associated with the last state-action pait\\n next_observation: only used when an episode is completed to ensure the last observation is captured\\n done: whether the last action completed an episode\\n done_no_max: whether the last action completed an episode without reaching the maximum allowed steps\\n env_reward: (optional) the reward given by the environment - stored and used to train the preference-learned\\n reward model when learning from synthetic feedback\\n image_observation: (optional) image-based observation -> should not be given is observations is also an image. This\\n should be used when you want to accumulate images separately from policy training.\\n image_next_observation: (optional) the image-based next observation -> should not be given when next_observation is also\\n and image. This should be used when you want to accumulate the images separately from the\\n trained policy.\\n \\\"\\\"\\\"\\n if self.observations is None:\\n self._start_trajectory(observation, action, reward, done, done_no_max, env_reward, image_observation)\\n elif done:\\n self._add_transition(observation, action, reward, done, done_no_max, env_reward, image_observation)\\n # the episode has ended, so we need to track the next observation\\n self.observations = np.concatenate([self.observations, np.expand_dims(next_observation, axis=0)], axis=0)\\n if image_next_observation is not None:\\n self.image_observations = np.concatenate([self.image_observations,\\n np.expand_dims(image_next_observation, axis=0)], axis=0)\\n # create the trajectory\\n self.trajectories.append(Trajectory(self.observations.astype(dtype=np.float32),\\n (self.image_observations.astype(dtype=np.float32)\\n if self.image_observations is not None\\n else None),\\n actions=self.actions.astype(dtype=np.float32),\\n rewards=self.rewards.astype(dtype=np.float32),\\n not_dones=self.not_dones.astype(dtype=np.float32),\\n not_dones_no_max=self.not_dones_no_max.astype(dtype=np.float32),\\n env_rewards=self.env_rewards.astype(dtype=np.float32)))\\n # check if the inclusion of the just completed trajectory puts the buffer at capacity\\n # if it does, remove the first trajectory as this is a FIFO buffer\\n if np.sum(self.trajectory_lengths) >= self.capacity:\\n self.trajectories = self.trajectories[1:]\\n self.trajectory_lengths = self.trajectory_lengths[1:]\\n self.observations = None\\n self.actions = None\\n self.rewards = None\\n self.not_dones = None\\n self.not_dones_no_max = None\\n self.env_rewards = None\\n self.image_observations = None\\n else:\\n self._add_transition(observation, action, reward, done, done_no_max, env_reward, image_observation)\\n\\n self.idx = (self.idx + 1) % self.capacity\\n self.full = self.full or self.idx == 0\\n\\n def relabel_with_predictor(self, predictor, state_action_formatter: PreProcessInference):\\n \\\"\\\"\\\"\\n Relabel the rewards stored in the replay buffer using the given predictor\\n\\n Args:\\n predictor: network that will consume state-action pairs and assign a reward\\n state_action_formatter: formats the states and actions for consumption by the reward model\\n \\\"\\\"\\\"\\n print(\\\"Relabelling the replay buffer with the updated reward model.\\\")\\n for trajectory in self.trajectories:\\n # the number of batches to run through the model\\n total_iter = int(len(trajectory) / self._RELABEL_BATCH_SIZE)\\n # handle the case where we have more transitions than is evenly divisible by the batch size\\n if len(trajectory) > self._RELABEL_BATCH_SIZE * total_iter:\\n total_iter += 1\\n # collect and process each batch to be passed through predictor\\n for index in range(total_iter):\\n start_indx = index * self._RELABEL_BATCH_SIZE\\n # make sure we don't have an end index that is after the end of the trajectory\\n end_indx = min((index + 1) * self._RELABEL_BATCH_SIZE, len(trajectory))\\n\\n # pull out the actions from the transitions that will be relabelled\\n actions = trajectory.actions[start_indx:end_indx]\\n # we need to handle the case where the reward model operates off of images\\n if predictor.image_observations:\\n observations = trajectory.all_image_observations[start_indx:end_indx]\\n else:\\n observations = trajectory.all_observations[start_indx:end_indx]\\n formatted_state_action = state_action_formatter.format_state_action(observations, actions, batch_sa=True)\\n pred_reward = predictor.r_hat_batch(formatted_state_action)\\n # update the rewards assigned to the transitions\\n trajectory.rewards[start_indx:end_indx] = pred_reward\\n\\n def sample(self, batch_size: int):\\n indxs = list(np.random.randint(0, np.sum(self.trajectory_lengths) - 1, size=batch_size))\\n observations, actions, rewards, next_observations, not_dones, not_dones_no_max, env_rewards, image_observations, next_image_observations = self[indxs]\\n observations = torch.as_tensor(observations, device=self.device).float()\\n actions = torch.as_tensor(actions, device=self.device)\\n rewards = torch.as_tensor(rewards, device=self.device)\\n next_observations = torch.as_tensor(next_observations, device=self.device).float()\\n not_dones = torch.as_tensor(not_dones, device=self.device)\\n not_dones_no_max = torch.as_tensor(not_dones_no_max, device=self.device)\\n env_rewards = torch.as_tensor(env_rewards, device=self.device)\\n image_observations = (torch.as_tensor(image_observations, device=self.device).float() if self._collect_image_observations else None)\\n next_image_observations = (torch.as_tensor(next_image_observations, device=self.device).float() if self._collect_image_observations else None)\\n return observations, actions, rewards, next_observations, not_dones, not_dones_no_max, env_rewards, image_observations, next_image_observations\\n\\n def sample_state_ent(self, batch_size: int):\\n observations, actions, rewards, next_observations, not_dones, not_dones_no_max, _, _, _ = self.sample(batch_size)\\n full_observation = torch.as_tensor(np.concatenate([traj.all_observations for traj in self.trajectories], axis=0),\\n device=self.device)\\n return observations, full_observation, actions, rewards, next_observations, not_dones, not_dones_no_max\\n\\n def save(self, out_directory: Path, env_id: str, step: int):\\n \\\"\\\"\\\"\\n Save the replay buffer to disk as a npz archive\\n Args:\\n out_directory: location where replay buffer will be saved\\n env_id: the environment within which the data was generated\\n step: the number of policy training steps taken to produce this dataset\\n \\\"\\\"\\\"\\n # create the ZipFile object\\n zip_obj = ZipFile(out_directory / f\\\"{env_id}_replay_buffer_{step}.zip\\\", \\\"w\\\")\\n\\n # write each trajectory file to disk and to the zip archive\\n for traj_id, trajectory in enumerate(self.trajectories):\\n trajectory.save(out_directory / f\\\"{traj_id}.npz\\\")\\n zip_obj.write(out_directory / f\\\"{traj_id}.npz\\\")\\n # close the Zip File\\n zip_obj.close()\\n\\n @staticmethod\\n def from_directory(directory_path: Path,\\n device: torch.device = 'cuda') -> \\\"TrajectoryReplayBuffer\\\":\\n \\\"\\\"\\\"\\n Create a TrajectoryReplay buffer from a directory of npz archive trajectories\\n\\n Args:\\n directory_path: the location of the npz_archive on disk\\n device: the device sampled transitions should be pushed to\\n Returns:\\n populated trajectory replay buffer\\n \\\"\\\"\\\"\\n # accumulate the trajectories\\n trajectories = []\\n trajectory_lengths = []\\n # determine how many transitions are in the replay buffer\\n capacity = 0\\n # load each trajectory from disk\\n for traj_filename in directory_path.iterdir():\\n # we only load data from npz archives, so we need to skip anything else\\n if not traj_filename.suffix == \\\".npz\\\": continue\\n # load the trajectory from disk\\n traj = Trajectory.from_npz(traj_filename)\\n # track the trajectory\\n trajectories.append(traj)\\n # track the trajectory's length\\n trajectory_lengths.append(len(traj))\\n # track the trajectory's length\\n capacity += len(traj)\\n # create the buffer\\n _buffer = TrajectoryReplayBuffer(capacity=capacity, device=device)\\n # add the trajectories to the buffer\\n _buffer.trajectories = trajectories\\n _buffer.trajectory_lengths = trajectory_lengths\\n\\n return _buffer\"\n },\n {\n \"identifier\": \"StateActionRewardModel\",\n \"path\": \"reed/models/reward_model.py\",\n \"snippet\": \"class StateActionRewardModel:\\n \\\"\\\"\\\"\\n Reward model that operates over state action pairs\\n \\\"\\\"\\\"\\n def __init__(self,\\n in_dim: t.Union[int, t.List[int]],\\n ensemble_size: int = 3,\\n hidden_dim: int = 256,\\n hidden_layers: int = 3,\\n final_activation: str = 'tanh',\\n lr: float = 3e-4,\\n optimizer: str = \\\"adam\\\",\\n reward_train_batch: int = 128,\\n size_segment: int = 1,\\n device: torch.device = \\\"cuda\\\",\\n multi_gpu: bool = False,\\n image_observations: bool = False,\\n image_encoder_architecture: str = \\\"pixl2r\\\",\\n image_hidden_num_channels: int = 32,\\n grayscale_images: bool = True):\\n # the device the model will be put on\\n self.device = device\\n # whether data parallelism should be used during model training\\n self.multi_gpu = multi_gpu\\n # reward model configuration\\n self.in_dim = in_dim\\n self.hidden_dim = hidden_dim\\n self.hidden_layers = hidden_layers\\n self.ensemble_size = ensemble_size\\n self.lr = lr\\n self.optimizer_type = optimizer\\n self.ensemble = []\\n self.paramlst = []\\n self.optimizer = None\\n self.model = None\\n self.final_activation = final_activation\\n self.size_segment = size_segment\\n\\n self.image_observations = image_observations\\n self.image_encoder_architecture = image_encoder_architecture\\n self.image_hidden_num_channels = image_hidden_num_channels\\n self.grayscale_images = grayscale_images\\n\\n # construct the reward ensemble\\n self.construct_ensemble()\\n\\n # parameters used to train the reward model on the preference labelled trajectories\\n self.train_batch_size = reward_train_batch\\n self.CEloss = nn.CrossEntropyLoss()\\n\\n def eval(self):\\n \\\"\\\"\\\"Set each reward model in the ensemble to evaluation mode\\\"\\\"\\\"\\n self.ensemble = [net.eval() for net in self.ensemble]\\n\\n def train(self):\\n \\\"\\\"\\\"Set each reward model in the ensemble to train mode\\\"\\\"\\\"\\n self.ensemble = [net.train() for net in self.ensemble]\\n\\n def softXEnt_loss(self, predicted: torch.Tensor, target: torch.Tensor):\\n logprobs = F.log_softmax(predicted, dim=1)\\n return -(target * logprobs).sum() / predicted.shape[0]\\n\\n def construct_ensemble(self):\\n for _ in range(self.ensemble_size):\\n if self.image_observations:\\n model = ImageStateActionNetwork(self.in_dim,\\n out_size=1,\\n hidden_dim=self.hidden_dim,\\n hidden_depth=self.hidden_layers,\\n final_activation=self.final_activation,\\n image_encoder_architecture=self.image_encoder_architecture,\\n image_hidden_num_channels=self.image_hidden_num_channels).float()\\n else:\\n model = StateActionNetwork(self.in_dim,\\n out_size=1,\\n hidden_dim=self.hidden_dim,\\n hidden_depth=self.hidden_layers,\\n final_activation=self.final_activation).float()\\n print(model)\\n # check if the model will be run with Data Parallelism\\n if self.multi_gpu:\\n print(f\\\"There are {torch.cuda.device_count()} GPU devices, so the reward ensemble WILL be trained \\\"\\n f\\\"using nn.DataParallel\\\")\\n self.ensemble.append(nn.DataParallel(model).to(self.device))\\n else:\\n print(f\\\"There are {torch.cuda.device_count()} GPU devices, so the reward ensemble will NOT be trained \\\"\\n f\\\"using nn.DataParallel\\\")\\n self.ensemble.append(model.to(self.device))\\n # track all model parameters\\n self.paramlst.extend(model.parameters())\\n # create a single optimizer applied to all ensemble members\\n if self.optimizer_type == \\\"adam\\\":\\n self.optimizer = torch.optim.Adam(self.paramlst, lr=self.lr)\\n elif self.optimizer_type == \\\"sgd\\\":\\n self.optimizer = torch.optim.SGD(self.paramlst, lr=self.lr)\\n else:\\n raise NotImplementedError(f\\\"{self.optimizer_type} is not implemented as a reward optimizer and must be \\\"\\n f\\\"one of 'adam' or 'sgd'.\\\")\\n\\n def format_state(self, obs: np.ndarray, batch_states: bool = False, by_trajectory: bool = False):\\n \\\"\\\"\\\"\\n Args:\\n obs: the state observations\\n batch_states: whether a batch of state is to be processed\\n by_trajectory: whether the batch of states is structured by trajectory -> should only be\\n True when batch_sa=True\\n Returns:\\n the state-action pairs as a single array\\n \\\"\\\"\\\"\\n if self.image_observations:\\n # check if the images needs to be converted to grayscale\\n if self.grayscale_images:\\n obs = _to_grayscale(obs, batch_states=batch_states)\\n if batch_states:\\n # permute the input so that the channels are in the first dimension\\n if by_trajectory:\\n obs = np.transpose(obs, (0, 1, 4, 2, 3))\\n else:\\n print(obs.shape)\\n obs = np.transpose(obs, (0, 3, 1, 2))\\n return obs\\n else:\\n # permute the input so that the channels are in the first dimension\\n obs = np.transpose(obs, (2, 0, 1))\\n # add a dimension along the front for concatenation into the buffer\\n return obs.reshape(1, *obs.shape)\\n else:\\n return obs.reshape(1, obs.shape[1:]) if batch_states else obs.reshape(1, obs.shape[0])\\n\\n def format_state_action(self, obs: np.ndarray, act: np.ndarray,\\n batch_sa: bool = False, by_trajectory: bool = False) -> np.ndarray:\\n \\\"\\\"\\\"\\n Args:\\n obs: the state observations\\n act: the actions associated with each state observation\\n batch_sa: whether a batch of state-action pairs is to be processed\\n by_trajectory: whether the batch of state-action pairs is structured by trajectory -> should only be\\n True when batch_sa=True\\n Returns:\\n the state-action pairs as a single array\\n \\\"\\\"\\\"\\n if self.image_observations:\\n # check if the images needs to be converted to grayscale\\n if self.grayscale_images:\\n obs = _to_grayscale(obs, batch_states=batch_sa)\\n if batch_sa:\\n obs_dim = obs.shape[1:]\\n # we concatenate the actions along channel dimension of the image\\n if by_trajectory:\\n repeated_actions = np.tile(act.reshape((act.shape[0], act.shape[1], 1, 1, act.shape[-1])),\\n (1, 1, obs_dim[0], obs_dim[1], 1))\\n else:\\n repeated_actions = np.tile(act.reshape((act.shape[0], 1, 1, act.shape[-1])),\\n (1, obs_dim[0], obs_dim[1], 1))\\n # now concatenate the two\\n sa_t = np.concatenate((obs, repeated_actions), axis=-1)\\n # permute the input so that the channels are in the first dimension\\n if by_trajectory:\\n sa_t = np.transpose(sa_t, (0, 1, 4, 2, 3))\\n else:\\n sa_t = np.transpose(sa_t, (0, 3, 1, 2))\\n return sa_t\\n else:\\n obs_dim = obs.shape\\n # we concatenate the actions along channel dimension of the image\\n repeated_actions = np.tile(act.reshape((1, 1, -1)), (obs_dim[0], obs_dim[1], 1))\\n # now concatenate the two\\n sa_t = np.concatenate((obs, repeated_actions), axis=-1)\\n # permute the input so that the channels are in the first dimension\\n sa_t = np.transpose(sa_t, (2, 0, 1))\\n # add a dimension along the front for concatenation into the buffer\\n return sa_t.reshape(1, *self.in_dim)\\n else:\\n sa_t = np.concatenate([obs, act], axis=-1)\\n if batch_sa:\\n return sa_t\\n else:\\n return sa_t.reshape(1, -1)\\n\\n def p_hat_member(self, x_1: np.ndarray, x_2: np.ndarray, member: int = -1):\\n # softmaxing to get the probabilities according to eqn 1\\n with torch.no_grad():\\n # if we are using image observations, we need to collapse along the batch and time dimensions to push\\n # a forward pass through the network\\n # to compute the probabilities when then need to re-construct the batch and time dimensions\\n if self.image_observations:\\n # we need to compute the probabilities in batches to avoid out of memory issues\\n # we use the train batch size as it should be an amount safe to put on the GPU's memory without causing\\n # issues\\n mb_size = self.train_batch_size\\n start_indx = 0\\n r_hat1 = None\\n r_hat2 = None\\n while start_indx < x_1.shape[0]:\\n # check if there is a mb_size worth of trajectories to still be processed\\n if start_indx + mb_size <= x_1.shape[0]:\\n mb_x_1 = x_1[start_indx:start_indx + mb_size].reshape((-1, *x_1.shape[2:]))\\n mb_x_2 = x_1[start_indx:start_indx + mb_size].reshape((-1, *x_1.shape[2:]))\\n else:\\n # process the leftover trajectories in a batch smaller than mb_size\\n mb_x_1 = x_1[start_indx:].reshape((-1, *x_1.shape[2:]))\\n mb_x_2 = x_2[start_indx:].reshape((-1, *x_2.shape[2:]))\\n # process the leftover trajectories in a batch smaller than mb_size\\n mb_rhat1 = self.r_hat_member(torch.from_numpy(mb_x_1).float().to(self.device),\\n member=member).detach().cpu().reshape((mb_size, x_1.shape[1], 1))\\n mb_rhat2 = self.r_hat_member(torch.from_numpy(mb_x_2).float().to(self.device),\\n member=member).detach().cpu().reshape((mb_size, x_2.shape[1], 1))\\n start_indx += mb_size\\n\\n # accumulate the rhats\\n if r_hat1 is None:\\n r_hat1 = mb_rhat1\\n r_hat2 = mb_rhat2\\n else:\\n r_hat1 = torch.concat((r_hat1, mb_rhat1), dim=0)\\n r_hat2 = torch.concat((r_hat2, mb_rhat2))\\n\\n else:\\n r_hat1 = self.r_hat_member(x_1, member=member).cpu()\\n r_hat2 = self.r_hat_member(x_2, member=member).cpu()\\n r_hat1 = r_hat1.sum(axis=1)\\n r_hat2 = r_hat2.sum(axis=1)\\n r_hat = torch.cat([r_hat1, r_hat2], axis=-1)\\n # taking 0 index for probability x_1 > x_2\\n return F.softmax(r_hat, dim=-1)[:, 0]\\n\\n def p_hat_entropy(self, x_1: np.ndarray, x_2: np.ndarray, member: int = -1):\\n # softmaxing to get the probabilities according to eqn 1\\n with torch.no_grad():\\n r_hat1 = self.r_hat_member(x_1, member=member)\\n r_hat2 = self.r_hat_member(x_2, member=member)\\n r_hat1 = r_hat1.sum(axis=1)\\n r_hat2 = r_hat2.sum(axis=1)\\n r_hat = torch.cat([r_hat1, r_hat2], axis=-1)\\n\\n ent = F.softmax(r_hat, dim=-1) * F.log_softmax(r_hat, dim=-1)\\n ent = ent.sum(axis=-1).abs()\\n return ent\\n\\n def r_hat_member(self, x: torch.Tensor, member: int = -1) -> torch.Tensor:\\n # the network parameterizes r hat in eqn 1 from the paper\\n # return self.ensemble[member](torch.from_numpy(x).float().to(device))\\n return self.ensemble[member](x)\\n\\n def r_hat(self, x: np.ndarray):\\n # they say they average the rewards from each member of the ensemble, but I think this only makes sense if the\\n # rewards are already normalized and I don't understand how the normalization should be happening right now :(\\n r_hats = []\\n for member in range(self.ensemble_size):\\n r_hats.append(self.r_hat_member(torch.from_numpy(x).float().to(self.device), member=member).detach().cpu().numpy())\\n r_hats = np.array(r_hats)\\n return np.mean(r_hats)\\n\\n def r_hat_batch(self, x: np.ndarray):\\n # they say they average the rewards from each member of the ensemble, but I think this only makes sense if the rewards are already normalized\\n # but I don't understand how the normalization should be happening right now :(\\n r_hats = []\\n for member in range(self.ensemble_size):\\n r_hats.append(self.r_hat_member(torch.from_numpy(x).float().to(self.device), member=member).detach().cpu().numpy())\\n r_hats = np.array(r_hats)\\n return np.mean(r_hats, axis=0)\\n\\n def save(self, model_dir: str, env_id: str, step: int):\\n \\\"\\\"\\\"\\n Save the reward ensemble to disk\\n\\n Args:\\n model_dir: path where the ensemble is to be saved\\n env_id: the environment on which the ensemble has been trained\\n step: the number of policy training steps\\n \\\"\\\"\\\"\\n for member in range(self.ensemble_size):\\n torch.save(\\n self.ensemble[member].state_dict(), f'{model_dir}/{env_id}_reward_model_{step}_{member}.pt'\\n )\\n\\n def train_reward(self,\\n preference_data_loader: PreferenceTripletEnsembleDataLoader,\\n num_epoch: int):\\n \\\"\\\"\\\"\\n Train the reward model on the given preference dataset.\\n\\n Args:\\n preference_data_loader: loads batches of preference triplets. Separated handles different preference\\n dataset permutations for each member of the reward's ensemble.\\n num_epoch: the number of training epochs to execute\\n \\\"\\\"\\\"\\n # track the accuracy and loss by ensemble member per epoch\\n ensemble_accuracies = np.zeros((num_epoch, self.ensemble_size))\\n ensemble_losses = np.zeros((num_epoch, self.ensemble_size))\\n\\n # train the reward model for the specified number of epochs\\n for epoch in range(num_epoch):\\n if epoch % 10 == 0:\\n print(f\\\"Running preference training epoch {epoch} of {num_epoch}\\\")\\n epoch_ensemble_losses = np.zeros(self.ensemble_size)\\n epoch_ensemble_acc = np.zeros(self.ensemble_size)\\n # train on each batch\\n for batch_indx, batch in enumerate(preference_data_loader):\\n # confirm there is either a single batch to be shared by all networks in the reward ensemble or\\n # a batch per network in the ensemble\\n assert len(batch) == 1 or len(batch) == self.ensemble_size\\n # we need to zero out the gradients before we begin to process this batch\\n self.optimizer.zero_grad()\\n # we will need to accumulate the loss across the ensemble members\\n batch_loss = 0.0\\n for member_indx, preference_triplet_batch in enumerate(batch):\\n # the predicted reward per transition in each trajectory\\n # check if we need to collapse the batch and time dimensions into one and then reconstruct the two\\n if self.image_observations:\\n # get the rewards for each transition in the trajectories one\\n traj_one_shape = preference_triplet_batch.trajectories_one.shape\\n formatted_trajectories_one = preference_triplet_batch.trajectories_one.reshape(\\n (-1, *traj_one_shape[2:]))\\n r_hat1 = self.r_hat_member(formatted_trajectories_one,\\n member=member_indx).reshape((traj_one_shape[0],\\n traj_one_shape[1], 1))\\n # get the rewards for each transition in the trajectories two\\n traj_two_shape = preference_triplet_batch.trajectories_two.shape\\n formatted_trajectories_two = preference_triplet_batch.trajectories_two.reshape(\\n (-1, *traj_two_shape[2:]))\\n r_hat2 = self.r_hat_member(formatted_trajectories_two,\\n member=member_indx).reshape((traj_two_shape[0],\\n traj_two_shape[1], 1))\\n else:\\n r_hat1 = self.r_hat_member(preference_triplet_batch.trajectories_one,\\n member=member_indx)\\n r_hat2 = self.r_hat_member(preference_triplet_batch.trajectories_two,\\n member=member_indx)\\n # compute the return per trajectory\\n r_hat1 = r_hat1.sum(axis=1)\\n r_hat2 = r_hat2.sum(axis=1)\\n\\n r_hat = torch.cat([r_hat1, r_hat2], dim=-1)\\n\\n # compute the ensemble member's loss\\n curr_loss = self.CEloss(r_hat, preference_triplet_batch.preference_labels.squeeze())\\n # add the loss from the ensemble member to the batch loss\\n batch_loss += curr_loss\\n # track the loss for this ensemble member\\n epoch_ensemble_losses[member_indx] += curr_loss.item()\\n\\n # compute the accuracy of the ensemble member's predictions\\n _, predicted = torch.max(r_hat.data, 1)\\n correct = (predicted == preference_triplet_batch.preference_labels.squeeze()).sum().item()\\n epoch_ensemble_acc[member_indx] += correct\\n # compute the gradients\\n batch_loss.backward()\\n # apply the gradients to the model\\n self.optimizer.step()\\n # compute the ensemble accuracy for this epoch\\n ensemble_accuracies[epoch] = epoch_ensemble_acc / preference_data_loader.dataset_length()\\n # compute the mean ensemble loss for this epoch\\n ensemble_losses[epoch] = epoch_ensemble_losses / preference_data_loader.dataset_length()\\n\\n if epoch % 10 == 0:\\n print(f\\\"Epoch {epoch} mean accuracy = {np.mean(ensemble_accuracies[:epoch + 1]):.2f}\\\")\\n\\n # check the current mean accuracy, if it is greater than 0.97 then terminate training\\n if np.mean(ensemble_accuracies[epoch]) >= 0.97:\\n print(f\\\"Epoch accuracy {np.mean(ensemble_accuracies[epoch]):.2f} \\\"\\n f\\\"after {epoch} epochs triggered early stopping.\\\")\\n return ensemble_accuracies[:epoch + 1], ensemble_losses[:epoch + 1]\\n\\n print(f\\\"Epoch {num_epoch} mean accuracy = {np.mean(ensemble_accuracies):.2f}\\\")\\n\\n return ensemble_accuracies, ensemble_losses\"\n },\n {\n \"identifier\": \"PreferenceDataset\",\n \"path\": \"reed/data/preference_dataset.py\",\n \"snippet\": \"class PreferenceDataset:\\n def __init__(self, observation_dim: t.Union[t.Tuple, int], action_dim: t.Union[t.Tuple, int], capacity: int,\\n size_segment: int, out_path: Path, image_observations: bool, grayscale_images: bool,\\n collect_image_pref_dataset: bool, state_action_formatter: PreProcessInference,\\n teacher_beta: float = -1, teacher_gamma: float = 1,\\n teacher_eps_mistake: float = 0, teacher_eps_skip: float = 0, teacher_eps_equal: float = 0):\\n \\\"\\\"\\\"\\n Args:\\n observation_dim: the dimensionality of the observations\\n action_dim: the dimensionality of the actions\\n capacity: the maximum number of trajectory pairs to include in the action_dimtaset\\n size_segment: the length of the trajectory segments\\n out_path: the location where the preference action_dimtaset will be written to disk during training\\n image_observations: whether the observations given to the reward model are images\\n grayscale_images: whether the image observations should be converted to grayscale instead of color\\n collect_image_pref_dataset: whether to collect the image preference dataset separate from the observations.\\n Should NOT be set to true if the observations are images.\\n state_action_formatter: function that maps states and actions to a single input\\n teacher_beta\\n teacher_gamma: used to determine how much influence each reward has on the preference label based on\\n order within the trajectory. Used to compute the return\\n teacher_eps_mistake: the frequency with which the teacher assigns an incorrect label\\n teacher_eps_skip: the frequency with which the teacher does not assign a label\\n teacher_eps_equal: the maximum difference between trajectory returns for the two trajectories to be labelled\\n as equally preferred\\n \\\"\\\"\\\"\\n self.observation_dim = observation_dim\\n self.action_dim = action_dim\\n self.capacity = capacity\\n self.size_segment = size_segment\\n self.out_path = out_path\\n self.image_observations = image_observations\\n self.grayscale_images = grayscale_images\\n # whether to collect the preference dataset as images\\n # only needs to be set to True if we are not learning the reward function from images\\n # if we are learning the reward function from images then we have an image dataset\\n self.collect_image_pref_dataset = collect_image_pref_dataset\\n\\n # formats the state-action pairs into a single input to the reward model\\n self.state_action_formatter = state_action_formatter\\n\\n # track where each preference triplet is written to disk\\n self._preference_triplet_tracker: t.List[Path] = []\\n\\n self.buffer_index = 0\\n self.buffer_full = False\\n\\n # create the preference labeller\\n self._preference_labeller = _PreferenceLabeller(teacher_beta=teacher_beta, teacher_gamma=teacher_gamma,\\n teacher_eps_mistake=teacher_eps_mistake,\\n teacher_eps_skip=teacher_eps_skip,\\n teacher_eps_equal=teacher_eps_equal)\\n\\n # make sure the outpath where the trajectories will be written exist\\n self.out_path.mkdir(parents=True, exist_ok=True)\\n\\n def __len__(self):\\n return len(self._preference_triplet_tracker)\\n\\n def __getitem__(self, item: int) -> PREFERENCE_TRIPLET:\\n \\\"\\\"\\\"\\n Load and return the preference triplet at the specified index in the buffer\\n\\n Args:\\n item: index of the triplet in the buffer\\n Returns:\\n trajectory one\\n trajectory two\\n preference label\\n \\\"\\\"\\\"\\n # get the location of the specified preference triplet and load it into memory\\n npz_archive = np.load(self._preference_triplet_tracker[item].as_posix())\\n\\n # grab the trajectories and preference labels\\n trajectory_one = npz_archive[\\\"trajectory_one\\\"]\\n trajectory_two = npz_archive[\\\"trajectory_two\\\"]\\n preference_label = npz_archive[\\\"preference_label\\\"]\\n\\n return trajectory_one, trajectory_two, preference_label\\n\\n def get_batch(self, indices: t.List[int]) -> PREFERENCE_TRIPLET_BATCH:\\n \\\"\\\"\\\"\\n Load and return the batch of preference triplets at the given indices in the buffer\\n\\n Args:\\n indices: the buffer indices of the preference triplets to load into memory\\n Returns:\\n batch of trajectories one\\n batch of trajectories two\\n batch of preference labels\\n \\\"\\\"\\\"\\n # accumulate the trajectory pairs and preference labels\\n trajectories_one = []\\n trajectories_two = []\\n preference_labels = []\\n # grab each preference triplet\\n for index in indices:\\n trajectory_one, trajectory_two, preference_label = self[index]\\n trajectories_one.append(np.expand_dims(trajectory_one, axis=0))\\n trajectories_two.append(np.expand_dims(trajectory_two, axis=0))\\n preference_labels.append(preference_label)\\n\\n return (np.concatenate(trajectories_one, axis=0), np.concatenate(trajectories_two, axis=0),\\n np.concatenate(preference_labels, axis=0))\\n\\n def _sample_trajectory_segments_uniform(self,\\n experience_buffer: TrajectoryReplayBuffer,\\n trajectory_count: int,\\n mini_batch_size: int) -> t.Tuple[np.ndarray, np.ndarray, t.Optional[np.ndarray]]:\\n \\\"\\\"\\\"\\n Uniformly sample trajectories and then uniformly sample a segment of the trajectory.\\n\\n Format and track the state-action pairs from each trajectory segment\\n Format and track rewards from each trajectory segment\\n\\n Combine the formatted state-action pairs and the rewards across trajectory segments\\n\\n Args:\\n experience_buffer: the replay buffer from which trajectory pairs will be drawn\\n trajectory_count: the number of trajectories to be sampled from\\n mini_batch_size: the number of trajectories to sample\\n\\n Returns:\\n the formatted state-action pairs from random trajectory segments from trajectories\\n the rewards from each random trajectory segment\\n (optionally) the image observations from each random trajectory segment - only returned when the flag to\\n collect image observations in the preference dataset is true and image observations are not\\n used to train the reward model\\n \\\"\\\"\\\"\\n # select the trajectories to be included in this batch of trajectory segments\\n trajectory_indices = np.random.choice(trajectory_count, size=mini_batch_size, replace=True)\\n\\n # accumulate the formatted state-action pairs and rewards from each trajectory segment\\n state_action_pairs = []\\n rewards = []\\n # optionally accumulate image observations\\n image_observations = ([] if self.collect_image_pref_dataset and not self.image_observations else None)\\n # extract each trajectory and randomly sample a segment\\n for traj_index in trajectory_indices:\\n # grab the trajectory\\n trajectory = experience_buffer.trajectories[traj_index]\\n # select a random segment from the trajectory\\n traj_segment = trajectory.random_segment(length=self.size_segment)\\n # track the rewards associated with the random segment\\n rewards.append(np.expand_dims(traj_segment.env_rewards, axis=0))\\n # format the state and action based on whether image observations are being used\\n if self.image_observations:\\n formatted_pair = self.state_action_formatter.format_state_action(\\n traj_segment.initial_image_observations,\\n traj_segment.actions,\\n batch_sa=True)\\n else:\\n formatted_pair = self.state_action_formatter.format_state_action(\\n traj_segment.initial_observations,\\n traj_segment.actions,\\n batch_sa=True)\\n if self.collect_image_pref_dataset:\\n image_observations.append(np.expand_dims(traj_segment.initial_image_observations, axis=0))\\n # add a dimension in the front so we can concatenate later and the track\\n state_action_pairs.append(np.expand_dims(formatted_pair, axis=0))\\n return (np.concatenate(state_action_pairs, axis=0),\\n np.concatenate(rewards, axis=0),\\n (np.concatenate(image_observations, axis=0) if image_observations is not None else None))\\n\\n @staticmethod\\n def get_rank_probability(trajectories_one: np.ndarray, trajectories_two: np.ndarray,\\n reward_model: torch.nn.Module):\\n \\\"\\\"\\\"\\n Compute the preference-prediction disagreement between the ensemble members for each trajectory pair\\n\\n Args:\\n trajectories_one: the trajectories one to be evaluated for ensemble disagreement\\n trajectories_two: the trajectories two to be evaluated for ensemble disagreement\\n reward_model: the ensemble of networks that will be used to compute disagreement\\n \\\"\\\"\\\"\\n\\n # get probability x_1 > x_2\\n probs = []\\n for member in range(len(reward_model.ensemble)):\\n probs.append(reward_model.p_hat_member(trajectories_one,\\n trajectories_two,\\n member=member).cpu().numpy())\\n probs = np.array(probs)\\n\\n return np.mean(probs, axis=0), np.std(probs, axis=0)\\n\\n def get_queries(self, experience_buffer: TrajectoryReplayBuffer, mb_size=20):\\n len_traj, max_len = experience_buffer.trajectory_lengths[0], experience_buffer.trajectory_count\\n\\n # if len(self.experience_buffer.trajectory_lengths[0][-1]) < len_traj:\\n # check that the last trajectory contains at least as many transitions as the target segment length\\n # we check the last trajectory, because it may be incomplete\\n # this is a carry over from the original code. The authors had an assumption that all \\\"completed\\\" trajectories\\n # will be at least as long as the target segment length\\n if experience_buffer.trajectory_lengths[-1] < self.size_segment:\\n max_len = max_len - 1\\n\\n # grab each trajectory, select a random segment from each, format the state-action pairs, and concatenate\\n # along the batch dimension\\n state_action_pair_traj_one, r_t_1, images_traj_one = self._sample_trajectory_segments_uniform(\\n experience_buffer=experience_buffer,\\n trajectory_count=max_len,\\n mini_batch_size=mb_size)\\n state_action_pair_traj_two, r_t_2, images_traj_two = self._sample_trajectory_segments_uniform(\\n experience_buffer=experience_buffer,\\n trajectory_count=max_len,\\n mini_batch_size=mb_size)\\n # confirm the image-specific variables are only populated when they should be\\n if not self.collect_image_pref_dataset and self.image_observations:\\n assert images_traj_one is None and images_traj_two is None\\n return state_action_pair_traj_one, state_action_pair_traj_two, r_t_1, r_t_2, images_traj_one, images_traj_two\\n\\n def put_queries(self, state_action_pair_traj_one: np.ndarray, state_action_pair_traj_two: np.ndarray,\\n preference_labels: np.ndarray,\\n images_traj_one: t.Optional[np.ndarray] = None, images_traj_two: t.Optional[np.ndarray] = None):\\n \\\"\\\"\\\"\\n Args:\\n state_action_pair_traj_one: the state-action pairs that make up the trajectories one in the queries\\n state_action_pair_traj_two: the state-action pairs that make up the trajectories two in the queries\\n preference_labels: the preference labels for each pair of trajectories\\n images_traj_one: the images for trajectories one\\n images_traj_two: the images for trajectories two\\n \\\"\\\"\\\"\\n # get the number of triplets to be stored\\n total_sample = state_action_pair_traj_one.shape[0]\\n # write each preference_triplet to disk\\n for batch_indx in range(total_sample):\\n # get the index of the triplet in the \\\"buffer\\\"\\n preference_triplet_index = self.buffer_index + batch_indx\\n # check if we need to wrap the buffer\\n if preference_triplet_index >= self.capacity:\\n preference_triplet_index -= self.capacity\\n elif not self.buffer_full:\\n # this is a previously unseen preference triplet buffer index, so we need to track the triplet location\\n self._preference_triplet_tracker.append(self.out_path / f\\\"preference_triplet_{preference_triplet_index}.npz\\\")\\n # save the preference triplet\\n np.savez((self.out_path / f\\\"preference_triplet_{preference_triplet_index}.npz\\\").as_posix(),\\n trajectory_one=state_action_pair_traj_one[batch_indx],\\n trajectory_two=state_action_pair_traj_two[batch_indx],\\n preference_label=preference_labels[batch_indx],\\n image_trajectory_one=(\\n None if images_traj_one is None else images_traj_one[batch_indx]),\\n image_trajectory_two=(\\n None if images_traj_two is None else images_traj_two[batch_indx]))\\n # set the new buffer index\\n next_index = self.buffer_index + total_sample\\n # check if the buffer has wrapped\\n if next_index >= self.capacity:\\n self.buffer_full = True\\n # wrap the buffer index\\n self.buffer_index = next_index - self.capacity\\n else:\\n self.buffer_index = next_index\\n\\n def uniform_sampling(self, experience_buffer: TrajectoryReplayBuffer, mb_size: int) -> int:\\n \\\"\\\"\\\"\\n Grow the preference dataset with preference triplets uniformly sampled from the experience buffer\\n\\n Args:\\n experience_buffer: the replay buffer from which to sample trajectory pairs\\n mb_size: target number of preference triplets to add to the preference dataset. Fewer than the target may\\n be added depending on the whether labeller skips labelling some trajectories.\\n Returns:\\n number of preference triplets added to the dataset\\n \\\"\\\"\\\"\\n # get queries\\n sa_t_1, sa_t_2, r_t_1, r_t_2, img_sa_t_1, img_sa_t_2 = self.get_queries(experience_buffer=experience_buffer,\\n mb_size=mb_size)\\n\\n # get labels\\n sa_t_1, sa_t_2, r_t_1, r_t_2, labels = self._preference_labeller.get_label(sa_t_1, sa_t_2, r_t_1, r_t_2)\\n if len(labels) > 0:\\n self.put_queries(sa_t_1, sa_t_2, labels, img_sa_t_1, img_sa_t_2)\\n\\n return len(labels)\\n\\n # TODO: refactor to break the circular import that would need to happen in order to specify that reward_model here\\n # should be BPref.reward_model.RewardModel\\n def disagreement_sampling(self, experience_buffer: TrajectoryReplayBuffer, mb_size: int, large_batch: int,\\n reward_model: torch.nn.Module) -> int:\\n \\\"\\\"\\\"\\n Grow the preference dataset with preference triplets from the experience buffer that the reward ensemble\\n disagrees about\\n\\n Args:\\n experience_buffer: the replay buffer from which to sample trajectory pairs\\n mb_size: target number of preference triplets to add to the preference dataset. Fewer than the target may\\n be added depending on the whether labeller skips labelling some trajectories.\\n large_batch: scales up the number of triplets to add to the preference dataset to uniformly select a large\\n number of trajectory pairs, which are then pruned based on which ones the reward ensemble\\n has the most disagreement over\\n reward_model: the ensemble of reward networks that will be used to assess disagreement.\\n Should be BPref.reward_model.RewardModel, but cannot import and reference from here right now\\n as it would lead to circular imports\\n Returns:\\n number of preference triplets added to the dataset\\n \\\"\\\"\\\"\\n # get queries\\n sa_t_1, sa_t_2, r_t_1, r_t_2, img_sa_t_1, img_sa_t_2 = self.get_queries(\\n experience_buffer=experience_buffer, mb_size=mb_size * large_batch)\\n\\n # get final queries based on ensemble member disagreement\\n _, disagree = self.get_rank_probability(sa_t_1, sa_t_2, reward_model=reward_model)\\n top_k_index = (-disagree).argsort()[:mb_size]\\n r_t_1, sa_t_1 = r_t_1[top_k_index], sa_t_1[top_k_index]\\n r_t_2, sa_t_2 = r_t_2[top_k_index], sa_t_2[top_k_index]\\n if img_sa_t_1 is not None:\\n img_sa_t_1 = img_sa_t_1[top_k_index]\\n img_sa_t_2 = img_sa_t_2[top_k_index]\\n\\n # get labels\\n sa_t_1, sa_t_2, r_t_1, r_t_2, labels = self._preference_labeller.get_label(\\n sa_t_1, sa_t_2, r_t_1, r_t_2)\\n if len(labels) > 0:\\n self.put_queries(sa_t_1, sa_t_2, labels, img_sa_t_1, img_sa_t_2)\\n\\n return len(labels)\\n\\n def set_teacher_thres_skip(self, new_margin):\\n self._preference_labeller.teacher_thres_skip = new_margin * self._preference_labeller.teacher_eps_skip\\n\\n def set_teacher_thres_equal(self, new_margin):\\n self._preference_labeller.teacher_eps_equal = new_margin * self._preference_labeller.teacher_eps_equal\\n\\n def save(self, dataset_dir: Path, env_id: str, step: int):\\n \\\"\\\"\\\"\\n Saves the preference dataset as a zip archive and the labeller configuration as a yaml to the specified location\\n\\n Args:\\n dataset_dir: path where the dataset is to be saved\\n env_id: the environment/task within which the data was generated\\n step: the number of policy training steps taken to produce this dataset\\n \\\"\\\"\\\"\\n # create the ZipFile object\\n zip_obj = ZipFile(dataset_dir / f\\\"{env_id}_preference_dataset_{step}.zip\\\", \\\"w\\\")\\n # the configuration for the online preference dataset\\n config = {\\\"teacher_params\\\": {\\\"teacher_beta\\\": self._preference_labeller.teacher_beta,\\n \\\"teacher_gamma\\\": self._preference_labeller.teacher_gamma,\\n \\\"teacher_eps_mistake\\\": self._preference_labeller.teacher_eps_mistake,\\n \\\"teacher_eps_equal\\\": self._preference_labeller.teacher_eps_equal,\\n \\\"teacher_eps_skip\\\": self._preference_labeller.teacher_eps_skip,\\n \\\"teacher_thres_skip\\\": self._preference_labeller.teacher_thres_skip,\\n \\\"teacher_thres_equal\\\": self._preference_labeller.teacher_thres_equal,\\n \\\"label_margin\\\": self._preference_labeller.label_margin,\\n \\\"label_target\\\": self._preference_labeller.label_target}}\\n with open((dataset_dir / f\\\"preference_dataset_config.yaml\\\").as_posix(), \\\"w+\\\") as f:\\n yaml.dump(config, f)\\n # write the labeller config to the preference dataset's zip archive\\n zip_obj.write(dataset_dir / f\\\"preference_dataset_config.yaml\\\")\\n\\n # add each preference triplet to the zip archive\\n for pref_triplet_path in self._preference_triplet_tracker:\\n zip_obj.write(pref_triplet_path)\\n # move the file from it temp location to the artifact directory\\n file_dest_path = dataset_dir / pref_triplet_path.name\\n shutil.move(pref_triplet_path, file_dest_path)\\n # close the Zip File\\n zip_obj.close()\"\n },\n {\n \"identifier\": \"PreferenceTripletEnsembleDataLoader\",\n \"path\": \"reed/data/preference_data_loader.py\",\n \"snippet\": \"class PreferenceTripletEnsembleDataLoader:\\n \\\"\\\"\\\"\\n Handles loading and generating batches of preference triplets.\\n\\n The special logic needed is to handle different batch orderings for different networks in the reward ensemble\\n \\\"\\\"\\\"\\n def __init__(self, dataset: PreferenceDataset, ensemble_size: int,\\n batch_size: int = 64, num_workers: int = 0, shuffle: bool = True, device: torch.device = \\\"cuda\\\"):\\n \\\"\\\"\\\"\\n Args:\\n\\n \\\"\\\"\\\"\\n # create a data loader per ensemble network\\n self.loader_ensemble = [DataLoader(dataset=dataset,\\n batch_size=batch_size,\\n shuffle=shuffle,\\n num_workers=num_workers)\\n for _ in range(ensemble_size)]\\n\\n self.device = device\\n\\n def _format_batch(self, batch: UNFORMATTED_PREFERENCE_TRIPLET_BATCH) -> FORMATTED_PREFERENCE_TRIPLET_BATCH:\\n \\\"\\\"\\\"\\n Format the preference batch so that the tensors are longs and on the correct device\\n \\\"\\\"\\\"\\n return [PreferenceTripletBatch(trajectories_one=member[0].float().to(self.device),\\n trajectories_two=member[1].float().to(self.device),\\n preference_labels=member[2].long().to(self.device))\\n for member in batch]\\n\\n def dataset_length(self) -> int:\\n return len(self.loader_ensemble[0].dataset)\\n\\n def __iter__(self) -> FORMATTED_PREFERENCE_TRIPLET_BATCH:\\n \\\"\\\"\\\"\\n Iterate through the preference triplet data loaders and return the batch per ensemble member\\n\\n Returns:\\n list of PreferenceTripletBatch\\n \\\"\\\"\\\"\\n # set up each loader as an iterator\\n iter_loader_ensemble = [iter(loader) for loader in self.loader_ensemble]\\n # for each data loader grab the next batch until there are no more batches to grab\\n while True:\\n # check if there is a next batch to return\\n try:\\n yield self._format_batch([next(dataloader_iterator) for dataloader_iterator in iter_loader_ensemble])\\n except StopIteration:\\n break\"\n },\n {\n \"identifier\": \"PreProcessInference\",\n \"path\": \"reed/data/preprocess_images.py\",\n \"snippet\": \"class PreProcessInference:\\n \\\"\\\"\\\"\\n Preprocess the data for inference by the reward, SSC, and SFC models\\n \\\"\\\"\\\"\\n def __init__(self,\\n image_observations: bool = False,\\n grayscale_images: bool = True,\\n normalize_images: bool = True,\\n environment_id: str = \\\"dmc\\\"):\\n \\\"\\\"\\\"\\n Args:\\n image_observations: whether the observations are images\\n grayscale_images: whether images observations should be in grayscale\\n normalize_images: whether the image observations should be normalized\\n environment_id: the environment from which the data is coming\\n \\\"\\\"\\\"\\n self.image_observations = image_observations\\n self.grayscale_images = grayscale_images\\n self.normalize_images = normalize_images\\n self.environment_id = environment_id\\n\\n @staticmethod\\n def _channel_first_to_last(observation: np.ndarray,\\n batch_states: bool = False,\\n by_trajectory: bool = False) -> np.ndarray:\\n \\\"\\\"\\\"\\n Move the channel from the first dimension to the last dimension\\n \\\"\\\"\\\"\\n if batch_states and by_trajectory:\\n return np.transpose(observation, (0, 1, 3, 4, 2))\\n elif batch_states:\\n return np.transpose(observation, (0, 2, 3, 1))\\n else:\\n return np.transpose(observation, (1, 2, 0))\\n\\n @staticmethod\\n def _channel_last_to_first(observation: np.ndarray, batch_states: bool = False,\\n by_trajectory: bool = False) -> np.ndarray:\\n \\\"\\\"\\\"\\n Move the channel from the last dimension to the first dimension\\n Args:\\n observation: the state observations\\n batch_states: whether a batch of state is to be processed\\n by_trajectory: whether the batch of states is structured by trajectory -> should only be\\n True when batch_sa=True\\n Returns:\\n the image with the channel dimension moved from first to last\\n \\\"\\\"\\\"\\n # permute the input so that the channels are in the first dimension of the images\\n if batch_states and by_trajectory:\\n return np.transpose(observation, (0, 1, 4, 2, 3))\\n elif batch_states:\\n return np.transpose(observation, (0, 3, 1, 2))\\n else:\\n # permute the input so that the channels are in the first dimension\\n obs = np.transpose(observation, (2, 0, 1))\\n # add a dimension along the front for concatenation into the buffer\\n return np.expand_dims(obs, axis=0)\\n\\n def format_state(self, obs: np.ndarray, batch_states: bool = False,\\n by_trajectory: bool = False, channel_first: bool = False) -> np.ndarray:\\n \\\"\\\"\\\"\\n Args:\\n obs: the state observations\\n batch_states: whether a batch of state is to be processed\\n by_trajectory: whether the batch of states is structured by trajectory -> should only be\\n True when batch_sa=True\\n channel_first: whether the channel dimension is first when the observations are images.\\n Returns:\\n the state-action pairs as a single array\\n \\\"\\\"\\\"\\n if self.image_observations:\\n if channel_first:\\n # move the channel dimension from first to last to avoid a bunch of logic in our formatting methods\\n # that handles variable locations for the channel dimension\\n obs = self._channel_first_to_last(observation=obs,\\n batch_states=batch_states,\\n by_trajectory=by_trajectory)\\n if self.grayscale_images:\\n obs = _to_grayscale(observation=obs)\\n if self.normalize_images:\\n # TODO: add normalization based on pixel mean and standard deviation instead of scaling 0 to 1\\n obs = np.divide(obs, 255.)\\n # move the channel dimension from first to last\\n return self._channel_last_to_first(observation=obs, batch_states=batch_states, by_trajectory=by_trajectory)\\n\\n else:\\n return obs.reshape(1, obs.shape[1:]) if batch_states else obs.reshape(1, obs.shape[0])\\n\\n def format_state_action(self, obs: np.ndarray, act: np.ndarray,\\n batch_sa: bool = False, by_trajectory: bool = False,\\n channel_first: bool = False) -> np.ndarray:\\n \\\"\\\"\\\"\\n Args:\\n obs: the state observations\\n act: the actions associated with each state observation\\n batch_sa: whether a batch of state-action pairs is to be processed\\n by_trajectory: whether the batch of state-action pairs is structured by trajectory -> should only be\\n True when batch_sa=True\\n channel_first: whether the channel dimension is first when the observations are images.\\n Returns:\\n the state-action pairs as a single array\\n \\\"\\\"\\\"\\n if self.image_observations:\\n if channel_first:\\n # move the channel dimension from first to last to avoid a bunch of logic in our formatting methods\\n # that handles variable locations for the channel dimension\\n obs = self._channel_first_to_last(observation=obs,\\n batch_states=batch_sa,\\n by_trajectory=by_trajectory)\\n if self.grayscale_images:\\n obs = _to_grayscale(observation=obs)\\n if self.normalize_images:\\n # TODO: add normalization based on pixel mean and standard deviation instead of scaling 0 to 1\\n obs = np.divide(obs, 255.)\\n\\n # get the dimensions of the image\\n obs_dim = obs.shape[-3:]\\n assert len(obs_dim) == 3\\n # add the actions to the image channels and permute the input so that the channels are in the first\\n # dimension of the images\\n if batch_sa and by_trajectory:\\n repeated_actions = np.tile(act.reshape((act.shape[0], act.shape[1], 1, 1, act.shape[-1])),\\n (1, 1, obs_dim[0], obs_dim[1], 1))\\n elif batch_sa:\\n repeated_actions = np.tile(act.reshape((act.shape[0], 1, 1, act.shape[-1])),\\n (1, obs_dim[0], obs_dim[1], 1))\\n else:\\n repeated_actions = np.tile(act.reshape((1, 1, -1)), (obs_dim[0], obs_dim[1], 1))\\n sa_t = np.concatenate((obs, repeated_actions), axis=-1)\\n return self._channel_last_to_first(sa_t, batch_states=batch_sa, by_trajectory=by_trajectory)\\n else:\\n sa_t = np.concatenate([obs, act], axis=-1)\\n if batch_sa:\\n return sa_t\\n else:\\n return sa_t.reshape(1, -1)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import typing as t\nimport time\nimport numpy as np\nimport torch\nimport hydra\nfrom pathlib import Path\nfrom omegaconf import dictconfig, OmegaConf\nfrom BPref import utils\nfrom BPref.logger import Logger\nfrom BPref.replay_buffer import TrajectoryReplayBuffer\nfrom collections import deque\nfrom reed.models.reward_model import StateActionRewardModel\nfrom reed.data.preference_dataset import PreferenceDataset\nfrom reed.data.preference_data_loader import PreferenceTripletEnsembleDataLoader\nfrom reed.data.preprocess_images import PreProcessInference"},"token_num":{"kind":"number","value":20852,"string":"20,852"},"cropped_code":{"kind":"string","value":" Determine the dimensionality of the inputs to the reward model\n\n Args:\n observation_dim: the dimensionality of agent observations. If the observation is an image, the\n dimensionality should have the following order: (num_channels, height, width)\n action_dim: the dimensionality of agent actions\n Returns:\n the dimensionality of the reward model's inputs\n \"\"\"\n # compute the dimensions of the input to the reward function\n if not self.experiment_config.reward_from_image_observations:\n return observation_dim + action_dim\n else:\n # we need to concatenate the actions to last dimension of the image\n # the input to the reward net also needs to have the channels first\n # the image dimensions are given to us a (height, width, channels)\n sample_shape = list(observation_dim)\n if self.experiment_config.grayscale_images:\n num_channels = action_dim + 1\n else:\n num_channels = sample_shape[0] + action_dim\n # update the number of channels\n sample_shape[0] = num_channels\n # the dimensions of the input to the reward model\n return sample_shape\n\n def _determine_reward_observation_dimensions(self) -> t.Union[int, np.ndarray]:\n \"\"\"\n Check if the reward will use the image observations.\n\n If so the reward input shape needs to be set accordingly\n\n Returns:\n the dimensionality of reward's observation space\n \"\"\"\n if self.experiment_config.reward_from_image_observations:\n # get a sample image rendering of the environment and get its shape\n self.env.reset()\n if \"metaworld\" in self.experiment_config.env:\n start_time = time.time()\n img_obs = self.env.render(camera_name=self.experiment_config.camera_name,\n resolution=(\n self.experiment_config.image_height, self.experiment_config.image_width))\n end_time = time.time()\n print(f\"Sample render time for metaworld is {end_time - start_time} seconds\")\n else:\n start_time = time.time()\n img_obs = self.env.render(mode=\"rgb_array\",\n height=self.experiment_config.image_height,\n width=self.experiment_config.image_width)\n end_time = time.time()\n print(f\"Sample render time for DMC is {end_time - start_time} seconds\")\n formatted_image_observation = self._reward_input_preprocessor.format_state(img_obs).squeeze(axis=0)\n observation_space = formatted_image_observation.shape\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"image observation shape\", observation_space)\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"--------------------------\")\n else:\n observation_space = self.env.observation_space.shape[0]\n\n return observation_space\n\n def _determine_observation_dimensions(self) -> t.Union[int, np.ndarray]:\n \"\"\"\n Check if the reward will use the image observations.\n\n If so the replay buffer needs to be set up to\n accumulate the image observations\n\n Returns:\n the dimensionality of reward's observation space\n \"\"\"\n if self.experiment_config.reward_from_image_observations:\n # get a sample image rendering of the environment and get its shape\n self.env.reset()\n if \"metaworld\" in self.experiment_config.env:\n start_time = time.time()\n img_obs = self.env.render(camera_name=self.experiment_config.camera_name,\n resolution=(\n self.experiment_config.image_height, self.experiment_config.image_width))\n end_time = time.time()\n print(f\"Sample render time for metaworld is {end_time - start_time} seconds\")\n else:\n start_time = time.time()\n img_obs = self.env.render(mode=\"rgb_array\",\n height=self.experiment_config.image_height,\n width=self.experiment_config.image_width)\n end_time = time.time()\n print(f\"Sample render time for DMC is {end_time - start_time} seconds\")\n observation_space = img_obs.shape\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"image observation shape\", observation_space)\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"--------------------------\")\n else:\n observation_space = self.env.observation_space.shape[0]\n\n return observation_space\n\n def _render_image_observation(self) -> np.ndarray:\n \"\"\"\n Render the current image observation\n \"\"\"\n if \"metaworld\" in self.experiment_config.env:\n img_obs = self.env.render(camera_name=self.experiment_config.camera_name,\n resolution=(\n self.experiment_config.image_height, self.experiment_config.image_width))\n else:\n img_obs = self.env.render(mode=\"rgb_array\",\n height=self.experiment_config.image_height,\n width=self.experiment_config.image_width)\n return img_obs\n\n"},"all_code":{"kind":"string","value":"#\n# For licensing see accompanying LICENSE file.\n# Copyright (C) 2023 Apple Inc. All Rights Reserved.\n#\n\n\n\n\n\n\n\nclass PEBBLE:\n \"\"\"\n Train a reward model in conjunction with policy training following the PEBBLE algorithm from (Lee et al. 2021)\n \"\"\"\n\n def __init__(self, experiment_config: dictconfig.DictConfig):\n \"\"\"\n Args:\n experiment_config: contains the configuration for the experiment to be run. Access like a dictionry\n \"\"\"\n # track the experimental configuration\n self.experiment_config = experiment_config\n\n # create the logger to track policy learning progress\n self.logger = Logger(\n self.experiment_config.out_dir,\n save_tb=self.experiment_config.log_save_tb,\n log_frequency=self.experiment_config.log_frequency,\n agent=self.experiment_config.agent.name)\n\n # used to track where we are in training\n # total amount of feedback the reward model has solicited\n self.total_feedback = 0\n # total amount of feedback given to the reward model\n self.labeled_feedback = 0\n # policy train step\n self.step = 0\n\n # we need to set the random seed for replication purposes\n utils.set_seed_everywhere(self.experiment_config.seed)\n\n # the device on which models will be trained\n self.device = torch.device(self.experiment_config.device)\n # flag to make sure we are handling multi-gpu training where we need to\n self.multi_gpu = torch.cuda.device_count() > 1\n print(\"----------------------------------------\")\n print(\"----------------------------------------\")\n print(\"----------------------------------------\")\n print(\"----------------------------------------\")\n print(f\"There is {torch.cuda.device_count()} GPU, so models will be trained with torch.nn.DataParallel.\")\n print(\"----------------------------------------\")\n print(\"----------------------------------------\")\n print(\"----------------------------------------\")\n print(\"----------------------------------------\")\n\n # make the environment\n if 'metaworld' in self.experiment_config.env:\n self.env = utils.make_metaworld_env(self.experiment_config)\n # we are not evaluating a domain where we need to log whether an agent has reached a goal state\n self.log_success = True\n else:\n self.env = utils.make_env(self.experiment_config)\n # we are not evaluating a domain where we need to log whether an agent has reached a goal state\n self.log_success = False\n print('----------------------')\n print('----------------------')\n print('----------------------')\n print('----------------------')\n print(\"observation space \", self.env.observation_space.shape[0])\n print(\"action space \", self.env.action_space.shape[0])\n print('----------------------')\n print('----------------------')\n print('----------------------')\n print('----------------------')\n # we need to set the policy's observation and action space\n self.experiment_config.agent.params.obs_dim = self.env.observation_space.shape[0]\n self.experiment_config.agent.params.action_dim = self.env.action_space.shape[0]\n self.experiment_config.agent.params.action_range = [\n float(self.env.action_space.low.min()),\n float(self.env.action_space.high.max())\n ]\n # create the agent specified in the configuration\n self.agent = hydra.utils.instantiate(self.experiment_config.agent)\n\n # the class that will format the observations and observation action pairs for consumption by the reward model\n self._reward_input_preprocessor = PreProcessInference(\n image_observations=self.experiment_config.reward_from_image_observations,\n grayscale_images=self.experiment_config.grayscale_images,\n normalize_images=self.experiment_config.normalized_images)\n\n # determine the reward's observation space\n # if the reward is trained on images then the reward's observation space differs from the policy's, which is\n # trained on the state space\n self._observation_dimensionality = self._determine_observation_dimensions()\n self._reward_observation_dimensionality = self._determine_reward_observation_dimensions()\n\n # create the agent's replay buffer setting if image observations will need to be tracked\n self.replay_buffer = TrajectoryReplayBuffer(\n int(self.experiment_config.replay_buffer_capacity),\n self.device,\n image_observations=(self._observation_dimensionality\n if (self.experiment_config.reward_from_image_observations\n or self.experiment_config.save_image_observations)\n else None)\n )\n\n # determine the dimensionality of the input to the reward function\n self.reward_in_dim = self._determine_reward_input_dimensions(\n observation_dim=self._reward_observation_dimensionality,\n action_dim=self.env.action_space.shape[0])\n\n # instantiating the reward model\n self.reward_model = self.construct_reward_ensemble()\n\n # create the preference dataset that will solicit and hold labelled preference triplets\n self.preference_dataset = PreferenceDataset(\n observation_dim=self._reward_observation_dimensionality,\n action_dim=self.env.action_space.shape[0],\n capacity=self.experiment_config.preference_dataset_capacity,\n size_segment=self.experiment_config.segment_size,\n out_path=Path(\"/tmp/preference_dataset/\"),\n image_observations=self.experiment_config.reward_from_image_observations,\n state_action_formatter=self._reward_input_preprocessor,\n grayscale_images=self.experiment_config.grayscale_images,\n collect_image_pref_dataset=self.experiment_config.save_image_observations,\n teacher_beta=self.experiment_config.teacher_beta,\n teacher_gamma=self.experiment_config.teacher_gamma,\n teacher_eps_mistake=self.experiment_config.teacher_eps_mistake,\n teacher_eps_skip=self.experiment_config.teacher_eps_skip,\n teacher_eps_equal=self.experiment_config.teacher_eps_equal\n )\n\n # save the experimental configuration\n with open(Path(self.experiment_config.out_dir) / \"experiment_config.yaml\", \"w+\") as f:\n OmegaConf.save(config=self.experiment_config, f=f)\n\n def _determine_reward_input_dimensions(self,\n observation_dim: t.Union[int, np.ndarray],\n action_dim: int) -> t.Union[int, t.Sequence]:\n \"\"\"\n Determine the dimensionality of the inputs to the reward model\n\n Args:\n observation_dim: the dimensionality of agent observations. If the observation is an image, the\n dimensionality should have the following order: (num_channels, height, width)\n action_dim: the dimensionality of agent actions\n Returns:\n the dimensionality of the reward model's inputs\n \"\"\"\n # compute the dimensions of the input to the reward function\n if not self.experiment_config.reward_from_image_observations:\n return observation_dim + action_dim\n else:\n # we need to concatenate the actions to last dimension of the image\n # the input to the reward net also needs to have the channels first\n # the image dimensions are given to us a (height, width, channels)\n sample_shape = list(observation_dim)\n if self.experiment_config.grayscale_images:\n num_channels = action_dim + 1\n else:\n num_channels = sample_shape[0] + action_dim\n # update the number of channels\n sample_shape[0] = num_channels\n # the dimensions of the input to the reward model\n return sample_shape\n\n def _determine_reward_observation_dimensions(self) -> t.Union[int, np.ndarray]:\n \"\"\"\n Check if the reward will use the image observations.\n\n If so the reward input shape needs to be set accordingly\n\n Returns:\n the dimensionality of reward's observation space\n \"\"\"\n if self.experiment_config.reward_from_image_observations:\n # get a sample image rendering of the environment and get its shape\n self.env.reset()\n if \"metaworld\" in self.experiment_config.env:\n start_time = time.time()\n img_obs = self.env.render(camera_name=self.experiment_config.camera_name,\n resolution=(\n self.experiment_config.image_height, self.experiment_config.image_width))\n end_time = time.time()\n print(f\"Sample render time for metaworld is {end_time - start_time} seconds\")\n else:\n start_time = time.time()\n img_obs = self.env.render(mode=\"rgb_array\",\n height=self.experiment_config.image_height,\n width=self.experiment_config.image_width)\n end_time = time.time()\n print(f\"Sample render time for DMC is {end_time - start_time} seconds\")\n formatted_image_observation = self._reward_input_preprocessor.format_state(img_obs).squeeze(axis=0)\n observation_space = formatted_image_observation.shape\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"image observation shape\", observation_space)\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"--------------------------\")\n else:\n observation_space = self.env.observation_space.shape[0]\n\n return observation_space\n\n def _determine_observation_dimensions(self) -> t.Union[int, np.ndarray]:\n \"\"\"\n Check if the reward will use the image observations.\n\n If so the replay buffer needs to be set up to\n accumulate the image observations\n\n Returns:\n the dimensionality of reward's observation space\n \"\"\"\n if self.experiment_config.reward_from_image_observations:\n # get a sample image rendering of the environment and get its shape\n self.env.reset()\n if \"metaworld\" in self.experiment_config.env:\n start_time = time.time()\n img_obs = self.env.render(camera_name=self.experiment_config.camera_name,\n resolution=(\n self.experiment_config.image_height, self.experiment_config.image_width))\n end_time = time.time()\n print(f\"Sample render time for metaworld is {end_time - start_time} seconds\")\n else:\n start_time = time.time()\n img_obs = self.env.render(mode=\"rgb_array\",\n height=self.experiment_config.image_height,\n width=self.experiment_config.image_width)\n end_time = time.time()\n print(f\"Sample render time for DMC is {end_time - start_time} seconds\")\n observation_space = img_obs.shape\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"image observation shape\", observation_space)\n print(\"--------------------------\")\n print(\"--------------------------\")\n print(\"--------------------------\")\n else:\n observation_space = self.env.observation_space.shape[0]\n\n return observation_space\n\n def _render_image_observation(self) -> np.ndarray:\n \"\"\"\n Render the current image observation\n \"\"\"\n if \"metaworld\" in self.experiment_config.env:\n img_obs = self.env.render(camera_name=self.experiment_config.camera_name,\n resolution=(\n self.experiment_config.image_height, self.experiment_config.image_width))\n else:\n img_obs = self.env.render(mode=\"rgb_array\",\n height=self.experiment_config.image_height,\n width=self.experiment_config.image_width)\n return img_obs\n"},"next_line":{"kind":"string","value":" def construct_reward_ensemble(self) -> StateActionRewardModel:"},"gold_snippet_index":{"kind":"number","value":3,"string":"3"},"created_at":{"kind":"string","value":"2023-11-06 23:14:20+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5282,"cells":{"repo_name":{"kind":"string","value":"allenai/unified-io-2"},"file_path":{"kind":"string","value":"t5x/train.py"},"context":{"kind":"list like","value":[{"identifier":"PackingStrategy","path":"t5x/examples/unified_io/packing.py","snippet":"class PackingStrategy:\n \"\"\"Defines how to pack data during training and handles batch-level constraints\n from the input/target encoders\"\"\"\n\n pack_max_len: Optional[Tuple[int, int]] = None\n \"\"\"If packing, max input/target length to to\"\"\"\n\n pack_pool_size: int = 10\n \"\"\"Pool to use when packing examples\"\"\"\n\n constraint_pool_size: int = 10\n \"\"\"Pool to use when matching batch constraints\"\"\"\n\n max_to_pack: int = 2\n \"\"\"Max examples to pack together\"\"\"\n\n @property\n def pack(self):\n return self.pack_max_len is not None\n\n def batch(self, ds, batch_size, drop_remainder=True, batch_constraints=None):\n if batch_constraints is None:\n batch_constraints = []\n for k, v in get_input_modalities().items():\n bound = v.get_constraints()\n if bound is not None:\n def _fn(ex):\n mask = tf.cast(ex[f\"inputs/{k}/mask\"], tf.bool)\n return tf.reduce_sum(tf.cast(tf.reduce_any(mask, -1), tf.int32))\n batch_bound = int(round(bound * batch_size))\n if self.pack_max_len is None:\n logging.info(f\"Adding batch constraint {k}/{bound} => \"\n f\"({batch_bound} per batch of {batch_size})\")\n else:\n bound *= self.max_to_pack\n logging.info(f\"Adding batch constraint {k}/{bound} => \"\n f\"({batch_bound} per batch of {batch_size} groups of {self.max_to_pack})\")\n batch_constraints.append((_fn, batch_bound))\n if self.pack:\n enc, dec = self.pack_max_len\n if self.max_to_pack == 2:\n ds = pair_examples(ds, enc, dec, self.pack_pool_size)\n else:\n raise NotImplementedError()\n if batch_constraints:\n ds = batch_with_constraints(ds, batch_size, self.constraint_pool_size, batch_constraints)\n else:\n ds = ds.batch(batch_size, drop_remainder=drop_remainder)\n return unfold(ds, batch_size, n=self.max_to_pack)\n else:\n if batch_constraints:\n return batch_with_constraints(ds, batch_size, self.constraint_pool_size, batch_constraints)\n else:\n return ds.batch(batch_size, drop_remainder=drop_remainder)"},{"identifier":"checkpoints","path":"t5x/checkpoints.py","snippet":"VERSION = 3\n_DESIRED_CHUNK_SIZE_BYTES = 64 * 1024 * 1024\n_TRAIN_DS_PREFIX = 'train_ds'\n_OPTIMIZER_KEY = 'optimizer'\n_VERSION_KEY = 'version'\n_CHECKPOINTS_SUBDIR = 'checkpoints'\n_STATE_KEY = 'state'\n_DATASET_KEY = 'dataset'\n_FLAX_CHECKPOINT_FILE = 'checkpoint'\ndef _choose_chunk_shape(write_shape: Sequence[int],\n target_elements: int) -> List[int]:\n def get_total_elements():\ndef _run_future_tree(future_tree):\ndef all_steps(checkpoints_dir: str) -> Sequence[int]:\ndef all_dataset_checkpoint_steps(checkpoints_dir: str) -> Sequence[int]:\ndef latest_step(checkpoints_dir: str) -> Optional[int]:\ndef _get_local_data(x):\ndef _sync_global_devices(name: str) -> None:\ndef get_checkpoint_dir(checkpoints_dir: str, step: int) -> str:\ndef _cast(target: PyTreeDef, dtype: jnp.dtype):\n def maybe_cast(x):\ndef _update_ts_path_from_relative_to_absolute(\n ckpt_dir: str, ts_spec_dict: MutableMapping[str, Any]):\ndef _maybe_update_ts_from_file_to_gcs(ckpt_contents):\n def _gfile_to_gcs_driver(arr_or_ts_spec_dict):\n def _is_leaf(value):\ndef _maybe_update_ts_from_gcs_to_file(ckpt_contents):\n def _gcs_to_file_driver(arr_or_ts_spec_dict):\n def _is_leaf(value):\n def __init__(self, num_bytes):\n async def wait_for_bytes(self, n_bytes):\n async def return_bytes(self, n_bytes):\n def __call__(self, state_dict: PyTreeDef,\n parameter_infos: PyTreeDef) -> Tuple[PyTreeDef, PyTreeDef]:\n def __call__(self,\n state_dict: PyTreeDef,\n target_state_dict: PyTreeDef,\n *,\n is_resuming: bool = False) -> PyTreeDef:\n def __init__(self, dataset_iterator: tf.data.Iterator):\n def save(self, filename: str):\n def load(self, filename: str):\n def __init__(self,\n train_state: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner,\n checkpoints_dir: str,\n dataset_iterator: Optional[\n Union[tf.data.Iterator,\n clu.data.dataset_iterator.DatasetIterator]] = None,\n *,\n keep: Optional[int] = None,\n save_dtype: jnp.dtype = np.float32,\n restore_dtype: Optional[jnp.dtype] = None,\n use_gda: Optional[bool] = True,\n keep_dataset_checkpoints: Optional[int] = None):\n def _get_state_dict_for_save(\n self,\n state_dict: Dict[str, Any],\n lazy_load: bool = True) -> MutableMapping[str, Any]:\n def _lazy_load_device_array(arr):\n def _get_parameter_infos(self):\n def _get_param_info(name: str, arr: Any, axes: partitioning.PartitionSpec):\n def _get_checkpoint_dir(self, step: int) -> str:\n def all_steps(self) -> Sequence[int]:\n def all_dataset_checkpoint_steps(self) -> Sequence[int]:\n def latest_step(self) -> Optional[int]:\n def _remove_old_dataset_checkpoints(self):\n def _remove_old_checkpoints(self):\n def save(self,\n train_state: train_state_lib.TrainState,\n state_transformation_fns: Sequence[SaveStateTransformationFn] = (),\n *,\n concurrent_gb: int = 128):\n def _write_state_to_tensorstore(\n self,\n ckpt_dir: str,\n train_state: train_state_lib.TrainState,\n concurrent_gb: int,\n state_transformation_fns: Sequence[SaveStateTransformationFn],\n ) -> Mapping[str, Any]:\n async def _write_array(maybe_arr: Any,\n param_info: Optional[_ParameterInfo],\n cast: bool = False):\n def _cast_arr_if_not_partitioned(maybe_arr, param_info):\n def _transform_state_and_infos(\n self,\n state_dict: PyTreeDef,\n parameter_infos: PyTreeDef,\n state_transformation_fns: Sequence[SaveStateTransformationFn],\n ) -> Tuple[PyTreeDef, PyTreeDef]:\n def restore(\n self,\n step: Optional[int] = None,\n path: Optional[str] = None,\n state_transformation_fns: Sequence[RestoreStateTransformationFn] = (),\n fallback_state: Optional[Mapping[str, Any]] = None,\n lazy_parameters: bool = False) -> train_state_lib.TrainState:\n def _restore_train_state(\n self,\n state_dict: optimizers.OptimizerStateType) -> train_state_lib.TrainState:\n def _create_lazy_awaitable_array(\n self, param_info: _ParameterInfo, maybe_ts_spec: Any, ckpt_path: str,\n restore_dtype: Optional[jnp.dtype]) -> LazyAwaitableArray:\n async def get_fn():\n def _read_state_from_tensorstore(\n self,\n ckpt_path: str,\n written_state_dict: Mapping[str, Any],\n restore_parameter_infos: Optional[Mapping[str, Any]] = None,\n lazy_parameters: bool = False,\n ) -> Mapping[str, Any]:\n def restore_from_tf_checkpoint(\n self,\n path_or_dir: str,\n strict: bool = True,\n translator: Optional[checkpoint_importer.CheckpointTranslator] = None\n ) -> train_state_lib.TrainState:\n def _partition_parameter(maybe_arr: Any, param_info: _ParameterInfo):\n def convert_from_tf_checkpoint(\n self,\n path_or_dir: str,\n *,\n state_transformation_fns: Sequence[SaveStateTransformationFn] = (),\n concurrent_gb: int = 16,\n translator: Optional[checkpoint_importer.CheckpointTranslator] = None):\n def _get_optimizer_state_dict(\n self, ckpt_contents: PyTreeDef,\n state_transformation_fns: Sequence[RestoreStateTransformationFn]):\n def __call__(self,\n train_state: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner,\n checkpoints_dir: str,\n dataset_iterator: Optional[tf.data.Iterator] = None,\n *,\n keep: Optional[int] = None,\n save_dtype: jnp.dtype = np.float32,\n restore_dtype: Optional[jnp.dtype] = None,\n use_gda: Optional[bool] = False,\n keep_dataset_checkpoints: Optional[int] = None) -> Checkpointer:\n def __init__(self,\n train_state: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner,\n checkpoints_dir: str,\n dataset_iterator: Optional[tf.data.Iterator] = None,\n *,\n keep: Optional[int] = None,\n save_dtype: jnp.dtype = np.float32,\n restore_dtype: Optional[jnp.dtype] = None,\n metric_name_to_monitor: str = 'train/accuracy',\n metric_mode: str = 'max',\n keep_checkpoints_without_metrics: bool = True,\n force_keep_period: Optional[int] = None,\n use_gda: bool = False,\n keep_dataset_checkpoints: Optional[int] = None):\n def _populate_metrics_for_steps(self,\n steps: Iterable[int]) -> Mapping[int, float]:\n def _try_fill_metric_run_and_tag_names(self, run_keys: Iterable[str]) -> bool:\n def _filter_out_force_keep_period_steps(self, existing_steps):\n def _remove_old_checkpoints(self):\ndef _get_optimizer_state_dict(\n ckpt_contents: PyTreeDef, optimizer_state: Mapping[str, Any],\n state_transformation_fns: Sequence[RestoreStateTransformationFn]):\ndef _transform_state_and_infos(\n state_dict: PyTreeDef,\n parameter_infos: PyTreeDef,\n state_transformation_fns: Sequence[SaveStateTransformationFn],\n) -> Tuple[PyTreeDef, PyTreeDef]:\nasync def _read_ts(param_info: _ParameterInfo,\n maybe_tspec: Any,\n ckpt_path: str,\n restore_dtype: Optional[jnp.dtype] = None,\n mesh: Optional[gda_lib.Shape] = None,\n axes: Optional[gda_lib.MeshAxes] = None):\ndef fake_param_info(maybe_tspec: Any) -> Optional[_ParameterInfo]:\ndef find_checkpoint(path: str, step: Optional[int] = None) -> str:\ndef load_t5x_checkpoint(\n path: str,\n step: Optional[int] = None,\n state_transformation_fns: Sequence[RestoreStateTransformationFn] = (),\n remap: bool = True,\n restore_dtype: Optional[jnp.dtype] = None,\n lazy_parameters: bool = False) -> PyTreeDef:\n def _create_lazy_awaitable_array(\n param_info: _ParameterInfo, maybe_ts_spec: Any, ckpt_path: str,\n restore_dtype: Optional[jnp.dtype]) -> LazyAwaitableArray:\ndef _transforms_from_state_transformation_fns(\n state_transformation_fns: Sequence[SaveStateTransformationFn]):\n def __init__(self, checkpoint_filename: str):\n def save(self, directory: epath.Path, item: tf.data.Iterator):\n def restore(self,\n directory: epath.Path,\n item: Optional[tf.data.Iterator] = None) -> tf.data.Iterator:\n def structure(self, directory: epath.Path) -> Any:\n def structure(self, directory: epath.Path) -> PyTreeDef:\n def tensorstore_spec_to_name(leaf):\n def save(self,\n directory: Union[str, epath.Path],\n item: Any,\n *args,\n force: bool = False,\n tmp_directory: Optional[Union[str, epath.Path]] = None,\n **kwargs):\n def __init__(self,\n directory: str,\n train_state_shape: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner,\n dataset_iterator: Optional[tf.data.Iterator] = None,\n save_dtype: Optional[jnp.dtype] = None,\n restore_dtype: Optional[jnp.dtype] = None,\n keep: Optional[int] = None,\n keep_dataset_checkpoints: Optional[int] = None):\n def all_steps(self) -> Sequence[int]:\n def _parameter_infos(self,\n train_state: train_state_lib.TrainState) -> PyTreeDef:\n def _get_save_directory(self,\n step: int,\n directory: epath.Path,\n key_name: Optional[str] = None) -> epath.Path:\n def save(self,\n train_state: train_state_lib.TrainState,\n state_transformation_fns: Sequence[SaveStateTransformationFn] = ()):\n def _save_args(param_info):\n def restore(\n self,\n step: int,\n fallback_state: Optional[Mapping[str, Any]] = None,\n state_transformation_fns: Sequence[SaveStateTransformationFn] = (),\n lazy_parameters: Optional[bool] = False) -> train_state_lib.TrainState:\n def _restore_args(param_info):\n def _add_checkpoint_info(self, step, metrics):\nclass _ParameterInfo:\nclass _BytesConditionVariable(object):\nclass SaveStateTransformationFn(typing_extensions.Protocol):\nclass RestoreStateTransformationFn(typing_extensions.Protocol):\nclass _TfDataCheckpointer:\nclass Checkpointer(object):\nclass CheckpointerConstructor(typing_extensions.Protocol):\nclass SaveBestCheckpointer(Checkpointer):\nclass _OrbaxParamInfo:\nclass DatasetCheckpointHandler(orbax.checkpoint.CheckpointHandler):\nclass TrainStateCheckpointHandler(orbax.checkpoint.PyTreeCheckpointHandler):\nclass NonAtomicCheckpointer(orbax.checkpoint.Checkpointer):\nclass CheckpointManager(orbax.checkpoint.CheckpointManager):"},{"identifier":"eval","path":"t5x/eval.py","snippet":"_DEFAULT_GIN_SEARCH_PATHS = [\n os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n]\n FLAGS = flags.FLAGS\nclass SummarizeConfigFn(Protocol):\nclass InferenceEvaluator:\n def __call__(self, model_dir: str,\n summary_writer: Optional[metric_writers.SummaryWriter],\n step: int) -> None:\n def __init__(\n self,\n infer_eval_dataset_cfg: utils.DatasetConfig,\n inference_evaluator_cls: utils.EvaluatorConstructor,\n model: models.BaseModel,\n partitioner: partitioning.BasePartitioner,\n log_dir: Optional[str] = None,\n verify_matching_vocabs_fn: Optional[\n Callable[[utils.DatasetConfig, models.BaseModel], None]]=None,\n ):\n def model_feature_shapes(self) -> Mapping[str, Tuple[int, ...]]:\n def eval_tasks(self) -> Sequence[seqio.Task]:\n def close(self):\n def evaluate(\n self,\n train_state: train_state_lib.TrainState,\n train_state_axes: train_state_lib.TrainState,\n ) -> seqio.evaluation.AllMetricsFuture:\ndef evaluate(\n *,\n model: models.BaseTransformerModel,\n dataset_cfg: utils.DatasetConfig,\n restore_checkpoint_cfg: utils.RestoreCheckpointConfig,\n partitioner: partitioning.BasePartitioner,\n output_dir: str,\n inference_evaluator_cls: utils.EvaluatorConstructor = UnifiedIOEvaluator,\n use_wandb = True,\n summarize_config_fn: SummarizeConfigFn = gin_utils.summarize_gin_config,\n train_state_initializer_cls: Type[\n utils.TrainStateInitializer] = utils.TrainStateInitializer,\n fallback_init_rng: Optional[int] = None,\n log_only=False\n):\n def main(argv: Sequence[str]):\n def _main(argv: Sequence[str]):"},{"identifier":"models","path":"t5x/models.py","snippet":"class TokensIdsToLogitsCallable(typing_extensions.Protocol):\nclass DecodeFnCallable(typing_extensions.Protocol):\nclass BaseModel(abc.ABC):\nclass BaseTransformerModel(BaseModel):\nclass EncoderDecoderModel(BaseTransformerModel):\nclass DecoderOnlyModel(BaseTransformerModel):\n def __call__(\n self, decoding_state: decoding.DecodingState\n ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\n def __call__(self, *, inputs: jnp.ndarray, cache: Mapping[str, jnp.ndarray],\n tokens_to_logits: TokensIdsToLogitsCallable, eos_id: int,\n num_decodes: int, decode_rng: Optional[jax.random.KeyArray],\n cache_offset: int, **kwargs) -> Tuple[jnp.ndarray, jnp.ndarray]:\n def __init__(self, optimizer_def: optimizers.OptimizerDefType):\n def loss_fn(\n self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n dropout_rng: Optional[jax.random.KeyArray],\n ) -> Tuple[jnp.ndarray, MetricsMap]:\n def eval_fn(\n self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n ) -> Tuple[jnp.ndarray, MetricsMap]:\n def predict_batch(self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n rng: Optional[jax.random.KeyArray] = None) -> jnp.ndarray:\n def predict_batch_with_aux(\n self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n rng: Optional[jax.random.KeyArray] = None,\n ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\n def score_batch(self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n return_intermediates: bool = False) -> jnp.ndarray:\n def get_initial_variables(\n self,\n rng: jax.random.KeyArray,\n input_shapes: Mapping[str, Array],\n input_types: Optional[Mapping[str, jnp.dtype]] = None\n ) -> flax_scope.FrozenVariableDict:\n def __init__(\n self,\n module: nn.Module,\n input_vocabulary: seqio.Vocabulary,\n output_vocabulary: seqio.Vocabulary,\n optimizer_def: optimizers.OptimizerDefType,\n decode_fn: Optional[DecodeFnCallable] = None,\n label_smoothing: float = 0.0,\n z_loss: float = 0.0,\n loss_normalizing_factor: Optional[Union[\n float, int, str, losses.SpecialLossNormalizingFactor]] = None,\n ):\n def input_vocabulary(self):\n def output_vocabulary(self):\n def decode_fn(self):\n def _compute_logits(\n self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n dropout_rng: Optional[jax.random.KeyArray] = None) -> jnp.ndarray:\n def loss_fn(\n self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n dropout_rng: Optional[jax.random.KeyArray],\n ) -> Tuple[jnp.ndarray, MetricsMap]:\n def _compute_metrics(\n self,\n logits: jnp.ndarray,\n targets: jnp.ndarray,\n mask: jnp.ndarray,\n loss: jnp.ndarray,\n z_loss: Optional[jnp.ndarray] = None,\n segment_ids: Optional[Mapping[str, jnp.ndarray]] = None,\n ) -> MetricsMap:\n def __init__(\n self,\n module: nn.Module,\n input_vocabulary: seqio.Vocabulary,\n output_vocabulary: seqio.Vocabulary,\n optimizer_def: optimizers.OptimizerDefType,\n decode_fn: DecodeFnCallable = decoding.beam_search,\n feature_converter_cls: Optional[Callable[...,\n seqio.FeatureConverter]] = None,\n label_smoothing: float = 0.0,\n z_loss: float = 0.0,\n loss_normalizing_factor: Optional[float] = None,\n ):\n def get_initial_variables(\n self,\n rng: jax.random.KeyArray,\n input_shapes: Mapping[str, Array],\n input_types: Optional[Mapping[str, jnp.dtype]] = None\n ) -> flax_scope.FrozenVariableDict:\n def _compute_logits(\n self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n dropout_rng: Optional[jax.random.KeyArray] = None,\n mutable: flax_scope.CollectionFilter = False,\n other_variables: Optional[PyTreeDef] = None,\n ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, flax_scope.FrozenVariableDict]]:\n def _compute_logits_from_slice(\n self, decoding_state: decoding.DecodingState, params: PyTreeDef,\n encoded_inputs: jnp.ndarray, raw_inputs: jnp.ndarray,\n max_decode_length: int) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\n def predict_batch_with_aux(\n self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n rng: Optional[jax.random.KeyArray] = None,\n decoder_params: Optional[MutableMapping[str, Any]] = None,\n return_all_decodes: bool = False,\n num_decodes: int = 1,\n prompt_with_targets: bool = False\n ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\n def score_batch(\n self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n return_intermediates: bool = False,\n ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Mapping[str, Any]]]:\n def __init__(\n self,\n module: nn.Module,\n vocabulary: seqio.Vocabulary,\n optimizer_def: optimizers.OptimizerDefType,\n decode_fn: DecodeFnCallable = decoding.temperature_sample,\n inputs_bidirectional_attention: bool = False,\n feature_converter_cls: Optional[Callable[...,\n seqio.FeatureConverter]] = None,\n label_smoothing: float = 0.0,\n z_loss: float = 0.0,\n loss_normalizing_factor: Optional[float] = None,\n ):\n def get_initial_variables(\n self,\n rng: jax.random.KeyArray,\n input_shapes: Mapping[str, Array],\n input_types: Optional[Mapping[str, jnp.dtype]] = None\n ) -> flax_scope.FrozenVariableDict:\n def _get_decoder_causal_attention(self, batch):\n def _compute_logits(\n self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n dropout_rng: Optional[jax.random.KeyArray] = None,\n mutable: flax_scope.CollectionFilter = False) -> jnp.ndarray:\n def _compute_logits_from_slice(\n self,\n decoding_state: decoding.DecodingState,\n params: PyTreeDef,\n max_decode_length: int,\n ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\n def score_batch(self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n return_intermediates: bool = False) -> jnp.ndarray:\n def _compute_kv_cache(\n self,\n params: PyTreeDef,\n inputs: jnp.ndarray,\n inputs_lengths: jnp.ndarray,\n decoder_causal_attention: jnp.ndarray,\n ) -> PyTreeDef:\n def predict_batch_with_aux(\n self,\n params: PyTreeDef,\n batch: Mapping[str, jnp.ndarray],\n rng: Optional[jax.random.KeyArray] = None,\n *,\n return_all_decodes: bool = False,\n num_decodes: int = 1,\n decoder_params: Optional[MutableMapping[str, Any]] = None,\n ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\ndef remove_prefix(sequence: jnp.ndarray,\n prefix_length: jnp.ndarray) -> jnp.ndarray:\ndef compute_weighted_accuracy(\n logits: jnp.ndarray,\n targets: jnp.ndarray,\n weights: Optional[jnp.ndarray] = None) -> Tuple[jnp.ndarray, jnp.ndarray]:\ndef compute_metrics(logits: jnp.ndarray, targets: jnp.ndarray,\n weights: jnp.ndarray, loss: jnp.ndarray,\n weight_sum: jnp.ndarray,\n additional_metrics: MetricsMap) -> MetricsMap:\ndef compute_base_metrics(\n logits: jnp.ndarray,\n targets: jnp.ndarray,\n mask: jnp.ndarray,\n loss: jnp.ndarray,\n z_loss: Optional[jnp.ndarray] = None,\n segment_ids: Optional[Mapping[str, jnp.ndarray]] = None,\n) -> MetricsMap:\ndef get_input_vocabulary(model: BaseTransformerModel) -> seqio.Vocabulary:\ndef get_output_vocabulary(model: BaseTransformerModel) -> seqio.Vocabulary:\n FEATURE_CONVERTER_CLS: Callable[..., seqio.FeatureConverter]\n FEATURE_CONVERTER_CLS = seqio.EncDecFeatureConverter\n FEATURE_CONVERTER_CLS = seqio.DecoderFeatureConverter"},{"identifier":"evaluator","path":"t5x/examples/unified_io/evaluator.py","snippet":"class UnifiedIOOutput:\nclass UnifiedIOEvaluator(seqio.Evaluator):\n def text(self):\n def text_tokens(self):\n def image_tokens(self):\n def image(self):\n def audio(self):\n def scores(self):\ndef build_uio_outputs(aux_values, vocab) -> List[UnifiedIOOutput]:\n def __init__(\n self,\n mixture_or_task_name: str,\n feature_converter,\n eval_split: str = \"validation\",\n use_cached: bool = False,\n seed: Optional[int] = 42,\n sequence_length: Optional[Mapping[str, int]] = None,\n num_examples: Optional[int] = None,\n shuffle: bool = False,\n logger_cls: Sequence = (),\n log_dir: Optional[str] = None,\n use_memory_cache: bool = True,\n target_field_name: str = \"targets\",\n ):\n def _compute_metrics(self,\n predicted_tokens: AllOutputTokensType,\n scores: AllOutputScoresType,\n all_aux_values: AllOutputAuxValuesType,\n step: Optional[int] = None) -> AllMetricsType:"},{"identifier":"partitioning","path":"t5x/partitioning.py","snippet":"class AxisNames(tuple):\nclass LocalChunkInfo:\nclass LocalChunker:\nclass DataLayout:\nclass BasePartitioner(metaclass=abc.ABCMeta):\nclass PjittedFnWithContext(PartitionedCallable):\nclass BasePjitPartitioner(BasePartitioner):\nclass PjitPartitioner(BasePjitPartitioner):\n def __new__(cls, *names):\n def __repr__(self):\ndef pjit(\n fun: Callable, # pylint: disable=g-bare-generic\n in_axis_resources,\n out_axis_resources,\n static_argnums: Union[int, Sequence[int]] = (),\n donate_argnums: Union[int, Sequence[int]] = (),\n backend: Optional[str] = None):\ndef pjit_with_cpu_fallback(\n fun: Callable, # pylint: disable=g-bare-generic\n in_axis_resources,\n out_axis_resources,\n static_argnums: Union[int, Sequence[int]] = (),\n donate_argnums: Union[int, Sequence[int]] = (),\n backend: Optional[str] = None):\ndef with_sharding_constraint(x, axis_resources):\ndef bounds_from_last_device(\n last_device: jax.lib.xla_client.Device) -> HardwareMesh:\ndef get_coords(device: jax.lib.xla_client.Device) -> HardwareMesh:\ndef global_mesh_defined():\ndef get_mesh(model_parallel_submesh: HardwareMesh,\n input_devices: Sequence[JaxDevice] = (),\n input_local_devices: Sequence[JaxDevice] = (),\n tile_by_host_if_needed: bool = True,\n backend: Optional[str] = None) -> Mesh:\n def dh_dd_mh_md(g: int, m: int, l: int) -> Tuple[int, int, int, int]:\ndef get_cpu_mesh() -> Mesh:\ndef get_gpu_mesh(num_partitions: int) -> Mesh:\ndef default_mesh(num_partitions: int,\n model_parallel_submesh: Optional[HardwareMesh] = None,\n backend: Optional[str] = None) -> Mesh:\n def __init__(self, global_mesh: Mesh):\n def get_local_chunk_info(\n self, global_shape: Tuple[int, ...],\n mesh_axes: Sequence[Optional[str]]) -> LocalChunkInfo:\ndef standard_logical_axis_rules(\n activation_partitioning_dims: int = 1,\n parameter_partitioning_dims: int = 1,\n additional_rules: Optional[LogicalAxisRules] = None) -> LogicalAxisRules:\ndef _id_fn(x, ix):\n def __init__(self,\n num_partitions: Optional[int] = None,\n model_parallel_submesh: Optional[HardwareMesh] = None,\n params_on_devices: bool = True,\n backend: Optional[str] = None):\n def mesh(self) -> Mesh:\n def data_partition_spec(self) -> PartitionSpec:\n def get_data_layout(self,\n batch_size: Optional[int] = None,\n host_index: Optional[int] = None) -> DataLayout:\n def get_local_chunk_info(\n self, global_shape: Tuple[int, ...],\n mesh_axes: Sequence[Optional[str]]) -> LocalChunkInfo:\n def params_on_devices(self):\n def move_params_to_devices(self, train_state: TrainState,\n train_state_axes: TrainState) -> TrainState:\n def _local_chunker(self):\n def get_logical_axes(self, train_state: TrainState) -> TrainState:\n def get_mesh_axes(self, train_state: TrainState) -> TrainState:\n def partition(\n self,\n fn: Callable, # pylint: disable=g-bare-generic\n in_axis_resources,\n out_axis_resources,\n static_argnums: Union[int, Sequence[int]] = (),\n donate_argnums: Union[int, Sequence[int]] = ()\n ) -> PartitionedCallable:\n def compile(self, partitioned_fn: PartitionedCallable,\n *args) -> CompiledPartitionedCallable:\n def __init__(self,\n pjitted_fn,\n partition_mesh: Mesh,\n logical_axis_rules: flax_partitioning.LogicalRules = ()):\n def __call__(self, *args):\n def lower(self, *args):\n def _local_chunker(self) -> LocalChunker:\n def mesh(self) -> Mesh:\n def partition(\n self,\n fn: Callable, # pylint: disable=g-bare-generic\n in_axis_resources,\n out_axis_resources,\n static_argnums: Union[int, Sequence[int]] = (),\n donate_argnums: Union[int, Sequence[int]] = ()\n ) -> PjittedFnWithContext:\n def compile(self, partitioned_fn: PjittedFnWithContext,\n *args) -> CompiledPartitionedCallable:\n def __init__(self,\n num_partitions: Optional[int] = None,\n model_parallel_submesh: Optional[HardwareMesh] = None,\n params_on_devices: bool = True,\n backend: Optional[str] = None,\n logical_axis_rules: Optional[LogicalAxisRules] = None,\n use_cpu_pjit: Optional[bool] = False):\n def partition(\n self,\n fn: Callable, # pylint: disable=g-bare-generic\n in_axis_resources,\n out_axis_resources,\n static_argnums: Union[int, Sequence[int]] = (),\n donate_argnums: Union[int, Sequence[int]] = ()\n ) -> PjittedFnWithContext:\n def logical_axis_rules(self):\n def get_logical_axes(self, train_state: TrainState) -> TrainState:\n def get_mesh_axes(self, train_state: TrainState) -> TrainState:\n def _logical_to_mesh_axes(param_name, logical_axes):"},{"identifier":"train_state","path":"t5x/train_state.py","snippet":"EMPTY_DICT = flax.core.freeze({})\nclass TrainState(typing_extensions.Protocol):\nclass FlaxOptimTrainState(flax.struct.PyTreeNode):\nclass GanOptimTrainState(FlaxOptimTrainState):\nclass InferenceState(flax.struct.PyTreeNode):\n def step(self) -> jnp.ndarray:\n def params(self) -> FrozenVariableDict:\n def param_states(self) -> FrozenVariableDict:\n def flax_mutables(self) -> FrozenVariableDict:\n def state_dict(self) -> MutableVariableDict:\n def restore_state(self, state_dict: Mapping[str, Any]) -> 'TrainState':\n def replace_params(self, params: VariableDict) -> 'TrainState':\n def replace_flax_mutables(self, flax_mutables: FrozenDict) -> 'TrainState':\n def replace_step(self, step: jnp.ndarray) -> 'TrainState':\n def apply_gradient(self,\n grads,\n learning_rate,\n flax_mutables=EMPTY_DICT) -> 'TrainState':\n def as_logical_axes(self) -> 'TrainState':\ndef _validate_params_axes(params_axes, params):\ndef _split_variables_and_axes(\n variables_and_axes: FrozenVariableDict\n) -> Tuple[FrozenVariableDict, FrozenVariableDict]:\n def create(cls, optimizer_def: optimizers.OptimizerDefType,\n model_variables: FrozenVariableDict) -> 'FlaxOptimTrainState':\n def step(self) -> jnp.ndarray:\n def params(self) -> FrozenVariableDict:\n def param_states(self) -> FrozenVariableDict:\n def state_dict(self) -> MutableVariableDict:\n def apply_gradient(self,\n grads,\n learning_rate,\n flax_mutables=EMPTY_DICT) -> 'FlaxOptimTrainState':\n def replace_params(self, params: VariableDict) -> 'FlaxOptimTrainState':\n def replace_flax_mutables(self,\n flax_mutables: FrozenDict) -> 'FlaxOptimTrainState':\n def replace_step(self, step: jnp.ndarray) -> 'FlaxOptimTrainState':\n def restore_state(self, state_dict: VariableDict) -> 'FlaxOptimTrainState':\n def as_logical_axes(self) -> 'FlaxOptimTrainState':\n def apply_gradient(self,\n grads,\n learning_rate,\n flax_mutables=EMPTY_DICT) -> 'FlaxOptimTrainState':\n def create(cls, model_variables: FrozenVariableDict) -> 'InferenceState':\n def param_states(self) -> FrozenVariableDict:\n def apply_gradient(self, *args, **kwargs) -> 'InferenceState':\n def state_dict(self) -> MutableMapping[str, Any]:\n def replace_step(self, step: jnp.ndarray) -> 'InferenceState':\n def replace_params(self, params: FrozenVariableDict) -> 'InferenceState':\n def replace_flax_mutables(self,\n flax_mutables: FrozenDict) -> 'InferenceState':\n def restore_state(self, state_dict: Mapping[str, Any]) -> 'InferenceState':\n def as_logical_axes(self) -> 'InferenceState':"},{"identifier":"trainer","path":"t5x/trainer.py","snippet":"def _merge_metrics(a, b):\ndef merge_metrics(a, b):\n def result(self) -> Mapping[str, Array]:\n def result(self) -> Mapping[str, clu.values.Value]:\n def result(self) -> float:\n def __call__(\n self,\n step: jnp.ndarray,\n ) -> jnp.ndarray:\n def __call__(self, metrics: MetricMapType, duration: float,\n num_steps: int) -> Mapping[str, jnp.ndarray]:\n def __call__(\n self, train_state: train_state_lib.TrainState,\n batch: BatchType) -> Tuple[train_state_lib.TrainState, MetricMapType]:\n def __call__(self, train_state: train_state_lib.TrainState,\n batch: jnp.ndarray) -> MetricMapType:\n def compute_metrics(\n self, gradients: ModelWeights,\n old_train_state: train_state_lib.TrainState,\n new_train_state: train_state_lib.TrainState) -> MutableMetricMapType:\n def _make_rms_metrics(name, tree):\n def _make_max_metrics(name, tree):\n def compute_metrics(\n self, gradients: ModelWeights,\n old_train_state: train_state_lib.TrainState,\n new_train_state: train_state_lib.TrainState) -> MutableMetricMapType:\n def __init__(self):\n def close(self):\n def __del__(self):\n def _get_completion_future(self, block_on: PyTreeDef = ()) -> TimeFuture:\n def _get_completion_time():\n def start(self, block_on: PyTreeDef = ()):\n def stop(self, block_on: PyTreeDef = ()) -> TimeFuture:\n def __init__(self, name: str, summary_dir: Optional[str] = None, log_to_wandb=False):\n def __del__(self):\n def close(self):\n def summary_writer(self) -> metric_writers.MetricWriter:\n def write_scalar(self, key: str, val: metric_writers.interface.Scalar,\n step: int):\n def write_scalars(self, step: int,\n scalars: Mapping[str, metric_writers.interface.Scalar]):\n def start_duration_timer(self, block_on: PyTreeDef = ()):\n def write_metrics_summary(self, metrics: MetricMapType, step: int,\n num_steps: int) -> MetricValueMapFuture:\n def _summarize_and_write():\n def _ensure_not_on_device(x):\n def flush(self):\n def __init__(self, model: models.BaseModel,\n train_state: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner,\n eval_names: Sequence[str], summary_dir: Optional[str],\n train_state_axes: Any, rng: Rng,\n use_wandb=False, packing_strategy=None, log_weights=None):\n def __enter__(self):\n def __exit__(self, exc_type, exc_value, traceback):\n def close(self):\n def _get_step_rng(self, step: int) -> Rng:\n def train_state(self):\n def train_state(self, train_state: PyTreeDef):\n def _weight_metric_fn(self):\n def _get_weight_metrics_fn(_params):\n def train(self,\n batch_iter: Union[Iterator[BatchType],\n clu.data.dataset_iterator.DatasetIterator],\n num_steps: int,\n start_step: Optional[int] = None) -> ArrayMapFuture:\n def compile_train(self, batch: ElementSpec) -> None:\n def eval(\n self, batch_iters: Mapping[str,\n Iterator[BatchType]], pbar_nsteps=None) -> Mapping[str, Array]:\n def compile_eval(self, batches: Mapping[str, BatchType]) -> None:\n def _partitioned_train_step(self) -> PartitionedTrainCallable:\n def _partitioned_eval_step(self) -> PartitionedEvalCallable:\ndef accumulate_grads_microbatched(\n model: models.BaseModel,\n train_state: train_state_lib.TrainState,\n batch: BatchType,\n dropout_rng: Rng,\n num_microbatches: Optional[int],\n data_partition_spec: PartitionSpec = PartitionSpec(\"data\"),\n loss_fn_args=None\n) -> Tuple[train_state_lib.TrainState, MutableMetricMapType,\n def get_microbatch(batch: BatchType, idx: int) -> Mapping[str, jnp.ndarray]:\n def metrics_and_grad(loop_cnt, dropout_rng, flax_mutables=None):\n def per_microbatch_train_step(\n loop_cnt: int, state: Tuple[jnp.ndarray, jnp.ndarray,\n Mapping[str, jnp.ndarray],\n Optional[FlaxMutables]]\n ) -> Tuple[jnp.ndarray, jnp.ndarray, Mapping[str, jnp.ndarray],\ndef apply_grads(\n train_state: train_state_lib.TrainState,\n grad_accum: ModelWeights,\n metrics: MutableMetricMapType,\n learning_rate: jnp.ndarray,\n weight_metrics_computer: Optional[WeightMetricsComputer],\n other_state_variables: Optional[Mapping[str, Any]] = None\n) -> Tuple[train_state_lib.TrainState, MetricMapType]:\ndef eval_step(model: models.BaseModel, train_state: train_state_lib.TrainState,\n batch: jnp.ndarray) -> MetricMapType:\ndef train_with_lr(\n train_state: train_state_lib.TrainState,\n batch: BatchType,\n learning_rate: jnp.ndarray,\n dropout_rng: Rng,\n model: models.BaseModel,\n num_microbatches: Optional[int],\n weight_metrics_computer: Optional[WeightMetricsComputer] = None,\n data_partition_spec: PartitionSpec = PartitionSpec(\"data\"),\n loss_fn_args=None\n):\n def __call__(self, model: models.BaseModel,\n train_state: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner,\n eval_names: Sequence[str], summary_dir: Optional[str],\n train_state_axes: Any, rng: Rng) -> BaseTrainer:\n def __init__(self,\n model: models.BaseModel,\n train_state: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner,\n eval_names: Sequence[str],\n summary_dir: Optional[str],\n train_state_axes: Any,\n rng: Rng,\n learning_rate_fn: LearningRateCallable,\n num_microbatches: Optional[int],\n weight_metrics_computer: Optional[WeightMetricsComputer] = None,\n use_wandb=True,\n packing_strategy=None,\n log_weights=False\n ):\n def _partitioned_train_step(self) -> PartitionedTrainCallable:\n def train_step(train_state: train_state_lib.TrainState, batch: BatchType, static_args=None):\n def _partitioned_eval_step(self) -> PartitionedEvalCallable:\ndef _warn_action_not_run(action, task, metric):\n def run(self, train_state: train_state_lib.TrainState,\n metrics_by_task: Mapping[str, MetricValueMapType]) -> bool:\n def __init__(self,\n metric: Tuple[str, str],\n mode: str,\n patience: int = 3,\n atol: float = 0.,\n rtol: float = 0.):\n def _compare_fn(self, current, previous):\n def run(self, train_state: train_state_lib.TrainState,\n metrics_by_task: Mapping[str, MetricValueMapType]) -> bool:\n def __init__(self, task: str, metric: str = \"loss\"):\n def run(self, train_state: train_state_lib.TrainState,\n metrics_by_task: Mapping[str, MetricValueMapType]) -> bool:\nclass ArrayMapFuture(typing_extensions.Protocol):\nclass MetricValueMapFuture(typing_extensions.Protocol):\nclass TimeFuture(typing_extensions.Protocol):\nclass LearningRateCallable(typing_extensions.Protocol):\nclass SummarizeMetricsCallable(typing_extensions.Protocol):\nclass PartitionedTrainCallable(typing_extensions.Protocol):\nclass PartitionedEvalCallable(typing_extensions.Protocol):\nclass GradNormComputer(object):\nclass WeightMetricsComputer(object):\nclass _AsyncTimer(object):\nclass MetricsManager(object):\nclass PreemptionError(Exception):\nclass BaseTrainer(abc.ABC):\nclass BaseTrainerConstructor(Protocol):\nclass Trainer(BaseTrainer):\nclass ActionMode(enum.Enum):\nclass BaseAction(abc.ABC):\nclass EarlyStoppingAction(BaseAction):\nclass TerminateOnNanAction(BaseAction):\n _WEIGHT_METRICS = [\n \"weight_rms\", \"weight_gradient_rms\", \"weight_update_rms\", \"weight_max\"\n ]\n TRAIN = 1\n TRAIN_EVAL = 2\n INFER_EVAL = 3"},{"identifier":"utils","path":"t5x/utils.py","snippet":"class EvaluatorConstructor(typing_extensions.Protocol):\nclass SaveCheckpointConfig:\nclass RestoreCheckpointConfig:\nclass CheckpointConfig:\nclass LegacyCheckpointer(orbax.checkpoint.Checkpointer):\nclass LegacyCheckpointManager(orbax.checkpoint.CheckpointManager):\nclass DatasetConfig:\nclass GDADatasetIterator(clu.data.dataset_iterator.DatasetIterator):\nclass InitFnCallable(typing_extensions.Protocol):\nclass LearningRateCallable(typing_extensions.Protocol):\nclass TrainStateInitializer:\nclass InferStepWithRngCallable(typing_extensions.Protocol):\nclass InferStepWithoutRngCallable(typing_extensions.Protocol):\nclass InferFnCallable(typing_extensions.Protocol):\nclass GetDatasetCallable(typing_extensions.Protocol):\nclass GetEvalDatasetCallable(typing_extensions.Protocol):\nclass _RegexMap(collections.abc.Mapping):\n def __call__(\n self,\n mixture_or_task_name: str,\n feature_converter: seqio.FeatureConverter,\n eval_split: str,\n use_cached: bool,\n seed: Optional[int],\n sequence_length: Optional[Mapping[str, int]],\n log_dir: Optional[str],\n use_memory_cache: bool,\n ) -> seqio.Evaluator:\n def __post_init__(self):\n def __post_init__(self):\n def __init__(self,\n *,\n save_checkpointer: Optional[checkpoints.Checkpointer] = None,\n restore_checkpointer: checkpoints.Checkpointer,\n strict: Optional[bool] = False):\n async def async_save(self, path: str, item: Any):\n async def async_restore(self, path: str, item: Optional[Any] = None) -> Any:\n def save(self,\n path: str,\n item: train_state_lib.TrainState,\n state_transformation_fns: Sequence[\n checkpoints.SaveStateTransformationFn] = (),\n *,\n concurrent_gb: int = 128):\n def restore(self,\n path: str,\n item: Optional[train_state_lib.TrainState],\n state_transformation_fns: Sequence[\n checkpoints.RestoreStateTransformationFn] = (),\n fallback_state: Optional[Mapping[str, Any]] = None,\n lazy_parameters: bool = False) -> train_state_lib.TrainState:\n def __init__(self,\n *,\n save_cfg: Optional[SaveCheckpointConfig] = None,\n restore_cfg: RestoreCheckpointConfig,\n train_state_shape: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner,\n ds_iter: Optional[\n Union[tf.data.Iterator,\n clu.data.dataset_iterator.DatasetIterator]] = None,\n model_dir: Optional[str] = None,\n use_gda: Optional[bool] = True):\n def save(self,\n train_state: train_state_lib.TrainState,\n state_transformation_fns: Sequence[\n checkpoints.SaveStateTransformationFn] = ()):\n def restore(\n self,\n paths: Sequence[str],\n restore_cfg: RestoreCheckpointConfig,\n fallback_state: Optional[Mapping[str, Any]] = None\n ) -> Union[train_state_lib.TrainState, Sequence[train_state_lib.TrainState]]:\ndef _get_index_mappings(device_to_idxs):\ndef _create_gda(partitioner: partitioning.BasePartitioner,\n global_shapes: PyTreeDef, host_arrays: PyTreeDef) -> PyTreeDef:\n def _put_to_devices(x, global_shape):\n def _gda(dbs, global_shape):\n def __init__(self, iterator: clu.data.dataset_iterator.DatasetIterator,\n partitioner: partitioning.BasePartitioner,\n global_shapes: PyTreeDef):\n def __next__(self):\n def reset(self):\n def element_spec(self):\n def save(self, filename):\n def restore(self, filename):\n def iterator(self):\ndef sync_global_devices(name: str) -> None:\ndef multihost_assert_equal(input_tree, fail_message: str = ''):\ndef _hardware_uniform(\n rng_key: Array,\n shape: Shape,\n dtype: jnp.dtype = np.float32,\n minval: Array = np.float32(0),\n maxval: Array = np.float32(1)\n) -> Array:\ndef _hardware_bernoulli(\n rng_key: Array, p: np.ndarray = np.float32(0.5),\n shape: Shape = ()) -> Array:\ndef set_hardware_rng_ops():\ndef get_zeros_batch_like_spec(\n batch_spec: Mapping[str,\n jax.ShapeDtypeStruct]) -> Mapping[str, jnp.ndarray]:\ndef get_zeros_batch_like_dataset(dataset: tf.data.Dataset,\n batch_size=None) -> Mapping[str, jnp.ndarray]:\n def __call__(\n self, rng: Array, input_shapes: Mapping[str, Array],\n input_types: Optional[Mapping[str,\n DType]]) -> flax_scope.FrozenVariableDict:\n def __call__(self, step: jnp.ndarray) -> jnp.ndarray:\ndef create_learning_rate_scheduler(\n factors: str = 'constant * linear_warmup * rsqrt_decay',\n base_learning_rate: float = 0.5,\n warmup_steps: int = 1000,\n decay_factor: float = 0.5,\n steps_per_decay: int = 20000,\n steps_per_cycle: int = 100000,\n step_offset: int = 0,\n min_learning_rate: float = 1e-8) -> LearningRateCallable:\n def step_fn(step: jnp.ndarray) -> jnp.ndarray:\ndef steps(prefix, config, data_size=None, batch_size=None, default=ValueError):\ndef create_vision_learning_rate_scheduler(\n total_steps, batch_size=None, data_size=None,\n base=1.0, decay_type=\"stair\",\n scale_with_batchsize=False, **kw):\n def step_fn(step):\ndef get_first_valid_restore_config_and_paths(\n restore_cfgs: Sequence[RestoreCheckpointConfig]\n) -> Tuple[Optional[RestoreCheckpointConfig], Sequence[str]]:\ndef get_fallback_state(restore_cfg: RestoreCheckpointConfig,\n init_fn: Callable[[jnp.ndarray], Mapping[str, Any]],\n init_rng: jnp.ndarray) -> Optional[Mapping[str, Any]]:\n def __init__(self,\n optimizer_def: Optional[optimizers.OptimizerDefType],\n init_fn: InitFnCallable,\n input_shapes: Mapping[str, Array],\n partitioner: partitioning.BasePartitioner,\n model=None,\n input_types: Optional[Mapping[str, DType]] = None):\n def initialize_train_state(rng: Array):\n def from_scratch(self, init_rng: Array) -> train_state_lib.TrainState:\n def from_checkpoints(\n self,\n restore_cfgs: Sequence[RestoreCheckpointConfig],\n ds_iter: Optional[tf.data.Iterator] = None,\n init_rng: Optional[jnp.ndarray] = None,\n ) -> Iterable[train_state_lib.TrainState]:\n def _restore_path(path, cfg):\n def from_checkpoint(\n self,\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\n *,\n ds_iter: Optional[tf.data.Iterator] = None,\n init_rng: Optional[jnp.ndarray] = None\n ) -> Optional[train_state_lib.TrainState]:\n def from_checkpoint_or_scratch(\n self,\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\n *,\n init_rng: Array,\n ds_iter: Optional[tf.data.Iterator] = None) -> train_state_lib.TrainState:\ndef log_model_info(log_file: Optional[str],\n full_train_state: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner):\n def _log_info_and_write_to_file(writer, format_str, *args):\n def _log_variable(name: str, arr: Optional[np.ndarray],\n logical_axes: Optional[partitioning.AxisNames],\n mesh_axes: Optional[partitioning.PartitionSpec]):\n def __call__(self,\n params: Mapping[str, Any],\n batch: Mapping[str, jnp.ndarray],\n rng: jnp.ndarray = None) -> PyTreeDef:\n def __call__(self, params: Mapping[str, Any],\n batch: Mapping[str, jnp.ndarray]) -> PyTreeDef:\n def __call__(\n self,\n ds: tf.data.Dataset,\n train_state: train_state_lib.TrainState,\n rng: Optional[jnp.ndarray] = None\n ) -> Union[_InferFnResult, _InferFnWithAuxResult]:\ndef _remove_padding(all_inferences, all_indices):\ndef get_infer_fn(infer_step: InferStepCallable, batch_size: int,\n train_state_axes: train_state_lib.TrainState,\n partitioner: partitioning.BasePartitioner, \n pbar=False) -> InferFnCallable:\n def infer_step_with_indices(params, batch, rng, indices):\n def infer_fn(ds: tf.data.Dataset,\n train_state: train_state_lib.TrainState,\n rng: Optional[jnp.ndarray] = None):\n def _copy_to_host_async(x):\ndef import_module(module: str):\ndef get_vocabulary(\n cfg: DatasetConfig) -> Tuple[seqio.Vocabulary, seqio.Vocabulary]:\ndef verify_matching_vocabs(cfg: DatasetConfig, model: Any):\ndef get_dataset(cfg: DatasetConfig,\n shard_id: int,\n num_shards: int,\n feature_converter_cls: Callable[..., seqio.FeatureConverter],\n num_epochs: Optional[int] = None,\n continue_from_last_checkpoint: bool = False,\n batching_fn=None) -> tf.data.Dataset:\ndef get_dataset_inner(cfg: DatasetConfig,\n shard_info: seqio.ShardInfo,\n feature_converter_cls: Callable[...,\n seqio.FeatureConverter],\n seed: Optional[int] = None,\n num_epochs: Optional[int] = None,\n batching_fn=None\n ):\n def __call__(\n self,\n cfg: DatasetConfig,\n shard_id: int,\n num_shards: int,\n feature_converter_cls: Callable[..., seqio.FeatureConverter],\n num_epochs: Optional[int] = None,\n continue_from_last_checkpoint: bool = True\n ) -> Union[clu.data.dataset_iterator.DatasetIterator, tf.data.Dataset]:\n def __call__(\n self, cfg: DatasetConfig, shard_id: int, num_shards: int, eval_steps: int,\n feature_converter_cls: Callable[..., seqio.FeatureConverter]\n ) -> Mapping[str, tf.data.Dataset]:\ndef get_training_eval_datasets(\n cfg: DatasetConfig,\n shard_id: int,\n num_shards: int,\n eval_steps: int,\n feature_converter_cls: Callable[..., seqio.FeatureConverter],\n deterministic: bool = False,\n model_dir: Optional[str] = None,\n start_step: int = 0,\n) -> Mapping[str, tf.data.Dataset]:\n def _repeat_shard_batch_take_cache(ds: tf.data.Dataset):\ndef round_vocab_size_to_multiple(vocabulary: seqio.Vocabulary,\n divisor: int = 128):\ndef flatten_dict_string_keys(x):\ndef flatten_lists(lsts: Iterable[Iterable]) -> Sequence:\n def __init__(self, kvs: Sequence[Tuple[str, Any]]):\n def __getitem__(self, key: str) -> Any:\n def __len__(self) -> int:\n def __iter__(self) -> Iterable[Tuple[re.Pattern, Any]]:\ndef override_params_axes_names(\n model_variables: flax_scope.FrozenVariableDict,\n params_axes_names_override: Sequence[Tuple[str, Tuple[str, ...]]] = ()\n) -> flax_scope.FrozenVariableDict:\ndef get_local_data(x):"},{"identifier":"init_wandb","path":"t5x/examples/unified_io/utils.py","snippet":"@gin.configurable()\ndef init_wandb(name=None, group=None, entity=None, project=None):\n utils.create_learning_rate_scheduler() # Makes sure this is registered in `operative_config`\n config_str = gin.operative_config_str()\n logging.info(f\"Init wandb with group={group} name={name}\")\n wandb.init(\n group=group,\n name=name,\n entity=entity,\n project=project,\n force=True,\n notes=config_str\n )"}],"string":"[\n {\n \"identifier\": \"PackingStrategy\",\n \"path\": \"t5x/examples/unified_io/packing.py\",\n \"snippet\": \"class PackingStrategy:\\n \\\"\\\"\\\"Defines how to pack data during training and handles batch-level constraints\\n from the input/target encoders\\\"\\\"\\\"\\n\\n pack_max_len: Optional[Tuple[int, int]] = None\\n \\\"\\\"\\\"If packing, max input/target length to to\\\"\\\"\\\"\\n\\n pack_pool_size: int = 10\\n \\\"\\\"\\\"Pool to use when packing examples\\\"\\\"\\\"\\n\\n constraint_pool_size: int = 10\\n \\\"\\\"\\\"Pool to use when matching batch constraints\\\"\\\"\\\"\\n\\n max_to_pack: int = 2\\n \\\"\\\"\\\"Max examples to pack together\\\"\\\"\\\"\\n\\n @property\\n def pack(self):\\n return self.pack_max_len is not None\\n\\n def batch(self, ds, batch_size, drop_remainder=True, batch_constraints=None):\\n if batch_constraints is None:\\n batch_constraints = []\\n for k, v in get_input_modalities().items():\\n bound = v.get_constraints()\\n if bound is not None:\\n def _fn(ex):\\n mask = tf.cast(ex[f\\\"inputs/{k}/mask\\\"], tf.bool)\\n return tf.reduce_sum(tf.cast(tf.reduce_any(mask, -1), tf.int32))\\n batch_bound = int(round(bound * batch_size))\\n if self.pack_max_len is None:\\n logging.info(f\\\"Adding batch constraint {k}/{bound} => \\\"\\n f\\\"({batch_bound} per batch of {batch_size})\\\")\\n else:\\n bound *= self.max_to_pack\\n logging.info(f\\\"Adding batch constraint {k}/{bound} => \\\"\\n f\\\"({batch_bound} per batch of {batch_size} groups of {self.max_to_pack})\\\")\\n batch_constraints.append((_fn, batch_bound))\\n if self.pack:\\n enc, dec = self.pack_max_len\\n if self.max_to_pack == 2:\\n ds = pair_examples(ds, enc, dec, self.pack_pool_size)\\n else:\\n raise NotImplementedError()\\n if batch_constraints:\\n ds = batch_with_constraints(ds, batch_size, self.constraint_pool_size, batch_constraints)\\n else:\\n ds = ds.batch(batch_size, drop_remainder=drop_remainder)\\n return unfold(ds, batch_size, n=self.max_to_pack)\\n else:\\n if batch_constraints:\\n return batch_with_constraints(ds, batch_size, self.constraint_pool_size, batch_constraints)\\n else:\\n return ds.batch(batch_size, drop_remainder=drop_remainder)\"\n },\n {\n \"identifier\": \"checkpoints\",\n \"path\": \"t5x/checkpoints.py\",\n \"snippet\": \"VERSION = 3\\n_DESIRED_CHUNK_SIZE_BYTES = 64 * 1024 * 1024\\n_TRAIN_DS_PREFIX = 'train_ds'\\n_OPTIMIZER_KEY = 'optimizer'\\n_VERSION_KEY = 'version'\\n_CHECKPOINTS_SUBDIR = 'checkpoints'\\n_STATE_KEY = 'state'\\n_DATASET_KEY = 'dataset'\\n_FLAX_CHECKPOINT_FILE = 'checkpoint'\\ndef _choose_chunk_shape(write_shape: Sequence[int],\\n target_elements: int) -> List[int]:\\n def get_total_elements():\\ndef _run_future_tree(future_tree):\\ndef all_steps(checkpoints_dir: str) -> Sequence[int]:\\ndef all_dataset_checkpoint_steps(checkpoints_dir: str) -> Sequence[int]:\\ndef latest_step(checkpoints_dir: str) -> Optional[int]:\\ndef _get_local_data(x):\\ndef _sync_global_devices(name: str) -> None:\\ndef get_checkpoint_dir(checkpoints_dir: str, step: int) -> str:\\ndef _cast(target: PyTreeDef, dtype: jnp.dtype):\\n def maybe_cast(x):\\ndef _update_ts_path_from_relative_to_absolute(\\n ckpt_dir: str, ts_spec_dict: MutableMapping[str, Any]):\\ndef _maybe_update_ts_from_file_to_gcs(ckpt_contents):\\n def _gfile_to_gcs_driver(arr_or_ts_spec_dict):\\n def _is_leaf(value):\\ndef _maybe_update_ts_from_gcs_to_file(ckpt_contents):\\n def _gcs_to_file_driver(arr_or_ts_spec_dict):\\n def _is_leaf(value):\\n def __init__(self, num_bytes):\\n async def wait_for_bytes(self, n_bytes):\\n async def return_bytes(self, n_bytes):\\n def __call__(self, state_dict: PyTreeDef,\\n parameter_infos: PyTreeDef) -> Tuple[PyTreeDef, PyTreeDef]:\\n def __call__(self,\\n state_dict: PyTreeDef,\\n target_state_dict: PyTreeDef,\\n *,\\n is_resuming: bool = False) -> PyTreeDef:\\n def __init__(self, dataset_iterator: tf.data.Iterator):\\n def save(self, filename: str):\\n def load(self, filename: str):\\n def __init__(self,\\n train_state: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner,\\n checkpoints_dir: str,\\n dataset_iterator: Optional[\\n Union[tf.data.Iterator,\\n clu.data.dataset_iterator.DatasetIterator]] = None,\\n *,\\n keep: Optional[int] = None,\\n save_dtype: jnp.dtype = np.float32,\\n restore_dtype: Optional[jnp.dtype] = None,\\n use_gda: Optional[bool] = True,\\n keep_dataset_checkpoints: Optional[int] = None):\\n def _get_state_dict_for_save(\\n self,\\n state_dict: Dict[str, Any],\\n lazy_load: bool = True) -> MutableMapping[str, Any]:\\n def _lazy_load_device_array(arr):\\n def _get_parameter_infos(self):\\n def _get_param_info(name: str, arr: Any, axes: partitioning.PartitionSpec):\\n def _get_checkpoint_dir(self, step: int) -> str:\\n def all_steps(self) -> Sequence[int]:\\n def all_dataset_checkpoint_steps(self) -> Sequence[int]:\\n def latest_step(self) -> Optional[int]:\\n def _remove_old_dataset_checkpoints(self):\\n def _remove_old_checkpoints(self):\\n def save(self,\\n train_state: train_state_lib.TrainState,\\n state_transformation_fns: Sequence[SaveStateTransformationFn] = (),\\n *,\\n concurrent_gb: int = 128):\\n def _write_state_to_tensorstore(\\n self,\\n ckpt_dir: str,\\n train_state: train_state_lib.TrainState,\\n concurrent_gb: int,\\n state_transformation_fns: Sequence[SaveStateTransformationFn],\\n ) -> Mapping[str, Any]:\\n async def _write_array(maybe_arr: Any,\\n param_info: Optional[_ParameterInfo],\\n cast: bool = False):\\n def _cast_arr_if_not_partitioned(maybe_arr, param_info):\\n def _transform_state_and_infos(\\n self,\\n state_dict: PyTreeDef,\\n parameter_infos: PyTreeDef,\\n state_transformation_fns: Sequence[SaveStateTransformationFn],\\n ) -> Tuple[PyTreeDef, PyTreeDef]:\\n def restore(\\n self,\\n step: Optional[int] = None,\\n path: Optional[str] = None,\\n state_transformation_fns: Sequence[RestoreStateTransformationFn] = (),\\n fallback_state: Optional[Mapping[str, Any]] = None,\\n lazy_parameters: bool = False) -> train_state_lib.TrainState:\\n def _restore_train_state(\\n self,\\n state_dict: optimizers.OptimizerStateType) -> train_state_lib.TrainState:\\n def _create_lazy_awaitable_array(\\n self, param_info: _ParameterInfo, maybe_ts_spec: Any, ckpt_path: str,\\n restore_dtype: Optional[jnp.dtype]) -> LazyAwaitableArray:\\n async def get_fn():\\n def _read_state_from_tensorstore(\\n self,\\n ckpt_path: str,\\n written_state_dict: Mapping[str, Any],\\n restore_parameter_infos: Optional[Mapping[str, Any]] = None,\\n lazy_parameters: bool = False,\\n ) -> Mapping[str, Any]:\\n def restore_from_tf_checkpoint(\\n self,\\n path_or_dir: str,\\n strict: bool = True,\\n translator: Optional[checkpoint_importer.CheckpointTranslator] = None\\n ) -> train_state_lib.TrainState:\\n def _partition_parameter(maybe_arr: Any, param_info: _ParameterInfo):\\n def convert_from_tf_checkpoint(\\n self,\\n path_or_dir: str,\\n *,\\n state_transformation_fns: Sequence[SaveStateTransformationFn] = (),\\n concurrent_gb: int = 16,\\n translator: Optional[checkpoint_importer.CheckpointTranslator] = None):\\n def _get_optimizer_state_dict(\\n self, ckpt_contents: PyTreeDef,\\n state_transformation_fns: Sequence[RestoreStateTransformationFn]):\\n def __call__(self,\\n train_state: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner,\\n checkpoints_dir: str,\\n dataset_iterator: Optional[tf.data.Iterator] = None,\\n *,\\n keep: Optional[int] = None,\\n save_dtype: jnp.dtype = np.float32,\\n restore_dtype: Optional[jnp.dtype] = None,\\n use_gda: Optional[bool] = False,\\n keep_dataset_checkpoints: Optional[int] = None) -> Checkpointer:\\n def __init__(self,\\n train_state: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner,\\n checkpoints_dir: str,\\n dataset_iterator: Optional[tf.data.Iterator] = None,\\n *,\\n keep: Optional[int] = None,\\n save_dtype: jnp.dtype = np.float32,\\n restore_dtype: Optional[jnp.dtype] = None,\\n metric_name_to_monitor: str = 'train/accuracy',\\n metric_mode: str = 'max',\\n keep_checkpoints_without_metrics: bool = True,\\n force_keep_period: Optional[int] = None,\\n use_gda: bool = False,\\n keep_dataset_checkpoints: Optional[int] = None):\\n def _populate_metrics_for_steps(self,\\n steps: Iterable[int]) -> Mapping[int, float]:\\n def _try_fill_metric_run_and_tag_names(self, run_keys: Iterable[str]) -> bool:\\n def _filter_out_force_keep_period_steps(self, existing_steps):\\n def _remove_old_checkpoints(self):\\ndef _get_optimizer_state_dict(\\n ckpt_contents: PyTreeDef, optimizer_state: Mapping[str, Any],\\n state_transformation_fns: Sequence[RestoreStateTransformationFn]):\\ndef _transform_state_and_infos(\\n state_dict: PyTreeDef,\\n parameter_infos: PyTreeDef,\\n state_transformation_fns: Sequence[SaveStateTransformationFn],\\n) -> Tuple[PyTreeDef, PyTreeDef]:\\nasync def _read_ts(param_info: _ParameterInfo,\\n maybe_tspec: Any,\\n ckpt_path: str,\\n restore_dtype: Optional[jnp.dtype] = None,\\n mesh: Optional[gda_lib.Shape] = None,\\n axes: Optional[gda_lib.MeshAxes] = None):\\ndef fake_param_info(maybe_tspec: Any) -> Optional[_ParameterInfo]:\\ndef find_checkpoint(path: str, step: Optional[int] = None) -> str:\\ndef load_t5x_checkpoint(\\n path: str,\\n step: Optional[int] = None,\\n state_transformation_fns: Sequence[RestoreStateTransformationFn] = (),\\n remap: bool = True,\\n restore_dtype: Optional[jnp.dtype] = None,\\n lazy_parameters: bool = False) -> PyTreeDef:\\n def _create_lazy_awaitable_array(\\n param_info: _ParameterInfo, maybe_ts_spec: Any, ckpt_path: str,\\n restore_dtype: Optional[jnp.dtype]) -> LazyAwaitableArray:\\ndef _transforms_from_state_transformation_fns(\\n state_transformation_fns: Sequence[SaveStateTransformationFn]):\\n def __init__(self, checkpoint_filename: str):\\n def save(self, directory: epath.Path, item: tf.data.Iterator):\\n def restore(self,\\n directory: epath.Path,\\n item: Optional[tf.data.Iterator] = None) -> tf.data.Iterator:\\n def structure(self, directory: epath.Path) -> Any:\\n def structure(self, directory: epath.Path) -> PyTreeDef:\\n def tensorstore_spec_to_name(leaf):\\n def save(self,\\n directory: Union[str, epath.Path],\\n item: Any,\\n *args,\\n force: bool = False,\\n tmp_directory: Optional[Union[str, epath.Path]] = None,\\n **kwargs):\\n def __init__(self,\\n directory: str,\\n train_state_shape: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner,\\n dataset_iterator: Optional[tf.data.Iterator] = None,\\n save_dtype: Optional[jnp.dtype] = None,\\n restore_dtype: Optional[jnp.dtype] = None,\\n keep: Optional[int] = None,\\n keep_dataset_checkpoints: Optional[int] = None):\\n def all_steps(self) -> Sequence[int]:\\n def _parameter_infos(self,\\n train_state: train_state_lib.TrainState) -> PyTreeDef:\\n def _get_save_directory(self,\\n step: int,\\n directory: epath.Path,\\n key_name: Optional[str] = None) -> epath.Path:\\n def save(self,\\n train_state: train_state_lib.TrainState,\\n state_transformation_fns: Sequence[SaveStateTransformationFn] = ()):\\n def _save_args(param_info):\\n def restore(\\n self,\\n step: int,\\n fallback_state: Optional[Mapping[str, Any]] = None,\\n state_transformation_fns: Sequence[SaveStateTransformationFn] = (),\\n lazy_parameters: Optional[bool] = False) -> train_state_lib.TrainState:\\n def _restore_args(param_info):\\n def _add_checkpoint_info(self, step, metrics):\\nclass _ParameterInfo:\\nclass _BytesConditionVariable(object):\\nclass SaveStateTransformationFn(typing_extensions.Protocol):\\nclass RestoreStateTransformationFn(typing_extensions.Protocol):\\nclass _TfDataCheckpointer:\\nclass Checkpointer(object):\\nclass CheckpointerConstructor(typing_extensions.Protocol):\\nclass SaveBestCheckpointer(Checkpointer):\\nclass _OrbaxParamInfo:\\nclass DatasetCheckpointHandler(orbax.checkpoint.CheckpointHandler):\\nclass TrainStateCheckpointHandler(orbax.checkpoint.PyTreeCheckpointHandler):\\nclass NonAtomicCheckpointer(orbax.checkpoint.Checkpointer):\\nclass CheckpointManager(orbax.checkpoint.CheckpointManager):\"\n },\n {\n \"identifier\": \"eval\",\n \"path\": \"t5x/eval.py\",\n \"snippet\": \"_DEFAULT_GIN_SEARCH_PATHS = [\\n os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\\n]\\n FLAGS = flags.FLAGS\\nclass SummarizeConfigFn(Protocol):\\nclass InferenceEvaluator:\\n def __call__(self, model_dir: str,\\n summary_writer: Optional[metric_writers.SummaryWriter],\\n step: int) -> None:\\n def __init__(\\n self,\\n infer_eval_dataset_cfg: utils.DatasetConfig,\\n inference_evaluator_cls: utils.EvaluatorConstructor,\\n model: models.BaseModel,\\n partitioner: partitioning.BasePartitioner,\\n log_dir: Optional[str] = None,\\n verify_matching_vocabs_fn: Optional[\\n Callable[[utils.DatasetConfig, models.BaseModel], None]]=None,\\n ):\\n def model_feature_shapes(self) -> Mapping[str, Tuple[int, ...]]:\\n def eval_tasks(self) -> Sequence[seqio.Task]:\\n def close(self):\\n def evaluate(\\n self,\\n train_state: train_state_lib.TrainState,\\n train_state_axes: train_state_lib.TrainState,\\n ) -> seqio.evaluation.AllMetricsFuture:\\ndef evaluate(\\n *,\\n model: models.BaseTransformerModel,\\n dataset_cfg: utils.DatasetConfig,\\n restore_checkpoint_cfg: utils.RestoreCheckpointConfig,\\n partitioner: partitioning.BasePartitioner,\\n output_dir: str,\\n inference_evaluator_cls: utils.EvaluatorConstructor = UnifiedIOEvaluator,\\n use_wandb = True,\\n summarize_config_fn: SummarizeConfigFn = gin_utils.summarize_gin_config,\\n train_state_initializer_cls: Type[\\n utils.TrainStateInitializer] = utils.TrainStateInitializer,\\n fallback_init_rng: Optional[int] = None,\\n log_only=False\\n):\\n def main(argv: Sequence[str]):\\n def _main(argv: Sequence[str]):\"\n },\n {\n \"identifier\": \"models\",\n \"path\": \"t5x/models.py\",\n \"snippet\": \"class TokensIdsToLogitsCallable(typing_extensions.Protocol):\\nclass DecodeFnCallable(typing_extensions.Protocol):\\nclass BaseModel(abc.ABC):\\nclass BaseTransformerModel(BaseModel):\\nclass EncoderDecoderModel(BaseTransformerModel):\\nclass DecoderOnlyModel(BaseTransformerModel):\\n def __call__(\\n self, decoding_state: decoding.DecodingState\\n ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\\n def __call__(self, *, inputs: jnp.ndarray, cache: Mapping[str, jnp.ndarray],\\n tokens_to_logits: TokensIdsToLogitsCallable, eos_id: int,\\n num_decodes: int, decode_rng: Optional[jax.random.KeyArray],\\n cache_offset: int, **kwargs) -> Tuple[jnp.ndarray, jnp.ndarray]:\\n def __init__(self, optimizer_def: optimizers.OptimizerDefType):\\n def loss_fn(\\n self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n dropout_rng: Optional[jax.random.KeyArray],\\n ) -> Tuple[jnp.ndarray, MetricsMap]:\\n def eval_fn(\\n self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n ) -> Tuple[jnp.ndarray, MetricsMap]:\\n def predict_batch(self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n rng: Optional[jax.random.KeyArray] = None) -> jnp.ndarray:\\n def predict_batch_with_aux(\\n self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n rng: Optional[jax.random.KeyArray] = None,\\n ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\\n def score_batch(self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n return_intermediates: bool = False) -> jnp.ndarray:\\n def get_initial_variables(\\n self,\\n rng: jax.random.KeyArray,\\n input_shapes: Mapping[str, Array],\\n input_types: Optional[Mapping[str, jnp.dtype]] = None\\n ) -> flax_scope.FrozenVariableDict:\\n def __init__(\\n self,\\n module: nn.Module,\\n input_vocabulary: seqio.Vocabulary,\\n output_vocabulary: seqio.Vocabulary,\\n optimizer_def: optimizers.OptimizerDefType,\\n decode_fn: Optional[DecodeFnCallable] = None,\\n label_smoothing: float = 0.0,\\n z_loss: float = 0.0,\\n loss_normalizing_factor: Optional[Union[\\n float, int, str, losses.SpecialLossNormalizingFactor]] = None,\\n ):\\n def input_vocabulary(self):\\n def output_vocabulary(self):\\n def decode_fn(self):\\n def _compute_logits(\\n self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n dropout_rng: Optional[jax.random.KeyArray] = None) -> jnp.ndarray:\\n def loss_fn(\\n self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n dropout_rng: Optional[jax.random.KeyArray],\\n ) -> Tuple[jnp.ndarray, MetricsMap]:\\n def _compute_metrics(\\n self,\\n logits: jnp.ndarray,\\n targets: jnp.ndarray,\\n mask: jnp.ndarray,\\n loss: jnp.ndarray,\\n z_loss: Optional[jnp.ndarray] = None,\\n segment_ids: Optional[Mapping[str, jnp.ndarray]] = None,\\n ) -> MetricsMap:\\n def __init__(\\n self,\\n module: nn.Module,\\n input_vocabulary: seqio.Vocabulary,\\n output_vocabulary: seqio.Vocabulary,\\n optimizer_def: optimizers.OptimizerDefType,\\n decode_fn: DecodeFnCallable = decoding.beam_search,\\n feature_converter_cls: Optional[Callable[...,\\n seqio.FeatureConverter]] = None,\\n label_smoothing: float = 0.0,\\n z_loss: float = 0.0,\\n loss_normalizing_factor: Optional[float] = None,\\n ):\\n def get_initial_variables(\\n self,\\n rng: jax.random.KeyArray,\\n input_shapes: Mapping[str, Array],\\n input_types: Optional[Mapping[str, jnp.dtype]] = None\\n ) -> flax_scope.FrozenVariableDict:\\n def _compute_logits(\\n self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n dropout_rng: Optional[jax.random.KeyArray] = None,\\n mutable: flax_scope.CollectionFilter = False,\\n other_variables: Optional[PyTreeDef] = None,\\n ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, flax_scope.FrozenVariableDict]]:\\n def _compute_logits_from_slice(\\n self, decoding_state: decoding.DecodingState, params: PyTreeDef,\\n encoded_inputs: jnp.ndarray, raw_inputs: jnp.ndarray,\\n max_decode_length: int) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\\n def predict_batch_with_aux(\\n self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n rng: Optional[jax.random.KeyArray] = None,\\n decoder_params: Optional[MutableMapping[str, Any]] = None,\\n return_all_decodes: bool = False,\\n num_decodes: int = 1,\\n prompt_with_targets: bool = False\\n ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\\n def score_batch(\\n self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n return_intermediates: bool = False,\\n ) -> Union[jnp.ndarray, Tuple[jnp.ndarray, Mapping[str, Any]]]:\\n def __init__(\\n self,\\n module: nn.Module,\\n vocabulary: seqio.Vocabulary,\\n optimizer_def: optimizers.OptimizerDefType,\\n decode_fn: DecodeFnCallable = decoding.temperature_sample,\\n inputs_bidirectional_attention: bool = False,\\n feature_converter_cls: Optional[Callable[...,\\n seqio.FeatureConverter]] = None,\\n label_smoothing: float = 0.0,\\n z_loss: float = 0.0,\\n loss_normalizing_factor: Optional[float] = None,\\n ):\\n def get_initial_variables(\\n self,\\n rng: jax.random.KeyArray,\\n input_shapes: Mapping[str, Array],\\n input_types: Optional[Mapping[str, jnp.dtype]] = None\\n ) -> flax_scope.FrozenVariableDict:\\n def _get_decoder_causal_attention(self, batch):\\n def _compute_logits(\\n self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n dropout_rng: Optional[jax.random.KeyArray] = None,\\n mutable: flax_scope.CollectionFilter = False) -> jnp.ndarray:\\n def _compute_logits_from_slice(\\n self,\\n decoding_state: decoding.DecodingState,\\n params: PyTreeDef,\\n max_decode_length: int,\\n ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\\n def score_batch(self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n return_intermediates: bool = False) -> jnp.ndarray:\\n def _compute_kv_cache(\\n self,\\n params: PyTreeDef,\\n inputs: jnp.ndarray,\\n inputs_lengths: jnp.ndarray,\\n decoder_causal_attention: jnp.ndarray,\\n ) -> PyTreeDef:\\n def predict_batch_with_aux(\\n self,\\n params: PyTreeDef,\\n batch: Mapping[str, jnp.ndarray],\\n rng: Optional[jax.random.KeyArray] = None,\\n *,\\n return_all_decodes: bool = False,\\n num_decodes: int = 1,\\n decoder_params: Optional[MutableMapping[str, Any]] = None,\\n ) -> Tuple[jnp.ndarray, Mapping[str, jnp.ndarray]]:\\ndef remove_prefix(sequence: jnp.ndarray,\\n prefix_length: jnp.ndarray) -> jnp.ndarray:\\ndef compute_weighted_accuracy(\\n logits: jnp.ndarray,\\n targets: jnp.ndarray,\\n weights: Optional[jnp.ndarray] = None) -> Tuple[jnp.ndarray, jnp.ndarray]:\\ndef compute_metrics(logits: jnp.ndarray, targets: jnp.ndarray,\\n weights: jnp.ndarray, loss: jnp.ndarray,\\n weight_sum: jnp.ndarray,\\n additional_metrics: MetricsMap) -> MetricsMap:\\ndef compute_base_metrics(\\n logits: jnp.ndarray,\\n targets: jnp.ndarray,\\n mask: jnp.ndarray,\\n loss: jnp.ndarray,\\n z_loss: Optional[jnp.ndarray] = None,\\n segment_ids: Optional[Mapping[str, jnp.ndarray]] = None,\\n) -> MetricsMap:\\ndef get_input_vocabulary(model: BaseTransformerModel) -> seqio.Vocabulary:\\ndef get_output_vocabulary(model: BaseTransformerModel) -> seqio.Vocabulary:\\n FEATURE_CONVERTER_CLS: Callable[..., seqio.FeatureConverter]\\n FEATURE_CONVERTER_CLS = seqio.EncDecFeatureConverter\\n FEATURE_CONVERTER_CLS = seqio.DecoderFeatureConverter\"\n },\n {\n \"identifier\": \"evaluator\",\n \"path\": \"t5x/examples/unified_io/evaluator.py\",\n \"snippet\": \"class UnifiedIOOutput:\\nclass UnifiedIOEvaluator(seqio.Evaluator):\\n def text(self):\\n def text_tokens(self):\\n def image_tokens(self):\\n def image(self):\\n def audio(self):\\n def scores(self):\\ndef build_uio_outputs(aux_values, vocab) -> List[UnifiedIOOutput]:\\n def __init__(\\n self,\\n mixture_or_task_name: str,\\n feature_converter,\\n eval_split: str = \\\"validation\\\",\\n use_cached: bool = False,\\n seed: Optional[int] = 42,\\n sequence_length: Optional[Mapping[str, int]] = None,\\n num_examples: Optional[int] = None,\\n shuffle: bool = False,\\n logger_cls: Sequence = (),\\n log_dir: Optional[str] = None,\\n use_memory_cache: bool = True,\\n target_field_name: str = \\\"targets\\\",\\n ):\\n def _compute_metrics(self,\\n predicted_tokens: AllOutputTokensType,\\n scores: AllOutputScoresType,\\n all_aux_values: AllOutputAuxValuesType,\\n step: Optional[int] = None) -> AllMetricsType:\"\n },\n {\n \"identifier\": \"partitioning\",\n \"path\": \"t5x/partitioning.py\",\n \"snippet\": \"class AxisNames(tuple):\\nclass LocalChunkInfo:\\nclass LocalChunker:\\nclass DataLayout:\\nclass BasePartitioner(metaclass=abc.ABCMeta):\\nclass PjittedFnWithContext(PartitionedCallable):\\nclass BasePjitPartitioner(BasePartitioner):\\nclass PjitPartitioner(BasePjitPartitioner):\\n def __new__(cls, *names):\\n def __repr__(self):\\ndef pjit(\\n fun: Callable, # pylint: disable=g-bare-generic\\n in_axis_resources,\\n out_axis_resources,\\n static_argnums: Union[int, Sequence[int]] = (),\\n donate_argnums: Union[int, Sequence[int]] = (),\\n backend: Optional[str] = None):\\ndef pjit_with_cpu_fallback(\\n fun: Callable, # pylint: disable=g-bare-generic\\n in_axis_resources,\\n out_axis_resources,\\n static_argnums: Union[int, Sequence[int]] = (),\\n donate_argnums: Union[int, Sequence[int]] = (),\\n backend: Optional[str] = None):\\ndef with_sharding_constraint(x, axis_resources):\\ndef bounds_from_last_device(\\n last_device: jax.lib.xla_client.Device) -> HardwareMesh:\\ndef get_coords(device: jax.lib.xla_client.Device) -> HardwareMesh:\\ndef global_mesh_defined():\\ndef get_mesh(model_parallel_submesh: HardwareMesh,\\n input_devices: Sequence[JaxDevice] = (),\\n input_local_devices: Sequence[JaxDevice] = (),\\n tile_by_host_if_needed: bool = True,\\n backend: Optional[str] = None) -> Mesh:\\n def dh_dd_mh_md(g: int, m: int, l: int) -> Tuple[int, int, int, int]:\\ndef get_cpu_mesh() -> Mesh:\\ndef get_gpu_mesh(num_partitions: int) -> Mesh:\\ndef default_mesh(num_partitions: int,\\n model_parallel_submesh: Optional[HardwareMesh] = None,\\n backend: Optional[str] = None) -> Mesh:\\n def __init__(self, global_mesh: Mesh):\\n def get_local_chunk_info(\\n self, global_shape: Tuple[int, ...],\\n mesh_axes: Sequence[Optional[str]]) -> LocalChunkInfo:\\ndef standard_logical_axis_rules(\\n activation_partitioning_dims: int = 1,\\n parameter_partitioning_dims: int = 1,\\n additional_rules: Optional[LogicalAxisRules] = None) -> LogicalAxisRules:\\ndef _id_fn(x, ix):\\n def __init__(self,\\n num_partitions: Optional[int] = None,\\n model_parallel_submesh: Optional[HardwareMesh] = None,\\n params_on_devices: bool = True,\\n backend: Optional[str] = None):\\n def mesh(self) -> Mesh:\\n def data_partition_spec(self) -> PartitionSpec:\\n def get_data_layout(self,\\n batch_size: Optional[int] = None,\\n host_index: Optional[int] = None) -> DataLayout:\\n def get_local_chunk_info(\\n self, global_shape: Tuple[int, ...],\\n mesh_axes: Sequence[Optional[str]]) -> LocalChunkInfo:\\n def params_on_devices(self):\\n def move_params_to_devices(self, train_state: TrainState,\\n train_state_axes: TrainState) -> TrainState:\\n def _local_chunker(self):\\n def get_logical_axes(self, train_state: TrainState) -> TrainState:\\n def get_mesh_axes(self, train_state: TrainState) -> TrainState:\\n def partition(\\n self,\\n fn: Callable, # pylint: disable=g-bare-generic\\n in_axis_resources,\\n out_axis_resources,\\n static_argnums: Union[int, Sequence[int]] = (),\\n donate_argnums: Union[int, Sequence[int]] = ()\\n ) -> PartitionedCallable:\\n def compile(self, partitioned_fn: PartitionedCallable,\\n *args) -> CompiledPartitionedCallable:\\n def __init__(self,\\n pjitted_fn,\\n partition_mesh: Mesh,\\n logical_axis_rules: flax_partitioning.LogicalRules = ()):\\n def __call__(self, *args):\\n def lower(self, *args):\\n def _local_chunker(self) -> LocalChunker:\\n def mesh(self) -> Mesh:\\n def partition(\\n self,\\n fn: Callable, # pylint: disable=g-bare-generic\\n in_axis_resources,\\n out_axis_resources,\\n static_argnums: Union[int, Sequence[int]] = (),\\n donate_argnums: Union[int, Sequence[int]] = ()\\n ) -> PjittedFnWithContext:\\n def compile(self, partitioned_fn: PjittedFnWithContext,\\n *args) -> CompiledPartitionedCallable:\\n def __init__(self,\\n num_partitions: Optional[int] = None,\\n model_parallel_submesh: Optional[HardwareMesh] = None,\\n params_on_devices: bool = True,\\n backend: Optional[str] = None,\\n logical_axis_rules: Optional[LogicalAxisRules] = None,\\n use_cpu_pjit: Optional[bool] = False):\\n def partition(\\n self,\\n fn: Callable, # pylint: disable=g-bare-generic\\n in_axis_resources,\\n out_axis_resources,\\n static_argnums: Union[int, Sequence[int]] = (),\\n donate_argnums: Union[int, Sequence[int]] = ()\\n ) -> PjittedFnWithContext:\\n def logical_axis_rules(self):\\n def get_logical_axes(self, train_state: TrainState) -> TrainState:\\n def get_mesh_axes(self, train_state: TrainState) -> TrainState:\\n def _logical_to_mesh_axes(param_name, logical_axes):\"\n },\n {\n \"identifier\": \"train_state\",\n \"path\": \"t5x/train_state.py\",\n \"snippet\": \"EMPTY_DICT = flax.core.freeze({})\\nclass TrainState(typing_extensions.Protocol):\\nclass FlaxOptimTrainState(flax.struct.PyTreeNode):\\nclass GanOptimTrainState(FlaxOptimTrainState):\\nclass InferenceState(flax.struct.PyTreeNode):\\n def step(self) -> jnp.ndarray:\\n def params(self) -> FrozenVariableDict:\\n def param_states(self) -> FrozenVariableDict:\\n def flax_mutables(self) -> FrozenVariableDict:\\n def state_dict(self) -> MutableVariableDict:\\n def restore_state(self, state_dict: Mapping[str, Any]) -> 'TrainState':\\n def replace_params(self, params: VariableDict) -> 'TrainState':\\n def replace_flax_mutables(self, flax_mutables: FrozenDict) -> 'TrainState':\\n def replace_step(self, step: jnp.ndarray) -> 'TrainState':\\n def apply_gradient(self,\\n grads,\\n learning_rate,\\n flax_mutables=EMPTY_DICT) -> 'TrainState':\\n def as_logical_axes(self) -> 'TrainState':\\ndef _validate_params_axes(params_axes, params):\\ndef _split_variables_and_axes(\\n variables_and_axes: FrozenVariableDict\\n) -> Tuple[FrozenVariableDict, FrozenVariableDict]:\\n def create(cls, optimizer_def: optimizers.OptimizerDefType,\\n model_variables: FrozenVariableDict) -> 'FlaxOptimTrainState':\\n def step(self) -> jnp.ndarray:\\n def params(self) -> FrozenVariableDict:\\n def param_states(self) -> FrozenVariableDict:\\n def state_dict(self) -> MutableVariableDict:\\n def apply_gradient(self,\\n grads,\\n learning_rate,\\n flax_mutables=EMPTY_DICT) -> 'FlaxOptimTrainState':\\n def replace_params(self, params: VariableDict) -> 'FlaxOptimTrainState':\\n def replace_flax_mutables(self,\\n flax_mutables: FrozenDict) -> 'FlaxOptimTrainState':\\n def replace_step(self, step: jnp.ndarray) -> 'FlaxOptimTrainState':\\n def restore_state(self, state_dict: VariableDict) -> 'FlaxOptimTrainState':\\n def as_logical_axes(self) -> 'FlaxOptimTrainState':\\n def apply_gradient(self,\\n grads,\\n learning_rate,\\n flax_mutables=EMPTY_DICT) -> 'FlaxOptimTrainState':\\n def create(cls, model_variables: FrozenVariableDict) -> 'InferenceState':\\n def param_states(self) -> FrozenVariableDict:\\n def apply_gradient(self, *args, **kwargs) -> 'InferenceState':\\n def state_dict(self) -> MutableMapping[str, Any]:\\n def replace_step(self, step: jnp.ndarray) -> 'InferenceState':\\n def replace_params(self, params: FrozenVariableDict) -> 'InferenceState':\\n def replace_flax_mutables(self,\\n flax_mutables: FrozenDict) -> 'InferenceState':\\n def restore_state(self, state_dict: Mapping[str, Any]) -> 'InferenceState':\\n def as_logical_axes(self) -> 'InferenceState':\"\n },\n {\n \"identifier\": \"trainer\",\n \"path\": \"t5x/trainer.py\",\n \"snippet\": \"def _merge_metrics(a, b):\\ndef merge_metrics(a, b):\\n def result(self) -> Mapping[str, Array]:\\n def result(self) -> Mapping[str, clu.values.Value]:\\n def result(self) -> float:\\n def __call__(\\n self,\\n step: jnp.ndarray,\\n ) -> jnp.ndarray:\\n def __call__(self, metrics: MetricMapType, duration: float,\\n num_steps: int) -> Mapping[str, jnp.ndarray]:\\n def __call__(\\n self, train_state: train_state_lib.TrainState,\\n batch: BatchType) -> Tuple[train_state_lib.TrainState, MetricMapType]:\\n def __call__(self, train_state: train_state_lib.TrainState,\\n batch: jnp.ndarray) -> MetricMapType:\\n def compute_metrics(\\n self, gradients: ModelWeights,\\n old_train_state: train_state_lib.TrainState,\\n new_train_state: train_state_lib.TrainState) -> MutableMetricMapType:\\n def _make_rms_metrics(name, tree):\\n def _make_max_metrics(name, tree):\\n def compute_metrics(\\n self, gradients: ModelWeights,\\n old_train_state: train_state_lib.TrainState,\\n new_train_state: train_state_lib.TrainState) -> MutableMetricMapType:\\n def __init__(self):\\n def close(self):\\n def __del__(self):\\n def _get_completion_future(self, block_on: PyTreeDef = ()) -> TimeFuture:\\n def _get_completion_time():\\n def start(self, block_on: PyTreeDef = ()):\\n def stop(self, block_on: PyTreeDef = ()) -> TimeFuture:\\n def __init__(self, name: str, summary_dir: Optional[str] = None, log_to_wandb=False):\\n def __del__(self):\\n def close(self):\\n def summary_writer(self) -> metric_writers.MetricWriter:\\n def write_scalar(self, key: str, val: metric_writers.interface.Scalar,\\n step: int):\\n def write_scalars(self, step: int,\\n scalars: Mapping[str, metric_writers.interface.Scalar]):\\n def start_duration_timer(self, block_on: PyTreeDef = ()):\\n def write_metrics_summary(self, metrics: MetricMapType, step: int,\\n num_steps: int) -> MetricValueMapFuture:\\n def _summarize_and_write():\\n def _ensure_not_on_device(x):\\n def flush(self):\\n def __init__(self, model: models.BaseModel,\\n train_state: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner,\\n eval_names: Sequence[str], summary_dir: Optional[str],\\n train_state_axes: Any, rng: Rng,\\n use_wandb=False, packing_strategy=None, log_weights=None):\\n def __enter__(self):\\n def __exit__(self, exc_type, exc_value, traceback):\\n def close(self):\\n def _get_step_rng(self, step: int) -> Rng:\\n def train_state(self):\\n def train_state(self, train_state: PyTreeDef):\\n def _weight_metric_fn(self):\\n def _get_weight_metrics_fn(_params):\\n def train(self,\\n batch_iter: Union[Iterator[BatchType],\\n clu.data.dataset_iterator.DatasetIterator],\\n num_steps: int,\\n start_step: Optional[int] = None) -> ArrayMapFuture:\\n def compile_train(self, batch: ElementSpec) -> None:\\n def eval(\\n self, batch_iters: Mapping[str,\\n Iterator[BatchType]], pbar_nsteps=None) -> Mapping[str, Array]:\\n def compile_eval(self, batches: Mapping[str, BatchType]) -> None:\\n def _partitioned_train_step(self) -> PartitionedTrainCallable:\\n def _partitioned_eval_step(self) -> PartitionedEvalCallable:\\ndef accumulate_grads_microbatched(\\n model: models.BaseModel,\\n train_state: train_state_lib.TrainState,\\n batch: BatchType,\\n dropout_rng: Rng,\\n num_microbatches: Optional[int],\\n data_partition_spec: PartitionSpec = PartitionSpec(\\\"data\\\"),\\n loss_fn_args=None\\n) -> Tuple[train_state_lib.TrainState, MutableMetricMapType,\\n def get_microbatch(batch: BatchType, idx: int) -> Mapping[str, jnp.ndarray]:\\n def metrics_and_grad(loop_cnt, dropout_rng, flax_mutables=None):\\n def per_microbatch_train_step(\\n loop_cnt: int, state: Tuple[jnp.ndarray, jnp.ndarray,\\n Mapping[str, jnp.ndarray],\\n Optional[FlaxMutables]]\\n ) -> Tuple[jnp.ndarray, jnp.ndarray, Mapping[str, jnp.ndarray],\\ndef apply_grads(\\n train_state: train_state_lib.TrainState,\\n grad_accum: ModelWeights,\\n metrics: MutableMetricMapType,\\n learning_rate: jnp.ndarray,\\n weight_metrics_computer: Optional[WeightMetricsComputer],\\n other_state_variables: Optional[Mapping[str, Any]] = None\\n) -> Tuple[train_state_lib.TrainState, MetricMapType]:\\ndef eval_step(model: models.BaseModel, train_state: train_state_lib.TrainState,\\n batch: jnp.ndarray) -> MetricMapType:\\ndef train_with_lr(\\n train_state: train_state_lib.TrainState,\\n batch: BatchType,\\n learning_rate: jnp.ndarray,\\n dropout_rng: Rng,\\n model: models.BaseModel,\\n num_microbatches: Optional[int],\\n weight_metrics_computer: Optional[WeightMetricsComputer] = None,\\n data_partition_spec: PartitionSpec = PartitionSpec(\\\"data\\\"),\\n loss_fn_args=None\\n):\\n def __call__(self, model: models.BaseModel,\\n train_state: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner,\\n eval_names: Sequence[str], summary_dir: Optional[str],\\n train_state_axes: Any, rng: Rng) -> BaseTrainer:\\n def __init__(self,\\n model: models.BaseModel,\\n train_state: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner,\\n eval_names: Sequence[str],\\n summary_dir: Optional[str],\\n train_state_axes: Any,\\n rng: Rng,\\n learning_rate_fn: LearningRateCallable,\\n num_microbatches: Optional[int],\\n weight_metrics_computer: Optional[WeightMetricsComputer] = None,\\n use_wandb=True,\\n packing_strategy=None,\\n log_weights=False\\n ):\\n def _partitioned_train_step(self) -> PartitionedTrainCallable:\\n def train_step(train_state: train_state_lib.TrainState, batch: BatchType, static_args=None):\\n def _partitioned_eval_step(self) -> PartitionedEvalCallable:\\ndef _warn_action_not_run(action, task, metric):\\n def run(self, train_state: train_state_lib.TrainState,\\n metrics_by_task: Mapping[str, MetricValueMapType]) -> bool:\\n def __init__(self,\\n metric: Tuple[str, str],\\n mode: str,\\n patience: int = 3,\\n atol: float = 0.,\\n rtol: float = 0.):\\n def _compare_fn(self, current, previous):\\n def run(self, train_state: train_state_lib.TrainState,\\n metrics_by_task: Mapping[str, MetricValueMapType]) -> bool:\\n def __init__(self, task: str, metric: str = \\\"loss\\\"):\\n def run(self, train_state: train_state_lib.TrainState,\\n metrics_by_task: Mapping[str, MetricValueMapType]) -> bool:\\nclass ArrayMapFuture(typing_extensions.Protocol):\\nclass MetricValueMapFuture(typing_extensions.Protocol):\\nclass TimeFuture(typing_extensions.Protocol):\\nclass LearningRateCallable(typing_extensions.Protocol):\\nclass SummarizeMetricsCallable(typing_extensions.Protocol):\\nclass PartitionedTrainCallable(typing_extensions.Protocol):\\nclass PartitionedEvalCallable(typing_extensions.Protocol):\\nclass GradNormComputer(object):\\nclass WeightMetricsComputer(object):\\nclass _AsyncTimer(object):\\nclass MetricsManager(object):\\nclass PreemptionError(Exception):\\nclass BaseTrainer(abc.ABC):\\nclass BaseTrainerConstructor(Protocol):\\nclass Trainer(BaseTrainer):\\nclass ActionMode(enum.Enum):\\nclass BaseAction(abc.ABC):\\nclass EarlyStoppingAction(BaseAction):\\nclass TerminateOnNanAction(BaseAction):\\n _WEIGHT_METRICS = [\\n \\\"weight_rms\\\", \\\"weight_gradient_rms\\\", \\\"weight_update_rms\\\", \\\"weight_max\\\"\\n ]\\n TRAIN = 1\\n TRAIN_EVAL = 2\\n INFER_EVAL = 3\"\n },\n {\n \"identifier\": \"utils\",\n \"path\": \"t5x/utils.py\",\n \"snippet\": \"class EvaluatorConstructor(typing_extensions.Protocol):\\nclass SaveCheckpointConfig:\\nclass RestoreCheckpointConfig:\\nclass CheckpointConfig:\\nclass LegacyCheckpointer(orbax.checkpoint.Checkpointer):\\nclass LegacyCheckpointManager(orbax.checkpoint.CheckpointManager):\\nclass DatasetConfig:\\nclass GDADatasetIterator(clu.data.dataset_iterator.DatasetIterator):\\nclass InitFnCallable(typing_extensions.Protocol):\\nclass LearningRateCallable(typing_extensions.Protocol):\\nclass TrainStateInitializer:\\nclass InferStepWithRngCallable(typing_extensions.Protocol):\\nclass InferStepWithoutRngCallable(typing_extensions.Protocol):\\nclass InferFnCallable(typing_extensions.Protocol):\\nclass GetDatasetCallable(typing_extensions.Protocol):\\nclass GetEvalDatasetCallable(typing_extensions.Protocol):\\nclass _RegexMap(collections.abc.Mapping):\\n def __call__(\\n self,\\n mixture_or_task_name: str,\\n feature_converter: seqio.FeatureConverter,\\n eval_split: str,\\n use_cached: bool,\\n seed: Optional[int],\\n sequence_length: Optional[Mapping[str, int]],\\n log_dir: Optional[str],\\n use_memory_cache: bool,\\n ) -> seqio.Evaluator:\\n def __post_init__(self):\\n def __post_init__(self):\\n def __init__(self,\\n *,\\n save_checkpointer: Optional[checkpoints.Checkpointer] = None,\\n restore_checkpointer: checkpoints.Checkpointer,\\n strict: Optional[bool] = False):\\n async def async_save(self, path: str, item: Any):\\n async def async_restore(self, path: str, item: Optional[Any] = None) -> Any:\\n def save(self,\\n path: str,\\n item: train_state_lib.TrainState,\\n state_transformation_fns: Sequence[\\n checkpoints.SaveStateTransformationFn] = (),\\n *,\\n concurrent_gb: int = 128):\\n def restore(self,\\n path: str,\\n item: Optional[train_state_lib.TrainState],\\n state_transformation_fns: Sequence[\\n checkpoints.RestoreStateTransformationFn] = (),\\n fallback_state: Optional[Mapping[str, Any]] = None,\\n lazy_parameters: bool = False) -> train_state_lib.TrainState:\\n def __init__(self,\\n *,\\n save_cfg: Optional[SaveCheckpointConfig] = None,\\n restore_cfg: RestoreCheckpointConfig,\\n train_state_shape: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner,\\n ds_iter: Optional[\\n Union[tf.data.Iterator,\\n clu.data.dataset_iterator.DatasetIterator]] = None,\\n model_dir: Optional[str] = None,\\n use_gda: Optional[bool] = True):\\n def save(self,\\n train_state: train_state_lib.TrainState,\\n state_transformation_fns: Sequence[\\n checkpoints.SaveStateTransformationFn] = ()):\\n def restore(\\n self,\\n paths: Sequence[str],\\n restore_cfg: RestoreCheckpointConfig,\\n fallback_state: Optional[Mapping[str, Any]] = None\\n ) -> Union[train_state_lib.TrainState, Sequence[train_state_lib.TrainState]]:\\ndef _get_index_mappings(device_to_idxs):\\ndef _create_gda(partitioner: partitioning.BasePartitioner,\\n global_shapes: PyTreeDef, host_arrays: PyTreeDef) -> PyTreeDef:\\n def _put_to_devices(x, global_shape):\\n def _gda(dbs, global_shape):\\n def __init__(self, iterator: clu.data.dataset_iterator.DatasetIterator,\\n partitioner: partitioning.BasePartitioner,\\n global_shapes: PyTreeDef):\\n def __next__(self):\\n def reset(self):\\n def element_spec(self):\\n def save(self, filename):\\n def restore(self, filename):\\n def iterator(self):\\ndef sync_global_devices(name: str) -> None:\\ndef multihost_assert_equal(input_tree, fail_message: str = ''):\\ndef _hardware_uniform(\\n rng_key: Array,\\n shape: Shape,\\n dtype: jnp.dtype = np.float32,\\n minval: Array = np.float32(0),\\n maxval: Array = np.float32(1)\\n) -> Array:\\ndef _hardware_bernoulli(\\n rng_key: Array, p: np.ndarray = np.float32(0.5),\\n shape: Shape = ()) -> Array:\\ndef set_hardware_rng_ops():\\ndef get_zeros_batch_like_spec(\\n batch_spec: Mapping[str,\\n jax.ShapeDtypeStruct]) -> Mapping[str, jnp.ndarray]:\\ndef get_zeros_batch_like_dataset(dataset: tf.data.Dataset,\\n batch_size=None) -> Mapping[str, jnp.ndarray]:\\n def __call__(\\n self, rng: Array, input_shapes: Mapping[str, Array],\\n input_types: Optional[Mapping[str,\\n DType]]) -> flax_scope.FrozenVariableDict:\\n def __call__(self, step: jnp.ndarray) -> jnp.ndarray:\\ndef create_learning_rate_scheduler(\\n factors: str = 'constant * linear_warmup * rsqrt_decay',\\n base_learning_rate: float = 0.5,\\n warmup_steps: int = 1000,\\n decay_factor: float = 0.5,\\n steps_per_decay: int = 20000,\\n steps_per_cycle: int = 100000,\\n step_offset: int = 0,\\n min_learning_rate: float = 1e-8) -> LearningRateCallable:\\n def step_fn(step: jnp.ndarray) -> jnp.ndarray:\\ndef steps(prefix, config, data_size=None, batch_size=None, default=ValueError):\\ndef create_vision_learning_rate_scheduler(\\n total_steps, batch_size=None, data_size=None,\\n base=1.0, decay_type=\\\"stair\\\",\\n scale_with_batchsize=False, **kw):\\n def step_fn(step):\\ndef get_first_valid_restore_config_and_paths(\\n restore_cfgs: Sequence[RestoreCheckpointConfig]\\n) -> Tuple[Optional[RestoreCheckpointConfig], Sequence[str]]:\\ndef get_fallback_state(restore_cfg: RestoreCheckpointConfig,\\n init_fn: Callable[[jnp.ndarray], Mapping[str, Any]],\\n init_rng: jnp.ndarray) -> Optional[Mapping[str, Any]]:\\n def __init__(self,\\n optimizer_def: Optional[optimizers.OptimizerDefType],\\n init_fn: InitFnCallable,\\n input_shapes: Mapping[str, Array],\\n partitioner: partitioning.BasePartitioner,\\n model=None,\\n input_types: Optional[Mapping[str, DType]] = None):\\n def initialize_train_state(rng: Array):\\n def from_scratch(self, init_rng: Array) -> train_state_lib.TrainState:\\n def from_checkpoints(\\n self,\\n restore_cfgs: Sequence[RestoreCheckpointConfig],\\n ds_iter: Optional[tf.data.Iterator] = None,\\n init_rng: Optional[jnp.ndarray] = None,\\n ) -> Iterable[train_state_lib.TrainState]:\\n def _restore_path(path, cfg):\\n def from_checkpoint(\\n self,\\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\\n *,\\n ds_iter: Optional[tf.data.Iterator] = None,\\n init_rng: Optional[jnp.ndarray] = None\\n ) -> Optional[train_state_lib.TrainState]:\\n def from_checkpoint_or_scratch(\\n self,\\n ckpt_cfgs: Sequence[RestoreCheckpointConfig],\\n *,\\n init_rng: Array,\\n ds_iter: Optional[tf.data.Iterator] = None) -> train_state_lib.TrainState:\\ndef log_model_info(log_file: Optional[str],\\n full_train_state: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner):\\n def _log_info_and_write_to_file(writer, format_str, *args):\\n def _log_variable(name: str, arr: Optional[np.ndarray],\\n logical_axes: Optional[partitioning.AxisNames],\\n mesh_axes: Optional[partitioning.PartitionSpec]):\\n def __call__(self,\\n params: Mapping[str, Any],\\n batch: Mapping[str, jnp.ndarray],\\n rng: jnp.ndarray = None) -> PyTreeDef:\\n def __call__(self, params: Mapping[str, Any],\\n batch: Mapping[str, jnp.ndarray]) -> PyTreeDef:\\n def __call__(\\n self,\\n ds: tf.data.Dataset,\\n train_state: train_state_lib.TrainState,\\n rng: Optional[jnp.ndarray] = None\\n ) -> Union[_InferFnResult, _InferFnWithAuxResult]:\\ndef _remove_padding(all_inferences, all_indices):\\ndef get_infer_fn(infer_step: InferStepCallable, batch_size: int,\\n train_state_axes: train_state_lib.TrainState,\\n partitioner: partitioning.BasePartitioner, \\n pbar=False) -> InferFnCallable:\\n def infer_step_with_indices(params, batch, rng, indices):\\n def infer_fn(ds: tf.data.Dataset,\\n train_state: train_state_lib.TrainState,\\n rng: Optional[jnp.ndarray] = None):\\n def _copy_to_host_async(x):\\ndef import_module(module: str):\\ndef get_vocabulary(\\n cfg: DatasetConfig) -> Tuple[seqio.Vocabulary, seqio.Vocabulary]:\\ndef verify_matching_vocabs(cfg: DatasetConfig, model: Any):\\ndef get_dataset(cfg: DatasetConfig,\\n shard_id: int,\\n num_shards: int,\\n feature_converter_cls: Callable[..., seqio.FeatureConverter],\\n num_epochs: Optional[int] = None,\\n continue_from_last_checkpoint: bool = False,\\n batching_fn=None) -> tf.data.Dataset:\\ndef get_dataset_inner(cfg: DatasetConfig,\\n shard_info: seqio.ShardInfo,\\n feature_converter_cls: Callable[...,\\n seqio.FeatureConverter],\\n seed: Optional[int] = None,\\n num_epochs: Optional[int] = None,\\n batching_fn=None\\n ):\\n def __call__(\\n self,\\n cfg: DatasetConfig,\\n shard_id: int,\\n num_shards: int,\\n feature_converter_cls: Callable[..., seqio.FeatureConverter],\\n num_epochs: Optional[int] = None,\\n continue_from_last_checkpoint: bool = True\\n ) -> Union[clu.data.dataset_iterator.DatasetIterator, tf.data.Dataset]:\\n def __call__(\\n self, cfg: DatasetConfig, shard_id: int, num_shards: int, eval_steps: int,\\n feature_converter_cls: Callable[..., seqio.FeatureConverter]\\n ) -> Mapping[str, tf.data.Dataset]:\\ndef get_training_eval_datasets(\\n cfg: DatasetConfig,\\n shard_id: int,\\n num_shards: int,\\n eval_steps: int,\\n feature_converter_cls: Callable[..., seqio.FeatureConverter],\\n deterministic: bool = False,\\n model_dir: Optional[str] = None,\\n start_step: int = 0,\\n) -> Mapping[str, tf.data.Dataset]:\\n def _repeat_shard_batch_take_cache(ds: tf.data.Dataset):\\ndef round_vocab_size_to_multiple(vocabulary: seqio.Vocabulary,\\n divisor: int = 128):\\ndef flatten_dict_string_keys(x):\\ndef flatten_lists(lsts: Iterable[Iterable]) -> Sequence:\\n def __init__(self, kvs: Sequence[Tuple[str, Any]]):\\n def __getitem__(self, key: str) -> Any:\\n def __len__(self) -> int:\\n def __iter__(self) -> Iterable[Tuple[re.Pattern, Any]]:\\ndef override_params_axes_names(\\n model_variables: flax_scope.FrozenVariableDict,\\n params_axes_names_override: Sequence[Tuple[str, Tuple[str, ...]]] = ()\\n) -> flax_scope.FrozenVariableDict:\\ndef get_local_data(x):\"\n },\n {\n \"identifier\": \"init_wandb\",\n \"path\": \"t5x/examples/unified_io/utils.py\",\n \"snippet\": \"@gin.configurable()\\ndef init_wandb(name=None, group=None, entity=None, project=None):\\n utils.create_learning_rate_scheduler() # Makes sure this is registered in `operative_config`\\n config_str = gin.operative_config_str()\\n logging.info(f\\\"Init wandb with group={group} name={name}\\\")\\n wandb.init(\\n group=group,\\n name=name,\\n entity=entity,\\n project=project,\\n force=True,\\n notes=config_str\\n )\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import functools\nimport math\nimport os\nimport time\nimport warnings\nimport clu.data\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nimport seqio\nimport tensorflow as tf\nimport jax.profiler\n import gin\nfrom typing import Callable, Sequence, Mapping, Tuple, Type, Optional\nfrom t5x.examples.unified_io.packing import PackingStrategy\nfrom absl import logging\nfrom clu import metric_writers\nfrom jax import random\nfrom jax.experimental import multihost_utils\nfrom jax.experimental.global_device_array import GlobalDeviceArray\nfrom t5x import checkpoints\nfrom t5x import eval as eval_lib\nfrom t5x import models\nfrom t5x.examples.unified_io import evaluator\nfrom t5x import partitioning\nfrom t5x import train_state as train_state_lib\nfrom t5x import trainer as trainer_lib\nfrom t5x import utils\nfrom os.path import expanduser\nfrom t5x.examples.unified_io.utils import init_wandb\n from t5x.examples.unified_io.metrics.metrics import null_metric\n from t5x.examples.unified_io.data.postprocessing import return_example\n from absl import app\n from absl import flags\n from t5x import gin_utils"},"token_num":{"kind":"number","value":15059,"string":"15,059"},"cropped_code":{"kind":"string","value":" metrics_by_task: A map of metrics keyed by task name.\n\n Returns:\n A bool indicating whether training should be halted.\n\n Raises:\n RuntimeError: When the metrics processed on host 0 is None.\n \"\"\"\n stop_training = False\n if jax.process_index() == 0:\n if not metrics_by_task:\n raise RuntimeError('Metric is unexpectedly empty on process 0')\n for action in actions.get(mode, []):\n stop_training |= action.run(train_state, metrics_by_task=metrics_by_task)\n # Broadcast result from host 0 to others.\n return bool(multihost_utils.broadcast_one_to_all(jnp.array(stop_training)))\n\ndef train(\n *,\n model: models.BaseTransformerModel,\n train_dataset_cfg: utils.DatasetConfig,\n train_eval_dataset_cfg: Optional[utils.DatasetConfig],\n infer_eval_dataset_cfg: Optional[utils.DatasetConfig],\n checkpoint_cfg: utils.CheckpointConfig,\n partitioner: partitioning.BasePartitioner,\n trainer_cls: trainer_lib.BaseTrainerConstructor,\n model_dir: str,\n total_steps: int,\n eval_steps: int,\n eval_period: int,\n stats_period: Optional[int] = None,\n random_seed: Optional[int],\n use_hardware_rng: bool = False,\n summarize_config_fn: Callable[[str, metric_writers.MetricWriter, int], None],\n inference_evaluator_cls: utils.EvaluatorConstructor = seqio.Evaluator,\n get_dataset_fn: utils.GetDatasetCallable = utils.get_dataset,\n concurrent_metrics: bool = True,\n actions: Optional[Mapping[str, Sequence[trainer_lib.BaseAction]]] = None,\n train_eval_get_dataset_fn: utils.GetEvalDatasetCallable = utils\n .get_training_eval_datasets,\n run_eval_before_training: bool = False,\n use_wandb = True, weight_metrics=\"norm\",\n packing_strategy: PackingStrategy = None,\n train_state_initializer_cls: Type[\n utils.TrainStateInitializer] = utils.TrainStateInitializer,\n use_gda: bool = True,\n verify_matching_vocabs_fn: Optional[\n Callable[[utils.DatasetConfig, models.BaseTransformerModel],\n None]] = utils.verify_matching_vocabs,\n shuffle_buffer_size=None,\n cycle_length=None,\n block_length=None\n) -> Tuple[int, train_state_lib.TrainState]:\n \"\"\"Train function.\n\n Args:\n model: The model object to use for training.\n train_dataset_cfg: Specification for the dataset to train with.\n train_eval_dataset_cfg: Specification for the dataset to evaluate with using\n the train metrics and no inference (e.g., uses teacher forcing). If None,\n train eval is disabled.\n infer_eval_dataset_cfg: Specification for the dataset to evaluate with using\n the inference metrics (e.g., uses sampled decoding). If None, inference\n eval is disabled.\n checkpoint_cfg: Specification for saving and restoring model parameters and\n dataset state to/from checkpoints.\n partitioner: Partitioner for model parameters and data across devices.\n trainer_cls: An implementation of BaseTrainer.\n model_dir: Path of directory to store checkpoints and metric summaries.\n total_steps: The step number to stop training after. The number of actual\n steps trained in this run will be this number minus the starting step from\n the checkpoint. If this is set to the starting step from the checkpoint,\n the model will not be compiled for training and training will not be run.\n This can be used in conjunction with `run_eval_before_training` to only\n evaluate a model.\n eval_steps: The number of batches to process for each train-eval loop.\n eval_period: The number of train steps between each evaluation (both\n train-eval and infer-eval).\n stats_period: The number of train steps between writing scalar stats. If\n None, defaults to eval_period.\n random_seed: A random seed to use for dropout and initialization. If None, a\n fast, non-deterministic hardware-based RNG is used.\n use_hardware_rng: Whether to force using the RngBitGenerator based hardware\n rng, which takes seeds and acts similarly to software PRNG in that it\n should be seed-deterministic. The new RngBitGenerator custom PRNG system\n should be reproducible for a given sharding, but the numbers will change\n for different shardings of the same model.\n summarize_config_fn: A function that takes in the model directory, a\n SummaryWriter, and the step number, and writes a summary of the\n inference_evaluator_cls: seqio.Evaluator class to use for inference\n evaluation, potentially with bound configuration args.\n get_dataset_fn: The callable use to get the train and train-eval datasets\n based on the DatasetConfig and shard information.\n concurrent_metrics: If True, allow metrics computation and logging to\n overlap with training. Will likely result in additional TPU memory usage.\n actions: A mapping of actions that runs after train, eval or infer_eval, to\n inspect the model and perform useful operations, e.g., early stopping. The\n key must have a 1:1 mapping to ActionMode enum. For EVAL actions to\n actually work, this requires `concurrent_metrics` to be turned off, since\n chaining futures and mutating states concurrently might be error-prone.\n train_eval_get_dataset_fn: Optional callable use to get the train-eval\n datasets based on the DatasetConfig and shard information. If missing, it\n defaults to `utils.get_training_eval_datasets`.\n run_eval_before_training: If True, calculate training eval and inference\n eval metrics before training begins.\n train_state_initializer_cls: t5x.utils.TrainStateInitializer class for\n initializing partitioned TrainState from checkpoints or scratch.\n use_gda: if True, uses GlobalDeviceArray. Experimental feature.\n verify_matching_vocabs_fn: Function to validate whether the task vocabulary\n matches the model vocabulary. Should raise an exception on error.\n\n Returns:\n The tuple of (last_step, last_train_state).\n \"\"\"\n\n if jax.process_index() == 0 and use_wandb:\n if not os.environ.get(\"WANDB_API_KEY\"):\n use_wandb = False\n logging.warning(\"WANDB_API_KEY not found, wandb will not be used\")\n else:\n"},"all_code":{"kind":"string","value":"# Copyright 2022 The T5X Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nr\"\"\"Script to pretrain or finetune in JAX using a SeqIO pipeline.\n\"\"\"\n\n\n# Set Linen to add profiling information when constructing Modules.\n# Must be set before flax imports.\n# pylint:disable=g-import-not-at-top\n\nos.environ['FLAX_PROFILE'] = 'true'\n# TODO(adarob): Re-enable once users are notified and tests are updated.\nos.environ['FLAX_LAZY_RNG'] = 'no'\n\n\n\nos.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = os.path.join(\n expanduser(\"~\"), \".config/gcloud/application_default_credentials.json\")\n\n# Automatically search for gin files relative to the T5X package.\n_DEFAULT_GIN_SEARCH_PATHS = [\n os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n]\nPyTreeDef = type(jax.tree_util.tree_structure(None))\nP = partitioning.PartitionSpec\n# Special key that used to distinguish train metrics.\nTRAIN_METRIC_KEY = 'train'\n# String keys that is acceptable from config.\n_ACTION_KEYS = frozenset(trainer_lib.ActionMode.__members__.keys())\n\ndef run_actions(\n mode: trainer_lib.ActionMode, actions: trainer_lib.ActionMapType,\n train_state: train_state_lib.TrainState,\n metrics_by_task: Mapping[str, trainer_lib.MetricValueMapType]) -> bool:\n \"\"\"Invokes all actions on the given mode on host 0, then broadcasts to all.\n\n Args:\n mode: The mode to run the actions. e.g., if mode is `train`, only actions\n configured to run with `train` mode will be invoked.\n actions: A mapping of actions that runs after train, eval or infer_eval, to\n inspect the model and perform useful operations, e.g., early stopping.\n train_state: The current train_state of the trainer.\n metrics_by_task: A map of metrics keyed by task name.\n\n Returns:\n A bool indicating whether training should be halted.\n\n Raises:\n RuntimeError: When the metrics processed on host 0 is None.\n \"\"\"\n stop_training = False\n if jax.process_index() == 0:\n if not metrics_by_task:\n raise RuntimeError('Metric is unexpectedly empty on process 0')\n for action in actions.get(mode, []):\n stop_training |= action.run(train_state, metrics_by_task=metrics_by_task)\n # Broadcast result from host 0 to others.\n return bool(multihost_utils.broadcast_one_to_all(jnp.array(stop_training)))\n\ndef train(\n *,\n model: models.BaseTransformerModel,\n train_dataset_cfg: utils.DatasetConfig,\n train_eval_dataset_cfg: Optional[utils.DatasetConfig],\n infer_eval_dataset_cfg: Optional[utils.DatasetConfig],\n checkpoint_cfg: utils.CheckpointConfig,\n partitioner: partitioning.BasePartitioner,\n trainer_cls: trainer_lib.BaseTrainerConstructor,\n model_dir: str,\n total_steps: int,\n eval_steps: int,\n eval_period: int,\n stats_period: Optional[int] = None,\n random_seed: Optional[int],\n use_hardware_rng: bool = False,\n summarize_config_fn: Callable[[str, metric_writers.MetricWriter, int], None],\n inference_evaluator_cls: utils.EvaluatorConstructor = seqio.Evaluator,\n get_dataset_fn: utils.GetDatasetCallable = utils.get_dataset,\n concurrent_metrics: bool = True,\n actions: Optional[Mapping[str, Sequence[trainer_lib.BaseAction]]] = None,\n train_eval_get_dataset_fn: utils.GetEvalDatasetCallable = utils\n .get_training_eval_datasets,\n run_eval_before_training: bool = False,\n use_wandb = True, weight_metrics=\"norm\",\n packing_strategy: PackingStrategy = None,\n train_state_initializer_cls: Type[\n utils.TrainStateInitializer] = utils.TrainStateInitializer,\n use_gda: bool = True,\n verify_matching_vocabs_fn: Optional[\n Callable[[utils.DatasetConfig, models.BaseTransformerModel],\n None]] = utils.verify_matching_vocabs,\n shuffle_buffer_size=None,\n cycle_length=None,\n block_length=None\n) -> Tuple[int, train_state_lib.TrainState]:\n \"\"\"Train function.\n\n Args:\n model: The model object to use for training.\n train_dataset_cfg: Specification for the dataset to train with.\n train_eval_dataset_cfg: Specification for the dataset to evaluate with using\n the train metrics and no inference (e.g., uses teacher forcing). If None,\n train eval is disabled.\n infer_eval_dataset_cfg: Specification for the dataset to evaluate with using\n the inference metrics (e.g., uses sampled decoding). If None, inference\n eval is disabled.\n checkpoint_cfg: Specification for saving and restoring model parameters and\n dataset state to/from checkpoints.\n partitioner: Partitioner for model parameters and data across devices.\n trainer_cls: An implementation of BaseTrainer.\n model_dir: Path of directory to store checkpoints and metric summaries.\n total_steps: The step number to stop training after. The number of actual\n steps trained in this run will be this number minus the starting step from\n the checkpoint. If this is set to the starting step from the checkpoint,\n the model will not be compiled for training and training will not be run.\n This can be used in conjunction with `run_eval_before_training` to only\n evaluate a model.\n eval_steps: The number of batches to process for each train-eval loop.\n eval_period: The number of train steps between each evaluation (both\n train-eval and infer-eval).\n stats_period: The number of train steps between writing scalar stats. If\n None, defaults to eval_period.\n random_seed: A random seed to use for dropout and initialization. If None, a\n fast, non-deterministic hardware-based RNG is used.\n use_hardware_rng: Whether to force using the RngBitGenerator based hardware\n rng, which takes seeds and acts similarly to software PRNG in that it\n should be seed-deterministic. The new RngBitGenerator custom PRNG system\n should be reproducible for a given sharding, but the numbers will change\n for different shardings of the same model.\n summarize_config_fn: A function that takes in the model directory, a\n SummaryWriter, and the step number, and writes a summary of the\n inference_evaluator_cls: seqio.Evaluator class to use for inference\n evaluation, potentially with bound configuration args.\n get_dataset_fn: The callable use to get the train and train-eval datasets\n based on the DatasetConfig and shard information.\n concurrent_metrics: If True, allow metrics computation and logging to\n overlap with training. Will likely result in additional TPU memory usage.\n actions: A mapping of actions that runs after train, eval or infer_eval, to\n inspect the model and perform useful operations, e.g., early stopping. The\n key must have a 1:1 mapping to ActionMode enum. For EVAL actions to\n actually work, this requires `concurrent_metrics` to be turned off, since\n chaining futures and mutating states concurrently might be error-prone.\n train_eval_get_dataset_fn: Optional callable use to get the train-eval\n datasets based on the DatasetConfig and shard information. If missing, it\n defaults to `utils.get_training_eval_datasets`.\n run_eval_before_training: If True, calculate training eval and inference\n eval metrics before training begins.\n train_state_initializer_cls: t5x.utils.TrainStateInitializer class for\n initializing partitioned TrainState from checkpoints or scratch.\n use_gda: if True, uses GlobalDeviceArray. Experimental feature.\n verify_matching_vocabs_fn: Function to validate whether the task vocabulary\n matches the model vocabulary. Should raise an exception on error.\n\n Returns:\n The tuple of (last_step, last_train_state).\n \"\"\"\n\n if jax.process_index() == 0 and use_wandb:\n if not os.environ.get(\"WANDB_API_KEY\"):\n use_wandb = False\n logging.warning(\"WANDB_API_KEY not found, wandb will not be used\")\n else:"},"next_line":{"kind":"string","value":" init_wandb()"},"gold_snippet_index":{"kind":"number","value":9,"string":"9"},"created_at":{"kind":"string","value":"2023-12-12 20:23:33+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5283,"cells":{"repo_name":{"kind":"string","value":"alibaba/animate-anything"},"file_path":{"kind":"string","value":"train.py"},"context":{"kind":"list like","value":[{"identifier":"VideoJsonDataset","path":"utils/dataset.py","snippet":"class VideoJsonDataset(Dataset):\n def __init__(\n self,\n tokenizer=None,\n width: int = 256,\n height: int = 256,\n n_sample_frames: int = 16,\n fps: int = 8,\n video_dir: str = \"./data\",\n video_json: str = \"\",\n fallback_prompt: str = \"\",\n use_bucketing: bool = False,\n cache_latents = False,\n motion_threshold = 50,\n **kwargs\n ):\n self.tokenizer = tokenizer\n self.use_bucketing = use_bucketing\n\n self.fallback_prompt = fallback_prompt\n self.video_dir = video_dir\n self.video_files = json.load(open(video_json))\n\n self.width = width\n self.height = height\n\n self.n_sample_frames = n_sample_frames\n self.fps = fps\n self.cache_latents = cache_latents\n self.motion_threshold = motion_threshold\n self.transform = T.Compose([\n #T.RandomResizedCrop(size=(height, width), scale=(0.8, 1.0), ratio=(width/height, width/height), antialias=False),\n T.Resize(min(height, width), antialias=False),\n T.CenterCrop([height, width])\n ])\n\n\n def get_frame_buckets(self, vr):\n _, h, w = vr[0].shape \n width, height = sensible_buckets(self.width, self.height, h, w)\n resize = T.transforms.Resize((height, width), antialias=True)\n\n return resize\n\n \n @staticmethod\n def __getname__(): return 'video_json'\n\n def __len__(self):\n return len(self.video_files)\n\n def __getitem__(self, index):\n mask = None\n try:\n item = self.video_files[index]\n video_path = os.path.join(self.video_dir, item['video'])\n cache_path = os.path.splitext(video_path)[0] + '.pt'\n if self.cache_latents and os.path.exists(cache_path):\n return torch.load(cache_path, map_location='cpu')\n\n prompt = item['caption']\n if self.fallback_prompt == \"<no_text>\":\n prompt = \"\"\n vr = decord.VideoReader(video_path)\n video = get_frame_batch(self.n_sample_frames, self.fps, vr, self.transform)\n except Exception as err:\n print(\"read video error\", err, video_path)\n return self.__getitem__(index+1)\n prompt_ids = get_prompt_ids(prompt, self.tokenizer)\n\n example = {\n \"pixel_values\": normalize_input(video), \n \"prompt_ids\": prompt_ids, \n \"text_prompt\": prompt, \n 'cache_path': cache_path,\n 'dataset': self.__getname__()\n }\n mask = get_moved_area_mask(video.permute([0,2,3,1]).numpy())\n example['motion'] = calculate_motion_score(video.permute([0,2,3,1]).numpy())\n if example['motion'] < self.motion_threshold:\n return self.__getitem__(random.randint(0, len(self)-1))\n return example"},{"identifier":"SingleVideoDataset","path":"utils/dataset.py","snippet":"class SingleVideoDataset(Dataset):\n def __init__(\n self,\n tokenizer = None,\n width: int = 256,\n height: int = 256,\n n_sample_frames: int = 4,\n frame_step: int = 1,\n single_video_path: str = \"\",\n single_video_prompt: str = \"\",\n use_caption: bool = False,\n use_bucketing: bool = False,\n **kwargs\n ):\n self.tokenizer = tokenizer\n self.use_bucketing = use_bucketing\n self.frames = []\n self.index = 1\n\n self.vid_types = (\".mp4\", \".avi\", \".mov\", \".webm\", \".flv\", \".mjpeg\")\n self.n_sample_frames = n_sample_frames\n self.frame_step = frame_step\n\n self.single_video_path = single_video_path\n self.single_video_prompt = single_video_prompt\n\n self.width = width\n self.height = height\n def create_video_chunks(self):\n # Create a list of frames separated by sample frames\n # [(1,2,3), (4,5,6), ...]\n vr = decord.VideoReader(self.single_video_path)\n vr_range = range(1, len(vr), self.frame_step)\n\n self.frames = list(self.chunk(vr_range, self.n_sample_frames))\n\n # Delete any list that contains an out of range index.\n for i, inner_frame_nums in enumerate(self.frames):\n for frame_num in inner_frame_nums:\n if frame_num > len(vr):\n print(f\"Removing out of range index list at position: {i}...\")\n del self.frames[i]\n\n return self.frames\n\n def chunk(self, it, size):\n it = iter(it)\n return iter(lambda: tuple(islice(it, size)), ())\n\n def get_frame_batch(self, vr, resize=None):\n index = self.index\n frames = vr.get_batch(self.frames[self.index])\n video = rearrange(frames, \"f h w c -> f c h w\")\n\n if resize is not None: video = resize(video)\n return video\n\n def get_frame_buckets(self, vr):\n _, h, w = vr[0].shape \n width, height = sensible_buckets(self.width, self.height, h, w)\n resize = T.transforms.Resize((height, width), antialias=True)\n\n return resize\n \n def process_video_wrapper(self, vid_path):\n video, vr = process_video(\n vid_path,\n self.use_bucketing,\n self.width, \n self.height, \n self.get_frame_buckets, \n self.get_frame_batch\n )\n \n return video, vr \n\n def single_video_batch(self, index):\n train_data = self.single_video_path\n self.index = index\n\n if train_data.endswith(self.vid_types):\n video, _ = self.process_video_wrapper(train_data)\n\n prompt = self.single_video_prompt\n prompt_ids = get_prompt_ids(prompt, self.tokenizer)\n\n return video, prompt, prompt_ids\n else:\n raise ValueError(f\"Single video is not a video type. Types: {self.vid_types}\")\n \n @staticmethod\n def __getname__(): return 'single_video'\n\n def __len__(self):\n \n return len(self.create_video_chunks())\n\n def __getitem__(self, index):\n\n video, prompt, prompt_ids = self.single_video_batch(index)\n\n example = {\n \"pixel_values\": normalize_input(video),\n \"prompt_ids\": prompt_ids,\n \"text_prompt\": prompt,\n 'dataset': self.__getname__()\n }\n\n return example"},{"identifier":"ImageDataset","path":"utils/dataset.py","snippet":"class ImageDataset(Dataset):\n \n def __init__(\n self,\n tokenizer = None,\n width: int = 256,\n height: int = 256,\n base_width: int = 256,\n base_height: int = 256,\n use_caption: bool = False,\n image_dir: str = '',\n single_img_prompt: str = '',\n use_bucketing: bool = False,\n fallback_prompt: str = '',\n **kwargs\n ):\n self.tokenizer = tokenizer\n self.img_types = (\".png\", \".jpg\", \".jpeg\", '.bmp')\n self.use_bucketing = use_bucketing\n #self.image_dir = self.get_images_list(image_dir)\n self.image_dir_path = image_dir\n self.image_dir = json.load(open(kwargs['image_json']))\n self.fallback_prompt = fallback_prompt\n\n self.use_caption = use_caption\n self.single_img_prompt = single_img_prompt\n\n self.width = width\n self.height = height\n\n def get_images_list(self, image_dir):\n if os.path.exists(image_dir):\n imgs = [x for x in os.listdir(image_dir) if x.endswith(self.img_types)]\n full_img_dir = []\n\n for img in imgs: \n full_img_dir.append(f\"{image_dir}/{img}\")\n\n return sorted(full_img_dir)\n\n return ['']\n\n def image_batch(self, index):\n train_data = self.image_dir[index]\n img, prompt = train_data['image'], train_data['caption']\n img = os.path.join(self.image_dir_path, img)\n try:\n img = torchvision.io.read_image(img, mode=torchvision.io.ImageReadMode.RGB)\n except:\n img = T.transforms.PILToTensor()(Image.open(img).convert(\"RGB\"))\n\n width = self.width\n height = self.height\n\n if self.use_bucketing:\n _, h, w = img.shape\n width, height = sensible_buckets(width, height, w, h)\n \n resize = T.transforms.Resize((height, width), antialias=True)\n\n img = resize(img) \n img = repeat(img, 'c h w -> f c h w', f=1)\n prompt_ids = get_prompt_ids(prompt, self.tokenizer)\n\n return img, prompt, prompt_ids\n\n @staticmethod\n def __getname__(): return 'image'\n \n def __len__(self):\n # Image directory\n return len(self.image_dir)\n\n def __getitem__(self, index):\n img, prompt, prompt_ids = self.image_batch(index)\n example = {\n \"pixel_values\": normalize_input(img),\n \"frames\": img,\n \"prompt_ids\": prompt_ids,\n \"text_prompt\": prompt, \n 'dataset': self.__getname__()\n }\n\n return example"},{"identifier":"VideoFolderDataset","path":"utils/dataset.py","snippet":"class VideoFolderDataset(Dataset):\n def __init__(\n self,\n tokenizer=None,\n width: int = 256,\n height: int = 256,\n n_sample_frames: int = 16,\n fps: int = 8,\n path: str = \"./data\",\n fallback_prompt: str = \"\",\n use_bucketing: bool = False,\n **kwargs\n ):\n self.tokenizer = tokenizer\n self.use_bucketing = use_bucketing\n\n self.fallback_prompt = fallback_prompt\n\n self.video_files = glob(f\"{path}/*.mp4\")\n\n self.width = width\n self.height = height\n\n self.n_sample_frames = n_sample_frames\n self.fps = fps\n\n def get_frame_buckets(self, vr):\n _, h, w = vr[0].shape \n width, height = sensible_buckets(self.width, self.height, h, w)\n resize = T.transforms.Resize((height, width), antialias=True)\n\n return resize\n\n def get_frame_batch(self, vr, resize=None):\n n_sample_frames = self.n_sample_frames\n native_fps = vr.get_avg_fps()\n \n every_nth_frame = max(1, round(native_fps / self.fps))\n every_nth_frame = min(len(vr), every_nth_frame)\n \n effective_length = len(vr) // every_nth_frame\n if effective_length < n_sample_frames:\n n_sample_frames = effective_length\n raise RuntimeError(\"not enough frames\")\n\n effective_idx = random.randint(0, (effective_length - n_sample_frames))\n idxs = every_nth_frame * np.arange(effective_idx, effective_idx + n_sample_frames)\n\n video = vr.get_batch(idxs)\n video = rearrange(video, \"f h w c -> f c h w\")\n\n if resize is not None: video = resize(video)\n return video, vr\n \n def process_video_wrapper(self, vid_path):\n video, vr = process_video(\n vid_path,\n self.use_bucketing,\n self.width, \n self.height, \n self.get_frame_buckets, \n self.get_frame_batch\n )\n return video, vr\n \n @staticmethod\n def __getname__(): return 'folder'\n\n def __len__(self):\n return len(self.video_files)\n\n def __getitem__(self, index):\n try:\n video, _ = self.process_video_wrapper(self.video_files[index])\n except Exception as err:\n print(\"read video error\", self.video_files[index])\n video, _ = self.process_video_wrapper(self.video_files[index+1])\n\n if os.path.exists(self.video_files[index].replace(\".mp4\", \".txt\")):\n with open(self.video_files[index].replace(\".mp4\", \".txt\"), \"r\") as f:\n lines = f.readlines()\n prompt = random.choice(lines)\n else:\n prompt = self.fallback_prompt\n\n prompt_ids = get_prompt_ids(prompt, self.tokenizer)\n\n return {\"pixel_values\": normalize_input(video[0]), \"frames\": video[0],\n \"prompt_ids\": prompt_ids, \"text_prompt\": prompt, 'dataset': self.__getname__()}"},{"identifier":"CachedDataset","path":"utils/dataset.py","snippet":"class CachedDataset(Dataset):\n def __init__(self,cache_dir: str = ''):\n self.cache_dir = cache_dir\n self.cached_data_list = self.get_files_list()\n\n def get_files_list(self):\n tensors_list = [f\"{self.cache_dir}/{x}\" for x in os.listdir(self.cache_dir) if x.endswith('.pt')]\n return sorted(tensors_list)\n\n def __len__(self):\n return len(self.cached_data_list)\n\n def __getitem__(self, index):\n cached_latent = torch.load(self.cached_data_list[index], map_location='cuda:0')\n return cached_latent"},{"identifier":"VideoBLIPDataset","path":"utils/dataset.py","snippet":"class VideoBLIPDataset(Dataset):\n def __init__(\n self,\n tokenizer = None,\n width: int = 256,\n height: int = 256,\n n_sample_frames: int = 4,\n sample_start_idx: int = 1,\n fps: int = 1,\n json_path: str =\"\",\n json_data = None,\n vid_data_key: str = \"video_path\",\n preprocessed: bool = False,\n use_bucketing: bool = False,\n cache_latents: bool = False,\n motion_threshold = 50,\n **kwargs\n ):\n self.vid_types = (\".mp4\", \".avi\", \".mov\", \".webm\", \".flv\", \".mjpeg\")\n self.use_bucketing = use_bucketing\n self.tokenizer = tokenizer\n self.preprocessed = preprocessed\n \n self.vid_data_key = vid_data_key\n self.train_data = self.load_from_json(json_path, json_data)\n self.cache_latents = cache_latents\n self.motion_threshold = motion_threshold\n self.width = width\n self.height = height\n\n self.n_sample_frames = n_sample_frames\n self.sample_start_idx = sample_start_idx\n self.fps = fps\n self.transform = T.Compose([\n #T.RandomResizedCrop(size=(height, width), scale=(0.8, 1.0), ratio=(width/height, width/height), antialias=False)\n T.Resize(min(height, width), antialias=False),\n T.CenterCrop([height, width])\n ])\n\n def build_json(self, json_data):\n extended_data = []\n for data in json_data['data']:\n for nested_data in data['data']:\n self.build_json_dict(\n data, \n nested_data, \n extended_data\n )\n json_data = extended_data\n return json_data\n\n def build_json_dict(self, data, nested_data, extended_data):\n clip_path = nested_data['clip_path'] if 'clip_path' in nested_data else None\n \n extended_data.append({\n self.vid_data_key: data[self.vid_data_key],\n 'frame_index': nested_data['frame_index'],\n 'prompt': nested_data['prompt'],\n 'clip_path': clip_path\n })\n \n def load_from_json(self, path, json_data):\n try:\n with open(path) as jpath:\n print(f\"Loading JSON from {path}\")\n json_data = json.load(jpath)\n\n return self.build_json(json_data)\n\n except:\n import traceback\n traceback.print_exc()\n self.train_data = []\n print(\"Non-existant JSON path. Skipping.\")\n \n def validate_json(self, base_path, path):\n return os.path.exists(f\"{base_path}/{path}\")\n\n def get_frame_buckets(self, vr):\n _, h, w = vr[0].shape \n width, height = sensible_buckets(self.width, self.height, h, w)\n resize = T.transforms.Resize((height, width), antialias=True)\n\n return resize\n\n def train_data_batch(self, index):\n vid_data = self.train_data[index]\n # Get video prompt\n prompt = vid_data['prompt']\n # If we are training on individual clips.\n if 'clip_path' in self.train_data[index] and \\\n self.train_data[index]['clip_path'] is not None:\n clip_path = vid_data['clip_path']\n else:\n clip_path = vid_data[self.vid_data_key]\n # Get the frame of the current index.\n self.sample_start_idx = vid_data['frame_index']\n cache_path = os.path.splitext(clip_path)[0] + '.pt'\n if self.cache_latents and os.path.exists(cache_path):\n return torch.load(cache_path, map_location='cpu')\n\n vr = decord.VideoReader(clip_path)\n video = get_frame_batch(self.n_sample_frames, self.fps, vr, self.transform)\n prompt_ids = get_prompt_ids(prompt, self.tokenizer)\n example = {\n \"pixel_values\": normalize_input(video),\n \"prompt_ids\": prompt_ids,\n \"text_prompt\": prompt,\n 'dataset': self.__getname__(),\n 'cache_path': cache_path,\n }\n mask = get_moved_area_mask(video.permute([0,2,3,1]).numpy())\n example['mask'] = mask\n example['motion'] = calculate_motion_score(video.permute([0,2,3,1]).numpy())\n return example\n \n\n @staticmethod\n def __getname__(): return 'video_blip'\n\n def __len__(self):\n if self.train_data is not None:\n return len(self.train_data)\n else: \n return 0\n\n def __getitem__(self, index):\n example = self.train_data_batch(index)\n if example['motion'] < self.motion_threshold:\n return self.__getitem__(random.randint(0, len(self)-1))\n return example"},{"identifier":"UNet3DConditionModel","path":"models/unet_3d_condition_mask.py","snippet":"class UNet3DConditionModel(ModelMixin, ConfigMixin):\n r\"\"\"\n UNet3DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep\n and returns sample shaped output.\n\n This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library\n implements for all the models (such as downloading or saving, etc.)\n\n Parameters:\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\n Height and width of input/output sample.\n in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.\n out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"CrossAttnDownBlock2D\", \"DownBlock2D\")`):\n The tuple of downsample blocks to use.\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\"UpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\", \"CrossAttnUpBlock2D\",)`):\n The tuple of upsample blocks to use.\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\n The tuple of output channels for each block.\n layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\n downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.\n mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.\n act_fn (`str`, *optional*, defaults to `\"silu\"`): The activation function to use.\n norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.\n If `None`, it will skip the normalization and activation layers in post-processing\n norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.\n cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.\n attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.\n \"\"\"\n\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\",\n ),\n up_block_types: Tuple[str] = (\"UpBlock3D\", \"CrossAttnUpBlock3D\", \"CrossAttnUpBlock3D\", \"CrossAttnUpBlock3D\"),\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1024,\n attention_head_dim: Union[int, Tuple[int]] = 64,\n motion_mask = False,\n motion_strength = False,\n ):\n super().__init__()\n self.motion_mask = motion_mask\n self.motion_strength = motion_strength\n print(f\"motion mask {self.motion_mask}, motion_strength {self.motion_strength}\")\n self.sample_size = sample_size\n self.gradient_checkpointing = False\n # Check inputs\n if len(down_block_types) != len(up_block_types):\n raise ValueError(\n f\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\"\n )\n\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n # input\n conv_in_kernel = 3\n conv_out_kernel = 3\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = nn.Conv2d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n self.conv_in2 = nn.Conv2d(\n 5, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n time_embed_dim = block_out_channels[0] * 4\n self.time_proj = Timesteps(block_out_channels[0], True, 0)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim,\n time_embed_dim,\n act_fn=act_fn,\n cond_proj_dim=block_out_channels[0],\n )\n\n self.motion_proj = Timesteps(block_out_channels[0], True, 0)\n self.motion_embedding = nn.Sequential(\n nn.Linear(timestep_input_dim, time_embed_dim), nn.SiLU(),\n nn.Linear(time_embed_dim, time_embed_dim))\n nn.init.zeros_(self.motion_embedding[-1].weight)\n nn.init.zeros_(self.motion_embedding[-1].bias)\n\n self.transformer_in = TransformerTemporalModel(\n num_attention_heads=8,\n attention_head_dim=attention_head_dim,\n in_channels=block_out_channels[0],\n num_layers=1,\n )\n\n # class embedding\n self.down_blocks = nn.ModuleList([])\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[i],\n downsample_padding=downsample_padding,\n dual_cross_attention=False,\n )\n self.down_blocks.append(down_block)\n\n # mid\n self.mid_block = UNetMidBlock3DCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=False,\n )\n # count how many layers upsample the images\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_attention_head_dim = list(reversed(attention_head_dim))\n\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=reversed_attention_head_dim[i],\n dual_cross_attention=False,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n if norm_num_groups is not None:\n self.conv_norm_out = nn.GroupNorm(\n num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps\n )\n self.conv_act = nn.SiLU()\n else:\n self.conv_norm_out = None\n self.conv_act = None\n\n conv_out_padding = (conv_out_kernel - 1) // 2\n self.conv_out = nn.Conv2d(\n block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding\n )\n\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, halves the input to the attention heads, so attention will be computed in two steps. If\n `\"max\"`, maxium amount of memory will be saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_slicable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_slicable_dims(module)\n\n num_slicable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_slicable_layers * [1]\n\n slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, value=False):\n self.gradient_checkpointing = value\n self.mid_block.gradient_checkpointing = value\n for module in self.down_blocks + self.up_blocks:\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\n module.gradient_checkpointing = value \n \n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n condition_latent: torch.Tensor,\n mask: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n motion = None,\n return_dict: bool = True,\n ) -> Union[UNet3DConditionOutput, Tuple]:\n r\"\"\"\n Args:\n sample (`torch.FloatTensor`): (batch, num_frames, channel, height, width) noisy inputs tensor\n timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps\n encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`models.unet_2d_condition.UNet3DConditionOutput`] instead of a plain tuple.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).\n\n Returns:\n [`~models.unet_2d_condition.UNet3DConditionOutput`] or `tuple`:\n [`~models.unet_2d_condition.UNet3DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2**self.num_upsamplers\n sample = torch.cat([condition_latent, sample], dim=2)\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\"Forward upsample size to force interpolation output size.\")\n forward_upsample_size = True\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n num_frames = sample.shape[2]\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n if self.motion_strength and motion is not None:\n timestep_cond = self.motion_proj(motion).to(dtype=self.dtype)\n emb = self.time_embedding(t_emb, timestep_cond)\n #emb += self.motion_embedding(m_emb)\n else:\n emb = self.time_embedding(t_emb, timestep_cond)\n emb = emb.repeat_interleave(repeats=num_frames, dim=0)\n encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)\n\n # 2. pre-process\n if self.motion_mask and mask is not None:\n mask = repeat(mask , 'b 1 1 h w -> (t b) 1 f h w', t=sample.shape[0]//mask.shape[0], f=sample.shape[2])\n sample = torch.cat([mask, sample], dim=1)\n sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])\n sample = self.conv_in2(sample)\n else:\n sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])\n sample = self.conv_in(sample)\n\n if num_frames > 1:\n if self.gradient_checkpointing:\n sample = transformer_g_c(self.transformer_in, sample, num_frames)\n else:\n sample = self.transformer_in(sample, num_frames=num_frames).sample\n\n # 3. down\n down_block_res_samples = (sample,)\n for i, downsample_block in enumerate(self.down_blocks):\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)\n \n down_block_res_samples += res_samples\n\n if down_block_additional_residuals is not None:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples += (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n if self.mid_block is not None:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n\n if mid_block_additional_residual is not None:\n sample = sample + mid_block_additional_residual\n\n # 5. up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n num_frames=num_frames,\n cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n upsample_size=upsample_size,\n num_frames=num_frames,\n )\n\n # 6. post-process\n if self.conv_norm_out:\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n\n sample = self.conv_out(sample)\n\n # reshape to (batch, channel, framerate, width, height)\n sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)\n sample = sample[:,:,1:]\n if not return_dict:\n return (sample,)\n\n return UNet3DConditionOutput(sample=sample)"},{"identifier":"LatentToVideoPipeline","path":"models/pipeline.py","snippet":"class LatentToVideoPipeline(TextToVideoSDPipeline):\n @torch.no_grad()\n def __call__(\n self,\n prompt = None,\n height= None,\n width= None,\n num_frames: int = 16,\n num_inference_steps: int = 50,\n guidance_scale= 9.0,\n negative_prompt= None,\n eta: float = 0.0,\n generator= None,\n latents= None,\n prompt_embeds= None,\n negative_prompt_embeds= None,\n output_type= \"np\",\n return_dict: bool = True,\n callback= None,\n callback_steps: int = 1,\n cross_attention_kwargs= None,\n condition_latent=None,\n mask=None,\n timesteps=None,\n motion=None,\n ):\n r\"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated video.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated video.\n num_frames (`int`, *optional*, defaults to 16):\n The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds\n amounts to 2 seconds of video.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality videos at the\n expense of slower inference.\n guidance_scale (`float`, *optional*, defaults to 7.5):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate videos that are closely linked to the text `prompt`,\n usually at the expense of lower video quality.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the video generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n eta (`float`, *optional*, defaults to 0.0):\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\n [`schedulers.DDIMScheduler`], will be ignored for others.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for video\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`. Latents should be of shape\n `(batch_size, num_channel, num_frames, height, width)`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"np\"`):\n The output format of the generate video. Choose between `torch.FloatTensor` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] instead of a\n plain tuple.\n callback (`Callable`, *optional*):\n A function that will be called every `callback_steps` steps during inference. The function will be\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.\n callback_steps (`int`, *optional*, defaults to 1):\n The frequency at which the `callback` function will be called. If not specified, the callback will be\n called at every step.\n cross_attention_kwargs (`dict`, *optional*):\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\n `self.processor` in\n [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).\n\n Examples:\n\n Returns:\n [`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] or `tuple`:\n [`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\n When returning a tuple, the first element is a list with the generated frames.\n \"\"\"\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n num_images_per_prompt = 1\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n #device = self._execution_device\n device = latents.device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds = self._encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n if timesteps is None:\n timesteps = self.scheduler.timesteps\n else:\n num_inference_steps = len(timesteps)\n # 5. Prepare latent variables. do nothing\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n uncondition_latent = condition_latent\n condition_latent = torch.cat([uncondition_latent, condition_latent]) if do_classifier_free_guidance else condition_latent \n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n if motion is not None:\n motion = torch.tensor(motion, device=device)\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n condition_latent=condition_latent,\n mask=mask,\n motion=motion\n ).sample\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # reshape latents\n bsz, channel, frames, width, height = latents.shape\n latents = latents.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)\n noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n # reshape latents back\n latents = latents[None, :].reshape(bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4)\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n video_tensor = self.decode_latents(latents)\n\n if output_type == \"pt\":\n video = video_tensor\n else:\n video = tensor2vid(video_tensor)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (video, latents)\n\n return TextToVideoSDPipelineOutput(frames=video)"},{"identifier":"LoraHandler","path":"utils/lora_handler.py","snippet":"class LoraHandler(object):\n def __init__(\n self, \n version: LORA_VERSIONS = LoraVersions.cloneofsimo, \n use_unet_lora: bool = False,\n use_text_lora: bool = False,\n save_for_webui: bool = False,\n only_for_webui: bool = False,\n lora_bias: str = 'none',\n unet_replace_modules: list = ['UNet3DConditionModel'],\n text_encoder_replace_modules: list = ['CLIPEncoderLayer']\n ):\n self.version = version\n self.lora_loader = self.get_lora_func(func_type=LoraFuncTypes.loader)\n self.lora_injector = self.get_lora_func(func_type=LoraFuncTypes.injector)\n self.lora_bias = lora_bias\n self.use_unet_lora = use_unet_lora\n self.use_text_lora = use_text_lora\n self.save_for_webui = save_for_webui\n self.only_for_webui = only_for_webui\n self.unet_replace_modules = unet_replace_modules\n self.text_encoder_replace_modules = text_encoder_replace_modules\n self.use_lora = any([use_text_lora, use_unet_lora])\n\n if self.use_lora:\n print(f\"Using LoRA Version: {self.version}\")\n\n def is_cloneofsimo_lora(self):\n return self.version == LoraVersions.cloneofsimo\n\n def is_stable_lora(self):\n return self.version == LoraVersions.stable_lora\n\n def get_lora_func(self, func_type: LORA_FUNC_TYPES = LoraFuncTypes.loader):\n\n if self.is_cloneofsimo_lora():\n\n if func_type == LoraFuncTypes.loader:\n return monkeypatch_or_replace_lora_extended\n\n if func_type == LoraFuncTypes.injector:\n return inject_trainable_lora_extended\n\n if self.is_stable_lora():\n\n if func_type == LoraFuncTypes.loader:\n return load_lora\n\n if func_type == LoraFuncTypes.injector:\n return add_lora_to\n \n assert \"LoRA Version does not exist.\"\n\n def check_lora_ext(self, lora_file: str):\n return lora_file.endswith(tuple(LORA_FILE_TYPES))\n\n def get_lora_file_path(\n self, \n lora_path: str, \n model: Union[UNet3DConditionModel, CLIPTextModel]\n ):\n if os.path.exists(lora_path):\n lora_filenames = [fns for fns in os.listdir(lora_path)]\n is_lora = self.check_lora_ext(lora_path)\n\n is_unet = isinstance(model, UNet3DConditionModel)\n is_text = isinstance(model, CLIPTextModel)\n idx = 0 if is_unet else 1\n\n base_name = FILE_BASENAMES[idx]\n \n for lora_filename in lora_filenames:\n is_lora = self.check_lora_ext(lora_filename)\n if not is_lora:\n continue\n \n if base_name in lora_filename:\n return os.path.join(lora_path, lora_filename)\n\n return None\n\n def handle_lora_load(self, file_name:str, lora_loader_args: dict = None):\n self.lora_loader(**lora_loader_args)\n print(f\"Successfully loaded LoRA from: {file_name}\")\n \n def load_lora(self, model, lora_path: str = '', lora_loader_args: dict = None,):\n try:\n lora_file = self.get_lora_file_path(lora_path, model)\n\n if lora_file is not None:\n lora_loader_args.update({\"lora_path\": lora_file})\n self.handle_lora_load(lora_file, lora_loader_args)\n\n else:\n print(f\"Could not load LoRAs for {model.__class__.__name__}. Injecting new ones instead...\")\n\n except Exception as e:\n print(f\"An error occured while loading a LoRA file: {e}\")\n \n def get_lora_func_args(self, lora_path, use_lora, model, replace_modules, r, dropout, lora_bias):\n return_dict = lora_args.copy()\n \n if self.is_cloneofsimo_lora():\n return_dict = filter_dict(return_dict, keys=CLONE_OF_SIMO_KEYS)\n return_dict.update({\n \"model\": model,\n \"loras\": self.get_lora_file_path(lora_path, model),\n \"target_replace_module\": replace_modules,\n \"r\": r\n })\n\n if self.is_stable_lora():\n KEYS = ['model', 'lora_path']\n return_dict = filter_dict(return_dict, KEYS)\n \n return_dict.update({'model': model, 'lora_path': lora_path})\n\n return return_dict\n\n def do_lora_injection(\n self, \n model, \n replace_modules, \n bias='none',\n dropout=0,\n r=4,\n lora_loader_args=None,\n ): \n REPLACE_MODULES = replace_modules\n\n params = None\n negation = None\n is_injection_hybrid = False\n \n if self.is_cloneofsimo_lora():\n is_injection_hybrid = True\n injector_args = lora_loader_args\n\n params, negation = self.lora_injector(**injector_args) \n for _up, _down in extract_lora_ups_down(\n model, \n target_replace_module=REPLACE_MODULES):\n\n if all(x is not None for x in [_up, _down]):\n print(f\"Lora successfully injected into {model.__class__.__name__}.\")\n\n break\n\n return params, negation, is_injection_hybrid\n\n if self.is_stable_lora():\n injector_args = lora_args.copy()\n injector_args = filter_dict(injector_args, keys=STABLE_LORA_KEYS)\n\n SEARCH_CLASS = [torch.nn.Linear, torch.nn.Conv2d, torch.nn.Conv3d, torch.nn.Embedding]\n\n injector_args.update({\n \"model\": model,\n \"target_module\": REPLACE_MODULES,\n \"search_class\": SEARCH_CLASS,\n \"r\": r,\n \"dropout\": dropout,\n \"lora_bias\": self.lora_bias\n })\n\n activator = self.lora_injector(**injector_args)\n activator()\n\n return params, negation, is_injection_hybrid\n\n def add_lora_to_model(self, use_lora, model, replace_modules, dropout=0.0, lora_path='', r=16):\n\n params = None\n negation = None\n\n lora_loader_args = self.get_lora_func_args(\n lora_path,\n use_lora,\n model,\n replace_modules,\n r,\n dropout,\n self.lora_bias\n )\n if use_lora:\n params, negation, is_injection_hybrid = self.do_lora_injection(\n model, \n replace_modules, \n bias=self.lora_bias,\n lora_loader_args=lora_loader_args,\n dropout=dropout,\n r=r\n )\n\n if not is_injection_hybrid:\n self.load_lora(model, lora_path=lora_path, lora_loader_args=lora_loader_args)\n \n params = model if params is None else params\n return params, negation\n \n\n def deactivate_lora_train(self, models, deactivate=True):\n \"\"\"\n Usage: Use before and after sampling previews.\n Currently only available for Stable LoRA.\n \"\"\"\n if self.is_stable_lora():\n set_mode_group(models, not deactivate)\n\n def save_cloneofsimo_lora(self, model, save_path, step):\n \n def save_lora(model, name, condition, replace_modules, step, save_path): \n if condition and replace_modules is not None:\n save_path = f\"{save_path}/{step}_{name}.pt\"\n save_lora_weight(model, save_path, replace_modules)\n\n save_lora(\n model.unet, \n FILE_BASENAMES[0], \n self.use_unet_lora, \n self.unet_replace_modules, \n step,\n save_path, \n )\n save_lora(\n model.text_encoder, \n FILE_BASENAMES[1], \n self.use_text_lora, \n self.text_encoder_replace_modules, \n step, \n save_path\n )\n\n train_patch_pipe(model, self.use_unet_lora, self.use_text_lora)\n\n def save_stable_lora(\n self, \n model, \n step, \n name, \n save_path = '', \n save_for_webui=False,\n only_for_webui=False\n ):\n import uuid\n\n save_filename = f\"{step}_{name}\"\n lora_metadata = metadata = {\n \"stable_lora_text_to_video\": \"v1\", \n \"lora_name\": name + \"_\" + uuid.uuid4().hex.lower()[:5]\n }\n save_lora(\n unet=model.unet,\n text_encoder=model.text_encoder,\n save_text_weights=self.use_text_lora,\n output_dir=save_path,\n lora_filename=save_filename,\n lora_bias=self.lora_bias,\n save_for_webui=self.save_for_webui,\n only_webui=self.only_for_webui,\n metadata=lora_metadata,\n unet_dict_converter=convert_unet_state_dict,\n text_dict_converter=convert_text_enc_state_dict_v20\n )\n\n def save_lora_weights(self, model: None, save_path: str ='',step: str = ''):\n save_path = f\"{save_path}/lora\"\n os.makedirs(save_path, exist_ok=True)\n\n if self.is_cloneofsimo_lora():\n if any([self.save_for_webui, self.only_for_webui]):\n warnings.warn(\n \"\"\"\n You have 'save_for_webui' enabled, but are using cloneofsimo's LoRA implemention.\n Only 'stable_lora' is supported for saving to a compatible webui file.\n \"\"\"\n )\n self.save_cloneofsimo_lora(model, save_path, step)\n\n if self.is_stable_lora():\n name = 'lora_text_to_video'\n self.save_stable_lora(model, step, name, save_path)"},{"identifier":"LORA_VERSIONS","path":"utils/lora_handler.py","snippet":"LORA_VERSIONS = [LoraVersions.stable_lora, LoraVersions.cloneofsimo]"},{"identifier":"read_mask","path":"utils/common.py","snippet":"def read_mask(json_path, label=[\"mask\"]):\n j = json.load(open(json_path)) \n if type(label) != list:\n labels = [label]\n height = j['imageHeight']\n width = j['imageWidth']\n mask = np.zeros([height, width], dtype=np.uint8)\n for shape in j['shapes']:\n if shape['label'] in label:\n x1, y1 = shape['points'][0]\n x2, y2 = shape['points'][1]\n mask[int(y1):int(y2), int(x1):int(x2)] = 255\n return mask"},{"identifier":"generate_random_mask","path":"utils/common.py","snippet":"def generate_random_mask(image):\n # Create a blank mask with the same size as the image\n b, c , h, w = image.shape\n mask = np.zeros([b, h, w], dtype=np.uint8)\n \n # Generate random coordinates for the mask\n num_points = np.random.randint(3, 10) # Randomly choose the number of points to generate\n points = np.random.randint(0, min(h, w), size=(num_points, 2)) # Randomly generate the points\n # Draw a filled polygon on the mask using the random points\n for i in range(b):\n width = random.randint(w//4, w)\n height = random.randint(h//4, h)\n x = random.randint(0, w-width)\n y = random.randint(0, h-height)\n points=np.array([[x, y], [x+width, y], [x+width, y+height], [x, y+height]])\n mask[i] = cv2.fillPoly(mask[i], [points], 255)\n \n # Apply the mask to the image\n #masked_image = cv2.bitwise_and(image, image, mask=mask)\n return mask "},{"identifier":"slerp","path":"utils/common.py","snippet":"def slerp(z1, z2, alpha):\n theta = torch.acos(torch.sum(z1 * z2) / (torch.norm(z1) * torch.norm(z2)))\n return (\n torch.sin((1 - alpha) * theta) / torch.sin(theta) * z1\n + torch.sin(alpha * theta) / torch.sin(theta) * z2\n )"},{"identifier":"calculate_motion_score","path":"utils/common.py","snippet":"def calculate_motion_score(frame_imgs, calculate_edges=False, color=\"RGB\") -> float:\n # Convert image into HSV colorspace.\n _last_frame = None\n\n _weights = [1.0, 1.0, 1.0, 0.0]\n score = 0\n for frame_img in frame_imgs:\n if color == \"RGB\":\n hue, sat, lum = cv2.split(cv2.cvtColor(frame_img, cv2.COLOR_RGB2HSV))\n else:\n hue, sat, lum = cv2.split(cv2.cvtColor(frame_img, cv2.COLOR_BGR2HSV))\n # Performance: Only calculate edges if we have to.\n edges = _detect_edges(lum) if calculate_edges else None\n if _last_frame == None:\n _last_frame = (hue, sat, lum, edges)\n continue\n\n score_components = [\n _mean_pixel_distance(hue, _last_frame[0]),\n _mean_pixel_distance(sat, _last_frame[1]),\n _mean_pixel_distance(lum, _last_frame[2]),\n 0.0 if edges is None else _mean_pixel_distance(edges, _last_frame[3]),\n ]\n\n frame_score: float = (\n sum(component * weight for (component, weight) in zip(score_components, _weights))\n / sum(abs(weight) for weight in _weights))\n score += frame_score\n _last_frame = (hue, sat, lum, edges)\n\n return round(score/(len(frame_imgs)-1) * 10)"},{"identifier":"read_video","path":"utils/common.py","snippet":"def read_video(video_path, frame_number=-1):\n # Open the video file\n cap = cv2.VideoCapture(video_path)\n count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) \n if frame_number == -1:\n frame_number = count\n else:\n frame_number = min(frame_number, count)\n frames = []\n for i in range(frame_number):\n ret, ref_frame = cap.read()\n ref_frame = cv2.cvtColor(ref_frame, cv2.COLOR_BGR2RGB)\n if not ret:\n raise ValueError(\"Failed to read video file\")\n frames.append(ref_frame)\n return frames"},{"identifier":"calculate_motion_precision","path":"utils/common.py","snippet":"def calculate_motion_precision(frames, mask):\n moved_mask = get_moved_area_mask(frames, move_th=20, th=0)\n moved = moved_mask == 255\n gt = mask == 255\n precision = np.sum(moved & gt) / np.sum(moved)\n return precision"},{"identifier":"calculate_latent_motion_score","path":"utils/common.py","snippet":"def calculate_latent_motion_score(latents):\n #latents b, c f, h, w\n diff=torch.abs(latents[:,:,1:]-latents[:,:,:-1])\n motion_score = torch.sum(torch.mean(diff, dim=[2,3,4]), dim=1) * 10\n return motion_score"},{"identifier":"DDPM_forward","path":"utils/common.py","snippet":"def DDPM_forward(x0, step, num_frames, scheduler):\n device = x0.device\n t = scheduler.timesteps[-1]\n xt = repeat(x0, 'b c 1 h w -> b c f h w', f = num_frames)\n\n eps = torch.randn_like(xt)\n alpha_vec = torch.prod(scheduler.alphas[t:])\n xt = torch.sqrt(alpha_vec) * xt + torch.sqrt(1-alpha_vec) * eps\n return xt, None"},{"identifier":"DDPM_forward_timesteps","path":"utils/common.py","snippet":"def DDPM_forward_timesteps(x0, step, num_frames, scheduler):\n '''larger step -> smaller t -> smaller alphas[t:] -> smaller xt -> smaller x0'''\n\n device = x0.device\n # timesteps are reversed\n timesteps = scheduler.timesteps[len(scheduler.timesteps)-step:]\n t = timesteps[0]\n\n if x0.shape[2] == 1:\n xt = repeat(x0, 'b c 1 h w -> b c f h w', f = num_frames)\n else:\n xt = x0\n noise = torch.randn(xt.shape, dtype=xt.dtype, device=device)\n # t to tensor of batch size \n t = torch.tensor([t]*xt.shape[0], device=device)\n xt = scheduler.add_noise(xt, noise, t)\n return xt, timesteps"},{"identifier":"DDPM_forward_mask","path":"utils/common.py","snippet":"def DDPM_forward_mask(x0, step, num_frames, scheduler, mask):\n '''larger step -> smaller t -> smaller alphas[t:] -> smaller xt -> smaller x0'''\n device = x0.device\n dtype = x0.dtype\n b, c, f, h, w = x0.shape\n\n move_xt, timesteps = DDPM_forward_timesteps(x0, step, num_frames, scheduler)\n mask = T.ToTensor()(mask).to(dtype).to(device)\n mask = T.Resize([h, w], antialias=False)(mask)\n mask = rearrange(mask, 'b h w -> b 1 1 h w')\n freeze_xt = repeat(x0, 'b c 1 h w -> b c f h w', f = num_frames)\n initial = freeze_xt * (1-mask) + move_xt * mask\n return initial, timesteps"},{"identifier":"motion_mask_loss","path":"utils/common.py","snippet":"def motion_mask_loss(latents, mask):\n diff = torch.abs(latents[:,:,1:] - latents[:,:,:-1])\n loss = torch.sum(torch.mean(diff * (1-mask), dim=[2,3,4]), dim=1)\n return loss"},{"identifier":"generate_center_mask","path":"utils/common.py","snippet":"def generate_center_mask(image):\n # Create a blank mask with the same size as the image\n b, c , h, w = image.shape\n mask = np.zeros([b, h, w], dtype=np.uint8)\n \n # Generate random coordinates for the mask\n for i in range(b):\n width = int(w/10)\n height = int(h/10)\n mask[i][height:-height,width:-width] = 255\n # Apply the mask to the image\n #masked_image = cv2.bitwise_and(image, image, mask=mask)\n return mask "},{"identifier":"tensor_to_vae_latent","path":"utils/common.py","snippet":"def tensor_to_vae_latent(t, vae):\n video_length = t.shape[1]\n\n t = rearrange(t, \"b f c h w -> (b f) c h w\")\n latents = vae.encode(t).latent_dist.sample()\n latents = rearrange(latents, \"(b f) c h w -> b c f h w\", f=video_length)\n latents = latents * 0.18215\n\n return latents"}],"string":"[\n {\n \"identifier\": \"VideoJsonDataset\",\n \"path\": \"utils/dataset.py\",\n \"snippet\": \"class VideoJsonDataset(Dataset):\\n def __init__(\\n self,\\n tokenizer=None,\\n width: int = 256,\\n height: int = 256,\\n n_sample_frames: int = 16,\\n fps: int = 8,\\n video_dir: str = \\\"./data\\\",\\n video_json: str = \\\"\\\",\\n fallback_prompt: str = \\\"\\\",\\n use_bucketing: bool = False,\\n cache_latents = False,\\n motion_threshold = 50,\\n **kwargs\\n ):\\n self.tokenizer = tokenizer\\n self.use_bucketing = use_bucketing\\n\\n self.fallback_prompt = fallback_prompt\\n self.video_dir = video_dir\\n self.video_files = json.load(open(video_json))\\n\\n self.width = width\\n self.height = height\\n\\n self.n_sample_frames = n_sample_frames\\n self.fps = fps\\n self.cache_latents = cache_latents\\n self.motion_threshold = motion_threshold\\n self.transform = T.Compose([\\n #T.RandomResizedCrop(size=(height, width), scale=(0.8, 1.0), ratio=(width/height, width/height), antialias=False),\\n T.Resize(min(height, width), antialias=False),\\n T.CenterCrop([height, width])\\n ])\\n\\n\\n def get_frame_buckets(self, vr):\\n _, h, w = vr[0].shape \\n width, height = sensible_buckets(self.width, self.height, h, w)\\n resize = T.transforms.Resize((height, width), antialias=True)\\n\\n return resize\\n\\n \\n @staticmethod\\n def __getname__(): return 'video_json'\\n\\n def __len__(self):\\n return len(self.video_files)\\n\\n def __getitem__(self, index):\\n mask = None\\n try:\\n item = self.video_files[index]\\n video_path = os.path.join(self.video_dir, item['video'])\\n cache_path = os.path.splitext(video_path)[0] + '.pt'\\n if self.cache_latents and os.path.exists(cache_path):\\n return torch.load(cache_path, map_location='cpu')\\n\\n prompt = item['caption']\\n if self.fallback_prompt == \\\"<no_text>\\\":\\n prompt = \\\"\\\"\\n vr = decord.VideoReader(video_path)\\n video = get_frame_batch(self.n_sample_frames, self.fps, vr, self.transform)\\n except Exception as err:\\n print(\\\"read video error\\\", err, video_path)\\n return self.__getitem__(index+1)\\n prompt_ids = get_prompt_ids(prompt, self.tokenizer)\\n\\n example = {\\n \\\"pixel_values\\\": normalize_input(video), \\n \\\"prompt_ids\\\": prompt_ids, \\n \\\"text_prompt\\\": prompt, \\n 'cache_path': cache_path,\\n 'dataset': self.__getname__()\\n }\\n mask = get_moved_area_mask(video.permute([0,2,3,1]).numpy())\\n example['motion'] = calculate_motion_score(video.permute([0,2,3,1]).numpy())\\n if example['motion'] < self.motion_threshold:\\n return self.__getitem__(random.randint(0, len(self)-1))\\n return example\"\n },\n {\n \"identifier\": \"SingleVideoDataset\",\n \"path\": \"utils/dataset.py\",\n \"snippet\": \"class SingleVideoDataset(Dataset):\\n def __init__(\\n self,\\n tokenizer = None,\\n width: int = 256,\\n height: int = 256,\\n n_sample_frames: int = 4,\\n frame_step: int = 1,\\n single_video_path: str = \\\"\\\",\\n single_video_prompt: str = \\\"\\\",\\n use_caption: bool = False,\\n use_bucketing: bool = False,\\n **kwargs\\n ):\\n self.tokenizer = tokenizer\\n self.use_bucketing = use_bucketing\\n self.frames = []\\n self.index = 1\\n\\n self.vid_types = (\\\".mp4\\\", \\\".avi\\\", \\\".mov\\\", \\\".webm\\\", \\\".flv\\\", \\\".mjpeg\\\")\\n self.n_sample_frames = n_sample_frames\\n self.frame_step = frame_step\\n\\n self.single_video_path = single_video_path\\n self.single_video_prompt = single_video_prompt\\n\\n self.width = width\\n self.height = height\\n def create_video_chunks(self):\\n # Create a list of frames separated by sample frames\\n # [(1,2,3), (4,5,6), ...]\\n vr = decord.VideoReader(self.single_video_path)\\n vr_range = range(1, len(vr), self.frame_step)\\n\\n self.frames = list(self.chunk(vr_range, self.n_sample_frames))\\n\\n # Delete any list that contains an out of range index.\\n for i, inner_frame_nums in enumerate(self.frames):\\n for frame_num in inner_frame_nums:\\n if frame_num > len(vr):\\n print(f\\\"Removing out of range index list at position: {i}...\\\")\\n del self.frames[i]\\n\\n return self.frames\\n\\n def chunk(self, it, size):\\n it = iter(it)\\n return iter(lambda: tuple(islice(it, size)), ())\\n\\n def get_frame_batch(self, vr, resize=None):\\n index = self.index\\n frames = vr.get_batch(self.frames[self.index])\\n video = rearrange(frames, \\\"f h w c -> f c h w\\\")\\n\\n if resize is not None: video = resize(video)\\n return video\\n\\n def get_frame_buckets(self, vr):\\n _, h, w = vr[0].shape \\n width, height = sensible_buckets(self.width, self.height, h, w)\\n resize = T.transforms.Resize((height, width), antialias=True)\\n\\n return resize\\n \\n def process_video_wrapper(self, vid_path):\\n video, vr = process_video(\\n vid_path,\\n self.use_bucketing,\\n self.width, \\n self.height, \\n self.get_frame_buckets, \\n self.get_frame_batch\\n )\\n \\n return video, vr \\n\\n def single_video_batch(self, index):\\n train_data = self.single_video_path\\n self.index = index\\n\\n if train_data.endswith(self.vid_types):\\n video, _ = self.process_video_wrapper(train_data)\\n\\n prompt = self.single_video_prompt\\n prompt_ids = get_prompt_ids(prompt, self.tokenizer)\\n\\n return video, prompt, prompt_ids\\n else:\\n raise ValueError(f\\\"Single video is not a video type. Types: {self.vid_types}\\\")\\n \\n @staticmethod\\n def __getname__(): return 'single_video'\\n\\n def __len__(self):\\n \\n return len(self.create_video_chunks())\\n\\n def __getitem__(self, index):\\n\\n video, prompt, prompt_ids = self.single_video_batch(index)\\n\\n example = {\\n \\\"pixel_values\\\": normalize_input(video),\\n \\\"prompt_ids\\\": prompt_ids,\\n \\\"text_prompt\\\": prompt,\\n 'dataset': self.__getname__()\\n }\\n\\n return example\"\n },\n {\n \"identifier\": \"ImageDataset\",\n \"path\": \"utils/dataset.py\",\n \"snippet\": \"class ImageDataset(Dataset):\\n \\n def __init__(\\n self,\\n tokenizer = None,\\n width: int = 256,\\n height: int = 256,\\n base_width: int = 256,\\n base_height: int = 256,\\n use_caption: bool = False,\\n image_dir: str = '',\\n single_img_prompt: str = '',\\n use_bucketing: bool = False,\\n fallback_prompt: str = '',\\n **kwargs\\n ):\\n self.tokenizer = tokenizer\\n self.img_types = (\\\".png\\\", \\\".jpg\\\", \\\".jpeg\\\", '.bmp')\\n self.use_bucketing = use_bucketing\\n #self.image_dir = self.get_images_list(image_dir)\\n self.image_dir_path = image_dir\\n self.image_dir = json.load(open(kwargs['image_json']))\\n self.fallback_prompt = fallback_prompt\\n\\n self.use_caption = use_caption\\n self.single_img_prompt = single_img_prompt\\n\\n self.width = width\\n self.height = height\\n\\n def get_images_list(self, image_dir):\\n if os.path.exists(image_dir):\\n imgs = [x for x in os.listdir(image_dir) if x.endswith(self.img_types)]\\n full_img_dir = []\\n\\n for img in imgs: \\n full_img_dir.append(f\\\"{image_dir}/{img}\\\")\\n\\n return sorted(full_img_dir)\\n\\n return ['']\\n\\n def image_batch(self, index):\\n train_data = self.image_dir[index]\\n img, prompt = train_data['image'], train_data['caption']\\n img = os.path.join(self.image_dir_path, img)\\n try:\\n img = torchvision.io.read_image(img, mode=torchvision.io.ImageReadMode.RGB)\\n except:\\n img = T.transforms.PILToTensor()(Image.open(img).convert(\\\"RGB\\\"))\\n\\n width = self.width\\n height = self.height\\n\\n if self.use_bucketing:\\n _, h, w = img.shape\\n width, height = sensible_buckets(width, height, w, h)\\n \\n resize = T.transforms.Resize((height, width), antialias=True)\\n\\n img = resize(img) \\n img = repeat(img, 'c h w -> f c h w', f=1)\\n prompt_ids = get_prompt_ids(prompt, self.tokenizer)\\n\\n return img, prompt, prompt_ids\\n\\n @staticmethod\\n def __getname__(): return 'image'\\n \\n def __len__(self):\\n # Image directory\\n return len(self.image_dir)\\n\\n def __getitem__(self, index):\\n img, prompt, prompt_ids = self.image_batch(index)\\n example = {\\n \\\"pixel_values\\\": normalize_input(img),\\n \\\"frames\\\": img,\\n \\\"prompt_ids\\\": prompt_ids,\\n \\\"text_prompt\\\": prompt, \\n 'dataset': self.__getname__()\\n }\\n\\n return example\"\n },\n {\n \"identifier\": \"VideoFolderDataset\",\n \"path\": \"utils/dataset.py\",\n \"snippet\": \"class VideoFolderDataset(Dataset):\\n def __init__(\\n self,\\n tokenizer=None,\\n width: int = 256,\\n height: int = 256,\\n n_sample_frames: int = 16,\\n fps: int = 8,\\n path: str = \\\"./data\\\",\\n fallback_prompt: str = \\\"\\\",\\n use_bucketing: bool = False,\\n **kwargs\\n ):\\n self.tokenizer = tokenizer\\n self.use_bucketing = use_bucketing\\n\\n self.fallback_prompt = fallback_prompt\\n\\n self.video_files = glob(f\\\"{path}/*.mp4\\\")\\n\\n self.width = width\\n self.height = height\\n\\n self.n_sample_frames = n_sample_frames\\n self.fps = fps\\n\\n def get_frame_buckets(self, vr):\\n _, h, w = vr[0].shape \\n width, height = sensible_buckets(self.width, self.height, h, w)\\n resize = T.transforms.Resize((height, width), antialias=True)\\n\\n return resize\\n\\n def get_frame_batch(self, vr, resize=None):\\n n_sample_frames = self.n_sample_frames\\n native_fps = vr.get_avg_fps()\\n \\n every_nth_frame = max(1, round(native_fps / self.fps))\\n every_nth_frame = min(len(vr), every_nth_frame)\\n \\n effective_length = len(vr) // every_nth_frame\\n if effective_length < n_sample_frames:\\n n_sample_frames = effective_length\\n raise RuntimeError(\\\"not enough frames\\\")\\n\\n effective_idx = random.randint(0, (effective_length - n_sample_frames))\\n idxs = every_nth_frame * np.arange(effective_idx, effective_idx + n_sample_frames)\\n\\n video = vr.get_batch(idxs)\\n video = rearrange(video, \\\"f h w c -> f c h w\\\")\\n\\n if resize is not None: video = resize(video)\\n return video, vr\\n \\n def process_video_wrapper(self, vid_path):\\n video, vr = process_video(\\n vid_path,\\n self.use_bucketing,\\n self.width, \\n self.height, \\n self.get_frame_buckets, \\n self.get_frame_batch\\n )\\n return video, vr\\n \\n @staticmethod\\n def __getname__(): return 'folder'\\n\\n def __len__(self):\\n return len(self.video_files)\\n\\n def __getitem__(self, index):\\n try:\\n video, _ = self.process_video_wrapper(self.video_files[index])\\n except Exception as err:\\n print(\\\"read video error\\\", self.video_files[index])\\n video, _ = self.process_video_wrapper(self.video_files[index+1])\\n\\n if os.path.exists(self.video_files[index].replace(\\\".mp4\\\", \\\".txt\\\")):\\n with open(self.video_files[index].replace(\\\".mp4\\\", \\\".txt\\\"), \\\"r\\\") as f:\\n lines = f.readlines()\\n prompt = random.choice(lines)\\n else:\\n prompt = self.fallback_prompt\\n\\n prompt_ids = get_prompt_ids(prompt, self.tokenizer)\\n\\n return {\\\"pixel_values\\\": normalize_input(video[0]), \\\"frames\\\": video[0],\\n \\\"prompt_ids\\\": prompt_ids, \\\"text_prompt\\\": prompt, 'dataset': self.__getname__()}\"\n },\n {\n \"identifier\": \"CachedDataset\",\n \"path\": \"utils/dataset.py\",\n \"snippet\": \"class CachedDataset(Dataset):\\n def __init__(self,cache_dir: str = ''):\\n self.cache_dir = cache_dir\\n self.cached_data_list = self.get_files_list()\\n\\n def get_files_list(self):\\n tensors_list = [f\\\"{self.cache_dir}/{x}\\\" for x in os.listdir(self.cache_dir) if x.endswith('.pt')]\\n return sorted(tensors_list)\\n\\n def __len__(self):\\n return len(self.cached_data_list)\\n\\n def __getitem__(self, index):\\n cached_latent = torch.load(self.cached_data_list[index], map_location='cuda:0')\\n return cached_latent\"\n },\n {\n \"identifier\": \"VideoBLIPDataset\",\n \"path\": \"utils/dataset.py\",\n \"snippet\": \"class VideoBLIPDataset(Dataset):\\n def __init__(\\n self,\\n tokenizer = None,\\n width: int = 256,\\n height: int = 256,\\n n_sample_frames: int = 4,\\n sample_start_idx: int = 1,\\n fps: int = 1,\\n json_path: str =\\\"\\\",\\n json_data = None,\\n vid_data_key: str = \\\"video_path\\\",\\n preprocessed: bool = False,\\n use_bucketing: bool = False,\\n cache_latents: bool = False,\\n motion_threshold = 50,\\n **kwargs\\n ):\\n self.vid_types = (\\\".mp4\\\", \\\".avi\\\", \\\".mov\\\", \\\".webm\\\", \\\".flv\\\", \\\".mjpeg\\\")\\n self.use_bucketing = use_bucketing\\n self.tokenizer = tokenizer\\n self.preprocessed = preprocessed\\n \\n self.vid_data_key = vid_data_key\\n self.train_data = self.load_from_json(json_path, json_data)\\n self.cache_latents = cache_latents\\n self.motion_threshold = motion_threshold\\n self.width = width\\n self.height = height\\n\\n self.n_sample_frames = n_sample_frames\\n self.sample_start_idx = sample_start_idx\\n self.fps = fps\\n self.transform = T.Compose([\\n #T.RandomResizedCrop(size=(height, width), scale=(0.8, 1.0), ratio=(width/height, width/height), antialias=False)\\n T.Resize(min(height, width), antialias=False),\\n T.CenterCrop([height, width])\\n ])\\n\\n def build_json(self, json_data):\\n extended_data = []\\n for data in json_data['data']:\\n for nested_data in data['data']:\\n self.build_json_dict(\\n data, \\n nested_data, \\n extended_data\\n )\\n json_data = extended_data\\n return json_data\\n\\n def build_json_dict(self, data, nested_data, extended_data):\\n clip_path = nested_data['clip_path'] if 'clip_path' in nested_data else None\\n \\n extended_data.append({\\n self.vid_data_key: data[self.vid_data_key],\\n 'frame_index': nested_data['frame_index'],\\n 'prompt': nested_data['prompt'],\\n 'clip_path': clip_path\\n })\\n \\n def load_from_json(self, path, json_data):\\n try:\\n with open(path) as jpath:\\n print(f\\\"Loading JSON from {path}\\\")\\n json_data = json.load(jpath)\\n\\n return self.build_json(json_data)\\n\\n except:\\n import traceback\\n traceback.print_exc()\\n self.train_data = []\\n print(\\\"Non-existant JSON path. Skipping.\\\")\\n \\n def validate_json(self, base_path, path):\\n return os.path.exists(f\\\"{base_path}/{path}\\\")\\n\\n def get_frame_buckets(self, vr):\\n _, h, w = vr[0].shape \\n width, height = sensible_buckets(self.width, self.height, h, w)\\n resize = T.transforms.Resize((height, width), antialias=True)\\n\\n return resize\\n\\n def train_data_batch(self, index):\\n vid_data = self.train_data[index]\\n # Get video prompt\\n prompt = vid_data['prompt']\\n # If we are training on individual clips.\\n if 'clip_path' in self.train_data[index] and \\\\\\n self.train_data[index]['clip_path'] is not None:\\n clip_path = vid_data['clip_path']\\n else:\\n clip_path = vid_data[self.vid_data_key]\\n # Get the frame of the current index.\\n self.sample_start_idx = vid_data['frame_index']\\n cache_path = os.path.splitext(clip_path)[0] + '.pt'\\n if self.cache_latents and os.path.exists(cache_path):\\n return torch.load(cache_path, map_location='cpu')\\n\\n vr = decord.VideoReader(clip_path)\\n video = get_frame_batch(self.n_sample_frames, self.fps, vr, self.transform)\\n prompt_ids = get_prompt_ids(prompt, self.tokenizer)\\n example = {\\n \\\"pixel_values\\\": normalize_input(video),\\n \\\"prompt_ids\\\": prompt_ids,\\n \\\"text_prompt\\\": prompt,\\n 'dataset': self.__getname__(),\\n 'cache_path': cache_path,\\n }\\n mask = get_moved_area_mask(video.permute([0,2,3,1]).numpy())\\n example['mask'] = mask\\n example['motion'] = calculate_motion_score(video.permute([0,2,3,1]).numpy())\\n return example\\n \\n\\n @staticmethod\\n def __getname__(): return 'video_blip'\\n\\n def __len__(self):\\n if self.train_data is not None:\\n return len(self.train_data)\\n else: \\n return 0\\n\\n def __getitem__(self, index):\\n example = self.train_data_batch(index)\\n if example['motion'] < self.motion_threshold:\\n return self.__getitem__(random.randint(0, len(self)-1))\\n return example\"\n },\n {\n \"identifier\": \"UNet3DConditionModel\",\n \"path\": \"models/unet_3d_condition_mask.py\",\n \"snippet\": \"class UNet3DConditionModel(ModelMixin, ConfigMixin):\\n r\\\"\\\"\\\"\\n UNet3DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep\\n and returns sample shaped output.\\n\\n This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library\\n implements for all the models (such as downloading or saving, etc.)\\n\\n Parameters:\\n sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):\\n Height and width of input/output sample.\\n in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.\\n out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.\\n down_block_types (`Tuple[str]`, *optional*, defaults to `(\\\"CrossAttnDownBlock2D\\\", \\\"CrossAttnDownBlock2D\\\", \\\"CrossAttnDownBlock2D\\\", \\\"DownBlock2D\\\")`):\\n The tuple of downsample blocks to use.\\n up_block_types (`Tuple[str]`, *optional*, defaults to `(\\\"UpBlock2D\\\", \\\"CrossAttnUpBlock2D\\\", \\\"CrossAttnUpBlock2D\\\", \\\"CrossAttnUpBlock2D\\\",)`):\\n The tuple of upsample blocks to use.\\n block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):\\n The tuple of output channels for each block.\\n layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.\\n downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.\\n mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.\\n act_fn (`str`, *optional*, defaults to `\\\"silu\\\"`): The activation function to use.\\n norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.\\n If `None`, it will skip the normalization and activation layers in post-processing\\n norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.\\n cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.\\n attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.\\n \\\"\\\"\\\"\\n\\n _supports_gradient_checkpointing = True\\n\\n @register_to_config\\n def __init__(\\n self,\\n sample_size: Optional[int] = None,\\n in_channels: int = 4,\\n out_channels: int = 4,\\n down_block_types: Tuple[str] = (\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"DownBlock3D\\\",\\n ),\\n up_block_types: Tuple[str] = (\\\"UpBlock3D\\\", \\\"CrossAttnUpBlock3D\\\", \\\"CrossAttnUpBlock3D\\\", \\\"CrossAttnUpBlock3D\\\"),\\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\\n layers_per_block: int = 2,\\n downsample_padding: int = 1,\\n mid_block_scale_factor: float = 1,\\n act_fn: str = \\\"silu\\\",\\n norm_num_groups: Optional[int] = 32,\\n norm_eps: float = 1e-5,\\n cross_attention_dim: int = 1024,\\n attention_head_dim: Union[int, Tuple[int]] = 64,\\n motion_mask = False,\\n motion_strength = False,\\n ):\\n super().__init__()\\n self.motion_mask = motion_mask\\n self.motion_strength = motion_strength\\n print(f\\\"motion mask {self.motion_mask}, motion_strength {self.motion_strength}\\\")\\n self.sample_size = sample_size\\n self.gradient_checkpointing = False\\n # Check inputs\\n if len(down_block_types) != len(up_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.\\\"\\n )\\n\\n if len(block_out_channels) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n # input\\n conv_in_kernel = 3\\n conv_out_kernel = 3\\n conv_in_padding = (conv_in_kernel - 1) // 2\\n self.conv_in = nn.Conv2d(\\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\\n )\\n self.conv_in2 = nn.Conv2d(\\n 5, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\\n )\\n\\n # time\\n time_embed_dim = block_out_channels[0] * 4\\n self.time_proj = Timesteps(block_out_channels[0], True, 0)\\n timestep_input_dim = block_out_channels[0]\\n\\n self.time_embedding = TimestepEmbedding(\\n timestep_input_dim,\\n time_embed_dim,\\n act_fn=act_fn,\\n cond_proj_dim=block_out_channels[0],\\n )\\n\\n self.motion_proj = Timesteps(block_out_channels[0], True, 0)\\n self.motion_embedding = nn.Sequential(\\n nn.Linear(timestep_input_dim, time_embed_dim), nn.SiLU(),\\n nn.Linear(time_embed_dim, time_embed_dim))\\n nn.init.zeros_(self.motion_embedding[-1].weight)\\n nn.init.zeros_(self.motion_embedding[-1].bias)\\n\\n self.transformer_in = TransformerTemporalModel(\\n num_attention_heads=8,\\n attention_head_dim=attention_head_dim,\\n in_channels=block_out_channels[0],\\n num_layers=1,\\n )\\n\\n # class embedding\\n self.down_blocks = nn.ModuleList([])\\n self.up_blocks = nn.ModuleList([])\\n\\n if isinstance(attention_head_dim, int):\\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\\n\\n # down\\n output_channel = block_out_channels[0]\\n for i, down_block_type in enumerate(down_block_types):\\n input_channel = output_channel\\n output_channel = block_out_channels[i]\\n is_final_block = i == len(block_out_channels) - 1\\n\\n down_block = get_down_block(\\n down_block_type,\\n num_layers=layers_per_block,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n temb_channels=time_embed_dim,\\n add_downsample=not is_final_block,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=attention_head_dim[i],\\n downsample_padding=downsample_padding,\\n dual_cross_attention=False,\\n )\\n self.down_blocks.append(down_block)\\n\\n # mid\\n self.mid_block = UNetMidBlock3DCrossAttn(\\n in_channels=block_out_channels[-1],\\n temb_channels=time_embed_dim,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n output_scale_factor=mid_block_scale_factor,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=attention_head_dim[-1],\\n resnet_groups=norm_num_groups,\\n dual_cross_attention=False,\\n )\\n # count how many layers upsample the images\\n self.num_upsamplers = 0\\n\\n # up\\n reversed_block_out_channels = list(reversed(block_out_channels))\\n reversed_attention_head_dim = list(reversed(attention_head_dim))\\n\\n output_channel = reversed_block_out_channels[0]\\n for i, up_block_type in enumerate(up_block_types):\\n is_final_block = i == len(block_out_channels) - 1\\n\\n prev_output_channel = output_channel\\n output_channel = reversed_block_out_channels[i]\\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\\n\\n # add upsample block for all BUT final layer\\n if not is_final_block:\\n add_upsample = True\\n self.num_upsamplers += 1\\n else:\\n add_upsample = False\\n\\n up_block = get_up_block(\\n up_block_type,\\n num_layers=layers_per_block + 1,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n prev_output_channel=prev_output_channel,\\n temb_channels=time_embed_dim,\\n add_upsample=add_upsample,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=reversed_attention_head_dim[i],\\n dual_cross_attention=False,\\n )\\n self.up_blocks.append(up_block)\\n prev_output_channel = output_channel\\n\\n # out\\n if norm_num_groups is not None:\\n self.conv_norm_out = nn.GroupNorm(\\n num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps\\n )\\n self.conv_act = nn.SiLU()\\n else:\\n self.conv_norm_out = None\\n self.conv_act = None\\n\\n conv_out_padding = (conv_out_kernel - 1) // 2\\n self.conv_out = nn.Conv2d(\\n block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding\\n )\\n\\n def set_attention_slice(self, slice_size):\\n r\\\"\\\"\\\"\\n Enable sliced attention computation.\\n\\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\\n\\n Args:\\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\\\"auto\\\"`):\\n When `\\\"auto\\\"`, halves the input to the attention heads, so attention will be computed in two steps. If\\n `\\\"max\\\"`, maxium amount of memory will be saved by running only one slice at a time. If a number is\\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\\n must be a multiple of `slice_size`.\\n \\\"\\\"\\\"\\n sliceable_head_dims = []\\n\\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n sliceable_head_dims.append(module.sliceable_head_dim)\\n\\n for child in module.children():\\n fn_recursive_retrieve_slicable_dims(child)\\n\\n # retrieve number of attention layers\\n for module in self.children():\\n fn_recursive_retrieve_slicable_dims(module)\\n\\n num_slicable_layers = len(sliceable_head_dims)\\n\\n if slice_size == \\\"auto\\\":\\n # half the attention head size is usually a good trade-off between\\n # speed and memory\\n slice_size = [dim // 2 for dim in sliceable_head_dims]\\n elif slice_size == \\\"max\\\":\\n # make smallest slice possible\\n slice_size = num_slicable_layers * [1]\\n\\n slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\\n\\n if len(slice_size) != len(sliceable_head_dims):\\n raise ValueError(\\n f\\\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\\\"\\n f\\\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\\\"\\n )\\n\\n for i in range(len(slice_size)):\\n size = slice_size[i]\\n dim = sliceable_head_dims[i]\\n if size is not None and size > dim:\\n raise ValueError(f\\\"size {size} has to be smaller or equal to {dim}.\\\")\\n\\n # Recursively walk through all the children.\\n # Any children which exposes the set_attention_slice method\\n # gets the message\\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n module.set_attention_slice(slice_size.pop())\\n\\n for child in module.children():\\n fn_recursive_set_attention_slice(child, slice_size)\\n\\n reversed_slice_size = list(reversed(slice_size))\\n for module in self.children():\\n fn_recursive_set_attention_slice(module, reversed_slice_size)\\n\\n def _set_gradient_checkpointing(self, value=False):\\n self.gradient_checkpointing = value\\n self.mid_block.gradient_checkpointing = value\\n for module in self.down_blocks + self.up_blocks:\\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\\n module.gradient_checkpointing = value \\n \\n def forward(\\n self,\\n sample: torch.FloatTensor,\\n timestep: Union[torch.Tensor, float, int],\\n encoder_hidden_states: torch.Tensor,\\n condition_latent: torch.Tensor,\\n mask: torch.Tensor,\\n class_labels: Optional[torch.Tensor] = None,\\n timestep_cond: Optional[torch.Tensor] = None,\\n attention_mask: Optional[torch.Tensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\\n mid_block_additional_residual: Optional[torch.Tensor] = None,\\n motion = None,\\n return_dict: bool = True,\\n ) -> Union[UNet3DConditionOutput, Tuple]:\\n r\\\"\\\"\\\"\\n Args:\\n sample (`torch.FloatTensor`): (batch, num_frames, channel, height, width) noisy inputs tensor\\n timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps\\n encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states\\n return_dict (`bool`, *optional*, defaults to `True`):\\n Whether or not to return a [`models.unet_2d_condition.UNet3DConditionOutput`] instead of a plain tuple.\\n cross_attention_kwargs (`dict`, *optional*):\\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\\n `self.processor` in\\n [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).\\n\\n Returns:\\n [`~models.unet_2d_condition.UNet3DConditionOutput`] or `tuple`:\\n [`~models.unet_2d_condition.UNet3DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When\\n returning a tuple, the first element is the sample tensor.\\n \\\"\\\"\\\"\\n # By default samples have to be AT least a multiple of the overall upsampling factor.\\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\\n # on the fly if necessary.\\n default_overall_up_factor = 2**self.num_upsamplers\\n sample = torch.cat([condition_latent, sample], dim=2)\\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\\n forward_upsample_size = False\\n upsample_size = None\\n\\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\\n logger.info(\\\"Forward upsample size to force interpolation output size.\\\")\\n forward_upsample_size = True\\n\\n # prepare attention_mask\\n if attention_mask is not None:\\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\\n attention_mask = attention_mask.unsqueeze(1)\\n\\n # 1. time\\n timesteps = timestep\\n if not torch.is_tensor(timesteps):\\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\\n # This would be a good case for the `match` statement (Python 3.10+)\\n is_mps = sample.device.type == \\\"mps\\\"\\n if isinstance(timestep, float):\\n dtype = torch.float32 if is_mps else torch.float64\\n else:\\n dtype = torch.int32 if is_mps else torch.int64\\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\\n elif len(timesteps.shape) == 0:\\n timesteps = timesteps[None].to(sample.device)\\n\\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\\n num_frames = sample.shape[2]\\n timesteps = timesteps.expand(sample.shape[0])\\n\\n t_emb = self.time_proj(timesteps)\\n\\n # timesteps does not contain any weights and will always return f32 tensors\\n # but time_embedding might actually be running in fp16. so we need to cast here.\\n # there might be better ways to encapsulate this.\\n t_emb = t_emb.to(dtype=self.dtype)\\n if self.motion_strength and motion is not None:\\n timestep_cond = self.motion_proj(motion).to(dtype=self.dtype)\\n emb = self.time_embedding(t_emb, timestep_cond)\\n #emb += self.motion_embedding(m_emb)\\n else:\\n emb = self.time_embedding(t_emb, timestep_cond)\\n emb = emb.repeat_interleave(repeats=num_frames, dim=0)\\n encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)\\n\\n # 2. pre-process\\n if self.motion_mask and mask is not None:\\n mask = repeat(mask , 'b 1 1 h w -> (t b) 1 f h w', t=sample.shape[0]//mask.shape[0], f=sample.shape[2])\\n sample = torch.cat([mask, sample], dim=1)\\n sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])\\n sample = self.conv_in2(sample)\\n else:\\n sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])\\n sample = self.conv_in(sample)\\n\\n if num_frames > 1:\\n if self.gradient_checkpointing:\\n sample = transformer_g_c(self.transformer_in, sample, num_frames)\\n else:\\n sample = self.transformer_in(sample, num_frames=num_frames).sample\\n\\n # 3. down\\n down_block_res_samples = (sample,)\\n for i, downsample_block in enumerate(self.down_blocks):\\n if hasattr(downsample_block, \\\"has_cross_attention\\\") and downsample_block.has_cross_attention:\\n sample, res_samples = downsample_block(\\n hidden_states=sample,\\n temb=emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n num_frames=num_frames,\\n cross_attention_kwargs=cross_attention_kwargs,\\n )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)\\n \\n down_block_res_samples += res_samples\\n\\n if down_block_additional_residuals is not None:\\n new_down_block_res_samples = ()\\n\\n for down_block_res_sample, down_block_additional_residual in zip(\\n down_block_res_samples, down_block_additional_residuals\\n ):\\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\\n new_down_block_res_samples += (down_block_res_sample,)\\n\\n down_block_res_samples = new_down_block_res_samples\\n\\n # 4. mid\\n if self.mid_block is not None:\\n sample = self.mid_block(\\n sample,\\n emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n num_frames=num_frames,\\n cross_attention_kwargs=cross_attention_kwargs,\\n )\\n\\n if mid_block_additional_residual is not None:\\n sample = sample + mid_block_additional_residual\\n\\n # 5. up\\n for i, upsample_block in enumerate(self.up_blocks):\\n is_final_block = i == len(self.up_blocks) - 1\\n\\n res_samples = down_block_res_samples[-len(upsample_block.resnets) :]\\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\\n\\n # if we have not reached the final block and need to forward the\\n # upsample size, we do it here\\n if not is_final_block and forward_upsample_size:\\n upsample_size = down_block_res_samples[-1].shape[2:]\\n\\n if hasattr(upsample_block, \\\"has_cross_attention\\\") and upsample_block.has_cross_attention:\\n sample = upsample_block(\\n hidden_states=sample,\\n temb=emb,\\n res_hidden_states_tuple=res_samples,\\n encoder_hidden_states=encoder_hidden_states,\\n upsample_size=upsample_size,\\n attention_mask=attention_mask,\\n num_frames=num_frames,\\n cross_attention_kwargs=cross_attention_kwargs,\\n )\\n else:\\n sample = upsample_block(\\n hidden_states=sample,\\n temb=emb,\\n res_hidden_states_tuple=res_samples,\\n upsample_size=upsample_size,\\n num_frames=num_frames,\\n )\\n\\n # 6. post-process\\n if self.conv_norm_out:\\n sample = self.conv_norm_out(sample)\\n sample = self.conv_act(sample)\\n\\n sample = self.conv_out(sample)\\n\\n # reshape to (batch, channel, framerate, width, height)\\n sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)\\n sample = sample[:,:,1:]\\n if not return_dict:\\n return (sample,)\\n\\n return UNet3DConditionOutput(sample=sample)\"\n },\n {\n \"identifier\": \"LatentToVideoPipeline\",\n \"path\": \"models/pipeline.py\",\n \"snippet\": \"class LatentToVideoPipeline(TextToVideoSDPipeline):\\n @torch.no_grad()\\n def __call__(\\n self,\\n prompt = None,\\n height= None,\\n width= None,\\n num_frames: int = 16,\\n num_inference_steps: int = 50,\\n guidance_scale= 9.0,\\n negative_prompt= None,\\n eta: float = 0.0,\\n generator= None,\\n latents= None,\\n prompt_embeds= None,\\n negative_prompt_embeds= None,\\n output_type= \\\"np\\\",\\n return_dict: bool = True,\\n callback= None,\\n callback_steps: int = 1,\\n cross_attention_kwargs= None,\\n condition_latent=None,\\n mask=None,\\n timesteps=None,\\n motion=None,\\n ):\\n r\\\"\\\"\\\"\\n Function invoked when calling the pipeline for generation.\\n\\n Args:\\n prompt (`str` or `List[str]`, *optional*):\\n The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds`.\\n instead.\\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\\n The height in pixels of the generated video.\\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\\n The width in pixels of the generated video.\\n num_frames (`int`, *optional*, defaults to 16):\\n The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds\\n amounts to 2 seconds of video.\\n num_inference_steps (`int`, *optional*, defaults to 50):\\n The number of denoising steps. More denoising steps usually lead to a higher quality videos at the\\n expense of slower inference.\\n guidance_scale (`float`, *optional*, defaults to 7.5):\\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\\n 1`. Higher guidance scale encourages to generate videos that are closely linked to the text `prompt`,\\n usually at the expense of lower video quality.\\n negative_prompt (`str` or `List[str]`, *optional*):\\n The prompt or prompts not to guide the video generation. If not defined, one has to pass\\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\\n less than `1`).\\n eta (`float`, *optional*, defaults to 0.0):\\n Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to\\n [`schedulers.DDIMScheduler`], will be ignored for others.\\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\\n to make generation deterministic.\\n latents (`torch.FloatTensor`, *optional*):\\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for video\\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\\n tensor will ge generated by sampling using the supplied random `generator`. Latents should be of shape\\n `(batch_size, num_channel, num_frames, height, width)`.\\n prompt_embeds (`torch.FloatTensor`, *optional*):\\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\\n provided, text embeddings will be generated from `prompt` input argument.\\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\\n argument.\\n output_type (`str`, *optional*, defaults to `\\\"np\\\"`):\\n The output format of the generate video. Choose between `torch.FloatTensor` or `np.array`.\\n return_dict (`bool`, *optional*, defaults to `True`):\\n Whether or not to return a [`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] instead of a\\n plain tuple.\\n callback (`Callable`, *optional*):\\n A function that will be called every `callback_steps` steps during inference. The function will be\\n called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.\\n callback_steps (`int`, *optional*, defaults to 1):\\n The frequency at which the `callback` function will be called. If not specified, the callback will be\\n called at every step.\\n cross_attention_kwargs (`dict`, *optional*):\\n A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under\\n `self.processor` in\\n [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).\\n\\n Examples:\\n\\n Returns:\\n [`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] or `tuple`:\\n [`~pipelines.stable_diffusion.TextToVideoSDPipelineOutput`] if `return_dict` is True, otherwise a `tuple.\\n When returning a tuple, the first element is a list with the generated frames.\\n \\\"\\\"\\\"\\n # 0. Default height and width to unet\\n height = height or self.unet.config.sample_size * self.vae_scale_factor\\n width = width or self.unet.config.sample_size * self.vae_scale_factor\\n\\n num_images_per_prompt = 1\\n\\n # 1. Check inputs. Raise error if not correct\\n self.check_inputs(\\n prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds\\n )\\n\\n # 2. Define call parameters\\n if prompt is not None and isinstance(prompt, str):\\n batch_size = 1\\n elif prompt is not None and isinstance(prompt, list):\\n batch_size = len(prompt)\\n else:\\n batch_size = prompt_embeds.shape[0]\\n\\n #device = self._execution_device\\n device = latents.device\\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\\n # corresponds to doing no classifier free guidance.\\n do_classifier_free_guidance = guidance_scale > 1.0\\n\\n # 3. Encode input prompt\\n text_encoder_lora_scale = (\\n cross_attention_kwargs.get(\\\"scale\\\", None) if cross_attention_kwargs is not None else None\\n )\\n prompt_embeds = self._encode_prompt(\\n prompt,\\n device,\\n num_images_per_prompt,\\n do_classifier_free_guidance,\\n negative_prompt,\\n prompt_embeds=prompt_embeds,\\n negative_prompt_embeds=negative_prompt_embeds,\\n lora_scale=text_encoder_lora_scale,\\n )\\n\\n # 4. Prepare timesteps\\n self.scheduler.set_timesteps(num_inference_steps, device=device)\\n if timesteps is None:\\n timesteps = self.scheduler.timesteps\\n else:\\n num_inference_steps = len(timesteps)\\n # 5. Prepare latent variables. do nothing\\n\\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\\n\\n # 7. Denoising loop\\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\\n uncondition_latent = condition_latent\\n condition_latent = torch.cat([uncondition_latent, condition_latent]) if do_classifier_free_guidance else condition_latent \\n with self.progress_bar(total=num_inference_steps) as progress_bar:\\n for i, t in enumerate(timesteps):\\n # expand the latents if we are doing classifier free guidance\\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\\n if motion is not None:\\n motion = torch.tensor(motion, device=device)\\n noise_pred = self.unet(\\n latent_model_input,\\n t,\\n encoder_hidden_states=prompt_embeds,\\n cross_attention_kwargs=cross_attention_kwargs,\\n condition_latent=condition_latent,\\n mask=mask,\\n motion=motion\\n ).sample\\n # perform guidance\\n if do_classifier_free_guidance:\\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\\n\\n # reshape latents\\n bsz, channel, frames, width, height = latents.shape\\n latents = latents.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)\\n noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)\\n\\n # compute the previous noisy sample x_t -> x_t-1\\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample\\n\\n # reshape latents back\\n latents = latents[None, :].reshape(bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4)\\n\\n # call the callback, if provided\\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\\n progress_bar.update()\\n if callback is not None and i % callback_steps == 0:\\n callback(i, t, latents)\\n\\n video_tensor = self.decode_latents(latents)\\n\\n if output_type == \\\"pt\\\":\\n video = video_tensor\\n else:\\n video = tensor2vid(video_tensor)\\n\\n # Offload last model to CPU\\n if hasattr(self, \\\"final_offload_hook\\\") and self.final_offload_hook is not None:\\n self.final_offload_hook.offload()\\n\\n if not return_dict:\\n return (video, latents)\\n\\n return TextToVideoSDPipelineOutput(frames=video)\"\n },\n {\n \"identifier\": \"LoraHandler\",\n \"path\": \"utils/lora_handler.py\",\n \"snippet\": \"class LoraHandler(object):\\n def __init__(\\n self, \\n version: LORA_VERSIONS = LoraVersions.cloneofsimo, \\n use_unet_lora: bool = False,\\n use_text_lora: bool = False,\\n save_for_webui: bool = False,\\n only_for_webui: bool = False,\\n lora_bias: str = 'none',\\n unet_replace_modules: list = ['UNet3DConditionModel'],\\n text_encoder_replace_modules: list = ['CLIPEncoderLayer']\\n ):\\n self.version = version\\n self.lora_loader = self.get_lora_func(func_type=LoraFuncTypes.loader)\\n self.lora_injector = self.get_lora_func(func_type=LoraFuncTypes.injector)\\n self.lora_bias = lora_bias\\n self.use_unet_lora = use_unet_lora\\n self.use_text_lora = use_text_lora\\n self.save_for_webui = save_for_webui\\n self.only_for_webui = only_for_webui\\n self.unet_replace_modules = unet_replace_modules\\n self.text_encoder_replace_modules = text_encoder_replace_modules\\n self.use_lora = any([use_text_lora, use_unet_lora])\\n\\n if self.use_lora:\\n print(f\\\"Using LoRA Version: {self.version}\\\")\\n\\n def is_cloneofsimo_lora(self):\\n return self.version == LoraVersions.cloneofsimo\\n\\n def is_stable_lora(self):\\n return self.version == LoraVersions.stable_lora\\n\\n def get_lora_func(self, func_type: LORA_FUNC_TYPES = LoraFuncTypes.loader):\\n\\n if self.is_cloneofsimo_lora():\\n\\n if func_type == LoraFuncTypes.loader:\\n return monkeypatch_or_replace_lora_extended\\n\\n if func_type == LoraFuncTypes.injector:\\n return inject_trainable_lora_extended\\n\\n if self.is_stable_lora():\\n\\n if func_type == LoraFuncTypes.loader:\\n return load_lora\\n\\n if func_type == LoraFuncTypes.injector:\\n return add_lora_to\\n \\n assert \\\"LoRA Version does not exist.\\\"\\n\\n def check_lora_ext(self, lora_file: str):\\n return lora_file.endswith(tuple(LORA_FILE_TYPES))\\n\\n def get_lora_file_path(\\n self, \\n lora_path: str, \\n model: Union[UNet3DConditionModel, CLIPTextModel]\\n ):\\n if os.path.exists(lora_path):\\n lora_filenames = [fns for fns in os.listdir(lora_path)]\\n is_lora = self.check_lora_ext(lora_path)\\n\\n is_unet = isinstance(model, UNet3DConditionModel)\\n is_text = isinstance(model, CLIPTextModel)\\n idx = 0 if is_unet else 1\\n\\n base_name = FILE_BASENAMES[idx]\\n \\n for lora_filename in lora_filenames:\\n is_lora = self.check_lora_ext(lora_filename)\\n if not is_lora:\\n continue\\n \\n if base_name in lora_filename:\\n return os.path.join(lora_path, lora_filename)\\n\\n return None\\n\\n def handle_lora_load(self, file_name:str, lora_loader_args: dict = None):\\n self.lora_loader(**lora_loader_args)\\n print(f\\\"Successfully loaded LoRA from: {file_name}\\\")\\n \\n def load_lora(self, model, lora_path: str = '', lora_loader_args: dict = None,):\\n try:\\n lora_file = self.get_lora_file_path(lora_path, model)\\n\\n if lora_file is not None:\\n lora_loader_args.update({\\\"lora_path\\\": lora_file})\\n self.handle_lora_load(lora_file, lora_loader_args)\\n\\n else:\\n print(f\\\"Could not load LoRAs for {model.__class__.__name__}. Injecting new ones instead...\\\")\\n\\n except Exception as e:\\n print(f\\\"An error occured while loading a LoRA file: {e}\\\")\\n \\n def get_lora_func_args(self, lora_path, use_lora, model, replace_modules, r, dropout, lora_bias):\\n return_dict = lora_args.copy()\\n \\n if self.is_cloneofsimo_lora():\\n return_dict = filter_dict(return_dict, keys=CLONE_OF_SIMO_KEYS)\\n return_dict.update({\\n \\\"model\\\": model,\\n \\\"loras\\\": self.get_lora_file_path(lora_path, model),\\n \\\"target_replace_module\\\": replace_modules,\\n \\\"r\\\": r\\n })\\n\\n if self.is_stable_lora():\\n KEYS = ['model', 'lora_path']\\n return_dict = filter_dict(return_dict, KEYS)\\n \\n return_dict.update({'model': model, 'lora_path': lora_path})\\n\\n return return_dict\\n\\n def do_lora_injection(\\n self, \\n model, \\n replace_modules, \\n bias='none',\\n dropout=0,\\n r=4,\\n lora_loader_args=None,\\n ): \\n REPLACE_MODULES = replace_modules\\n\\n params = None\\n negation = None\\n is_injection_hybrid = False\\n \\n if self.is_cloneofsimo_lora():\\n is_injection_hybrid = True\\n injector_args = lora_loader_args\\n\\n params, negation = self.lora_injector(**injector_args) \\n for _up, _down in extract_lora_ups_down(\\n model, \\n target_replace_module=REPLACE_MODULES):\\n\\n if all(x is not None for x in [_up, _down]):\\n print(f\\\"Lora successfully injected into {model.__class__.__name__}.\\\")\\n\\n break\\n\\n return params, negation, is_injection_hybrid\\n\\n if self.is_stable_lora():\\n injector_args = lora_args.copy()\\n injector_args = filter_dict(injector_args, keys=STABLE_LORA_KEYS)\\n\\n SEARCH_CLASS = [torch.nn.Linear, torch.nn.Conv2d, torch.nn.Conv3d, torch.nn.Embedding]\\n\\n injector_args.update({\\n \\\"model\\\": model,\\n \\\"target_module\\\": REPLACE_MODULES,\\n \\\"search_class\\\": SEARCH_CLASS,\\n \\\"r\\\": r,\\n \\\"dropout\\\": dropout,\\n \\\"lora_bias\\\": self.lora_bias\\n })\\n\\n activator = self.lora_injector(**injector_args)\\n activator()\\n\\n return params, negation, is_injection_hybrid\\n\\n def add_lora_to_model(self, use_lora, model, replace_modules, dropout=0.0, lora_path='', r=16):\\n\\n params = None\\n negation = None\\n\\n lora_loader_args = self.get_lora_func_args(\\n lora_path,\\n use_lora,\\n model,\\n replace_modules,\\n r,\\n dropout,\\n self.lora_bias\\n )\\n if use_lora:\\n params, negation, is_injection_hybrid = self.do_lora_injection(\\n model, \\n replace_modules, \\n bias=self.lora_bias,\\n lora_loader_args=lora_loader_args,\\n dropout=dropout,\\n r=r\\n )\\n\\n if not is_injection_hybrid:\\n self.load_lora(model, lora_path=lora_path, lora_loader_args=lora_loader_args)\\n \\n params = model if params is None else params\\n return params, negation\\n \\n\\n def deactivate_lora_train(self, models, deactivate=True):\\n \\\"\\\"\\\"\\n Usage: Use before and after sampling previews.\\n Currently only available for Stable LoRA.\\n \\\"\\\"\\\"\\n if self.is_stable_lora():\\n set_mode_group(models, not deactivate)\\n\\n def save_cloneofsimo_lora(self, model, save_path, step):\\n \\n def save_lora(model, name, condition, replace_modules, step, save_path): \\n if condition and replace_modules is not None:\\n save_path = f\\\"{save_path}/{step}_{name}.pt\\\"\\n save_lora_weight(model, save_path, replace_modules)\\n\\n save_lora(\\n model.unet, \\n FILE_BASENAMES[0], \\n self.use_unet_lora, \\n self.unet_replace_modules, \\n step,\\n save_path, \\n )\\n save_lora(\\n model.text_encoder, \\n FILE_BASENAMES[1], \\n self.use_text_lora, \\n self.text_encoder_replace_modules, \\n step, \\n save_path\\n )\\n\\n train_patch_pipe(model, self.use_unet_lora, self.use_text_lora)\\n\\n def save_stable_lora(\\n self, \\n model, \\n step, \\n name, \\n save_path = '', \\n save_for_webui=False,\\n only_for_webui=False\\n ):\\n import uuid\\n\\n save_filename = f\\\"{step}_{name}\\\"\\n lora_metadata = metadata = {\\n \\\"stable_lora_text_to_video\\\": \\\"v1\\\", \\n \\\"lora_name\\\": name + \\\"_\\\" + uuid.uuid4().hex.lower()[:5]\\n }\\n save_lora(\\n unet=model.unet,\\n text_encoder=model.text_encoder,\\n save_text_weights=self.use_text_lora,\\n output_dir=save_path,\\n lora_filename=save_filename,\\n lora_bias=self.lora_bias,\\n save_for_webui=self.save_for_webui,\\n only_webui=self.only_for_webui,\\n metadata=lora_metadata,\\n unet_dict_converter=convert_unet_state_dict,\\n text_dict_converter=convert_text_enc_state_dict_v20\\n )\\n\\n def save_lora_weights(self, model: None, save_path: str ='',step: str = ''):\\n save_path = f\\\"{save_path}/lora\\\"\\n os.makedirs(save_path, exist_ok=True)\\n\\n if self.is_cloneofsimo_lora():\\n if any([self.save_for_webui, self.only_for_webui]):\\n warnings.warn(\\n \\\"\\\"\\\"\\n You have 'save_for_webui' enabled, but are using cloneofsimo's LoRA implemention.\\n Only 'stable_lora' is supported for saving to a compatible webui file.\\n \\\"\\\"\\\"\\n )\\n self.save_cloneofsimo_lora(model, save_path, step)\\n\\n if self.is_stable_lora():\\n name = 'lora_text_to_video'\\n self.save_stable_lora(model, step, name, save_path)\"\n },\n {\n \"identifier\": \"LORA_VERSIONS\",\n \"path\": \"utils/lora_handler.py\",\n \"snippet\": \"LORA_VERSIONS = [LoraVersions.stable_lora, LoraVersions.cloneofsimo]\"\n },\n {\n \"identifier\": \"read_mask\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def read_mask(json_path, label=[\\\"mask\\\"]):\\n j = json.load(open(json_path)) \\n if type(label) != list:\\n labels = [label]\\n height = j['imageHeight']\\n width = j['imageWidth']\\n mask = np.zeros([height, width], dtype=np.uint8)\\n for shape in j['shapes']:\\n if shape['label'] in label:\\n x1, y1 = shape['points'][0]\\n x2, y2 = shape['points'][1]\\n mask[int(y1):int(y2), int(x1):int(x2)] = 255\\n return mask\"\n },\n {\n \"identifier\": \"generate_random_mask\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def generate_random_mask(image):\\n # Create a blank mask with the same size as the image\\n b, c , h, w = image.shape\\n mask = np.zeros([b, h, w], dtype=np.uint8)\\n \\n # Generate random coordinates for the mask\\n num_points = np.random.randint(3, 10) # Randomly choose the number of points to generate\\n points = np.random.randint(0, min(h, w), size=(num_points, 2)) # Randomly generate the points\\n # Draw a filled polygon on the mask using the random points\\n for i in range(b):\\n width = random.randint(w//4, w)\\n height = random.randint(h//4, h)\\n x = random.randint(0, w-width)\\n y = random.randint(0, h-height)\\n points=np.array([[x, y], [x+width, y], [x+width, y+height], [x, y+height]])\\n mask[i] = cv2.fillPoly(mask[i], [points], 255)\\n \\n # Apply the mask to the image\\n #masked_image = cv2.bitwise_and(image, image, mask=mask)\\n return mask \"\n },\n {\n \"identifier\": \"slerp\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def slerp(z1, z2, alpha):\\n theta = torch.acos(torch.sum(z1 * z2) / (torch.norm(z1) * torch.norm(z2)))\\n return (\\n torch.sin((1 - alpha) * theta) / torch.sin(theta) * z1\\n + torch.sin(alpha * theta) / torch.sin(theta) * z2\\n )\"\n },\n {\n \"identifier\": \"calculate_motion_score\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def calculate_motion_score(frame_imgs, calculate_edges=False, color=\\\"RGB\\\") -> float:\\n # Convert image into HSV colorspace.\\n _last_frame = None\\n\\n _weights = [1.0, 1.0, 1.0, 0.0]\\n score = 0\\n for frame_img in frame_imgs:\\n if color == \\\"RGB\\\":\\n hue, sat, lum = cv2.split(cv2.cvtColor(frame_img, cv2.COLOR_RGB2HSV))\\n else:\\n hue, sat, lum = cv2.split(cv2.cvtColor(frame_img, cv2.COLOR_BGR2HSV))\\n # Performance: Only calculate edges if we have to.\\n edges = _detect_edges(lum) if calculate_edges else None\\n if _last_frame == None:\\n _last_frame = (hue, sat, lum, edges)\\n continue\\n\\n score_components = [\\n _mean_pixel_distance(hue, _last_frame[0]),\\n _mean_pixel_distance(sat, _last_frame[1]),\\n _mean_pixel_distance(lum, _last_frame[2]),\\n 0.0 if edges is None else _mean_pixel_distance(edges, _last_frame[3]),\\n ]\\n\\n frame_score: float = (\\n sum(component * weight for (component, weight) in zip(score_components, _weights))\\n / sum(abs(weight) for weight in _weights))\\n score += frame_score\\n _last_frame = (hue, sat, lum, edges)\\n\\n return round(score/(len(frame_imgs)-1) * 10)\"\n },\n {\n \"identifier\": \"read_video\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def read_video(video_path, frame_number=-1):\\n # Open the video file\\n cap = cv2.VideoCapture(video_path)\\n count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) \\n if frame_number == -1:\\n frame_number = count\\n else:\\n frame_number = min(frame_number, count)\\n frames = []\\n for i in range(frame_number):\\n ret, ref_frame = cap.read()\\n ref_frame = cv2.cvtColor(ref_frame, cv2.COLOR_BGR2RGB)\\n if not ret:\\n raise ValueError(\\\"Failed to read video file\\\")\\n frames.append(ref_frame)\\n return frames\"\n },\n {\n \"identifier\": \"calculate_motion_precision\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def calculate_motion_precision(frames, mask):\\n moved_mask = get_moved_area_mask(frames, move_th=20, th=0)\\n moved = moved_mask == 255\\n gt = mask == 255\\n precision = np.sum(moved & gt) / np.sum(moved)\\n return precision\"\n },\n {\n \"identifier\": \"calculate_latent_motion_score\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def calculate_latent_motion_score(latents):\\n #latents b, c f, h, w\\n diff=torch.abs(latents[:,:,1:]-latents[:,:,:-1])\\n motion_score = torch.sum(torch.mean(diff, dim=[2,3,4]), dim=1) * 10\\n return motion_score\"\n },\n {\n \"identifier\": \"DDPM_forward\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def DDPM_forward(x0, step, num_frames, scheduler):\\n device = x0.device\\n t = scheduler.timesteps[-1]\\n xt = repeat(x0, 'b c 1 h w -> b c f h w', f = num_frames)\\n\\n eps = torch.randn_like(xt)\\n alpha_vec = torch.prod(scheduler.alphas[t:])\\n xt = torch.sqrt(alpha_vec) * xt + torch.sqrt(1-alpha_vec) * eps\\n return xt, None\"\n },\n {\n \"identifier\": \"DDPM_forward_timesteps\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def DDPM_forward_timesteps(x0, step, num_frames, scheduler):\\n '''larger step -> smaller t -> smaller alphas[t:] -> smaller xt -> smaller x0'''\\n\\n device = x0.device\\n # timesteps are reversed\\n timesteps = scheduler.timesteps[len(scheduler.timesteps)-step:]\\n t = timesteps[0]\\n\\n if x0.shape[2] == 1:\\n xt = repeat(x0, 'b c 1 h w -> b c f h w', f = num_frames)\\n else:\\n xt = x0\\n noise = torch.randn(xt.shape, dtype=xt.dtype, device=device)\\n # t to tensor of batch size \\n t = torch.tensor([t]*xt.shape[0], device=device)\\n xt = scheduler.add_noise(xt, noise, t)\\n return xt, timesteps\"\n },\n {\n \"identifier\": \"DDPM_forward_mask\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def DDPM_forward_mask(x0, step, num_frames, scheduler, mask):\\n '''larger step -> smaller t -> smaller alphas[t:] -> smaller xt -> smaller x0'''\\n device = x0.device\\n dtype = x0.dtype\\n b, c, f, h, w = x0.shape\\n\\n move_xt, timesteps = DDPM_forward_timesteps(x0, step, num_frames, scheduler)\\n mask = T.ToTensor()(mask).to(dtype).to(device)\\n mask = T.Resize([h, w], antialias=False)(mask)\\n mask = rearrange(mask, 'b h w -> b 1 1 h w')\\n freeze_xt = repeat(x0, 'b c 1 h w -> b c f h w', f = num_frames)\\n initial = freeze_xt * (1-mask) + move_xt * mask\\n return initial, timesteps\"\n },\n {\n \"identifier\": \"motion_mask_loss\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def motion_mask_loss(latents, mask):\\n diff = torch.abs(latents[:,:,1:] - latents[:,:,:-1])\\n loss = torch.sum(torch.mean(diff * (1-mask), dim=[2,3,4]), dim=1)\\n return loss\"\n },\n {\n \"identifier\": \"generate_center_mask\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def generate_center_mask(image):\\n # Create a blank mask with the same size as the image\\n b, c , h, w = image.shape\\n mask = np.zeros([b, h, w], dtype=np.uint8)\\n \\n # Generate random coordinates for the mask\\n for i in range(b):\\n width = int(w/10)\\n height = int(h/10)\\n mask[i][height:-height,width:-width] = 255\\n # Apply the mask to the image\\n #masked_image = cv2.bitwise_and(image, image, mask=mask)\\n return mask \"\n },\n {\n \"identifier\": \"tensor_to_vae_latent\",\n \"path\": \"utils/common.py\",\n \"snippet\": \"def tensor_to_vae_latent(t, vae):\\n video_length = t.shape[1]\\n\\n t = rearrange(t, \\\"b f c h w -> (b f) c h w\\\")\\n latents = vae.encode(t).latent_dist.sample()\\n latents = rearrange(latents, \\\"(b f) c h w -> b c f h w\\\", f=video_length)\\n latents = latents * 0.18215\\n\\n return latents\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import argparse\nimport datetime\nimport logging\nimport inspect\nimport math\nimport os\nimport json\nimport gc\nimport copy\nimport random\nimport cv2\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nimport torchvision.transforms as T\nimport diffusers\nimport transformers\nimport numpy as np\nimport imageio\n import itertools\n import bitsandbytes as bnb\nfrom typing import Dict, Optional, Tuple\nfrom omegaconf import OmegaConf\nfrom tqdm.auto import tqdm\nfrom PIL import Image\nfrom accelerate import Accelerator\nfrom accelerate.logging import get_logger\nfrom accelerate.utils import set_seed\nfrom diffusers.models import AutoencoderKL\nfrom diffusers import DPMSolverMultistepScheduler, DDPMScheduler\nfrom diffusers.image_processor import VaeImageProcessor\nfrom diffusers.optimization import get_scheduler\nfrom diffusers.utils import check_min_version, export_to_video\nfrom diffusers.utils.import_utils import is_xformers_available\nfrom diffusers.models.attention_processor import AttnProcessor2_0, Attention\nfrom diffusers.models.attention import BasicTransformerBlock\nfrom diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth import tensor2vid\nfrom transformers import CLIPTextModel, CLIPTokenizer\nfrom transformers.models.clip.modeling_clip import CLIPEncoder\nfrom utils.dataset import VideoJsonDataset, SingleVideoDataset, \\\n ImageDataset, VideoFolderDataset, CachedDataset, VideoBLIPDataset\nfrom einops import rearrange, repeat\nfrom models.unet_3d_condition_mask import UNet3DConditionModel\nfrom models.pipeline import LatentToVideoPipeline\nfrom utils.lora_handler import LoraHandler, LORA_VERSIONS\nfrom utils.common import read_mask, generate_random_mask, slerp, calculate_motion_score, \\\n read_video, calculate_motion_precision, calculate_latent_motion_score, \\\n DDPM_forward, DDPM_forward_timesteps, DDPM_forward_mask, motion_mask_loss, \\\n generate_center_mask, tensor_to_vae_latent\n from xformers.ops import MemoryEfficientAttentionFlashAttentionOp"},"token_num":{"kind":"number","value":17639,"string":"17,639"},"cropped_code":{"kind":"string","value":"\n\n\n\n\n\n\n\n\nalready_printed_trainables = False\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\ndef create_logging(logging, logger, accelerator):\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n\ndef accelerate_set_verbose(accelerator):\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\ndef get_train_dataset(dataset_types, train_data, tokenizer):\n train_datasets = []\n"},"all_code":{"kind":"string","value":"\n\n\n\n\n\n\n\n\nalready_printed_trainables = False\n\nlogger = get_logger(__name__, log_level=\"INFO\")\n\ndef create_logging(logging, logger, accelerator):\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO,\n )\n logger.info(accelerator.state, main_process_only=False)\n\ndef accelerate_set_verbose(accelerator):\n if accelerator.is_local_main_process:\n transformers.utils.logging.set_verbosity_warning()\n diffusers.utils.logging.set_verbosity_info()\n else:\n transformers.utils.logging.set_verbosity_error()\n diffusers.utils.logging.set_verbosity_error()\n\ndef get_train_dataset(dataset_types, train_data, tokenizer):\n train_datasets = []"},"next_line":{"kind":"string","value":" dataset_cls = [VideoJsonDataset, SingleVideoDataset, ImageDataset, VideoFolderDataset, VideoBLIPDataset]"},"gold_snippet_index":{"kind":"number","value":5,"string":"5"},"created_at":{"kind":"string","value":"2023-12-07 08:26:29+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5284,"cells":{"repo_name":{"kind":"string","value":"allenai/Holodeck"},"file_path":{"kind":"string","value":"modules/object_selector.py"},"context":{"kind":"list like","value":[{"identifier":"DFS_Solver_Floor","path":"modules/floor_objects.py","snippet":"class DFS_Solver_Floor():\n def __init__(self, grid_size, random_seed=0, max_duration=5, constraint_bouns=0.2):\n self.grid_size = grid_size\n self.random_seed = random_seed\n self.max_duration = max_duration # maximum allowed time in seconds\n self.constraint_bouns = constraint_bouns\n self.start_time = None\n self.solutions = []\n self.vistualize = False\n\n # Define the functions in a dictionary to avoid if-else conditions\n self.func_dict = {\n \"global\": {\n \"edge\": self.place_edge\n },\n \"relative\": self.place_relative,\n \"direction\": self.place_face,\n \"alignment\": self.place_alignment_center,\n \"distance\": self.place_distance\n }\n\n self.constraint_type2weight = {\n \"global\": 1.0,\n \"relative\": 0.5,\n \"direction\": 0.5,\n \"alignment\": 0.5,\n \"distance\": 1.8,\n }\n\n self.edge_bouns = 0.0 # worth more than one constraint\n\n\n def get_solution(self, bounds, objects_list, constraints, initial_state, use_milp=False):\n self.start_time = time.time()\n if use_milp:\n # iterate through the constraints list\n # for each constraint type \"distance\", add the same constraint to the target object\n new_constraints = constraints.copy()\n for object_name, object_constraints in constraints.items():\n for constraint in object_constraints:\n if constraint[\"type\"] == \"distance\":\n target_object_name = constraint[\"target\"]\n if target_object_name in constraints.keys():\n # if there is already a distance constraint of target object_name, continue\n if any(constraint[\"type\"] == \"distance\" and constraint[\"target\"] == object_name for constraint in constraints[target_object_name]): continue\n new_constraint = constraint.copy()\n new_constraint[\"target\"] = object_name\n new_constraints[target_object_name].append(new_constraint)\n # iterate through the constraints list\n # for each constraint type \"left of\" or \"right of\", add the same constraint to the target object\n #for object_name, object_constraints in constraints.items():\n # for constraint in object_constraints: if constraint[\"type\"] == \"relative\":\n # if constraint[\"constraint\"] == \"left of\":\n constraints = new_constraints\n\n try:\n self.milp_dfs(bounds, objects_list, constraints, initial_state, 10)\n except SolutionFound as e:\n print(f\"Time taken: {time.time() - self.start_time}\")\n \n else:\n grid_points = self.create_grids(bounds)\n grid_points = self.remove_points(grid_points, initial_state)\n try:\n self.dfs(bounds, objects_list, constraints, grid_points, initial_state, 30)\n except SolutionFound as e:\n print(f\"Time taken: {time.time() - self.start_time}\")\n \n print(f\"Number of solutions found: {len(self.solutions)}\")\n max_solution = self.get_max_solution(self.solutions)\n\n if not use_milp and self.vistualize:\n self.visualize_grid(bounds, grid_points, max_solution)\n\n return max_solution\n \n\n def get_max_solution(self, solutions):\n path_weights = []\n for solution in solutions:\n path_weights.append(sum([obj[-1] for obj in solution.values()]))\n max_index = np.argmax(path_weights)\n return solutions[max_index]\n\n\n def dfs(self, room_poly, objects_list, constraints, grid_points, placed_objects, branch_factor):\n if len(objects_list) == 0:\n self.solutions.append(placed_objects)\n return placed_objects\n \n if time.time() - self.start_time > self.max_duration:\n print(f\"Time limit reached.\")\n raise SolutionFound(self.solutions)\n \n object_name, object_dim = objects_list[0]\n placements = self.get_possible_placements(room_poly, object_dim, constraints[object_name], grid_points, placed_objects)\n \n if len(placements) == 0 and len(placed_objects) != 0:\n self.solutions.append(placed_objects)\n\n paths = []\n if branch_factor > 1: random.shuffle(placements) # shuffle the placements of the first object\n\n for placement in placements[:branch_factor]:\n placed_objects_updated = copy.deepcopy(placed_objects)\n placed_objects_updated[object_name] = placement\n grid_points_updated = self.remove_points(grid_points, placed_objects_updated)\n\n sub_paths = self.dfs(room_poly, objects_list[1:], constraints, grid_points_updated, placed_objects_updated, 1)\n paths.extend(sub_paths)\n\n return paths\n\n \n def get_possible_placements(self, room_poly, object_dim, constraints, grid_points, placed_objects):\n solutions = self.filter_collision(placed_objects, self.get_all_solutions(room_poly, grid_points, object_dim))\n solutions = self.filter_facing_wall(room_poly, solutions, object_dim)\n edge_solutions = self.place_edge(room_poly, copy.deepcopy(solutions), object_dim)\n\n if len(edge_solutions) == 0: return edge_solutions\n\n global_constraint = next((constraint for constraint in constraints if constraint[\"type\"] == \"global\"), None)\n\n if global_constraint is None: global_constraint = {\"type\": \"global\", \"constraint\": \"edge\"}\n\n if global_constraint[\"constraint\"] == \"edge\":\n candidate_solutions = copy.deepcopy(edge_solutions) # edge is hard constraint\n else:\n if len(constraints) > 1: candidate_solutions = solutions + edge_solutions # edge is soft constraint\n else: candidate_solutions = copy.deepcopy(solutions) # the first object\n\n candidate_solutions = self.filter_collision(placed_objects, candidate_solutions) # filter again after global constraint\n\n if candidate_solutions == []: return candidate_solutions\n random.shuffle(candidate_solutions)\n placement2score = {tuple(solution[:3]): solution[-1] for solution in candidate_solutions}\n\n # add a bias to edge solutions\n for solution in candidate_solutions:\n if solution in edge_solutions and len(constraints) >= 1:\n placement2score[tuple(solution[:3])] += self.edge_bouns\n \n for constraint in constraints:\n if \"target\" not in constraint: continue\n\n func = self.func_dict.get(constraint[\"type\"])\n valid_solutions = func(constraint[\"constraint\"], placed_objects[constraint[\"target\"]], candidate_solutions)\n \n weight = self.constraint_type2weight[constraint[\"type\"]]\n if constraint[\"type\"] == \"distance\":\n for solution in valid_solutions:\n bouns = solution[-1]\n placement2score[tuple(solution[:3])] += bouns * weight\n else:\n for solution in valid_solutions:\n placement2score[tuple(solution[:3])] += self.constraint_bouns * weight\n\n # normalize the scores\n for placement in placement2score: placement2score[placement] /= max(len(constraints), 1)\n\n sorted_placements = sorted(placement2score, key=placement2score.get, reverse=True)\n sorted_solutions = [list(placement) + [placement2score[placement]] for placement in sorted_placements]\n\n return sorted_solutions\n\n\n def create_grids(self, room_poly):\n # get the min and max bounds of the room\n min_x, min_z, max_x, max_z = room_poly.bounds\n\n # create grid points\n grid_points = []\n for x in range(int(min_x), int(max_x), self.grid_size):\n for y in range(int(min_z), int(max_z), self.grid_size):\n point = Point(x, y)\n if room_poly.contains(point):\n grid_points.append((x, y))\n\n return grid_points\n \n\n def remove_points(self, grid_points, objects_dict):\n # Create an r-tree index\n idx = index.Index()\n\n # Populate the index with bounding boxes of the objects\n for i, (_, _, obj, _) in enumerate(objects_dict.values()):\n idx.insert(i, Polygon(obj).bounds)\n \n # Create Shapely Polygon objects only once\n polygons = [Polygon(obj) for _, _, obj, _ in objects_dict.values()]\n\n valid_points = []\n \n for point in grid_points:\n p = Point(point)\n # Get a list of potential candidates\n candidates = [polygons[i] for i in idx.intersection(p.bounds)]\n # Check if point is in any of the candidate polygons\n if not any(candidate.contains(p) for candidate in candidates):\n valid_points.append(point)\n \n return valid_points\n \n\n def get_all_solutions(self, room_poly, grid_points, object_dim):\n obj_length, obj_width = object_dim\n obj_half_length, obj_half_width = obj_length / 2, obj_width / 2\n\n rotation_adjustments = {\n 0: ((-obj_half_length, -obj_half_width), (obj_half_length, obj_half_width)),\n 90: ((-obj_half_width, -obj_half_length), (obj_half_width, obj_half_length)),\n 180: ((-obj_half_length, obj_half_width), (obj_half_length, -obj_half_width)),\n 270: ((obj_half_width, -obj_half_length), (-obj_half_width, obj_half_length)),\n }\n\n solutions = []\n for rotation in [0, 90, 180, 270]:\n for point in grid_points:\n center_x, center_y = point\n lower_left_adjustment, upper_right_adjustment = rotation_adjustments[rotation]\n lower_left = (center_x + lower_left_adjustment[0], center_y + lower_left_adjustment[1])\n upper_right = (center_x + upper_right_adjustment[0], center_y + upper_right_adjustment[1])\n obj_box = box(*lower_left, *upper_right)\n\n if room_poly.contains(obj_box):\n solutions.append([point, rotation, tuple(obj_box.exterior.coords[:]), 1])\n \n return solutions\n \n\n def filter_collision(self, objects_dict, solutions):\n valid_solutions = []\n object_polygons = [Polygon(obj_coords) for _, _, obj_coords, _ in list(objects_dict.values())]\n for solution in solutions:\n sol_obj_coords = solution[2]\n sol_obj = Polygon(sol_obj_coords)\n if not any(sol_obj.intersects(obj) for obj in object_polygons):\n valid_solutions.append(solution)\n return valid_solutions\n\n \n def filter_facing_wall(self, room_poly, solutions, obj_dim):\n valid_solutions = []\n obj_width = obj_dim[1]\n obj_half_width = obj_width / 2\n\n front_center_adjustments = {\n 0: (0, obj_half_width),\n 90: (obj_half_width, 0),\n 180: (0, -obj_half_width),\n 270: (-obj_half_width, 0),\n }\n\n valid_solutions = []\n for solution in solutions:\n center_x, center_y = solution[0]\n rotation = solution[1]\n\n front_center_adjustment = front_center_adjustments[rotation]\n front_center_x, front_center_y = center_x + front_center_adjustment[0], center_y + front_center_adjustment[1]\n\n front_center_distance = room_poly.boundary.distance(Point(front_center_x, front_center_y))\n\n if front_center_distance >= 30: # TODO: make this a parameter\n valid_solutions.append(solution)\n\n return valid_solutions\n \n\n def place_edge(self, room_poly, solutions, obj_dim):\n valid_solutions = []\n obj_width = obj_dim[1]\n obj_half_width = obj_width / 2\n\n back_center_adjustments = {\n 0: (0, -obj_half_width),\n 90: (-obj_half_width, 0),\n 180: (0, obj_half_width),\n 270: (obj_half_width, 0),\n }\n\n for solution in solutions:\n center_x, center_y = solution[0]\n rotation = solution[1]\n\n back_center_adjustment = back_center_adjustments[rotation]\n back_center_x, back_center_y = center_x + back_center_adjustment[0], center_y + back_center_adjustment[1]\n\n back_center_distance = room_poly.boundary.distance(Point(back_center_x, back_center_y))\n center_distance = room_poly.boundary.distance(Point(center_x, center_y))\n\n if back_center_distance <= self.grid_size and back_center_distance < center_distance:\n solution[-1] += self.constraint_bouns\n # valid_solutions.append(solution) # those are still valid solutions, but we need to move the object to the edge\n\n # move the object to the edge\n center2back_vector = np.array([back_center_x - center_x, back_center_y - center_y])\n center2back_vector /= np.linalg.norm(center2back_vector)\n offset = center2back_vector * (back_center_distance + 4.5) # add a small distance to avoid the object cross the wall\n solution[0] = (center_x + offset[0], center_y + offset[1])\n solution[2] = ((solution[2][0][0] + offset[0], solution[2][0][1] + offset[1]), \\\n (solution[2][1][0] + offset[0], solution[2][1][1] + offset[1]), \\\n (solution[2][2][0] + offset[0], solution[2][2][1] + offset[1]), \\\n (solution[2][3][0] + offset[0], solution[2][3][1] + offset[1]))\n valid_solutions.append(solution)\n\n return valid_solutions\n \n\n def place_corner(self, room_poly, solutions, obj_dim):\n obj_length, obj_width = obj_dim\n obj_half_length, _ = obj_length / 2, obj_width / 2\n\n rotation_center_adjustments = {\n 0: ((-obj_half_length, 0), (obj_half_length, 0)),\n 90: ((0, obj_half_length), (0, -obj_half_length)),\n 180: ((obj_half_length, 0), (-obj_half_length, 0)),\n 270: ((0, -obj_half_length), (0, obj_half_length))\n }\n\n edge_solutions = self.place_edge(room_poly, solutions, obj_dim)\n\n valid_solutions = []\n\n for solution in edge_solutions:\n (center_x, center_y), rotation = solution[:2]\n (dx_left, dy_left), (dx_right, dy_right) = rotation_center_adjustments[rotation]\n\n left_center_x, left_center_y = center_x + dx_left, center_y + dy_left\n right_center_x, right_center_y = center_x + dx_right, center_y + dy_right\n \n left_center_distance = room_poly.boundary.distance(Point(left_center_x, left_center_y))\n right_center_distance = room_poly.boundary.distance(Point(right_center_x, right_center_y))\n\n if min(left_center_distance, right_center_distance) < self.grid_size:\n solution[-1] += self.constraint_bouns\n valid_solutions.append(solution)\n\n return valid_solutions\n \n\n def place_relative(self, place_type, target_object, solutions):\n valid_solutions = []\n _, target_rotation, target_coords, _ = target_object\n target_polygon = Polygon(target_coords)\n\n min_x, min_y, max_x, max_y = target_polygon.bounds\n mean_x = (min_x + max_x) / 2\n mean_y = (min_y + max_y) / 2\n\n comparison_dict = {\n 'left of': {\n 0: lambda sol_center: sol_center[0] < min_x and min_y <= sol_center[1] <= max_y,\n 90: lambda sol_center: sol_center[1] > max_y and min_x <= sol_center[0] <= max_x,\n 180: lambda sol_center: sol_center[0] > max_x and min_y <= sol_center[1] <= max_y,\n 270: lambda sol_center: sol_center[1] < min_y and min_x <= sol_center[0] <= max_x,\n },\n 'right of': {\n 0: lambda sol_center: sol_center[0] > max_x and min_y <= sol_center[1] <= max_y,\n 90: lambda sol_center: sol_center[1] < min_y and min_x <= sol_center[0] <= max_x,\n 180: lambda sol_center: sol_center[0] < min_x and min_y <= sol_center[1] <= max_y,\n 270: lambda sol_center: sol_center[1] > max_y and min_x <= sol_center[0] <= max_x,\n },\n 'in front of': {\n 0: lambda sol_center: sol_center[1] > max_y and mean_x - self.grid_size < sol_center[0] < mean_x + self.grid_size, # in front of and centered\n 90: lambda sol_center: sol_center[0] > max_x and mean_y - self.grid_size < sol_center[1] < mean_y + self.grid_size,\n 180: lambda sol_center: sol_center[1] < min_y and mean_x - self.grid_size < sol_center[0] < mean_x + self.grid_size,\n 270: lambda sol_center: sol_center[0] < min_x and mean_y - self.grid_size < sol_center[1] < mean_y + self.grid_size,\n },\n 'behind': {\n 0: lambda sol_center: sol_center[1] < min_y and min_x <= sol_center[0] <= max_x,\n 90: lambda sol_center: sol_center[0] < min_x and min_y <= sol_center[1] <= max_y,\n 180: lambda sol_center: sol_center[1] > max_y and min_x <= sol_center[0] <= max_x,\n 270: lambda sol_center: sol_center[0] > max_x and min_y <= sol_center[1] <= max_y,\n },\n \"side of\": {\n 0: lambda sol_center: min_y <= sol_center[1] <= max_y,\n 90: lambda sol_center: min_x <= sol_center[0] <= max_x,\n 180: lambda sol_center: min_y <= sol_center[1] <= max_y,\n 270: lambda sol_center: min_x <= sol_center[0] <= max_x\n }\n }\n \n compare_func = comparison_dict.get(place_type).get(target_rotation)\n\n for solution in solutions:\n sol_center = solution[0]\n\n if compare_func(sol_center):\n solution[-1] += self.constraint_bouns\n valid_solutions.append(solution)\n \n return valid_solutions\n \n\n def place_distance(self, distance_type, target_object, solutions):\n target_coords = target_object[2]\n target_poly = Polygon(target_coords)\n distances = []\n valid_solutions = []\n for solution in solutions:\n sol_coords = solution[2]\n sol_poly = Polygon(sol_coords)\n distance = target_poly.distance(sol_poly)\n distances.append(distance)\n\n solution[-1] = distance\n valid_solutions.append(solution)\n \n min_distance = min(distances)\n max_distance = max(distances)\n\n if distance_type == \"near\":\n if min_distance < 80:\n points = [(min_distance, 1), (80, 0), (max_distance, 0)]\n else:\n points = [(min_distance, 0), (max_distance, 0)]\n\n elif distance_type == \"far\":\n points = [(min_distance, 0), (max_distance, 1)]\n \n x = [point[0] for point in points]\n y = [point[1] for point in points]\n\n f = interp1d(x, y, kind='linear', fill_value='extrapolate')\n \n for solution in valid_solutions:\n distance = solution[-1]\n solution[-1] = float(f(distance))\n\n return valid_solutions\n \n\n def place_face(self, face_type, target_object, solutions):\n if face_type == \"face to\":\n return self.place_face_to(target_object, solutions)\n \n elif face_type == \"face same as\":\n return self.place_face_same(target_object, solutions)\n \n elif face_type == \"face opposite to\":\n return self.place_face_opposite(target_object, solutions)\n \n\n def place_face_to(self, target_object, solutions):\n # Define unit vectors for each rotation\n unit_vectors = {\n 0: np.array([0., 1.]), # Facing up\n 90: np.array([1., 0.]), # Facing right\n 180: np.array([0., -1.]), # Facing down\n 270: np.array([-1., 0.]) # Facing left\n }\n \n target_coords = target_object[2]\n target_poly = Polygon(target_coords)\n \n valid_solutions = []\n \n for solution in solutions:\n sol_center = solution[0]\n sol_rotation = solution[1]\n\n # Define an arbitrarily large point in the direction of the solution's rotation\n far_point = sol_center + 1e6 * unit_vectors[sol_rotation]\n\n # Create a half-line from the solution's center to the far point\n half_line = LineString([sol_center, far_point])\n\n # Check if the half-line intersects with the target polygon\n if half_line.intersects(target_poly):\n solution[-1] += self.constraint_bouns\n valid_solutions.append(solution)\n \n return valid_solutions\n \n\n def place_face_same(self, target_object, solutions):\n target_rotation = target_object[1]\n valid_solutions = []\n \n for solution in solutions:\n sol_rotation = solution[1]\n if sol_rotation == target_rotation:\n solution[-1] += self.constraint_bouns\n valid_solutions.append(solution)\n\n return valid_solutions\n \n\n def place_face_opposite(self, target_object, solutions):\n target_rotation = (target_object[1] + 180) % 360\n valid_solutions = []\n \n for solution in solutions:\n sol_rotation = solution[1]\n if sol_rotation == target_rotation:\n solution[-1] += self.constraint_bouns\n valid_solutions.append(solution)\n \n return valid_solutions\n\n\n def place_alignment_center(self, alignment_type, target_object, solutions):\n target_center = target_object[0]\n valid_solutions = []\n eps = 5\n for solution in solutions:\n sol_center = solution[0]\n if abs(sol_center[0] - target_center[0]) < eps or abs(sol_center[1] - target_center[1]) < eps:\n solution[-1] += self.constraint_bouns\n valid_solutions.append(solution)\n return valid_solutions\n\n\n def visualize_grid(self, room_poly, grid_points, solutions):\n plt.rcParams[\"font.family\"] = \"Times New Roman\"\n plt.rcParams[\"font.size\"] = 22\n\n # create a new figure\n fig, ax = plt.subplots()\n\n # draw the room\n x, y = room_poly.exterior.xy\n ax.plot(x, y, '-', label='Room', color='black', linewidth=2)\n\n # draw the grid points\n grid_x = [point[0] for point in grid_points]\n grid_y = [point[1] for point in grid_points]\n ax.plot(grid_x, grid_y, 'o', markersize=2, color=\"grey\")\n\n # draw the solutions\n for object_name, solution in solutions.items():\n center, rotation, box_coords = solution[:3]\n center_x, center_y = center\n\n # create a polygon for the solution\n obj_poly = Polygon(box_coords)\n x, y = obj_poly.exterior.xy\n ax.plot(x, y, '-', linewidth=2, color='black')\n\n # ax.text(center_x, center_y, object_name, fontsize=18, ha='center')\n\n # set arrow direction based on rotation\n if rotation == 0:\n ax.arrow(center_x, center_y, 0, 25, head_width=10, fc='black')\n elif rotation == 90:\n ax.arrow(center_x, center_y, 25, 0, head_width=10, fc='black')\n elif rotation == 180:\n ax.arrow(center_x, center_y, 0, -25, head_width=10, fc='black')\n elif rotation == 270:\n ax.arrow(center_x, center_y, -25, 0, head_width=10, fc='black')\n # axis off\n ax.axis('off')\n ax.set_aspect('equal', 'box') # to keep the ratios equal along x and y axis\n create_time = str(datetime.datetime.now()).replace(\" \", \"-\").replace(\":\", \"-\").replace(\".\", \"-\")\n plt.savefig(f\"{create_time}.pdf\", bbox_inches='tight', dpi=300)\n plt.show()\n \n\n def milp_dfs(self, room_poly, all_objects_list, constraints, placed_objects, branch_factor=1):\n if len(all_objects_list) == 0:\n self.solutions.append(placed_objects)\n return placed_objects\n \n if time.time() - self.start_time > self.max_duration:\n print(f\"Time limit reached.\")\n raise SolutionFound(self.solutions)\n \n def milp_solve(soft_constraints_list, hard_constraints_list, verbose=False):\n problem = cp.Problem(cp.Maximize(sum(soft_constraints_list)), hard_constraints_list)\n if verbose:\n print('solving milp using GUROBI ...')\n problem.solve(solver=cp.GUROBI, reoptimize=True, verbose=False)\n return problem.value\n \n def parse_object_properties(object_properties):\n x, y = object_properties[0]\n rotation = int(object_properties[1] or 0)\n # set rotation to the closest 90 degree\n rotation = int(round(rotation / 90) * 90)\n assert rotation in [0, 90, 180, 270]\n object_bbox = object_properties[2]\n min_x = min([point[0] for point in object_bbox])\n max_x = max([point[0] for point in object_bbox])\n min_y = min([point[1] for point in object_bbox])\n max_y = max([point[1] for point in object_bbox])\n object_dim = (max_x - min_x, max_y - min_y) if rotation == 0 or rotation == 180 else (max_y - min_y, max_x - min_x)\n return x, y, rotation, object_dim\n\n def find_object_dim(target_object_name, objects_list, placed_objects):\n target_object_dim = None\n for object_name_1, object_dim_1 in objects_list:\n if object_name_1 == target_object_name: \n target_object_dim = object_dim_1\n return target_object_dim\n\n if not None: \n for object_name_1, object_properties in placed_objects.items():\n if object_name_1 == target_object_name:\n x, y, rotation, target_object_dim = parse_object_properties(object_properties)\n return target_object_dim\n return None\n\n\n found_a_solution = False\n # randomly select a set of objects from all_objects_list\n # start with the largest object + more objects --> gradually reduce the number of objects \n for branch_idx in range(branch_factor):\n # sample a set of objects from a list that contains the first object\n \n k = random.randint(0, min(5, len(all_objects_list)-1))\n objects_list = [all_objects_list[0]] + random.sample(all_objects_list[1:], k)\n\n hard_constraints_list = []\n soft_constraints_list = [0]\n\n # formulate the milp problem\n # object_name, object_dim = objects_list[0]\n # x, y, rotate_180, rotate_90\n variables_dict = {object[0]: [cp.Variable(), cp.Variable(), cp.Variable(boolean=True), cp.Variable(boolean=True)] for object in objects_list}\n # add placed objects into variables dict even though they are not variables\n for object, object_properties in placed_objects.items():\n x, y = object_properties[0]\n rotation = int(object_properties[1])\n variables_dict[object] = [x, y, rotation == 180, rotation == 90 or rotation == 270]\n\n # Initialize a list of variables, each variable represents the coordinate for each object\n room_min_x, room_min_y, room_max_x, room_max_y = room_poly.bounds\n # Add boundary constraints to all objects\n for object_name, object_dim in objects_list:\n hard_constraints_list.extend(create_boundary_constraints(variables_dict[object_name],\n object_dim,\n (room_min_x, room_min_y, room_max_x, room_max_y)))\n # Add pariwise collision constraints\n for object_name_1, object_dim_1 in objects_list:\n for object_name_2, object_dim_2 in objects_list:\n if object_name_1 == object_name_2: continue\n # collision constraints should be hard constraints\n hard_constraints_list.extend(create_nooverlap_constraints(variables_dict[object_name_1],\n variables_dict[object_name_2],\n object_dim_1,\n object_dim_2))\n\n # Add pariwise collision constraints with placed objects\n for object_name_1, object_dim_1 in objects_list:\n for object_name_2, object_properties_2 in placed_objects.items():\n # bbox is a list of four points\n x, y, rotation, object_dim_2 = parse_object_properties(object_properties_2)\n\n hard_constraints_list.extend(create_nooverlap_constraints(variables_dict[object_name_1],\n [x, y, rotation == 180, rotation == 90 or rotation == 270],\n object_dim_1, object_dim_2))\n\n # default constraints / heuristics?\n for object_name, object_dim in objects_list:\n # encourage dispersement of assets\n all_other_objects_list = [x[0] for x in objects_list if x[0] != object_name] + list(placed_objects.keys())\n for target_object_name in all_other_objects_list:\n hard_constraints, soft_constraints = create_distance_constraints(variables_dict[object_name],\n variables_dict[target_object_name],\n upper_bound=[room_max_x-room_min_x, room_max_y-room_min_y],\n type='far')\n assert len(soft_constraints) == 1\n # soft_constraints[0] *= 0.001\n hard_constraints_list.extend(hard_constraints)\n soft_constraints_list.extend(soft_constraints)\n\n\n # use cvxpy to solve for the hard constraints\n for object_name, object_dim in objects_list:\n\n # by default - add soft edge constraints although this might make the solver take a longer time\n if not any(constraint['type'] == 'global' for constraint in constraints[object_name]):\n hard_constraints, soft_constraints = create_edge_constraints(variables_dict[object_name],\n object_dim,\n room_dim=(room_min_x, room_min_y, room_max_x, room_max_y),\n hard=False)\n soft_constraints[0] *= 100\n hard_constraints_list.extend(hard_constraints)\n soft_constraints_list.extend(soft_constraints)\n\n\n\n for constraint in constraints[object_name]:\n if constraint['type'] == 'global': \n if constraint['constraint'] == 'edge': # hard constraints\n hard_constraints, soft_constraints = create_edge_constraints(variables_dict[object_name],\n object_dim,\n room_dim=(room_min_x, room_min_y, room_max_x, room_max_y),\n hard=True)\n hard_constraints_list.extend(hard_constraints)\n soft_constraints_list.extend(soft_constraints)\n\n if constraint['type'] == 'direction':\n assert constraint['constraint'] == 'face to'\n target_object_name = constraint['target']\n target_object_dim = find_object_dim(target_object_name, objects_list, placed_objects)\n if target_object_dim:\n hard_constraints_list.extend(create_directional_constraints(variables_dict[object_name],\n variables_dict[target_object_name],\n object_dim,\n target_object_dim))\n\n if constraint['type'] == 'alignment':\n assert constraint['constraint'] == 'center aligned'\n target_object_name = constraint['target']\n target_object_dim = find_object_dim(target_object_name, objects_list, placed_objects)\n if target_object_dim:\n hard_constraints_list.extend(create_alignment_constraints(variables_dict[object_name],\n variables_dict[target_object_name],\n object_dim,\n target_object_dim))\n\n if constraint['type'] == 'distance':\n target_object_name = constraint['target']\n target_object_dim = find_object_dim(target_object_name, objects_list, placed_objects)\n if target_object_dim:\n hard_constraints, soft_constraints = create_distance_constraints(variables_dict[object_name],\n variables_dict[target_object_name],\n upper_bound=[room_max_x-room_min_x, room_max_y-room_min_y],\n type=constraint['constraint'])\n hard_constraints_list.extend(hard_constraints)\n soft_constraints_list.extend(soft_constraints)\n assert len(soft_constraints) == 1\n # higher weighting\n soft_constraints[0] *= 0.01\n\n if constraint['type'] == 'relative':\n target_object_name = constraint['target']\n target_object_dim = find_object_dim(target_object_name, objects_list, placed_objects)\n if target_object_dim:\n hard_constraints_list.extend(create_relative_constraints(variables_dict[object_name],\n variables_dict[target_object_name],\n object_dim,\n target_object_dim,\n constraint['constraint']))\n\n result = milp_solve(soft_constraints_list, hard_constraints_list, verbose=False)\n if result is None or math.isnan(result) or math.isinf(result):\n continue\n\n found_a_solution = True\n print(result, [x[0] for x in objects_list])\n\n # we fonud a valid solution\n # convert the placements to the same format as the dfs solver\n placed_objects_updated = copy.deepcopy(placed_objects)\n for object_name, object_dim in objects_list:\n # (x, y), rotation, bbox, score\n x = variables_dict[object_name][0].value.item()\n y = variables_dict[object_name][1].value.item()\n rotate_180 = variables_dict[object_name][2].value\n rotate_90 = variables_dict[object_name][3].value \n if not rotate_180: rotate_180 = 0\n if not rotate_90: rotate_90 = 0\n\n # bbox has taken into account of the rotation\n if rotate_90:\n bbox = [(x - object_dim[1]/2, y - object_dim[0]/2),\n (x + object_dim[1]/2, y - object_dim[0]/2),\n (x + object_dim[1]/2, y + object_dim[0]/2),\n (x - object_dim[1]/2, y + object_dim[0]/2)]\n else:\n bbox = [(x - object_dim[0]/2, y - object_dim[1]/2),\n (x + object_dim[0]/2, y - object_dim[1]/2),\n (x + object_dim[0]/2, y + object_dim[1]/2),\n (x - object_dim[0]/2, y + object_dim[1]/2)]\n\n placed_objects_updated[object_name] = [(x,y), rotate_180 * 180 + rotate_90 * 90, bbox,\n len(constraints[object_name])]\n\n # remove all elemnts in objects_list from all_objects_list\n self.milp_dfs(room_poly, [x for x in all_objects_list if x not in objects_list], constraints, placed_objects_updated, branch_factor=1)\n \n if not found_a_solution and len(placed_objects) != 0:\n self.solutions.append(placed_objects)\n \n\n def test_dfs_placement(self):\n room_vertices = ((0, 0), (0, 500), (500, 500), (500, 0))\n room_poly = Polygon(room_vertices)\n grid_points = self.create_grids(room_poly)\n objects = {\"door\": ((50, 50), 0, ((0, 0), (100, 0), (100, 100), (0, 100)), 1)}\n grid_points = self.remove_points(grid_points, objects)\n # self.visualize_grid(room_poly, grid_points, objects)\n\n object_dim = (200, 100)\n solutions = self.get_all_solutions(room_poly, grid_points, object_dim)\n solutions = self.filter_collision(objects, solutions)\n solutions = self.place_edge(room_poly, solutions, object_dim)\n\n # for i, solution in enumerate(solutions):\n # objects[f\"sofa-{i}\"] = solution\n # self.visualize_grid(room_poly, grid_points, objects)\n\n random.seed(0)\n objects[\"sofa\"] = random.choice(solutions)\n # self.visualize_grid(room_poly, grid_points, objects)\n object_1_dim = (100, 50)\n\n solutions_1 = self.get_all_solutions(room_poly, grid_points, object_1_dim)\n solutions_1 = self.filter_collision(objects, solutions_1)\n\n # random.seed(42)\n # for i, solution in enumerate(random.sample(solutions_1, 25)):\n # objects[f\"coffee table-{i}\"] = solution\n \n # objects[f\"coffee table\"] = [(300, 350), 0, ((350.0, 325.0), (350.0, 375.0), (250.0, 375.0), (250.0, 325.0), (350.0, 325.0)), 1.0]\n # self.visualize_grid(room_poly, grid_points, objects)\n \n solutions_1 = self.place_face_to(objects[\"sofa\"], solutions_1)\n solutions_1 = self.place_relative(\"in front of\", objects[\"sofa\"], solutions_1)\n solutions_1 = self.place_alignment_center(\"center alignment\", objects[\"sofa\"], solutions_1)\n solutions_1 = self.place_distance(\"near\", objects[\"sofa\"], solutions_1)\n objects[f\"coffee table\"] = solutions_1[-1]\n self.visualize_grid(room_poly, grid_points, objects)\n\n\n def test_milp_placement(self, simple=False, use_milp=True):\n room_vertices = ((0, 0), (0, 600), (800, 600), (800, 0))\n room_poly = Polygon(room_vertices)\n grid_points = self.create_grids(room_poly)\n\n if not simple:\n constraints = {'sofa-0': [{'type': 'global', 'constraint': 'edge'}],\n 'sofa-1': [{'type': 'global', 'constraint': 'edge'},\n {'type': 'distance', 'constraint': 'near', 'target': 'sofa-0'},\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-0'}],\n 'tv stand-0': [{'type': 'global', 'constraint': 'edge'},\n {'type': 'distance', 'constraint': 'far', 'target': 'sofa-1'},\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-1'}],\n 'coffee table-0': [{'type': 'global', 'constraint': 'middle'},\n {'type': 'distance', 'constraint': 'near', 'target': 'sofa-0'},\n {'type': 'relative', 'constraint': 'in front of', 'target': 'sofa-0'},\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-0'},\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-0'},\n {'type': 'direction', 'constraint': 'face to', 'target': 'tv stand-0'}],\n 'coffee table-1': [{'type': 'global', 'constraint': 'middle'},\n {'type': 'distance', 'constraint': 'near', 'target': 'sofa-1'},\n {'type': 'relative', 'constraint': 'in front of', 'target': 'sofa-1'},\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-1'},\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-1'},\n {'type': 'direction', 'constraint': 'face to', 'target': 'tv stand-0'}],\n 'side table-0': [{'type': 'global', 'constraint': 'edge'},\n {'type': 'distance', 'constraint': 'near', 'target': 'sofa-0'},\n {'type': 'relative', 'constraint': 'side of', 'target': 'sofa-0'}],\n 'side table-1': [{'type': 'global', 'constraint': 'edge'},\n {'type': 'distance', 'constraint': 'near', 'target': 'sofa-1'},\n {'type': 'relative', 'constraint': 'side of', 'target': 'sofa-1'}],\n 'armchair-0': [{'type': 'global', 'constraint': 'middle'},\n {'type': 'distance', 'constraint': 'near', 'target': 'coffee table-0'},\n {'type': 'direction', 'constraint': 'face to', 'target': 'coffee table-0'},\n {'type': 'direction', 'constraint': 'face to', 'target': 'coffee table-0'}],\n 'armchair-1': [{'type': 'global', 'constraint': 'middle'},\n {'type': 'distance', 'constraint': 'near', 'target': 'coffee table-1'},\n {'type': 'direction', 'constraint': 'face to', 'target': 'coffee table-1'},\n {'type': 'direction', 'constraint': 'face to', 'target': 'coffee table-1'}],\n 'bookshelf-0': [{'type': 'global', 'constraint': 'edge'},\n {'type': 'distance', 'constraint': 'far', 'target': 'tv stand-0'}],\n 'bookshelf-1': [{'type': 'global', 'constraint': 'edge'},\n {'type': 'distance', 'constraint': 'far', 'target': 'bookshelf-0'},\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'bookshelf-0'}]}\n\n initial_state = {'door-0': ((586.7550200520433, 550.0), 0, [(640.8300346432603, 500.0), (532.6800054608262, 500.0), (532.6800054608262, 600.0), (640.8300346432603, 600.0)], 1)}\n\n objects = [('sofa-0', (301.6667297651499, 106.48952360032415)),\n ('sofa-1', (301.6667297651499, 106.48952360032415)),\n ('tv stand-0', (201.0964714933229, 59.39910836195032)),\n ('coffee table-0', (69.15754261308616, 126.69169450358964)),\n ('coffee table-1', (69.15754261308616, 126.69169450358964)),\n ('side table-0', (61.74632023132328, 61.74453745262855)),\n ('side table-1', (61.74632023132328, 61.74453745262855)),\n ('armchair-0', (79.0368498902692, 89.4893987892571)),\n ('armchair-1', (79.0368498902692, 89.4893987892571)),\n ('bookshelf-0', (67.94689517917222, 43.8934937031396)),\n ('bookshelf-1', (67.94689517917222, 43.8934937031396))]\n solution = self.get_solution(room_poly, objects, constraints, initial_state, use_milp=use_milp)\n else:\n constraints = {'dining table': [{'type': 'global', 'constraint': 'edge'},\n {'type': 'distance', 'constraint': 'far', 'target': 'door'},\n {'type': 'distance', 'constraint': 'near', 'target': 'chair'}],\n 'chair': [{'type': 'relative', 'constraint': 'side of', 'target': 'dining table'}]\n }\n initial_state = {\"door\": ((50, 50), 0, ((0, 0), (100, 0), (100, 100), (0, 100)), 1)}\n objects = [(\"dining table\", (100, 50)), (\"chair\", (50, 50))]\n solution = self.get_solution(room_poly, objects, constraints, initial_state, use_milp=use_milp)\n\n print('milp solution:', len(solution))\n for object_name, object_properties in solution.items():\n print(object_name, object_properties)\n # if object_properties[2] == 90 or object_properties[2] == 270:\n self.visualize_grid(room_poly, grid_points, solution)"},{"identifier":"DFS_Solver_Wall","path":"modules/wall_objects.py","snippet":"class DFS_Solver_Wall():\n def __init__(self, grid_size, random_seed=0, max_duration=5, constraint_bouns=100):\n self.grid_size = grid_size\n self.random_seed = random_seed\n self.max_duration = max_duration # maximum allowed time in seconds\n self.constraint_bouns = constraint_bouns\n self.start_time = None\n self.solutions = []\n self.visualize = False\n\n\n def get_solution(self, room_poly, wall_objects_list, constraints, initial_state):\n grid_points = self.create_grids(room_poly)\n\n self.start_time = time.time()\n try:\n self.dfs(room_poly, wall_objects_list, constraints, grid_points, initial_state)\n except SolutionFound as e:\n print(f\"Time taken: {time.time() - self.start_time}\")\n \n max_solution = self.get_max_solution(self.solutions)\n \n if self.visualize: self.visualize_grid(room_poly, grid_points, max_solution)\n return max_solution\n\n\n def get_max_solution(self, solutions):\n path_weights = []\n for solution in solutions:\n path_weights.append(sum([obj[-1] for obj in solution.values()]))\n max_index = np.argmax(path_weights)\n return solutions[max_index]\n\n\n def dfs(self, room_poly, wall_objects_list, constraints, grid_points, placed_objects):\n if len(wall_objects_list) == 0:\n self.solutions.append(placed_objects)\n return placed_objects\n \n if time.time() - self.start_time > self.max_duration:\n print(f\"Time limit reached.\")\n raise SolutionFound(self.solutions)\n \n object_name, object_dim = wall_objects_list[0]\n placements = self.get_possible_placements(room_poly, object_dim, constraints[object_name], grid_points, placed_objects)\n \n if len(placements) == 0:\n self.solutions.append(placed_objects)\n\n paths = []\n for placement in placements:\n placed_objects_updated = copy.deepcopy(placed_objects)\n placed_objects_updated[object_name] = placement\n\n sub_paths = self.dfs(room_poly, wall_objects_list[1:], constraints, grid_points, placed_objects_updated)\n paths.extend(sub_paths)\n\n return paths\n \n\n def get_possible_placements(self, room_poly, object_dim, constraint, grid_points, placed_objects):\n all_solutions = self.filter_collision(placed_objects, self.get_all_solutions(room_poly, grid_points, object_dim, constraint[\"height\"]))\n random.shuffle(all_solutions)\n target_floor_object_name = constraint[\"target_floor_object_name\"]\n if target_floor_object_name is not None and target_floor_object_name in placed_objects:\n all_solutions = self.score_solution_by_distance(all_solutions, placed_objects[target_floor_object_name])\n # order solutions by distance to target floor object\n all_solutions = sorted(all_solutions, key=lambda x: x[-1], reverse=True)\n return all_solutions\n\n\n def create_grids(self, room_poly):\n # Get the coordinates of the polygon\n poly_coords = list(room_poly.exterior.coords)\n\n grid_points = []\n # Iterate over each pair of points (edges of the polygon)\n for i in range(len(poly_coords) - 1):\n line = LineString([poly_coords[i], poly_coords[i + 1]])\n line_length = line.length\n\n # Create points along the edge at intervals of grid size\n for j in range(0, int(line_length), self.grid_size):\n point_on_line = substring(line, j, j) # Get a point at distance j from the start of the line\n if point_on_line:\n grid_points.append((point_on_line.x, point_on_line.y))\n \n return grid_points\n \n\n def get_all_solutions(self, room_poly, grid_points, object_dim, height):\n obj_length, obj_height, obj_width = object_dim\n obj_half_length = obj_length / 2\n\n rotation_adjustments = {\n 0: ((-obj_half_length, 0), (obj_half_length, obj_width)),\n 90: ((0, -obj_half_length), (obj_width, obj_half_length)),\n 180: ((-obj_half_length, -obj_width), (obj_half_length, 0)),\n 270: ((-obj_width, -obj_half_length), (0, obj_half_length))\n }\n\n solutions = []\n for rotation in [0, 90, 180, 270]:\n for point in grid_points:\n center_x, center_y = point\n lower_left_adjustment, upper_right_adjustment = rotation_adjustments[rotation]\n lower_left = (center_x + lower_left_adjustment[0], center_y + lower_left_adjustment[1])\n upper_right = (center_x + upper_right_adjustment[0], center_y + upper_right_adjustment[1])\n obj_box = box(*lower_left, *upper_right)\n\n if room_poly.contains(obj_box):\n object_coords = obj_box.exterior.coords[:]\n coordinates_on_edge = [coord for coord in object_coords if room_poly.boundary.contains(Point(coord))]\n coordinates_on_edge = list(set(coordinates_on_edge))\n if len(coordinates_on_edge) >= 2:\n vertex_min = (lower_left[0], height, lower_left[1])\n vertex_max = (upper_right[0], height + obj_height, upper_right[1])\n\n solutions.append([vertex_min, vertex_max, rotation, tuple(obj_box.exterior.coords[:]), 1])\n \n return solutions\n \n\n def filter_collision(self, placed_objects, solutions):\n def intersect_3d(box1, box2):\n # box1 and box2 are dictionaries with 'min' and 'max' keys,\n # which are tuples representing the minimum and maximum corners of the 3D box.\n for i in range(3):\n if box1['max'][i] < box2['min'][i] or box1['min'][i] > box2['max'][i]:\n return False\n return True\n\n valid_solutions = []\n boxes = [{\"min\": vertex_min, \"max\": vertex_max} for vertex_min, vertex_max, rotation, box_coords, path_weight in placed_objects.values()]\n\n for solution in solutions:\n for box in boxes:\n if intersect_3d(box, {\"min\": solution[0], \"max\": solution[1]}):\n break\n else:\n valid_solutions.append(solution)\n \n return valid_solutions\n \n\n def score_solution_by_distance(self, solutions, target_object):\n distances = []\n scored_solutions = []\n for solution in solutions:\n center_x, center_y, center_z = (solution[0][0]+solution[1][0])/2, (solution[0][1]+solution[1][1])/2, (solution[0][2]+solution[1][2])/2\n target_x, target_y, target_z = (target_object[0][0]+target_object[1][0])/2, (target_object[0][1]+target_object[1][1])/2, (target_object[0][2]+target_object[1][2])/2\n distance = np.sqrt((center_x - target_x)**2 + (center_y - target_y)**2 + (center_z - target_z)**2)\n distances.append(distance)\n scored_solution = solution.copy()\n scored_solution[-1] = solution[-1] + self.constraint_bouns * (1/distance)\n scored_solutions.append(scored_solution)\n return scored_solutions\n \n \n def visualize_grid(self, room_poly, grid_points, solutions):\n # create a new figure\n fig, ax = plt.subplots()\n\n # draw the room\n x, y = room_poly.exterior.xy\n ax.plot(x, y, 'b-', label='Room')\n\n # draw the grid points\n grid_x = [point[0] for point in grid_points]\n grid_y = [point[1] for point in grid_points]\n ax.plot(grid_x, grid_y, 'ro', markersize=2)\n\n # draw the solutions\n for object_name, solution in solutions.items():\n vertex_min, vertex_max, rotation, box_coords = solution[:-1]\n center_x, center_y = (vertex_min[0]+vertex_max[0])/2, (vertex_min[2]+vertex_max[2])/2\n\n # create a polygon for the solution\n obj_poly = Polygon(box_coords)\n x, y = obj_poly.exterior.xy\n ax.plot(x, y, 'g-', linewidth=2)\n\n ax.text(center_x, center_y, object_name, fontsize=12, ha='center')\n\n # set arrow direction based on rotation\n if rotation == 0:\n ax.arrow(center_x, center_y, 0, 25, head_width=10, fc='g')\n elif rotation == 90:\n ax.arrow(center_x, center_y, 25, 0, head_width=10, fc='g')\n elif rotation == 180:\n ax.arrow(center_x, center_y, 0, -25, head_width=10, fc='g')\n elif rotation == 270:\n ax.arrow(center_x, center_y, -25, 0, head_width=10, fc='g')\n\n ax.set_aspect('equal', 'box') # to keep the ratios equal along x and y axis\n plt.show()"}],"string":"[\n {\n \"identifier\": \"DFS_Solver_Floor\",\n \"path\": \"modules/floor_objects.py\",\n \"snippet\": \"class DFS_Solver_Floor():\\n def __init__(self, grid_size, random_seed=0, max_duration=5, constraint_bouns=0.2):\\n self.grid_size = grid_size\\n self.random_seed = random_seed\\n self.max_duration = max_duration # maximum allowed time in seconds\\n self.constraint_bouns = constraint_bouns\\n self.start_time = None\\n self.solutions = []\\n self.vistualize = False\\n\\n # Define the functions in a dictionary to avoid if-else conditions\\n self.func_dict = {\\n \\\"global\\\": {\\n \\\"edge\\\": self.place_edge\\n },\\n \\\"relative\\\": self.place_relative,\\n \\\"direction\\\": self.place_face,\\n \\\"alignment\\\": self.place_alignment_center,\\n \\\"distance\\\": self.place_distance\\n }\\n\\n self.constraint_type2weight = {\\n \\\"global\\\": 1.0,\\n \\\"relative\\\": 0.5,\\n \\\"direction\\\": 0.5,\\n \\\"alignment\\\": 0.5,\\n \\\"distance\\\": 1.8,\\n }\\n\\n self.edge_bouns = 0.0 # worth more than one constraint\\n\\n\\n def get_solution(self, bounds, objects_list, constraints, initial_state, use_milp=False):\\n self.start_time = time.time()\\n if use_milp:\\n # iterate through the constraints list\\n # for each constraint type \\\"distance\\\", add the same constraint to the target object\\n new_constraints = constraints.copy()\\n for object_name, object_constraints in constraints.items():\\n for constraint in object_constraints:\\n if constraint[\\\"type\\\"] == \\\"distance\\\":\\n target_object_name = constraint[\\\"target\\\"]\\n if target_object_name in constraints.keys():\\n # if there is already a distance constraint of target object_name, continue\\n if any(constraint[\\\"type\\\"] == \\\"distance\\\" and constraint[\\\"target\\\"] == object_name for constraint in constraints[target_object_name]): continue\\n new_constraint = constraint.copy()\\n new_constraint[\\\"target\\\"] = object_name\\n new_constraints[target_object_name].append(new_constraint)\\n # iterate through the constraints list\\n # for each constraint type \\\"left of\\\" or \\\"right of\\\", add the same constraint to the target object\\n #for object_name, object_constraints in constraints.items():\\n # for constraint in object_constraints: if constraint[\\\"type\\\"] == \\\"relative\\\":\\n # if constraint[\\\"constraint\\\"] == \\\"left of\\\":\\n constraints = new_constraints\\n\\n try:\\n self.milp_dfs(bounds, objects_list, constraints, initial_state, 10)\\n except SolutionFound as e:\\n print(f\\\"Time taken: {time.time() - self.start_time}\\\")\\n \\n else:\\n grid_points = self.create_grids(bounds)\\n grid_points = self.remove_points(grid_points, initial_state)\\n try:\\n self.dfs(bounds, objects_list, constraints, grid_points, initial_state, 30)\\n except SolutionFound as e:\\n print(f\\\"Time taken: {time.time() - self.start_time}\\\")\\n \\n print(f\\\"Number of solutions found: {len(self.solutions)}\\\")\\n max_solution = self.get_max_solution(self.solutions)\\n\\n if not use_milp and self.vistualize:\\n self.visualize_grid(bounds, grid_points, max_solution)\\n\\n return max_solution\\n \\n\\n def get_max_solution(self, solutions):\\n path_weights = []\\n for solution in solutions:\\n path_weights.append(sum([obj[-1] for obj in solution.values()]))\\n max_index = np.argmax(path_weights)\\n return solutions[max_index]\\n\\n\\n def dfs(self, room_poly, objects_list, constraints, grid_points, placed_objects, branch_factor):\\n if len(objects_list) == 0:\\n self.solutions.append(placed_objects)\\n return placed_objects\\n \\n if time.time() - self.start_time > self.max_duration:\\n print(f\\\"Time limit reached.\\\")\\n raise SolutionFound(self.solutions)\\n \\n object_name, object_dim = objects_list[0]\\n placements = self.get_possible_placements(room_poly, object_dim, constraints[object_name], grid_points, placed_objects)\\n \\n if len(placements) == 0 and len(placed_objects) != 0:\\n self.solutions.append(placed_objects)\\n\\n paths = []\\n if branch_factor > 1: random.shuffle(placements) # shuffle the placements of the first object\\n\\n for placement in placements[:branch_factor]:\\n placed_objects_updated = copy.deepcopy(placed_objects)\\n placed_objects_updated[object_name] = placement\\n grid_points_updated = self.remove_points(grid_points, placed_objects_updated)\\n\\n sub_paths = self.dfs(room_poly, objects_list[1:], constraints, grid_points_updated, placed_objects_updated, 1)\\n paths.extend(sub_paths)\\n\\n return paths\\n\\n \\n def get_possible_placements(self, room_poly, object_dim, constraints, grid_points, placed_objects):\\n solutions = self.filter_collision(placed_objects, self.get_all_solutions(room_poly, grid_points, object_dim))\\n solutions = self.filter_facing_wall(room_poly, solutions, object_dim)\\n edge_solutions = self.place_edge(room_poly, copy.deepcopy(solutions), object_dim)\\n\\n if len(edge_solutions) == 0: return edge_solutions\\n\\n global_constraint = next((constraint for constraint in constraints if constraint[\\\"type\\\"] == \\\"global\\\"), None)\\n\\n if global_constraint is None: global_constraint = {\\\"type\\\": \\\"global\\\", \\\"constraint\\\": \\\"edge\\\"}\\n\\n if global_constraint[\\\"constraint\\\"] == \\\"edge\\\":\\n candidate_solutions = copy.deepcopy(edge_solutions) # edge is hard constraint\\n else:\\n if len(constraints) > 1: candidate_solutions = solutions + edge_solutions # edge is soft constraint\\n else: candidate_solutions = copy.deepcopy(solutions) # the first object\\n\\n candidate_solutions = self.filter_collision(placed_objects, candidate_solutions) # filter again after global constraint\\n\\n if candidate_solutions == []: return candidate_solutions\\n random.shuffle(candidate_solutions)\\n placement2score = {tuple(solution[:3]): solution[-1] for solution in candidate_solutions}\\n\\n # add a bias to edge solutions\\n for solution in candidate_solutions:\\n if solution in edge_solutions and len(constraints) >= 1:\\n placement2score[tuple(solution[:3])] += self.edge_bouns\\n \\n for constraint in constraints:\\n if \\\"target\\\" not in constraint: continue\\n\\n func = self.func_dict.get(constraint[\\\"type\\\"])\\n valid_solutions = func(constraint[\\\"constraint\\\"], placed_objects[constraint[\\\"target\\\"]], candidate_solutions)\\n \\n weight = self.constraint_type2weight[constraint[\\\"type\\\"]]\\n if constraint[\\\"type\\\"] == \\\"distance\\\":\\n for solution in valid_solutions:\\n bouns = solution[-1]\\n placement2score[tuple(solution[:3])] += bouns * weight\\n else:\\n for solution in valid_solutions:\\n placement2score[tuple(solution[:3])] += self.constraint_bouns * weight\\n\\n # normalize the scores\\n for placement in placement2score: placement2score[placement] /= max(len(constraints), 1)\\n\\n sorted_placements = sorted(placement2score, key=placement2score.get, reverse=True)\\n sorted_solutions = [list(placement) + [placement2score[placement]] for placement in sorted_placements]\\n\\n return sorted_solutions\\n\\n\\n def create_grids(self, room_poly):\\n # get the min and max bounds of the room\\n min_x, min_z, max_x, max_z = room_poly.bounds\\n\\n # create grid points\\n grid_points = []\\n for x in range(int(min_x), int(max_x), self.grid_size):\\n for y in range(int(min_z), int(max_z), self.grid_size):\\n point = Point(x, y)\\n if room_poly.contains(point):\\n grid_points.append((x, y))\\n\\n return grid_points\\n \\n\\n def remove_points(self, grid_points, objects_dict):\\n # Create an r-tree index\\n idx = index.Index()\\n\\n # Populate the index with bounding boxes of the objects\\n for i, (_, _, obj, _) in enumerate(objects_dict.values()):\\n idx.insert(i, Polygon(obj).bounds)\\n \\n # Create Shapely Polygon objects only once\\n polygons = [Polygon(obj) for _, _, obj, _ in objects_dict.values()]\\n\\n valid_points = []\\n \\n for point in grid_points:\\n p = Point(point)\\n # Get a list of potential candidates\\n candidates = [polygons[i] for i in idx.intersection(p.bounds)]\\n # Check if point is in any of the candidate polygons\\n if not any(candidate.contains(p) for candidate in candidates):\\n valid_points.append(point)\\n \\n return valid_points\\n \\n\\n def get_all_solutions(self, room_poly, grid_points, object_dim):\\n obj_length, obj_width = object_dim\\n obj_half_length, obj_half_width = obj_length / 2, obj_width / 2\\n\\n rotation_adjustments = {\\n 0: ((-obj_half_length, -obj_half_width), (obj_half_length, obj_half_width)),\\n 90: ((-obj_half_width, -obj_half_length), (obj_half_width, obj_half_length)),\\n 180: ((-obj_half_length, obj_half_width), (obj_half_length, -obj_half_width)),\\n 270: ((obj_half_width, -obj_half_length), (-obj_half_width, obj_half_length)),\\n }\\n\\n solutions = []\\n for rotation in [0, 90, 180, 270]:\\n for point in grid_points:\\n center_x, center_y = point\\n lower_left_adjustment, upper_right_adjustment = rotation_adjustments[rotation]\\n lower_left = (center_x + lower_left_adjustment[0], center_y + lower_left_adjustment[1])\\n upper_right = (center_x + upper_right_adjustment[0], center_y + upper_right_adjustment[1])\\n obj_box = box(*lower_left, *upper_right)\\n\\n if room_poly.contains(obj_box):\\n solutions.append([point, rotation, tuple(obj_box.exterior.coords[:]), 1])\\n \\n return solutions\\n \\n\\n def filter_collision(self, objects_dict, solutions):\\n valid_solutions = []\\n object_polygons = [Polygon(obj_coords) for _, _, obj_coords, _ in list(objects_dict.values())]\\n for solution in solutions:\\n sol_obj_coords = solution[2]\\n sol_obj = Polygon(sol_obj_coords)\\n if not any(sol_obj.intersects(obj) for obj in object_polygons):\\n valid_solutions.append(solution)\\n return valid_solutions\\n\\n \\n def filter_facing_wall(self, room_poly, solutions, obj_dim):\\n valid_solutions = []\\n obj_width = obj_dim[1]\\n obj_half_width = obj_width / 2\\n\\n front_center_adjustments = {\\n 0: (0, obj_half_width),\\n 90: (obj_half_width, 0),\\n 180: (0, -obj_half_width),\\n 270: (-obj_half_width, 0),\\n }\\n\\n valid_solutions = []\\n for solution in solutions:\\n center_x, center_y = solution[0]\\n rotation = solution[1]\\n\\n front_center_adjustment = front_center_adjustments[rotation]\\n front_center_x, front_center_y = center_x + front_center_adjustment[0], center_y + front_center_adjustment[1]\\n\\n front_center_distance = room_poly.boundary.distance(Point(front_center_x, front_center_y))\\n\\n if front_center_distance >= 30: # TODO: make this a parameter\\n valid_solutions.append(solution)\\n\\n return valid_solutions\\n \\n\\n def place_edge(self, room_poly, solutions, obj_dim):\\n valid_solutions = []\\n obj_width = obj_dim[1]\\n obj_half_width = obj_width / 2\\n\\n back_center_adjustments = {\\n 0: (0, -obj_half_width),\\n 90: (-obj_half_width, 0),\\n 180: (0, obj_half_width),\\n 270: (obj_half_width, 0),\\n }\\n\\n for solution in solutions:\\n center_x, center_y = solution[0]\\n rotation = solution[1]\\n\\n back_center_adjustment = back_center_adjustments[rotation]\\n back_center_x, back_center_y = center_x + back_center_adjustment[0], center_y + back_center_adjustment[1]\\n\\n back_center_distance = room_poly.boundary.distance(Point(back_center_x, back_center_y))\\n center_distance = room_poly.boundary.distance(Point(center_x, center_y))\\n\\n if back_center_distance <= self.grid_size and back_center_distance < center_distance:\\n solution[-1] += self.constraint_bouns\\n # valid_solutions.append(solution) # those are still valid solutions, but we need to move the object to the edge\\n\\n # move the object to the edge\\n center2back_vector = np.array([back_center_x - center_x, back_center_y - center_y])\\n center2back_vector /= np.linalg.norm(center2back_vector)\\n offset = center2back_vector * (back_center_distance + 4.5) # add a small distance to avoid the object cross the wall\\n solution[0] = (center_x + offset[0], center_y + offset[1])\\n solution[2] = ((solution[2][0][0] + offset[0], solution[2][0][1] + offset[1]), \\\\\\n (solution[2][1][0] + offset[0], solution[2][1][1] + offset[1]), \\\\\\n (solution[2][2][0] + offset[0], solution[2][2][1] + offset[1]), \\\\\\n (solution[2][3][0] + offset[0], solution[2][3][1] + offset[1]))\\n valid_solutions.append(solution)\\n\\n return valid_solutions\\n \\n\\n def place_corner(self, room_poly, solutions, obj_dim):\\n obj_length, obj_width = obj_dim\\n obj_half_length, _ = obj_length / 2, obj_width / 2\\n\\n rotation_center_adjustments = {\\n 0: ((-obj_half_length, 0), (obj_half_length, 0)),\\n 90: ((0, obj_half_length), (0, -obj_half_length)),\\n 180: ((obj_half_length, 0), (-obj_half_length, 0)),\\n 270: ((0, -obj_half_length), (0, obj_half_length))\\n }\\n\\n edge_solutions = self.place_edge(room_poly, solutions, obj_dim)\\n\\n valid_solutions = []\\n\\n for solution in edge_solutions:\\n (center_x, center_y), rotation = solution[:2]\\n (dx_left, dy_left), (dx_right, dy_right) = rotation_center_adjustments[rotation]\\n\\n left_center_x, left_center_y = center_x + dx_left, center_y + dy_left\\n right_center_x, right_center_y = center_x + dx_right, center_y + dy_right\\n \\n left_center_distance = room_poly.boundary.distance(Point(left_center_x, left_center_y))\\n right_center_distance = room_poly.boundary.distance(Point(right_center_x, right_center_y))\\n\\n if min(left_center_distance, right_center_distance) < self.grid_size:\\n solution[-1] += self.constraint_bouns\\n valid_solutions.append(solution)\\n\\n return valid_solutions\\n \\n\\n def place_relative(self, place_type, target_object, solutions):\\n valid_solutions = []\\n _, target_rotation, target_coords, _ = target_object\\n target_polygon = Polygon(target_coords)\\n\\n min_x, min_y, max_x, max_y = target_polygon.bounds\\n mean_x = (min_x + max_x) / 2\\n mean_y = (min_y + max_y) / 2\\n\\n comparison_dict = {\\n 'left of': {\\n 0: lambda sol_center: sol_center[0] < min_x and min_y <= sol_center[1] <= max_y,\\n 90: lambda sol_center: sol_center[1] > max_y and min_x <= sol_center[0] <= max_x,\\n 180: lambda sol_center: sol_center[0] > max_x and min_y <= sol_center[1] <= max_y,\\n 270: lambda sol_center: sol_center[1] < min_y and min_x <= sol_center[0] <= max_x,\\n },\\n 'right of': {\\n 0: lambda sol_center: sol_center[0] > max_x and min_y <= sol_center[1] <= max_y,\\n 90: lambda sol_center: sol_center[1] < min_y and min_x <= sol_center[0] <= max_x,\\n 180: lambda sol_center: sol_center[0] < min_x and min_y <= sol_center[1] <= max_y,\\n 270: lambda sol_center: sol_center[1] > max_y and min_x <= sol_center[0] <= max_x,\\n },\\n 'in front of': {\\n 0: lambda sol_center: sol_center[1] > max_y and mean_x - self.grid_size < sol_center[0] < mean_x + self.grid_size, # in front of and centered\\n 90: lambda sol_center: sol_center[0] > max_x and mean_y - self.grid_size < sol_center[1] < mean_y + self.grid_size,\\n 180: lambda sol_center: sol_center[1] < min_y and mean_x - self.grid_size < sol_center[0] < mean_x + self.grid_size,\\n 270: lambda sol_center: sol_center[0] < min_x and mean_y - self.grid_size < sol_center[1] < mean_y + self.grid_size,\\n },\\n 'behind': {\\n 0: lambda sol_center: sol_center[1] < min_y and min_x <= sol_center[0] <= max_x,\\n 90: lambda sol_center: sol_center[0] < min_x and min_y <= sol_center[1] <= max_y,\\n 180: lambda sol_center: sol_center[1] > max_y and min_x <= sol_center[0] <= max_x,\\n 270: lambda sol_center: sol_center[0] > max_x and min_y <= sol_center[1] <= max_y,\\n },\\n \\\"side of\\\": {\\n 0: lambda sol_center: min_y <= sol_center[1] <= max_y,\\n 90: lambda sol_center: min_x <= sol_center[0] <= max_x,\\n 180: lambda sol_center: min_y <= sol_center[1] <= max_y,\\n 270: lambda sol_center: min_x <= sol_center[0] <= max_x\\n }\\n }\\n \\n compare_func = comparison_dict.get(place_type).get(target_rotation)\\n\\n for solution in solutions:\\n sol_center = solution[0]\\n\\n if compare_func(sol_center):\\n solution[-1] += self.constraint_bouns\\n valid_solutions.append(solution)\\n \\n return valid_solutions\\n \\n\\n def place_distance(self, distance_type, target_object, solutions):\\n target_coords = target_object[2]\\n target_poly = Polygon(target_coords)\\n distances = []\\n valid_solutions = []\\n for solution in solutions:\\n sol_coords = solution[2]\\n sol_poly = Polygon(sol_coords)\\n distance = target_poly.distance(sol_poly)\\n distances.append(distance)\\n\\n solution[-1] = distance\\n valid_solutions.append(solution)\\n \\n min_distance = min(distances)\\n max_distance = max(distances)\\n\\n if distance_type == \\\"near\\\":\\n if min_distance < 80:\\n points = [(min_distance, 1), (80, 0), (max_distance, 0)]\\n else:\\n points = [(min_distance, 0), (max_distance, 0)]\\n\\n elif distance_type == \\\"far\\\":\\n points = [(min_distance, 0), (max_distance, 1)]\\n \\n x = [point[0] for point in points]\\n y = [point[1] for point in points]\\n\\n f = interp1d(x, y, kind='linear', fill_value='extrapolate')\\n \\n for solution in valid_solutions:\\n distance = solution[-1]\\n solution[-1] = float(f(distance))\\n\\n return valid_solutions\\n \\n\\n def place_face(self, face_type, target_object, solutions):\\n if face_type == \\\"face to\\\":\\n return self.place_face_to(target_object, solutions)\\n \\n elif face_type == \\\"face same as\\\":\\n return self.place_face_same(target_object, solutions)\\n \\n elif face_type == \\\"face opposite to\\\":\\n return self.place_face_opposite(target_object, solutions)\\n \\n\\n def place_face_to(self, target_object, solutions):\\n # Define unit vectors for each rotation\\n unit_vectors = {\\n 0: np.array([0., 1.]), # Facing up\\n 90: np.array([1., 0.]), # Facing right\\n 180: np.array([0., -1.]), # Facing down\\n 270: np.array([-1., 0.]) # Facing left\\n }\\n \\n target_coords = target_object[2]\\n target_poly = Polygon(target_coords)\\n \\n valid_solutions = []\\n \\n for solution in solutions:\\n sol_center = solution[0]\\n sol_rotation = solution[1]\\n\\n # Define an arbitrarily large point in the direction of the solution's rotation\\n far_point = sol_center + 1e6 * unit_vectors[sol_rotation]\\n\\n # Create a half-line from the solution's center to the far point\\n half_line = LineString([sol_center, far_point])\\n\\n # Check if the half-line intersects with the target polygon\\n if half_line.intersects(target_poly):\\n solution[-1] += self.constraint_bouns\\n valid_solutions.append(solution)\\n \\n return valid_solutions\\n \\n\\n def place_face_same(self, target_object, solutions):\\n target_rotation = target_object[1]\\n valid_solutions = []\\n \\n for solution in solutions:\\n sol_rotation = solution[1]\\n if sol_rotation == target_rotation:\\n solution[-1] += self.constraint_bouns\\n valid_solutions.append(solution)\\n\\n return valid_solutions\\n \\n\\n def place_face_opposite(self, target_object, solutions):\\n target_rotation = (target_object[1] + 180) % 360\\n valid_solutions = []\\n \\n for solution in solutions:\\n sol_rotation = solution[1]\\n if sol_rotation == target_rotation:\\n solution[-1] += self.constraint_bouns\\n valid_solutions.append(solution)\\n \\n return valid_solutions\\n\\n\\n def place_alignment_center(self, alignment_type, target_object, solutions):\\n target_center = target_object[0]\\n valid_solutions = []\\n eps = 5\\n for solution in solutions:\\n sol_center = solution[0]\\n if abs(sol_center[0] - target_center[0]) < eps or abs(sol_center[1] - target_center[1]) < eps:\\n solution[-1] += self.constraint_bouns\\n valid_solutions.append(solution)\\n return valid_solutions\\n\\n\\n def visualize_grid(self, room_poly, grid_points, solutions):\\n plt.rcParams[\\\"font.family\\\"] = \\\"Times New Roman\\\"\\n plt.rcParams[\\\"font.size\\\"] = 22\\n\\n # create a new figure\\n fig, ax = plt.subplots()\\n\\n # draw the room\\n x, y = room_poly.exterior.xy\\n ax.plot(x, y, '-', label='Room', color='black', linewidth=2)\\n\\n # draw the grid points\\n grid_x = [point[0] for point in grid_points]\\n grid_y = [point[1] for point in grid_points]\\n ax.plot(grid_x, grid_y, 'o', markersize=2, color=\\\"grey\\\")\\n\\n # draw the solutions\\n for object_name, solution in solutions.items():\\n center, rotation, box_coords = solution[:3]\\n center_x, center_y = center\\n\\n # create a polygon for the solution\\n obj_poly = Polygon(box_coords)\\n x, y = obj_poly.exterior.xy\\n ax.plot(x, y, '-', linewidth=2, color='black')\\n\\n # ax.text(center_x, center_y, object_name, fontsize=18, ha='center')\\n\\n # set arrow direction based on rotation\\n if rotation == 0:\\n ax.arrow(center_x, center_y, 0, 25, head_width=10, fc='black')\\n elif rotation == 90:\\n ax.arrow(center_x, center_y, 25, 0, head_width=10, fc='black')\\n elif rotation == 180:\\n ax.arrow(center_x, center_y, 0, -25, head_width=10, fc='black')\\n elif rotation == 270:\\n ax.arrow(center_x, center_y, -25, 0, head_width=10, fc='black')\\n # axis off\\n ax.axis('off')\\n ax.set_aspect('equal', 'box') # to keep the ratios equal along x and y axis\\n create_time = str(datetime.datetime.now()).replace(\\\" \\\", \\\"-\\\").replace(\\\":\\\", \\\"-\\\").replace(\\\".\\\", \\\"-\\\")\\n plt.savefig(f\\\"{create_time}.pdf\\\", bbox_inches='tight', dpi=300)\\n plt.show()\\n \\n\\n def milp_dfs(self, room_poly, all_objects_list, constraints, placed_objects, branch_factor=1):\\n if len(all_objects_list) == 0:\\n self.solutions.append(placed_objects)\\n return placed_objects\\n \\n if time.time() - self.start_time > self.max_duration:\\n print(f\\\"Time limit reached.\\\")\\n raise SolutionFound(self.solutions)\\n \\n def milp_solve(soft_constraints_list, hard_constraints_list, verbose=False):\\n problem = cp.Problem(cp.Maximize(sum(soft_constraints_list)), hard_constraints_list)\\n if verbose:\\n print('solving milp using GUROBI ...')\\n problem.solve(solver=cp.GUROBI, reoptimize=True, verbose=False)\\n return problem.value\\n \\n def parse_object_properties(object_properties):\\n x, y = object_properties[0]\\n rotation = int(object_properties[1] or 0)\\n # set rotation to the closest 90 degree\\n rotation = int(round(rotation / 90) * 90)\\n assert rotation in [0, 90, 180, 270]\\n object_bbox = object_properties[2]\\n min_x = min([point[0] for point in object_bbox])\\n max_x = max([point[0] for point in object_bbox])\\n min_y = min([point[1] for point in object_bbox])\\n max_y = max([point[1] for point in object_bbox])\\n object_dim = (max_x - min_x, max_y - min_y) if rotation == 0 or rotation == 180 else (max_y - min_y, max_x - min_x)\\n return x, y, rotation, object_dim\\n\\n def find_object_dim(target_object_name, objects_list, placed_objects):\\n target_object_dim = None\\n for object_name_1, object_dim_1 in objects_list:\\n if object_name_1 == target_object_name: \\n target_object_dim = object_dim_1\\n return target_object_dim\\n\\n if not None: \\n for object_name_1, object_properties in placed_objects.items():\\n if object_name_1 == target_object_name:\\n x, y, rotation, target_object_dim = parse_object_properties(object_properties)\\n return target_object_dim\\n return None\\n\\n\\n found_a_solution = False\\n # randomly select a set of objects from all_objects_list\\n # start with the largest object + more objects --> gradually reduce the number of objects \\n for branch_idx in range(branch_factor):\\n # sample a set of objects from a list that contains the first object\\n \\n k = random.randint(0, min(5, len(all_objects_list)-1))\\n objects_list = [all_objects_list[0]] + random.sample(all_objects_list[1:], k)\\n\\n hard_constraints_list = []\\n soft_constraints_list = [0]\\n\\n # formulate the milp problem\\n # object_name, object_dim = objects_list[0]\\n # x, y, rotate_180, rotate_90\\n variables_dict = {object[0]: [cp.Variable(), cp.Variable(), cp.Variable(boolean=True), cp.Variable(boolean=True)] for object in objects_list}\\n # add placed objects into variables dict even though they are not variables\\n for object, object_properties in placed_objects.items():\\n x, y = object_properties[0]\\n rotation = int(object_properties[1])\\n variables_dict[object] = [x, y, rotation == 180, rotation == 90 or rotation == 270]\\n\\n # Initialize a list of variables, each variable represents the coordinate for each object\\n room_min_x, room_min_y, room_max_x, room_max_y = room_poly.bounds\\n # Add boundary constraints to all objects\\n for object_name, object_dim in objects_list:\\n hard_constraints_list.extend(create_boundary_constraints(variables_dict[object_name],\\n object_dim,\\n (room_min_x, room_min_y, room_max_x, room_max_y)))\\n # Add pariwise collision constraints\\n for object_name_1, object_dim_1 in objects_list:\\n for object_name_2, object_dim_2 in objects_list:\\n if object_name_1 == object_name_2: continue\\n # collision constraints should be hard constraints\\n hard_constraints_list.extend(create_nooverlap_constraints(variables_dict[object_name_1],\\n variables_dict[object_name_2],\\n object_dim_1,\\n object_dim_2))\\n\\n # Add pariwise collision constraints with placed objects\\n for object_name_1, object_dim_1 in objects_list:\\n for object_name_2, object_properties_2 in placed_objects.items():\\n # bbox is a list of four points\\n x, y, rotation, object_dim_2 = parse_object_properties(object_properties_2)\\n\\n hard_constraints_list.extend(create_nooverlap_constraints(variables_dict[object_name_1],\\n [x, y, rotation == 180, rotation == 90 or rotation == 270],\\n object_dim_1, object_dim_2))\\n\\n # default constraints / heuristics?\\n for object_name, object_dim in objects_list:\\n # encourage dispersement of assets\\n all_other_objects_list = [x[0] for x in objects_list if x[0] != object_name] + list(placed_objects.keys())\\n for target_object_name in all_other_objects_list:\\n hard_constraints, soft_constraints = create_distance_constraints(variables_dict[object_name],\\n variables_dict[target_object_name],\\n upper_bound=[room_max_x-room_min_x, room_max_y-room_min_y],\\n type='far')\\n assert len(soft_constraints) == 1\\n # soft_constraints[0] *= 0.001\\n hard_constraints_list.extend(hard_constraints)\\n soft_constraints_list.extend(soft_constraints)\\n\\n\\n # use cvxpy to solve for the hard constraints\\n for object_name, object_dim in objects_list:\\n\\n # by default - add soft edge constraints although this might make the solver take a longer time\\n if not any(constraint['type'] == 'global' for constraint in constraints[object_name]):\\n hard_constraints, soft_constraints = create_edge_constraints(variables_dict[object_name],\\n object_dim,\\n room_dim=(room_min_x, room_min_y, room_max_x, room_max_y),\\n hard=False)\\n soft_constraints[0] *= 100\\n hard_constraints_list.extend(hard_constraints)\\n soft_constraints_list.extend(soft_constraints)\\n\\n\\n\\n for constraint in constraints[object_name]:\\n if constraint['type'] == 'global': \\n if constraint['constraint'] == 'edge': # hard constraints\\n hard_constraints, soft_constraints = create_edge_constraints(variables_dict[object_name],\\n object_dim,\\n room_dim=(room_min_x, room_min_y, room_max_x, room_max_y),\\n hard=True)\\n hard_constraints_list.extend(hard_constraints)\\n soft_constraints_list.extend(soft_constraints)\\n\\n if constraint['type'] == 'direction':\\n assert constraint['constraint'] == 'face to'\\n target_object_name = constraint['target']\\n target_object_dim = find_object_dim(target_object_name, objects_list, placed_objects)\\n if target_object_dim:\\n hard_constraints_list.extend(create_directional_constraints(variables_dict[object_name],\\n variables_dict[target_object_name],\\n object_dim,\\n target_object_dim))\\n\\n if constraint['type'] == 'alignment':\\n assert constraint['constraint'] == 'center aligned'\\n target_object_name = constraint['target']\\n target_object_dim = find_object_dim(target_object_name, objects_list, placed_objects)\\n if target_object_dim:\\n hard_constraints_list.extend(create_alignment_constraints(variables_dict[object_name],\\n variables_dict[target_object_name],\\n object_dim,\\n target_object_dim))\\n\\n if constraint['type'] == 'distance':\\n target_object_name = constraint['target']\\n target_object_dim = find_object_dim(target_object_name, objects_list, placed_objects)\\n if target_object_dim:\\n hard_constraints, soft_constraints = create_distance_constraints(variables_dict[object_name],\\n variables_dict[target_object_name],\\n upper_bound=[room_max_x-room_min_x, room_max_y-room_min_y],\\n type=constraint['constraint'])\\n hard_constraints_list.extend(hard_constraints)\\n soft_constraints_list.extend(soft_constraints)\\n assert len(soft_constraints) == 1\\n # higher weighting\\n soft_constraints[0] *= 0.01\\n\\n if constraint['type'] == 'relative':\\n target_object_name = constraint['target']\\n target_object_dim = find_object_dim(target_object_name, objects_list, placed_objects)\\n if target_object_dim:\\n hard_constraints_list.extend(create_relative_constraints(variables_dict[object_name],\\n variables_dict[target_object_name],\\n object_dim,\\n target_object_dim,\\n constraint['constraint']))\\n\\n result = milp_solve(soft_constraints_list, hard_constraints_list, verbose=False)\\n if result is None or math.isnan(result) or math.isinf(result):\\n continue\\n\\n found_a_solution = True\\n print(result, [x[0] for x in objects_list])\\n\\n # we fonud a valid solution\\n # convert the placements to the same format as the dfs solver\\n placed_objects_updated = copy.deepcopy(placed_objects)\\n for object_name, object_dim in objects_list:\\n # (x, y), rotation, bbox, score\\n x = variables_dict[object_name][0].value.item()\\n y = variables_dict[object_name][1].value.item()\\n rotate_180 = variables_dict[object_name][2].value\\n rotate_90 = variables_dict[object_name][3].value \\n if not rotate_180: rotate_180 = 0\\n if not rotate_90: rotate_90 = 0\\n\\n # bbox has taken into account of the rotation\\n if rotate_90:\\n bbox = [(x - object_dim[1]/2, y - object_dim[0]/2),\\n (x + object_dim[1]/2, y - object_dim[0]/2),\\n (x + object_dim[1]/2, y + object_dim[0]/2),\\n (x - object_dim[1]/2, y + object_dim[0]/2)]\\n else:\\n bbox = [(x - object_dim[0]/2, y - object_dim[1]/2),\\n (x + object_dim[0]/2, y - object_dim[1]/2),\\n (x + object_dim[0]/2, y + object_dim[1]/2),\\n (x - object_dim[0]/2, y + object_dim[1]/2)]\\n\\n placed_objects_updated[object_name] = [(x,y), rotate_180 * 180 + rotate_90 * 90, bbox,\\n len(constraints[object_name])]\\n\\n # remove all elemnts in objects_list from all_objects_list\\n self.milp_dfs(room_poly, [x for x in all_objects_list if x not in objects_list], constraints, placed_objects_updated, branch_factor=1)\\n \\n if not found_a_solution and len(placed_objects) != 0:\\n self.solutions.append(placed_objects)\\n \\n\\n def test_dfs_placement(self):\\n room_vertices = ((0, 0), (0, 500), (500, 500), (500, 0))\\n room_poly = Polygon(room_vertices)\\n grid_points = self.create_grids(room_poly)\\n objects = {\\\"door\\\": ((50, 50), 0, ((0, 0), (100, 0), (100, 100), (0, 100)), 1)}\\n grid_points = self.remove_points(grid_points, objects)\\n # self.visualize_grid(room_poly, grid_points, objects)\\n\\n object_dim = (200, 100)\\n solutions = self.get_all_solutions(room_poly, grid_points, object_dim)\\n solutions = self.filter_collision(objects, solutions)\\n solutions = self.place_edge(room_poly, solutions, object_dim)\\n\\n # for i, solution in enumerate(solutions):\\n # objects[f\\\"sofa-{i}\\\"] = solution\\n # self.visualize_grid(room_poly, grid_points, objects)\\n\\n random.seed(0)\\n objects[\\\"sofa\\\"] = random.choice(solutions)\\n # self.visualize_grid(room_poly, grid_points, objects)\\n object_1_dim = (100, 50)\\n\\n solutions_1 = self.get_all_solutions(room_poly, grid_points, object_1_dim)\\n solutions_1 = self.filter_collision(objects, solutions_1)\\n\\n # random.seed(42)\\n # for i, solution in enumerate(random.sample(solutions_1, 25)):\\n # objects[f\\\"coffee table-{i}\\\"] = solution\\n \\n # objects[f\\\"coffee table\\\"] = [(300, 350), 0, ((350.0, 325.0), (350.0, 375.0), (250.0, 375.0), (250.0, 325.0), (350.0, 325.0)), 1.0]\\n # self.visualize_grid(room_poly, grid_points, objects)\\n \\n solutions_1 = self.place_face_to(objects[\\\"sofa\\\"], solutions_1)\\n solutions_1 = self.place_relative(\\\"in front of\\\", objects[\\\"sofa\\\"], solutions_1)\\n solutions_1 = self.place_alignment_center(\\\"center alignment\\\", objects[\\\"sofa\\\"], solutions_1)\\n solutions_1 = self.place_distance(\\\"near\\\", objects[\\\"sofa\\\"], solutions_1)\\n objects[f\\\"coffee table\\\"] = solutions_1[-1]\\n self.visualize_grid(room_poly, grid_points, objects)\\n\\n\\n def test_milp_placement(self, simple=False, use_milp=True):\\n room_vertices = ((0, 0), (0, 600), (800, 600), (800, 0))\\n room_poly = Polygon(room_vertices)\\n grid_points = self.create_grids(room_poly)\\n\\n if not simple:\\n constraints = {'sofa-0': [{'type': 'global', 'constraint': 'edge'}],\\n 'sofa-1': [{'type': 'global', 'constraint': 'edge'},\\n {'type': 'distance', 'constraint': 'near', 'target': 'sofa-0'},\\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-0'}],\\n 'tv stand-0': [{'type': 'global', 'constraint': 'edge'},\\n {'type': 'distance', 'constraint': 'far', 'target': 'sofa-1'},\\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-1'}],\\n 'coffee table-0': [{'type': 'global', 'constraint': 'middle'},\\n {'type': 'distance', 'constraint': 'near', 'target': 'sofa-0'},\\n {'type': 'relative', 'constraint': 'in front of', 'target': 'sofa-0'},\\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-0'},\\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-0'},\\n {'type': 'direction', 'constraint': 'face to', 'target': 'tv stand-0'}],\\n 'coffee table-1': [{'type': 'global', 'constraint': 'middle'},\\n {'type': 'distance', 'constraint': 'near', 'target': 'sofa-1'},\\n {'type': 'relative', 'constraint': 'in front of', 'target': 'sofa-1'},\\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-1'},\\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'sofa-1'},\\n {'type': 'direction', 'constraint': 'face to', 'target': 'tv stand-0'}],\\n 'side table-0': [{'type': 'global', 'constraint': 'edge'},\\n {'type': 'distance', 'constraint': 'near', 'target': 'sofa-0'},\\n {'type': 'relative', 'constraint': 'side of', 'target': 'sofa-0'}],\\n 'side table-1': [{'type': 'global', 'constraint': 'edge'},\\n {'type': 'distance', 'constraint': 'near', 'target': 'sofa-1'},\\n {'type': 'relative', 'constraint': 'side of', 'target': 'sofa-1'}],\\n 'armchair-0': [{'type': 'global', 'constraint': 'middle'},\\n {'type': 'distance', 'constraint': 'near', 'target': 'coffee table-0'},\\n {'type': 'direction', 'constraint': 'face to', 'target': 'coffee table-0'},\\n {'type': 'direction', 'constraint': 'face to', 'target': 'coffee table-0'}],\\n 'armchair-1': [{'type': 'global', 'constraint': 'middle'},\\n {'type': 'distance', 'constraint': 'near', 'target': 'coffee table-1'},\\n {'type': 'direction', 'constraint': 'face to', 'target': 'coffee table-1'},\\n {'type': 'direction', 'constraint': 'face to', 'target': 'coffee table-1'}],\\n 'bookshelf-0': [{'type': 'global', 'constraint': 'edge'},\\n {'type': 'distance', 'constraint': 'far', 'target': 'tv stand-0'}],\\n 'bookshelf-1': [{'type': 'global', 'constraint': 'edge'},\\n {'type': 'distance', 'constraint': 'far', 'target': 'bookshelf-0'},\\n {'type': 'alignment', 'constraint': 'center aligned', 'target': 'bookshelf-0'}]}\\n\\n initial_state = {'door-0': ((586.7550200520433, 550.0), 0, [(640.8300346432603, 500.0), (532.6800054608262, 500.0), (532.6800054608262, 600.0), (640.8300346432603, 600.0)], 1)}\\n\\n objects = [('sofa-0', (301.6667297651499, 106.48952360032415)),\\n ('sofa-1', (301.6667297651499, 106.48952360032415)),\\n ('tv stand-0', (201.0964714933229, 59.39910836195032)),\\n ('coffee table-0', (69.15754261308616, 126.69169450358964)),\\n ('coffee table-1', (69.15754261308616, 126.69169450358964)),\\n ('side table-0', (61.74632023132328, 61.74453745262855)),\\n ('side table-1', (61.74632023132328, 61.74453745262855)),\\n ('armchair-0', (79.0368498902692, 89.4893987892571)),\\n ('armchair-1', (79.0368498902692, 89.4893987892571)),\\n ('bookshelf-0', (67.94689517917222, 43.8934937031396)),\\n ('bookshelf-1', (67.94689517917222, 43.8934937031396))]\\n solution = self.get_solution(room_poly, objects, constraints, initial_state, use_milp=use_milp)\\n else:\\n constraints = {'dining table': [{'type': 'global', 'constraint': 'edge'},\\n {'type': 'distance', 'constraint': 'far', 'target': 'door'},\\n {'type': 'distance', 'constraint': 'near', 'target': 'chair'}],\\n 'chair': [{'type': 'relative', 'constraint': 'side of', 'target': 'dining table'}]\\n }\\n initial_state = {\\\"door\\\": ((50, 50), 0, ((0, 0), (100, 0), (100, 100), (0, 100)), 1)}\\n objects = [(\\\"dining table\\\", (100, 50)), (\\\"chair\\\", (50, 50))]\\n solution = self.get_solution(room_poly, objects, constraints, initial_state, use_milp=use_milp)\\n\\n print('milp solution:', len(solution))\\n for object_name, object_properties in solution.items():\\n print(object_name, object_properties)\\n # if object_properties[2] == 90 or object_properties[2] == 270:\\n self.visualize_grid(room_poly, grid_points, solution)\"\n },\n {\n \"identifier\": \"DFS_Solver_Wall\",\n \"path\": \"modules/wall_objects.py\",\n \"snippet\": \"class DFS_Solver_Wall():\\n def __init__(self, grid_size, random_seed=0, max_duration=5, constraint_bouns=100):\\n self.grid_size = grid_size\\n self.random_seed = random_seed\\n self.max_duration = max_duration # maximum allowed time in seconds\\n self.constraint_bouns = constraint_bouns\\n self.start_time = None\\n self.solutions = []\\n self.visualize = False\\n\\n\\n def get_solution(self, room_poly, wall_objects_list, constraints, initial_state):\\n grid_points = self.create_grids(room_poly)\\n\\n self.start_time = time.time()\\n try:\\n self.dfs(room_poly, wall_objects_list, constraints, grid_points, initial_state)\\n except SolutionFound as e:\\n print(f\\\"Time taken: {time.time() - self.start_time}\\\")\\n \\n max_solution = self.get_max_solution(self.solutions)\\n \\n if self.visualize: self.visualize_grid(room_poly, grid_points, max_solution)\\n return max_solution\\n\\n\\n def get_max_solution(self, solutions):\\n path_weights = []\\n for solution in solutions:\\n path_weights.append(sum([obj[-1] for obj in solution.values()]))\\n max_index = np.argmax(path_weights)\\n return solutions[max_index]\\n\\n\\n def dfs(self, room_poly, wall_objects_list, constraints, grid_points, placed_objects):\\n if len(wall_objects_list) == 0:\\n self.solutions.append(placed_objects)\\n return placed_objects\\n \\n if time.time() - self.start_time > self.max_duration:\\n print(f\\\"Time limit reached.\\\")\\n raise SolutionFound(self.solutions)\\n \\n object_name, object_dim = wall_objects_list[0]\\n placements = self.get_possible_placements(room_poly, object_dim, constraints[object_name], grid_points, placed_objects)\\n \\n if len(placements) == 0:\\n self.solutions.append(placed_objects)\\n\\n paths = []\\n for placement in placements:\\n placed_objects_updated = copy.deepcopy(placed_objects)\\n placed_objects_updated[object_name] = placement\\n\\n sub_paths = self.dfs(room_poly, wall_objects_list[1:], constraints, grid_points, placed_objects_updated)\\n paths.extend(sub_paths)\\n\\n return paths\\n \\n\\n def get_possible_placements(self, room_poly, object_dim, constraint, grid_points, placed_objects):\\n all_solutions = self.filter_collision(placed_objects, self.get_all_solutions(room_poly, grid_points, object_dim, constraint[\\\"height\\\"]))\\n random.shuffle(all_solutions)\\n target_floor_object_name = constraint[\\\"target_floor_object_name\\\"]\\n if target_floor_object_name is not None and target_floor_object_name in placed_objects:\\n all_solutions = self.score_solution_by_distance(all_solutions, placed_objects[target_floor_object_name])\\n # order solutions by distance to target floor object\\n all_solutions = sorted(all_solutions, key=lambda x: x[-1], reverse=True)\\n return all_solutions\\n\\n\\n def create_grids(self, room_poly):\\n # Get the coordinates of the polygon\\n poly_coords = list(room_poly.exterior.coords)\\n\\n grid_points = []\\n # Iterate over each pair of points (edges of the polygon)\\n for i in range(len(poly_coords) - 1):\\n line = LineString([poly_coords[i], poly_coords[i + 1]])\\n line_length = line.length\\n\\n # Create points along the edge at intervals of grid size\\n for j in range(0, int(line_length), self.grid_size):\\n point_on_line = substring(line, j, j) # Get a point at distance j from the start of the line\\n if point_on_line:\\n grid_points.append((point_on_line.x, point_on_line.y))\\n \\n return grid_points\\n \\n\\n def get_all_solutions(self, room_poly, grid_points, object_dim, height):\\n obj_length, obj_height, obj_width = object_dim\\n obj_half_length = obj_length / 2\\n\\n rotation_adjustments = {\\n 0: ((-obj_half_length, 0), (obj_half_length, obj_width)),\\n 90: ((0, -obj_half_length), (obj_width, obj_half_length)),\\n 180: ((-obj_half_length, -obj_width), (obj_half_length, 0)),\\n 270: ((-obj_width, -obj_half_length), (0, obj_half_length))\\n }\\n\\n solutions = []\\n for rotation in [0, 90, 180, 270]:\\n for point in grid_points:\\n center_x, center_y = point\\n lower_left_adjustment, upper_right_adjustment = rotation_adjustments[rotation]\\n lower_left = (center_x + lower_left_adjustment[0], center_y + lower_left_adjustment[1])\\n upper_right = (center_x + upper_right_adjustment[0], center_y + upper_right_adjustment[1])\\n obj_box = box(*lower_left, *upper_right)\\n\\n if room_poly.contains(obj_box):\\n object_coords = obj_box.exterior.coords[:]\\n coordinates_on_edge = [coord for coord in object_coords if room_poly.boundary.contains(Point(coord))]\\n coordinates_on_edge = list(set(coordinates_on_edge))\\n if len(coordinates_on_edge) >= 2:\\n vertex_min = (lower_left[0], height, lower_left[1])\\n vertex_max = (upper_right[0], height + obj_height, upper_right[1])\\n\\n solutions.append([vertex_min, vertex_max, rotation, tuple(obj_box.exterior.coords[:]), 1])\\n \\n return solutions\\n \\n\\n def filter_collision(self, placed_objects, solutions):\\n def intersect_3d(box1, box2):\\n # box1 and box2 are dictionaries with 'min' and 'max' keys,\\n # which are tuples representing the minimum and maximum corners of the 3D box.\\n for i in range(3):\\n if box1['max'][i] < box2['min'][i] or box1['min'][i] > box2['max'][i]:\\n return False\\n return True\\n\\n valid_solutions = []\\n boxes = [{\\\"min\\\": vertex_min, \\\"max\\\": vertex_max} for vertex_min, vertex_max, rotation, box_coords, path_weight in placed_objects.values()]\\n\\n for solution in solutions:\\n for box in boxes:\\n if intersect_3d(box, {\\\"min\\\": solution[0], \\\"max\\\": solution[1]}):\\n break\\n else:\\n valid_solutions.append(solution)\\n \\n return valid_solutions\\n \\n\\n def score_solution_by_distance(self, solutions, target_object):\\n distances = []\\n scored_solutions = []\\n for solution in solutions:\\n center_x, center_y, center_z = (solution[0][0]+solution[1][0])/2, (solution[0][1]+solution[1][1])/2, (solution[0][2]+solution[1][2])/2\\n target_x, target_y, target_z = (target_object[0][0]+target_object[1][0])/2, (target_object[0][1]+target_object[1][1])/2, (target_object[0][2]+target_object[1][2])/2\\n distance = np.sqrt((center_x - target_x)**2 + (center_y - target_y)**2 + (center_z - target_z)**2)\\n distances.append(distance)\\n scored_solution = solution.copy()\\n scored_solution[-1] = solution[-1] + self.constraint_bouns * (1/distance)\\n scored_solutions.append(scored_solution)\\n return scored_solutions\\n \\n \\n def visualize_grid(self, room_poly, grid_points, solutions):\\n # create a new figure\\n fig, ax = plt.subplots()\\n\\n # draw the room\\n x, y = room_poly.exterior.xy\\n ax.plot(x, y, 'b-', label='Room')\\n\\n # draw the grid points\\n grid_x = [point[0] for point in grid_points]\\n grid_y = [point[1] for point in grid_points]\\n ax.plot(grid_x, grid_y, 'ro', markersize=2)\\n\\n # draw the solutions\\n for object_name, solution in solutions.items():\\n vertex_min, vertex_max, rotation, box_coords = solution[:-1]\\n center_x, center_y = (vertex_min[0]+vertex_max[0])/2, (vertex_min[2]+vertex_max[2])/2\\n\\n # create a polygon for the solution\\n obj_poly = Polygon(box_coords)\\n x, y = obj_poly.exterior.xy\\n ax.plot(x, y, 'g-', linewidth=2)\\n\\n ax.text(center_x, center_y, object_name, fontsize=12, ha='center')\\n\\n # set arrow direction based on rotation\\n if rotation == 0:\\n ax.arrow(center_x, center_y, 0, 25, head_width=10, fc='g')\\n elif rotation == 90:\\n ax.arrow(center_x, center_y, 25, 0, head_width=10, fc='g')\\n elif rotation == 180:\\n ax.arrow(center_x, center_y, 0, -25, head_width=10, fc='g')\\n elif rotation == 270:\\n ax.arrow(center_x, center_y, -25, 0, head_width=10, fc='g')\\n\\n ax.set_aspect('equal', 'box') # to keep the ratios equal along x and y axis\\n plt.show()\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import re\nimport copy\nimport json\nimport torch\nimport random\nimport multiprocessing\nimport torch.nn.functional as F\nimport modules.prompts as prompts\nfrom typing import Dict\nfrom colorama import Fore\nfrom shapely import Polygon\nfrom langchain import PromptTemplate\nfrom modules.floor_objects import DFS_Solver_Floor\nfrom modules.wall_objects import DFS_Solver_Wall"},"token_num":{"kind":"number","value":16351,"string":"16,351"},"cropped_code":{"kind":"string","value":" valid_candidates = []\n for candidate in candidates:\n dimension = self.database[candidate[0]][\"assetMetadata\"][\"boundingBox\"]\n size = [dimension[\"x\"], dimension[\"y\"], dimension[\"z\"]]\n if size[2] > size[0]: size = [size[2], size[1], size[0]] # make sure that x > z\n\n if size[0] > room_size[0] * self.object_size_tolerance: continue\n if size[1] > room_size[1] * self.object_size_tolerance: continue\n if size[2] > room_size[2] * self.object_size_tolerance: continue\n if size[0] * size[2] > room_size[0] * room_size[2] * 0.5: continue # TODO: consider using the floor area instead of the room area\n\n valid_candidates.append(candidate)\n\n return valid_candidates\n \n\n def check_thin_object(self, candidates):\n valid_candidates = []\n for candidate in candidates:\n dimension = self.database[candidate[0]][\"assetMetadata\"][\"boundingBox\"]\n size = [dimension[\"x\"], dimension[\"y\"], dimension[\"z\"]]\n if size[2] > min(size[0], size[1]) * self.thin_threshold: continue\n valid_candidates.append(candidate)\n return valid_candidates\n\n\n def random_select(self, candidates):\n if self.random_selection:\n selected_candidate = random.choice(candidates)\n else:\n scores = [candidate[1] for candidate in candidates]\n scores_tensor = torch.Tensor(scores)\n probas = F.softmax(scores_tensor, dim=0) # TODO: consider using normalized scores\n selected_index = torch.multinomial(probas, 1).item()\n selected_candidate = candidates[selected_index]\n return selected_candidate\n\n\n def update_floor_capacity(self, room2floor_capacity, scene):\n for room in scene[\"rooms\"]:\n room_vertices = room[\"vertices\"]\n room_poly = Polygon(room_vertices)\n for door in scene[\"doors\"]:\n for door_vertices in door[\"doorBoxes\"]:\n door_poly = Polygon(door_vertices)\n door_center = door_poly.centroid\n door_area = door_poly.area\n if room_poly.contains(door_center):\n room2floor_capacity[room[\"id\"]][1] += door_area * 0.6\n \n if scene[\"open_walls\"] != []:\n for open_wall_vertices in scene[\"open_walls\"][\"openWallBoxes\"]:\n open_wall_poly = Polygon(open_wall_vertices)\n open_wall_center = open_wall_poly.centroid\n if room_poly.contains(open_wall_center):\n room2floor_capacity[room[\"id\"]][1] += open_wall_poly.area * 0.6\n \n return room2floor_capacity\n \n\n def update_wall_capacity(self, room2wall_capacity, scene):\n for room in scene[\"rooms\"]:\n room_vertices = room[\"vertices\"]\n room_poly = Polygon(room_vertices)\n for window in scene[\"windows\"]:\n for window_vertices in window[\"windowBoxes\"]:\n window_poly = Polygon(window_vertices)\n window_center = window_poly.centroid\n window_x = window_poly.bounds[2] - window_poly.bounds[0]\n window_y = window_poly.bounds[3] - window_poly.bounds[1]\n window_width = max(window_x, window_y)\n if room_poly.contains(window_center):\n room2wall_capacity[room[\"id\"]][1] += window_width * 0.6\n \n if scene[\"open_walls\"] != []:\n for open_wall_vertices in scene[\"open_walls\"][\"openWallBoxes\"]:\n open_wall_poly = Polygon(open_wall_vertices)\n open_wall_center = open_wall_poly.centroid\n open_wall_x = open_wall_poly.bounds[2] - open_wall_poly.bounds[0]\n open_wall_y = open_wall_poly.bounds[3] - open_wall_poly.bounds[1]\n open_wall_width = max(open_wall_x, open_wall_y)\n if room_poly.contains(open_wall_center):\n room2wall_capacity[room[\"id\"]][1] += open_wall_width * 0.6\n \n return room2wall_capacity\n\n \n def check_floor_placement(self, candidates, room_vertices, scene):\n room_x = max([vertex[0] for vertex in room_vertices]) - min([vertex[0] for vertex in room_vertices])\n room_z = max([vertex[1] for vertex in room_vertices]) - min([vertex[1] for vertex in room_vertices])\n grid_size = int(max(room_x // 20, room_z // 20))\n \n solver = DFS_Solver_Floor(grid_size=grid_size)\n\n room_poly = Polygon(room_vertices)\n initial_state = self.get_initial_state_floor(room_vertices, scene, add_window=False)\n\n grid_points = solver.create_grids(room_poly)\n grid_points = solver.remove_points(grid_points, initial_state)\n\n valid_candidates = []\n for candidate in candidates:\n object_size = self.database[candidate[0]][\"assetMetadata\"][\"boundingBox\"]\n object_dim = (object_size[\"x\"]*100 + self.size_buffer, object_size[\"z\"]*100 + self.size_buffer)\n\n solutions = solver.get_all_solutions(room_poly, grid_points, object_dim)\n solutions = solver.filter_collision(initial_state, solutions)\n solutions = solver.place_edge(room_poly, solutions, object_dim)\n\n if solutions != []: valid_candidates.append(candidate)\n else: print(f\"Floor Object {candidate[0]} (size: {object_dim}) cannot be placed in room\"); continue\n\n return valid_candidates\n\n\n def check_wall_placement(self, candidates, room_vertices, scene):\n room_x = max([vertex[0] for vertex in room_vertices]) - min([vertex[0] for vertex in room_vertices])\n room_z = max([vertex[1] for vertex in room_vertices]) - min([vertex[1] for vertex in room_vertices])\n grid_size = int(max(room_x // 20, room_z // 20))\n\n"},"all_code":{"kind":"string","value":"\n\nclass ObjectSelector:\n def __init__(self, object_retriever, llm):\n # object retriever\n self.object_retriever = object_retriever\n self.database = object_retriever.database\n\n # language model and prompt templates\n self.llm = llm\n self.object_selection_template_1 = prompts.object_selection_prompt_new_1\n self.object_selection_template_2 = PromptTemplate(input_variables=[\"object_selection_prompt_new_1\", \"object_selection_1\", \"room\"], template=prompts.object_selection_prompt_new_2)\n\n # hyperparameters\n self.floor_capacity_ratio = 0.4\n self.wall_capacity_ratio = 0.5\n self.object_size_tolerance = 0.8\n self.similarity_threshold_floor = 31 # need to be tuned\n self.similarity_threshold_wall = 31 # need to be tuned\n self.thin_threshold = 3\n self.used_assets = []\n self.consider_size = True\n self.size_buffer = 10\n\n self.random_selection = False\n self.reuse_selection = False\n self.multiprocessing = True\n\n\n def select_objects(self, scene, additional_requirements=\"N/A\"):\n rooms_types = [room[\"roomType\"] for room in scene[\"rooms\"]]\n room2area = {room[\"roomType\"]: self.get_room_area(room) for room in scene[\"rooms\"]}\n room2size = {room[\"roomType\"]: self.get_room_size(room, scene[\"wall_height\"]) for room in scene[\"rooms\"]}\n room2perimeter = {room[\"roomType\"]: self.get_room_perimeter(room) for room in scene[\"rooms\"]}\n room2vertices = {room[\"roomType\"]: [(x * 100, y * 100) for (x, y) in room[\"vertices\"]] for room in scene[\"rooms\"]}\n\n room2floor_capacity = {room_type: [room_area * self.floor_capacity_ratio, 0] for room_type, room_area in room2area.items()}\n room2floor_capacity = self.update_floor_capacity(room2floor_capacity, scene)\n room2wall_capacity = {room_type: [room_perimeter * self.wall_capacity_ratio, 0] for room_type, room_perimeter in room2perimeter.items()}\n selected_objects = {room[\"roomType\"]: {\"floor\": [], \"wall\": []} for room in scene[\"rooms\"]}\n\n if \"object_selection_plan\" in scene:\n object_selection_plan = scene[\"object_selection_plan\"]\n if self.reuse_selection:\n selected_objects = scene[\"selected_objects\"]\n else:\n for room_type in rooms_types:\n floor_objects, _, wall_objects, _ = self.get_objects_by_room(object_selection_plan[room_type], scene, room2size[room_type], room2floor_capacity[room_type], room2wall_capacity[room_type], room2vertices[room_type])\n selected_objects[room_type][\"floor\"] = floor_objects\n selected_objects[room_type][\"wall\"] = wall_objects\n else:\n object_selection_plan = {room[\"roomType\"]: [] for room in scene[\"rooms\"]}\n packed_args = [(room_type, scene, additional_requirements, room2size, room2floor_capacity, room2wall_capacity, room2vertices) for room_type in rooms_types]\n\n if self.multiprocessing:\n pool = multiprocessing.Pool(processes=4)\n results = pool.map(self.plan_room, packed_args)\n pool.close()\n pool.join()\n else:\n results = [self.plan_room(args) for args in packed_args]\n\n for room_type, result in results:\n selected_objects[room_type][\"floor\"] = result[\"floor\"]\n selected_objects[room_type][\"wall\"] = result[\"wall\"]\n object_selection_plan[room_type] = result[\"plan\"]\n \n print(f\"\\n{Fore.GREEN}AI: Here is the object selection plan:\\n{object_selection_plan}{Fore.RESET}\")\n return object_selection_plan, selected_objects\n \n\n def plan_room(self, args):\n room_type, scene, additional_requirements, room2size, room2floor_capacity, room2wall_capacity, room2vertices = args\n print(f\"\\n{Fore.GREEN}AI: Selecting objects for {room_type}...{Fore.RESET}\\n\")\n \n result = {}\n room_size_str = f\"{int(room2size[room_type][0])*100}cm in length, {int(room2size[room_type][1])*100}cm in width, {int(room2size[room_type][2])*100}cm in height\"\n \n prompt_1 = self.object_selection_template_1.replace(\"INPUT\", scene[\"query\"]).replace(\"ROOM_TYPE\", room_type).replace(\"ROOM_SIZE\", room_size_str).replace(\"REQUIREMENTS\", additional_requirements)\n # print(f\"\\nUser: {prompt_1}\\n\")\n \n output_1 = self.llm(prompt_1).lower()\n plan_1 = self.extract_json(output_1)\n\n if plan_1 is None:\n print(f\"Error while extracting the JSON for {room_type}.\")\n return result\n\n floor_objects, floor_capacity, wall_objects, wall_capacity = self.get_objects_by_room(plan_1, scene, room2size[room_type], room2floor_capacity[room_type], room2wall_capacity[room_type], room2vertices[room_type])\n\n if floor_capacity[1] / floor_capacity[0] >= 0.8:\n result[\"floor\"] = floor_objects\n result[\"wall\"] = wall_objects\n result[\"plan\"] = plan_1\n else:\n print(f\"{Fore.RED}AI: The floor capacity of {room_type} is {floor_capacity[1]}m^2, which is less than 70% of the total floor capacity {floor_capacity[0]}m^2.{Fore.RESET}\")\n prompt_2 = self.object_selection_template_2.format(object_selection_prompt_new_1=prompt_1, object_selection_1=output_1, room=room_type)\n output_2 = self.llm(prompt_2).lower()\n plan_2 = self.extract_json(output_2)\n\n new_plan = copy.deepcopy(plan_1)\n for object in plan_2: new_plan[object] = plan_2[object]\n \n floor_objects, _, wall_objects, _ = self.get_objects_by_room(new_plan, scene, room2size[room_type], room2floor_capacity[room_type], room2wall_capacity[room_type], room2vertices[room_type])\n result[\"floor\"] = floor_objects\n result[\"wall\"] = wall_objects\n result[\"plan\"] = new_plan\n\n return room_type, result\n \n \n def extract_json(self, input_string):\n # Using regex to identify the JSON structure in the string\n json_match = re.search(r'{.*}', input_string, re.DOTALL)\n if json_match:\n extracted_json = json_match.group(0)\n try:\n # Convert the extracted JSON string into a Python dictionary\n json_dict = json.loads(extracted_json)\n json_dict = self.check_dict(json_dict)\n return json_dict\n except json.JSONDecodeError:\n print(input_string)\n print(\"Error while decoding the JSON.\")\n return None\n else:\n print(\"No valid JSON found.\")\n return None\n \n\n def check_dict(self, dict):\n valid = True\n attributes = [\"description\", \"location\", \"size\", \"quantity\", \"variance_type\", \"objects_on_top\"]\n for key, value in dict.items():\n if not isinstance(key, str): valid = False; break\n\n if not isinstance(value, Dict): valid = False; break\n\n for attribute in attributes:\n if attribute not in value: valid = False; break\n \n if not isinstance(value[\"description\"], str): valid = False; break\n\n if value[\"location\"] not in [\"floor\", \"wall\"]: dict[key][\"location\"] = \"floor\"\n\n if not isinstance(value[\"size\"], list) or len(value[\"size\"]) != 3 or not all(isinstance(i, int) for i in value[\"size\"]): dict[key][\"size\"] = None\n\n if not isinstance(value[\"quantity\"], int): dict[key][\"quantity\"] = 1\n\n if not isinstance(value[\"variance_type\"], str) or value[\"variance_type\"] not in [\"same\", \"varied\"]: dict[key][\"variance_type\"] = \"same\"\n\n if not isinstance(value[\"objects_on_top\"], list): dict[key][\"objects_on_top\"] = []\n\n for i, child in enumerate(value[\"objects_on_top\"]):\n if not isinstance(child, Dict): valid = False; break\n \n for attribute in [\"object_name\", \"quantity\", \"variance_type\"]:\n if attribute not in child: valid = False; break\n \n if not isinstance(child[\"object_name\"], str): valid = False; break\n\n if not isinstance(child[\"quantity\"], int): dict[key][\"objects_on_top\"][i][\"quantity\"] = 1\n\n if not isinstance(child[\"variance_type\"], str) or child[\"variance_type\"] not in [\"same\", \"varied\"]: dict[key][\"objects_on_top\"][i][\"variance_type\"] = \"same\"\n\n if not valid: return None\n else: return dict\n \n\n def get_objects_by_room(self, parsed_plan, scene, room_size, floor_capacity, wall_capacity, vertices):\n # get the floor and wall objects\n floor_object_list = []\n wall_object_list = []\n for object_name, object_info in parsed_plan.items():\n object_info[\"object_name\"] = object_name\n if object_info[\"location\"] == \"floor\": floor_object_list.append(object_info)\n else: wall_object_list.append(object_info)\n \n floor_objects, floor_capacity = self.get_floor_objects(floor_object_list, floor_capacity, room_size, vertices, scene)\n wall_objects, wall_capacity = self.get_wall_objects(wall_object_list, wall_capacity, room_size, vertices, scene)\n\n return floor_objects, floor_capacity, wall_objects, wall_capacity\n\n \n def get_room_size(self, room, wall_height):\n floor_polygon = room[\"floorPolygon\"]\n x_values = [point['x'] for point in floor_polygon]\n z_values = [point['z'] for point in floor_polygon]\n x_dim = max(x_values) - min(x_values)\n z_dim = max(z_values) - min(z_values)\n\n if x_dim > z_dim: return (x_dim, wall_height, z_dim)\n else: return (z_dim, wall_height, x_dim)\n\n\n def get_room_area(self, room):\n room_vertices = room[\"vertices\"]\n room_polygon = Polygon(room_vertices)\n return room_polygon.area\n \n\n def get_room_perimeter(self, room):\n room_vertices = room[\"vertices\"]\n room_polygon = Polygon(room_vertices)\n return room_polygon.length\n\n\n def get_floor_objects(self, floor_object_list, floor_capacity, room_size, room_vertices, scene):\n selected_floor_objects_all = []\n for floor_object in floor_object_list:\n object_type = floor_object[\"object_name\"]\n object_description = floor_object[\"description\"]\n object_size = floor_object[\"size\"]\n quantity = min(floor_object[\"quantity\"], 10)\n variance_type = floor_object[\"variance_type\"]\n\n candidates = self.object_retriever.retrieve([f\"a 3D model of {object_type}, {object_description}\"], self.similarity_threshold_floor)\n\n # check on floor objects\n candidates = [candidate for candidate in candidates if self.database[candidate[0]][\"annotations\"][\"onFloor\"] == True] # only select objects on the floor\n candidates = [candidate for candidate in candidates if self.database[candidate[0]][\"annotations\"][\"onCeiling\"] == False] # only select objects not on the ceiling\n \n # ignore doors and windows and frames\n candidates = [candidate for candidate in candidates if \"door\" not in self.database[candidate[0]][\"annotations\"][\"category\"].lower()]\n candidates = [candidate for candidate in candidates if \"window\" not in self.database[candidate[0]][\"annotations\"][\"category\"].lower()]\n candidates = [candidate for candidate in candidates if \"frame\" not in self.database[candidate[0]][\"annotations\"][\"category\"].lower()]\n\n # check if the object is too big\n candidates = self.check_object_size(candidates, room_size)\n\n # check if object can be placed on the floor\n candidates = self.check_floor_placement(candidates[:20], room_vertices, scene)\n\n # No candidates found\n if len(candidates) == 0: print(\"No candidates found for {} {}\".format(object_type, object_description)); continue\n\n # remove used assets\n top_one_candidate = candidates[0]\n if len(candidates) > 1: candidates = [candidate for candidate in candidates if candidate[0] not in self.used_assets]\n if len(candidates) == 0: candidates = [top_one_candidate]\n \n # consider object size difference\n if object_size is not None and self.consider_size:\n candidates = self.object_retriever.compute_size_difference(object_size, candidates)\n\n candidates = candidates[:10] # only select top 10 candidates\n\n selected_asset_ids = []\n if variance_type == \"same\":\n selected_candidate = self.random_select(candidates)\n selected_asset_id = selected_candidate[0]\n selected_asset_ids = [selected_asset_id] * quantity\n\n elif variance_type == \"varied\":\n for i in range(quantity):\n selected_candidate = self.random_select(candidates)\n selected_asset_id = selected_candidate[0]\n selected_asset_ids.append(selected_asset_id)\n if len(candidates) > 1: candidates.remove(selected_candidate)\n\n for i in range(quantity):\n selected_asset_id = selected_asset_ids[i]\n object_name = f\"{object_type}-{i}\"\n selected_floor_objects_all.append((object_name, selected_asset_id))\n\n # reselect objects if they exceed floor capacity, consider the diversity of objects\n selected_floor_objects = []\n while True:\n if len(selected_floor_objects_all) == 0: break\n current_selected_asset_ids = []\n current_number_of_objects = len(selected_floor_objects)\n for object_name, selected_asset_id in selected_floor_objects_all:\n if selected_asset_id not in current_selected_asset_ids:\n selected_asset_size = self.database[selected_asset_id][\"assetMetadata\"][\"boundingBox\"]\n selected_asset_capacity = selected_asset_size[\"x\"] * selected_asset_size[\"z\"]\n if floor_capacity[1] + selected_asset_capacity > floor_capacity[0] and len(selected_floor_objects) > 0:\n print(f\"{object_type} {object_description} exceeds floor capacity\")\n else:\n current_selected_asset_ids.append(selected_asset_id)\n selected_floor_objects.append((object_name, selected_asset_id))\n selected_floor_objects_all.remove((object_name, selected_asset_id))\n floor_capacity = (floor_capacity[0], floor_capacity[1] + selected_asset_capacity)\n if len(selected_floor_objects) == current_number_of_objects: print(\"No more objects can be added\"); break\n \n # sort objects by object type\n object_type2objects = {}\n for object_name, selected_asset_id in selected_floor_objects:\n object_type = object_name.split(\"-\")[0]\n if object_type not in object_type2objects: object_type2objects[object_type] = []\n object_type2objects[object_type].append((object_name, selected_asset_id))\n \n selected_floor_objects_ordered = []\n for object_type in object_type2objects:\n selected_floor_objects_ordered += sorted(object_type2objects[object_type])\n\n return selected_floor_objects_ordered, floor_capacity\n \n\n def get_wall_objects(self, wall_object_list, wall_capacity, room_size, room_vertices, scene):\n selected_wall_objects_all = []\n for wall_object in wall_object_list:\n object_type = wall_object[\"object_name\"]\n object_description = wall_object[\"description\"]\n object_size = wall_object[\"size\"]\n quantity = min(wall_object[\"quantity\"], 10)\n variance_type = wall_object[\"variance_type\"]\n\n candidates = self.object_retriever.retrieve([f\"a 3D model of {object_type}, {object_description}\"], self.similarity_threshold_wall)\n\n # check on wall objects\n candidates = [candidate for candidate in candidates if self.database[candidate[0]][\"annotations\"][\"onWall\"] == True] # only select objects on the wall\n\n # ignore doors and windows\n candidates = [candidate for candidate in candidates if \"door\" not in self.database[candidate[0]][\"annotations\"][\"category\"].lower()]\n candidates = [candidate for candidate in candidates if \"window\" not in self.database[candidate[0]][\"annotations\"][\"category\"].lower()]\n\n # check if the object is too big\n candidates = self.check_object_size(candidates, room_size)\n\n # check thin objects\n candidates = self.check_thin_object(candidates)\n\n # check if object can be placed on the wall\n candidates = self.check_wall_placement(candidates[:20], room_vertices, scene)\n\n if len(candidates) == 0: print(\"No candidates found for {} {}\".format(object_type, object_description)); continue\n\n # remove used assets\n top_one_candidate = candidates[0]\n if len(candidates) > 1: candidates = [candidate for candidate in candidates if candidate[0] not in self.used_assets]\n if len(candidates) == 0: candidates = [top_one_candidate]\n\n # consider object size difference\n if object_size is not None and self.consider_size:\n candidates = self.object_retriever.compute_size_difference(object_size, candidates)\n\n candidates = candidates[:10] # only select top 10 candidates\n\n selected_asset_ids = []\n if variance_type == \"same\":\n selected_candidate = self.random_select(candidates)\n selected_asset_id = selected_candidate[0]\n selected_asset_ids = [selected_asset_id] * quantity\n\n elif variance_type == \"varied\":\n for i in range(quantity):\n selected_candidate = self.random_select(candidates)\n selected_asset_id = selected_candidate[0]\n selected_asset_ids.append(selected_asset_id)\n if len(candidates) > 1: candidates.remove(selected_candidate)\n\n for i in range(quantity):\n selected_asset_id = selected_asset_ids[i]\n object_name = f\"{object_type}-{i}\"\n selected_wall_objects_all.append((object_name, selected_asset_id))\n\n # reselect objects if they exceed wall capacity, consider the diversity of objects\n selected_wall_objects = []\n while True:\n if len(selected_wall_objects_all) == 0: break\n current_selected_asset_ids = []\n current_number_of_objects = len(selected_wall_objects)\n for object_name, selected_asset_id in selected_wall_objects_all:\n if selected_asset_id not in current_selected_asset_ids:\n selected_asset_size = self.database[selected_asset_id][\"assetMetadata\"][\"boundingBox\"]\n selected_asset_capacity = selected_asset_size[\"x\"]\n if wall_capacity[1] + selected_asset_capacity > wall_capacity[0] and len(selected_wall_objects) > 0:\n print(f\"{object_type} {object_description} exceeds wall capacity\")\n else:\n current_selected_asset_ids.append(selected_asset_id)\n selected_wall_objects.append((object_name, selected_asset_id))\n selected_wall_objects_all.remove((object_name, selected_asset_id))\n wall_capacity = (wall_capacity[0], wall_capacity[1] + selected_asset_capacity)\n if len(selected_wall_objects) == current_number_of_objects: print(\"No more objects can be added\"); break\n \n # sort objects by object type\n object_type2objects = {}\n for object_name, selected_asset_id in selected_wall_objects:\n object_type = object_name.split(\"-\")[0]\n if object_type not in object_type2objects: object_type2objects[object_type] = []\n object_type2objects[object_type].append((object_name, selected_asset_id))\n \n selected_wall_objects_ordered = []\n for object_type in object_type2objects:\n selected_wall_objects_ordered += sorted(object_type2objects[object_type])\n \n return selected_wall_objects_ordered, wall_capacity\n\n\n def check_object_size(self, candidates, room_size):\n valid_candidates = []\n for candidate in candidates:\n dimension = self.database[candidate[0]][\"assetMetadata\"][\"boundingBox\"]\n size = [dimension[\"x\"], dimension[\"y\"], dimension[\"z\"]]\n if size[2] > size[0]: size = [size[2], size[1], size[0]] # make sure that x > z\n\n if size[0] > room_size[0] * self.object_size_tolerance: continue\n if size[1] > room_size[1] * self.object_size_tolerance: continue\n if size[2] > room_size[2] * self.object_size_tolerance: continue\n if size[0] * size[2] > room_size[0] * room_size[2] * 0.5: continue # TODO: consider using the floor area instead of the room area\n\n valid_candidates.append(candidate)\n\n return valid_candidates\n \n\n def check_thin_object(self, candidates):\n valid_candidates = []\n for candidate in candidates:\n dimension = self.database[candidate[0]][\"assetMetadata\"][\"boundingBox\"]\n size = [dimension[\"x\"], dimension[\"y\"], dimension[\"z\"]]\n if size[2] > min(size[0], size[1]) * self.thin_threshold: continue\n valid_candidates.append(candidate)\n return valid_candidates\n\n\n def random_select(self, candidates):\n if self.random_selection:\n selected_candidate = random.choice(candidates)\n else:\n scores = [candidate[1] for candidate in candidates]\n scores_tensor = torch.Tensor(scores)\n probas = F.softmax(scores_tensor, dim=0) # TODO: consider using normalized scores\n selected_index = torch.multinomial(probas, 1).item()\n selected_candidate = candidates[selected_index]\n return selected_candidate\n\n\n def update_floor_capacity(self, room2floor_capacity, scene):\n for room in scene[\"rooms\"]:\n room_vertices = room[\"vertices\"]\n room_poly = Polygon(room_vertices)\n for door in scene[\"doors\"]:\n for door_vertices in door[\"doorBoxes\"]:\n door_poly = Polygon(door_vertices)\n door_center = door_poly.centroid\n door_area = door_poly.area\n if room_poly.contains(door_center):\n room2floor_capacity[room[\"id\"]][1] += door_area * 0.6\n \n if scene[\"open_walls\"] != []:\n for open_wall_vertices in scene[\"open_walls\"][\"openWallBoxes\"]:\n open_wall_poly = Polygon(open_wall_vertices)\n open_wall_center = open_wall_poly.centroid\n if room_poly.contains(open_wall_center):\n room2floor_capacity[room[\"id\"]][1] += open_wall_poly.area * 0.6\n \n return room2floor_capacity\n \n\n def update_wall_capacity(self, room2wall_capacity, scene):\n for room in scene[\"rooms\"]:\n room_vertices = room[\"vertices\"]\n room_poly = Polygon(room_vertices)\n for window in scene[\"windows\"]:\n for window_vertices in window[\"windowBoxes\"]:\n window_poly = Polygon(window_vertices)\n window_center = window_poly.centroid\n window_x = window_poly.bounds[2] - window_poly.bounds[0]\n window_y = window_poly.bounds[3] - window_poly.bounds[1]\n window_width = max(window_x, window_y)\n if room_poly.contains(window_center):\n room2wall_capacity[room[\"id\"]][1] += window_width * 0.6\n \n if scene[\"open_walls\"] != []:\n for open_wall_vertices in scene[\"open_walls\"][\"openWallBoxes\"]:\n open_wall_poly = Polygon(open_wall_vertices)\n open_wall_center = open_wall_poly.centroid\n open_wall_x = open_wall_poly.bounds[2] - open_wall_poly.bounds[0]\n open_wall_y = open_wall_poly.bounds[3] - open_wall_poly.bounds[1]\n open_wall_width = max(open_wall_x, open_wall_y)\n if room_poly.contains(open_wall_center):\n room2wall_capacity[room[\"id\"]][1] += open_wall_width * 0.6\n \n return room2wall_capacity\n\n \n def check_floor_placement(self, candidates, room_vertices, scene):\n room_x = max([vertex[0] for vertex in room_vertices]) - min([vertex[0] for vertex in room_vertices])\n room_z = max([vertex[1] for vertex in room_vertices]) - min([vertex[1] for vertex in room_vertices])\n grid_size = int(max(room_x // 20, room_z // 20))\n \n solver = DFS_Solver_Floor(grid_size=grid_size)\n\n room_poly = Polygon(room_vertices)\n initial_state = self.get_initial_state_floor(room_vertices, scene, add_window=False)\n\n grid_points = solver.create_grids(room_poly)\n grid_points = solver.remove_points(grid_points, initial_state)\n\n valid_candidates = []\n for candidate in candidates:\n object_size = self.database[candidate[0]][\"assetMetadata\"][\"boundingBox\"]\n object_dim = (object_size[\"x\"]*100 + self.size_buffer, object_size[\"z\"]*100 + self.size_buffer)\n\n solutions = solver.get_all_solutions(room_poly, grid_points, object_dim)\n solutions = solver.filter_collision(initial_state, solutions)\n solutions = solver.place_edge(room_poly, solutions, object_dim)\n\n if solutions != []: valid_candidates.append(candidate)\n else: print(f\"Floor Object {candidate[0]} (size: {object_dim}) cannot be placed in room\"); continue\n\n return valid_candidates\n\n\n def check_wall_placement(self, candidates, room_vertices, scene):\n room_x = max([vertex[0] for vertex in room_vertices]) - min([vertex[0] for vertex in room_vertices])\n room_z = max([vertex[1] for vertex in room_vertices]) - min([vertex[1] for vertex in room_vertices])\n grid_size = int(max(room_x // 20, room_z // 20))\n"},"next_line":{"kind":"string","value":" solver = DFS_Solver_Wall(grid_size=grid_size)"},"gold_snippet_index":{"kind":"number","value":1,"string":"1"},"created_at":{"kind":"string","value":"2023-12-08 19:19:57+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5285,"cells":{"repo_name":{"kind":"string","value":"modelscope/richdreamer"},"file_path":{"kind":"string","value":"threestudio/models/geometry/tetrahedra_sdf_grid.py"},"context":{"kind":"list like","value":[{"identifier":"BaseExplicitGeometry","path":"threestudio/models/geometry/base.py","snippet":"class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Float[Tensor, \"2 3\"]\n self.register_buffer(\n \"bbox\",\n torch.as_tensor(\n [\n [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],\n [self.cfg.radius, self.cfg.radius, self.cfg.radius],\n ],\n dtype=torch.float32,\n ),\n )"},{"identifier":"BaseGeometry","path":"threestudio/models/geometry/base.py","snippet":"class BaseGeometry(BaseModule):\n @dataclass\n class Config(BaseModule.Config):\n pass\n\n cfg: Config\n\n @staticmethod\n def create_from(\n other: \"BaseGeometry\", cfg: Optional[Union[dict, DictConfig]] = None, **kwargs\n ) -> \"BaseGeometry\":\n raise TypeError(\n f\"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}\"\n )\n\n def export(self, *args, **kwargs) -> Dict[str, Any]:\n return {}"},{"identifier":"contract_to_unisphere","path":"threestudio/models/geometry/base.py","snippet":"def contract_to_unisphere(\n x: Float[Tensor, \"... 3\"], bbox: Float[Tensor, \"2 3\"], unbounded: bool = False\n) -> Float[Tensor, \"... 3\"]:\n if unbounded:\n x = scale_tensor(x, bbox, (0, 1))\n x = x * 2 - 1 # aabb is at [-1, 1]\n mag = x.norm(dim=-1, keepdim=True)\n mask = mag.squeeze(-1) > 1\n x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask])\n x = x / 4 + 0.5 # [-inf, inf] is at [0, 1]\n else:\n x = scale_tensor(x, bbox, (0, 1))\n return x"},{"identifier":"ImplicitSDF","path":"threestudio/models/geometry/implicit_sdf.py","snippet":"class ImplicitSDF(BaseImplicitGeometry):\n @dataclass\n class Config(BaseImplicitGeometry.Config):\n n_input_dims: int = 3\n n_feature_dims: int = 3\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n normal_type: Optional[\n str\n ] = \"finite_difference\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\n finite_difference_normal_eps: Union[\n float, str\n ] = 0.01 # in [float, \"progressive\"]\n shape_init: Optional[str] = None\n shape_init_params: Optional[Any] = None\n shape_init_mesh_up: str = \"+z\"\n shape_init_mesh_front: str = \"+x\"\n force_shape_init: bool = False\n sdf_bias: Union[float, str] = 0.0\n sdf_bias_params: Optional[Any] = None\n\n # no need to removal outlier for SDF\n isosurface_remove_outliers: bool = False\n\n cfg: Config\n\n def configure(self) -> None:\n super().configure()\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.sdf_network = get_mlp(\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\n )\n\n if self.cfg.n_feature_dims > 0:\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n\n if self.cfg.normal_type == \"pred\":\n self.normal_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n if self.cfg.isosurface_deformable_grid:\n assert (\n self.cfg.isosurface_method == \"mt\"\n ), \"isosurface_deformable_grid only works with mt\"\n self.deformation_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n\n self.finite_difference_normal_eps: Optional[float] = None\n\n def initialize_shape(self) -> None:\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\n return\n\n # do not initialize shape if weights are provided\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\n return\n\n if self.cfg.sdf_bias != 0.0:\n threestudio.warn(\n \"shape_init and sdf_bias are both specified, which may lead to unexpected results.\"\n )\n\n get_gt_sdf: Callable[[Float[Tensor, \"N 3\"]], Float[Tensor, \"N 1\"]]\n assert isinstance(self.cfg.shape_init, str)\n if self.cfg.shape_init == \"ellipsoid\":\n assert (\n isinstance(self.cfg.shape_init_params, Sized)\n and len(self.cfg.shape_init_params) == 3\n )\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return ((points_rand / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n\n get_gt_sdf = func\n elif self.cfg.shape_init == \"sphere\":\n assert isinstance(self.cfg.shape_init_params, float)\n radius = self.cfg.shape_init_params\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\n\n get_gt_sdf = func\n elif self.cfg.shape_init.startswith(\"mesh:\"):\n assert isinstance(self.cfg.shape_init_params, float)\n mesh_path = self.cfg.shape_init[5:]\n if not os.path.exists(mesh_path):\n raise ValueError(f\"Mesh file {mesh_path} does not exist.\")\n\n import trimesh\n\n scene = trimesh.load(mesh_path)\n if isinstance(scene, trimesh.Trimesh):\n mesh = scene\n elif isinstance(scene, trimesh.scene.Scene):\n mesh = trimesh.Trimesh()\n for obj in scene.geometry.values():\n mesh = trimesh.util.concatenate([mesh, obj])\n else:\n raise ValueError(f\"Unknown mesh type at {mesh_path}.\")\n\n # move to center\n centroid = mesh.vertices.mean(0)\n mesh.vertices = mesh.vertices - centroid\n\n # align to up-z and front-x\n dirs = [\"+x\", \"+y\", \"+z\", \"-x\", \"-y\", \"-z\"]\n dir2vec = {\n \"+x\": np.array([1, 0, 0]),\n \"+y\": np.array([0, 1, 0]),\n \"+z\": np.array([0, 0, 1]),\n \"-x\": np.array([-1, 0, 0]),\n \"-y\": np.array([0, -1, 0]),\n \"-z\": np.array([0, 0, -1]),\n }\n if (\n self.cfg.shape_init_mesh_up not in dirs\n or self.cfg.shape_init_mesh_front not in dirs\n ):\n raise ValueError(\n f\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\"\n )\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\n raise ValueError(\n \"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\"\n )\n z_, x_ = (\n dir2vec[self.cfg.shape_init_mesh_up],\n dir2vec[self.cfg.shape_init_mesh_front],\n )\n y_ = np.cross(z_, x_)\n std2mesh = np.stack([x_, y_, z_], axis=0).T\n mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n from pysdf import SDF\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n # Initialize SDF to a given shape when no weights are provided or force_shape_init is True\n optim = torch.optim.Adam(self.parameters(), lr=1e-3)\n from tqdm import tqdm\n\n for _ in tqdm(\n range(1000),\n desc=f\"Initializing SDF to a(n) {self.cfg.shape_init}:\",\n disable=get_rank() != 0,\n ):\n points_rand = (\n torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0\n )\n sdf_gt = get_gt_sdf(points_rand)\n sdf_pred = self.forward_sdf(points_rand)\n loss = F.mse_loss(sdf_pred, sdf_gt)\n optim.zero_grad()\n loss.backward()\n optim.step()\n\n # explicit broadcast to ensure param consistency across ranks\n for param in self.parameters():\n broadcast(param, src=0)\n\n def get_shifted_sdf(\n self, points: Float[Tensor, \"*N Di\"], sdf: Float[Tensor, \"*N 1\"]\n ) -> Float[Tensor, \"*N 1\"]:\n sdf_bias: Union[float, Float[Tensor, \"*N 1\"]]\n if self.cfg.sdf_bias == \"ellipsoid\":\n assert (\n isinstance(self.cfg.sdf_bias_params, Sized)\n and len(self.cfg.sdf_bias_params) == 3\n )\n size = torch.as_tensor(self.cfg.sdf_bias_params).to(points)\n sdf_bias = ((points / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n elif self.cfg.sdf_bias == \"sphere\":\n assert isinstance(self.cfg.sdf_bias_params, float)\n radius = self.cfg.sdf_bias_params\n sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius\n elif isinstance(self.cfg.sdf_bias, float):\n sdf_bias = self.cfg.sdf_bias\n else:\n raise ValueError(f\"Unknown sdf bias {self.cfg.sdf_bias}\")\n return sdf + sdf_bias\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n grad_enabled = torch.is_grad_enabled()\n\n if output_normal and self.cfg.normal_type == \"analytic\":\n torch.set_grad_enabled(True)\n points.requires_grad_(True)\n\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(\n points, self.bbox, self.unbounded\n ) # points normalized to (0, 1)\n\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n sdf = self.sdf_network(enc).view(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n output = {\"sdf\": sdf}\n\n if self.cfg.n_feature_dims > 0:\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n output.update({\"features\": features})\n\n if output_normal:\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n assert self.finite_difference_normal_eps is not None\n eps: float = self.finite_difference_normal_eps\n if self.cfg.normal_type == \"finite_difference_laplacian\":\n offsets: Float[Tensor, \"6 3\"] = torch.as_tensor(\n [\n [eps, 0.0, 0.0],\n [-eps, 0.0, 0.0],\n [0.0, eps, 0.0],\n [0.0, -eps, 0.0],\n [0.0, 0.0, eps],\n [0.0, 0.0, -eps],\n ]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 6 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n sdf_offset: Float[Tensor, \"... 6 1\"] = self.forward_sdf(\n points_offset\n )\n sdf_grad = (\n 0.5\n * (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0])\n / eps\n )\n else:\n offsets: Float[Tensor, \"3 3\"] = torch.as_tensor(\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 3 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n sdf_offset: Float[Tensor, \"... 3 1\"] = self.forward_sdf(\n points_offset\n )\n sdf_grad = (sdf_offset[..., 0::1, 0] - sdf) / eps\n normal = F.normalize(sdf_grad, dim=-1)\n elif self.cfg.normal_type == \"pred\":\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\n normal = F.normalize(normal, dim=-1)\n sdf_grad = normal\n elif self.cfg.normal_type == \"analytic\":\n sdf_grad = -torch.autograd.grad(\n sdf,\n points_unscaled,\n grad_outputs=torch.ones_like(sdf),\n create_graph=True,\n )[0]\n normal = F.normalize(sdf_grad, dim=-1)\n if not grad_enabled:\n sdf_grad = sdf_grad.detach()\n normal = normal.detach()\n else:\n raise AttributeError(f\"Unknown normal type {self.cfg.normal_type}\")\n output.update(\n {\"normal\": normal, \"shading_normal\": normal, \"sdf_grad\": sdf_grad}\n )\n return output\n\n def forward_sdf(self, points: Float[Tensor, \"*N Di\"]) -> Float[Tensor, \"*N 1\"]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n\n sdf = self.sdf_network(\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n ).reshape(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n return sdf\n\n def forward_field(\n self, points: Float[Tensor, \"*N Di\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Optional[Float[Tensor, \"*N 3\"]]]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1)\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\n deformation: Optional[Float[Tensor, \"*N 3\"]] = None\n if self.cfg.isosurface_deformable_grid:\n deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3)\n return sdf, deformation\n\n def forward_level(\n self, field: Float[Tensor, \"*N 1\"], threshold: float\n ) -> Float[Tensor, \"*N 1\"]:\n return field - threshold\n\n def export(self, points: Float[Tensor, \"*N Di\"], **kwargs) -> Dict[str, Any]:\n out: Dict[str, Any] = {}\n if self.cfg.n_feature_dims == 0:\n return out\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n out.update(\n {\n \"features\": features,\n }\n )\n return out\n\n def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n if isinstance(self.cfg.finite_difference_normal_eps, float):\n self.finite_difference_normal_eps = (\n self.cfg.finite_difference_normal_eps\n )\n elif self.cfg.finite_difference_normal_eps == \"progressive\":\n # progressive finite difference eps from Neuralangelo\n # https://arxiv.org/abs/2306.03092\n hg_conf: Any = self.cfg.pos_encoding_config\n assert (\n hg_conf.otype == \"ProgressiveBandHashGrid\"\n ), \"finite_difference_normal_eps=progressive only works with ProgressiveBandHashGrid\"\n current_level = min(\n hg_conf.start_level\n + max(global_step - hg_conf.start_step, 0) // hg_conf.update_steps,\n hg_conf.n_levels,\n )\n grid_res = hg_conf.base_resolution * hg_conf.per_level_scale ** (\n current_level - 1\n )\n grid_size = 2 * self.cfg.radius / grid_res\n if grid_size != self.finite_difference_normal_eps:\n threestudio.info(\n f\"Update finite_difference_normal_eps to {grid_size}\"\n )\n self.finite_difference_normal_eps = grid_size\n else:\n raise ValueError(\n f\"Unknown finite_difference_normal_eps={self.cfg.finite_difference_normal_eps}\"\n )\n\n @staticmethod\n @torch.no_grad()\n def create_from(\n other: BaseGeometry, cfg: Optional[Union[dict, DictConfig]] = None, **kwargs\n ) -> BaseGeometry:\n return other"},{"identifier":"ImplicitVolume","path":"threestudio/models/geometry/implicit_volume.py","snippet":"class ImplicitVolume(BaseImplicitGeometry):\n @dataclass\n class Config(BaseImplicitGeometry.Config):\n n_input_dims: int = 3\n n_feature_dims: int = 3\n density_activation: Optional[str] = \"softplus\"\n density_bias: Union[float, str] = \"blob_magic3d\"\n density_blob_scale: float = 10.0\n density_blob_std: float = 0.5\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n normal_type: Optional[\n str\n ] = \"finite_difference\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\n finite_difference_normal_eps: float = 0.01\n\n # automatically determine the threshold\n isosurface_threshold: Union[float, str] = 25.0\n\n cfg: Config\n\n def configure(self) -> None:\n super().configure()\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.density_network = get_mlp(\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\n )\n if self.cfg.n_feature_dims > 0:\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n if self.cfg.normal_type == \"pred\":\n self.normal_network = get_mlp(\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\n )\n\n def get_activated_density(\n self, points: Float[Tensor, \"*N Di\"], density: Float[Tensor, \"*N 1\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Float[Tensor, \"*N 1\"]]:\n density_bias: Union[float, Float[Tensor, \"*N 1\"]]\n if self.cfg.density_bias == \"blob_dreamfusion\":\n # pre-activation density bias\n density_bias = (\n self.cfg.density_blob_scale\n * torch.exp(\n -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2\n )[..., None]\n )\n elif self.cfg.density_bias == \"blob_magic3d\":\n # pre-activation density bias\n density_bias = (\n self.cfg.density_blob_scale\n * (\n 1\n - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std\n )[..., None]\n )\n elif isinstance(self.cfg.density_bias, float):\n density_bias = self.cfg.density_bias\n else:\n raise ValueError(f\"Unknown density bias {self.cfg.density_bias}\")\n raw_density: Float[Tensor, \"*N 1\"] = density + density_bias\n density = get_activation(self.cfg.density_activation)(raw_density)\n return raw_density, density\n\n def initialize_shape(self) -> None:\n pass\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n grad_enabled = torch.is_grad_enabled()\n\n if output_normal and self.cfg.normal_type == \"analytic\":\n torch.set_grad_enabled(True)\n points.requires_grad_(True)\n\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(\n points, self.bbox, self.unbounded\n ) # points normalized to (0, 1)\n\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n density = self.density_network(enc).view(*points.shape[:-1], 1)\n raw_density, density = self.get_activated_density(points_unscaled, density)\n\n output = {\n \"density\": density,\n \"points_scaled\": points.view(-1, self.cfg.n_input_dims),\n }\n\n if self.cfg.n_feature_dims > 0:\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n output.update({\"features\": features})\n\n if output_normal:\n if (\n self.cfg.normal_type == \"finite_difference\"\n or self.cfg.normal_type == \"finite_difference_laplacian\"\n ):\n # TODO: use raw density\n eps = self.cfg.finite_difference_normal_eps\n if self.cfg.normal_type == \"finite_difference_laplacian\":\n offsets: Float[Tensor, \"6 3\"] = torch.as_tensor(\n [\n [eps, 0.0, 0.0],\n [-eps, 0.0, 0.0],\n [0.0, eps, 0.0],\n [0.0, -eps, 0.0],\n [0.0, 0.0, eps],\n [0.0, 0.0, -eps],\n ]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 6 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n density_offset: Float[Tensor, \"... 6 1\"] = self.forward_density(\n points_offset\n )\n normal = (\n -0.5\n * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])\n / eps\n )\n else:\n offsets: Float[Tensor, \"3 3\"] = torch.as_tensor(\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\n ).to(points_unscaled)\n points_offset: Float[Tensor, \"... 3 3\"] = (\n points_unscaled[..., None, :] + offsets\n ).clamp(-self.cfg.radius, self.cfg.radius)\n density_offset: Float[Tensor, \"... 3 1\"] = self.forward_density(\n points_offset\n )\n normal = -(density_offset[..., 0::1, 0] - density) / eps\n normal = F.normalize(normal, dim=-1)\n elif self.cfg.normal_type == \"pred\":\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\n normal = F.normalize(normal, dim=-1)\n elif self.cfg.normal_type == \"analytic\":\n normal = -torch.autograd.grad(\n density,\n points_unscaled,\n grad_outputs=torch.ones_like(density),\n create_graph=True,\n )[0]\n normal = F.normalize(normal, dim=-1)\n if not grad_enabled:\n normal = normal.detach()\n else:\n raise AttributeError(f\"Unknown normal type {self.cfg.normal_type}\")\n output.update({\"normal\": normal, \"shading_normal\": normal})\n\n torch.set_grad_enabled(grad_enabled)\n return output\n\n def forward_density(self, points: Float[Tensor, \"*N Di\"]) -> Float[Tensor, \"*N 1\"]:\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n\n density = self.density_network(\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n ).reshape(*points.shape[:-1], 1)\n\n _, density = self.get_activated_density(points_unscaled, density)\n return density\n\n def forward_field(\n self, points: Float[Tensor, \"*N Di\"]\n ) -> Tuple[Float[Tensor, \"*N 1\"], Optional[Float[Tensor, \"*N 3\"]]]:\n if self.cfg.isosurface_deformable_grid:\n threestudio.warn(\n f\"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring.\"\n )\n density = self.forward_density(points)\n return density, None\n\n def forward_level(\n self, field: Float[Tensor, \"*N 1\"], threshold: float\n ) -> Float[Tensor, \"*N 1\"]:\n return -(field - threshold)\n\n def export(self, points: Float[Tensor, \"*N Di\"], **kwargs) -> Dict[str, Any]:\n out: Dict[str, Any] = {}\n if self.cfg.n_feature_dims == 0:\n return out\n points_unscaled = points\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n out.update(\n {\n \"features\": features,\n }\n )\n return out\n\n @staticmethod\n @torch.no_grad()\n def create_from(\n other: BaseGeometry,\n cfg: Optional[Union[dict, DictConfig]] = None,\n copy_net: bool = True,\n **kwargs,\n ) -> \"ImplicitVolume\":\n if isinstance(other, ImplicitVolume):\n instance = ImplicitVolume(cfg, **kwargs)\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.density_network.load_state_dict(other.density_network.state_dict())\n if copy_net:\n if (\n instance.cfg.n_feature_dims > 0\n and other.cfg.n_feature_dims == instance.cfg.n_feature_dims\n ):\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n if (\n instance.cfg.normal_type == \"pred\"\n and other.cfg.normal_type == \"pred\"\n ):\n instance.normal_network.load_state_dict(\n other.normal_network.state_dict()\n )\n return instance\n else:\n raise TypeError(\n f\"Cannot create {ImplicitVolume.__name__} from {other.__class__.__name__}\"\n )"},{"identifier":"MarchingTetrahedraHelper","path":"threestudio/models/isosurface.py","snippet":"class MarchingTetrahedraHelper(IsosurfaceHelper):\n def __init__(self, resolution: int, tets_path: str):\n super().__init__()\n self.resolution = resolution\n self.tets_path = tets_path\n\n self.triangle_table: Float[Tensor, \"...\"]\n self.register_buffer(\n \"triangle_table\",\n torch.as_tensor(\n [\n [-1, -1, -1, -1, -1, -1],\n [1, 0, 2, -1, -1, -1],\n [4, 0, 3, -1, -1, -1],\n [1, 4, 2, 1, 3, 4],\n [3, 1, 5, -1, -1, -1],\n [2, 3, 0, 2, 5, 3],\n [1, 4, 0, 1, 5, 4],\n [4, 2, 5, -1, -1, -1],\n [4, 5, 2, -1, -1, -1],\n [4, 1, 0, 4, 5, 1],\n [3, 2, 0, 3, 5, 2],\n [1, 3, 5, -1, -1, -1],\n [4, 1, 2, 4, 3, 1],\n [3, 0, 4, -1, -1, -1],\n [2, 0, 1, -1, -1, -1],\n [-1, -1, -1, -1, -1, -1],\n ],\n dtype=torch.long,\n ),\n persistent=False,\n )\n self.num_triangles_table: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"num_triangles_table\",\n torch.as_tensor(\n [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long\n ),\n persistent=False,\n )\n self.base_tet_edges: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"base_tet_edges\",\n torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),\n persistent=False,\n )\n\n tets = np.load(self.tets_path)\n self._grid_vertices: Float[Tensor, \"...\"]\n self.register_buffer(\n \"_grid_vertices\",\n torch.from_numpy(tets[\"vertices\"]).float(),\n persistent=False,\n )\n self.indices: Integer[Tensor, \"...\"]\n self.register_buffer(\n \"indices\", torch.from_numpy(tets[\"indices\"]).long(), persistent=False\n )\n\n self._all_edges: Optional[Integer[Tensor, \"Ne 2\"]] = None\n\n def normalize_grid_deformation(\n self, grid_vertex_offsets: Float[Tensor, \"Nv 3\"]\n ) -> Float[Tensor, \"Nv 3\"]:\n return (\n (self.points_range[1] - self.points_range[0])\n / (self.resolution) # half tet size is approximately 1 / self.resolution\n * torch.tanh(grid_vertex_offsets)\n ) # FIXME: hard-coded activation\n\n @property\n def grid_vertices(self) -> Float[Tensor, \"Nv 3\"]:\n return self._grid_vertices\n\n @property\n def all_edges(self) -> Integer[Tensor, \"Ne 2\"]:\n if self._all_edges is None:\n # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)\n edges = torch.tensor(\n [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],\n dtype=torch.long,\n device=self.indices.device,\n )\n _all_edges = self.indices[:, edges].reshape(-1, 2)\n _all_edges_sorted = torch.sort(_all_edges, dim=1)[0]\n _all_edges = torch.unique(_all_edges_sorted, dim=0)\n self._all_edges = _all_edges\n return self._all_edges\n\n def sort_edges(self, edges_ex2):\n with torch.no_grad():\n order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()\n order = order.unsqueeze(dim=1)\n\n a = torch.gather(input=edges_ex2, index=order, dim=1)\n b = torch.gather(input=edges_ex2, index=1 - order, dim=1)\n\n return torch.stack([a, b], -1)\n\n def _forward(self, pos_nx3, sdf_n, tet_fx4):\n with torch.no_grad():\n occ_n = sdf_n > 0\n occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)\n occ_sum = torch.sum(occ_fx4, -1)\n valid_tets = (occ_sum > 0) & (occ_sum < 4)\n occ_sum = occ_sum[valid_tets]\n\n # find all vertices\n all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)\n all_edges = self.sort_edges(all_edges)\n unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)\n\n unique_edges = unique_edges.long()\n mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1\n mapping = (\n torch.ones(\n (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device\n )\n * -1\n )\n mapping[mask_edges] = torch.arange(\n mask_edges.sum(), dtype=torch.long, device=pos_nx3.device\n )\n idx_map = mapping[idx_map] # map edges to verts\n\n interp_v = unique_edges[mask_edges]\n edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)\n edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)\n edges_to_interp_sdf[:, -1] *= -1\n\n denominator = edges_to_interp_sdf.sum(1, keepdim=True)\n\n edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator\n verts = (edges_to_interp * edges_to_interp_sdf).sum(1)\n\n idx_map = idx_map.reshape(-1, 6)\n\n v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))\n tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)\n num_triangles = self.num_triangles_table[tetindex]\n\n # Generate triangle indices\n faces = torch.cat(\n (\n torch.gather(\n input=idx_map[num_triangles == 1],\n dim=1,\n index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],\n ).reshape(-1, 3),\n torch.gather(\n input=idx_map[num_triangles == 2],\n dim=1,\n index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],\n ).reshape(-1, 3),\n ),\n dim=0,\n )\n\n return verts, faces\n\n def forward(\n self,\n level: Float[Tensor, \"N3 1\"],\n deformation: Optional[Float[Tensor, \"N3 3\"]] = None,\n ) -> Mesh:\n if deformation is not None:\n grid_vertices = self.grid_vertices + self.normalize_grid_deformation(\n deformation\n )\n else:\n grid_vertices = self.grid_vertices\n\n v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)\n\n mesh = Mesh(\n v_pos=v_pos,\n t_pos_idx=t_pos_idx,\n # extras\n grid_vertices=grid_vertices,\n tet_edges=self.all_edges,\n grid_level=level,\n grid_deformation=deformation,\n )\n\n return mesh"},{"identifier":"Mesh","path":"threestudio/models/mesh.py","snippet":"class Mesh:\n def __init__(\n self, v_pos: Float[Tensor, \"Nv 3\"], t_pos_idx: Integer[Tensor, \"Nf 3\"], **kwargs\n ) -> None:\n self.v_pos: Float[Tensor, \"Nv 3\"] = v_pos\n self.t_pos_idx: Integer[Tensor, \"Nf 3\"] = t_pos_idx\n self._v_nrm: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._v_tng: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._v_tex: Optional[Float[Tensor, \"Nt 3\"]] = None\n self._t_tex_idx: Optional[Float[Tensor, \"Nf 3\"]] = None\n self._v_rgb: Optional[Float[Tensor, \"Nv 3\"]] = None\n self._edges: Optional[Integer[Tensor, \"Ne 2\"]] = None\n self.extras: Dict[str, Any] = {}\n for k, v in kwargs.items():\n self.add_extra(k, v)\n\n def add_extra(self, k, v) -> None:\n self.extras[k] = v\n\n def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]) -> Mesh:\n if self.requires_grad:\n threestudio.debug(\"Mesh is differentiable, not removing outliers\")\n return self\n\n # use trimesh to first split the mesh into connected components\n # then remove the components with less than n_face_threshold faces\n import trimesh\n\n # construct a trimesh object\n mesh = trimesh.Trimesh(\n vertices=self.v_pos.detach().cpu().numpy(),\n faces=self.t_pos_idx.detach().cpu().numpy(),\n )\n\n # split the mesh into connected components\n components = mesh.split(only_watertight=False)\n # log the number of faces in each component\n threestudio.debug(\n \"Mesh has {} components, with faces: {}\".format(\n len(components), [c.faces.shape[0] for c in components]\n )\n )\n\n n_faces_threshold: int\n if isinstance(outlier_n_faces_threshold, float):\n # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold\n n_faces_threshold = int(\n max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold\n )\n else:\n # set the threshold directly to outlier_n_faces_threshold\n n_faces_threshold = outlier_n_faces_threshold\n\n # log the threshold\n threestudio.debug(\n \"Removing components with less than {} faces\".format(n_faces_threshold)\n )\n\n # remove the components with less than n_face_threshold faces\n components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]\n\n # log the number of faces in each component after removing outliers\n threestudio.debug(\n \"Mesh has {} components after removing outliers, with faces: {}\".format(\n len(components), [c.faces.shape[0] for c in components]\n )\n )\n # merge the components\n mesh = trimesh.util.concatenate(components)\n\n # convert back to our mesh format\n v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)\n t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)\n\n clean_mesh = Mesh(v_pos, t_pos_idx)\n # keep the extras unchanged\n\n if len(self.extras) > 0:\n clean_mesh.extras = self.extras\n threestudio.debug(\n f\"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}\"\n )\n return clean_mesh\n\n @property\n def requires_grad(self):\n return self.v_pos.requires_grad\n\n @property\n def v_nrm(self):\n if self._v_nrm is None:\n self._v_nrm = self._compute_vertex_normal()\n return self._v_nrm\n\n @property\n def v_tng(self):\n if self._v_tng is None:\n self._v_tng = self._compute_vertex_tangent()\n return self._v_tng\n\n @property\n def v_tex(self):\n if self._v_tex is None:\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\n return self._v_tex\n\n @property\n def t_tex_idx(self):\n if self._t_tex_idx is None:\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\n return self._t_tex_idx\n\n @property\n def v_rgb(self):\n return self._v_rgb\n\n @property\n def edges(self):\n if self._edges is None:\n self._edges = self._compute_edges()\n return self._edges\n\n def _compute_vertex_normal(self):\n i0 = self.t_pos_idx[:, 0]\n i1 = self.t_pos_idx[:, 1]\n i2 = self.t_pos_idx[:, 2]\n\n v0 = self.v_pos[i0, :]\n v1 = self.v_pos[i1, :]\n v2 = self.v_pos[i2, :]\n\n face_normals = torch.cross(v1 - v0, v2 - v0)\n\n # Splat face normals to vertices\n v_nrm = torch.zeros_like(self.v_pos)\n v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)\n v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)\n v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)\n\n # Normalize, replace zero (degenerated) normals with some default value\n v_nrm = torch.where(\n dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)\n )\n v_nrm = F.normalize(v_nrm, dim=1)\n\n if torch.is_anomaly_enabled():\n assert torch.all(torch.isfinite(v_nrm))\n\n return v_nrm\n\n def _compute_vertex_tangent(self):\n vn_idx = [None] * 3\n pos = [None] * 3\n tex = [None] * 3\n for i in range(0, 3):\n pos[i] = self.v_pos[self.t_pos_idx[:, i]]\n tex[i] = self.v_tex[self.t_tex_idx[:, i]]\n # t_nrm_idx is always the same as t_pos_idx\n vn_idx[i] = self.t_pos_idx[:, i]\n\n tangents = torch.zeros_like(self.v_nrm)\n tansum = torch.zeros_like(self.v_nrm)\n\n # Compute tangent space for each triangle\n uve1 = tex[1] - tex[0]\n uve2 = tex[2] - tex[0]\n pe1 = pos[1] - pos[0]\n pe2 = pos[2] - pos[0]\n\n nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]\n denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]\n\n # Avoid division by zero for degenerated texture coordinates\n tang = nom / torch.where(\n denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)\n )\n\n # Update all 3 vertices\n for i in range(0, 3):\n idx = vn_idx[i][:, None].repeat(1, 3)\n tangents.scatter_add_(0, idx, tang) # tangents[n_i] = tangents[n_i] + tang\n tansum.scatter_add_(\n 0, idx, torch.ones_like(tang)\n ) # tansum[n_i] = tansum[n_i] + 1\n tangents = tangents / tansum\n\n # Normalize and make sure tangent is perpendicular to normal\n tangents = F.normalize(tangents, dim=1)\n tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)\n\n if torch.is_anomaly_enabled():\n assert torch.all(torch.isfinite(tangents))\n\n return tangents\n\n def _unwrap_uv(\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\n ):\n threestudio.info(\"Using xatlas to perform UV unwrapping, may take a while ...\")\n\n import xatlas\n\n atlas = xatlas.Atlas()\n atlas.add_mesh(\n self.v_pos.detach().cpu().numpy(),\n self.t_pos_idx.cpu().numpy(),\n )\n co = xatlas.ChartOptions()\n po = xatlas.PackOptions()\n for k, v in xatlas_chart_options.items():\n setattr(co, k, v)\n for k, v in xatlas_pack_options.items():\n setattr(po, k, v)\n atlas.generate(co, po)\n vmapping, indices, uvs = atlas.get_mesh(0)\n vmapping = (\n torch.from_numpy(\n vmapping.astype(np.uint64, casting=\"same_kind\").view(np.int64)\n )\n .to(self.v_pos.device)\n .long()\n )\n uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()\n indices = (\n torch.from_numpy(\n indices.astype(np.uint64, casting=\"same_kind\").view(np.int64)\n )\n .to(self.v_pos.device)\n .long()\n )\n return uvs, indices\n\n def unwrap_uv(\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\n ):\n self._v_tex, self._t_tex_idx = self._unwrap_uv(\n xatlas_chart_options, xatlas_pack_options\n )\n\n def set_vertex_color(self, v_rgb):\n assert v_rgb.shape[0] == self.v_pos.shape[0]\n self._v_rgb = v_rgb\n\n def _compute_edges(self):\n # Compute edges\n edges = torch.cat(\n [\n self.t_pos_idx[:, [0, 1]],\n self.t_pos_idx[:, [1, 2]],\n self.t_pos_idx[:, [2, 0]],\n ],\n dim=0,\n )\n edges = edges.sort()[0]\n edges = torch.unique(edges, dim=0)\n return edges\n\n def normal_consistency(self) -> Float[Tensor, \"\"]:\n edge_nrm: Float[Tensor, \"Ne 2 3\"] = self.v_nrm[self.edges]\n nc = (\n 1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)\n ).mean()\n return nc\n\n def _laplacian_uniform(self):\n # from stable-dreamfusion\n # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224\n verts, faces = self.v_pos, self.t_pos_idx\n\n V = verts.shape[0]\n F = faces.shape[0]\n\n # Neighbor indices\n ii = faces[:, [1, 2, 0]].flatten()\n jj = faces[:, [2, 0, 1]].flatten()\n adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(\n dim=1\n )\n adj_values = torch.ones(adj.shape[1]).to(verts)\n\n # Diagonal indices\n diag_idx = adj[0]\n\n # Build the sparse matrix\n idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)\n values = torch.cat((-adj_values, adj_values))\n\n # The coalesce operation sums the duplicate indices, resulting in the\n # correct diagonal\n return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()\n\n def laplacian(self) -> Float[Tensor, \"\"]:\n with torch.no_grad():\n L = self._laplacian_uniform()\n loss = L.mm(self.v_pos)\n loss = loss.norm(dim=1)\n loss = loss.mean()\n return loss"},{"identifier":"get_encoding","path":"threestudio/models/networks.py","snippet":"def get_encoding(n_input_dims: int, config) -> nn.Module:\n # input suppose to be range [0, 1]\n encoding: nn.Module\n if config.otype == \"ProgressiveBandFrequency\":\n encoding = ProgressiveBandFrequency(n_input_dims, config_to_primitive(config))\n elif config.otype == \"ProgressiveBandHashGrid\":\n encoding = ProgressiveBandHashGrid(n_input_dims, config_to_primitive(config))\n else:\n encoding = TCNNEncoding(n_input_dims, config_to_primitive(config))\n encoding = CompositeEncoding(\n encoding,\n include_xyz=config.get(\"include_xyz\", False),\n xyz_scale=2.0,\n xyz_offset=-1.0,\n ) # FIXME: hard coded\n return encoding"},{"identifier":"get_mlp","path":"threestudio/models/networks.py","snippet":"def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module:\n network: nn.Module\n if config.otype == \"VanillaMLP\":\n network = VanillaMLP(n_input_dims, n_output_dims, config_to_primitive(config))\n elif config.otype == \"SphereInitVanillaMLP\":\n network = SphereInitVanillaMLP(\n n_input_dims, n_output_dims, config_to_primitive(config)\n )\n else:\n assert (\n config.get(\"sphere_init\", False) is False\n ), \"sphere_init=True only supported by VanillaMLP\"\n network = TCNNNetwork(n_input_dims, n_output_dims, config_to_primitive(config))\n return network"},{"identifier":"broadcast","path":"threestudio/utils/misc.py","snippet":"def broadcast(tensor, src=0):\n if not _distributed_available():\n return tensor\n else:\n torch.distributed.broadcast(tensor, src=src)\n return tensor"},{"identifier":"get_rank","path":"threestudio/utils/misc.py","snippet":"def get_rank():\n # SLURM_PROCID can be set even if SLURM is not managing the multiprocessing,\n # therefore LOCAL_RANK needs to be checked first\n rank_keys = (\"RANK\", \"LOCAL_RANK\", \"SLURM_PROCID\", \"JSM_NAMESPACE_RANK\")\n for key in rank_keys:\n rank = os.environ.get(key)\n if rank is not None:\n return int(rank)\n return 0"},{"identifier":"scale_tensor","path":"threestudio/utils/ops.py","snippet":"def scale_tensor(\n dat: Num[Tensor, \"... D\"], inp_scale: ValidScale, tgt_scale: ValidScale\n):\n if inp_scale is None:\n inp_scale = (0, 1)\n if tgt_scale is None:\n tgt_scale = (0, 1)\n if isinstance(tgt_scale, Tensor):\n assert dat.shape[-1] == tgt_scale.shape[-1]\n dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])\n dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]\n return dat"}],"string":"[\n {\n \"identifier\": \"BaseExplicitGeometry\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"class BaseExplicitGeometry(BaseGeometry):\\n @dataclass\\n class Config(BaseGeometry.Config):\\n radius: float = 1.0\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n self.bbox: Float[Tensor, \\\"2 3\\\"]\\n self.register_buffer(\\n \\\"bbox\\\",\\n torch.as_tensor(\\n [\\n [-self.cfg.radius, -self.cfg.radius, -self.cfg.radius],\\n [self.cfg.radius, self.cfg.radius, self.cfg.radius],\\n ],\\n dtype=torch.float32,\\n ),\\n )\"\n },\n {\n \"identifier\": \"BaseGeometry\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"class BaseGeometry(BaseModule):\\n @dataclass\\n class Config(BaseModule.Config):\\n pass\\n\\n cfg: Config\\n\\n @staticmethod\\n def create_from(\\n other: \\\"BaseGeometry\\\", cfg: Optional[Union[dict, DictConfig]] = None, **kwargs\\n ) -> \\\"BaseGeometry\\\":\\n raise TypeError(\\n f\\\"Cannot create {BaseGeometry.__name__} from {other.__class__.__name__}\\\"\\n )\\n\\n def export(self, *args, **kwargs) -> Dict[str, Any]:\\n return {}\"\n },\n {\n \"identifier\": \"contract_to_unisphere\",\n \"path\": \"threestudio/models/geometry/base.py\",\n \"snippet\": \"def contract_to_unisphere(\\n x: Float[Tensor, \\\"... 3\\\"], bbox: Float[Tensor, \\\"2 3\\\"], unbounded: bool = False\\n) -> Float[Tensor, \\\"... 3\\\"]:\\n if unbounded:\\n x = scale_tensor(x, bbox, (0, 1))\\n x = x * 2 - 1 # aabb is at [-1, 1]\\n mag = x.norm(dim=-1, keepdim=True)\\n mask = mag.squeeze(-1) > 1\\n x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask])\\n x = x / 4 + 0.5 # [-inf, inf] is at [0, 1]\\n else:\\n x = scale_tensor(x, bbox, (0, 1))\\n return x\"\n },\n {\n \"identifier\": \"ImplicitSDF\",\n \"path\": \"threestudio/models/geometry/implicit_sdf.py\",\n \"snippet\": \"class ImplicitSDF(BaseImplicitGeometry):\\n @dataclass\\n class Config(BaseImplicitGeometry.Config):\\n n_input_dims: int = 3\\n n_feature_dims: int = 3\\n pos_encoding_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"HashGrid\\\",\\n \\\"n_levels\\\": 16,\\n \\\"n_features_per_level\\\": 2,\\n \\\"log2_hashmap_size\\\": 19,\\n \\\"base_resolution\\\": 16,\\n \\\"per_level_scale\\\": 1.447269237440378,\\n }\\n )\\n mlp_network_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"VanillaMLP\\\",\\n \\\"activation\\\": \\\"ReLU\\\",\\n \\\"output_activation\\\": \\\"none\\\",\\n \\\"n_neurons\\\": 64,\\n \\\"n_hidden_layers\\\": 1,\\n }\\n )\\n normal_type: Optional[\\n str\\n ] = \\\"finite_difference\\\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\\n finite_difference_normal_eps: Union[\\n float, str\\n ] = 0.01 # in [float, \\\"progressive\\\"]\\n shape_init: Optional[str] = None\\n shape_init_params: Optional[Any] = None\\n shape_init_mesh_up: str = \\\"+z\\\"\\n shape_init_mesh_front: str = \\\"+x\\\"\\n force_shape_init: bool = False\\n sdf_bias: Union[float, str] = 0.0\\n sdf_bias_params: Optional[Any] = None\\n\\n # no need to removal outlier for SDF\\n isosurface_remove_outliers: bool = False\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n super().configure()\\n self.encoding = get_encoding(\\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\\n )\\n self.sdf_network = get_mlp(\\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\\n )\\n\\n if self.cfg.n_feature_dims > 0:\\n self.feature_network = get_mlp(\\n self.encoding.n_output_dims,\\n self.cfg.n_feature_dims,\\n self.cfg.mlp_network_config,\\n )\\n\\n if self.cfg.normal_type == \\\"pred\\\":\\n self.normal_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n if self.cfg.isosurface_deformable_grid:\\n assert (\\n self.cfg.isosurface_method == \\\"mt\\\"\\n ), \\\"isosurface_deformable_grid only works with mt\\\"\\n self.deformation_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n\\n self.finite_difference_normal_eps: Optional[float] = None\\n\\n def initialize_shape(self) -> None:\\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\\n return\\n\\n # do not initialize shape if weights are provided\\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\\n return\\n\\n if self.cfg.sdf_bias != 0.0:\\n threestudio.warn(\\n \\\"shape_init and sdf_bias are both specified, which may lead to unexpected results.\\\"\\n )\\n\\n get_gt_sdf: Callable[[Float[Tensor, \\\"N 3\\\"]], Float[Tensor, \\\"N 1\\\"]]\\n assert isinstance(self.cfg.shape_init, str)\\n if self.cfg.shape_init == \\\"ellipsoid\\\":\\n assert (\\n isinstance(self.cfg.shape_init_params, Sized)\\n and len(self.cfg.shape_init_params) == 3\\n )\\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n return ((points_rand / size) ** 2).sum(\\n dim=-1, keepdim=True\\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\\n\\n get_gt_sdf = func\\n elif self.cfg.shape_init == \\\"sphere\\\":\\n assert isinstance(self.cfg.shape_init_params, float)\\n radius = self.cfg.shape_init_params\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\\n\\n get_gt_sdf = func\\n elif self.cfg.shape_init.startswith(\\\"mesh:\\\"):\\n assert isinstance(self.cfg.shape_init_params, float)\\n mesh_path = self.cfg.shape_init[5:]\\n if not os.path.exists(mesh_path):\\n raise ValueError(f\\\"Mesh file {mesh_path} does not exist.\\\")\\n\\n import trimesh\\n\\n scene = trimesh.load(mesh_path)\\n if isinstance(scene, trimesh.Trimesh):\\n mesh = scene\\n elif isinstance(scene, trimesh.scene.Scene):\\n mesh = trimesh.Trimesh()\\n for obj in scene.geometry.values():\\n mesh = trimesh.util.concatenate([mesh, obj])\\n else:\\n raise ValueError(f\\\"Unknown mesh type at {mesh_path}.\\\")\\n\\n # move to center\\n centroid = mesh.vertices.mean(0)\\n mesh.vertices = mesh.vertices - centroid\\n\\n # align to up-z and front-x\\n dirs = [\\\"+x\\\", \\\"+y\\\", \\\"+z\\\", \\\"-x\\\", \\\"-y\\\", \\\"-z\\\"]\\n dir2vec = {\\n \\\"+x\\\": np.array([1, 0, 0]),\\n \\\"+y\\\": np.array([0, 1, 0]),\\n \\\"+z\\\": np.array([0, 0, 1]),\\n \\\"-x\\\": np.array([-1, 0, 0]),\\n \\\"-y\\\": np.array([0, -1, 0]),\\n \\\"-z\\\": np.array([0, 0, -1]),\\n }\\n if (\\n self.cfg.shape_init_mesh_up not in dirs\\n or self.cfg.shape_init_mesh_front not in dirs\\n ):\\n raise ValueError(\\n f\\\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\\\"\\n )\\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\\n raise ValueError(\\n \\\"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\\\"\\n )\\n z_, x_ = (\\n dir2vec[self.cfg.shape_init_mesh_up],\\n dir2vec[self.cfg.shape_init_mesh_front],\\n )\\n y_ = np.cross(z_, x_)\\n std2mesh = np.stack([x_, y_, z_], axis=0).T\\n mesh2std = np.linalg.inv(std2mesh)\\n\\n # scaling\\n scale = np.abs(mesh.vertices).max()\\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\\n\\n from pysdf import SDF\\n\\n sdf = SDF(mesh.vertices, mesh.faces)\\n\\n def func(points_rand: Float[Tensor, \\\"N 3\\\"]) -> Float[Tensor, \\\"N 1\\\"]:\\n # add a negative signed here\\n # as in pysdf the inside of the shape has positive signed distance\\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\\n points_rand\\n )[..., None]\\n\\n get_gt_sdf = func\\n\\n else:\\n raise ValueError(\\n f\\\"Unknown shape initialization type: {self.cfg.shape_init}\\\"\\n )\\n\\n # Initialize SDF to a given shape when no weights are provided or force_shape_init is True\\n optim = torch.optim.Adam(self.parameters(), lr=1e-3)\\n from tqdm import tqdm\\n\\n for _ in tqdm(\\n range(1000),\\n desc=f\\\"Initializing SDF to a(n) {self.cfg.shape_init}:\\\",\\n disable=get_rank() != 0,\\n ):\\n points_rand = (\\n torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0\\n )\\n sdf_gt = get_gt_sdf(points_rand)\\n sdf_pred = self.forward_sdf(points_rand)\\n loss = F.mse_loss(sdf_pred, sdf_gt)\\n optim.zero_grad()\\n loss.backward()\\n optim.step()\\n\\n # explicit broadcast to ensure param consistency across ranks\\n for param in self.parameters():\\n broadcast(param, src=0)\\n\\n def get_shifted_sdf(\\n self, points: Float[Tensor, \\\"*N Di\\\"], sdf: Float[Tensor, \\\"*N 1\\\"]\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n sdf_bias: Union[float, Float[Tensor, \\\"*N 1\\\"]]\\n if self.cfg.sdf_bias == \\\"ellipsoid\\\":\\n assert (\\n isinstance(self.cfg.sdf_bias_params, Sized)\\n and len(self.cfg.sdf_bias_params) == 3\\n )\\n size = torch.as_tensor(self.cfg.sdf_bias_params).to(points)\\n sdf_bias = ((points / size) ** 2).sum(\\n dim=-1, keepdim=True\\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\\n elif self.cfg.sdf_bias == \\\"sphere\\\":\\n assert isinstance(self.cfg.sdf_bias_params, float)\\n radius = self.cfg.sdf_bias_params\\n sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius\\n elif isinstance(self.cfg.sdf_bias, float):\\n sdf_bias = self.cfg.sdf_bias\\n else:\\n raise ValueError(f\\\"Unknown sdf bias {self.cfg.sdf_bias}\\\")\\n return sdf + sdf_bias\\n\\n def forward(\\n self, points: Float[Tensor, \\\"*N Di\\\"], output_normal: bool = False\\n ) -> Dict[str, Float[Tensor, \\\"...\\\"]]:\\n grad_enabled = torch.is_grad_enabled()\\n\\n if output_normal and self.cfg.normal_type == \\\"analytic\\\":\\n torch.set_grad_enabled(True)\\n points.requires_grad_(True)\\n\\n points_unscaled = points # points in the original scale\\n points = contract_to_unisphere(\\n points, self.bbox, self.unbounded\\n ) # points normalized to (0, 1)\\n\\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\\n sdf = self.sdf_network(enc).view(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n output = {\\\"sdf\\\": sdf}\\n\\n if self.cfg.n_feature_dims > 0:\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n output.update({\\\"features\\\": features})\\n\\n if output_normal:\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n assert self.finite_difference_normal_eps is not None\\n eps: float = self.finite_difference_normal_eps\\n if self.cfg.normal_type == \\\"finite_difference_laplacian\\\":\\n offsets: Float[Tensor, \\\"6 3\\\"] = torch.as_tensor(\\n [\\n [eps, 0.0, 0.0],\\n [-eps, 0.0, 0.0],\\n [0.0, eps, 0.0],\\n [0.0, -eps, 0.0],\\n [0.0, 0.0, eps],\\n [0.0, 0.0, -eps],\\n ]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 6 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n sdf_offset: Float[Tensor, \\\"... 6 1\\\"] = self.forward_sdf(\\n points_offset\\n )\\n sdf_grad = (\\n 0.5\\n * (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0])\\n / eps\\n )\\n else:\\n offsets: Float[Tensor, \\\"3 3\\\"] = torch.as_tensor(\\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 3 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n sdf_offset: Float[Tensor, \\\"... 3 1\\\"] = self.forward_sdf(\\n points_offset\\n )\\n sdf_grad = (sdf_offset[..., 0::1, 0] - sdf) / eps\\n normal = F.normalize(sdf_grad, dim=-1)\\n elif self.cfg.normal_type == \\\"pred\\\":\\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\\n normal = F.normalize(normal, dim=-1)\\n sdf_grad = normal\\n elif self.cfg.normal_type == \\\"analytic\\\":\\n sdf_grad = -torch.autograd.grad(\\n sdf,\\n points_unscaled,\\n grad_outputs=torch.ones_like(sdf),\\n create_graph=True,\\n )[0]\\n normal = F.normalize(sdf_grad, dim=-1)\\n if not grad_enabled:\\n sdf_grad = sdf_grad.detach()\\n normal = normal.detach()\\n else:\\n raise AttributeError(f\\\"Unknown normal type {self.cfg.normal_type}\\\")\\n output.update(\\n {\\\"normal\\\": normal, \\\"shading_normal\\\": normal, \\\"sdf_grad\\\": sdf_grad}\\n )\\n return output\\n\\n def forward_sdf(self, points: Float[Tensor, \\\"*N Di\\\"]) -> Float[Tensor, \\\"*N 1\\\"]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n\\n sdf = self.sdf_network(\\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n ).reshape(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n return sdf\\n\\n def forward_field(\\n self, points: Float[Tensor, \\\"*N Di\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Optional[Float[Tensor, \\\"*N 3\\\"]]]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1)\\n sdf = self.get_shifted_sdf(points_unscaled, sdf)\\n deformation: Optional[Float[Tensor, \\\"*N 3\\\"]] = None\\n if self.cfg.isosurface_deformable_grid:\\n deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3)\\n return sdf, deformation\\n\\n def forward_level(\\n self, field: Float[Tensor, \\\"*N 1\\\"], threshold: float\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n return field - threshold\\n\\n def export(self, points: Float[Tensor, \\\"*N Di\\\"], **kwargs) -> Dict[str, Any]:\\n out: Dict[str, Any] = {}\\n if self.cfg.n_feature_dims == 0:\\n return out\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n out.update(\\n {\\n \\\"features\\\": features,\\n }\\n )\\n return out\\n\\n def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n if isinstance(self.cfg.finite_difference_normal_eps, float):\\n self.finite_difference_normal_eps = (\\n self.cfg.finite_difference_normal_eps\\n )\\n elif self.cfg.finite_difference_normal_eps == \\\"progressive\\\":\\n # progressive finite difference eps from Neuralangelo\\n # https://arxiv.org/abs/2306.03092\\n hg_conf: Any = self.cfg.pos_encoding_config\\n assert (\\n hg_conf.otype == \\\"ProgressiveBandHashGrid\\\"\\n ), \\\"finite_difference_normal_eps=progressive only works with ProgressiveBandHashGrid\\\"\\n current_level = min(\\n hg_conf.start_level\\n + max(global_step - hg_conf.start_step, 0) // hg_conf.update_steps,\\n hg_conf.n_levels,\\n )\\n grid_res = hg_conf.base_resolution * hg_conf.per_level_scale ** (\\n current_level - 1\\n )\\n grid_size = 2 * self.cfg.radius / grid_res\\n if grid_size != self.finite_difference_normal_eps:\\n threestudio.info(\\n f\\\"Update finite_difference_normal_eps to {grid_size}\\\"\\n )\\n self.finite_difference_normal_eps = grid_size\\n else:\\n raise ValueError(\\n f\\\"Unknown finite_difference_normal_eps={self.cfg.finite_difference_normal_eps}\\\"\\n )\\n\\n @staticmethod\\n @torch.no_grad()\\n def create_from(\\n other: BaseGeometry, cfg: Optional[Union[dict, DictConfig]] = None, **kwargs\\n ) -> BaseGeometry:\\n return other\"\n },\n {\n \"identifier\": \"ImplicitVolume\",\n \"path\": \"threestudio/models/geometry/implicit_volume.py\",\n \"snippet\": \"class ImplicitVolume(BaseImplicitGeometry):\\n @dataclass\\n class Config(BaseImplicitGeometry.Config):\\n n_input_dims: int = 3\\n n_feature_dims: int = 3\\n density_activation: Optional[str] = \\\"softplus\\\"\\n density_bias: Union[float, str] = \\\"blob_magic3d\\\"\\n density_blob_scale: float = 10.0\\n density_blob_std: float = 0.5\\n pos_encoding_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"HashGrid\\\",\\n \\\"n_levels\\\": 16,\\n \\\"n_features_per_level\\\": 2,\\n \\\"log2_hashmap_size\\\": 19,\\n \\\"base_resolution\\\": 16,\\n \\\"per_level_scale\\\": 1.447269237440378,\\n }\\n )\\n mlp_network_config: dict = field(\\n default_factory=lambda: {\\n \\\"otype\\\": \\\"VanillaMLP\\\",\\n \\\"activation\\\": \\\"ReLU\\\",\\n \\\"output_activation\\\": \\\"none\\\",\\n \\\"n_neurons\\\": 64,\\n \\\"n_hidden_layers\\\": 1,\\n }\\n )\\n normal_type: Optional[\\n str\\n ] = \\\"finite_difference\\\" # in ['pred', 'finite_difference', 'finite_difference_laplacian']\\n finite_difference_normal_eps: float = 0.01\\n\\n # automatically determine the threshold\\n isosurface_threshold: Union[float, str] = 25.0\\n\\n cfg: Config\\n\\n def configure(self) -> None:\\n super().configure()\\n self.encoding = get_encoding(\\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\\n )\\n self.density_network = get_mlp(\\n self.encoding.n_output_dims, 1, self.cfg.mlp_network_config\\n )\\n if self.cfg.n_feature_dims > 0:\\n self.feature_network = get_mlp(\\n self.encoding.n_output_dims,\\n self.cfg.n_feature_dims,\\n self.cfg.mlp_network_config,\\n )\\n if self.cfg.normal_type == \\\"pred\\\":\\n self.normal_network = get_mlp(\\n self.encoding.n_output_dims, 3, self.cfg.mlp_network_config\\n )\\n\\n def get_activated_density(\\n self, points: Float[Tensor, \\\"*N Di\\\"], density: Float[Tensor, \\\"*N 1\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Float[Tensor, \\\"*N 1\\\"]]:\\n density_bias: Union[float, Float[Tensor, \\\"*N 1\\\"]]\\n if self.cfg.density_bias == \\\"blob_dreamfusion\\\":\\n # pre-activation density bias\\n density_bias = (\\n self.cfg.density_blob_scale\\n * torch.exp(\\n -0.5 * (points**2).sum(dim=-1) / self.cfg.density_blob_std**2\\n )[..., None]\\n )\\n elif self.cfg.density_bias == \\\"blob_magic3d\\\":\\n # pre-activation density bias\\n density_bias = (\\n self.cfg.density_blob_scale\\n * (\\n 1\\n - torch.sqrt((points**2).sum(dim=-1)) / self.cfg.density_blob_std\\n )[..., None]\\n )\\n elif isinstance(self.cfg.density_bias, float):\\n density_bias = self.cfg.density_bias\\n else:\\n raise ValueError(f\\\"Unknown density bias {self.cfg.density_bias}\\\")\\n raw_density: Float[Tensor, \\\"*N 1\\\"] = density + density_bias\\n density = get_activation(self.cfg.density_activation)(raw_density)\\n return raw_density, density\\n\\n def initialize_shape(self) -> None:\\n pass\\n\\n def forward(\\n self, points: Float[Tensor, \\\"*N Di\\\"], output_normal: bool = False\\n ) -> Dict[str, Float[Tensor, \\\"...\\\"]]:\\n grad_enabled = torch.is_grad_enabled()\\n\\n if output_normal and self.cfg.normal_type == \\\"analytic\\\":\\n torch.set_grad_enabled(True)\\n points.requires_grad_(True)\\n\\n points_unscaled = points # points in the original scale\\n points = contract_to_unisphere(\\n points, self.bbox, self.unbounded\\n ) # points normalized to (0, 1)\\n\\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\\n density = self.density_network(enc).view(*points.shape[:-1], 1)\\n raw_density, density = self.get_activated_density(points_unscaled, density)\\n\\n output = {\\n \\\"density\\\": density,\\n \\\"points_scaled\\\": points.view(-1, self.cfg.n_input_dims),\\n }\\n\\n if self.cfg.n_feature_dims > 0:\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n output.update({\\\"features\\\": features})\\n\\n if output_normal:\\n if (\\n self.cfg.normal_type == \\\"finite_difference\\\"\\n or self.cfg.normal_type == \\\"finite_difference_laplacian\\\"\\n ):\\n # TODO: use raw density\\n eps = self.cfg.finite_difference_normal_eps\\n if self.cfg.normal_type == \\\"finite_difference_laplacian\\\":\\n offsets: Float[Tensor, \\\"6 3\\\"] = torch.as_tensor(\\n [\\n [eps, 0.0, 0.0],\\n [-eps, 0.0, 0.0],\\n [0.0, eps, 0.0],\\n [0.0, -eps, 0.0],\\n [0.0, 0.0, eps],\\n [0.0, 0.0, -eps],\\n ]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 6 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n density_offset: Float[Tensor, \\\"... 6 1\\\"] = self.forward_density(\\n points_offset\\n )\\n normal = (\\n -0.5\\n * (density_offset[..., 0::2, 0] - density_offset[..., 1::2, 0])\\n / eps\\n )\\n else:\\n offsets: Float[Tensor, \\\"3 3\\\"] = torch.as_tensor(\\n [[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]\\n ).to(points_unscaled)\\n points_offset: Float[Tensor, \\\"... 3 3\\\"] = (\\n points_unscaled[..., None, :] + offsets\\n ).clamp(-self.cfg.radius, self.cfg.radius)\\n density_offset: Float[Tensor, \\\"... 3 1\\\"] = self.forward_density(\\n points_offset\\n )\\n normal = -(density_offset[..., 0::1, 0] - density) / eps\\n normal = F.normalize(normal, dim=-1)\\n elif self.cfg.normal_type == \\\"pred\\\":\\n normal = self.normal_network(enc).view(*points.shape[:-1], 3)\\n normal = F.normalize(normal, dim=-1)\\n elif self.cfg.normal_type == \\\"analytic\\\":\\n normal = -torch.autograd.grad(\\n density,\\n points_unscaled,\\n grad_outputs=torch.ones_like(density),\\n create_graph=True,\\n )[0]\\n normal = F.normalize(normal, dim=-1)\\n if not grad_enabled:\\n normal = normal.detach()\\n else:\\n raise AttributeError(f\\\"Unknown normal type {self.cfg.normal_type}\\\")\\n output.update({\\\"normal\\\": normal, \\\"shading_normal\\\": normal})\\n\\n torch.set_grad_enabled(grad_enabled)\\n return output\\n\\n def forward_density(self, points: Float[Tensor, \\\"*N Di\\\"]) -> Float[Tensor, \\\"*N 1\\\"]:\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n\\n density = self.density_network(\\n self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n ).reshape(*points.shape[:-1], 1)\\n\\n _, density = self.get_activated_density(points_unscaled, density)\\n return density\\n\\n def forward_field(\\n self, points: Float[Tensor, \\\"*N Di\\\"]\\n ) -> Tuple[Float[Tensor, \\\"*N 1\\\"], Optional[Float[Tensor, \\\"*N 3\\\"]]]:\\n if self.cfg.isosurface_deformable_grid:\\n threestudio.warn(\\n f\\\"{self.__class__.__name__} does not support isosurface_deformable_grid. Ignoring.\\\"\\n )\\n density = self.forward_density(points)\\n return density, None\\n\\n def forward_level(\\n self, field: Float[Tensor, \\\"*N 1\\\"], threshold: float\\n ) -> Float[Tensor, \\\"*N 1\\\"]:\\n return -(field - threshold)\\n\\n def export(self, points: Float[Tensor, \\\"*N Di\\\"], **kwargs) -> Dict[str, Any]:\\n out: Dict[str, Any] = {}\\n if self.cfg.n_feature_dims == 0:\\n return out\\n points_unscaled = points\\n points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)\\n enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))\\n features = self.feature_network(enc).view(\\n *points.shape[:-1], self.cfg.n_feature_dims\\n )\\n out.update(\\n {\\n \\\"features\\\": features,\\n }\\n )\\n return out\\n\\n @staticmethod\\n @torch.no_grad()\\n def create_from(\\n other: BaseGeometry,\\n cfg: Optional[Union[dict, DictConfig]] = None,\\n copy_net: bool = True,\\n **kwargs,\\n ) -> \\\"ImplicitVolume\\\":\\n if isinstance(other, ImplicitVolume):\\n instance = ImplicitVolume(cfg, **kwargs)\\n instance.encoding.load_state_dict(other.encoding.state_dict())\\n instance.density_network.load_state_dict(other.density_network.state_dict())\\n if copy_net:\\n if (\\n instance.cfg.n_feature_dims > 0\\n and other.cfg.n_feature_dims == instance.cfg.n_feature_dims\\n ):\\n instance.feature_network.load_state_dict(\\n other.feature_network.state_dict()\\n )\\n if (\\n instance.cfg.normal_type == \\\"pred\\\"\\n and other.cfg.normal_type == \\\"pred\\\"\\n ):\\n instance.normal_network.load_state_dict(\\n other.normal_network.state_dict()\\n )\\n return instance\\n else:\\n raise TypeError(\\n f\\\"Cannot create {ImplicitVolume.__name__} from {other.__class__.__name__}\\\"\\n )\"\n },\n {\n \"identifier\": \"MarchingTetrahedraHelper\",\n \"path\": \"threestudio/models/isosurface.py\",\n \"snippet\": \"class MarchingTetrahedraHelper(IsosurfaceHelper):\\n def __init__(self, resolution: int, tets_path: str):\\n super().__init__()\\n self.resolution = resolution\\n self.tets_path = tets_path\\n\\n self.triangle_table: Float[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"triangle_table\\\",\\n torch.as_tensor(\\n [\\n [-1, -1, -1, -1, -1, -1],\\n [1, 0, 2, -1, -1, -1],\\n [4, 0, 3, -1, -1, -1],\\n [1, 4, 2, 1, 3, 4],\\n [3, 1, 5, -1, -1, -1],\\n [2, 3, 0, 2, 5, 3],\\n [1, 4, 0, 1, 5, 4],\\n [4, 2, 5, -1, -1, -1],\\n [4, 5, 2, -1, -1, -1],\\n [4, 1, 0, 4, 5, 1],\\n [3, 2, 0, 3, 5, 2],\\n [1, 3, 5, -1, -1, -1],\\n [4, 1, 2, 4, 3, 1],\\n [3, 0, 4, -1, -1, -1],\\n [2, 0, 1, -1, -1, -1],\\n [-1, -1, -1, -1, -1, -1],\\n ],\\n dtype=torch.long,\\n ),\\n persistent=False,\\n )\\n self.num_triangles_table: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"num_triangles_table\\\",\\n torch.as_tensor(\\n [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long\\n ),\\n persistent=False,\\n )\\n self.base_tet_edges: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"base_tet_edges\\\",\\n torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),\\n persistent=False,\\n )\\n\\n tets = np.load(self.tets_path)\\n self._grid_vertices: Float[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"_grid_vertices\\\",\\n torch.from_numpy(tets[\\\"vertices\\\"]).float(),\\n persistent=False,\\n )\\n self.indices: Integer[Tensor, \\\"...\\\"]\\n self.register_buffer(\\n \\\"indices\\\", torch.from_numpy(tets[\\\"indices\\\"]).long(), persistent=False\\n )\\n\\n self._all_edges: Optional[Integer[Tensor, \\\"Ne 2\\\"]] = None\\n\\n def normalize_grid_deformation(\\n self, grid_vertex_offsets: Float[Tensor, \\\"Nv 3\\\"]\\n ) -> Float[Tensor, \\\"Nv 3\\\"]:\\n return (\\n (self.points_range[1] - self.points_range[0])\\n / (self.resolution) # half tet size is approximately 1 / self.resolution\\n * torch.tanh(grid_vertex_offsets)\\n ) # FIXME: hard-coded activation\\n\\n @property\\n def grid_vertices(self) -> Float[Tensor, \\\"Nv 3\\\"]:\\n return self._grid_vertices\\n\\n @property\\n def all_edges(self) -> Integer[Tensor, \\\"Ne 2\\\"]:\\n if self._all_edges is None:\\n # compute edges on GPU, or it would be VERY SLOW (basically due to the unique operation)\\n edges = torch.tensor(\\n [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3],\\n dtype=torch.long,\\n device=self.indices.device,\\n )\\n _all_edges = self.indices[:, edges].reshape(-1, 2)\\n _all_edges_sorted = torch.sort(_all_edges, dim=1)[0]\\n _all_edges = torch.unique(_all_edges_sorted, dim=0)\\n self._all_edges = _all_edges\\n return self._all_edges\\n\\n def sort_edges(self, edges_ex2):\\n with torch.no_grad():\\n order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()\\n order = order.unsqueeze(dim=1)\\n\\n a = torch.gather(input=edges_ex2, index=order, dim=1)\\n b = torch.gather(input=edges_ex2, index=1 - order, dim=1)\\n\\n return torch.stack([a, b], -1)\\n\\n def _forward(self, pos_nx3, sdf_n, tet_fx4):\\n with torch.no_grad():\\n occ_n = sdf_n > 0\\n occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)\\n occ_sum = torch.sum(occ_fx4, -1)\\n valid_tets = (occ_sum > 0) & (occ_sum < 4)\\n occ_sum = occ_sum[valid_tets]\\n\\n # find all vertices\\n all_edges = tet_fx4[valid_tets][:, self.base_tet_edges].reshape(-1, 2)\\n all_edges = self.sort_edges(all_edges)\\n unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)\\n\\n unique_edges = unique_edges.long()\\n mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1\\n mapping = (\\n torch.ones(\\n (unique_edges.shape[0]), dtype=torch.long, device=pos_nx3.device\\n )\\n * -1\\n )\\n mapping[mask_edges] = torch.arange(\\n mask_edges.sum(), dtype=torch.long, device=pos_nx3.device\\n )\\n idx_map = mapping[idx_map] # map edges to verts\\n\\n interp_v = unique_edges[mask_edges]\\n edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)\\n edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)\\n edges_to_interp_sdf[:, -1] *= -1\\n\\n denominator = edges_to_interp_sdf.sum(1, keepdim=True)\\n\\n edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator\\n verts = (edges_to_interp * edges_to_interp_sdf).sum(1)\\n\\n idx_map = idx_map.reshape(-1, 6)\\n\\n v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=pos_nx3.device))\\n tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)\\n num_triangles = self.num_triangles_table[tetindex]\\n\\n # Generate triangle indices\\n faces = torch.cat(\\n (\\n torch.gather(\\n input=idx_map[num_triangles == 1],\\n dim=1,\\n index=self.triangle_table[tetindex[num_triangles == 1]][:, :3],\\n ).reshape(-1, 3),\\n torch.gather(\\n input=idx_map[num_triangles == 2],\\n dim=1,\\n index=self.triangle_table[tetindex[num_triangles == 2]][:, :6],\\n ).reshape(-1, 3),\\n ),\\n dim=0,\\n )\\n\\n return verts, faces\\n\\n def forward(\\n self,\\n level: Float[Tensor, \\\"N3 1\\\"],\\n deformation: Optional[Float[Tensor, \\\"N3 3\\\"]] = None,\\n ) -> Mesh:\\n if deformation is not None:\\n grid_vertices = self.grid_vertices + self.normalize_grid_deformation(\\n deformation\\n )\\n else:\\n grid_vertices = self.grid_vertices\\n\\n v_pos, t_pos_idx = self._forward(grid_vertices, level, self.indices)\\n\\n mesh = Mesh(\\n v_pos=v_pos,\\n t_pos_idx=t_pos_idx,\\n # extras\\n grid_vertices=grid_vertices,\\n tet_edges=self.all_edges,\\n grid_level=level,\\n grid_deformation=deformation,\\n )\\n\\n return mesh\"\n },\n {\n \"identifier\": \"Mesh\",\n \"path\": \"threestudio/models/mesh.py\",\n \"snippet\": \"class Mesh:\\n def __init__(\\n self, v_pos: Float[Tensor, \\\"Nv 3\\\"], t_pos_idx: Integer[Tensor, \\\"Nf 3\\\"], **kwargs\\n ) -> None:\\n self.v_pos: Float[Tensor, \\\"Nv 3\\\"] = v_pos\\n self.t_pos_idx: Integer[Tensor, \\\"Nf 3\\\"] = t_pos_idx\\n self._v_nrm: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._v_tng: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._v_tex: Optional[Float[Tensor, \\\"Nt 3\\\"]] = None\\n self._t_tex_idx: Optional[Float[Tensor, \\\"Nf 3\\\"]] = None\\n self._v_rgb: Optional[Float[Tensor, \\\"Nv 3\\\"]] = None\\n self._edges: Optional[Integer[Tensor, \\\"Ne 2\\\"]] = None\\n self.extras: Dict[str, Any] = {}\\n for k, v in kwargs.items():\\n self.add_extra(k, v)\\n\\n def add_extra(self, k, v) -> None:\\n self.extras[k] = v\\n\\n def remove_outlier(self, outlier_n_faces_threshold: Union[int, float]) -> Mesh:\\n if self.requires_grad:\\n threestudio.debug(\\\"Mesh is differentiable, not removing outliers\\\")\\n return self\\n\\n # use trimesh to first split the mesh into connected components\\n # then remove the components with less than n_face_threshold faces\\n import trimesh\\n\\n # construct a trimesh object\\n mesh = trimesh.Trimesh(\\n vertices=self.v_pos.detach().cpu().numpy(),\\n faces=self.t_pos_idx.detach().cpu().numpy(),\\n )\\n\\n # split the mesh into connected components\\n components = mesh.split(only_watertight=False)\\n # log the number of faces in each component\\n threestudio.debug(\\n \\\"Mesh has {} components, with faces: {}\\\".format(\\n len(components), [c.faces.shape[0] for c in components]\\n )\\n )\\n\\n n_faces_threshold: int\\n if isinstance(outlier_n_faces_threshold, float):\\n # set the threshold to the number of faces in the largest component multiplied by outlier_n_faces_threshold\\n n_faces_threshold = int(\\n max([c.faces.shape[0] for c in components]) * outlier_n_faces_threshold\\n )\\n else:\\n # set the threshold directly to outlier_n_faces_threshold\\n n_faces_threshold = outlier_n_faces_threshold\\n\\n # log the threshold\\n threestudio.debug(\\n \\\"Removing components with less than {} faces\\\".format(n_faces_threshold)\\n )\\n\\n # remove the components with less than n_face_threshold faces\\n components = [c for c in components if c.faces.shape[0] >= n_faces_threshold]\\n\\n # log the number of faces in each component after removing outliers\\n threestudio.debug(\\n \\\"Mesh has {} components after removing outliers, with faces: {}\\\".format(\\n len(components), [c.faces.shape[0] for c in components]\\n )\\n )\\n # merge the components\\n mesh = trimesh.util.concatenate(components)\\n\\n # convert back to our mesh format\\n v_pos = torch.from_numpy(mesh.vertices).to(self.v_pos)\\n t_pos_idx = torch.from_numpy(mesh.faces).to(self.t_pos_idx)\\n\\n clean_mesh = Mesh(v_pos, t_pos_idx)\\n # keep the extras unchanged\\n\\n if len(self.extras) > 0:\\n clean_mesh.extras = self.extras\\n threestudio.debug(\\n f\\\"The following extra attributes are inherited from the original mesh unchanged: {list(self.extras.keys())}\\\"\\n )\\n return clean_mesh\\n\\n @property\\n def requires_grad(self):\\n return self.v_pos.requires_grad\\n\\n @property\\n def v_nrm(self):\\n if self._v_nrm is None:\\n self._v_nrm = self._compute_vertex_normal()\\n return self._v_nrm\\n\\n @property\\n def v_tng(self):\\n if self._v_tng is None:\\n self._v_tng = self._compute_vertex_tangent()\\n return self._v_tng\\n\\n @property\\n def v_tex(self):\\n if self._v_tex is None:\\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\\n return self._v_tex\\n\\n @property\\n def t_tex_idx(self):\\n if self._t_tex_idx is None:\\n self._v_tex, self._t_tex_idx = self._unwrap_uv()\\n return self._t_tex_idx\\n\\n @property\\n def v_rgb(self):\\n return self._v_rgb\\n\\n @property\\n def edges(self):\\n if self._edges is None:\\n self._edges = self._compute_edges()\\n return self._edges\\n\\n def _compute_vertex_normal(self):\\n i0 = self.t_pos_idx[:, 0]\\n i1 = self.t_pos_idx[:, 1]\\n i2 = self.t_pos_idx[:, 2]\\n\\n v0 = self.v_pos[i0, :]\\n v1 = self.v_pos[i1, :]\\n v2 = self.v_pos[i2, :]\\n\\n face_normals = torch.cross(v1 - v0, v2 - v0)\\n\\n # Splat face normals to vertices\\n v_nrm = torch.zeros_like(self.v_pos)\\n v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)\\n v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)\\n v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)\\n\\n # Normalize, replace zero (degenerated) normals with some default value\\n v_nrm = torch.where(\\n dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)\\n )\\n v_nrm = F.normalize(v_nrm, dim=1)\\n\\n if torch.is_anomaly_enabled():\\n assert torch.all(torch.isfinite(v_nrm))\\n\\n return v_nrm\\n\\n def _compute_vertex_tangent(self):\\n vn_idx = [None] * 3\\n pos = [None] * 3\\n tex = [None] * 3\\n for i in range(0, 3):\\n pos[i] = self.v_pos[self.t_pos_idx[:, i]]\\n tex[i] = self.v_tex[self.t_tex_idx[:, i]]\\n # t_nrm_idx is always the same as t_pos_idx\\n vn_idx[i] = self.t_pos_idx[:, i]\\n\\n tangents = torch.zeros_like(self.v_nrm)\\n tansum = torch.zeros_like(self.v_nrm)\\n\\n # Compute tangent space for each triangle\\n uve1 = tex[1] - tex[0]\\n uve2 = tex[2] - tex[0]\\n pe1 = pos[1] - pos[0]\\n pe2 = pos[2] - pos[0]\\n\\n nom = pe1 * uve2[..., 1:2] - pe2 * uve1[..., 1:2]\\n denom = uve1[..., 0:1] * uve2[..., 1:2] - uve1[..., 1:2] * uve2[..., 0:1]\\n\\n # Avoid division by zero for degenerated texture coordinates\\n tang = nom / torch.where(\\n denom > 0.0, torch.clamp(denom, min=1e-6), torch.clamp(denom, max=-1e-6)\\n )\\n\\n # Update all 3 vertices\\n for i in range(0, 3):\\n idx = vn_idx[i][:, None].repeat(1, 3)\\n tangents.scatter_add_(0, idx, tang) # tangents[n_i] = tangents[n_i] + tang\\n tansum.scatter_add_(\\n 0, idx, torch.ones_like(tang)\\n ) # tansum[n_i] = tansum[n_i] + 1\\n tangents = tangents / tansum\\n\\n # Normalize and make sure tangent is perpendicular to normal\\n tangents = F.normalize(tangents, dim=1)\\n tangents = F.normalize(tangents - dot(tangents, self.v_nrm) * self.v_nrm)\\n\\n if torch.is_anomaly_enabled():\\n assert torch.all(torch.isfinite(tangents))\\n\\n return tangents\\n\\n def _unwrap_uv(\\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\\n ):\\n threestudio.info(\\\"Using xatlas to perform UV unwrapping, may take a while ...\\\")\\n\\n import xatlas\\n\\n atlas = xatlas.Atlas()\\n atlas.add_mesh(\\n self.v_pos.detach().cpu().numpy(),\\n self.t_pos_idx.cpu().numpy(),\\n )\\n co = xatlas.ChartOptions()\\n po = xatlas.PackOptions()\\n for k, v in xatlas_chart_options.items():\\n setattr(co, k, v)\\n for k, v in xatlas_pack_options.items():\\n setattr(po, k, v)\\n atlas.generate(co, po)\\n vmapping, indices, uvs = atlas.get_mesh(0)\\n vmapping = (\\n torch.from_numpy(\\n vmapping.astype(np.uint64, casting=\\\"same_kind\\\").view(np.int64)\\n )\\n .to(self.v_pos.device)\\n .long()\\n )\\n uvs = torch.from_numpy(uvs).to(self.v_pos.device).float()\\n indices = (\\n torch.from_numpy(\\n indices.astype(np.uint64, casting=\\\"same_kind\\\").view(np.int64)\\n )\\n .to(self.v_pos.device)\\n .long()\\n )\\n return uvs, indices\\n\\n def unwrap_uv(\\n self, xatlas_chart_options: dict = {}, xatlas_pack_options: dict = {}\\n ):\\n self._v_tex, self._t_tex_idx = self._unwrap_uv(\\n xatlas_chart_options, xatlas_pack_options\\n )\\n\\n def set_vertex_color(self, v_rgb):\\n assert v_rgb.shape[0] == self.v_pos.shape[0]\\n self._v_rgb = v_rgb\\n\\n def _compute_edges(self):\\n # Compute edges\\n edges = torch.cat(\\n [\\n self.t_pos_idx[:, [0, 1]],\\n self.t_pos_idx[:, [1, 2]],\\n self.t_pos_idx[:, [2, 0]],\\n ],\\n dim=0,\\n )\\n edges = edges.sort()[0]\\n edges = torch.unique(edges, dim=0)\\n return edges\\n\\n def normal_consistency(self) -> Float[Tensor, \\\"\\\"]:\\n edge_nrm: Float[Tensor, \\\"Ne 2 3\\\"] = self.v_nrm[self.edges]\\n nc = (\\n 1.0 - torch.cosine_similarity(edge_nrm[:, 0], edge_nrm[:, 1], dim=-1)\\n ).mean()\\n return nc\\n\\n def _laplacian_uniform(self):\\n # from stable-dreamfusion\\n # https://github.com/ashawkey/stable-dreamfusion/blob/8fb3613e9e4cd1ded1066b46e80ca801dfb9fd06/nerf/renderer.py#L224\\n verts, faces = self.v_pos, self.t_pos_idx\\n\\n V = verts.shape[0]\\n F = faces.shape[0]\\n\\n # Neighbor indices\\n ii = faces[:, [1, 2, 0]].flatten()\\n jj = faces[:, [2, 0, 1]].flatten()\\n adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(\\n dim=1\\n )\\n adj_values = torch.ones(adj.shape[1]).to(verts)\\n\\n # Diagonal indices\\n diag_idx = adj[0]\\n\\n # Build the sparse matrix\\n idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1)\\n values = torch.cat((-adj_values, adj_values))\\n\\n # The coalesce operation sums the duplicate indices, resulting in the\\n # correct diagonal\\n return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()\\n\\n def laplacian(self) -> Float[Tensor, \\\"\\\"]:\\n with torch.no_grad():\\n L = self._laplacian_uniform()\\n loss = L.mm(self.v_pos)\\n loss = loss.norm(dim=1)\\n loss = loss.mean()\\n return loss\"\n },\n {\n \"identifier\": \"get_encoding\",\n \"path\": \"threestudio/models/networks.py\",\n \"snippet\": \"def get_encoding(n_input_dims: int, config) -> nn.Module:\\n # input suppose to be range [0, 1]\\n encoding: nn.Module\\n if config.otype == \\\"ProgressiveBandFrequency\\\":\\n encoding = ProgressiveBandFrequency(n_input_dims, config_to_primitive(config))\\n elif config.otype == \\\"ProgressiveBandHashGrid\\\":\\n encoding = ProgressiveBandHashGrid(n_input_dims, config_to_primitive(config))\\n else:\\n encoding = TCNNEncoding(n_input_dims, config_to_primitive(config))\\n encoding = CompositeEncoding(\\n encoding,\\n include_xyz=config.get(\\\"include_xyz\\\", False),\\n xyz_scale=2.0,\\n xyz_offset=-1.0,\\n ) # FIXME: hard coded\\n return encoding\"\n },\n {\n \"identifier\": \"get_mlp\",\n \"path\": \"threestudio/models/networks.py\",\n \"snippet\": \"def get_mlp(n_input_dims, n_output_dims, config) -> nn.Module:\\n network: nn.Module\\n if config.otype == \\\"VanillaMLP\\\":\\n network = VanillaMLP(n_input_dims, n_output_dims, config_to_primitive(config))\\n elif config.otype == \\\"SphereInitVanillaMLP\\\":\\n network = SphereInitVanillaMLP(\\n n_input_dims, n_output_dims, config_to_primitive(config)\\n )\\n else:\\n assert (\\n config.get(\\\"sphere_init\\\", False) is False\\n ), \\\"sphere_init=True only supported by VanillaMLP\\\"\\n network = TCNNNetwork(n_input_dims, n_output_dims, config_to_primitive(config))\\n return network\"\n },\n {\n \"identifier\": \"broadcast\",\n \"path\": \"threestudio/utils/misc.py\",\n \"snippet\": \"def broadcast(tensor, src=0):\\n if not _distributed_available():\\n return tensor\\n else:\\n torch.distributed.broadcast(tensor, src=src)\\n return tensor\"\n },\n {\n \"identifier\": \"get_rank\",\n \"path\": \"threestudio/utils/misc.py\",\n \"snippet\": \"def get_rank():\\n # SLURM_PROCID can be set even if SLURM is not managing the multiprocessing,\\n # therefore LOCAL_RANK needs to be checked first\\n rank_keys = (\\\"RANK\\\", \\\"LOCAL_RANK\\\", \\\"SLURM_PROCID\\\", \\\"JSM_NAMESPACE_RANK\\\")\\n for key in rank_keys:\\n rank = os.environ.get(key)\\n if rank is not None:\\n return int(rank)\\n return 0\"\n },\n {\n \"identifier\": \"scale_tensor\",\n \"path\": \"threestudio/utils/ops.py\",\n \"snippet\": \"def scale_tensor(\\n dat: Num[Tensor, \\\"... D\\\"], inp_scale: ValidScale, tgt_scale: ValidScale\\n):\\n if inp_scale is None:\\n inp_scale = (0, 1)\\n if tgt_scale is None:\\n tgt_scale = (0, 1)\\n if isinstance(tgt_scale, Tensor):\\n assert dat.shape[-1] == tgt_scale.shape[-1]\\n dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])\\n dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]\\n return dat\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import numpy as np\nimport os\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport threestudio\n import trimesh\nfrom dataclasses import dataclass, field\nfrom threestudio.models.geometry.base import (BaseExplicitGeometry,\n BaseGeometry,\n contract_to_unisphere,)\nfrom threestudio.models.geometry.implicit_sdf import ImplicitSDF\nfrom threestudio.models.geometry.implicit_volume import ImplicitVolume\nfrom threestudio.models.isosurface import MarchingTetrahedraHelper\nfrom threestudio.models.mesh import Mesh\nfrom threestudio.models.networks import get_encoding, get_mlp\nfrom threestudio.utils.misc import broadcast, get_rank\nfrom threestudio.utils.ops import scale_tensor\nfrom threestudio.utils.typing import *\n from pysdf import SDF"},"token_num":{"kind":"number","value":15743,"string":"15,743"},"cropped_code":{"kind":"string","value":"\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n sdf_gt = get_gt_sdf(\n scale_tensor(\n self.isosurface_helper.grid_vertices,\n self.isosurface_helper.points_range,\n self.isosurface_bbox,\n )\n )\n self.sdf.data = sdf_gt\n\n # explicit broadcast to ensure param consistency across ranks\n for param in self.parameters():\n broadcast(param, src=0)\n\n def isosurface(self) -> Mesh:\n # return cached mesh if fix_geometry is True to save computation\n if self.cfg.fix_geometry and self.mesh is not None:\n return self.mesh\n mesh = self.isosurface_helper(self.sdf, self.deformation)\n mesh.v_pos = scale_tensor(\n mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox\n )\n if self.cfg.isosurface_remove_outliers:\n mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)\n self.mesh = mesh\n return mesh\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n if self.cfg.geometry_only:\n return {}\n assert (\n output_normal == False\n ), f\"Normal output is not supported for {self.__class__.__name__}\"\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n return {\"features\": features}\n\n @staticmethod\n @torch.no_grad()\n def create_from(\n other: BaseGeometry,\n cfg: Optional[Union[dict, DictConfig]] = None,\n copy_net: bool = True,\n **kwargs,\n ) -> \"TetrahedraSDFGrid\":\n if isinstance(other, TetrahedraSDFGrid):\n instance = TetrahedraSDFGrid(cfg, **kwargs)\n assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution\n instance.isosurface_bbox = other.isosurface_bbox.clone()\n instance.sdf.data = other.sdf.data.clone()\n if (\n instance.cfg.isosurface_deformable_grid\n and other.cfg.isosurface_deformable_grid\n ):\n assert (\n instance.deformation is not None and other.deformation is not None\n )\n instance.deformation.data = other.deformation.data.clone()\n if (\n not instance.cfg.geometry_only\n and not other.cfg.geometry_only\n and copy_net\n ):\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n return instance\n elif isinstance(other, ImplicitVolume):\n instance = TetrahedraSDFGrid(cfg, **kwargs)\n if other.cfg.isosurface_method != \"mt\":\n other.cfg.isosurface_method = \"mt\"\n threestudio.warn(\n f\"Override isosurface_method of the source geometry to 'mt'\"\n )\n if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:\n other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution\n threestudio.warn(\n f\"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}\"\n )\n mesh = other.isosurface()\n # instance.isosurface_bbox = mesh.extras[\"bbox\"]\n instance.isosurface_bbox = mesh.extras[\"bbox\"] * instance.cfg.nerf_scale\n instance.sdf.data = (\n mesh.extras[\"grid_level\"].to(instance.sdf.data).clamp(-1, 1)\n )\n if not instance.cfg.geometry_only and copy_net:\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n return instance\n"},"all_code":{"kind":"string","value":"\n\n\n@threestudio.register(\"tetrahedra-sdf-grid\")\nclass TetrahedraSDFGrid(BaseExplicitGeometry):\n @dataclass\n class Config(BaseExplicitGeometry.Config):\n isosurface_resolution: int = 128\n isosurface_deformable_grid: bool = True\n isosurface_remove_outliers: bool = False\n isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01\n nerf_scale: float = 1.0\n\n n_input_dims: int = 3\n n_feature_dims: int = 3\n pos_encoding_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"HashGrid\",\n \"n_levels\": 16,\n \"n_features_per_level\": 2,\n \"log2_hashmap_size\": 19,\n \"base_resolution\": 16,\n \"per_level_scale\": 1.447269237440378,\n }\n )\n mlp_network_config: dict = field(\n default_factory=lambda: {\n \"otype\": \"VanillaMLP\",\n \"activation\": \"ReLU\",\n \"output_activation\": \"none\",\n \"n_neurons\": 64,\n \"n_hidden_layers\": 1,\n }\n )\n shape_init: Optional[str] = None\n shape_init_params: Optional[Any] = None\n shape_init_mesh_up: str = \"+z\"\n shape_init_mesh_front: str = \"+x\"\n force_shape_init: bool = False\n geometry_only: bool = False\n fix_geometry: bool = False\n\n # sdf_bias: Union[float, str] = 0.0\n # sdf_bias_params: Optional[Any] = None\n\n cfg: Config\n\n def configure(self) -> None:\n super().configure()\n\n # this should be saved to state_dict, register as buffer\n self.isosurface_bbox: Float[Tensor, \"2 3\"]\n self.register_buffer(\"isosurface_bbox\", self.bbox.clone())\n\n self.isosurface_helper = MarchingTetrahedraHelper(\n self.cfg.isosurface_resolution,\n f\"load/tets/{self.cfg.isosurface_resolution}_tets.npz\",\n )\n\n self.sdf: Float[Tensor, \"Nv 1\"]\n self.deformation: Optional[Float[Tensor, \"Nv 3\"]]\n\n if not self.cfg.fix_geometry:\n self.register_parameter(\n \"sdf\",\n nn.Parameter(\n torch.zeros(\n (self.isosurface_helper.grid_vertices.shape[0], 1),\n dtype=torch.float32,\n )\n ),\n )\n if self.cfg.isosurface_deformable_grid:\n self.register_parameter(\n \"deformation\",\n nn.Parameter(\n torch.zeros_like(self.isosurface_helper.grid_vertices)\n ),\n )\n else:\n self.deformation = None\n else:\n self.register_buffer(\n \"sdf\",\n torch.zeros(\n (self.isosurface_helper.grid_vertices.shape[0], 1),\n dtype=torch.float32,\n ),\n )\n if self.cfg.isosurface_deformable_grid:\n self.register_buffer(\n \"deformation\",\n torch.zeros_like(self.isosurface_helper.grid_vertices),\n )\n else:\n self.deformation = None\n\n if not self.cfg.geometry_only:\n self.encoding = get_encoding(\n self.cfg.n_input_dims, self.cfg.pos_encoding_config\n )\n self.feature_network = get_mlp(\n self.encoding.n_output_dims,\n self.cfg.n_feature_dims,\n self.cfg.mlp_network_config,\n )\n\n self.mesh: Optional[Mesh] = None\n\n def initialize_shape(self) -> None:\n if self.cfg.shape_init is None and not self.cfg.force_shape_init:\n return\n\n # do not initialize shape if weights are provided\n if self.cfg.weights is not None and not self.cfg.force_shape_init:\n return\n\n get_gt_sdf: Callable[[Float[Tensor, \"N 3\"]], Float[Tensor, \"N 1\"]]\n assert isinstance(self.cfg.shape_init, str)\n if self.cfg.shape_init == \"ellipsoid\":\n assert (\n isinstance(self.cfg.shape_init_params, Sized)\n and len(self.cfg.shape_init_params) == 3\n )\n size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return ((points_rand / size) ** 2).sum(\n dim=-1, keepdim=True\n ).sqrt() - 1.0 # pseudo signed distance of an ellipsoid\n\n get_gt_sdf = func\n elif self.cfg.shape_init == \"sphere\":\n assert isinstance(self.cfg.shape_init_params, float)\n radius = self.cfg.shape_init_params\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius\n\n get_gt_sdf = func\n elif self.cfg.shape_init.startswith(\"mesh:\"):\n assert isinstance(self.cfg.shape_init_params, float)\n mesh_path = self.cfg.shape_init[5:]\n if not os.path.exists(mesh_path):\n raise ValueError(f\"Mesh file {mesh_path} does not exist.\")\n\n\n mesh = trimesh.load(mesh_path)\n\n # move to center\n centroid = mesh.vertices.mean(0)\n mesh.vertices = mesh.vertices - centroid\n\n # align to up-z and front-x\n dirs = [\"+x\", \"+y\", \"+z\", \"-x\", \"-y\", \"-z\"]\n dir2vec = {\n \"+x\": np.array([1, 0, 0]),\n \"+y\": np.array([0, 1, 0]),\n \"+z\": np.array([0, 0, 1]),\n \"-x\": np.array([-1, 0, 0]),\n \"-y\": np.array([0, -1, 0]),\n \"-z\": np.array([0, 0, -1]),\n }\n if (\n self.cfg.shape_init_mesh_up not in dirs\n or self.cfg.shape_init_mesh_front not in dirs\n ):\n raise ValueError(\n f\"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}.\"\n )\n if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:\n raise ValueError(\n \"shape_init_mesh_up and shape_init_mesh_front must be orthogonal.\"\n )\n z_, x_ = (\n dir2vec[self.cfg.shape_init_mesh_up],\n dir2vec[self.cfg.shape_init_mesh_front],\n )\n y_ = np.cross(z_, x_)\n std2mesh = np.stack([x_, y_, z_], axis=0).T\n mesh2std = np.linalg.inv(std2mesh)\n\n # scaling\n scale = np.abs(mesh.vertices).max()\n mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params\n mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T\n\n\n sdf = SDF(mesh.vertices, mesh.faces)\n\n def func(points_rand: Float[Tensor, \"N 3\"]) -> Float[Tensor, \"N 1\"]:\n # add a negative signed here\n # as in pysdf the inside of the shape has positive signed distance\n return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(\n points_rand\n )[..., None]\n\n get_gt_sdf = func\n\n else:\n raise ValueError(\n f\"Unknown shape initialization type: {self.cfg.shape_init}\"\n )\n\n sdf_gt = get_gt_sdf(\n scale_tensor(\n self.isosurface_helper.grid_vertices,\n self.isosurface_helper.points_range,\n self.isosurface_bbox,\n )\n )\n self.sdf.data = sdf_gt\n\n # explicit broadcast to ensure param consistency across ranks\n for param in self.parameters():\n broadcast(param, src=0)\n\n def isosurface(self) -> Mesh:\n # return cached mesh if fix_geometry is True to save computation\n if self.cfg.fix_geometry and self.mesh is not None:\n return self.mesh\n mesh = self.isosurface_helper(self.sdf, self.deformation)\n mesh.v_pos = scale_tensor(\n mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox\n )\n if self.cfg.isosurface_remove_outliers:\n mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)\n self.mesh = mesh\n return mesh\n\n def forward(\n self, points: Float[Tensor, \"*N Di\"], output_normal: bool = False\n ) -> Dict[str, Float[Tensor, \"...\"]]:\n if self.cfg.geometry_only:\n return {}\n assert (\n output_normal == False\n ), f\"Normal output is not supported for {self.__class__.__name__}\"\n points_unscaled = points # points in the original scale\n points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)\n enc = self.encoding(points.view(-1, self.cfg.n_input_dims))\n features = self.feature_network(enc).view(\n *points.shape[:-1], self.cfg.n_feature_dims\n )\n return {\"features\": features}\n\n @staticmethod\n @torch.no_grad()\n def create_from(\n other: BaseGeometry,\n cfg: Optional[Union[dict, DictConfig]] = None,\n copy_net: bool = True,\n **kwargs,\n ) -> \"TetrahedraSDFGrid\":\n if isinstance(other, TetrahedraSDFGrid):\n instance = TetrahedraSDFGrid(cfg, **kwargs)\n assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution\n instance.isosurface_bbox = other.isosurface_bbox.clone()\n instance.sdf.data = other.sdf.data.clone()\n if (\n instance.cfg.isosurface_deformable_grid\n and other.cfg.isosurface_deformable_grid\n ):\n assert (\n instance.deformation is not None and other.deformation is not None\n )\n instance.deformation.data = other.deformation.data.clone()\n if (\n not instance.cfg.geometry_only\n and not other.cfg.geometry_only\n and copy_net\n ):\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n return instance\n elif isinstance(other, ImplicitVolume):\n instance = TetrahedraSDFGrid(cfg, **kwargs)\n if other.cfg.isosurface_method != \"mt\":\n other.cfg.isosurface_method = \"mt\"\n threestudio.warn(\n f\"Override isosurface_method of the source geometry to 'mt'\"\n )\n if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:\n other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution\n threestudio.warn(\n f\"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}\"\n )\n mesh = other.isosurface()\n # instance.isosurface_bbox = mesh.extras[\"bbox\"]\n instance.isosurface_bbox = mesh.extras[\"bbox\"] * instance.cfg.nerf_scale\n instance.sdf.data = (\n mesh.extras[\"grid_level\"].to(instance.sdf.data).clamp(-1, 1)\n )\n if not instance.cfg.geometry_only and copy_net:\n instance.encoding.load_state_dict(other.encoding.state_dict())\n instance.feature_network.load_state_dict(\n other.feature_network.state_dict()\n )\n return instance"},"next_line":{"kind":"string","value":" elif isinstance(other, ImplicitSDF):"},"gold_snippet_index":{"kind":"number","value":3,"string":"3"},"created_at":{"kind":"string","value":"2023-12-06 07:53:11+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5286,"cells":{"repo_name":{"kind":"string","value":"rehg-lab/RAVE"},"file_path":{"kind":"string","value":"annotator/oneformer/detectron2/modeling/meta_arch/retinanet.py"},"context":{"kind":"list like","value":[{"identifier":"configurable","path":"annotator/oneformer/detectron2/config/config.py","snippet":"def configurable(init_func=None, *, from_config=None):\r\n \"\"\"\r\n Decorate a function or a class's __init__ method so that it can be called\r\n with a :class:`CfgNode` object using a :func:`from_config` function that translates\r\n :class:`CfgNode` to arguments.\r\n\r\n Examples:\r\n ::\r\n # Usage 1: Decorator on __init__:\r\n class A:\r\n @configurable\r\n def __init__(self, a, b=2, c=3):\r\n pass\r\n\r\n @classmethod\r\n def from_config(cls, cfg): # 'cfg' must be the first argument\r\n # Returns kwargs to be passed to __init__\r\n return {\"a\": cfg.A, \"b\": cfg.B}\r\n\r\n a1 = A(a=1, b=2) # regular construction\r\n a2 = A(cfg) # construct with a cfg\r\n a3 = A(cfg, b=3, c=4) # construct with extra overwrite\r\n\r\n # Usage 2: Decorator on any function. Needs an extra from_config argument:\r\n @configurable(from_config=lambda cfg: {\"a: cfg.A, \"b\": cfg.B})\r\n def a_func(a, b=2, c=3):\r\n pass\r\n\r\n a1 = a_func(a=1, b=2) # regular call\r\n a2 = a_func(cfg) # call with a cfg\r\n a3 = a_func(cfg, b=3, c=4) # call with extra overwrite\r\n\r\n Args:\r\n init_func (callable): a class's ``__init__`` method in usage 1. The\r\n class must have a ``from_config`` classmethod which takes `cfg` as\r\n the first argument.\r\n from_config (callable): the from_config function in usage 2. It must take `cfg`\r\n as its first argument.\r\n \"\"\"\r\n\r\n if init_func is not None:\r\n assert (\r\n inspect.isfunction(init_func)\r\n and from_config is None\r\n and init_func.__name__ == \"__init__\"\r\n ), \"Incorrect use of @configurable. Check API documentation for examples.\"\r\n\r\n @functools.wraps(init_func)\r\n def wrapped(self, *args, **kwargs):\r\n try:\r\n from_config_func = type(self).from_config\r\n except AttributeError as e:\r\n raise AttributeError(\r\n \"Class with @configurable must have a 'from_config' classmethod.\"\r\n ) from e\r\n if not inspect.ismethod(from_config_func):\r\n raise TypeError(\"Class with @configurable must have a 'from_config' classmethod.\")\r\n\r\n if _called_with_cfg(*args, **kwargs):\r\n explicit_args = _get_args_from_config(from_config_func, *args, **kwargs)\r\n init_func(self, **explicit_args)\r\n else:\r\n init_func(self, *args, **kwargs)\r\n\r\n return wrapped\r\n\r\n else:\r\n if from_config is None:\r\n return configurable # @configurable() is made equivalent to @configurable\r\n assert inspect.isfunction(\r\n from_config\r\n ), \"from_config argument of configurable must be a function!\"\r\n\r\n def wrapper(orig_func):\r\n @functools.wraps(orig_func)\r\n def wrapped(*args, **kwargs):\r\n if _called_with_cfg(*args, **kwargs):\r\n explicit_args = _get_args_from_config(from_config, *args, **kwargs)\r\n return orig_func(**explicit_args)\r\n else:\r\n return orig_func(*args, **kwargs)\r\n\r\n wrapped.from_config = from_config\r\n return wrapped\r\n\r\n return wrapper\r"},{"identifier":"get_norm","path":"annotator/oneformer/detectron2/layers/batch_norm.py","snippet":"def get_norm(norm, out_channels):\r\n \"\"\"\r\n Args:\r\n norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;\r\n or a callable that takes a channel number and returns\r\n the normalization layer as a nn.Module.\r\n\r\n Returns:\r\n nn.Module or None: the normalization layer\r\n \"\"\"\r\n if norm is None:\r\n return None\r\n if isinstance(norm, str):\r\n if len(norm) == 0:\r\n return None\r\n norm = {\r\n \"BN\": BatchNorm2d,\r\n # Fixed in https://github.com/pytorch/pytorch/pull/36382\r\n \"SyncBN\": NaiveSyncBatchNorm if env.TORCH_VERSION <= (1, 5) else nn.SyncBatchNorm,\r\n \"FrozenBN\": FrozenBatchNorm2d,\r\n \"GN\": lambda channels: nn.GroupNorm(32, channels),\r\n # for debugging:\r\n \"nnSyncBN\": nn.SyncBatchNorm,\r\n \"naiveSyncBN\": NaiveSyncBatchNorm,\r\n # expose stats_mode N as an option to caller, required for zero-len inputs\r\n \"naiveSyncBN_N\": lambda channels: NaiveSyncBatchNorm(channels, stats_mode=\"N\"),\r\n \"LN\": lambda channels: LayerNorm(channels),\r\n }[norm]\r\n return norm(out_channels)\r"},{"identifier":"CycleBatchNormList","path":"annotator/oneformer/detectron2/layers/batch_norm.py","snippet":"class CycleBatchNormList(nn.ModuleList):\r\n \"\"\"\r\n Implement domain-specific BatchNorm by cycling.\r\n\r\n When a BatchNorm layer is used for multiple input domains or input\r\n features, it might need to maintain a separate test-time statistics\r\n for each domain. See Sec 5.2 in :paper:`rethinking-batchnorm`.\r\n\r\n This module implements it by using N separate BN layers\r\n and it cycles through them every time a forward() is called.\r\n\r\n NOTE: The caller of this module MUST guarantee to always call\r\n this module by multiple of N times. Otherwise its test-time statistics\r\n will be incorrect.\r\n \"\"\"\r\n\r\n def __init__(self, length: int, bn_class=nn.BatchNorm2d, **kwargs):\r\n \"\"\"\r\n Args:\r\n length: number of BatchNorm layers to cycle.\r\n bn_class: the BatchNorm class to use\r\n kwargs: arguments of the BatchNorm class, such as num_features.\r\n \"\"\"\r\n self._affine = kwargs.pop(\"affine\", True)\r\n super().__init__([bn_class(**kwargs, affine=False) for k in range(length)])\r\n if self._affine:\r\n # shared affine, domain-specific BN\r\n channels = self[0].num_features\r\n self.weight = nn.Parameter(torch.ones(channels))\r\n self.bias = nn.Parameter(torch.zeros(channels))\r\n self._pos = 0\r\n\r\n def forward(self, x):\r\n ret = self[self._pos](x)\r\n self._pos = (self._pos + 1) % len(self)\r\n\r\n if self._affine:\r\n w = self.weight.reshape(1, -1, 1, 1)\r\n b = self.bias.reshape(1, -1, 1, 1)\r\n return ret * w + b\r\n else:\r\n return ret\r\n\r\n def extra_repr(self):\r\n return f\"affine={self._affine}\"\r"},{"identifier":"batched_nms","path":"annotator/oneformer/detectron2/layers/nms.py","snippet":"def batched_nms(\r\n boxes: torch.Tensor, scores: torch.Tensor, idxs: torch.Tensor, iou_threshold: float\r\n):\r\n \"\"\"\r\n Same as torchvision.ops.boxes.batched_nms, but with float().\r\n \"\"\"\r\n assert boxes.shape[-1] == 4\r\n # Note: Torchvision already has a strategy (https://github.com/pytorch/vision/issues/1311)\r\n # to decide whether to use coordinate trick or for loop to implement batched_nms. So we\r\n # just call it directly.\r\n # Fp16 does not have enough range for batched NMS, so adding float().\r\n return box_ops.batched_nms(boxes.float(), scores, idxs, iou_threshold)\r"},{"identifier":"ShapeSpec","path":"annotator/oneformer/detectron2/layers/shape_spec.py","snippet":"class ShapeSpec:\r\n \"\"\"\r\n A simple structure that contains basic shape specification about a tensor.\r\n It is often used as the auxiliary inputs/outputs of models,\r\n to complement the lack of shape inference ability among pytorch modules.\r\n \"\"\"\r\n\r\n channels: Optional[int] = None\r\n height: Optional[int] = None\r\n width: Optional[int] = None\r\n stride: Optional[int] = None\r"},{"identifier":"cat","path":"annotator/oneformer/detectron2/layers/wrappers.py","snippet":"def cat(tensors: List[torch.Tensor], dim: int = 0):\r\n \"\"\"\r\n Efficient version of torch.cat that avoids a copy if there is only a single element in a list\r\n \"\"\"\r\n assert isinstance(tensors, (list, tuple))\r\n if len(tensors) == 1:\r\n return tensors[0]\r\n return torch.cat(tensors, dim)\r"},{"identifier":"Boxes","path":"annotator/oneformer/detectron2/structures/boxes.py","snippet":"class Boxes:\r\n \"\"\"\r\n This structure stores a list of boxes as a Nx4 torch.Tensor.\r\n It supports some common methods about boxes\r\n (`area`, `clip`, `nonempty`, etc),\r\n and also behaves like a Tensor\r\n (support indexing, `to(device)`, `.device`, and iteration over all boxes)\r\n\r\n Attributes:\r\n tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).\r\n \"\"\"\r\n\r\n def __init__(self, tensor: torch.Tensor):\r\n \"\"\"\r\n Args:\r\n tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2).\r\n \"\"\"\r\n if not isinstance(tensor, torch.Tensor):\r\n tensor = torch.as_tensor(tensor, dtype=torch.float32, device=torch.device(\"cpu\"))\r\n else:\r\n tensor = tensor.to(torch.float32)\r\n if tensor.numel() == 0:\r\n # Use reshape, so we don't end up creating a new tensor that does not depend on\r\n # the inputs (and consequently confuses jit)\r\n tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32)\r\n assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()\r\n\r\n self.tensor = tensor\r\n\r\n def clone(self) -> \"Boxes\":\r\n \"\"\"\r\n Clone the Boxes.\r\n\r\n Returns:\r\n Boxes\r\n \"\"\"\r\n return Boxes(self.tensor.clone())\r\n\r\n def to(self, device: torch.device):\r\n # Boxes are assumed float32 and does not support to(dtype)\r\n return Boxes(self.tensor.to(device=device))\r\n\r\n def area(self) -> torch.Tensor:\r\n \"\"\"\r\n Computes the area of all the boxes.\r\n\r\n Returns:\r\n torch.Tensor: a vector with areas of each box.\r\n \"\"\"\r\n box = self.tensor\r\n area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])\r\n return area\r\n\r\n def clip(self, box_size: Tuple[int, int]) -> None:\r\n \"\"\"\r\n Clip (in place) the boxes by limiting x coordinates to the range [0, width]\r\n and y coordinates to the range [0, height].\r\n\r\n Args:\r\n box_size (height, width): The clipping box's size.\r\n \"\"\"\r\n assert torch.isfinite(self.tensor).all(), \"Box tensor contains infinite or NaN!\"\r\n h, w = box_size\r\n x1 = self.tensor[:, 0].clamp(min=0, max=w)\r\n y1 = self.tensor[:, 1].clamp(min=0, max=h)\r\n x2 = self.tensor[:, 2].clamp(min=0, max=w)\r\n y2 = self.tensor[:, 3].clamp(min=0, max=h)\r\n self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)\r\n\r\n def nonempty(self, threshold: float = 0.0) -> torch.Tensor:\r\n \"\"\"\r\n Find boxes that are non-empty.\r\n A box is considered empty, if either of its side is no larger than threshold.\r\n\r\n Returns:\r\n Tensor:\r\n a binary vector which represents whether each box is empty\r\n (False) or non-empty (True).\r\n \"\"\"\r\n box = self.tensor\r\n widths = box[:, 2] - box[:, 0]\r\n heights = box[:, 3] - box[:, 1]\r\n keep = (widths > threshold) & (heights > threshold)\r\n return keep\r\n\r\n def __getitem__(self, item) -> \"Boxes\":\r\n \"\"\"\r\n Args:\r\n item: int, slice, or a BoolTensor\r\n\r\n Returns:\r\n Boxes: Create a new :class:`Boxes` by indexing.\r\n\r\n The following usage are allowed:\r\n\r\n 1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.\r\n 2. `new_boxes = boxes[2:10]`: return a slice of boxes.\r\n 3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor\r\n with `length = len(boxes)`. Nonzero elements in the vector will be selected.\r\n\r\n Note that the returned Boxes might share storage with this Boxes,\r\n subject to Pytorch's indexing semantics.\r\n \"\"\"\r\n if isinstance(item, int):\r\n return Boxes(self.tensor[item].view(1, -1))\r\n b = self.tensor[item]\r\n assert b.dim() == 2, \"Indexing on Boxes with {} failed to return a matrix!\".format(item)\r\n return Boxes(b)\r\n\r\n def __len__(self) -> int:\r\n return self.tensor.shape[0]\r\n\r\n def __repr__(self) -> str:\r\n return \"Boxes(\" + str(self.tensor) + \")\"\r\n\r\n def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:\r\n \"\"\"\r\n Args:\r\n box_size (height, width): Size of the reference box.\r\n boundary_threshold (int): Boxes that extend beyond the reference box\r\n boundary by more than boundary_threshold are considered \"outside\".\r\n\r\n Returns:\r\n a binary vector, indicating whether each box is inside the reference box.\r\n \"\"\"\r\n height, width = box_size\r\n inds_inside = (\r\n (self.tensor[..., 0] >= -boundary_threshold)\r\n & (self.tensor[..., 1] >= -boundary_threshold)\r\n & (self.tensor[..., 2] < width + boundary_threshold)\r\n & (self.tensor[..., 3] < height + boundary_threshold)\r\n )\r\n return inds_inside\r\n\r\n def get_centers(self) -> torch.Tensor:\r\n \"\"\"\r\n Returns:\r\n The box centers in a Nx2 array of (x, y).\r\n \"\"\"\r\n return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2\r\n\r\n def scale(self, scale_x: float, scale_y: float) -> None:\r\n \"\"\"\r\n Scale the box with horizontal and vertical scaling factors\r\n \"\"\"\r\n self.tensor[:, 0::2] *= scale_x\r\n self.tensor[:, 1::2] *= scale_y\r\n\r\n @classmethod\r\n def cat(cls, boxes_list: List[\"Boxes\"]) -> \"Boxes\":\r\n \"\"\"\r\n Concatenates a list of Boxes into a single Boxes\r\n\r\n Arguments:\r\n boxes_list (list[Boxes])\r\n\r\n Returns:\r\n Boxes: the concatenated Boxes\r\n \"\"\"\r\n assert isinstance(boxes_list, (list, tuple))\r\n if len(boxes_list) == 0:\r\n return cls(torch.empty(0))\r\n assert all([isinstance(box, Boxes) for box in boxes_list])\r\n\r\n # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input\r\n cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))\r\n return cat_boxes\r\n\r\n @property\r\n def device(self) -> device:\r\n return self.tensor.device\r\n\r\n # type \"Iterator[torch.Tensor]\", yield, and iter() not supported by torchscript\r\n # https://github.com/pytorch/pytorch/issues/18627\r\n @torch.jit.unused\r\n def __iter__(self):\r\n \"\"\"\r\n Yield a box as a Tensor of shape (4,) at a time.\r\n \"\"\"\r\n yield from self.tensor\r"},{"identifier":"pairwise_iou","path":"annotator/oneformer/detectron2/structures/boxes.py","snippet":"def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:\r\n \"\"\"\r\n Given two lists of boxes of size N and M, compute the IoU\r\n (intersection over union) between **all** N x M pairs of boxes.\r\n The box order must be (xmin, ymin, xmax, ymax).\r\n\r\n Args:\r\n boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.\r\n\r\n Returns:\r\n Tensor: IoU, sized [N,M].\r\n \"\"\"\r\n area1 = boxes1.area() # [N]\r\n area2 = boxes2.area() # [M]\r\n inter = pairwise_intersection(boxes1, boxes2)\r\n\r\n # handle empty boxes\r\n iou = torch.where(\r\n inter > 0,\r\n inter / (area1[:, None] + area2 - inter),\r\n torch.zeros(1, dtype=inter.dtype, device=inter.device),\r\n )\r\n return iou\r"},{"identifier":"ImageList","path":"annotator/oneformer/detectron2/structures/image_list.py","snippet":"class ImageList(object):\r\n \"\"\"\r\n Structure that holds a list of images (of possibly\r\n varying sizes) as a single tensor.\r\n This works by padding the images to the same size.\r\n The original sizes of each image is stored in `image_sizes`.\r\n\r\n Attributes:\r\n image_sizes (list[tuple[int, int]]): each tuple is (h, w).\r\n During tracing, it becomes list[Tensor] instead.\r\n \"\"\"\r\n\r\n def __init__(self, tensor: torch.Tensor, image_sizes: List[Tuple[int, int]]):\r\n \"\"\"\r\n Arguments:\r\n tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1\r\n image_sizes (list[tuple[int, int]]): Each tuple is (h, w). It can\r\n be smaller than (H, W) due to padding.\r\n \"\"\"\r\n self.tensor = tensor\r\n self.image_sizes = image_sizes\r\n\r\n def __len__(self) -> int:\r\n return len(self.image_sizes)\r\n\r\n def __getitem__(self, idx) -> torch.Tensor:\r\n \"\"\"\r\n Access the individual image in its original size.\r\n\r\n Args:\r\n idx: int or slice\r\n\r\n Returns:\r\n Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1\r\n \"\"\"\r\n size = self.image_sizes[idx]\r\n return self.tensor[idx, ..., : size[0], : size[1]]\r\n\r\n @torch.jit.unused\r\n def to(self, *args: Any, **kwargs: Any) -> \"ImageList\":\r\n cast_tensor = self.tensor.to(*args, **kwargs)\r\n return ImageList(cast_tensor, self.image_sizes)\r\n\r\n @property\r\n def device(self) -> device:\r\n return self.tensor.device\r\n\r\n @staticmethod\r\n def from_tensors(\r\n tensors: List[torch.Tensor],\r\n size_divisibility: int = 0,\r\n pad_value: float = 0.0,\r\n padding_constraints: Optional[Dict[str, int]] = None,\r\n ) -> \"ImageList\":\r\n \"\"\"\r\n Args:\r\n tensors: a tuple or list of `torch.Tensor`, each of shape (Hi, Wi) or\r\n (C_1, ..., C_K, Hi, Wi) where K >= 1. The Tensors will be padded\r\n to the same shape with `pad_value`.\r\n size_divisibility (int): If `size_divisibility > 0`, add padding to ensure\r\n the common height and width is divisible by `size_divisibility`.\r\n This depends on the model and many models need a divisibility of 32.\r\n pad_value (float): value to pad.\r\n padding_constraints (optional[Dict]): If given, it would follow the format as\r\n {\"size_divisibility\": int, \"square_size\": int}, where `size_divisibility` will\r\n overwrite the above one if presented and `square_size` indicates the\r\n square padding size if `square_size` > 0.\r\n Returns:\r\n an `ImageList`.\r\n \"\"\"\r\n assert len(tensors) > 0\r\n assert isinstance(tensors, (tuple, list))\r\n for t in tensors:\r\n assert isinstance(t, torch.Tensor), type(t)\r\n assert t.shape[:-2] == tensors[0].shape[:-2], t.shape\r\n\r\n image_sizes = [(im.shape[-2], im.shape[-1]) for im in tensors]\r\n image_sizes_tensor = [shapes_to_tensor(x) for x in image_sizes]\r\n max_size = torch.stack(image_sizes_tensor).max(0).values\r\n\r\n if padding_constraints is not None:\r\n square_size = padding_constraints.get(\"square_size\", 0)\r\n if square_size > 0:\r\n # pad to square.\r\n max_size[0] = max_size[1] = square_size\r\n if \"size_divisibility\" in padding_constraints:\r\n size_divisibility = padding_constraints[\"size_divisibility\"]\r\n if size_divisibility > 1:\r\n stride = size_divisibility\r\n # the last two dims are H,W, both subject to divisibility requirement\r\n max_size = (max_size + (stride - 1)).div(stride, rounding_mode=\"floor\") * stride\r\n\r\n # handle weirdness of scripting and tracing ...\r\n if torch.jit.is_scripting():\r\n max_size: List[int] = max_size.to(dtype=torch.long).tolist()\r\n else:\r\n if torch.jit.is_tracing():\r\n image_sizes = image_sizes_tensor\r\n\r\n if len(tensors) == 1:\r\n # This seems slightly (2%) faster.\r\n # TODO: check whether it's faster for multiple images as well\r\n image_size = image_sizes[0]\r\n padding_size = [0, max_size[-1] - image_size[1], 0, max_size[-2] - image_size[0]]\r\n batched_imgs = F.pad(tensors[0], padding_size, value=pad_value).unsqueeze_(0)\r\n else:\r\n # max_size can be a tensor in tracing mode, therefore convert to list\r\n batch_shape = [len(tensors)] + list(tensors[0].shape[:-2]) + list(max_size)\r\n device = (\r\n None if torch.jit.is_scripting() else (\"cpu\" if torch.jit.is_tracing() else None)\r\n )\r\n batched_imgs = tensors[0].new_full(batch_shape, pad_value, device=device)\r\n batched_imgs = move_device_like(batched_imgs, tensors[0])\r\n for i, img in enumerate(tensors):\r\n # Use `batched_imgs` directly instead of `img, pad_img = zip(tensors, batched_imgs)`\r\n # Tracing mode cannot capture `copy_()` of temporary locals\r\n batched_imgs[i, ..., : img.shape[-2], : img.shape[-1]].copy_(img)\r\n\r\n return ImageList(batched_imgs.contiguous(), image_sizes)\r"},{"identifier":"Instances","path":"annotator/oneformer/detectron2/structures/instances.py","snippet":"class Instances:\r\n \"\"\"\r\n This class represents a list of instances in an image.\r\n It stores the attributes of instances (e.g., boxes, masks, labels, scores) as \"fields\".\r\n All fields must have the same ``__len__`` which is the number of instances.\r\n\r\n All other (non-field) attributes of this class are considered private:\r\n they must start with '_' and are not modifiable by a user.\r\n\r\n Some basic usage:\r\n\r\n 1. Set/get/check a field:\r\n\r\n .. code-block:: python\r\n\r\n instances.gt_boxes = Boxes(...)\r\n print(instances.pred_masks) # a tensor of shape (N, H, W)\r\n print('gt_masks' in instances)\r\n\r\n 2. ``len(instances)`` returns the number of instances\r\n 3. Indexing: ``instances[indices]`` will apply the indexing on all the fields\r\n and returns a new :class:`Instances`.\r\n Typically, ``indices`` is a integer vector of indices,\r\n or a binary mask of length ``num_instances``\r\n\r\n .. code-block:: python\r\n\r\n category_3_detections = instances[instances.pred_classes == 3]\r\n confident_detections = instances[instances.scores > 0.9]\r\n \"\"\"\r\n\r\n def __init__(self, image_size: Tuple[int, int], **kwargs: Any):\r\n \"\"\"\r\n Args:\r\n image_size (height, width): the spatial size of the image.\r\n kwargs: fields to add to this `Instances`.\r\n \"\"\"\r\n self._image_size = image_size\r\n self._fields: Dict[str, Any] = {}\r\n for k, v in kwargs.items():\r\n self.set(k, v)\r\n\r\n @property\r\n def image_size(self) -> Tuple[int, int]:\r\n \"\"\"\r\n Returns:\r\n tuple: height, width\r\n \"\"\"\r\n return self._image_size\r\n\r\n def __setattr__(self, name: str, val: Any) -> None:\r\n if name.startswith(\"_\"):\r\n super().__setattr__(name, val)\r\n else:\r\n self.set(name, val)\r\n\r\n def __getattr__(self, name: str) -> Any:\r\n if name == \"_fields\" or name not in self._fields:\r\n raise AttributeError(\"Cannot find field '{}' in the given Instances!\".format(name))\r\n return self._fields[name]\r\n\r\n def set(self, name: str, value: Any) -> None:\r\n \"\"\"\r\n Set the field named `name` to `value`.\r\n The length of `value` must be the number of instances,\r\n and must agree with other existing fields in this object.\r\n \"\"\"\r\n with warnings.catch_warnings(record=True):\r\n data_len = len(value)\r\n if len(self._fields):\r\n assert (\r\n len(self) == data_len\r\n ), \"Adding a field of length {} to a Instances of length {}\".format(data_len, len(self))\r\n self._fields[name] = value\r\n\r\n def has(self, name: str) -> bool:\r\n \"\"\"\r\n Returns:\r\n bool: whether the field called `name` exists.\r\n \"\"\"\r\n return name in self._fields\r\n\r\n def remove(self, name: str) -> None:\r\n \"\"\"\r\n Remove the field called `name`.\r\n \"\"\"\r\n del self._fields[name]\r\n\r\n def get(self, name: str) -> Any:\r\n \"\"\"\r\n Returns the field called `name`.\r\n \"\"\"\r\n return self._fields[name]\r\n\r\n def get_fields(self) -> Dict[str, Any]:\r\n \"\"\"\r\n Returns:\r\n dict: a dict which maps names (str) to data of the fields\r\n\r\n Modifying the returned dict will modify this instance.\r\n \"\"\"\r\n return self._fields\r\n\r\n # Tensor-like methods\r\n def to(self, *args: Any, **kwargs: Any) -> \"Instances\":\r\n \"\"\"\r\n Returns:\r\n Instances: all fields are called with a `to(device)`, if the field has this method.\r\n \"\"\"\r\n ret = Instances(self._image_size)\r\n for k, v in self._fields.items():\r\n if hasattr(v, \"to\"):\r\n v = v.to(*args, **kwargs)\r\n ret.set(k, v)\r\n return ret\r\n\r\n def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> \"Instances\":\r\n \"\"\"\r\n Args:\r\n item: an index-like object and will be used to index all the fields.\r\n\r\n Returns:\r\n If `item` is a string, return the data in the corresponding field.\r\n Otherwise, returns an `Instances` where all fields are indexed by `item`.\r\n \"\"\"\r\n if type(item) == int:\r\n if item >= len(self) or item < -len(self):\r\n raise IndexError(\"Instances index out of range!\")\r\n else:\r\n item = slice(item, None, len(self))\r\n\r\n ret = Instances(self._image_size)\r\n for k, v in self._fields.items():\r\n ret.set(k, v[item])\r\n return ret\r\n\r\n def __len__(self) -> int:\r\n for v in self._fields.values():\r\n # use __len__ because len() has to be int and is not friendly to tracing\r\n return v.__len__()\r\n raise NotImplementedError(\"Empty Instances does not support __len__!\")\r\n\r\n def __iter__(self):\r\n raise NotImplementedError(\"`Instances` object is not iterable!\")\r\n\r\n @staticmethod\r\n def cat(instance_lists: List[\"Instances\"]) -> \"Instances\":\r\n \"\"\"\r\n Args:\r\n instance_lists (list[Instances])\r\n\r\n Returns:\r\n Instances\r\n \"\"\"\r\n assert all(isinstance(i, Instances) for i in instance_lists)\r\n assert len(instance_lists) > 0\r\n if len(instance_lists) == 1:\r\n return instance_lists[0]\r\n\r\n image_size = instance_lists[0].image_size\r\n if not isinstance(image_size, torch.Tensor): # could be a tensor in tracing\r\n for i in instance_lists[1:]:\r\n assert i.image_size == image_size\r\n ret = Instances(image_size)\r\n for k in instance_lists[0]._fields.keys():\r\n values = [i.get(k) for i in instance_lists]\r\n v0 = values[0]\r\n if isinstance(v0, torch.Tensor):\r\n values = torch.cat(values, dim=0)\r\n elif isinstance(v0, list):\r\n values = list(itertools.chain(*values))\r\n elif hasattr(type(v0), \"cat\"):\r\n values = type(v0).cat(values)\r\n else:\r\n raise ValueError(\"Unsupported type {} for concatenation\".format(type(v0)))\r\n ret.set(k, values)\r\n return ret\r\n\r\n def __str__(self) -> str:\r\n s = self.__class__.__name__ + \"(\"\r\n s += \"num_instances={}, \".format(len(self))\r\n s += \"image_height={}, \".format(self._image_size[0])\r\n s += \"image_width={}, \".format(self._image_size[1])\r\n s += \"fields=[{}])\".format(\", \".join((f\"{k}: {v}\" for k, v in self._fields.items())))\r\n return s\r\n\r\n __repr__ = __str__\r"},{"identifier":"get_event_storage","path":"annotator/oneformer/detectron2/utils/events.py","snippet":"def get_event_storage():\r\n \"\"\"\r\n Returns:\r\n The :class:`EventStorage` object that's currently being used.\r\n Throws an error if no :class:`EventStorage` is currently enabled.\r\n \"\"\"\r\n assert len(\r\n _CURRENT_STORAGE_STACK\r\n ), \"get_event_storage() has to be called inside a 'with EventStorage(...)' context!\"\r\n return _CURRENT_STORAGE_STACK[-1]\r"},{"identifier":"build_anchor_generator","path":"annotator/oneformer/detectron2/modeling/anchor_generator.py","snippet":"def build_anchor_generator(cfg, input_shape):\r\n \"\"\"\r\n Built an anchor generator from `cfg.MODEL.ANCHOR_GENERATOR.NAME`.\r\n \"\"\"\r\n anchor_generator = cfg.MODEL.ANCHOR_GENERATOR.NAME\r\n return ANCHOR_GENERATOR_REGISTRY.get(anchor_generator)(cfg, input_shape)\r"},{"identifier":"build_backbone","path":"annotator/oneformer/detectron2/modeling/backbone/build.py","snippet":"def build_backbone(cfg, input_shape=None):\r\n \"\"\"\r\n Build a backbone from `cfg.MODEL.BACKBONE.NAME`.\r\n\r\n Returns:\r\n an instance of :class:`Backbone`\r\n \"\"\"\r\n if input_shape is None:\r\n input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))\r\n\r\n backbone_name = cfg.MODEL.BACKBONE.NAME\r\n backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape)\r\n assert isinstance(backbone, Backbone)\r\n return backbone\r"},{"identifier":"Backbone","path":"annotator/oneformer/detectron2/modeling/backbone/backbone.py","snippet":"class Backbone(nn.Module, metaclass=ABCMeta):\r\n \"\"\"\r\n Abstract base class for network backbones.\r\n \"\"\"\r\n\r\n def __init__(self):\r\n \"\"\"\r\n The `__init__` method of any subclass can specify its own set of arguments.\r\n \"\"\"\r\n super().__init__()\r\n\r\n @abstractmethod\r\n def forward(self):\r\n \"\"\"\r\n Subclasses must override this method, but adhere to the same return type.\r\n\r\n Returns:\r\n dict[str->Tensor]: mapping from feature name (e.g., \"res2\") to tensor\r\n \"\"\"\r\n pass\r\n\r\n @property\r\n def size_divisibility(self) -> int:\r\n \"\"\"\r\n Some backbones require the input height and width to be divisible by a\r\n specific integer. This is typically true for encoder / decoder type networks\r\n with lateral connection (e.g., FPN) for which feature maps need to match\r\n dimension in the \"bottom up\" and \"top down\" paths. Set to 0 if no specific\r\n input size divisibility is required.\r\n \"\"\"\r\n return 0\r\n\r\n @property\r\n def padding_constraints(self) -> Dict[str, int]:\r\n \"\"\"\r\n This property is a generalization of size_divisibility. Some backbones and training\r\n recipes require specific padding constraints, such as enforcing divisibility by a specific\r\n integer (e.g., FPN) or padding to a square (e.g., ViTDet with large-scale jitter\r\n in :paper:vitdet). `padding_constraints` contains these optional items like:\r\n {\r\n \"size_divisibility\": int,\r\n \"square_size\": int,\r\n # Future options are possible\r\n }\r\n `size_divisibility` will read from here if presented and `square_size` indicates the\r\n square padding size if `square_size` > 0.\r\n\r\n TODO: use type of Dict[str, int] to avoid torchscipt issues. The type of padding_constraints\r\n could be generalized as TypedDict (Python 3.8+) to support more types in the future.\r\n \"\"\"\r\n return {}\r\n\r\n def output_shape(self):\r\n \"\"\"\r\n Returns:\r\n dict[str->ShapeSpec]\r\n \"\"\"\r\n # this is a backward-compatible default\r\n return {\r\n name: ShapeSpec(\r\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\r\n )\r\n for name in self._out_features\r\n }\r"},{"identifier":"Box2BoxTransform","path":"annotator/oneformer/detectron2/modeling/box_regression.py","snippet":"class Box2BoxTransform(object):\r\n \"\"\"\r\n The box-to-box transform defined in R-CNN. The transformation is parameterized\r\n by 4 deltas: (dx, dy, dw, dh). The transformation scales the box's width and height\r\n by exp(dw), exp(dh) and shifts a box's center by the offset (dx * width, dy * height).\r\n \"\"\"\r\n\r\n def __init__(\r\n self, weights: Tuple[float, float, float, float], scale_clamp: float = _DEFAULT_SCALE_CLAMP\r\n ):\r\n \"\"\"\r\n Args:\r\n weights (4-element tuple): Scaling factors that are applied to the\r\n (dx, dy, dw, dh) deltas. In Fast R-CNN, these were originally set\r\n such that the deltas have unit variance; now they are treated as\r\n hyperparameters of the system.\r\n scale_clamp (float): When predicting deltas, the predicted box scaling\r\n factors (dw and dh) are clamped such that they are <= scale_clamp.\r\n \"\"\"\r\n self.weights = weights\r\n self.scale_clamp = scale_clamp\r\n\r\n def get_deltas(self, src_boxes, target_boxes):\r\n \"\"\"\r\n Get box regression transformation deltas (dx, dy, dw, dh) that can be used\r\n to transform the `src_boxes` into the `target_boxes`. That is, the relation\r\n ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless\r\n any delta is too large and is clamped).\r\n\r\n Args:\r\n src_boxes (Tensor): source boxes, e.g., object proposals\r\n target_boxes (Tensor): target of the transformation, e.g., ground-truth\r\n boxes.\r\n \"\"\"\r\n assert isinstance(src_boxes, torch.Tensor), type(src_boxes)\r\n assert isinstance(target_boxes, torch.Tensor), type(target_boxes)\r\n\r\n src_widths = src_boxes[:, 2] - src_boxes[:, 0]\r\n src_heights = src_boxes[:, 3] - src_boxes[:, 1]\r\n src_ctr_x = src_boxes[:, 0] + 0.5 * src_widths\r\n src_ctr_y = src_boxes[:, 1] + 0.5 * src_heights\r\n\r\n target_widths = target_boxes[:, 2] - target_boxes[:, 0]\r\n target_heights = target_boxes[:, 3] - target_boxes[:, 1]\r\n target_ctr_x = target_boxes[:, 0] + 0.5 * target_widths\r\n target_ctr_y = target_boxes[:, 1] + 0.5 * target_heights\r\n\r\n wx, wy, ww, wh = self.weights\r\n dx = wx * (target_ctr_x - src_ctr_x) / src_widths\r\n dy = wy * (target_ctr_y - src_ctr_y) / src_heights\r\n dw = ww * torch.log(target_widths / src_widths)\r\n dh = wh * torch.log(target_heights / src_heights)\r\n\r\n deltas = torch.stack((dx, dy, dw, dh), dim=1)\r\n assert (src_widths > 0).all().item(), \"Input boxes to Box2BoxTransform are not valid!\"\r\n return deltas\r\n\r\n def apply_deltas(self, deltas, boxes):\r\n \"\"\"\r\n Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`.\r\n\r\n Args:\r\n deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1.\r\n deltas[i] represents k potentially different class-specific\r\n box transformations for the single box boxes[i].\r\n boxes (Tensor): boxes to transform, of shape (N, 4)\r\n \"\"\"\r\n deltas = deltas.float() # ensure fp32 for decoding precision\r\n boxes = boxes.to(deltas.dtype)\r\n\r\n widths = boxes[:, 2] - boxes[:, 0]\r\n heights = boxes[:, 3] - boxes[:, 1]\r\n ctr_x = boxes[:, 0] + 0.5 * widths\r\n ctr_y = boxes[:, 1] + 0.5 * heights\r\n\r\n wx, wy, ww, wh = self.weights\r\n dx = deltas[:, 0::4] / wx\r\n dy = deltas[:, 1::4] / wy\r\n dw = deltas[:, 2::4] / ww\r\n dh = deltas[:, 3::4] / wh\r\n\r\n # Prevent sending too large values into torch.exp()\r\n dw = torch.clamp(dw, max=self.scale_clamp)\r\n dh = torch.clamp(dh, max=self.scale_clamp)\r\n\r\n pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]\r\n pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]\r\n pred_w = torch.exp(dw) * widths[:, None]\r\n pred_h = torch.exp(dh) * heights[:, None]\r\n\r\n x1 = pred_ctr_x - 0.5 * pred_w\r\n y1 = pred_ctr_y - 0.5 * pred_h\r\n x2 = pred_ctr_x + 0.5 * pred_w\r\n y2 = pred_ctr_y + 0.5 * pred_h\r\n pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)\r\n return pred_boxes.reshape(deltas.shape)\r"},{"identifier":"_dense_box_regression_loss","path":"annotator/oneformer/detectron2/modeling/box_regression.py","snippet":"def _dense_box_regression_loss(\r\n anchors: List[Union[Boxes, torch.Tensor]],\r\n box2box_transform: Box2BoxTransform,\r\n pred_anchor_deltas: List[torch.Tensor],\r\n gt_boxes: List[torch.Tensor],\r\n fg_mask: torch.Tensor,\r\n box_reg_loss_type=\"smooth_l1\",\r\n smooth_l1_beta=0.0,\r\n):\r\n \"\"\"\r\n Compute loss for dense multi-level box regression.\r\n Loss is accumulated over ``fg_mask``.\r\n\r\n Args:\r\n anchors: #lvl anchor boxes, each is (HixWixA, 4)\r\n pred_anchor_deltas: #lvl predictions, each is (N, HixWixA, 4)\r\n gt_boxes: N ground truth boxes, each has shape (R, 4) (R = sum(Hi * Wi * A))\r\n fg_mask: the foreground boolean mask of shape (N, R) to compute loss on\r\n box_reg_loss_type (str): Loss type to use. Supported losses: \"smooth_l1\", \"giou\",\r\n \"diou\", \"ciou\".\r\n smooth_l1_beta (float): beta parameter for the smooth L1 regression loss. Default to\r\n use L1 loss. Only used when `box_reg_loss_type` is \"smooth_l1\"\r\n \"\"\"\r\n if isinstance(anchors[0], Boxes):\r\n anchors = type(anchors[0]).cat(anchors).tensor # (R, 4)\r\n else:\r\n anchors = cat(anchors)\r\n if box_reg_loss_type == \"smooth_l1\":\r\n gt_anchor_deltas = [box2box_transform.get_deltas(anchors, k) for k in gt_boxes]\r\n gt_anchor_deltas = torch.stack(gt_anchor_deltas) # (N, R, 4)\r\n loss_box_reg = smooth_l1_loss(\r\n cat(pred_anchor_deltas, dim=1)[fg_mask],\r\n gt_anchor_deltas[fg_mask],\r\n beta=smooth_l1_beta,\r\n reduction=\"sum\",\r\n )\r\n elif box_reg_loss_type == \"giou\":\r\n pred_boxes = [\r\n box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1)\r\n ]\r\n loss_box_reg = giou_loss(\r\n torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction=\"sum\"\r\n )\r\n elif box_reg_loss_type == \"diou\":\r\n pred_boxes = [\r\n box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1)\r\n ]\r\n loss_box_reg = diou_loss(\r\n torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction=\"sum\"\r\n )\r\n elif box_reg_loss_type == \"ciou\":\r\n pred_boxes = [\r\n box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1)\r\n ]\r\n loss_box_reg = ciou_loss(\r\n torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction=\"sum\"\r\n )\r\n else:\r\n raise ValueError(f\"Invalid dense box regression loss type '{box_reg_loss_type}'\")\r\n return loss_box_reg\r"},{"identifier":"Matcher","path":"annotator/oneformer/detectron2/modeling/matcher.py","snippet":"class Matcher(object):\r\n \"\"\"\r\n This class assigns to each predicted \"element\" (e.g., a box) a ground-truth\r\n element. Each predicted element will have exactly zero or one matches; each\r\n ground-truth element may be matched to zero or more predicted elements.\r\n\r\n The matching is determined by the MxN match_quality_matrix, that characterizes\r\n how well each (ground-truth, prediction)-pair match each other. For example,\r\n if the elements are boxes, this matrix may contain box intersection-over-union\r\n overlap values.\r\n\r\n The matcher returns (a) a vector of length N containing the index of the\r\n ground-truth element m in [0, M) that matches to prediction n in [0, N).\r\n (b) a vector of length N containing the labels for each prediction.\r\n \"\"\"\r\n\r\n def __init__(\r\n self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False\r\n ):\r\n \"\"\"\r\n Args:\r\n thresholds (list): a list of thresholds used to stratify predictions\r\n into levels.\r\n labels (list): a list of values to label predictions belonging at\r\n each level. A label can be one of {-1, 0, 1} signifying\r\n {ignore, negative class, positive class}, respectively.\r\n allow_low_quality_matches (bool): if True, produce additional matches\r\n for predictions with maximum match quality lower than high_threshold.\r\n See set_low_quality_matches_ for more details.\r\n\r\n For example,\r\n thresholds = [0.3, 0.5]\r\n labels = [0, -1, 1]\r\n All predictions with iou < 0.3 will be marked with 0 and\r\n thus will be considered as false positives while training.\r\n All predictions with 0.3 <= iou < 0.5 will be marked with -1 and\r\n thus will be ignored.\r\n All predictions with 0.5 <= iou will be marked with 1 and\r\n thus will be considered as true positives.\r\n \"\"\"\r\n # Add -inf and +inf to first and last position in thresholds\r\n thresholds = thresholds[:]\r\n assert thresholds[0] > 0\r\n thresholds.insert(0, -float(\"inf\"))\r\n thresholds.append(float(\"inf\"))\r\n # Currently torchscript does not support all + generator\r\n assert all([low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])])\r\n assert all([l in [-1, 0, 1] for l in labels])\r\n assert len(labels) == len(thresholds) - 1\r\n self.thresholds = thresholds\r\n self.labels = labels\r\n self.allow_low_quality_matches = allow_low_quality_matches\r\n\r\n def __call__(self, match_quality_matrix):\r\n \"\"\"\r\n Args:\r\n match_quality_matrix (Tensor[float]): an MxN tensor, containing the\r\n pairwise quality between M ground-truth elements and N predicted\r\n elements. All elements must be >= 0 (due to the us of `torch.nonzero`\r\n for selecting indices in :meth:`set_low_quality_matches_`).\r\n\r\n Returns:\r\n matches (Tensor[int64]): a vector of length N, where matches[i] is a matched\r\n ground-truth index in [0, M)\r\n match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates\r\n whether a prediction is a true or false positive or ignored\r\n \"\"\"\r\n assert match_quality_matrix.dim() == 2\r\n if match_quality_matrix.numel() == 0:\r\n default_matches = match_quality_matrix.new_full(\r\n (match_quality_matrix.size(1),), 0, dtype=torch.int64\r\n )\r\n # When no gt boxes exist, we define IOU = 0 and therefore set labels\r\n # to `self.labels[0]`, which usually defaults to background class 0\r\n # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds\r\n default_match_labels = match_quality_matrix.new_full(\r\n (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8\r\n )\r\n return default_matches, default_match_labels\r\n\r\n assert torch.all(match_quality_matrix >= 0)\r\n\r\n # match_quality_matrix is M (gt) x N (predicted)\r\n # Max over gt elements (dim 0) to find best gt candidate for each prediction\r\n matched_vals, matches = match_quality_matrix.max(dim=0)\r\n\r\n match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)\r\n\r\n for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):\r\n low_high = (matched_vals >= low) & (matched_vals < high)\r\n match_labels[low_high] = l\r\n\r\n if self.allow_low_quality_matches:\r\n self.set_low_quality_matches_(match_labels, match_quality_matrix)\r\n\r\n return matches, match_labels\r\n\r\n def set_low_quality_matches_(self, match_labels, match_quality_matrix):\r\n \"\"\"\r\n Produce additional matches for predictions that have only low-quality matches.\r\n Specifically, for each ground-truth G find the set of predictions that have\r\n maximum overlap with it (including ties); for each prediction in that set, if\r\n it is unmatched, then match it to the ground-truth G.\r\n\r\n This function implements the RPN assignment case (i) in Sec. 3.1.2 of\r\n :paper:`Faster R-CNN`.\r\n \"\"\"\r\n # For each gt, find the prediction with which it has highest quality\r\n highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)\r\n # Find the highest quality match available, even if it is low, including ties.\r\n # Note that the matches qualities must be positive due to the use of\r\n # `torch.nonzero`.\r\n _, pred_inds_with_highest_quality = nonzero_tuple(\r\n match_quality_matrix == highest_quality_foreach_gt[:, None]\r\n )\r\n # If an anchor was labeled positive only due to a low-quality match\r\n # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B.\r\n # This follows the implementation in Detectron, and is found to have no significant impact.\r\n match_labels[pred_inds_with_highest_quality] = 1\r"},{"identifier":"META_ARCH_REGISTRY","path":"annotator/oneformer/detectron2/modeling/meta_arch/build.py","snippet":"META_ARCH_REGISTRY = Registry(\"META_ARCH\") # noqa F401 isort:skip\r"},{"identifier":"DenseDetector","path":"annotator/oneformer/detectron2/modeling/meta_arch/dense_detector.py","snippet":"class DenseDetector(nn.Module):\r\n \"\"\"\r\n Base class for dense detector. We define a dense detector as a fully-convolutional model that\r\n makes per-pixel (i.e. dense) predictions.\r\n \"\"\"\r\n\r\n def __init__(\r\n self,\r\n backbone: Backbone,\r\n head: nn.Module,\r\n head_in_features: Optional[List[str]] = None,\r\n *,\r\n pixel_mean,\r\n pixel_std,\r\n ):\r\n \"\"\"\r\n Args:\r\n backbone: backbone module\r\n head: head module\r\n head_in_features: backbone features to use in head. Default to all backbone features.\r\n pixel_mean (Tuple[float]):\r\n Values to be used for image normalization (BGR order).\r\n To train on images of different number of channels, set different mean & std.\r\n Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]\r\n pixel_std (Tuple[float]):\r\n When using pre-trained models in Detectron1 or any MSRA models,\r\n std has been absorbed into its conv1 weights, so the std needs to be set 1.\r\n Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)\r\n \"\"\"\r\n super().__init__()\r\n\r\n self.backbone = backbone\r\n self.head = head\r\n if head_in_features is None:\r\n shapes = self.backbone.output_shape()\r\n self.head_in_features = sorted(shapes.keys(), key=lambda x: shapes[x].stride)\r\n else:\r\n self.head_in_features = head_in_features\r\n self.register_buffer(\"pixel_mean\", torch.tensor(pixel_mean).view(-1, 1, 1), False)\r\n self.register_buffer(\"pixel_std\", torch.tensor(pixel_std).view(-1, 1, 1), False)\r\n\r\n @property\r\n def device(self):\r\n return self.pixel_mean.device\r\n\r\n def _move_to_current_device(self, x):\r\n return move_device_like(x, self.pixel_mean)\r\n\r\n def forward(self, batched_inputs: List[Dict[str, Tensor]]):\r\n \"\"\"\r\n Args:\r\n batched_inputs: a list, batched outputs of :class:`DatasetMapper` .\r\n Each item in the list contains the inputs for one image.\r\n For now, each item in the list is a dict that contains:\r\n\r\n * image: Tensor, image in (C, H, W) format.\r\n * instances: Instances\r\n\r\n Other information that's included in the original dicts, such as:\r\n\r\n * \"height\", \"width\" (int): the output resolution of the model, used in inference.\r\n See :meth:`postprocess` for details.\r\n\r\n Returns:\r\n In training, dict[str, Tensor]: mapping from a named loss to a tensor storing the\r\n loss. Used during training only. In inference, the standard output format, described\r\n in :doc:`/tutorials/models`.\r\n \"\"\"\r\n images = self.preprocess_image(batched_inputs)\r\n features = self.backbone(images.tensor)\r\n features = [features[f] for f in self.head_in_features]\r\n predictions = self.head(features)\r\n\r\n if self.training:\r\n assert not torch.jit.is_scripting(), \"Not supported\"\r\n assert \"instances\" in batched_inputs[0], \"Instance annotations are missing in training!\"\r\n gt_instances = [x[\"instances\"].to(self.device) for x in batched_inputs]\r\n return self.forward_training(images, features, predictions, gt_instances)\r\n else:\r\n results = self.forward_inference(images, features, predictions)\r\n if torch.jit.is_scripting():\r\n return results\r\n\r\n processed_results = []\r\n for results_per_image, input_per_image, image_size in zip(\r\n results, batched_inputs, images.image_sizes\r\n ):\r\n height = input_per_image.get(\"height\", image_size[0])\r\n width = input_per_image.get(\"width\", image_size[1])\r\n r = detector_postprocess(results_per_image, height, width)\r\n processed_results.append({\"instances\": r})\r\n return processed_results\r\n\r\n def forward_training(self, images, features, predictions, gt_instances):\r\n raise NotImplementedError()\r\n\r\n def preprocess_image(self, batched_inputs: List[Dict[str, Tensor]]):\r\n \"\"\"\r\n Normalize, pad and batch the input images.\r\n \"\"\"\r\n images = [self._move_to_current_device(x[\"image\"]) for x in batched_inputs]\r\n images = [(x - self.pixel_mean) / self.pixel_std for x in images]\r\n images = ImageList.from_tensors(\r\n images,\r\n self.backbone.size_divisibility,\r\n padding_constraints=self.backbone.padding_constraints,\r\n )\r\n return images\r\n\r\n def _transpose_dense_predictions(\r\n self, predictions: List[List[Tensor]], dims_per_anchor: List[int]\r\n ) -> List[List[Tensor]]:\r\n \"\"\"\r\n Transpose the dense per-level predictions.\r\n\r\n Args:\r\n predictions: a list of outputs, each is a list of per-level\r\n predictions with shape (N, Ai x K, Hi, Wi), where N is the\r\n number of images, Ai is the number of anchors per location on\r\n level i, K is the dimension of predictions per anchor.\r\n dims_per_anchor: the value of K for each predictions. e.g. 4 for\r\n box prediction, #classes for classification prediction.\r\n\r\n Returns:\r\n List[List[Tensor]]: each prediction is transposed to (N, Hi x Wi x Ai, K).\r\n \"\"\"\r\n assert len(predictions) == len(dims_per_anchor)\r\n res: List[List[Tensor]] = []\r\n for pred, dim_per_anchor in zip(predictions, dims_per_anchor):\r\n pred = [permute_to_N_HWA_K(x, dim_per_anchor) for x in pred]\r\n res.append(pred)\r\n return res\r\n\r\n def _ema_update(self, name: str, value: float, initial_value: float, momentum: float = 0.9):\r\n \"\"\"\r\n Apply EMA update to `self.name` using `value`.\r\n\r\n This is mainly used for loss normalizer. In Detectron1, loss is normalized by number\r\n of foreground samples in the batch. When batch size is 1 per GPU, #foreground has a\r\n large variance and using it lead to lower performance. Therefore we maintain an EMA of\r\n #foreground to stabilize the normalizer.\r\n\r\n Args:\r\n name: name of the normalizer\r\n value: the new value to update\r\n initial_value: the initial value to start with\r\n momentum: momentum of EMA\r\n\r\n Returns:\r\n float: the updated EMA value\r\n \"\"\"\r\n if hasattr(self, name):\r\n old = getattr(self, name)\r\n else:\r\n old = initial_value\r\n new = old * momentum + value * (1 - momentum)\r\n setattr(self, name, new)\r\n return new\r\n\r\n def _decode_per_level_predictions(\r\n self,\r\n anchors: Boxes,\r\n pred_scores: Tensor,\r\n pred_deltas: Tensor,\r\n score_thresh: float,\r\n topk_candidates: int,\r\n image_size: Tuple[int, int],\r\n ) -> Instances:\r\n \"\"\"\r\n Decode boxes and classification predictions of one featuer level, by\r\n the following steps:\r\n 1. filter the predictions based on score threshold and top K scores.\r\n 2. transform the box regression outputs\r\n 3. return the predicted scores, classes and boxes\r\n\r\n Args:\r\n anchors: Boxes, anchor for this feature level\r\n pred_scores: HxWxA,K\r\n pred_deltas: HxWxA,4\r\n\r\n Returns:\r\n Instances: with field \"scores\", \"pred_boxes\", \"pred_classes\".\r\n \"\"\"\r\n # Apply two filtering to make NMS faster.\r\n # 1. Keep boxes with confidence score higher than threshold\r\n keep_idxs = pred_scores > score_thresh\r\n pred_scores = pred_scores[keep_idxs]\r\n topk_idxs = torch.nonzero(keep_idxs) # Kx2\r\n\r\n # 2. Keep top k top scoring boxes only\r\n topk_idxs_size = topk_idxs.shape[0]\r\n if isinstance(topk_idxs_size, Tensor):\r\n # It's a tensor in tracing\r\n num_topk = torch.clamp(topk_idxs_size, max=topk_candidates)\r\n else:\r\n num_topk = min(topk_idxs_size, topk_candidates)\r\n pred_scores, idxs = pred_scores.topk(num_topk)\r\n topk_idxs = topk_idxs[idxs]\r\n\r\n anchor_idxs, classes_idxs = topk_idxs.unbind(dim=1)\r\n\r\n pred_boxes = self.box2box_transform.apply_deltas(\r\n pred_deltas[anchor_idxs], anchors.tensor[anchor_idxs]\r\n )\r\n return Instances(\r\n image_size, pred_boxes=Boxes(pred_boxes), scores=pred_scores, pred_classes=classes_idxs\r\n )\r\n\r\n def _decode_multi_level_predictions(\r\n self,\r\n anchors: List[Boxes],\r\n pred_scores: List[Tensor],\r\n pred_deltas: List[Tensor],\r\n score_thresh: float,\r\n topk_candidates: int,\r\n image_size: Tuple[int, int],\r\n ) -> Instances:\r\n \"\"\"\r\n Run `_decode_per_level_predictions` for all feature levels and concat the results.\r\n \"\"\"\r\n predictions = [\r\n self._decode_per_level_predictions(\r\n anchors_i,\r\n box_cls_i,\r\n box_reg_i,\r\n self.test_score_thresh,\r\n self.test_topk_candidates,\r\n image_size,\r\n )\r\n # Iterate over every feature level\r\n for box_cls_i, box_reg_i, anchors_i in zip(pred_scores, pred_deltas, anchors)\r\n ]\r\n return predictions[0].cat(predictions) # 'Instances.cat' is not scriptale but this is\r\n\r\n def visualize_training(self, batched_inputs, results):\r\n \"\"\"\r\n A function used to visualize ground truth images and final network predictions.\r\n It shows ground truth bounding boxes on the original image and up to 20\r\n predicted object bounding boxes on the original image.\r\n\r\n Args:\r\n batched_inputs (list): a list that contains input to the model.\r\n results (List[Instances]): a list of #images elements returned by forward_inference().\r\n \"\"\"\r\n from annotator.oneformer.detectron2.utils.visualizer import Visualizer\r\n\r\n assert len(batched_inputs) == len(\r\n results\r\n ), \"Cannot visualize inputs and results of different sizes\"\r\n storage = get_event_storage()\r\n max_boxes = 20\r\n\r\n image_index = 0 # only visualize a single image\r\n img = batched_inputs[image_index][\"image\"]\r\n img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format)\r\n v_gt = Visualizer(img, None)\r\n v_gt = v_gt.overlay_instances(boxes=batched_inputs[image_index][\"instances\"].gt_boxes)\r\n anno_img = v_gt.get_image()\r\n processed_results = detector_postprocess(results[image_index], img.shape[0], img.shape[1])\r\n predicted_boxes = processed_results.pred_boxes.tensor.detach().cpu().numpy()\r\n\r\n v_pred = Visualizer(img, None)\r\n v_pred = v_pred.overlay_instances(boxes=predicted_boxes[0:max_boxes])\r\n prop_img = v_pred.get_image()\r\n vis_img = np.vstack((anno_img, prop_img))\r\n vis_img = vis_img.transpose(2, 0, 1)\r\n vis_name = f\"Top: GT bounding boxes; Bottom: {max_boxes} Highest Scoring Results\"\r\n storage.put_image(vis_name, vis_img)\r"},{"identifier":"permute_to_N_HWA_K","path":"annotator/oneformer/detectron2/modeling/meta_arch/dense_detector.py","snippet":"def permute_to_N_HWA_K(tensor, K: int):\r\n \"\"\"\r\n Transpose/reshape a tensor from (N, (Ai x K), H, W) to (N, (HxWxAi), K)\r\n \"\"\"\r\n assert tensor.dim() == 4, tensor.shape\r\n N, _, H, W = tensor.shape\r\n tensor = tensor.view(N, -1, K, H, W)\r\n tensor = tensor.permute(0, 3, 4, 1, 2)\r\n tensor = tensor.reshape(N, -1, K) # Size=(N,HWA,K)\r\n return tensor\r"}],"string":"[\n {\n \"identifier\": \"configurable\",\n \"path\": \"annotator/oneformer/detectron2/config/config.py\",\n \"snippet\": \"def configurable(init_func=None, *, from_config=None):\\r\\n \\\"\\\"\\\"\\r\\n Decorate a function or a class's __init__ method so that it can be called\\r\\n with a :class:`CfgNode` object using a :func:`from_config` function that translates\\r\\n :class:`CfgNode` to arguments.\\r\\n\\r\\n Examples:\\r\\n ::\\r\\n # Usage 1: Decorator on __init__:\\r\\n class A:\\r\\n @configurable\\r\\n def __init__(self, a, b=2, c=3):\\r\\n pass\\r\\n\\r\\n @classmethod\\r\\n def from_config(cls, cfg): # 'cfg' must be the first argument\\r\\n # Returns kwargs to be passed to __init__\\r\\n return {\\\"a\\\": cfg.A, \\\"b\\\": cfg.B}\\r\\n\\r\\n a1 = A(a=1, b=2) # regular construction\\r\\n a2 = A(cfg) # construct with a cfg\\r\\n a3 = A(cfg, b=3, c=4) # construct with extra overwrite\\r\\n\\r\\n # Usage 2: Decorator on any function. Needs an extra from_config argument:\\r\\n @configurable(from_config=lambda cfg: {\\\"a: cfg.A, \\\"b\\\": cfg.B})\\r\\n def a_func(a, b=2, c=3):\\r\\n pass\\r\\n\\r\\n a1 = a_func(a=1, b=2) # regular call\\r\\n a2 = a_func(cfg) # call with a cfg\\r\\n a3 = a_func(cfg, b=3, c=4) # call with extra overwrite\\r\\n\\r\\n Args:\\r\\n init_func (callable): a class's ``__init__`` method in usage 1. The\\r\\n class must have a ``from_config`` classmethod which takes `cfg` as\\r\\n the first argument.\\r\\n from_config (callable): the from_config function in usage 2. It must take `cfg`\\r\\n as its first argument.\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n if init_func is not None:\\r\\n assert (\\r\\n inspect.isfunction(init_func)\\r\\n and from_config is None\\r\\n and init_func.__name__ == \\\"__init__\\\"\\r\\n ), \\\"Incorrect use of @configurable. Check API documentation for examples.\\\"\\r\\n\\r\\n @functools.wraps(init_func)\\r\\n def wrapped(self, *args, **kwargs):\\r\\n try:\\r\\n from_config_func = type(self).from_config\\r\\n except AttributeError as e:\\r\\n raise AttributeError(\\r\\n \\\"Class with @configurable must have a 'from_config' classmethod.\\\"\\r\\n ) from e\\r\\n if not inspect.ismethod(from_config_func):\\r\\n raise TypeError(\\\"Class with @configurable must have a 'from_config' classmethod.\\\")\\r\\n\\r\\n if _called_with_cfg(*args, **kwargs):\\r\\n explicit_args = _get_args_from_config(from_config_func, *args, **kwargs)\\r\\n init_func(self, **explicit_args)\\r\\n else:\\r\\n init_func(self, *args, **kwargs)\\r\\n\\r\\n return wrapped\\r\\n\\r\\n else:\\r\\n if from_config is None:\\r\\n return configurable # @configurable() is made equivalent to @configurable\\r\\n assert inspect.isfunction(\\r\\n from_config\\r\\n ), \\\"from_config argument of configurable must be a function!\\\"\\r\\n\\r\\n def wrapper(orig_func):\\r\\n @functools.wraps(orig_func)\\r\\n def wrapped(*args, **kwargs):\\r\\n if _called_with_cfg(*args, **kwargs):\\r\\n explicit_args = _get_args_from_config(from_config, *args, **kwargs)\\r\\n return orig_func(**explicit_args)\\r\\n else:\\r\\n return orig_func(*args, **kwargs)\\r\\n\\r\\n wrapped.from_config = from_config\\r\\n return wrapped\\r\\n\\r\\n return wrapper\\r\"\n },\n {\n \"identifier\": \"get_norm\",\n \"path\": \"annotator/oneformer/detectron2/layers/batch_norm.py\",\n \"snippet\": \"def get_norm(norm, out_channels):\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;\\r\\n or a callable that takes a channel number and returns\\r\\n the normalization layer as a nn.Module.\\r\\n\\r\\n Returns:\\r\\n nn.Module or None: the normalization layer\\r\\n \\\"\\\"\\\"\\r\\n if norm is None:\\r\\n return None\\r\\n if isinstance(norm, str):\\r\\n if len(norm) == 0:\\r\\n return None\\r\\n norm = {\\r\\n \\\"BN\\\": BatchNorm2d,\\r\\n # Fixed in https://github.com/pytorch/pytorch/pull/36382\\r\\n \\\"SyncBN\\\": NaiveSyncBatchNorm if env.TORCH_VERSION <= (1, 5) else nn.SyncBatchNorm,\\r\\n \\\"FrozenBN\\\": FrozenBatchNorm2d,\\r\\n \\\"GN\\\": lambda channels: nn.GroupNorm(32, channels),\\r\\n # for debugging:\\r\\n \\\"nnSyncBN\\\": nn.SyncBatchNorm,\\r\\n \\\"naiveSyncBN\\\": NaiveSyncBatchNorm,\\r\\n # expose stats_mode N as an option to caller, required for zero-len inputs\\r\\n \\\"naiveSyncBN_N\\\": lambda channels: NaiveSyncBatchNorm(channels, stats_mode=\\\"N\\\"),\\r\\n \\\"LN\\\": lambda channels: LayerNorm(channels),\\r\\n }[norm]\\r\\n return norm(out_channels)\\r\"\n },\n {\n \"identifier\": \"CycleBatchNormList\",\n \"path\": \"annotator/oneformer/detectron2/layers/batch_norm.py\",\n \"snippet\": \"class CycleBatchNormList(nn.ModuleList):\\r\\n \\\"\\\"\\\"\\r\\n Implement domain-specific BatchNorm by cycling.\\r\\n\\r\\n When a BatchNorm layer is used for multiple input domains or input\\r\\n features, it might need to maintain a separate test-time statistics\\r\\n for each domain. See Sec 5.2 in :paper:`rethinking-batchnorm`.\\r\\n\\r\\n This module implements it by using N separate BN layers\\r\\n and it cycles through them every time a forward() is called.\\r\\n\\r\\n NOTE: The caller of this module MUST guarantee to always call\\r\\n this module by multiple of N times. Otherwise its test-time statistics\\r\\n will be incorrect.\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n def __init__(self, length: int, bn_class=nn.BatchNorm2d, **kwargs):\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n length: number of BatchNorm layers to cycle.\\r\\n bn_class: the BatchNorm class to use\\r\\n kwargs: arguments of the BatchNorm class, such as num_features.\\r\\n \\\"\\\"\\\"\\r\\n self._affine = kwargs.pop(\\\"affine\\\", True)\\r\\n super().__init__([bn_class(**kwargs, affine=False) for k in range(length)])\\r\\n if self._affine:\\r\\n # shared affine, domain-specific BN\\r\\n channels = self[0].num_features\\r\\n self.weight = nn.Parameter(torch.ones(channels))\\r\\n self.bias = nn.Parameter(torch.zeros(channels))\\r\\n self._pos = 0\\r\\n\\r\\n def forward(self, x):\\r\\n ret = self[self._pos](x)\\r\\n self._pos = (self._pos + 1) % len(self)\\r\\n\\r\\n if self._affine:\\r\\n w = self.weight.reshape(1, -1, 1, 1)\\r\\n b = self.bias.reshape(1, -1, 1, 1)\\r\\n return ret * w + b\\r\\n else:\\r\\n return ret\\r\\n\\r\\n def extra_repr(self):\\r\\n return f\\\"affine={self._affine}\\\"\\r\"\n },\n {\n \"identifier\": \"batched_nms\",\n \"path\": \"annotator/oneformer/detectron2/layers/nms.py\",\n \"snippet\": \"def batched_nms(\\r\\n boxes: torch.Tensor, scores: torch.Tensor, idxs: torch.Tensor, iou_threshold: float\\r\\n):\\r\\n \\\"\\\"\\\"\\r\\n Same as torchvision.ops.boxes.batched_nms, but with float().\\r\\n \\\"\\\"\\\"\\r\\n assert boxes.shape[-1] == 4\\r\\n # Note: Torchvision already has a strategy (https://github.com/pytorch/vision/issues/1311)\\r\\n # to decide whether to use coordinate trick or for loop to implement batched_nms. So we\\r\\n # just call it directly.\\r\\n # Fp16 does not have enough range for batched NMS, so adding float().\\r\\n return box_ops.batched_nms(boxes.float(), scores, idxs, iou_threshold)\\r\"\n },\n {\n \"identifier\": \"ShapeSpec\",\n \"path\": \"annotator/oneformer/detectron2/layers/shape_spec.py\",\n \"snippet\": \"class ShapeSpec:\\r\\n \\\"\\\"\\\"\\r\\n A simple structure that contains basic shape specification about a tensor.\\r\\n It is often used as the auxiliary inputs/outputs of models,\\r\\n to complement the lack of shape inference ability among pytorch modules.\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n channels: Optional[int] = None\\r\\n height: Optional[int] = None\\r\\n width: Optional[int] = None\\r\\n stride: Optional[int] = None\\r\"\n },\n {\n \"identifier\": \"cat\",\n \"path\": \"annotator/oneformer/detectron2/layers/wrappers.py\",\n \"snippet\": \"def cat(tensors: List[torch.Tensor], dim: int = 0):\\r\\n \\\"\\\"\\\"\\r\\n Efficient version of torch.cat that avoids a copy if there is only a single element in a list\\r\\n \\\"\\\"\\\"\\r\\n assert isinstance(tensors, (list, tuple))\\r\\n if len(tensors) == 1:\\r\\n return tensors[0]\\r\\n return torch.cat(tensors, dim)\\r\"\n },\n {\n \"identifier\": \"Boxes\",\n \"path\": \"annotator/oneformer/detectron2/structures/boxes.py\",\n \"snippet\": \"class Boxes:\\r\\n \\\"\\\"\\\"\\r\\n This structure stores a list of boxes as a Nx4 torch.Tensor.\\r\\n It supports some common methods about boxes\\r\\n (`area`, `clip`, `nonempty`, etc),\\r\\n and also behaves like a Tensor\\r\\n (support indexing, `to(device)`, `.device`, and iteration over all boxes)\\r\\n\\r\\n Attributes:\\r\\n tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n def __init__(self, tensor: torch.Tensor):\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2).\\r\\n \\\"\\\"\\\"\\r\\n if not isinstance(tensor, torch.Tensor):\\r\\n tensor = torch.as_tensor(tensor, dtype=torch.float32, device=torch.device(\\\"cpu\\\"))\\r\\n else:\\r\\n tensor = tensor.to(torch.float32)\\r\\n if tensor.numel() == 0:\\r\\n # Use reshape, so we don't end up creating a new tensor that does not depend on\\r\\n # the inputs (and consequently confuses jit)\\r\\n tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32)\\r\\n assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()\\r\\n\\r\\n self.tensor = tensor\\r\\n\\r\\n def clone(self) -> \\\"Boxes\\\":\\r\\n \\\"\\\"\\\"\\r\\n Clone the Boxes.\\r\\n\\r\\n Returns:\\r\\n Boxes\\r\\n \\\"\\\"\\\"\\r\\n return Boxes(self.tensor.clone())\\r\\n\\r\\n def to(self, device: torch.device):\\r\\n # Boxes are assumed float32 and does not support to(dtype)\\r\\n return Boxes(self.tensor.to(device=device))\\r\\n\\r\\n def area(self) -> torch.Tensor:\\r\\n \\\"\\\"\\\"\\r\\n Computes the area of all the boxes.\\r\\n\\r\\n Returns:\\r\\n torch.Tensor: a vector with areas of each box.\\r\\n \\\"\\\"\\\"\\r\\n box = self.tensor\\r\\n area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])\\r\\n return area\\r\\n\\r\\n def clip(self, box_size: Tuple[int, int]) -> None:\\r\\n \\\"\\\"\\\"\\r\\n Clip (in place) the boxes by limiting x coordinates to the range [0, width]\\r\\n and y coordinates to the range [0, height].\\r\\n\\r\\n Args:\\r\\n box_size (height, width): The clipping box's size.\\r\\n \\\"\\\"\\\"\\r\\n assert torch.isfinite(self.tensor).all(), \\\"Box tensor contains infinite or NaN!\\\"\\r\\n h, w = box_size\\r\\n x1 = self.tensor[:, 0].clamp(min=0, max=w)\\r\\n y1 = self.tensor[:, 1].clamp(min=0, max=h)\\r\\n x2 = self.tensor[:, 2].clamp(min=0, max=w)\\r\\n y2 = self.tensor[:, 3].clamp(min=0, max=h)\\r\\n self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)\\r\\n\\r\\n def nonempty(self, threshold: float = 0.0) -> torch.Tensor:\\r\\n \\\"\\\"\\\"\\r\\n Find boxes that are non-empty.\\r\\n A box is considered empty, if either of its side is no larger than threshold.\\r\\n\\r\\n Returns:\\r\\n Tensor:\\r\\n a binary vector which represents whether each box is empty\\r\\n (False) or non-empty (True).\\r\\n \\\"\\\"\\\"\\r\\n box = self.tensor\\r\\n widths = box[:, 2] - box[:, 0]\\r\\n heights = box[:, 3] - box[:, 1]\\r\\n keep = (widths > threshold) & (heights > threshold)\\r\\n return keep\\r\\n\\r\\n def __getitem__(self, item) -> \\\"Boxes\\\":\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n item: int, slice, or a BoolTensor\\r\\n\\r\\n Returns:\\r\\n Boxes: Create a new :class:`Boxes` by indexing.\\r\\n\\r\\n The following usage are allowed:\\r\\n\\r\\n 1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.\\r\\n 2. `new_boxes = boxes[2:10]`: return a slice of boxes.\\r\\n 3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor\\r\\n with `length = len(boxes)`. Nonzero elements in the vector will be selected.\\r\\n\\r\\n Note that the returned Boxes might share storage with this Boxes,\\r\\n subject to Pytorch's indexing semantics.\\r\\n \\\"\\\"\\\"\\r\\n if isinstance(item, int):\\r\\n return Boxes(self.tensor[item].view(1, -1))\\r\\n b = self.tensor[item]\\r\\n assert b.dim() == 2, \\\"Indexing on Boxes with {} failed to return a matrix!\\\".format(item)\\r\\n return Boxes(b)\\r\\n\\r\\n def __len__(self) -> int:\\r\\n return self.tensor.shape[0]\\r\\n\\r\\n def __repr__(self) -> str:\\r\\n return \\\"Boxes(\\\" + str(self.tensor) + \\\")\\\"\\r\\n\\r\\n def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n box_size (height, width): Size of the reference box.\\r\\n boundary_threshold (int): Boxes that extend beyond the reference box\\r\\n boundary by more than boundary_threshold are considered \\\"outside\\\".\\r\\n\\r\\n Returns:\\r\\n a binary vector, indicating whether each box is inside the reference box.\\r\\n \\\"\\\"\\\"\\r\\n height, width = box_size\\r\\n inds_inside = (\\r\\n (self.tensor[..., 0] >= -boundary_threshold)\\r\\n & (self.tensor[..., 1] >= -boundary_threshold)\\r\\n & (self.tensor[..., 2] < width + boundary_threshold)\\r\\n & (self.tensor[..., 3] < height + boundary_threshold)\\r\\n )\\r\\n return inds_inside\\r\\n\\r\\n def get_centers(self) -> torch.Tensor:\\r\\n \\\"\\\"\\\"\\r\\n Returns:\\r\\n The box centers in a Nx2 array of (x, y).\\r\\n \\\"\\\"\\\"\\r\\n return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2\\r\\n\\r\\n def scale(self, scale_x: float, scale_y: float) -> None:\\r\\n \\\"\\\"\\\"\\r\\n Scale the box with horizontal and vertical scaling factors\\r\\n \\\"\\\"\\\"\\r\\n self.tensor[:, 0::2] *= scale_x\\r\\n self.tensor[:, 1::2] *= scale_y\\r\\n\\r\\n @classmethod\\r\\n def cat(cls, boxes_list: List[\\\"Boxes\\\"]) -> \\\"Boxes\\\":\\r\\n \\\"\\\"\\\"\\r\\n Concatenates a list of Boxes into a single Boxes\\r\\n\\r\\n Arguments:\\r\\n boxes_list (list[Boxes])\\r\\n\\r\\n Returns:\\r\\n Boxes: the concatenated Boxes\\r\\n \\\"\\\"\\\"\\r\\n assert isinstance(boxes_list, (list, tuple))\\r\\n if len(boxes_list) == 0:\\r\\n return cls(torch.empty(0))\\r\\n assert all([isinstance(box, Boxes) for box in boxes_list])\\r\\n\\r\\n # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input\\r\\n cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))\\r\\n return cat_boxes\\r\\n\\r\\n @property\\r\\n def device(self) -> device:\\r\\n return self.tensor.device\\r\\n\\r\\n # type \\\"Iterator[torch.Tensor]\\\", yield, and iter() not supported by torchscript\\r\\n # https://github.com/pytorch/pytorch/issues/18627\\r\\n @torch.jit.unused\\r\\n def __iter__(self):\\r\\n \\\"\\\"\\\"\\r\\n Yield a box as a Tensor of shape (4,) at a time.\\r\\n \\\"\\\"\\\"\\r\\n yield from self.tensor\\r\"\n },\n {\n \"identifier\": \"pairwise_iou\",\n \"path\": \"annotator/oneformer/detectron2/structures/boxes.py\",\n \"snippet\": \"def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:\\r\\n \\\"\\\"\\\"\\r\\n Given two lists of boxes of size N and M, compute the IoU\\r\\n (intersection over union) between **all** N x M pairs of boxes.\\r\\n The box order must be (xmin, ymin, xmax, ymax).\\r\\n\\r\\n Args:\\r\\n boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.\\r\\n\\r\\n Returns:\\r\\n Tensor: IoU, sized [N,M].\\r\\n \\\"\\\"\\\"\\r\\n area1 = boxes1.area() # [N]\\r\\n area2 = boxes2.area() # [M]\\r\\n inter = pairwise_intersection(boxes1, boxes2)\\r\\n\\r\\n # handle empty boxes\\r\\n iou = torch.where(\\r\\n inter > 0,\\r\\n inter / (area1[:, None] + area2 - inter),\\r\\n torch.zeros(1, dtype=inter.dtype, device=inter.device),\\r\\n )\\r\\n return iou\\r\"\n },\n {\n \"identifier\": \"ImageList\",\n \"path\": \"annotator/oneformer/detectron2/structures/image_list.py\",\n \"snippet\": \"class ImageList(object):\\r\\n \\\"\\\"\\\"\\r\\n Structure that holds a list of images (of possibly\\r\\n varying sizes) as a single tensor.\\r\\n This works by padding the images to the same size.\\r\\n The original sizes of each image is stored in `image_sizes`.\\r\\n\\r\\n Attributes:\\r\\n image_sizes (list[tuple[int, int]]): each tuple is (h, w).\\r\\n During tracing, it becomes list[Tensor] instead.\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n def __init__(self, tensor: torch.Tensor, image_sizes: List[Tuple[int, int]]):\\r\\n \\\"\\\"\\\"\\r\\n Arguments:\\r\\n tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1\\r\\n image_sizes (list[tuple[int, int]]): Each tuple is (h, w). It can\\r\\n be smaller than (H, W) due to padding.\\r\\n \\\"\\\"\\\"\\r\\n self.tensor = tensor\\r\\n self.image_sizes = image_sizes\\r\\n\\r\\n def __len__(self) -> int:\\r\\n return len(self.image_sizes)\\r\\n\\r\\n def __getitem__(self, idx) -> torch.Tensor:\\r\\n \\\"\\\"\\\"\\r\\n Access the individual image in its original size.\\r\\n\\r\\n Args:\\r\\n idx: int or slice\\r\\n\\r\\n Returns:\\r\\n Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1\\r\\n \\\"\\\"\\\"\\r\\n size = self.image_sizes[idx]\\r\\n return self.tensor[idx, ..., : size[0], : size[1]]\\r\\n\\r\\n @torch.jit.unused\\r\\n def to(self, *args: Any, **kwargs: Any) -> \\\"ImageList\\\":\\r\\n cast_tensor = self.tensor.to(*args, **kwargs)\\r\\n return ImageList(cast_tensor, self.image_sizes)\\r\\n\\r\\n @property\\r\\n def device(self) -> device:\\r\\n return self.tensor.device\\r\\n\\r\\n @staticmethod\\r\\n def from_tensors(\\r\\n tensors: List[torch.Tensor],\\r\\n size_divisibility: int = 0,\\r\\n pad_value: float = 0.0,\\r\\n padding_constraints: Optional[Dict[str, int]] = None,\\r\\n ) -> \\\"ImageList\\\":\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n tensors: a tuple or list of `torch.Tensor`, each of shape (Hi, Wi) or\\r\\n (C_1, ..., C_K, Hi, Wi) where K >= 1. The Tensors will be padded\\r\\n to the same shape with `pad_value`.\\r\\n size_divisibility (int): If `size_divisibility > 0`, add padding to ensure\\r\\n the common height and width is divisible by `size_divisibility`.\\r\\n This depends on the model and many models need a divisibility of 32.\\r\\n pad_value (float): value to pad.\\r\\n padding_constraints (optional[Dict]): If given, it would follow the format as\\r\\n {\\\"size_divisibility\\\": int, \\\"square_size\\\": int}, where `size_divisibility` will\\r\\n overwrite the above one if presented and `square_size` indicates the\\r\\n square padding size if `square_size` > 0.\\r\\n Returns:\\r\\n an `ImageList`.\\r\\n \\\"\\\"\\\"\\r\\n assert len(tensors) > 0\\r\\n assert isinstance(tensors, (tuple, list))\\r\\n for t in tensors:\\r\\n assert isinstance(t, torch.Tensor), type(t)\\r\\n assert t.shape[:-2] == tensors[0].shape[:-2], t.shape\\r\\n\\r\\n image_sizes = [(im.shape[-2], im.shape[-1]) for im in tensors]\\r\\n image_sizes_tensor = [shapes_to_tensor(x) for x in image_sizes]\\r\\n max_size = torch.stack(image_sizes_tensor).max(0).values\\r\\n\\r\\n if padding_constraints is not None:\\r\\n square_size = padding_constraints.get(\\\"square_size\\\", 0)\\r\\n if square_size > 0:\\r\\n # pad to square.\\r\\n max_size[0] = max_size[1] = square_size\\r\\n if \\\"size_divisibility\\\" in padding_constraints:\\r\\n size_divisibility = padding_constraints[\\\"size_divisibility\\\"]\\r\\n if size_divisibility > 1:\\r\\n stride = size_divisibility\\r\\n # the last two dims are H,W, both subject to divisibility requirement\\r\\n max_size = (max_size + (stride - 1)).div(stride, rounding_mode=\\\"floor\\\") * stride\\r\\n\\r\\n # handle weirdness of scripting and tracing ...\\r\\n if torch.jit.is_scripting():\\r\\n max_size: List[int] = max_size.to(dtype=torch.long).tolist()\\r\\n else:\\r\\n if torch.jit.is_tracing():\\r\\n image_sizes = image_sizes_tensor\\r\\n\\r\\n if len(tensors) == 1:\\r\\n # This seems slightly (2%) faster.\\r\\n # TODO: check whether it's faster for multiple images as well\\r\\n image_size = image_sizes[0]\\r\\n padding_size = [0, max_size[-1] - image_size[1], 0, max_size[-2] - image_size[0]]\\r\\n batched_imgs = F.pad(tensors[0], padding_size, value=pad_value).unsqueeze_(0)\\r\\n else:\\r\\n # max_size can be a tensor in tracing mode, therefore convert to list\\r\\n batch_shape = [len(tensors)] + list(tensors[0].shape[:-2]) + list(max_size)\\r\\n device = (\\r\\n None if torch.jit.is_scripting() else (\\\"cpu\\\" if torch.jit.is_tracing() else None)\\r\\n )\\r\\n batched_imgs = tensors[0].new_full(batch_shape, pad_value, device=device)\\r\\n batched_imgs = move_device_like(batched_imgs, tensors[0])\\r\\n for i, img in enumerate(tensors):\\r\\n # Use `batched_imgs` directly instead of `img, pad_img = zip(tensors, batched_imgs)`\\r\\n # Tracing mode cannot capture `copy_()` of temporary locals\\r\\n batched_imgs[i, ..., : img.shape[-2], : img.shape[-1]].copy_(img)\\r\\n\\r\\n return ImageList(batched_imgs.contiguous(), image_sizes)\\r\"\n },\n {\n \"identifier\": \"Instances\",\n \"path\": \"annotator/oneformer/detectron2/structures/instances.py\",\n \"snippet\": \"class Instances:\\r\\n \\\"\\\"\\\"\\r\\n This class represents a list of instances in an image.\\r\\n It stores the attributes of instances (e.g., boxes, masks, labels, scores) as \\\"fields\\\".\\r\\n All fields must have the same ``__len__`` which is the number of instances.\\r\\n\\r\\n All other (non-field) attributes of this class are considered private:\\r\\n they must start with '_' and are not modifiable by a user.\\r\\n\\r\\n Some basic usage:\\r\\n\\r\\n 1. Set/get/check a field:\\r\\n\\r\\n .. code-block:: python\\r\\n\\r\\n instances.gt_boxes = Boxes(...)\\r\\n print(instances.pred_masks) # a tensor of shape (N, H, W)\\r\\n print('gt_masks' in instances)\\r\\n\\r\\n 2. ``len(instances)`` returns the number of instances\\r\\n 3. Indexing: ``instances[indices]`` will apply the indexing on all the fields\\r\\n and returns a new :class:`Instances`.\\r\\n Typically, ``indices`` is a integer vector of indices,\\r\\n or a binary mask of length ``num_instances``\\r\\n\\r\\n .. code-block:: python\\r\\n\\r\\n category_3_detections = instances[instances.pred_classes == 3]\\r\\n confident_detections = instances[instances.scores > 0.9]\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n def __init__(self, image_size: Tuple[int, int], **kwargs: Any):\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n image_size (height, width): the spatial size of the image.\\r\\n kwargs: fields to add to this `Instances`.\\r\\n \\\"\\\"\\\"\\r\\n self._image_size = image_size\\r\\n self._fields: Dict[str, Any] = {}\\r\\n for k, v in kwargs.items():\\r\\n self.set(k, v)\\r\\n\\r\\n @property\\r\\n def image_size(self) -> Tuple[int, int]:\\r\\n \\\"\\\"\\\"\\r\\n Returns:\\r\\n tuple: height, width\\r\\n \\\"\\\"\\\"\\r\\n return self._image_size\\r\\n\\r\\n def __setattr__(self, name: str, val: Any) -> None:\\r\\n if name.startswith(\\\"_\\\"):\\r\\n super().__setattr__(name, val)\\r\\n else:\\r\\n self.set(name, val)\\r\\n\\r\\n def __getattr__(self, name: str) -> Any:\\r\\n if name == \\\"_fields\\\" or name not in self._fields:\\r\\n raise AttributeError(\\\"Cannot find field '{}' in the given Instances!\\\".format(name))\\r\\n return self._fields[name]\\r\\n\\r\\n def set(self, name: str, value: Any) -> None:\\r\\n \\\"\\\"\\\"\\r\\n Set the field named `name` to `value`.\\r\\n The length of `value` must be the number of instances,\\r\\n and must agree with other existing fields in this object.\\r\\n \\\"\\\"\\\"\\r\\n with warnings.catch_warnings(record=True):\\r\\n data_len = len(value)\\r\\n if len(self._fields):\\r\\n assert (\\r\\n len(self) == data_len\\r\\n ), \\\"Adding a field of length {} to a Instances of length {}\\\".format(data_len, len(self))\\r\\n self._fields[name] = value\\r\\n\\r\\n def has(self, name: str) -> bool:\\r\\n \\\"\\\"\\\"\\r\\n Returns:\\r\\n bool: whether the field called `name` exists.\\r\\n \\\"\\\"\\\"\\r\\n return name in self._fields\\r\\n\\r\\n def remove(self, name: str) -> None:\\r\\n \\\"\\\"\\\"\\r\\n Remove the field called `name`.\\r\\n \\\"\\\"\\\"\\r\\n del self._fields[name]\\r\\n\\r\\n def get(self, name: str) -> Any:\\r\\n \\\"\\\"\\\"\\r\\n Returns the field called `name`.\\r\\n \\\"\\\"\\\"\\r\\n return self._fields[name]\\r\\n\\r\\n def get_fields(self) -> Dict[str, Any]:\\r\\n \\\"\\\"\\\"\\r\\n Returns:\\r\\n dict: a dict which maps names (str) to data of the fields\\r\\n\\r\\n Modifying the returned dict will modify this instance.\\r\\n \\\"\\\"\\\"\\r\\n return self._fields\\r\\n\\r\\n # Tensor-like methods\\r\\n def to(self, *args: Any, **kwargs: Any) -> \\\"Instances\\\":\\r\\n \\\"\\\"\\\"\\r\\n Returns:\\r\\n Instances: all fields are called with a `to(device)`, if the field has this method.\\r\\n \\\"\\\"\\\"\\r\\n ret = Instances(self._image_size)\\r\\n for k, v in self._fields.items():\\r\\n if hasattr(v, \\\"to\\\"):\\r\\n v = v.to(*args, **kwargs)\\r\\n ret.set(k, v)\\r\\n return ret\\r\\n\\r\\n def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> \\\"Instances\\\":\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n item: an index-like object and will be used to index all the fields.\\r\\n\\r\\n Returns:\\r\\n If `item` is a string, return the data in the corresponding field.\\r\\n Otherwise, returns an `Instances` where all fields are indexed by `item`.\\r\\n \\\"\\\"\\\"\\r\\n if type(item) == int:\\r\\n if item >= len(self) or item < -len(self):\\r\\n raise IndexError(\\\"Instances index out of range!\\\")\\r\\n else:\\r\\n item = slice(item, None, len(self))\\r\\n\\r\\n ret = Instances(self._image_size)\\r\\n for k, v in self._fields.items():\\r\\n ret.set(k, v[item])\\r\\n return ret\\r\\n\\r\\n def __len__(self) -> int:\\r\\n for v in self._fields.values():\\r\\n # use __len__ because len() has to be int and is not friendly to tracing\\r\\n return v.__len__()\\r\\n raise NotImplementedError(\\\"Empty Instances does not support __len__!\\\")\\r\\n\\r\\n def __iter__(self):\\r\\n raise NotImplementedError(\\\"`Instances` object is not iterable!\\\")\\r\\n\\r\\n @staticmethod\\r\\n def cat(instance_lists: List[\\\"Instances\\\"]) -> \\\"Instances\\\":\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n instance_lists (list[Instances])\\r\\n\\r\\n Returns:\\r\\n Instances\\r\\n \\\"\\\"\\\"\\r\\n assert all(isinstance(i, Instances) for i in instance_lists)\\r\\n assert len(instance_lists) > 0\\r\\n if len(instance_lists) == 1:\\r\\n return instance_lists[0]\\r\\n\\r\\n image_size = instance_lists[0].image_size\\r\\n if not isinstance(image_size, torch.Tensor): # could be a tensor in tracing\\r\\n for i in instance_lists[1:]:\\r\\n assert i.image_size == image_size\\r\\n ret = Instances(image_size)\\r\\n for k in instance_lists[0]._fields.keys():\\r\\n values = [i.get(k) for i in instance_lists]\\r\\n v0 = values[0]\\r\\n if isinstance(v0, torch.Tensor):\\r\\n values = torch.cat(values, dim=0)\\r\\n elif isinstance(v0, list):\\r\\n values = list(itertools.chain(*values))\\r\\n elif hasattr(type(v0), \\\"cat\\\"):\\r\\n values = type(v0).cat(values)\\r\\n else:\\r\\n raise ValueError(\\\"Unsupported type {} for concatenation\\\".format(type(v0)))\\r\\n ret.set(k, values)\\r\\n return ret\\r\\n\\r\\n def __str__(self) -> str:\\r\\n s = self.__class__.__name__ + \\\"(\\\"\\r\\n s += \\\"num_instances={}, \\\".format(len(self))\\r\\n s += \\\"image_height={}, \\\".format(self._image_size[0])\\r\\n s += \\\"image_width={}, \\\".format(self._image_size[1])\\r\\n s += \\\"fields=[{}])\\\".format(\\\", \\\".join((f\\\"{k}: {v}\\\" for k, v in self._fields.items())))\\r\\n return s\\r\\n\\r\\n __repr__ = __str__\\r\"\n },\n {\n \"identifier\": \"get_event_storage\",\n \"path\": \"annotator/oneformer/detectron2/utils/events.py\",\n \"snippet\": \"def get_event_storage():\\r\\n \\\"\\\"\\\"\\r\\n Returns:\\r\\n The :class:`EventStorage` object that's currently being used.\\r\\n Throws an error if no :class:`EventStorage` is currently enabled.\\r\\n \\\"\\\"\\\"\\r\\n assert len(\\r\\n _CURRENT_STORAGE_STACK\\r\\n ), \\\"get_event_storage() has to be called inside a 'with EventStorage(...)' context!\\\"\\r\\n return _CURRENT_STORAGE_STACK[-1]\\r\"\n },\n {\n \"identifier\": \"build_anchor_generator\",\n \"path\": \"annotator/oneformer/detectron2/modeling/anchor_generator.py\",\n \"snippet\": \"def build_anchor_generator(cfg, input_shape):\\r\\n \\\"\\\"\\\"\\r\\n Built an anchor generator from `cfg.MODEL.ANCHOR_GENERATOR.NAME`.\\r\\n \\\"\\\"\\\"\\r\\n anchor_generator = cfg.MODEL.ANCHOR_GENERATOR.NAME\\r\\n return ANCHOR_GENERATOR_REGISTRY.get(anchor_generator)(cfg, input_shape)\\r\"\n },\n {\n \"identifier\": \"build_backbone\",\n \"path\": \"annotator/oneformer/detectron2/modeling/backbone/build.py\",\n \"snippet\": \"def build_backbone(cfg, input_shape=None):\\r\\n \\\"\\\"\\\"\\r\\n Build a backbone from `cfg.MODEL.BACKBONE.NAME`.\\r\\n\\r\\n Returns:\\r\\n an instance of :class:`Backbone`\\r\\n \\\"\\\"\\\"\\r\\n if input_shape is None:\\r\\n input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))\\r\\n\\r\\n backbone_name = cfg.MODEL.BACKBONE.NAME\\r\\n backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape)\\r\\n assert isinstance(backbone, Backbone)\\r\\n return backbone\\r\"\n },\n {\n \"identifier\": \"Backbone\",\n \"path\": \"annotator/oneformer/detectron2/modeling/backbone/backbone.py\",\n \"snippet\": \"class Backbone(nn.Module, metaclass=ABCMeta):\\r\\n \\\"\\\"\\\"\\r\\n Abstract base class for network backbones.\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n def __init__(self):\\r\\n \\\"\\\"\\\"\\r\\n The `__init__` method of any subclass can specify its own set of arguments.\\r\\n \\\"\\\"\\\"\\r\\n super().__init__()\\r\\n\\r\\n @abstractmethod\\r\\n def forward(self):\\r\\n \\\"\\\"\\\"\\r\\n Subclasses must override this method, but adhere to the same return type.\\r\\n\\r\\n Returns:\\r\\n dict[str->Tensor]: mapping from feature name (e.g., \\\"res2\\\") to tensor\\r\\n \\\"\\\"\\\"\\r\\n pass\\r\\n\\r\\n @property\\r\\n def size_divisibility(self) -> int:\\r\\n \\\"\\\"\\\"\\r\\n Some backbones require the input height and width to be divisible by a\\r\\n specific integer. This is typically true for encoder / decoder type networks\\r\\n with lateral connection (e.g., FPN) for which feature maps need to match\\r\\n dimension in the \\\"bottom up\\\" and \\\"top down\\\" paths. Set to 0 if no specific\\r\\n input size divisibility is required.\\r\\n \\\"\\\"\\\"\\r\\n return 0\\r\\n\\r\\n @property\\r\\n def padding_constraints(self) -> Dict[str, int]:\\r\\n \\\"\\\"\\\"\\r\\n This property is a generalization of size_divisibility. Some backbones and training\\r\\n recipes require specific padding constraints, such as enforcing divisibility by a specific\\r\\n integer (e.g., FPN) or padding to a square (e.g., ViTDet with large-scale jitter\\r\\n in :paper:vitdet). `padding_constraints` contains these optional items like:\\r\\n {\\r\\n \\\"size_divisibility\\\": int,\\r\\n \\\"square_size\\\": int,\\r\\n # Future options are possible\\r\\n }\\r\\n `size_divisibility` will read from here if presented and `square_size` indicates the\\r\\n square padding size if `square_size` > 0.\\r\\n\\r\\n TODO: use type of Dict[str, int] to avoid torchscipt issues. The type of padding_constraints\\r\\n could be generalized as TypedDict (Python 3.8+) to support more types in the future.\\r\\n \\\"\\\"\\\"\\r\\n return {}\\r\\n\\r\\n def output_shape(self):\\r\\n \\\"\\\"\\\"\\r\\n Returns:\\r\\n dict[str->ShapeSpec]\\r\\n \\\"\\\"\\\"\\r\\n # this is a backward-compatible default\\r\\n return {\\r\\n name: ShapeSpec(\\r\\n channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]\\r\\n )\\r\\n for name in self._out_features\\r\\n }\\r\"\n },\n {\n \"identifier\": \"Box2BoxTransform\",\n \"path\": \"annotator/oneformer/detectron2/modeling/box_regression.py\",\n \"snippet\": \"class Box2BoxTransform(object):\\r\\n \\\"\\\"\\\"\\r\\n The box-to-box transform defined in R-CNN. The transformation is parameterized\\r\\n by 4 deltas: (dx, dy, dw, dh). The transformation scales the box's width and height\\r\\n by exp(dw), exp(dh) and shifts a box's center by the offset (dx * width, dy * height).\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n def __init__(\\r\\n self, weights: Tuple[float, float, float, float], scale_clamp: float = _DEFAULT_SCALE_CLAMP\\r\\n ):\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n weights (4-element tuple): Scaling factors that are applied to the\\r\\n (dx, dy, dw, dh) deltas. In Fast R-CNN, these were originally set\\r\\n such that the deltas have unit variance; now they are treated as\\r\\n hyperparameters of the system.\\r\\n scale_clamp (float): When predicting deltas, the predicted box scaling\\r\\n factors (dw and dh) are clamped such that they are <= scale_clamp.\\r\\n \\\"\\\"\\\"\\r\\n self.weights = weights\\r\\n self.scale_clamp = scale_clamp\\r\\n\\r\\n def get_deltas(self, src_boxes, target_boxes):\\r\\n \\\"\\\"\\\"\\r\\n Get box regression transformation deltas (dx, dy, dw, dh) that can be used\\r\\n to transform the `src_boxes` into the `target_boxes`. That is, the relation\\r\\n ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless\\r\\n any delta is too large and is clamped).\\r\\n\\r\\n Args:\\r\\n src_boxes (Tensor): source boxes, e.g., object proposals\\r\\n target_boxes (Tensor): target of the transformation, e.g., ground-truth\\r\\n boxes.\\r\\n \\\"\\\"\\\"\\r\\n assert isinstance(src_boxes, torch.Tensor), type(src_boxes)\\r\\n assert isinstance(target_boxes, torch.Tensor), type(target_boxes)\\r\\n\\r\\n src_widths = src_boxes[:, 2] - src_boxes[:, 0]\\r\\n src_heights = src_boxes[:, 3] - src_boxes[:, 1]\\r\\n src_ctr_x = src_boxes[:, 0] + 0.5 * src_widths\\r\\n src_ctr_y = src_boxes[:, 1] + 0.5 * src_heights\\r\\n\\r\\n target_widths = target_boxes[:, 2] - target_boxes[:, 0]\\r\\n target_heights = target_boxes[:, 3] - target_boxes[:, 1]\\r\\n target_ctr_x = target_boxes[:, 0] + 0.5 * target_widths\\r\\n target_ctr_y = target_boxes[:, 1] + 0.5 * target_heights\\r\\n\\r\\n wx, wy, ww, wh = self.weights\\r\\n dx = wx * (target_ctr_x - src_ctr_x) / src_widths\\r\\n dy = wy * (target_ctr_y - src_ctr_y) / src_heights\\r\\n dw = ww * torch.log(target_widths / src_widths)\\r\\n dh = wh * torch.log(target_heights / src_heights)\\r\\n\\r\\n deltas = torch.stack((dx, dy, dw, dh), dim=1)\\r\\n assert (src_widths > 0).all().item(), \\\"Input boxes to Box2BoxTransform are not valid!\\\"\\r\\n return deltas\\r\\n\\r\\n def apply_deltas(self, deltas, boxes):\\r\\n \\\"\\\"\\\"\\r\\n Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`.\\r\\n\\r\\n Args:\\r\\n deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1.\\r\\n deltas[i] represents k potentially different class-specific\\r\\n box transformations for the single box boxes[i].\\r\\n boxes (Tensor): boxes to transform, of shape (N, 4)\\r\\n \\\"\\\"\\\"\\r\\n deltas = deltas.float() # ensure fp32 for decoding precision\\r\\n boxes = boxes.to(deltas.dtype)\\r\\n\\r\\n widths = boxes[:, 2] - boxes[:, 0]\\r\\n heights = boxes[:, 3] - boxes[:, 1]\\r\\n ctr_x = boxes[:, 0] + 0.5 * widths\\r\\n ctr_y = boxes[:, 1] + 0.5 * heights\\r\\n\\r\\n wx, wy, ww, wh = self.weights\\r\\n dx = deltas[:, 0::4] / wx\\r\\n dy = deltas[:, 1::4] / wy\\r\\n dw = deltas[:, 2::4] / ww\\r\\n dh = deltas[:, 3::4] / wh\\r\\n\\r\\n # Prevent sending too large values into torch.exp()\\r\\n dw = torch.clamp(dw, max=self.scale_clamp)\\r\\n dh = torch.clamp(dh, max=self.scale_clamp)\\r\\n\\r\\n pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]\\r\\n pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]\\r\\n pred_w = torch.exp(dw) * widths[:, None]\\r\\n pred_h = torch.exp(dh) * heights[:, None]\\r\\n\\r\\n x1 = pred_ctr_x - 0.5 * pred_w\\r\\n y1 = pred_ctr_y - 0.5 * pred_h\\r\\n x2 = pred_ctr_x + 0.5 * pred_w\\r\\n y2 = pred_ctr_y + 0.5 * pred_h\\r\\n pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)\\r\\n return pred_boxes.reshape(deltas.shape)\\r\"\n },\n {\n \"identifier\": \"_dense_box_regression_loss\",\n \"path\": \"annotator/oneformer/detectron2/modeling/box_regression.py\",\n \"snippet\": \"def _dense_box_regression_loss(\\r\\n anchors: List[Union[Boxes, torch.Tensor]],\\r\\n box2box_transform: Box2BoxTransform,\\r\\n pred_anchor_deltas: List[torch.Tensor],\\r\\n gt_boxes: List[torch.Tensor],\\r\\n fg_mask: torch.Tensor,\\r\\n box_reg_loss_type=\\\"smooth_l1\\\",\\r\\n smooth_l1_beta=0.0,\\r\\n):\\r\\n \\\"\\\"\\\"\\r\\n Compute loss for dense multi-level box regression.\\r\\n Loss is accumulated over ``fg_mask``.\\r\\n\\r\\n Args:\\r\\n anchors: #lvl anchor boxes, each is (HixWixA, 4)\\r\\n pred_anchor_deltas: #lvl predictions, each is (N, HixWixA, 4)\\r\\n gt_boxes: N ground truth boxes, each has shape (R, 4) (R = sum(Hi * Wi * A))\\r\\n fg_mask: the foreground boolean mask of shape (N, R) to compute loss on\\r\\n box_reg_loss_type (str): Loss type to use. Supported losses: \\\"smooth_l1\\\", \\\"giou\\\",\\r\\n \\\"diou\\\", \\\"ciou\\\".\\r\\n smooth_l1_beta (float): beta parameter for the smooth L1 regression loss. Default to\\r\\n use L1 loss. Only used when `box_reg_loss_type` is \\\"smooth_l1\\\"\\r\\n \\\"\\\"\\\"\\r\\n if isinstance(anchors[0], Boxes):\\r\\n anchors = type(anchors[0]).cat(anchors).tensor # (R, 4)\\r\\n else:\\r\\n anchors = cat(anchors)\\r\\n if box_reg_loss_type == \\\"smooth_l1\\\":\\r\\n gt_anchor_deltas = [box2box_transform.get_deltas(anchors, k) for k in gt_boxes]\\r\\n gt_anchor_deltas = torch.stack(gt_anchor_deltas) # (N, R, 4)\\r\\n loss_box_reg = smooth_l1_loss(\\r\\n cat(pred_anchor_deltas, dim=1)[fg_mask],\\r\\n gt_anchor_deltas[fg_mask],\\r\\n beta=smooth_l1_beta,\\r\\n reduction=\\\"sum\\\",\\r\\n )\\r\\n elif box_reg_loss_type == \\\"giou\\\":\\r\\n pred_boxes = [\\r\\n box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1)\\r\\n ]\\r\\n loss_box_reg = giou_loss(\\r\\n torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction=\\\"sum\\\"\\r\\n )\\r\\n elif box_reg_loss_type == \\\"diou\\\":\\r\\n pred_boxes = [\\r\\n box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1)\\r\\n ]\\r\\n loss_box_reg = diou_loss(\\r\\n torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction=\\\"sum\\\"\\r\\n )\\r\\n elif box_reg_loss_type == \\\"ciou\\\":\\r\\n pred_boxes = [\\r\\n box2box_transform.apply_deltas(k, anchors) for k in cat(pred_anchor_deltas, dim=1)\\r\\n ]\\r\\n loss_box_reg = ciou_loss(\\r\\n torch.stack(pred_boxes)[fg_mask], torch.stack(gt_boxes)[fg_mask], reduction=\\\"sum\\\"\\r\\n )\\r\\n else:\\r\\n raise ValueError(f\\\"Invalid dense box regression loss type '{box_reg_loss_type}'\\\")\\r\\n return loss_box_reg\\r\"\n },\n {\n \"identifier\": \"Matcher\",\n \"path\": \"annotator/oneformer/detectron2/modeling/matcher.py\",\n \"snippet\": \"class Matcher(object):\\r\\n \\\"\\\"\\\"\\r\\n This class assigns to each predicted \\\"element\\\" (e.g., a box) a ground-truth\\r\\n element. Each predicted element will have exactly zero or one matches; each\\r\\n ground-truth element may be matched to zero or more predicted elements.\\r\\n\\r\\n The matching is determined by the MxN match_quality_matrix, that characterizes\\r\\n how well each (ground-truth, prediction)-pair match each other. For example,\\r\\n if the elements are boxes, this matrix may contain box intersection-over-union\\r\\n overlap values.\\r\\n\\r\\n The matcher returns (a) a vector of length N containing the index of the\\r\\n ground-truth element m in [0, M) that matches to prediction n in [0, N).\\r\\n (b) a vector of length N containing the labels for each prediction.\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n def __init__(\\r\\n self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False\\r\\n ):\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n thresholds (list): a list of thresholds used to stratify predictions\\r\\n into levels.\\r\\n labels (list): a list of values to label predictions belonging at\\r\\n each level. A label can be one of {-1, 0, 1} signifying\\r\\n {ignore, negative class, positive class}, respectively.\\r\\n allow_low_quality_matches (bool): if True, produce additional matches\\r\\n for predictions with maximum match quality lower than high_threshold.\\r\\n See set_low_quality_matches_ for more details.\\r\\n\\r\\n For example,\\r\\n thresholds = [0.3, 0.5]\\r\\n labels = [0, -1, 1]\\r\\n All predictions with iou < 0.3 will be marked with 0 and\\r\\n thus will be considered as false positives while training.\\r\\n All predictions with 0.3 <= iou < 0.5 will be marked with -1 and\\r\\n thus will be ignored.\\r\\n All predictions with 0.5 <= iou will be marked with 1 and\\r\\n thus will be considered as true positives.\\r\\n \\\"\\\"\\\"\\r\\n # Add -inf and +inf to first and last position in thresholds\\r\\n thresholds = thresholds[:]\\r\\n assert thresholds[0] > 0\\r\\n thresholds.insert(0, -float(\\\"inf\\\"))\\r\\n thresholds.append(float(\\\"inf\\\"))\\r\\n # Currently torchscript does not support all + generator\\r\\n assert all([low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])])\\r\\n assert all([l in [-1, 0, 1] for l in labels])\\r\\n assert len(labels) == len(thresholds) - 1\\r\\n self.thresholds = thresholds\\r\\n self.labels = labels\\r\\n self.allow_low_quality_matches = allow_low_quality_matches\\r\\n\\r\\n def __call__(self, match_quality_matrix):\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n match_quality_matrix (Tensor[float]): an MxN tensor, containing the\\r\\n pairwise quality between M ground-truth elements and N predicted\\r\\n elements. All elements must be >= 0 (due to the us of `torch.nonzero`\\r\\n for selecting indices in :meth:`set_low_quality_matches_`).\\r\\n\\r\\n Returns:\\r\\n matches (Tensor[int64]): a vector of length N, where matches[i] is a matched\\r\\n ground-truth index in [0, M)\\r\\n match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates\\r\\n whether a prediction is a true or false positive or ignored\\r\\n \\\"\\\"\\\"\\r\\n assert match_quality_matrix.dim() == 2\\r\\n if match_quality_matrix.numel() == 0:\\r\\n default_matches = match_quality_matrix.new_full(\\r\\n (match_quality_matrix.size(1),), 0, dtype=torch.int64\\r\\n )\\r\\n # When no gt boxes exist, we define IOU = 0 and therefore set labels\\r\\n # to `self.labels[0]`, which usually defaults to background class 0\\r\\n # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds\\r\\n default_match_labels = match_quality_matrix.new_full(\\r\\n (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8\\r\\n )\\r\\n return default_matches, default_match_labels\\r\\n\\r\\n assert torch.all(match_quality_matrix >= 0)\\r\\n\\r\\n # match_quality_matrix is M (gt) x N (predicted)\\r\\n # Max over gt elements (dim 0) to find best gt candidate for each prediction\\r\\n matched_vals, matches = match_quality_matrix.max(dim=0)\\r\\n\\r\\n match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)\\r\\n\\r\\n for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):\\r\\n low_high = (matched_vals >= low) & (matched_vals < high)\\r\\n match_labels[low_high] = l\\r\\n\\r\\n if self.allow_low_quality_matches:\\r\\n self.set_low_quality_matches_(match_labels, match_quality_matrix)\\r\\n\\r\\n return matches, match_labels\\r\\n\\r\\n def set_low_quality_matches_(self, match_labels, match_quality_matrix):\\r\\n \\\"\\\"\\\"\\r\\n Produce additional matches for predictions that have only low-quality matches.\\r\\n Specifically, for each ground-truth G find the set of predictions that have\\r\\n maximum overlap with it (including ties); for each prediction in that set, if\\r\\n it is unmatched, then match it to the ground-truth G.\\r\\n\\r\\n This function implements the RPN assignment case (i) in Sec. 3.1.2 of\\r\\n :paper:`Faster R-CNN`.\\r\\n \\\"\\\"\\\"\\r\\n # For each gt, find the prediction with which it has highest quality\\r\\n highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)\\r\\n # Find the highest quality match available, even if it is low, including ties.\\r\\n # Note that the matches qualities must be positive due to the use of\\r\\n # `torch.nonzero`.\\r\\n _, pred_inds_with_highest_quality = nonzero_tuple(\\r\\n match_quality_matrix == highest_quality_foreach_gt[:, None]\\r\\n )\\r\\n # If an anchor was labeled positive only due to a low-quality match\\r\\n # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B.\\r\\n # This follows the implementation in Detectron, and is found to have no significant impact.\\r\\n match_labels[pred_inds_with_highest_quality] = 1\\r\"\n },\n {\n \"identifier\": \"META_ARCH_REGISTRY\",\n \"path\": \"annotator/oneformer/detectron2/modeling/meta_arch/build.py\",\n \"snippet\": \"META_ARCH_REGISTRY = Registry(\\\"META_ARCH\\\") # noqa F401 isort:skip\\r\"\n },\n {\n \"identifier\": \"DenseDetector\",\n \"path\": \"annotator/oneformer/detectron2/modeling/meta_arch/dense_detector.py\",\n \"snippet\": \"class DenseDetector(nn.Module):\\r\\n \\\"\\\"\\\"\\r\\n Base class for dense detector. We define a dense detector as a fully-convolutional model that\\r\\n makes per-pixel (i.e. dense) predictions.\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n def __init__(\\r\\n self,\\r\\n backbone: Backbone,\\r\\n head: nn.Module,\\r\\n head_in_features: Optional[List[str]] = None,\\r\\n *,\\r\\n pixel_mean,\\r\\n pixel_std,\\r\\n ):\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n backbone: backbone module\\r\\n head: head module\\r\\n head_in_features: backbone features to use in head. Default to all backbone features.\\r\\n pixel_mean (Tuple[float]):\\r\\n Values to be used for image normalization (BGR order).\\r\\n To train on images of different number of channels, set different mean & std.\\r\\n Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]\\r\\n pixel_std (Tuple[float]):\\r\\n When using pre-trained models in Detectron1 or any MSRA models,\\r\\n std has been absorbed into its conv1 weights, so the std needs to be set 1.\\r\\n Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)\\r\\n \\\"\\\"\\\"\\r\\n super().__init__()\\r\\n\\r\\n self.backbone = backbone\\r\\n self.head = head\\r\\n if head_in_features is None:\\r\\n shapes = self.backbone.output_shape()\\r\\n self.head_in_features = sorted(shapes.keys(), key=lambda x: shapes[x].stride)\\r\\n else:\\r\\n self.head_in_features = head_in_features\\r\\n self.register_buffer(\\\"pixel_mean\\\", torch.tensor(pixel_mean).view(-1, 1, 1), False)\\r\\n self.register_buffer(\\\"pixel_std\\\", torch.tensor(pixel_std).view(-1, 1, 1), False)\\r\\n\\r\\n @property\\r\\n def device(self):\\r\\n return self.pixel_mean.device\\r\\n\\r\\n def _move_to_current_device(self, x):\\r\\n return move_device_like(x, self.pixel_mean)\\r\\n\\r\\n def forward(self, batched_inputs: List[Dict[str, Tensor]]):\\r\\n \\\"\\\"\\\"\\r\\n Args:\\r\\n batched_inputs: a list, batched outputs of :class:`DatasetMapper` .\\r\\n Each item in the list contains the inputs for one image.\\r\\n For now, each item in the list is a dict that contains:\\r\\n\\r\\n * image: Tensor, image in (C, H, W) format.\\r\\n * instances: Instances\\r\\n\\r\\n Other information that's included in the original dicts, such as:\\r\\n\\r\\n * \\\"height\\\", \\\"width\\\" (int): the output resolution of the model, used in inference.\\r\\n See :meth:`postprocess` for details.\\r\\n\\r\\n Returns:\\r\\n In training, dict[str, Tensor]: mapping from a named loss to a tensor storing the\\r\\n loss. Used during training only. In inference, the standard output format, described\\r\\n in :doc:`/tutorials/models`.\\r\\n \\\"\\\"\\\"\\r\\n images = self.preprocess_image(batched_inputs)\\r\\n features = self.backbone(images.tensor)\\r\\n features = [features[f] for f in self.head_in_features]\\r\\n predictions = self.head(features)\\r\\n\\r\\n if self.training:\\r\\n assert not torch.jit.is_scripting(), \\\"Not supported\\\"\\r\\n assert \\\"instances\\\" in batched_inputs[0], \\\"Instance annotations are missing in training!\\\"\\r\\n gt_instances = [x[\\\"instances\\\"].to(self.device) for x in batched_inputs]\\r\\n return self.forward_training(images, features, predictions, gt_instances)\\r\\n else:\\r\\n results = self.forward_inference(images, features, predictions)\\r\\n if torch.jit.is_scripting():\\r\\n return results\\r\\n\\r\\n processed_results = []\\r\\n for results_per_image, input_per_image, image_size in zip(\\r\\n results, batched_inputs, images.image_sizes\\r\\n ):\\r\\n height = input_per_image.get(\\\"height\\\", image_size[0])\\r\\n width = input_per_image.get(\\\"width\\\", image_size[1])\\r\\n r = detector_postprocess(results_per_image, height, width)\\r\\n processed_results.append({\\\"instances\\\": r})\\r\\n return processed_results\\r\\n\\r\\n def forward_training(self, images, features, predictions, gt_instances):\\r\\n raise NotImplementedError()\\r\\n\\r\\n def preprocess_image(self, batched_inputs: List[Dict[str, Tensor]]):\\r\\n \\\"\\\"\\\"\\r\\n Normalize, pad and batch the input images.\\r\\n \\\"\\\"\\\"\\r\\n images = [self._move_to_current_device(x[\\\"image\\\"]) for x in batched_inputs]\\r\\n images = [(x - self.pixel_mean) / self.pixel_std for x in images]\\r\\n images = ImageList.from_tensors(\\r\\n images,\\r\\n self.backbone.size_divisibility,\\r\\n padding_constraints=self.backbone.padding_constraints,\\r\\n )\\r\\n return images\\r\\n\\r\\n def _transpose_dense_predictions(\\r\\n self, predictions: List[List[Tensor]], dims_per_anchor: List[int]\\r\\n ) -> List[List[Tensor]]:\\r\\n \\\"\\\"\\\"\\r\\n Transpose the dense per-level predictions.\\r\\n\\r\\n Args:\\r\\n predictions: a list of outputs, each is a list of per-level\\r\\n predictions with shape (N, Ai x K, Hi, Wi), where N is the\\r\\n number of images, Ai is the number of anchors per location on\\r\\n level i, K is the dimension of predictions per anchor.\\r\\n dims_per_anchor: the value of K for each predictions. e.g. 4 for\\r\\n box prediction, #classes for classification prediction.\\r\\n\\r\\n Returns:\\r\\n List[List[Tensor]]: each prediction is transposed to (N, Hi x Wi x Ai, K).\\r\\n \\\"\\\"\\\"\\r\\n assert len(predictions) == len(dims_per_anchor)\\r\\n res: List[List[Tensor]] = []\\r\\n for pred, dim_per_anchor in zip(predictions, dims_per_anchor):\\r\\n pred = [permute_to_N_HWA_K(x, dim_per_anchor) for x in pred]\\r\\n res.append(pred)\\r\\n return res\\r\\n\\r\\n def _ema_update(self, name: str, value: float, initial_value: float, momentum: float = 0.9):\\r\\n \\\"\\\"\\\"\\r\\n Apply EMA update to `self.name` using `value`.\\r\\n\\r\\n This is mainly used for loss normalizer. In Detectron1, loss is normalized by number\\r\\n of foreground samples in the batch. When batch size is 1 per GPU, #foreground has a\\r\\n large variance and using it lead to lower performance. Therefore we maintain an EMA of\\r\\n #foreground to stabilize the normalizer.\\r\\n\\r\\n Args:\\r\\n name: name of the normalizer\\r\\n value: the new value to update\\r\\n initial_value: the initial value to start with\\r\\n momentum: momentum of EMA\\r\\n\\r\\n Returns:\\r\\n float: the updated EMA value\\r\\n \\\"\\\"\\\"\\r\\n if hasattr(self, name):\\r\\n old = getattr(self, name)\\r\\n else:\\r\\n old = initial_value\\r\\n new = old * momentum + value * (1 - momentum)\\r\\n setattr(self, name, new)\\r\\n return new\\r\\n\\r\\n def _decode_per_level_predictions(\\r\\n self,\\r\\n anchors: Boxes,\\r\\n pred_scores: Tensor,\\r\\n pred_deltas: Tensor,\\r\\n score_thresh: float,\\r\\n topk_candidates: int,\\r\\n image_size: Tuple[int, int],\\r\\n ) -> Instances:\\r\\n \\\"\\\"\\\"\\r\\n Decode boxes and classification predictions of one featuer level, by\\r\\n the following steps:\\r\\n 1. filter the predictions based on score threshold and top K scores.\\r\\n 2. transform the box regression outputs\\r\\n 3. return the predicted scores, classes and boxes\\r\\n\\r\\n Args:\\r\\n anchors: Boxes, anchor for this feature level\\r\\n pred_scores: HxWxA,K\\r\\n pred_deltas: HxWxA,4\\r\\n\\r\\n Returns:\\r\\n Instances: with field \\\"scores\\\", \\\"pred_boxes\\\", \\\"pred_classes\\\".\\r\\n \\\"\\\"\\\"\\r\\n # Apply two filtering to make NMS faster.\\r\\n # 1. Keep boxes with confidence score higher than threshold\\r\\n keep_idxs = pred_scores > score_thresh\\r\\n pred_scores = pred_scores[keep_idxs]\\r\\n topk_idxs = torch.nonzero(keep_idxs) # Kx2\\r\\n\\r\\n # 2. Keep top k top scoring boxes only\\r\\n topk_idxs_size = topk_idxs.shape[0]\\r\\n if isinstance(topk_idxs_size, Tensor):\\r\\n # It's a tensor in tracing\\r\\n num_topk = torch.clamp(topk_idxs_size, max=topk_candidates)\\r\\n else:\\r\\n num_topk = min(topk_idxs_size, topk_candidates)\\r\\n pred_scores, idxs = pred_scores.topk(num_topk)\\r\\n topk_idxs = topk_idxs[idxs]\\r\\n\\r\\n anchor_idxs, classes_idxs = topk_idxs.unbind(dim=1)\\r\\n\\r\\n pred_boxes = self.box2box_transform.apply_deltas(\\r\\n pred_deltas[anchor_idxs], anchors.tensor[anchor_idxs]\\r\\n )\\r\\n return Instances(\\r\\n image_size, pred_boxes=Boxes(pred_boxes), scores=pred_scores, pred_classes=classes_idxs\\r\\n )\\r\\n\\r\\n def _decode_multi_level_predictions(\\r\\n self,\\r\\n anchors: List[Boxes],\\r\\n pred_scores: List[Tensor],\\r\\n pred_deltas: List[Tensor],\\r\\n score_thresh: float,\\r\\n topk_candidates: int,\\r\\n image_size: Tuple[int, int],\\r\\n ) -> Instances:\\r\\n \\\"\\\"\\\"\\r\\n Run `_decode_per_level_predictions` for all feature levels and concat the results.\\r\\n \\\"\\\"\\\"\\r\\n predictions = [\\r\\n self._decode_per_level_predictions(\\r\\n anchors_i,\\r\\n box_cls_i,\\r\\n box_reg_i,\\r\\n self.test_score_thresh,\\r\\n self.test_topk_candidates,\\r\\n image_size,\\r\\n )\\r\\n # Iterate over every feature level\\r\\n for box_cls_i, box_reg_i, anchors_i in zip(pred_scores, pred_deltas, anchors)\\r\\n ]\\r\\n return predictions[0].cat(predictions) # 'Instances.cat' is not scriptale but this is\\r\\n\\r\\n def visualize_training(self, batched_inputs, results):\\r\\n \\\"\\\"\\\"\\r\\n A function used to visualize ground truth images and final network predictions.\\r\\n It shows ground truth bounding boxes on the original image and up to 20\\r\\n predicted object bounding boxes on the original image.\\r\\n\\r\\n Args:\\r\\n batched_inputs (list): a list that contains input to the model.\\r\\n results (List[Instances]): a list of #images elements returned by forward_inference().\\r\\n \\\"\\\"\\\"\\r\\n from annotator.oneformer.detectron2.utils.visualizer import Visualizer\\r\\n\\r\\n assert len(batched_inputs) == len(\\r\\n results\\r\\n ), \\\"Cannot visualize inputs and results of different sizes\\\"\\r\\n storage = get_event_storage()\\r\\n max_boxes = 20\\r\\n\\r\\n image_index = 0 # only visualize a single image\\r\\n img = batched_inputs[image_index][\\\"image\\\"]\\r\\n img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format)\\r\\n v_gt = Visualizer(img, None)\\r\\n v_gt = v_gt.overlay_instances(boxes=batched_inputs[image_index][\\\"instances\\\"].gt_boxes)\\r\\n anno_img = v_gt.get_image()\\r\\n processed_results = detector_postprocess(results[image_index], img.shape[0], img.shape[1])\\r\\n predicted_boxes = processed_results.pred_boxes.tensor.detach().cpu().numpy()\\r\\n\\r\\n v_pred = Visualizer(img, None)\\r\\n v_pred = v_pred.overlay_instances(boxes=predicted_boxes[0:max_boxes])\\r\\n prop_img = v_pred.get_image()\\r\\n vis_img = np.vstack((anno_img, prop_img))\\r\\n vis_img = vis_img.transpose(2, 0, 1)\\r\\n vis_name = f\\\"Top: GT bounding boxes; Bottom: {max_boxes} Highest Scoring Results\\\"\\r\\n storage.put_image(vis_name, vis_img)\\r\"\n },\n {\n \"identifier\": \"permute_to_N_HWA_K\",\n \"path\": \"annotator/oneformer/detectron2/modeling/meta_arch/dense_detector.py\",\n \"snippet\": \"def permute_to_N_HWA_K(tensor, K: int):\\r\\n \\\"\\\"\\\"\\r\\n Transpose/reshape a tensor from (N, (Ai x K), H, W) to (N, (HxWxAi), K)\\r\\n \\\"\\\"\\\"\\r\\n assert tensor.dim() == 4, tensor.shape\\r\\n N, _, H, W = tensor.shape\\r\\n tensor = tensor.view(N, -1, K, H, W)\\r\\n tensor = tensor.permute(0, 3, 4, 1, 2)\\r\\n tensor = tensor.reshape(N, -1, K) # Size=(N,HWA,K)\\r\\n return tensor\\r\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import logging\r\nimport math\r\nimport torch\r\nfrom typing import List, Tuple\r\nfrom fvcore.nn import sigmoid_focal_loss_jit\r\nfrom torch import Tensor, nn\r\nfrom torch.nn import functional as F\r\nfrom annotator.oneformer.detectron2.config import configurable\r\nfrom annotator.oneformer.detectron2.layers import CycleBatchNormList, ShapeSpec, batched_nms, cat, get_norm\r\nfrom annotator.oneformer.detectron2.structures import Boxes, ImageList, Instances, pairwise_iou\r\nfrom annotator.oneformer.detectron2.utils.events import get_event_storage\r\nfrom ..anchor_generator import build_anchor_generator\r\nfrom ..backbone import Backbone, build_backbone\r\nfrom ..box_regression import Box2BoxTransform, _dense_box_regression_loss\r\nfrom ..matcher import Matcher\r\nfrom .build import META_ARCH_REGISTRY\r\nfrom .dense_detector import DenseDetector, permute_to_N_HWA_K # noqa\r"},"token_num":{"kind":"number","value":16849,"string":"16,849"},"cropped_code":{"kind":"string","value":" # Vis parameters\r\n self.vis_period = vis_period\r\n self.input_format = input_format\r\n\r\n @classmethod\r\n def from_config(cls, cfg):\r\n backbone = build_backbone(cfg)\r\n backbone_shape = backbone.output_shape()\r\n feature_shapes = [backbone_shape[f] for f in cfg.MODEL.RETINANET.IN_FEATURES]\r\n head = RetinaNetHead(cfg, feature_shapes)\r\n anchor_generator = build_anchor_generator(cfg, feature_shapes)\r\n return {\r\n \"backbone\": backbone,\r\n \"head\": head,\r\n \"anchor_generator\": anchor_generator,\r\n \"box2box_transform\": Box2BoxTransform(weights=cfg.MODEL.RETINANET.BBOX_REG_WEIGHTS),\r\n \"anchor_matcher\": Matcher(\r\n cfg.MODEL.RETINANET.IOU_THRESHOLDS,\r\n cfg.MODEL.RETINANET.IOU_LABELS,\r\n allow_low_quality_matches=True,\r\n ),\r\n \"pixel_mean\": cfg.MODEL.PIXEL_MEAN,\r\n \"pixel_std\": cfg.MODEL.PIXEL_STD,\r\n \"num_classes\": cfg.MODEL.RETINANET.NUM_CLASSES,\r\n \"head_in_features\": cfg.MODEL.RETINANET.IN_FEATURES,\r\n # Loss parameters:\r\n \"focal_loss_alpha\": cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA,\r\n \"focal_loss_gamma\": cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA,\r\n \"smooth_l1_beta\": cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA,\r\n \"box_reg_loss_type\": cfg.MODEL.RETINANET.BBOX_REG_LOSS_TYPE,\r\n # Inference parameters:\r\n \"test_score_thresh\": cfg.MODEL.RETINANET.SCORE_THRESH_TEST,\r\n \"test_topk_candidates\": cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST,\r\n \"test_nms_thresh\": cfg.MODEL.RETINANET.NMS_THRESH_TEST,\r\n \"max_detections_per_image\": cfg.TEST.DETECTIONS_PER_IMAGE,\r\n # Vis parameters\r\n \"vis_period\": cfg.VIS_PERIOD,\r\n \"input_format\": cfg.INPUT.FORMAT,\r\n }\r\n\r\n def forward_training(self, images, features, predictions, gt_instances):\r\n # Transpose the Hi*Wi*A dimension to the middle:\r\n pred_logits, pred_anchor_deltas = self._transpose_dense_predictions(\r\n predictions, [self.num_classes, 4]\r\n )\r\n anchors = self.anchor_generator(features)\r\n gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances)\r\n return self.losses(anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes)\r\n\r\n def losses(self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes):\r\n \"\"\"\r\n Args:\r\n anchors (list[Boxes]): a list of #feature level Boxes\r\n gt_labels, gt_boxes: see output of :meth:`RetinaNet.label_anchors`.\r\n Their shapes are (N, R) and (N, R, 4), respectively, where R is\r\n the total number of anchors across levels, i.e. sum(Hi x Wi x Ai)\r\n pred_logits, pred_anchor_deltas: both are list[Tensor]. Each element in the\r\n list corresponds to one level and has shape (N, Hi * Wi * Ai, K or 4).\r\n Where K is the number of classes used in `pred_logits`.\r\n\r\n Returns:\r\n dict[str, Tensor]:\r\n mapping from a named loss to a scalar tensor storing the loss.\r\n Used during training only. The dict keys are: \"loss_cls\" and \"loss_box_reg\"\r\n \"\"\"\r\n num_images = len(gt_labels)\r\n gt_labels = torch.stack(gt_labels) # (N, R)\r\n\r\n valid_mask = gt_labels >= 0\r\n pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes)\r\n num_pos_anchors = pos_mask.sum().item()\r\n get_event_storage().put_scalar(\"num_pos_anchors\", num_pos_anchors / num_images)\r\n normalizer = self._ema_update(\"loss_normalizer\", max(num_pos_anchors, 1), 100)\r\n\r\n # classification and regression loss\r\n gt_labels_target = F.one_hot(gt_labels[valid_mask], num_classes=self.num_classes + 1)[\r\n :, :-1\r\n ] # no loss for the last (background) class\r\n loss_cls = sigmoid_focal_loss_jit(\r\n cat(pred_logits, dim=1)[valid_mask],\r\n gt_labels_target.to(pred_logits[0].dtype),\r\n alpha=self.focal_loss_alpha,\r\n gamma=self.focal_loss_gamma,\r\n reduction=\"sum\",\r\n )\r\n\r\n loss_box_reg = _dense_box_regression_loss(\r\n anchors,\r\n self.box2box_transform,\r\n pred_anchor_deltas,\r\n gt_boxes,\r\n pos_mask,\r\n box_reg_loss_type=self.box_reg_loss_type,\r\n smooth_l1_beta=self.smooth_l1_beta,\r\n )\r\n\r\n return {\r\n \"loss_cls\": loss_cls / normalizer,\r\n \"loss_box_reg\": loss_box_reg / normalizer,\r\n }\r\n\r\n @torch.no_grad()\r\n def label_anchors(self, anchors, gt_instances):\r\n \"\"\"\r\n Args:\r\n anchors (list[Boxes]): A list of #feature level Boxes.\r\n The Boxes contains anchors of this image on the specific feature level.\r\n gt_instances (list[Instances]): a list of N `Instances`s. The i-th\r\n `Instances` contains the ground-truth per-instance annotations\r\n for the i-th input image.\r\n\r\n Returns:\r\n list[Tensor]: List of #img tensors. i-th element is a vector of labels whose length is\r\n the total number of anchors across all feature maps (sum(Hi * Wi * A)).\r\n Label values are in {-1, 0, ..., K}, with -1 means ignore, and K means background.\r\n\r\n list[Tensor]: i-th element is a Rx4 tensor, where R is the total number of anchors\r\n across feature maps. The values are the matched gt boxes for each anchor.\r\n Values are undefined for those anchors not labeled as foreground.\r\n \"\"\"\r\n"},"all_code":{"kind":"string","value":"# Copyright (c) Facebook, Inc. and its affiliates.\r\n\r\n\r\n\r\n__all__ = [\"RetinaNet\"]\r\n\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\n@META_ARCH_REGISTRY.register()\r\nclass RetinaNet(DenseDetector):\r\n \"\"\"\r\n Implement RetinaNet in :paper:`RetinaNet`.\r\n \"\"\"\r\n\r\n @configurable\r\n def __init__(\r\n self,\r\n *,\r\n backbone: Backbone,\r\n head: nn.Module,\r\n head_in_features,\r\n anchor_generator,\r\n box2box_transform,\r\n anchor_matcher,\r\n num_classes,\r\n focal_loss_alpha=0.25,\r\n focal_loss_gamma=2.0,\r\n smooth_l1_beta=0.0,\r\n box_reg_loss_type=\"smooth_l1\",\r\n test_score_thresh=0.05,\r\n test_topk_candidates=1000,\r\n test_nms_thresh=0.5,\r\n max_detections_per_image=100,\r\n pixel_mean,\r\n pixel_std,\r\n vis_period=0,\r\n input_format=\"BGR\",\r\n ):\r\n \"\"\"\r\n NOTE: this interface is experimental.\r\n\r\n Args:\r\n backbone: a backbone module, must follow detectron2's backbone interface\r\n head (nn.Module): a module that predicts logits and regression deltas\r\n for each level from a list of per-level features\r\n head_in_features (Tuple[str]): Names of the input feature maps to be used in head\r\n anchor_generator (nn.Module): a module that creates anchors from a\r\n list of features. Usually an instance of :class:`AnchorGenerator`\r\n box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to\r\n instance boxes\r\n anchor_matcher (Matcher): label the anchors by matching them with ground truth.\r\n num_classes (int): number of classes. Used to label background proposals.\r\n\r\n # Loss parameters:\r\n focal_loss_alpha (float): focal_loss_alpha\r\n focal_loss_gamma (float): focal_loss_gamma\r\n smooth_l1_beta (float): smooth_l1_beta\r\n box_reg_loss_type (str): Options are \"smooth_l1\", \"giou\", \"diou\", \"ciou\"\r\n\r\n # Inference parameters:\r\n test_score_thresh (float): Inference cls score threshold, only anchors with\r\n score > INFERENCE_TH are considered for inference (to improve speed)\r\n test_topk_candidates (int): Select topk candidates before NMS\r\n test_nms_thresh (float): Overlap threshold used for non-maximum suppression\r\n (suppress boxes with IoU >= this threshold)\r\n max_detections_per_image (int):\r\n Maximum number of detections to return per image during inference\r\n (100 is based on the limit established for the COCO dataset).\r\n\r\n pixel_mean, pixel_std: see :class:`DenseDetector`.\r\n \"\"\"\r\n super().__init__(\r\n backbone, head, head_in_features, pixel_mean=pixel_mean, pixel_std=pixel_std\r\n )\r\n self.num_classes = num_classes\r\n\r\n # Anchors\r\n self.anchor_generator = anchor_generator\r\n self.box2box_transform = box2box_transform\r\n self.anchor_matcher = anchor_matcher\r\n\r\n # Loss parameters:\r\n self.focal_loss_alpha = focal_loss_alpha\r\n self.focal_loss_gamma = focal_loss_gamma\r\n self.smooth_l1_beta = smooth_l1_beta\r\n self.box_reg_loss_type = box_reg_loss_type\r\n # Inference parameters:\r\n self.test_score_thresh = test_score_thresh\r\n self.test_topk_candidates = test_topk_candidates\r\n self.test_nms_thresh = test_nms_thresh\r\n self.max_detections_per_image = max_detections_per_image\r\n # Vis parameters\r\n self.vis_period = vis_period\r\n self.input_format = input_format\r\n\r\n @classmethod\r\n def from_config(cls, cfg):\r\n backbone = build_backbone(cfg)\r\n backbone_shape = backbone.output_shape()\r\n feature_shapes = [backbone_shape[f] for f in cfg.MODEL.RETINANET.IN_FEATURES]\r\n head = RetinaNetHead(cfg, feature_shapes)\r\n anchor_generator = build_anchor_generator(cfg, feature_shapes)\r\n return {\r\n \"backbone\": backbone,\r\n \"head\": head,\r\n \"anchor_generator\": anchor_generator,\r\n \"box2box_transform\": Box2BoxTransform(weights=cfg.MODEL.RETINANET.BBOX_REG_WEIGHTS),\r\n \"anchor_matcher\": Matcher(\r\n cfg.MODEL.RETINANET.IOU_THRESHOLDS,\r\n cfg.MODEL.RETINANET.IOU_LABELS,\r\n allow_low_quality_matches=True,\r\n ),\r\n \"pixel_mean\": cfg.MODEL.PIXEL_MEAN,\r\n \"pixel_std\": cfg.MODEL.PIXEL_STD,\r\n \"num_classes\": cfg.MODEL.RETINANET.NUM_CLASSES,\r\n \"head_in_features\": cfg.MODEL.RETINANET.IN_FEATURES,\r\n # Loss parameters:\r\n \"focal_loss_alpha\": cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA,\r\n \"focal_loss_gamma\": cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA,\r\n \"smooth_l1_beta\": cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA,\r\n \"box_reg_loss_type\": cfg.MODEL.RETINANET.BBOX_REG_LOSS_TYPE,\r\n # Inference parameters:\r\n \"test_score_thresh\": cfg.MODEL.RETINANET.SCORE_THRESH_TEST,\r\n \"test_topk_candidates\": cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST,\r\n \"test_nms_thresh\": cfg.MODEL.RETINANET.NMS_THRESH_TEST,\r\n \"max_detections_per_image\": cfg.TEST.DETECTIONS_PER_IMAGE,\r\n # Vis parameters\r\n \"vis_period\": cfg.VIS_PERIOD,\r\n \"input_format\": cfg.INPUT.FORMAT,\r\n }\r\n\r\n def forward_training(self, images, features, predictions, gt_instances):\r\n # Transpose the Hi*Wi*A dimension to the middle:\r\n pred_logits, pred_anchor_deltas = self._transpose_dense_predictions(\r\n predictions, [self.num_classes, 4]\r\n )\r\n anchors = self.anchor_generator(features)\r\n gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances)\r\n return self.losses(anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes)\r\n\r\n def losses(self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes):\r\n \"\"\"\r\n Args:\r\n anchors (list[Boxes]): a list of #feature level Boxes\r\n gt_labels, gt_boxes: see output of :meth:`RetinaNet.label_anchors`.\r\n Their shapes are (N, R) and (N, R, 4), respectively, where R is\r\n the total number of anchors across levels, i.e. sum(Hi x Wi x Ai)\r\n pred_logits, pred_anchor_deltas: both are list[Tensor]. Each element in the\r\n list corresponds to one level and has shape (N, Hi * Wi * Ai, K or 4).\r\n Where K is the number of classes used in `pred_logits`.\r\n\r\n Returns:\r\n dict[str, Tensor]:\r\n mapping from a named loss to a scalar tensor storing the loss.\r\n Used during training only. The dict keys are: \"loss_cls\" and \"loss_box_reg\"\r\n \"\"\"\r\n num_images = len(gt_labels)\r\n gt_labels = torch.stack(gt_labels) # (N, R)\r\n\r\n valid_mask = gt_labels >= 0\r\n pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes)\r\n num_pos_anchors = pos_mask.sum().item()\r\n get_event_storage().put_scalar(\"num_pos_anchors\", num_pos_anchors / num_images)\r\n normalizer = self._ema_update(\"loss_normalizer\", max(num_pos_anchors, 1), 100)\r\n\r\n # classification and regression loss\r\n gt_labels_target = F.one_hot(gt_labels[valid_mask], num_classes=self.num_classes + 1)[\r\n :, :-1\r\n ] # no loss for the last (background) class\r\n loss_cls = sigmoid_focal_loss_jit(\r\n cat(pred_logits, dim=1)[valid_mask],\r\n gt_labels_target.to(pred_logits[0].dtype),\r\n alpha=self.focal_loss_alpha,\r\n gamma=self.focal_loss_gamma,\r\n reduction=\"sum\",\r\n )\r\n\r\n loss_box_reg = _dense_box_regression_loss(\r\n anchors,\r\n self.box2box_transform,\r\n pred_anchor_deltas,\r\n gt_boxes,\r\n pos_mask,\r\n box_reg_loss_type=self.box_reg_loss_type,\r\n smooth_l1_beta=self.smooth_l1_beta,\r\n )\r\n\r\n return {\r\n \"loss_cls\": loss_cls / normalizer,\r\n \"loss_box_reg\": loss_box_reg / normalizer,\r\n }\r\n\r\n @torch.no_grad()\r\n def label_anchors(self, anchors, gt_instances):\r\n \"\"\"\r\n Args:\r\n anchors (list[Boxes]): A list of #feature level Boxes.\r\n The Boxes contains anchors of this image on the specific feature level.\r\n gt_instances (list[Instances]): a list of N `Instances`s. The i-th\r\n `Instances` contains the ground-truth per-instance annotations\r\n for the i-th input image.\r\n\r\n Returns:\r\n list[Tensor]: List of #img tensors. i-th element is a vector of labels whose length is\r\n the total number of anchors across all feature maps (sum(Hi * Wi * A)).\r\n Label values are in {-1, 0, ..., K}, with -1 means ignore, and K means background.\r\n\r\n list[Tensor]: i-th element is a Rx4 tensor, where R is the total number of anchors\r\n across feature maps. The values are the matched gt boxes for each anchor.\r\n Values are undefined for those anchors not labeled as foreground.\r\n \"\"\"\r"},"next_line":{"kind":"string","value":" anchors = Boxes.cat(anchors) # Rx4\r"},"gold_snippet_index":{"kind":"number","value":6,"string":"6"},"created_at":{"kind":"string","value":"2023-12-05 02:51:53+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5287,"cells":{"repo_name":{"kind":"string","value":"DiffusionLight/DiffusionLight"},"file_path":{"kind":"string","value":"relighting/inpainter.py"},"context":{"kind":"list like","value":[{"identifier":"CustomStableDiffusionControlNetInpaintPipeline","path":"relighting/pipeline.py","snippet":"class CustomStableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetInpaintPipeline):\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n control_image: PipelineImageInput = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 1.0,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 0.5,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n newx: int = 0,\n newy: int = 0,\n newr: int = 256,\n current_seed=0,\n use_noise_moving=True,\n ):\n # OVERWRITE METHODS\n self.prepare_mask_latents = custom_prepare_mask_latents.__get__(self, CustomStableDiffusionControlNetInpaintPipeline)\n self.prepare_latents = custom_prepare_latents.__get__(self, CustomStableDiffusionControlNetInpaintPipeline)\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [\n control_guidance_end\n ]\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n prompt,\n control_image,\n height,\n width,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n global_pool_conditions = (\n controlnet.config.global_pool_conditions\n if isinstance(controlnet, ControlNetModel)\n else controlnet.nets[0].config.global_pool_conditions\n )\n guess_mode = guess_mode or global_pool_conditions\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. Prepare image\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n assert False\n\n # 4. Preprocess mask and image - resizes image and mask w.r.t height and width\n init_image = self.image_processor.preprocess(image, height=height, width=width)\n init_image = init_image.to(dtype=torch.float32)\n\n mask = self.mask_processor.preprocess(mask_image, height=height, width=width)\n\n masked_image = init_image * (mask < 0.5)\n _, _, height, width = init_image.shape\n\n # 5. Prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps=num_inference_steps, strength=strength, device=device\n )\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n # EDITED HERE\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n newx=newx,\n newy=newy,\n newr=newr,\n current_seed=current_seed,\n use_noise_moving=use_noise_moving,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7.1 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\n\n # 8. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n # controlnet(s) inference\n if guess_mode and do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n return_dict=False,\n )\n\n if guess_mode and do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n # predict the noise residual\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents[:1]\n init_mask = mask[:1]\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)"},{"identifier":"CustomStableDiffusionInpaintPipeline","path":"relighting/pipeline_inpaintonly.py","snippet":"class CustomStableDiffusionInpaintPipeline(StableDiffusionInpaintPipeline):\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n masked_image_latents: torch.FloatTensor = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 1.0,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n newx: int = 0,\n newy: int = 0,\n newr: int = 256,\n current_seed=0,\n use_noise_moving=True,\n ):\n # OVERWRITE METHODS\n self.prepare_mask_latents = custom_prepare_mask_latents.__get__(self, CustomStableDiffusionInpaintPipeline)\n self.prepare_latents = custom_prepare_latents.__get__(self, CustomStableDiffusionInpaintPipeline)\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n height,\n width,\n strength,\n callback_steps,\n negative_prompt,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt,\n device,\n num_images_per_prompt,\n do_classifier_free_guidance,\n negative_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n # For classifier free guidance, we need to do two forward passes.\n # Here we concatenate the unconditional and text embeddings into a single batch\n # to avoid doing two forward passes\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\n\n # 4. set timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps=num_inference_steps, strength=strength, device=device\n )\n # check that number of inference steps is not < 1 - as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\"\n f\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\"\n )\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 5. Preprocess mask and image\n\n init_image = self.image_processor.preprocess(image, height=height, width=width)\n init_image = init_image.to(dtype=torch.float32)\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n newx=newx,\n newy=newy,\n newr=newr,\n current_seed=current_seed,\n use_noise_moving=use_noise_moving,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask_condition = self.mask_processor.preprocess(mask_image, height=height, width=width)\n\n if masked_image_latents is None:\n masked_image = init_image * (mask_condition < 0.5)\n else:\n masked_image = masked_image_latents\n\n mask, masked_image_latents = self.prepare_mask_latents(\n mask_condition,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 10. Denoising loop\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents[:1]\n init_mask = mask[:1]\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n if not output_type == \"latent\":\n condition_kwargs = {}\n if isinstance(self.vae, AsymmetricAutoencoderKL):\n init_image = init_image.to(device=device, dtype=masked_image_latents.dtype)\n init_image_condition = init_image.clone()\n init_image = self._encode_vae_image(init_image, generator=generator)\n mask_condition = mask_condition.to(device=device, dtype=masked_image_latents.dtype)\n condition_kwargs = {\"image\": init_image_condition, \"mask\": mask_condition}\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, **condition_kwargs)[0]\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\n else:\n image = latents\n has_nsfw_concept = None\n\n if has_nsfw_concept is None:\n do_denormalize = [True] * image.shape[0]\n else:\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\n\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image, has_nsfw_concept)\n\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)"},{"identifier":"CustomStableDiffusionXLInpaintPipeline","path":"relighting/pipeline_inpaintonly.py","snippet":"class CustomStableDiffusionXLInpaintPipeline(StableDiffusionXLInpaintPipeline):\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n masked_image_latents: torch.FloatTensor = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.9999,\n num_inference_steps: int = 50,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n guidance_rescale: float = 0.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n negative_original_size: Optional[Tuple[int, int]] = None,\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\n negative_target_size: Optional[Tuple[int, int]] = None,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n newx: int = 0,\n newy: int = 0,\n newr: int = 256,\n current_seed=0,\n use_noise_moving=True,\n ):\n # OVERWRITE METHODS\n self.prepare_mask_latents = custom_prepare_mask_latents.__get__(self, CustomStableDiffusionXLInpaintPipeline)\n self.prepare_latents = custom_prepare_latents.__get__(self, CustomStableDiffusionXLInpaintPipeline)\n\n # 0. Default height and width to unet\n height = height or self.unet.config.sample_size * self.vae_scale_factor\n width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n prompt_2,\n height,\n width,\n strength,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. set timesteps\n def denoising_value_valid(dnv):\n return isinstance(denoising_end, float) and 0 < dnv < 1\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None\n )\n # check that number of inference steps is not < 1 - as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\"\n f\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\"\n )\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 5. Preprocess mask and image\n init_image = self.image_processor.preprocess(image, height=height, width=width)\n init_image = init_image.to(dtype=torch.float32)\n\n mask = self.mask_processor.preprocess(mask_image, height=height, width=width)\n\n if masked_image_latents is not None:\n masked_image = masked_image_latents\n elif init_image.shape[1] == 4:\n # if images are in latent space, we can't mask it\n masked_image = None\n else:\n masked_image = init_image * (mask < 0.5)\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n # add_noise = True if denoising_start is None else False\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n newx=newx,\n newy=newy,\n newr=newr,\n current_seed=current_seed,\n use_noise_moving=use_noise_moving,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n # 8.1 Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n height, width = latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 10. Prepare added time ids & embeddings\n if negative_original_size is None:\n negative_original_size = original_size\n if negative_target_size is None:\n negative_target_size = target_size\n\n add_text_embeds = pooled_prompt_embeds\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n negative_original_size,\n negative_crops_coords_top_left,\n negative_target_size,\n dtype=prompt_embeds.dtype,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device)\n\n # 11. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n if (\n denoising_end is not None\n and denoising_start is not None\n and denoising_value_valid(denoising_end)\n and denoising_value_valid(denoising_start)\n and denoising_start >= denoising_end\n ):\n raise ValueError(\n f\"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: \"\n + f\" {denoising_end} when using type float.\"\n )\n elif denoising_end is not None and denoising_value_valid(denoising_end):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # predict the noise residual\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents[:1]\n init_mask = mask[:1]\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n if not output_type == \"latent\":\n # make sure the VAE is in float32 mode, as it overflows in float16\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\n\n if needs_upcasting:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # cast back to fp16 if needed\n if needs_upcasting:\n self.vae.to(dtype=torch.float16)\n else:\n return StableDiffusionXLPipelineOutput(images=latents)\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)"},{"identifier":"SAMPLERS","path":"relighting/argument.py","snippet":"SAMPLERS = {\n \"ddim\": DDIMScheduler,\n \"ddpm\": DDPMScheduler,\n \"unipc\": UniPCMultistepScheduler,\n}"},{"identifier":"VAE_MODELS","path":"relighting/argument.py","snippet":"VAE_MODELS = {\n \"sdxl\": \"madebyollin/sdxl-vae-fp16-fix\",\n \"sdxl_fast\": \"madebyollin/sdxl-vae-fp16-fix\",\n}"},{"identifier":"DEPTH_ESTIMATOR","path":"relighting/argument.py","snippet":"DEPTH_ESTIMATOR = \"Intel/dpt-hybrid-midas\""},{"identifier":"get_control_signal_type","path":"relighting/argument.py","snippet":"def get_control_signal_type(controlnet):\n if \"normal\" in controlnet:\n return \"normal\"\n elif \"depth\" in controlnet:\n return \"depth\"\n else:\n raise NotImplementedError"},{"identifier":"estimate_scene_depth","path":"relighting/image_processor.py","snippet":"def estimate_scene_depth(image, depth_estimator):\n #image = feature_extractor(images=image, return_tensors=\"pt\").pixel_values.to(\"cuda\")\n #with torch.no_grad(), torch.autocast(\"cuda\"):\n # depth_map = depth_estimator(image).predicted_depth\n\n depth_map = depth_estimator(image)['predicted_depth']\n W, H = image.size\n depth_map = torch.nn.functional.interpolate(\n depth_map.unsqueeze(1),\n size=(H, W),\n mode=\"bicubic\",\n align_corners=False,\n )\n depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)\n depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)\n depth_map = (depth_map - depth_min) / (depth_max - depth_min)\n image = torch.cat([depth_map] * 3, dim=1)\n\n image = image.permute(0, 2, 3, 1).cpu().numpy()[0]\n image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))\n return image"},{"identifier":"estimate_scene_normal","path":"relighting/image_processor.py","snippet":"def estimate_scene_normal(image, depth_estimator):\n # can be improve speed do not going back and float between numpy and torch\n normal_image = depth_estimator(image)['predicted_depth'][0]\n\n normal_image = normal_image.numpy()\n\n # upsizing image depth to match input\n hw = np.array(image).shape[:2]\n normal_image = skimage.transform.resize(normal_image, hw, preserve_range=True)\n\n image_depth = normal_image.copy()\n image_depth -= np.min(image_depth)\n image_depth /= np.max(image_depth)\n \n bg_threhold = 0.4\n\n x = cv2.Sobel(normal_image, cv2.CV_32F, 1, 0, ksize=3)\n x[image_depth < bg_threhold] = 0\n\n y = cv2.Sobel(normal_image, cv2.CV_32F, 0, 1, ksize=3)\n y[image_depth < bg_threhold] = 0\n\n z = np.ones_like(x) * np.pi * 2.0\n\n normal_image = np.stack([x, y, z], axis=2)\n normal_image /= np.sum(normal_image ** 2.0, axis=2, keepdims=True) ** 0.5\n\n # rescale back to image size\n return normal_image"},{"identifier":"merge_normal_map","path":"relighting/image_processor.py","snippet":"def merge_normal_map(normal_map, normal_ball, mask_ball, x, y):\n \"\"\"\n Merge a ball to normal map using mask\n @params\n normal_amp (np.array) - normal map of the scene [height, width, 3]\n normal_ball (np.array) - normal map of the ball [ball_height, ball_width, 3]\n mask_ball (np.array) - mask of the ball [ball_height, ball_width]\n x (int) - x position of the ball (top-left)\n y (int) - y position of the ball (top-left)\n @return\n normal_mapthe merge normal map [height, width, 3] \n \"\"\"\n result = normal_map.copy()\n\n mask_ball = mask_ball[..., None]\n ball = (normal_ball * mask_ball) # alpha blending the ball\n unball = (normal_map[y:y+normal_ball.shape[0], x:x+normal_ball.shape[1]] * (1 - mask_ball)) # alpha blending the normal map\n result[y:y+normal_ball.shape[0], x:x+normal_ball.shape[1]] = ball+unball # add them together\n return result"},{"identifier":"fill_depth_circular","path":"relighting/image_processor.py","snippet":"def fill_depth_circular(depth_image, x, y, r):\n depth_image = np.array(depth_image)\n\n for i in range(depth_image.shape[0]):\n for j in range(depth_image.shape[1]):\n xy = (i - x - r//2)**2 + (j - y - r//2)**2\n # if xy <= rr**2:\n # depth_image[j, i, :] = 255\n # depth_image[j, i, :] = int(minv + (maxv - minv) * z)\n if xy <= (r // 2)**2:\n depth_image[j, i, :] = 255\n \n depth_image = Image.fromarray(depth_image)\n return depth_image"},{"identifier":"get_ideal_normal_ball","path":"relighting/ball_processor.py","snippet":"def get_ideal_normal_ball(size, flip_x=True):\n \"\"\"\n Generate normal ball for specific size \n Normal map is x \"left\", y up, z into the screen \n (we flip X to match sobel operator)\n @params\n - size (int) - single value of height and width\n @return:\n - normal_map (np.array) - normal map [size, size, 3]\n - mask (np.array) - mask that make a valid normal map [size,size]\n \"\"\"\n # we flip x to match sobel operator\n x = torch.linspace(1, -1, size)\n y = torch.linspace(1, -1, size)\n x = x.flip(dims=(-1,)) if not flip_x else x\n\n y, x = torch.meshgrid(y, x)\n z = (1 - x**2 - y**2)\n mask = z >= 0\n\n # clean up invalid value outsize the mask\n x = x * mask\n y = y * mask\n z = z * mask\n \n # get real z value\n z = torch.sqrt(z)\n \n # clean up normal map value outside mask \n normal_map = torch.cat([x[..., None], y[..., None], z[..., None]], dim=-1)\n normal_map = normal_map.numpy()\n mask = mask.numpy()\n return normal_map, mask"},{"identifier":"crop_ball","path":"relighting/ball_processor.py","snippet":"def crop_ball(image, mask_ball, x, y, size, apply_mask=True, bg_color = (0, 0, 0)):\n if isinstance(image, Image.Image):\n result = np.array(image)\n else:\n result = image.copy()\n \n result = result[y:y+size, x:x+size]\n if apply_mask:\n result[~mask_ball] = bg_color\n return result"},{"identifier":"CustomStableDiffusionXLControlNetInpaintPipeline","path":"relighting/pipeline_xl.py","snippet":"class CustomStableDiffusionXLControlNetInpaintPipeline(StableDiffusionXLControlNetInpaintPipeline):\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] = None,\n prompt_2: Optional[Union[str, List[str]]] = None,\n image: PipelineImageInput = None,\n mask_image: PipelineImageInput = None,\n control_image: Union[\n PipelineImageInput,\n List[PipelineImageInput],\n ] = None,\n height: Optional[int] = None,\n width: Optional[int] = None,\n strength: float = 0.9999,\n num_inference_steps: int = 50,\n denoising_start: Optional[float] = None,\n denoising_end: Optional[float] = None,\n guidance_scale: float = 5.0,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\n num_images_per_prompt: Optional[int] = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: Optional[str] = \"pil\",\n return_dict: bool = True,\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\n callback_steps: int = 1,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\n guess_mode: bool = False,\n control_guidance_start: Union[float, List[float]] = 0.0,\n control_guidance_end: Union[float, List[float]] = 1.0,\n guidance_rescale: float = 0.0,\n original_size: Tuple[int, int] = None,\n crops_coords_top_left: Tuple[int, int] = (0, 0),\n target_size: Tuple[int, int] = None,\n aesthetic_score: float = 6.0,\n negative_aesthetic_score: float = 2.5,\n newx: int = 0,\n newy: int = 0,\n newr: int = 256,\n current_seed=0,\n use_noise_moving=True,\n ):\n # OVERWRITE METHODS\n self.prepare_mask_latents = custom_prepare_mask_latents.__get__(self, CustomStableDiffusionXLControlNetInpaintPipeline)\n self.prepare_latents = custom_prepare_latents.__get__(self, CustomStableDiffusionXLControlNetInpaintPipeline)\n\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\n\n # align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [\n control_guidance_end\n ]\n\n # # 0.0 Default height and width to unet\n # height = height or self.unet.config.sample_size * self.vae_scale_factor\n # width = width or self.unet.config.sample_size * self.vae_scale_factor\n\n # 0.1 align format for control guidance\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\n control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [\n control_guidance_end\n ]\n\n # 1. Check inputs\n self.check_inputs(\n prompt,\n prompt_2,\n control_image,\n strength,\n num_inference_steps,\n callback_steps,\n negative_prompt,\n negative_prompt_2,\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n controlnet_conditioning_scale,\n control_guidance_start,\n control_guidance_end,\n )\n\n # 2. Define call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\n\n # 3. Encode input prompt\n text_encoder_lora_scale = (\n cross_attention_kwargs.get(\"scale\", None) if cross_attention_kwargs is not None else None\n )\n\n (\n prompt_embeds,\n negative_prompt_embeds,\n pooled_prompt_embeds,\n negative_pooled_prompt_embeds,\n ) = self.encode_prompt(\n prompt=prompt,\n prompt_2=prompt_2,\n device=device,\n num_images_per_prompt=num_images_per_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n negative_prompt=negative_prompt,\n negative_prompt_2=negative_prompt_2,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n pooled_prompt_embeds=pooled_prompt_embeds,\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\n lora_scale=text_encoder_lora_scale,\n )\n\n # 4. set timesteps\n def denoising_value_valid(dnv):\n return isinstance(denoising_end, float) and 0 < dnv < 1\n\n self.scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps, num_inference_steps = self.get_timesteps(\n num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None\n )\n # check that number of inference steps is not < 1 - as this doesn't make sense\n if num_inference_steps < 1:\n raise ValueError(\n f\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\"\n f\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\"\n )\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\n is_strength_max = strength == 1.0\n\n # 5. Preprocess mask and image - resizes image and mask w.r.t height and width\n # 5.1 Prepare init image\n init_image = self.image_processor.preprocess(image, height=height, width=width)\n init_image = init_image.to(dtype=torch.float32)\n\n # 5.2 Prepare control images\n if isinstance(controlnet, ControlNetModel):\n control_image = self.prepare_control_image(\n image=control_image,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n elif isinstance(controlnet, MultiControlNetModel):\n control_images = []\n\n for control_image_ in control_image:\n control_image_ = self.prepare_control_image(\n image=control_image_,\n width=width,\n height=height,\n batch_size=batch_size * num_images_per_prompt,\n num_images_per_prompt=num_images_per_prompt,\n device=device,\n dtype=controlnet.dtype,\n do_classifier_free_guidance=do_classifier_free_guidance,\n guess_mode=guess_mode,\n )\n\n control_images.append(control_image_)\n\n control_image = control_images\n else:\n raise ValueError(f\"{controlnet.__class__} is not supported.\")\n\n # 5.3 Prepare mask\n mask = self.mask_processor.preprocess(mask_image, height=height, width=width)\n\n masked_image = init_image * (mask < 0.5)\n _, _, height, width = init_image.shape\n\n # 6. Prepare latent variables\n num_channels_latents = self.vae.config.latent_channels\n num_channels_unet = self.unet.config.in_channels\n return_image_latents = num_channels_unet == 4\n\n add_noise = True if denoising_start is None else False\n latents_outputs = self.prepare_latents(\n batch_size * num_images_per_prompt,\n num_channels_latents,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n image=init_image,\n timestep=latent_timestep,\n is_strength_max=is_strength_max,\n return_noise=True,\n return_image_latents=return_image_latents,\n newx=newx,\n newy=newy,\n newr=newr,\n current_seed=current_seed,\n use_noise_moving=use_noise_moving,\n )\n\n if return_image_latents:\n latents, noise, image_latents = latents_outputs\n else:\n latents, noise = latents_outputs\n\n # 7. Prepare mask latent variables\n mask, masked_image_latents = self.prepare_mask_latents(\n mask,\n masked_image,\n batch_size * num_images_per_prompt,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n do_classifier_free_guidance,\n )\n\n # 8. Check that sizes of mask, masked image and latents match\n if num_channels_unet == 9:\n # default case for runwayml/stable-diffusion-inpainting\n num_channels_mask = mask.shape[1]\n num_channels_masked_image = masked_image_latents.shape[1]\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\n raise ValueError(\n f\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\"\n f\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\"\n f\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\"\n f\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\"\n \" `pipeline.unet` or your `mask_image` or `image` input.\"\n )\n elif num_channels_unet != 4:\n raise ValueError(\n f\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\"\n )\n # 8.1 Prepare extra step kwargs.\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 8.2 Create tensor stating which controlnets to keep\n controlnet_keep = []\n for i in range(len(timesteps)):\n keeps = [\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\n for s, e in zip(control_guidance_start, control_guidance_end)\n ]\n if isinstance(self.controlnet, MultiControlNetModel):\n controlnet_keep.append(keeps)\n else:\n controlnet_keep.append(keeps[0])\n\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n height, width = latents.shape[-2:]\n height = height * self.vae_scale_factor\n width = width * self.vae_scale_factor\n\n original_size = original_size or (height, width)\n target_size = target_size or (height, width)\n\n # 10. Prepare added time ids & embeddings\n add_text_embeds = pooled_prompt_embeds\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\n original_size,\n crops_coords_top_left,\n target_size,\n aesthetic_score,\n negative_aesthetic_score,\n dtype=prompt_embeds.dtype,\n )\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\n\n prompt_embeds = prompt_embeds.to(device)\n add_text_embeds = add_text_embeds.to(device)\n add_time_ids = add_time_ids.to(device)\n\n # 11. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n if (\n denoising_end is not None\n and denoising_start is not None\n and denoising_value_valid(denoising_end)\n and denoising_value_valid(denoising_start)\n and denoising_start >= denoising_end\n ):\n raise ValueError(\n f\"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: \"\n + f\" {denoising_end} when using type float.\"\n )\n elif denoising_end is not None and denoising_value_valid(denoising_end):\n discrete_timestep_cutoff = int(\n round(\n self.scheduler.config.num_train_timesteps\n - (denoising_end * self.scheduler.config.num_train_timesteps)\n )\n )\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\n timesteps = timesteps[:num_inference_steps]\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n for i, t in enumerate(timesteps):\n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n\n # concat latents, mask, masked_image_latents in the channel dimension\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n added_cond_kwargs = {\"text_embeds\": add_text_embeds, \"time_ids\": add_time_ids}\n\n # controlnet(s) inference\n if guess_mode and do_classifier_free_guidance:\n # Infer ControlNet only for the conditional batch.\n control_model_input = latents\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\n controlnet_added_cond_kwargs = {\n \"text_embeds\": add_text_embeds.chunk(2)[1],\n \"time_ids\": add_time_ids.chunk(2)[1],\n }\n else:\n control_model_input = latent_model_input\n controlnet_prompt_embeds = prompt_embeds\n controlnet_added_cond_kwargs = added_cond_kwargs\n\n if isinstance(controlnet_keep[i], list):\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\n else:\n controlnet_cond_scale = controlnet_conditioning_scale\n if isinstance(controlnet_cond_scale, list):\n controlnet_cond_scale = controlnet_cond_scale[0]\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\n\n # # Resize control_image to match the size of the input to the controlnet\n # if control_image.shape[-2:] != control_model_input.shape[-2:]:\n # control_image = F.interpolate(control_image, size=control_model_input.shape[-2:], mode=\"bilinear\", align_corners=False)\n\n down_block_res_samples, mid_block_res_sample = self.controlnet(\n control_model_input,\n t,\n encoder_hidden_states=controlnet_prompt_embeds,\n controlnet_cond=control_image,\n conditioning_scale=cond_scale,\n guess_mode=guess_mode,\n added_cond_kwargs=controlnet_added_cond_kwargs,\n return_dict=False,\n )\n\n if guess_mode and do_classifier_free_guidance:\n # Infered ControlNet only for the conditional batch.\n # To apply the output of ControlNet to both the unconditional and conditional batches,\n # add 0 to the unconditional batch to keep it unchanged.\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\n\n if num_channels_unet == 9:\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\n\n # predict the noise residual\n noise_pred = self.unet(\n latent_model_input,\n t,\n encoder_hidden_states=prompt_embeds,\n cross_attention_kwargs=cross_attention_kwargs,\n down_block_additional_residuals=down_block_res_samples,\n mid_block_additional_residual=mid_block_res_sample,\n added_cond_kwargs=added_cond_kwargs,\n return_dict=False,\n )[0]\n\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n if do_classifier_free_guidance and guidance_rescale > 0.0:\n print(\"rescale: \", guidance_rescale)\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n\n if num_channels_unet == 4:\n init_latents_proper = image_latents[:1]\n init_mask = mask[:1]\n\n if i < len(timesteps) - 1:\n noise_timestep = timesteps[i + 1]\n init_latents_proper = self.scheduler.add_noise(\n init_latents_proper, noise, torch.tensor([noise_timestep])\n )\n\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\n\n # call the callback, if provided\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n if callback is not None and i % callback_steps == 0:\n callback(i, t, latents)\n\n # make sure the VAE is in float32 mode, as it overflows in float16\n if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:\n self.upcast_vae()\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\n\n # If we do sequential model offloading, let's offload unet and controlnet\n # manually for max memory savings\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.unet.to(\"cpu\")\n self.controlnet.to(\"cpu\")\n torch.cuda.empty_cache()\n\n if not output_type == \"latent\":\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n else:\n return StableDiffusionXLPipelineOutput(images=latents)\n\n # apply watermark if available\n if self.watermark is not None:\n image = self.watermark.apply_watermark(image)\n\n image = self.image_processor.postprocess(image, output_type=output_type)\n\n # Offload last model to CPU\n if hasattr(self, \"final_offload_hook\") and self.final_offload_hook is not None:\n self.final_offload_hook.offload()\n\n if not return_dict:\n return (image,)\n\n return StableDiffusionXLPipelineOutput(images=image)"}],"string":"[\n {\n \"identifier\": \"CustomStableDiffusionControlNetInpaintPipeline\",\n \"path\": \"relighting/pipeline.py\",\n \"snippet\": \"class CustomStableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetInpaintPipeline):\\n @torch.no_grad()\\n def __call__(\\n self,\\n prompt: Union[str, List[str]] = None,\\n image: PipelineImageInput = None,\\n mask_image: PipelineImageInput = None,\\n control_image: PipelineImageInput = None,\\n height: Optional[int] = None,\\n width: Optional[int] = None,\\n strength: float = 1.0,\\n num_inference_steps: int = 50,\\n guidance_scale: float = 7.5,\\n negative_prompt: Optional[Union[str, List[str]]] = None,\\n num_images_per_prompt: Optional[int] = 1,\\n eta: float = 0.0,\\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\\n latents: Optional[torch.FloatTensor] = None,\\n prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\\n output_type: Optional[str] = \\\"pil\\\",\\n return_dict: bool = True,\\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\\n callback_steps: int = 1,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n controlnet_conditioning_scale: Union[float, List[float]] = 0.5,\\n guess_mode: bool = False,\\n control_guidance_start: Union[float, List[float]] = 0.0,\\n control_guidance_end: Union[float, List[float]] = 1.0,\\n newx: int = 0,\\n newy: int = 0,\\n newr: int = 256,\\n current_seed=0,\\n use_noise_moving=True,\\n ):\\n # OVERWRITE METHODS\\n self.prepare_mask_latents = custom_prepare_mask_latents.__get__(self, CustomStableDiffusionControlNetInpaintPipeline)\\n self.prepare_latents = custom_prepare_latents.__get__(self, CustomStableDiffusionControlNetInpaintPipeline)\\n\\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\\n\\n # align format for control guidance\\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\\n control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [\\n control_guidance_end\\n ]\\n\\n # 1. Check inputs. Raise error if not correct\\n self.check_inputs(\\n prompt,\\n control_image,\\n height,\\n width,\\n callback_steps,\\n negative_prompt,\\n prompt_embeds,\\n negative_prompt_embeds,\\n controlnet_conditioning_scale,\\n control_guidance_start,\\n control_guidance_end,\\n )\\n\\n # 2. Define call parameters\\n if prompt is not None and isinstance(prompt, str):\\n batch_size = 1\\n elif prompt is not None and isinstance(prompt, list):\\n batch_size = len(prompt)\\n else:\\n batch_size = prompt_embeds.shape[0]\\n\\n device = self._execution_device\\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\\n # corresponds to doing no classifier free guidance.\\n do_classifier_free_guidance = guidance_scale > 1.0\\n\\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\\n\\n global_pool_conditions = (\\n controlnet.config.global_pool_conditions\\n if isinstance(controlnet, ControlNetModel)\\n else controlnet.nets[0].config.global_pool_conditions\\n )\\n guess_mode = guess_mode or global_pool_conditions\\n\\n # 3. Encode input prompt\\n text_encoder_lora_scale = (\\n cross_attention_kwargs.get(\\\"scale\\\", None) if cross_attention_kwargs is not None else None\\n )\\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\\n prompt,\\n device,\\n num_images_per_prompt,\\n do_classifier_free_guidance,\\n negative_prompt,\\n prompt_embeds=prompt_embeds,\\n negative_prompt_embeds=negative_prompt_embeds,\\n lora_scale=text_encoder_lora_scale,\\n )\\n # For classifier free guidance, we need to do two forward passes.\\n # Here we concatenate the unconditional and text embeddings into a single batch\\n # to avoid doing two forward passes\\n if do_classifier_free_guidance:\\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\\n\\n # 4. Prepare image\\n if isinstance(controlnet, ControlNetModel):\\n control_image = self.prepare_control_image(\\n image=control_image,\\n width=width,\\n height=height,\\n batch_size=batch_size * num_images_per_prompt,\\n num_images_per_prompt=num_images_per_prompt,\\n device=device,\\n dtype=controlnet.dtype,\\n do_classifier_free_guidance=do_classifier_free_guidance,\\n guess_mode=guess_mode,\\n )\\n elif isinstance(controlnet, MultiControlNetModel):\\n control_images = []\\n\\n for control_image_ in control_image:\\n control_image_ = self.prepare_control_image(\\n image=control_image_,\\n width=width,\\n height=height,\\n batch_size=batch_size * num_images_per_prompt,\\n num_images_per_prompt=num_images_per_prompt,\\n device=device,\\n dtype=controlnet.dtype,\\n do_classifier_free_guidance=do_classifier_free_guidance,\\n guess_mode=guess_mode,\\n )\\n\\n control_images.append(control_image_)\\n\\n control_image = control_images\\n else:\\n assert False\\n\\n # 4. Preprocess mask and image - resizes image and mask w.r.t height and width\\n init_image = self.image_processor.preprocess(image, height=height, width=width)\\n init_image = init_image.to(dtype=torch.float32)\\n\\n mask = self.mask_processor.preprocess(mask_image, height=height, width=width)\\n\\n masked_image = init_image * (mask < 0.5)\\n _, _, height, width = init_image.shape\\n\\n # 5. Prepare timesteps\\n self.scheduler.set_timesteps(num_inference_steps, device=device)\\n timesteps, num_inference_steps = self.get_timesteps(\\n num_inference_steps=num_inference_steps, strength=strength, device=device\\n )\\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\\n is_strength_max = strength == 1.0\\n\\n # 6. Prepare latent variables\\n num_channels_latents = self.vae.config.latent_channels\\n num_channels_unet = self.unet.config.in_channels\\n return_image_latents = num_channels_unet == 4\\n\\n # EDITED HERE\\n latents_outputs = self.prepare_latents(\\n batch_size * num_images_per_prompt,\\n num_channels_latents,\\n height,\\n width,\\n prompt_embeds.dtype,\\n device,\\n generator,\\n latents,\\n image=init_image,\\n timestep=latent_timestep,\\n is_strength_max=is_strength_max,\\n return_noise=True,\\n return_image_latents=return_image_latents,\\n newx=newx,\\n newy=newy,\\n newr=newr,\\n current_seed=current_seed,\\n use_noise_moving=use_noise_moving,\\n )\\n\\n if return_image_latents:\\n latents, noise, image_latents = latents_outputs\\n else:\\n latents, noise = latents_outputs\\n\\n # 7. Prepare mask latent variables\\n mask, masked_image_latents = self.prepare_mask_latents(\\n mask,\\n masked_image,\\n batch_size * num_images_per_prompt,\\n height,\\n width,\\n prompt_embeds.dtype,\\n device,\\n generator,\\n do_classifier_free_guidance,\\n )\\n\\n # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\\n\\n # 7.1 Create tensor stating which controlnets to keep\\n controlnet_keep = []\\n for i in range(len(timesteps)):\\n keeps = [\\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\\n for s, e in zip(control_guidance_start, control_guidance_end)\\n ]\\n controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)\\n\\n # 8. Denoising loop\\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\\n with self.progress_bar(total=num_inference_steps) as progress_bar:\\n for i, t in enumerate(timesteps):\\n # expand the latents if we are doing classifier free guidance\\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\\n\\n # controlnet(s) inference\\n if guess_mode and do_classifier_free_guidance:\\n # Infer ControlNet only for the conditional batch.\\n control_model_input = latents\\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\\n else:\\n control_model_input = latent_model_input\\n controlnet_prompt_embeds = prompt_embeds\\n\\n if isinstance(controlnet_keep[i], list):\\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\\n else:\\n controlnet_cond_scale = controlnet_conditioning_scale\\n if isinstance(controlnet_cond_scale, list):\\n controlnet_cond_scale = controlnet_cond_scale[0]\\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\\n\\n down_block_res_samples, mid_block_res_sample = self.controlnet(\\n control_model_input,\\n t,\\n encoder_hidden_states=controlnet_prompt_embeds,\\n controlnet_cond=control_image,\\n conditioning_scale=cond_scale,\\n guess_mode=guess_mode,\\n return_dict=False,\\n )\\n\\n if guess_mode and do_classifier_free_guidance:\\n # Infered ControlNet only for the conditional batch.\\n # To apply the output of ControlNet to both the unconditional and conditional batches,\\n # add 0 to the unconditional batch to keep it unchanged.\\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\\n\\n # predict the noise residual\\n if num_channels_unet == 9:\\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\\n\\n noise_pred = self.unet(\\n latent_model_input,\\n t,\\n encoder_hidden_states=prompt_embeds,\\n cross_attention_kwargs=cross_attention_kwargs,\\n down_block_additional_residuals=down_block_res_samples,\\n mid_block_additional_residual=mid_block_res_sample,\\n return_dict=False,\\n )[0]\\n\\n # perform guidance\\n if do_classifier_free_guidance:\\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\\n\\n # compute the previous noisy sample x_t -> x_t-1\\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\\n\\n if num_channels_unet == 4:\\n init_latents_proper = image_latents[:1]\\n init_mask = mask[:1]\\n\\n if i < len(timesteps) - 1:\\n noise_timestep = timesteps[i + 1]\\n init_latents_proper = self.scheduler.add_noise(\\n init_latents_proper, noise, torch.tensor([noise_timestep])\\n )\\n\\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\\n\\n # call the callback, if provided\\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\\n progress_bar.update()\\n if callback is not None and i % callback_steps == 0:\\n callback(i, t, latents)\\n\\n # If we do sequential model offloading, let's offload unet and controlnet\\n # manually for max memory savings\\n if hasattr(self, \\\"final_offload_hook\\\") and self.final_offload_hook is not None:\\n self.unet.to(\\\"cpu\\\")\\n self.controlnet.to(\\\"cpu\\\")\\n torch.cuda.empty_cache()\\n\\n if not output_type == \\\"latent\\\":\\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\\n else:\\n image = latents\\n has_nsfw_concept = None\\n\\n if has_nsfw_concept is None:\\n do_denormalize = [True] * image.shape[0]\\n else:\\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\\n\\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\\n\\n # Offload all models\\n self.maybe_free_model_hooks()\\n\\n if not return_dict:\\n return (image, has_nsfw_concept)\\n\\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\"\n },\n {\n \"identifier\": \"CustomStableDiffusionInpaintPipeline\",\n \"path\": \"relighting/pipeline_inpaintonly.py\",\n \"snippet\": \"class CustomStableDiffusionInpaintPipeline(StableDiffusionInpaintPipeline):\\n @torch.no_grad()\\n def __call__(\\n self,\\n prompt: Union[str, List[str]] = None,\\n image: PipelineImageInput = None,\\n mask_image: PipelineImageInput = None,\\n masked_image_latents: torch.FloatTensor = None,\\n height: Optional[int] = None,\\n width: Optional[int] = None,\\n strength: float = 1.0,\\n num_inference_steps: int = 50,\\n guidance_scale: float = 7.5,\\n negative_prompt: Optional[Union[str, List[str]]] = None,\\n num_images_per_prompt: Optional[int] = 1,\\n eta: float = 0.0,\\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\\n latents: Optional[torch.FloatTensor] = None,\\n prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\\n output_type: Optional[str] = \\\"pil\\\",\\n return_dict: bool = True,\\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\\n callback_steps: int = 1,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n newx: int = 0,\\n newy: int = 0,\\n newr: int = 256,\\n current_seed=0,\\n use_noise_moving=True,\\n ):\\n # OVERWRITE METHODS\\n self.prepare_mask_latents = custom_prepare_mask_latents.__get__(self, CustomStableDiffusionInpaintPipeline)\\n self.prepare_latents = custom_prepare_latents.__get__(self, CustomStableDiffusionInpaintPipeline)\\n\\n # 0. Default height and width to unet\\n height = height or self.unet.config.sample_size * self.vae_scale_factor\\n width = width or self.unet.config.sample_size * self.vae_scale_factor\\n\\n # 1. Check inputs\\n self.check_inputs(\\n prompt,\\n height,\\n width,\\n strength,\\n callback_steps,\\n negative_prompt,\\n prompt_embeds,\\n negative_prompt_embeds,\\n )\\n\\n # 2. Define call parameters\\n if prompt is not None and isinstance(prompt, str):\\n batch_size = 1\\n elif prompt is not None and isinstance(prompt, list):\\n batch_size = len(prompt)\\n else:\\n batch_size = prompt_embeds.shape[0]\\n\\n device = self._execution_device\\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\\n # corresponds to doing no classifier free guidance.\\n do_classifier_free_guidance = guidance_scale > 1.0\\n\\n # 3. Encode input prompt\\n text_encoder_lora_scale = (\\n cross_attention_kwargs.get(\\\"scale\\\", None) if cross_attention_kwargs is not None else None\\n )\\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\\n prompt,\\n device,\\n num_images_per_prompt,\\n do_classifier_free_guidance,\\n negative_prompt,\\n prompt_embeds=prompt_embeds,\\n negative_prompt_embeds=negative_prompt_embeds,\\n lora_scale=text_encoder_lora_scale,\\n )\\n # For classifier free guidance, we need to do two forward passes.\\n # Here we concatenate the unconditional and text embeddings into a single batch\\n # to avoid doing two forward passes\\n if do_classifier_free_guidance:\\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])\\n\\n # 4. set timesteps\\n self.scheduler.set_timesteps(num_inference_steps, device=device)\\n timesteps, num_inference_steps = self.get_timesteps(\\n num_inference_steps=num_inference_steps, strength=strength, device=device\\n )\\n # check that number of inference steps is not < 1 - as this doesn't make sense\\n if num_inference_steps < 1:\\n raise ValueError(\\n f\\\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\\\"\\n f\\\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\\\"\\n )\\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\\n is_strength_max = strength == 1.0\\n\\n # 5. Preprocess mask and image\\n\\n init_image = self.image_processor.preprocess(image, height=height, width=width)\\n init_image = init_image.to(dtype=torch.float32)\\n\\n # 6. Prepare latent variables\\n num_channels_latents = self.vae.config.latent_channels\\n num_channels_unet = self.unet.config.in_channels\\n return_image_latents = num_channels_unet == 4\\n\\n latents_outputs = self.prepare_latents(\\n batch_size * num_images_per_prompt,\\n num_channels_latents,\\n height,\\n width,\\n prompt_embeds.dtype,\\n device,\\n generator,\\n latents,\\n image=init_image,\\n timestep=latent_timestep,\\n is_strength_max=is_strength_max,\\n return_noise=True,\\n return_image_latents=return_image_latents,\\n newx=newx,\\n newy=newy,\\n newr=newr,\\n current_seed=current_seed,\\n use_noise_moving=use_noise_moving,\\n )\\n\\n if return_image_latents:\\n latents, noise, image_latents = latents_outputs\\n else:\\n latents, noise = latents_outputs\\n\\n # 7. Prepare mask latent variables\\n mask_condition = self.mask_processor.preprocess(mask_image, height=height, width=width)\\n\\n if masked_image_latents is None:\\n masked_image = init_image * (mask_condition < 0.5)\\n else:\\n masked_image = masked_image_latents\\n\\n mask, masked_image_latents = self.prepare_mask_latents(\\n mask_condition,\\n masked_image,\\n batch_size * num_images_per_prompt,\\n height,\\n width,\\n prompt_embeds.dtype,\\n device,\\n generator,\\n do_classifier_free_guidance,\\n )\\n\\n # 8. Check that sizes of mask, masked image and latents match\\n if num_channels_unet == 9:\\n # default case for runwayml/stable-diffusion-inpainting\\n num_channels_mask = mask.shape[1]\\n num_channels_masked_image = masked_image_latents.shape[1]\\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\\n raise ValueError(\\n f\\\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\\\"\\n f\\\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\\\"\\n f\\\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\\\"\\n f\\\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\\\"\\n \\\" `pipeline.unet` or your `mask_image` or `image` input.\\\"\\n )\\n elif num_channels_unet != 4:\\n raise ValueError(\\n f\\\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\\\"\\n )\\n\\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\\n\\n # 10. Denoising loop\\n num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order\\n with self.progress_bar(total=num_inference_steps) as progress_bar:\\n for i, t in enumerate(timesteps):\\n # expand the latents if we are doing classifier free guidance\\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\\n\\n # concat latents, mask, masked_image_latents in the channel dimension\\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\\n\\n if num_channels_unet == 9:\\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\\n\\n # predict the noise residual\\n noise_pred = self.unet(\\n latent_model_input,\\n t,\\n encoder_hidden_states=prompt_embeds,\\n cross_attention_kwargs=cross_attention_kwargs,\\n return_dict=False,\\n )[0]\\n\\n # perform guidance\\n if do_classifier_free_guidance:\\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\\n\\n # compute the previous noisy sample x_t -> x_t-1\\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\\n\\n if num_channels_unet == 4:\\n init_latents_proper = image_latents[:1]\\n init_mask = mask[:1]\\n\\n if i < len(timesteps) - 1:\\n noise_timestep = timesteps[i + 1]\\n init_latents_proper = self.scheduler.add_noise(\\n init_latents_proper, noise, torch.tensor([noise_timestep])\\n )\\n\\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\\n\\n # call the callback, if provided\\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\\n progress_bar.update()\\n if callback is not None and i % callback_steps == 0:\\n callback(i, t, latents)\\n\\n if not output_type == \\\"latent\\\":\\n condition_kwargs = {}\\n if isinstance(self.vae, AsymmetricAutoencoderKL):\\n init_image = init_image.to(device=device, dtype=masked_image_latents.dtype)\\n init_image_condition = init_image.clone()\\n init_image = self._encode_vae_image(init_image, generator=generator)\\n mask_condition = mask_condition.to(device=device, dtype=masked_image_latents.dtype)\\n condition_kwargs = {\\\"image\\\": init_image_condition, \\\"mask\\\": mask_condition}\\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, **condition_kwargs)[0]\\n image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)\\n else:\\n image = latents\\n has_nsfw_concept = None\\n\\n if has_nsfw_concept is None:\\n do_denormalize = [True] * image.shape[0]\\n else:\\n do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]\\n\\n image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)\\n\\n # Offload all models\\n self.maybe_free_model_hooks()\\n\\n if not return_dict:\\n return (image, has_nsfw_concept)\\n\\n return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)\"\n },\n {\n \"identifier\": \"CustomStableDiffusionXLInpaintPipeline\",\n \"path\": \"relighting/pipeline_inpaintonly.py\",\n \"snippet\": \"class CustomStableDiffusionXLInpaintPipeline(StableDiffusionXLInpaintPipeline):\\n @torch.no_grad()\\n def __call__(\\n self,\\n prompt: Union[str, List[str]] = None,\\n prompt_2: Optional[Union[str, List[str]]] = None,\\n image: PipelineImageInput = None,\\n mask_image: PipelineImageInput = None,\\n masked_image_latents: torch.FloatTensor = None,\\n height: Optional[int] = None,\\n width: Optional[int] = None,\\n strength: float = 0.9999,\\n num_inference_steps: int = 50,\\n denoising_start: Optional[float] = None,\\n denoising_end: Optional[float] = None,\\n guidance_scale: float = 7.5,\\n negative_prompt: Optional[Union[str, List[str]]] = None,\\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\\n num_images_per_prompt: Optional[int] = 1,\\n eta: float = 0.0,\\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\\n latents: Optional[torch.FloatTensor] = None,\\n prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\\n output_type: Optional[str] = \\\"pil\\\",\\n return_dict: bool = True,\\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\\n callback_steps: int = 1,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n guidance_rescale: float = 0.0,\\n original_size: Tuple[int, int] = None,\\n crops_coords_top_left: Tuple[int, int] = (0, 0),\\n target_size: Tuple[int, int] = None,\\n negative_original_size: Optional[Tuple[int, int]] = None,\\n negative_crops_coords_top_left: Tuple[int, int] = (0, 0),\\n negative_target_size: Optional[Tuple[int, int]] = None,\\n aesthetic_score: float = 6.0,\\n negative_aesthetic_score: float = 2.5,\\n newx: int = 0,\\n newy: int = 0,\\n newr: int = 256,\\n current_seed=0,\\n use_noise_moving=True,\\n ):\\n # OVERWRITE METHODS\\n self.prepare_mask_latents = custom_prepare_mask_latents.__get__(self, CustomStableDiffusionXLInpaintPipeline)\\n self.prepare_latents = custom_prepare_latents.__get__(self, CustomStableDiffusionXLInpaintPipeline)\\n\\n # 0. Default height and width to unet\\n height = height or self.unet.config.sample_size * self.vae_scale_factor\\n width = width or self.unet.config.sample_size * self.vae_scale_factor\\n\\n # 1. Check inputs\\n self.check_inputs(\\n prompt,\\n prompt_2,\\n height,\\n width,\\n strength,\\n callback_steps,\\n negative_prompt,\\n negative_prompt_2,\\n prompt_embeds,\\n negative_prompt_embeds,\\n )\\n\\n # 2. Define call parameters\\n if prompt is not None and isinstance(prompt, str):\\n batch_size = 1\\n elif prompt is not None and isinstance(prompt, list):\\n batch_size = len(prompt)\\n else:\\n batch_size = prompt_embeds.shape[0]\\n\\n device = self._execution_device\\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\\n # corresponds to doing no classifier free guidance.\\n do_classifier_free_guidance = guidance_scale > 1.0\\n\\n # 3. Encode input prompt\\n text_encoder_lora_scale = (\\n cross_attention_kwargs.get(\\\"scale\\\", None) if cross_attention_kwargs is not None else None\\n )\\n\\n (\\n prompt_embeds,\\n negative_prompt_embeds,\\n pooled_prompt_embeds,\\n negative_pooled_prompt_embeds,\\n ) = self.encode_prompt(\\n prompt=prompt,\\n prompt_2=prompt_2,\\n device=device,\\n num_images_per_prompt=num_images_per_prompt,\\n do_classifier_free_guidance=do_classifier_free_guidance,\\n negative_prompt=negative_prompt,\\n negative_prompt_2=negative_prompt_2,\\n prompt_embeds=prompt_embeds,\\n negative_prompt_embeds=negative_prompt_embeds,\\n pooled_prompt_embeds=pooled_prompt_embeds,\\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\\n lora_scale=text_encoder_lora_scale,\\n )\\n\\n # 4. set timesteps\\n def denoising_value_valid(dnv):\\n return isinstance(denoising_end, float) and 0 < dnv < 1\\n\\n self.scheduler.set_timesteps(num_inference_steps, device=device)\\n timesteps, num_inference_steps = self.get_timesteps(\\n num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None\\n )\\n # check that number of inference steps is not < 1 - as this doesn't make sense\\n if num_inference_steps < 1:\\n raise ValueError(\\n f\\\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\\\"\\n f\\\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\\\"\\n )\\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\\n is_strength_max = strength == 1.0\\n\\n # 5. Preprocess mask and image\\n init_image = self.image_processor.preprocess(image, height=height, width=width)\\n init_image = init_image.to(dtype=torch.float32)\\n\\n mask = self.mask_processor.preprocess(mask_image, height=height, width=width)\\n\\n if masked_image_latents is not None:\\n masked_image = masked_image_latents\\n elif init_image.shape[1] == 4:\\n # if images are in latent space, we can't mask it\\n masked_image = None\\n else:\\n masked_image = init_image * (mask < 0.5)\\n\\n # 6. Prepare latent variables\\n num_channels_latents = self.vae.config.latent_channels\\n num_channels_unet = self.unet.config.in_channels\\n return_image_latents = num_channels_unet == 4\\n\\n # add_noise = True if denoising_start is None else False\\n latents_outputs = self.prepare_latents(\\n batch_size * num_images_per_prompt,\\n num_channels_latents,\\n height,\\n width,\\n prompt_embeds.dtype,\\n device,\\n generator,\\n latents,\\n image=init_image,\\n timestep=latent_timestep,\\n is_strength_max=is_strength_max,\\n return_noise=True,\\n return_image_latents=return_image_latents,\\n newx=newx,\\n newy=newy,\\n newr=newr,\\n current_seed=current_seed,\\n use_noise_moving=use_noise_moving,\\n )\\n\\n if return_image_latents:\\n latents, noise, image_latents = latents_outputs\\n else:\\n latents, noise = latents_outputs\\n\\n # 7. Prepare mask latent variables\\n mask, masked_image_latents = self.prepare_mask_latents(\\n mask,\\n masked_image,\\n batch_size * num_images_per_prompt,\\n height,\\n width,\\n prompt_embeds.dtype,\\n device,\\n generator,\\n do_classifier_free_guidance,\\n )\\n\\n # 8. Check that sizes of mask, masked image and latents match\\n if num_channels_unet == 9:\\n # default case for runwayml/stable-diffusion-inpainting\\n num_channels_mask = mask.shape[1]\\n num_channels_masked_image = masked_image_latents.shape[1]\\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\\n raise ValueError(\\n f\\\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\\\"\\n f\\\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\\\"\\n f\\\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\\\"\\n f\\\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\\\"\\n \\\" `pipeline.unet` or your `mask_image` or `image` input.\\\"\\n )\\n elif num_channels_unet != 4:\\n raise ValueError(\\n f\\\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\\\"\\n )\\n # 8.1 Prepare extra step kwargs.\\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\\n\\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\\n height, width = latents.shape[-2:]\\n height = height * self.vae_scale_factor\\n width = width * self.vae_scale_factor\\n\\n original_size = original_size or (height, width)\\n target_size = target_size or (height, width)\\n\\n # 10. Prepare added time ids & embeddings\\n if negative_original_size is None:\\n negative_original_size = original_size\\n if negative_target_size is None:\\n negative_target_size = target_size\\n\\n add_text_embeds = pooled_prompt_embeds\\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\\n original_size,\\n crops_coords_top_left,\\n target_size,\\n aesthetic_score,\\n negative_aesthetic_score,\\n negative_original_size,\\n negative_crops_coords_top_left,\\n negative_target_size,\\n dtype=prompt_embeds.dtype,\\n )\\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\\n\\n if do_classifier_free_guidance:\\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\\n\\n prompt_embeds = prompt_embeds.to(device)\\n add_text_embeds = add_text_embeds.to(device)\\n add_time_ids = add_time_ids.to(device)\\n\\n # 11. Denoising loop\\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\\n\\n if (\\n denoising_end is not None\\n and denoising_start is not None\\n and denoising_value_valid(denoising_end)\\n and denoising_value_valid(denoising_start)\\n and denoising_start >= denoising_end\\n ):\\n raise ValueError(\\n f\\\"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: \\\"\\n + f\\\" {denoising_end} when using type float.\\\"\\n )\\n elif denoising_end is not None and denoising_value_valid(denoising_end):\\n discrete_timestep_cutoff = int(\\n round(\\n self.scheduler.config.num_train_timesteps\\n - (denoising_end * self.scheduler.config.num_train_timesteps)\\n )\\n )\\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\\n timesteps = timesteps[:num_inference_steps]\\n\\n with self.progress_bar(total=num_inference_steps) as progress_bar:\\n for i, t in enumerate(timesteps):\\n # expand the latents if we are doing classifier free guidance\\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\\n\\n # concat latents, mask, masked_image_latents in the channel dimension\\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\\n\\n if num_channels_unet == 9:\\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\\n\\n # predict the noise residual\\n added_cond_kwargs = {\\\"text_embeds\\\": add_text_embeds, \\\"time_ids\\\": add_time_ids}\\n noise_pred = self.unet(\\n latent_model_input,\\n t,\\n encoder_hidden_states=prompt_embeds,\\n cross_attention_kwargs=cross_attention_kwargs,\\n added_cond_kwargs=added_cond_kwargs,\\n return_dict=False,\\n )[0]\\n\\n # perform guidance\\n if do_classifier_free_guidance:\\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\\n\\n if do_classifier_free_guidance and guidance_rescale > 0.0:\\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\\n\\n # compute the previous noisy sample x_t -> x_t-1\\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\\n\\n if num_channels_unet == 4:\\n init_latents_proper = image_latents[:1]\\n init_mask = mask[:1]\\n\\n if i < len(timesteps) - 1:\\n noise_timestep = timesteps[i + 1]\\n init_latents_proper = self.scheduler.add_noise(\\n init_latents_proper, noise, torch.tensor([noise_timestep])\\n )\\n\\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\\n\\n # call the callback, if provided\\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\\n progress_bar.update()\\n if callback is not None and i % callback_steps == 0:\\n callback(i, t, latents)\\n\\n if not output_type == \\\"latent\\\":\\n # make sure the VAE is in float32 mode, as it overflows in float16\\n needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast\\n\\n if needs_upcasting:\\n self.upcast_vae()\\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\\n\\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\\n\\n # cast back to fp16 if needed\\n if needs_upcasting:\\n self.vae.to(dtype=torch.float16)\\n else:\\n return StableDiffusionXLPipelineOutput(images=latents)\\n\\n # apply watermark if available\\n if self.watermark is not None:\\n image = self.watermark.apply_watermark(image)\\n\\n image = self.image_processor.postprocess(image, output_type=output_type)\\n\\n # Offload all models\\n self.maybe_free_model_hooks()\\n\\n if not return_dict:\\n return (image,)\\n\\n return StableDiffusionXLPipelineOutput(images=image)\"\n },\n {\n \"identifier\": \"SAMPLERS\",\n \"path\": \"relighting/argument.py\",\n \"snippet\": \"SAMPLERS = {\\n \\\"ddim\\\": DDIMScheduler,\\n \\\"ddpm\\\": DDPMScheduler,\\n \\\"unipc\\\": UniPCMultistepScheduler,\\n}\"\n },\n {\n \"identifier\": \"VAE_MODELS\",\n \"path\": \"relighting/argument.py\",\n \"snippet\": \"VAE_MODELS = {\\n \\\"sdxl\\\": \\\"madebyollin/sdxl-vae-fp16-fix\\\",\\n \\\"sdxl_fast\\\": \\\"madebyollin/sdxl-vae-fp16-fix\\\",\\n}\"\n },\n {\n \"identifier\": \"DEPTH_ESTIMATOR\",\n \"path\": \"relighting/argument.py\",\n \"snippet\": \"DEPTH_ESTIMATOR = \\\"Intel/dpt-hybrid-midas\\\"\"\n },\n {\n \"identifier\": \"get_control_signal_type\",\n \"path\": \"relighting/argument.py\",\n \"snippet\": \"def get_control_signal_type(controlnet):\\n if \\\"normal\\\" in controlnet:\\n return \\\"normal\\\"\\n elif \\\"depth\\\" in controlnet:\\n return \\\"depth\\\"\\n else:\\n raise NotImplementedError\"\n },\n {\n \"identifier\": \"estimate_scene_depth\",\n \"path\": \"relighting/image_processor.py\",\n \"snippet\": \"def estimate_scene_depth(image, depth_estimator):\\n #image = feature_extractor(images=image, return_tensors=\\\"pt\\\").pixel_values.to(\\\"cuda\\\")\\n #with torch.no_grad(), torch.autocast(\\\"cuda\\\"):\\n # depth_map = depth_estimator(image).predicted_depth\\n\\n depth_map = depth_estimator(image)['predicted_depth']\\n W, H = image.size\\n depth_map = torch.nn.functional.interpolate(\\n depth_map.unsqueeze(1),\\n size=(H, W),\\n mode=\\\"bicubic\\\",\\n align_corners=False,\\n )\\n depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)\\n depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)\\n depth_map = (depth_map - depth_min) / (depth_max - depth_min)\\n image = torch.cat([depth_map] * 3, dim=1)\\n\\n image = image.permute(0, 2, 3, 1).cpu().numpy()[0]\\n image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))\\n return image\"\n },\n {\n \"identifier\": \"estimate_scene_normal\",\n \"path\": \"relighting/image_processor.py\",\n \"snippet\": \"def estimate_scene_normal(image, depth_estimator):\\n # can be improve speed do not going back and float between numpy and torch\\n normal_image = depth_estimator(image)['predicted_depth'][0]\\n\\n normal_image = normal_image.numpy()\\n\\n # upsizing image depth to match input\\n hw = np.array(image).shape[:2]\\n normal_image = skimage.transform.resize(normal_image, hw, preserve_range=True)\\n\\n image_depth = normal_image.copy()\\n image_depth -= np.min(image_depth)\\n image_depth /= np.max(image_depth)\\n \\n bg_threhold = 0.4\\n\\n x = cv2.Sobel(normal_image, cv2.CV_32F, 1, 0, ksize=3)\\n x[image_depth < bg_threhold] = 0\\n\\n y = cv2.Sobel(normal_image, cv2.CV_32F, 0, 1, ksize=3)\\n y[image_depth < bg_threhold] = 0\\n\\n z = np.ones_like(x) * np.pi * 2.0\\n\\n normal_image = np.stack([x, y, z], axis=2)\\n normal_image /= np.sum(normal_image ** 2.0, axis=2, keepdims=True) ** 0.5\\n\\n # rescale back to image size\\n return normal_image\"\n },\n {\n \"identifier\": \"merge_normal_map\",\n \"path\": \"relighting/image_processor.py\",\n \"snippet\": \"def merge_normal_map(normal_map, normal_ball, mask_ball, x, y):\\n \\\"\\\"\\\"\\n Merge a ball to normal map using mask\\n @params\\n normal_amp (np.array) - normal map of the scene [height, width, 3]\\n normal_ball (np.array) - normal map of the ball [ball_height, ball_width, 3]\\n mask_ball (np.array) - mask of the ball [ball_height, ball_width]\\n x (int) - x position of the ball (top-left)\\n y (int) - y position of the ball (top-left)\\n @return\\n normal_mapthe merge normal map [height, width, 3] \\n \\\"\\\"\\\"\\n result = normal_map.copy()\\n\\n mask_ball = mask_ball[..., None]\\n ball = (normal_ball * mask_ball) # alpha blending the ball\\n unball = (normal_map[y:y+normal_ball.shape[0], x:x+normal_ball.shape[1]] * (1 - mask_ball)) # alpha blending the normal map\\n result[y:y+normal_ball.shape[0], x:x+normal_ball.shape[1]] = ball+unball # add them together\\n return result\"\n },\n {\n \"identifier\": \"fill_depth_circular\",\n \"path\": \"relighting/image_processor.py\",\n \"snippet\": \"def fill_depth_circular(depth_image, x, y, r):\\n depth_image = np.array(depth_image)\\n\\n for i in range(depth_image.shape[0]):\\n for j in range(depth_image.shape[1]):\\n xy = (i - x - r//2)**2 + (j - y - r//2)**2\\n # if xy <= rr**2:\\n # depth_image[j, i, :] = 255\\n # depth_image[j, i, :] = int(minv + (maxv - minv) * z)\\n if xy <= (r // 2)**2:\\n depth_image[j, i, :] = 255\\n \\n depth_image = Image.fromarray(depth_image)\\n return depth_image\"\n },\n {\n \"identifier\": \"get_ideal_normal_ball\",\n \"path\": \"relighting/ball_processor.py\",\n \"snippet\": \"def get_ideal_normal_ball(size, flip_x=True):\\n \\\"\\\"\\\"\\n Generate normal ball for specific size \\n Normal map is x \\\"left\\\", y up, z into the screen \\n (we flip X to match sobel operator)\\n @params\\n - size (int) - single value of height and width\\n @return:\\n - normal_map (np.array) - normal map [size, size, 3]\\n - mask (np.array) - mask that make a valid normal map [size,size]\\n \\\"\\\"\\\"\\n # we flip x to match sobel operator\\n x = torch.linspace(1, -1, size)\\n y = torch.linspace(1, -1, size)\\n x = x.flip(dims=(-1,)) if not flip_x else x\\n\\n y, x = torch.meshgrid(y, x)\\n z = (1 - x**2 - y**2)\\n mask = z >= 0\\n\\n # clean up invalid value outsize the mask\\n x = x * mask\\n y = y * mask\\n z = z * mask\\n \\n # get real z value\\n z = torch.sqrt(z)\\n \\n # clean up normal map value outside mask \\n normal_map = torch.cat([x[..., None], y[..., None], z[..., None]], dim=-1)\\n normal_map = normal_map.numpy()\\n mask = mask.numpy()\\n return normal_map, mask\"\n },\n {\n \"identifier\": \"crop_ball\",\n \"path\": \"relighting/ball_processor.py\",\n \"snippet\": \"def crop_ball(image, mask_ball, x, y, size, apply_mask=True, bg_color = (0, 0, 0)):\\n if isinstance(image, Image.Image):\\n result = np.array(image)\\n else:\\n result = image.copy()\\n \\n result = result[y:y+size, x:x+size]\\n if apply_mask:\\n result[~mask_ball] = bg_color\\n return result\"\n },\n {\n \"identifier\": \"CustomStableDiffusionXLControlNetInpaintPipeline\",\n \"path\": \"relighting/pipeline_xl.py\",\n \"snippet\": \"class CustomStableDiffusionXLControlNetInpaintPipeline(StableDiffusionXLControlNetInpaintPipeline):\\n @torch.no_grad()\\n def __call__(\\n self,\\n prompt: Union[str, List[str]] = None,\\n prompt_2: Optional[Union[str, List[str]]] = None,\\n image: PipelineImageInput = None,\\n mask_image: PipelineImageInput = None,\\n control_image: Union[\\n PipelineImageInput,\\n List[PipelineImageInput],\\n ] = None,\\n height: Optional[int] = None,\\n width: Optional[int] = None,\\n strength: float = 0.9999,\\n num_inference_steps: int = 50,\\n denoising_start: Optional[float] = None,\\n denoising_end: Optional[float] = None,\\n guidance_scale: float = 5.0,\\n negative_prompt: Optional[Union[str, List[str]]] = None,\\n negative_prompt_2: Optional[Union[str, List[str]]] = None,\\n num_images_per_prompt: Optional[int] = 1,\\n eta: float = 0.0,\\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\\n latents: Optional[torch.FloatTensor] = None,\\n prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\\n pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\\n negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,\\n output_type: Optional[str] = \\\"pil\\\",\\n return_dict: bool = True,\\n callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,\\n callback_steps: int = 1,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n controlnet_conditioning_scale: Union[float, List[float]] = 1.0,\\n guess_mode: bool = False,\\n control_guidance_start: Union[float, List[float]] = 0.0,\\n control_guidance_end: Union[float, List[float]] = 1.0,\\n guidance_rescale: float = 0.0,\\n original_size: Tuple[int, int] = None,\\n crops_coords_top_left: Tuple[int, int] = (0, 0),\\n target_size: Tuple[int, int] = None,\\n aesthetic_score: float = 6.0,\\n negative_aesthetic_score: float = 2.5,\\n newx: int = 0,\\n newy: int = 0,\\n newr: int = 256,\\n current_seed=0,\\n use_noise_moving=True,\\n ):\\n # OVERWRITE METHODS\\n self.prepare_mask_latents = custom_prepare_mask_latents.__get__(self, CustomStableDiffusionXLControlNetInpaintPipeline)\\n self.prepare_latents = custom_prepare_latents.__get__(self, CustomStableDiffusionXLControlNetInpaintPipeline)\\n\\n controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet\\n\\n # align format for control guidance\\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\\n control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [\\n control_guidance_end\\n ]\\n\\n # # 0.0 Default height and width to unet\\n # height = height or self.unet.config.sample_size * self.vae_scale_factor\\n # width = width or self.unet.config.sample_size * self.vae_scale_factor\\n\\n # 0.1 align format for control guidance\\n if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):\\n control_guidance_start = len(control_guidance_end) * [control_guidance_start]\\n elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):\\n control_guidance_end = len(control_guidance_start) * [control_guidance_end]\\n elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):\\n mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1\\n control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [\\n control_guidance_end\\n ]\\n\\n # 1. Check inputs\\n self.check_inputs(\\n prompt,\\n prompt_2,\\n control_image,\\n strength,\\n num_inference_steps,\\n callback_steps,\\n negative_prompt,\\n negative_prompt_2,\\n prompt_embeds,\\n negative_prompt_embeds,\\n pooled_prompt_embeds,\\n negative_pooled_prompt_embeds,\\n controlnet_conditioning_scale,\\n control_guidance_start,\\n control_guidance_end,\\n )\\n\\n # 2. Define call parameters\\n if prompt is not None and isinstance(prompt, str):\\n batch_size = 1\\n elif prompt is not None and isinstance(prompt, list):\\n batch_size = len(prompt)\\n else:\\n batch_size = prompt_embeds.shape[0]\\n\\n device = self._execution_device\\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\\n # corresponds to doing no classifier free guidance.\\n do_classifier_free_guidance = guidance_scale > 1.0\\n\\n if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):\\n controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)\\n\\n # 3. Encode input prompt\\n text_encoder_lora_scale = (\\n cross_attention_kwargs.get(\\\"scale\\\", None) if cross_attention_kwargs is not None else None\\n )\\n\\n (\\n prompt_embeds,\\n negative_prompt_embeds,\\n pooled_prompt_embeds,\\n negative_pooled_prompt_embeds,\\n ) = self.encode_prompt(\\n prompt=prompt,\\n prompt_2=prompt_2,\\n device=device,\\n num_images_per_prompt=num_images_per_prompt,\\n do_classifier_free_guidance=do_classifier_free_guidance,\\n negative_prompt=negative_prompt,\\n negative_prompt_2=negative_prompt_2,\\n prompt_embeds=prompt_embeds,\\n negative_prompt_embeds=negative_prompt_embeds,\\n pooled_prompt_embeds=pooled_prompt_embeds,\\n negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,\\n lora_scale=text_encoder_lora_scale,\\n )\\n\\n # 4. set timesteps\\n def denoising_value_valid(dnv):\\n return isinstance(denoising_end, float) and 0 < dnv < 1\\n\\n self.scheduler.set_timesteps(num_inference_steps, device=device)\\n timesteps, num_inference_steps = self.get_timesteps(\\n num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None\\n )\\n # check that number of inference steps is not < 1 - as this doesn't make sense\\n if num_inference_steps < 1:\\n raise ValueError(\\n f\\\"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline\\\"\\n f\\\"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline.\\\"\\n )\\n # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)\\n latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)\\n # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise\\n is_strength_max = strength == 1.0\\n\\n # 5. Preprocess mask and image - resizes image and mask w.r.t height and width\\n # 5.1 Prepare init image\\n init_image = self.image_processor.preprocess(image, height=height, width=width)\\n init_image = init_image.to(dtype=torch.float32)\\n\\n # 5.2 Prepare control images\\n if isinstance(controlnet, ControlNetModel):\\n control_image = self.prepare_control_image(\\n image=control_image,\\n width=width,\\n height=height,\\n batch_size=batch_size * num_images_per_prompt,\\n num_images_per_prompt=num_images_per_prompt,\\n device=device,\\n dtype=controlnet.dtype,\\n do_classifier_free_guidance=do_classifier_free_guidance,\\n guess_mode=guess_mode,\\n )\\n elif isinstance(controlnet, MultiControlNetModel):\\n control_images = []\\n\\n for control_image_ in control_image:\\n control_image_ = self.prepare_control_image(\\n image=control_image_,\\n width=width,\\n height=height,\\n batch_size=batch_size * num_images_per_prompt,\\n num_images_per_prompt=num_images_per_prompt,\\n device=device,\\n dtype=controlnet.dtype,\\n do_classifier_free_guidance=do_classifier_free_guidance,\\n guess_mode=guess_mode,\\n )\\n\\n control_images.append(control_image_)\\n\\n control_image = control_images\\n else:\\n raise ValueError(f\\\"{controlnet.__class__} is not supported.\\\")\\n\\n # 5.3 Prepare mask\\n mask = self.mask_processor.preprocess(mask_image, height=height, width=width)\\n\\n masked_image = init_image * (mask < 0.5)\\n _, _, height, width = init_image.shape\\n\\n # 6. Prepare latent variables\\n num_channels_latents = self.vae.config.latent_channels\\n num_channels_unet = self.unet.config.in_channels\\n return_image_latents = num_channels_unet == 4\\n\\n add_noise = True if denoising_start is None else False\\n latents_outputs = self.prepare_latents(\\n batch_size * num_images_per_prompt,\\n num_channels_latents,\\n height,\\n width,\\n prompt_embeds.dtype,\\n device,\\n generator,\\n latents,\\n image=init_image,\\n timestep=latent_timestep,\\n is_strength_max=is_strength_max,\\n return_noise=True,\\n return_image_latents=return_image_latents,\\n newx=newx,\\n newy=newy,\\n newr=newr,\\n current_seed=current_seed,\\n use_noise_moving=use_noise_moving,\\n )\\n\\n if return_image_latents:\\n latents, noise, image_latents = latents_outputs\\n else:\\n latents, noise = latents_outputs\\n\\n # 7. Prepare mask latent variables\\n mask, masked_image_latents = self.prepare_mask_latents(\\n mask,\\n masked_image,\\n batch_size * num_images_per_prompt,\\n height,\\n width,\\n prompt_embeds.dtype,\\n device,\\n generator,\\n do_classifier_free_guidance,\\n )\\n\\n # 8. Check that sizes of mask, masked image and latents match\\n if num_channels_unet == 9:\\n # default case for runwayml/stable-diffusion-inpainting\\n num_channels_mask = mask.shape[1]\\n num_channels_masked_image = masked_image_latents.shape[1]\\n if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:\\n raise ValueError(\\n f\\\"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects\\\"\\n f\\\" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +\\\"\\n f\\\" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}\\\"\\n f\\\" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of\\\"\\n \\\" `pipeline.unet` or your `mask_image` or `image` input.\\\"\\n )\\n elif num_channels_unet != 4:\\n raise ValueError(\\n f\\\"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}.\\\"\\n )\\n # 8.1 Prepare extra step kwargs.\\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\\n\\n # 8.2 Create tensor stating which controlnets to keep\\n controlnet_keep = []\\n for i in range(len(timesteps)):\\n keeps = [\\n 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)\\n for s, e in zip(control_guidance_start, control_guidance_end)\\n ]\\n if isinstance(self.controlnet, MultiControlNetModel):\\n controlnet_keep.append(keeps)\\n else:\\n controlnet_keep.append(keeps[0])\\n\\n # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\\n height, width = latents.shape[-2:]\\n height = height * self.vae_scale_factor\\n width = width * self.vae_scale_factor\\n\\n original_size = original_size or (height, width)\\n target_size = target_size or (height, width)\\n\\n # 10. Prepare added time ids & embeddings\\n add_text_embeds = pooled_prompt_embeds\\n add_time_ids, add_neg_time_ids = self._get_add_time_ids(\\n original_size,\\n crops_coords_top_left,\\n target_size,\\n aesthetic_score,\\n negative_aesthetic_score,\\n dtype=prompt_embeds.dtype,\\n )\\n add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)\\n\\n if do_classifier_free_guidance:\\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\\n add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)\\n add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)\\n add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)\\n\\n prompt_embeds = prompt_embeds.to(device)\\n add_text_embeds = add_text_embeds.to(device)\\n add_time_ids = add_time_ids.to(device)\\n\\n # 11. Denoising loop\\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\\n\\n if (\\n denoising_end is not None\\n and denoising_start is not None\\n and denoising_value_valid(denoising_end)\\n and denoising_value_valid(denoising_start)\\n and denoising_start >= denoising_end\\n ):\\n raise ValueError(\\n f\\\"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: \\\"\\n + f\\\" {denoising_end} when using type float.\\\"\\n )\\n elif denoising_end is not None and denoising_value_valid(denoising_end):\\n discrete_timestep_cutoff = int(\\n round(\\n self.scheduler.config.num_train_timesteps\\n - (denoising_end * self.scheduler.config.num_train_timesteps)\\n )\\n )\\n num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))\\n timesteps = timesteps[:num_inference_steps]\\n\\n with self.progress_bar(total=num_inference_steps) as progress_bar:\\n for i, t in enumerate(timesteps):\\n # expand the latents if we are doing classifier free guidance\\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\\n\\n # concat latents, mask, masked_image_latents in the channel dimension\\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\\n\\n added_cond_kwargs = {\\\"text_embeds\\\": add_text_embeds, \\\"time_ids\\\": add_time_ids}\\n\\n # controlnet(s) inference\\n if guess_mode and do_classifier_free_guidance:\\n # Infer ControlNet only for the conditional batch.\\n control_model_input = latents\\n control_model_input = self.scheduler.scale_model_input(control_model_input, t)\\n controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]\\n controlnet_added_cond_kwargs = {\\n \\\"text_embeds\\\": add_text_embeds.chunk(2)[1],\\n \\\"time_ids\\\": add_time_ids.chunk(2)[1],\\n }\\n else:\\n control_model_input = latent_model_input\\n controlnet_prompt_embeds = prompt_embeds\\n controlnet_added_cond_kwargs = added_cond_kwargs\\n\\n if isinstance(controlnet_keep[i], list):\\n cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]\\n else:\\n controlnet_cond_scale = controlnet_conditioning_scale\\n if isinstance(controlnet_cond_scale, list):\\n controlnet_cond_scale = controlnet_cond_scale[0]\\n cond_scale = controlnet_cond_scale * controlnet_keep[i]\\n\\n # # Resize control_image to match the size of the input to the controlnet\\n # if control_image.shape[-2:] != control_model_input.shape[-2:]:\\n # control_image = F.interpolate(control_image, size=control_model_input.shape[-2:], mode=\\\"bilinear\\\", align_corners=False)\\n\\n down_block_res_samples, mid_block_res_sample = self.controlnet(\\n control_model_input,\\n t,\\n encoder_hidden_states=controlnet_prompt_embeds,\\n controlnet_cond=control_image,\\n conditioning_scale=cond_scale,\\n guess_mode=guess_mode,\\n added_cond_kwargs=controlnet_added_cond_kwargs,\\n return_dict=False,\\n )\\n\\n if guess_mode and do_classifier_free_guidance:\\n # Infered ControlNet only for the conditional batch.\\n # To apply the output of ControlNet to both the unconditional and conditional batches,\\n # add 0 to the unconditional batch to keep it unchanged.\\n down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]\\n mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])\\n\\n if num_channels_unet == 9:\\n latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)\\n\\n # predict the noise residual\\n noise_pred = self.unet(\\n latent_model_input,\\n t,\\n encoder_hidden_states=prompt_embeds,\\n cross_attention_kwargs=cross_attention_kwargs,\\n down_block_additional_residuals=down_block_res_samples,\\n mid_block_additional_residual=mid_block_res_sample,\\n added_cond_kwargs=added_cond_kwargs,\\n return_dict=False,\\n )[0]\\n\\n # perform guidance\\n if do_classifier_free_guidance:\\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\\n noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\\n\\n if do_classifier_free_guidance and guidance_rescale > 0.0:\\n print(\\\"rescale: \\\", guidance_rescale)\\n # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf\\n noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)\\n\\n # compute the previous noisy sample x_t -> x_t-1\\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\\n\\n if num_channels_unet == 4:\\n init_latents_proper = image_latents[:1]\\n init_mask = mask[:1]\\n\\n if i < len(timesteps) - 1:\\n noise_timestep = timesteps[i + 1]\\n init_latents_proper = self.scheduler.add_noise(\\n init_latents_proper, noise, torch.tensor([noise_timestep])\\n )\\n\\n latents = (1 - init_mask) * init_latents_proper + init_mask * latents\\n\\n # call the callback, if provided\\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\\n progress_bar.update()\\n if callback is not None and i % callback_steps == 0:\\n callback(i, t, latents)\\n\\n # make sure the VAE is in float32 mode, as it overflows in float16\\n if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:\\n self.upcast_vae()\\n latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)\\n\\n # If we do sequential model offloading, let's offload unet and controlnet\\n # manually for max memory savings\\n if hasattr(self, \\\"final_offload_hook\\\") and self.final_offload_hook is not None:\\n self.unet.to(\\\"cpu\\\")\\n self.controlnet.to(\\\"cpu\\\")\\n torch.cuda.empty_cache()\\n\\n if not output_type == \\\"latent\\\":\\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\\n else:\\n return StableDiffusionXLPipelineOutput(images=latents)\\n\\n # apply watermark if available\\n if self.watermark is not None:\\n image = self.watermark.apply_watermark(image)\\n\\n image = self.image_processor.postprocess(image, output_type=output_type)\\n\\n # Offload last model to CPU\\n if hasattr(self, \\\"final_offload_hook\\\") and self.final_offload_hook is not None:\\n self.final_offload_hook.offload()\\n\\n if not return_dict:\\n return (image,)\\n\\n return StableDiffusionXLPipelineOutput(images=image)\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import torch\nimport numpy as np\nimport os\nimport pickle\nfrom diffusers import ControlNetModel, AutoencoderKL\nfrom PIL import Image\nfrom tqdm.auto import tqdm\nfrom transformers import pipeline as transformers_pipeline\nfrom relighting.pipeline import CustomStableDiffusionControlNetInpaintPipeline\nfrom relighting.pipeline_inpaintonly import CustomStableDiffusionInpaintPipeline, CustomStableDiffusionXLInpaintPipeline\nfrom relighting.argument import SAMPLERS, VAE_MODELS, DEPTH_ESTIMATOR, get_control_signal_type\nfrom relighting.image_processor import (\n estimate_scene_depth,\n estimate_scene_normal,\n merge_normal_map,\n fill_depth_circular\n)\nfrom relighting.ball_processor import get_ideal_normal_ball, crop_ball\nfrom relighting.pipeline_xl import CustomStableDiffusionXLControlNetInpaintPipeline"},"token_num":{"kind":"number","value":18556,"string":"18,556"},"cropped_code":{"kind":"string","value":" \n control_image = fill_depth_circular(control_image, x, y, r)\n return control_image\n\n def process_sdxl_depth(self, input_image, normal_ball=None, mask_ball=None, x=None, y=None, r=None):\n if getattr(self, 'depth_estimator', None) is None:\n self.depth_estimator = transformers_pipeline(\"depth-estimation\", model=DEPTH_ESTIMATOR, device=self.device.index)\n\n control_image = estimate_scene_depth(input_image, depth_estimator=self.depth_estimator)\n xs = [x] if not isinstance(x, list) else x\n ys = [y] if not isinstance(y, list) else y\n rs = [r] if not isinstance(r, list) else r\n \n for x, y, r in zip(xs, ys, rs):\n #print(f\"depth at {x}, {y}, {r}\")\n control_image = fill_depth_circular(control_image, x, y, r)\n return control_image\n\n def process_sd_normal(self, input_image, normal_ball, mask_ball, x, y, r=None, normal_ball_path=None):\n if getattr(self, 'depth_estimator', None) is None:\n self.depth_estimator = transformers_pipeline(\"depth-estimation\", model=DEPTH_ESTIMATOR, device=self.device.index)\n\n normal_scene = estimate_scene_normal(input_image, depth_estimator=self.depth_estimator)\n normal_image = merge_normal_map(normal_scene, normal_ball, mask_ball, x, y)\n normal_image = (normal_image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)\n control_image = Image.fromarray(normal_image)\n return control_image\n\n def __call__(self, *args, **kwargs):\n process_fn = getattr(self, f\"process_{self.sd_arch}_{self.control_signal_type}\", None)\n if process_fn is None:\n raise ValueError\n else:\n return process_fn(*args, **kwargs)\n \n\nclass BallInpainter():\n def __init__(self, pipeline, sd_arch, control_generator, disable_water_mask=True):\n self.pipeline = pipeline\n self.sd_arch = sd_arch\n self.control_generator = control_generator\n self.median = {}\n if disable_water_mask:\n self._disable_water_mask()\n\n def _disable_water_mask(self):\n if hasattr(self.pipeline, \"watermark\"):\n self.pipeline.watermark = NoWaterMark()\n print(\"Disabled watermasking\")\n\n @classmethod\n def from_sd(cls, \n model, \n controlnet=None, \n device=0, \n sampler=\"unipc\", \n torch_dtype=torch.float16,\n disable_water_mask=True,\n offload=False\n ):\n if controlnet is not None:\n control_signal_type = get_control_signal_type(controlnet)\n controlnet = ControlNetModel.from_pretrained(controlnet, torch_dtype=torch.float16)\n pipe = CustomStableDiffusionControlNetInpaintPipeline.from_pretrained(\n model,\n controlnet=controlnet,\n torch_dtype=torch_dtype,\n ).to(device)\n control_generator = ControlSignalGenerator(\"sd\", control_signal_type, device=device)\n else:\n pipe = CustomStableDiffusionInpaintPipeline.from_pretrained(\n model,\n torch_dtype=torch_dtype,\n ).to(device)\n control_generator = None\n \n try:\n if torch_dtype==torch.float16 and device != torch.device(\"cpu\"):\n pipe.enable_xformers_memory_efficient_attention()\n except:\n pass\n pipe.set_progress_bar_config(disable=True)\n \n pipe.scheduler = SAMPLERS[sampler].from_config(pipe.scheduler.config)\n \n return BallInpainter(pipe, \"sd\", control_generator, disable_water_mask)\n\n @classmethod\n def from_sdxl(cls, \n model, \n controlnet=None, \n device=0, \n sampler=\"unipc\", \n torch_dtype=torch.float16,\n disable_water_mask=True,\n use_fixed_vae=True,\n offload=False\n ):\n vae = VAE_MODELS[\"sdxl\"]\n vae = AutoencoderKL.from_pretrained(vae, torch_dtype=torch_dtype).to(device) if use_fixed_vae else None\n extra_kwargs = {\"vae\": vae} if vae is not None else {}\n \n if controlnet is not None:\n control_signal_type = get_control_signal_type(controlnet)\n controlnet = ControlNetModel.from_pretrained(\n controlnet,\n variant=\"fp16\" if torch_dtype == torch.float16 else None,\n use_safetensors=True,\n torch_dtype=torch_dtype,\n ).to(device)\n pipe = CustomStableDiffusionXLControlNetInpaintPipeline.from_pretrained(\n model,\n controlnet=controlnet,\n variant=\"fp16\" if torch_dtype == torch.float16 else None,\n use_safetensors=True,\n torch_dtype=torch_dtype,\n **extra_kwargs,\n ).to(device)\n control_generator = ControlSignalGenerator(\"sdxl\", control_signal_type, device=device)\n else:\n"},"all_code":{"kind":"string","value":"\n\n\nclass NoWaterMark:\n def apply_watermark(self, *args, **kwargs):\n return args[0]\n\nclass ControlSignalGenerator():\n def __init__(self, sd_arch, control_signal_type, device):\n self.sd_arch = sd_arch\n self.control_signal_type = control_signal_type\n self.device = device\n\n def process_sd_depth(self, input_image, normal_ball=None, mask_ball=None, x=None, y=None, r=None):\n if getattr(self, 'depth_estimator', None) is None:\n self.depth_estimator = transformers_pipeline(\"depth-estimation\", device=self.device.index)\n\n control_image = self.depth_estimator(input_image)['depth']\n control_image = np.array(control_image)\n control_image = control_image[:, :, None]\n control_image = np.concatenate([control_image, control_image, control_image], axis=2)\n control_image = Image.fromarray(control_image)\n \n control_image = fill_depth_circular(control_image, x, y, r)\n return control_image\n\n def process_sdxl_depth(self, input_image, normal_ball=None, mask_ball=None, x=None, y=None, r=None):\n if getattr(self, 'depth_estimator', None) is None:\n self.depth_estimator = transformers_pipeline(\"depth-estimation\", model=DEPTH_ESTIMATOR, device=self.device.index)\n\n control_image = estimate_scene_depth(input_image, depth_estimator=self.depth_estimator)\n xs = [x] if not isinstance(x, list) else x\n ys = [y] if not isinstance(y, list) else y\n rs = [r] if not isinstance(r, list) else r\n \n for x, y, r in zip(xs, ys, rs):\n #print(f\"depth at {x}, {y}, {r}\")\n control_image = fill_depth_circular(control_image, x, y, r)\n return control_image\n\n def process_sd_normal(self, input_image, normal_ball, mask_ball, x, y, r=None, normal_ball_path=None):\n if getattr(self, 'depth_estimator', None) is None:\n self.depth_estimator = transformers_pipeline(\"depth-estimation\", model=DEPTH_ESTIMATOR, device=self.device.index)\n\n normal_scene = estimate_scene_normal(input_image, depth_estimator=self.depth_estimator)\n normal_image = merge_normal_map(normal_scene, normal_ball, mask_ball, x, y)\n normal_image = (normal_image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)\n control_image = Image.fromarray(normal_image)\n return control_image\n\n def __call__(self, *args, **kwargs):\n process_fn = getattr(self, f\"process_{self.sd_arch}_{self.control_signal_type}\", None)\n if process_fn is None:\n raise ValueError\n else:\n return process_fn(*args, **kwargs)\n \n\nclass BallInpainter():\n def __init__(self, pipeline, sd_arch, control_generator, disable_water_mask=True):\n self.pipeline = pipeline\n self.sd_arch = sd_arch\n self.control_generator = control_generator\n self.median = {}\n if disable_water_mask:\n self._disable_water_mask()\n\n def _disable_water_mask(self):\n if hasattr(self.pipeline, \"watermark\"):\n self.pipeline.watermark = NoWaterMark()\n print(\"Disabled watermasking\")\n\n @classmethod\n def from_sd(cls, \n model, \n controlnet=None, \n device=0, \n sampler=\"unipc\", \n torch_dtype=torch.float16,\n disable_water_mask=True,\n offload=False\n ):\n if controlnet is not None:\n control_signal_type = get_control_signal_type(controlnet)\n controlnet = ControlNetModel.from_pretrained(controlnet, torch_dtype=torch.float16)\n pipe = CustomStableDiffusionControlNetInpaintPipeline.from_pretrained(\n model,\n controlnet=controlnet,\n torch_dtype=torch_dtype,\n ).to(device)\n control_generator = ControlSignalGenerator(\"sd\", control_signal_type, device=device)\n else:\n pipe = CustomStableDiffusionInpaintPipeline.from_pretrained(\n model,\n torch_dtype=torch_dtype,\n ).to(device)\n control_generator = None\n \n try:\n if torch_dtype==torch.float16 and device != torch.device(\"cpu\"):\n pipe.enable_xformers_memory_efficient_attention()\n except:\n pass\n pipe.set_progress_bar_config(disable=True)\n \n pipe.scheduler = SAMPLERS[sampler].from_config(pipe.scheduler.config)\n \n return BallInpainter(pipe, \"sd\", control_generator, disable_water_mask)\n\n @classmethod\n def from_sdxl(cls, \n model, \n controlnet=None, \n device=0, \n sampler=\"unipc\", \n torch_dtype=torch.float16,\n disable_water_mask=True,\n use_fixed_vae=True,\n offload=False\n ):\n vae = VAE_MODELS[\"sdxl\"]\n vae = AutoencoderKL.from_pretrained(vae, torch_dtype=torch_dtype).to(device) if use_fixed_vae else None\n extra_kwargs = {\"vae\": vae} if vae is not None else {}\n \n if controlnet is not None:\n control_signal_type = get_control_signal_type(controlnet)\n controlnet = ControlNetModel.from_pretrained(\n controlnet,\n variant=\"fp16\" if torch_dtype == torch.float16 else None,\n use_safetensors=True,\n torch_dtype=torch_dtype,\n ).to(device)\n pipe = CustomStableDiffusionXLControlNetInpaintPipeline.from_pretrained(\n model,\n controlnet=controlnet,\n variant=\"fp16\" if torch_dtype == torch.float16 else None,\n use_safetensors=True,\n torch_dtype=torch_dtype,\n **extra_kwargs,\n ).to(device)\n control_generator = ControlSignalGenerator(\"sdxl\", control_signal_type, device=device)\n else:"},"next_line":{"kind":"string","value":" pipe = CustomStableDiffusionXLInpaintPipeline.from_pretrained("},"gold_snippet_index":{"kind":"number","value":2,"string":"2"},"created_at":{"kind":"string","value":"2023-12-07 14:03:31+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5288,"cells":{"repo_name":{"kind":"string","value":"modelscope/normal-depth-diffusion"},"file_path":{"kind":"string","value":"ldm/models/diffusion/mv_ddpm.py"},"context":{"kind":"list like","value":[{"identifier":"AutoencoderKL","path":"ldm/models/autoencoder.py","snippet":"class AutoencoderKL(pl.LightningModule):\n\n def __init__(self,\n ddconfig,\n lossconfig,\n embed_dim,\n ckpt_path=None,\n ignore_keys=[],\n image_key='image',\n colorize_nlabels=None,\n monitor=None,\n prior_model=None,\n prior_normal=None,\n using_rgb=True):\n super().__init__()\n self.image_key = image_key\n self.encoder = Encoder(**ddconfig)\n self.decoder = Decoder(**ddconfig)\n self.loss = instantiate_from_config(lossconfig)\n self.prior_model = prior_model\n self.using_rgb = using_rgb\n\n assert ddconfig['double_z']\n self.quant_conv = torch.nn.Conv2d(2 * ddconfig['z_channels'],\n 2 * embed_dim, 1)\n self.post_quant_conv = torch.nn.Conv2d(embed_dim,\n ddconfig['z_channels'], 1)\n self.embed_dim = embed_dim\n if colorize_nlabels is not None:\n assert type(colorize_nlabels) == int\n self.register_buffer('colorize',\n torch.randn(3, colorize_nlabels, 1, 1))\n if monitor is not None:\n self.monitor = monitor\n\n if prior_model is not None:\n self.prior_model = instantiate_from_config(prior_model)\n if prior_normal is not None:\n self.prior_normal = instantiate_from_config(prior_normal)\n\n if ckpt_path is not None:\n self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)\n\n def init_from_ckpt(self, path, ignore_keys=list()):\n try:\n sd = torch.load(path, map_location='cpu')['state_dict']\n except:\n sd = torch.load(path, map_location='cpu')\n\n keys = list(sd.keys())\n for k in keys:\n for ik in ignore_keys:\n if k.startswith(ik):\n print('Deleting key {} from state_dict.'.format(k))\n del sd[k]\n m, u = self.load_state_dict(sd, strict=False)\n if len(m) > 0:\n print('missing keys:')\n print(m)\n if len(u) > 0:\n print('unexpected keys:')\n print(u)\n\n print(f'Restored from {path}')\n\n def encode(self, x):\n h = self.encoder(x)\n moments = self.quant_conv(h)\n posterior = DiagonalGaussianDistribution(moments)\n return posterior\n\n def decode(self, z):\n z = self.post_quant_conv(z)\n dec = self.decoder(z)\n return dec\n\n def prior_to_eval(self):\n\n if self.prior_model is not None:\n self.prior_model.eval()\n\n if self.prior_normal is not None:\n self.prior_normal.eval()\n\n @torch.no_grad()\n def prior_inference(self, inputs, prior_inputs):\n # depth prior model\n # midas or zoe is 384 model\n prior_results = {}\n\n self.prior_to_eval()\n\n model_prior_results = self.prior_model(prior_inputs)\n prior_results.update(model_prior_results)\n\n # using normal map\n if not self.using_rgb:\n normal_prior = self.prior_normal(prior_inputs)\n prior_results.update(normal_prior)\n\n resize_prior_results = {}\n _, __, h, w = inputs.shape\n\n for key in prior_results.keys():\n resize_prior_results[key] = F.interpolate(\n prior_results[key], (w, h), mode='bilinear')\n\n if self.using_rgb:\n return torch.cat([inputs, resize_prior_results['depth']], dim=1)\n else:\n return torch.cat([\n resize_prior_results['normal'], resize_prior_results['depth']\n ],\n dim=1)\n\n def forward(self, input, sample_posterior=True):\n\n posterior = self.encode(input)\n if sample_posterior:\n z = posterior.sample()\n else:\n z = posterior.mode()\n dec = self.decode(z)\n return dec, posterior\n\n def get_input(self, batch, k):\n x = batch[k]\n if len(x.shape) == 3:\n x = x[..., None]\n x = x.permute(0, 3, 1,\n 2).to(memory_format=torch.contiguous_format).float()\n return x\n\n def training_step(self, batch, batch_idx, optimizer_idx):\n\n inputs = self.get_input(batch, self.image_key)\n if self.prior_model is not None:\n inputs = self.prior_inference(inputs, batch['prior'])\n\n reconstructions, posterior = self(inputs)\n\n if optimizer_idx == 0:\n # train encoder+decoder+logvar\n aeloss, log_dict_ae = self.loss(\n inputs,\n reconstructions,\n posterior,\n optimizer_idx,\n self.global_step,\n last_layer=self.get_last_layer(),\n split='train')\n\n self.log(\n 'rec_loss',\n log_dict_ae['train/rec_loss'],\n prog_bar=True,\n logger=True,\n on_step=True,\n on_epoch=True)\n self.log(\n 'aeloss',\n aeloss,\n prog_bar=True,\n logger=True,\n on_step=True,\n on_epoch=True)\n self.log_dict(\n log_dict_ae,\n prog_bar=False,\n logger=True,\n on_step=True,\n on_epoch=False)\n return aeloss\n\n if optimizer_idx == 1:\n # train the discriminator\n discloss, log_dict_disc = self.loss(\n inputs,\n reconstructions,\n posterior,\n optimizer_idx,\n self.global_step,\n last_layer=self.get_last_layer(),\n split='train')\n\n self.log(\n 'discloss',\n discloss,\n prog_bar=True,\n logger=True,\n on_step=True,\n on_epoch=True)\n self.log_dict(\n log_dict_disc,\n prog_bar=False,\n logger=True,\n on_step=True,\n on_epoch=False)\n return discloss\n\n def validation_step(self, batch, batch_idx):\n inputs = self.get_input(batch, self.image_key)\n reconstructions, posterior = self(inputs)\n aeloss, log_dict_ae = self.loss(\n inputs,\n reconstructions,\n posterior,\n 0,\n self.global_step,\n last_layer=self.get_last_layer(),\n split='val')\n\n discloss, log_dict_disc = self.loss(\n inputs,\n reconstructions,\n posterior,\n 1,\n self.global_step,\n last_layer=self.get_last_layer(),\n split='val')\n\n self.log('val/rec_loss', log_dict_ae['val/rec_loss'])\n self.log_dict(log_dict_ae)\n self.log_dict(log_dict_disc)\n return self.log_dict\n\n @torch.no_grad()\n def test_step(self, batch, batch_idx):\n pass\n\n @torch.no_grad()\n def sample_imgs(self, batch):\n '''using to test for sampling image\n\n '''\n inputs = self.get_input(batch, self.image_key)\n reconstructions, posterior = self(inputs)\n\n return {'samples': reconstructions}\n\n def configure_optimizers(self):\n lr = self.learning_rate\n opt_ae = torch.optim.Adam(\n list(self.encoder.parameters()) + list(self.decoder.parameters())\n + list(self.quant_conv.parameters())\n + list(self.post_quant_conv.parameters()),\n lr=lr,\n betas=(0.5, 0.9))\n opt_disc = torch.optim.Adam(\n self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9))\n\n return [opt_ae, opt_disc], []\n\n def get_last_layer(self):\n return self.decoder.conv_out.weight\n\n @torch.no_grad()\n def log_images(self, batch, only_inputs=False, **kwargs):\n log = dict()\n x = self.get_input(batch, self.image_key)\n x = x.to(self.device)\n if not only_inputs:\n xrec, posterior = self(x)\n xrec = repeat(xrec[:, 0, ...], 'b h w -> b c h w', c=3)\n\n if x.shape[1] > 3:\n # colorize with random projection\n assert xrec.shape[1] > 3\n x = self.to_rgb(x)\n xrec = self.to_rgb(xrec)\n samples = self.decode(torch.randn_like(posterior.sample()))\n samples = repeat(samples[:, 0, ...], 'b h w -> b c h w', c=3)\n log['samples'] = samples\n\n log['reconstructions'] = xrec\n log['inputs'] = x\n return log\n\n @torch.no_grad()\n def log_rgbd(self, batch, only_inputs=False, **kwargs):\n log = dict()\n x = self.get_input(batch, self.image_key)\n\n if x.shape[1] == 3:\n if self.prior_model is not None:\n x = self.prior_inference(x, batch['prior'])\n\n x = x.to(self.device)\n if not only_inputs:\n xrec, posterior = self(x)\n samples = self.decode(torch.randn_like(posterior.sample()))\n log['samples'] = samples\n log['reconstructions'] = xrec\n log['inputs'] = x\n return log\n\n def to_rgb(self, x):\n assert self.image_key == 'segmentation'\n if not hasattr(self, 'colorize'):\n self.register_buffer('colorize',\n torch.randn(3, x.shape[1], 1, 1).to(x))\n x = F.conv2d(x, weight=self.colorize)\n x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.\n return x"},{"identifier":"IdentityFirstStage","path":"ldm/models/autoencoder.py","snippet":"class IdentityFirstStage(torch.nn.Module):\n\n def __init__(self, *args, vq_interface=False, **kwargs):\n self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff\n super().__init__()\n\n def encode(self, x, *args, **kwargs):\n return x\n\n def decode(self, x, *args, **kwargs):\n return x\n\n def quantize(self, x, *args, **kwargs):\n if self.vq_interface:\n return x, None, [None, None, None]\n return x\n\n def forward(self, x, *args, **kwargs):\n return x"},{"identifier":"VQModelInterface","path":"ldm/models/autoencoder.py","snippet":"class VQModelInterface(VQModel):\n\n def __init__(self, embed_dim, *args, **kwargs):\n super().__init__(embed_dim=embed_dim, *args, **kwargs)\n self.embed_dim = embed_dim\n\n def encode(self, x):\n h = self.encoder(x)\n h = self.quant_conv(h)\n return h\n\n def decode(self, h, force_not_quantize=False):\n # also go through quantization layer\n if not force_not_quantize:\n quant, emb_loss, info = self.quantize(h)\n else:\n quant = h\n quant = self.post_quant_conv(quant)\n dec = self.decoder(quant)\n return dec"},{"identifier":"DDIMSampler","path":"ldm/models/diffusion/ddim.py","snippet":"class DDIMSampler(object):\n\n def __init__(self, model, schedule='linear', **kwargs):\n super().__init__()\n self.model = model\n self.ddpm_num_timesteps = model.num_timesteps\n self.schedule = schedule\n\n def register_buffer(self, name, attr):\n if type(attr) == torch.Tensor:\n if attr.device != torch.device('cuda'):\n attr = attr.to(torch.device('cuda'))\n setattr(self, name, attr)\n\n def make_schedule(self,\n ddim_num_steps,\n ddim_discretize='uniform',\n ddim_eta=0.,\n verbose=True):\n self.ddim_timesteps = make_ddim_timesteps(\n ddim_discr_method=ddim_discretize,\n num_ddim_timesteps=ddim_num_steps,\n num_ddpm_timesteps=self.ddpm_num_timesteps,\n verbose=verbose)\n alphas_cumprod = self.model.alphas_cumprod\n assert alphas_cumprod.shape[\n 0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model\n .device)\n\n self.register_buffer('betas', to_torch(self.model.betas))\n self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\n self.register_buffer('alphas_cumprod_prev',\n to_torch(self.model.alphas_cumprod_prev))\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer('sqrt_alphas_cumprod',\n to_torch(np.sqrt(alphas_cumprod.cpu())))\n self.register_buffer('sqrt_one_minus_alphas_cumprod',\n to_torch(np.sqrt(1. - alphas_cumprod.cpu())))\n self.register_buffer('log_one_minus_alphas_cumprod',\n to_torch(np.log(1. - alphas_cumprod.cpu())))\n self.register_buffer('sqrt_recip_alphas_cumprod',\n to_torch(np.sqrt(1. / alphas_cumprod.cpu())))\n self.register_buffer('sqrt_recipm1_alphas_cumprod',\n to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))\n\n # ddim sampling parameters\n ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(\n alphacums=alphas_cumprod.cpu(),\n ddim_timesteps=self.ddim_timesteps,\n eta=ddim_eta,\n verbose=verbose)\n self.register_buffer('ddim_sigmas', ddim_sigmas)\n self.register_buffer('ddim_alphas', ddim_alphas)\n self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)\n self.register_buffer('ddim_sqrt_one_minus_alphas',\n np.sqrt(1. - ddim_alphas))\n sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\n (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) *\n (1 - self.alphas_cumprod / self.alphas_cumprod_prev))\n self.register_buffer('ddim_sigmas_for_original_num_steps',\n sigmas_for_original_sampling_steps)\n\n @torch.no_grad()\n def sample(\n self,\n S,\n batch_size,\n shape,\n conditioning=None,\n callback=None,\n normals_sequence=None,\n img_callback=None,\n quantize_x0=False,\n eta=0.,\n mask=None,\n x0=None,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n verbose=True,\n x_T=None,\n log_every_t=100,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None,\n # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\n **kwargs):\n if conditioning is not None:\n if isinstance(conditioning, dict):\n cbs = conditioning[list(conditioning.keys())[0]].shape[0]\n if cbs != batch_size:\n print(\n f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'\n )\n else:\n if conditioning.shape[0] != batch_size:\n print(\n f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'\n )\n\n self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\n # sampling\n C, H, W = shape\n size = (batch_size, C, H, W)\n\n samples, intermediates = self.ddim_sampling(\n conditioning,\n size,\n callback=callback,\n img_callback=img_callback,\n quantize_denoised=quantize_x0,\n mask=mask,\n x0=x0,\n ddim_use_original_steps=False,\n noise_dropout=noise_dropout,\n temperature=temperature,\n score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n x_T=x_T,\n log_every_t=log_every_t,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n **kwargs)\n return samples, intermediates\n\n @torch.no_grad()\n def ddim_sampling(self,\n cond,\n shape,\n x_T=None,\n ddim_use_original_steps=False,\n callback=None,\n timesteps=None,\n quantize_denoised=False,\n mask=None,\n x0=None,\n img_callback=None,\n log_every_t=100,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None,\n **kwargs):\n device = self.model.betas.device\n b = shape[0]\n if x_T is None:\n img = torch.randn(shape, device=device)\n else:\n img = x_T\n\n if timesteps is None:\n timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps\n elif timesteps is not None and not ddim_use_original_steps:\n subset_end = int(\n min(timesteps / self.ddim_timesteps.shape[0], 1)\n * self.ddim_timesteps.shape[0]) - 1\n timesteps = self.ddim_timesteps[:subset_end]\n\n intermediates = {'x_inter': [img], 'pred_x0': [img]}\n time_range = reversed(range(\n 0, timesteps)) if ddim_use_original_steps else np.flip(timesteps)\n total_steps = timesteps if ddim_use_original_steps else timesteps.shape[\n 0]\n\n iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)\n\n for i, step in enumerate(iterator):\n index = total_steps - i - 1\n ts = torch.full((b, ), step, device=device, dtype=torch.long)\n\n if mask is not None:\n assert x0 is not None\n img_orig = self.model.q_sample(\n x0, ts) # TODO: deterministic forward pass?\n img = img_orig * mask + (1. - mask) * img\n\n outs = self.p_sample_ddim(\n img,\n cond,\n ts,\n index=index,\n use_original_steps=ddim_use_original_steps,\n quantize_denoised=quantize_denoised,\n temperature=temperature,\n noise_dropout=noise_dropout,\n score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n **kwargs)\n img, pred_x0 = outs\n if callback: callback(i)\n if img_callback: img_callback(pred_x0, i)\n\n if index % log_every_t == 0 or index == total_steps - 1:\n intermediates['x_inter'].append(img)\n intermediates['pred_x0'].append(pred_x0)\n\n return img, intermediates\n\n @torch.no_grad()\n def p_sample_ddim(self,\n x,\n c,\n t,\n index,\n repeat_noise=False,\n use_original_steps=False,\n quantize_denoised=False,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None,\n dynamic_threshold=None,\n **kwargs):\n b, *_, device = *x.shape, x.device\n\n if unconditional_conditioning is None or unconditional_guidance_scale == 1.:\n model_output = self.model.apply_model(x, t, c)\n else:\n x_in = torch.cat([x] * 2)\n t_in = torch.cat([t] * 2)\n if isinstance(c, dict):\n assert isinstance(unconditional_conditioning, dict)\n c_in = dict()\n for k in c:\n if isinstance(c[k], list):\n c_in[k] = [\n torch.cat(\n [unconditional_conditioning[k][i], c[k][i]])\n for i in range(len(c[k]))\n ]\n elif isinstance(c[k], torch.Tensor):\n c_in[k] = torch.cat(\n [unconditional_conditioning[k], c[k]])\n else:\n assert c[k] == unconditional_conditioning[k]\n c_in[k] = c[k]\n elif isinstance(c, list):\n c_in = list()\n assert isinstance(unconditional_conditioning, list)\n for i in range(len(c)):\n c_in.append(\n torch.cat([unconditional_conditioning[i], c[i]]))\n else:\n c_in = torch.cat([unconditional_conditioning, c])\n model_uncond, model_t = self.model.apply_model(x_in, t_in,\n c_in).chunk(2)\n # model_t = self.model.apply_model(x, t, c, **kwargs)\n # model_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)\n model_output = model_uncond + unconditional_guidance_scale * (\n model_t - model_uncond)\n\n if self.model.parameterization == 'v':\n print('using v!')\n e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)\n else:\n e_t = model_output\n\n if score_corrector is not None:\n assert self.model.parameterization == 'eps', 'not implemented'\n e_t = score_corrector.modify_score(self.model, e_t, x, t, c,\n **corrector_kwargs)\n\n alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\n alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev\n sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas\n sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas\n # select parameters corresponding to the currently considered timestep\n a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)\n a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)\n sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)\n sqrt_one_minus_at = torch.full((b, 1, 1, 1),\n sqrt_one_minus_alphas[index],\n device=device)\n\n # current prediction for x_0\n if self.model.parameterization != 'v':\n pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\n else:\n pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)\n\n if quantize_denoised:\n pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)\n\n if dynamic_threshold is not None:\n raise NotImplementedError()\n\n # direction pointing to x_t\n dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t\n noise = sigma_t * noise_like(x.shape, device,\n repeat_noise) * temperature\n if noise_dropout > 0.:\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\n x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise\n return x_prev, pred_x0\n\n @torch.no_grad()\n def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):\n # fast, but does not allow for exact reconstruction\n # t serves as an index to gather the correct alphas\n if use_original_steps:\n sqrt_alphas_cumprod = self.sqrt_alphas_cumprod\n sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod\n else:\n sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)\n sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas\n\n if noise is None:\n noise = torch.randn_like(x0)\n return (\n extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0\n + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape)\n * noise)\n\n @torch.no_grad()\n def decode(self,\n x_latent,\n cond,\n t_start,\n unconditional_guidance_scale=1.0,\n unconditional_conditioning=None,\n use_original_steps=False,\n **kwargs):\n\n timesteps = np.arange(self.ddpm_num_timesteps\n ) if use_original_steps else self.ddim_timesteps\n timesteps = timesteps[:t_start]\n\n time_range = np.flip(timesteps)\n total_steps = timesteps.shape[0]\n\n iterator = tqdm(time_range, desc='Decoding image', total=total_steps)\n x_dec = x_latent\n for i, step in enumerate(iterator):\n index = total_steps - i - 1\n ts = torch.full((x_latent.shape[0], ),\n step,\n device=x_latent.device,\n dtype=torch.long)\n x_dec, _ = self.p_sample_ddim(\n x_dec,\n cond,\n ts,\n index=index,\n use_original_steps=use_original_steps,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n **kwargs)\n return x_dec"},{"identifier":"DPMSolverSampler","path":"ldm/models/diffusion/dpm_solver/sampler.py","snippet":"class DPMSolverSampler(object):\n\n def __init__(self, model, **kwargs):\n super().__init__()\n self.model = model\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.\n device)\n self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))\n\n def register_buffer(self, name, attr):\n if type(attr) == torch.Tensor:\n if attr.device != torch.device('cuda'):\n attr = attr.to(torch.device('cuda'))\n setattr(self, name, attr)\n\n @torch.no_grad()\n def sample(\n self,\n S,\n batch_size,\n shape,\n conditioning=None,\n callback=None,\n normals_sequence=None,\n img_callback=None,\n quantize_x0=False,\n eta=0.,\n mask=None,\n x0=None,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n verbose=True,\n x_T=None,\n log_every_t=100,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None,\n # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\n **kwargs):\n if conditioning is not None:\n if isinstance(conditioning, dict):\n cbs = conditioning[list(conditioning.keys())[0]].shape[0]\n if cbs != batch_size:\n print(\n f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'\n )\n else:\n if conditioning.shape[0] != batch_size:\n print(\n f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'\n )\n\n # sampling\n C, H, W = shape\n size = (batch_size, C, H, W)\n\n # print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')\n\n device = self.model.betas.device\n if x_T is None:\n img = torch.randn(size, device=device)\n else:\n img = x_T\n\n ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)\n\n model_fn = model_wrapper(\n lambda x, t, c: self.model.apply_model(x, t, c),\n ns,\n model_type='noise',\n guidance_type='classifier-free',\n condition=conditioning,\n unconditional_condition=unconditional_conditioning,\n guidance_scale=unconditional_guidance_scale,\n )\n\n dpm_solver = DPM_Solver(\n model_fn, ns, predict_x0=True, thresholding=False)\n x = dpm_solver.sample(\n img,\n steps=S,\n skip_type='time_uniform',\n method='multistep',\n order=2,\n lower_order_final=True)\n\n return x.to(device), None"},{"identifier":"PLMSSampler","path":"ldm/models/diffusion/plms.py","snippet":"class PLMSSampler(object):\n\n def __init__(self, model, schedule='linear', **kwargs):\n super().__init__()\n self.model = model\n self.ddpm_num_timesteps = model.num_timesteps\n self.schedule = schedule\n\n def register_buffer(self, name, attr):\n if type(attr) == torch.Tensor:\n if attr.device != torch.device('cuda'):\n attr = attr.to(torch.device('cuda'))\n setattr(self, name, attr)\n\n def make_schedule(self,\n ddim_num_steps,\n ddim_discretize='uniform',\n ddim_eta=0.,\n verbose=True):\n if ddim_eta != 0:\n raise ValueError('ddim_eta must be 0 for PLMS')\n self.ddim_timesteps = make_ddim_timesteps(\n ddim_discr_method=ddim_discretize,\n num_ddim_timesteps=ddim_num_steps,\n num_ddpm_timesteps=self.ddpm_num_timesteps,\n verbose=verbose)\n alphas_cumprod = self.model.alphas_cumprod\n assert alphas_cumprod.shape[\n 0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model\n .device)\n\n self.register_buffer('betas', to_torch(self.model.betas))\n self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\n self.register_buffer('alphas_cumprod_prev',\n to_torch(self.model.alphas_cumprod_prev))\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer('sqrt_alphas_cumprod',\n to_torch(np.sqrt(alphas_cumprod.cpu())))\n self.register_buffer('sqrt_one_minus_alphas_cumprod',\n to_torch(np.sqrt(1. - alphas_cumprod.cpu())))\n self.register_buffer('log_one_minus_alphas_cumprod',\n to_torch(np.log(1. - alphas_cumprod.cpu())))\n self.register_buffer('sqrt_recip_alphas_cumprod',\n to_torch(np.sqrt(1. / alphas_cumprod.cpu())))\n self.register_buffer('sqrt_recipm1_alphas_cumprod',\n to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))\n\n # ddim sampling parameters\n ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(\n alphacums=alphas_cumprod.cpu(),\n ddim_timesteps=self.ddim_timesteps,\n eta=ddim_eta,\n verbose=verbose)\n self.register_buffer('ddim_sigmas', ddim_sigmas)\n self.register_buffer('ddim_alphas', ddim_alphas)\n self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)\n self.register_buffer('ddim_sqrt_one_minus_alphas',\n np.sqrt(1. - ddim_alphas))\n sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\n (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) *\n (1 - self.alphas_cumprod / self.alphas_cumprod_prev))\n self.register_buffer('ddim_sigmas_for_original_num_steps',\n sigmas_for_original_sampling_steps)\n\n @torch.no_grad()\n def sample(\n self,\n S,\n batch_size,\n shape,\n conditioning=None,\n callback=None,\n normals_sequence=None,\n img_callback=None,\n quantize_x0=False,\n eta=0.,\n mask=None,\n x0=None,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n verbose=True,\n x_T=None,\n log_every_t=100,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None,\n # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\n **kwargs):\n if conditioning is not None:\n if isinstance(conditioning, dict):\n cbs = conditioning[list(conditioning.keys())[0]].shape[0]\n if cbs != batch_size:\n print(\n f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'\n )\n else:\n if conditioning.shape[0] != batch_size:\n print(\n f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'\n )\n\n self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\n # sampling\n C, H, W = shape\n size = (batch_size, C, H, W)\n print(f'Data shape for PLMS sampling is {size}')\n\n samples, intermediates = self.plms_sampling(\n conditioning,\n size,\n callback=callback,\n img_callback=img_callback,\n quantize_denoised=quantize_x0,\n mask=mask,\n x0=x0,\n ddim_use_original_steps=False,\n noise_dropout=noise_dropout,\n temperature=temperature,\n score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n x_T=x_T,\n log_every_t=log_every_t,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n )\n return samples, intermediates\n\n @torch.no_grad()\n def plms_sampling(\n self,\n cond,\n shape,\n x_T=None,\n ddim_use_original_steps=False,\n callback=None,\n timesteps=None,\n quantize_denoised=False,\n mask=None,\n x0=None,\n img_callback=None,\n log_every_t=100,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None,\n ):\n device = self.model.betas.device\n b = shape[0]\n if x_T is None:\n img = torch.randn(shape, device=device)\n else:\n img = x_T\n\n if timesteps is None:\n timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps\n elif timesteps is not None and not ddim_use_original_steps:\n subset_end = int(\n min(timesteps / self.ddim_timesteps.shape[0], 1)\n * self.ddim_timesteps.shape[0]) - 1\n timesteps = self.ddim_timesteps[:subset_end]\n\n intermediates = {'x_inter': [img], 'pred_x0': [img]}\n time_range = list(reversed(range(\n 0, timesteps))) if ddim_use_original_steps else np.flip(timesteps)\n total_steps = timesteps if ddim_use_original_steps else timesteps.shape[\n 0]\n print(f'Running PLMS Sampling with {total_steps} timesteps')\n\n iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)\n old_eps = []\n\n for i, step in enumerate(iterator):\n index = total_steps - i - 1\n ts = torch.full((b, ), step, device=device, dtype=torch.long)\n ts_next = torch.full((b, ),\n time_range[min(i + 1,\n len(time_range) - 1)],\n device=device,\n dtype=torch.long)\n\n if mask is not None:\n assert x0 is not None\n img_orig = self.model.q_sample(\n x0, ts) # TODO: deterministic forward pass?\n img = img_orig * mask + (1. - mask) * img\n\n outs = self.p_sample_plms(\n img,\n cond,\n ts,\n index=index,\n use_original_steps=ddim_use_original_steps,\n quantize_denoised=quantize_denoised,\n temperature=temperature,\n noise_dropout=noise_dropout,\n score_corrector=score_corrector,\n corrector_kwargs=corrector_kwargs,\n unconditional_guidance_scale=unconditional_guidance_scale,\n unconditional_conditioning=unconditional_conditioning,\n old_eps=old_eps,\n t_next=ts_next)\n img, pred_x0, e_t = outs\n old_eps.append(e_t)\n if len(old_eps) >= 4:\n old_eps.pop(0)\n if callback: callback(i)\n if img_callback: img_callback(pred_x0, i)\n\n if index % log_every_t == 0 or index == total_steps - 1:\n intermediates['x_inter'].append(img)\n intermediates['pred_x0'].append(pred_x0)\n\n return img, intermediates\n\n @torch.no_grad()\n def p_sample_plms(self,\n x,\n c,\n t,\n index,\n repeat_noise=False,\n use_original_steps=False,\n quantize_denoised=False,\n temperature=1.,\n noise_dropout=0.,\n score_corrector=None,\n corrector_kwargs=None,\n unconditional_guidance_scale=1.,\n unconditional_conditioning=None,\n old_eps=None,\n t_next=None):\n b, *_, device = *x.shape, x.device\n\n def get_model_output(x, t):\n if unconditional_conditioning is None or unconditional_guidance_scale == 1.:\n e_t = self.model.apply_model(x, t, c)\n else:\n x_in = torch.cat([x] * 2)\n t_in = torch.cat([t] * 2)\n c_in = torch.cat([unconditional_conditioning, c])\n e_t_uncond, e_t = self.model.apply_model(x_in, t_in,\n c_in).chunk(2)\n e_t = e_t_uncond + unconditional_guidance_scale * (\n e_t - e_t_uncond)\n\n if score_corrector is not None:\n assert self.model.parameterization == 'eps'\n e_t = score_corrector.modify_score(self.model, e_t, x, t, c,\n **corrector_kwargs)\n\n return e_t\n\n alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\n alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev\n sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas\n sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas\n\n def get_x_prev_and_pred_x0(e_t, index):\n # select parameters corresponding to the currently considered timestep\n a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)\n a_prev = torch.full((b, 1, 1, 1),\n alphas_prev[index],\n device=device)\n sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)\n sqrt_one_minus_at = torch.full((b, 1, 1, 1),\n sqrt_one_minus_alphas[index],\n device=device)\n\n # current prediction for x_0\n pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\n if quantize_denoised:\n pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)\n # direction pointing to x_t\n dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t\n noise = sigma_t * noise_like(x.shape, device,\n repeat_noise) * temperature\n if noise_dropout > 0.:\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\n x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise\n return x_prev, pred_x0\n\n e_t = get_model_output(x, t)\n if len(old_eps) == 0:\n # Pseudo Improved Euler (2nd order)\n x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)\n e_t_next = get_model_output(x_prev, t_next)\n e_t_prime = (e_t + e_t_next) / 2\n elif len(old_eps) == 1:\n # 2nd order Pseudo Linear Multistep (Adams-Bashforth)\n e_t_prime = (3 * e_t - old_eps[-1]) / 2\n elif len(old_eps) == 2:\n # 3nd order Pseudo Linear Multistep (Adams-Bashforth)\n e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12\n elif len(old_eps) >= 3:\n # 4nd order Pseudo Linear Multistep (Adams-Bashforth)\n e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2]\n - 9 * old_eps[-3]) / 24\n\n x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)\n\n return x_prev, pred_x0, e_t"},{"identifier":"CrossAttention","path":"ldm/modules/attention.py","snippet":"class CrossAttention(nn.Module):\n\n def __init__(self,\n query_dim,\n context_dim=None,\n heads=8,\n dim_head=64,\n dropout=0.):\n super().__init__()\n inner_dim = dim_head * heads\n context_dim = default(context_dim, query_dim)\n\n self.scale = dim_head**-0.5\n self.heads = heads\n\n self.to_q = nn.Linear(query_dim, inner_dim, bias=False)\n self.to_k = nn.Linear(context_dim, inner_dim, bias=False)\n self.to_v = nn.Linear(context_dim, inner_dim, bias=False)\n\n self.to_out = nn.Sequential(\n nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))\n\n def forward(self, x, context=None, mask=None):\n h = self.heads\n\n q = self.to_q(x)\n context = default(context, x)\n k = self.to_k(context)\n v = self.to_v(context)\n\n q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h),\n (q, k, v))\n\n sim = einsum('b i d, b j d -> b i j', q, k) * self.scale\n\n if exists(mask):\n mask = rearrange(mask, 'b ... -> b (...)')\n max_neg_value = -torch.finfo(sim.dtype).max\n mask = repeat(mask, 'b j -> (b h) () j', h=h)\n sim.masked_fill_(~mask, max_neg_value)\n\n # attention, what we cannot get enough of\n attn = sim.softmax(dim=-1)\n\n out = einsum('b i j, b j d -> b i d', attn, v)\n out = rearrange(out, '(b h) n d -> b n (h d)', h=h)\n return self.to_out(out)"},{"identifier":"extract_into_tensor","path":"ldm/modules/diffusionmodules/util.py","snippet":"def extract_into_tensor(a, t, x_shape):\n b, *_ = t.shape\n out = a.gather(-1, t)\n return out.reshape(b, *((1, ) * (len(x_shape) - 1)))"},{"identifier":"make_beta_schedule","path":"ldm/modules/diffusionmodules/util.py","snippet":"def make_beta_schedule(schedule,\n n_timestep,\n linear_start=1e-4,\n linear_end=2e-2,\n cosine_s=8e-3):\n if schedule == 'linear':\n betas = (\n torch.linspace(\n linear_start**0.5,\n linear_end**0.5,\n n_timestep,\n dtype=torch.float64)**2)\n\n elif schedule == 'cosine':\n timesteps = (\n torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep\n + cosine_s)\n alphas = timesteps / (1 + cosine_s) * np.pi / 2\n alphas = torch.cos(alphas).pow(2)\n alphas = alphas / alphas[0]\n betas = 1 - alphas[1:] / alphas[:-1]\n betas = np.clip(betas, a_min=0, a_max=0.999)\n\n elif schedule == 'sqrt_linear':\n betas = torch.linspace(\n linear_start, linear_end, n_timestep, dtype=torch.float64)\n elif schedule == 'sqrt':\n betas = torch.linspace(\n linear_start, linear_end, n_timestep, dtype=torch.float64)**0.5\n else:\n raise ValueError(f\"schedule '{schedule}' unknown.\")\n return betas.numpy()"},{"identifier":"noise_like","path":"ldm/modules/diffusionmodules/util.py","snippet":"def noise_like(shape, device, repeat=False):\n repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(\n shape[0], *((1, ) * (len(shape) - 1)))\n noise = lambda: torch.randn(shape, device=device)\n return repeat_noise() if repeat else noise()"},{"identifier":"DiagonalGaussianDistribution","path":"ldm/modules/distributions/distributions.py","snippet":"class DiagonalGaussianDistribution(object):\n\n def __init__(self, parameters, deterministic=False):\n self.parameters = parameters\n self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)\n self.logvar = torch.clamp(self.logvar, -30.0, 20.0)\n self.deterministic = deterministic\n self.std = torch.exp(0.5 * self.logvar)\n self.var = torch.exp(self.logvar)\n if self.deterministic:\n self.var = self.std = torch.zeros_like(\n self.mean).to(device=self.parameters.device)\n\n def sample(self):\n x = self.mean + self.std * torch.randn(\n self.mean.shape).to(device=self.parameters.device)\n return x\n\n def kl(self, other=None):\n if self.deterministic:\n return torch.Tensor([0.])\n else:\n if other is None:\n return 0.5 * torch.sum(\n torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,\n dim=[1, 2, 3])\n else:\n return 0.5 * torch.sum(\n torch.pow(self.mean - other.mean, 2) / other.var\n + self.var / other.var - 1.0 - self.logvar + other.logvar,\n dim=[1, 2, 3])\n\n def nll(self, sample, dims=[1, 2, 3]):\n if self.deterministic:\n return torch.Tensor([0.])\n logtwopi = np.log(2.0 * np.pi)\n return 0.5 * torch.sum(\n logtwopi + self.logvar\n + torch.pow(sample - self.mean, 2) / self.var,\n dim=dims)\n\n def mode(self):\n return self.mean"},{"identifier":"normal_kl","path":"ldm/modules/distributions/distributions.py","snippet":"def normal_kl(mean1, logvar1, mean2, logvar2):\n \"\"\"\n source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12\n Compute the KL divergence between two gaussians.\n Shapes are automatically broadcasted, so batches can be compared to\n scalars, among other use cases.\n \"\"\"\n tensor = None\n for obj in (mean1, logvar1, mean2, logvar2):\n if isinstance(obj, torch.Tensor):\n tensor = obj\n break\n assert tensor is not None, 'at least one argument must be a Tensor'\n\n # Force variances to be Tensors. Broadcasting helps convert scalars to\n # Tensors, but it does not work for torch.exp().\n logvar1, logvar2 = [\n x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)\n for x in (logvar1, logvar2)\n ]\n\n return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) +\n ((mean1 - mean2)**2) * torch.exp(-logvar2))"},{"identifier":"LitEma","path":"ldm/modules/ema.py","snippet":"class LitEma(nn.Module):\n\n def __init__(self, model, decay=0.9999, use_num_upates=True):\n super().__init__()\n if decay < 0.0 or decay > 1.0:\n raise ValueError('Decay must be between 0 and 1')\n\n self.m_name2s_name = {}\n self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))\n self.register_buffer(\n 'num_updates',\n torch.tensor(0, dtype=torch.int)\n if use_num_upates else torch.tensor(-1, dtype=torch.int))\n\n for name, p in model.named_parameters():\n if p.requires_grad:\n #remove as '.'-character is not allowed in buffers\n s_name = name.replace('.', '')\n self.m_name2s_name.update({name: s_name})\n self.register_buffer(s_name, p.clone().detach().data)\n\n self.collected_params = []\n\n def forward(self, model):\n decay = self.decay\n\n if self.num_updates >= 0:\n self.num_updates += 1\n decay = min(self.decay, (1 + self.num_updates) /\n (10 + self.num_updates))\n\n one_minus_decay = 1.0 - decay\n\n with torch.no_grad():\n m_param = dict(model.named_parameters())\n shadow_params = dict(self.named_buffers())\n\n for key in m_param:\n if m_param[key].requires_grad:\n sname = self.m_name2s_name[key]\n shadow_params[sname] = shadow_params[sname].type_as(\n m_param[key])\n shadow_params[sname].sub_(\n one_minus_decay *\n (shadow_params[sname] - m_param[key]))\n else:\n assert not key in self.m_name2s_name\n\n def copy_to(self, model):\n m_param = dict(model.named_parameters())\n shadow_params = dict(self.named_buffers())\n for key in m_param:\n if m_param[key].requires_grad:\n m_param[key].data.copy_(\n shadow_params[self.m_name2s_name[key]].data)\n else:\n assert not key in self.m_name2s_name\n\n def store(self, parameters):\n \"\"\"\n Save the current parameters for restoring later.\n Args:\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n temporarily stored.\n \"\"\"\n self.collected_params = [param.clone() for param in parameters]\n\n def restore(self, parameters):\n \"\"\"\n Restore the parameters stored with the `store` method.\n Useful to validate the model with EMA parameters without affecting the\n original optimization process. Store the parameters before the\n `copy_to` method. After validation (or model saving), use this to\n restore the former parameters.\n Args:\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\n updated with the stored parameters.\n \"\"\"\n for c_param, param in zip(self.collected_params, parameters):\n param.data.copy_(c_param.data)"},{"identifier":"count_params","path":"ldm/util.py","snippet":"def count_params(model, verbose=False):\n total_params = sum(p.numel() for p in model.parameters())\n if verbose:\n print(\n f'{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.'\n )\n return total_params"},{"identifier":"default","path":"ldm/util.py","snippet":"def default(val, d):\n if exists(val):\n return val\n return d() if isfunction(d) else d"},{"identifier":"exists","path":"ldm/util.py","snippet":"def exists(x):\n return x is not None"},{"identifier":"filter_nan_loss","path":"ldm/util.py","snippet":"def filter_nan_loss(loss):\n fake_loss = torch.isnan(loss)\n loss = loss[torch.logical_not(fake_loss)]\n\n if loss.shape[0] == 0:\n return loss.sum()\n else:\n return loss"},{"identifier":"instantiate_from_config","path":"ldm/util.py","snippet":"def instantiate_from_config(config):\n if not 'target' in config:\n\n print(config)\n if config == '__is_first_stage__':\n return None\n elif config == '__is_unconditional__':\n return None\n raise KeyError('Expected key `target` to instantiate.')\n return get_obj_from_str(config['target'])(**config.get('params', dict()))"},{"identifier":"isimage","path":"ldm/util.py","snippet":"def isimage(x):\n if not isinstance(x, torch.Tensor):\n return False\n return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)"},{"identifier":"ismap","path":"ldm/util.py","snippet":"def ismap(x):\n if not isinstance(x, torch.Tensor):\n return False\n return (len(x.shape) == 4) and (x.shape[1] > 3)"},{"identifier":"log_txt_as_img","path":"ldm/util.py","snippet":"def log_txt_as_img(wh, xc, size=20):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new('RGB', wh, color='white')\n draw = ImageDraw.Draw(txt)\n font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)\n nc = int(10 * (wh[0] / 256))\n lines = '\\n'.join(xc[bi][start:start + nc]\n for start in range(0, len(xc[bi]), nc))\n\n try:\n draw.text((0, 0), lines, fill='black', font=font)\n except UnicodeEncodeError:\n print('Cant encode string for logging. Skipping.')\n\n txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0\n txts.append(txt)\n txts = np.stack(txts)\n txts = torch.tensor(txts)\n return txts"},{"identifier":"mean_flat","path":"ldm/util.py","snippet":"def mean_flat(tensor):\n \"\"\"\n https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86\n Take the mean over all non-batch dimensions.\n \"\"\"\n return tensor.mean(dim=list(range(1, len(tensor.shape))))"}],"string":"[\n {\n \"identifier\": \"AutoencoderKL\",\n \"path\": \"ldm/models/autoencoder.py\",\n \"snippet\": \"class AutoencoderKL(pl.LightningModule):\\n\\n def __init__(self,\\n ddconfig,\\n lossconfig,\\n embed_dim,\\n ckpt_path=None,\\n ignore_keys=[],\\n image_key='image',\\n colorize_nlabels=None,\\n monitor=None,\\n prior_model=None,\\n prior_normal=None,\\n using_rgb=True):\\n super().__init__()\\n self.image_key = image_key\\n self.encoder = Encoder(**ddconfig)\\n self.decoder = Decoder(**ddconfig)\\n self.loss = instantiate_from_config(lossconfig)\\n self.prior_model = prior_model\\n self.using_rgb = using_rgb\\n\\n assert ddconfig['double_z']\\n self.quant_conv = torch.nn.Conv2d(2 * ddconfig['z_channels'],\\n 2 * embed_dim, 1)\\n self.post_quant_conv = torch.nn.Conv2d(embed_dim,\\n ddconfig['z_channels'], 1)\\n self.embed_dim = embed_dim\\n if colorize_nlabels is not None:\\n assert type(colorize_nlabels) == int\\n self.register_buffer('colorize',\\n torch.randn(3, colorize_nlabels, 1, 1))\\n if monitor is not None:\\n self.monitor = monitor\\n\\n if prior_model is not None:\\n self.prior_model = instantiate_from_config(prior_model)\\n if prior_normal is not None:\\n self.prior_normal = instantiate_from_config(prior_normal)\\n\\n if ckpt_path is not None:\\n self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)\\n\\n def init_from_ckpt(self, path, ignore_keys=list()):\\n try:\\n sd = torch.load(path, map_location='cpu')['state_dict']\\n except:\\n sd = torch.load(path, map_location='cpu')\\n\\n keys = list(sd.keys())\\n for k in keys:\\n for ik in ignore_keys:\\n if k.startswith(ik):\\n print('Deleting key {} from state_dict.'.format(k))\\n del sd[k]\\n m, u = self.load_state_dict(sd, strict=False)\\n if len(m) > 0:\\n print('missing keys:')\\n print(m)\\n if len(u) > 0:\\n print('unexpected keys:')\\n print(u)\\n\\n print(f'Restored from {path}')\\n\\n def encode(self, x):\\n h = self.encoder(x)\\n moments = self.quant_conv(h)\\n posterior = DiagonalGaussianDistribution(moments)\\n return posterior\\n\\n def decode(self, z):\\n z = self.post_quant_conv(z)\\n dec = self.decoder(z)\\n return dec\\n\\n def prior_to_eval(self):\\n\\n if self.prior_model is not None:\\n self.prior_model.eval()\\n\\n if self.prior_normal is not None:\\n self.prior_normal.eval()\\n\\n @torch.no_grad()\\n def prior_inference(self, inputs, prior_inputs):\\n # depth prior model\\n # midas or zoe is 384 model\\n prior_results = {}\\n\\n self.prior_to_eval()\\n\\n model_prior_results = self.prior_model(prior_inputs)\\n prior_results.update(model_prior_results)\\n\\n # using normal map\\n if not self.using_rgb:\\n normal_prior = self.prior_normal(prior_inputs)\\n prior_results.update(normal_prior)\\n\\n resize_prior_results = {}\\n _, __, h, w = inputs.shape\\n\\n for key in prior_results.keys():\\n resize_prior_results[key] = F.interpolate(\\n prior_results[key], (w, h), mode='bilinear')\\n\\n if self.using_rgb:\\n return torch.cat([inputs, resize_prior_results['depth']], dim=1)\\n else:\\n return torch.cat([\\n resize_prior_results['normal'], resize_prior_results['depth']\\n ],\\n dim=1)\\n\\n def forward(self, input, sample_posterior=True):\\n\\n posterior = self.encode(input)\\n if sample_posterior:\\n z = posterior.sample()\\n else:\\n z = posterior.mode()\\n dec = self.decode(z)\\n return dec, posterior\\n\\n def get_input(self, batch, k):\\n x = batch[k]\\n if len(x.shape) == 3:\\n x = x[..., None]\\n x = x.permute(0, 3, 1,\\n 2).to(memory_format=torch.contiguous_format).float()\\n return x\\n\\n def training_step(self, batch, batch_idx, optimizer_idx):\\n\\n inputs = self.get_input(batch, self.image_key)\\n if self.prior_model is not None:\\n inputs = self.prior_inference(inputs, batch['prior'])\\n\\n reconstructions, posterior = self(inputs)\\n\\n if optimizer_idx == 0:\\n # train encoder+decoder+logvar\\n aeloss, log_dict_ae = self.loss(\\n inputs,\\n reconstructions,\\n posterior,\\n optimizer_idx,\\n self.global_step,\\n last_layer=self.get_last_layer(),\\n split='train')\\n\\n self.log(\\n 'rec_loss',\\n log_dict_ae['train/rec_loss'],\\n prog_bar=True,\\n logger=True,\\n on_step=True,\\n on_epoch=True)\\n self.log(\\n 'aeloss',\\n aeloss,\\n prog_bar=True,\\n logger=True,\\n on_step=True,\\n on_epoch=True)\\n self.log_dict(\\n log_dict_ae,\\n prog_bar=False,\\n logger=True,\\n on_step=True,\\n on_epoch=False)\\n return aeloss\\n\\n if optimizer_idx == 1:\\n # train the discriminator\\n discloss, log_dict_disc = self.loss(\\n inputs,\\n reconstructions,\\n posterior,\\n optimizer_idx,\\n self.global_step,\\n last_layer=self.get_last_layer(),\\n split='train')\\n\\n self.log(\\n 'discloss',\\n discloss,\\n prog_bar=True,\\n logger=True,\\n on_step=True,\\n on_epoch=True)\\n self.log_dict(\\n log_dict_disc,\\n prog_bar=False,\\n logger=True,\\n on_step=True,\\n on_epoch=False)\\n return discloss\\n\\n def validation_step(self, batch, batch_idx):\\n inputs = self.get_input(batch, self.image_key)\\n reconstructions, posterior = self(inputs)\\n aeloss, log_dict_ae = self.loss(\\n inputs,\\n reconstructions,\\n posterior,\\n 0,\\n self.global_step,\\n last_layer=self.get_last_layer(),\\n split='val')\\n\\n discloss, log_dict_disc = self.loss(\\n inputs,\\n reconstructions,\\n posterior,\\n 1,\\n self.global_step,\\n last_layer=self.get_last_layer(),\\n split='val')\\n\\n self.log('val/rec_loss', log_dict_ae['val/rec_loss'])\\n self.log_dict(log_dict_ae)\\n self.log_dict(log_dict_disc)\\n return self.log_dict\\n\\n @torch.no_grad()\\n def test_step(self, batch, batch_idx):\\n pass\\n\\n @torch.no_grad()\\n def sample_imgs(self, batch):\\n '''using to test for sampling image\\n\\n '''\\n inputs = self.get_input(batch, self.image_key)\\n reconstructions, posterior = self(inputs)\\n\\n return {'samples': reconstructions}\\n\\n def configure_optimizers(self):\\n lr = self.learning_rate\\n opt_ae = torch.optim.Adam(\\n list(self.encoder.parameters()) + list(self.decoder.parameters())\\n + list(self.quant_conv.parameters())\\n + list(self.post_quant_conv.parameters()),\\n lr=lr,\\n betas=(0.5, 0.9))\\n opt_disc = torch.optim.Adam(\\n self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9))\\n\\n return [opt_ae, opt_disc], []\\n\\n def get_last_layer(self):\\n return self.decoder.conv_out.weight\\n\\n @torch.no_grad()\\n def log_images(self, batch, only_inputs=False, **kwargs):\\n log = dict()\\n x = self.get_input(batch, self.image_key)\\n x = x.to(self.device)\\n if not only_inputs:\\n xrec, posterior = self(x)\\n xrec = repeat(xrec[:, 0, ...], 'b h w -> b c h w', c=3)\\n\\n if x.shape[1] > 3:\\n # colorize with random projection\\n assert xrec.shape[1] > 3\\n x = self.to_rgb(x)\\n xrec = self.to_rgb(xrec)\\n samples = self.decode(torch.randn_like(posterior.sample()))\\n samples = repeat(samples[:, 0, ...], 'b h w -> b c h w', c=3)\\n log['samples'] = samples\\n\\n log['reconstructions'] = xrec\\n log['inputs'] = x\\n return log\\n\\n @torch.no_grad()\\n def log_rgbd(self, batch, only_inputs=False, **kwargs):\\n log = dict()\\n x = self.get_input(batch, self.image_key)\\n\\n if x.shape[1] == 3:\\n if self.prior_model is not None:\\n x = self.prior_inference(x, batch['prior'])\\n\\n x = x.to(self.device)\\n if not only_inputs:\\n xrec, posterior = self(x)\\n samples = self.decode(torch.randn_like(posterior.sample()))\\n log['samples'] = samples\\n log['reconstructions'] = xrec\\n log['inputs'] = x\\n return log\\n\\n def to_rgb(self, x):\\n assert self.image_key == 'segmentation'\\n if not hasattr(self, 'colorize'):\\n self.register_buffer('colorize',\\n torch.randn(3, x.shape[1], 1, 1).to(x))\\n x = F.conv2d(x, weight=self.colorize)\\n x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.\\n return x\"\n },\n {\n \"identifier\": \"IdentityFirstStage\",\n \"path\": \"ldm/models/autoencoder.py\",\n \"snippet\": \"class IdentityFirstStage(torch.nn.Module):\\n\\n def __init__(self, *args, vq_interface=False, **kwargs):\\n self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff\\n super().__init__()\\n\\n def encode(self, x, *args, **kwargs):\\n return x\\n\\n def decode(self, x, *args, **kwargs):\\n return x\\n\\n def quantize(self, x, *args, **kwargs):\\n if self.vq_interface:\\n return x, None, [None, None, None]\\n return x\\n\\n def forward(self, x, *args, **kwargs):\\n return x\"\n },\n {\n \"identifier\": \"VQModelInterface\",\n \"path\": \"ldm/models/autoencoder.py\",\n \"snippet\": \"class VQModelInterface(VQModel):\\n\\n def __init__(self, embed_dim, *args, **kwargs):\\n super().__init__(embed_dim=embed_dim, *args, **kwargs)\\n self.embed_dim = embed_dim\\n\\n def encode(self, x):\\n h = self.encoder(x)\\n h = self.quant_conv(h)\\n return h\\n\\n def decode(self, h, force_not_quantize=False):\\n # also go through quantization layer\\n if not force_not_quantize:\\n quant, emb_loss, info = self.quantize(h)\\n else:\\n quant = h\\n quant = self.post_quant_conv(quant)\\n dec = self.decoder(quant)\\n return dec\"\n },\n {\n \"identifier\": \"DDIMSampler\",\n \"path\": \"ldm/models/diffusion/ddim.py\",\n \"snippet\": \"class DDIMSampler(object):\\n\\n def __init__(self, model, schedule='linear', **kwargs):\\n super().__init__()\\n self.model = model\\n self.ddpm_num_timesteps = model.num_timesteps\\n self.schedule = schedule\\n\\n def register_buffer(self, name, attr):\\n if type(attr) == torch.Tensor:\\n if attr.device != torch.device('cuda'):\\n attr = attr.to(torch.device('cuda'))\\n setattr(self, name, attr)\\n\\n def make_schedule(self,\\n ddim_num_steps,\\n ddim_discretize='uniform',\\n ddim_eta=0.,\\n verbose=True):\\n self.ddim_timesteps = make_ddim_timesteps(\\n ddim_discr_method=ddim_discretize,\\n num_ddim_timesteps=ddim_num_steps,\\n num_ddpm_timesteps=self.ddpm_num_timesteps,\\n verbose=verbose)\\n alphas_cumprod = self.model.alphas_cumprod\\n assert alphas_cumprod.shape[\\n 0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'\\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model\\n .device)\\n\\n self.register_buffer('betas', to_torch(self.model.betas))\\n self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\\n self.register_buffer('alphas_cumprod_prev',\\n to_torch(self.model.alphas_cumprod_prev))\\n\\n # calculations for diffusion q(x_t | x_{t-1}) and others\\n self.register_buffer('sqrt_alphas_cumprod',\\n to_torch(np.sqrt(alphas_cumprod.cpu())))\\n self.register_buffer('sqrt_one_minus_alphas_cumprod',\\n to_torch(np.sqrt(1. - alphas_cumprod.cpu())))\\n self.register_buffer('log_one_minus_alphas_cumprod',\\n to_torch(np.log(1. - alphas_cumprod.cpu())))\\n self.register_buffer('sqrt_recip_alphas_cumprod',\\n to_torch(np.sqrt(1. / alphas_cumprod.cpu())))\\n self.register_buffer('sqrt_recipm1_alphas_cumprod',\\n to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))\\n\\n # ddim sampling parameters\\n ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(\\n alphacums=alphas_cumprod.cpu(),\\n ddim_timesteps=self.ddim_timesteps,\\n eta=ddim_eta,\\n verbose=verbose)\\n self.register_buffer('ddim_sigmas', ddim_sigmas)\\n self.register_buffer('ddim_alphas', ddim_alphas)\\n self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)\\n self.register_buffer('ddim_sqrt_one_minus_alphas',\\n np.sqrt(1. - ddim_alphas))\\n sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\\n (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) *\\n (1 - self.alphas_cumprod / self.alphas_cumprod_prev))\\n self.register_buffer('ddim_sigmas_for_original_num_steps',\\n sigmas_for_original_sampling_steps)\\n\\n @torch.no_grad()\\n def sample(\\n self,\\n S,\\n batch_size,\\n shape,\\n conditioning=None,\\n callback=None,\\n normals_sequence=None,\\n img_callback=None,\\n quantize_x0=False,\\n eta=0.,\\n mask=None,\\n x0=None,\\n temperature=1.,\\n noise_dropout=0.,\\n score_corrector=None,\\n corrector_kwargs=None,\\n verbose=True,\\n x_T=None,\\n log_every_t=100,\\n unconditional_guidance_scale=1.,\\n unconditional_conditioning=None,\\n # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\\n **kwargs):\\n if conditioning is not None:\\n if isinstance(conditioning, dict):\\n cbs = conditioning[list(conditioning.keys())[0]].shape[0]\\n if cbs != batch_size:\\n print(\\n f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'\\n )\\n else:\\n if conditioning.shape[0] != batch_size:\\n print(\\n f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'\\n )\\n\\n self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\\n # sampling\\n C, H, W = shape\\n size = (batch_size, C, H, W)\\n\\n samples, intermediates = self.ddim_sampling(\\n conditioning,\\n size,\\n callback=callback,\\n img_callback=img_callback,\\n quantize_denoised=quantize_x0,\\n mask=mask,\\n x0=x0,\\n ddim_use_original_steps=False,\\n noise_dropout=noise_dropout,\\n temperature=temperature,\\n score_corrector=score_corrector,\\n corrector_kwargs=corrector_kwargs,\\n x_T=x_T,\\n log_every_t=log_every_t,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning,\\n **kwargs)\\n return samples, intermediates\\n\\n @torch.no_grad()\\n def ddim_sampling(self,\\n cond,\\n shape,\\n x_T=None,\\n ddim_use_original_steps=False,\\n callback=None,\\n timesteps=None,\\n quantize_denoised=False,\\n mask=None,\\n x0=None,\\n img_callback=None,\\n log_every_t=100,\\n temperature=1.,\\n noise_dropout=0.,\\n score_corrector=None,\\n corrector_kwargs=None,\\n unconditional_guidance_scale=1.,\\n unconditional_conditioning=None,\\n **kwargs):\\n device = self.model.betas.device\\n b = shape[0]\\n if x_T is None:\\n img = torch.randn(shape, device=device)\\n else:\\n img = x_T\\n\\n if timesteps is None:\\n timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps\\n elif timesteps is not None and not ddim_use_original_steps:\\n subset_end = int(\\n min(timesteps / self.ddim_timesteps.shape[0], 1)\\n * self.ddim_timesteps.shape[0]) - 1\\n timesteps = self.ddim_timesteps[:subset_end]\\n\\n intermediates = {'x_inter': [img], 'pred_x0': [img]}\\n time_range = reversed(range(\\n 0, timesteps)) if ddim_use_original_steps else np.flip(timesteps)\\n total_steps = timesteps if ddim_use_original_steps else timesteps.shape[\\n 0]\\n\\n iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)\\n\\n for i, step in enumerate(iterator):\\n index = total_steps - i - 1\\n ts = torch.full((b, ), step, device=device, dtype=torch.long)\\n\\n if mask is not None:\\n assert x0 is not None\\n img_orig = self.model.q_sample(\\n x0, ts) # TODO: deterministic forward pass?\\n img = img_orig * mask + (1. - mask) * img\\n\\n outs = self.p_sample_ddim(\\n img,\\n cond,\\n ts,\\n index=index,\\n use_original_steps=ddim_use_original_steps,\\n quantize_denoised=quantize_denoised,\\n temperature=temperature,\\n noise_dropout=noise_dropout,\\n score_corrector=score_corrector,\\n corrector_kwargs=corrector_kwargs,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning,\\n **kwargs)\\n img, pred_x0 = outs\\n if callback: callback(i)\\n if img_callback: img_callback(pred_x0, i)\\n\\n if index % log_every_t == 0 or index == total_steps - 1:\\n intermediates['x_inter'].append(img)\\n intermediates['pred_x0'].append(pred_x0)\\n\\n return img, intermediates\\n\\n @torch.no_grad()\\n def p_sample_ddim(self,\\n x,\\n c,\\n t,\\n index,\\n repeat_noise=False,\\n use_original_steps=False,\\n quantize_denoised=False,\\n temperature=1.,\\n noise_dropout=0.,\\n score_corrector=None,\\n corrector_kwargs=None,\\n unconditional_guidance_scale=1.,\\n unconditional_conditioning=None,\\n dynamic_threshold=None,\\n **kwargs):\\n b, *_, device = *x.shape, x.device\\n\\n if unconditional_conditioning is None or unconditional_guidance_scale == 1.:\\n model_output = self.model.apply_model(x, t, c)\\n else:\\n x_in = torch.cat([x] * 2)\\n t_in = torch.cat([t] * 2)\\n if isinstance(c, dict):\\n assert isinstance(unconditional_conditioning, dict)\\n c_in = dict()\\n for k in c:\\n if isinstance(c[k], list):\\n c_in[k] = [\\n torch.cat(\\n [unconditional_conditioning[k][i], c[k][i]])\\n for i in range(len(c[k]))\\n ]\\n elif isinstance(c[k], torch.Tensor):\\n c_in[k] = torch.cat(\\n [unconditional_conditioning[k], c[k]])\\n else:\\n assert c[k] == unconditional_conditioning[k]\\n c_in[k] = c[k]\\n elif isinstance(c, list):\\n c_in = list()\\n assert isinstance(unconditional_conditioning, list)\\n for i in range(len(c)):\\n c_in.append(\\n torch.cat([unconditional_conditioning[i], c[i]]))\\n else:\\n c_in = torch.cat([unconditional_conditioning, c])\\n model_uncond, model_t = self.model.apply_model(x_in, t_in,\\n c_in).chunk(2)\\n # model_t = self.model.apply_model(x, t, c, **kwargs)\\n # model_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)\\n model_output = model_uncond + unconditional_guidance_scale * (\\n model_t - model_uncond)\\n\\n if self.model.parameterization == 'v':\\n print('using v!')\\n e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)\\n else:\\n e_t = model_output\\n\\n if score_corrector is not None:\\n assert self.model.parameterization == 'eps', 'not implemented'\\n e_t = score_corrector.modify_score(self.model, e_t, x, t, c,\\n **corrector_kwargs)\\n\\n alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\\n alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev\\n sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas\\n sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas\\n # select parameters corresponding to the currently considered timestep\\n a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)\\n a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)\\n sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)\\n sqrt_one_minus_at = torch.full((b, 1, 1, 1),\\n sqrt_one_minus_alphas[index],\\n device=device)\\n\\n # current prediction for x_0\\n if self.model.parameterization != 'v':\\n pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\\n else:\\n pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)\\n\\n if quantize_denoised:\\n pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)\\n\\n if dynamic_threshold is not None:\\n raise NotImplementedError()\\n\\n # direction pointing to x_t\\n dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t\\n noise = sigma_t * noise_like(x.shape, device,\\n repeat_noise) * temperature\\n if noise_dropout > 0.:\\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\\n x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise\\n return x_prev, pred_x0\\n\\n @torch.no_grad()\\n def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):\\n # fast, but does not allow for exact reconstruction\\n # t serves as an index to gather the correct alphas\\n if use_original_steps:\\n sqrt_alphas_cumprod = self.sqrt_alphas_cumprod\\n sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod\\n else:\\n sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)\\n sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas\\n\\n if noise is None:\\n noise = torch.randn_like(x0)\\n return (\\n extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0\\n + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape)\\n * noise)\\n\\n @torch.no_grad()\\n def decode(self,\\n x_latent,\\n cond,\\n t_start,\\n unconditional_guidance_scale=1.0,\\n unconditional_conditioning=None,\\n use_original_steps=False,\\n **kwargs):\\n\\n timesteps = np.arange(self.ddpm_num_timesteps\\n ) if use_original_steps else self.ddim_timesteps\\n timesteps = timesteps[:t_start]\\n\\n time_range = np.flip(timesteps)\\n total_steps = timesteps.shape[0]\\n\\n iterator = tqdm(time_range, desc='Decoding image', total=total_steps)\\n x_dec = x_latent\\n for i, step in enumerate(iterator):\\n index = total_steps - i - 1\\n ts = torch.full((x_latent.shape[0], ),\\n step,\\n device=x_latent.device,\\n dtype=torch.long)\\n x_dec, _ = self.p_sample_ddim(\\n x_dec,\\n cond,\\n ts,\\n index=index,\\n use_original_steps=use_original_steps,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning,\\n **kwargs)\\n return x_dec\"\n },\n {\n \"identifier\": \"DPMSolverSampler\",\n \"path\": \"ldm/models/diffusion/dpm_solver/sampler.py\",\n \"snippet\": \"class DPMSolverSampler(object):\\n\\n def __init__(self, model, **kwargs):\\n super().__init__()\\n self.model = model\\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.\\n device)\\n self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))\\n\\n def register_buffer(self, name, attr):\\n if type(attr) == torch.Tensor:\\n if attr.device != torch.device('cuda'):\\n attr = attr.to(torch.device('cuda'))\\n setattr(self, name, attr)\\n\\n @torch.no_grad()\\n def sample(\\n self,\\n S,\\n batch_size,\\n shape,\\n conditioning=None,\\n callback=None,\\n normals_sequence=None,\\n img_callback=None,\\n quantize_x0=False,\\n eta=0.,\\n mask=None,\\n x0=None,\\n temperature=1.,\\n noise_dropout=0.,\\n score_corrector=None,\\n corrector_kwargs=None,\\n verbose=True,\\n x_T=None,\\n log_every_t=100,\\n unconditional_guidance_scale=1.,\\n unconditional_conditioning=None,\\n # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\\n **kwargs):\\n if conditioning is not None:\\n if isinstance(conditioning, dict):\\n cbs = conditioning[list(conditioning.keys())[0]].shape[0]\\n if cbs != batch_size:\\n print(\\n f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'\\n )\\n else:\\n if conditioning.shape[0] != batch_size:\\n print(\\n f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'\\n )\\n\\n # sampling\\n C, H, W = shape\\n size = (batch_size, C, H, W)\\n\\n # print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')\\n\\n device = self.model.betas.device\\n if x_T is None:\\n img = torch.randn(size, device=device)\\n else:\\n img = x_T\\n\\n ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)\\n\\n model_fn = model_wrapper(\\n lambda x, t, c: self.model.apply_model(x, t, c),\\n ns,\\n model_type='noise',\\n guidance_type='classifier-free',\\n condition=conditioning,\\n unconditional_condition=unconditional_conditioning,\\n guidance_scale=unconditional_guidance_scale,\\n )\\n\\n dpm_solver = DPM_Solver(\\n model_fn, ns, predict_x0=True, thresholding=False)\\n x = dpm_solver.sample(\\n img,\\n steps=S,\\n skip_type='time_uniform',\\n method='multistep',\\n order=2,\\n lower_order_final=True)\\n\\n return x.to(device), None\"\n },\n {\n \"identifier\": \"PLMSSampler\",\n \"path\": \"ldm/models/diffusion/plms.py\",\n \"snippet\": \"class PLMSSampler(object):\\n\\n def __init__(self, model, schedule='linear', **kwargs):\\n super().__init__()\\n self.model = model\\n self.ddpm_num_timesteps = model.num_timesteps\\n self.schedule = schedule\\n\\n def register_buffer(self, name, attr):\\n if type(attr) == torch.Tensor:\\n if attr.device != torch.device('cuda'):\\n attr = attr.to(torch.device('cuda'))\\n setattr(self, name, attr)\\n\\n def make_schedule(self,\\n ddim_num_steps,\\n ddim_discretize='uniform',\\n ddim_eta=0.,\\n verbose=True):\\n if ddim_eta != 0:\\n raise ValueError('ddim_eta must be 0 for PLMS')\\n self.ddim_timesteps = make_ddim_timesteps(\\n ddim_discr_method=ddim_discretize,\\n num_ddim_timesteps=ddim_num_steps,\\n num_ddpm_timesteps=self.ddpm_num_timesteps,\\n verbose=verbose)\\n alphas_cumprod = self.model.alphas_cumprod\\n assert alphas_cumprod.shape[\\n 0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'\\n to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model\\n .device)\\n\\n self.register_buffer('betas', to_torch(self.model.betas))\\n self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))\\n self.register_buffer('alphas_cumprod_prev',\\n to_torch(self.model.alphas_cumprod_prev))\\n\\n # calculations for diffusion q(x_t | x_{t-1}) and others\\n self.register_buffer('sqrt_alphas_cumprod',\\n to_torch(np.sqrt(alphas_cumprod.cpu())))\\n self.register_buffer('sqrt_one_minus_alphas_cumprod',\\n to_torch(np.sqrt(1. - alphas_cumprod.cpu())))\\n self.register_buffer('log_one_minus_alphas_cumprod',\\n to_torch(np.log(1. - alphas_cumprod.cpu())))\\n self.register_buffer('sqrt_recip_alphas_cumprod',\\n to_torch(np.sqrt(1. / alphas_cumprod.cpu())))\\n self.register_buffer('sqrt_recipm1_alphas_cumprod',\\n to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))\\n\\n # ddim sampling parameters\\n ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(\\n alphacums=alphas_cumprod.cpu(),\\n ddim_timesteps=self.ddim_timesteps,\\n eta=ddim_eta,\\n verbose=verbose)\\n self.register_buffer('ddim_sigmas', ddim_sigmas)\\n self.register_buffer('ddim_alphas', ddim_alphas)\\n self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)\\n self.register_buffer('ddim_sqrt_one_minus_alphas',\\n np.sqrt(1. - ddim_alphas))\\n sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(\\n (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) *\\n (1 - self.alphas_cumprod / self.alphas_cumprod_prev))\\n self.register_buffer('ddim_sigmas_for_original_num_steps',\\n sigmas_for_original_sampling_steps)\\n\\n @torch.no_grad()\\n def sample(\\n self,\\n S,\\n batch_size,\\n shape,\\n conditioning=None,\\n callback=None,\\n normals_sequence=None,\\n img_callback=None,\\n quantize_x0=False,\\n eta=0.,\\n mask=None,\\n x0=None,\\n temperature=1.,\\n noise_dropout=0.,\\n score_corrector=None,\\n corrector_kwargs=None,\\n verbose=True,\\n x_T=None,\\n log_every_t=100,\\n unconditional_guidance_scale=1.,\\n unconditional_conditioning=None,\\n # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...\\n **kwargs):\\n if conditioning is not None:\\n if isinstance(conditioning, dict):\\n cbs = conditioning[list(conditioning.keys())[0]].shape[0]\\n if cbs != batch_size:\\n print(\\n f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'\\n )\\n else:\\n if conditioning.shape[0] != batch_size:\\n print(\\n f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'\\n )\\n\\n self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)\\n # sampling\\n C, H, W = shape\\n size = (batch_size, C, H, W)\\n print(f'Data shape for PLMS sampling is {size}')\\n\\n samples, intermediates = self.plms_sampling(\\n conditioning,\\n size,\\n callback=callback,\\n img_callback=img_callback,\\n quantize_denoised=quantize_x0,\\n mask=mask,\\n x0=x0,\\n ddim_use_original_steps=False,\\n noise_dropout=noise_dropout,\\n temperature=temperature,\\n score_corrector=score_corrector,\\n corrector_kwargs=corrector_kwargs,\\n x_T=x_T,\\n log_every_t=log_every_t,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning,\\n )\\n return samples, intermediates\\n\\n @torch.no_grad()\\n def plms_sampling(\\n self,\\n cond,\\n shape,\\n x_T=None,\\n ddim_use_original_steps=False,\\n callback=None,\\n timesteps=None,\\n quantize_denoised=False,\\n mask=None,\\n x0=None,\\n img_callback=None,\\n log_every_t=100,\\n temperature=1.,\\n noise_dropout=0.,\\n score_corrector=None,\\n corrector_kwargs=None,\\n unconditional_guidance_scale=1.,\\n unconditional_conditioning=None,\\n ):\\n device = self.model.betas.device\\n b = shape[0]\\n if x_T is None:\\n img = torch.randn(shape, device=device)\\n else:\\n img = x_T\\n\\n if timesteps is None:\\n timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps\\n elif timesteps is not None and not ddim_use_original_steps:\\n subset_end = int(\\n min(timesteps / self.ddim_timesteps.shape[0], 1)\\n * self.ddim_timesteps.shape[0]) - 1\\n timesteps = self.ddim_timesteps[:subset_end]\\n\\n intermediates = {'x_inter': [img], 'pred_x0': [img]}\\n time_range = list(reversed(range(\\n 0, timesteps))) if ddim_use_original_steps else np.flip(timesteps)\\n total_steps = timesteps if ddim_use_original_steps else timesteps.shape[\\n 0]\\n print(f'Running PLMS Sampling with {total_steps} timesteps')\\n\\n iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)\\n old_eps = []\\n\\n for i, step in enumerate(iterator):\\n index = total_steps - i - 1\\n ts = torch.full((b, ), step, device=device, dtype=torch.long)\\n ts_next = torch.full((b, ),\\n time_range[min(i + 1,\\n len(time_range) - 1)],\\n device=device,\\n dtype=torch.long)\\n\\n if mask is not None:\\n assert x0 is not None\\n img_orig = self.model.q_sample(\\n x0, ts) # TODO: deterministic forward pass?\\n img = img_orig * mask + (1. - mask) * img\\n\\n outs = self.p_sample_plms(\\n img,\\n cond,\\n ts,\\n index=index,\\n use_original_steps=ddim_use_original_steps,\\n quantize_denoised=quantize_denoised,\\n temperature=temperature,\\n noise_dropout=noise_dropout,\\n score_corrector=score_corrector,\\n corrector_kwargs=corrector_kwargs,\\n unconditional_guidance_scale=unconditional_guidance_scale,\\n unconditional_conditioning=unconditional_conditioning,\\n old_eps=old_eps,\\n t_next=ts_next)\\n img, pred_x0, e_t = outs\\n old_eps.append(e_t)\\n if len(old_eps) >= 4:\\n old_eps.pop(0)\\n if callback: callback(i)\\n if img_callback: img_callback(pred_x0, i)\\n\\n if index % log_every_t == 0 or index == total_steps - 1:\\n intermediates['x_inter'].append(img)\\n intermediates['pred_x0'].append(pred_x0)\\n\\n return img, intermediates\\n\\n @torch.no_grad()\\n def p_sample_plms(self,\\n x,\\n c,\\n t,\\n index,\\n repeat_noise=False,\\n use_original_steps=False,\\n quantize_denoised=False,\\n temperature=1.,\\n noise_dropout=0.,\\n score_corrector=None,\\n corrector_kwargs=None,\\n unconditional_guidance_scale=1.,\\n unconditional_conditioning=None,\\n old_eps=None,\\n t_next=None):\\n b, *_, device = *x.shape, x.device\\n\\n def get_model_output(x, t):\\n if unconditional_conditioning is None or unconditional_guidance_scale == 1.:\\n e_t = self.model.apply_model(x, t, c)\\n else:\\n x_in = torch.cat([x] * 2)\\n t_in = torch.cat([t] * 2)\\n c_in = torch.cat([unconditional_conditioning, c])\\n e_t_uncond, e_t = self.model.apply_model(x_in, t_in,\\n c_in).chunk(2)\\n e_t = e_t_uncond + unconditional_guidance_scale * (\\n e_t - e_t_uncond)\\n\\n if score_corrector is not None:\\n assert self.model.parameterization == 'eps'\\n e_t = score_corrector.modify_score(self.model, e_t, x, t, c,\\n **corrector_kwargs)\\n\\n return e_t\\n\\n alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas\\n alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev\\n sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas\\n sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas\\n\\n def get_x_prev_and_pred_x0(e_t, index):\\n # select parameters corresponding to the currently considered timestep\\n a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)\\n a_prev = torch.full((b, 1, 1, 1),\\n alphas_prev[index],\\n device=device)\\n sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)\\n sqrt_one_minus_at = torch.full((b, 1, 1, 1),\\n sqrt_one_minus_alphas[index],\\n device=device)\\n\\n # current prediction for x_0\\n pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\\n if quantize_denoised:\\n pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)\\n # direction pointing to x_t\\n dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t\\n noise = sigma_t * noise_like(x.shape, device,\\n repeat_noise) * temperature\\n if noise_dropout > 0.:\\n noise = torch.nn.functional.dropout(noise, p=noise_dropout)\\n x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise\\n return x_prev, pred_x0\\n\\n e_t = get_model_output(x, t)\\n if len(old_eps) == 0:\\n # Pseudo Improved Euler (2nd order)\\n x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)\\n e_t_next = get_model_output(x_prev, t_next)\\n e_t_prime = (e_t + e_t_next) / 2\\n elif len(old_eps) == 1:\\n # 2nd order Pseudo Linear Multistep (Adams-Bashforth)\\n e_t_prime = (3 * e_t - old_eps[-1]) / 2\\n elif len(old_eps) == 2:\\n # 3nd order Pseudo Linear Multistep (Adams-Bashforth)\\n e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12\\n elif len(old_eps) >= 3:\\n # 4nd order Pseudo Linear Multistep (Adams-Bashforth)\\n e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2]\\n - 9 * old_eps[-3]) / 24\\n\\n x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)\\n\\n return x_prev, pred_x0, e_t\"\n },\n {\n \"identifier\": \"CrossAttention\",\n \"path\": \"ldm/modules/attention.py\",\n \"snippet\": \"class CrossAttention(nn.Module):\\n\\n def __init__(self,\\n query_dim,\\n context_dim=None,\\n heads=8,\\n dim_head=64,\\n dropout=0.):\\n super().__init__()\\n inner_dim = dim_head * heads\\n context_dim = default(context_dim, query_dim)\\n\\n self.scale = dim_head**-0.5\\n self.heads = heads\\n\\n self.to_q = nn.Linear(query_dim, inner_dim, bias=False)\\n self.to_k = nn.Linear(context_dim, inner_dim, bias=False)\\n self.to_v = nn.Linear(context_dim, inner_dim, bias=False)\\n\\n self.to_out = nn.Sequential(\\n nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))\\n\\n def forward(self, x, context=None, mask=None):\\n h = self.heads\\n\\n q = self.to_q(x)\\n context = default(context, x)\\n k = self.to_k(context)\\n v = self.to_v(context)\\n\\n q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h),\\n (q, k, v))\\n\\n sim = einsum('b i d, b j d -> b i j', q, k) * self.scale\\n\\n if exists(mask):\\n mask = rearrange(mask, 'b ... -> b (...)')\\n max_neg_value = -torch.finfo(sim.dtype).max\\n mask = repeat(mask, 'b j -> (b h) () j', h=h)\\n sim.masked_fill_(~mask, max_neg_value)\\n\\n # attention, what we cannot get enough of\\n attn = sim.softmax(dim=-1)\\n\\n out = einsum('b i j, b j d -> b i d', attn, v)\\n out = rearrange(out, '(b h) n d -> b n (h d)', h=h)\\n return self.to_out(out)\"\n },\n {\n \"identifier\": \"extract_into_tensor\",\n \"path\": \"ldm/modules/diffusionmodules/util.py\",\n \"snippet\": \"def extract_into_tensor(a, t, x_shape):\\n b, *_ = t.shape\\n out = a.gather(-1, t)\\n return out.reshape(b, *((1, ) * (len(x_shape) - 1)))\"\n },\n {\n \"identifier\": \"make_beta_schedule\",\n \"path\": \"ldm/modules/diffusionmodules/util.py\",\n \"snippet\": \"def make_beta_schedule(schedule,\\n n_timestep,\\n linear_start=1e-4,\\n linear_end=2e-2,\\n cosine_s=8e-3):\\n if schedule == 'linear':\\n betas = (\\n torch.linspace(\\n linear_start**0.5,\\n linear_end**0.5,\\n n_timestep,\\n dtype=torch.float64)**2)\\n\\n elif schedule == 'cosine':\\n timesteps = (\\n torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep\\n + cosine_s)\\n alphas = timesteps / (1 + cosine_s) * np.pi / 2\\n alphas = torch.cos(alphas).pow(2)\\n alphas = alphas / alphas[0]\\n betas = 1 - alphas[1:] / alphas[:-1]\\n betas = np.clip(betas, a_min=0, a_max=0.999)\\n\\n elif schedule == 'sqrt_linear':\\n betas = torch.linspace(\\n linear_start, linear_end, n_timestep, dtype=torch.float64)\\n elif schedule == 'sqrt':\\n betas = torch.linspace(\\n linear_start, linear_end, n_timestep, dtype=torch.float64)**0.5\\n else:\\n raise ValueError(f\\\"schedule '{schedule}' unknown.\\\")\\n return betas.numpy()\"\n },\n {\n \"identifier\": \"noise_like\",\n \"path\": \"ldm/modules/diffusionmodules/util.py\",\n \"snippet\": \"def noise_like(shape, device, repeat=False):\\n repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(\\n shape[0], *((1, ) * (len(shape) - 1)))\\n noise = lambda: torch.randn(shape, device=device)\\n return repeat_noise() if repeat else noise()\"\n },\n {\n \"identifier\": \"DiagonalGaussianDistribution\",\n \"path\": \"ldm/modules/distributions/distributions.py\",\n \"snippet\": \"class DiagonalGaussianDistribution(object):\\n\\n def __init__(self, parameters, deterministic=False):\\n self.parameters = parameters\\n self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)\\n self.logvar = torch.clamp(self.logvar, -30.0, 20.0)\\n self.deterministic = deterministic\\n self.std = torch.exp(0.5 * self.logvar)\\n self.var = torch.exp(self.logvar)\\n if self.deterministic:\\n self.var = self.std = torch.zeros_like(\\n self.mean).to(device=self.parameters.device)\\n\\n def sample(self):\\n x = self.mean + self.std * torch.randn(\\n self.mean.shape).to(device=self.parameters.device)\\n return x\\n\\n def kl(self, other=None):\\n if self.deterministic:\\n return torch.Tensor([0.])\\n else:\\n if other is None:\\n return 0.5 * torch.sum(\\n torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,\\n dim=[1, 2, 3])\\n else:\\n return 0.5 * torch.sum(\\n torch.pow(self.mean - other.mean, 2) / other.var\\n + self.var / other.var - 1.0 - self.logvar + other.logvar,\\n dim=[1, 2, 3])\\n\\n def nll(self, sample, dims=[1, 2, 3]):\\n if self.deterministic:\\n return torch.Tensor([0.])\\n logtwopi = np.log(2.0 * np.pi)\\n return 0.5 * torch.sum(\\n logtwopi + self.logvar\\n + torch.pow(sample - self.mean, 2) / self.var,\\n dim=dims)\\n\\n def mode(self):\\n return self.mean\"\n },\n {\n \"identifier\": \"normal_kl\",\n \"path\": \"ldm/modules/distributions/distributions.py\",\n \"snippet\": \"def normal_kl(mean1, logvar1, mean2, logvar2):\\n \\\"\\\"\\\"\\n source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12\\n Compute the KL divergence between two gaussians.\\n Shapes are automatically broadcasted, so batches can be compared to\\n scalars, among other use cases.\\n \\\"\\\"\\\"\\n tensor = None\\n for obj in (mean1, logvar1, mean2, logvar2):\\n if isinstance(obj, torch.Tensor):\\n tensor = obj\\n break\\n assert tensor is not None, 'at least one argument must be a Tensor'\\n\\n # Force variances to be Tensors. Broadcasting helps convert scalars to\\n # Tensors, but it does not work for torch.exp().\\n logvar1, logvar2 = [\\n x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)\\n for x in (logvar1, logvar2)\\n ]\\n\\n return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) +\\n ((mean1 - mean2)**2) * torch.exp(-logvar2))\"\n },\n {\n \"identifier\": \"LitEma\",\n \"path\": \"ldm/modules/ema.py\",\n \"snippet\": \"class LitEma(nn.Module):\\n\\n def __init__(self, model, decay=0.9999, use_num_upates=True):\\n super().__init__()\\n if decay < 0.0 or decay > 1.0:\\n raise ValueError('Decay must be between 0 and 1')\\n\\n self.m_name2s_name = {}\\n self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))\\n self.register_buffer(\\n 'num_updates',\\n torch.tensor(0, dtype=torch.int)\\n if use_num_upates else torch.tensor(-1, dtype=torch.int))\\n\\n for name, p in model.named_parameters():\\n if p.requires_grad:\\n #remove as '.'-character is not allowed in buffers\\n s_name = name.replace('.', '')\\n self.m_name2s_name.update({name: s_name})\\n self.register_buffer(s_name, p.clone().detach().data)\\n\\n self.collected_params = []\\n\\n def forward(self, model):\\n decay = self.decay\\n\\n if self.num_updates >= 0:\\n self.num_updates += 1\\n decay = min(self.decay, (1 + self.num_updates) /\\n (10 + self.num_updates))\\n\\n one_minus_decay = 1.0 - decay\\n\\n with torch.no_grad():\\n m_param = dict(model.named_parameters())\\n shadow_params = dict(self.named_buffers())\\n\\n for key in m_param:\\n if m_param[key].requires_grad:\\n sname = self.m_name2s_name[key]\\n shadow_params[sname] = shadow_params[sname].type_as(\\n m_param[key])\\n shadow_params[sname].sub_(\\n one_minus_decay *\\n (shadow_params[sname] - m_param[key]))\\n else:\\n assert not key in self.m_name2s_name\\n\\n def copy_to(self, model):\\n m_param = dict(model.named_parameters())\\n shadow_params = dict(self.named_buffers())\\n for key in m_param:\\n if m_param[key].requires_grad:\\n m_param[key].data.copy_(\\n shadow_params[self.m_name2s_name[key]].data)\\n else:\\n assert not key in self.m_name2s_name\\n\\n def store(self, parameters):\\n \\\"\\\"\\\"\\n Save the current parameters for restoring later.\\n Args:\\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\\n temporarily stored.\\n \\\"\\\"\\\"\\n self.collected_params = [param.clone() for param in parameters]\\n\\n def restore(self, parameters):\\n \\\"\\\"\\\"\\n Restore the parameters stored with the `store` method.\\n Useful to validate the model with EMA parameters without affecting the\\n original optimization process. Store the parameters before the\\n `copy_to` method. After validation (or model saving), use this to\\n restore the former parameters.\\n Args:\\n parameters: Iterable of `torch.nn.Parameter`; the parameters to be\\n updated with the stored parameters.\\n \\\"\\\"\\\"\\n for c_param, param in zip(self.collected_params, parameters):\\n param.data.copy_(c_param.data)\"\n },\n {\n \"identifier\": \"count_params\",\n \"path\": \"ldm/util.py\",\n \"snippet\": \"def count_params(model, verbose=False):\\n total_params = sum(p.numel() for p in model.parameters())\\n if verbose:\\n print(\\n f'{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.'\\n )\\n return total_params\"\n },\n {\n \"identifier\": \"default\",\n \"path\": \"ldm/util.py\",\n \"snippet\": \"def default(val, d):\\n if exists(val):\\n return val\\n return d() if isfunction(d) else d\"\n },\n {\n \"identifier\": \"exists\",\n \"path\": \"ldm/util.py\",\n \"snippet\": \"def exists(x):\\n return x is not None\"\n },\n {\n \"identifier\": \"filter_nan_loss\",\n \"path\": \"ldm/util.py\",\n \"snippet\": \"def filter_nan_loss(loss):\\n fake_loss = torch.isnan(loss)\\n loss = loss[torch.logical_not(fake_loss)]\\n\\n if loss.shape[0] == 0:\\n return loss.sum()\\n else:\\n return loss\"\n },\n {\n \"identifier\": \"instantiate_from_config\",\n \"path\": \"ldm/util.py\",\n \"snippet\": \"def instantiate_from_config(config):\\n if not 'target' in config:\\n\\n print(config)\\n if config == '__is_first_stage__':\\n return None\\n elif config == '__is_unconditional__':\\n return None\\n raise KeyError('Expected key `target` to instantiate.')\\n return get_obj_from_str(config['target'])(**config.get('params', dict()))\"\n },\n {\n \"identifier\": \"isimage\",\n \"path\": \"ldm/util.py\",\n \"snippet\": \"def isimage(x):\\n if not isinstance(x, torch.Tensor):\\n return False\\n return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)\"\n },\n {\n \"identifier\": \"ismap\",\n \"path\": \"ldm/util.py\",\n \"snippet\": \"def ismap(x):\\n if not isinstance(x, torch.Tensor):\\n return False\\n return (len(x.shape) == 4) and (x.shape[1] > 3)\"\n },\n {\n \"identifier\": \"log_txt_as_img\",\n \"path\": \"ldm/util.py\",\n \"snippet\": \"def log_txt_as_img(wh, xc, size=20):\\n # wh a tuple of (width, height)\\n # xc a list of captions to plot\\n b = len(xc)\\n txts = list()\\n for bi in range(b):\\n txt = Image.new('RGB', wh, color='white')\\n draw = ImageDraw.Draw(txt)\\n font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)\\n nc = int(10 * (wh[0] / 256))\\n lines = '\\\\n'.join(xc[bi][start:start + nc]\\n for start in range(0, len(xc[bi]), nc))\\n\\n try:\\n draw.text((0, 0), lines, fill='black', font=font)\\n except UnicodeEncodeError:\\n print('Cant encode string for logging. Skipping.')\\n\\n txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0\\n txts.append(txt)\\n txts = np.stack(txts)\\n txts = torch.tensor(txts)\\n return txts\"\n },\n {\n \"identifier\": \"mean_flat\",\n \"path\": \"ldm/util.py\",\n \"snippet\": \"def mean_flat(tensor):\\n \\\"\\\"\\\"\\n https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86\\n Take the mean over all non-batch dimensions.\\n \\\"\\\"\\\"\\n return tensor.mean(dim=list(range(1, len(tensor.shape))))\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import pdb\nimport numpy as np\nimport pytorch_lightning as pl\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom contextlib import contextmanager\nfrom functools import partial\nfrom einops import rearrange, repeat\nfrom ldm.models.autoencoder import (AutoencoderKL, IdentityFirstStage,\n VQModelInterface)\nfrom ldm.models.diffusion.ddim import DDIMSampler\nfrom ldm.models.diffusion.dpm_solver import DPMSolverSampler\nfrom ldm.models.diffusion.plms import PLMSSampler\nfrom ldm.modules.attention import CrossAttention\nfrom ldm.modules.diffusionmodules.util import (extract_into_tensor,\n make_beta_schedule, noise_like)\nfrom ldm.modules.distributions.distributions import (\n DiagonalGaussianDistribution, normal_kl)\nfrom ldm.modules.ema import LitEma\nfrom ldm.util import (count_params, default, exists, filter_nan_loss,\n instantiate_from_config, isimage, ismap, log_txt_as_img,\n mean_flat)\nfrom torch.optim.lr_scheduler import LambdaLR\nfrom torchvision.utils import make_grid\nfrom tqdm import tqdm\n from pytorch_lightning.utilities.distributed import rank_zero_only\n from pytorch_lightning.utilities.rank_zero import rank_zero_only"},"token_num":{"kind":"number","value":15149,"string":"15,149"},"cropped_code":{"kind":"string","value":" else:\n return int(self.start_steps + self.start_steps\n * upper_bound(self.anneal_global_step, global_step))\n\n\nclass DDPM(pl.LightningModule):\n # classic DDPM with Gaussian diffusion, in image space\n def __init__(\n self,\n unet_config,\n timesteps=1000,\n beta_schedule='linear',\n loss_type='l2',\n ckpt_path=None,\n ignore_keys=[],\n load_only_unet=False,\n monitor='val/loss',\n use_ema=True,\n first_stage_key='image',\n image_size=256,\n channels=3,\n log_every_t=100,\n clip_denoised=True,\n linear_start=1e-4,\n linear_end=2e-2,\n cosine_s=8e-3,\n given_betas=None,\n original_elbo_weight=0.,\n v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta\n l_simple_weight=1.,\n conditioning_key=None,\n parameterization='eps', # all assuming fixed variance schedules\n scheduler_config=None,\n use_positional_encodings=False,\n learn_logvar=False,\n logvar_init=0.,\n anneal_t=False, # we find at the begining, smaller t, larger denoise mse loss.\n anneal_global_step=[],\n anneal_ratio=0.9,\n prior_model=None,\n prior_normal=None,\n input_keys=['rgb'],\n ):\n super().__init__()\n assert parameterization in [\n 'eps', 'x0'\n ], 'currently only supporting \"eps\" and \"x0\"'\n self.parameterization = parameterization\n print(\n f'{self.__class__.__name__}: Running in {self.parameterization}-prediction mode'\n )\n self.cond_stage_model = None\n self.clip_denoised = clip_denoised\n self.log_every_t = log_every_t\n self.first_stage_key = first_stage_key\n self.image_size = image_size # try conv?\n self.channels = channels\n self.use_positional_encodings = use_positional_encodings\n self.model = DiffusionWrapper(unet_config, conditioning_key)\n count_params(self.model, verbose=True)\n self.use_ema = use_ema\n if self.use_ema:\n self.model_ema = LitEma(self.model)\n print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')\n\n self.use_scheduler = scheduler_config is not None\n if self.use_scheduler:\n self.scheduler_config = scheduler_config\n\n self.v_posterior = v_posterior\n self.original_elbo_weight = original_elbo_weight\n self.l_simple_weight = l_simple_weight\n self.input_keys = input_keys\n\n if monitor is not None:\n self.monitor = monitor\n if ckpt_path is not None:\n self.init_from_ckpt(\n ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)\n\n self.register_schedule(\n given_betas=given_betas,\n beta_schedule=beta_schedule,\n timesteps=timesteps,\n linear_start=linear_start,\n linear_end=linear_end,\n cosine_s=cosine_s)\n\n self.loss_type = loss_type\n\n self.learn_logvar = learn_logvar\n self.logvar = torch.full(\n fill_value=logvar_init, size=(self.num_timesteps, ))\n if self.learn_logvar:\n self.logvar = nn.Parameter(self.logvar, requires_grad=True)\n\n ### anneal t function\n if not anneal_t:\n self.anneal_func = anneal_identity()\n else:\n self.anneal_func = anneal_warmup(anneal_ratio, anneal_global_step,\n self.num_timesteps)\n\n if prior_model is not None:\n self.prior_model = instantiate_from_config(prior_model)\n else:\n self.prior_model = None\n\n if prior_normal is not None:\n self.prior_normal = instantiate_from_config(prior_normal)\n else:\n self.prior_normal = None\n\n def register_schedule(self,\n given_betas=None,\n beta_schedule='linear',\n timesteps=1000,\n linear_start=1e-4,\n linear_end=2e-2,\n cosine_s=8e-3):\n"},"all_code":{"kind":"string","value":"\"\"\"\nwild mixture of\nhttps://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py\nhttps://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py\nhttps://github.com/CompVis/taming-transformers\n-- merci\n\"\"\"\n\n\ntry:\nexcept:\n\n__conditioning_keys__ = {\n 'concat': 'c_concat',\n 'crossattn': 'c_crossattn',\n 'adm': 'y'\n}\n\n\ndef disabled_train(self, mode=True):\n \"\"\"Overwrite model.train with this function to make sure train/eval mode\n does not change anymore.\"\"\"\n return self\n\n\ndef uniform_on_device(r1, r2, shape, device):\n return (r1 - r2) * torch.rand(*shape, device=device) + r2\n\n\nclass anneal_identity():\n\n def __call__(self, x, global_step):\n return x\n\n\ndef upper_bound(arr, key):\n left = 0\n right = len(arr)\n while left < right:\n mid = (left + right) >> 1\n if arr[mid] < key:\n left = mid + 1\n else:\n right = mid\n\n return left\n\n\nclass anneal_warmup():\n\n def __init__(self, anneal_ratio, anneal_global_step, num_steps):\n self.anneal_ratio = anneal_ratio\n self.anneal_global_step = anneal_global_step\n self.steps = num_steps // (len(anneal_global_step) + 1)\n self.start_steps = self.steps\n\n def __call__(self, x, global_step):\n\n if (torch.rand(1) > self.anneal_ratio).item():\n return x\n else:\n return int(self.start_steps + self.start_steps\n * upper_bound(self.anneal_global_step, global_step))\n\n\nclass DDPM(pl.LightningModule):\n # classic DDPM with Gaussian diffusion, in image space\n def __init__(\n self,\n unet_config,\n timesteps=1000,\n beta_schedule='linear',\n loss_type='l2',\n ckpt_path=None,\n ignore_keys=[],\n load_only_unet=False,\n monitor='val/loss',\n use_ema=True,\n first_stage_key='image',\n image_size=256,\n channels=3,\n log_every_t=100,\n clip_denoised=True,\n linear_start=1e-4,\n linear_end=2e-2,\n cosine_s=8e-3,\n given_betas=None,\n original_elbo_weight=0.,\n v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta\n l_simple_weight=1.,\n conditioning_key=None,\n parameterization='eps', # all assuming fixed variance schedules\n scheduler_config=None,\n use_positional_encodings=False,\n learn_logvar=False,\n logvar_init=0.,\n anneal_t=False, # we find at the begining, smaller t, larger denoise mse loss.\n anneal_global_step=[],\n anneal_ratio=0.9,\n prior_model=None,\n prior_normal=None,\n input_keys=['rgb'],\n ):\n super().__init__()\n assert parameterization in [\n 'eps', 'x0'\n ], 'currently only supporting \"eps\" and \"x0\"'\n self.parameterization = parameterization\n print(\n f'{self.__class__.__name__}: Running in {self.parameterization}-prediction mode'\n )\n self.cond_stage_model = None\n self.clip_denoised = clip_denoised\n self.log_every_t = log_every_t\n self.first_stage_key = first_stage_key\n self.image_size = image_size # try conv?\n self.channels = channels\n self.use_positional_encodings = use_positional_encodings\n self.model = DiffusionWrapper(unet_config, conditioning_key)\n count_params(self.model, verbose=True)\n self.use_ema = use_ema\n if self.use_ema:\n self.model_ema = LitEma(self.model)\n print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')\n\n self.use_scheduler = scheduler_config is not None\n if self.use_scheduler:\n self.scheduler_config = scheduler_config\n\n self.v_posterior = v_posterior\n self.original_elbo_weight = original_elbo_weight\n self.l_simple_weight = l_simple_weight\n self.input_keys = input_keys\n\n if monitor is not None:\n self.monitor = monitor\n if ckpt_path is not None:\n self.init_from_ckpt(\n ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)\n\n self.register_schedule(\n given_betas=given_betas,\n beta_schedule=beta_schedule,\n timesteps=timesteps,\n linear_start=linear_start,\n linear_end=linear_end,\n cosine_s=cosine_s)\n\n self.loss_type = loss_type\n\n self.learn_logvar = learn_logvar\n self.logvar = torch.full(\n fill_value=logvar_init, size=(self.num_timesteps, ))\n if self.learn_logvar:\n self.logvar = nn.Parameter(self.logvar, requires_grad=True)\n\n ### anneal t function\n if not anneal_t:\n self.anneal_func = anneal_identity()\n else:\n self.anneal_func = anneal_warmup(anneal_ratio, anneal_global_step,\n self.num_timesteps)\n\n if prior_model is not None:\n self.prior_model = instantiate_from_config(prior_model)\n else:\n self.prior_model = None\n\n if prior_normal is not None:\n self.prior_normal = instantiate_from_config(prior_normal)\n else:\n self.prior_normal = None\n\n def register_schedule(self,\n given_betas=None,\n beta_schedule='linear',\n timesteps=1000,\n linear_start=1e-4,\n linear_end=2e-2,\n cosine_s=8e-3):"},"next_line":{"kind":"string","value":" if exists(given_betas):"},"gold_snippet_index":{"kind":"number","value":15,"string":"15"},"created_at":{"kind":"string","value":"2023-12-06 07:29:34+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5289,"cells":{"repo_name":{"kind":"string","value":"RobertCsordas/moe_attention"},"file_path":{"kind":"string","value":"tasks/simple/language_model/transformer_lm_mixin.py"},"context":{"kind":"list like","value":[{"identifier":"TransformerLanguageModel","path":"models/transformer_language_model.py","snippet":"class TransformerLanguageModel(LoggingLayer, torch.nn.Module):\n def __init__(self, voc_size: int, embedding_size: Optional[int], state_size: int, dropout: float,\n tied_embedding: bool, layers: List[torch.nn.Module], n_prev_states: int,\n n_prev_states_test: Optional[int] = None, adaptive_cutoffs: List[int] = [],\n same_length_eval: bool = True, norm_before_output: bool = False,\n p_drop_layer: float = 0.0, use_last_state: bool = False, same_length: bool = False):\n\n super().__init__()\n\n self.embedding = torch.nn.Embedding(voc_size, embedding_size or state_size)\n torch.nn.init.kaiming_normal_(self.embedding.weight, mode=\"fan_in\", nonlinearity=\"linear\")\n\n self.shared_layers = all([la is layers[0] for la in layers])\n\n if embedding_size is None:\n self.embedding_adapter = lambda x: x\n else:\n self.embedding_adapter = torch.nn.Linear(embedding_size, state_size)\n\n self.dropout = torch.nn.Dropout(dropout)\n self.layers = layers\n self.unique_layers = torch.nn.ModuleList(unique_obejcts(layers))\n self.output_adapter = lambda x: x\n self.n_prev_states = n_prev_states\n self.n_prev_states_test = n_prev_states_test or n_prev_states\n self.same_length_eval = same_length_eval\n self.embedding_scale = math.sqrt(state_size)\n self.p_drop_layer = p_drop_layer\n self.use_last_state = use_last_state\n self.same_length = same_length\n self.iter = 0\n\n self.adaptive = bool(adaptive_cutoffs)\n\n out_proj_size = (embedding_size or state_size) if tied_embedding else state_size\n if self.adaptive:\n self.output = framework.layers.CustomAdaptiveLogSoftmaxWithLoss(\n out_proj_size, voc_size, adaptive_cutoffs, div_value=1,\n tied_to=self.embedding if tied_embedding else None)\n else:\n self.output = torch.nn.Linear(out_proj_size, voc_size)\n\n if norm_before_output:\n self.out_norm = torch.nn.LayerNorm(state_size)\n else:\n self.out_norm = lambda x: x\n\n if tied_embedding:\n if not self.adaptive:\n self.output.weight = self.embedding.weight\n if embedding_size is not None:\n self.output_adapter = torch.nn.Linear(state_size, embedding_size)\n\n @staticmethod\n def generate_history_mask(sz: int, device: torch.device) -> torch.Tensor:\n return torch.tril(torch.ones(sz, sz, dtype=torch.bool, device=device), diagonal=-1)\n\n def gen_output(self, x: torch.Tensor, target: Optional[torch.Tensor]) -> torch.Tensor:\n net = self.out_norm(x)\n net = self.output_adapter(net)\n net = self.dropout(net)\n\n if self.adaptive:\n net = self.output(net.transpose(0, 1), target)\n else:\n net = self.output(net.transpose(0, 1))\n\n return net\n\n def forward(self, x: torch.Tensor, target: Optional[torch.Tensor], state) -> Tuple[torch.Tensor, Any]:\n causality_mask = Transformer.generate_square_subsequent_mask(x.shape[0], x.device)\n\n net = self.dropout(self.embedding(x.T.long()))\n net = self.embedding_adapter(net)\n net = net * self.embedding_scale\n\n new_state = []\n features = [net]\n\n n_prev_states = self.n_prev_states if self.training else self.n_prev_states_test\n\n same_length = self.same_length or ((not self.training) and self.same_length_eval)\n if same_length and state is not None:\n causality_mask = [self.generate_history_mask(x.shape[0], x.device)] + \\\n [torch.zeros_like(causality_mask)] * (len(state[0]) - 1) + [causality_mask]\n causality_mask = torch.cat(causality_mask, -1)\n\n\n plot_cossim = (self.iter % 100 == 0 and self.training)\n for li, l in enumerate(self.layers):\n if n_prev_states > 0:\n if li == 0:\n # Pos offset should be constant for all layers\n pos_offset = sum(s.shape[1] for s in state[0]) if state is not None else 0\n\n # Concatenate the new state with the previous states\n li_r = -1 if self.use_last_state else li\n s = (state[li_r] + [net]) if state is not None else [net]\n attend_to = torch.cat(s, 1)\n\n if not self.use_last_state:\n s[-1] = s[-1].detach()\n new_state.append(s[-n_prev_states:])\n else:\n pos_offset = None\n attend_to = None\n\n net_o = l(net, mask=AttentionMask(None, causality_mask), attend_to=attend_to,\n pos_offset=pos_offset)\n\n if plot_cossim:\n features.append(net_o)\n\n with torch.no_grad():\n ndiff = torch.norm(net_o - net, p=2, dim=-1)\n n_in = torch.norm(net, p=2, dim=-1)\n self.log(f\"activation_norm/abs_update_layer_{li}\", ndiff.mean())\n self.log(f\"activation_norm/in_layer_{li}\", n_in.mean())\n self.log(f\"activation_norm/rel_update_layer_{li}\", (ndiff/n_in.clamp(min=torch.finfo(n_in.dtype).eps)).mean())\n\n if self.training and self.p_drop_layer > 0.0:\n net = torch.where(torch.rand_like(net_o[..., 0:1]) < self.p_drop_layer, net, net_o)\n else:\n net = net_o\n\n if self.use_last_state and n_prev_states > 0:\n # If we carry over the last state, save it here\n new_state = [((state[0] if state is not None else []) + [net.detach()])[-n_prev_states:]]\n\n if plot_cossim:\n with torch.no_grad():\n f_sample = [f.view(-1, f.shape[-1])[:1024] for f in features]\n f_sample_all = torch.stack(f_sample, -2)\n scores = framework.utils.cossim(f_sample_all, f_sample_all).mean(0)\n self.log(\"feature_cossim\", framework.visualize.plot.Heatmap(scores, range=(0, 1), textval=False))\n\n outs = F.softmax(self.gen_output(f_sample_all, target).transpose(0, 1), -1)\n scores = framework.utils.cossim(outs, outs).mean(0)\n self.log(\"out_dist_cossim\", framework.visualize.plot.Heatmap(scores, range=(0, 1), textval=False))\n\n real_out = outs[:, -1]\n for i in range(outs.shape[-2] - 1):\n self.log(f\"out_diff_{i}\", (outs[:, i] - real_out).norm(dim=-1, p=1).mean())\n\n del outs\n\n\n del features\n\n net = self.gen_output(net, target)\n self.iter += 1\n\n return net, new_state"},{"identifier":"task","path":"tasks/task_db.py","snippet":"def task(name: Optional[str] = None):\n def wrapper(cls):\n n = TASK_PREFIX + (name or camel_to_snake(cls.__name__))\n assert n not in TASKS, f\"Task {n} already exists\"\n TASKS[n] = cls\n return cls\n return wrapper"},{"identifier":"args","path":"tasks/task_db.py","snippet":"def args(fn):\n global ARGS_REGISTERS\n ARGS_REGISTERS.append(fn)\n return fn"},{"identifier":"RelativeTransformerEncoderLayer","path":"layers/transformer/relative_transformer.py","snippet":"class RelativeTransformerEncoderLayer(torch.nn.Module):\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation: ActivationFunction = F.relu,\n attention_dropout=0, test_pos_clamp: Optional[int] = None, drop_expand: bool = True,\n head_projection_size: Optional[int] = None, ln_after_attention: bool = True):\n super().__init__()\n self.ln_after_attention = ln_after_attention\n self.self_attn = FixedRelativeMultiheadAttention(\n d_model, nhead, dropout=attention_dropout, test_pos_clamp=test_pos_clamp,\n projection_size=head_projection_size)\n self.linear1 = torch.nn.Linear(d_model, dim_feedforward)\n self.dropout = torch.nn.Dropout(dropout) if drop_expand else lambda x: x\n self.linear2 = torch.nn.Linear(dim_feedforward, d_model)\n\n if ln_after_attention:\n self.norm1 = torch.nn.LayerNorm(d_model)\n self.norm2 = torch.nn.LayerNorm(d_model)\n self.dropout1 = torch.nn.Dropout(dropout)\n self.dropout2 = torch.nn.Dropout(dropout)\n\n self.activation = activation\n self.reset_parameters()\n\n def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None, attend_to: Optional[torch.Tensor] = None,\n pos_offset: Optional[int] = None) -> torch.Tensor:\n src2 = self.self_attn(src, attend_to if attend_to is not None else src, mask, pos_offset=pos_offset)\n src = src + self.dropout1(src2)\n src = self.norm1(src) if self.ln_after_attention else src\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))\n src = src + self.dropout2(src2)\n src = self.norm2(src)\n return src\n\n def reset_parameters(self):\n torch.nn.init.xavier_normal_(self.linear1.weight, gain=torch.nn.init.calculate_gain('relu')\n if self.activation is F.relu else 1.0)\n torch.nn.init.xavier_uniform_(self.linear2.weight)"},{"identifier":"PrelnRelativeTransformerEncoderLayer","path":"layers/transformer/relative_preln_transformer.py","snippet":"class PrelnRelativeTransformerEncoderLayer(RelativeTransformerEncoderLayer):\n is_preln = True\n\n def __init__(self, d_model, nhead, n_layers: int, dim_feedforward=2048, dropout=0.1,\n activation: ActivationFunction = F.relu, attention_dropout=0, test_pos_clamp: Optional[int] = None,\n drop_expand: bool = True, head_projection_size: Optional[int] = None):\n super().__init__(\n d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout,\n activation=activation, attention_dropout=attention_dropout, test_pos_clamp=test_pos_clamp,\n drop_expand=drop_expand, head_projection_size=head_projection_size)\n\n reset_prenorm_params(self, n_layers)\n\n def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None, attend_to: Optional[torch.Tensor] = None,\n pos_offset: Optional[int] = None) -> torch.Tensor:\n src2 = self.norm1(src)\n src2 = self.self_attn(src2, self.norm1(attend_to) if attend_to is not None else src2, mask,\n pos_offset=pos_offset)\n src = src + self.dropout1(src2)\n src2 = self.norm2(src)\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\n src = src + self.dropout2(src2)\n return src"},{"identifier":"RelativeMoeTransformerEncoderLayer","path":"layers/transformer/relative_moe_transformer.py","snippet":"class RelativeMoeTransformerEncoderLayer(LoggingLayer, torch.nn.Module):\n def __init__(self, d_model, nhead, n_experts: int, expert_size: int, n_layers: int,\n dropout=0.1, activation: ActivationFunction = F.relu, attention_dropout=0,\n test_pos_clamp: Optional[int] = None,\n dropout_mode: str = \"none\", selection_mode: str = \"add\",\n perplexity_reg: float = 0.0,\n n_heads: int = 1, norm_keys: bool = False, perplexity_reg_mode: str=\"step\",\n n_random: int = 0, reg_type: str = \"normal\",\n topk_mode: str = \"full\", head_projection_size: Optional[int] = None,\n activation_after_topk: bool = False,\n drop_parallel: bool = True,\n normalize_expert_sel_init: bool = False, norm_key_init: bool = False, norm_value_init: bool = False,\n identical_init: bool = False,\n sel_norm: str = \"none\",\n preln: bool = True, ln_affine: bool = True,\n moe_dropout_factor: float = 1.0,\n drop_expert: float = 0.0, sync_distributed: bool = True,\n modulation_amplitude: float = 0.5, moe_init_scale: float = 1.0,\n moe_att_n_experts: int = 4, moe_att_expert_dropout: Optional[float] = None,\n moe_att_selection_mode: str = \"sigmoid\",\n moe_att_k: Optional[int] = None, moe_att_ppl_reg: Optional[float] = None,\n q_expert: bool = True, k_expert: bool = True, v_expert: bool = True,\n o_expert: bool = True,\n v_projection_size: Optional[int] = None,\n qside_n_experts: Optional[int] = None,\n moe_attention: bool = False, moe_att_variant: str = \"full\",\n moe_att_shared_experts: bool = False,\n moe_att_kq_n_experts: Optional[int] = None, moe_att_separate_kq_sel: bool = False,\n moe_att_norm_init: bool = False, moe_att_same_sel: bool = False, moe_att_norm_retrieval: bool = False,\n rotate_fraction: float = 0.5, rope_base: float = 10000):\n super().__init__()\n self.preln = preln\n self.i = 0\n\n if moe_attention:\n if moe_att_variant == \"full\":\n self.self_attn = FullMoeRelativeAttention(\n d_model, nhead, dropout=attention_dropout,\n projection_size=head_projection_size, init_std_scale=math.sqrt(2 / n_layers) if preln else 1.0,\n n_experts=moe_att_n_experts,\n perplexity_reg=perplexity_reg if moe_att_ppl_reg is None else moe_att_ppl_reg,\n expert_dropout=drop_expert if moe_att_expert_dropout is None else moe_att_expert_dropout,\n selection_mode=moe_att_selection_mode, q_expert=q_expert, k_expert=k_expert, v_expert=v_expert,\n moe_k=n_heads if moe_att_k is None else moe_att_k, o_expert=o_expert, qside_n_experts=qside_n_experts,\n v_projection_size=v_projection_size, shared_experts=moe_att_shared_experts,\n kq_n_experts=moe_att_kq_n_experts, separate_kq_sel=moe_att_separate_kq_sel,\n normalize_init=moe_att_norm_init,\n same_sel=moe_att_same_sel, normalize_retrieval=moe_att_norm_retrieval,\n )\n elif moe_att_variant == \"full_rope\":\n self.self_attn = FullMoeRopeAttention(\n d_model, nhead, dropout=attention_dropout,\n projection_size=head_projection_size, init_std_scale=math.sqrt(2 / n_layers) if preln else 1.0,\n n_experts=moe_att_n_experts,\n perplexity_reg=perplexity_reg if moe_att_ppl_reg is None else moe_att_ppl_reg,\n expert_dropout=drop_expert if moe_att_expert_dropout is None else moe_att_expert_dropout,\n selection_mode=moe_att_selection_mode, q_expert=q_expert, k_expert=k_expert, v_expert=v_expert,\n moe_k=n_heads if moe_att_k is None else moe_att_k, o_expert=o_expert, qside_n_experts=qside_n_experts,\n v_projection_size=v_projection_size, shared_experts=moe_att_shared_experts,\n kq_n_experts=moe_att_kq_n_experts, separate_kq_sel=moe_att_separate_kq_sel,\n normalize_init=moe_att_norm_init, normalize_retrieval=moe_att_norm_retrieval,\n rotate_fraction=rotate_fraction, rope_base=rope_base,\n )\n else:\n raise ValueError(f\"Unknown attention variant {moe_att_variant}\")\n else:\n self.self_attn = FixedRelativeMultiheadAttention(\n d_model, nhead, dropout=attention_dropout, test_pos_clamp=test_pos_clamp,\n projection_size=head_projection_size)\n\n std_scale = math.sqrt(2.0 / n_layers) if preln else 1.0\n std_scale *= math.sqrt(moe_init_scale)\n\n self.pkm = MoE(\n d_model, n_experts, expert_size, dropout=dropout * moe_dropout_factor, dropout_mode=dropout_mode,\n weight_scale=std_scale, selection_mode=selection_mode,\n perplexity_reg=perplexity_reg, n_heads=n_heads,\n norm_keys=norm_keys, perplexity_reg_mode=perplexity_reg_mode, n_random=n_random,\n reg_type=reg_type, topk_mode=topk_mode,\n activation_after_topk=activation_after_topk,\n activation=activation,\n normalize_expert_sel_init=normalize_expert_sel_init, norm_key_init=norm_key_init,\n norm_value_init=norm_value_init, identical_init=identical_init,\n sel_norm=sel_norm,\n expert_dropout=drop_expert,\n sync_distributed=sync_distributed,\n modulation_amplitude=modulation_amplitude)\n\n self.norm1 = torch.nn.LayerNorm(d_model, elementwise_affine=ln_affine)\n self.norm2 = torch.nn.LayerNorm(d_model, elementwise_affine=ln_affine)\n self.dropout = torch.nn.Dropout(dropout)\n\n self.activation = activation\n self.drop_parallel = drop_parallel\n\n if preln:\n reset_prenorm_params(self, n_layers)\n\n def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None, attend_to: Optional[torch.Tensor] = None,\n pos_offset: Optional[int] = None) -> torch.Tensor:\n\n src2 = self.norm1(src) if self.preln else src\n src2 = self.self_attn(src2, self.norm1(attend_to) if attend_to is not None else src2, mask,\n pos_offset=pos_offset)\n src = src + self.dropout(src2)\n\n if self.preln:\n src2 = self.norm2(src)\n else:\n src = src2 = self.norm1(src)\n\n src3 = self.pkm(src2)\n\n src = src + self.dropout(src3)\n if not self.preln:\n src = self.norm2(src)\n return src"},{"identifier":"FastRopeTransformerEncoderLayer","path":"layers/transformer/fast_rope_transformer.py","snippet":"class FastRopeTransformerEncoderLayer(torch.nn.Module):\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation: ActivationFunction = F.relu,\n attention_dropout=0, drop_expand: bool = True,\n head_projection_size: Optional[int] = None, preln: bool = False, n_layers: Optional[int] = None,\n rotate_fraction: float = 0.5, rope_base: float = 10000):\n super().__init__()\n self.preln = preln\n self.self_attn = FastRopeAttention(\n d_model, nhead, dropout=attention_dropout,\n projection_size=head_projection_size, rotate_fraction=rotate_fraction,\n rope_base=rope_base)\n self.linear1 = torch.nn.Linear(d_model, dim_feedforward)\n self.dropout = torch.nn.Dropout(dropout) if drop_expand else lambda x: x\n self.linear2 = torch.nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = torch.nn.LayerNorm(d_model)\n self.norm2 = torch.nn.LayerNorm(d_model)\n self.dropout1 = torch.nn.Dropout(dropout)\n self.dropout2 = torch.nn.Dropout(dropout)\n\n self.activation = activation\n\n if preln:\n if n_layers is None:\n raise ValueError(\"n_layers must be specified when using preln\")\n reset_prenorm_params(self, n_layers)\n else:\n self.reset_parameters()\n\n def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None, attend_to: Optional[torch.Tensor] = None,\n pos_offset: Optional[int] = None) -> torch.Tensor:\n src2 = self.norm1(src) if self.preln else src\n src2 = self.self_attn(src2, self.norm1(attend_to) if attend_to is not None else src2, mask, pos_offset=pos_offset)\n src = src + self.dropout1(src2)\n\n if self.preln:\n src2 = self.norm2(src)\n else:\n src2 = src = self.norm1(src)\n\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\n src = src + self.dropout2(src2)\n\n if not self.preln:\n src = self.norm2(src)\n return src\n\n def reset_parameters(self):\n torch.nn.init.xavier_normal_(self.linear1.weight, gain=torch.nn.init.calculate_gain('relu')\n if self.activation is F.relu else 1.0)\n torch.nn.init.xavier_uniform_(self.linear2.weight)"},{"identifier":"MoeAttentionRelativeTransformerEncoderLayer","path":"layers/transformer/moe_attention_relative_transformer.py","snippet":"class MoeAttentionRelativeTransformerEncoderLayer(torch.nn.Module):\n def __init__(self, d_model, nhead, moe_att_n_experts, dim_feedforward=2048, dropout=0.1, activation: ActivationFunction = F.relu,\n attention_dropout=0, drop_expand: bool = True,\n head_projection_size: Optional[int] = None, preln: bool = False, n_layers: Optional[int] = None,\n att_perplexity_reg: float = 0.0, expert_dropout: float = 0.0, att_selection_mode=\"sigmoid\",\n attention_variant=\"moa\", q_expert: bool = True, k_expert: bool = True, v_expert: bool = True,\n o_expert: bool = True, moe_k: int = 2,\n norm_qk_score: bool = False, v_projection_size: Optional[int] = None, same_sel: bool = False,\n qside_n_experts: Optional[int] = None, shared_experts: bool = False,\n kq_n_experts: Optional[int] = None, separate_kq_sel: bool = False,\n cvloss: float = 0.0, switchloss: float = 0.0, zloss: float = 0.0,\n moa_mode: str = \"my\", rotate_fraction: float = 0.5, rope_base: float = 10000,\n moeatt_norm_init: bool = False):\n super().__init__()\n self.is_preln = preln\n if attention_variant not in {\"full\", \"full_rope\"} and (not q_expert):\n raise ValueError(\"q_expert can be disabled only when using qside attention\")\n\n if attention_variant == \"moa\":\n self.self_attn = MoA(\n d_model, nhead, dropout=attention_dropout,\n projection_size=head_projection_size, init_std_scale=math.sqrt(2 / n_layers) if preln else 1.0,\n n_experts=moe_att_n_experts, perplexity_reg=att_perplexity_reg, expert_dropout=expert_dropout,\n selection_mode=att_selection_mode, mode=moa_mode, cvloss=cvloss, switchloss=switchloss, zloss=zloss\n )\n elif attention_variant == \"full\":\n self.self_attn = FullMoeRelativeAttention(\n d_model, nhead, dropout=attention_dropout,\n projection_size=head_projection_size, init_std_scale=math.sqrt(2 / n_layers) if preln else 1.0,\n n_experts=moe_att_n_experts, perplexity_reg=att_perplexity_reg, expert_dropout=expert_dropout,\n selection_mode=att_selection_mode, q_expert=q_expert, k_expert=k_expert, v_expert=v_expert,\n norm_qk_score=norm_qk_score, v_projection_size=v_projection_size, same_sel=same_sel,\n o_expert=o_expert, moe_k=moe_k, qside_n_experts=qside_n_experts,\n shared_experts=shared_experts, kq_n_experts=kq_n_experts, separate_kq_sel=separate_kq_sel,\n normalize_init=moeatt_norm_init\n )\n elif attention_variant == \"full_rope\":\n self.self_attn = FullMoeRopeAttention(\n d_model, nhead, dropout=attention_dropout,\n projection_size=head_projection_size, init_std_scale=math.sqrt(2 / n_layers) if preln else 1.0,\n n_experts=moe_att_n_experts, perplexity_reg=att_perplexity_reg, expert_dropout=expert_dropout,\n selection_mode=att_selection_mode, q_expert=q_expert, k_expert=k_expert, v_expert=v_expert,\n norm_qk_score=norm_qk_score, v_projection_size=v_projection_size, same_sel=same_sel,\n o_expert=o_expert, moe_k=moe_k, qside_n_experts=qside_n_experts,\n shared_experts=shared_experts, kq_n_experts=kq_n_experts, separate_kq_sel=separate_kq_sel,\n rotate_fraction=rotate_fraction, rope_base=rope_base,\n normalize_init=moeatt_norm_init\n )\n else:\n raise ValueError(f\"Unknown attention variant: {attention_variant}\")\n\n self.linear1 = torch.nn.Linear(d_model, dim_feedforward)\n self.dropout = torch.nn.Dropout(dropout) if drop_expand else lambda x: x\n self.linear2 = torch.nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = torch.nn.LayerNorm(d_model)\n self.norm2 = torch.nn.LayerNorm(d_model)\n self.dropout1 = torch.nn.Dropout(dropout)\n self.dropout2 = torch.nn.Dropout(dropout)\n\n self.activation = activation\n\n if preln:\n if n_layers is None:\n raise ValueError(\"n_layers must be specified when using preln\")\n reset_prenorm_params(self, n_layers)\n else:\n self.reset_parameters()\n\n def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None, attend_to: Optional[torch.Tensor] = None,\n pos_offset: Optional[int] = None) -> torch.Tensor:\n src2 = self.norm1(src) if self.is_preln else src\n src2 = self.self_attn(src2, self.norm1(attend_to) if attend_to is not None else src2, mask, pos_offset=pos_offset)\n src = src + self.dropout1(src2)\n\n if self.is_preln:\n src2 = self.norm2(src)\n else:\n src2 = src = self.norm1(src)\n\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\n src = src + self.dropout2(src2)\n\n if not self.is_preln:\n src = self.norm2(src)\n return src\n\n def reset_parameters(self):\n torch.nn.init.xavier_normal_(self.linear1.weight, gain=torch.nn.init.calculate_gain('relu')\n if self.activation is F.relu else 1.0)\n torch.nn.init.xavier_uniform_(self.linear2.weight)"},{"identifier":"MoE","path":"layers/moe_layer.py","snippet":"class MoE(LoggingLayer, RegularizedLayer, OncePerIterLayer, torch.nn.Module):\n def __init__(self, dmodel: int, n_experts: int, expert_size: int, n_heads: int,\n dropout: float = 0, weight_scale: float = 1.0,\n dropout_mode: str = \"none\", selection_mode: str = \"sigmoid\", perplexity_reg: float = 0.0,\n norm_keys: bool = False,\n perplexity_reg_mode: str=\"step\", n_random: int = 0, reg_type: str = \"entropy\",\n topk_mode: str = \"full\", activation_after_topk: bool = False,\n activation = lambda x: F.relu(x, inplace=True),\n normalize_expert_sel_init: bool = False, norm_key_init: bool = False, norm_value_init: bool = False,\n identical_init: bool = False,\n rescale_normed: bool = False, sel_norm: str = \"none\",\n v_dim: Optional[int] = None,\n expert_dropout: float = 0.0,\n sync_distributed: bool = False,\n modulation_amplitude: float = 0.5,\n ppl_past_blocks: int = 0):\n\n super().__init__()\n self.k_dim = dmodel\n self.v_dim = v_dim if v_dim is not None else dmodel\n self.n_experts = n_experts\n self.expert_size = expert_size\n self.size = self.n_experts * self.expert_size\n self.dropout = dropout\n self.dropout_mode = dropout_mode\n self.selection_mode = selection_mode\n self.perplexity_reg = perplexity_reg\n self.k_vec_dim = self.k_dim\n self.n_heads = n_heads\n self.norm_keys = norm_keys\n self.perplexity_reg_mode = perplexity_reg_mode\n self.n_random = n_random\n self.reg_type = reg_type\n self.topk_mode = topk_mode\n self.activation_after_topk = activation_after_topk\n self.activation = activation\n self.weight_scale = weight_scale\n self.normalize_expert_sel_init = normalize_expert_sel_init\n self.norm_key_init = norm_key_init\n self.norm_value_init = norm_value_init\n self.identical_init = identical_init\n self.layer = 0\n self.initalized = False\n self.rescale_normed = rescale_normed\n self.sel_norm = sel_norm\n self.was_training = True\n self.expert_dropout = expert_dropout\n self.reg_counts = 0\n self.sync_distributed = sync_distributed and torch.distributed.is_initialized()\n self.modulation_amplitude = modulation_amplitude\n self.record_all_expert_sel_counts = False\n self.ppl_past_blocks = ppl_past_blocks\n self.blocks_for_ppl = []\n self.recorded_inputs = []\n\n self.coocurence = None\n\n assert self.selection_mode in {\"gate\", \"sigmoid\", \"sinkhorn\", \"sinkhorn2\", \"sinkmoid\", \"sinkmax\", \"sinkhorn_local\", \"mul\", \"sinkmoid2\", \"sinkmax2\"}\n assert self.perplexity_reg_mode in {\"step\", \"global\", \"time\", \"global_time\"}\n assert self.dropout_mode in {\"none\", \"score\"}\n assert self.reg_type in {\"perplexity\", \"variance\", \"entropy\", \"l2\", \"switch\"}\n assert self.topk_mode in {\"full\", \"l1_approx\", \"approx\"}\n assert self.sel_norm in {\"none\", \"cos\", \"input\", \"weights\"}\n\n self.register_buffer(\"iter\", torch.tensor(0, dtype=torch.int64), persistent=False)\n\n if selection_mode in {\"mul\"} and activation_after_topk:\n raise ValueError(\"Activation after topk is not supported with mul selection\")\n\n self.keys = torch.nn.Parameter(torch.empty(self.n_experts, self.k_vec_dim, self.expert_size))\n\n self.values = torch.nn.Parameter(torch.empty(self.n_experts, self.expert_size, self.v_dim))\n\n self.expert_sel = torch.nn.Parameter(torch.empty(self.n_experts, self.k_vec_dim))\n self.sel = lambda x: F.linear(x, self.expert_sel)\n\n torch.nn.init.normal_(self.expert_sel, std=self.k_vec_dim ** -0.5 * weight_scale)\n torch.nn.init.normal_(self.keys, std=dmodel ** -0.5 * weight_scale)\n torch.nn.init.normal_(self.values, std=self.size ** -0.5 * weight_scale)\n self.sel_hist = []\n self.index_sel_counts = 0\n self.index_sel_norm = 0\n\n self.index_sel_counts_100 = 0\n self.index_sel_norm_100 = 0\n\n self.sel_count_log = None\n\n self.all_expert_sel_counts = []\n self.all_expert_sel_soft = []\n\n self.register_buffer(\"kv_sel_counts\", torch.zeros(self.n_experts, self.expert_size), persistent=False)\n self.register_buffer(\"kv_sel_counts_100\", torch.zeros_like(self.kv_sel_counts))\n\n if self.rescale_normed and self.sel_norm != \"none\":\n self.sel_scale = torch.nn.Parameter(torch.ones([1]))\n else:\n self.sel_scale = 1.0\n\n self.register_buffer(\"seq\", torch.arange(max(self.n_heads, self.n_experts, self.k_dim, self.v_dim), dtype=torch.long), persistent=False)\n self.regroup_weights()\n\n if self.ppl_past_blocks > 0 and self.reg_type not in {\"perplexity\", \"entropy\"}:\n print(f\"Warning: ppl_past_blocks>0 (currently {self.ppl_past_blocks}) is only supported with perplexity and entropy regularization\")\n\n def keys_to_logical_order(self, keys: torch.Tensor) -> torch.Tensor:\n k = keys.view(self.n_experts, self.k_vec_dim, self.expert_size)\n return k.permute(0, 2, 1).contiguous().view(-1, self.k_vec_dim)\n\n def keys_from_logical_order(self, keys: torch.Tensor) -> torch.Tensor:\n return keys.view(self.n_experts, self.expert_size, self.k_vec_dim).permute(0, 2, 1).contiguous().view(self.n_experts * self.k_vec_dim, self.expert_size)\n\n def renorm_keep_std(self, weight: torch.Tensor, dim: int = 0):\n with torch.no_grad():\n std = weight.std()\n weight.div_(weight.norm(dim=dim, keepdim=True))\n weight.mul_(std / weight.std())\n\n def regroup_weights(self) -> Optional[torch.Tensor]:\n with torch.no_grad():\n if self.norm_key_init:\n self.renorm_keep_std(self.keys.view(self.n_experts, self.k_vec_dim, self.expert_size), dim=1)\n\n if self.norm_value_init:\n self.renorm_keep_std(self.values, dim=1)\n\n if self.identical_init:\n k = self.keys.view(self.n_experts, self.k_vec_dim, self.expert_size)\n self.keys.set_(k[:1].expand_as(k).reshape_as(self.keys))\n\n v = self.values.view(self.n_experts, self.expert_size, self.v_dim)\n self.values.set_(v[:1].expand_as(v).reshape_as(self.values))\n\n if self.normalize_expert_sel_init:\n self.renorm_keep_std(self.expert_sel, dim=1)\n\n def ani(self, x: torch.Tensor) -> torch.Tensor:\n assert x.ndim == 2\n chunk_size = 32\n\n xnorm = F.normalize(x, 2, dim=-1)\n\n accu = 0\n for i in range(0, x.shape[0], chunk_size):\n a = xnorm[i: i + chunk_size]\n sims = xnorm @ a.T\n sims[i : i + chunk_size].fill_diagonal_(0)\n accu += sims.sum()\n\n return accu / (x.shape[0] * (x.shape[0] - 1))\n\n def log_expert_sel_usage(self, prefix: str, channel_sel_counts: torch.Tensor):\n sel_nonzero = (channel_sel_counts != 0).type(torch.float).sum(axis=-1) / self.expert_size\n self.log(f\"{prefix}/mean\", sel_nonzero.mean())\n self.log(f\"{prefix}/min\", sel_nonzero.min())\n self.log(f\"{prefix}/max\", sel_nonzero.max())\n\n\n def pre_train_forward(self):\n if self.norm_keys:\n with torch.no_grad():\n self.keys.div_(self.keys.norm(dim=-1, keepdim=True))\n\n if self.training and not self.was_training:\n sorted_counts = self.index_sel_counts.sort(descending=True).values\n self.log(\"test_exert_channel_usage\", framework.visualize.plot.Barplot(sorted_counts, xlabel=\"expert\", ylabel=\"usage count\"), drop_old=True)\n\n self.layer = 0\n if self.sel_hist:\n self.sel_hist = []\n self.index_sel_counts = 0\n self.index_sel_norm = 0\n self.reg_counts = 0\n\n def before_loss(self):\n if self.sel_hist:\n # Concatenate against time dimension. Important for the within-batch regularization\n sel = torch.cat(self.sel_hist, -2)\n self.add_perplexity_reg(sel)\n\n self.sel_hist = []\n\n if self.index_sel_norm > 0:\n if self.training:\n with torch.no_grad():\n self.log(\"usag_rel_perplexity_all_layers\", utils.relative_perplexity(self.index_sel_counts / self.index_sel_norm))\n self.log(\"dead_expert_proportion_all_layers\", (self.index_sel_counts == 0).float().sum() / self.n_experts)\n\n self.log_expert_sel_usage(\"exert_channel_usage\", self.kv_sel_counts)\n\n self.kv_sel_counts_100.add_(self.kv_sel_counts)\n self.kv_sel_counts.zero_()\n\n self.index_sel_counts_100 = self.index_sel_counts_100 + self.index_sel_counts\n self.index_sel_norm_100 = self.index_sel_norm_100 + self.index_sel_norm\n\n if self.training and self.iter % 100 == 0:\n norm_cnt = self.index_sel_counts_100 / self.index_sel_norm_100\n self.log(\"usag_rel_perplexity_100\", utils.relative_perplexity(norm_cnt))\n self.log(\"dead_expert_proportion_100\", (self.index_sel_counts_100 == 0).float().sum() / self.n_experts)\n\n sorted_counts = self.index_sel_counts_100.sort(descending=True).values\n self.log(\"usage_counts_100\", framework.visualize.plot.Barplot(sorted_counts, xlabel=\"expert\", ylabel=\"usage count\"), drop_old=True)\n\n\n self.log_expert_sel_usage(\"exert_channel_usage_100\", self.kv_sel_counts_100)\n self.kv_sel_counts_100.zero_()\n\n self.index_sel_counts_100 = 0\n self.index_sel_norm_100 = 0\n\n self.log(\"ani/keys\", self.ani(self.keys_to_logical_order(self.keys)))\n self.log(\"ani/values\", self.ani(self.values.flatten(0, -2)))\n self.log(\"ani/expert_sel\", self.ani(self.expert_sel.T))\n\n if self.training:\n self.iter += 1\n\n def topk(self, x: torch.Tensor, k: int, approx: bool) -> Tuple[torch.Tensor, torch.Tensor]:\n if approx:\n x = x.view(*x.shape[:-1], k, -1)\n scores, ind = x.max(-1)\n return scores, self.seq[:k] * x.shape[-1] + ind\n else:\n return x.topk(k, dim=-1, sorted=False)\n\n def rolling_logsumexp(self, x: torch.Tensor) -> torch.Tensor:\n # Simulate calculating logsumexp over a bigger batch than the current one. Will have stale values, but that\n # should not matter much later in training.\n if self.ppl_past_blocks == 0 or not self.training:\n return F.log_softmax(x, dim=-1)\n else:\n if len(self.blocks_for_ppl) == self.ppl_past_blocks:\n self.blocks_for_ppl.pop(0)\n\n self.blocks_for_ppl.append(x)\n res = F.log_softmax(torch.cat(self.blocks_for_ppl, dim=0), dim=-1)\n self.blocks_for_ppl[-1] = self.blocks_for_ppl[-1].detach()\n return res\n\n def add_perplexity_reg(self, sel: torch.Tensor):\n sync_distributed = self.sync_distributed and (self.perplexity_reg_mode not in {\"time\", \"global_time\"})\n\n if self.perplexity_reg_mode in {\"time\", \"global_time\"}:\n sel = sel.flatten(0, -3)\n else:\n sel = sel.flatten(0, -2)\n\n # Note: sel are raw logits, no matter what activation is used\n if self.perplexity_reg > 0:\n if self.reg_type == \"perplexity\":\n sel_d = self.rolling_logsumexp(sel)\n sel_d = framework.utils.distributed_ops.log_mean(sel_d, -2, self.sync_distributed)\n loss = lambda: self.perplexity_reg * ( - utils.relative_perplexity_l(sel_d).mean())\n elif self.reg_type == \"entropy\":\n sel_d = self.rolling_logsumexp(sel)\n sel_d = framework.utils.distributed_ops.log_mean(sel_d, -2, self.sync_distributed)\n loss = lambda: self.perplexity_reg * ( - utils.entropy_l(sel_d).mean())\n elif self.reg_type == \"variance\":\n if sync_distributed:\n raise NotImplementedError(\"Variance regularization is not supported in distributed mode\")\n avg_sel = sel.mean(-2)\n loss = lambda: self.perplexity_reg * avg_sel.var(-1).mean()\n elif self.reg_type == \"l2\":\n loss = lambda: self.perplexity_reg * sel.pow(2).mean()\n elif self.reg_type == \"switch\":\n if sync_distributed:\n torch.distributed.all_reduce(self.reg_counts, op=torch.distributed.ReduceOp.SUM)\n\n p_sel_real = self.reg_counts / self.reg_counts.sum(-1, keepdims=True)\n if self.perplexity_reg_mode in {\"time\", \"global_time\"}:\n p_sel_real = p_sel_real.unsqueeze(-2)\n\n loss = lambda: self.perplexity_reg * (F.softmax(sel, dim=-1) * p_sel_real).mean()\n self.reg_counts = 0\n else:\n assert False\n\n self.add_reg(loss, \"moe\")\n\n def compute_scores(self, input: torch.Tensor, index: CVMMSel, expert_scores: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n scores = cvmm(input, index, self.keys)\n\n if self.selection_mode in {\"mul\"}:\n scores = scores * expert_scores[..., None]\n elif self.selection_mode in {\"gate\", \"sigmoid\", \"sinkhorn\", \"sinkhorn2\", \"sinkmoid\", \"sinkmax\", \"sinkmoid2\"}:\n # Handle it later\n pass\n\n scores = self.activation(scores)\n\n plot_training = self.train and self.iter % 10 == 0\n if plot_training:\n with torch.no_grad():\n gt0 = (scores > 0).float()\n gt0_s = gt0.sum()\n\n if plot_training:\n self.log(\"relu_pass_rate\", gt0_s / scores.numel())\n\n self.kv_sel_counts.index_add_(0, index.raw_sel.flatten(), gt0.flatten(end_dim=-2))\n\n if self.dropout > 0 and self.dropout_mode != \"none\":\n scores = F.dropout(scores, self.dropout, training=self.training)\n\n return scores\n\n def sel_activation(self, sel: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:\n reg_sel = sel\n if self.selection_mode in {\"sigmoid\"}:\n sel = torch.sigmoid(sel)\n elif self.selection_mode in {\"mul\"}:\n sel = sel.abs()\n reg_sel = sel\n elif self.selection_mode in {\"gate\"}:\n sel = F.softmax(sel, dim=-1)\n with torch.no_grad():\n self.log(\"expert_rel_perplexity_per_selection\", utils.relative_perplexity(sel).mean())\n else:\n assert False\n\n return sel, reg_sel\n\n def forward(self, input: torch.Tensor) -> torch.Tensor:\n out = 0\n\n in1 = in2 = input\n\n sel = self.sel(in1)\n # sel=sel.float()\n\n if self.sel_norm == \"cos\":\n sel = sel / (in1.norm(dim=-1, keepdim=True) * self.expert_sel.norm(dim=-1)[None]) * self.sel_scale\n elif self.sel_norm == \"weights\":\n sel = sel * (self.sel_scale / self.expert_sel.norm(dim=-1)[None])\n elif self.sel_norm == \"input\":\n sel = sel * (self.sel_scale / in1.norm(dim=-1, keepdim=True))\n\n sel_raw = reg_sel = sel\n\n inv_val = float(\"-inf\")\n\n if not self.activation_after_topk:\n # Sinkhorn should be always applied before top-k\n sel, reg_sel = self.sel_activation(sel, input.shape[-2])\n inv_val = 0\n\n if self.training and self.expert_dropout > 0:\n if self.selection_mode not in {\"sigmoid\", \"gate\"}:\n raise ValueError(\"Expert dropout not supported in this mode\")\n\n mask = torch.rand_like(sel) < self.expert_dropout\n sel2 = sel.masked_fill(mask, inv_val)\n else:\n sel2 = sel\n\n sel_val, sel_index = self.topk(sel2, self.n_heads, self.topk_mode in {\"l1_approx\", \"approx\"})\n\n if self.activation_after_topk or (self.selection_mode in {\"mul\"}):\n sel_val = torch.gather(sel_raw, -1, sel_index)\n sel_val, reg_sel = self.sel_activation(sel_val, input.shape[-2])\n\n\n record_counts_now = (self.training and self.iter % 10 == 0) or (not self.training) or (self.record_all_expert_sel_counts)\n\n if not self.training:\n sel_index_flat = sel_index.flatten(end_dim=-2)\n if self.coocurence is None:\n self.coocurence = torch.zeros([self.n_experts, self.n_experts], device=sel_index_flat.device, dtype=torch.long)\n\n for h1 in range(self.n_heads):\n for h2 in range(self.n_heads):\n ind_flat = sel_index_flat[..., h1] * self.n_experts + sel_index_flat[..., h2]\n values = torch.tensor([1], device=self.coocurence.device, dtype=self.coocurence.dtype).expand_as(ind_flat)\n # values = sel_val[..., h2].flatten()\n self.coocurence.flatten().put_(ind_flat, values, accumulate=True)\n # self.coocurence[sel_index_flat[..., h1], sel_index_flat[..., h2]] += 1\n\n if record_counts_now or self.reg_type == \"switch\":\n reg_counts = F.one_hot(sel_index, self.n_experts).type_as(input)\n\n if self.reg_type == \"switch\":\n reg_counts2 = reg_counts.view(*input.shape[:-2], input.shape[-2] * self.n_heads, self.n_experts)\n if self.perplexity_reg_mode == \"time\":\n reg_counts2 = reg_counts2.sum(-2)\n else:\n reg_counts2 = reg_counts2.flatten(end_dim=-2).sum(0)\n\n self.reg_counts = self.reg_counts + reg_counts2\n\n if record_counts_now:\n with torch.no_grad():\n sel_counts = reg_counts.flatten(end_dim=-2).sum(0)\n cnt = sel_index.nelement()\n\n p_expert_sel = sel_counts / cnt\n\n self.index_sel_counts = self.index_sel_counts + sel_counts\n self.index_sel_norm = self.index_sel_norm + cnt\n\n if self.record_all_expert_sel_counts:\n softcnt = torch.zeros_like(sel_counts, dtype=sel_val.dtype)\n softcnt.index_add_(0, sel_index.flatten(), sel_val.flatten())\n\n self.all_expert_sel_soft.append(softcnt)\n self.all_expert_sel_counts.append(sel_counts)\n\n if self.training:\n self.log(\"min_sel_score\", sel_val.min(dim=-1).values.mean())\n self.log(\"max_sel_score\", sel_val.max(dim=-1).values.mean())\n\n sel_oh = F.one_hot(sel_index, self.n_experts).sum(-2).bool()\n if self.layer >= 1 and self.training:\n self.log(f\"layer_sel_overlap_{self.layer}\", ((self.prev_sel_oh & sel_oh).sum(-1).float() / self.n_heads).mean())\n\n self.prev_sel_oh = sel_oh\n\n ppl = utils.relative_perplexity(p_expert_sel)\n self.log(\"usage_rel_perplexity\", ppl)\n self.log(\"dead_expert_proportion\", (p_expert_sel == 0).float().sum() / self.n_experts)\n\n if self.perplexity_reg_mode in {\"step\", \"time\"}:\n self.add_perplexity_reg(reg_sel)\n elif self.perplexity_reg > 0 and self.training:\n self.sel_hist.append(reg_sel)\n\n sel_indices = cvmm_prepare_sel2(sel_index.int())\n\n scores = self.compute_scores(in2, sel_indices, sel_val)\n\n sel_indices = sel_indices.clone()\n sel_indices.reduction_weight = sel_val\n sel_indices.sel_index = sel_indices.out_index\n sel_indices.out_index = None\n\n if self.selection_mode not in {\"gate\", \"sigmoid\"}:\n sel_indices.reduction_weight = torch.ones_like(sel_indices.reduction_weight)\n\n out = cvmm(scores, sel_indices, self.values)\n\n self.layer += 1\n\n self.was_training = self.training\n res = out.view(*input.shape[:-1], self.v_dim)\n return res\n\n def dump_logs(self, save_dir: str):\n if self.coocurence is not None:\n os.makedirs(save_dir, exist_ok=True)\n torch.save(self.coocurence, os.path.join(save_dir, \"coocurence.pt\"))\n\n def get_logs(self) -> Dict[str, Any]:\n res = super().get_logs()\n\n if self.coocurence is not None:\n coo = self.coocurence / self.coocurence.diagonal().clamp(min=1)[:, None]\n res[\"expert_coocurence\"] = framework.visualize.plot.Heatmap(coo, xlabel=\"expert\", ylabel=\"expert\", textval=False)\n self.coocurence = None\n return res"},{"identifier":"Result","path":"interfaces/result.py","snippet":"class Result:\n outputs: torch.Tensor\n loss: torch.Tensor\n\n batch_dim = 0\n\n def plot(self) -> Dict[str, Any]:\n return {}\n\n @property\n def batch_size(self) -> int:\n return self.outputs.shape[self.batch_dim]\n\n @staticmethod\n def merge(l: List, batch_weights: Optional[List[float]] = None):\n if len(l) == 1:\n return l[0]\n batch_weights = batch_weights if batch_weights is not None else [1] * len(l)\n loss = sum([r.loss * w for r, w in zip(l, batch_weights)]) / sum(batch_weights)\n out = torch.cat([r.outputs for r in l], l[0].batch_dim)\n return l[0].__class__(out, loss)"},{"identifier":"LayerVisualizer","path":"layers/layer_with_visualization.py","snippet":"class LayerVisualizer:\n def __init__(self, module: torch.nn.Module, options: Dict[str, Any] = {}):\n self.modules = []\n self.options = options\n self.curr_options = None\n for n, m in module.named_modules():\n if isinstance(m, LayerWithVisualization):\n self.modules.append((n, m))\n\n def plot(self) -> Dict[str, Any]:\n res = {}\n for n, m in self.modules:\n res.update({f\"{n}/{k}\": v for k, v in m.plot(self.curr_options).items()})\n m.visualization_enabled = False\n\n self.curr_options = None\n return res\n\n def prepare(self, options: Dict[str, Any] = {}):\n self.curr_options = self.options.copy()\n self.curr_options.update(options)\n\n for _, m in self.modules:\n m.prepare()\n m.visualization_enabled = True"},{"identifier":"FullMoeRelativeAttentionCore","path":"layers/transformer/full_moe_relative_attention.py","snippet":"class FullMoeRelativeAttentionCore(LayerWithVisualization, LoggingLayer, RegularizedLayer, OncePerIterLayer, torch.nn.Module):\n def __init__(self, state_size: int, n_heads: int, n_experts: int, dropout: float = 0.0, input_size: Optional[int] = None,\n projection_size: Optional[int] = None, output_size: Optional[int] = None, init_std_scale: float = 1.0,\n perplexity_reg: float = 0, share_pk: bool = True, expert_dropout: float = 0.0,\n selection_mode: str = \"sigmoid\", moe_k: int = 2, q_expert: bool = True,\n k_expert: bool = True, v_expert: bool = True, o_expert: bool = True, norm_qk_score: bool = False,\n v_projection_size: Optional[int] = None, same_sel: bool = False,\n qside_n_experts: Optional[int] = None, shared_experts: bool = False,\n kq_n_experts: Optional[int] = None, separate_kq_sel: bool = False,\n normalize_init: bool = False, normalize_retrieval: bool = False):\n\n super().__init__()\n\n self.input_size = input_size or state_size\n self.output_size = output_size or state_size\n self.pe_size = self.input_size\n self.perplexity_reg = perplexity_reg\n self.share_pk = share_pk\n self.expert_dropout = expert_dropout\n self.selection_mode = selection_mode\n self.iter = 0\n self.moe_k = moe_k\n self.norm_qk_score = norm_qk_score\n self.same_sel = same_sel\n self.shared_experts = shared_experts\n self.init_std_scale = init_std_scale\n self.normalize_init = normalize_init\n self.attention_to_visualize = []\n self.selections_to_visualize = {}\n\n self.is_expert = {\n \"k\": k_expert,\n \"q\": q_expert,\n \"v\": v_expert,\n \"o\": o_expert\n }\n self.n_experts = {\n \"k\": kq_n_experts or n_experts,\n \"q\": kq_n_experts or qside_n_experts or n_experts,\n \"v\": n_experts,\n \"o\": qside_n_experts or n_experts\n }\n\n self.separate_k_sel = separate_kq_sel or (self.n_experts[\"k\"] != self.n_experts[\"v\"])\n self.separate_q_sel = separate_kq_sel or (self.n_experts[\"q\"] != self.n_experts[\"o\"])\n\n self.sel_hist = {}\n self.sel_counts_100 = {}\n\n self.n_heads = n_heads\n self.dropout = torch.nn.Dropout(dropout) if dropout > 0 else lambda x: x\n self.projection_size = projection_size or (state_size // n_heads)\n self.v_projection_size = v_projection_size or self.projection_size\n\n self.std_in = init_std_scale * math.sqrt(1 / self.input_size)\n std_out = init_std_scale * math.sqrt(1 / (n_heads * self.v_projection_size))\n\n self.create_selection_logic()\n\n self.src_side_maps = {\"k\", \"v\"}\n\n self.projections = torch.nn.ParameterDict({\n \"q\": self.create_param_block(\"q\", self.input_size, self.projection_size, self.std_in),\n \"k\": self.create_param_block(\"k\", self.input_size, self.projection_size, self.std_in),\n \"v\": self.create_param_block(\"v\", self.input_size, self.v_projection_size, self.std_in),\n \"o\": self.create_param_block(\"o\", self.v_projection_size, self.output_size, std_out),\n })\n\n if normalize_retrieval:\n self.norm_ret = torch.nn.LayerNorm(self.projection_size)\n else:\n self.norm_ret = lambda x: x\n\n self.sel_correlation = 0\n\n self.register_buffer(\"scale\", torch.full([1], 1.0 / math.sqrt(self.projection_size)), persistent=False)\n\n def renorm_keep_std(self, weight: torch.Tensor, dim: int = 0):\n with torch.no_grad():\n std = weight.std()\n weight.div_(weight.norm(dim=dim, keepdim=True))\n weight.mul_(std / weight.std())\n\n def get_n_copies(self, name: str):\n return self.n_heads\n\n def create_param_block(self, name: str, in_size: int, out_size: int, std: float):\n n_copies = self.get_n_copies(name)\n\n if self.is_expert[name]:\n exp_mul = 1 if self.shared_experts else n_copies\n p = torch.nn.Parameter(torch.randn(exp_mul * self.n_experts[name], in_size, out_size) * std)\n if self.normalize_init:\n self.renorm_keep_std(p, dim=0)\n return p\n else:\n if name == \"o\":\n in_size = n_copies * in_size\n else:\n out_size = n_copies * out_size\n return torch.nn.Parameter(torch.randn(out_size, in_size) * std)\n\n def create_selection_logic(self):\n sels_params = {}\n self.sel_map = {}\n\n def register_remap(dest: str, src: str) -> bool:\n if not (src in sels_params or src in self.sel_map):\n # src is not defined\n return False\n\n assert self.n_experts[src] == self.n_experts[dest]\n self.sel_map[dest] = self.sel_map.get(src, src)\n return True\n\n if self.is_expert[\"o\"]:\n sels_params[\"o\"] = self.init_sel(\"o\", self.std_in)\n\n if self.is_expert[\"q\"] and (self.separate_q_sel or not register_remap(\"q\", \"o\")):\n sels_params[\"q\"] = self.init_sel(\"q\", self.std_in)\n\n if self.is_expert[\"v\"] and ((not self.same_sel) or not register_remap(\"v\", \"o\")):\n sels_params[\"v\"] = self.init_sel(\"v\", self.std_in)\n\n if self.is_expert[\"k\"]:\n if (not (self.same_sel and self.separate_k_sel and register_remap(\"k\", \"q\"))) and (self.separate_k_sel or not register_remap(\"k\", \"v\")):\n sels_params[\"k\"] = self.init_sel(\"k\", self.std_in)\n\n self.selections = torch.nn.ParameterDict(sels_params)\n\n def init_sel(self, name: str, std: float) -> torch.nn.Parameter:\n n_copies = self.get_n_copies(name)\n n_experts = self.n_experts[name]\n sel = torch.nn.Parameter(torch.randn(n_experts*n_copies, self.input_size) * std)\n self.renorm_rows(sel)\n return sel\n\n def renorm_rows(self, x: torch.Tensor):\n with torch.no_grad():\n std_t = x.std(dim=-1, keepdim=True)\n x.div_(x.norm(dim=-1, keepdim=True))\n x.mul_(std_t / x.std())\n\n\n def project_to_torch_order(self, x: torch.Tensor):\n return x.view(*x.shape[:-1], self.get_n_copies(\"k\"), -1).transpose(-2, -3)\n\n def get_mask_tensor(self, src_len: int, mask: Optional[AttentionMask]) -> Optional[torch.Tensor]:\n if mask is None or (mask.position_mask is None and mask.src_length_mask is None):\n return None\n\n # mask.position_mask: [..., N_out, N_in]\n # mask.src_length_mask: [B, ...., N_in]\n # True where it has to be masked\n\n if mask.position_mask is not None:\n n_pad = src_len - mask.position_mask.shape[-1]\n if n_pad > 0:\n pm = F.pad(mask.position_mask, (n_pad, 0), 'constant', value=False)\n else:\n pm = mask.position_mask\n\n if mask.position_mask is None:\n m = mask.src_length_mask.unsqueeze(-2).unsqueeze(-2)\n elif mask.src_length_mask is None:\n m = pm\n else:\n m = mask.src_length_mask.unsqueeze(-2).unsqueeze(-2) | pm\n\n return m\n\n def train(self, mode: bool = True):\n self.sel_hist = {}\n return super().train(mode)\n\n def get_lost_on_hist(self, l: List[torch.Tensor]) -> torch.Tensor:\n assert l[0].ndim == 4\n l = [t.flatten(1,2) for t in l]\n sel = torch.cat(l, -2)\n sel_d = F.log_softmax(sel, dim=-1)\n sel_d = framework.utils.distributed_ops.log_mean(sel_d, -2, sync_distributed=False)\n return self.perplexity_reg * ( - utils.entropy_l(sel_d).mean())\n\n def get_reg_loss(self) -> Dict[str, torch.Tensor]:\n l = super().get_reg_loss()\n for k, v in self.sel_hist.items():\n l[f\"moe_att_entropy/{k}\"] = self.get_lost_on_hist(v)\n\n self.sel_hist = {}\n return l\n\n def get_sel(self, t: torch.Tensor, w: torch.Tensor, name: str) -> Selection:\n n_experts = self.n_experts[name]\n n_copies = self.get_n_copies(name)\n\n sel = F.linear(t, w).float()\n sel = sel.view(*sel.shape[:-1], n_copies, -1)\n with torch.no_grad():\n if self.expert_dropout > 0 and self.training:\n mask = torch.rand_like(sel) < self.expert_dropout\n sel2 = sel.masked_fill(mask, float('-inf'))\n else:\n sel2 = sel\n _, sel_index = sel2.topk(self.moe_k, dim=-1, sorted=False)\n sel_val = torch.gather(sel, -1, sel_index)\n\n if self.selection_mode == \"softmax\":\n sel_val = sel_val.softmax(-1)\n elif self.selection_mode == \"sigmoid\":\n sel_val = sel_val.sigmoid()\n else:\n raise ValueError(\"Unknown selection mode: \" + self.selection_mode)\n\n exp_shift = 0 if self.shared_experts else n_experts\n\n sel_index_shifted = (torch.arange(n_copies, device=sel_index.device, dtype=sel_index.dtype) * exp_shift).unsqueeze(-1) + sel_index\n sel_index_pp = cvmm_prepare_sel2(sel_index_shifted.flatten(-2,-1), sel_val)\n\n return Selection(sel, sel_val, sel_index, sel_index_pp)\n\n def before_loss(self):\n self.iter += 1\n if self.iter % 100 == 0:\n for k, v in self.sel_counts_100.items():\n sorted_counts = v.sort(descending=True).values\n self.log(f\"sel_counts/{k}\", framework.visualize.plot.Barplot(sorted_counts, xlabel=\"expert\", ylabel=\"usage count\"), drop_old=True)\n\n self.sel_counts_100 = {}\n\n def exp_proj(self, x: torch.Tensor, w: torch.Tensor, sel: Selection) -> torch.Tensor:\n return cvmm(x, sel.sel_index, w)\n\n def compute_sel(self, curr_state: torch.Tensor, attend_to: torch.Tensor) -> Dict[str, Selection]:\n self.selection_mode\n outs = {}\n done = {}\n cross_atten = curr_state is not attend_to\n\n for name in (set(self.selections.keys()) | set(self.sel_map.keys())):\n name_actual = self.sel_map.get(name, name)\n\n # There coukd be 2 versions of everything: source side and destination side. Check if they are different,\n # and if not, use the cached version, my_id is the unique identifier for this transformation\n is_src_side = (name in self.src_side_maps) or not cross_atten\n my_id = (name_actual, is_src_side)\n\n cached = done.get(my_id)\n if cached is not None:\n outs[name] = cached\n continue\n\n # No cache, actually compute\n inp = attend_to if is_src_side else curr_state\n v = self.selections[name_actual]\n outs[name] = self.get_sel(inp, v, name)\n\n # Save history for regularization\n if self.perplexity_reg > 0 and self.training:\n if name not in self.sel_hist:\n self.sel_hist[name] = []\n self.sel_hist[name].append(outs[name].raw_sel)\n\n # Visualize statistics\n if self.training and self.iter % 10 == 0:\n self.sel_counts_100[name] = self.sel_counts_100.get(name, 0) + \\\n F.one_hot(outs[name].raw_sel_index.flatten(), self.n_experts[name]).sum(0)\n\n done[my_id] = outs[name]\n\n return outs\n\n def project(self, name: str, src: torch.Tensor, sel: Dict[str, Selection]) -> torch.Tensor:\n if name in sel:\n sv = sel[name]\n if self.norm_qk_score and name in {\"q\", \"k\"}:\n sv.sel_index.reduction_weight = F.normalize(sv.sel_index.reduction_weight, p=1, dim=-1)\n return self.exp_proj(src, self.projections[name], sv)\n else:\n return F.linear(src, self.projections[name])\n\n def attend(self, curr_state: torch.Tensor, attend_to: torch.Tensor, pos_offset: int, v: torch.Tensor,\n k: torch.Tensor, q: torch.Tensor, mask: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:\n raise NotImplementedError()\n\n def attention_proj(self, att: torch.Tensor, v: torch.Tensor,\n mask: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:\n if mask is not None:\n att.masked_fill_(mask, float('-inf'))\n\n att = F.softmax(att, dim=-1)\n\n res = att @ v\n return res, att\n\n def forward(self, curr_state: torch.Tensor, attend_to: torch.Tensor, mask: Optional[AttentionMask],\n pos_offset: Optional[int] = None, need_weights: bool = False):\n # curr_state: [batch_size, out_len, c]\n # attend_to: [batch_size, in_len, c]\n\n if pos_offset is None:\n assert curr_state.shape[1] == attend_to.shape[1], \"If attend_to has different shape than curr_state, pos_offset should be provided\"\n pos_offset = 0\n\n sel = self.compute_sel(curr_state, attend_to)\n\n # scale q and k with sqrt(scale) before the attention. This should save memory, be faster, and\n # keep the range of k and v better. It should make attention NaNs better with float16.\n scale = self.scale.sqrt()\n\n q = self.project(\"q\", curr_state, sel)\n q = q * scale.type_as(q)\n k = self.project(\"k\", attend_to, sel)\n k = k * scale.type_as(k)\n v = self.project(\"v\", attend_to, sel)\n\n q = self.project_to_torch_order(q) if \"q\" not in sel else q.transpose(-2,-3)\n k = self.project_to_torch_order(k) if \"k\" not in sel else k.transpose(-2,-3)\n v = self.project_to_torch_order(v) if \"v\" not in sel else v.transpose(-2,-3)\n\n k = self.dropout(k)\n\n res, att = self.attend(curr_state, attend_to, pos_offset, v, k, q, self.get_mask_tensor(attend_to.shape[-2], mask))\n res = self.norm_ret(res)\n\n if self.visualization_enabled:\n self.attention_to_visualize.append(att[0].detach())\n for k, s in sel.items():\n if k not in self.selections_to_visualize:\n self.selections_to_visualize[k] = []\n\n with torch.no_grad():\n m = torch.zeros([*s.sel_val[0].shape[:-1], self.n_experts[k]], device=s.sel_val.device, dtype=s.sel_val.dtype)\n m.scatter_(-1, s.raw_sel_index[0], s.sel_val[0])\n\n self.selections_to_visualize[k].append(m)\n\n if self.get_n_copies(\"k\") != self.get_n_copies(\"v\"):\n res = res.view(\n *res.shape[:-1], self.get_n_copies(\"v\") // self.get_n_copies(\"k\"), -1).transpose(2,3).flatten(1,2).contiguous()\n\n if self.is_expert[\"o\"]:\n res = res.transpose(-2, -3)\n # The output selection indices are calculated from the current state and are also used for projecting \"q\".\n # But that projection needs to create multiple copies for the different heads. Here we already have the\n # heads, but we have to create copies for the top-k elements. We can calculate that from the reduction\n # weight. We also want to compute not only the weighted average between the top-k elements, but also\n # of the different heads. So reshape the reduction weight accordingly.\n o_sel = sel[\"o\"].sel_index.clone()\n o_sel.sel_index = o_sel.out_index // o_sel.reduction_weight.shape[-1]\n o_sel.reduction_weight = o_sel.reduction_weight.flatten(-2)\n out = cvmm(res, o_sel, self.projections[\"o\"])\n else:\n res = res.transpose(-2, -3)\n out = F.linear(res.contiguous().view(*curr_state.shape[:-1], -1), self.projections[\"o\"])\n\n return out\n\n def plot(self, options: Dict[str, Any]) -> Dict[str, Any]:\n r = {}\n marks = options.get(\"steplabel\")\n n_steps = options.get(\"n_steps\") or 9999999\n y_marks = options.get(\"target_labels\", marks)\n\n ns1 = (self.attention_to_visualize[0].shape[-2] + n_steps) if n_steps < 0 else 0\n ns1_e = self.attention_to_visualize[0].shape[-2] if n_steps < 0 else n_steps\n ns2 = (self.attention_to_visualize[0].shape[-1] + n_steps) if n_steps < 0 else 0\n ns2_e = self.attention_to_visualize[0].shape[-1] if n_steps < 0 else n_steps\n\n if marks is not None:\n assert len(marks) == self.attention_to_visualize[0].shape[-1]\n marks = marks[ns2:ns2_e]\n\n if y_marks is not None:\n assert len(y_marks) == self.attention_to_visualize[0].shape[-2]\n y_marks = y_marks[ns1:ns1_e]\n\n if options.get(\"mha.plot_head_details\") and self.attention_to_visualize[0].shape[0] > 1:\n for head in range(self.attention_to_visualize[0].shape[0]):\n sel_map = {k: [e[:, head][ns1:ns1_e] if k in {'q', 'o'} else e[:, head][ns2:ns2_e] for e in v] for k, v in self.selections_to_visualize.items()}\n selections = {k: torch.stack(v, 0).cpu() for k, v in sel_map.items()}\n\n x_selections = {k: v for k, v in selections.items() if k in {'k', 'v'}}\n y_selections = {k: v for k, v in selections.items() if k in {'q', 'o'}}\n\n r[f\"head_{head}\"] = MoEAttentionPlot(\n torch.stack([layer[head][ns1:ns1_e, ns2:ns2_e] for _, layer in enumerate(self.attention_to_visualize)], 0),\n x_selections, y_selections,\n ylabel=\"dest\", xlabel=\"src\", x_marks=marks, y_marks=y_marks)\n\n r[\"attention_max\"] = framework.visualize.plot.AnimatedHeatmap(\n torch.stack([layer.max(0)[0][ns1:ns1_e, ns2:ns2_e] for _, layer in enumerate(self.attention_to_visualize)], 0),\n ylabel=\"dest\", xlabel=\"src\", textval=False, x_marks=marks, y_marks=y_marks, ignore_wrong_marks=True)\n\n self.attention_to_visualize = []\n self.selections_to_visualize = {}\n return r\n\n def dump_logs(self, save_dir: str):\n if torch.is_tensor(self.sel_correlation):\n os.makedirs(save_dir, exist_ok=True)\n torch.save(self.sel_correlation, os.path.join(save_dir, \"sel_correlation.pt\"))\n\n def get_logs(self) -> Dict[str, Any]:\n res = super().get_logs()\n\n if torch.is_tensor(self.sel_correlation):\n coo = self.sel_correlation / self.sel_correlation.flatten(1).sum(-1).clamp(min=1)[:, None, None]\n for h in range(self.n_heads):\n res[f\"expert_coocurence_{h}\"] = framework.visualize.plot.Heatmap(coo[h], xlabel=\"o expert\", ylabel=\"v expert\", textval=False)\n self.sel_correlation = 0\n return res"}],"string":"[\n {\n \"identifier\": \"TransformerLanguageModel\",\n \"path\": \"models/transformer_language_model.py\",\n \"snippet\": \"class TransformerLanguageModel(LoggingLayer, torch.nn.Module):\\n def __init__(self, voc_size: int, embedding_size: Optional[int], state_size: int, dropout: float,\\n tied_embedding: bool, layers: List[torch.nn.Module], n_prev_states: int,\\n n_prev_states_test: Optional[int] = None, adaptive_cutoffs: List[int] = [],\\n same_length_eval: bool = True, norm_before_output: bool = False,\\n p_drop_layer: float = 0.0, use_last_state: bool = False, same_length: bool = False):\\n\\n super().__init__()\\n\\n self.embedding = torch.nn.Embedding(voc_size, embedding_size or state_size)\\n torch.nn.init.kaiming_normal_(self.embedding.weight, mode=\\\"fan_in\\\", nonlinearity=\\\"linear\\\")\\n\\n self.shared_layers = all([la is layers[0] for la in layers])\\n\\n if embedding_size is None:\\n self.embedding_adapter = lambda x: x\\n else:\\n self.embedding_adapter = torch.nn.Linear(embedding_size, state_size)\\n\\n self.dropout = torch.nn.Dropout(dropout)\\n self.layers = layers\\n self.unique_layers = torch.nn.ModuleList(unique_obejcts(layers))\\n self.output_adapter = lambda x: x\\n self.n_prev_states = n_prev_states\\n self.n_prev_states_test = n_prev_states_test or n_prev_states\\n self.same_length_eval = same_length_eval\\n self.embedding_scale = math.sqrt(state_size)\\n self.p_drop_layer = p_drop_layer\\n self.use_last_state = use_last_state\\n self.same_length = same_length\\n self.iter = 0\\n\\n self.adaptive = bool(adaptive_cutoffs)\\n\\n out_proj_size = (embedding_size or state_size) if tied_embedding else state_size\\n if self.adaptive:\\n self.output = framework.layers.CustomAdaptiveLogSoftmaxWithLoss(\\n out_proj_size, voc_size, adaptive_cutoffs, div_value=1,\\n tied_to=self.embedding if tied_embedding else None)\\n else:\\n self.output = torch.nn.Linear(out_proj_size, voc_size)\\n\\n if norm_before_output:\\n self.out_norm = torch.nn.LayerNorm(state_size)\\n else:\\n self.out_norm = lambda x: x\\n\\n if tied_embedding:\\n if not self.adaptive:\\n self.output.weight = self.embedding.weight\\n if embedding_size is not None:\\n self.output_adapter = torch.nn.Linear(state_size, embedding_size)\\n\\n @staticmethod\\n def generate_history_mask(sz: int, device: torch.device) -> torch.Tensor:\\n return torch.tril(torch.ones(sz, sz, dtype=torch.bool, device=device), diagonal=-1)\\n\\n def gen_output(self, x: torch.Tensor, target: Optional[torch.Tensor]) -> torch.Tensor:\\n net = self.out_norm(x)\\n net = self.output_adapter(net)\\n net = self.dropout(net)\\n\\n if self.adaptive:\\n net = self.output(net.transpose(0, 1), target)\\n else:\\n net = self.output(net.transpose(0, 1))\\n\\n return net\\n\\n def forward(self, x: torch.Tensor, target: Optional[torch.Tensor], state) -> Tuple[torch.Tensor, Any]:\\n causality_mask = Transformer.generate_square_subsequent_mask(x.shape[0], x.device)\\n\\n net = self.dropout(self.embedding(x.T.long()))\\n net = self.embedding_adapter(net)\\n net = net * self.embedding_scale\\n\\n new_state = []\\n features = [net]\\n\\n n_prev_states = self.n_prev_states if self.training else self.n_prev_states_test\\n\\n same_length = self.same_length or ((not self.training) and self.same_length_eval)\\n if same_length and state is not None:\\n causality_mask = [self.generate_history_mask(x.shape[0], x.device)] + \\\\\\n [torch.zeros_like(causality_mask)] * (len(state[0]) - 1) + [causality_mask]\\n causality_mask = torch.cat(causality_mask, -1)\\n\\n\\n plot_cossim = (self.iter % 100 == 0 and self.training)\\n for li, l in enumerate(self.layers):\\n if n_prev_states > 0:\\n if li == 0:\\n # Pos offset should be constant for all layers\\n pos_offset = sum(s.shape[1] for s in state[0]) if state is not None else 0\\n\\n # Concatenate the new state with the previous states\\n li_r = -1 if self.use_last_state else li\\n s = (state[li_r] + [net]) if state is not None else [net]\\n attend_to = torch.cat(s, 1)\\n\\n if not self.use_last_state:\\n s[-1] = s[-1].detach()\\n new_state.append(s[-n_prev_states:])\\n else:\\n pos_offset = None\\n attend_to = None\\n\\n net_o = l(net, mask=AttentionMask(None, causality_mask), attend_to=attend_to,\\n pos_offset=pos_offset)\\n\\n if plot_cossim:\\n features.append(net_o)\\n\\n with torch.no_grad():\\n ndiff = torch.norm(net_o - net, p=2, dim=-1)\\n n_in = torch.norm(net, p=2, dim=-1)\\n self.log(f\\\"activation_norm/abs_update_layer_{li}\\\", ndiff.mean())\\n self.log(f\\\"activation_norm/in_layer_{li}\\\", n_in.mean())\\n self.log(f\\\"activation_norm/rel_update_layer_{li}\\\", (ndiff/n_in.clamp(min=torch.finfo(n_in.dtype).eps)).mean())\\n\\n if self.training and self.p_drop_layer > 0.0:\\n net = torch.where(torch.rand_like(net_o[..., 0:1]) < self.p_drop_layer, net, net_o)\\n else:\\n net = net_o\\n\\n if self.use_last_state and n_prev_states > 0:\\n # If we carry over the last state, save it here\\n new_state = [((state[0] if state is not None else []) + [net.detach()])[-n_prev_states:]]\\n\\n if plot_cossim:\\n with torch.no_grad():\\n f_sample = [f.view(-1, f.shape[-1])[:1024] for f in features]\\n f_sample_all = torch.stack(f_sample, -2)\\n scores = framework.utils.cossim(f_sample_all, f_sample_all).mean(0)\\n self.log(\\\"feature_cossim\\\", framework.visualize.plot.Heatmap(scores, range=(0, 1), textval=False))\\n\\n outs = F.softmax(self.gen_output(f_sample_all, target).transpose(0, 1), -1)\\n scores = framework.utils.cossim(outs, outs).mean(0)\\n self.log(\\\"out_dist_cossim\\\", framework.visualize.plot.Heatmap(scores, range=(0, 1), textval=False))\\n\\n real_out = outs[:, -1]\\n for i in range(outs.shape[-2] - 1):\\n self.log(f\\\"out_diff_{i}\\\", (outs[:, i] - real_out).norm(dim=-1, p=1).mean())\\n\\n del outs\\n\\n\\n del features\\n\\n net = self.gen_output(net, target)\\n self.iter += 1\\n\\n return net, new_state\"\n },\n {\n \"identifier\": \"task\",\n \"path\": \"tasks/task_db.py\",\n \"snippet\": \"def task(name: Optional[str] = None):\\n def wrapper(cls):\\n n = TASK_PREFIX + (name or camel_to_snake(cls.__name__))\\n assert n not in TASKS, f\\\"Task {n} already exists\\\"\\n TASKS[n] = cls\\n return cls\\n return wrapper\"\n },\n {\n \"identifier\": \"args\",\n \"path\": \"tasks/task_db.py\",\n \"snippet\": \"def args(fn):\\n global ARGS_REGISTERS\\n ARGS_REGISTERS.append(fn)\\n return fn\"\n },\n {\n \"identifier\": \"RelativeTransformerEncoderLayer\",\n \"path\": \"layers/transformer/relative_transformer.py\",\n \"snippet\": \"class RelativeTransformerEncoderLayer(torch.nn.Module):\\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation: ActivationFunction = F.relu,\\n attention_dropout=0, test_pos_clamp: Optional[int] = None, drop_expand: bool = True,\\n head_projection_size: Optional[int] = None, ln_after_attention: bool = True):\\n super().__init__()\\n self.ln_after_attention = ln_after_attention\\n self.self_attn = FixedRelativeMultiheadAttention(\\n d_model, nhead, dropout=attention_dropout, test_pos_clamp=test_pos_clamp,\\n projection_size=head_projection_size)\\n self.linear1 = torch.nn.Linear(d_model, dim_feedforward)\\n self.dropout = torch.nn.Dropout(dropout) if drop_expand else lambda x: x\\n self.linear2 = torch.nn.Linear(dim_feedforward, d_model)\\n\\n if ln_after_attention:\\n self.norm1 = torch.nn.LayerNorm(d_model)\\n self.norm2 = torch.nn.LayerNorm(d_model)\\n self.dropout1 = torch.nn.Dropout(dropout)\\n self.dropout2 = torch.nn.Dropout(dropout)\\n\\n self.activation = activation\\n self.reset_parameters()\\n\\n def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None, attend_to: Optional[torch.Tensor] = None,\\n pos_offset: Optional[int] = None) -> torch.Tensor:\\n src2 = self.self_attn(src, attend_to if attend_to is not None else src, mask, pos_offset=pos_offset)\\n src = src + self.dropout1(src2)\\n src = self.norm1(src) if self.ln_after_attention else src\\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))\\n src = src + self.dropout2(src2)\\n src = self.norm2(src)\\n return src\\n\\n def reset_parameters(self):\\n torch.nn.init.xavier_normal_(self.linear1.weight, gain=torch.nn.init.calculate_gain('relu')\\n if self.activation is F.relu else 1.0)\\n torch.nn.init.xavier_uniform_(self.linear2.weight)\"\n },\n {\n \"identifier\": \"PrelnRelativeTransformerEncoderLayer\",\n \"path\": \"layers/transformer/relative_preln_transformer.py\",\n \"snippet\": \"class PrelnRelativeTransformerEncoderLayer(RelativeTransformerEncoderLayer):\\n is_preln = True\\n\\n def __init__(self, d_model, nhead, n_layers: int, dim_feedforward=2048, dropout=0.1,\\n activation: ActivationFunction = F.relu, attention_dropout=0, test_pos_clamp: Optional[int] = None,\\n drop_expand: bool = True, head_projection_size: Optional[int] = None):\\n super().__init__(\\n d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout,\\n activation=activation, attention_dropout=attention_dropout, test_pos_clamp=test_pos_clamp,\\n drop_expand=drop_expand, head_projection_size=head_projection_size)\\n\\n reset_prenorm_params(self, n_layers)\\n\\n def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None, attend_to: Optional[torch.Tensor] = None,\\n pos_offset: Optional[int] = None) -> torch.Tensor:\\n src2 = self.norm1(src)\\n src2 = self.self_attn(src2, self.norm1(attend_to) if attend_to is not None else src2, mask,\\n pos_offset=pos_offset)\\n src = src + self.dropout1(src2)\\n src2 = self.norm2(src)\\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\\n src = src + self.dropout2(src2)\\n return src\"\n },\n {\n \"identifier\": \"RelativeMoeTransformerEncoderLayer\",\n \"path\": \"layers/transformer/relative_moe_transformer.py\",\n \"snippet\": \"class RelativeMoeTransformerEncoderLayer(LoggingLayer, torch.nn.Module):\\n def __init__(self, d_model, nhead, n_experts: int, expert_size: int, n_layers: int,\\n dropout=0.1, activation: ActivationFunction = F.relu, attention_dropout=0,\\n test_pos_clamp: Optional[int] = None,\\n dropout_mode: str = \\\"none\\\", selection_mode: str = \\\"add\\\",\\n perplexity_reg: float = 0.0,\\n n_heads: int = 1, norm_keys: bool = False, perplexity_reg_mode: str=\\\"step\\\",\\n n_random: int = 0, reg_type: str = \\\"normal\\\",\\n topk_mode: str = \\\"full\\\", head_projection_size: Optional[int] = None,\\n activation_after_topk: bool = False,\\n drop_parallel: bool = True,\\n normalize_expert_sel_init: bool = False, norm_key_init: bool = False, norm_value_init: bool = False,\\n identical_init: bool = False,\\n sel_norm: str = \\\"none\\\",\\n preln: bool = True, ln_affine: bool = True,\\n moe_dropout_factor: float = 1.0,\\n drop_expert: float = 0.0, sync_distributed: bool = True,\\n modulation_amplitude: float = 0.5, moe_init_scale: float = 1.0,\\n moe_att_n_experts: int = 4, moe_att_expert_dropout: Optional[float] = None,\\n moe_att_selection_mode: str = \\\"sigmoid\\\",\\n moe_att_k: Optional[int] = None, moe_att_ppl_reg: Optional[float] = None,\\n q_expert: bool = True, k_expert: bool = True, v_expert: bool = True,\\n o_expert: bool = True,\\n v_projection_size: Optional[int] = None,\\n qside_n_experts: Optional[int] = None,\\n moe_attention: bool = False, moe_att_variant: str = \\\"full\\\",\\n moe_att_shared_experts: bool = False,\\n moe_att_kq_n_experts: Optional[int] = None, moe_att_separate_kq_sel: bool = False,\\n moe_att_norm_init: bool = False, moe_att_same_sel: bool = False, moe_att_norm_retrieval: bool = False,\\n rotate_fraction: float = 0.5, rope_base: float = 10000):\\n super().__init__()\\n self.preln = preln\\n self.i = 0\\n\\n if moe_attention:\\n if moe_att_variant == \\\"full\\\":\\n self.self_attn = FullMoeRelativeAttention(\\n d_model, nhead, dropout=attention_dropout,\\n projection_size=head_projection_size, init_std_scale=math.sqrt(2 / n_layers) if preln else 1.0,\\n n_experts=moe_att_n_experts,\\n perplexity_reg=perplexity_reg if moe_att_ppl_reg is None else moe_att_ppl_reg,\\n expert_dropout=drop_expert if moe_att_expert_dropout is None else moe_att_expert_dropout,\\n selection_mode=moe_att_selection_mode, q_expert=q_expert, k_expert=k_expert, v_expert=v_expert,\\n moe_k=n_heads if moe_att_k is None else moe_att_k, o_expert=o_expert, qside_n_experts=qside_n_experts,\\n v_projection_size=v_projection_size, shared_experts=moe_att_shared_experts,\\n kq_n_experts=moe_att_kq_n_experts, separate_kq_sel=moe_att_separate_kq_sel,\\n normalize_init=moe_att_norm_init,\\n same_sel=moe_att_same_sel, normalize_retrieval=moe_att_norm_retrieval,\\n )\\n elif moe_att_variant == \\\"full_rope\\\":\\n self.self_attn = FullMoeRopeAttention(\\n d_model, nhead, dropout=attention_dropout,\\n projection_size=head_projection_size, init_std_scale=math.sqrt(2 / n_layers) if preln else 1.0,\\n n_experts=moe_att_n_experts,\\n perplexity_reg=perplexity_reg if moe_att_ppl_reg is None else moe_att_ppl_reg,\\n expert_dropout=drop_expert if moe_att_expert_dropout is None else moe_att_expert_dropout,\\n selection_mode=moe_att_selection_mode, q_expert=q_expert, k_expert=k_expert, v_expert=v_expert,\\n moe_k=n_heads if moe_att_k is None else moe_att_k, o_expert=o_expert, qside_n_experts=qside_n_experts,\\n v_projection_size=v_projection_size, shared_experts=moe_att_shared_experts,\\n kq_n_experts=moe_att_kq_n_experts, separate_kq_sel=moe_att_separate_kq_sel,\\n normalize_init=moe_att_norm_init, normalize_retrieval=moe_att_norm_retrieval,\\n rotate_fraction=rotate_fraction, rope_base=rope_base,\\n )\\n else:\\n raise ValueError(f\\\"Unknown attention variant {moe_att_variant}\\\")\\n else:\\n self.self_attn = FixedRelativeMultiheadAttention(\\n d_model, nhead, dropout=attention_dropout, test_pos_clamp=test_pos_clamp,\\n projection_size=head_projection_size)\\n\\n std_scale = math.sqrt(2.0 / n_layers) if preln else 1.0\\n std_scale *= math.sqrt(moe_init_scale)\\n\\n self.pkm = MoE(\\n d_model, n_experts, expert_size, dropout=dropout * moe_dropout_factor, dropout_mode=dropout_mode,\\n weight_scale=std_scale, selection_mode=selection_mode,\\n perplexity_reg=perplexity_reg, n_heads=n_heads,\\n norm_keys=norm_keys, perplexity_reg_mode=perplexity_reg_mode, n_random=n_random,\\n reg_type=reg_type, topk_mode=topk_mode,\\n activation_after_topk=activation_after_topk,\\n activation=activation,\\n normalize_expert_sel_init=normalize_expert_sel_init, norm_key_init=norm_key_init,\\n norm_value_init=norm_value_init, identical_init=identical_init,\\n sel_norm=sel_norm,\\n expert_dropout=drop_expert,\\n sync_distributed=sync_distributed,\\n modulation_amplitude=modulation_amplitude)\\n\\n self.norm1 = torch.nn.LayerNorm(d_model, elementwise_affine=ln_affine)\\n self.norm2 = torch.nn.LayerNorm(d_model, elementwise_affine=ln_affine)\\n self.dropout = torch.nn.Dropout(dropout)\\n\\n self.activation = activation\\n self.drop_parallel = drop_parallel\\n\\n if preln:\\n reset_prenorm_params(self, n_layers)\\n\\n def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None, attend_to: Optional[torch.Tensor] = None,\\n pos_offset: Optional[int] = None) -> torch.Tensor:\\n\\n src2 = self.norm1(src) if self.preln else src\\n src2 = self.self_attn(src2, self.norm1(attend_to) if attend_to is not None else src2, mask,\\n pos_offset=pos_offset)\\n src = src + self.dropout(src2)\\n\\n if self.preln:\\n src2 = self.norm2(src)\\n else:\\n src = src2 = self.norm1(src)\\n\\n src3 = self.pkm(src2)\\n\\n src = src + self.dropout(src3)\\n if not self.preln:\\n src = self.norm2(src)\\n return src\"\n },\n {\n \"identifier\": \"FastRopeTransformerEncoderLayer\",\n \"path\": \"layers/transformer/fast_rope_transformer.py\",\n \"snippet\": \"class FastRopeTransformerEncoderLayer(torch.nn.Module):\\n def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation: ActivationFunction = F.relu,\\n attention_dropout=0, drop_expand: bool = True,\\n head_projection_size: Optional[int] = None, preln: bool = False, n_layers: Optional[int] = None,\\n rotate_fraction: float = 0.5, rope_base: float = 10000):\\n super().__init__()\\n self.preln = preln\\n self.self_attn = FastRopeAttention(\\n d_model, nhead, dropout=attention_dropout,\\n projection_size=head_projection_size, rotate_fraction=rotate_fraction,\\n rope_base=rope_base)\\n self.linear1 = torch.nn.Linear(d_model, dim_feedforward)\\n self.dropout = torch.nn.Dropout(dropout) if drop_expand else lambda x: x\\n self.linear2 = torch.nn.Linear(dim_feedforward, d_model)\\n\\n self.norm1 = torch.nn.LayerNorm(d_model)\\n self.norm2 = torch.nn.LayerNorm(d_model)\\n self.dropout1 = torch.nn.Dropout(dropout)\\n self.dropout2 = torch.nn.Dropout(dropout)\\n\\n self.activation = activation\\n\\n if preln:\\n if n_layers is None:\\n raise ValueError(\\\"n_layers must be specified when using preln\\\")\\n reset_prenorm_params(self, n_layers)\\n else:\\n self.reset_parameters()\\n\\n def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None, attend_to: Optional[torch.Tensor] = None,\\n pos_offset: Optional[int] = None) -> torch.Tensor:\\n src2 = self.norm1(src) if self.preln else src\\n src2 = self.self_attn(src2, self.norm1(attend_to) if attend_to is not None else src2, mask, pos_offset=pos_offset)\\n src = src + self.dropout1(src2)\\n\\n if self.preln:\\n src2 = self.norm2(src)\\n else:\\n src2 = src = self.norm1(src)\\n\\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\\n src = src + self.dropout2(src2)\\n\\n if not self.preln:\\n src = self.norm2(src)\\n return src\\n\\n def reset_parameters(self):\\n torch.nn.init.xavier_normal_(self.linear1.weight, gain=torch.nn.init.calculate_gain('relu')\\n if self.activation is F.relu else 1.0)\\n torch.nn.init.xavier_uniform_(self.linear2.weight)\"\n },\n {\n \"identifier\": \"MoeAttentionRelativeTransformerEncoderLayer\",\n \"path\": \"layers/transformer/moe_attention_relative_transformer.py\",\n \"snippet\": \"class MoeAttentionRelativeTransformerEncoderLayer(torch.nn.Module):\\n def __init__(self, d_model, nhead, moe_att_n_experts, dim_feedforward=2048, dropout=0.1, activation: ActivationFunction = F.relu,\\n attention_dropout=0, drop_expand: bool = True,\\n head_projection_size: Optional[int] = None, preln: bool = False, n_layers: Optional[int] = None,\\n att_perplexity_reg: float = 0.0, expert_dropout: float = 0.0, att_selection_mode=\\\"sigmoid\\\",\\n attention_variant=\\\"moa\\\", q_expert: bool = True, k_expert: bool = True, v_expert: bool = True,\\n o_expert: bool = True, moe_k: int = 2,\\n norm_qk_score: bool = False, v_projection_size: Optional[int] = None, same_sel: bool = False,\\n qside_n_experts: Optional[int] = None, shared_experts: bool = False,\\n kq_n_experts: Optional[int] = None, separate_kq_sel: bool = False,\\n cvloss: float = 0.0, switchloss: float = 0.0, zloss: float = 0.0,\\n moa_mode: str = \\\"my\\\", rotate_fraction: float = 0.5, rope_base: float = 10000,\\n moeatt_norm_init: bool = False):\\n super().__init__()\\n self.is_preln = preln\\n if attention_variant not in {\\\"full\\\", \\\"full_rope\\\"} and (not q_expert):\\n raise ValueError(\\\"q_expert can be disabled only when using qside attention\\\")\\n\\n if attention_variant == \\\"moa\\\":\\n self.self_attn = MoA(\\n d_model, nhead, dropout=attention_dropout,\\n projection_size=head_projection_size, init_std_scale=math.sqrt(2 / n_layers) if preln else 1.0,\\n n_experts=moe_att_n_experts, perplexity_reg=att_perplexity_reg, expert_dropout=expert_dropout,\\n selection_mode=att_selection_mode, mode=moa_mode, cvloss=cvloss, switchloss=switchloss, zloss=zloss\\n )\\n elif attention_variant == \\\"full\\\":\\n self.self_attn = FullMoeRelativeAttention(\\n d_model, nhead, dropout=attention_dropout,\\n projection_size=head_projection_size, init_std_scale=math.sqrt(2 / n_layers) if preln else 1.0,\\n n_experts=moe_att_n_experts, perplexity_reg=att_perplexity_reg, expert_dropout=expert_dropout,\\n selection_mode=att_selection_mode, q_expert=q_expert, k_expert=k_expert, v_expert=v_expert,\\n norm_qk_score=norm_qk_score, v_projection_size=v_projection_size, same_sel=same_sel,\\n o_expert=o_expert, moe_k=moe_k, qside_n_experts=qside_n_experts,\\n shared_experts=shared_experts, kq_n_experts=kq_n_experts, separate_kq_sel=separate_kq_sel,\\n normalize_init=moeatt_norm_init\\n )\\n elif attention_variant == \\\"full_rope\\\":\\n self.self_attn = FullMoeRopeAttention(\\n d_model, nhead, dropout=attention_dropout,\\n projection_size=head_projection_size, init_std_scale=math.sqrt(2 / n_layers) if preln else 1.0,\\n n_experts=moe_att_n_experts, perplexity_reg=att_perplexity_reg, expert_dropout=expert_dropout,\\n selection_mode=att_selection_mode, q_expert=q_expert, k_expert=k_expert, v_expert=v_expert,\\n norm_qk_score=norm_qk_score, v_projection_size=v_projection_size, same_sel=same_sel,\\n o_expert=o_expert, moe_k=moe_k, qside_n_experts=qside_n_experts,\\n shared_experts=shared_experts, kq_n_experts=kq_n_experts, separate_kq_sel=separate_kq_sel,\\n rotate_fraction=rotate_fraction, rope_base=rope_base,\\n normalize_init=moeatt_norm_init\\n )\\n else:\\n raise ValueError(f\\\"Unknown attention variant: {attention_variant}\\\")\\n\\n self.linear1 = torch.nn.Linear(d_model, dim_feedforward)\\n self.dropout = torch.nn.Dropout(dropout) if drop_expand else lambda x: x\\n self.linear2 = torch.nn.Linear(dim_feedforward, d_model)\\n\\n self.norm1 = torch.nn.LayerNorm(d_model)\\n self.norm2 = torch.nn.LayerNorm(d_model)\\n self.dropout1 = torch.nn.Dropout(dropout)\\n self.dropout2 = torch.nn.Dropout(dropout)\\n\\n self.activation = activation\\n\\n if preln:\\n if n_layers is None:\\n raise ValueError(\\\"n_layers must be specified when using preln\\\")\\n reset_prenorm_params(self, n_layers)\\n else:\\n self.reset_parameters()\\n\\n def forward(self, src: torch.Tensor, mask: Optional[AttentionMask] = None, attend_to: Optional[torch.Tensor] = None,\\n pos_offset: Optional[int] = None) -> torch.Tensor:\\n src2 = self.norm1(src) if self.is_preln else src\\n src2 = self.self_attn(src2, self.norm1(attend_to) if attend_to is not None else src2, mask, pos_offset=pos_offset)\\n src = src + self.dropout1(src2)\\n\\n if self.is_preln:\\n src2 = self.norm2(src)\\n else:\\n src2 = src = self.norm1(src)\\n\\n src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\\n src = src + self.dropout2(src2)\\n\\n if not self.is_preln:\\n src = self.norm2(src)\\n return src\\n\\n def reset_parameters(self):\\n torch.nn.init.xavier_normal_(self.linear1.weight, gain=torch.nn.init.calculate_gain('relu')\\n if self.activation is F.relu else 1.0)\\n torch.nn.init.xavier_uniform_(self.linear2.weight)\"\n },\n {\n \"identifier\": \"MoE\",\n \"path\": \"layers/moe_layer.py\",\n \"snippet\": \"class MoE(LoggingLayer, RegularizedLayer, OncePerIterLayer, torch.nn.Module):\\n def __init__(self, dmodel: int, n_experts: int, expert_size: int, n_heads: int,\\n dropout: float = 0, weight_scale: float = 1.0,\\n dropout_mode: str = \\\"none\\\", selection_mode: str = \\\"sigmoid\\\", perplexity_reg: float = 0.0,\\n norm_keys: bool = False,\\n perplexity_reg_mode: str=\\\"step\\\", n_random: int = 0, reg_type: str = \\\"entropy\\\",\\n topk_mode: str = \\\"full\\\", activation_after_topk: bool = False,\\n activation = lambda x: F.relu(x, inplace=True),\\n normalize_expert_sel_init: bool = False, norm_key_init: bool = False, norm_value_init: bool = False,\\n identical_init: bool = False,\\n rescale_normed: bool = False, sel_norm: str = \\\"none\\\",\\n v_dim: Optional[int] = None,\\n expert_dropout: float = 0.0,\\n sync_distributed: bool = False,\\n modulation_amplitude: float = 0.5,\\n ppl_past_blocks: int = 0):\\n\\n super().__init__()\\n self.k_dim = dmodel\\n self.v_dim = v_dim if v_dim is not None else dmodel\\n self.n_experts = n_experts\\n self.expert_size = expert_size\\n self.size = self.n_experts * self.expert_size\\n self.dropout = dropout\\n self.dropout_mode = dropout_mode\\n self.selection_mode = selection_mode\\n self.perplexity_reg = perplexity_reg\\n self.k_vec_dim = self.k_dim\\n self.n_heads = n_heads\\n self.norm_keys = norm_keys\\n self.perplexity_reg_mode = perplexity_reg_mode\\n self.n_random = n_random\\n self.reg_type = reg_type\\n self.topk_mode = topk_mode\\n self.activation_after_topk = activation_after_topk\\n self.activation = activation\\n self.weight_scale = weight_scale\\n self.normalize_expert_sel_init = normalize_expert_sel_init\\n self.norm_key_init = norm_key_init\\n self.norm_value_init = norm_value_init\\n self.identical_init = identical_init\\n self.layer = 0\\n self.initalized = False\\n self.rescale_normed = rescale_normed\\n self.sel_norm = sel_norm\\n self.was_training = True\\n self.expert_dropout = expert_dropout\\n self.reg_counts = 0\\n self.sync_distributed = sync_distributed and torch.distributed.is_initialized()\\n self.modulation_amplitude = modulation_amplitude\\n self.record_all_expert_sel_counts = False\\n self.ppl_past_blocks = ppl_past_blocks\\n self.blocks_for_ppl = []\\n self.recorded_inputs = []\\n\\n self.coocurence = None\\n\\n assert self.selection_mode in {\\\"gate\\\", \\\"sigmoid\\\", \\\"sinkhorn\\\", \\\"sinkhorn2\\\", \\\"sinkmoid\\\", \\\"sinkmax\\\", \\\"sinkhorn_local\\\", \\\"mul\\\", \\\"sinkmoid2\\\", \\\"sinkmax2\\\"}\\n assert self.perplexity_reg_mode in {\\\"step\\\", \\\"global\\\", \\\"time\\\", \\\"global_time\\\"}\\n assert self.dropout_mode in {\\\"none\\\", \\\"score\\\"}\\n assert self.reg_type in {\\\"perplexity\\\", \\\"variance\\\", \\\"entropy\\\", \\\"l2\\\", \\\"switch\\\"}\\n assert self.topk_mode in {\\\"full\\\", \\\"l1_approx\\\", \\\"approx\\\"}\\n assert self.sel_norm in {\\\"none\\\", \\\"cos\\\", \\\"input\\\", \\\"weights\\\"}\\n\\n self.register_buffer(\\\"iter\\\", torch.tensor(0, dtype=torch.int64), persistent=False)\\n\\n if selection_mode in {\\\"mul\\\"} and activation_after_topk:\\n raise ValueError(\\\"Activation after topk is not supported with mul selection\\\")\\n\\n self.keys = torch.nn.Parameter(torch.empty(self.n_experts, self.k_vec_dim, self.expert_size))\\n\\n self.values = torch.nn.Parameter(torch.empty(self.n_experts, self.expert_size, self.v_dim))\\n\\n self.expert_sel = torch.nn.Parameter(torch.empty(self.n_experts, self.k_vec_dim))\\n self.sel = lambda x: F.linear(x, self.expert_sel)\\n\\n torch.nn.init.normal_(self.expert_sel, std=self.k_vec_dim ** -0.5 * weight_scale)\\n torch.nn.init.normal_(self.keys, std=dmodel ** -0.5 * weight_scale)\\n torch.nn.init.normal_(self.values, std=self.size ** -0.5 * weight_scale)\\n self.sel_hist = []\\n self.index_sel_counts = 0\\n self.index_sel_norm = 0\\n\\n self.index_sel_counts_100 = 0\\n self.index_sel_norm_100 = 0\\n\\n self.sel_count_log = None\\n\\n self.all_expert_sel_counts = []\\n self.all_expert_sel_soft = []\\n\\n self.register_buffer(\\\"kv_sel_counts\\\", torch.zeros(self.n_experts, self.expert_size), persistent=False)\\n self.register_buffer(\\\"kv_sel_counts_100\\\", torch.zeros_like(self.kv_sel_counts))\\n\\n if self.rescale_normed and self.sel_norm != \\\"none\\\":\\n self.sel_scale = torch.nn.Parameter(torch.ones([1]))\\n else:\\n self.sel_scale = 1.0\\n\\n self.register_buffer(\\\"seq\\\", torch.arange(max(self.n_heads, self.n_experts, self.k_dim, self.v_dim), dtype=torch.long), persistent=False)\\n self.regroup_weights()\\n\\n if self.ppl_past_blocks > 0 and self.reg_type not in {\\\"perplexity\\\", \\\"entropy\\\"}:\\n print(f\\\"Warning: ppl_past_blocks>0 (currently {self.ppl_past_blocks}) is only supported with perplexity and entropy regularization\\\")\\n\\n def keys_to_logical_order(self, keys: torch.Tensor) -> torch.Tensor:\\n k = keys.view(self.n_experts, self.k_vec_dim, self.expert_size)\\n return k.permute(0, 2, 1).contiguous().view(-1, self.k_vec_dim)\\n\\n def keys_from_logical_order(self, keys: torch.Tensor) -> torch.Tensor:\\n return keys.view(self.n_experts, self.expert_size, self.k_vec_dim).permute(0, 2, 1).contiguous().view(self.n_experts * self.k_vec_dim, self.expert_size)\\n\\n def renorm_keep_std(self, weight: torch.Tensor, dim: int = 0):\\n with torch.no_grad():\\n std = weight.std()\\n weight.div_(weight.norm(dim=dim, keepdim=True))\\n weight.mul_(std / weight.std())\\n\\n def regroup_weights(self) -> Optional[torch.Tensor]:\\n with torch.no_grad():\\n if self.norm_key_init:\\n self.renorm_keep_std(self.keys.view(self.n_experts, self.k_vec_dim, self.expert_size), dim=1)\\n\\n if self.norm_value_init:\\n self.renorm_keep_std(self.values, dim=1)\\n\\n if self.identical_init:\\n k = self.keys.view(self.n_experts, self.k_vec_dim, self.expert_size)\\n self.keys.set_(k[:1].expand_as(k).reshape_as(self.keys))\\n\\n v = self.values.view(self.n_experts, self.expert_size, self.v_dim)\\n self.values.set_(v[:1].expand_as(v).reshape_as(self.values))\\n\\n if self.normalize_expert_sel_init:\\n self.renorm_keep_std(self.expert_sel, dim=1)\\n\\n def ani(self, x: torch.Tensor) -> torch.Tensor:\\n assert x.ndim == 2\\n chunk_size = 32\\n\\n xnorm = F.normalize(x, 2, dim=-1)\\n\\n accu = 0\\n for i in range(0, x.shape[0], chunk_size):\\n a = xnorm[i: i + chunk_size]\\n sims = xnorm @ a.T\\n sims[i : i + chunk_size].fill_diagonal_(0)\\n accu += sims.sum()\\n\\n return accu / (x.shape[0] * (x.shape[0] - 1))\\n\\n def log_expert_sel_usage(self, prefix: str, channel_sel_counts: torch.Tensor):\\n sel_nonzero = (channel_sel_counts != 0).type(torch.float).sum(axis=-1) / self.expert_size\\n self.log(f\\\"{prefix}/mean\\\", sel_nonzero.mean())\\n self.log(f\\\"{prefix}/min\\\", sel_nonzero.min())\\n self.log(f\\\"{prefix}/max\\\", sel_nonzero.max())\\n\\n\\n def pre_train_forward(self):\\n if self.norm_keys:\\n with torch.no_grad():\\n self.keys.div_(self.keys.norm(dim=-1, keepdim=True))\\n\\n if self.training and not self.was_training:\\n sorted_counts = self.index_sel_counts.sort(descending=True).values\\n self.log(\\\"test_exert_channel_usage\\\", framework.visualize.plot.Barplot(sorted_counts, xlabel=\\\"expert\\\", ylabel=\\\"usage count\\\"), drop_old=True)\\n\\n self.layer = 0\\n if self.sel_hist:\\n self.sel_hist = []\\n self.index_sel_counts = 0\\n self.index_sel_norm = 0\\n self.reg_counts = 0\\n\\n def before_loss(self):\\n if self.sel_hist:\\n # Concatenate against time dimension. Important for the within-batch regularization\\n sel = torch.cat(self.sel_hist, -2)\\n self.add_perplexity_reg(sel)\\n\\n self.sel_hist = []\\n\\n if self.index_sel_norm > 0:\\n if self.training:\\n with torch.no_grad():\\n self.log(\\\"usag_rel_perplexity_all_layers\\\", utils.relative_perplexity(self.index_sel_counts / self.index_sel_norm))\\n self.log(\\\"dead_expert_proportion_all_layers\\\", (self.index_sel_counts == 0).float().sum() / self.n_experts)\\n\\n self.log_expert_sel_usage(\\\"exert_channel_usage\\\", self.kv_sel_counts)\\n\\n self.kv_sel_counts_100.add_(self.kv_sel_counts)\\n self.kv_sel_counts.zero_()\\n\\n self.index_sel_counts_100 = self.index_sel_counts_100 + self.index_sel_counts\\n self.index_sel_norm_100 = self.index_sel_norm_100 + self.index_sel_norm\\n\\n if self.training and self.iter % 100 == 0:\\n norm_cnt = self.index_sel_counts_100 / self.index_sel_norm_100\\n self.log(\\\"usag_rel_perplexity_100\\\", utils.relative_perplexity(norm_cnt))\\n self.log(\\\"dead_expert_proportion_100\\\", (self.index_sel_counts_100 == 0).float().sum() / self.n_experts)\\n\\n sorted_counts = self.index_sel_counts_100.sort(descending=True).values\\n self.log(\\\"usage_counts_100\\\", framework.visualize.plot.Barplot(sorted_counts, xlabel=\\\"expert\\\", ylabel=\\\"usage count\\\"), drop_old=True)\\n\\n\\n self.log_expert_sel_usage(\\\"exert_channel_usage_100\\\", self.kv_sel_counts_100)\\n self.kv_sel_counts_100.zero_()\\n\\n self.index_sel_counts_100 = 0\\n self.index_sel_norm_100 = 0\\n\\n self.log(\\\"ani/keys\\\", self.ani(self.keys_to_logical_order(self.keys)))\\n self.log(\\\"ani/values\\\", self.ani(self.values.flatten(0, -2)))\\n self.log(\\\"ani/expert_sel\\\", self.ani(self.expert_sel.T))\\n\\n if self.training:\\n self.iter += 1\\n\\n def topk(self, x: torch.Tensor, k: int, approx: bool) -> Tuple[torch.Tensor, torch.Tensor]:\\n if approx:\\n x = x.view(*x.shape[:-1], k, -1)\\n scores, ind = x.max(-1)\\n return scores, self.seq[:k] * x.shape[-1] + ind\\n else:\\n return x.topk(k, dim=-1, sorted=False)\\n\\n def rolling_logsumexp(self, x: torch.Tensor) -> torch.Tensor:\\n # Simulate calculating logsumexp over a bigger batch than the current one. Will have stale values, but that\\n # should not matter much later in training.\\n if self.ppl_past_blocks == 0 or not self.training:\\n return F.log_softmax(x, dim=-1)\\n else:\\n if len(self.blocks_for_ppl) == self.ppl_past_blocks:\\n self.blocks_for_ppl.pop(0)\\n\\n self.blocks_for_ppl.append(x)\\n res = F.log_softmax(torch.cat(self.blocks_for_ppl, dim=0), dim=-1)\\n self.blocks_for_ppl[-1] = self.blocks_for_ppl[-1].detach()\\n return res\\n\\n def add_perplexity_reg(self, sel: torch.Tensor):\\n sync_distributed = self.sync_distributed and (self.perplexity_reg_mode not in {\\\"time\\\", \\\"global_time\\\"})\\n\\n if self.perplexity_reg_mode in {\\\"time\\\", \\\"global_time\\\"}:\\n sel = sel.flatten(0, -3)\\n else:\\n sel = sel.flatten(0, -2)\\n\\n # Note: sel are raw logits, no matter what activation is used\\n if self.perplexity_reg > 0:\\n if self.reg_type == \\\"perplexity\\\":\\n sel_d = self.rolling_logsumexp(sel)\\n sel_d = framework.utils.distributed_ops.log_mean(sel_d, -2, self.sync_distributed)\\n loss = lambda: self.perplexity_reg * ( - utils.relative_perplexity_l(sel_d).mean())\\n elif self.reg_type == \\\"entropy\\\":\\n sel_d = self.rolling_logsumexp(sel)\\n sel_d = framework.utils.distributed_ops.log_mean(sel_d, -2, self.sync_distributed)\\n loss = lambda: self.perplexity_reg * ( - utils.entropy_l(sel_d).mean())\\n elif self.reg_type == \\\"variance\\\":\\n if sync_distributed:\\n raise NotImplementedError(\\\"Variance regularization is not supported in distributed mode\\\")\\n avg_sel = sel.mean(-2)\\n loss = lambda: self.perplexity_reg * avg_sel.var(-1).mean()\\n elif self.reg_type == \\\"l2\\\":\\n loss = lambda: self.perplexity_reg * sel.pow(2).mean()\\n elif self.reg_type == \\\"switch\\\":\\n if sync_distributed:\\n torch.distributed.all_reduce(self.reg_counts, op=torch.distributed.ReduceOp.SUM)\\n\\n p_sel_real = self.reg_counts / self.reg_counts.sum(-1, keepdims=True)\\n if self.perplexity_reg_mode in {\\\"time\\\", \\\"global_time\\\"}:\\n p_sel_real = p_sel_real.unsqueeze(-2)\\n\\n loss = lambda: self.perplexity_reg * (F.softmax(sel, dim=-1) * p_sel_real).mean()\\n self.reg_counts = 0\\n else:\\n assert False\\n\\n self.add_reg(loss, \\\"moe\\\")\\n\\n def compute_scores(self, input: torch.Tensor, index: CVMMSel, expert_scores: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\\n scores = cvmm(input, index, self.keys)\\n\\n if self.selection_mode in {\\\"mul\\\"}:\\n scores = scores * expert_scores[..., None]\\n elif self.selection_mode in {\\\"gate\\\", \\\"sigmoid\\\", \\\"sinkhorn\\\", \\\"sinkhorn2\\\", \\\"sinkmoid\\\", \\\"sinkmax\\\", \\\"sinkmoid2\\\"}:\\n # Handle it later\\n pass\\n\\n scores = self.activation(scores)\\n\\n plot_training = self.train and self.iter % 10 == 0\\n if plot_training:\\n with torch.no_grad():\\n gt0 = (scores > 0).float()\\n gt0_s = gt0.sum()\\n\\n if plot_training:\\n self.log(\\\"relu_pass_rate\\\", gt0_s / scores.numel())\\n\\n self.kv_sel_counts.index_add_(0, index.raw_sel.flatten(), gt0.flatten(end_dim=-2))\\n\\n if self.dropout > 0 and self.dropout_mode != \\\"none\\\":\\n scores = F.dropout(scores, self.dropout, training=self.training)\\n\\n return scores\\n\\n def sel_activation(self, sel: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]:\\n reg_sel = sel\\n if self.selection_mode in {\\\"sigmoid\\\"}:\\n sel = torch.sigmoid(sel)\\n elif self.selection_mode in {\\\"mul\\\"}:\\n sel = sel.abs()\\n reg_sel = sel\\n elif self.selection_mode in {\\\"gate\\\"}:\\n sel = F.softmax(sel, dim=-1)\\n with torch.no_grad():\\n self.log(\\\"expert_rel_perplexity_per_selection\\\", utils.relative_perplexity(sel).mean())\\n else:\\n assert False\\n\\n return sel, reg_sel\\n\\n def forward(self, input: torch.Tensor) -> torch.Tensor:\\n out = 0\\n\\n in1 = in2 = input\\n\\n sel = self.sel(in1)\\n # sel=sel.float()\\n\\n if self.sel_norm == \\\"cos\\\":\\n sel = sel / (in1.norm(dim=-1, keepdim=True) * self.expert_sel.norm(dim=-1)[None]) * self.sel_scale\\n elif self.sel_norm == \\\"weights\\\":\\n sel = sel * (self.sel_scale / self.expert_sel.norm(dim=-1)[None])\\n elif self.sel_norm == \\\"input\\\":\\n sel = sel * (self.sel_scale / in1.norm(dim=-1, keepdim=True))\\n\\n sel_raw = reg_sel = sel\\n\\n inv_val = float(\\\"-inf\\\")\\n\\n if not self.activation_after_topk:\\n # Sinkhorn should be always applied before top-k\\n sel, reg_sel = self.sel_activation(sel, input.shape[-2])\\n inv_val = 0\\n\\n if self.training and self.expert_dropout > 0:\\n if self.selection_mode not in {\\\"sigmoid\\\", \\\"gate\\\"}:\\n raise ValueError(\\\"Expert dropout not supported in this mode\\\")\\n\\n mask = torch.rand_like(sel) < self.expert_dropout\\n sel2 = sel.masked_fill(mask, inv_val)\\n else:\\n sel2 = sel\\n\\n sel_val, sel_index = self.topk(sel2, self.n_heads, self.topk_mode in {\\\"l1_approx\\\", \\\"approx\\\"})\\n\\n if self.activation_after_topk or (self.selection_mode in {\\\"mul\\\"}):\\n sel_val = torch.gather(sel_raw, -1, sel_index)\\n sel_val, reg_sel = self.sel_activation(sel_val, input.shape[-2])\\n\\n\\n record_counts_now = (self.training and self.iter % 10 == 0) or (not self.training) or (self.record_all_expert_sel_counts)\\n\\n if not self.training:\\n sel_index_flat = sel_index.flatten(end_dim=-2)\\n if self.coocurence is None:\\n self.coocurence = torch.zeros([self.n_experts, self.n_experts], device=sel_index_flat.device, dtype=torch.long)\\n\\n for h1 in range(self.n_heads):\\n for h2 in range(self.n_heads):\\n ind_flat = sel_index_flat[..., h1] * self.n_experts + sel_index_flat[..., h2]\\n values = torch.tensor([1], device=self.coocurence.device, dtype=self.coocurence.dtype).expand_as(ind_flat)\\n # values = sel_val[..., h2].flatten()\\n self.coocurence.flatten().put_(ind_flat, values, accumulate=True)\\n # self.coocurence[sel_index_flat[..., h1], sel_index_flat[..., h2]] += 1\\n\\n if record_counts_now or self.reg_type == \\\"switch\\\":\\n reg_counts = F.one_hot(sel_index, self.n_experts).type_as(input)\\n\\n if self.reg_type == \\\"switch\\\":\\n reg_counts2 = reg_counts.view(*input.shape[:-2], input.shape[-2] * self.n_heads, self.n_experts)\\n if self.perplexity_reg_mode == \\\"time\\\":\\n reg_counts2 = reg_counts2.sum(-2)\\n else:\\n reg_counts2 = reg_counts2.flatten(end_dim=-2).sum(0)\\n\\n self.reg_counts = self.reg_counts + reg_counts2\\n\\n if record_counts_now:\\n with torch.no_grad():\\n sel_counts = reg_counts.flatten(end_dim=-2).sum(0)\\n cnt = sel_index.nelement()\\n\\n p_expert_sel = sel_counts / cnt\\n\\n self.index_sel_counts = self.index_sel_counts + sel_counts\\n self.index_sel_norm = self.index_sel_norm + cnt\\n\\n if self.record_all_expert_sel_counts:\\n softcnt = torch.zeros_like(sel_counts, dtype=sel_val.dtype)\\n softcnt.index_add_(0, sel_index.flatten(), sel_val.flatten())\\n\\n self.all_expert_sel_soft.append(softcnt)\\n self.all_expert_sel_counts.append(sel_counts)\\n\\n if self.training:\\n self.log(\\\"min_sel_score\\\", sel_val.min(dim=-1).values.mean())\\n self.log(\\\"max_sel_score\\\", sel_val.max(dim=-1).values.mean())\\n\\n sel_oh = F.one_hot(sel_index, self.n_experts).sum(-2).bool()\\n if self.layer >= 1 and self.training:\\n self.log(f\\\"layer_sel_overlap_{self.layer}\\\", ((self.prev_sel_oh & sel_oh).sum(-1).float() / self.n_heads).mean())\\n\\n self.prev_sel_oh = sel_oh\\n\\n ppl = utils.relative_perplexity(p_expert_sel)\\n self.log(\\\"usage_rel_perplexity\\\", ppl)\\n self.log(\\\"dead_expert_proportion\\\", (p_expert_sel == 0).float().sum() / self.n_experts)\\n\\n if self.perplexity_reg_mode in {\\\"step\\\", \\\"time\\\"}:\\n self.add_perplexity_reg(reg_sel)\\n elif self.perplexity_reg > 0 and self.training:\\n self.sel_hist.append(reg_sel)\\n\\n sel_indices = cvmm_prepare_sel2(sel_index.int())\\n\\n scores = self.compute_scores(in2, sel_indices, sel_val)\\n\\n sel_indices = sel_indices.clone()\\n sel_indices.reduction_weight = sel_val\\n sel_indices.sel_index = sel_indices.out_index\\n sel_indices.out_index = None\\n\\n if self.selection_mode not in {\\\"gate\\\", \\\"sigmoid\\\"}:\\n sel_indices.reduction_weight = torch.ones_like(sel_indices.reduction_weight)\\n\\n out = cvmm(scores, sel_indices, self.values)\\n\\n self.layer += 1\\n\\n self.was_training = self.training\\n res = out.view(*input.shape[:-1], self.v_dim)\\n return res\\n\\n def dump_logs(self, save_dir: str):\\n if self.coocurence is not None:\\n os.makedirs(save_dir, exist_ok=True)\\n torch.save(self.coocurence, os.path.join(save_dir, \\\"coocurence.pt\\\"))\\n\\n def get_logs(self) -> Dict[str, Any]:\\n res = super().get_logs()\\n\\n if self.coocurence is not None:\\n coo = self.coocurence / self.coocurence.diagonal().clamp(min=1)[:, None]\\n res[\\\"expert_coocurence\\\"] = framework.visualize.plot.Heatmap(coo, xlabel=\\\"expert\\\", ylabel=\\\"expert\\\", textval=False)\\n self.coocurence = None\\n return res\"\n },\n {\n \"identifier\": \"Result\",\n \"path\": \"interfaces/result.py\",\n \"snippet\": \"class Result:\\n outputs: torch.Tensor\\n loss: torch.Tensor\\n\\n batch_dim = 0\\n\\n def plot(self) -> Dict[str, Any]:\\n return {}\\n\\n @property\\n def batch_size(self) -> int:\\n return self.outputs.shape[self.batch_dim]\\n\\n @staticmethod\\n def merge(l: List, batch_weights: Optional[List[float]] = None):\\n if len(l) == 1:\\n return l[0]\\n batch_weights = batch_weights if batch_weights is not None else [1] * len(l)\\n loss = sum([r.loss * w for r, w in zip(l, batch_weights)]) / sum(batch_weights)\\n out = torch.cat([r.outputs for r in l], l[0].batch_dim)\\n return l[0].__class__(out, loss)\"\n },\n {\n \"identifier\": \"LayerVisualizer\",\n \"path\": \"layers/layer_with_visualization.py\",\n \"snippet\": \"class LayerVisualizer:\\n def __init__(self, module: torch.nn.Module, options: Dict[str, Any] = {}):\\n self.modules = []\\n self.options = options\\n self.curr_options = None\\n for n, m in module.named_modules():\\n if isinstance(m, LayerWithVisualization):\\n self.modules.append((n, m))\\n\\n def plot(self) -> Dict[str, Any]:\\n res = {}\\n for n, m in self.modules:\\n res.update({f\\\"{n}/{k}\\\": v for k, v in m.plot(self.curr_options).items()})\\n m.visualization_enabled = False\\n\\n self.curr_options = None\\n return res\\n\\n def prepare(self, options: Dict[str, Any] = {}):\\n self.curr_options = self.options.copy()\\n self.curr_options.update(options)\\n\\n for _, m in self.modules:\\n m.prepare()\\n m.visualization_enabled = True\"\n },\n {\n \"identifier\": \"FullMoeRelativeAttentionCore\",\n \"path\": \"layers/transformer/full_moe_relative_attention.py\",\n \"snippet\": \"class FullMoeRelativeAttentionCore(LayerWithVisualization, LoggingLayer, RegularizedLayer, OncePerIterLayer, torch.nn.Module):\\n def __init__(self, state_size: int, n_heads: int, n_experts: int, dropout: float = 0.0, input_size: Optional[int] = None,\\n projection_size: Optional[int] = None, output_size: Optional[int] = None, init_std_scale: float = 1.0,\\n perplexity_reg: float = 0, share_pk: bool = True, expert_dropout: float = 0.0,\\n selection_mode: str = \\\"sigmoid\\\", moe_k: int = 2, q_expert: bool = True,\\n k_expert: bool = True, v_expert: bool = True, o_expert: bool = True, norm_qk_score: bool = False,\\n v_projection_size: Optional[int] = None, same_sel: bool = False,\\n qside_n_experts: Optional[int] = None, shared_experts: bool = False,\\n kq_n_experts: Optional[int] = None, separate_kq_sel: bool = False,\\n normalize_init: bool = False, normalize_retrieval: bool = False):\\n\\n super().__init__()\\n\\n self.input_size = input_size or state_size\\n self.output_size = output_size or state_size\\n self.pe_size = self.input_size\\n self.perplexity_reg = perplexity_reg\\n self.share_pk = share_pk\\n self.expert_dropout = expert_dropout\\n self.selection_mode = selection_mode\\n self.iter = 0\\n self.moe_k = moe_k\\n self.norm_qk_score = norm_qk_score\\n self.same_sel = same_sel\\n self.shared_experts = shared_experts\\n self.init_std_scale = init_std_scale\\n self.normalize_init = normalize_init\\n self.attention_to_visualize = []\\n self.selections_to_visualize = {}\\n\\n self.is_expert = {\\n \\\"k\\\": k_expert,\\n \\\"q\\\": q_expert,\\n \\\"v\\\": v_expert,\\n \\\"o\\\": o_expert\\n }\\n self.n_experts = {\\n \\\"k\\\": kq_n_experts or n_experts,\\n \\\"q\\\": kq_n_experts or qside_n_experts or n_experts,\\n \\\"v\\\": n_experts,\\n \\\"o\\\": qside_n_experts or n_experts\\n }\\n\\n self.separate_k_sel = separate_kq_sel or (self.n_experts[\\\"k\\\"] != self.n_experts[\\\"v\\\"])\\n self.separate_q_sel = separate_kq_sel or (self.n_experts[\\\"q\\\"] != self.n_experts[\\\"o\\\"])\\n\\n self.sel_hist = {}\\n self.sel_counts_100 = {}\\n\\n self.n_heads = n_heads\\n self.dropout = torch.nn.Dropout(dropout) if dropout > 0 else lambda x: x\\n self.projection_size = projection_size or (state_size // n_heads)\\n self.v_projection_size = v_projection_size or self.projection_size\\n\\n self.std_in = init_std_scale * math.sqrt(1 / self.input_size)\\n std_out = init_std_scale * math.sqrt(1 / (n_heads * self.v_projection_size))\\n\\n self.create_selection_logic()\\n\\n self.src_side_maps = {\\\"k\\\", \\\"v\\\"}\\n\\n self.projections = torch.nn.ParameterDict({\\n \\\"q\\\": self.create_param_block(\\\"q\\\", self.input_size, self.projection_size, self.std_in),\\n \\\"k\\\": self.create_param_block(\\\"k\\\", self.input_size, self.projection_size, self.std_in),\\n \\\"v\\\": self.create_param_block(\\\"v\\\", self.input_size, self.v_projection_size, self.std_in),\\n \\\"o\\\": self.create_param_block(\\\"o\\\", self.v_projection_size, self.output_size, std_out),\\n })\\n\\n if normalize_retrieval:\\n self.norm_ret = torch.nn.LayerNorm(self.projection_size)\\n else:\\n self.norm_ret = lambda x: x\\n\\n self.sel_correlation = 0\\n\\n self.register_buffer(\\\"scale\\\", torch.full([1], 1.0 / math.sqrt(self.projection_size)), persistent=False)\\n\\n def renorm_keep_std(self, weight: torch.Tensor, dim: int = 0):\\n with torch.no_grad():\\n std = weight.std()\\n weight.div_(weight.norm(dim=dim, keepdim=True))\\n weight.mul_(std / weight.std())\\n\\n def get_n_copies(self, name: str):\\n return self.n_heads\\n\\n def create_param_block(self, name: str, in_size: int, out_size: int, std: float):\\n n_copies = self.get_n_copies(name)\\n\\n if self.is_expert[name]:\\n exp_mul = 1 if self.shared_experts else n_copies\\n p = torch.nn.Parameter(torch.randn(exp_mul * self.n_experts[name], in_size, out_size) * std)\\n if self.normalize_init:\\n self.renorm_keep_std(p, dim=0)\\n return p\\n else:\\n if name == \\\"o\\\":\\n in_size = n_copies * in_size\\n else:\\n out_size = n_copies * out_size\\n return torch.nn.Parameter(torch.randn(out_size, in_size) * std)\\n\\n def create_selection_logic(self):\\n sels_params = {}\\n self.sel_map = {}\\n\\n def register_remap(dest: str, src: str) -> bool:\\n if not (src in sels_params or src in self.sel_map):\\n # src is not defined\\n return False\\n\\n assert self.n_experts[src] == self.n_experts[dest]\\n self.sel_map[dest] = self.sel_map.get(src, src)\\n return True\\n\\n if self.is_expert[\\\"o\\\"]:\\n sels_params[\\\"o\\\"] = self.init_sel(\\\"o\\\", self.std_in)\\n\\n if self.is_expert[\\\"q\\\"] and (self.separate_q_sel or not register_remap(\\\"q\\\", \\\"o\\\")):\\n sels_params[\\\"q\\\"] = self.init_sel(\\\"q\\\", self.std_in)\\n\\n if self.is_expert[\\\"v\\\"] and ((not self.same_sel) or not register_remap(\\\"v\\\", \\\"o\\\")):\\n sels_params[\\\"v\\\"] = self.init_sel(\\\"v\\\", self.std_in)\\n\\n if self.is_expert[\\\"k\\\"]:\\n if (not (self.same_sel and self.separate_k_sel and register_remap(\\\"k\\\", \\\"q\\\"))) and (self.separate_k_sel or not register_remap(\\\"k\\\", \\\"v\\\")):\\n sels_params[\\\"k\\\"] = self.init_sel(\\\"k\\\", self.std_in)\\n\\n self.selections = torch.nn.ParameterDict(sels_params)\\n\\n def init_sel(self, name: str, std: float) -> torch.nn.Parameter:\\n n_copies = self.get_n_copies(name)\\n n_experts = self.n_experts[name]\\n sel = torch.nn.Parameter(torch.randn(n_experts*n_copies, self.input_size) * std)\\n self.renorm_rows(sel)\\n return sel\\n\\n def renorm_rows(self, x: torch.Tensor):\\n with torch.no_grad():\\n std_t = x.std(dim=-1, keepdim=True)\\n x.div_(x.norm(dim=-1, keepdim=True))\\n x.mul_(std_t / x.std())\\n\\n\\n def project_to_torch_order(self, x: torch.Tensor):\\n return x.view(*x.shape[:-1], self.get_n_copies(\\\"k\\\"), -1).transpose(-2, -3)\\n\\n def get_mask_tensor(self, src_len: int, mask: Optional[AttentionMask]) -> Optional[torch.Tensor]:\\n if mask is None or (mask.position_mask is None and mask.src_length_mask is None):\\n return None\\n\\n # mask.position_mask: [..., N_out, N_in]\\n # mask.src_length_mask: [B, ...., N_in]\\n # True where it has to be masked\\n\\n if mask.position_mask is not None:\\n n_pad = src_len - mask.position_mask.shape[-1]\\n if n_pad > 0:\\n pm = F.pad(mask.position_mask, (n_pad, 0), 'constant', value=False)\\n else:\\n pm = mask.position_mask\\n\\n if mask.position_mask is None:\\n m = mask.src_length_mask.unsqueeze(-2).unsqueeze(-2)\\n elif mask.src_length_mask is None:\\n m = pm\\n else:\\n m = mask.src_length_mask.unsqueeze(-2).unsqueeze(-2) | pm\\n\\n return m\\n\\n def train(self, mode: bool = True):\\n self.sel_hist = {}\\n return super().train(mode)\\n\\n def get_lost_on_hist(self, l: List[torch.Tensor]) -> torch.Tensor:\\n assert l[0].ndim == 4\\n l = [t.flatten(1,2) for t in l]\\n sel = torch.cat(l, -2)\\n sel_d = F.log_softmax(sel, dim=-1)\\n sel_d = framework.utils.distributed_ops.log_mean(sel_d, -2, sync_distributed=False)\\n return self.perplexity_reg * ( - utils.entropy_l(sel_d).mean())\\n\\n def get_reg_loss(self) -> Dict[str, torch.Tensor]:\\n l = super().get_reg_loss()\\n for k, v in self.sel_hist.items():\\n l[f\\\"moe_att_entropy/{k}\\\"] = self.get_lost_on_hist(v)\\n\\n self.sel_hist = {}\\n return l\\n\\n def get_sel(self, t: torch.Tensor, w: torch.Tensor, name: str) -> Selection:\\n n_experts = self.n_experts[name]\\n n_copies = self.get_n_copies(name)\\n\\n sel = F.linear(t, w).float()\\n sel = sel.view(*sel.shape[:-1], n_copies, -1)\\n with torch.no_grad():\\n if self.expert_dropout > 0 and self.training:\\n mask = torch.rand_like(sel) < self.expert_dropout\\n sel2 = sel.masked_fill(mask, float('-inf'))\\n else:\\n sel2 = sel\\n _, sel_index = sel2.topk(self.moe_k, dim=-1, sorted=False)\\n sel_val = torch.gather(sel, -1, sel_index)\\n\\n if self.selection_mode == \\\"softmax\\\":\\n sel_val = sel_val.softmax(-1)\\n elif self.selection_mode == \\\"sigmoid\\\":\\n sel_val = sel_val.sigmoid()\\n else:\\n raise ValueError(\\\"Unknown selection mode: \\\" + self.selection_mode)\\n\\n exp_shift = 0 if self.shared_experts else n_experts\\n\\n sel_index_shifted = (torch.arange(n_copies, device=sel_index.device, dtype=sel_index.dtype) * exp_shift).unsqueeze(-1) + sel_index\\n sel_index_pp = cvmm_prepare_sel2(sel_index_shifted.flatten(-2,-1), sel_val)\\n\\n return Selection(sel, sel_val, sel_index, sel_index_pp)\\n\\n def before_loss(self):\\n self.iter += 1\\n if self.iter % 100 == 0:\\n for k, v in self.sel_counts_100.items():\\n sorted_counts = v.sort(descending=True).values\\n self.log(f\\\"sel_counts/{k}\\\", framework.visualize.plot.Barplot(sorted_counts, xlabel=\\\"expert\\\", ylabel=\\\"usage count\\\"), drop_old=True)\\n\\n self.sel_counts_100 = {}\\n\\n def exp_proj(self, x: torch.Tensor, w: torch.Tensor, sel: Selection) -> torch.Tensor:\\n return cvmm(x, sel.sel_index, w)\\n\\n def compute_sel(self, curr_state: torch.Tensor, attend_to: torch.Tensor) -> Dict[str, Selection]:\\n self.selection_mode\\n outs = {}\\n done = {}\\n cross_atten = curr_state is not attend_to\\n\\n for name in (set(self.selections.keys()) | set(self.sel_map.keys())):\\n name_actual = self.sel_map.get(name, name)\\n\\n # There coukd be 2 versions of everything: source side and destination side. Check if they are different,\\n # and if not, use the cached version, my_id is the unique identifier for this transformation\\n is_src_side = (name in self.src_side_maps) or not cross_atten\\n my_id = (name_actual, is_src_side)\\n\\n cached = done.get(my_id)\\n if cached is not None:\\n outs[name] = cached\\n continue\\n\\n # No cache, actually compute\\n inp = attend_to if is_src_side else curr_state\\n v = self.selections[name_actual]\\n outs[name] = self.get_sel(inp, v, name)\\n\\n # Save history for regularization\\n if self.perplexity_reg > 0 and self.training:\\n if name not in self.sel_hist:\\n self.sel_hist[name] = []\\n self.sel_hist[name].append(outs[name].raw_sel)\\n\\n # Visualize statistics\\n if self.training and self.iter % 10 == 0:\\n self.sel_counts_100[name] = self.sel_counts_100.get(name, 0) + \\\\\\n F.one_hot(outs[name].raw_sel_index.flatten(), self.n_experts[name]).sum(0)\\n\\n done[my_id] = outs[name]\\n\\n return outs\\n\\n def project(self, name: str, src: torch.Tensor, sel: Dict[str, Selection]) -> torch.Tensor:\\n if name in sel:\\n sv = sel[name]\\n if self.norm_qk_score and name in {\\\"q\\\", \\\"k\\\"}:\\n sv.sel_index.reduction_weight = F.normalize(sv.sel_index.reduction_weight, p=1, dim=-1)\\n return self.exp_proj(src, self.projections[name], sv)\\n else:\\n return F.linear(src, self.projections[name])\\n\\n def attend(self, curr_state: torch.Tensor, attend_to: torch.Tensor, pos_offset: int, v: torch.Tensor,\\n k: torch.Tensor, q: torch.Tensor, mask: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:\\n raise NotImplementedError()\\n\\n def attention_proj(self, att: torch.Tensor, v: torch.Tensor,\\n mask: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:\\n if mask is not None:\\n att.masked_fill_(mask, float('-inf'))\\n\\n att = F.softmax(att, dim=-1)\\n\\n res = att @ v\\n return res, att\\n\\n def forward(self, curr_state: torch.Tensor, attend_to: torch.Tensor, mask: Optional[AttentionMask],\\n pos_offset: Optional[int] = None, need_weights: bool = False):\\n # curr_state: [batch_size, out_len, c]\\n # attend_to: [batch_size, in_len, c]\\n\\n if pos_offset is None:\\n assert curr_state.shape[1] == attend_to.shape[1], \\\"If attend_to has different shape than curr_state, pos_offset should be provided\\\"\\n pos_offset = 0\\n\\n sel = self.compute_sel(curr_state, attend_to)\\n\\n # scale q and k with sqrt(scale) before the attention. This should save memory, be faster, and\\n # keep the range of k and v better. It should make attention NaNs better with float16.\\n scale = self.scale.sqrt()\\n\\n q = self.project(\\\"q\\\", curr_state, sel)\\n q = q * scale.type_as(q)\\n k = self.project(\\\"k\\\", attend_to, sel)\\n k = k * scale.type_as(k)\\n v = self.project(\\\"v\\\", attend_to, sel)\\n\\n q = self.project_to_torch_order(q) if \\\"q\\\" not in sel else q.transpose(-2,-3)\\n k = self.project_to_torch_order(k) if \\\"k\\\" not in sel else k.transpose(-2,-3)\\n v = self.project_to_torch_order(v) if \\\"v\\\" not in sel else v.transpose(-2,-3)\\n\\n k = self.dropout(k)\\n\\n res, att = self.attend(curr_state, attend_to, pos_offset, v, k, q, self.get_mask_tensor(attend_to.shape[-2], mask))\\n res = self.norm_ret(res)\\n\\n if self.visualization_enabled:\\n self.attention_to_visualize.append(att[0].detach())\\n for k, s in sel.items():\\n if k not in self.selections_to_visualize:\\n self.selections_to_visualize[k] = []\\n\\n with torch.no_grad():\\n m = torch.zeros([*s.sel_val[0].shape[:-1], self.n_experts[k]], device=s.sel_val.device, dtype=s.sel_val.dtype)\\n m.scatter_(-1, s.raw_sel_index[0], s.sel_val[0])\\n\\n self.selections_to_visualize[k].append(m)\\n\\n if self.get_n_copies(\\\"k\\\") != self.get_n_copies(\\\"v\\\"):\\n res = res.view(\\n *res.shape[:-1], self.get_n_copies(\\\"v\\\") // self.get_n_copies(\\\"k\\\"), -1).transpose(2,3).flatten(1,2).contiguous()\\n\\n if self.is_expert[\\\"o\\\"]:\\n res = res.transpose(-2, -3)\\n # The output selection indices are calculated from the current state and are also used for projecting \\\"q\\\".\\n # But that projection needs to create multiple copies for the different heads. Here we already have the\\n # heads, but we have to create copies for the top-k elements. We can calculate that from the reduction\\n # weight. We also want to compute not only the weighted average between the top-k elements, but also\\n # of the different heads. So reshape the reduction weight accordingly.\\n o_sel = sel[\\\"o\\\"].sel_index.clone()\\n o_sel.sel_index = o_sel.out_index // o_sel.reduction_weight.shape[-1]\\n o_sel.reduction_weight = o_sel.reduction_weight.flatten(-2)\\n out = cvmm(res, o_sel, self.projections[\\\"o\\\"])\\n else:\\n res = res.transpose(-2, -3)\\n out = F.linear(res.contiguous().view(*curr_state.shape[:-1], -1), self.projections[\\\"o\\\"])\\n\\n return out\\n\\n def plot(self, options: Dict[str, Any]) -> Dict[str, Any]:\\n r = {}\\n marks = options.get(\\\"steplabel\\\")\\n n_steps = options.get(\\\"n_steps\\\") or 9999999\\n y_marks = options.get(\\\"target_labels\\\", marks)\\n\\n ns1 = (self.attention_to_visualize[0].shape[-2] + n_steps) if n_steps < 0 else 0\\n ns1_e = self.attention_to_visualize[0].shape[-2] if n_steps < 0 else n_steps\\n ns2 = (self.attention_to_visualize[0].shape[-1] + n_steps) if n_steps < 0 else 0\\n ns2_e = self.attention_to_visualize[0].shape[-1] if n_steps < 0 else n_steps\\n\\n if marks is not None:\\n assert len(marks) == self.attention_to_visualize[0].shape[-1]\\n marks = marks[ns2:ns2_e]\\n\\n if y_marks is not None:\\n assert len(y_marks) == self.attention_to_visualize[0].shape[-2]\\n y_marks = y_marks[ns1:ns1_e]\\n\\n if options.get(\\\"mha.plot_head_details\\\") and self.attention_to_visualize[0].shape[0] > 1:\\n for head in range(self.attention_to_visualize[0].shape[0]):\\n sel_map = {k: [e[:, head][ns1:ns1_e] if k in {'q', 'o'} else e[:, head][ns2:ns2_e] for e in v] for k, v in self.selections_to_visualize.items()}\\n selections = {k: torch.stack(v, 0).cpu() for k, v in sel_map.items()}\\n\\n x_selections = {k: v for k, v in selections.items() if k in {'k', 'v'}}\\n y_selections = {k: v for k, v in selections.items() if k in {'q', 'o'}}\\n\\n r[f\\\"head_{head}\\\"] = MoEAttentionPlot(\\n torch.stack([layer[head][ns1:ns1_e, ns2:ns2_e] for _, layer in enumerate(self.attention_to_visualize)], 0),\\n x_selections, y_selections,\\n ylabel=\\\"dest\\\", xlabel=\\\"src\\\", x_marks=marks, y_marks=y_marks)\\n\\n r[\\\"attention_max\\\"] = framework.visualize.plot.AnimatedHeatmap(\\n torch.stack([layer.max(0)[0][ns1:ns1_e, ns2:ns2_e] for _, layer in enumerate(self.attention_to_visualize)], 0),\\n ylabel=\\\"dest\\\", xlabel=\\\"src\\\", textval=False, x_marks=marks, y_marks=y_marks, ignore_wrong_marks=True)\\n\\n self.attention_to_visualize = []\\n self.selections_to_visualize = {}\\n return r\\n\\n def dump_logs(self, save_dir: str):\\n if torch.is_tensor(self.sel_correlation):\\n os.makedirs(save_dir, exist_ok=True)\\n torch.save(self.sel_correlation, os.path.join(save_dir, \\\"sel_correlation.pt\\\"))\\n\\n def get_logs(self) -> Dict[str, Any]:\\n res = super().get_logs()\\n\\n if torch.is_tensor(self.sel_correlation):\\n coo = self.sel_correlation / self.sel_correlation.flatten(1).sum(-1).clamp(min=1)[:, None, None]\\n for h in range(self.n_heads):\\n res[f\\\"expert_coocurence_{h}\\\"] = framework.visualize.plot.Heatmap(coo[h], xlabel=\\\"o expert\\\", ylabel=\\\"v expert\\\", textval=False)\\n self.sel_correlation = 0\\n return res\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import framework\nimport torch\nimport torch.nn\nimport torch.nn.functional as F\nimport torch.utils.data\nimport math\nfrom typing import List, Tuple, Dict, Any\nfrom models import TransformerLanguageModel\nfrom ... import task, args\nfrom layers.transformer import RelativeTransformerEncoderLayer, PrelnRelativeTransformerEncoderLayer\nfrom layers.transformer.relative_moe_transformer import RelativeMoeTransformerEncoderLayer\nfrom layers.transformer.fast_rope_transformer import FastRopeTransformerEncoderLayer\nfrom layers.transformer.moe_attention_relative_transformer import MoeAttentionRelativeTransformerEncoderLayer\nfrom layers.moe_layer import MoE\nfrom interfaces import Result\nfrom layers import LayerVisualizer\nfrom layers.transformer.full_moe_relative_attention import FullMoeRelativeAttentionCore"},"token_num":{"kind":"number","value":20164,"string":"20,164"},"cropped_code":{"kind":"string","value":" parser.add_argument(\"-moe.att.n_experts\", default=4)\n parser.add_argument(\"-moe.att.variant\", default=\"moa\", choice=[\"moa\", \"simple\", \"qside\", \"full\", \"full_rope\", \"seq\", \"target\"])\n parser.add_argument(\"-moe.att.enable\", default=False)\n parser.add_argument(\"-moe.att.q_expert\", default=True)\n parser.add_argument(\"-moe.att.k_expert\", default=True)\n parser.add_argument(\"-moe.att.v_expert\", default=True)\n parser.add_argument(\"-moe.att.o_expert\", default=True)\n parser.add_argument(\"-moe.att.k\", default=2)\n parser.add_argument(\"-moe.att.norm_qk\", default=False)\n parser.add_argument(\"-moe.att.v_size\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-moe.att.same_sel\", default=False)\n parser.add_argument(\"-moe.att.expert_dropout\", default=\"none\", parser=parser.float_or_none_parser)\n parser.add_argument(\"-moe.att.selection_mode\", default=\"sigmoid\", choice=[\"sigmoid\", \"softmax\"])\n parser.add_argument(\"-moe.att.perplexity_reg\", default=\"none\", parser=parser.float_or_none_parser)\n parser.add_argument(\"-moe.att.qside_n_experts\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-moe.att.k\", default=2)\n parser.add_argument(\"-moe.att.norm_ret\", default=False)\n parser.add_argument(\"-moe.att.shared_experts\", default=False)\n parser.add_argument(\"-moe.att.drop_expert\", default=\"none\", parser=parser.float_or_none_parser)\n parser.add_argument(\"-moe.att.kq_n_experts\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-moe.att.separate_kq_sel\", default=False)\n parser.add_argument(\"-moe.att.norm_init\", default=False)\n parser.add_argument(\"-rope.rotate_fraction\", default=0.5)\n parser.add_argument(\"-rope.base\", default=10000.0)\n parser.add_argument(\"-moa.mode\", default=\"my\", choice=[\"my\", \"moa\"])\n parser.add_argument(\"-moa.cvloss\", default=0.0)\n parser.add_argument(\"-moa.switchloss\", default=0.0)\n parser.add_argument(\"-moa.zloss\", default=0.0)\n parser.add_argument(\"-debug_plot_interval\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-transformer.plot_head_details\", default=False)\n parser.add_argument(\"-plot.n_steps\", default=-128)\n\n@task()\nclass TransformerLMMixin:\n helper: framework.helpers.TrainingHelper\n\n def is_preln(self) -> bool:\n return \"preln\" in self.helper.args.transformer.variant\n\n def topk_activation(self, x: torch.Tensor) -> torch.Tensor:\n nx = -x\n return torch.masked_fill(x, nx <= nx.kthvalue(self.helper.args.transformer.topk_value, keepdim=True)[0], 0)\n\n def get_layers(self) -> List[torch.nn.Module]:\n # pyright: reportOptionalMemberAccess=false\n if self.helper.args.transformer.activation == \"relu\":\n activation = F.relu\n elif self.helper.args.transformer.activation == \"topk\":\n activation = self.topk_activation\n elif self.helper.args.transformer.activation == \"identity\":\n activation = lambda x: x\n elif self.helper.args.transformer.activation == \"sigmoid\":\n activation = torch.sigmoid\n elif self.helper.args.transformer.activation == \"gelu\":\n activation = F.gelu\n elif self.helper.args.transformer.activation == \"softmax\":\n activation = lambda x: F.softmax(x, dim=-1)\n else:\n raise ValueError(f\"Invalid activation: {self.helper.args.transformer.activation}\")\n\n base_args = dict(\n d_model=self.helper.args.state_size,\n nhead=self.helper.args.transformer.n_heads,\n dropout=self.helper.args.dropout,\n activation=activation\n )\n\n if self.helper.args.transformer.variant not in {\"preln_moe\", \"moe\"}:\n base_args[\"dim_feedforward\"]=int(self.helper.args.state_size * self.helper.args.transformer.ff_multiplier)\n\n\n extra_args = {} if not self.helper.args.transformer.variant.endswith(\"_gelu\") else {\n \"activation\": F.gelu,\n \"drop_expand\": False\n }\n\n\n if self.helper.args.transformer.variant in {\"preln_relative\"}:\n mklayer = lambda: PrelnRelativeTransformerEncoderLayer(\n **base_args, **extra_args, test_pos_clamp=self.helper.args.lm.trafo.test_pos_clamp,\n n_layers=self.helper.args.transformer.encoder_n_layers,\n head_projection_size=self.helper.args.transformer.head_projection_size,)\n elif self.helper.args.transformer.variant in {\"preln_moeatt\"}:\n mklayer = lambda: MoeAttentionRelativeTransformerEncoderLayer(\n **base_args, **extra_args, moe_att_n_experts=self.helper.args.moe.att.n_experts,\n n_layers=self.helper.args.transformer.encoder_n_layers,\n head_projection_size=self.helper.args.transformer.head_projection_size,\n att_perplexity_reg=self.helper.args.moe.perplexity_reg if self.helper.args.moe.att.perplexity_reg is None else self.helper.args.moe.att.perplexity_reg,\n expert_dropout=self.helper.args.moe.drop_expert if self.helper.args.moe.att.drop_expert is None else self.helper.args.moe.att.drop_expert,\n att_selection_mode=self.helper.args.moe.att.selection_mode,\n preln=self.is_preln(),\n attention_variant=self.helper.args.moe.att.variant,\n q_expert=self.helper.args.moe.att.q_expert,\n k_expert=self.helper.args.moe.att.k_expert,\n v_expert=self.helper.args.moe.att.v_expert,\n o_expert=self.helper.args.moe.att.o_expert,\n norm_qk_score=self.helper.args.moe.att.norm_qk,\n v_projection_size=self.helper.args.moe.att.v_size,\n same_sel=self.helper.args.moe.att.same_sel,\n moe_k=self.helper.args.moe.att.k,\n qside_n_experts=self.helper.args.moe.att.qside_n_experts,\n shared_experts=self.helper.args.moe.att.shared_experts,\n kq_n_experts=self.helper.args.moe.att.kq_n_experts,\n separate_kq_sel=self.helper.args.moe.att.separate_kq_sel,\n moa_mode=self.helper.args.moa.mode,\n cvloss=self.helper.args.moa.cvloss,\n switchloss=self.helper.args.moa.switchloss,\n zloss=self.helper.args.moa.zloss,\n rotate_fraction=self.helper.args.rope.rotate_fraction,\n rope_base=self.helper.args.rope.base,\n moeatt_norm_init=self.helper.args.moe.att.norm_init)\n elif self.helper.args.transformer.variant in {\"preln_rope\", \"rope\"}:\n mklayer = lambda: FastRopeTransformerEncoderLayer(\n **base_args, **extra_args,\n n_layers=self.helper.args.transformer.encoder_n_layers,\n head_projection_size=self.helper.args.transformer.head_projection_size,\n preln=self.is_preln(), rotate_fraction = self.helper.args.rope.rotate_fraction,\n rope_base=self.helper.args.rope.base)\n elif self.helper.args.transformer.variant in {\"preln_moe\", \"moe\"}:\n # def __init__(self, d_model, nhead, n_bins: int, bin_size: int, n_layers: int, dim_feedforward=2048,\n"},"all_code":{"kind":"string","value":"\n\n@args\ndef a(parser: framework.helpers.ArgumentParser):\n parser.add_argument(\"-lm.trafo.context_blocks\", default=1)\n parser.add_argument(\"-lm.trafo.test_context_blocks\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-lm.trafo.test_pos_clamp\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-lm.trafo.same_length_eval\", default=False)\n parser.add_argument(\"-lm.trafo.same_length\", default=False)\n parser.add_argument(\"-lm.trafo.last_layer_context\", default=False)\n parser.add_argument(\"-lm.trafo.xl_init\", default=False)\n parser.add_argument(\"-lm.trafo.embedding_mode_init\", default=\"default\", choice=[\"default\", \"scale_to_sqrt_dmodel\", \"init_to_sqrt_dmodel\", \"one_and_scale_to_sqrt_dmodel\", \"like_preln\"])\n parser.add_argument(\"-pkm.n_heads\", default=1)\n parser.add_argument(\"-moe.n_experts\", default=128)\n parser.add_argument(\"-moe.expert_size\", default=128)\n parser.add_argument(\"-moe.selection_mode\", default=\"sigmoid\", choice=[\"gate\", \"sigmoid\", \"mul\"])\n parser.add_argument(\"-moe.perplexity_reg\", default=0.0)\n parser.add_argument(\"-moe.perplexity_reg_mode\", default=\"step\", choice=[\"step\", \"global\", \"time\", \"global_time\"])\n parser.add_argument(\"-moe.reg_type\", default=\"entropy\", choice=[\"perplexity\", \"variance\", \"entropy\", \"l2\", \"switch\", \"normal\"])\n parser.add_argument(\"-moe.norm_keys\", default=False)\n parser.add_argument(\"-moe.n_random\", default=0)\n parser.add_argument(\"-moe.topk_mode\", default=\"full\", choice=[\"full\", \"l1_approx\", \"approx\"])\n parser.add_argument(\"-moe.activation_after_topk\", default=False)\n parser.add_argument(\"-moe.drop_parallel\", default=True)\n parser.add_argument(\"-moe.norm_key_init\", default=False)\n parser.add_argument(\"-moe.norm_value_init\", default=False)\n parser.add_argument(\"-moe.identical_init\", default=False)\n parser.add_argument(\"-moe.sel_lr_multipler\", default=1.0)\n parser.add_argument(\"-moe.expert_lr_multipler\", default=1.0)\n parser.add_argument(\"-moe.sel_norm\", default=\"none\", choice=[\"none\", \"cos\", \"input\", \"weights\"])\n parser.add_argument(\"-moe.dropout_factor\", default=1.0)\n parser.add_argument(\"-moe.drop_expert\", default=0.0)\n parser.add_argument(\"-moe.sync_distributed\", default=True)\n parser.add_argument(\"-moe.modulation_amplitude\", default=0.5)\n parser.add_argument(\"-moe.init_scale\", default=1.0)\n parser.add_argument(\"-moe.norm_expert_sel_init\", default=False)\n parser.add_argument(\"-kvmem.dropout\", default=\"none\", choice=[\"none\", \"early\", \"late\", \"weight\", \"score\"])\n parser.add_argument(\"-kvmem.norm_values\", default=False)\n parser.add_argument(\"-transformer.topk_value\", default=32)\n parser.add_argument(\"-transformer.activation\", default=\"relu\", choice=[\"relu\", \"topk\", \"gelu\", \"identity\", \"sigmoid\", \"softmax\"])\n parser.add_argument(\"-transformer.p_drop_layer\", default=0.0)\n parser.add_argument(\"-transformer.head_projection_size\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-transformer.ln_affine\", default=True)\n parser.add_argument(\"-transformer.ln_after_attention\", default=True)\n parser.add_argument(\"-moe.att.n_experts\", default=4)\n parser.add_argument(\"-moe.att.variant\", default=\"moa\", choice=[\"moa\", \"simple\", \"qside\", \"full\", \"full_rope\", \"seq\", \"target\"])\n parser.add_argument(\"-moe.att.enable\", default=False)\n parser.add_argument(\"-moe.att.q_expert\", default=True)\n parser.add_argument(\"-moe.att.k_expert\", default=True)\n parser.add_argument(\"-moe.att.v_expert\", default=True)\n parser.add_argument(\"-moe.att.o_expert\", default=True)\n parser.add_argument(\"-moe.att.k\", default=2)\n parser.add_argument(\"-moe.att.norm_qk\", default=False)\n parser.add_argument(\"-moe.att.v_size\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-moe.att.same_sel\", default=False)\n parser.add_argument(\"-moe.att.expert_dropout\", default=\"none\", parser=parser.float_or_none_parser)\n parser.add_argument(\"-moe.att.selection_mode\", default=\"sigmoid\", choice=[\"sigmoid\", \"softmax\"])\n parser.add_argument(\"-moe.att.perplexity_reg\", default=\"none\", parser=parser.float_or_none_parser)\n parser.add_argument(\"-moe.att.qside_n_experts\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-moe.att.k\", default=2)\n parser.add_argument(\"-moe.att.norm_ret\", default=False)\n parser.add_argument(\"-moe.att.shared_experts\", default=False)\n parser.add_argument(\"-moe.att.drop_expert\", default=\"none\", parser=parser.float_or_none_parser)\n parser.add_argument(\"-moe.att.kq_n_experts\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-moe.att.separate_kq_sel\", default=False)\n parser.add_argument(\"-moe.att.norm_init\", default=False)\n parser.add_argument(\"-rope.rotate_fraction\", default=0.5)\n parser.add_argument(\"-rope.base\", default=10000.0)\n parser.add_argument(\"-moa.mode\", default=\"my\", choice=[\"my\", \"moa\"])\n parser.add_argument(\"-moa.cvloss\", default=0.0)\n parser.add_argument(\"-moa.switchloss\", default=0.0)\n parser.add_argument(\"-moa.zloss\", default=0.0)\n parser.add_argument(\"-debug_plot_interval\", default=\"none\", parser=parser.int_or_none_parser)\n parser.add_argument(\"-transformer.plot_head_details\", default=False)\n parser.add_argument(\"-plot.n_steps\", default=-128)\n\n@task()\nclass TransformerLMMixin:\n helper: framework.helpers.TrainingHelper\n\n def is_preln(self) -> bool:\n return \"preln\" in self.helper.args.transformer.variant\n\n def topk_activation(self, x: torch.Tensor) -> torch.Tensor:\n nx = -x\n return torch.masked_fill(x, nx <= nx.kthvalue(self.helper.args.transformer.topk_value, keepdim=True)[0], 0)\n\n def get_layers(self) -> List[torch.nn.Module]:\n # pyright: reportOptionalMemberAccess=false\n if self.helper.args.transformer.activation == \"relu\":\n activation = F.relu\n elif self.helper.args.transformer.activation == \"topk\":\n activation = self.topk_activation\n elif self.helper.args.transformer.activation == \"identity\":\n activation = lambda x: x\n elif self.helper.args.transformer.activation == \"sigmoid\":\n activation = torch.sigmoid\n elif self.helper.args.transformer.activation == \"gelu\":\n activation = F.gelu\n elif self.helper.args.transformer.activation == \"softmax\":\n activation = lambda x: F.softmax(x, dim=-1)\n else:\n raise ValueError(f\"Invalid activation: {self.helper.args.transformer.activation}\")\n\n base_args = dict(\n d_model=self.helper.args.state_size,\n nhead=self.helper.args.transformer.n_heads,\n dropout=self.helper.args.dropout,\n activation=activation\n )\n\n if self.helper.args.transformer.variant not in {\"preln_moe\", \"moe\"}:\n base_args[\"dim_feedforward\"]=int(self.helper.args.state_size * self.helper.args.transformer.ff_multiplier)\n\n\n extra_args = {} if not self.helper.args.transformer.variant.endswith(\"_gelu\") else {\n \"activation\": F.gelu,\n \"drop_expand\": False\n }\n\n\n if self.helper.args.transformer.variant in {\"preln_relative\"}:\n mklayer = lambda: PrelnRelativeTransformerEncoderLayer(\n **base_args, **extra_args, test_pos_clamp=self.helper.args.lm.trafo.test_pos_clamp,\n n_layers=self.helper.args.transformer.encoder_n_layers,\n head_projection_size=self.helper.args.transformer.head_projection_size,)\n elif self.helper.args.transformer.variant in {\"preln_moeatt\"}:\n mklayer = lambda: MoeAttentionRelativeTransformerEncoderLayer(\n **base_args, **extra_args, moe_att_n_experts=self.helper.args.moe.att.n_experts,\n n_layers=self.helper.args.transformer.encoder_n_layers,\n head_projection_size=self.helper.args.transformer.head_projection_size,\n att_perplexity_reg=self.helper.args.moe.perplexity_reg if self.helper.args.moe.att.perplexity_reg is None else self.helper.args.moe.att.perplexity_reg,\n expert_dropout=self.helper.args.moe.drop_expert if self.helper.args.moe.att.drop_expert is None else self.helper.args.moe.att.drop_expert,\n att_selection_mode=self.helper.args.moe.att.selection_mode,\n preln=self.is_preln(),\n attention_variant=self.helper.args.moe.att.variant,\n q_expert=self.helper.args.moe.att.q_expert,\n k_expert=self.helper.args.moe.att.k_expert,\n v_expert=self.helper.args.moe.att.v_expert,\n o_expert=self.helper.args.moe.att.o_expert,\n norm_qk_score=self.helper.args.moe.att.norm_qk,\n v_projection_size=self.helper.args.moe.att.v_size,\n same_sel=self.helper.args.moe.att.same_sel,\n moe_k=self.helper.args.moe.att.k,\n qside_n_experts=self.helper.args.moe.att.qside_n_experts,\n shared_experts=self.helper.args.moe.att.shared_experts,\n kq_n_experts=self.helper.args.moe.att.kq_n_experts,\n separate_kq_sel=self.helper.args.moe.att.separate_kq_sel,\n moa_mode=self.helper.args.moa.mode,\n cvloss=self.helper.args.moa.cvloss,\n switchloss=self.helper.args.moa.switchloss,\n zloss=self.helper.args.moa.zloss,\n rotate_fraction=self.helper.args.rope.rotate_fraction,\n rope_base=self.helper.args.rope.base,\n moeatt_norm_init=self.helper.args.moe.att.norm_init)\n elif self.helper.args.transformer.variant in {\"preln_rope\", \"rope\"}:\n mklayer = lambda: FastRopeTransformerEncoderLayer(\n **base_args, **extra_args,\n n_layers=self.helper.args.transformer.encoder_n_layers,\n head_projection_size=self.helper.args.transformer.head_projection_size,\n preln=self.is_preln(), rotate_fraction = self.helper.args.rope.rotate_fraction,\n rope_base=self.helper.args.rope.base)\n elif self.helper.args.transformer.variant in {\"preln_moe\", \"moe\"}:\n # def __init__(self, d_model, nhead, n_bins: int, bin_size: int, n_layers: int, dim_feedforward=2048,"},"next_line":{"kind":"string","value":" mklayer = lambda: RelativeMoeTransformerEncoderLayer("},"gold_snippet_index":{"kind":"number","value":5,"string":"5"},"created_at":{"kind":"string","value":"2023-12-13 08:45:02+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5290,"cells":{"repo_name":{"kind":"string","value":"AIFSH/NativeDancer"},"file_path":{"kind":"string","value":"nativedancer/third_part/detectron2/utils/visualizer.py"},"context":{"kind":"list like","value":[{"identifier":"MetadataCatalog","path":"nativedancer/third_part/detectron2/data/catalog.py","snippet":"class _DatasetCatalog(UserDict):\nclass Metadata(types.SimpleNamespace):\nclass _MetadataCatalog(UserDict):\n def register(self, name, func):\n def get(self, name):\n def list(self) -> List[str]:\n def remove(self, name):\n def __str__(self):\n def __getattr__(self, key):\n def __setattr__(self, key, val):\n def as_dict(self):\n def set(self, **kwargs):\n def get(self, key, default=None):\n def get(self, name):\n def list(self):\n def remove(self, name):\n def __str__(self):\n _RENAMED = {\n \"class_names\": \"thing_classes\",\n \"dataset_id_to_contiguous_id\": \"thing_dataset_id_to_contiguous_id\",\n \"stuff_class_names\": \"stuff_classes\",\n }"},{"identifier":"Boxes","path":"nativedancer/third_part/detectron2/structures/boxes.py","snippet":"class Boxes:\n \"\"\"\n This structure stores a list of boxes as a Nx4 torch.Tensor.\n It supports some common methods about boxes\n (`area`, `clip`, `nonempty`, etc),\n and also behaves like a Tensor\n (support indexing, `to(device)`, `.device`, and iteration over all boxes)\n\n Attributes:\n tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).\n \"\"\"\n\n def __init__(self, tensor: torch.Tensor):\n \"\"\"\n Args:\n tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2).\n \"\"\"\n if not isinstance(tensor, torch.Tensor):\n tensor = torch.as_tensor(tensor, dtype=torch.float32, device=torch.device(\"cpu\"))\n else:\n tensor = tensor.to(torch.float32)\n if tensor.numel() == 0:\n # Use reshape, so we don't end up creating a new tensor that does not depend on\n # the inputs (and consequently confuses jit)\n tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32)\n assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()\n\n self.tensor = tensor\n\n def clone(self) -> \"Boxes\":\n \"\"\"\n Clone the Boxes.\n\n Returns:\n Boxes\n \"\"\"\n return Boxes(self.tensor.clone())\n\n def to(self, device: torch.device):\n # Boxes are assumed float32 and does not support to(dtype)\n return Boxes(self.tensor.to(device=device))\n\n def area(self) -> torch.Tensor:\n \"\"\"\n Computes the area of all the boxes.\n\n Returns:\n torch.Tensor: a vector with areas of each box.\n \"\"\"\n box = self.tensor\n area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])\n return area\n\n def clip(self, box_size: Tuple[int, int]) -> None:\n \"\"\"\n Clip (in place) the boxes by limiting x coordinates to the range [0, width]\n and y coordinates to the range [0, height].\n\n Args:\n box_size (height, width): The clipping box's size.\n \"\"\"\n assert torch.isfinite(self.tensor).all(), \"Box tensor contains infinite or NaN!\"\n h, w = box_size\n x1 = self.tensor[:, 0].clamp(min=0, max=w)\n y1 = self.tensor[:, 1].clamp(min=0, max=h)\n x2 = self.tensor[:, 2].clamp(min=0, max=w)\n y2 = self.tensor[:, 3].clamp(min=0, max=h)\n self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)\n\n def nonempty(self, threshold: float = 0.0) -> torch.Tensor:\n \"\"\"\n Find boxes that are non-empty.\n A box is considered empty, if either of its side is no larger than threshold.\n\n Returns:\n Tensor:\n a binary vector which represents whether each box is empty\n (False) or non-empty (True).\n \"\"\"\n box = self.tensor\n widths = box[:, 2] - box[:, 0]\n heights = box[:, 3] - box[:, 1]\n keep = (widths > threshold) & (heights > threshold)\n return keep\n\n def __getitem__(self, item) -> \"Boxes\":\n \"\"\"\n Args:\n item: int, slice, or a BoolTensor\n\n Returns:\n Boxes: Create a new :class:`Boxes` by indexing.\n\n The following usage are allowed:\n\n 1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.\n 2. `new_boxes = boxes[2:10]`: return a slice of boxes.\n 3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor\n with `length = len(boxes)`. Nonzero elements in the vector will be selected.\n\n Note that the returned Boxes might share storage with this Boxes,\n subject to Pytorch's indexing semantics.\n \"\"\"\n if isinstance(item, int):\n return Boxes(self.tensor[item].view(1, -1))\n b = self.tensor[item]\n assert b.dim() == 2, \"Indexing on Boxes with {} failed to return a matrix!\".format(item)\n return Boxes(b)\n\n def __len__(self) -> int:\n return self.tensor.shape[0]\n\n def __repr__(self) -> str:\n return \"Boxes(\" + str(self.tensor) + \")\"\n\n def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:\n \"\"\"\n Args:\n box_size (height, width): Size of the reference box.\n boundary_threshold (int): Boxes that extend beyond the reference box\n boundary by more than boundary_threshold are considered \"outside\".\n\n Returns:\n a binary vector, indicating whether each box is inside the reference box.\n \"\"\"\n height, width = box_size\n inds_inside = (\n (self.tensor[..., 0] >= -boundary_threshold)\n & (self.tensor[..., 1] >= -boundary_threshold)\n & (self.tensor[..., 2] < width + boundary_threshold)\n & (self.tensor[..., 3] < height + boundary_threshold)\n )\n return inds_inside\n\n def get_centers(self) -> torch.Tensor:\n \"\"\"\n Returns:\n The box centers in a Nx2 array of (x, y).\n \"\"\"\n return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2\n\n def scale(self, scale_x: float, scale_y: float) -> None:\n \"\"\"\n Scale the box with horizontal and vertical scaling factors\n \"\"\"\n self.tensor[:, 0::2] *= scale_x\n self.tensor[:, 1::2] *= scale_y\n\n @classmethod\n def cat(cls, boxes_list: List[\"Boxes\"]) -> \"Boxes\":\n \"\"\"\n Concatenates a list of Boxes into a single Boxes\n\n Arguments:\n boxes_list (list[Boxes])\n\n Returns:\n Boxes: the concatenated Boxes\n \"\"\"\n assert isinstance(boxes_list, (list, tuple))\n if len(boxes_list) == 0:\n return cls(torch.empty(0))\n assert all([isinstance(box, Boxes) for box in boxes_list])\n\n # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input\n cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))\n return cat_boxes\n\n @property\n def device(self) -> device:\n return self.tensor.device\n\n # type \"Iterator[torch.Tensor]\", yield, and iter() not supported by torchscript\n # https://github.com/pytorch/pytorch/issues/18627\n @torch.jit.unused\n def __iter__(self):\n \"\"\"\n Yield a box as a Tensor of shape (4,) at a time.\n \"\"\"\n yield from self.tensor"},{"identifier":"BoxMode","path":"nativedancer/third_part/detectron2/structures/boxes.py","snippet":"class BoxMode(IntEnum):\n \"\"\"\n Enum of different ways to represent a box.\n \"\"\"\n\n XYXY_ABS = 0\n \"\"\"\n (x0, y0, x1, y1) in absolute floating points coordinates.\n The coordinates in range [0, width or height].\n \"\"\"\n XYWH_ABS = 1\n \"\"\"\n (x0, y0, w, h) in absolute floating points coordinates.\n \"\"\"\n XYXY_REL = 2\n \"\"\"\n Not yet supported!\n (x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image.\n \"\"\"\n XYWH_REL = 3\n \"\"\"\n Not yet supported!\n (x0, y0, w, h) in range [0, 1]. They are relative to the size of the image.\n \"\"\"\n XYWHA_ABS = 4\n \"\"\"\n (xc, yc, w, h, a) in absolute floating points coordinates.\n (xc, yc) is the center of the rotated box, and the angle a is in degrees ccw.\n \"\"\"\n\n @staticmethod\n def convert(box: _RawBoxType, from_mode: \"BoxMode\", to_mode: \"BoxMode\") -> _RawBoxType:\n \"\"\"\n Args:\n box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5\n from_mode, to_mode (BoxMode)\n\n Returns:\n The converted box of the same type.\n \"\"\"\n if from_mode == to_mode:\n return box\n\n original_type = type(box)\n is_numpy = isinstance(box, np.ndarray)\n single_box = isinstance(box, (list, tuple))\n if single_box:\n assert len(box) == 4 or len(box) == 5, (\n \"BoxMode.convert takes either a k-tuple/list or an Nxk array/tensor,\"\n \" where k == 4 or 5\"\n )\n arr = torch.tensor(box)[None, :]\n else:\n # avoid modifying the input box\n if is_numpy:\n arr = torch.from_numpy(np.asarray(box)).clone()\n else:\n arr = box.clone()\n\n assert to_mode not in [BoxMode.XYXY_REL, BoxMode.XYWH_REL] and from_mode not in [\n BoxMode.XYXY_REL,\n BoxMode.XYWH_REL,\n ], \"Relative mode not yet supported!\"\n\n if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS:\n assert (\n arr.shape[-1] == 5\n ), \"The last dimension of input shape must be 5 for XYWHA format\"\n original_dtype = arr.dtype\n arr = arr.double()\n\n w = arr[:, 2]\n h = arr[:, 3]\n a = arr[:, 4]\n c = torch.abs(torch.cos(a * math.pi / 180.0))\n s = torch.abs(torch.sin(a * math.pi / 180.0))\n # This basically computes the horizontal bounding rectangle of the rotated box\n new_w = c * w + s * h\n new_h = c * h + s * w\n\n # convert center to top-left corner\n arr[:, 0] -= new_w / 2.0\n arr[:, 1] -= new_h / 2.0\n # bottom-right corner\n arr[:, 2] = arr[:, 0] + new_w\n arr[:, 3] = arr[:, 1] + new_h\n\n arr = arr[:, :4].to(dtype=original_dtype)\n elif from_mode == BoxMode.XYWH_ABS and to_mode == BoxMode.XYWHA_ABS:\n original_dtype = arr.dtype\n arr = arr.double()\n arr[:, 0] += arr[:, 2] / 2.0\n arr[:, 1] += arr[:, 3] / 2.0\n angles = torch.zeros((arr.shape[0], 1), dtype=arr.dtype)\n arr = torch.cat((arr, angles), axis=1).to(dtype=original_dtype)\n else:\n if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS:\n arr[:, 2] += arr[:, 0]\n arr[:, 3] += arr[:, 1]\n elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS:\n arr[:, 2] -= arr[:, 0]\n arr[:, 3] -= arr[:, 1]\n else:\n raise NotImplementedError(\n \"Conversion from BoxMode {} to {} is not supported yet\".format(\n from_mode, to_mode\n )\n )\n\n if single_box:\n return original_type(arr.flatten().tolist())\n if is_numpy:\n return arr.numpy()\n else:\n return arr"},{"identifier":"Keypoints","path":"nativedancer/third_part/detectron2/structures/keypoints.py","snippet":"class Keypoints:\n \"\"\"\n Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property\n containing the x,y location and visibility flag of each keypoint. This tensor has shape\n (N, K, 3) where N is the number of instances and K is the number of keypoints per instance.\n\n The visibility flag follows the COCO format and must be one of three integers:\n\n * v=0: not labeled (in which case x=y=0)\n * v=1: labeled but not visible\n * v=2: labeled and visible\n \"\"\"\n\n def __init__(self, keypoints: Union[torch.Tensor, np.ndarray, List[List[float]]]):\n \"\"\"\n Arguments:\n keypoints: A Tensor, numpy array, or list of the x, y, and visibility of each keypoint.\n The shape should be (N, K, 3) where N is the number of\n instances, and K is the number of keypoints per instance.\n \"\"\"\n device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device(\"cpu\")\n keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device)\n assert keypoints.dim() == 3 and keypoints.shape[2] == 3, keypoints.shape\n self.tensor = keypoints\n\n def __len__(self) -> int:\n return self.tensor.size(0)\n\n def to(self, *args: Any, **kwargs: Any) -> \"Keypoints\":\n return type(self)(self.tensor.to(*args, **kwargs))\n\n @property\n def device(self) -> torch.device:\n return self.tensor.device\n\n def to_heatmap(self, boxes: torch.Tensor, heatmap_size: int) -> torch.Tensor:\n \"\"\"\n Convert keypoint annotations to a heatmap of one-hot labels for training,\n as described in :paper:`Mask R-CNN`.\n\n Arguments:\n boxes: Nx4 tensor, the boxes to draw the keypoints to\n\n Returns:\n heatmaps:\n A tensor of shape (N, K), each element is integer spatial label\n in the range [0, heatmap_size**2 - 1] for each keypoint in the input.\n valid:\n A tensor of shape (N, K) containing whether each keypoint is in the roi or not.\n \"\"\"\n return _keypoints_to_heatmap(self.tensor, boxes, heatmap_size)\n\n def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> \"Keypoints\":\n \"\"\"\n Create a new `Keypoints` by indexing on this `Keypoints`.\n\n The following usage are allowed:\n\n 1. `new_kpts = kpts[3]`: return a `Keypoints` which contains only one instance.\n 2. `new_kpts = kpts[2:10]`: return a slice of key points.\n 3. `new_kpts = kpts[vector]`, where vector is a torch.ByteTensor\n with `length = len(kpts)`. Nonzero elements in the vector will be selected.\n\n Note that the returned Keypoints might share storage with this Keypoints,\n subject to Pytorch's indexing semantics.\n \"\"\"\n if isinstance(item, int):\n return Keypoints([self.tensor[item]])\n return Keypoints(self.tensor[item])\n\n def __repr__(self) -> str:\n s = self.__class__.__name__ + \"(\"\n s += \"num_instances={})\".format(len(self.tensor))\n return s\n\n @staticmethod\n def cat(keypoints_list: List[\"Keypoints\"]) -> \"Keypoints\":\n \"\"\"\n Concatenates a list of Keypoints into a single Keypoints\n\n Arguments:\n keypoints_list (list[Keypoints])\n\n Returns:\n Keypoints: the concatenated Keypoints\n \"\"\"\n assert isinstance(keypoints_list, (list, tuple))\n assert len(keypoints_list) > 0\n assert all(isinstance(keypoints, Keypoints) for keypoints in keypoints_list)\n\n cat_kpts = type(keypoints_list[0])(\n torch.cat([kpts.tensor for kpts in keypoints_list], dim=0)\n )\n return cat_kpts"},{"identifier":"BitMasks","path":"nativedancer/third_part/detectron2/structures/masks.py","snippet":"class BitMasks:\n \"\"\"\n This class stores the segmentation masks for all objects in one image, in\n the form of bitmaps.\n\n Attributes:\n tensor: bool Tensor of N,H,W, representing N instances in the image.\n \"\"\"\n\n def __init__(self, tensor: Union[torch.Tensor, np.ndarray]):\n \"\"\"\n Args:\n tensor: bool Tensor of N,H,W, representing N instances in the image.\n \"\"\"\n if isinstance(tensor, torch.Tensor):\n tensor = tensor.to(torch.bool)\n else:\n tensor = torch.as_tensor(tensor, dtype=torch.bool, device=torch.device(\"cpu\"))\n assert tensor.dim() == 3, tensor.size()\n self.image_size = tensor.shape[1:]\n self.tensor = tensor\n\n @torch.jit.unused\n def to(self, *args: Any, **kwargs: Any) -> \"BitMasks\":\n return BitMasks(self.tensor.to(*args, **kwargs))\n\n @property\n def device(self) -> torch.device:\n return self.tensor.device\n\n @torch.jit.unused\n def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> \"BitMasks\":\n \"\"\"\n Returns:\n BitMasks: Create a new :class:`BitMasks` by indexing.\n\n The following usage are allowed:\n\n 1. `new_masks = masks[3]`: return a `BitMasks` which contains only one mask.\n 2. `new_masks = masks[2:10]`: return a slice of masks.\n 3. `new_masks = masks[vector]`, where vector is a torch.BoolTensor\n with `length = len(masks)`. Nonzero elements in the vector will be selected.\n\n Note that the returned object might share storage with this object,\n subject to Pytorch's indexing semantics.\n \"\"\"\n if isinstance(item, int):\n return BitMasks(self.tensor[item].unsqueeze(0))\n m = self.tensor[item]\n assert m.dim() == 3, \"Indexing on BitMasks with {} returns a tensor with shape {}!\".format(\n item, m.shape\n )\n return BitMasks(m)\n\n @torch.jit.unused\n def __iter__(self) -> torch.Tensor:\n yield from self.tensor\n\n @torch.jit.unused\n def __repr__(self) -> str:\n s = self.__class__.__name__ + \"(\"\n s += \"num_instances={})\".format(len(self.tensor))\n return s\n\n def __len__(self) -> int:\n return self.tensor.shape[0]\n\n def nonempty(self) -> torch.Tensor:\n \"\"\"\n Find masks that are non-empty.\n\n Returns:\n Tensor: a BoolTensor which represents\n whether each mask is empty (False) or non-empty (True).\n \"\"\"\n return self.tensor.flatten(1).any(dim=1)\n\n @staticmethod\n def from_polygon_masks(\n polygon_masks: Union[\"PolygonMasks\", List[List[np.ndarray]]], height: int, width: int\n ) -> \"BitMasks\":\n \"\"\"\n Args:\n polygon_masks (list[list[ndarray]] or PolygonMasks)\n height, width (int)\n \"\"\"\n if isinstance(polygon_masks, PolygonMasks):\n polygon_masks = polygon_masks.polygons\n masks = [polygons_to_bitmask(p, height, width) for p in polygon_masks]\n if len(masks):\n return BitMasks(torch.stack([torch.from_numpy(x) for x in masks]))\n else:\n return BitMasks(torch.empty(0, height, width, dtype=torch.bool))\n\n @staticmethod\n def from_roi_masks(roi_masks: \"ROIMasks\", height: int, width: int) -> \"BitMasks\":\n \"\"\"\n Args:\n roi_masks:\n height, width (int):\n \"\"\"\n return roi_masks.to_bitmasks(height, width)\n\n def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor:\n \"\"\"\n Crop each bitmask by the given box, and resize results to (mask_size, mask_size).\n This can be used to prepare training targets for Mask R-CNN.\n It has less reconstruction error compared to rasterization with polygons.\n However we observe no difference in accuracy,\n but BitMasks requires more memory to store all the masks.\n\n Args:\n boxes (Tensor): Nx4 tensor storing the boxes for each mask\n mask_size (int): the size of the rasterized mask.\n\n Returns:\n Tensor:\n A bool tensor of shape (N, mask_size, mask_size), where\n N is the number of predicted boxes for this image.\n \"\"\"\n assert len(boxes) == len(self), \"{} != {}\".format(len(boxes), len(self))\n device = self.tensor.device\n\n batch_inds = torch.arange(len(boxes), device=device).to(dtype=boxes.dtype)[:, None]\n rois = torch.cat([batch_inds, boxes], dim=1) # Nx5\n\n bit_masks = self.tensor.to(dtype=torch.float32)\n rois = rois.to(device=device)\n output = (\n ROIAlign((mask_size, mask_size), 1.0, 0, aligned=True)\n .forward(bit_masks[:, None, :, :], rois)\n .squeeze(1)\n )\n output = output >= 0.5\n return output\n\n def get_bounding_boxes(self) -> Boxes:\n \"\"\"\n Returns:\n Boxes: tight bounding boxes around bitmasks.\n If a mask is empty, it's bounding box will be all zero.\n \"\"\"\n boxes = torch.zeros(self.tensor.shape[0], 4, dtype=torch.float32)\n x_any = torch.any(self.tensor, dim=1)\n y_any = torch.any(self.tensor, dim=2)\n for idx in range(self.tensor.shape[0]):\n x = torch.where(x_any[idx, :])[0]\n y = torch.where(y_any[idx, :])[0]\n if len(x) > 0 and len(y) > 0:\n boxes[idx, :] = torch.as_tensor(\n [x[0], y[0], x[-1] + 1, y[-1] + 1], dtype=torch.float32\n )\n return Boxes(boxes)\n\n @staticmethod\n def cat(bitmasks_list: List[\"BitMasks\"]) -> \"BitMasks\":\n \"\"\"\n Concatenates a list of BitMasks into a single BitMasks\n\n Arguments:\n bitmasks_list (list[BitMasks])\n\n Returns:\n BitMasks: the concatenated BitMasks\n \"\"\"\n assert isinstance(bitmasks_list, (list, tuple))\n assert len(bitmasks_list) > 0\n assert all(isinstance(bitmask, BitMasks) for bitmask in bitmasks_list)\n\n cat_bitmasks = type(bitmasks_list[0])(torch.cat([bm.tensor for bm in bitmasks_list], dim=0))\n return cat_bitmasks"},{"identifier":"PolygonMasks","path":"nativedancer/third_part/detectron2/structures/masks.py","snippet":"class PolygonMasks:\n \"\"\"\n This class stores the segmentation masks for all objects in one image, in the form of polygons.\n\n Attributes:\n polygons: list[list[ndarray]]. Each ndarray is a float64 vector representing a polygon.\n \"\"\"\n\n def __init__(self, polygons: List[List[Union[torch.Tensor, np.ndarray]]]):\n \"\"\"\n Arguments:\n polygons (list[list[np.ndarray]]): The first\n level of the list correspond to individual instances,\n the second level to all the polygons that compose the\n instance, and the third level to the polygon coordinates.\n The third level array should have the format of\n [x0, y0, x1, y1, ..., xn, yn] (n >= 3).\n \"\"\"\n if not isinstance(polygons, list):\n raise ValueError(\n \"Cannot create PolygonMasks: Expect a list of list of polygons per image. \"\n \"Got '{}' instead.\".format(type(polygons))\n )\n\n def _make_array(t: Union[torch.Tensor, np.ndarray]) -> np.ndarray:\n # Use float64 for higher precision, because why not?\n # Always put polygons on CPU (self.to is a no-op) since they\n # are supposed to be small tensors.\n # May need to change this assumption if GPU placement becomes useful\n if isinstance(t, torch.Tensor):\n t = t.cpu().numpy()\n return np.asarray(t).astype(\"float64\")\n\n def process_polygons(\n polygons_per_instance: List[Union[torch.Tensor, np.ndarray]]\n ) -> List[np.ndarray]:\n if not isinstance(polygons_per_instance, list):\n raise ValueError(\n \"Cannot create polygons: Expect a list of polygons per instance. \"\n \"Got '{}' instead.\".format(type(polygons_per_instance))\n )\n # transform each polygon to a numpy array\n polygons_per_instance = [_make_array(p) for p in polygons_per_instance]\n for polygon in polygons_per_instance:\n if len(polygon) % 2 != 0 or len(polygon) < 6:\n raise ValueError(f\"Cannot create a polygon from {len(polygon)} coordinates.\")\n return polygons_per_instance\n\n self.polygons: List[List[np.ndarray]] = [\n process_polygons(polygons_per_instance) for polygons_per_instance in polygons\n ]\n\n def to(self, *args: Any, **kwargs: Any) -> \"PolygonMasks\":\n return self\n\n @property\n def device(self) -> torch.device:\n return torch.device(\"cpu\")\n\n def get_bounding_boxes(self) -> Boxes:\n \"\"\"\n Returns:\n Boxes: tight bounding boxes around polygon masks.\n \"\"\"\n boxes = torch.zeros(len(self.polygons), 4, dtype=torch.float32)\n for idx, polygons_per_instance in enumerate(self.polygons):\n minxy = torch.as_tensor([float(\"inf\"), float(\"inf\")], dtype=torch.float32)\n maxxy = torch.zeros(2, dtype=torch.float32)\n for polygon in polygons_per_instance:\n coords = torch.from_numpy(polygon).view(-1, 2).to(dtype=torch.float32)\n minxy = torch.min(minxy, torch.min(coords, dim=0).values)\n maxxy = torch.max(maxxy, torch.max(coords, dim=0).values)\n boxes[idx, :2] = minxy\n boxes[idx, 2:] = maxxy\n return Boxes(boxes)\n\n def nonempty(self) -> torch.Tensor:\n \"\"\"\n Find masks that are non-empty.\n\n Returns:\n Tensor:\n a BoolTensor which represents whether each mask is empty (False) or not (True).\n \"\"\"\n keep = [1 if len(polygon) > 0 else 0 for polygon in self.polygons]\n return torch.from_numpy(np.asarray(keep, dtype=bool))\n\n def __getitem__(self, item: Union[int, slice, List[int], torch.BoolTensor]) -> \"PolygonMasks\":\n \"\"\"\n Support indexing over the instances and return a `PolygonMasks` object.\n `item` can be:\n\n 1. An integer. It will return an object with only one instance.\n 2. A slice. It will return an object with the selected instances.\n 3. A list[int]. It will return an object with the selected instances,\n correpsonding to the indices in the list.\n 4. A vector mask of type BoolTensor, whose length is num_instances.\n It will return an object with the instances whose mask is nonzero.\n \"\"\"\n if isinstance(item, int):\n selected_polygons = [self.polygons[item]]\n elif isinstance(item, slice):\n selected_polygons = self.polygons[item]\n elif isinstance(item, list):\n selected_polygons = [self.polygons[i] for i in item]\n elif isinstance(item, torch.Tensor):\n # Polygons is a list, so we have to move the indices back to CPU.\n if item.dtype == torch.bool:\n assert item.dim() == 1, item.shape\n item = item.nonzero().squeeze(1).cpu().numpy().tolist()\n elif item.dtype in [torch.int32, torch.int64]:\n item = item.cpu().numpy().tolist()\n else:\n raise ValueError(\"Unsupported tensor dtype={} for indexing!\".format(item.dtype))\n selected_polygons = [self.polygons[i] for i in item]\n return PolygonMasks(selected_polygons)\n\n def __iter__(self) -> Iterator[List[np.ndarray]]:\n \"\"\"\n Yields:\n list[ndarray]: the polygons for one instance.\n Each Tensor is a float64 vector representing a polygon.\n \"\"\"\n return iter(self.polygons)\n\n def __repr__(self) -> str:\n s = self.__class__.__name__ + \"(\"\n s += \"num_instances={})\".format(len(self.polygons))\n return s\n\n def __len__(self) -> int:\n return len(self.polygons)\n\n def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor:\n \"\"\"\n Crop each mask by the given box, and resize results to (mask_size, mask_size).\n This can be used to prepare training targets for Mask R-CNN.\n\n Args:\n boxes (Tensor): Nx4 tensor storing the boxes for each mask\n mask_size (int): the size of the rasterized mask.\n\n Returns:\n Tensor: A bool tensor of shape (N, mask_size, mask_size), where\n N is the number of predicted boxes for this image.\n \"\"\"\n assert len(boxes) == len(self), \"{} != {}\".format(len(boxes), len(self))\n\n device = boxes.device\n # Put boxes on the CPU, as the polygon representation is not efficient GPU-wise\n # (several small tensors for representing a single instance mask)\n boxes = boxes.to(torch.device(\"cpu\"))\n\n results = [\n rasterize_polygons_within_box(poly, box.numpy(), mask_size)\n for poly, box in zip(self.polygons, boxes)\n ]\n \"\"\"\n poly: list[list[float]], the polygons for one instance\n box: a tensor of shape (4,)\n \"\"\"\n if len(results) == 0:\n return torch.empty(0, mask_size, mask_size, dtype=torch.bool, device=device)\n return torch.stack(results, dim=0).to(device=device)\n\n def area(self):\n \"\"\"\n Computes area of the mask.\n Only works with Polygons, using the shoelace formula:\n https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates\n\n Returns:\n Tensor: a vector, area for each instance\n \"\"\"\n\n area = []\n for polygons_per_instance in self.polygons:\n area_per_instance = 0\n for p in polygons_per_instance:\n area_per_instance += polygon_area(p[0::2], p[1::2])\n area.append(area_per_instance)\n\n return torch.tensor(area)\n\n @staticmethod\n def cat(polymasks_list: List[\"PolygonMasks\"]) -> \"PolygonMasks\":\n \"\"\"\n Concatenates a list of PolygonMasks into a single PolygonMasks\n\n Arguments:\n polymasks_list (list[PolygonMasks])\n\n Returns:\n PolygonMasks: the concatenated PolygonMasks\n \"\"\"\n assert isinstance(polymasks_list, (list, tuple))\n assert len(polymasks_list) > 0\n assert all(isinstance(polymask, PolygonMasks) for polymask in polymasks_list)\n\n cat_polymasks = type(polymasks_list[0])(\n list(itertools.chain.from_iterable(pm.polygons for pm in polymasks_list))\n )\n return cat_polymasks"},{"identifier":"RotatedBoxes","path":"nativedancer/third_part/detectron2/structures/rotated_boxes.py","snippet":"class RotatedBoxes(Boxes):\n \"\"\"\n This structure stores a list of rotated boxes as a Nx5 torch.Tensor.\n It supports some common methods about boxes\n (`area`, `clip`, `nonempty`, etc),\n and also behaves like a Tensor\n (support indexing, `to(device)`, `.device`, and iteration over all boxes)\n \"\"\"\n\n def __init__(self, tensor: torch.Tensor):\n \"\"\"\n Args:\n tensor (Tensor[float]): a Nx5 matrix. Each row is\n (x_center, y_center, width, height, angle),\n in which angle is represented in degrees.\n While there's no strict range restriction for it,\n the recommended principal range is between [-180, 180) degrees.\n\n Assume we have a horizontal box B = (x_center, y_center, width, height),\n where width is along the x-axis and height is along the y-axis.\n The rotated box B_rot (x_center, y_center, width, height, angle)\n can be seen as:\n\n 1. When angle == 0:\n B_rot == B\n 2. When angle > 0:\n B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CCW;\n 3. When angle < 0:\n B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CW.\n\n Mathematically, since the right-handed coordinate system for image space\n is (y, x), where y is top->down and x is left->right, the 4 vertices of the\n rotated rectangle :math:`(yr_i, xr_i)` (i = 1, 2, 3, 4) can be obtained from\n the vertices of the horizontal rectangle :math:`(y_i, x_i)` (i = 1, 2, 3, 4)\n in the following way (:math:`\\\\theta = angle*\\\\pi/180` is the angle in radians,\n :math:`(y_c, x_c)` is the center of the rectangle):\n\n .. math::\n\n yr_i = \\\\cos(\\\\theta) (y_i - y_c) - \\\\sin(\\\\theta) (x_i - x_c) + y_c,\n\n xr_i = \\\\sin(\\\\theta) (y_i - y_c) + \\\\cos(\\\\theta) (x_i - x_c) + x_c,\n\n which is the standard rigid-body rotation transformation.\n\n Intuitively, the angle is\n (1) the rotation angle from y-axis in image space\n to the height vector (top->down in the box's local coordinate system)\n of the box in CCW, and\n (2) the rotation angle from x-axis in image space\n to the width vector (left->right in the box's local coordinate system)\n of the box in CCW.\n\n More intuitively, consider the following horizontal box ABCD represented\n in (x1, y1, x2, y2): (3, 2, 7, 4),\n covering the [3, 7] x [2, 4] region of the continuous coordinate system\n which looks like this:\n\n .. code:: none\n\n O--------> x\n |\n | A---B\n | | |\n | D---C\n |\n v y\n\n Note that each capital letter represents one 0-dimensional geometric point\n instead of a 'square pixel' here.\n\n In the example above, using (x, y) to represent a point we have:\n\n .. math::\n\n O = (0, 0), A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4)\n\n We name vector AB = vector DC as the width vector in box's local coordinate system, and\n vector AD = vector BC as the height vector in box's local coordinate system. Initially,\n when angle = 0 degree, they're aligned with the positive directions of x-axis and y-axis\n in the image space, respectively.\n\n For better illustration, we denote the center of the box as E,\n\n .. code:: none\n\n O--------> x\n |\n | A---B\n | | E |\n | D---C\n |\n v y\n\n where the center E = ((3+7)/2, (2+4)/2) = (5, 3).\n\n Also,\n\n .. math::\n\n width = |AB| = |CD| = 7 - 3 = 4,\n height = |AD| = |BC| = 4 - 2 = 2.\n\n Therefore, the corresponding representation for the same shape in rotated box in\n (x_center, y_center, width, height, angle) format is:\n\n (5, 3, 4, 2, 0),\n\n Now, let's consider (5, 3, 4, 2, 90), which is rotated by 90 degrees\n CCW (counter-clockwise) by definition. It looks like this:\n\n .. code:: none\n\n O--------> x\n | B-C\n | | |\n | |E|\n | | |\n | A-D\n v y\n\n The center E is still located at the same point (5, 3), while the vertices\n ABCD are rotated by 90 degrees CCW with regard to E:\n A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5)\n\n Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to\n vector AD or vector BC (the top->down height vector in box's local coordinate system),\n or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right\n width vector in box's local coordinate system).\n\n .. math::\n\n width = |AB| = |CD| = 5 - 1 = 4,\n height = |AD| = |BC| = 6 - 4 = 2.\n\n Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise)\n by definition? It looks like this:\n\n .. code:: none\n\n O--------> x\n | D-A\n | | |\n | |E|\n | | |\n | C-B\n v y\n\n The center E is still located at the same point (5, 3), while the vertices\n ABCD are rotated by 90 degrees CW with regard to E:\n A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1)\n\n .. math::\n\n width = |AB| = |CD| = 5 - 1 = 4,\n height = |AD| = |BC| = 6 - 4 = 2.\n\n This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU\n will be 1. However, these two will generate different RoI Pooling results and\n should not be treated as an identical box.\n\n On the other hand, it's easy to see that (X, Y, W, H, A) is identical to\n (X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be\n identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is\n equivalent to rotating the same shape 90 degrees CW.\n\n We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180):\n\n .. code:: none\n\n O--------> x\n |\n | C---D\n | | E |\n | B---A\n |\n v y\n\n .. math::\n\n A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2),\n\n width = |AB| = |CD| = 7 - 3 = 4,\n height = |AD| = |BC| = 4 - 2 = 2.\n\n Finally, this is a very inaccurate (heavily quantized) illustration of\n how (5, 3, 4, 2, 60) looks like in case anyone wonders:\n\n .. code:: none\n\n O--------> x\n | B\\\n | / C\n | /E /\n | A /\n | `D\n v y\n\n It's still a rectangle with center of (5, 3), width of 4 and height of 2,\n but its angle (and thus orientation) is somewhere between\n (5, 3, 4, 2, 0) and (5, 3, 4, 2, 90).\n \"\"\"\n device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device(\"cpu\")\n tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)\n if tensor.numel() == 0:\n # Use reshape, so we don't end up creating a new tensor that does not depend on\n # the inputs (and consequently confuses jit)\n tensor = tensor.reshape((0, 5)).to(dtype=torch.float32, device=device)\n assert tensor.dim() == 2 and tensor.size(-1) == 5, tensor.size()\n\n self.tensor = tensor\n\n def clone(self) -> \"RotatedBoxes\":\n \"\"\"\n Clone the RotatedBoxes.\n\n Returns:\n RotatedBoxes\n \"\"\"\n return RotatedBoxes(self.tensor.clone())\n\n def to(self, device: torch.device):\n # Boxes are assumed float32 and does not support to(dtype)\n return RotatedBoxes(self.tensor.to(device=device))\n\n def area(self) -> torch.Tensor:\n \"\"\"\n Computes the area of all the boxes.\n\n Returns:\n torch.Tensor: a vector with areas of each box.\n \"\"\"\n box = self.tensor\n area = box[:, 2] * box[:, 3]\n return area\n\n # Avoid in-place operations so that we can torchscript; NOTE: this creates a new tensor\n def normalize_angles(self) -> None:\n \"\"\"\n Restrict angles to the range of [-180, 180) degrees\n \"\"\"\n angle_tensor = (self.tensor[:, 4] + 180.0) % 360.0 - 180.0\n self.tensor = torch.cat((self.tensor[:, :4], angle_tensor[:, None]), dim=1)\n\n def clip(self, box_size: Tuple[int, int], clip_angle_threshold: float = 1.0) -> None:\n \"\"\"\n Clip (in place) the boxes by limiting x coordinates to the range [0, width]\n and y coordinates to the range [0, height].\n\n For RRPN:\n Only clip boxes that are almost horizontal with a tolerance of\n clip_angle_threshold to maintain backward compatibility.\n\n Rotated boxes beyond this threshold are not clipped for two reasons:\n\n 1. There are potentially multiple ways to clip a rotated box to make it\n fit within the image.\n 2. It's tricky to make the entire rectangular box fit within the image\n and still be able to not leave out pixels of interest.\n\n Therefore we rely on ops like RoIAlignRotated to safely handle this.\n\n Args:\n box_size (height, width): The clipping box's size.\n clip_angle_threshold:\n Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees),\n we do the clipping as horizontal boxes.\n \"\"\"\n h, w = box_size\n\n # normalize angles to be within (-180, 180] degrees\n self.normalize_angles()\n\n idx = torch.where(torch.abs(self.tensor[:, 4]) <= clip_angle_threshold)[0]\n\n # convert to (x1, y1, x2, y2)\n x1 = self.tensor[idx, 0] - self.tensor[idx, 2] / 2.0\n y1 = self.tensor[idx, 1] - self.tensor[idx, 3] / 2.0\n x2 = self.tensor[idx, 0] + self.tensor[idx, 2] / 2.0\n y2 = self.tensor[idx, 1] + self.tensor[idx, 3] / 2.0\n\n # clip\n x1.clamp_(min=0, max=w)\n y1.clamp_(min=0, max=h)\n x2.clamp_(min=0, max=w)\n y2.clamp_(min=0, max=h)\n\n # convert back to (xc, yc, w, h)\n self.tensor[idx, 0] = (x1 + x2) / 2.0\n self.tensor[idx, 1] = (y1 + y2) / 2.0\n # make sure widths and heights do not increase due to numerical errors\n self.tensor[idx, 2] = torch.min(self.tensor[idx, 2], x2 - x1)\n self.tensor[idx, 3] = torch.min(self.tensor[idx, 3], y2 - y1)\n\n def nonempty(self, threshold: float = 0.0) -> torch.Tensor:\n \"\"\"\n Find boxes that are non-empty.\n A box is considered empty, if either of its side is no larger than threshold.\n\n Returns:\n Tensor: a binary vector which represents\n whether each box is empty (False) or non-empty (True).\n \"\"\"\n box = self.tensor\n widths = box[:, 2]\n heights = box[:, 3]\n keep = (widths > threshold) & (heights > threshold)\n return keep\n\n def __getitem__(self, item) -> \"RotatedBoxes\":\n \"\"\"\n Returns:\n RotatedBoxes: Create a new :class:`RotatedBoxes` by indexing.\n\n The following usage are allowed:\n\n 1. `new_boxes = boxes[3]`: return a `RotatedBoxes` which contains only one box.\n 2. `new_boxes = boxes[2:10]`: return a slice of boxes.\n 3. `new_boxes = boxes[vector]`, where vector is a torch.ByteTensor\n with `length = len(boxes)`. Nonzero elements in the vector will be selected.\n\n Note that the returned RotatedBoxes might share storage with this RotatedBoxes,\n subject to Pytorch's indexing semantics.\n \"\"\"\n if isinstance(item, int):\n return RotatedBoxes(self.tensor[item].view(1, -1))\n b = self.tensor[item]\n assert b.dim() == 2, \"Indexing on RotatedBoxes with {} failed to return a matrix!\".format(\n item\n )\n return RotatedBoxes(b)\n\n def __len__(self) -> int:\n return self.tensor.shape[0]\n\n def __repr__(self) -> str:\n return \"RotatedBoxes(\" + str(self.tensor) + \")\"\n\n def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:\n \"\"\"\n Args:\n box_size (height, width): Size of the reference box covering\n [0, width] x [0, height]\n boundary_threshold (int): Boxes that extend beyond the reference box\n boundary by more than boundary_threshold are considered \"outside\".\n\n For RRPN, it might not be necessary to call this function since it's common\n for rotated box to extend to outside of the image boundaries\n (the clip function only clips the near-horizontal boxes)\n\n Returns:\n a binary vector, indicating whether each box is inside the reference box.\n \"\"\"\n height, width = box_size\n\n cnt_x = self.tensor[..., 0]\n cnt_y = self.tensor[..., 1]\n half_w = self.tensor[..., 2] / 2.0\n half_h = self.tensor[..., 3] / 2.0\n a = self.tensor[..., 4]\n c = torch.abs(torch.cos(a * math.pi / 180.0))\n s = torch.abs(torch.sin(a * math.pi / 180.0))\n # This basically computes the horizontal bounding rectangle of the rotated box\n max_rect_dx = c * half_w + s * half_h\n max_rect_dy = c * half_h + s * half_w\n\n inds_inside = (\n (cnt_x - max_rect_dx >= -boundary_threshold)\n & (cnt_y - max_rect_dy >= -boundary_threshold)\n & (cnt_x + max_rect_dx < width + boundary_threshold)\n & (cnt_y + max_rect_dy < height + boundary_threshold)\n )\n\n return inds_inside\n\n def get_centers(self) -> torch.Tensor:\n \"\"\"\n Returns:\n The box centers in a Nx2 array of (x, y).\n \"\"\"\n return self.tensor[:, :2]\n\n def scale(self, scale_x: float, scale_y: float) -> None:\n \"\"\"\n Scale the rotated box with horizontal and vertical scaling factors\n Note: when scale_factor_x != scale_factor_y,\n the rotated box does not preserve the rectangular shape when the angle\n is not a multiple of 90 degrees under resize transformation.\n Instead, the shape is a parallelogram (that has skew)\n Here we make an approximation by fitting a rotated rectangle to the parallelogram.\n \"\"\"\n self.tensor[:, 0] *= scale_x\n self.tensor[:, 1] *= scale_y\n theta = self.tensor[:, 4] * math.pi / 180.0\n c = torch.cos(theta)\n s = torch.sin(theta)\n\n # In image space, y is top->down and x is left->right\n # Consider the local coordintate system for the rotated box,\n # where the box center is located at (0, 0), and the four vertices ABCD are\n # A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2)\n # the midpoint of the left edge AD of the rotated box E is:\n # E = (A+D)/2 = (-w / 2, 0)\n # the midpoint of the top edge AB of the rotated box F is:\n # F(0, -h / 2)\n # To get the old coordinates in the global system, apply the rotation transformation\n # (Note: the right-handed coordinate system for image space is yOx):\n # (old_x, old_y) = (s * y + c * x, c * y - s * x)\n # E(old) = (s * 0 + c * (-w/2), c * 0 - s * (-w/2)) = (-c * w / 2, s * w / 2)\n # F(old) = (s * (-h / 2) + c * 0, c * (-h / 2) - s * 0) = (-s * h / 2, -c * h / 2)\n # After applying the scaling factor (sfx, sfy):\n # E(new) = (-sfx * c * w / 2, sfy * s * w / 2)\n # F(new) = (-sfx * s * h / 2, -sfy * c * h / 2)\n # The new width after scaling tranformation becomes:\n\n # w(new) = |E(new) - O| * 2\n # = sqrt[(sfx * c * w / 2)^2 + (sfy * s * w / 2)^2] * 2\n # = sqrt[(sfx * c)^2 + (sfy * s)^2] * w\n # i.e., scale_factor_w = sqrt[(sfx * c)^2 + (sfy * s)^2]\n #\n # For example,\n # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_w == scale_factor_x;\n # when |angle| = 90, c = 0, |s| = 1, scale_factor_w == scale_factor_y\n self.tensor[:, 2] *= torch.sqrt((scale_x * c) ** 2 + (scale_y * s) ** 2)\n\n # h(new) = |F(new) - O| * 2\n # = sqrt[(sfx * s * h / 2)^2 + (sfy * c * h / 2)^2] * 2\n # = sqrt[(sfx * s)^2 + (sfy * c)^2] * h\n # i.e., scale_factor_h = sqrt[(sfx * s)^2 + (sfy * c)^2]\n #\n # For example,\n # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_h == scale_factor_y;\n # when |angle| = 90, c = 0, |s| = 1, scale_factor_h == scale_factor_x\n self.tensor[:, 3] *= torch.sqrt((scale_x * s) ** 2 + (scale_y * c) ** 2)\n\n # The angle is the rotation angle from y-axis in image space to the height\n # vector (top->down in the box's local coordinate system) of the box in CCW.\n #\n # angle(new) = angle_yOx(O - F(new))\n # = angle_yOx( (sfx * s * h / 2, sfy * c * h / 2) )\n # = atan2(sfx * s * h / 2, sfy * c * h / 2)\n # = atan2(sfx * s, sfy * c)\n #\n # For example,\n # when sfx == sfy, angle(new) == atan2(s, c) == angle(old)\n self.tensor[:, 4] = torch.atan2(scale_x * s, scale_y * c) * 180 / math.pi\n\n @classmethod\n def cat(cls, boxes_list: List[\"RotatedBoxes\"]) -> \"RotatedBoxes\":\n \"\"\"\n Concatenates a list of RotatedBoxes into a single RotatedBoxes\n\n Arguments:\n boxes_list (list[RotatedBoxes])\n\n Returns:\n RotatedBoxes: the concatenated RotatedBoxes\n \"\"\"\n assert isinstance(boxes_list, (list, tuple))\n if len(boxes_list) == 0:\n return cls(torch.empty(0))\n assert all([isinstance(box, RotatedBoxes) for box in boxes_list])\n\n # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input\n cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))\n return cat_boxes\n\n @property\n def device(self) -> torch.device:\n return self.tensor.device\n\n @torch.jit.unused\n def __iter__(self):\n \"\"\"\n Yield a box as a Tensor of shape (5,) at a time.\n \"\"\"\n yield from self.tensor"},{"identifier":"PathManager","path":"nativedancer/third_part/detectron2/utils/file_io.py","snippet":"class Detectron2Handler(PathHandler):\n PREFIX = \"detectron2://\"\n S3_DETECTRON2_PREFIX = \"https://dl.fbaipublicfiles.com/detectron2/\"\n def _get_supported_prefixes(self):\n def _get_local_path(self, path, **kwargs):\n def _open(self, path, mode=\"r\", **kwargs):"},{"identifier":"random_color","path":"nativedancer/third_part/detectron2/utils/colormap.py","snippet":"def random_color(rgb=False, maximum=255):\n \"\"\"\n Args:\n rgb (bool): whether to return RGB colors or BGR colors.\n maximum (int): either 255 or 1\n\n Returns:\n ndarray: a vector of 3 numbers\n \"\"\"\n idx = np.random.randint(0, len(_COLORS))\n ret = _COLORS[idx] * maximum\n if not rgb:\n ret = ret[::-1]\n return ret"}],"string":"[\n {\n \"identifier\": \"MetadataCatalog\",\n \"path\": \"nativedancer/third_part/detectron2/data/catalog.py\",\n \"snippet\": \"class _DatasetCatalog(UserDict):\\nclass Metadata(types.SimpleNamespace):\\nclass _MetadataCatalog(UserDict):\\n def register(self, name, func):\\n def get(self, name):\\n def list(self) -> List[str]:\\n def remove(self, name):\\n def __str__(self):\\n def __getattr__(self, key):\\n def __setattr__(self, key, val):\\n def as_dict(self):\\n def set(self, **kwargs):\\n def get(self, key, default=None):\\n def get(self, name):\\n def list(self):\\n def remove(self, name):\\n def __str__(self):\\n _RENAMED = {\\n \\\"class_names\\\": \\\"thing_classes\\\",\\n \\\"dataset_id_to_contiguous_id\\\": \\\"thing_dataset_id_to_contiguous_id\\\",\\n \\\"stuff_class_names\\\": \\\"stuff_classes\\\",\\n }\"\n },\n {\n \"identifier\": \"Boxes\",\n \"path\": \"nativedancer/third_part/detectron2/structures/boxes.py\",\n \"snippet\": \"class Boxes:\\n \\\"\\\"\\\"\\n This structure stores a list of boxes as a Nx4 torch.Tensor.\\n It supports some common methods about boxes\\n (`area`, `clip`, `nonempty`, etc),\\n and also behaves like a Tensor\\n (support indexing, `to(device)`, `.device`, and iteration over all boxes)\\n\\n Attributes:\\n tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).\\n \\\"\\\"\\\"\\n\\n def __init__(self, tensor: torch.Tensor):\\n \\\"\\\"\\\"\\n Args:\\n tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2).\\n \\\"\\\"\\\"\\n if not isinstance(tensor, torch.Tensor):\\n tensor = torch.as_tensor(tensor, dtype=torch.float32, device=torch.device(\\\"cpu\\\"))\\n else:\\n tensor = tensor.to(torch.float32)\\n if tensor.numel() == 0:\\n # Use reshape, so we don't end up creating a new tensor that does not depend on\\n # the inputs (and consequently confuses jit)\\n tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32)\\n assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()\\n\\n self.tensor = tensor\\n\\n def clone(self) -> \\\"Boxes\\\":\\n \\\"\\\"\\\"\\n Clone the Boxes.\\n\\n Returns:\\n Boxes\\n \\\"\\\"\\\"\\n return Boxes(self.tensor.clone())\\n\\n def to(self, device: torch.device):\\n # Boxes are assumed float32 and does not support to(dtype)\\n return Boxes(self.tensor.to(device=device))\\n\\n def area(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Computes the area of all the boxes.\\n\\n Returns:\\n torch.Tensor: a vector with areas of each box.\\n \\\"\\\"\\\"\\n box = self.tensor\\n area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])\\n return area\\n\\n def clip(self, box_size: Tuple[int, int]) -> None:\\n \\\"\\\"\\\"\\n Clip (in place) the boxes by limiting x coordinates to the range [0, width]\\n and y coordinates to the range [0, height].\\n\\n Args:\\n box_size (height, width): The clipping box's size.\\n \\\"\\\"\\\"\\n assert torch.isfinite(self.tensor).all(), \\\"Box tensor contains infinite or NaN!\\\"\\n h, w = box_size\\n x1 = self.tensor[:, 0].clamp(min=0, max=w)\\n y1 = self.tensor[:, 1].clamp(min=0, max=h)\\n x2 = self.tensor[:, 2].clamp(min=0, max=w)\\n y2 = self.tensor[:, 3].clamp(min=0, max=h)\\n self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)\\n\\n def nonempty(self, threshold: float = 0.0) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Find boxes that are non-empty.\\n A box is considered empty, if either of its side is no larger than threshold.\\n\\n Returns:\\n Tensor:\\n a binary vector which represents whether each box is empty\\n (False) or non-empty (True).\\n \\\"\\\"\\\"\\n box = self.tensor\\n widths = box[:, 2] - box[:, 0]\\n heights = box[:, 3] - box[:, 1]\\n keep = (widths > threshold) & (heights > threshold)\\n return keep\\n\\n def __getitem__(self, item) -> \\\"Boxes\\\":\\n \\\"\\\"\\\"\\n Args:\\n item: int, slice, or a BoolTensor\\n\\n Returns:\\n Boxes: Create a new :class:`Boxes` by indexing.\\n\\n The following usage are allowed:\\n\\n 1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.\\n 2. `new_boxes = boxes[2:10]`: return a slice of boxes.\\n 3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor\\n with `length = len(boxes)`. Nonzero elements in the vector will be selected.\\n\\n Note that the returned Boxes might share storage with this Boxes,\\n subject to Pytorch's indexing semantics.\\n \\\"\\\"\\\"\\n if isinstance(item, int):\\n return Boxes(self.tensor[item].view(1, -1))\\n b = self.tensor[item]\\n assert b.dim() == 2, \\\"Indexing on Boxes with {} failed to return a matrix!\\\".format(item)\\n return Boxes(b)\\n\\n def __len__(self) -> int:\\n return self.tensor.shape[0]\\n\\n def __repr__(self) -> str:\\n return \\\"Boxes(\\\" + str(self.tensor) + \\\")\\\"\\n\\n def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Args:\\n box_size (height, width): Size of the reference box.\\n boundary_threshold (int): Boxes that extend beyond the reference box\\n boundary by more than boundary_threshold are considered \\\"outside\\\".\\n\\n Returns:\\n a binary vector, indicating whether each box is inside the reference box.\\n \\\"\\\"\\\"\\n height, width = box_size\\n inds_inside = (\\n (self.tensor[..., 0] >= -boundary_threshold)\\n & (self.tensor[..., 1] >= -boundary_threshold)\\n & (self.tensor[..., 2] < width + boundary_threshold)\\n & (self.tensor[..., 3] < height + boundary_threshold)\\n )\\n return inds_inside\\n\\n def get_centers(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Returns:\\n The box centers in a Nx2 array of (x, y).\\n \\\"\\\"\\\"\\n return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2\\n\\n def scale(self, scale_x: float, scale_y: float) -> None:\\n \\\"\\\"\\\"\\n Scale the box with horizontal and vertical scaling factors\\n \\\"\\\"\\\"\\n self.tensor[:, 0::2] *= scale_x\\n self.tensor[:, 1::2] *= scale_y\\n\\n @classmethod\\n def cat(cls, boxes_list: List[\\\"Boxes\\\"]) -> \\\"Boxes\\\":\\n \\\"\\\"\\\"\\n Concatenates a list of Boxes into a single Boxes\\n\\n Arguments:\\n boxes_list (list[Boxes])\\n\\n Returns:\\n Boxes: the concatenated Boxes\\n \\\"\\\"\\\"\\n assert isinstance(boxes_list, (list, tuple))\\n if len(boxes_list) == 0:\\n return cls(torch.empty(0))\\n assert all([isinstance(box, Boxes) for box in boxes_list])\\n\\n # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input\\n cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))\\n return cat_boxes\\n\\n @property\\n def device(self) -> device:\\n return self.tensor.device\\n\\n # type \\\"Iterator[torch.Tensor]\\\", yield, and iter() not supported by torchscript\\n # https://github.com/pytorch/pytorch/issues/18627\\n @torch.jit.unused\\n def __iter__(self):\\n \\\"\\\"\\\"\\n Yield a box as a Tensor of shape (4,) at a time.\\n \\\"\\\"\\\"\\n yield from self.tensor\"\n },\n {\n \"identifier\": \"BoxMode\",\n \"path\": \"nativedancer/third_part/detectron2/structures/boxes.py\",\n \"snippet\": \"class BoxMode(IntEnum):\\n \\\"\\\"\\\"\\n Enum of different ways to represent a box.\\n \\\"\\\"\\\"\\n\\n XYXY_ABS = 0\\n \\\"\\\"\\\"\\n (x0, y0, x1, y1) in absolute floating points coordinates.\\n The coordinates in range [0, width or height].\\n \\\"\\\"\\\"\\n XYWH_ABS = 1\\n \\\"\\\"\\\"\\n (x0, y0, w, h) in absolute floating points coordinates.\\n \\\"\\\"\\\"\\n XYXY_REL = 2\\n \\\"\\\"\\\"\\n Not yet supported!\\n (x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image.\\n \\\"\\\"\\\"\\n XYWH_REL = 3\\n \\\"\\\"\\\"\\n Not yet supported!\\n (x0, y0, w, h) in range [0, 1]. They are relative to the size of the image.\\n \\\"\\\"\\\"\\n XYWHA_ABS = 4\\n \\\"\\\"\\\"\\n (xc, yc, w, h, a) in absolute floating points coordinates.\\n (xc, yc) is the center of the rotated box, and the angle a is in degrees ccw.\\n \\\"\\\"\\\"\\n\\n @staticmethod\\n def convert(box: _RawBoxType, from_mode: \\\"BoxMode\\\", to_mode: \\\"BoxMode\\\") -> _RawBoxType:\\n \\\"\\\"\\\"\\n Args:\\n box: can be a k-tuple, k-list or an Nxk array/tensor, where k = 4 or 5\\n from_mode, to_mode (BoxMode)\\n\\n Returns:\\n The converted box of the same type.\\n \\\"\\\"\\\"\\n if from_mode == to_mode:\\n return box\\n\\n original_type = type(box)\\n is_numpy = isinstance(box, np.ndarray)\\n single_box = isinstance(box, (list, tuple))\\n if single_box:\\n assert len(box) == 4 or len(box) == 5, (\\n \\\"BoxMode.convert takes either a k-tuple/list or an Nxk array/tensor,\\\"\\n \\\" where k == 4 or 5\\\"\\n )\\n arr = torch.tensor(box)[None, :]\\n else:\\n # avoid modifying the input box\\n if is_numpy:\\n arr = torch.from_numpy(np.asarray(box)).clone()\\n else:\\n arr = box.clone()\\n\\n assert to_mode not in [BoxMode.XYXY_REL, BoxMode.XYWH_REL] and from_mode not in [\\n BoxMode.XYXY_REL,\\n BoxMode.XYWH_REL,\\n ], \\\"Relative mode not yet supported!\\\"\\n\\n if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS:\\n assert (\\n arr.shape[-1] == 5\\n ), \\\"The last dimension of input shape must be 5 for XYWHA format\\\"\\n original_dtype = arr.dtype\\n arr = arr.double()\\n\\n w = arr[:, 2]\\n h = arr[:, 3]\\n a = arr[:, 4]\\n c = torch.abs(torch.cos(a * math.pi / 180.0))\\n s = torch.abs(torch.sin(a * math.pi / 180.0))\\n # This basically computes the horizontal bounding rectangle of the rotated box\\n new_w = c * w + s * h\\n new_h = c * h + s * w\\n\\n # convert center to top-left corner\\n arr[:, 0] -= new_w / 2.0\\n arr[:, 1] -= new_h / 2.0\\n # bottom-right corner\\n arr[:, 2] = arr[:, 0] + new_w\\n arr[:, 3] = arr[:, 1] + new_h\\n\\n arr = arr[:, :4].to(dtype=original_dtype)\\n elif from_mode == BoxMode.XYWH_ABS and to_mode == BoxMode.XYWHA_ABS:\\n original_dtype = arr.dtype\\n arr = arr.double()\\n arr[:, 0] += arr[:, 2] / 2.0\\n arr[:, 1] += arr[:, 3] / 2.0\\n angles = torch.zeros((arr.shape[0], 1), dtype=arr.dtype)\\n arr = torch.cat((arr, angles), axis=1).to(dtype=original_dtype)\\n else:\\n if to_mode == BoxMode.XYXY_ABS and from_mode == BoxMode.XYWH_ABS:\\n arr[:, 2] += arr[:, 0]\\n arr[:, 3] += arr[:, 1]\\n elif from_mode == BoxMode.XYXY_ABS and to_mode == BoxMode.XYWH_ABS:\\n arr[:, 2] -= arr[:, 0]\\n arr[:, 3] -= arr[:, 1]\\n else:\\n raise NotImplementedError(\\n \\\"Conversion from BoxMode {} to {} is not supported yet\\\".format(\\n from_mode, to_mode\\n )\\n )\\n\\n if single_box:\\n return original_type(arr.flatten().tolist())\\n if is_numpy:\\n return arr.numpy()\\n else:\\n return arr\"\n },\n {\n \"identifier\": \"Keypoints\",\n \"path\": \"nativedancer/third_part/detectron2/structures/keypoints.py\",\n \"snippet\": \"class Keypoints:\\n \\\"\\\"\\\"\\n Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property\\n containing the x,y location and visibility flag of each keypoint. This tensor has shape\\n (N, K, 3) where N is the number of instances and K is the number of keypoints per instance.\\n\\n The visibility flag follows the COCO format and must be one of three integers:\\n\\n * v=0: not labeled (in which case x=y=0)\\n * v=1: labeled but not visible\\n * v=2: labeled and visible\\n \\\"\\\"\\\"\\n\\n def __init__(self, keypoints: Union[torch.Tensor, np.ndarray, List[List[float]]]):\\n \\\"\\\"\\\"\\n Arguments:\\n keypoints: A Tensor, numpy array, or list of the x, y, and visibility of each keypoint.\\n The shape should be (N, K, 3) where N is the number of\\n instances, and K is the number of keypoints per instance.\\n \\\"\\\"\\\"\\n device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device(\\\"cpu\\\")\\n keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device)\\n assert keypoints.dim() == 3 and keypoints.shape[2] == 3, keypoints.shape\\n self.tensor = keypoints\\n\\n def __len__(self) -> int:\\n return self.tensor.size(0)\\n\\n def to(self, *args: Any, **kwargs: Any) -> \\\"Keypoints\\\":\\n return type(self)(self.tensor.to(*args, **kwargs))\\n\\n @property\\n def device(self) -> torch.device:\\n return self.tensor.device\\n\\n def to_heatmap(self, boxes: torch.Tensor, heatmap_size: int) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Convert keypoint annotations to a heatmap of one-hot labels for training,\\n as described in :paper:`Mask R-CNN`.\\n\\n Arguments:\\n boxes: Nx4 tensor, the boxes to draw the keypoints to\\n\\n Returns:\\n heatmaps:\\n A tensor of shape (N, K), each element is integer spatial label\\n in the range [0, heatmap_size**2 - 1] for each keypoint in the input.\\n valid:\\n A tensor of shape (N, K) containing whether each keypoint is in the roi or not.\\n \\\"\\\"\\\"\\n return _keypoints_to_heatmap(self.tensor, boxes, heatmap_size)\\n\\n def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> \\\"Keypoints\\\":\\n \\\"\\\"\\\"\\n Create a new `Keypoints` by indexing on this `Keypoints`.\\n\\n The following usage are allowed:\\n\\n 1. `new_kpts = kpts[3]`: return a `Keypoints` which contains only one instance.\\n 2. `new_kpts = kpts[2:10]`: return a slice of key points.\\n 3. `new_kpts = kpts[vector]`, where vector is a torch.ByteTensor\\n with `length = len(kpts)`. Nonzero elements in the vector will be selected.\\n\\n Note that the returned Keypoints might share storage with this Keypoints,\\n subject to Pytorch's indexing semantics.\\n \\\"\\\"\\\"\\n if isinstance(item, int):\\n return Keypoints([self.tensor[item]])\\n return Keypoints(self.tensor[item])\\n\\n def __repr__(self) -> str:\\n s = self.__class__.__name__ + \\\"(\\\"\\n s += \\\"num_instances={})\\\".format(len(self.tensor))\\n return s\\n\\n @staticmethod\\n def cat(keypoints_list: List[\\\"Keypoints\\\"]) -> \\\"Keypoints\\\":\\n \\\"\\\"\\\"\\n Concatenates a list of Keypoints into a single Keypoints\\n\\n Arguments:\\n keypoints_list (list[Keypoints])\\n\\n Returns:\\n Keypoints: the concatenated Keypoints\\n \\\"\\\"\\\"\\n assert isinstance(keypoints_list, (list, tuple))\\n assert len(keypoints_list) > 0\\n assert all(isinstance(keypoints, Keypoints) for keypoints in keypoints_list)\\n\\n cat_kpts = type(keypoints_list[0])(\\n torch.cat([kpts.tensor for kpts in keypoints_list], dim=0)\\n )\\n return cat_kpts\"\n },\n {\n \"identifier\": \"BitMasks\",\n \"path\": \"nativedancer/third_part/detectron2/structures/masks.py\",\n \"snippet\": \"class BitMasks:\\n \\\"\\\"\\\"\\n This class stores the segmentation masks for all objects in one image, in\\n the form of bitmaps.\\n\\n Attributes:\\n tensor: bool Tensor of N,H,W, representing N instances in the image.\\n \\\"\\\"\\\"\\n\\n def __init__(self, tensor: Union[torch.Tensor, np.ndarray]):\\n \\\"\\\"\\\"\\n Args:\\n tensor: bool Tensor of N,H,W, representing N instances in the image.\\n \\\"\\\"\\\"\\n if isinstance(tensor, torch.Tensor):\\n tensor = tensor.to(torch.bool)\\n else:\\n tensor = torch.as_tensor(tensor, dtype=torch.bool, device=torch.device(\\\"cpu\\\"))\\n assert tensor.dim() == 3, tensor.size()\\n self.image_size = tensor.shape[1:]\\n self.tensor = tensor\\n\\n @torch.jit.unused\\n def to(self, *args: Any, **kwargs: Any) -> \\\"BitMasks\\\":\\n return BitMasks(self.tensor.to(*args, **kwargs))\\n\\n @property\\n def device(self) -> torch.device:\\n return self.tensor.device\\n\\n @torch.jit.unused\\n def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> \\\"BitMasks\\\":\\n \\\"\\\"\\\"\\n Returns:\\n BitMasks: Create a new :class:`BitMasks` by indexing.\\n\\n The following usage are allowed:\\n\\n 1. `new_masks = masks[3]`: return a `BitMasks` which contains only one mask.\\n 2. `new_masks = masks[2:10]`: return a slice of masks.\\n 3. `new_masks = masks[vector]`, where vector is a torch.BoolTensor\\n with `length = len(masks)`. Nonzero elements in the vector will be selected.\\n\\n Note that the returned object might share storage with this object,\\n subject to Pytorch's indexing semantics.\\n \\\"\\\"\\\"\\n if isinstance(item, int):\\n return BitMasks(self.tensor[item].unsqueeze(0))\\n m = self.tensor[item]\\n assert m.dim() == 3, \\\"Indexing on BitMasks with {} returns a tensor with shape {}!\\\".format(\\n item, m.shape\\n )\\n return BitMasks(m)\\n\\n @torch.jit.unused\\n def __iter__(self) -> torch.Tensor:\\n yield from self.tensor\\n\\n @torch.jit.unused\\n def __repr__(self) -> str:\\n s = self.__class__.__name__ + \\\"(\\\"\\n s += \\\"num_instances={})\\\".format(len(self.tensor))\\n return s\\n\\n def __len__(self) -> int:\\n return self.tensor.shape[0]\\n\\n def nonempty(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Find masks that are non-empty.\\n\\n Returns:\\n Tensor: a BoolTensor which represents\\n whether each mask is empty (False) or non-empty (True).\\n \\\"\\\"\\\"\\n return self.tensor.flatten(1).any(dim=1)\\n\\n @staticmethod\\n def from_polygon_masks(\\n polygon_masks: Union[\\\"PolygonMasks\\\", List[List[np.ndarray]]], height: int, width: int\\n ) -> \\\"BitMasks\\\":\\n \\\"\\\"\\\"\\n Args:\\n polygon_masks (list[list[ndarray]] or PolygonMasks)\\n height, width (int)\\n \\\"\\\"\\\"\\n if isinstance(polygon_masks, PolygonMasks):\\n polygon_masks = polygon_masks.polygons\\n masks = [polygons_to_bitmask(p, height, width) for p in polygon_masks]\\n if len(masks):\\n return BitMasks(torch.stack([torch.from_numpy(x) for x in masks]))\\n else:\\n return BitMasks(torch.empty(0, height, width, dtype=torch.bool))\\n\\n @staticmethod\\n def from_roi_masks(roi_masks: \\\"ROIMasks\\\", height: int, width: int) -> \\\"BitMasks\\\":\\n \\\"\\\"\\\"\\n Args:\\n roi_masks:\\n height, width (int):\\n \\\"\\\"\\\"\\n return roi_masks.to_bitmasks(height, width)\\n\\n def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Crop each bitmask by the given box, and resize results to (mask_size, mask_size).\\n This can be used to prepare training targets for Mask R-CNN.\\n It has less reconstruction error compared to rasterization with polygons.\\n However we observe no difference in accuracy,\\n but BitMasks requires more memory to store all the masks.\\n\\n Args:\\n boxes (Tensor): Nx4 tensor storing the boxes for each mask\\n mask_size (int): the size of the rasterized mask.\\n\\n Returns:\\n Tensor:\\n A bool tensor of shape (N, mask_size, mask_size), where\\n N is the number of predicted boxes for this image.\\n \\\"\\\"\\\"\\n assert len(boxes) == len(self), \\\"{} != {}\\\".format(len(boxes), len(self))\\n device = self.tensor.device\\n\\n batch_inds = torch.arange(len(boxes), device=device).to(dtype=boxes.dtype)[:, None]\\n rois = torch.cat([batch_inds, boxes], dim=1) # Nx5\\n\\n bit_masks = self.tensor.to(dtype=torch.float32)\\n rois = rois.to(device=device)\\n output = (\\n ROIAlign((mask_size, mask_size), 1.0, 0, aligned=True)\\n .forward(bit_masks[:, None, :, :], rois)\\n .squeeze(1)\\n )\\n output = output >= 0.5\\n return output\\n\\n def get_bounding_boxes(self) -> Boxes:\\n \\\"\\\"\\\"\\n Returns:\\n Boxes: tight bounding boxes around bitmasks.\\n If a mask is empty, it's bounding box will be all zero.\\n \\\"\\\"\\\"\\n boxes = torch.zeros(self.tensor.shape[0], 4, dtype=torch.float32)\\n x_any = torch.any(self.tensor, dim=1)\\n y_any = torch.any(self.tensor, dim=2)\\n for idx in range(self.tensor.shape[0]):\\n x = torch.where(x_any[idx, :])[0]\\n y = torch.where(y_any[idx, :])[0]\\n if len(x) > 0 and len(y) > 0:\\n boxes[idx, :] = torch.as_tensor(\\n [x[0], y[0], x[-1] + 1, y[-1] + 1], dtype=torch.float32\\n )\\n return Boxes(boxes)\\n\\n @staticmethod\\n def cat(bitmasks_list: List[\\\"BitMasks\\\"]) -> \\\"BitMasks\\\":\\n \\\"\\\"\\\"\\n Concatenates a list of BitMasks into a single BitMasks\\n\\n Arguments:\\n bitmasks_list (list[BitMasks])\\n\\n Returns:\\n BitMasks: the concatenated BitMasks\\n \\\"\\\"\\\"\\n assert isinstance(bitmasks_list, (list, tuple))\\n assert len(bitmasks_list) > 0\\n assert all(isinstance(bitmask, BitMasks) for bitmask in bitmasks_list)\\n\\n cat_bitmasks = type(bitmasks_list[0])(torch.cat([bm.tensor for bm in bitmasks_list], dim=0))\\n return cat_bitmasks\"\n },\n {\n \"identifier\": \"PolygonMasks\",\n \"path\": \"nativedancer/third_part/detectron2/structures/masks.py\",\n \"snippet\": \"class PolygonMasks:\\n \\\"\\\"\\\"\\n This class stores the segmentation masks for all objects in one image, in the form of polygons.\\n\\n Attributes:\\n polygons: list[list[ndarray]]. Each ndarray is a float64 vector representing a polygon.\\n \\\"\\\"\\\"\\n\\n def __init__(self, polygons: List[List[Union[torch.Tensor, np.ndarray]]]):\\n \\\"\\\"\\\"\\n Arguments:\\n polygons (list[list[np.ndarray]]): The first\\n level of the list correspond to individual instances,\\n the second level to all the polygons that compose the\\n instance, and the third level to the polygon coordinates.\\n The third level array should have the format of\\n [x0, y0, x1, y1, ..., xn, yn] (n >= 3).\\n \\\"\\\"\\\"\\n if not isinstance(polygons, list):\\n raise ValueError(\\n \\\"Cannot create PolygonMasks: Expect a list of list of polygons per image. \\\"\\n \\\"Got '{}' instead.\\\".format(type(polygons))\\n )\\n\\n def _make_array(t: Union[torch.Tensor, np.ndarray]) -> np.ndarray:\\n # Use float64 for higher precision, because why not?\\n # Always put polygons on CPU (self.to is a no-op) since they\\n # are supposed to be small tensors.\\n # May need to change this assumption if GPU placement becomes useful\\n if isinstance(t, torch.Tensor):\\n t = t.cpu().numpy()\\n return np.asarray(t).astype(\\\"float64\\\")\\n\\n def process_polygons(\\n polygons_per_instance: List[Union[torch.Tensor, np.ndarray]]\\n ) -> List[np.ndarray]:\\n if not isinstance(polygons_per_instance, list):\\n raise ValueError(\\n \\\"Cannot create polygons: Expect a list of polygons per instance. \\\"\\n \\\"Got '{}' instead.\\\".format(type(polygons_per_instance))\\n )\\n # transform each polygon to a numpy array\\n polygons_per_instance = [_make_array(p) for p in polygons_per_instance]\\n for polygon in polygons_per_instance:\\n if len(polygon) % 2 != 0 or len(polygon) < 6:\\n raise ValueError(f\\\"Cannot create a polygon from {len(polygon)} coordinates.\\\")\\n return polygons_per_instance\\n\\n self.polygons: List[List[np.ndarray]] = [\\n process_polygons(polygons_per_instance) for polygons_per_instance in polygons\\n ]\\n\\n def to(self, *args: Any, **kwargs: Any) -> \\\"PolygonMasks\\\":\\n return self\\n\\n @property\\n def device(self) -> torch.device:\\n return torch.device(\\\"cpu\\\")\\n\\n def get_bounding_boxes(self) -> Boxes:\\n \\\"\\\"\\\"\\n Returns:\\n Boxes: tight bounding boxes around polygon masks.\\n \\\"\\\"\\\"\\n boxes = torch.zeros(len(self.polygons), 4, dtype=torch.float32)\\n for idx, polygons_per_instance in enumerate(self.polygons):\\n minxy = torch.as_tensor([float(\\\"inf\\\"), float(\\\"inf\\\")], dtype=torch.float32)\\n maxxy = torch.zeros(2, dtype=torch.float32)\\n for polygon in polygons_per_instance:\\n coords = torch.from_numpy(polygon).view(-1, 2).to(dtype=torch.float32)\\n minxy = torch.min(minxy, torch.min(coords, dim=0).values)\\n maxxy = torch.max(maxxy, torch.max(coords, dim=0).values)\\n boxes[idx, :2] = minxy\\n boxes[idx, 2:] = maxxy\\n return Boxes(boxes)\\n\\n def nonempty(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Find masks that are non-empty.\\n\\n Returns:\\n Tensor:\\n a BoolTensor which represents whether each mask is empty (False) or not (True).\\n \\\"\\\"\\\"\\n keep = [1 if len(polygon) > 0 else 0 for polygon in self.polygons]\\n return torch.from_numpy(np.asarray(keep, dtype=bool))\\n\\n def __getitem__(self, item: Union[int, slice, List[int], torch.BoolTensor]) -> \\\"PolygonMasks\\\":\\n \\\"\\\"\\\"\\n Support indexing over the instances and return a `PolygonMasks` object.\\n `item` can be:\\n\\n 1. An integer. It will return an object with only one instance.\\n 2. A slice. It will return an object with the selected instances.\\n 3. A list[int]. It will return an object with the selected instances,\\n correpsonding to the indices in the list.\\n 4. A vector mask of type BoolTensor, whose length is num_instances.\\n It will return an object with the instances whose mask is nonzero.\\n \\\"\\\"\\\"\\n if isinstance(item, int):\\n selected_polygons = [self.polygons[item]]\\n elif isinstance(item, slice):\\n selected_polygons = self.polygons[item]\\n elif isinstance(item, list):\\n selected_polygons = [self.polygons[i] for i in item]\\n elif isinstance(item, torch.Tensor):\\n # Polygons is a list, so we have to move the indices back to CPU.\\n if item.dtype == torch.bool:\\n assert item.dim() == 1, item.shape\\n item = item.nonzero().squeeze(1).cpu().numpy().tolist()\\n elif item.dtype in [torch.int32, torch.int64]:\\n item = item.cpu().numpy().tolist()\\n else:\\n raise ValueError(\\\"Unsupported tensor dtype={} for indexing!\\\".format(item.dtype))\\n selected_polygons = [self.polygons[i] for i in item]\\n return PolygonMasks(selected_polygons)\\n\\n def __iter__(self) -> Iterator[List[np.ndarray]]:\\n \\\"\\\"\\\"\\n Yields:\\n list[ndarray]: the polygons for one instance.\\n Each Tensor is a float64 vector representing a polygon.\\n \\\"\\\"\\\"\\n return iter(self.polygons)\\n\\n def __repr__(self) -> str:\\n s = self.__class__.__name__ + \\\"(\\\"\\n s += \\\"num_instances={})\\\".format(len(self.polygons))\\n return s\\n\\n def __len__(self) -> int:\\n return len(self.polygons)\\n\\n def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Crop each mask by the given box, and resize results to (mask_size, mask_size).\\n This can be used to prepare training targets for Mask R-CNN.\\n\\n Args:\\n boxes (Tensor): Nx4 tensor storing the boxes for each mask\\n mask_size (int): the size of the rasterized mask.\\n\\n Returns:\\n Tensor: A bool tensor of shape (N, mask_size, mask_size), where\\n N is the number of predicted boxes for this image.\\n \\\"\\\"\\\"\\n assert len(boxes) == len(self), \\\"{} != {}\\\".format(len(boxes), len(self))\\n\\n device = boxes.device\\n # Put boxes on the CPU, as the polygon representation is not efficient GPU-wise\\n # (several small tensors for representing a single instance mask)\\n boxes = boxes.to(torch.device(\\\"cpu\\\"))\\n\\n results = [\\n rasterize_polygons_within_box(poly, box.numpy(), mask_size)\\n for poly, box in zip(self.polygons, boxes)\\n ]\\n \\\"\\\"\\\"\\n poly: list[list[float]], the polygons for one instance\\n box: a tensor of shape (4,)\\n \\\"\\\"\\\"\\n if len(results) == 0:\\n return torch.empty(0, mask_size, mask_size, dtype=torch.bool, device=device)\\n return torch.stack(results, dim=0).to(device=device)\\n\\n def area(self):\\n \\\"\\\"\\\"\\n Computes area of the mask.\\n Only works with Polygons, using the shoelace formula:\\n https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates\\n\\n Returns:\\n Tensor: a vector, area for each instance\\n \\\"\\\"\\\"\\n\\n area = []\\n for polygons_per_instance in self.polygons:\\n area_per_instance = 0\\n for p in polygons_per_instance:\\n area_per_instance += polygon_area(p[0::2], p[1::2])\\n area.append(area_per_instance)\\n\\n return torch.tensor(area)\\n\\n @staticmethod\\n def cat(polymasks_list: List[\\\"PolygonMasks\\\"]) -> \\\"PolygonMasks\\\":\\n \\\"\\\"\\\"\\n Concatenates a list of PolygonMasks into a single PolygonMasks\\n\\n Arguments:\\n polymasks_list (list[PolygonMasks])\\n\\n Returns:\\n PolygonMasks: the concatenated PolygonMasks\\n \\\"\\\"\\\"\\n assert isinstance(polymasks_list, (list, tuple))\\n assert len(polymasks_list) > 0\\n assert all(isinstance(polymask, PolygonMasks) for polymask in polymasks_list)\\n\\n cat_polymasks = type(polymasks_list[0])(\\n list(itertools.chain.from_iterable(pm.polygons for pm in polymasks_list))\\n )\\n return cat_polymasks\"\n },\n {\n \"identifier\": \"RotatedBoxes\",\n \"path\": \"nativedancer/third_part/detectron2/structures/rotated_boxes.py\",\n \"snippet\": \"class RotatedBoxes(Boxes):\\n \\\"\\\"\\\"\\n This structure stores a list of rotated boxes as a Nx5 torch.Tensor.\\n It supports some common methods about boxes\\n (`area`, `clip`, `nonempty`, etc),\\n and also behaves like a Tensor\\n (support indexing, `to(device)`, `.device`, and iteration over all boxes)\\n \\\"\\\"\\\"\\n\\n def __init__(self, tensor: torch.Tensor):\\n \\\"\\\"\\\"\\n Args:\\n tensor (Tensor[float]): a Nx5 matrix. Each row is\\n (x_center, y_center, width, height, angle),\\n in which angle is represented in degrees.\\n While there's no strict range restriction for it,\\n the recommended principal range is between [-180, 180) degrees.\\n\\n Assume we have a horizontal box B = (x_center, y_center, width, height),\\n where width is along the x-axis and height is along the y-axis.\\n The rotated box B_rot (x_center, y_center, width, height, angle)\\n can be seen as:\\n\\n 1. When angle == 0:\\n B_rot == B\\n 2. When angle > 0:\\n B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CCW;\\n 3. When angle < 0:\\n B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CW.\\n\\n Mathematically, since the right-handed coordinate system for image space\\n is (y, x), where y is top->down and x is left->right, the 4 vertices of the\\n rotated rectangle :math:`(yr_i, xr_i)` (i = 1, 2, 3, 4) can be obtained from\\n the vertices of the horizontal rectangle :math:`(y_i, x_i)` (i = 1, 2, 3, 4)\\n in the following way (:math:`\\\\\\\\theta = angle*\\\\\\\\pi/180` is the angle in radians,\\n :math:`(y_c, x_c)` is the center of the rectangle):\\n\\n .. math::\\n\\n yr_i = \\\\\\\\cos(\\\\\\\\theta) (y_i - y_c) - \\\\\\\\sin(\\\\\\\\theta) (x_i - x_c) + y_c,\\n\\n xr_i = \\\\\\\\sin(\\\\\\\\theta) (y_i - y_c) + \\\\\\\\cos(\\\\\\\\theta) (x_i - x_c) + x_c,\\n\\n which is the standard rigid-body rotation transformation.\\n\\n Intuitively, the angle is\\n (1) the rotation angle from y-axis in image space\\n to the height vector (top->down in the box's local coordinate system)\\n of the box in CCW, and\\n (2) the rotation angle from x-axis in image space\\n to the width vector (left->right in the box's local coordinate system)\\n of the box in CCW.\\n\\n More intuitively, consider the following horizontal box ABCD represented\\n in (x1, y1, x2, y2): (3, 2, 7, 4),\\n covering the [3, 7] x [2, 4] region of the continuous coordinate system\\n which looks like this:\\n\\n .. code:: none\\n\\n O--------> x\\n |\\n | A---B\\n | | |\\n | D---C\\n |\\n v y\\n\\n Note that each capital letter represents one 0-dimensional geometric point\\n instead of a 'square pixel' here.\\n\\n In the example above, using (x, y) to represent a point we have:\\n\\n .. math::\\n\\n O = (0, 0), A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4)\\n\\n We name vector AB = vector DC as the width vector in box's local coordinate system, and\\n vector AD = vector BC as the height vector in box's local coordinate system. Initially,\\n when angle = 0 degree, they're aligned with the positive directions of x-axis and y-axis\\n in the image space, respectively.\\n\\n For better illustration, we denote the center of the box as E,\\n\\n .. code:: none\\n\\n O--------> x\\n |\\n | A---B\\n | | E |\\n | D---C\\n |\\n v y\\n\\n where the center E = ((3+7)/2, (2+4)/2) = (5, 3).\\n\\n Also,\\n\\n .. math::\\n\\n width = |AB| = |CD| = 7 - 3 = 4,\\n height = |AD| = |BC| = 4 - 2 = 2.\\n\\n Therefore, the corresponding representation for the same shape in rotated box in\\n (x_center, y_center, width, height, angle) format is:\\n\\n (5, 3, 4, 2, 0),\\n\\n Now, let's consider (5, 3, 4, 2, 90), which is rotated by 90 degrees\\n CCW (counter-clockwise) by definition. It looks like this:\\n\\n .. code:: none\\n\\n O--------> x\\n | B-C\\n | | |\\n | |E|\\n | | |\\n | A-D\\n v y\\n\\n The center E is still located at the same point (5, 3), while the vertices\\n ABCD are rotated by 90 degrees CCW with regard to E:\\n A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5)\\n\\n Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to\\n vector AD or vector BC (the top->down height vector in box's local coordinate system),\\n or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right\\n width vector in box's local coordinate system).\\n\\n .. math::\\n\\n width = |AB| = |CD| = 5 - 1 = 4,\\n height = |AD| = |BC| = 6 - 4 = 2.\\n\\n Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise)\\n by definition? It looks like this:\\n\\n .. code:: none\\n\\n O--------> x\\n | D-A\\n | | |\\n | |E|\\n | | |\\n | C-B\\n v y\\n\\n The center E is still located at the same point (5, 3), while the vertices\\n ABCD are rotated by 90 degrees CW with regard to E:\\n A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1)\\n\\n .. math::\\n\\n width = |AB| = |CD| = 5 - 1 = 4,\\n height = |AD| = |BC| = 6 - 4 = 2.\\n\\n This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU\\n will be 1. However, these two will generate different RoI Pooling results and\\n should not be treated as an identical box.\\n\\n On the other hand, it's easy to see that (X, Y, W, H, A) is identical to\\n (X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be\\n identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is\\n equivalent to rotating the same shape 90 degrees CW.\\n\\n We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180):\\n\\n .. code:: none\\n\\n O--------> x\\n |\\n | C---D\\n | | E |\\n | B---A\\n |\\n v y\\n\\n .. math::\\n\\n A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2),\\n\\n width = |AB| = |CD| = 7 - 3 = 4,\\n height = |AD| = |BC| = 4 - 2 = 2.\\n\\n Finally, this is a very inaccurate (heavily quantized) illustration of\\n how (5, 3, 4, 2, 60) looks like in case anyone wonders:\\n\\n .. code:: none\\n\\n O--------> x\\n | B\\\\\\n | / C\\n | /E /\\n | A /\\n | `D\\n v y\\n\\n It's still a rectangle with center of (5, 3), width of 4 and height of 2,\\n but its angle (and thus orientation) is somewhere between\\n (5, 3, 4, 2, 0) and (5, 3, 4, 2, 90).\\n \\\"\\\"\\\"\\n device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device(\\\"cpu\\\")\\n tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)\\n if tensor.numel() == 0:\\n # Use reshape, so we don't end up creating a new tensor that does not depend on\\n # the inputs (and consequently confuses jit)\\n tensor = tensor.reshape((0, 5)).to(dtype=torch.float32, device=device)\\n assert tensor.dim() == 2 and tensor.size(-1) == 5, tensor.size()\\n\\n self.tensor = tensor\\n\\n def clone(self) -> \\\"RotatedBoxes\\\":\\n \\\"\\\"\\\"\\n Clone the RotatedBoxes.\\n\\n Returns:\\n RotatedBoxes\\n \\\"\\\"\\\"\\n return RotatedBoxes(self.tensor.clone())\\n\\n def to(self, device: torch.device):\\n # Boxes are assumed float32 and does not support to(dtype)\\n return RotatedBoxes(self.tensor.to(device=device))\\n\\n def area(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Computes the area of all the boxes.\\n\\n Returns:\\n torch.Tensor: a vector with areas of each box.\\n \\\"\\\"\\\"\\n box = self.tensor\\n area = box[:, 2] * box[:, 3]\\n return area\\n\\n # Avoid in-place operations so that we can torchscript; NOTE: this creates a new tensor\\n def normalize_angles(self) -> None:\\n \\\"\\\"\\\"\\n Restrict angles to the range of [-180, 180) degrees\\n \\\"\\\"\\\"\\n angle_tensor = (self.tensor[:, 4] + 180.0) % 360.0 - 180.0\\n self.tensor = torch.cat((self.tensor[:, :4], angle_tensor[:, None]), dim=1)\\n\\n def clip(self, box_size: Tuple[int, int], clip_angle_threshold: float = 1.0) -> None:\\n \\\"\\\"\\\"\\n Clip (in place) the boxes by limiting x coordinates to the range [0, width]\\n and y coordinates to the range [0, height].\\n\\n For RRPN:\\n Only clip boxes that are almost horizontal with a tolerance of\\n clip_angle_threshold to maintain backward compatibility.\\n\\n Rotated boxes beyond this threshold are not clipped for two reasons:\\n\\n 1. There are potentially multiple ways to clip a rotated box to make it\\n fit within the image.\\n 2. It's tricky to make the entire rectangular box fit within the image\\n and still be able to not leave out pixels of interest.\\n\\n Therefore we rely on ops like RoIAlignRotated to safely handle this.\\n\\n Args:\\n box_size (height, width): The clipping box's size.\\n clip_angle_threshold:\\n Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees),\\n we do the clipping as horizontal boxes.\\n \\\"\\\"\\\"\\n h, w = box_size\\n\\n # normalize angles to be within (-180, 180] degrees\\n self.normalize_angles()\\n\\n idx = torch.where(torch.abs(self.tensor[:, 4]) <= clip_angle_threshold)[0]\\n\\n # convert to (x1, y1, x2, y2)\\n x1 = self.tensor[idx, 0] - self.tensor[idx, 2] / 2.0\\n y1 = self.tensor[idx, 1] - self.tensor[idx, 3] / 2.0\\n x2 = self.tensor[idx, 0] + self.tensor[idx, 2] / 2.0\\n y2 = self.tensor[idx, 1] + self.tensor[idx, 3] / 2.0\\n\\n # clip\\n x1.clamp_(min=0, max=w)\\n y1.clamp_(min=0, max=h)\\n x2.clamp_(min=0, max=w)\\n y2.clamp_(min=0, max=h)\\n\\n # convert back to (xc, yc, w, h)\\n self.tensor[idx, 0] = (x1 + x2) / 2.0\\n self.tensor[idx, 1] = (y1 + y2) / 2.0\\n # make sure widths and heights do not increase due to numerical errors\\n self.tensor[idx, 2] = torch.min(self.tensor[idx, 2], x2 - x1)\\n self.tensor[idx, 3] = torch.min(self.tensor[idx, 3], y2 - y1)\\n\\n def nonempty(self, threshold: float = 0.0) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Find boxes that are non-empty.\\n A box is considered empty, if either of its side is no larger than threshold.\\n\\n Returns:\\n Tensor: a binary vector which represents\\n whether each box is empty (False) or non-empty (True).\\n \\\"\\\"\\\"\\n box = self.tensor\\n widths = box[:, 2]\\n heights = box[:, 3]\\n keep = (widths > threshold) & (heights > threshold)\\n return keep\\n\\n def __getitem__(self, item) -> \\\"RotatedBoxes\\\":\\n \\\"\\\"\\\"\\n Returns:\\n RotatedBoxes: Create a new :class:`RotatedBoxes` by indexing.\\n\\n The following usage are allowed:\\n\\n 1. `new_boxes = boxes[3]`: return a `RotatedBoxes` which contains only one box.\\n 2. `new_boxes = boxes[2:10]`: return a slice of boxes.\\n 3. `new_boxes = boxes[vector]`, where vector is a torch.ByteTensor\\n with `length = len(boxes)`. Nonzero elements in the vector will be selected.\\n\\n Note that the returned RotatedBoxes might share storage with this RotatedBoxes,\\n subject to Pytorch's indexing semantics.\\n \\\"\\\"\\\"\\n if isinstance(item, int):\\n return RotatedBoxes(self.tensor[item].view(1, -1))\\n b = self.tensor[item]\\n assert b.dim() == 2, \\\"Indexing on RotatedBoxes with {} failed to return a matrix!\\\".format(\\n item\\n )\\n return RotatedBoxes(b)\\n\\n def __len__(self) -> int:\\n return self.tensor.shape[0]\\n\\n def __repr__(self) -> str:\\n return \\\"RotatedBoxes(\\\" + str(self.tensor) + \\\")\\\"\\n\\n def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Args:\\n box_size (height, width): Size of the reference box covering\\n [0, width] x [0, height]\\n boundary_threshold (int): Boxes that extend beyond the reference box\\n boundary by more than boundary_threshold are considered \\\"outside\\\".\\n\\n For RRPN, it might not be necessary to call this function since it's common\\n for rotated box to extend to outside of the image boundaries\\n (the clip function only clips the near-horizontal boxes)\\n\\n Returns:\\n a binary vector, indicating whether each box is inside the reference box.\\n \\\"\\\"\\\"\\n height, width = box_size\\n\\n cnt_x = self.tensor[..., 0]\\n cnt_y = self.tensor[..., 1]\\n half_w = self.tensor[..., 2] / 2.0\\n half_h = self.tensor[..., 3] / 2.0\\n a = self.tensor[..., 4]\\n c = torch.abs(torch.cos(a * math.pi / 180.0))\\n s = torch.abs(torch.sin(a * math.pi / 180.0))\\n # This basically computes the horizontal bounding rectangle of the rotated box\\n max_rect_dx = c * half_w + s * half_h\\n max_rect_dy = c * half_h + s * half_w\\n\\n inds_inside = (\\n (cnt_x - max_rect_dx >= -boundary_threshold)\\n & (cnt_y - max_rect_dy >= -boundary_threshold)\\n & (cnt_x + max_rect_dx < width + boundary_threshold)\\n & (cnt_y + max_rect_dy < height + boundary_threshold)\\n )\\n\\n return inds_inside\\n\\n def get_centers(self) -> torch.Tensor:\\n \\\"\\\"\\\"\\n Returns:\\n The box centers in a Nx2 array of (x, y).\\n \\\"\\\"\\\"\\n return self.tensor[:, :2]\\n\\n def scale(self, scale_x: float, scale_y: float) -> None:\\n \\\"\\\"\\\"\\n Scale the rotated box with horizontal and vertical scaling factors\\n Note: when scale_factor_x != scale_factor_y,\\n the rotated box does not preserve the rectangular shape when the angle\\n is not a multiple of 90 degrees under resize transformation.\\n Instead, the shape is a parallelogram (that has skew)\\n Here we make an approximation by fitting a rotated rectangle to the parallelogram.\\n \\\"\\\"\\\"\\n self.tensor[:, 0] *= scale_x\\n self.tensor[:, 1] *= scale_y\\n theta = self.tensor[:, 4] * math.pi / 180.0\\n c = torch.cos(theta)\\n s = torch.sin(theta)\\n\\n # In image space, y is top->down and x is left->right\\n # Consider the local coordintate system for the rotated box,\\n # where the box center is located at (0, 0), and the four vertices ABCD are\\n # A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2)\\n # the midpoint of the left edge AD of the rotated box E is:\\n # E = (A+D)/2 = (-w / 2, 0)\\n # the midpoint of the top edge AB of the rotated box F is:\\n # F(0, -h / 2)\\n # To get the old coordinates in the global system, apply the rotation transformation\\n # (Note: the right-handed coordinate system for image space is yOx):\\n # (old_x, old_y) = (s * y + c * x, c * y - s * x)\\n # E(old) = (s * 0 + c * (-w/2), c * 0 - s * (-w/2)) = (-c * w / 2, s * w / 2)\\n # F(old) = (s * (-h / 2) + c * 0, c * (-h / 2) - s * 0) = (-s * h / 2, -c * h / 2)\\n # After applying the scaling factor (sfx, sfy):\\n # E(new) = (-sfx * c * w / 2, sfy * s * w / 2)\\n # F(new) = (-sfx * s * h / 2, -sfy * c * h / 2)\\n # The new width after scaling tranformation becomes:\\n\\n # w(new) = |E(new) - O| * 2\\n # = sqrt[(sfx * c * w / 2)^2 + (sfy * s * w / 2)^2] * 2\\n # = sqrt[(sfx * c)^2 + (sfy * s)^2] * w\\n # i.e., scale_factor_w = sqrt[(sfx * c)^2 + (sfy * s)^2]\\n #\\n # For example,\\n # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_w == scale_factor_x;\\n # when |angle| = 90, c = 0, |s| = 1, scale_factor_w == scale_factor_y\\n self.tensor[:, 2] *= torch.sqrt((scale_x * c) ** 2 + (scale_y * s) ** 2)\\n\\n # h(new) = |F(new) - O| * 2\\n # = sqrt[(sfx * s * h / 2)^2 + (sfy * c * h / 2)^2] * 2\\n # = sqrt[(sfx * s)^2 + (sfy * c)^2] * h\\n # i.e., scale_factor_h = sqrt[(sfx * s)^2 + (sfy * c)^2]\\n #\\n # For example,\\n # when angle = 0 or 180, |c| = 1, s = 0, scale_factor_h == scale_factor_y;\\n # when |angle| = 90, c = 0, |s| = 1, scale_factor_h == scale_factor_x\\n self.tensor[:, 3] *= torch.sqrt((scale_x * s) ** 2 + (scale_y * c) ** 2)\\n\\n # The angle is the rotation angle from y-axis in image space to the height\\n # vector (top->down in the box's local coordinate system) of the box in CCW.\\n #\\n # angle(new) = angle_yOx(O - F(new))\\n # = angle_yOx( (sfx * s * h / 2, sfy * c * h / 2) )\\n # = atan2(sfx * s * h / 2, sfy * c * h / 2)\\n # = atan2(sfx * s, sfy * c)\\n #\\n # For example,\\n # when sfx == sfy, angle(new) == atan2(s, c) == angle(old)\\n self.tensor[:, 4] = torch.atan2(scale_x * s, scale_y * c) * 180 / math.pi\\n\\n @classmethod\\n def cat(cls, boxes_list: List[\\\"RotatedBoxes\\\"]) -> \\\"RotatedBoxes\\\":\\n \\\"\\\"\\\"\\n Concatenates a list of RotatedBoxes into a single RotatedBoxes\\n\\n Arguments:\\n boxes_list (list[RotatedBoxes])\\n\\n Returns:\\n RotatedBoxes: the concatenated RotatedBoxes\\n \\\"\\\"\\\"\\n assert isinstance(boxes_list, (list, tuple))\\n if len(boxes_list) == 0:\\n return cls(torch.empty(0))\\n assert all([isinstance(box, RotatedBoxes) for box in boxes_list])\\n\\n # use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input\\n cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))\\n return cat_boxes\\n\\n @property\\n def device(self) -> torch.device:\\n return self.tensor.device\\n\\n @torch.jit.unused\\n def __iter__(self):\\n \\\"\\\"\\\"\\n Yield a box as a Tensor of shape (5,) at a time.\\n \\\"\\\"\\\"\\n yield from self.tensor\"\n },\n {\n \"identifier\": \"PathManager\",\n \"path\": \"nativedancer/third_part/detectron2/utils/file_io.py\",\n \"snippet\": \"class Detectron2Handler(PathHandler):\\n PREFIX = \\\"detectron2://\\\"\\n S3_DETECTRON2_PREFIX = \\\"https://dl.fbaipublicfiles.com/detectron2/\\\"\\n def _get_supported_prefixes(self):\\n def _get_local_path(self, path, **kwargs):\\n def _open(self, path, mode=\\\"r\\\", **kwargs):\"\n },\n {\n \"identifier\": \"random_color\",\n \"path\": \"nativedancer/third_part/detectron2/utils/colormap.py\",\n \"snippet\": \"def random_color(rgb=False, maximum=255):\\n \\\"\\\"\\\"\\n Args:\\n rgb (bool): whether to return RGB colors or BGR colors.\\n maximum (int): either 255 or 1\\n\\n Returns:\\n ndarray: a vector of 3 numbers\\n \\\"\\\"\\\"\\n idx = np.random.randint(0, len(_COLORS))\\n ret = _COLORS[idx] * maximum\\n if not rgb:\\n ret = ret[::-1]\\n return ret\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import colorsys\nimport logging\nimport math\nimport numpy as np\nimport cv2\nimport matplotlib as mpl\nimport matplotlib.colors as mplc\nimport matplotlib.figure as mplfigure\nimport pycocotools.mask as mask_util\nimport torch\nfrom enum import Enum, unique\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg\nfrom PIL import Image\nfrom ..data import MetadataCatalog\nfrom ..structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes\nfrom ..utils.file_io import PathManager\nfrom .colormap import random_color\n from panopticapi.utils import rgb2id"},"token_num":{"kind":"number","value":17119,"string":"17,119"},"cropped_code":{"kind":"string","value":" polygon = mpl.patches.Polygon(\n segment,\n fill=True,\n facecolor=mplc.to_rgb(color) + (alpha,),\n edgecolor=edge_color,\n linewidth=max(self._default_font_size // 15 * self.output.scale, 1),\n )\n self.output.ax.add_patch(polygon)\n return self.output\n\n \"\"\"\n Internal methods:\n \"\"\"\n\n def _jitter(self, color):\n \"\"\"\n Randomly modifies given color to produce a slightly different color than the color given.\n\n Args:\n color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color\n picked. The values in the list are in the [0.0, 1.0] range.\n\n Returns:\n jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the\n color after being jittered. The values in the list are in the [0.0, 1.0] range.\n \"\"\"\n color = mplc.to_rgb(color)\n vec = np.random.rand(3)\n # better to do it in another color space\n vec = vec / np.linalg.norm(vec) * 0.5\n res = np.clip(vec + color, 0, 1)\n return tuple(res)\n\n def _create_grayscale_image(self, mask=None):\n \"\"\"\n Create a grayscale version of the original image.\n The colors in masked area, if given, will be kept.\n \"\"\"\n img_bw = self.img.astype(\"f4\").mean(axis=2)\n img_bw = np.stack([img_bw] * 3, axis=2)\n if mask is not None:\n img_bw[mask] = self.img[mask]\n return img_bw\n\n def _change_color_brightness(self, color, brightness_factor):\n \"\"\"\n Depending on the brightness_factor, gives a lighter or darker color i.e. a color with\n less or more saturation than the original color.\n\n Args:\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of\n 0 will correspond to no change, a factor in [-1.0, 0) range will result in\n a darker color and a factor in (0, 1.0] range will result in a lighter color.\n\n Returns:\n modified_color (tuple[double]): a tuple containing the RGB values of the\n modified color. Each value in the tuple is in the [0.0, 1.0] range.\n \"\"\"\n assert brightness_factor >= -1.0 and brightness_factor <= 1.0\n color = mplc.to_rgb(color)\n polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))\n modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])\n modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness\n modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness\n modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])\n return tuple(np.clip(modified_color, 0.0, 1.0))\n\n def _convert_boxes(self, boxes):\n \"\"\"\n Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.\n \"\"\"\n if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):\n return boxes.tensor.detach().numpy()\n else:\n return np.asarray(boxes)\n\n def _convert_masks(self, masks_or_polygons):\n \"\"\"\n Convert different format of masks or polygons to a tuple of masks and polygons.\n\n Returns:\n list[GenericMask]:\n \"\"\"\n\n m = masks_or_polygons\n if isinstance(m, PolygonMasks):\n m = m.polygons\n if isinstance(m, BitMasks):\n m = m.tensor.numpy()\n if isinstance(m, torch.Tensor):\n m = m.numpy()\n ret = []\n for x in m:\n if isinstance(x, GenericMask):\n ret.append(x)\n else:\n ret.append(GenericMask(x, self.output.height, self.output.width))\n return ret\n\n def _draw_text_in_mask(self, binary_mask, text, color):\n \"\"\"\n Find proper places to draw text given a binary mask.\n \"\"\"\n # TODO sometimes drawn on wrong objects. the heuristics here can improve.\n _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)\n if stats[1:, -1].size == 0:\n return\n largest_component_id = np.argmax(stats[1:, -1]) + 1\n\n # draw text on the largest component, as well as other very large components.\n for cid in range(1, _num_cc):\n if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:\n # median is more stable than centroid\n # center = centroids[largest_component_id]\n center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]\n self.draw_text(text, center, color=color)\n\n def _convert_keypoints(self, keypoints):\n"},"all_code":{"kind":"string","value":"# Copyright (c) Facebook, Inc. and its affiliates.\n\n\n\nlogger = logging.getLogger(__name__)\n\n__all__ = [\"ColorMode\", \"VisImage\", \"Visualizer\"]\n\n\n_SMALL_OBJECT_AREA_THRESH = 1000\n_LARGE_MASK_AREA_THRESH = 120000\n_OFF_WHITE = (1.0, 1.0, 240.0 / 255)\n_BLACK = (0, 0, 0)\n_RED = (1.0, 0, 0)\n\n_KEYPOINT_THRESHOLD = 0.05\n\n\n@unique\nclass ColorMode(Enum):\n \"\"\"\n Enum of different color modes to use for instance visualizations.\n \"\"\"\n\n IMAGE = 0\n \"\"\"\n Picks a random color for every instance and overlay segmentations with low opacity.\n \"\"\"\n SEGMENTATION = 1\n \"\"\"\n Let instances of the same category have similar colors\n (from metadata.thing_colors), and overlay them with\n high opacity. This provides more attention on the quality of segmentation.\n \"\"\"\n IMAGE_BW = 2\n \"\"\"\n Same as IMAGE, but convert all areas without masks to gray-scale.\n Only available for drawing per-instance mask predictions.\n \"\"\"\n\n\nclass GenericMask:\n \"\"\"\n Attribute:\n polygons (list[ndarray]): list[ndarray]: polygons for this mask.\n Each ndarray has format [x, y, x, y, ...]\n mask (ndarray): a binary mask\n \"\"\"\n\n def __init__(self, mask_or_polygons, height, width):\n self._mask = self._polygons = self._has_holes = None\n self.height = height\n self.width = width\n\n m = mask_or_polygons\n if isinstance(m, dict):\n # RLEs\n assert \"counts\" in m and \"size\" in m\n if isinstance(m[\"counts\"], list): # uncompressed RLEs\n h, w = m[\"size\"]\n assert h == height and w == width\n m = mask_util.frPyObjects(m, h, w)\n self._mask = mask_util.decode(m)[:, :]\n return\n\n if isinstance(m, list): # list[ndarray]\n self._polygons = [np.asarray(x).reshape(-1) for x in m]\n return\n\n if isinstance(m, np.ndarray): # assumed to be a binary mask\n assert m.shape[1] != 2, m.shape\n assert m.shape == (\n height,\n width,\n ), f\"mask shape: {m.shape}, target dims: {height}, {width}\"\n self._mask = m.astype(\"uint8\")\n return\n\n raise ValueError(\"GenericMask cannot handle object {} of type '{}'\".format(m, type(m)))\n\n @property\n def mask(self):\n if self._mask is None:\n self._mask = self.polygons_to_mask(self._polygons)\n return self._mask\n\n @property\n def polygons(self):\n if self._polygons is None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n return self._polygons\n\n @property\n def has_holes(self):\n if self._has_holes is None:\n if self._mask is not None:\n self._polygons, self._has_holes = self.mask_to_polygons(self._mask)\n else:\n self._has_holes = False # if original format is polygon, does not have holes\n return self._has_holes\n\n def mask_to_polygons(self, mask):\n # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level\n # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.\n # Internal contours (holes) are placed in hierarchy-2.\n # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.\n mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr\n res = cv2.findContours(mask.astype(\"uint8\"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\n hierarchy = res[-1]\n if hierarchy is None: # empty mask\n return [], False\n has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0\n res = res[-2]\n res = [x.flatten() for x in res]\n # These coordinates from OpenCV are integers in range [0, W-1 or H-1].\n # We add 0.5 to turn them into real-value coordinate space. A better solution\n # would be to first +0.5 and then dilate the returned polygon by 0.5.\n res = [x + 0.5 for x in res if len(x) >= 6]\n return res, has_holes\n\n def polygons_to_mask(self, polygons):\n rle = mask_util.frPyObjects(polygons, self.height, self.width)\n rle = mask_util.merge(rle)\n return mask_util.decode(rle)[:, :]\n\n def area(self):\n return self.mask.sum()\n\n def bbox(self):\n p = mask_util.frPyObjects(self.polygons, self.height, self.width)\n p = mask_util.merge(p)\n bbox = mask_util.toBbox(p)\n bbox[2] += bbox[0]\n bbox[3] += bbox[1]\n return bbox\n\n\nclass _PanopticPrediction:\n \"\"\"\n Unify different panoptic annotation/prediction formats\n \"\"\"\n\n def __init__(self, panoptic_seg, segments_info, metadata=None):\n if segments_info is None:\n assert metadata is not None\n # If \"segments_info\" is None, we assume \"panoptic_img\" is a\n # H*W int32 image storing the panoptic_id in the format of\n # category_id * label_divisor + instance_id. We reserve -1 for\n # VOID label.\n label_divisor = metadata.label_divisor\n segments_info = []\n for panoptic_label in np.unique(panoptic_seg.numpy()):\n if panoptic_label == -1:\n # VOID region.\n continue\n pred_class = panoptic_label // label_divisor\n isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()\n segments_info.append(\n {\n \"id\": int(panoptic_label),\n \"category_id\": int(pred_class),\n \"isthing\": bool(isthing),\n }\n )\n del metadata\n\n self._seg = panoptic_seg\n\n self._sinfo = {s[\"id\"]: s for s in segments_info} # seg id -> seg info\n segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)\n areas = areas.numpy()\n sorted_idxs = np.argsort(-areas)\n self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]\n self._seg_ids = self._seg_ids.tolist()\n for sid, area in zip(self._seg_ids, self._seg_areas):\n if sid in self._sinfo:\n self._sinfo[sid][\"area\"] = float(area)\n\n def non_empty_mask(self):\n \"\"\"\n Returns:\n (H, W) array, a mask for all pixels that have a prediction\n \"\"\"\n empty_ids = []\n for id in self._seg_ids:\n if id not in self._sinfo:\n empty_ids.append(id)\n if len(empty_ids) == 0:\n return np.zeros(self._seg.shape, dtype=np.uint8)\n assert (\n len(empty_ids) == 1\n ), \">1 ids corresponds to no labels. This is currently not supported\"\n return (self._seg != empty_ids[0]).numpy().astype(bool)\n\n def semantic_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or sinfo[\"isthing\"]:\n # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.\n continue\n yield (self._seg == sid).numpy().astype(bool), sinfo\n\n def instance_masks(self):\n for sid in self._seg_ids:\n sinfo = self._sinfo.get(sid)\n if sinfo is None or not sinfo[\"isthing\"]:\n continue\n mask = (self._seg == sid).numpy().astype(bool)\n if mask.sum() > 0:\n yield mask, sinfo\n\n\ndef _create_text_labels(classes, scores, class_names, is_crowd=None):\n \"\"\"\n Args:\n classes (list[int] or None):\n scores (list[float] or None):\n class_names (list[str] or None):\n is_crowd (list[bool] or None):\n\n Returns:\n list[str] or None\n \"\"\"\n labels = None\n if classes is not None:\n if class_names is not None and len(class_names) > 0:\n labels = [class_names[i] for i in classes]\n else:\n labels = [str(i) for i in classes]\n if scores is not None:\n if labels is None:\n labels = [\"{:.0f}%\".format(s * 100) for s in scores]\n else:\n labels = [\"{} {:.0f}%\".format(l, s * 100) for l, s in zip(labels, scores)]\n if labels is not None and is_crowd is not None:\n labels = [l + (\"|crowd\" if crowd else \"\") for l, crowd in zip(labels, is_crowd)]\n return labels\n\n\nclass VisImage:\n def __init__(self, img, scale=1.0):\n \"\"\"\n Args:\n img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].\n scale (float): scale the input image\n \"\"\"\n self.img = img\n self.scale = scale\n self.width, self.height = img.shape[1], img.shape[0]\n self._setup_figure(img)\n\n def _setup_figure(self, img):\n \"\"\"\n Args:\n Same as in :meth:`__init__()`.\n\n Returns:\n fig (matplotlib.pyplot.figure): top level container for all the image plot elements.\n ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.\n \"\"\"\n fig = mplfigure.Figure(frameon=False)\n self.dpi = fig.get_dpi()\n # add a small 1e-2 to avoid precision lost due to matplotlib's truncation\n # (https://github.com/matplotlib/matplotlib/issues/15363)\n fig.set_size_inches(\n (self.width * self.scale + 1e-2) / self.dpi,\n (self.height * self.scale + 1e-2) / self.dpi,\n )\n self.canvas = FigureCanvasAgg(fig)\n # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)\n ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n ax.axis(\"off\")\n self.fig = fig\n self.ax = ax\n self.reset_image(img)\n\n def reset_image(self, img):\n \"\"\"\n Args:\n img: same as in __init__\n \"\"\"\n img = img.astype(\"uint8\")\n self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation=\"nearest\")\n\n def save(self, filepath):\n \"\"\"\n Args:\n filepath (str): a string that contains the absolute path, including the file name, where\n the visualized image will be saved.\n \"\"\"\n self.fig.savefig(filepath)\n\n def get_image(self):\n \"\"\"\n Returns:\n ndarray:\n the visualized image of shape (H, W, 3) (RGB) in uint8 type.\n The shape is scaled w.r.t the input image using the given `scale` argument.\n \"\"\"\n canvas = self.canvas\n s, (width, height) = canvas.print_to_buffer()\n # buf = io.BytesIO() # works for cairo backend\n # canvas.print_rgba(buf)\n # width, height = self.width, self.height\n # s = buf.getvalue()\n\n buffer = np.frombuffer(s, dtype=\"uint8\")\n\n img_rgba = buffer.reshape(height, width, 4)\n rgb, alpha = np.split(img_rgba, [3], axis=2)\n return rgb.astype(\"uint8\")\n\n\nclass Visualizer:\n \"\"\"\n Visualizer that draws data about detection/segmentation on images.\n\n It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`\n that draw primitive objects to images, as well as high-level wrappers like\n `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`\n that draw composite data in some pre-defined style.\n\n Note that the exact visualization style for the high-level wrappers are subject to change.\n Style such as color, opacity, label contents, visibility of labels, or even the visibility\n of objects themselves (e.g. when the object is too small) may change according\n to different heuristics, as long as the results still look visually reasonable.\n\n To obtain a consistent style, you can implement custom drawing functions with the\n abovementioned primitive methods instead. If you need more customized visualization\n styles, you can process the data yourself following their format documented in\n tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not\n intend to satisfy everyone's preference on drawing styles.\n\n This visualizer focuses on high rendering quality rather than performance. It is not\n designed to be used for real-time applications.\n \"\"\"\n\n # TODO implement a fast, rasterized version using OpenCV\n\n def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):\n \"\"\"\n Args:\n img_rgb: a numpy array of shape (H, W, C), where H and W correspond to\n the height and width of the image respectively. C is the number of\n color channels. The image is required to be in RGB format since that\n is a requirement of the Matplotlib library. The image is also expected\n to be in the range [0, 255].\n metadata (Metadata): dataset metadata (e.g. class names and colors)\n instance_mode (ColorMode): defines one of the pre-defined style for drawing\n instances on an image.\n \"\"\"\n self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)\n if metadata is None:\n metadata = MetadataCatalog.get(\"__nonexist__\")\n self.metadata = metadata\n self.output = VisImage(self.img, scale=scale)\n self.cpu_device = torch.device(\"cpu\")\n\n # too small texts are useless, therefore clamp to 9\n self._default_font_size = max(\n np.sqrt(self.output.height * self.output.width) // 90, 10 // scale\n )\n self._instance_mode = instance_mode\n self.keypoint_threshold = _KEYPOINT_THRESHOLD\n\n def draw_instance_predictions(self, predictions):\n \"\"\"\n Draw instance-level prediction results on an image.\n\n Args:\n predictions (Instances): the output of an instance detection/segmentation\n model. Following fields will be used to draw:\n \"pred_boxes\", \"pred_classes\", \"scores\", \"pred_masks\" (or \"pred_masks_rle\").\n\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n boxes = predictions.pred_boxes if predictions.has(\"pred_boxes\") else None\n scores = predictions.scores if predictions.has(\"scores\") else None\n classes = predictions.pred_classes.tolist() if predictions.has(\"pred_classes\") else None\n labels = _create_text_labels(classes, scores, self.metadata.get(\"thing_classes\", None))\n keypoints = predictions.pred_keypoints if predictions.has(\"pred_keypoints\") else None\n\n if predictions.has(\"pred_masks\"):\n masks = np.asarray(predictions.pred_masks)\n masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]\n else:\n masks = None\n\n if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get(\"thing_colors\"):\n colors = [\n self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes\n ]\n alpha = 0.8\n else:\n colors = None\n alpha = 0.5\n\n if self._instance_mode == ColorMode.IMAGE_BW:\n self.output.reset_image(\n self._create_grayscale_image(\n (predictions.pred_masks.any(dim=0) > 0).numpy()\n if predictions.has(\"pred_masks\")\n else None\n )\n )\n alpha = 0.3\n\n self.overlay_instances(\n masks=masks,\n boxes=boxes,\n labels=labels,\n keypoints=keypoints,\n assigned_colors=colors,\n alpha=alpha,\n )\n return self.output\n\n def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):\n \"\"\"\n Draw semantic segmentation predictions/labels.\n\n Args:\n sem_seg (Tensor or ndarray): the segmentation of shape (H, W).\n Each value is the integer label of the pixel.\n area_threshold (int): segments with less than `area_threshold` are not drawn.\n alpha (float): the larger it is, the more opaque the segmentations are.\n\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n if isinstance(sem_seg, torch.Tensor):\n sem_seg = sem_seg.numpy()\n labels, areas = np.unique(sem_seg, return_counts=True)\n sorted_idxs = np.argsort(-areas).tolist()\n labels = labels[sorted_idxs]\n for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):\n try:\n mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]\n except (AttributeError, IndexError):\n mask_color = None\n\n binary_mask = (sem_seg == label).astype(np.uint8)\n text = self.metadata.stuff_classes[label]\n self.draw_binary_mask(\n binary_mask,\n color=mask_color,\n edge_color=_OFF_WHITE,\n text=text,\n alpha=alpha,\n area_threshold=area_threshold,\n )\n return self.output\n\n def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7):\n \"\"\"\n Draw panoptic prediction annotations or results.\n\n Args:\n panoptic_seg (Tensor): of shape (height, width) where the values are ids for each\n segment.\n segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.\n If it is a ``list[dict]``, each dict contains keys \"id\", \"category_id\".\n If None, category id of each pixel is computed by\n ``pixel // metadata.label_divisor``.\n area_threshold (int): stuff segments with less than `area_threshold` are not drawn.\n\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)\n\n if self._instance_mode == ColorMode.IMAGE_BW:\n self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))\n\n # draw mask for all semantic segments first i.e. \"stuff\"\n for mask, sinfo in pred.semantic_masks():\n category_idx = sinfo[\"category_id\"]\n try:\n mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]\n except AttributeError:\n mask_color = None\n\n text = self.metadata.stuff_classes[category_idx]\n self.draw_binary_mask(\n mask,\n color=mask_color,\n edge_color=_OFF_WHITE,\n text=text,\n alpha=alpha,\n area_threshold=area_threshold,\n )\n\n # draw mask for all instances second\n all_instances = list(pred.instance_masks())\n if len(all_instances) == 0:\n return self.output\n masks, sinfo = list(zip(*all_instances))\n category_ids = [x[\"category_id\"] for x in sinfo]\n\n try:\n scores = [x[\"score\"] for x in sinfo]\n except KeyError:\n scores = None\n labels = _create_text_labels(\n category_ids, scores, self.metadata.thing_classes, [x.get(\"iscrowd\", 0) for x in sinfo]\n )\n\n try:\n colors = [\n self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in category_ids\n ]\n except AttributeError:\n colors = None\n self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha)\n\n return self.output\n\n draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility\n\n def draw_dataset_dict(self, dic):\n \"\"\"\n Draw annotations/segmentations in Detectron2 Dataset format.\n\n Args:\n dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.\n\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n annos = dic.get(\"annotations\", None)\n if annos:\n if \"segmentation\" in annos[0]:\n masks = [x[\"segmentation\"] for x in annos]\n else:\n masks = None\n if \"keypoints\" in annos[0]:\n keypts = [x[\"keypoints\"] for x in annos]\n keypts = np.array(keypts).reshape(len(annos), -1, 3)\n else:\n keypts = None\n\n boxes = [\n BoxMode.convert(x[\"bbox\"], x[\"bbox_mode\"], BoxMode.XYXY_ABS)\n if len(x[\"bbox\"]) == 4\n else x[\"bbox\"]\n for x in annos\n ]\n\n colors = None\n category_ids = [x[\"category_id\"] for x in annos]\n if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get(\"thing_colors\"):\n colors = [\n self._jitter([x / 255 for x in self.metadata.thing_colors[c]])\n for c in category_ids\n ]\n names = self.metadata.get(\"thing_classes\", None)\n labels = _create_text_labels(\n category_ids,\n scores=None,\n class_names=names,\n is_crowd=[x.get(\"iscrowd\", 0) for x in annos],\n )\n self.overlay_instances(\n labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors\n )\n\n sem_seg = dic.get(\"sem_seg\", None)\n if sem_seg is None and \"sem_seg_file_name\" in dic:\n with PathManager.open(dic[\"sem_seg_file_name\"], \"rb\") as f:\n sem_seg = Image.open(f)\n sem_seg = np.asarray(sem_seg, dtype=\"uint8\")\n if sem_seg is not None:\n self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)\n\n pan_seg = dic.get(\"pan_seg\", None)\n if pan_seg is None and \"pan_seg_file_name\" in dic:\n with PathManager.open(dic[\"pan_seg_file_name\"], \"rb\") as f:\n pan_seg = Image.open(f)\n pan_seg = np.asarray(pan_seg)\n\n pan_seg = rgb2id(pan_seg)\n if pan_seg is not None:\n segments_info = dic[\"segments_info\"]\n pan_seg = torch.tensor(pan_seg)\n self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)\n return self.output\n\n def overlay_instances(\n self,\n *,\n boxes=None,\n labels=None,\n masks=None,\n keypoints=None,\n assigned_colors=None,\n alpha=0.5,\n ):\n \"\"\"\n Args:\n boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,\n or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,\n or a :class:`RotatedBoxes`,\n or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image,\n labels (list[str]): the text to be displayed for each instance.\n masks (masks-like object): Supported types are:\n\n * :class:`detectron2.structures.PolygonMasks`,\n :class:`detectron2.structures.BitMasks`.\n * list[list[ndarray]]: contains the segmentation masks for all objects in one image.\n The first level of the list corresponds to individual instances. The second\n level to all the polygon that compose the instance, and the third level\n to the polygon coordinates. The third level should have the format of\n [x0, y0, x1, y1, ..., xn, yn] (n >= 3).\n * list[ndarray]: each ndarray is a binary mask of shape (H, W).\n * list[dict]: each dict is a COCO-style RLE.\n keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),\n where the N is the number of instances and K is the number of keypoints.\n The last dimension corresponds to (x, y, visibility or score).\n assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n for full list of formats that the colors are accepted in.\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n num_instances = 0\n if boxes is not None:\n boxes = self._convert_boxes(boxes)\n num_instances = len(boxes)\n if masks is not None:\n masks = self._convert_masks(masks)\n if num_instances:\n assert len(masks) == num_instances\n else:\n num_instances = len(masks)\n if keypoints is not None:\n if num_instances:\n assert len(keypoints) == num_instances\n else:\n num_instances = len(keypoints)\n keypoints = self._convert_keypoints(keypoints)\n if labels is not None:\n assert len(labels) == num_instances\n if assigned_colors is None:\n assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]\n if num_instances == 0:\n return self.output\n if boxes is not None and boxes.shape[1] == 5:\n return self.overlay_rotated_instances(\n boxes=boxes, labels=labels, assigned_colors=assigned_colors\n )\n\n # Display in largest to smallest order to reduce occlusion.\n areas = None\n if boxes is not None:\n areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)\n elif masks is not None:\n areas = np.asarray([x.area() for x in masks])\n\n if areas is not None:\n sorted_idxs = np.argsort(-areas).tolist()\n # Re-order overlapped instances in descending order.\n boxes = boxes[sorted_idxs] if boxes is not None else None\n labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None\n assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]\n keypoints = keypoints[sorted_idxs] if keypoints is not None else None\n\n for i in range(num_instances):\n color = assigned_colors[i]\n if boxes is not None:\n self.draw_box(boxes[i], edge_color=color)\n\n if masks is not None:\n for segment in masks[i].polygons:\n self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha)\n\n if labels is not None:\n # first get a box\n if boxes is not None:\n x0, y0, x1, y1 = boxes[i]\n text_pos = (x0, y0) # if drawing boxes, put text on the box corner.\n horiz_align = \"left\"\n elif masks is not None:\n # skip small mask without polygon\n if len(masks[i].polygons) == 0:\n continue\n\n x0, y0, x1, y1 = masks[i].bbox()\n\n # draw text in the center (defined by median) when box is not drawn\n # median is less sensitive to outliers.\n text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1]\n horiz_align = \"center\"\n else:\n continue # drawing the box confidence for keypoints isn't very useful.\n # for small objects, draw text at the side to avoid occlusion\n instance_area = (y1 - y0) * (x1 - x0)\n if (\n instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale\n or y1 - y0 < 40 * self.output.scale\n ):\n if y1 >= self.output.height - 5:\n text_pos = (x1, y0)\n else:\n text_pos = (x0, y1)\n\n height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n font_size = (\n np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)\n * 0.5\n * self._default_font_size\n )\n self.draw_text(\n labels[i],\n text_pos,\n color=lighter_color,\n horizontal_alignment=horiz_align,\n font_size=font_size,\n )\n\n # draw keypoints\n if keypoints is not None:\n for keypoints_per_instance in keypoints:\n self.draw_and_connect_keypoints(keypoints_per_instance)\n\n return self.output\n\n def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):\n \"\"\"\n Args:\n boxes (ndarray): an Nx5 numpy array of\n (x_center, y_center, width, height, angle_degrees) format\n for the N objects in a single image.\n labels (list[str]): the text to be displayed for each instance.\n assigned_colors (list[matplotlib.colors]): a list of colors, where each color\n corresponds to each mask or box in the image. Refer to 'matplotlib.colors'\n for full list of formats that the colors are accepted in.\n\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n num_instances = len(boxes)\n\n if assigned_colors is None:\n assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]\n if num_instances == 0:\n return self.output\n\n # Display in largest to smallest order to reduce occlusion.\n if boxes is not None:\n areas = boxes[:, 2] * boxes[:, 3]\n\n sorted_idxs = np.argsort(-areas).tolist()\n # Re-order overlapped instances in descending order.\n boxes = boxes[sorted_idxs]\n labels = [labels[k] for k in sorted_idxs] if labels is not None else None\n colors = [assigned_colors[idx] for idx in sorted_idxs]\n\n for i in range(num_instances):\n self.draw_rotated_box_with_label(\n boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None\n )\n\n return self.output\n\n def draw_and_connect_keypoints(self, keypoints):\n \"\"\"\n Draws keypoints of an instance and follows the rules for keypoint connections\n to draw lines between appropriate keypoints. This follows color heuristics for\n line color.\n\n Args:\n keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints\n and the last dimension corresponds to (x, y, probability).\n\n Returns:\n output (VisImage): image object with visualizations.\n \"\"\"\n visible = {}\n keypoint_names = self.metadata.get(\"keypoint_names\")\n for idx, keypoint in enumerate(keypoints):\n\n # draw keypoint\n x, y, prob = keypoint\n if prob > self.keypoint_threshold:\n self.draw_circle((x, y), color=_RED)\n if keypoint_names:\n keypoint_name = keypoint_names[idx]\n visible[keypoint_name] = (x, y)\n\n if self.metadata.get(\"keypoint_connection_rules\"):\n for kp0, kp1, color in self.metadata.keypoint_connection_rules:\n if kp0 in visible and kp1 in visible:\n x0, y0 = visible[kp0]\n x1, y1 = visible[kp1]\n color = tuple(x / 255.0 for x in color)\n self.draw_line([x0, x1], [y0, y1], color=color)\n\n # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip\n # Note that this strategy is specific to person keypoints.\n # For other keypoints, it should just do nothing\n try:\n ls_x, ls_y = visible[\"left_shoulder\"]\n rs_x, rs_y = visible[\"right_shoulder\"]\n mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2\n except KeyError:\n pass\n else:\n # draw line from nose to mid-shoulder\n nose_x, nose_y = visible.get(\"nose\", (None, None))\n if nose_x is not None:\n self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED)\n\n try:\n # draw line from mid-shoulder to mid-hip\n lh_x, lh_y = visible[\"left_hip\"]\n rh_x, rh_y = visible[\"right_hip\"]\n except KeyError:\n pass\n else:\n mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2\n self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED)\n return self.output\n\n \"\"\"\n Primitive drawing functions:\n \"\"\"\n\n def draw_text(\n self,\n text,\n position,\n *,\n font_size=None,\n color=\"g\",\n horizontal_alignment=\"center\",\n rotation=0,\n ):\n \"\"\"\n Args:\n text (str): class label\n position (tuple): a tuple of the x and y coordinates to place text on image.\n font_size (int, optional): font of the text. If not provided, a font size\n proportional to the image width is calculated and used.\n color: color of the text. Refer to `matplotlib.colors` for full list\n of formats that are accepted.\n horizontal_alignment (str): see `matplotlib.text.Text`\n rotation: rotation angle in degrees CCW\n\n Returns:\n output (VisImage): image object with text drawn.\n \"\"\"\n if not font_size:\n font_size = self._default_font_size\n\n # since the text background is dark, we don't want the text to be dark\n color = np.maximum(list(mplc.to_rgb(color)), 0.2)\n color[np.argmax(color)] = max(0.8, np.max(color))\n\n x, y = position\n self.output.ax.text(\n x,\n y,\n text,\n size=font_size * self.output.scale,\n family=\"sans-serif\",\n bbox={\"facecolor\": \"black\", \"alpha\": 0.8, \"pad\": 0.7, \"edgecolor\": \"none\"},\n verticalalignment=\"top\",\n horizontalalignment=horizontal_alignment,\n color=color,\n zorder=10,\n rotation=rotation,\n )\n return self.output\n\n def draw_box(self, box_coord, alpha=0.5, edge_color=\"g\", line_style=\"-\"):\n \"\"\"\n Args:\n box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0\n are the coordinates of the image's top left corner. x1 and y1 are the\n coordinates of the image's bottom right corner.\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n edge_color: color of the outline of the box. Refer to `matplotlib.colors`\n for full list of formats that are accepted.\n line_style (string): the string to use to create the outline of the boxes.\n\n Returns:\n output (VisImage): image object with box drawn.\n \"\"\"\n x0, y0, x1, y1 = box_coord\n width = x1 - x0\n height = y1 - y0\n\n linewidth = max(self._default_font_size / 4, 1)\n\n self.output.ax.add_patch(\n mpl.patches.Rectangle(\n (x0, y0),\n width,\n height,\n fill=False,\n edgecolor=edge_color,\n linewidth=linewidth * self.output.scale,\n alpha=alpha,\n linestyle=line_style,\n )\n )\n return self.output\n\n def draw_rotated_box_with_label(\n self, rotated_box, alpha=0.5, edge_color=\"g\", line_style=\"-\", label=None\n ):\n \"\"\"\n Draw a rotated box with label on its top-left corner.\n\n Args:\n rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle),\n where cnt_x and cnt_y are the center coordinates of the box.\n w and h are the width and height of the box. angle represents how\n many degrees the box is rotated CCW with regard to the 0-degree box.\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n edge_color: color of the outline of the box. Refer to `matplotlib.colors`\n for full list of formats that are accepted.\n line_style (string): the string to use to create the outline of the boxes.\n label (string): label for rotated box. It will not be rendered when set to None.\n\n Returns:\n output (VisImage): image object with box drawn.\n \"\"\"\n cnt_x, cnt_y, w, h, angle = rotated_box\n area = w * h\n # use thinner lines when the box is small\n linewidth = self._default_font_size / (\n 6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3\n )\n\n theta = angle * math.pi / 180.0\n c = math.cos(theta)\n s = math.sin(theta)\n rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)]\n # x: left->right ; y: top->down\n rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect]\n for k in range(4):\n j = (k + 1) % 4\n self.draw_line(\n [rotated_rect[k][0], rotated_rect[j][0]],\n [rotated_rect[k][1], rotated_rect[j][1]],\n color=edge_color,\n linestyle=\"--\" if k == 1 else line_style,\n linewidth=linewidth,\n )\n\n if label is not None:\n text_pos = rotated_rect[1] # topleft corner\n\n height_ratio = h / np.sqrt(self.output.height * self.output.width)\n label_color = self._change_color_brightness(edge_color, brightness_factor=0.7)\n font_size = (\n np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size\n )\n self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle)\n\n return self.output\n\n def draw_circle(self, circle_coord, color, radius=3):\n \"\"\"\n Args:\n circle_coord (list(int) or tuple(int)): contains the x and y coordinates\n of the center of the circle.\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n radius (int): radius of the circle.\n\n Returns:\n output (VisImage): image object with box drawn.\n \"\"\"\n x, y = circle_coord\n self.output.ax.add_patch(\n mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)\n )\n return self.output\n\n def draw_line(self, x_data, y_data, color, linestyle=\"-\", linewidth=None):\n \"\"\"\n Args:\n x_data (list[int]): a list containing x values of all the points being drawn.\n Length of list should match the length of y_data.\n y_data (list[int]): a list containing y values of all the points being drawn.\n Length of list should match the length of x_data.\n color: color of the line. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n linestyle: style of the line. Refer to `matplotlib.lines.Line2D`\n for a full list of formats that are accepted.\n linewidth (float or None): width of the line. When it's None,\n a default value will be computed and used.\n\n Returns:\n output (VisImage): image object with line drawn.\n \"\"\"\n if linewidth is None:\n linewidth = self._default_font_size / 3\n linewidth = max(linewidth, 1)\n self.output.ax.add_line(\n mpl.lines.Line2D(\n x_data,\n y_data,\n linewidth=linewidth * self.output.scale,\n color=color,\n linestyle=linestyle,\n )\n )\n return self.output\n\n def draw_binary_mask(\n self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10\n ):\n \"\"\"\n Args:\n binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and\n W is the image width. Each value in the array is either a 0 or 1 value of uint8\n type.\n color: color of the mask. Refer to `matplotlib.colors` for a full list of\n formats that are accepted. If None, will pick a random color.\n edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a\n full list of formats that are accepted.\n text (str): if None, will be drawn on the object\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n area_threshold (float): a connected component smaller than this area will not be shown.\n\n Returns:\n output (VisImage): image object with mask drawn.\n \"\"\"\n if color is None:\n color = random_color(rgb=True, maximum=1)\n color = mplc.to_rgb(color)\n\n has_valid_segment = False\n binary_mask = binary_mask.astype(\"uint8\") # opencv needs uint8\n mask = GenericMask(binary_mask, self.output.height, self.output.width)\n shape2d = (binary_mask.shape[0], binary_mask.shape[1])\n\n if not mask.has_holes:\n # draw polygons for regular masks\n for segment in mask.polygons:\n area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1]))\n if area < (area_threshold or 0):\n continue\n has_valid_segment = True\n segment = segment.reshape(-1, 2)\n self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha)\n else:\n # TODO: Use Path/PathPatch to draw vector graphics:\n # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon\n rgba = np.zeros(shape2d + (4,), dtype=\"float32\")\n rgba[:, :, :3] = color\n rgba[:, :, 3] = (mask.mask == 1).astype(\"float32\") * alpha\n has_valid_segment = True\n self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))\n\n if text is not None and has_valid_segment:\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n self._draw_text_in_mask(binary_mask, text, lighter_color)\n return self.output\n\n def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):\n \"\"\"\n Args:\n soft_mask (ndarray): float array of shape (H, W), each value in [0, 1].\n color: color of the mask. Refer to `matplotlib.colors` for a full list of\n formats that are accepted. If None, will pick a random color.\n text (str): if None, will be drawn on the object\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n\n Returns:\n output (VisImage): image object with mask drawn.\n \"\"\"\n if color is None:\n color = random_color(rgb=True, maximum=1)\n color = mplc.to_rgb(color)\n\n shape2d = (soft_mask.shape[0], soft_mask.shape[1])\n rgba = np.zeros(shape2d + (4,), dtype=\"float32\")\n rgba[:, :, :3] = color\n rgba[:, :, 3] = soft_mask * alpha\n self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))\n\n if text is not None:\n lighter_color = self._change_color_brightness(color, brightness_factor=0.7)\n binary_mask = (soft_mask > 0.5).astype(\"uint8\")\n self._draw_text_in_mask(binary_mask, text, lighter_color)\n return self.output\n\n def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):\n \"\"\"\n Args:\n segment: numpy array of shape Nx2, containing all the points in the polygon.\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a\n full list of formats that are accepted. If not provided, a darker shade\n of the polygon color will be used instead.\n alpha (float): blending efficient. Smaller values lead to more transparent masks.\n\n Returns:\n output (VisImage): image object with polygon drawn.\n \"\"\"\n if edge_color is None:\n # make edge color darker than the polygon color\n if alpha > 0.8:\n edge_color = self._change_color_brightness(color, brightness_factor=-0.7)\n else:\n edge_color = color\n edge_color = mplc.to_rgb(edge_color) + (1,)\n\n polygon = mpl.patches.Polygon(\n segment,\n fill=True,\n facecolor=mplc.to_rgb(color) + (alpha,),\n edgecolor=edge_color,\n linewidth=max(self._default_font_size // 15 * self.output.scale, 1),\n )\n self.output.ax.add_patch(polygon)\n return self.output\n\n \"\"\"\n Internal methods:\n \"\"\"\n\n def _jitter(self, color):\n \"\"\"\n Randomly modifies given color to produce a slightly different color than the color given.\n\n Args:\n color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color\n picked. The values in the list are in the [0.0, 1.0] range.\n\n Returns:\n jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the\n color after being jittered. The values in the list are in the [0.0, 1.0] range.\n \"\"\"\n color = mplc.to_rgb(color)\n vec = np.random.rand(3)\n # better to do it in another color space\n vec = vec / np.linalg.norm(vec) * 0.5\n res = np.clip(vec + color, 0, 1)\n return tuple(res)\n\n def _create_grayscale_image(self, mask=None):\n \"\"\"\n Create a grayscale version of the original image.\n The colors in masked area, if given, will be kept.\n \"\"\"\n img_bw = self.img.astype(\"f4\").mean(axis=2)\n img_bw = np.stack([img_bw] * 3, axis=2)\n if mask is not None:\n img_bw[mask] = self.img[mask]\n return img_bw\n\n def _change_color_brightness(self, color, brightness_factor):\n \"\"\"\n Depending on the brightness_factor, gives a lighter or darker color i.e. a color with\n less or more saturation than the original color.\n\n Args:\n color: color of the polygon. Refer to `matplotlib.colors` for a full list of\n formats that are accepted.\n brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of\n 0 will correspond to no change, a factor in [-1.0, 0) range will result in\n a darker color and a factor in (0, 1.0] range will result in a lighter color.\n\n Returns:\n modified_color (tuple[double]): a tuple containing the RGB values of the\n modified color. Each value in the tuple is in the [0.0, 1.0] range.\n \"\"\"\n assert brightness_factor >= -1.0 and brightness_factor <= 1.0\n color = mplc.to_rgb(color)\n polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))\n modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])\n modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness\n modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness\n modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])\n return tuple(np.clip(modified_color, 0.0, 1.0))\n\n def _convert_boxes(self, boxes):\n \"\"\"\n Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.\n \"\"\"\n if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):\n return boxes.tensor.detach().numpy()\n else:\n return np.asarray(boxes)\n\n def _convert_masks(self, masks_or_polygons):\n \"\"\"\n Convert different format of masks or polygons to a tuple of masks and polygons.\n\n Returns:\n list[GenericMask]:\n \"\"\"\n\n m = masks_or_polygons\n if isinstance(m, PolygonMasks):\n m = m.polygons\n if isinstance(m, BitMasks):\n m = m.tensor.numpy()\n if isinstance(m, torch.Tensor):\n m = m.numpy()\n ret = []\n for x in m:\n if isinstance(x, GenericMask):\n ret.append(x)\n else:\n ret.append(GenericMask(x, self.output.height, self.output.width))\n return ret\n\n def _draw_text_in_mask(self, binary_mask, text, color):\n \"\"\"\n Find proper places to draw text given a binary mask.\n \"\"\"\n # TODO sometimes drawn on wrong objects. the heuristics here can improve.\n _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)\n if stats[1:, -1].size == 0:\n return\n largest_component_id = np.argmax(stats[1:, -1]) + 1\n\n # draw text on the largest component, as well as other very large components.\n for cid in range(1, _num_cc):\n if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:\n # median is more stable than centroid\n # center = centroids[largest_component_id]\n center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]\n self.draw_text(text, center, color=color)\n\n def _convert_keypoints(self, keypoints):"},"next_line":{"kind":"string","value":" if isinstance(keypoints, Keypoints):"},"gold_snippet_index":{"kind":"number","value":3,"string":"3"},"created_at":{"kind":"string","value":"2023-12-10 20:14:00+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5291,"cells":{"repo_name":{"kind":"string","value":"mkang315/ASF-YOLO"},"file_path":{"kind":"string","value":"segment/val.py"},"context":{"kind":"list like","value":[{"identifier":"DetectMultiBackend","path":"models/common.py","snippet":"class DetectMultiBackend(nn.Module):\n # YOLOv5 MultiBackend class for python inference on various backends\n def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):\n # Usage:\n # PyTorch: weights = *.pt\n # TorchScript: *.torchscript\n # ONNX Runtime: *.onnx\n # ONNX OpenCV DNN: *.onnx --dnn\n # OpenVINO: *_openvino_model\n # CoreML: *.mlmodel\n # TensorRT: *.engine\n # TensorFlow SavedModel: *_saved_model\n # TensorFlow GraphDef: *.pb\n # TensorFlow Lite: *.tflite\n # TensorFlow Edge TPU: *_edgetpu.tflite\n # PaddlePaddle: *_paddle_model\n from models.experimental import attempt_download, attempt_load # scoped to avoid circular import\n\n super().__init__()\n w = str(weights[0] if isinstance(weights, list) else weights)\n pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)\n fp16 &= pt or jit or onnx or engine # FP16\n nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)\n stride = 32 # default stride\n cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA\n if not (pt or triton):\n w = attempt_download(w) # download if not local\n\n if pt: # PyTorch\n model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)\n stride = max(int(model.stride.max()), 32) # model stride\n names = model.module.names if hasattr(model, 'module') else model.names # get class names\n model.half() if fp16 else model.float()\n self.model = model # explicitly assign for to(), cpu(), cuda(), half()\n elif jit: # TorchScript\n LOGGER.info(f'Loading {w} for TorchScript inference...')\n extra_files = {'config.txt': ''} # model metadata\n model = torch.jit.load(w, _extra_files=extra_files, map_location=device)\n model.half() if fp16 else model.float()\n if extra_files['config.txt']: # load metadata dict\n d = json.loads(extra_files['config.txt'],\n object_hook=lambda d: {int(k) if k.isdigit() else k: v\n for k, v in d.items()})\n stride, names = int(d['stride']), d['names']\n elif dnn: # ONNX OpenCV DNN\n LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')\n check_requirements('opencv-python>=4.5.4')\n net = cv2.dnn.readNetFromONNX(w)\n elif onnx: # ONNX Runtime\n LOGGER.info(f'Loading {w} for ONNX Runtime inference...')\n check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))\n import onnxruntime\n providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']\n session = onnxruntime.InferenceSession(w, providers=providers)\n output_names = [x.name for x in session.get_outputs()]\n meta = session.get_modelmeta().custom_metadata_map # metadata\n if 'stride' in meta:\n stride, names = int(meta['stride']), eval(meta['names'])\n elif xml: # OpenVINO\n LOGGER.info(f'Loading {w} for OpenVINO inference...')\n check_requirements('openvino') # requires openvino-dev: https://pypi.org/project/openvino-dev/\n from openvino.runtime import Core, Layout, get_batch\n ie = Core()\n if not Path(w).is_file(): # if not *.xml\n w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir\n network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))\n if network.get_parameters()[0].get_layout().empty:\n network.get_parameters()[0].set_layout(Layout(\"NCHW\"))\n batch_dim = get_batch(network)\n if batch_dim.is_static:\n batch_size = batch_dim.get_length()\n executable_network = ie.compile_model(network, device_name=\"CPU\") # device_name=\"MYRIAD\" for Intel NCS2\n stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata\n elif engine: # TensorRT\n LOGGER.info(f'Loading {w} for TensorRT inference...')\n import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download\n check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0\n if device.type == 'cpu':\n device = torch.device('cuda:0')\n Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))\n logger = trt.Logger(trt.Logger.INFO)\n with open(w, 'rb') as f, trt.Runtime(logger) as runtime:\n model = runtime.deserialize_cuda_engine(f.read())\n context = model.create_execution_context()\n bindings = OrderedDict()\n output_names = []\n fp16 = False # default updated below\n dynamic = False\n for i in range(model.num_bindings):\n name = model.get_binding_name(i)\n dtype = trt.nptype(model.get_binding_dtype(i))\n if model.binding_is_input(i):\n if -1 in tuple(model.get_binding_shape(i)): # dynamic\n dynamic = True\n context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))\n if dtype == np.float16:\n fp16 = True\n else: # output\n output_names.append(name)\n shape = tuple(context.get_binding_shape(i))\n im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)\n bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))\n binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())\n batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size\n elif coreml: # CoreML\n LOGGER.info(f'Loading {w} for CoreML inference...')\n import coremltools as ct\n model = ct.models.MLModel(w)\n elif saved_model: # TF SavedModel\n LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')\n import tensorflow as tf\n keras = False # assume TF1 saved_model\n model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)\n elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt\n LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')\n import tensorflow as tf\n\n def wrap_frozen_graph(gd, inputs, outputs):\n x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=\"\"), []) # wrapped\n ge = x.graph.as_graph_element\n return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))\n\n def gd_outputs(gd):\n name_list, input_list = [], []\n for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef\n name_list.append(node.name)\n input_list.extend(node.input)\n return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))\n\n gd = tf.Graph().as_graph_def() # TF GraphDef\n with open(w, 'rb') as f:\n gd.ParseFromString(f.read())\n frozen_func = wrap_frozen_graph(gd, inputs=\"x:0\", outputs=gd_outputs(gd))\n elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python\n try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu\n from tflite_runtime.interpreter import Interpreter, load_delegate\n except ImportError:\n import tensorflow as tf\n Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,\n if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime\n LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')\n delegate = {\n 'Linux': 'libedgetpu.so.1',\n 'Darwin': 'libedgetpu.1.dylib',\n 'Windows': 'edgetpu.dll'}[platform.system()]\n interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])\n else: # TFLite\n LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')\n interpreter = Interpreter(model_path=w) # load TFLite model\n interpreter.allocate_tensors() # allocate\n input_details = interpreter.get_input_details() # inputs\n output_details = interpreter.get_output_details() # outputs\n # load metadata\n with contextlib.suppress(zipfile.BadZipFile):\n with zipfile.ZipFile(w, \"r\") as model:\n meta_file = model.namelist()[0]\n meta = ast.literal_eval(model.read(meta_file).decode(\"utf-8\"))\n stride, names = int(meta['stride']), meta['names']\n elif tfjs: # TF.js\n raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')\n elif paddle: # PaddlePaddle\n LOGGER.info(f'Loading {w} for PaddlePaddle inference...')\n check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')\n import paddle.inference as pdi\n if not Path(w).is_file(): # if not *.pdmodel\n w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir\n weights = Path(w).with_suffix('.pdiparams')\n config = pdi.Config(str(w), str(weights))\n if cuda:\n config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)\n predictor = pdi.create_predictor(config)\n input_handle = predictor.get_input_handle(predictor.get_input_names()[0])\n output_names = predictor.get_output_names()\n elif triton: # NVIDIA Triton Inference Server\n LOGGER.info(f'Using {w} as Triton Inference Server...')\n check_requirements('tritonclient[all]')\n from utils.triton import TritonRemoteModel\n model = TritonRemoteModel(url=w)\n nhwc = model.runtime.startswith(\"tensorflow\")\n else:\n raise NotImplementedError(f'ERROR: {w} is not a supported format')\n\n # class names\n if 'names' not in locals():\n names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}\n if names[0] == 'n01440764' and len(names) == 1000: # ImageNet\n names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names\n\n self.__dict__.update(locals()) # assign all variables to self\n\n def forward(self, im, augment=False, visualize=False):\n # YOLOv5 MultiBackend inference\n b, ch, h, w = im.shape # batch, channel, height, width\n if self.fp16 and im.dtype != torch.float16:\n im = im.half() # to FP16\n if self.nhwc:\n im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3)\n\n if self.pt: # PyTorch\n y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)\n elif self.jit: # TorchScript\n y = self.model(im)\n elif self.dnn: # ONNX OpenCV DNN\n im = im.cpu().numpy() # torch to numpy\n self.net.setInput(im)\n y = self.net.forward()\n elif self.onnx: # ONNX Runtime\n im = im.cpu().numpy() # torch to numpy\n y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})\n elif self.xml: # OpenVINO\n im = im.cpu().numpy() # FP32\n y = list(self.executable_network([im]).values())\n elif self.engine: # TensorRT\n if self.dynamic and im.shape != self.bindings['images'].shape:\n i = self.model.get_binding_index('images')\n self.context.set_binding_shape(i, im.shape) # reshape if dynamic\n self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)\n for name in self.output_names:\n i = self.model.get_binding_index(name)\n self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))\n s = self.bindings['images'].shape\n assert im.shape == s, f\"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}\"\n self.binding_addrs['images'] = int(im.data_ptr())\n self.context.execute_v2(list(self.binding_addrs.values()))\n y = [self.bindings[x].data for x in sorted(self.output_names)]\n elif self.coreml: # CoreML\n im = im.cpu().numpy()\n im = Image.fromarray((im[0] * 255).astype('uint8'))\n # im = im.resize((192, 320), Image.ANTIALIAS)\n y = self.model.predict({'image': im}) # coordinates are xywh normalized\n if 'confidence' in y:\n box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels\n conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)\n y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)\n else:\n y = list(reversed(y.values())) # reversed for segmentation models (pred, proto)\n elif self.paddle: # PaddlePaddle\n im = im.cpu().numpy().astype(np.float32)\n self.input_handle.copy_from_cpu(im)\n self.predictor.run()\n y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]\n elif self.triton: # NVIDIA Triton Inference Server\n y = self.model(im)\n else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)\n im = im.cpu().numpy()\n if self.saved_model: # SavedModel\n y = self.model(im, training=False) if self.keras else self.model(im)\n elif self.pb: # GraphDef\n y = self.frozen_func(x=self.tf.constant(im))\n else: # Lite or Edge TPU\n input = self.input_details[0]\n int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model\n if int8:\n scale, zero_point = input['quantization']\n im = (im / scale + zero_point).astype(np.uint8) # de-scale\n self.interpreter.set_tensor(input['index'], im)\n self.interpreter.invoke()\n y = []\n for output in self.output_details:\n x = self.interpreter.get_tensor(output['index'])\n if int8:\n scale, zero_point = output['quantization']\n x = (x.astype(np.float32) - zero_point) * scale # re-scale\n y.append(x)\n y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]\n y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels\n\n if isinstance(y, (list, tuple)):\n return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]\n else:\n return self.from_numpy(y)\n\n def from_numpy(self, x):\n return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x\n\n def warmup(self, imgsz=(1, 3, 640, 640)):\n # Warmup model by running inference once\n warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton\n if any(warmup_types) and (self.device.type != 'cpu' or self.triton):\n im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input\n for _ in range(2 if self.jit else 1): #\n self.forward(im) # warmup\n\n @staticmethod\n def _model_type(p='path/to/model.pt'):\n # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx\n # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]\n from export import export_formats\n from utils.downloads import is_url\n sf = list(export_formats().Suffix) # export suffixes\n if not is_url(p, check=False):\n check_suffix(p, sf) # checks\n url = urlparse(p) # if url may be Triton inference server\n types = [s in Path(p).name for s in sf]\n types[8] &= not types[9] # tflite &= not edgetpu\n triton = not any(types) and all([any(s in url.scheme for s in [\"http\", \"grpc\"]), url.netloc])\n return types + [triton]\n\n @staticmethod\n def _load_metadata(f=Path('path/to/meta.yaml')):\n # Load metadata from meta.yaml if it exists\n if f.exists():\n d = yaml_load(f)\n return d['stride'], d['names'] # assign stride, names\n return None, None"},{"identifier":"SegmentationModel","path":"models/yolo.py","snippet":"class SegmentationModel(DetectionModel):\n # YOLOv5 segmentation model\n def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None):\n super().__init__(cfg, ch, nc, anchors)"},{"identifier":"Callbacks","path":"utils/callbacks.py","snippet":"class Callbacks:\n \"\"\"\"\n Handles all registered callbacks for YOLOv5 Hooks\n \"\"\"\n\n def __init__(self):\n # Define the available callbacks\n self._callbacks = {\n 'on_pretrain_routine_start': [],\n 'on_pretrain_routine_end': [],\n 'on_train_start': [],\n 'on_train_epoch_start': [],\n 'on_train_batch_start': [],\n 'optimizer_step': [],\n 'on_before_zero_grad': [],\n 'on_train_batch_end': [],\n 'on_train_epoch_end': [],\n 'on_val_start': [],\n 'on_val_batch_start': [],\n 'on_val_image_end': [],\n 'on_val_batch_end': [],\n 'on_val_end': [],\n 'on_fit_epoch_end': [], # fit = train + val\n 'on_model_save': [],\n 'on_train_end': [],\n 'on_params_update': [],\n 'teardown': [],}\n self.stop_training = False # set True to interrupt training\n\n def register_action(self, hook, name='', callback=None):\n \"\"\"\n Register a new action to a callback hook\n\n Args:\n hook: The callback hook name to register the action to\n name: The name of the action for later reference\n callback: The callback to fire\n \"\"\"\n assert hook in self._callbacks, f\"hook '{hook}' not found in callbacks {self._callbacks}\"\n assert callable(callback), f\"callback '{callback}' is not callable\"\n self._callbacks[hook].append({'name': name, 'callback': callback})\n\n def get_registered_actions(self, hook=None):\n \"\"\"\"\n Returns all the registered actions by callback hook\n\n Args:\n hook: The name of the hook to check, defaults to all\n \"\"\"\n return self._callbacks[hook] if hook else self._callbacks\n\n def run(self, hook, *args, thread=False, **kwargs):\n \"\"\"\n Loop through the registered actions and fire all callbacks on main thread\n\n Args:\n hook: The name of the hook to check, defaults to all\n args: Arguments to receive from YOLOv5\n thread: (boolean) Run callbacks in daemon thread\n kwargs: Keyword Arguments to receive from YOLOv5\n \"\"\"\n\n assert hook in self._callbacks, f\"hook '{hook}' not found in callbacks {self._callbacks}\"\n for logger in self._callbacks[hook]:\n if thread:\n threading.Thread(target=logger['callback'], args=args, kwargs=kwargs, daemon=True).start()\n else:\n logger['callback'](*args, **kwargs)"},{"identifier":"LOGGER","path":"utils/general.py","snippet":"LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.)"},{"identifier":"NUM_THREADS","path":"utils/general.py","snippet":"NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads"},{"identifier":"TQDM_BAR_FORMAT","path":"utils/general.py","snippet":"TQDM_BAR_FORMAT = '{l_bar}{bar:10}| {n_fmt}/{total_fmt} {elapsed}' # tqdm bar format"},{"identifier":"Profile","path":"utils/general.py","snippet":"class Profile(contextlib.ContextDecorator):\n # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager\n def __init__(self, t=0.0):\n self.t = t\n self.cuda = torch.cuda.is_available()\n\n def __enter__(self):\n self.start = self.time()\n return self\n\n def __exit__(self, type, value, traceback):\n self.dt = self.time() - self.start # delta-time\n self.t += self.dt # accumulate dt\n\n def time(self):\n if self.cuda:\n torch.cuda.synchronize()\n return time.time()"},{"identifier":"check_dataset","path":"utils/general.py","snippet":"def check_dataset(data, autodownload=True):\n # Download, check and/or unzip dataset if not found locally\n\n # Download (optional)\n extract_dir = ''\n if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):\n download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1)\n data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))\n extract_dir, autodownload = data.parent, False\n\n # Read yaml (optional)\n if isinstance(data, (str, Path)):\n data = yaml_load(data) # dictionary\n\n # Checks\n for k in 'train', 'val', 'names':\n assert k in data, emojis(f\"data.yaml '{k}:' field missing ❌\")\n if isinstance(data['names'], (list, tuple)): # old array format\n data['names'] = dict(enumerate(data['names'])) # convert to dict\n assert all(isinstance(k, int) for k in data['names'].keys()), 'data.yaml names keys must be integers, i.e. 2: car'\n data['nc'] = len(data['names'])\n\n # Resolve paths\n path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.'\n if not path.is_absolute():\n path = (ROOT / path).resolve()\n data['path'] = path # download scripts\n for k in 'train', 'val', 'test':\n if data.get(k): # prepend path\n if isinstance(data[k], str):\n x = (path / data[k]).resolve()\n if not x.exists() and data[k].startswith('../'):\n x = (path / data[k][3:]).resolve()\n data[k] = str(x)\n else:\n data[k] = [str((path / x).resolve()) for x in data[k]]\n\n # Parse yaml\n train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))\n if val:\n val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path\n if not all(x.exists() for x in val):\n LOGGER.info('\\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()])\n if not s or not autodownload:\n raise Exception('Dataset not found ❌')\n t = time.time()\n if s.startswith('http') and s.endswith('.zip'): # URL\n f = Path(s).name # filename\n LOGGER.info(f'Downloading {s} to {f}...')\n torch.hub.download_url_to_file(s, f)\n Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root\n unzip_file(f, path=DATASETS_DIR) # unzip\n Path(f).unlink() # remove zip\n r = None # success\n elif s.startswith('bash '): # bash script\n LOGGER.info(f'Running {s} ...')\n r = os.system(s)\n else: # python script\n r = exec(s, {'yaml': data}) # return None\n dt = f'({round(time.time() - t, 1)}s)'\n s = f\"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}\" if r in (0, None) else f\"failure {dt} ❌\"\n LOGGER.info(f\"Dataset download {s}\")\n check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts\n return data # dictionary"},{"identifier":"check_img_size","path":"utils/general.py","snippet":"def check_img_size(imgsz, s=32, floor=0):\n # Verify image size is a multiple of stride s in each dimension\n if isinstance(imgsz, int): # integer i.e. img_size=640\n new_size = max(make_divisible(imgsz, int(s)), floor)\n else: # list i.e. img_size=[640, 480]\n imgsz = list(imgsz) # convert to list if tuple\n new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]\n if new_size != imgsz:\n LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')\n return new_size"},{"identifier":"check_requirements","path":"utils/general.py","snippet":"@TryExcept()\ndef check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=''):\n # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages or single package str)\n prefix = colorstr('red', 'bold', 'requirements:')\n check_python() # check python version\n if isinstance(requirements, Path): # requirements.txt file\n file = requirements.resolve()\n assert file.exists(), f\"{prefix} {file} not found, check failed.\"\n with file.open() as f:\n requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]\n elif isinstance(requirements, str):\n requirements = [requirements]\n\n s = ''\n n = 0\n for r in requirements:\n try:\n pkg.require(r)\n except (pkg.VersionConflict, pkg.DistributionNotFound): # exception if requirements not met\n s += f'\"{r}\" '\n n += 1\n\n if s and install and AUTOINSTALL: # check environment variable\n LOGGER.info(f\"{prefix} YOLOv5 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...\")\n try:\n # assert check_online(), \"AutoUpdate skipped (offline)\"\n LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode())\n source = file if 'file' in locals() else requirements\n s = f\"{prefix} {n} package{'s' * (n > 1)} updated per {source}\\n\" \\\n f\"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\\n\"\n LOGGER.info(s)\n except Exception as e:\n LOGGER.warning(f'{prefix} ❌ {e}')"},{"identifier":"check_yaml","path":"utils/general.py","snippet":"def check_yaml(file, suffix=('.yaml', '.yml')):\n # Search/download YAML file (if necessary) and return path, checking suffix\n return check_file(file, suffix)"},{"identifier":"coco80_to_coco91_class","path":"utils/general.py","snippet":"def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)\n # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/\n # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\\n')\n # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\\n')\n # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco\n # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet\n return [\n 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,\n 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,\n 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]"},{"identifier":"colorstr","path":"utils/general.py","snippet":"def colorstr(*input):\n # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')\n *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string\n colors = {\n 'black': '\\033[30m', # basic colors\n 'red': '\\033[31m',\n 'green': '\\033[32m',\n 'yellow': '\\033[33m',\n 'blue': '\\033[34m',\n 'magenta': '\\033[35m',\n 'cyan': '\\033[36m',\n 'white': '\\033[37m',\n 'bright_black': '\\033[90m', # bright colors\n 'bright_red': '\\033[91m',\n 'bright_green': '\\033[92m',\n 'bright_yellow': '\\033[93m',\n 'bright_blue': '\\033[94m',\n 'bright_magenta': '\\033[95m',\n 'bright_cyan': '\\033[96m',\n 'bright_white': '\\033[97m',\n 'end': '\\033[0m', # misc\n 'bold': '\\033[1m',\n 'underline': '\\033[4m'}\n return ''.join(colors[x] for x in args) + f'{string}' + colors['end']"},{"identifier":"increment_path","path":"utils/general.py","snippet":"def increment_path(path, exist_ok=False, sep='', mkdir=False):\n # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.\n path = Path(path) # os-agnostic\n if path.exists() and not exist_ok:\n path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')\n\n # Method 1\n for n in range(2, 9999):\n p = f'{path}{sep}{n}{suffix}' # increment path\n if not os.path.exists(p): #\n break\n path = Path(p)\n\n # Method 2 (deprecated)\n # dirs = glob.glob(f\"{path}{sep}*\") # similar paths\n # matches = [re.search(rf\"{path.stem}{sep}(\\d+)\", d) for d in dirs]\n # i = [int(m.groups()[0]) for m in matches if m] # indices\n # n = max(i) + 1 if i else 2 # increment number\n # path = Path(f\"{path}{sep}{n}{suffix}\") # increment path\n\n if mkdir:\n path.mkdir(parents=True, exist_ok=True) # make directory\n\n return path"},{"identifier":"non_max_suppression","path":"utils/general.py","snippet":"def non_max_suppression(\n prediction,\n conf_thres=0.25,\n iou_thres=0.45,\n classes=None,\n agnostic=False,\n multi_label=False,\n labels=(),\n max_det=300,\n nm=0, # number of masks\n):\n \"\"\"Non-Maximum Suppression (NMS) on inference results to reject overlapping detections\n\n Returns:\n list of detections, on (n,6) tensor per image [xyxy, conf, cls]\n \"\"\"\n\n if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out)\n prediction = prediction[0] # select only inference output\n\n device = prediction.device\n mps = 'mps' in device.type # Apple MPS\n if mps: # MPS not fully supported yet, convert tensors to CPU before NMS\n prediction = prediction.cpu()\n bs = prediction.shape[0] # batch size\n nc = prediction.shape[2] - nm - 5 # number of classes\n xc = prediction[..., 4] > conf_thres # candidates\n\n # Checks\n assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'\n assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'\n\n # Settings\n # min_wh = 2 # (pixels) minimum box width and height\n max_wh = 7680 # (pixels) maximum box width and height\n max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()\n time_limit = 0.5 + 0.05 * bs # seconds to quit after\n redundant = True # require redundant detections\n multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)\n merge = False # use merge-NMS\n\n t = time.time()\n mi = 5 + nc # mask start index\n output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs\n for xi, x in enumerate(prediction): # image index, image inference\n # Apply constraints\n # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height\n x = x[xc[xi]] # confidence\n\n # Cat apriori labels if autolabelling\n if labels and len(labels[xi]):\n lb = labels[xi]\n v = torch.zeros((len(lb), nc + nm + 5), device=x.device)\n v[:, :4] = lb[:, 1:5] # box\n v[:, 4] = 1.0 # conf\n v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls\n x = torch.cat((x, v), 0)\n\n # If none remain process next image\n if not x.shape[0]:\n continue\n\n # Compute conf\n x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf\n\n # Box/Mask\n box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)\n mask = x[:, mi:] # zero columns if no masks\n\n # Detections matrix nx6 (xyxy, conf, cls)\n if multi_label:\n i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T\n x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)\n else: # best class only\n conf, j = x[:, 5:mi].max(1, keepdim=True)\n x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]\n\n # Filter by class\n if classes is not None:\n x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]\n\n # Apply finite constraint\n # if not torch.isfinite(x).all():\n # x = x[torch.isfinite(x).all(1)]\n\n # Check shape\n n = x.shape[0] # number of boxes\n if not n: # no boxes\n continue\n elif n > max_nms: # excess boxes\n x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence\n else:\n x = x[x[:, 4].argsort(descending=True)] # sort by confidence\n\n # Batched NMS\n c = x[:, 5:6] * (0 if agnostic else max_wh) # classes\n boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores\n i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS\n #i = my_soft_nms(boxes, scores, iou_thres) \n if i.shape[0] > max_det: # limit detections\n i = i[:max_det]\n if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)\n # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)\n iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix\n weights = iou * scores[None] # box weights\n x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes\n if redundant:\n i = i[iou.sum(1) > 1] # require redundancy\n\n output[xi] = x[i]\n if mps:\n output[xi] = output[xi].to(device)\n if (time.time() - t) > time_limit:\n LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded')\n break # time limit exceeded\n\n return output"},{"identifier":"print_args","path":"utils/general.py","snippet":"def print_args(args: Optional[dict] = None, show_file=True, show_func=False):\n # Print function arguments (optional args dict)\n x = inspect.currentframe().f_back # previous frame\n file, _, func, _, _ = inspect.getframeinfo(x)\n if args is None: # get args automatically\n args, _, _, frm = inspect.getargvalues(x)\n args = {k: v for k, v in frm.items() if k in args}\n try:\n file = Path(file).resolve().relative_to(ROOT).with_suffix('')\n except ValueError:\n file = Path(file).stem\n s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '')\n LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))"},{"identifier":"scale_boxes","path":"utils/general.py","snippet":"def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):\n # Rescale boxes (xyxy) from img1_shape to img0_shape\n if ratio_pad is None: # calculate from img0_shape\n gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new\n pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding\n else:\n gain = ratio_pad[0][0]\n pad = ratio_pad[1]\n\n boxes[:, [0, 2]] -= pad[0] # x padding\n boxes[:, [1, 3]] -= pad[1] # y padding\n boxes[:, :4] /= gain\n clip_boxes(boxes, img0_shape)\n return boxes"},{"identifier":"xywh2xyxy","path":"utils/general.py","snippet":"def xywh2xyxy(x):\n # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right\n y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x\n y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y\n y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x\n y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y\n return y"},{"identifier":"xyxy2xywh","path":"utils/general.py","snippet":"def xyxy2xywh(x):\n # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right\n y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\n y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center\n y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center\n y[:, 2] = x[:, 2] - x[:, 0] # width\n y[:, 3] = x[:, 3] - x[:, 1] # height\n return y"},{"identifier":"ConfusionMatrix","path":"utils/metrics.py","snippet":"class ConfusionMatrix:\n # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix\n def __init__(self, nc, conf=0.25, iou_thres=0.45):\n self.matrix = np.zeros((nc + 1, nc + 1))\n self.nc = nc # number of classes\n self.conf = conf\n self.iou_thres = iou_thres\n\n def process_batch(self, detections, labels):\n \"\"\"\n Return intersection-over-union (Jaccard index) of boxes.\n Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n Arguments:\n detections (Array[N, 6]), x1, y1, x2, y2, conf, class\n labels (Array[M, 5]), class, x1, y1, x2, y2\n Returns:\n None, updates confusion matrix accordingly\n \"\"\"\n if detections is None:\n gt_classes = labels.int()\n for gc in gt_classes:\n self.matrix[self.nc, gc] += 1 # background FN\n return\n\n detections = detections[detections[:, 4] > self.conf]\n gt_classes = labels[:, 0].int()\n detection_classes = detections[:, 5].int()\n iou = box_iou(labels[:, 1:], detections[:, :4])\n\n x = torch.where(iou > self.iou_thres)\n if x[0].shape[0]:\n matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()\n if x[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n else:\n matches = np.zeros((0, 3))\n\n n = matches.shape[0] > 0\n m0, m1, _ = matches.transpose().astype(int)\n for i, gc in enumerate(gt_classes):\n j = m0 == i\n if n and sum(j) == 1:\n self.matrix[detection_classes[m1[j]], gc] += 1 # correct\n else:\n self.matrix[self.nc, gc] += 1 # true background\n\n if n:\n for i, dc in enumerate(detection_classes):\n if not any(m1 == i):\n self.matrix[dc, self.nc] += 1 # predicted background\n\n def tp_fp(self):\n tp = self.matrix.diagonal() # true positives\n fp = self.matrix.sum(1) - tp # false positives\n # fn = self.matrix.sum(0) - tp # false negatives (missed detections)\n return tp[:-1], fp[:-1] # remove background class\n\n @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')\n def plot(self, normalize=True, save_dir='', names=()):\n import seaborn as sn\n\n array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns\n array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)\n\n fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)\n nc, nn = self.nc, len(names) # number of classes, names\n sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size\n labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels\n ticklabels = (names + ['background']) if labels else \"auto\"\n with warnings.catch_warnings():\n warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered\n sn.heatmap(array,\n ax=ax,\n annot=nc < 30,\n annot_kws={\n \"size\": 8},\n cmap='Blues',\n fmt='.2f',\n square=True,\n vmin=0.0,\n xticklabels=ticklabels,\n yticklabels=ticklabels).set_facecolor((1, 1, 1))\n ax.set_ylabel('True')\n ax.set_ylabel('Predicted')\n ax.set_title('Confusion Matrix')\n fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)\n plt.close(fig)\n\n def print(self):\n for i in range(self.nc + 1):\n print(' '.join(map(str, self.matrix[i])))"},{"identifier":"box_iou","path":"utils/metrics.py","snippet":"def box_iou(box1, box2, eps=1e-7):\n # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py\n \"\"\"\n Return intersection-over-union (Jaccard index) of boxes.\n Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n Arguments:\n box1 (Tensor[N, 4])\n box2 (Tensor[M, 4])\n Returns:\n iou (Tensor[N, M]): the NxM matrix containing the pairwise\n IoU values for every element in boxes1 and boxes2\n \"\"\"\n\n # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)\n (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)\n inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)\n\n # IoU = inter / (area1 + area2 - inter)\n return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)"},{"identifier":"output_to_target","path":"utils/plots.py","snippet":"def output_to_target(output, max_det=300):\n # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting\n targets = []\n for i, o in enumerate(output):\n box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)\n j = torch.full((conf.shape[0], 1), i)\n targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))\n return torch.cat(targets, 0).numpy()"},{"identifier":"plot_val_study","path":"utils/plots.py","snippet":"def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study()\n # Plot file=study.txt generated by val.py (or plot all study*.txt in dir)\n save_dir = Path(file).parent if file else Path(dir)\n plot2 = False # plot additional results\n if plot2:\n ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()\n\n fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)\n # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:\n for f in sorted(save_dir.glob('study*.txt')):\n y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T\n x = np.arange(y.shape[1]) if x is None else np.array(x)\n if plot2:\n s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']\n for i in range(7):\n ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)\n ax[i].set_title(s[i])\n\n j = y[3].argmax() + 1\n ax2.plot(y[5, 1:j],\n y[3, 1:j] * 1E2,\n '.-',\n linewidth=2,\n markersize=8,\n label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))\n\n ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],\n 'k.-',\n linewidth=2,\n markersize=8,\n alpha=.25,\n label='EfficientDet')\n\n ax2.grid(alpha=0.2)\n ax2.set_yticks(np.arange(20, 60, 5))\n ax2.set_xlim(0, 57)\n ax2.set_ylim(25, 55)\n ax2.set_xlabel('GPU Speed (ms/img)')\n ax2.set_ylabel('COCO AP val')\n ax2.legend(loc='lower right')\n f = save_dir / 'study.png'\n print(f'Saving {f}...')\n plt.savefig(f, dpi=300)"},{"identifier":"create_dataloader","path":"utils/segment/dataloaders.py","snippet":"def create_dataloader(path,\n imgsz,\n batch_size,\n stride,\n single_cls=False,\n hyp=None,\n augment=False,\n cache=False,\n pad=0.0,\n rect=False,\n rank=-1,\n workers=8,\n image_weights=False,\n quad=False,\n prefix='',\n shuffle=False,\n mask_downsample_ratio=1,\n overlap_mask=False):\n if rect and shuffle:\n LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False')\n shuffle = False\n with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP\n dataset = LoadImagesAndLabelsAndMasks(\n path,\n imgsz,\n batch_size,\n augment=augment, # augmentation\n hyp=hyp, # hyperparameters\n rect=rect, # rectangular batches\n cache_images=cache,\n single_cls=single_cls,\n stride=int(stride),\n pad=pad,\n image_weights=image_weights,\n prefix=prefix,\n downsample_ratio=mask_downsample_ratio,\n overlap=overlap_mask)\n\n batch_size = min(batch_size, len(dataset))\n nd = torch.cuda.device_count() # number of CUDA devices\n nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers\n sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)\n loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates\n generator = torch.Generator()\n generator.manual_seed(6148914691236517205 + RANK)\n return loader(\n dataset,\n batch_size=batch_size,\n shuffle=shuffle and sampler is None,\n num_workers=nw,\n sampler=sampler,\n pin_memory=True,\n collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn,\n worker_init_fn=seed_worker,\n generator=generator,\n ), dataset"},{"identifier":"mask_iou","path":"utils/segment/general.py","snippet":"def mask_iou(mask1, mask2, eps=1e-7):\n \"\"\"\n mask1: [N, n] m1 means number of predicted objects\n mask2: [M, n] m2 means number of gt objects\n Note: n means image_w x image_h\n\n return: masks iou, [N, M]\n \"\"\"\n intersection = torch.matmul(mask1, mask2.t()).clamp(0)\n union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection\n return intersection / (union + eps)"},{"identifier":"process_mask","path":"utils/segment/general.py","snippet":"def process_mask(protos, masks_in, bboxes, shape, upsample=False):\n \"\"\"\n Crop before upsample.\n proto_out: [mask_dim, mask_h, mask_w]\n out_masks: [n, mask_dim], n is number of masks after nms\n bboxes: [n, 4], n is number of masks after nms\n shape:input_image_size, (h, w)\n\n return: h, w, n\n \"\"\"\n\n c, mh, mw = protos.shape # CHW\n ih, iw = shape\n masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW\n\n downsampled_bboxes = bboxes.clone()\n downsampled_bboxes[:, 0] *= mw / iw\n downsampled_bboxes[:, 2] *= mw / iw\n downsampled_bboxes[:, 3] *= mh / ih\n downsampled_bboxes[:, 1] *= mh / ih\n\n masks = crop_mask(masks, downsampled_bboxes) # CHW\n if upsample:\n masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW\n return masks.gt_(0.5)"},{"identifier":"process_mask_upsample","path":"utils/segment/general.py","snippet":"def process_mask_upsample(protos, masks_in, bboxes, shape):\n \"\"\"\n Crop after upsample.\n proto_out: [mask_dim, mask_h, mask_w]\n out_masks: [n, mask_dim], n is number of masks after nms\n bboxes: [n, 4], n is number of masks after nms\n shape:input_image_size, (h, w)\n\n return: h, w, n\n \"\"\"\n\n c, mh, mw = protos.shape # CHW\n masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)\n masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW\n masks = crop_mask(masks, bboxes) # CHW\n return masks.gt_(0.5)"},{"identifier":"scale_image","path":"utils/segment/general.py","snippet":"def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):\n \"\"\"\n img1_shape: model input shape, [h, w]\n img0_shape: origin pic shape, [h, w, 3]\n masks: [h, w, num]\n \"\"\"\n # Rescale coordinates (xyxy) from im1_shape to im0_shape\n if ratio_pad is None: # calculate from im0_shape\n gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new\n pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding\n else:\n pad = ratio_pad[1]\n top, left = int(pad[1]), int(pad[0]) # y, x\n bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])\n\n if len(masks.shape) < 2:\n raise ValueError(f'\"len of masks shape\" should be 2 or 3, but got {len(masks.shape)}')\n masks = masks[top:bottom, left:right]\n # masks = masks.permute(2, 0, 1).contiguous()\n # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]\n # masks = masks.permute(1, 2, 0).contiguous()\n masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))\n\n if len(masks.shape) == 2:\n masks = masks[:, :, None]\n return masks"},{"identifier":"Metrics","path":"utils/segment/metrics.py","snippet":"class Metrics:\n \"\"\"Metric for boxes and masks.\"\"\"\n\n def __init__(self) -> None:\n self.metric_box = Metric()\n self.metric_mask = Metric()\n\n def update(self, results):\n \"\"\"\n Args:\n results: Dict{'boxes': Dict{}, 'masks': Dict{}}\n \"\"\"\n self.metric_box.update(list(results[\"boxes\"].values()))\n self.metric_mask.update(list(results[\"masks\"].values()))\n\n def mean_results(self):\n return self.metric_box.mean_results() + self.metric_mask.mean_results()\n\n def class_result(self, i):\n return self.metric_box.class_result(i) + self.metric_mask.class_result(i)\n\n def get_maps(self, nc):\n return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)\n\n @property\n def ap_class_index(self):\n # boxes and masks have the same ap_class_index\n return self.metric_box.ap_class_index"},{"identifier":"ap_per_class_box_and_mask","path":"utils/segment/metrics.py","snippet":"def ap_per_class_box_and_mask(\n tp_m,\n tp_b,\n conf,\n pred_cls,\n target_cls,\n plot=False,\n save_dir=\".\",\n names=(),\n):\n \"\"\"\n Args:\n tp_b: tp of boxes.\n tp_m: tp of masks.\n other arguments see `func: ap_per_class`.\n \"\"\"\n results_boxes = ap_per_class(tp_b,\n conf,\n pred_cls,\n target_cls,\n plot=plot,\n save_dir=save_dir,\n names=names,\n prefix=\"Box\")[2:]\n results_masks = ap_per_class(tp_m,\n conf,\n pred_cls,\n target_cls,\n plot=plot,\n save_dir=save_dir,\n names=names,\n prefix=\"Mask\")[2:]\n\n results = {\n \"boxes\": {\n \"p\": results_boxes[0],\n \"r\": results_boxes[1],\n \"ap\": results_boxes[3],\n \"f1\": results_boxes[2],\n \"ap_class\": results_boxes[4]},\n \"masks\": {\n \"p\": results_masks[0],\n \"r\": results_masks[1],\n \"ap\": results_masks[3],\n \"f1\": results_masks[2],\n \"ap_class\": results_masks[4]}}\n return results"},{"identifier":"plot_images_and_masks","path":"utils/segment/plots.py","snippet":"@threaded\ndef plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None):\n # Plot image grid with labels\n if isinstance(images, torch.Tensor):\n images = images.cpu().float().numpy()\n if isinstance(targets, torch.Tensor):\n targets = targets.cpu().numpy()\n if isinstance(masks, torch.Tensor):\n masks = masks.cpu().numpy().astype(int)\n\n max_size = 1920 # max image size\n max_subplots = 16 # max image subplots, i.e. 4x4\n bs, _, h, w = images.shape # batch size, _, height, width\n bs = min(bs, max_subplots) # limit plot images\n ns = np.ceil(bs ** 0.5) # number of subplots (square)\n if np.max(images[0]) <= 1:\n images *= 255 # de-normalise (optional)\n\n # Build Image\n mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init\n for i, im in enumerate(images):\n if i == max_subplots: # if last batch has fewer images than we expect\n break\n x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin\n im = im.transpose(1, 2, 0)\n mosaic[y:y + h, x:x + w, :] = im\n\n # Resize (optional)\n scale = max_size / ns / max(h, w)\n if scale < 1:\n h = math.ceil(scale * h)\n w = math.ceil(scale * w)\n mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))\n\n # Annotate\n fs = int((h + w) * ns * 0.01) # font size\n annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)\n for i in range(i + 1):\n x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin\n annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders\n if paths:\n annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames\n if len(targets) > 0:\n idx = targets[:, 0] == i\n ti = targets[idx] # image targets\n\n boxes = xywh2xyxy(ti[:, 2:6]).T\n classes = ti[:, 1].astype('int')\n labels = ti.shape[1] == 6 # labels if no conf column\n conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)\n\n if boxes.shape[1]:\n if boxes.max() <= 1.01: # if normalized with tolerance 0.01\n boxes[[0, 2]] *= w # scale to pixels\n boxes[[1, 3]] *= h\n elif scale < 1: # absolute coords need scale if image scales\n boxes *= scale\n boxes[[0, 2]] += x\n boxes[[1, 3]] += y\n for j, box in enumerate(boxes.T.tolist()):\n cls = classes[j]\n color = colors(cls)\n cls = names[cls] if names else cls\n if labels or conf[j] > 0.25: # 0.25 conf thresh\n label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'\n annotator.box_label(box, label, color=color)\n\n # Plot masks\n if len(masks):\n if masks.max() > 1.0: # mean that masks are overlap\n image_masks = masks[[i]] # (1, 640, 640)\n nl = len(ti)\n index = np.arange(nl).reshape(nl, 1, 1) + 1\n image_masks = np.repeat(image_masks, nl, axis=0)\n image_masks = np.where(image_masks == index, 1.0, 0.0)\n else:\n image_masks = masks[idx]\n\n im = np.asarray(annotator.im).copy()\n for j, box in enumerate(boxes.T.tolist()):\n if labels or conf[j] > 0.25: # 0.25 conf thresh\n color = colors(classes[j])\n mh, mw = image_masks[j].shape\n if mh != h or mw != w:\n mask = image_masks[j].astype(np.uint8)\n mask = cv2.resize(mask, (w, h))\n mask = mask.astype(bool)\n else:\n mask = image_masks[j].astype(bool)\n with contextlib.suppress(Exception):\n im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6\n annotator.fromarray(im)\n annotator.im.save(fname) # save"},{"identifier":"de_parallel","path":"utils/torch_utils.py","snippet":"def de_parallel(model):\n # De-parallelize a model: returns single-GPU model if model is of type DP or DDP\n return model.module if is_parallel(model) else model"},{"identifier":"select_device","path":"utils/torch_utils.py","snippet":"def select_device(device='', batch_size=0, newline=True):\n # device = None or 'cpu' or 0 or '0' or '0,1,2,3'\n s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '\n device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0'\n cpu = device == 'cpu'\n mps = device == 'mps' # Apple Metal Performance Shaders (MPS)\n if cpu or mps:\n os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False\n elif device: # non-cpu device requested\n os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()\n assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \\\n f\"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)\"\n\n if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available\n devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7\n n = len(devices) # device count\n if n > 1 and batch_size > 0: # check batch_size is divisible by device_count\n assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'\n space = ' ' * (len(s) + 1)\n for i, d in enumerate(devices):\n p = torch.cuda.get_device_properties(i)\n s += f\"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\\n\" # bytes to MB\n arg = 'cuda:0'\n elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available\n s += 'MPS\\n'\n arg = 'mps'\n else: # revert to CPU\n s += 'CPU\\n'\n arg = 'cpu'\n\n if not newline:\n s = s.rstrip()\n LOGGER.info(s)\n return torch.device(arg)"},{"identifier":"smart_inference_mode","path":"utils/torch_utils.py","snippet":"def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):\n # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator\n def decorate(fn):\n return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)\n\n return decorate"}],"string":"[\n {\n \"identifier\": \"DetectMultiBackend\",\n \"path\": \"models/common.py\",\n \"snippet\": \"class DetectMultiBackend(nn.Module):\\n # YOLOv5 MultiBackend class for python inference on various backends\\n def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):\\n # Usage:\\n # PyTorch: weights = *.pt\\n # TorchScript: *.torchscript\\n # ONNX Runtime: *.onnx\\n # ONNX OpenCV DNN: *.onnx --dnn\\n # OpenVINO: *_openvino_model\\n # CoreML: *.mlmodel\\n # TensorRT: *.engine\\n # TensorFlow SavedModel: *_saved_model\\n # TensorFlow GraphDef: *.pb\\n # TensorFlow Lite: *.tflite\\n # TensorFlow Edge TPU: *_edgetpu.tflite\\n # PaddlePaddle: *_paddle_model\\n from models.experimental import attempt_download, attempt_load # scoped to avoid circular import\\n\\n super().__init__()\\n w = str(weights[0] if isinstance(weights, list) else weights)\\n pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)\\n fp16 &= pt or jit or onnx or engine # FP16\\n nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)\\n stride = 32 # default stride\\n cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA\\n if not (pt or triton):\\n w = attempt_download(w) # download if not local\\n\\n if pt: # PyTorch\\n model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)\\n stride = max(int(model.stride.max()), 32) # model stride\\n names = model.module.names if hasattr(model, 'module') else model.names # get class names\\n model.half() if fp16 else model.float()\\n self.model = model # explicitly assign for to(), cpu(), cuda(), half()\\n elif jit: # TorchScript\\n LOGGER.info(f'Loading {w} for TorchScript inference...')\\n extra_files = {'config.txt': ''} # model metadata\\n model = torch.jit.load(w, _extra_files=extra_files, map_location=device)\\n model.half() if fp16 else model.float()\\n if extra_files['config.txt']: # load metadata dict\\n d = json.loads(extra_files['config.txt'],\\n object_hook=lambda d: {int(k) if k.isdigit() else k: v\\n for k, v in d.items()})\\n stride, names = int(d['stride']), d['names']\\n elif dnn: # ONNX OpenCV DNN\\n LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')\\n check_requirements('opencv-python>=4.5.4')\\n net = cv2.dnn.readNetFromONNX(w)\\n elif onnx: # ONNX Runtime\\n LOGGER.info(f'Loading {w} for ONNX Runtime inference...')\\n check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))\\n import onnxruntime\\n providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']\\n session = onnxruntime.InferenceSession(w, providers=providers)\\n output_names = [x.name for x in session.get_outputs()]\\n meta = session.get_modelmeta().custom_metadata_map # metadata\\n if 'stride' in meta:\\n stride, names = int(meta['stride']), eval(meta['names'])\\n elif xml: # OpenVINO\\n LOGGER.info(f'Loading {w} for OpenVINO inference...')\\n check_requirements('openvino') # requires openvino-dev: https://pypi.org/project/openvino-dev/\\n from openvino.runtime import Core, Layout, get_batch\\n ie = Core()\\n if not Path(w).is_file(): # if not *.xml\\n w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir\\n network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))\\n if network.get_parameters()[0].get_layout().empty:\\n network.get_parameters()[0].set_layout(Layout(\\\"NCHW\\\"))\\n batch_dim = get_batch(network)\\n if batch_dim.is_static:\\n batch_size = batch_dim.get_length()\\n executable_network = ie.compile_model(network, device_name=\\\"CPU\\\") # device_name=\\\"MYRIAD\\\" for Intel NCS2\\n stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata\\n elif engine: # TensorRT\\n LOGGER.info(f'Loading {w} for TensorRT inference...')\\n import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download\\n check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0\\n if device.type == 'cpu':\\n device = torch.device('cuda:0')\\n Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))\\n logger = trt.Logger(trt.Logger.INFO)\\n with open(w, 'rb') as f, trt.Runtime(logger) as runtime:\\n model = runtime.deserialize_cuda_engine(f.read())\\n context = model.create_execution_context()\\n bindings = OrderedDict()\\n output_names = []\\n fp16 = False # default updated below\\n dynamic = False\\n for i in range(model.num_bindings):\\n name = model.get_binding_name(i)\\n dtype = trt.nptype(model.get_binding_dtype(i))\\n if model.binding_is_input(i):\\n if -1 in tuple(model.get_binding_shape(i)): # dynamic\\n dynamic = True\\n context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))\\n if dtype == np.float16:\\n fp16 = True\\n else: # output\\n output_names.append(name)\\n shape = tuple(context.get_binding_shape(i))\\n im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)\\n bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))\\n binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())\\n batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size\\n elif coreml: # CoreML\\n LOGGER.info(f'Loading {w} for CoreML inference...')\\n import coremltools as ct\\n model = ct.models.MLModel(w)\\n elif saved_model: # TF SavedModel\\n LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')\\n import tensorflow as tf\\n keras = False # assume TF1 saved_model\\n model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)\\n elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt\\n LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')\\n import tensorflow as tf\\n\\n def wrap_frozen_graph(gd, inputs, outputs):\\n x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=\\\"\\\"), []) # wrapped\\n ge = x.graph.as_graph_element\\n return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))\\n\\n def gd_outputs(gd):\\n name_list, input_list = [], []\\n for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef\\n name_list.append(node.name)\\n input_list.extend(node.input)\\n return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))\\n\\n gd = tf.Graph().as_graph_def() # TF GraphDef\\n with open(w, 'rb') as f:\\n gd.ParseFromString(f.read())\\n frozen_func = wrap_frozen_graph(gd, inputs=\\\"x:0\\\", outputs=gd_outputs(gd))\\n elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python\\n try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu\\n from tflite_runtime.interpreter import Interpreter, load_delegate\\n except ImportError:\\n import tensorflow as tf\\n Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,\\n if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime\\n LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')\\n delegate = {\\n 'Linux': 'libedgetpu.so.1',\\n 'Darwin': 'libedgetpu.1.dylib',\\n 'Windows': 'edgetpu.dll'}[platform.system()]\\n interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])\\n else: # TFLite\\n LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')\\n interpreter = Interpreter(model_path=w) # load TFLite model\\n interpreter.allocate_tensors() # allocate\\n input_details = interpreter.get_input_details() # inputs\\n output_details = interpreter.get_output_details() # outputs\\n # load metadata\\n with contextlib.suppress(zipfile.BadZipFile):\\n with zipfile.ZipFile(w, \\\"r\\\") as model:\\n meta_file = model.namelist()[0]\\n meta = ast.literal_eval(model.read(meta_file).decode(\\\"utf-8\\\"))\\n stride, names = int(meta['stride']), meta['names']\\n elif tfjs: # TF.js\\n raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')\\n elif paddle: # PaddlePaddle\\n LOGGER.info(f'Loading {w} for PaddlePaddle inference...')\\n check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')\\n import paddle.inference as pdi\\n if not Path(w).is_file(): # if not *.pdmodel\\n w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir\\n weights = Path(w).with_suffix('.pdiparams')\\n config = pdi.Config(str(w), str(weights))\\n if cuda:\\n config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)\\n predictor = pdi.create_predictor(config)\\n input_handle = predictor.get_input_handle(predictor.get_input_names()[0])\\n output_names = predictor.get_output_names()\\n elif triton: # NVIDIA Triton Inference Server\\n LOGGER.info(f'Using {w} as Triton Inference Server...')\\n check_requirements('tritonclient[all]')\\n from utils.triton import TritonRemoteModel\\n model = TritonRemoteModel(url=w)\\n nhwc = model.runtime.startswith(\\\"tensorflow\\\")\\n else:\\n raise NotImplementedError(f'ERROR: {w} is not a supported format')\\n\\n # class names\\n if 'names' not in locals():\\n names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}\\n if names[0] == 'n01440764' and len(names) == 1000: # ImageNet\\n names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names\\n\\n self.__dict__.update(locals()) # assign all variables to self\\n\\n def forward(self, im, augment=False, visualize=False):\\n # YOLOv5 MultiBackend inference\\n b, ch, h, w = im.shape # batch, channel, height, width\\n if self.fp16 and im.dtype != torch.float16:\\n im = im.half() # to FP16\\n if self.nhwc:\\n im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3)\\n\\n if self.pt: # PyTorch\\n y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)\\n elif self.jit: # TorchScript\\n y = self.model(im)\\n elif self.dnn: # ONNX OpenCV DNN\\n im = im.cpu().numpy() # torch to numpy\\n self.net.setInput(im)\\n y = self.net.forward()\\n elif self.onnx: # ONNX Runtime\\n im = im.cpu().numpy() # torch to numpy\\n y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})\\n elif self.xml: # OpenVINO\\n im = im.cpu().numpy() # FP32\\n y = list(self.executable_network([im]).values())\\n elif self.engine: # TensorRT\\n if self.dynamic and im.shape != self.bindings['images'].shape:\\n i = self.model.get_binding_index('images')\\n self.context.set_binding_shape(i, im.shape) # reshape if dynamic\\n self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)\\n for name in self.output_names:\\n i = self.model.get_binding_index(name)\\n self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))\\n s = self.bindings['images'].shape\\n assert im.shape == s, f\\\"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}\\\"\\n self.binding_addrs['images'] = int(im.data_ptr())\\n self.context.execute_v2(list(self.binding_addrs.values()))\\n y = [self.bindings[x].data for x in sorted(self.output_names)]\\n elif self.coreml: # CoreML\\n im = im.cpu().numpy()\\n im = Image.fromarray((im[0] * 255).astype('uint8'))\\n # im = im.resize((192, 320), Image.ANTIALIAS)\\n y = self.model.predict({'image': im}) # coordinates are xywh normalized\\n if 'confidence' in y:\\n box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels\\n conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)\\n y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)\\n else:\\n y = list(reversed(y.values())) # reversed for segmentation models (pred, proto)\\n elif self.paddle: # PaddlePaddle\\n im = im.cpu().numpy().astype(np.float32)\\n self.input_handle.copy_from_cpu(im)\\n self.predictor.run()\\n y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]\\n elif self.triton: # NVIDIA Triton Inference Server\\n y = self.model(im)\\n else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)\\n im = im.cpu().numpy()\\n if self.saved_model: # SavedModel\\n y = self.model(im, training=False) if self.keras else self.model(im)\\n elif self.pb: # GraphDef\\n y = self.frozen_func(x=self.tf.constant(im))\\n else: # Lite or Edge TPU\\n input = self.input_details[0]\\n int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model\\n if int8:\\n scale, zero_point = input['quantization']\\n im = (im / scale + zero_point).astype(np.uint8) # de-scale\\n self.interpreter.set_tensor(input['index'], im)\\n self.interpreter.invoke()\\n y = []\\n for output in self.output_details:\\n x = self.interpreter.get_tensor(output['index'])\\n if int8:\\n scale, zero_point = output['quantization']\\n x = (x.astype(np.float32) - zero_point) * scale # re-scale\\n y.append(x)\\n y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]\\n y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels\\n\\n if isinstance(y, (list, tuple)):\\n return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]\\n else:\\n return self.from_numpy(y)\\n\\n def from_numpy(self, x):\\n return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x\\n\\n def warmup(self, imgsz=(1, 3, 640, 640)):\\n # Warmup model by running inference once\\n warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton\\n if any(warmup_types) and (self.device.type != 'cpu' or self.triton):\\n im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input\\n for _ in range(2 if self.jit else 1): #\\n self.forward(im) # warmup\\n\\n @staticmethod\\n def _model_type(p='path/to/model.pt'):\\n # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx\\n # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]\\n from export import export_formats\\n from utils.downloads import is_url\\n sf = list(export_formats().Suffix) # export suffixes\\n if not is_url(p, check=False):\\n check_suffix(p, sf) # checks\\n url = urlparse(p) # if url may be Triton inference server\\n types = [s in Path(p).name for s in sf]\\n types[8] &= not types[9] # tflite &= not edgetpu\\n triton = not any(types) and all([any(s in url.scheme for s in [\\\"http\\\", \\\"grpc\\\"]), url.netloc])\\n return types + [triton]\\n\\n @staticmethod\\n def _load_metadata(f=Path('path/to/meta.yaml')):\\n # Load metadata from meta.yaml if it exists\\n if f.exists():\\n d = yaml_load(f)\\n return d['stride'], d['names'] # assign stride, names\\n return None, None\"\n },\n {\n \"identifier\": \"SegmentationModel\",\n \"path\": \"models/yolo.py\",\n \"snippet\": \"class SegmentationModel(DetectionModel):\\n # YOLOv5 segmentation model\\n def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None):\\n super().__init__(cfg, ch, nc, anchors)\"\n },\n {\n \"identifier\": \"Callbacks\",\n \"path\": \"utils/callbacks.py\",\n \"snippet\": \"class Callbacks:\\n \\\"\\\"\\\"\\\"\\n Handles all registered callbacks for YOLOv5 Hooks\\n \\\"\\\"\\\"\\n\\n def __init__(self):\\n # Define the available callbacks\\n self._callbacks = {\\n 'on_pretrain_routine_start': [],\\n 'on_pretrain_routine_end': [],\\n 'on_train_start': [],\\n 'on_train_epoch_start': [],\\n 'on_train_batch_start': [],\\n 'optimizer_step': [],\\n 'on_before_zero_grad': [],\\n 'on_train_batch_end': [],\\n 'on_train_epoch_end': [],\\n 'on_val_start': [],\\n 'on_val_batch_start': [],\\n 'on_val_image_end': [],\\n 'on_val_batch_end': [],\\n 'on_val_end': [],\\n 'on_fit_epoch_end': [], # fit = train + val\\n 'on_model_save': [],\\n 'on_train_end': [],\\n 'on_params_update': [],\\n 'teardown': [],}\\n self.stop_training = False # set True to interrupt training\\n\\n def register_action(self, hook, name='', callback=None):\\n \\\"\\\"\\\"\\n Register a new action to a callback hook\\n\\n Args:\\n hook: The callback hook name to register the action to\\n name: The name of the action for later reference\\n callback: The callback to fire\\n \\\"\\\"\\\"\\n assert hook in self._callbacks, f\\\"hook '{hook}' not found in callbacks {self._callbacks}\\\"\\n assert callable(callback), f\\\"callback '{callback}' is not callable\\\"\\n self._callbacks[hook].append({'name': name, 'callback': callback})\\n\\n def get_registered_actions(self, hook=None):\\n \\\"\\\"\\\"\\\"\\n Returns all the registered actions by callback hook\\n\\n Args:\\n hook: The name of the hook to check, defaults to all\\n \\\"\\\"\\\"\\n return self._callbacks[hook] if hook else self._callbacks\\n\\n def run(self, hook, *args, thread=False, **kwargs):\\n \\\"\\\"\\\"\\n Loop through the registered actions and fire all callbacks on main thread\\n\\n Args:\\n hook: The name of the hook to check, defaults to all\\n args: Arguments to receive from YOLOv5\\n thread: (boolean) Run callbacks in daemon thread\\n kwargs: Keyword Arguments to receive from YOLOv5\\n \\\"\\\"\\\"\\n\\n assert hook in self._callbacks, f\\\"hook '{hook}' not found in callbacks {self._callbacks}\\\"\\n for logger in self._callbacks[hook]:\\n if thread:\\n threading.Thread(target=logger['callback'], args=args, kwargs=kwargs, daemon=True).start()\\n else:\\n logger['callback'](*args, **kwargs)\"\n },\n {\n \"identifier\": \"LOGGER\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.)\"\n },\n {\n \"identifier\": \"NUM_THREADS\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads\"\n },\n {\n \"identifier\": \"TQDM_BAR_FORMAT\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"TQDM_BAR_FORMAT = '{l_bar}{bar:10}| {n_fmt}/{total_fmt} {elapsed}' # tqdm bar format\"\n },\n {\n \"identifier\": \"Profile\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"class Profile(contextlib.ContextDecorator):\\n # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager\\n def __init__(self, t=0.0):\\n self.t = t\\n self.cuda = torch.cuda.is_available()\\n\\n def __enter__(self):\\n self.start = self.time()\\n return self\\n\\n def __exit__(self, type, value, traceback):\\n self.dt = self.time() - self.start # delta-time\\n self.t += self.dt # accumulate dt\\n\\n def time(self):\\n if self.cuda:\\n torch.cuda.synchronize()\\n return time.time()\"\n },\n {\n \"identifier\": \"check_dataset\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def check_dataset(data, autodownload=True):\\n # Download, check and/or unzip dataset if not found locally\\n\\n # Download (optional)\\n extract_dir = ''\\n if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):\\n download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1)\\n data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))\\n extract_dir, autodownload = data.parent, False\\n\\n # Read yaml (optional)\\n if isinstance(data, (str, Path)):\\n data = yaml_load(data) # dictionary\\n\\n # Checks\\n for k in 'train', 'val', 'names':\\n assert k in data, emojis(f\\\"data.yaml '{k}:' field missing ❌\\\")\\n if isinstance(data['names'], (list, tuple)): # old array format\\n data['names'] = dict(enumerate(data['names'])) # convert to dict\\n assert all(isinstance(k, int) for k in data['names'].keys()), 'data.yaml names keys must be integers, i.e. 2: car'\\n data['nc'] = len(data['names'])\\n\\n # Resolve paths\\n path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.'\\n if not path.is_absolute():\\n path = (ROOT / path).resolve()\\n data['path'] = path # download scripts\\n for k in 'train', 'val', 'test':\\n if data.get(k): # prepend path\\n if isinstance(data[k], str):\\n x = (path / data[k]).resolve()\\n if not x.exists() and data[k].startswith('../'):\\n x = (path / data[k][3:]).resolve()\\n data[k] = str(x)\\n else:\\n data[k] = [str((path / x).resolve()) for x in data[k]]\\n\\n # Parse yaml\\n train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))\\n if val:\\n val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path\\n if not all(x.exists() for x in val):\\n LOGGER.info('\\\\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()])\\n if not s or not autodownload:\\n raise Exception('Dataset not found ❌')\\n t = time.time()\\n if s.startswith('http') and s.endswith('.zip'): # URL\\n f = Path(s).name # filename\\n LOGGER.info(f'Downloading {s} to {f}...')\\n torch.hub.download_url_to_file(s, f)\\n Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root\\n unzip_file(f, path=DATASETS_DIR) # unzip\\n Path(f).unlink() # remove zip\\n r = None # success\\n elif s.startswith('bash '): # bash script\\n LOGGER.info(f'Running {s} ...')\\n r = os.system(s)\\n else: # python script\\n r = exec(s, {'yaml': data}) # return None\\n dt = f'({round(time.time() - t, 1)}s)'\\n s = f\\\"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}\\\" if r in (0, None) else f\\\"failure {dt} ❌\\\"\\n LOGGER.info(f\\\"Dataset download {s}\\\")\\n check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts\\n return data # dictionary\"\n },\n {\n \"identifier\": \"check_img_size\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def check_img_size(imgsz, s=32, floor=0):\\n # Verify image size is a multiple of stride s in each dimension\\n if isinstance(imgsz, int): # integer i.e. img_size=640\\n new_size = max(make_divisible(imgsz, int(s)), floor)\\n else: # list i.e. img_size=[640, 480]\\n imgsz = list(imgsz) # convert to list if tuple\\n new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]\\n if new_size != imgsz:\\n LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')\\n return new_size\"\n },\n {\n \"identifier\": \"check_requirements\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"@TryExcept()\\ndef check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=''):\\n # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages or single package str)\\n prefix = colorstr('red', 'bold', 'requirements:')\\n check_python() # check python version\\n if isinstance(requirements, Path): # requirements.txt file\\n file = requirements.resolve()\\n assert file.exists(), f\\\"{prefix} {file} not found, check failed.\\\"\\n with file.open() as f:\\n requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]\\n elif isinstance(requirements, str):\\n requirements = [requirements]\\n\\n s = ''\\n n = 0\\n for r in requirements:\\n try:\\n pkg.require(r)\\n except (pkg.VersionConflict, pkg.DistributionNotFound): # exception if requirements not met\\n s += f'\\\"{r}\\\" '\\n n += 1\\n\\n if s and install and AUTOINSTALL: # check environment variable\\n LOGGER.info(f\\\"{prefix} YOLOv5 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...\\\")\\n try:\\n # assert check_online(), \\\"AutoUpdate skipped (offline)\\\"\\n LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode())\\n source = file if 'file' in locals() else requirements\\n s = f\\\"{prefix} {n} package{'s' * (n > 1)} updated per {source}\\\\n\\\" \\\\\\n f\\\"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\\\\n\\\"\\n LOGGER.info(s)\\n except Exception as e:\\n LOGGER.warning(f'{prefix} ❌ {e}')\"\n },\n {\n \"identifier\": \"check_yaml\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def check_yaml(file, suffix=('.yaml', '.yml')):\\n # Search/download YAML file (if necessary) and return path, checking suffix\\n return check_file(file, suffix)\"\n },\n {\n \"identifier\": \"coco80_to_coco91_class\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)\\n # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/\\n # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\\\\n')\\n # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\\\\n')\\n # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco\\n # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet\\n return [\\n 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,\\n 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,\\n 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]\"\n },\n {\n \"identifier\": \"colorstr\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def colorstr(*input):\\n # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')\\n *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string\\n colors = {\\n 'black': '\\\\033[30m', # basic colors\\n 'red': '\\\\033[31m',\\n 'green': '\\\\033[32m',\\n 'yellow': '\\\\033[33m',\\n 'blue': '\\\\033[34m',\\n 'magenta': '\\\\033[35m',\\n 'cyan': '\\\\033[36m',\\n 'white': '\\\\033[37m',\\n 'bright_black': '\\\\033[90m', # bright colors\\n 'bright_red': '\\\\033[91m',\\n 'bright_green': '\\\\033[92m',\\n 'bright_yellow': '\\\\033[93m',\\n 'bright_blue': '\\\\033[94m',\\n 'bright_magenta': '\\\\033[95m',\\n 'bright_cyan': '\\\\033[96m',\\n 'bright_white': '\\\\033[97m',\\n 'end': '\\\\033[0m', # misc\\n 'bold': '\\\\033[1m',\\n 'underline': '\\\\033[4m'}\\n return ''.join(colors[x] for x in args) + f'{string}' + colors['end']\"\n },\n {\n \"identifier\": \"increment_path\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def increment_path(path, exist_ok=False, sep='', mkdir=False):\\n # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.\\n path = Path(path) # os-agnostic\\n if path.exists() and not exist_ok:\\n path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')\\n\\n # Method 1\\n for n in range(2, 9999):\\n p = f'{path}{sep}{n}{suffix}' # increment path\\n if not os.path.exists(p): #\\n break\\n path = Path(p)\\n\\n # Method 2 (deprecated)\\n # dirs = glob.glob(f\\\"{path}{sep}*\\\") # similar paths\\n # matches = [re.search(rf\\\"{path.stem}{sep}(\\\\d+)\\\", d) for d in dirs]\\n # i = [int(m.groups()[0]) for m in matches if m] # indices\\n # n = max(i) + 1 if i else 2 # increment number\\n # path = Path(f\\\"{path}{sep}{n}{suffix}\\\") # increment path\\n\\n if mkdir:\\n path.mkdir(parents=True, exist_ok=True) # make directory\\n\\n return path\"\n },\n {\n \"identifier\": \"non_max_suppression\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def non_max_suppression(\\n prediction,\\n conf_thres=0.25,\\n iou_thres=0.45,\\n classes=None,\\n agnostic=False,\\n multi_label=False,\\n labels=(),\\n max_det=300,\\n nm=0, # number of masks\\n):\\n \\\"\\\"\\\"Non-Maximum Suppression (NMS) on inference results to reject overlapping detections\\n\\n Returns:\\n list of detections, on (n,6) tensor per image [xyxy, conf, cls]\\n \\\"\\\"\\\"\\n\\n if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out)\\n prediction = prediction[0] # select only inference output\\n\\n device = prediction.device\\n mps = 'mps' in device.type # Apple MPS\\n if mps: # MPS not fully supported yet, convert tensors to CPU before NMS\\n prediction = prediction.cpu()\\n bs = prediction.shape[0] # batch size\\n nc = prediction.shape[2] - nm - 5 # number of classes\\n xc = prediction[..., 4] > conf_thres # candidates\\n\\n # Checks\\n assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'\\n assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'\\n\\n # Settings\\n # min_wh = 2 # (pixels) minimum box width and height\\n max_wh = 7680 # (pixels) maximum box width and height\\n max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()\\n time_limit = 0.5 + 0.05 * bs # seconds to quit after\\n redundant = True # require redundant detections\\n multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)\\n merge = False # use merge-NMS\\n\\n t = time.time()\\n mi = 5 + nc # mask start index\\n output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs\\n for xi, x in enumerate(prediction): # image index, image inference\\n # Apply constraints\\n # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height\\n x = x[xc[xi]] # confidence\\n\\n # Cat apriori labels if autolabelling\\n if labels and len(labels[xi]):\\n lb = labels[xi]\\n v = torch.zeros((len(lb), nc + nm + 5), device=x.device)\\n v[:, :4] = lb[:, 1:5] # box\\n v[:, 4] = 1.0 # conf\\n v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls\\n x = torch.cat((x, v), 0)\\n\\n # If none remain process next image\\n if not x.shape[0]:\\n continue\\n\\n # Compute conf\\n x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf\\n\\n # Box/Mask\\n box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)\\n mask = x[:, mi:] # zero columns if no masks\\n\\n # Detections matrix nx6 (xyxy, conf, cls)\\n if multi_label:\\n i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T\\n x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)\\n else: # best class only\\n conf, j = x[:, 5:mi].max(1, keepdim=True)\\n x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]\\n\\n # Filter by class\\n if classes is not None:\\n x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]\\n\\n # Apply finite constraint\\n # if not torch.isfinite(x).all():\\n # x = x[torch.isfinite(x).all(1)]\\n\\n # Check shape\\n n = x.shape[0] # number of boxes\\n if not n: # no boxes\\n continue\\n elif n > max_nms: # excess boxes\\n x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence\\n else:\\n x = x[x[:, 4].argsort(descending=True)] # sort by confidence\\n\\n # Batched NMS\\n c = x[:, 5:6] * (0 if agnostic else max_wh) # classes\\n boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores\\n i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS\\n #i = my_soft_nms(boxes, scores, iou_thres) \\n if i.shape[0] > max_det: # limit detections\\n i = i[:max_det]\\n if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)\\n # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)\\n iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix\\n weights = iou * scores[None] # box weights\\n x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes\\n if redundant:\\n i = i[iou.sum(1) > 1] # require redundancy\\n\\n output[xi] = x[i]\\n if mps:\\n output[xi] = output[xi].to(device)\\n if (time.time() - t) > time_limit:\\n LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded')\\n break # time limit exceeded\\n\\n return output\"\n },\n {\n \"identifier\": \"print_args\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def print_args(args: Optional[dict] = None, show_file=True, show_func=False):\\n # Print function arguments (optional args dict)\\n x = inspect.currentframe().f_back # previous frame\\n file, _, func, _, _ = inspect.getframeinfo(x)\\n if args is None: # get args automatically\\n args, _, _, frm = inspect.getargvalues(x)\\n args = {k: v for k, v in frm.items() if k in args}\\n try:\\n file = Path(file).resolve().relative_to(ROOT).with_suffix('')\\n except ValueError:\\n file = Path(file).stem\\n s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '')\\n LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items()))\"\n },\n {\n \"identifier\": \"scale_boxes\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):\\n # Rescale boxes (xyxy) from img1_shape to img0_shape\\n if ratio_pad is None: # calculate from img0_shape\\n gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new\\n pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding\\n else:\\n gain = ratio_pad[0][0]\\n pad = ratio_pad[1]\\n\\n boxes[:, [0, 2]] -= pad[0] # x padding\\n boxes[:, [1, 3]] -= pad[1] # y padding\\n boxes[:, :4] /= gain\\n clip_boxes(boxes, img0_shape)\\n return boxes\"\n },\n {\n \"identifier\": \"xywh2xyxy\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def xywh2xyxy(x):\\n # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right\\n y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\\n y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x\\n y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y\\n y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x\\n y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y\\n return y\"\n },\n {\n \"identifier\": \"xyxy2xywh\",\n \"path\": \"utils/general.py\",\n \"snippet\": \"def xyxy2xywh(x):\\n # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right\\n y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\\n y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center\\n y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center\\n y[:, 2] = x[:, 2] - x[:, 0] # width\\n y[:, 3] = x[:, 3] - x[:, 1] # height\\n return y\"\n },\n {\n \"identifier\": \"ConfusionMatrix\",\n \"path\": \"utils/metrics.py\",\n \"snippet\": \"class ConfusionMatrix:\\n # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix\\n def __init__(self, nc, conf=0.25, iou_thres=0.45):\\n self.matrix = np.zeros((nc + 1, nc + 1))\\n self.nc = nc # number of classes\\n self.conf = conf\\n self.iou_thres = iou_thres\\n\\n def process_batch(self, detections, labels):\\n \\\"\\\"\\\"\\n Return intersection-over-union (Jaccard index) of boxes.\\n Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\\n Arguments:\\n detections (Array[N, 6]), x1, y1, x2, y2, conf, class\\n labels (Array[M, 5]), class, x1, y1, x2, y2\\n Returns:\\n None, updates confusion matrix accordingly\\n \\\"\\\"\\\"\\n if detections is None:\\n gt_classes = labels.int()\\n for gc in gt_classes:\\n self.matrix[self.nc, gc] += 1 # background FN\\n return\\n\\n detections = detections[detections[:, 4] > self.conf]\\n gt_classes = labels[:, 0].int()\\n detection_classes = detections[:, 5].int()\\n iou = box_iou(labels[:, 1:], detections[:, :4])\\n\\n x = torch.where(iou > self.iou_thres)\\n if x[0].shape[0]:\\n matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()\\n if x[0].shape[0] > 1:\\n matches = matches[matches[:, 2].argsort()[::-1]]\\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\\n matches = matches[matches[:, 2].argsort()[::-1]]\\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\\n else:\\n matches = np.zeros((0, 3))\\n\\n n = matches.shape[0] > 0\\n m0, m1, _ = matches.transpose().astype(int)\\n for i, gc in enumerate(gt_classes):\\n j = m0 == i\\n if n and sum(j) == 1:\\n self.matrix[detection_classes[m1[j]], gc] += 1 # correct\\n else:\\n self.matrix[self.nc, gc] += 1 # true background\\n\\n if n:\\n for i, dc in enumerate(detection_classes):\\n if not any(m1 == i):\\n self.matrix[dc, self.nc] += 1 # predicted background\\n\\n def tp_fp(self):\\n tp = self.matrix.diagonal() # true positives\\n fp = self.matrix.sum(1) - tp # false positives\\n # fn = self.matrix.sum(0) - tp # false negatives (missed detections)\\n return tp[:-1], fp[:-1] # remove background class\\n\\n @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')\\n def plot(self, normalize=True, save_dir='', names=()):\\n import seaborn as sn\\n\\n array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns\\n array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)\\n\\n fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)\\n nc, nn = self.nc, len(names) # number of classes, names\\n sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size\\n labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels\\n ticklabels = (names + ['background']) if labels else \\\"auto\\\"\\n with warnings.catch_warnings():\\n warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered\\n sn.heatmap(array,\\n ax=ax,\\n annot=nc < 30,\\n annot_kws={\\n \\\"size\\\": 8},\\n cmap='Blues',\\n fmt='.2f',\\n square=True,\\n vmin=0.0,\\n xticklabels=ticklabels,\\n yticklabels=ticklabels).set_facecolor((1, 1, 1))\\n ax.set_ylabel('True')\\n ax.set_ylabel('Predicted')\\n ax.set_title('Confusion Matrix')\\n fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)\\n plt.close(fig)\\n\\n def print(self):\\n for i in range(self.nc + 1):\\n print(' '.join(map(str, self.matrix[i])))\"\n },\n {\n \"identifier\": \"box_iou\",\n \"path\": \"utils/metrics.py\",\n \"snippet\": \"def box_iou(box1, box2, eps=1e-7):\\n # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py\\n \\\"\\\"\\\"\\n Return intersection-over-union (Jaccard index) of boxes.\\n Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\\n Arguments:\\n box1 (Tensor[N, 4])\\n box2 (Tensor[M, 4])\\n Returns:\\n iou (Tensor[N, M]): the NxM matrix containing the pairwise\\n IoU values for every element in boxes1 and boxes2\\n \\\"\\\"\\\"\\n\\n # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)\\n (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)\\n inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)\\n\\n # IoU = inter / (area1 + area2 - inter)\\n return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)\"\n },\n {\n \"identifier\": \"output_to_target\",\n \"path\": \"utils/plots.py\",\n \"snippet\": \"def output_to_target(output, max_det=300):\\n # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting\\n targets = []\\n for i, o in enumerate(output):\\n box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)\\n j = torch.full((conf.shape[0], 1), i)\\n targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))\\n return torch.cat(targets, 0).numpy()\"\n },\n {\n \"identifier\": \"plot_val_study\",\n \"path\": \"utils/plots.py\",\n \"snippet\": \"def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study()\\n # Plot file=study.txt generated by val.py (or plot all study*.txt in dir)\\n save_dir = Path(file).parent if file else Path(dir)\\n plot2 = False # plot additional results\\n if plot2:\\n ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()\\n\\n fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)\\n # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:\\n for f in sorted(save_dir.glob('study*.txt')):\\n y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T\\n x = np.arange(y.shape[1]) if x is None else np.array(x)\\n if plot2:\\n s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']\\n for i in range(7):\\n ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)\\n ax[i].set_title(s[i])\\n\\n j = y[3].argmax() + 1\\n ax2.plot(y[5, 1:j],\\n y[3, 1:j] * 1E2,\\n '.-',\\n linewidth=2,\\n markersize=8,\\n label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))\\n\\n ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],\\n 'k.-',\\n linewidth=2,\\n markersize=8,\\n alpha=.25,\\n label='EfficientDet')\\n\\n ax2.grid(alpha=0.2)\\n ax2.set_yticks(np.arange(20, 60, 5))\\n ax2.set_xlim(0, 57)\\n ax2.set_ylim(25, 55)\\n ax2.set_xlabel('GPU Speed (ms/img)')\\n ax2.set_ylabel('COCO AP val')\\n ax2.legend(loc='lower right')\\n f = save_dir / 'study.png'\\n print(f'Saving {f}...')\\n plt.savefig(f, dpi=300)\"\n },\n {\n \"identifier\": \"create_dataloader\",\n \"path\": \"utils/segment/dataloaders.py\",\n \"snippet\": \"def create_dataloader(path,\\n imgsz,\\n batch_size,\\n stride,\\n single_cls=False,\\n hyp=None,\\n augment=False,\\n cache=False,\\n pad=0.0,\\n rect=False,\\n rank=-1,\\n workers=8,\\n image_weights=False,\\n quad=False,\\n prefix='',\\n shuffle=False,\\n mask_downsample_ratio=1,\\n overlap_mask=False):\\n if rect and shuffle:\\n LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False')\\n shuffle = False\\n with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP\\n dataset = LoadImagesAndLabelsAndMasks(\\n path,\\n imgsz,\\n batch_size,\\n augment=augment, # augmentation\\n hyp=hyp, # hyperparameters\\n rect=rect, # rectangular batches\\n cache_images=cache,\\n single_cls=single_cls,\\n stride=int(stride),\\n pad=pad,\\n image_weights=image_weights,\\n prefix=prefix,\\n downsample_ratio=mask_downsample_ratio,\\n overlap=overlap_mask)\\n\\n batch_size = min(batch_size, len(dataset))\\n nd = torch.cuda.device_count() # number of CUDA devices\\n nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers\\n sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)\\n loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates\\n generator = torch.Generator()\\n generator.manual_seed(6148914691236517205 + RANK)\\n return loader(\\n dataset,\\n batch_size=batch_size,\\n shuffle=shuffle and sampler is None,\\n num_workers=nw,\\n sampler=sampler,\\n pin_memory=True,\\n collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn,\\n worker_init_fn=seed_worker,\\n generator=generator,\\n ), dataset\"\n },\n {\n \"identifier\": \"mask_iou\",\n \"path\": \"utils/segment/general.py\",\n \"snippet\": \"def mask_iou(mask1, mask2, eps=1e-7):\\n \\\"\\\"\\\"\\n mask1: [N, n] m1 means number of predicted objects\\n mask2: [M, n] m2 means number of gt objects\\n Note: n means image_w x image_h\\n\\n return: masks iou, [N, M]\\n \\\"\\\"\\\"\\n intersection = torch.matmul(mask1, mask2.t()).clamp(0)\\n union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection\\n return intersection / (union + eps)\"\n },\n {\n \"identifier\": \"process_mask\",\n \"path\": \"utils/segment/general.py\",\n \"snippet\": \"def process_mask(protos, masks_in, bboxes, shape, upsample=False):\\n \\\"\\\"\\\"\\n Crop before upsample.\\n proto_out: [mask_dim, mask_h, mask_w]\\n out_masks: [n, mask_dim], n is number of masks after nms\\n bboxes: [n, 4], n is number of masks after nms\\n shape:input_image_size, (h, w)\\n\\n return: h, w, n\\n \\\"\\\"\\\"\\n\\n c, mh, mw = protos.shape # CHW\\n ih, iw = shape\\n masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW\\n\\n downsampled_bboxes = bboxes.clone()\\n downsampled_bboxes[:, 0] *= mw / iw\\n downsampled_bboxes[:, 2] *= mw / iw\\n downsampled_bboxes[:, 3] *= mh / ih\\n downsampled_bboxes[:, 1] *= mh / ih\\n\\n masks = crop_mask(masks, downsampled_bboxes) # CHW\\n if upsample:\\n masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW\\n return masks.gt_(0.5)\"\n },\n {\n \"identifier\": \"process_mask_upsample\",\n \"path\": \"utils/segment/general.py\",\n \"snippet\": \"def process_mask_upsample(protos, masks_in, bboxes, shape):\\n \\\"\\\"\\\"\\n Crop after upsample.\\n proto_out: [mask_dim, mask_h, mask_w]\\n out_masks: [n, mask_dim], n is number of masks after nms\\n bboxes: [n, 4], n is number of masks after nms\\n shape:input_image_size, (h, w)\\n\\n return: h, w, n\\n \\\"\\\"\\\"\\n\\n c, mh, mw = protos.shape # CHW\\n masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)\\n masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW\\n masks = crop_mask(masks, bboxes) # CHW\\n return masks.gt_(0.5)\"\n },\n {\n \"identifier\": \"scale_image\",\n \"path\": \"utils/segment/general.py\",\n \"snippet\": \"def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):\\n \\\"\\\"\\\"\\n img1_shape: model input shape, [h, w]\\n img0_shape: origin pic shape, [h, w, 3]\\n masks: [h, w, num]\\n \\\"\\\"\\\"\\n # Rescale coordinates (xyxy) from im1_shape to im0_shape\\n if ratio_pad is None: # calculate from im0_shape\\n gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new\\n pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding\\n else:\\n pad = ratio_pad[1]\\n top, left = int(pad[1]), int(pad[0]) # y, x\\n bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])\\n\\n if len(masks.shape) < 2:\\n raise ValueError(f'\\\"len of masks shape\\\" should be 2 or 3, but got {len(masks.shape)}')\\n masks = masks[top:bottom, left:right]\\n # masks = masks.permute(2, 0, 1).contiguous()\\n # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]\\n # masks = masks.permute(1, 2, 0).contiguous()\\n masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))\\n\\n if len(masks.shape) == 2:\\n masks = masks[:, :, None]\\n return masks\"\n },\n {\n \"identifier\": \"Metrics\",\n \"path\": \"utils/segment/metrics.py\",\n \"snippet\": \"class Metrics:\\n \\\"\\\"\\\"Metric for boxes and masks.\\\"\\\"\\\"\\n\\n def __init__(self) -> None:\\n self.metric_box = Metric()\\n self.metric_mask = Metric()\\n\\n def update(self, results):\\n \\\"\\\"\\\"\\n Args:\\n results: Dict{'boxes': Dict{}, 'masks': Dict{}}\\n \\\"\\\"\\\"\\n self.metric_box.update(list(results[\\\"boxes\\\"].values()))\\n self.metric_mask.update(list(results[\\\"masks\\\"].values()))\\n\\n def mean_results(self):\\n return self.metric_box.mean_results() + self.metric_mask.mean_results()\\n\\n def class_result(self, i):\\n return self.metric_box.class_result(i) + self.metric_mask.class_result(i)\\n\\n def get_maps(self, nc):\\n return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)\\n\\n @property\\n def ap_class_index(self):\\n # boxes and masks have the same ap_class_index\\n return self.metric_box.ap_class_index\"\n },\n {\n \"identifier\": \"ap_per_class_box_and_mask\",\n \"path\": \"utils/segment/metrics.py\",\n \"snippet\": \"def ap_per_class_box_and_mask(\\n tp_m,\\n tp_b,\\n conf,\\n pred_cls,\\n target_cls,\\n plot=False,\\n save_dir=\\\".\\\",\\n names=(),\\n):\\n \\\"\\\"\\\"\\n Args:\\n tp_b: tp of boxes.\\n tp_m: tp of masks.\\n other arguments see `func: ap_per_class`.\\n \\\"\\\"\\\"\\n results_boxes = ap_per_class(tp_b,\\n conf,\\n pred_cls,\\n target_cls,\\n plot=plot,\\n save_dir=save_dir,\\n names=names,\\n prefix=\\\"Box\\\")[2:]\\n results_masks = ap_per_class(tp_m,\\n conf,\\n pred_cls,\\n target_cls,\\n plot=plot,\\n save_dir=save_dir,\\n names=names,\\n prefix=\\\"Mask\\\")[2:]\\n\\n results = {\\n \\\"boxes\\\": {\\n \\\"p\\\": results_boxes[0],\\n \\\"r\\\": results_boxes[1],\\n \\\"ap\\\": results_boxes[3],\\n \\\"f1\\\": results_boxes[2],\\n \\\"ap_class\\\": results_boxes[4]},\\n \\\"masks\\\": {\\n \\\"p\\\": results_masks[0],\\n \\\"r\\\": results_masks[1],\\n \\\"ap\\\": results_masks[3],\\n \\\"f1\\\": results_masks[2],\\n \\\"ap_class\\\": results_masks[4]}}\\n return results\"\n },\n {\n \"identifier\": \"plot_images_and_masks\",\n \"path\": \"utils/segment/plots.py\",\n \"snippet\": \"@threaded\\ndef plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None):\\n # Plot image grid with labels\\n if isinstance(images, torch.Tensor):\\n images = images.cpu().float().numpy()\\n if isinstance(targets, torch.Tensor):\\n targets = targets.cpu().numpy()\\n if isinstance(masks, torch.Tensor):\\n masks = masks.cpu().numpy().astype(int)\\n\\n max_size = 1920 # max image size\\n max_subplots = 16 # max image subplots, i.e. 4x4\\n bs, _, h, w = images.shape # batch size, _, height, width\\n bs = min(bs, max_subplots) # limit plot images\\n ns = np.ceil(bs ** 0.5) # number of subplots (square)\\n if np.max(images[0]) <= 1:\\n images *= 255 # de-normalise (optional)\\n\\n # Build Image\\n mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init\\n for i, im in enumerate(images):\\n if i == max_subplots: # if last batch has fewer images than we expect\\n break\\n x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin\\n im = im.transpose(1, 2, 0)\\n mosaic[y:y + h, x:x + w, :] = im\\n\\n # Resize (optional)\\n scale = max_size / ns / max(h, w)\\n if scale < 1:\\n h = math.ceil(scale * h)\\n w = math.ceil(scale * w)\\n mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))\\n\\n # Annotate\\n fs = int((h + w) * ns * 0.01) # font size\\n annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)\\n for i in range(i + 1):\\n x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin\\n annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders\\n if paths:\\n annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames\\n if len(targets) > 0:\\n idx = targets[:, 0] == i\\n ti = targets[idx] # image targets\\n\\n boxes = xywh2xyxy(ti[:, 2:6]).T\\n classes = ti[:, 1].astype('int')\\n labels = ti.shape[1] == 6 # labels if no conf column\\n conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)\\n\\n if boxes.shape[1]:\\n if boxes.max() <= 1.01: # if normalized with tolerance 0.01\\n boxes[[0, 2]] *= w # scale to pixels\\n boxes[[1, 3]] *= h\\n elif scale < 1: # absolute coords need scale if image scales\\n boxes *= scale\\n boxes[[0, 2]] += x\\n boxes[[1, 3]] += y\\n for j, box in enumerate(boxes.T.tolist()):\\n cls = classes[j]\\n color = colors(cls)\\n cls = names[cls] if names else cls\\n if labels or conf[j] > 0.25: # 0.25 conf thresh\\n label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'\\n annotator.box_label(box, label, color=color)\\n\\n # Plot masks\\n if len(masks):\\n if masks.max() > 1.0: # mean that masks are overlap\\n image_masks = masks[[i]] # (1, 640, 640)\\n nl = len(ti)\\n index = np.arange(nl).reshape(nl, 1, 1) + 1\\n image_masks = np.repeat(image_masks, nl, axis=0)\\n image_masks = np.where(image_masks == index, 1.0, 0.0)\\n else:\\n image_masks = masks[idx]\\n\\n im = np.asarray(annotator.im).copy()\\n for j, box in enumerate(boxes.T.tolist()):\\n if labels or conf[j] > 0.25: # 0.25 conf thresh\\n color = colors(classes[j])\\n mh, mw = image_masks[j].shape\\n if mh != h or mw != w:\\n mask = image_masks[j].astype(np.uint8)\\n mask = cv2.resize(mask, (w, h))\\n mask = mask.astype(bool)\\n else:\\n mask = image_masks[j].astype(bool)\\n with contextlib.suppress(Exception):\\n im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6\\n annotator.fromarray(im)\\n annotator.im.save(fname) # save\"\n },\n {\n \"identifier\": \"de_parallel\",\n \"path\": \"utils/torch_utils.py\",\n \"snippet\": \"def de_parallel(model):\\n # De-parallelize a model: returns single-GPU model if model is of type DP or DDP\\n return model.module if is_parallel(model) else model\"\n },\n {\n \"identifier\": \"select_device\",\n \"path\": \"utils/torch_utils.py\",\n \"snippet\": \"def select_device(device='', batch_size=0, newline=True):\\n # device = None or 'cpu' or 0 or '0' or '0,1,2,3'\\n s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '\\n device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0'\\n cpu = device == 'cpu'\\n mps = device == 'mps' # Apple Metal Performance Shaders (MPS)\\n if cpu or mps:\\n os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False\\n elif device: # non-cpu device requested\\n os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()\\n assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \\\\\\n f\\\"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)\\\"\\n\\n if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available\\n devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7\\n n = len(devices) # device count\\n if n > 1 and batch_size > 0: # check batch_size is divisible by device_count\\n assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'\\n space = ' ' * (len(s) + 1)\\n for i, d in enumerate(devices):\\n p = torch.cuda.get_device_properties(i)\\n s += f\\\"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\\\\n\\\" # bytes to MB\\n arg = 'cuda:0'\\n elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available\\n s += 'MPS\\\\n'\\n arg = 'mps'\\n else: # revert to CPU\\n s += 'CPU\\\\n'\\n arg = 'cpu'\\n\\n if not newline:\\n s = s.rstrip()\\n LOGGER.info(s)\\n return torch.device(arg)\"\n },\n {\n \"identifier\": \"smart_inference_mode\",\n \"path\": \"utils/torch_utils.py\",\n \"snippet\": \"def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):\\n # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator\\n def decorate(fn):\\n return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)\\n\\n return decorate\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import argparse\nimport json\nimport os\nimport sys\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\n import time\nfrom multiprocessing.pool import ThreadPool\nfrom pathlib import Path\nfrom tqdm import tqdm\nfrom models.common import DetectMultiBackend\nfrom models.yolo import SegmentationModel\nfrom utils.callbacks import Callbacks\nfrom utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size,\n check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path,\n non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh)\nfrom utils.metrics import ConfusionMatrix, box_iou\nfrom utils.plots import output_to_target, plot_val_study\nfrom utils.segment.dataloaders import create_dataloader\nfrom utils.segment.general import mask_iou, process_mask, process_mask_upsample, scale_image\nfrom utils.segment.metrics import Metrics, ap_per_class_box_and_mask\nfrom utils.segment.plots import plot_images_and_masks\nfrom utils.torch_utils import de_parallel, select_device, smart_inference_mode\n from pycocotools.mask import encode\n from pycocotools.coco import COCO\n from pycocotools.cocoeval import COCOeval"},"token_num":{"kind":"number","value":21426,"string":"21,426"},"cropped_code":{"kind":"string","value":" labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels\n correct_bboxes = process_batch(predn, labelsn, iouv)\n correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)\n if plots:\n confusion_matrix.process_batch(predn, labelsn)\n stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)\n\n pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)\n if plots and batch_i < 3:\n plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot\n\n # Save/log\n if save_txt:\n save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')\n if save_json:\n pred_masks = scale_image(im[si].shape[1:],\n pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1])\n save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary\n # callbacks.run('on_val_image_end', pred, predn, path, names, im[si])\n\n # Plot images\n if plots and batch_i < 3:\n if len(plot_masks):\n plot_masks = torch.cat(plot_masks, dim=0)\n plot_images_and_masks(im, targets, masks, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names)\n plot_images_and_masks(im, output_to_target(preds, max_det=15), plot_masks, paths,\n save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred\n\n # callbacks.run('on_val_batch_end')\n\n # Compute metrics\n stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy\n if len(stats) and stats[0].any():\n results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names)\n metrics.update(results)\n nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class\n\n # Print results\n pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format\n LOGGER.info(pf % (\"all\", seen, nt.sum(), *metrics.mean_results()))\n if nt.sum() == 0:\n LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels')\n\n # Print results per class\n if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):\n for i, c in enumerate(metrics.ap_class_index):\n LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i)))\n\n # Print speeds\n t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image\n if not training:\n shape = (batch_size, 3, imgsz, imgsz)\n LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)\n\n # Plots\n if plots:\n confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))\n # callbacks.run('on_val_end')\n\n mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results()\n\n # Save JSON\n if save_json and len(jdict):\n w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights\n anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json\n pred_json = str(save_dir / f\"{w}_predictions.json\") # predictions json\n LOGGER.info(f'\\nEvaluating pycocotools mAP... saving {pred_json}...')\n with open(pred_json, 'w') as f:\n json.dump(jdict, f)\n\n try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb\n\n anno = COCO(anno_json) # init annotations api\n pred = anno.loadRes(pred_json) # init predictions api\n results = []\n for eval in COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm'):\n if is_coco:\n eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate\n eval.evaluate()\n eval.accumulate()\n eval.summarize()\n results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5)\n map_bbox, map50_bbox, map_mask, map50_mask = results\n except Exception as e:\n LOGGER.info(f'pycocotools unable to run: {e}')\n\n # Return results\n model.float() # for training\n if not training:\n s = f\"\\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}\" if save_txt else ''\n LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}{s}\")\n final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask\n return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t\n\n\ndef parse_opt():\n parser = argparse.ArgumentParser()\n parser.add_argument('--data', type=str, default=ROOT / 'data/BCC.yaml', help='dataset.yaml path')\n parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs_2/train-seg/base/weights/best.pt', help='model path(s)')\n parser.add_argument('--batch-size', type=int, default=1, help='batch size')\n parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')\n parser.add_argument('--conf-thres', type=float, default=0.01, help='confidence threshold')\n parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')\n parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image')\n parser.add_argument('--task', default='val', help='train, val, test, speed or study')\n parser.add_argument('--device', default='6', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')\n parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')\n parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')\n parser.add_argument('--augment', action='store_true', help='augmented inference')\n parser.add_argument('--verbose', action='store_true', help='report mAP by class')\n parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')\n parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')\n parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')\n parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')\n parser.add_argument('--project', default=ROOT / 'runs_2/val_test', help='save results to project/name')\n parser.add_argument('--name', default='base', help='save to project/name')\n parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')\n parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')\n parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')\n opt = parser.parse_args()\n"},"all_code":{"kind":"string","value":"# YOLOv5 🚀 by Ultralytics, GPL-3.0 license\n\"\"\"\nValidate a trained YOLOv5 segment model on a segment dataset\n\nUsage:\n $ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images)\n $ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments\n\nUsage - formats:\n $ python segment/val.py --weights yolov5s-seg.pt # PyTorch\n yolov5s-seg.torchscript # TorchScript\n yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn\n yolov5s-seg_openvino_label # OpenVINO\n yolov5s-seg.engine # TensorRT\n yolov5s-seg.mlmodel # CoreML (macOS-only)\n yolov5s-seg_saved_model # TensorFlow SavedModel\n yolov5s-seg.pb # TensorFlow GraphDef\n yolov5s-seg.tflite # TensorFlow Lite\n yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU\n yolov5s-seg_paddle_model # PaddlePaddle\n\"\"\"\n\n\n\nFILE = Path(__file__).resolve()\nROOT = FILE.parents[1] # YOLOv5 root directory\nif str(ROOT) not in sys.path:\n sys.path.append(str(ROOT)) # add ROOT to PATH\nROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative\n\n\n\n\ndef save_one_txt(predn, save_conf, shape, file):\n # Save one txt result\n gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh\n for *xyxy, conf, cls in predn.tolist():\n xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh\n line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format\n with open(file, 'a') as f:\n f.write(('%g ' * len(line)).rstrip() % line + '\\n')\n\n\ndef save_one_json(predn, jdict, path, class_map, pred_masks):\n # Save one JSON result {\"image_id\": 42, \"category_id\": 18, \"bbox\": [258.15, 41.29, 348.26, 243.78], \"score\": 0.236}\n\n def single_encode(x):\n rle = encode(np.asarray(x[:, :, None], order=\"F\", dtype=\"uint8\"))[0]\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\")\n return rle\n\n image_id = int(path.stem) if path.stem.isnumeric() else path.stem\n box = xyxy2xywh(predn[:, :4]) # xywh\n box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner\n pred_masks = np.transpose(pred_masks, (2, 0, 1))\n with ThreadPool(NUM_THREADS) as pool:\n rles = pool.map(single_encode, pred_masks)\n for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())):\n jdict.append({\n 'image_id': image_id,\n 'category_id': class_map[int(p[5])],\n 'bbox': [round(x, 3) for x in b],\n 'score': round(p[4], 5),\n 'segmentation': rles[i]})\n\n\ndef process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):\n \"\"\"\n Return correct prediction matrix\n Arguments:\n detections (array[N, 6]), x1, y1, x2, y2, conf, class\n labels (array[M, 5]), class, x1, y1, x2, y2\n Returns:\n correct (array[N, 10]), for 10 IoU levels\n \"\"\"\n if masks:\n if overlap:\n nl = len(labels)\n index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1\n gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)\n gt_masks = torch.where(gt_masks == index, 1.0, 0.0)\n if gt_masks.shape[1:] != pred_masks.shape[1:]:\n gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode=\"bilinear\", align_corners=False)[0]\n gt_masks = gt_masks.gt_(0.5)\n iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))\n else: # boxes\n iou = box_iou(labels[:, 1:], detections[:, :4])\n\n correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)\n correct_class = labels[:, 0:1] == detections[:, 5]\n for i in range(len(iouv)):\n x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match\n if x[0].shape[0]:\n matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]\n if x[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n # matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n correct[matches[:, 1].astype(int), i] = True\n return torch.tensor(correct, dtype=torch.bool, device=iouv.device)\n\n\n@smart_inference_mode()\ndef run(\n data,\n weights=None, # model.pt path(s)\n batch_size=32, # batch size\n imgsz=640, # inference size (pixels)\n conf_thres=0.001, # confidence threshold\n iou_thres=0.6, # NMS IoU threshold\n max_det=300, # maximum detections per image\n task='val', # train, val, test, speed or study\n device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu\n workers=8, # max dataloader workers (per RANK in DDP mode)\n single_cls=False, # treat as single-class dataset\n augment=False, # augmented inference\n verbose=False, # verbose output\n save_txt=False, # save results to *.txt\n save_hybrid=False, # save label+prediction hybrid results to *.txt\n save_conf=False, # save confidences in --save-txt labels\n save_json=False, # save a COCO-JSON results file\n project=ROOT / 'runs/val-seg', # save to project/name\n name='exp', # save to project/name\n exist_ok=False, # existing project/name ok, do not increment\n half=True, # use FP16 half-precision inference\n dnn=False, # use OpenCV DNN for ONNX inference\n model=None,\n dataloader=None,\n save_dir=Path(''),\n plots=True,\n overlap=False,\n mask_downsample_ratio=1,\n compute_loss=None,\n callbacks=Callbacks(),\n):\n if save_json:\n check_requirements(['pycocotools'])\n process = process_mask_upsample # more accurate\n else:\n process = process_mask # faster\n\n # Initialize/load model and set device\n training = model is not None\n if training: # called by train.py\n device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model\n half &= device.type != 'cpu' # half precision only supported on CUDA\n model.half() if half else model.float()\n nm = de_parallel(model).model[-1].nm # number of masks\n else: # called directly\n device = select_device(device, batch_size=batch_size)\n\n # Directories\n save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run\n (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir\n\n # Load model\n model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)\n stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine\n imgsz = check_img_size(imgsz, s=stride) # check image size\n half = model.fp16 # FP16 supported on limited backends with CUDA\n nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks\n if engine:\n batch_size = model.batch_size\n else:\n device = model.device\n if not (pt or jit):\n batch_size = 1 # export.py models default to batch-size 1\n LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')\n\n # Data\n data = check_dataset(data) # check\n\n # Configure\n model.eval()\n cuda = device.type != 'cpu'\n is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset\n nc = 1 if single_cls else int(data['nc']) # number of classes\n iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95\n niou = iouv.numel()\n\n # Dataloader\n if not training:\n if pt and not single_cls: # check --weights are trained on --data\n ncm = model.model.nc\n assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \\\n f'classes). Pass correct combination of --weights and --data that are trained together.'\n model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup\n pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks\n task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images\n dataloader = create_dataloader(data[task],\n imgsz,\n batch_size,\n stride,\n single_cls,\n pad=pad,\n rect=rect,\n workers=workers,\n prefix=colorstr(f'{task}: '),\n overlap_mask=overlap,\n mask_downsample_ratio=mask_downsample_ratio)[0]\n\n seen = 0\n confusion_matrix = ConfusionMatrix(nc=nc)\n names = model.names if hasattr(model, 'names') else model.module.names # get class names\n if isinstance(names, (list, tuple)): # old format\n names = dict(enumerate(names))\n class_map = coco80_to_coco91_class() if is_coco else list(range(1000))\n s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', \"R\", \"mAP50\", \"mAP50-95)\", \"Mask(P\", \"R\",\n \"mAP50\", \"mAP50-95)\")\n dt = Profile(), Profile(), Profile()\n metrics = Metrics()\n loss = torch.zeros(4, device=device)\n jdict, stats = [], []\n # callbacks.run('on_val_start')\n pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar\n for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar):\n # callbacks.run('on_val_batch_start')\n with dt[0]:\n if cuda:\n im = im.to(device, non_blocking=True)\n targets = targets.to(device)\n masks = masks.to(device)\n masks = masks.float()\n im = im.half() if half else im.float() # uint8 to fp16/32\n im /= 255 # 0 - 255 to 0.0 - 1.0\n nb, _, height, width = im.shape # batch size, channels, height, width\n\n # Inference\n with dt[1]:\n act = time.time()\n preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None)\n #print('time.time():',time.time()-act)\n\n # Loss\n if compute_loss:\n loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls\n\n # NMS\n targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels\n lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling\n with dt[2]:\n preds = non_max_suppression(preds,\n conf_thres,\n iou_thres,\n labels=lb,\n multi_label=True,\n agnostic=single_cls,\n max_det=max_det,\n nm=nm)\n\n # Metrics\n plot_masks = [] # masks for plotting\n for si, (pred, proto) in enumerate(zip(preds, protos)):\n labels = targets[targets[:, 0] == si, 1:]\n nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions\n path, shape = Path(paths[si]), shapes[si][0]\n correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init\n correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init\n seen += 1\n\n if npr == 0:\n if nl:\n stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0]))\n if plots:\n confusion_matrix.process_batch(detections=None, labels=labels[:, 0])\n continue\n\n # Masks\n midx = [si] if overlap else targets[:, 0] == si\n gt_masks = masks[midx]\n pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:])\n\n # Predictions\n if single_cls:\n pred[:, 5] = 0\n predn = pred.clone()\n scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred\n\n # Evaluate\n if nl:\n tbox = xywh2xyxy(labels[:, 1:5]) # target boxes\n scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels\n labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels\n correct_bboxes = process_batch(predn, labelsn, iouv)\n correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)\n if plots:\n confusion_matrix.process_batch(predn, labelsn)\n stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)\n\n pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)\n if plots and batch_i < 3:\n plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot\n\n # Save/log\n if save_txt:\n save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')\n if save_json:\n pred_masks = scale_image(im[si].shape[1:],\n pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1])\n save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary\n # callbacks.run('on_val_image_end', pred, predn, path, names, im[si])\n\n # Plot images\n if plots and batch_i < 3:\n if len(plot_masks):\n plot_masks = torch.cat(plot_masks, dim=0)\n plot_images_and_masks(im, targets, masks, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names)\n plot_images_and_masks(im, output_to_target(preds, max_det=15), plot_masks, paths,\n save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred\n\n # callbacks.run('on_val_batch_end')\n\n # Compute metrics\n stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy\n if len(stats) and stats[0].any():\n results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names)\n metrics.update(results)\n nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class\n\n # Print results\n pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format\n LOGGER.info(pf % (\"all\", seen, nt.sum(), *metrics.mean_results()))\n if nt.sum() == 0:\n LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels')\n\n # Print results per class\n if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):\n for i, c in enumerate(metrics.ap_class_index):\n LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i)))\n\n # Print speeds\n t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image\n if not training:\n shape = (batch_size, 3, imgsz, imgsz)\n LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)\n\n # Plots\n if plots:\n confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))\n # callbacks.run('on_val_end')\n\n mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results()\n\n # Save JSON\n if save_json and len(jdict):\n w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights\n anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json\n pred_json = str(save_dir / f\"{w}_predictions.json\") # predictions json\n LOGGER.info(f'\\nEvaluating pycocotools mAP... saving {pred_json}...')\n with open(pred_json, 'w') as f:\n json.dump(jdict, f)\n\n try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb\n\n anno = COCO(anno_json) # init annotations api\n pred = anno.loadRes(pred_json) # init predictions api\n results = []\n for eval in COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm'):\n if is_coco:\n eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate\n eval.evaluate()\n eval.accumulate()\n eval.summarize()\n results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5)\n map_bbox, map50_bbox, map_mask, map50_mask = results\n except Exception as e:\n LOGGER.info(f'pycocotools unable to run: {e}')\n\n # Return results\n model.float() # for training\n if not training:\n s = f\"\\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}\" if save_txt else ''\n LOGGER.info(f\"Results saved to {colorstr('bold', save_dir)}{s}\")\n final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask\n return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t\n\n\ndef parse_opt():\n parser = argparse.ArgumentParser()\n parser.add_argument('--data', type=str, default=ROOT / 'data/BCC.yaml', help='dataset.yaml path')\n parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs_2/train-seg/base/weights/best.pt', help='model path(s)')\n parser.add_argument('--batch-size', type=int, default=1, help='batch size')\n parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')\n parser.add_argument('--conf-thres', type=float, default=0.01, help='confidence threshold')\n parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')\n parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image')\n parser.add_argument('--task', default='val', help='train, val, test, speed or study')\n parser.add_argument('--device', default='6', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')\n parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')\n parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')\n parser.add_argument('--augment', action='store_true', help='augmented inference')\n parser.add_argument('--verbose', action='store_true', help='report mAP by class')\n parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')\n parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')\n parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')\n parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')\n parser.add_argument('--project', default=ROOT / 'runs_2/val_test', help='save results to project/name')\n parser.add_argument('--name', default='base', help='save to project/name')\n parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')\n parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')\n parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')\n opt = parser.parse_args()"},"next_line":{"kind":"string","value":" opt.data = check_yaml(opt.data) # check YAML"},"gold_snippet_index":{"kind":"number","value":10,"string":"10"},"created_at":{"kind":"string","value":"2023-12-10 14:18:29+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5292,"cells":{"repo_name":{"kind":"string","value":"youngskkim/CRN"},"file_path":{"kind":"string","value":"exps/base_exp.py"},"context":{"kind":"list like","value":[{"identifier":"NuscDatasetRadarDet","path":"datasets/nusc_det_dataset.py","snippet":"class NuscDatasetRadarDet(Dataset):\n def __init__(self,\n ida_aug_conf,\n bda_aug_conf,\n rda_aug_conf,\n classes,\n data_root,\n info_paths,\n is_train,\n load_interval=1,\n num_sweeps=1,\n img_conf=dict(img_mean=[123.675, 116.28, 103.53],\n img_std=[58.395, 57.12, 57.375],\n to_rgb=True),\n img_backbone_conf=dict(\n x_bound=[-51.2, 51.2, 0.8],\n y_bound=[-51.2, 51.2, 0.8],\n z_bound=[-5, 3, 8],\n d_bound=[2.0, 58.0, 0.5]\n ),\n drop_aug_conf=None,\n return_image=True,\n return_depth=False,\n return_radar_pv=False,\n depth_path='depth_gt',\n radar_pv_path='radar_pv_filter',\n remove_z_axis=False,\n use_cbgs=False,\n gt_for_radar_only=False,\n sweep_idxes=list(),\n key_idxes=list()):\n \"\"\"Dataset used for bevdetection task.\n Args:\n ida_aug_conf (dict): Config for ida augmentation.\n bda_aug_conf (dict): Config for bda augmentation.\n classes (list): Class names.\n use_cbgs (bool): Whether to use cbgs strategy,\n Default: False.\n num_sweeps (int): Number of sweeps to be used for each sample.\n default: 1.\n img_conf (dict): Config for image.\n return_depth (bool): Whether to use depth gt.\n default: False.\n sweep_idxes (list): List of sweep idxes to be used.\n default: list().\n key_idxes (list): List of key idxes to be used.\n default: list().\n \"\"\"\n super().__init__()\n if isinstance(info_paths, list):\n self.infos = list()\n for info_path in info_paths:\n self.infos.extend(mmcv.load(info_path))\n else:\n self.infos = mmcv.load(info_paths)\n self.is_train = is_train\n self.ida_aug_conf = ida_aug_conf\n self.bda_aug_conf = bda_aug_conf\n self.rda_aug_conf = rda_aug_conf\n self.drop_aug_conf = drop_aug_conf\n self.data_root = data_root\n self.classes = classes\n self.use_cbgs = use_cbgs\n if self.use_cbgs:\n self.cat2id = {name: i for i, name in enumerate(self.classes)}\n self.sample_indices = self._get_sample_indices()\n self.num_sweeps = num_sweeps\n self.img_mean = np.array(img_conf['img_mean'], np.float32)\n self.img_std = np.array(img_conf['img_std'], np.float32)\n self.to_rgb = img_conf['to_rgb']\n self.img_backbone_conf = img_backbone_conf\n\n self.return_image = return_image\n self.return_depth = return_depth\n self.return_radar_pv = return_radar_pv\n\n self.remove_z_axis = remove_z_axis\n self.gt_for_radar_only = gt_for_radar_only\n\n assert sum([sweep_idx >= 0 for sweep_idx in sweep_idxes]) \\\n == len(sweep_idxes), 'All `sweep_idxes` must greater \\\n than or equal to 0.'\n\n self.sweeps_idx = sweep_idxes\n assert sum([key_idx < 0 for key_idx in key_idxes]) == len(key_idxes),\\\n 'All `key_idxes` must less than 0.'\n self.key_idxes = [0] + key_idxes\n if load_interval > 1:\n self.infos = self.infos[::load_interval]\n self.depth_path = depth_path\n self.radar_pv_path = radar_pv_path\n\n self.max_radar_points_pv = 1536\n self.max_distance_pv = self.img_backbone_conf['d_bound'][1]\n\n def _get_sample_indices(self):\n \"\"\"Load annotations from ann_file.\n\n Args:\n ann_file (str): Path of the annotation file.\n\n Returns:\n list[dict]: List of annotations after class sampling.\n \"\"\"\n class_sample_idxs = {cat_id: [] for cat_id in self.cat2id.values()}\n for idx, info in enumerate(self.infos):\n gt_names = set(\n [ann_info['category_name'] for ann_info in info['ann_infos']])\n for gt_name in gt_names:\n gt_name = map_name_from_general_to_detection[gt_name]\n if gt_name not in self.classes:\n continue\n class_sample_idxs[self.cat2id[gt_name]].append(idx)\n duplicated_samples = sum(\n [len(v) for _, v in class_sample_idxs.items()])\n class_distribution = {\n k: len(v) / duplicated_samples\n for k, v in class_sample_idxs.items()\n }\n\n sample_indices = []\n\n frac = 1.0 / len(self.classes)\n ratios = [frac / v for v in class_distribution.values()]\n for cls_inds, ratio in zip(list(class_sample_idxs.values()), ratios):\n sample_indices += np.random.choice(cls_inds,\n int(len(cls_inds) *\n ratio)).tolist()\n return sample_indices\n\n def sample_ida_augmentation(self):\n \"\"\"Generate ida augmentation values based on ida_config.\"\"\"\n H, W = self.ida_aug_conf['H'], self.ida_aug_conf['W']\n fH, fW = self.ida_aug_conf['final_dim']\n if self.is_train:\n resize = np.random.uniform(*self.ida_aug_conf['resize_lim'])\n resize_dims = (int(W * resize), int(H * resize))\n newW, newH = resize_dims\n crop_h = int(\n (1 - np.random.uniform(*self.ida_aug_conf['bot_pct_lim'])) *\n newH) - fH\n crop_w = int(np.random.uniform(0, max(0, newW - fW)))\n crop = (crop_w, crop_h, crop_w + fW, crop_h + fH)\n flip = False\n if self.ida_aug_conf['rand_flip'] and np.random.choice([0, 1]):\n flip = True\n rotate_ida = np.random.uniform(*self.ida_aug_conf['rot_lim'])\n else:\n resize = max(fH / H, fW / W)\n resize_dims = (int(W * resize), int(H * resize))\n newW, newH = resize_dims\n crop_h = int(\n (1 - np.mean(self.ida_aug_conf['bot_pct_lim'])) * newH) - fH\n crop_w = int(max(0, newW - fW) / 2)\n crop = (crop_w, crop_h, crop_w + fW, crop_h + fH)\n flip = False\n rotate_ida = 0\n return resize, resize_dims, crop, flip, rotate_ida\n\n def sample_bda_augmentation(self):\n \"\"\"Generate bda augmentation values based on bda_config.\"\"\"\n if self.is_train:\n if np.random.uniform() < self.bda_aug_conf['rot_ratio']:\n rotate_bda = np.random.uniform(*self.bda_aug_conf['rot_lim'])\n else:\n rotate_bda = 0\n scale_bda = np.random.uniform(*self.bda_aug_conf['scale_lim'])\n flip_dx = np.random.uniform() < self.bda_aug_conf['flip_dx_ratio']\n flip_dy = np.random.uniform() < self.bda_aug_conf['flip_dy_ratio']\n else:\n rotate_bda = 0\n scale_bda = 1.0\n flip_dx = False\n flip_dy = False\n return rotate_bda, scale_bda, flip_dx, flip_dy\n\n def sample_radar_augmentation(self):\n \"\"\"Generate bda augmentation values based on bda_config.\"\"\"\n if self.is_train:\n radar_idx = np.random.choice(self.rda_aug_conf['N_sweeps'],\n self.rda_aug_conf['N_use'],\n replace=False)\n else:\n radar_idx = np.arange(self.rda_aug_conf['N_sweeps'])\n return radar_idx\n\n def transform_radar_pv(self, points, resize, resize_dims, crop, flip, rotate, radar_idx):\n points = points[points[:, 2] < self.max_distance_pv, :]\n\n H, W = resize_dims\n points[:, :2] = points[:, :2] * resize\n points[:, 0] -= crop[0]\n points[:, 1] -= crop[1]\n if flip:\n points[:, 0] = resize_dims[1] - points[:, 0]\n\n points[:, 0] -= W / 2.0\n points[:, 1] -= H / 2.0\n\n h = rotate / 180 * np.pi\n rot_matrix = [\n [np.cos(h), np.sin(h)],\n [-np.sin(h), np.cos(h)],\n ]\n points[:, :2] = np.matmul(rot_matrix, points[:, :2].T).T\n\n points[:, 0] += W / 2.0\n points[:, 1] += H / 2.0\n\n depth_coords = points[:, :2].astype(np.int16)\n\n valid_mask = ((depth_coords[:, 1] < resize_dims[0])\n & (depth_coords[:, 0] < resize_dims[1])\n & (depth_coords[:, 1] >= 0)\n & (depth_coords[:, 0] >= 0))\n\n points = torch.Tensor(points[valid_mask])\n\n if self.remove_z_axis:\n points[:, 1] = 1. # dummy height value\n\n points_save = []\n for i in radar_idx:\n points_save.append(points[points[:, 6] == i])\n points = torch.cat(points_save, dim=0)\n\n # mean, std of rcs and speed are from train set\n points[:, 3] = (points[:, 3] - 4.783) / 7.576\n points[:, 4] = (torch.norm(points[:, 4:6], dim=1) - 0.677) / 1.976\n\n if self.is_train:\n drop_idx = np.random.uniform(size=points.shape[0]) # randomly drop points\n points = points[drop_idx > self.rda_aug_conf['drop_ratio']]\n\n num_points, num_feat = points.shape\n if num_points > self.max_radar_points_pv:\n choices = np.random.choice(num_points, self.max_radar_points_pv, replace=False)\n points = points[choices]\n else:\n num_append = self.max_radar_points_pv - num_points\n points = torch.cat([points, -999*torch.ones(num_append, num_feat)], dim=0)\n\n if num_points == 0:\n points[0, :] = points.new_tensor([0.1, 0.1, self.max_distance_pv-1, 0, 0, 0, 0])\n\n points[..., [0, 1, 2]] = points[..., [0, 2, 1]] # convert [w, h, d] to [w, d, h]\n\n return points[..., :5]\n\n def depth_transform(self, cam_depth, resize, resize_dims, crop, flip, rotate):\n \"\"\"Transform depth based on ida augmentation configuration.\n\n Args:\n cam_depth (np array): Nx3, 3: x,y,d.\n resize (float): Resize factor.\n resize_dims (tuple): Final dimension.\n crop (tuple): x1, y1, x2, y2\n flip (bool): Whether to flip.\n rotate (float): Rotation value.\n\n Returns:\n np array: [h/down_ratio, w/down_ratio, d]\n \"\"\"\n valid_depth = cam_depth[:, 2] < self.img_backbone_conf['d_bound'][1]\n cam_depth = cam_depth[valid_depth, :]\n\n H, W = resize_dims\n cam_depth[:, :2] = cam_depth[:, :2] * resize\n cam_depth[:, 0] -= crop[0]\n cam_depth[:, 1] -= crop[1]\n if flip:\n cam_depth[:, 0] = resize_dims[1] - cam_depth[:, 0]\n\n cam_depth[:, 0] -= W / 2.0\n cam_depth[:, 1] -= H / 2.0\n\n h = rotate / 180 * np.pi\n rot_matrix = [\n [np.cos(h), np.sin(h)],\n [-np.sin(h), np.cos(h)],\n ]\n cam_depth[:, :2] = np.matmul(rot_matrix, cam_depth[:, :2].T).T\n\n cam_depth[:, 0] += W / 2.0\n cam_depth[:, 1] += H / 2.0\n\n depth_coords = cam_depth[:, :2].astype(np.int16)\n\n depth_map = np.zeros(resize_dims)\n valid_mask = ((depth_coords[:, 1] < resize_dims[0])\n & (depth_coords[:, 0] < resize_dims[1])\n & (depth_coords[:, 1] >= 0)\n & (depth_coords[:, 0] >= 0))\n depth_map[depth_coords[valid_mask, 1],\n depth_coords[valid_mask, 0]] = cam_depth[valid_mask, 2]\n\n return torch.Tensor(depth_map)\n\n def get_image(self, cam_infos, cams):\n \"\"\"Given data and cam_names, return image data needed.\n\n Args:\n sweeps_data (list): Raw data used to generate the data we needed.\n cams (list): Camera names.\n\n Returns:\n Tensor: Image data after processing.\n Tensor: Transformation matrix from camera to ego.\n Tensor: Intrinsic matrix.\n Tensor: Transformation matrix for ida.\n Tensor: Transformation matrix from key\n frame camera to sweep frame camera.\n Tensor: timestamps.\n dict: meta infos needed for evaluation.\n \"\"\"\n assert len(cam_infos) > 0\n sweep_imgs = list()\n sweep_sensor2ego_mats = list()\n sweep_intrin_mats = list()\n sweep_ida_mats = list()\n sweep_sensor2sensor_mats = list()\n sweep_timestamps = list()\n sweep_gt_depths = list()\n sweep_radar_points = list()\n for cam in cams:\n imgs = list()\n sensor2ego_mats = list()\n intrin_mats = list()\n ida_mats = list()\n sensor2sensor_mats = list()\n timestamps = list()\n gt_depths = list()\n radar_points = list()\n key_info = cam_infos[0]\n resize, resize_dims, crop, flip, \\\n rotate_ida = self.sample_ida_augmentation()\n radar_idx = self.sample_radar_augmentation()\n\n for sweep_idx, cam_info in enumerate(cam_infos):\n img = Image.open(\n os.path.join(self.data_root, cam_info[cam]['filename']))\n\n w, x, y, z = cam_info[cam]['calibrated_sensor']['rotation']\n # sweep sensor to sweep ego\n sweepsensor2sweepego_rot = torch.Tensor(\n Quaternion(w, x, y, z).rotation_matrix)\n sweepsensor2sweepego_tran = torch.Tensor(\n cam_info[cam]['calibrated_sensor']['translation'])\n sweepsensor2sweepego = sweepsensor2sweepego_rot.new_zeros(\n (4, 4))\n sweepsensor2sweepego[3, 3] = 1\n sweepsensor2sweepego[:3, :3] = sweepsensor2sweepego_rot\n sweepsensor2sweepego[:3, -1] = sweepsensor2sweepego_tran\n # sweep ego to global\n w, x, y, z = cam_info[cam]['ego_pose']['rotation']\n sweepego2global_rot = torch.Tensor(\n Quaternion(w, x, y, z).rotation_matrix)\n sweepego2global_tran = torch.Tensor(\n cam_info[cam]['ego_pose']['translation'])\n sweepego2global = sweepego2global_rot.new_zeros((4, 4))\n sweepego2global[3, 3] = 1\n sweepego2global[:3, :3] = sweepego2global_rot\n sweepego2global[:3, -1] = sweepego2global_tran\n\n # global sensor to cur ego\n w, x, y, z = key_info[cam]['ego_pose']['rotation']\n keyego2global_rot = torch.Tensor(\n Quaternion(w, x, y, z).rotation_matrix)\n keyego2global_tran = torch.Tensor(\n key_info[cam]['ego_pose']['translation'])\n keyego2global = keyego2global_rot.new_zeros((4, 4))\n keyego2global[3, 3] = 1\n keyego2global[:3, :3] = keyego2global_rot\n keyego2global[:3, -1] = keyego2global_tran\n global2keyego = keyego2global.inverse()\n\n # cur ego to sensor\n w, x, y, z = key_info[cam]['calibrated_sensor']['rotation']\n keysensor2keyego_rot = torch.Tensor(\n Quaternion(w, x, y, z).rotation_matrix)\n keysensor2keyego_tran = torch.Tensor(\n key_info[cam]['calibrated_sensor']['translation'])\n keysensor2keyego = keysensor2keyego_rot.new_zeros((4, 4))\n keysensor2keyego[3, 3] = 1\n keysensor2keyego[:3, :3] = keysensor2keyego_rot\n keysensor2keyego[:3, -1] = keysensor2keyego_tran\n keyego2keysensor = keysensor2keyego.inverse()\n keysensor2sweepsensor = (\n keyego2keysensor @ global2keyego @ sweepego2global\n @ sweepsensor2sweepego).inverse()\n sweepsensor2keyego = global2keyego @ sweepego2global @\\\n sweepsensor2sweepego\n sensor2ego_mats.append(sweepsensor2keyego)\n sensor2sensor_mats.append(keysensor2sweepsensor)\n intrin_mat = torch.zeros((4, 4))\n intrin_mat[3, 3] = 1\n intrin_mat[:3, :3] = torch.Tensor(\n cam_info[cam]['calibrated_sensor']['camera_intrinsic'])\n\n file_name = os.path.split(cam_info[cam]['filename'])[-1]\n if self.return_depth:\n point_depth = np.fromfile(os.path.join(\n self.data_root, self.depth_path, f'{file_name}.bin'),\n dtype=np.float32,\n count=-1)\n point_depth = point_depth.reshape(-1, 3)\n point_depth_augmented = self.depth_transform(\n point_depth, resize, self.ida_aug_conf['final_dim'],\n crop, flip, rotate_ida)\n gt_depths.append(point_depth_augmented)\n\n if self.return_radar_pv:\n radar_point = np.fromfile(os.path.join(\n self.data_root, self.radar_pv_path, f'{file_name}.bin'),\n dtype=np.float32,\n count=-1).reshape(-1, 7)\n radar_point_augmented = self.transform_radar_pv(\n radar_point, resize, self.ida_aug_conf['final_dim'],\n crop, flip, rotate_ida, radar_idx)\n radar_points.append(radar_point_augmented)\n\n img, ida_mat = img_transform(\n img,\n resize=resize,\n resize_dims=resize_dims,\n crop=crop,\n flip=flip,\n rotate=rotate_ida,\n )\n ida_mats.append(ida_mat)\n img = mmcv.imnormalize(np.array(img), self.img_mean,\n self.img_std, self.to_rgb)\n img = torch.from_numpy(img).permute(2, 0, 1)\n imgs.append(img)\n intrin_mats.append(intrin_mat)\n timestamps.append(cam_info[cam]['timestamp'])\n sweep_imgs.append(torch.stack(imgs))\n sweep_sensor2ego_mats.append(torch.stack(sensor2ego_mats))\n sweep_intrin_mats.append(torch.stack(intrin_mats))\n sweep_ida_mats.append(torch.stack(ida_mats))\n sweep_sensor2sensor_mats.append(torch.stack(sensor2sensor_mats))\n sweep_timestamps.append(torch.tensor(timestamps))\n if self.return_depth:\n sweep_gt_depths.append(torch.stack(gt_depths))\n if self.return_radar_pv:\n sweep_radar_points.append(torch.stack(radar_points))\n\n ret_list = [\n torch.stack(sweep_imgs).permute(1, 0, 2, 3, 4),\n torch.stack(sweep_sensor2ego_mats).permute(1, 0, 2, 3),\n torch.stack(sweep_intrin_mats).permute(1, 0, 2, 3),\n torch.stack(sweep_ida_mats).permute(1, 0, 2, 3),\n torch.stack(sweep_sensor2sensor_mats).permute(1, 0, 2, 3),\n torch.stack(sweep_timestamps).permute(1, 0),\n ]\n if self.return_depth:\n ret_list.append(torch.stack(sweep_gt_depths).permute(1, 0, 2, 3),)\n else:\n ret_list.append(None)\n if self.return_radar_pv:\n ret_list.append(torch.stack(sweep_radar_points).permute(1, 0, 2, 3),)\n else:\n ret_list.append(None)\n return ret_list\n\n def get_image_meta(self, cam_infos, cams):\n key_info = cam_infos[0]\n\n # Get mean pose of all cams.\n ego2global_rotation = np.mean(\n [key_info[cam]['ego_pose']['rotation'] for cam in cams], 0)\n ego2global_translation = np.mean(\n [key_info[cam]['ego_pose']['translation'] for cam in cams], 0)\n img_metas = dict(\n box_type_3d=LiDARInstance3DBoxes,\n ego2global_translation=ego2global_translation,\n ego2global_rotation=ego2global_rotation,\n )\n return img_metas\n\n def get_image_sensor2ego_mats(self, cam_infos, cams):\n sweep_sensor2ego_mats = list()\n for cam in cams:\n sensor2ego_mats = list()\n key_info = cam_infos[0]\n for sweep_idx, cam_info in enumerate(cam_infos):\n w, x, y, z = cam_info[cam]['calibrated_sensor']['rotation']\n # sweep sensor to sweep ego\n sweepsensor2sweepego_rot = torch.Tensor(\n Quaternion(w, x, y, z).rotation_matrix)\n sweepsensor2sweepego_tran = torch.Tensor(\n cam_info[cam]['calibrated_sensor']['translation'])\n sweepsensor2sweepego = sweepsensor2sweepego_rot.new_zeros(\n (4, 4))\n sweepsensor2sweepego[3, 3] = 1\n sweepsensor2sweepego[:3, :3] = sweepsensor2sweepego_rot\n sweepsensor2sweepego[:3, -1] = sweepsensor2sweepego_tran\n # sweep ego to global\n w, x, y, z = cam_info[cam]['ego_pose']['rotation']\n sweepego2global_rot = torch.Tensor(\n Quaternion(w, x, y, z).rotation_matrix)\n sweepego2global_tran = torch.Tensor(\n cam_info[cam]['ego_pose']['translation'])\n sweepego2global = sweepego2global_rot.new_zeros((4, 4))\n sweepego2global[3, 3] = 1\n sweepego2global[:3, :3] = sweepego2global_rot\n sweepego2global[:3, -1] = sweepego2global_tran\n\n # global sensor to cur ego\n w, x, y, z = key_info[cam]['ego_pose']['rotation']\n keyego2global_rot = torch.Tensor(\n Quaternion(w, x, y, z).rotation_matrix)\n keyego2global_tran = torch.Tensor(\n key_info[cam]['ego_pose']['translation'])\n keyego2global = keyego2global_rot.new_zeros((4, 4))\n keyego2global[3, 3] = 1\n keyego2global[:3, :3] = keyego2global_rot\n keyego2global[:3, -1] = keyego2global_tran\n global2keyego = keyego2global.inverse()\n\n # cur ego to sensor\n w, x, y, z = key_info[cam]['calibrated_sensor']['rotation']\n keysensor2keyego_rot = torch.Tensor(\n Quaternion(w, x, y, z).rotation_matrix)\n keysensor2keyego_tran = torch.Tensor(\n key_info[cam]['calibrated_sensor']['translation'])\n keysensor2keyego = keysensor2keyego_rot.new_zeros((4, 4))\n keysensor2keyego[3, 3] = 1\n keysensor2keyego[:3, :3] = keysensor2keyego_rot\n keysensor2keyego[:3, -1] = keysensor2keyego_tran\n sweepsensor2keyego = global2keyego @ sweepego2global @\\\n sweepsensor2sweepego\n sensor2ego_mats.append(sweepsensor2keyego)\n sweep_sensor2ego_mats.append(torch.stack(sensor2ego_mats))\n return torch.stack(sweep_sensor2ego_mats).permute(1, 0, 2, 3)\n\n def get_gt(self, info, cams, return_corners=False):\n \"\"\"Generate gt labels from info.\n\n Args:\n info(dict): Infos needed to generate gt labels.\n cams(list): Camera names.\n\n Returns:\n Tensor: GT bboxes.\n Tensor: GT labels.\n \"\"\"\n ego2global_rotation = np.mean(\n [info['cam_infos'][cam]['ego_pose']['rotation'] for cam in cams],\n 0)\n ego2global_translation = np.mean([\n info['cam_infos'][cam]['ego_pose']['translation'] for cam in cams\n ], 0)\n trans = -np.array(ego2global_translation)\n rot = Quaternion(ego2global_rotation).inverse\n gt_boxes = list()\n gt_labels = list()\n if return_corners: # for debugging and visualization\n gt_corners = list()\n else:\n gt_corners = None\n for ann_info in info['ann_infos']:\n # Use ego coordinate.\n if self.gt_for_radar_only:\n if ann_info['num_radar_pts'] == 0:\n continue\n if map_name_from_general_to_detection[ann_info['category_name']] not in self.classes:\n continue\n if ann_info['num_lidar_pts'] + ann_info['num_radar_pts'] == 0:\n continue\n\n box = Box(\n ann_info['translation'],\n ann_info['size'],\n Quaternion(ann_info['rotation']),\n velocity=ann_info['velocity'],\n )\n box.translate(trans)\n box.rotate(rot)\n box_xyz = np.array(box.center)\n box_dxdydz = np.array(box.wlh)[[1, 0, 2]]\n box_yaw = np.array([box.orientation.yaw_pitch_roll[0]])\n box_velo = np.array(box.velocity[:2])\n gt_box = np.concatenate([box_xyz, box_dxdydz, box_yaw, box_velo])\n gt_boxes.append(gt_box)\n gt_labels.append(\n self.classes.index(map_name_from_general_to_detection[\n ann_info['category_name']]))\n if return_corners: # for debugging and visualization\n gt_corners.append(box.corners())\n\n return torch.Tensor(gt_boxes), torch.tensor(gt_labels), gt_corners\n\n def choose_cams(self):\n \"\"\"Choose cameras randomly.\n\n Returns:\n list: Cameras to be used.\n \"\"\"\n if self.is_train and self.ida_aug_conf['Ncams'] < len(\n self.ida_aug_conf['cams']):\n cams = np.random.choice(self.ida_aug_conf['cams'],\n self.ida_aug_conf['Ncams'],\n replace=False)\n else:\n cams = self.ida_aug_conf['cams']\n return cams\n\n def __getitem__(self, idx):\n if self.use_cbgs:\n idx = self.sample_indices[idx]\n cam_infos = list()\n pts_infos = list()\n cams = self.choose_cams()\n for key_idx in self.key_idxes:\n cur_idx = key_idx + idx\n # Handle scenarios when current idx doesn't have previous key\n # frame or previous key frame is from another scene.\n while self.infos[cur_idx]['scene_token'] != self.infos[idx]['scene_token']:\n cur_idx += 1\n info = self.infos[cur_idx]\n cam_infos.append(info['cam_infos'])\n pts_infos.append([info['lidar_infos']] + info['lidar_sweeps'])\n for sweep_idx in self.sweeps_idx:\n if len(info['cam_sweeps']) == 0:\n cam_infos.append(info['cam_infos'])\n else:\n # Handle scenarios when current sweep doesn't have all cam keys.\n for i in range(min(len(info['cam_sweeps']) - 1, sweep_idx), -1,\n -1):\n if sum([cam in info['cam_sweeps'][i]\n for cam in cams]) == len(cams):\n cam_infos.append(info['cam_sweeps'][i])\n break\n\n if self.return_image or self.return_depth or self.return_radar_pv:\n image_data_list = self.get_image(cam_infos, cams)\n (\n sweep_imgs,\n sweep_sensor2ego_mats,\n sweep_intrins,\n sweep_ida_mats,\n sweep_sensor2sensor_mats,\n sweep_timestamps,\n ) = image_data_list[:6]\n else:\n (\n sweep_imgs,\n sweep_intrins,\n sweep_ida_mats,\n sweep_sensor2sensor_mats,\n sweep_timestamps,\n ) = None, None, None, None, None\n sweep_sensor2ego_mats = self.get_image_sensor2ego_mats(cam_infos, cams)\n\n img_metas = self.get_image_meta(cam_infos, cams)\n img_metas['token'] = self.infos[idx]['sample_token']\n gt_boxes_3d, gt_labels_3d, gt_corners = self.get_gt(self.infos[idx], cams, return_corners=False)\n\n rotate_bda, scale_bda, flip_dx, flip_dy = self.sample_bda_augmentation()\n gt_boxes_3d, bda_rot = bev_det_transform(gt_boxes_3d, rotate_bda, scale_bda, flip_dx, flip_dy)\n\n bda_mat = torch.zeros(4, 4, dtype=torch.float32)\n bda_mat[:3, :3] = bda_rot\n bda_mat[3, 3] = 1\n\n ret_list = [\n sweep_imgs,\n sweep_sensor2ego_mats,\n sweep_intrins,\n sweep_ida_mats,\n sweep_sensor2sensor_mats,\n bda_mat,\n sweep_timestamps,\n img_metas,\n gt_boxes_3d,\n gt_labels_3d,\n ]\n\n if self.return_depth:\n ret_list.append(image_data_list[6])\n else:\n ret_list.append(None)\n if self.return_radar_pv:\n ret_list.append(image_data_list[7])\n else:\n ret_list.append(None)\n\n return ret_list\n\n def __str__(self):\n return f\"\"\"NuscData: {len(self)} samples. Split: \\\n {\"train\" if self.is_train else \"val\"}.\n Augmentation Conf: {self.ida_aug_conf}\"\"\"\n\n def __len__(self):\n if self.use_cbgs:\n return len(self.sample_indices)\n else:\n return len(self.infos)"},{"identifier":"collate_fn","path":"datasets/nusc_det_dataset.py","snippet":"def collate_fn(data,\n is_return_image=True,\n is_return_depth=False,\n is_return_radar_pv=False):\n assert (is_return_image or is_return_depth or is_return_radar_pv) is True\n imgs_batch = list()\n sensor2ego_mats_batch = list()\n intrin_mats_batch = list()\n ida_mats_batch = list()\n sensor2sensor_mats_batch = list()\n bda_mat_batch = list()\n gt_boxes_3d_batch = list()\n gt_labels_3d_batch = list()\n img_metas_batch = list()\n depth_labels_batch = list()\n radar_pv_batch = list()\n\n for iter_data in data:\n (\n sweep_imgs,\n sweep_sensor2ego_mats,\n sweep_intrins,\n sweep_ida_mats,\n sweep_sensor2sensor_mats,\n bda_mat,\n sweep_timestamps,\n img_metas,\n gt_boxes,\n gt_labels,\n ) = iter_data[:10]\n if is_return_depth:\n gt_depth = iter_data[10]\n depth_labels_batch.append(gt_depth)\n if is_return_radar_pv:\n radar_pv = iter_data[11]\n radar_pv_batch.append(radar_pv)\n\n imgs_batch.append(sweep_imgs)\n sensor2ego_mats_batch.append(sweep_sensor2ego_mats)\n intrin_mats_batch.append(sweep_intrins)\n ida_mats_batch.append(sweep_ida_mats)\n sensor2sensor_mats_batch.append(sweep_sensor2sensor_mats)\n bda_mat_batch.append(bda_mat)\n img_metas_batch.append(img_metas)\n gt_boxes_3d_batch.append(gt_boxes)\n gt_labels_3d_batch.append(gt_labels)\n\n if is_return_image:\n mats_dict = dict()\n mats_dict['sensor2ego_mats'] = torch.stack(sensor2ego_mats_batch)\n mats_dict['intrin_mats'] = torch.stack(intrin_mats_batch)\n mats_dict['ida_mats'] = torch.stack(ida_mats_batch)\n mats_dict['sensor2sensor_mats'] = torch.stack(sensor2sensor_mats_batch)\n mats_dict['bda_mat'] = torch.stack(bda_mat_batch)\n ret_list = [\n torch.stack(imgs_batch),\n mats_dict,\n img_metas_batch,\n gt_boxes_3d_batch,\n gt_labels_3d_batch,\n None, # reserve for segmentation\n ]\n else:\n ret_list = [\n None,\n None,\n img_metas_batch,\n gt_boxes_3d_batch,\n gt_labels_3d_batch,\n None,\n ]\n if is_return_depth:\n ret_list.append(torch.stack(depth_labels_batch))\n else:\n ret_list.append(None)\n if is_return_radar_pv:\n ret_list.append(torch.stack(radar_pv_batch))\n else:\n ret_list.append(None)\n\n return ret_list"},{"identifier":"DetNuscEvaluator","path":"evaluators/det_evaluators.py","snippet":"class DetNuscEvaluator():\n ErrNameMapping = {\n 'trans_err': 'mATE',\n 'scale_err': 'mASE',\n 'orient_err': 'mAOE',\n 'vel_err': 'mAVE',\n 'attr_err': 'mAAE',\n }\n\n DefaultAttribute = {\n 'car': 'vehicle.parked',\n 'pedestrian': 'pedestrian.moving',\n 'trailer': 'vehicle.parked',\n 'truck': 'vehicle.parked',\n 'bus': 'vehicle.moving',\n 'motorcycle': 'cycle.without_rider',\n 'construction_vehicle': 'vehicle.parked',\n 'bicycle': 'cycle.without_rider',\n 'barrier': '',\n 'traffic_cone': '',\n }\n\n def __init__(\n self,\n class_names,\n eval_version='detection_cvpr_2019',\n data_root='./data/nuScenes',\n version='v1.0-trainval',\n modality=dict(use_lidar=False,\n use_camera=True,\n use_radar=True,\n use_map=False,\n use_external=False),\n output_dir=None,\n ) -> None:\n self.eval_version = eval_version\n self.data_root = data_root\n\n # Load config file and deserialize it.\n this_dir = osp.dirname(osp.abspath(__file__))\n with open(osp.join(this_dir, 'configs', '%s.json' % eval_version), 'r') as f:\n data = json.load(f)\n self.eval_detection_configs = DetectionConfig.deserialize(data)\n\n self.version = version\n self.class_names = class_names\n self.modality = modality\n self.output_dir = output_dir\n\n def _evaluate_single(self,\n result_path,\n logger=None,\n metric='bbox',\n result_name='pts_bbox'):\n \"\"\"Evaluation for a single model in nuScenes protocol.\n\n Args:\n result_path (str): Path of the result file.\n logger (logging.Logger | str | None): Logger used for printing\n related information during evaluation. Default: None.\n metric (str): Metric name used for evaluation. Default: 'bbox'.\n result_name (str): Result name in the metric prefix.\n Default: 'pts_bbox'.\n\n Returns:\n dict: Dictionary of evaluation details.\n \"\"\"\n from nuscenes import NuScenes\n from nuscenes.eval.detection.evaluate import NuScenesEval\n\n output_dir = osp.join(*osp.split(result_path)[:-1])\n nusc = NuScenes(version=self.version,\n dataroot=self.data_root,\n verbose=False)\n eval_set_map = {\n 'v1.0-mini': 'mini_val',\n 'v1.0-trainval': 'val',\n }\n nusc_eval = NuScenesEval(nusc,\n config=self.eval_detection_configs,\n result_path=result_path,\n eval_set=eval_set_map[self.version],\n output_dir=output_dir,\n verbose=False)\n nusc_eval.main(render_curves=False)\n # nusc_eval.main(render_curves=True, plot_examples=40)\n\n # record metrics\n metrics = mmcv.load(osp.join(output_dir, 'metrics_summary.json'))\n detail = dict()\n metric_prefix = f'{result_name}_NuScenes'\n for class_name in self.class_names:\n for k, v in metrics['label_aps'][class_name].items():\n val = float('{:.4f}'.format(v))\n detail['{}/{}_AP_dist_{}'.format(metric_prefix, class_name,\n k)] = val\n for k, v in metrics['label_tp_errors'][class_name].items():\n val = float('{:.4f}'.format(v))\n detail['{}/{}_{}'.format(metric_prefix, class_name, k)] = val\n for k, v in metrics['tp_errors'].items():\n val = float('{:.4f}'.format(v))\n detail['{}/{}'.format(metric_prefix,\n self.ErrNameMapping[k])] = val\n\n detail['{}/NDS'.format(metric_prefix)] = metrics['nd_score']\n detail['{}/mAP'.format(metric_prefix)] = metrics['mean_ap']\n return detail\n\n def format_results(self,\n results,\n img_metas,\n result_names=['img_bbox'],\n jsonfile_prefix=None,\n **kwargs):\n \"\"\"Format the results to json (standard format for COCO evaluation).\n\n Args:\n results (list[tuple | numpy.ndarray]): Testing results of the\n dataset.\n jsonfile_prefix (str | None): The prefix of json files. It includes\n the file path and the prefix of filename, e.g., \"a/b/prefix\".\n If not specified, a temp file will be created. Default: None.\n\n Returns:\n tuple: (result_files, tmp_dir), result_files is a dict containing \\\n the json filepaths, tmp_dir is the temporal directory created \\\n for saving json files when jsonfile_prefix is not specified.\n \"\"\"\n assert isinstance(results, list), 'results must be a list'\n\n if jsonfile_prefix is None:\n tmp_dir = tempfile.TemporaryDirectory()\n jsonfile_prefix = osp.join(tmp_dir.name, 'results')\n else:\n tmp_dir = None\n\n # currently the output prediction results could be in two formats\n # 1. list of dict('boxes_3d': ..., 'scores_3d': ..., 'labels_3d': ...)\n # 2. list of dict('pts_bbox' or 'img_bbox':\n # dict('boxes_3d': ..., 'scores_3d': ..., 'labels_3d': ...))\n # this is a workaround to enable evaluation of both formats on nuScenes\n # refer to https://github.com/open-mmlab/mmdetection3d/issues/449\n # should take the inner dict out of 'pts_bbox' or 'img_bbox' dict\n result_files = dict()\n # refactor this.\n for rasult_name in result_names:\n # not evaluate 2D predictions on nuScenes\n if '2d' in rasult_name:\n continue\n print(f'\\nFormating bboxes of {rasult_name}')\n tmp_file_ = osp.join(jsonfile_prefix, rasult_name)\n if self.output_dir:\n result_files.update({\n rasult_name:\n self._format_bbox(results, img_metas, self.output_dir)\n })\n else:\n result_files.update({\n rasult_name:\n self._format_bbox(results, img_metas, tmp_file_)\n })\n return result_files, tmp_dir\n\n def evaluate(\n self,\n results,\n img_metas,\n metric='bbox',\n logger=None,\n jsonfile_prefix=None,\n result_names=['img_bbox'],\n show=False,\n out_dir=None,\n pipeline=None,\n ):\n \"\"\"Evaluation in nuScenes protocol.\n\n Args:\n results (list[dict]): Testing results of the dataset.\n metric (str | list[str]): Metrics to be evaluated.\n logger (logging.Logger | str | None): Logger used for printing\n related information during evaluation. Default: None.\n jsonfile_prefix (str | None): The prefix of json files. It includes\n the file path and the prefix of filename, e.g., \"a/b/prefix\".\n If not specified, a temp file will be created. Default: None.\n show (bool): Whether to visualize.\n Default: False.\n out_dir (str): Path to save the visualization results.\n Default: None.\n pipeline (list[dict], optional): raw data loading for showing.\n Default: None.\n\n Returns:\n dict[str, float]: Results of each evaluation metric.\n \"\"\"\n result_files, tmp_dir = self.format_results(results, img_metas,\n result_names,\n jsonfile_prefix)\n if isinstance(result_files, dict):\n for name in result_names:\n print('Evaluating bboxes of {}'.format(name))\n print()\n self._evaluate_single(result_files[name])\n elif isinstance(result_files, str):\n self._evaluate_single(result_files)\n\n if tmp_dir is not None:\n tmp_dir.cleanup()\n\n def _format_bbox(self, results, img_metas, jsonfile_prefix=None):\n \"\"\"Convert the results to the standard format.\n\n Args:\n results (list[dict]): Testing results of the dataset.\n jsonfile_prefix (str): The prefix of the output jsonfile.\n You can specify the output directory/filename by\n modifying the jsonfile_prefix. Default: None.\n\n Returns:\n str: Path of the output json file.\n \"\"\"\n nusc_annos = {}\n mapped_class_names = self.class_names\n\n print('Start to convert detection format...')\n\n for sample_id, det in enumerate(mmcv.track_iter_progress(results)):\n boxes, scores, labels = det\n\n order = np.argsort(scores)[::-1]\n order = order[:500]\n\n boxes = boxes[order]\n scores = scores[order]\n labels = labels[order]\n\n sample_token = img_metas[sample_id]['token']\n trans = np.array(img_metas[sample_id]['ego2global_translation'])\n rot = Quaternion(img_metas[sample_id]['ego2global_rotation'])\n annos = list()\n for i, box in enumerate(boxes):\n name = mapped_class_names[labels[i]]\n center = box[:3]\n wlh = box[[4, 3, 5]]\n box_yaw = box[6]\n box_vel = box[7:].tolist()\n box_vel.append(0)\n quat = pyquaternion.Quaternion(axis=[0, 0, 1], radians=box_yaw)\n nusc_box = Box(center, wlh, quat, velocity=box_vel)\n nusc_box.rotate(rot)\n nusc_box.translate(trans)\n if np.sqrt(nusc_box.velocity[0]**2 +\n nusc_box.velocity[1]**2) > 0.2:\n if name in [\n 'car',\n 'construction_vehicle',\n 'bus',\n 'truck',\n 'trailer',\n ]:\n attr = 'vehicle.moving'\n elif name in ['bicycle', 'motorcycle']:\n attr = 'cycle.with_rider'\n else:\n attr = self.DefaultAttribute[name]\n else:\n if name in ['pedestrian']:\n attr = 'pedestrian.standing'\n elif name in ['bus']:\n attr = 'vehicle.stopped'\n else:\n attr = self.DefaultAttribute[name]\n nusc_anno = dict(\n sample_token=sample_token,\n translation=nusc_box.center.tolist(),\n size=nusc_box.wlh.tolist(),\n rotation=nusc_box.orientation.elements.tolist(),\n velocity=nusc_box.velocity[:2],\n detection_name=name,\n detection_score=float(scores[i]),\n attribute_name=attr,\n )\n annos.append(nusc_anno)\n # other views results of the same frame should be concatenated\n if sample_token in nusc_annos:\n nusc_annos[sample_token].extend(annos)\n else:\n nusc_annos[sample_token] = annos\n nusc_submissions = {\n 'meta': self.modality,\n 'results': nusc_annos,\n }\n mmcv.mkdir_or_exist(jsonfile_prefix)\n res_path = osp.join(jsonfile_prefix, 'results_nusc.json')\n print('Results writes to', res_path)\n mmcv.dump(nusc_submissions, res_path)\n return res_path"},{"identifier":"BaseBEVDepth","path":"models/base_bev_depth.py","snippet":"class BaseBEVDepth(nn.Module):\n \"\"\"Source code of `BEVDepth`, `https://arxiv.org/abs/2112.11790`.\n\n Args:\n backbone_conf (dict): Config of backbone.\n head_conf (dict): Config of head.\n \"\"\"\n\n def __init__(self, backbone_conf, head_conf):\n super(BaseBEVDepth, self).__init__()\n self.backbone_img = BaseLSSFPN(**backbone_conf)\n self.head = BEVDepthHead(**head_conf)\n\n # for inference time measurement\n self.idx = 0\n self.times_dict = {\n 'img': [],\n 'img_backbone': [],\n 'img_dep': [],\n 'img_transform': [],\n 'img_pool': [],\n\n 'head': [],\n 'head_backbone': [],\n 'head_head': [],\n }\n\n def forward(self,\n sweep_imgs,\n mats_dict,\n is_train=False\n ):\n \"\"\"Forward function for BEVDepth\n\n Args:\n sweep_imgs (Tensor): Input images.\n mats_dict(dict):\n sensor2ego_mats(Tensor): Transformation matrix from\n camera to ego with shape of (B, num_sweeps,\n num_cameras, 4, 4).\n intrin_mats(Tensor): Intrinsic matrix with shape\n of (B, num_sweeps, num_cameras, 4, 4).\n ida_mats(Tensor): Transformation matrix for ida with\n shape of (B, num_sweeps, num_cameras, 4, 4).\n sensor2sensor_mats(Tensor): Transformation matrix\n from key frame camera to sweep frame camera with\n shape of (B, num_sweeps, num_cameras, 4, 4).\n bda_mat(Tensor): Rotation matrix for bda with shape\n of (B, 4, 4).\n\n Returns:\n tuple(list[dict]): Output results for tasks.\n \"\"\"\n if is_train:\n self.time = None\n\n x, depth, _ = self.backbone_img(sweep_imgs, mats_dict,\n is_return_depth=True)\n preds, _ = self.head(x)\n return preds, depth\n else:\n if self.idx < 100: # skip few iterations for warmup\n self.times = None\n elif self.idx == 100:\n self.times = self.times_dict\n\n x, self.times = self.backbone_img(sweep_imgs, mats_dict,\n times=self.times)\n preds, self.times = self.head(x, times=self.times)\n\n if self.idx == 1000:\n time_mean = {}\n for k, v in self.times.items():\n time_mean[k] = sum(v) / len(v)\n print('img: %.2f' % time_mean['img'])\n print(' img_backbone: %.2f' % time_mean['img_backbone'])\n print(' img_dep: %.2f' % time_mean['img_dep'])\n print(' img_transform: %.2f' % time_mean['img_transform'])\n print(' img_pool: %.2f' % time_mean['img_pool'])\n print('head: %.2f' % time_mean['head'])\n print(' head_backbone: %.2f' % time_mean['head_backbone'])\n print(' head_head: %.2f' % time_mean['head_head'])\n total = time_mean['img'] + time_mean['head']\n print('total: %.2f' % total)\n print(' ')\n print('FPS: %.2f' % (1000/total))\n\n self.idx += 1\n return preds\n\n def get_targets(self, gt_boxes, gt_labels):\n \"\"\"Generate training targets for a single sample.\n\n Args:\n gt_bboxes_3d (:obj:`LiDARInstance3DBoxes`): Ground truth gt boxes.\n gt_labels_3d (torch.Tensor): Labels of boxes.\n\n Returns:\n tuple[list[torch.Tensor]]: Tuple of target including \\\n the following results in order.\n\n - list[torch.Tensor]: Heatmap scores.\n - list[torch.Tensor]: Ground truth boxes.\n - list[torch.Tensor]: Indexes indicating the position \\\n of the valid boxes.\n - list[torch.Tensor]: Masks indicating which boxes \\\n are valid.\n \"\"\"\n return self.head.get_targets(gt_boxes, gt_labels)\n\n def loss(self, targets, preds_dicts):\n \"\"\"Loss function for BEVDepth.\n\n Args:\n gt_bboxes_3d (list[:obj:`LiDARInstance3DBoxes`]): Ground\n truth gt boxes.\n gt_labels_3d (list[torch.Tensor]): Labels of boxes.\n preds_dicts (dict): Output of forward function.\n\n Returns:\n dict[str:torch.Tensor]: Loss of heatmap and bbox of each task.\n \"\"\"\n return self.head.loss(targets, preds_dicts)\n\n def get_bboxes(self, preds_dicts, img_metas=None, img=None, rescale=False):\n \"\"\"Generate bboxes from bbox head predictions.\n\n Args:\n preds_dicts (tuple[list[dict]]): Prediction results.\n img_metas (list[dict]): Point cloud and image's meta info.\n\n Returns:\n list[dict]: Decoded bbox, scores and labels after nms.\n \"\"\"\n return self.head.get_bboxes(preds_dicts, img_metas, img, rescale)"},{"identifier":"all_gather_object","path":"utils/torch_dist.py","snippet":"def all_gather_object(obj):\n world_size = get_world_size()\n if world_size < 2:\n return [obj]\n output = [None for _ in range(world_size)]\n dist.all_gather_object(output, obj)\n return output"},{"identifier":"synchronize","path":"utils/torch_dist.py","snippet":"def synchronize():\n \"\"\"Helper function to synchronize (barrier)\n among all processes when using distributed training\"\"\"\n if not dist.is_available():\n return\n if not dist.is_initialized():\n return\n current_world_size = dist.get_world_size()\n if current_world_size == 1:\n return\n dist.barrier()"}],"string":"[\n {\n \"identifier\": \"NuscDatasetRadarDet\",\n \"path\": \"datasets/nusc_det_dataset.py\",\n \"snippet\": \"class NuscDatasetRadarDet(Dataset):\\n def __init__(self,\\n ida_aug_conf,\\n bda_aug_conf,\\n rda_aug_conf,\\n classes,\\n data_root,\\n info_paths,\\n is_train,\\n load_interval=1,\\n num_sweeps=1,\\n img_conf=dict(img_mean=[123.675, 116.28, 103.53],\\n img_std=[58.395, 57.12, 57.375],\\n to_rgb=True),\\n img_backbone_conf=dict(\\n x_bound=[-51.2, 51.2, 0.8],\\n y_bound=[-51.2, 51.2, 0.8],\\n z_bound=[-5, 3, 8],\\n d_bound=[2.0, 58.0, 0.5]\\n ),\\n drop_aug_conf=None,\\n return_image=True,\\n return_depth=False,\\n return_radar_pv=False,\\n depth_path='depth_gt',\\n radar_pv_path='radar_pv_filter',\\n remove_z_axis=False,\\n use_cbgs=False,\\n gt_for_radar_only=False,\\n sweep_idxes=list(),\\n key_idxes=list()):\\n \\\"\\\"\\\"Dataset used for bevdetection task.\\n Args:\\n ida_aug_conf (dict): Config for ida augmentation.\\n bda_aug_conf (dict): Config for bda augmentation.\\n classes (list): Class names.\\n use_cbgs (bool): Whether to use cbgs strategy,\\n Default: False.\\n num_sweeps (int): Number of sweeps to be used for each sample.\\n default: 1.\\n img_conf (dict): Config for image.\\n return_depth (bool): Whether to use depth gt.\\n default: False.\\n sweep_idxes (list): List of sweep idxes to be used.\\n default: list().\\n key_idxes (list): List of key idxes to be used.\\n default: list().\\n \\\"\\\"\\\"\\n super().__init__()\\n if isinstance(info_paths, list):\\n self.infos = list()\\n for info_path in info_paths:\\n self.infos.extend(mmcv.load(info_path))\\n else:\\n self.infos = mmcv.load(info_paths)\\n self.is_train = is_train\\n self.ida_aug_conf = ida_aug_conf\\n self.bda_aug_conf = bda_aug_conf\\n self.rda_aug_conf = rda_aug_conf\\n self.drop_aug_conf = drop_aug_conf\\n self.data_root = data_root\\n self.classes = classes\\n self.use_cbgs = use_cbgs\\n if self.use_cbgs:\\n self.cat2id = {name: i for i, name in enumerate(self.classes)}\\n self.sample_indices = self._get_sample_indices()\\n self.num_sweeps = num_sweeps\\n self.img_mean = np.array(img_conf['img_mean'], np.float32)\\n self.img_std = np.array(img_conf['img_std'], np.float32)\\n self.to_rgb = img_conf['to_rgb']\\n self.img_backbone_conf = img_backbone_conf\\n\\n self.return_image = return_image\\n self.return_depth = return_depth\\n self.return_radar_pv = return_radar_pv\\n\\n self.remove_z_axis = remove_z_axis\\n self.gt_for_radar_only = gt_for_radar_only\\n\\n assert sum([sweep_idx >= 0 for sweep_idx in sweep_idxes]) \\\\\\n == len(sweep_idxes), 'All `sweep_idxes` must greater \\\\\\n than or equal to 0.'\\n\\n self.sweeps_idx = sweep_idxes\\n assert sum([key_idx < 0 for key_idx in key_idxes]) == len(key_idxes),\\\\\\n 'All `key_idxes` must less than 0.'\\n self.key_idxes = [0] + key_idxes\\n if load_interval > 1:\\n self.infos = self.infos[::load_interval]\\n self.depth_path = depth_path\\n self.radar_pv_path = radar_pv_path\\n\\n self.max_radar_points_pv = 1536\\n self.max_distance_pv = self.img_backbone_conf['d_bound'][1]\\n\\n def _get_sample_indices(self):\\n \\\"\\\"\\\"Load annotations from ann_file.\\n\\n Args:\\n ann_file (str): Path of the annotation file.\\n\\n Returns:\\n list[dict]: List of annotations after class sampling.\\n \\\"\\\"\\\"\\n class_sample_idxs = {cat_id: [] for cat_id in self.cat2id.values()}\\n for idx, info in enumerate(self.infos):\\n gt_names = set(\\n [ann_info['category_name'] for ann_info in info['ann_infos']])\\n for gt_name in gt_names:\\n gt_name = map_name_from_general_to_detection[gt_name]\\n if gt_name not in self.classes:\\n continue\\n class_sample_idxs[self.cat2id[gt_name]].append(idx)\\n duplicated_samples = sum(\\n [len(v) for _, v in class_sample_idxs.items()])\\n class_distribution = {\\n k: len(v) / duplicated_samples\\n for k, v in class_sample_idxs.items()\\n }\\n\\n sample_indices = []\\n\\n frac = 1.0 / len(self.classes)\\n ratios = [frac / v for v in class_distribution.values()]\\n for cls_inds, ratio in zip(list(class_sample_idxs.values()), ratios):\\n sample_indices += np.random.choice(cls_inds,\\n int(len(cls_inds) *\\n ratio)).tolist()\\n return sample_indices\\n\\n def sample_ida_augmentation(self):\\n \\\"\\\"\\\"Generate ida augmentation values based on ida_config.\\\"\\\"\\\"\\n H, W = self.ida_aug_conf['H'], self.ida_aug_conf['W']\\n fH, fW = self.ida_aug_conf['final_dim']\\n if self.is_train:\\n resize = np.random.uniform(*self.ida_aug_conf['resize_lim'])\\n resize_dims = (int(W * resize), int(H * resize))\\n newW, newH = resize_dims\\n crop_h = int(\\n (1 - np.random.uniform(*self.ida_aug_conf['bot_pct_lim'])) *\\n newH) - fH\\n crop_w = int(np.random.uniform(0, max(0, newW - fW)))\\n crop = (crop_w, crop_h, crop_w + fW, crop_h + fH)\\n flip = False\\n if self.ida_aug_conf['rand_flip'] and np.random.choice([0, 1]):\\n flip = True\\n rotate_ida = np.random.uniform(*self.ida_aug_conf['rot_lim'])\\n else:\\n resize = max(fH / H, fW / W)\\n resize_dims = (int(W * resize), int(H * resize))\\n newW, newH = resize_dims\\n crop_h = int(\\n (1 - np.mean(self.ida_aug_conf['bot_pct_lim'])) * newH) - fH\\n crop_w = int(max(0, newW - fW) / 2)\\n crop = (crop_w, crop_h, crop_w + fW, crop_h + fH)\\n flip = False\\n rotate_ida = 0\\n return resize, resize_dims, crop, flip, rotate_ida\\n\\n def sample_bda_augmentation(self):\\n \\\"\\\"\\\"Generate bda augmentation values based on bda_config.\\\"\\\"\\\"\\n if self.is_train:\\n if np.random.uniform() < self.bda_aug_conf['rot_ratio']:\\n rotate_bda = np.random.uniform(*self.bda_aug_conf['rot_lim'])\\n else:\\n rotate_bda = 0\\n scale_bda = np.random.uniform(*self.bda_aug_conf['scale_lim'])\\n flip_dx = np.random.uniform() < self.bda_aug_conf['flip_dx_ratio']\\n flip_dy = np.random.uniform() < self.bda_aug_conf['flip_dy_ratio']\\n else:\\n rotate_bda = 0\\n scale_bda = 1.0\\n flip_dx = False\\n flip_dy = False\\n return rotate_bda, scale_bda, flip_dx, flip_dy\\n\\n def sample_radar_augmentation(self):\\n \\\"\\\"\\\"Generate bda augmentation values based on bda_config.\\\"\\\"\\\"\\n if self.is_train:\\n radar_idx = np.random.choice(self.rda_aug_conf['N_sweeps'],\\n self.rda_aug_conf['N_use'],\\n replace=False)\\n else:\\n radar_idx = np.arange(self.rda_aug_conf['N_sweeps'])\\n return radar_idx\\n\\n def transform_radar_pv(self, points, resize, resize_dims, crop, flip, rotate, radar_idx):\\n points = points[points[:, 2] < self.max_distance_pv, :]\\n\\n H, W = resize_dims\\n points[:, :2] = points[:, :2] * resize\\n points[:, 0] -= crop[0]\\n points[:, 1] -= crop[1]\\n if flip:\\n points[:, 0] = resize_dims[1] - points[:, 0]\\n\\n points[:, 0] -= W / 2.0\\n points[:, 1] -= H / 2.0\\n\\n h = rotate / 180 * np.pi\\n rot_matrix = [\\n [np.cos(h), np.sin(h)],\\n [-np.sin(h), np.cos(h)],\\n ]\\n points[:, :2] = np.matmul(rot_matrix, points[:, :2].T).T\\n\\n points[:, 0] += W / 2.0\\n points[:, 1] += H / 2.0\\n\\n depth_coords = points[:, :2].astype(np.int16)\\n\\n valid_mask = ((depth_coords[:, 1] < resize_dims[0])\\n & (depth_coords[:, 0] < resize_dims[1])\\n & (depth_coords[:, 1] >= 0)\\n & (depth_coords[:, 0] >= 0))\\n\\n points = torch.Tensor(points[valid_mask])\\n\\n if self.remove_z_axis:\\n points[:, 1] = 1. # dummy height value\\n\\n points_save = []\\n for i in radar_idx:\\n points_save.append(points[points[:, 6] == i])\\n points = torch.cat(points_save, dim=0)\\n\\n # mean, std of rcs and speed are from train set\\n points[:, 3] = (points[:, 3] - 4.783) / 7.576\\n points[:, 4] = (torch.norm(points[:, 4:6], dim=1) - 0.677) / 1.976\\n\\n if self.is_train:\\n drop_idx = np.random.uniform(size=points.shape[0]) # randomly drop points\\n points = points[drop_idx > self.rda_aug_conf['drop_ratio']]\\n\\n num_points, num_feat = points.shape\\n if num_points > self.max_radar_points_pv:\\n choices = np.random.choice(num_points, self.max_radar_points_pv, replace=False)\\n points = points[choices]\\n else:\\n num_append = self.max_radar_points_pv - num_points\\n points = torch.cat([points, -999*torch.ones(num_append, num_feat)], dim=0)\\n\\n if num_points == 0:\\n points[0, :] = points.new_tensor([0.1, 0.1, self.max_distance_pv-1, 0, 0, 0, 0])\\n\\n points[..., [0, 1, 2]] = points[..., [0, 2, 1]] # convert [w, h, d] to [w, d, h]\\n\\n return points[..., :5]\\n\\n def depth_transform(self, cam_depth, resize, resize_dims, crop, flip, rotate):\\n \\\"\\\"\\\"Transform depth based on ida augmentation configuration.\\n\\n Args:\\n cam_depth (np array): Nx3, 3: x,y,d.\\n resize (float): Resize factor.\\n resize_dims (tuple): Final dimension.\\n crop (tuple): x1, y1, x2, y2\\n flip (bool): Whether to flip.\\n rotate (float): Rotation value.\\n\\n Returns:\\n np array: [h/down_ratio, w/down_ratio, d]\\n \\\"\\\"\\\"\\n valid_depth = cam_depth[:, 2] < self.img_backbone_conf['d_bound'][1]\\n cam_depth = cam_depth[valid_depth, :]\\n\\n H, W = resize_dims\\n cam_depth[:, :2] = cam_depth[:, :2] * resize\\n cam_depth[:, 0] -= crop[0]\\n cam_depth[:, 1] -= crop[1]\\n if flip:\\n cam_depth[:, 0] = resize_dims[1] - cam_depth[:, 0]\\n\\n cam_depth[:, 0] -= W / 2.0\\n cam_depth[:, 1] -= H / 2.0\\n\\n h = rotate / 180 * np.pi\\n rot_matrix = [\\n [np.cos(h), np.sin(h)],\\n [-np.sin(h), np.cos(h)],\\n ]\\n cam_depth[:, :2] = np.matmul(rot_matrix, cam_depth[:, :2].T).T\\n\\n cam_depth[:, 0] += W / 2.0\\n cam_depth[:, 1] += H / 2.0\\n\\n depth_coords = cam_depth[:, :2].astype(np.int16)\\n\\n depth_map = np.zeros(resize_dims)\\n valid_mask = ((depth_coords[:, 1] < resize_dims[0])\\n & (depth_coords[:, 0] < resize_dims[1])\\n & (depth_coords[:, 1] >= 0)\\n & (depth_coords[:, 0] >= 0))\\n depth_map[depth_coords[valid_mask, 1],\\n depth_coords[valid_mask, 0]] = cam_depth[valid_mask, 2]\\n\\n return torch.Tensor(depth_map)\\n\\n def get_image(self, cam_infos, cams):\\n \\\"\\\"\\\"Given data and cam_names, return image data needed.\\n\\n Args:\\n sweeps_data (list): Raw data used to generate the data we needed.\\n cams (list): Camera names.\\n\\n Returns:\\n Tensor: Image data after processing.\\n Tensor: Transformation matrix from camera to ego.\\n Tensor: Intrinsic matrix.\\n Tensor: Transformation matrix for ida.\\n Tensor: Transformation matrix from key\\n frame camera to sweep frame camera.\\n Tensor: timestamps.\\n dict: meta infos needed for evaluation.\\n \\\"\\\"\\\"\\n assert len(cam_infos) > 0\\n sweep_imgs = list()\\n sweep_sensor2ego_mats = list()\\n sweep_intrin_mats = list()\\n sweep_ida_mats = list()\\n sweep_sensor2sensor_mats = list()\\n sweep_timestamps = list()\\n sweep_gt_depths = list()\\n sweep_radar_points = list()\\n for cam in cams:\\n imgs = list()\\n sensor2ego_mats = list()\\n intrin_mats = list()\\n ida_mats = list()\\n sensor2sensor_mats = list()\\n timestamps = list()\\n gt_depths = list()\\n radar_points = list()\\n key_info = cam_infos[0]\\n resize, resize_dims, crop, flip, \\\\\\n rotate_ida = self.sample_ida_augmentation()\\n radar_idx = self.sample_radar_augmentation()\\n\\n for sweep_idx, cam_info in enumerate(cam_infos):\\n img = Image.open(\\n os.path.join(self.data_root, cam_info[cam]['filename']))\\n\\n w, x, y, z = cam_info[cam]['calibrated_sensor']['rotation']\\n # sweep sensor to sweep ego\\n sweepsensor2sweepego_rot = torch.Tensor(\\n Quaternion(w, x, y, z).rotation_matrix)\\n sweepsensor2sweepego_tran = torch.Tensor(\\n cam_info[cam]['calibrated_sensor']['translation'])\\n sweepsensor2sweepego = sweepsensor2sweepego_rot.new_zeros(\\n (4, 4))\\n sweepsensor2sweepego[3, 3] = 1\\n sweepsensor2sweepego[:3, :3] = sweepsensor2sweepego_rot\\n sweepsensor2sweepego[:3, -1] = sweepsensor2sweepego_tran\\n # sweep ego to global\\n w, x, y, z = cam_info[cam]['ego_pose']['rotation']\\n sweepego2global_rot = torch.Tensor(\\n Quaternion(w, x, y, z).rotation_matrix)\\n sweepego2global_tran = torch.Tensor(\\n cam_info[cam]['ego_pose']['translation'])\\n sweepego2global = sweepego2global_rot.new_zeros((4, 4))\\n sweepego2global[3, 3] = 1\\n sweepego2global[:3, :3] = sweepego2global_rot\\n sweepego2global[:3, -1] = sweepego2global_tran\\n\\n # global sensor to cur ego\\n w, x, y, z = key_info[cam]['ego_pose']['rotation']\\n keyego2global_rot = torch.Tensor(\\n Quaternion(w, x, y, z).rotation_matrix)\\n keyego2global_tran = torch.Tensor(\\n key_info[cam]['ego_pose']['translation'])\\n keyego2global = keyego2global_rot.new_zeros((4, 4))\\n keyego2global[3, 3] = 1\\n keyego2global[:3, :3] = keyego2global_rot\\n keyego2global[:3, -1] = keyego2global_tran\\n global2keyego = keyego2global.inverse()\\n\\n # cur ego to sensor\\n w, x, y, z = key_info[cam]['calibrated_sensor']['rotation']\\n keysensor2keyego_rot = torch.Tensor(\\n Quaternion(w, x, y, z).rotation_matrix)\\n keysensor2keyego_tran = torch.Tensor(\\n key_info[cam]['calibrated_sensor']['translation'])\\n keysensor2keyego = keysensor2keyego_rot.new_zeros((4, 4))\\n keysensor2keyego[3, 3] = 1\\n keysensor2keyego[:3, :3] = keysensor2keyego_rot\\n keysensor2keyego[:3, -1] = keysensor2keyego_tran\\n keyego2keysensor = keysensor2keyego.inverse()\\n keysensor2sweepsensor = (\\n keyego2keysensor @ global2keyego @ sweepego2global\\n @ sweepsensor2sweepego).inverse()\\n sweepsensor2keyego = global2keyego @ sweepego2global @\\\\\\n sweepsensor2sweepego\\n sensor2ego_mats.append(sweepsensor2keyego)\\n sensor2sensor_mats.append(keysensor2sweepsensor)\\n intrin_mat = torch.zeros((4, 4))\\n intrin_mat[3, 3] = 1\\n intrin_mat[:3, :3] = torch.Tensor(\\n cam_info[cam]['calibrated_sensor']['camera_intrinsic'])\\n\\n file_name = os.path.split(cam_info[cam]['filename'])[-1]\\n if self.return_depth:\\n point_depth = np.fromfile(os.path.join(\\n self.data_root, self.depth_path, f'{file_name}.bin'),\\n dtype=np.float32,\\n count=-1)\\n point_depth = point_depth.reshape(-1, 3)\\n point_depth_augmented = self.depth_transform(\\n point_depth, resize, self.ida_aug_conf['final_dim'],\\n crop, flip, rotate_ida)\\n gt_depths.append(point_depth_augmented)\\n\\n if self.return_radar_pv:\\n radar_point = np.fromfile(os.path.join(\\n self.data_root, self.radar_pv_path, f'{file_name}.bin'),\\n dtype=np.float32,\\n count=-1).reshape(-1, 7)\\n radar_point_augmented = self.transform_radar_pv(\\n radar_point, resize, self.ida_aug_conf['final_dim'],\\n crop, flip, rotate_ida, radar_idx)\\n radar_points.append(radar_point_augmented)\\n\\n img, ida_mat = img_transform(\\n img,\\n resize=resize,\\n resize_dims=resize_dims,\\n crop=crop,\\n flip=flip,\\n rotate=rotate_ida,\\n )\\n ida_mats.append(ida_mat)\\n img = mmcv.imnormalize(np.array(img), self.img_mean,\\n self.img_std, self.to_rgb)\\n img = torch.from_numpy(img).permute(2, 0, 1)\\n imgs.append(img)\\n intrin_mats.append(intrin_mat)\\n timestamps.append(cam_info[cam]['timestamp'])\\n sweep_imgs.append(torch.stack(imgs))\\n sweep_sensor2ego_mats.append(torch.stack(sensor2ego_mats))\\n sweep_intrin_mats.append(torch.stack(intrin_mats))\\n sweep_ida_mats.append(torch.stack(ida_mats))\\n sweep_sensor2sensor_mats.append(torch.stack(sensor2sensor_mats))\\n sweep_timestamps.append(torch.tensor(timestamps))\\n if self.return_depth:\\n sweep_gt_depths.append(torch.stack(gt_depths))\\n if self.return_radar_pv:\\n sweep_radar_points.append(torch.stack(radar_points))\\n\\n ret_list = [\\n torch.stack(sweep_imgs).permute(1, 0, 2, 3, 4),\\n torch.stack(sweep_sensor2ego_mats).permute(1, 0, 2, 3),\\n torch.stack(sweep_intrin_mats).permute(1, 0, 2, 3),\\n torch.stack(sweep_ida_mats).permute(1, 0, 2, 3),\\n torch.stack(sweep_sensor2sensor_mats).permute(1, 0, 2, 3),\\n torch.stack(sweep_timestamps).permute(1, 0),\\n ]\\n if self.return_depth:\\n ret_list.append(torch.stack(sweep_gt_depths).permute(1, 0, 2, 3),)\\n else:\\n ret_list.append(None)\\n if self.return_radar_pv:\\n ret_list.append(torch.stack(sweep_radar_points).permute(1, 0, 2, 3),)\\n else:\\n ret_list.append(None)\\n return ret_list\\n\\n def get_image_meta(self, cam_infos, cams):\\n key_info = cam_infos[0]\\n\\n # Get mean pose of all cams.\\n ego2global_rotation = np.mean(\\n [key_info[cam]['ego_pose']['rotation'] for cam in cams], 0)\\n ego2global_translation = np.mean(\\n [key_info[cam]['ego_pose']['translation'] for cam in cams], 0)\\n img_metas = dict(\\n box_type_3d=LiDARInstance3DBoxes,\\n ego2global_translation=ego2global_translation,\\n ego2global_rotation=ego2global_rotation,\\n )\\n return img_metas\\n\\n def get_image_sensor2ego_mats(self, cam_infos, cams):\\n sweep_sensor2ego_mats = list()\\n for cam in cams:\\n sensor2ego_mats = list()\\n key_info = cam_infos[0]\\n for sweep_idx, cam_info in enumerate(cam_infos):\\n w, x, y, z = cam_info[cam]['calibrated_sensor']['rotation']\\n # sweep sensor to sweep ego\\n sweepsensor2sweepego_rot = torch.Tensor(\\n Quaternion(w, x, y, z).rotation_matrix)\\n sweepsensor2sweepego_tran = torch.Tensor(\\n cam_info[cam]['calibrated_sensor']['translation'])\\n sweepsensor2sweepego = sweepsensor2sweepego_rot.new_zeros(\\n (4, 4))\\n sweepsensor2sweepego[3, 3] = 1\\n sweepsensor2sweepego[:3, :3] = sweepsensor2sweepego_rot\\n sweepsensor2sweepego[:3, -1] = sweepsensor2sweepego_tran\\n # sweep ego to global\\n w, x, y, z = cam_info[cam]['ego_pose']['rotation']\\n sweepego2global_rot = torch.Tensor(\\n Quaternion(w, x, y, z).rotation_matrix)\\n sweepego2global_tran = torch.Tensor(\\n cam_info[cam]['ego_pose']['translation'])\\n sweepego2global = sweepego2global_rot.new_zeros((4, 4))\\n sweepego2global[3, 3] = 1\\n sweepego2global[:3, :3] = sweepego2global_rot\\n sweepego2global[:3, -1] = sweepego2global_tran\\n\\n # global sensor to cur ego\\n w, x, y, z = key_info[cam]['ego_pose']['rotation']\\n keyego2global_rot = torch.Tensor(\\n Quaternion(w, x, y, z).rotation_matrix)\\n keyego2global_tran = torch.Tensor(\\n key_info[cam]['ego_pose']['translation'])\\n keyego2global = keyego2global_rot.new_zeros((4, 4))\\n keyego2global[3, 3] = 1\\n keyego2global[:3, :3] = keyego2global_rot\\n keyego2global[:3, -1] = keyego2global_tran\\n global2keyego = keyego2global.inverse()\\n\\n # cur ego to sensor\\n w, x, y, z = key_info[cam]['calibrated_sensor']['rotation']\\n keysensor2keyego_rot = torch.Tensor(\\n Quaternion(w, x, y, z).rotation_matrix)\\n keysensor2keyego_tran = torch.Tensor(\\n key_info[cam]['calibrated_sensor']['translation'])\\n keysensor2keyego = keysensor2keyego_rot.new_zeros((4, 4))\\n keysensor2keyego[3, 3] = 1\\n keysensor2keyego[:3, :3] = keysensor2keyego_rot\\n keysensor2keyego[:3, -1] = keysensor2keyego_tran\\n sweepsensor2keyego = global2keyego @ sweepego2global @\\\\\\n sweepsensor2sweepego\\n sensor2ego_mats.append(sweepsensor2keyego)\\n sweep_sensor2ego_mats.append(torch.stack(sensor2ego_mats))\\n return torch.stack(sweep_sensor2ego_mats).permute(1, 0, 2, 3)\\n\\n def get_gt(self, info, cams, return_corners=False):\\n \\\"\\\"\\\"Generate gt labels from info.\\n\\n Args:\\n info(dict): Infos needed to generate gt labels.\\n cams(list): Camera names.\\n\\n Returns:\\n Tensor: GT bboxes.\\n Tensor: GT labels.\\n \\\"\\\"\\\"\\n ego2global_rotation = np.mean(\\n [info['cam_infos'][cam]['ego_pose']['rotation'] for cam in cams],\\n 0)\\n ego2global_translation = np.mean([\\n info['cam_infos'][cam]['ego_pose']['translation'] for cam in cams\\n ], 0)\\n trans = -np.array(ego2global_translation)\\n rot = Quaternion(ego2global_rotation).inverse\\n gt_boxes = list()\\n gt_labels = list()\\n if return_corners: # for debugging and visualization\\n gt_corners = list()\\n else:\\n gt_corners = None\\n for ann_info in info['ann_infos']:\\n # Use ego coordinate.\\n if self.gt_for_radar_only:\\n if ann_info['num_radar_pts'] == 0:\\n continue\\n if map_name_from_general_to_detection[ann_info['category_name']] not in self.classes:\\n continue\\n if ann_info['num_lidar_pts'] + ann_info['num_radar_pts'] == 0:\\n continue\\n\\n box = Box(\\n ann_info['translation'],\\n ann_info['size'],\\n Quaternion(ann_info['rotation']),\\n velocity=ann_info['velocity'],\\n )\\n box.translate(trans)\\n box.rotate(rot)\\n box_xyz = np.array(box.center)\\n box_dxdydz = np.array(box.wlh)[[1, 0, 2]]\\n box_yaw = np.array([box.orientation.yaw_pitch_roll[0]])\\n box_velo = np.array(box.velocity[:2])\\n gt_box = np.concatenate([box_xyz, box_dxdydz, box_yaw, box_velo])\\n gt_boxes.append(gt_box)\\n gt_labels.append(\\n self.classes.index(map_name_from_general_to_detection[\\n ann_info['category_name']]))\\n if return_corners: # for debugging and visualization\\n gt_corners.append(box.corners())\\n\\n return torch.Tensor(gt_boxes), torch.tensor(gt_labels), gt_corners\\n\\n def choose_cams(self):\\n \\\"\\\"\\\"Choose cameras randomly.\\n\\n Returns:\\n list: Cameras to be used.\\n \\\"\\\"\\\"\\n if self.is_train and self.ida_aug_conf['Ncams'] < len(\\n self.ida_aug_conf['cams']):\\n cams = np.random.choice(self.ida_aug_conf['cams'],\\n self.ida_aug_conf['Ncams'],\\n replace=False)\\n else:\\n cams = self.ida_aug_conf['cams']\\n return cams\\n\\n def __getitem__(self, idx):\\n if self.use_cbgs:\\n idx = self.sample_indices[idx]\\n cam_infos = list()\\n pts_infos = list()\\n cams = self.choose_cams()\\n for key_idx in self.key_idxes:\\n cur_idx = key_idx + idx\\n # Handle scenarios when current idx doesn't have previous key\\n # frame or previous key frame is from another scene.\\n while self.infos[cur_idx]['scene_token'] != self.infos[idx]['scene_token']:\\n cur_idx += 1\\n info = self.infos[cur_idx]\\n cam_infos.append(info['cam_infos'])\\n pts_infos.append([info['lidar_infos']] + info['lidar_sweeps'])\\n for sweep_idx in self.sweeps_idx:\\n if len(info['cam_sweeps']) == 0:\\n cam_infos.append(info['cam_infos'])\\n else:\\n # Handle scenarios when current sweep doesn't have all cam keys.\\n for i in range(min(len(info['cam_sweeps']) - 1, sweep_idx), -1,\\n -1):\\n if sum([cam in info['cam_sweeps'][i]\\n for cam in cams]) == len(cams):\\n cam_infos.append(info['cam_sweeps'][i])\\n break\\n\\n if self.return_image or self.return_depth or self.return_radar_pv:\\n image_data_list = self.get_image(cam_infos, cams)\\n (\\n sweep_imgs,\\n sweep_sensor2ego_mats,\\n sweep_intrins,\\n sweep_ida_mats,\\n sweep_sensor2sensor_mats,\\n sweep_timestamps,\\n ) = image_data_list[:6]\\n else:\\n (\\n sweep_imgs,\\n sweep_intrins,\\n sweep_ida_mats,\\n sweep_sensor2sensor_mats,\\n sweep_timestamps,\\n ) = None, None, None, None, None\\n sweep_sensor2ego_mats = self.get_image_sensor2ego_mats(cam_infos, cams)\\n\\n img_metas = self.get_image_meta(cam_infos, cams)\\n img_metas['token'] = self.infos[idx]['sample_token']\\n gt_boxes_3d, gt_labels_3d, gt_corners = self.get_gt(self.infos[idx], cams, return_corners=False)\\n\\n rotate_bda, scale_bda, flip_dx, flip_dy = self.sample_bda_augmentation()\\n gt_boxes_3d, bda_rot = bev_det_transform(gt_boxes_3d, rotate_bda, scale_bda, flip_dx, flip_dy)\\n\\n bda_mat = torch.zeros(4, 4, dtype=torch.float32)\\n bda_mat[:3, :3] = bda_rot\\n bda_mat[3, 3] = 1\\n\\n ret_list = [\\n sweep_imgs,\\n sweep_sensor2ego_mats,\\n sweep_intrins,\\n sweep_ida_mats,\\n sweep_sensor2sensor_mats,\\n bda_mat,\\n sweep_timestamps,\\n img_metas,\\n gt_boxes_3d,\\n gt_labels_3d,\\n ]\\n\\n if self.return_depth:\\n ret_list.append(image_data_list[6])\\n else:\\n ret_list.append(None)\\n if self.return_radar_pv:\\n ret_list.append(image_data_list[7])\\n else:\\n ret_list.append(None)\\n\\n return ret_list\\n\\n def __str__(self):\\n return f\\\"\\\"\\\"NuscData: {len(self)} samples. Split: \\\\\\n {\\\"train\\\" if self.is_train else \\\"val\\\"}.\\n Augmentation Conf: {self.ida_aug_conf}\\\"\\\"\\\"\\n\\n def __len__(self):\\n if self.use_cbgs:\\n return len(self.sample_indices)\\n else:\\n return len(self.infos)\"\n },\n {\n \"identifier\": \"collate_fn\",\n \"path\": \"datasets/nusc_det_dataset.py\",\n \"snippet\": \"def collate_fn(data,\\n is_return_image=True,\\n is_return_depth=False,\\n is_return_radar_pv=False):\\n assert (is_return_image or is_return_depth or is_return_radar_pv) is True\\n imgs_batch = list()\\n sensor2ego_mats_batch = list()\\n intrin_mats_batch = list()\\n ida_mats_batch = list()\\n sensor2sensor_mats_batch = list()\\n bda_mat_batch = list()\\n gt_boxes_3d_batch = list()\\n gt_labels_3d_batch = list()\\n img_metas_batch = list()\\n depth_labels_batch = list()\\n radar_pv_batch = list()\\n\\n for iter_data in data:\\n (\\n sweep_imgs,\\n sweep_sensor2ego_mats,\\n sweep_intrins,\\n sweep_ida_mats,\\n sweep_sensor2sensor_mats,\\n bda_mat,\\n sweep_timestamps,\\n img_metas,\\n gt_boxes,\\n gt_labels,\\n ) = iter_data[:10]\\n if is_return_depth:\\n gt_depth = iter_data[10]\\n depth_labels_batch.append(gt_depth)\\n if is_return_radar_pv:\\n radar_pv = iter_data[11]\\n radar_pv_batch.append(radar_pv)\\n\\n imgs_batch.append(sweep_imgs)\\n sensor2ego_mats_batch.append(sweep_sensor2ego_mats)\\n intrin_mats_batch.append(sweep_intrins)\\n ida_mats_batch.append(sweep_ida_mats)\\n sensor2sensor_mats_batch.append(sweep_sensor2sensor_mats)\\n bda_mat_batch.append(bda_mat)\\n img_metas_batch.append(img_metas)\\n gt_boxes_3d_batch.append(gt_boxes)\\n gt_labels_3d_batch.append(gt_labels)\\n\\n if is_return_image:\\n mats_dict = dict()\\n mats_dict['sensor2ego_mats'] = torch.stack(sensor2ego_mats_batch)\\n mats_dict['intrin_mats'] = torch.stack(intrin_mats_batch)\\n mats_dict['ida_mats'] = torch.stack(ida_mats_batch)\\n mats_dict['sensor2sensor_mats'] = torch.stack(sensor2sensor_mats_batch)\\n mats_dict['bda_mat'] = torch.stack(bda_mat_batch)\\n ret_list = [\\n torch.stack(imgs_batch),\\n mats_dict,\\n img_metas_batch,\\n gt_boxes_3d_batch,\\n gt_labels_3d_batch,\\n None, # reserve for segmentation\\n ]\\n else:\\n ret_list = [\\n None,\\n None,\\n img_metas_batch,\\n gt_boxes_3d_batch,\\n gt_labels_3d_batch,\\n None,\\n ]\\n if is_return_depth:\\n ret_list.append(torch.stack(depth_labels_batch))\\n else:\\n ret_list.append(None)\\n if is_return_radar_pv:\\n ret_list.append(torch.stack(radar_pv_batch))\\n else:\\n ret_list.append(None)\\n\\n return ret_list\"\n },\n {\n \"identifier\": \"DetNuscEvaluator\",\n \"path\": \"evaluators/det_evaluators.py\",\n \"snippet\": \"class DetNuscEvaluator():\\n ErrNameMapping = {\\n 'trans_err': 'mATE',\\n 'scale_err': 'mASE',\\n 'orient_err': 'mAOE',\\n 'vel_err': 'mAVE',\\n 'attr_err': 'mAAE',\\n }\\n\\n DefaultAttribute = {\\n 'car': 'vehicle.parked',\\n 'pedestrian': 'pedestrian.moving',\\n 'trailer': 'vehicle.parked',\\n 'truck': 'vehicle.parked',\\n 'bus': 'vehicle.moving',\\n 'motorcycle': 'cycle.without_rider',\\n 'construction_vehicle': 'vehicle.parked',\\n 'bicycle': 'cycle.without_rider',\\n 'barrier': '',\\n 'traffic_cone': '',\\n }\\n\\n def __init__(\\n self,\\n class_names,\\n eval_version='detection_cvpr_2019',\\n data_root='./data/nuScenes',\\n version='v1.0-trainval',\\n modality=dict(use_lidar=False,\\n use_camera=True,\\n use_radar=True,\\n use_map=False,\\n use_external=False),\\n output_dir=None,\\n ) -> None:\\n self.eval_version = eval_version\\n self.data_root = data_root\\n\\n # Load config file and deserialize it.\\n this_dir = osp.dirname(osp.abspath(__file__))\\n with open(osp.join(this_dir, 'configs', '%s.json' % eval_version), 'r') as f:\\n data = json.load(f)\\n self.eval_detection_configs = DetectionConfig.deserialize(data)\\n\\n self.version = version\\n self.class_names = class_names\\n self.modality = modality\\n self.output_dir = output_dir\\n\\n def _evaluate_single(self,\\n result_path,\\n logger=None,\\n metric='bbox',\\n result_name='pts_bbox'):\\n \\\"\\\"\\\"Evaluation for a single model in nuScenes protocol.\\n\\n Args:\\n result_path (str): Path of the result file.\\n logger (logging.Logger | str | None): Logger used for printing\\n related information during evaluation. Default: None.\\n metric (str): Metric name used for evaluation. Default: 'bbox'.\\n result_name (str): Result name in the metric prefix.\\n Default: 'pts_bbox'.\\n\\n Returns:\\n dict: Dictionary of evaluation details.\\n \\\"\\\"\\\"\\n from nuscenes import NuScenes\\n from nuscenes.eval.detection.evaluate import NuScenesEval\\n\\n output_dir = osp.join(*osp.split(result_path)[:-1])\\n nusc = NuScenes(version=self.version,\\n dataroot=self.data_root,\\n verbose=False)\\n eval_set_map = {\\n 'v1.0-mini': 'mini_val',\\n 'v1.0-trainval': 'val',\\n }\\n nusc_eval = NuScenesEval(nusc,\\n config=self.eval_detection_configs,\\n result_path=result_path,\\n eval_set=eval_set_map[self.version],\\n output_dir=output_dir,\\n verbose=False)\\n nusc_eval.main(render_curves=False)\\n # nusc_eval.main(render_curves=True, plot_examples=40)\\n\\n # record metrics\\n metrics = mmcv.load(osp.join(output_dir, 'metrics_summary.json'))\\n detail = dict()\\n metric_prefix = f'{result_name}_NuScenes'\\n for class_name in self.class_names:\\n for k, v in metrics['label_aps'][class_name].items():\\n val = float('{:.4f}'.format(v))\\n detail['{}/{}_AP_dist_{}'.format(metric_prefix, class_name,\\n k)] = val\\n for k, v in metrics['label_tp_errors'][class_name].items():\\n val = float('{:.4f}'.format(v))\\n detail['{}/{}_{}'.format(metric_prefix, class_name, k)] = val\\n for k, v in metrics['tp_errors'].items():\\n val = float('{:.4f}'.format(v))\\n detail['{}/{}'.format(metric_prefix,\\n self.ErrNameMapping[k])] = val\\n\\n detail['{}/NDS'.format(metric_prefix)] = metrics['nd_score']\\n detail['{}/mAP'.format(metric_prefix)] = metrics['mean_ap']\\n return detail\\n\\n def format_results(self,\\n results,\\n img_metas,\\n result_names=['img_bbox'],\\n jsonfile_prefix=None,\\n **kwargs):\\n \\\"\\\"\\\"Format the results to json (standard format for COCO evaluation).\\n\\n Args:\\n results (list[tuple | numpy.ndarray]): Testing results of the\\n dataset.\\n jsonfile_prefix (str | None): The prefix of json files. It includes\\n the file path and the prefix of filename, e.g., \\\"a/b/prefix\\\".\\n If not specified, a temp file will be created. Default: None.\\n\\n Returns:\\n tuple: (result_files, tmp_dir), result_files is a dict containing \\\\\\n the json filepaths, tmp_dir is the temporal directory created \\\\\\n for saving json files when jsonfile_prefix is not specified.\\n \\\"\\\"\\\"\\n assert isinstance(results, list), 'results must be a list'\\n\\n if jsonfile_prefix is None:\\n tmp_dir = tempfile.TemporaryDirectory()\\n jsonfile_prefix = osp.join(tmp_dir.name, 'results')\\n else:\\n tmp_dir = None\\n\\n # currently the output prediction results could be in two formats\\n # 1. list of dict('boxes_3d': ..., 'scores_3d': ..., 'labels_3d': ...)\\n # 2. list of dict('pts_bbox' or 'img_bbox':\\n # dict('boxes_3d': ..., 'scores_3d': ..., 'labels_3d': ...))\\n # this is a workaround to enable evaluation of both formats on nuScenes\\n # refer to https://github.com/open-mmlab/mmdetection3d/issues/449\\n # should take the inner dict out of 'pts_bbox' or 'img_bbox' dict\\n result_files = dict()\\n # refactor this.\\n for rasult_name in result_names:\\n # not evaluate 2D predictions on nuScenes\\n if '2d' in rasult_name:\\n continue\\n print(f'\\\\nFormating bboxes of {rasult_name}')\\n tmp_file_ = osp.join(jsonfile_prefix, rasult_name)\\n if self.output_dir:\\n result_files.update({\\n rasult_name:\\n self._format_bbox(results, img_metas, self.output_dir)\\n })\\n else:\\n result_files.update({\\n rasult_name:\\n self._format_bbox(results, img_metas, tmp_file_)\\n })\\n return result_files, tmp_dir\\n\\n def evaluate(\\n self,\\n results,\\n img_metas,\\n metric='bbox',\\n logger=None,\\n jsonfile_prefix=None,\\n result_names=['img_bbox'],\\n show=False,\\n out_dir=None,\\n pipeline=None,\\n ):\\n \\\"\\\"\\\"Evaluation in nuScenes protocol.\\n\\n Args:\\n results (list[dict]): Testing results of the dataset.\\n metric (str | list[str]): Metrics to be evaluated.\\n logger (logging.Logger | str | None): Logger used for printing\\n related information during evaluation. Default: None.\\n jsonfile_prefix (str | None): The prefix of json files. It includes\\n the file path and the prefix of filename, e.g., \\\"a/b/prefix\\\".\\n If not specified, a temp file will be created. Default: None.\\n show (bool): Whether to visualize.\\n Default: False.\\n out_dir (str): Path to save the visualization results.\\n Default: None.\\n pipeline (list[dict], optional): raw data loading for showing.\\n Default: None.\\n\\n Returns:\\n dict[str, float]: Results of each evaluation metric.\\n \\\"\\\"\\\"\\n result_files, tmp_dir = self.format_results(results, img_metas,\\n result_names,\\n jsonfile_prefix)\\n if isinstance(result_files, dict):\\n for name in result_names:\\n print('Evaluating bboxes of {}'.format(name))\\n print()\\n self._evaluate_single(result_files[name])\\n elif isinstance(result_files, str):\\n self._evaluate_single(result_files)\\n\\n if tmp_dir is not None:\\n tmp_dir.cleanup()\\n\\n def _format_bbox(self, results, img_metas, jsonfile_prefix=None):\\n \\\"\\\"\\\"Convert the results to the standard format.\\n\\n Args:\\n results (list[dict]): Testing results of the dataset.\\n jsonfile_prefix (str): The prefix of the output jsonfile.\\n You can specify the output directory/filename by\\n modifying the jsonfile_prefix. Default: None.\\n\\n Returns:\\n str: Path of the output json file.\\n \\\"\\\"\\\"\\n nusc_annos = {}\\n mapped_class_names = self.class_names\\n\\n print('Start to convert detection format...')\\n\\n for sample_id, det in enumerate(mmcv.track_iter_progress(results)):\\n boxes, scores, labels = det\\n\\n order = np.argsort(scores)[::-1]\\n order = order[:500]\\n\\n boxes = boxes[order]\\n scores = scores[order]\\n labels = labels[order]\\n\\n sample_token = img_metas[sample_id]['token']\\n trans = np.array(img_metas[sample_id]['ego2global_translation'])\\n rot = Quaternion(img_metas[sample_id]['ego2global_rotation'])\\n annos = list()\\n for i, box in enumerate(boxes):\\n name = mapped_class_names[labels[i]]\\n center = box[:3]\\n wlh = box[[4, 3, 5]]\\n box_yaw = box[6]\\n box_vel = box[7:].tolist()\\n box_vel.append(0)\\n quat = pyquaternion.Quaternion(axis=[0, 0, 1], radians=box_yaw)\\n nusc_box = Box(center, wlh, quat, velocity=box_vel)\\n nusc_box.rotate(rot)\\n nusc_box.translate(trans)\\n if np.sqrt(nusc_box.velocity[0]**2 +\\n nusc_box.velocity[1]**2) > 0.2:\\n if name in [\\n 'car',\\n 'construction_vehicle',\\n 'bus',\\n 'truck',\\n 'trailer',\\n ]:\\n attr = 'vehicle.moving'\\n elif name in ['bicycle', 'motorcycle']:\\n attr = 'cycle.with_rider'\\n else:\\n attr = self.DefaultAttribute[name]\\n else:\\n if name in ['pedestrian']:\\n attr = 'pedestrian.standing'\\n elif name in ['bus']:\\n attr = 'vehicle.stopped'\\n else:\\n attr = self.DefaultAttribute[name]\\n nusc_anno = dict(\\n sample_token=sample_token,\\n translation=nusc_box.center.tolist(),\\n size=nusc_box.wlh.tolist(),\\n rotation=nusc_box.orientation.elements.tolist(),\\n velocity=nusc_box.velocity[:2],\\n detection_name=name,\\n detection_score=float(scores[i]),\\n attribute_name=attr,\\n )\\n annos.append(nusc_anno)\\n # other views results of the same frame should be concatenated\\n if sample_token in nusc_annos:\\n nusc_annos[sample_token].extend(annos)\\n else:\\n nusc_annos[sample_token] = annos\\n nusc_submissions = {\\n 'meta': self.modality,\\n 'results': nusc_annos,\\n }\\n mmcv.mkdir_or_exist(jsonfile_prefix)\\n res_path = osp.join(jsonfile_prefix, 'results_nusc.json')\\n print('Results writes to', res_path)\\n mmcv.dump(nusc_submissions, res_path)\\n return res_path\"\n },\n {\n \"identifier\": \"BaseBEVDepth\",\n \"path\": \"models/base_bev_depth.py\",\n \"snippet\": \"class BaseBEVDepth(nn.Module):\\n \\\"\\\"\\\"Source code of `BEVDepth`, `https://arxiv.org/abs/2112.11790`.\\n\\n Args:\\n backbone_conf (dict): Config of backbone.\\n head_conf (dict): Config of head.\\n \\\"\\\"\\\"\\n\\n def __init__(self, backbone_conf, head_conf):\\n super(BaseBEVDepth, self).__init__()\\n self.backbone_img = BaseLSSFPN(**backbone_conf)\\n self.head = BEVDepthHead(**head_conf)\\n\\n # for inference time measurement\\n self.idx = 0\\n self.times_dict = {\\n 'img': [],\\n 'img_backbone': [],\\n 'img_dep': [],\\n 'img_transform': [],\\n 'img_pool': [],\\n\\n 'head': [],\\n 'head_backbone': [],\\n 'head_head': [],\\n }\\n\\n def forward(self,\\n sweep_imgs,\\n mats_dict,\\n is_train=False\\n ):\\n \\\"\\\"\\\"Forward function for BEVDepth\\n\\n Args:\\n sweep_imgs (Tensor): Input images.\\n mats_dict(dict):\\n sensor2ego_mats(Tensor): Transformation matrix from\\n camera to ego with shape of (B, num_sweeps,\\n num_cameras, 4, 4).\\n intrin_mats(Tensor): Intrinsic matrix with shape\\n of (B, num_sweeps, num_cameras, 4, 4).\\n ida_mats(Tensor): Transformation matrix for ida with\\n shape of (B, num_sweeps, num_cameras, 4, 4).\\n sensor2sensor_mats(Tensor): Transformation matrix\\n from key frame camera to sweep frame camera with\\n shape of (B, num_sweeps, num_cameras, 4, 4).\\n bda_mat(Tensor): Rotation matrix for bda with shape\\n of (B, 4, 4).\\n\\n Returns:\\n tuple(list[dict]): Output results for tasks.\\n \\\"\\\"\\\"\\n if is_train:\\n self.time = None\\n\\n x, depth, _ = self.backbone_img(sweep_imgs, mats_dict,\\n is_return_depth=True)\\n preds, _ = self.head(x)\\n return preds, depth\\n else:\\n if self.idx < 100: # skip few iterations for warmup\\n self.times = None\\n elif self.idx == 100:\\n self.times = self.times_dict\\n\\n x, self.times = self.backbone_img(sweep_imgs, mats_dict,\\n times=self.times)\\n preds, self.times = self.head(x, times=self.times)\\n\\n if self.idx == 1000:\\n time_mean = {}\\n for k, v in self.times.items():\\n time_mean[k] = sum(v) / len(v)\\n print('img: %.2f' % time_mean['img'])\\n print(' img_backbone: %.2f' % time_mean['img_backbone'])\\n print(' img_dep: %.2f' % time_mean['img_dep'])\\n print(' img_transform: %.2f' % time_mean['img_transform'])\\n print(' img_pool: %.2f' % time_mean['img_pool'])\\n print('head: %.2f' % time_mean['head'])\\n print(' head_backbone: %.2f' % time_mean['head_backbone'])\\n print(' head_head: %.2f' % time_mean['head_head'])\\n total = time_mean['img'] + time_mean['head']\\n print('total: %.2f' % total)\\n print(' ')\\n print('FPS: %.2f' % (1000/total))\\n\\n self.idx += 1\\n return preds\\n\\n def get_targets(self, gt_boxes, gt_labels):\\n \\\"\\\"\\\"Generate training targets for a single sample.\\n\\n Args:\\n gt_bboxes_3d (:obj:`LiDARInstance3DBoxes`): Ground truth gt boxes.\\n gt_labels_3d (torch.Tensor): Labels of boxes.\\n\\n Returns:\\n tuple[list[torch.Tensor]]: Tuple of target including \\\\\\n the following results in order.\\n\\n - list[torch.Tensor]: Heatmap scores.\\n - list[torch.Tensor]: Ground truth boxes.\\n - list[torch.Tensor]: Indexes indicating the position \\\\\\n of the valid boxes.\\n - list[torch.Tensor]: Masks indicating which boxes \\\\\\n are valid.\\n \\\"\\\"\\\"\\n return self.head.get_targets(gt_boxes, gt_labels)\\n\\n def loss(self, targets, preds_dicts):\\n \\\"\\\"\\\"Loss function for BEVDepth.\\n\\n Args:\\n gt_bboxes_3d (list[:obj:`LiDARInstance3DBoxes`]): Ground\\n truth gt boxes.\\n gt_labels_3d (list[torch.Tensor]): Labels of boxes.\\n preds_dicts (dict): Output of forward function.\\n\\n Returns:\\n dict[str:torch.Tensor]: Loss of heatmap and bbox of each task.\\n \\\"\\\"\\\"\\n return self.head.loss(targets, preds_dicts)\\n\\n def get_bboxes(self, preds_dicts, img_metas=None, img=None, rescale=False):\\n \\\"\\\"\\\"Generate bboxes from bbox head predictions.\\n\\n Args:\\n preds_dicts (tuple[list[dict]]): Prediction results.\\n img_metas (list[dict]): Point cloud and image's meta info.\\n\\n Returns:\\n list[dict]: Decoded bbox, scores and labels after nms.\\n \\\"\\\"\\\"\\n return self.head.get_bboxes(preds_dicts, img_metas, img, rescale)\"\n },\n {\n \"identifier\": \"all_gather_object\",\n \"path\": \"utils/torch_dist.py\",\n \"snippet\": \"def all_gather_object(obj):\\n world_size = get_world_size()\\n if world_size < 2:\\n return [obj]\\n output = [None for _ in range(world_size)]\\n dist.all_gather_object(output, obj)\\n return output\"\n },\n {\n \"identifier\": \"synchronize\",\n \"path\": \"utils/torch_dist.py\",\n \"snippet\": \"def synchronize():\\n \\\"\\\"\\\"Helper function to synchronize (barrier)\\n among all processes when using distributed training\\\"\\\"\\\"\\n if not dist.is_available():\\n return\\n if not dist.is_initialized():\\n return\\n current_world_size = dist.get_world_size()\\n if current_world_size == 1:\\n return\\n dist.barrier()\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from functools import partial\nfrom pytorch_lightning.core import LightningModule\nfrom torch.cuda.amp.autocast_mode import autocast\nfrom torch.optim.lr_scheduler import MultiStepLR\nfrom mmcv.runner import build_optimizer\nfrom datasets.nusc_det_dataset import NuscDatasetRadarDet, collate_fn\nfrom evaluators.det_evaluators import DetNuscEvaluator\nfrom models.base_bev_depth import BaseBEVDepth\nfrom utils.torch_dist import all_gather_object, synchronize\nimport mmcv\nimport torch\nimport torch.nn.functional as F\nimport torch.nn.parallel\nimport torch.utils.data\nimport torch.utils.data.distributed\nimport torchvision.models as models"},"token_num":{"kind":"number","value":16127,"string":"16,127"},"cropped_code":{"kind":"string","value":" Output:\n gt_depths: [B*N*h*w, d]\n \"\"\"\n B, N, H, W = gt_depths.shape\n gt_depths = gt_depths.view(\n B * N,\n H // self.downsample_factor,\n self.downsample_factor,\n W // self.downsample_factor,\n self.downsample_factor,\n 1,\n )\n gt_depths = gt_depths.permute(0, 1, 3, 5, 2, 4).contiguous()\n gt_depths = gt_depths.view(\n -1, self.downsample_factor * self.downsample_factor)\n gt_depths_tmp = torch.where(gt_depths == 0.0,\n 1e5 * torch.ones_like(gt_depths),\n gt_depths)\n gt_depths = torch.min(gt_depths_tmp, dim=-1).values\n gt_depths = gt_depths.view(B * N, H // self.downsample_factor,\n W // self.downsample_factor)\n\n gt_depths = (gt_depths -\n (self.dbound[0] - self.dbound[2])) / self.dbound[2]\n gt_depths = torch.where(\n (gt_depths < self.depth_channels + 1) & (gt_depths > 0.),\n gt_depths, torch.zeros_like(gt_depths))\n gt_depths = F.one_hot(gt_depths.long(),\n num_classes=self.depth_channels + 1).view(\n -1, self.depth_channels + 1)[:, 1:]\n return gt_depths.float()\n\n def eval_step(self, batch, batch_idx, prefix: str):\n (sweep_imgs, mats, img_metas, _, _, _, _, pts_pv) = batch\n if torch.cuda.is_available():\n if self.return_image:\n sweep_imgs = sweep_imgs.cuda()\n for key, value in mats.items():\n mats[key] = value.cuda()\n if self.return_radar_pv:\n pts_pv = pts_pv.cuda()\n preds = self(sweep_imgs, mats,\n pts_pv=pts_pv,\n is_train=False)\n if isinstance(self.model, torch.nn.parallel.DistributedDataParallel):\n results = self.model.module.get_bboxes(preds, img_metas)\n else:\n results = self.model.get_bboxes(preds, img_metas)\n for i in range(len(results)):\n results[i][0] = results[i][0].tensor.detach().cpu().numpy()\n results[i][1] = results[i][1].detach().cpu().numpy()\n results[i][2] = results[i][2].detach().cpu().numpy()\n results[i].append(img_metas[i])\n return results\n\n def validation_epoch_end(self, validation_step_outputs):\n detection_losses = list()\n heatmap_losses = list()\n bbox_losses = list()\n depth_losses = list()\n for validation_step_output in validation_step_outputs:\n detection_losses.append(validation_step_output[0])\n heatmap_losses.append(validation_step_output[1])\n bbox_losses.append(validation_step_output[2])\n depth_losses.append(validation_step_output[3])\n synchronize()\n\n self.log('val/detection', torch.mean(torch.stack(detection_losses)), on_epoch=True)\n self.log('val/heatmap', torch.mean(torch.stack(heatmap_losses)), on_epoch=True)\n self.log('val/bbox', torch.mean(torch.stack(bbox_losses)), on_epoch=True)\n self.log('val/depth', torch.mean(torch.stack(depth_losses)), on_epoch=True)\n\n def validation_step(self, batch, batch_idx):\n (sweep_imgs, mats, _, gt_boxes_3d, gt_labels_3d, _, depth_labels, pts_pv) = batch\n if torch.cuda.is_available():\n if self.return_image:\n sweep_imgs = sweep_imgs.cuda()\n for key, value in mats.items():\n mats[key] = value.cuda()\n if self.return_radar_pv:\n pts_pv = pts_pv.cuda()\n gt_boxes_3d = [gt_box.cuda() for gt_box in gt_boxes_3d]\n gt_labels_3d = [gt_label.cuda() for gt_label in gt_labels_3d]\n with torch.no_grad():\n preds, depth_preds = self(sweep_imgs, mats,\n pts_pv=pts_pv,\n is_train=True)\n targets = self.model.get_targets(gt_boxes_3d, gt_labels_3d)\n loss_detection, loss_heatmap, loss_bbox = self.model.loss(targets, preds)\n\n if len(depth_labels.shape) == 5:\n # only key-frame will calculate depth loss\n depth_labels = depth_labels[:, 0, ...].contiguous()\n loss_depth = self.get_depth_loss(depth_labels.cuda(), depth_preds, weight=3.)\n return loss_detection, loss_heatmap, loss_bbox, loss_depth\n\n def test_epoch_end(self, test_step_outputs):\n all_pred_results = list()\n all_img_metas = list()\n for test_step_output in test_step_outputs:\n for i in range(len(test_step_output)):\n all_pred_results.append(test_step_output[i][:3])\n all_img_metas.append(test_step_output[i][3])\n synchronize()\n # TODO: Change another way.\n dataset_length = len(self.val_dataloader().dataset)\n all_pred_results = sum(\n map(list, zip(*all_gather_object(all_pred_results))),\n [])[:dataset_length]\n all_img_metas = sum(map(list, zip(*all_gather_object(all_img_metas))),\n [])[:dataset_length]\n if self.global_rank == 0:\n self.evaluator.evaluate(all_pred_results, all_img_metas)\n\n def configure_optimizers(self):\n optimizer = build_optimizer(self.model, self.optimizer_config)\n scheduler = MultiStepLR(optimizer, [19, 23])\n return [[optimizer], [scheduler]]\n\n def train_dataloader(self):\n"},"all_code":{"kind":"string","value":"# Copyright (c) Megvii Inc. All rights reserved.\n\n\n\npretrain_config = dict(\n img_model_path=None,\n img_load_key=[],\n img_freeze_key=None,\n pts_model_path=None,\n pts_load_key=[])\n\noptimizer_config = dict(\n type='AdamW',\n lr=2e-4,\n weight_decay=1e-2)\n\nH = 900\nW = 1600\nfinal_dim = (256, 704)\nimg_conf = dict(img_mean=[123.675, 116.28, 103.53],\n img_std=[58.395, 57.12, 57.375],\n to_rgb=True)\n\nida_aug_conf = {\n 'resize_lim': (0.386, 0.55),\n 'final_dim': final_dim,\n 'rot_lim': (-5.4, 5.4),\n 'H': 900,\n 'W': 1600,\n 'rand_flip': True,\n 'bot_pct_lim': (0.0, 0.0),\n 'cams': ['CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT',\n 'CAM_BACK_LEFT', 'CAM_BACK', 'CAM_BACK_RIGHT'],\n 'Ncams': 6,\n}\nbda_aug_conf = {\n 'rot_ratio': 1.0,\n 'rot_lim': (-22.5, 22.5),\n 'scale_lim': (0.95, 1.05),\n 'flip_dx_ratio': 0.5,\n 'flip_dy_ratio': 0.5\n}\nrda_aug_conf = {\n 'N_sweeps': 6,\n 'N_use': 5,\n 'drop_ratio': 0.1,\n}\n\nbackbone_img_conf = {\n 'x_bound': [-51.2, 51.2, 0.8],\n 'y_bound': [-51.2, 51.2, 0.8],\n 'z_bound': [-5, 3, 8],\n 'd_bound': [2.0, 58.0, 0.8],\n 'final_dim': final_dim,\n 'output_channels': 80,\n 'downsample_factor': 16,\n 'img_backbone_conf': dict(\n type='ResNet',\n depth=50,\n frozen_stages=0,\n out_indices=[0, 1, 2, 3],\n norm_eval=False,\n init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),\n ),\n 'img_neck_conf': dict(\n type='SECONDFPN',\n in_channels=[256, 512, 1024, 2048],\n upsample_strides=[0.25, 0.5, 1, 2],\n out_channels=[128, 128, 128, 128],\n ),\n 'depth_net_conf':\n dict(in_channels=512, mid_channels=512),\n 'camera_aware': True\n}\n\nCLASSES = [\n 'car', 'truck', 'construction_vehicle', 'bus', 'trailer',\n 'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone',\n]\n\nhead_conf = {\n 'bev_backbone_conf': dict(\n type='ResNet',\n in_channels=80,\n depth=18,\n num_stages=3,\n strides=(1, 2, 2),\n dilations=(1, 1, 1),\n out_indices=[0, 1, 2],\n norm_eval=False,\n base_channels=160),\n 'bev_neck_conf': dict(\n type='SECONDFPN',\n in_channels=[80, 160, 320, 640],\n upsample_strides=[1, 2, 4, 8],\n out_channels=[64, 64, 64, 64]),\n 'tasks': [\n dict(num_class=1, class_names=['car']),\n dict(num_class=2, class_names=['truck', 'construction_vehicle']),\n dict(num_class=2, class_names=['bus', 'trailer']),\n dict(num_class=1, class_names=['barrier']),\n dict(num_class=2, class_names=['motorcycle', 'bicycle']),\n dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),],\n 'common_heads': dict(\n reg=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),\n 'bbox_coder': dict(\n type='CenterPointBBoxCoder',\n post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],\n max_num=500,\n score_threshold=0.1,\n out_size_factor=4,\n voxel_size=[0.2, 0.2, 8],\n pc_range=[-51.2, -51.2, -5, 51.2, 51.2, 3],\n code_size=9),\n 'train_cfg': dict(\n point_cloud_range=[-51.2, -51.2, -5, 51.2, 51.2, 3],\n grid_size=[512, 512, 1],\n voxel_size=[0.2, 0.2, 8],\n out_size_factor=4,\n dense_reg=1,\n gaussian_overlap=0.1,\n max_objs=500,\n min_radius=2,\n code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5, 0.5]),\n 'test_cfg': dict(\n post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],\n max_per_img=500,\n max_pool_nms=False,\n min_radius=[4, 12, 10, 1, 0.85, 0.175],\n score_threshold=0.1,\n out_size_factor=4,\n voxel_size=[0.2, 0.2, 8],\n nms_type='circle',\n pre_max_size=1000,\n post_max_size=83,\n nms_thr=0.2),\n 'in_channels': 256, # Equal to bev_neck output_channels.\n 'loss_cls': dict(type='GaussianFocalLoss', reduction='mean'),\n 'loss_bbox': dict(type='L1Loss', reduction='mean', loss_weight=0.25),\n 'gaussian_overlap': 0.1,\n 'min_radius': 2,\n}\n\n\nclass BEVDepthLightningModel(LightningModule):\n MODEL_NAMES = sorted(name for name in models.__dict__\n if name.islower() and not name.startswith('__')\n and callable(models.__dict__[name]))\n\n def __init__(self,\n gpus: int = 1,\n data_root='data/nuScenes',\n eval_interval=1,\n batch_size_per_device=8,\n class_names=CLASSES,\n backbone_img_conf=backbone_img_conf,\n head_conf=head_conf,\n ida_aug_conf=ida_aug_conf,\n bda_aug_conf=bda_aug_conf,\n rda_aug_conf=rda_aug_conf,\n default_root_dir='./outputs/',\n **kwargs):\n super().__init__()\n self.save_hyperparameters()\n self.gpus = gpus\n self.optimizer_config = optimizer_config\n self.pretrain_config = pretrain_config\n self.eval_interval = eval_interval\n self.batch_size_per_device = batch_size_per_device\n self.data_root = data_root\n self.class_names = class_names\n self.backbone_img_conf = backbone_img_conf\n self.head_conf = head_conf\n self.ida_aug_conf = ida_aug_conf\n self.bda_aug_conf = bda_aug_conf\n self.rda_aug_conf = rda_aug_conf\n mmcv.mkdir_or_exist(default_root_dir)\n self.default_root_dir = default_root_dir\n self.evaluator = DetNuscEvaluator(class_names=self.class_names,\n output_dir=self.default_root_dir)\n self.model = BaseBEVDepth(self.backbone_img_conf,\n self.head_conf)\n self.mode = 'valid'\n self.img_conf = img_conf\n self.data_use_cbgs = False\n self.load_interval = 1\n self.num_sweeps = 1\n self.sweep_idxes = list()\n self.key_idxes = list()\n self.data_return_depth = True\n self.downsample_factor = self.backbone_img_conf['downsample_factor']\n self.dbound = self.backbone_img_conf['d_bound']\n self.depth_channels = int(\n (self.dbound[1] - self.dbound[0]) / self.dbound[2])\n self.use_fusion = False\n self.train_info_paths = 'data/nuScenes/nuscenes_infos_train.pkl'\n self.val_info_paths = 'data/nuScenes/nuscenes_infos_val.pkl'\n self.predict_info_paths = 'data/nuScenes/nuscenes_infos_test.pkl'\n\n self.return_image = True\n self.return_depth = True\n self.return_radar_pv = False\n\n self.remove_z_axis = True\n\n def forward(self, sweep_imgs, mats, is_train=False, **inputs):\n return self.model(sweep_imgs, mats, is_train=is_train)\n\n def training_step(self, batch):\n if self.global_rank == 0:\n for pg in self.trainer.optimizers[0].param_groups:\n self.log('learning_rate', pg[\"lr\"])\n\n (sweep_imgs, mats, _, gt_boxes_3d, gt_labels_3d, _, depth_labels, pts_pv) = batch\n if torch.cuda.is_available():\n if self.return_image:\n sweep_imgs = sweep_imgs.cuda()\n for key, value in mats.items():\n mats[key] = value.cuda()\n if self.return_radar_pv:\n pts_pv = pts_pv.cuda()\n gt_boxes_3d = [gt_box.cuda() for gt_box in gt_boxes_3d]\n gt_labels_3d = [gt_label.cuda() for gt_label in gt_labels_3d]\n preds, depth_preds = self(sweep_imgs, mats,\n pts_pv=pts_pv,\n is_train=True)\n targets = self.model.get_targets(gt_boxes_3d, gt_labels_3d)\n loss_detection, loss_heatmap, loss_bbox = self.model.loss(targets, preds)\n\n if len(depth_labels.shape) == 5:\n # only key-frame will calculate depth loss\n depth_labels = depth_labels[:, 0, ...].contiguous()\n loss_depth = self.get_depth_loss(depth_labels.cuda(), depth_preds)\n self.log('train/detection', loss_detection)\n self.log('train/heatmap', loss_heatmap)\n self.log('train/bbox', loss_bbox)\n self.log('train/depth', loss_depth)\n\n return loss_detection + loss_depth\n\n def get_depth_loss(self, depth_labels, depth_preds, weight=3.):\n depth_labels = self.get_downsampled_gt_depth(depth_labels)\n depth_preds = depth_preds.permute(0, 2, 3, 1).contiguous().view(\n -1, self.depth_channels)\n fg_mask = torch.max(depth_labels, dim=1).values > 0.0\n\n with autocast(enabled=False):\n loss_depth = (F.binary_cross_entropy(\n depth_preds[fg_mask],\n depth_labels[fg_mask],\n reduction='none',\n ).sum() / max(1.0, fg_mask.sum()))\n\n return weight * loss_depth\n\n def get_downsampled_gt_depth(self, gt_depths):\n \"\"\"\n Input:\n gt_depths: [B, N, H, W]\n Output:\n gt_depths: [B*N*h*w, d]\n \"\"\"\n B, N, H, W = gt_depths.shape\n gt_depths = gt_depths.view(\n B * N,\n H // self.downsample_factor,\n self.downsample_factor,\n W // self.downsample_factor,\n self.downsample_factor,\n 1,\n )\n gt_depths = gt_depths.permute(0, 1, 3, 5, 2, 4).contiguous()\n gt_depths = gt_depths.view(\n -1, self.downsample_factor * self.downsample_factor)\n gt_depths_tmp = torch.where(gt_depths == 0.0,\n 1e5 * torch.ones_like(gt_depths),\n gt_depths)\n gt_depths = torch.min(gt_depths_tmp, dim=-1).values\n gt_depths = gt_depths.view(B * N, H // self.downsample_factor,\n W // self.downsample_factor)\n\n gt_depths = (gt_depths -\n (self.dbound[0] - self.dbound[2])) / self.dbound[2]\n gt_depths = torch.where(\n (gt_depths < self.depth_channels + 1) & (gt_depths > 0.),\n gt_depths, torch.zeros_like(gt_depths))\n gt_depths = F.one_hot(gt_depths.long(),\n num_classes=self.depth_channels + 1).view(\n -1, self.depth_channels + 1)[:, 1:]\n return gt_depths.float()\n\n def eval_step(self, batch, batch_idx, prefix: str):\n (sweep_imgs, mats, img_metas, _, _, _, _, pts_pv) = batch\n if torch.cuda.is_available():\n if self.return_image:\n sweep_imgs = sweep_imgs.cuda()\n for key, value in mats.items():\n mats[key] = value.cuda()\n if self.return_radar_pv:\n pts_pv = pts_pv.cuda()\n preds = self(sweep_imgs, mats,\n pts_pv=pts_pv,\n is_train=False)\n if isinstance(self.model, torch.nn.parallel.DistributedDataParallel):\n results = self.model.module.get_bboxes(preds, img_metas)\n else:\n results = self.model.get_bboxes(preds, img_metas)\n for i in range(len(results)):\n results[i][0] = results[i][0].tensor.detach().cpu().numpy()\n results[i][1] = results[i][1].detach().cpu().numpy()\n results[i][2] = results[i][2].detach().cpu().numpy()\n results[i].append(img_metas[i])\n return results\n\n def validation_epoch_end(self, validation_step_outputs):\n detection_losses = list()\n heatmap_losses = list()\n bbox_losses = list()\n depth_losses = list()\n for validation_step_output in validation_step_outputs:\n detection_losses.append(validation_step_output[0])\n heatmap_losses.append(validation_step_output[1])\n bbox_losses.append(validation_step_output[2])\n depth_losses.append(validation_step_output[3])\n synchronize()\n\n self.log('val/detection', torch.mean(torch.stack(detection_losses)), on_epoch=True)\n self.log('val/heatmap', torch.mean(torch.stack(heatmap_losses)), on_epoch=True)\n self.log('val/bbox', torch.mean(torch.stack(bbox_losses)), on_epoch=True)\n self.log('val/depth', torch.mean(torch.stack(depth_losses)), on_epoch=True)\n\n def validation_step(self, batch, batch_idx):\n (sweep_imgs, mats, _, gt_boxes_3d, gt_labels_3d, _, depth_labels, pts_pv) = batch\n if torch.cuda.is_available():\n if self.return_image:\n sweep_imgs = sweep_imgs.cuda()\n for key, value in mats.items():\n mats[key] = value.cuda()\n if self.return_radar_pv:\n pts_pv = pts_pv.cuda()\n gt_boxes_3d = [gt_box.cuda() for gt_box in gt_boxes_3d]\n gt_labels_3d = [gt_label.cuda() for gt_label in gt_labels_3d]\n with torch.no_grad():\n preds, depth_preds = self(sweep_imgs, mats,\n pts_pv=pts_pv,\n is_train=True)\n targets = self.model.get_targets(gt_boxes_3d, gt_labels_3d)\n loss_detection, loss_heatmap, loss_bbox = self.model.loss(targets, preds)\n\n if len(depth_labels.shape) == 5:\n # only key-frame will calculate depth loss\n depth_labels = depth_labels[:, 0, ...].contiguous()\n loss_depth = self.get_depth_loss(depth_labels.cuda(), depth_preds, weight=3.)\n return loss_detection, loss_heatmap, loss_bbox, loss_depth\n\n def test_epoch_end(self, test_step_outputs):\n all_pred_results = list()\n all_img_metas = list()\n for test_step_output in test_step_outputs:\n for i in range(len(test_step_output)):\n all_pred_results.append(test_step_output[i][:3])\n all_img_metas.append(test_step_output[i][3])\n synchronize()\n # TODO: Change another way.\n dataset_length = len(self.val_dataloader().dataset)\n all_pred_results = sum(\n map(list, zip(*all_gather_object(all_pred_results))),\n [])[:dataset_length]\n all_img_metas = sum(map(list, zip(*all_gather_object(all_img_metas))),\n [])[:dataset_length]\n if self.global_rank == 0:\n self.evaluator.evaluate(all_pred_results, all_img_metas)\n\n def configure_optimizers(self):\n optimizer = build_optimizer(self.model, self.optimizer_config)\n scheduler = MultiStepLR(optimizer, [19, 23])\n return [[optimizer], [scheduler]]\n\n def train_dataloader(self):"},"next_line":{"kind":"string","value":" train_dataset = NuscDatasetRadarDet("},"gold_snippet_index":{"kind":"number","value":0,"string":"0"},"created_at":{"kind":"string","value":"2023-12-06 14:57:49+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5293,"cells":{"repo_name":{"kind":"string","value":"jinxixiang/magic_animate_unofficial"},"file_path":{"kind":"string","value":"animatediff/magic_animate/pipeline.py"},"context":{"kind":"list like","value":[{"identifier":"UNet3DConditionModel","path":"animatediff/magic_animate/unet_controlnet.py","snippet":"class UNet3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optional[int] = None,\n in_channels: int = 4,\n out_channels: int = 4,\n center_input_sample: bool = False,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\",\n ),\n mid_block_type: str = \"UNetMidBlock3DCrossAttn\",\n up_block_types: Tuple[str] = (\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\"\n ),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: int = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1280,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n dual_cross_attention: bool = False,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n\n # Additional\n use_motion_module=False,\n motion_module_resolutions=(1, 2, 4, 8),\n motion_module_mid_block=False,\n motion_module_decoder_only=False,\n motion_module_type=None,\n motion_module_kwargs={},\n unet_use_cross_frame_attention=None,\n unet_use_temporal_attention=None,\n\n # Addition for image embeddings\n use_image_condition=False,\n # Additional for dwpose adapter\n use_dwpose_adapter=False,\n ):\n super().__init__()\n\n self.sample_size = sample_size\n time_embed_dim = block_out_channels[0] * 4\n\n # input\n self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))\n\n # time\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n # dwpose condition\n if use_dwpose_adapter:\n self.dwpose_adapter = ControlNetConditioningEmbedding(conditioning_embedding_channels=4) # pose guider net\n else:\n self.dwpose_adapter = None\n\n self.use_image_condition = False\n if use_image_condition:\n self.use_image_condition = True\n self.image_proj_model = Resampler(\n dim=cross_attention_dim,\n depth=4,\n dim_head=64,\n heads=12,\n num_queries=16,\n embedding_dim=1024,\n output_dim=cross_attention_dim,\n ff_mult=4,\n )\n\n self.down_blocks = nn.ModuleList([])\n self.mid_block = None\n self.up_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n for i, down_block_type in enumerate(down_block_types):\n res = 2 ** i\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[i],\n downsample_padding=downsample_padding,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n\n use_motion_module=use_motion_module and (res in motion_module_resolutions) and (\n not motion_module_decoder_only),\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n self.down_blocks.append(down_block)\n\n # mid\n if mid_block_type == \"UNetMidBlock3DCrossAttn\":\n self.mid_block = UNetMidBlock3DCrossAttn(\n in_channels=block_out_channels[-1],\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n\n use_motion_module=use_motion_module and motion_module_mid_block,\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n else:\n raise ValueError(f\"unknown mid_block_type : {mid_block_type}\")\n\n # count how many layers upsample the videos\n self.num_upsamplers = 0\n\n # up\n reversed_block_out_channels = list(reversed(block_out_channels))\n reversed_attention_head_dim = list(reversed(attention_head_dim))\n only_cross_attention = list(reversed(only_cross_attention))\n output_channel = reversed_block_out_channels[0]\n for i, up_block_type in enumerate(up_block_types):\n res = 2 ** (3 - i)\n is_final_block = i == len(block_out_channels) - 1\n\n prev_output_channel = output_channel\n output_channel = reversed_block_out_channels[i]\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\n\n # add upsample block for all BUT final layer\n if not is_final_block:\n add_upsample = True\n self.num_upsamplers += 1\n else:\n add_upsample = False\n\n up_block = get_up_block(\n up_block_type,\n num_layers=layers_per_block + 1,\n in_channels=input_channel,\n out_channels=output_channel,\n prev_output_channel=prev_output_channel,\n temb_channels=time_embed_dim,\n add_upsample=add_upsample,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n attn_num_head_channels=reversed_attention_head_dim[i],\n dual_cross_attention=dual_cross_attention,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\n unet_use_temporal_attention=unet_use_temporal_attention,\n\n use_motion_module=use_motion_module and (res in motion_module_resolutions),\n motion_module_type=motion_module_type,\n motion_module_kwargs=motion_module_kwargs,\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)\n self.conv_act = nn.SiLU()\n self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)\n\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, halves the input to the attention heads, so attention will be computed in two steps. If\n `\"max\"`, maxium amount of memory will be saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_slicable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_slicable_dims(module)\n\n num_slicable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_slicable_layers * [1]\n\n slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n class_labels: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n # for controlnet\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n # for pose_guider\n dwpose_conditions: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNet3DConditionOutput, Tuple]:\n r\"\"\"\n Args:\n sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor\n timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps\n encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.\n\n Returns:\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\n [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When\n returning a tuple, the first element is the sample tensor.\n \"\"\"\n # By default samples have to be AT least a multiple of the overall upsampling factor.\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\n # on the fly if necessary.\n default_overall_up_factor = 2 ** self.num_upsamplers\n\n # if self.use_image_condition:\n # # project global image to 16 tokens for cross-attention\n # encoder_hidden_states = self.image_proj(encoder_hidden_states)\n # encoder_hidden_states = encoder_hidden_states.reshape(-1, 16, 768)\n # encoder_hidden_states = self.image_norm(encoder_hidden_states)\n\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\n forward_upsample_size = False\n upsample_size = None\n\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n logger.info(\"Forward upsample size to force interpolation output size.\")\n forward_upsample_size = True\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # center input if necessary\n if self.config.center_input_sample:\n sample = 2 * sample - 1.0\n\n # time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n emb = self.time_embedding(t_emb)\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n\n # add pose conditions\n if dwpose_conditions is not None:\n conditions = self.dwpose_adapter(dwpose_conditions)\n sample += conditions\n\n # pre-process\n sample = self.conv_in(sample)\n\n # down\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\n\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb,\n encoder_hidden_states=encoder_hidden_states)\n\n down_block_res_samples += res_samples\n\n if is_controlnet:\n new_down_block_res_samples = ()\n\n for down_block_res_sample, down_block_additional_residual in zip(\n down_block_res_samples, down_block_additional_residuals\n ):\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # mid\n sample = self.mid_block(\n sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\n )\n\n if is_controlnet:\n sample = sample + mid_block_additional_residual\n\n # up\n for i, upsample_block in enumerate(self.up_blocks):\n is_final_block = i == len(self.up_blocks) - 1\n\n res_samples = down_block_res_samples[-len(upsample_block.resnets):]\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\n\n # if we have not reached the final block and need to forward the\n # upsample size, we do it here\n if not is_final_block and forward_upsample_size:\n upsample_size = down_block_res_samples[-1].shape[2:]\n\n if hasattr(upsample_block, \"has_cross_attention\") and upsample_block.has_cross_attention:\n sample = upsample_block(\n hidden_states=sample,\n temb=emb,\n res_hidden_states_tuple=res_samples,\n encoder_hidden_states=encoder_hidden_states,\n upsample_size=upsample_size,\n attention_mask=attention_mask,\n )\n else:\n sample = upsample_block(\n hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size,\n encoder_hidden_states=encoder_hidden_states,\n )\n\n # post-process\n sample = self.conv_norm_out(sample)\n sample = self.conv_act(sample)\n sample = self.conv_out(sample)\n\n if not return_dict:\n return (sample,)\n\n return UNet3DConditionOutput(sample=sample)\n\n @classmethod\n def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):\n if subfolder is not None:\n pretrained_model_path = os.path.join(pretrained_model_path, subfolder)\n print(f\"loaded temporal unet's pretrained weights from {pretrained_model_path} ...\")\n\n config_file = os.path.join(pretrained_model_path, 'config.json')\n if not os.path.isfile(config_file):\n raise RuntimeError(f\"{config_file} does not exist\")\n with open(config_file, \"r\") as f:\n config = json.load(f)\n config[\"_class_name\"] = cls.__name__\n config[\"down_block_types\"] = [\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"CrossAttnDownBlock3D\",\n \"DownBlock3D\"\n ]\n config[\"up_block_types\"] = [\n \"UpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\",\n \"CrossAttnUpBlock3D\"\n ]\n # config[\"mid_block_type\"] = \"UNetMidBlock3DCrossAttn\"\n\n from diffusers.utils import WEIGHTS_NAME\n model = cls.from_config(config, **unet_additional_kwargs)\n model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)\n if not os.path.isfile(model_file):\n raise RuntimeError(f\"{model_file} does not exist\")\n state_dict = torch.load(model_file, map_location=\"cpu\")\n\n m, u = model.load_state_dict(state_dict, strict=False)\n print(f\"### missing keys: {len(m)}; \\n### unexpected keys: {len(u)};\")\n # print(f\"### missing keys:\\n{m}\\n### unexpected keys:\\n{u}\\n\")\n\n params = [p.numel() if \"temporal\" in n else 0 for n, p in model.named_parameters()]\n print(f\"### Temporal Module Parameters: {sum(params) / 1e6} M\")\n\n return model"},{"identifier":"ControlNetModel","path":"animatediff/magic_animate/controlnet.py","snippet":"class ControlNetModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n in_channels: int = 4,\n flip_sin_to_cos: bool = True,\n freq_shift: int = 0,\n down_block_types: Tuple[str] = (\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"CrossAttnDownBlock2D\",\n \"DownBlock2D\",\n ),\n only_cross_attention: Union[bool, Tuple[bool]] = False,\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\n layers_per_block: int = 2,\n downsample_padding: int = 1,\n mid_block_scale_factor: float = 1,\n act_fn: str = \"silu\",\n norm_num_groups: Optional[int] = 32,\n norm_eps: float = 1e-5,\n cross_attention_dim: int = 1280,\n attention_head_dim: Union[int, Tuple[int]] = 8,\n use_linear_projection: bool = False,\n class_embed_type: Optional[str] = None,\n num_class_embeds: Optional[int] = None,\n upcast_attention: bool = False,\n resnet_time_scale_shift: str = \"default\",\n projection_class_embeddings_input_dim: Optional[int] = None,\n controlnet_conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\n ):\n super().__init__()\n\n # Check inputs\n if len(block_out_channels) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}.\"\n )\n\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\n raise ValueError(\n f\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\"\n )\n\n # input\n conv_in_kernel = 3\n conv_in_padding = (conv_in_kernel - 1) // 2\n self.conv_in = nn.Conv2d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n )\n\n # time\n time_embed_dim = block_out_channels[0] * 4\n\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\n timestep_input_dim = block_out_channels[0]\n\n self.time_embedding = TimestepEmbedding(\n timestep_input_dim,\n time_embed_dim,\n act_fn=act_fn,\n )\n\n # class embedding\n if class_embed_type is None and num_class_embeds is not None:\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\n elif class_embed_type == \"timestep\":\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\n elif class_embed_type == \"identity\":\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\n elif class_embed_type == \"projection\":\n if projection_class_embeddings_input_dim is None:\n raise ValueError(\n \"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set\"\n )\n # The projection `class_embed_type` is the same as the timestep `class_embed_type` except\n # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings\n # 2. it projects from an arbitrary input dimension.\n #\n # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.\n # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.\n # As a result, `TimestepEmbedding` can be passed arbitrary vectors.\n self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\n else:\n self.class_embedding = None\n\n # control net conditioning embedding\n self.controlnet_cond_embedding = ControlNetConditioningEmbedding(\n conditioning_embedding_channels=block_out_channels[0],\n block_out_channels=conditioning_embedding_out_channels,\n )\n\n self.down_blocks = nn.ModuleList([])\n self.controlnet_down_blocks = nn.ModuleList([])\n\n if isinstance(only_cross_attention, bool):\n only_cross_attention = [only_cross_attention] * len(down_block_types)\n\n if isinstance(attention_head_dim, int):\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\n\n # down\n output_channel = block_out_channels[0]\n\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n for i, down_block_type in enumerate(down_block_types):\n input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n\n down_block = get_down_block(\n down_block_type,\n num_layers=layers_per_block,\n in_channels=input_channel,\n out_channels=output_channel,\n temb_channels=time_embed_dim,\n add_downsample=not is_final_block,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n resnet_groups=norm_num_groups,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=attention_head_dim[i],\n downsample_padding=downsample_padding,\n use_linear_projection=use_linear_projection,\n only_cross_attention=only_cross_attention[i],\n upcast_attention=upcast_attention,\n resnet_time_scale_shift=resnet_time_scale_shift,\n )\n self.down_blocks.append(down_block)\n\n for _ in range(layers_per_block):\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n if not is_final_block:\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_down_blocks.append(controlnet_block)\n\n # mid\n mid_block_channel = block_out_channels[-1]\n\n controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)\n controlnet_block = zero_module(controlnet_block)\n self.controlnet_mid_block = controlnet_block\n\n self.mid_block = UNetMidBlock2DCrossAttn(\n in_channels=mid_block_channel,\n temb_channels=time_embed_dim,\n resnet_eps=norm_eps,\n resnet_act_fn=act_fn,\n output_scale_factor=mid_block_scale_factor,\n resnet_time_scale_shift=resnet_time_scale_shift,\n cross_attention_dim=cross_attention_dim,\n num_attention_heads=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n )\n\n @classmethod\n def from_unet(\n cls,\n unet: UNet2DConditionModel,\n controlnet_conditioning_channel_order: str = \"rgb\",\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\n load_weights_from_unet: bool = True,\n ):\n r\"\"\"\n Instantiate Controlnet class from UNet2DConditionModel.\n\n Parameters:\n unet (`UNet2DConditionModel`):\n UNet model which weights are copied to the ControlNet. Note that all configuration options are also\n copied where applicable.\n \"\"\"\n controlnet = cls(\n in_channels=unet.config.in_channels,\n flip_sin_to_cos=unet.config.flip_sin_to_cos,\n freq_shift=unet.config.freq_shift,\n down_block_types=unet.config.down_block_types,\n only_cross_attention=unet.config.only_cross_attention,\n block_out_channels=unet.config.block_out_channels,\n layers_per_block=unet.config.layers_per_block,\n downsample_padding=unet.config.downsample_padding,\n mid_block_scale_factor=unet.config.mid_block_scale_factor,\n act_fn=unet.config.act_fn,\n norm_num_groups=unet.config.norm_num_groups,\n norm_eps=unet.config.norm_eps,\n cross_attention_dim=unet.config.cross_attention_dim,\n attention_head_dim=unet.config.attention_head_dim,\n use_linear_projection=unet.config.use_linear_projection,\n class_embed_type=unet.config.class_embed_type,\n num_class_embeds=unet.config.num_class_embeds,\n upcast_attention=unet.config.upcast_attention,\n resnet_time_scale_shift=unet.config.resnet_time_scale_shift,\n projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,\n controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,\n conditioning_embedding_out_channels=conditioning_embedding_out_channels,\n )\n\n if load_weights_from_unet:\n controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())\n controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())\n controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())\n\n if controlnet.class_embedding:\n controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())\n\n controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())\n controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())\n\n return controlnet\n\n # @property\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors\n # def attn_processors(self) -> Dict[str, AttentionProcessor]:\n # r\"\"\"\n # Returns:\n # `dict` of attention processors: A dictionary containing all attention processors used in the model with\n # indexed by its weight name.\n # \"\"\"\n # # set recursively\n # processors = {}\n\n # def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\n # if hasattr(module, \"set_processor\"):\n # processors[f\"{name}.processor\"] = module.processor\n\n # for sub_name, child in module.named_children():\n # fn_recursive_add_processors(f\"{name}.{sub_name}\", child, processors)\n\n # return processors\n\n # for name, module in self.named_children():\n # fn_recursive_add_processors(name, module, processors)\n\n # return processors\n\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor\n # def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\n # r\"\"\"\n # Parameters:\n # `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):\n # The instantiated processor class or a dictionary of processor classes that will be set as the processor\n # of **all** `Attention` layers.\n # In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:\n\n # \"\"\"\n # count = len(self.attn_processors.keys())\n\n # if isinstance(processor, dict) and len(processor) != count:\n # raise ValueError(\n # f\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\"\n # f\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\"\n # )\n\n # def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\n # if hasattr(module, \"set_processor\"):\n # if not isinstance(processor, dict):\n # module.set_processor(processor)\n # else:\n # module.set_processor(processor.pop(f\"{name}.processor\"))\n\n # for sub_name, child in module.named_children():\n # fn_recursive_attn_processor(f\"{name}.{sub_name}\", child, processor)\n\n # for name, module in self.named_children():\n # fn_recursive_attn_processor(name, module, processor)\n\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\n # def set_default_attn_processor(self):\n # \"\"\"\n # Disables custom attention processors and sets the default attention implementation.\n # \"\"\"\n # self.set_attn_processor(AttnProcessor())\n\n # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice\n def set_attention_slice(self, slice_size):\n r\"\"\"\n Enable sliced attention computation.\n\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\n\n Args:\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\"auto\"`):\n When `\"auto\"`, halves the input to the attention heads, so attention will be computed in two steps. If\n `\"max\"`, maximum amount of memory will be saved by running only one slice at a time. If a number is\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\n must be a multiple of `slice_size`.\n \"\"\"\n sliceable_head_dims = []\n\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\n if hasattr(module, \"set_attention_slice\"):\n sliceable_head_dims.append(module.sliceable_head_dim)\n\n for child in module.children():\n fn_recursive_retrieve_sliceable_dims(child)\n\n # retrieve number of attention layers\n for module in self.children():\n fn_recursive_retrieve_sliceable_dims(module)\n\n num_sliceable_layers = len(sliceable_head_dims)\n\n if slice_size == \"auto\":\n # half the attention head size is usually a good trade-off between\n # speed and memory\n slice_size = [dim // 2 for dim in sliceable_head_dims]\n elif slice_size == \"max\":\n # make smallest slice possible\n slice_size = num_sliceable_layers * [1]\n\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n\n if len(slice_size) != len(sliceable_head_dims):\n raise ValueError(\n f\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\"\n f\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\"\n )\n\n for i in range(len(slice_size)):\n size = slice_size[i]\n dim = sliceable_head_dims[i]\n if size is not None and size > dim:\n raise ValueError(f\"size {size} has to be smaller or equal to {dim}.\")\n\n # Recursively walk through all the children.\n # Any children which exposes the set_attention_slice method\n # gets the message\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\n if hasattr(module, \"set_attention_slice\"):\n module.set_attention_slice(slice_size.pop())\n\n for child in module.children():\n fn_recursive_set_attention_slice(child, slice_size)\n\n reversed_slice_size = list(reversed(slice_size))\n for module in self.children():\n fn_recursive_set_attention_slice(module, reversed_slice_size)\n\n def _set_gradient_checkpointing(self, module, value=False):\n if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):\n module.gradient_checkpointing = value\n\n def forward(\n self,\n sample: torch.FloatTensor,\n timestep: Union[torch.Tensor, float, int],\n encoder_hidden_states: torch.Tensor,\n controlnet_cond: torch.FloatTensor,\n conditioning_scale: float = 1.0,\n class_labels: Optional[torch.Tensor] = None,\n timestep_cond: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n return_dict: bool = True,\n ) -> Union[ControlNetOutput, Tuple]:\n # check channel order\n channel_order = self.config.controlnet_conditioning_channel_order\n\n if channel_order == \"rgb\":\n # in rgb order by default\n ...\n elif channel_order == \"bgr\":\n controlnet_cond = torch.flip(controlnet_cond, dims=[1])\n else:\n raise ValueError(f\"unknown `controlnet_conditioning_channel_order`: {channel_order}\")\n\n # prepare attention_mask\n if attention_mask is not None:\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # 1. time\n timesteps = timestep\n if not torch.is_tensor(timesteps):\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\n # This would be a good case for the `match` statement (Python 3.10+)\n is_mps = sample.device.type == \"mps\"\n if isinstance(timestep, float):\n dtype = torch.float32 if is_mps else torch.float64\n else:\n dtype = torch.int32 if is_mps else torch.int64\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\n elif len(timesteps.shape) == 0:\n timesteps = timesteps[None].to(sample.device)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timesteps = timesteps.expand(sample.shape[0])\n\n t_emb = self.time_proj(timesteps)\n\n # timesteps does not contain any weights and will always return f32 tensors\n # but time_embedding might actually be running in fp16. so we need to cast here.\n # there might be better ways to encapsulate this.\n t_emb = t_emb.to(dtype=self.dtype)\n\n emb = self.time_embedding(t_emb, timestep_cond)\n\n if self.class_embedding is not None:\n if class_labels is None:\n raise ValueError(\"class_labels should be provided when num_class_embeds > 0\")\n\n if self.config.class_embed_type == \"timestep\":\n class_labels = self.time_proj(class_labels)\n\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\n emb = emb + class_emb\n\n # 2. pre-process\n sample = self.conv_in(sample)\n\n controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)\n\n sample += controlnet_cond\n\n # 3. down\n down_block_res_samples = (sample,)\n for downsample_block in self.down_blocks:\n if hasattr(downsample_block, \"has_cross_attention\") and downsample_block.has_cross_attention:\n sample, res_samples = downsample_block(\n hidden_states=sample,\n temb=emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n # cross_attention_kwargs=cross_attention_kwargs,\n )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\n\n down_block_res_samples += res_samples\n\n # 4. mid\n if self.mid_block is not None:\n sample = self.mid_block(\n sample,\n emb,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=attention_mask,\n # cross_attention_kwargs=cross_attention_kwargs,\n )\n\n # 5. Control net blocks\n\n controlnet_down_block_res_samples = ()\n\n for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):\n down_block_res_sample = controlnet_block(down_block_res_sample)\n controlnet_down_block_res_samples += (down_block_res_sample,)\n\n down_block_res_samples = controlnet_down_block_res_samples\n\n mid_block_res_sample = self.controlnet_mid_block(sample)\n\n # 6. scaling\n down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]\n mid_block_res_sample *= conditioning_scale\n\n if not return_dict:\n return (down_block_res_samples, mid_block_res_sample)\n\n return ControlNetOutput(\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\n )"},{"identifier":"ReferenceAttentionControl","path":"animatediff/magic_animate/mutual_self_attention.py","snippet":"class ReferenceAttentionControl():\n \n def __init__(self, \n unet,\n mode=\"write\",\n do_classifier_free_guidance=False,\n attention_auto_machine_weight = float('inf'),\n gn_auto_machine_weight = 1.0,\n style_fidelity = 1.0,\n reference_attn=True,\n reference_adain=False,\n fusion_blocks=\"midup\",\n batch_size=1,\n clip_length=8,\n is_image=False,\n ) -> None:\n # 10. Modify self attention and group norm\n self.unet = unet\n assert mode in [\"read\", \"write\"]\n assert fusion_blocks in [\"midup\", \"full\"]\n self.reference_attn = reference_attn\n self.reference_adain = reference_adain\n self.fusion_blocks = fusion_blocks\n self.register_reference_hooks(\n mode, \n do_classifier_free_guidance,\n clip_length,\n attention_auto_machine_weight,\n gn_auto_machine_weight,\n style_fidelity,\n reference_attn,\n reference_adain,\n fusion_blocks=fusion_blocks,\n batch_size=batch_size,\n is_image=is_image,\n )\n\n def register_reference_hooks(\n self, \n mode, \n do_classifier_free_guidance,\n clip_length,\n attention_auto_machine_weight,\n gn_auto_machine_weight,\n style_fidelity,\n reference_attn,\n reference_adain,\n dtype=torch.float16,\n batch_size=1, \n num_images_per_prompt=1, \n device=torch.device(\"cpu\"), \n fusion_blocks='midup',\n is_image=False,\n ):\n MODE = mode\n do_classifier_free_guidance = do_classifier_free_guidance\n attention_auto_machine_weight = attention_auto_machine_weight\n gn_auto_machine_weight = gn_auto_machine_weight\n style_fidelity = style_fidelity\n reference_attn = reference_attn\n reference_adain = reference_adain\n fusion_blocks = fusion_blocks\n num_images_per_prompt = num_images_per_prompt\n dtype=dtype\n if do_classifier_free_guidance:\n # uc_mask = (\n # torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)\n # .to(device)\n # .bool()\n # )\n\n uc_mask = (\n torch.Tensor(\n [1] * batch_size * num_images_per_prompt * clip_length + [0] * batch_size * num_images_per_prompt * clip_length)\n .to(device)\n .bool()\n )\n\n else:\n uc_mask = (\n torch.Tensor([0] * batch_size * num_images_per_prompt * 2)\n .to(device)\n .bool()\n )\n \n def hacked_basic_transformer_inner_forward(\n self,\n hidden_states: torch.FloatTensor,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n timestep: Optional[torch.LongTensor] = None,\n cross_attention_kwargs: Dict[str, Any] = None,\n class_labels: Optional[torch.LongTensor] = None,\n video_length=None,\n ):\n if self.use_ada_layer_norm:\n norm_hidden_states = self.norm1(hidden_states, timestep)\n elif self.use_ada_layer_norm_zero:\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\n )\n else:\n norm_hidden_states = self.norm1(hidden_states)\n\n # 1. Self-Attention\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\n if self.only_cross_attention:\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n else:\n if MODE == \"write\":\n self.bank.append(norm_hidden_states.clone())\n attn_output = self.attn1(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\n attention_mask=attention_mask,\n **cross_attention_kwargs,\n )\n if MODE == \"read\":\n if not is_image:\n self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), \"b t l c -> (b t) l c\")[:hidden_states.shape[0]] for d in self.bank]\n\n hidden_states_uc = self.attn1(norm_hidden_states,\n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\n attention_mask=attention_mask) + hidden_states\n hidden_states_c = hidden_states_uc.clone()\n _uc_mask = uc_mask.clone()\n if do_classifier_free_guidance:\n if hidden_states.shape[0] != _uc_mask.shape[0]:\n _uc_mask = (\n torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))\n .to(device)\n .bool()\n )\n hidden_states_c[_uc_mask] = self.attn1(\n norm_hidden_states[_uc_mask],\n encoder_hidden_states=norm_hidden_states[_uc_mask],\n attention_mask=attention_mask,\n ) + hidden_states[_uc_mask]\n hidden_states = hidden_states_c.clone()\n \n self.bank.clear()\n if self.attn2 is not None:\n # Cross-Attention\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n hidden_states = (\n self.attn2(\n norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\n )\n + hidden_states\n )\n\n # Feed-forward\n hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states\n\n # Temporal-Attention\n if not is_image:\n if self.unet_use_temporal_attention:\n d = hidden_states.shape[1]\n hidden_states = rearrange(hidden_states, \"(b f) d c -> (b d) f c\", f=video_length)\n norm_hidden_states = (\n self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)\n )\n hidden_states = self.attn_temp(norm_hidden_states) + hidden_states\n hidden_states = rearrange(hidden_states, \"(b d) f c -> (b f) d c\", d=d)\n\n return hidden_states\n \n if self.use_ada_layer_norm_zero:\n attn_output = gate_msa.unsqueeze(1) * attn_output\n hidden_states = attn_output + hidden_states\n\n if self.attn2 is not None:\n norm_hidden_states = (\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\n )\n\n # 2. Cross-Attention\n attn_output = self.attn2(\n norm_hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n attention_mask=encoder_attention_mask,\n **cross_attention_kwargs,\n )\n hidden_states = attn_output + hidden_states\n\n # 3. Feed-forward\n norm_hidden_states = self.norm3(hidden_states)\n\n if self.use_ada_layer_norm_zero:\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\n\n ff_output = self.ff(norm_hidden_states)\n\n if self.use_ada_layer_norm_zero:\n ff_output = gate_mlp.unsqueeze(1) * ff_output\n\n hidden_states = ff_output + hidden_states\n\n return hidden_states\n\n def hacked_mid_forward(self, *args, **kwargs):\n eps = 1e-6\n x = self.original_forward(*args, **kwargs)\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append(mean)\n self.var_bank.append(var)\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\n x_c = x_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n x_c[uc_mask] = x[uc_mask]\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\n self.mean_bank = []\n self.var_bank = []\n return x\n\n def hack_CrossAttnDownBlock2D_forward(\n self,\n hidden_states: torch.FloatTensor,\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ):\n eps = 1e-6\n\n # TODO(Patrick, William) - attention mask is not used\n output_states = ()\n\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_DownBlock2D_forward(self, hidden_states, temb=None):\n eps = 1e-6\n\n output_states = ()\n\n for i, resnet in enumerate(self.resnets):\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n output_states = output_states + (hidden_states,)\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.downsamplers is not None:\n for downsampler in self.downsamplers:\n hidden_states = downsampler(hidden_states)\n\n output_states = output_states + (hidden_states,)\n\n return hidden_states, output_states\n\n def hacked_CrossAttnUpBlock2D_forward(\n self,\n hidden_states: torch.FloatTensor,\n res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],\n temb: Optional[torch.FloatTensor] = None,\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\n upsample_size: Optional[int] = None,\n attention_mask: Optional[torch.FloatTensor] = None,\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\n ):\n eps = 1e-6\n # TODO(Patrick, William) - attention mask is not used\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n hidden_states = attn(\n hidden_states,\n encoder_hidden_states=encoder_hidden_states,\n cross_attention_kwargs=cross_attention_kwargs,\n attention_mask=attention_mask,\n encoder_attention_mask=encoder_attention_mask,\n return_dict=False,\n )[0]\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):\n eps = 1e-6\n for i, resnet in enumerate(self.resnets):\n # pop res hidden states\n res_hidden_states = res_hidden_states_tuple[-1]\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\n hidden_states = resnet(hidden_states, temb)\n\n if MODE == \"write\":\n if gn_auto_machine_weight >= self.gn_weight:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n self.mean_bank.append([mean])\n self.var_bank.append([var])\n if MODE == \"read\":\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\n hidden_states_c = hidden_states_uc.clone()\n if do_classifier_free_guidance and style_fidelity > 0:\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\n\n if MODE == \"read\":\n self.mean_bank = []\n self.var_bank = []\n\n if self.upsamplers is not None:\n for upsampler in self.upsamplers:\n hidden_states = upsampler(hidden_states, upsample_size)\n\n return hidden_states\n\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)] \n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n\n for i, module in enumerate(attn_modules):\n module._original_inner_forward = module.forward\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\n module.bank = []\n module.attn_weight = float(i) / float(len(attn_modules))\n\n if self.reference_adain:\n gn_modules = [self.unet.mid_block]\n self.unet.mid_block.gn_weight = 0\n\n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\n gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n module.gn_weight = float(w) / float(len(up_blocks))\n gn_modules.append(module)\n\n for i, module in enumerate(gn_modules):\n if getattr(module, \"original_forward\", None) is None:\n module.original_forward = module.forward\n if i == 0:\n # mid_block\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\n elif isinstance(module, CrossAttnDownBlock2D):\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\n elif isinstance(module, DownBlock2D):\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\n elif isinstance(module, CrossAttnUpBlock2D):\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\n elif isinstance(module, UpBlock2D):\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\n module.mean_bank = []\n module.var_bank = []\n module.gn_weight *= 2\n \n def update(self, writer, dtype=torch.float16):\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]\n writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]\n writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]) \n writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n for r, w in zip(reader_attn_modules, writer_attn_modules):\n r.bank = [v.clone().to(dtype) for v in w.bank]\n # w.bank.clear()\n if self.reference_adain:\n reader_gn_modules = [self.unet.mid_block]\n \n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n reader_gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n reader_gn_modules.append(module)\n \n writer_gn_modules = [writer.unet.mid_block]\n \n down_blocks = writer.unet.down_blocks\n for w, module in enumerate(down_blocks):\n writer_gn_modules.append(module)\n\n up_blocks = writer.unet.up_blocks\n for w, module in enumerate(up_blocks):\n writer_gn_modules.append(module)\n \n for r, w in zip(reader_gn_modules, writer_gn_modules):\n if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):\n r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]\n r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]\n else:\n r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]\n r.var_bank = [v.clone().to(dtype) for v in w.var_bank]\n \n def clear(self):\n if self.reference_attn:\n if self.fusion_blocks == \"midup\":\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n elif self.fusion_blocks == \"full\":\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\n for r in reader_attn_modules:\n r.bank.clear()\n if self.reference_adain:\n reader_gn_modules = [self.unet.mid_block]\n \n down_blocks = self.unet.down_blocks\n for w, module in enumerate(down_blocks):\n reader_gn_modules.append(module)\n\n up_blocks = self.unet.up_blocks\n for w, module in enumerate(up_blocks):\n reader_gn_modules.append(module)\n \n for r in reader_gn_modules:\n r.mean_bank.clear()\n r.var_bank.clear()"},{"identifier":"get_context_scheduler","path":"animatediff/magic_animate/context.py","snippet":"def get_context_scheduler(name: str) -> Callable:\n if name == \"uniform\":\n return uniform\n else:\n raise ValueError(f\"Unknown context_overlap policy {name}\")"},{"identifier":"get_total_steps","path":"animatediff/magic_animate/context.py","snippet":"def get_total_steps(\n scheduler,\n timesteps: List[int],\n num_steps: Optional[int] = None,\n num_frames: int = ...,\n context_size: Optional[int] = None,\n context_stride: int = 3,\n context_overlap: int = 4,\n closed_loop: bool = True,\n):\n return sum(\n len(\n list(\n scheduler(\n i,\n num_steps,\n num_frames,\n context_size,\n context_stride,\n context_overlap,\n )\n )\n )\n for i in range(len(timesteps))\n )"},{"identifier":"get_tensor_interpolation_method","path":"animatediff/utils/util.py","snippet":"def get_tensor_interpolation_method():\n return tensor_interpolation"}],"string":"[\n {\n \"identifier\": \"UNet3DConditionModel\",\n \"path\": \"animatediff/magic_animate/unet_controlnet.py\",\n \"snippet\": \"class UNet3DConditionModel(ModelMixin, ConfigMixin):\\n _supports_gradient_checkpointing = True\\n\\n @register_to_config\\n def __init__(\\n self,\\n sample_size: Optional[int] = None,\\n in_channels: int = 4,\\n out_channels: int = 4,\\n center_input_sample: bool = False,\\n flip_sin_to_cos: bool = True,\\n freq_shift: int = 0,\\n down_block_types: Tuple[str] = (\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"DownBlock3D\\\",\\n ),\\n mid_block_type: str = \\\"UNetMidBlock3DCrossAttn\\\",\\n up_block_types: Tuple[str] = (\\n \\\"UpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\"\\n ),\\n only_cross_attention: Union[bool, Tuple[bool]] = False,\\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\\n layers_per_block: int = 2,\\n downsample_padding: int = 1,\\n mid_block_scale_factor: float = 1,\\n act_fn: str = \\\"silu\\\",\\n norm_num_groups: int = 32,\\n norm_eps: float = 1e-5,\\n cross_attention_dim: int = 1280,\\n attention_head_dim: Union[int, Tuple[int]] = 8,\\n dual_cross_attention: bool = False,\\n use_linear_projection: bool = False,\\n class_embed_type: Optional[str] = None,\\n num_class_embeds: Optional[int] = None,\\n upcast_attention: bool = False,\\n resnet_time_scale_shift: str = \\\"default\\\",\\n\\n # Additional\\n use_motion_module=False,\\n motion_module_resolutions=(1, 2, 4, 8),\\n motion_module_mid_block=False,\\n motion_module_decoder_only=False,\\n motion_module_type=None,\\n motion_module_kwargs={},\\n unet_use_cross_frame_attention=None,\\n unet_use_temporal_attention=None,\\n\\n # Addition for image embeddings\\n use_image_condition=False,\\n # Additional for dwpose adapter\\n use_dwpose_adapter=False,\\n ):\\n super().__init__()\\n\\n self.sample_size = sample_size\\n time_embed_dim = block_out_channels[0] * 4\\n\\n # input\\n self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))\\n\\n # time\\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\\n timestep_input_dim = block_out_channels[0]\\n\\n self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n\\n # class embedding\\n if class_embed_type is None and num_class_embeds is not None:\\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\\n elif class_embed_type == \\\"timestep\\\":\\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n elif class_embed_type == \\\"identity\\\":\\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\\n else:\\n self.class_embedding = None\\n\\n # dwpose condition\\n if use_dwpose_adapter:\\n self.dwpose_adapter = ControlNetConditioningEmbedding(conditioning_embedding_channels=4) # pose guider net\\n else:\\n self.dwpose_adapter = None\\n\\n self.use_image_condition = False\\n if use_image_condition:\\n self.use_image_condition = True\\n self.image_proj_model = Resampler(\\n dim=cross_attention_dim,\\n depth=4,\\n dim_head=64,\\n heads=12,\\n num_queries=16,\\n embedding_dim=1024,\\n output_dim=cross_attention_dim,\\n ff_mult=4,\\n )\\n\\n self.down_blocks = nn.ModuleList([])\\n self.mid_block = None\\n self.up_blocks = nn.ModuleList([])\\n\\n if isinstance(only_cross_attention, bool):\\n only_cross_attention = [only_cross_attention] * len(down_block_types)\\n\\n if isinstance(attention_head_dim, int):\\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\\n\\n # down\\n output_channel = block_out_channels[0]\\n for i, down_block_type in enumerate(down_block_types):\\n res = 2 ** i\\n input_channel = output_channel\\n output_channel = block_out_channels[i]\\n is_final_block = i == len(block_out_channels) - 1\\n\\n down_block = get_down_block(\\n down_block_type,\\n num_layers=layers_per_block,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n temb_channels=time_embed_dim,\\n add_downsample=not is_final_block,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=attention_head_dim[i],\\n downsample_padding=downsample_padding,\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n\\n use_motion_module=use_motion_module and (res in motion_module_resolutions) and (\\n not motion_module_decoder_only),\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n self.down_blocks.append(down_block)\\n\\n # mid\\n if mid_block_type == \\\"UNetMidBlock3DCrossAttn\\\":\\n self.mid_block = UNetMidBlock3DCrossAttn(\\n in_channels=block_out_channels[-1],\\n temb_channels=time_embed_dim,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n output_scale_factor=mid_block_scale_factor,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=attention_head_dim[-1],\\n resnet_groups=norm_num_groups,\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n upcast_attention=upcast_attention,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n\\n use_motion_module=use_motion_module and motion_module_mid_block,\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n else:\\n raise ValueError(f\\\"unknown mid_block_type : {mid_block_type}\\\")\\n\\n # count how many layers upsample the videos\\n self.num_upsamplers = 0\\n\\n # up\\n reversed_block_out_channels = list(reversed(block_out_channels))\\n reversed_attention_head_dim = list(reversed(attention_head_dim))\\n only_cross_attention = list(reversed(only_cross_attention))\\n output_channel = reversed_block_out_channels[0]\\n for i, up_block_type in enumerate(up_block_types):\\n res = 2 ** (3 - i)\\n is_final_block = i == len(block_out_channels) - 1\\n\\n prev_output_channel = output_channel\\n output_channel = reversed_block_out_channels[i]\\n input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]\\n\\n # add upsample block for all BUT final layer\\n if not is_final_block:\\n add_upsample = True\\n self.num_upsamplers += 1\\n else:\\n add_upsample = False\\n\\n up_block = get_up_block(\\n up_block_type,\\n num_layers=layers_per_block + 1,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n prev_output_channel=prev_output_channel,\\n temb_channels=time_embed_dim,\\n add_upsample=add_upsample,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n attn_num_head_channels=reversed_attention_head_dim[i],\\n dual_cross_attention=dual_cross_attention,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n\\n unet_use_cross_frame_attention=unet_use_cross_frame_attention,\\n unet_use_temporal_attention=unet_use_temporal_attention,\\n\\n use_motion_module=use_motion_module and (res in motion_module_resolutions),\\n motion_module_type=motion_module_type,\\n motion_module_kwargs=motion_module_kwargs,\\n )\\n self.up_blocks.append(up_block)\\n prev_output_channel = output_channel\\n\\n # out\\n self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)\\n self.conv_act = nn.SiLU()\\n self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)\\n\\n def set_attention_slice(self, slice_size):\\n r\\\"\\\"\\\"\\n Enable sliced attention computation.\\n\\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\\n\\n Args:\\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\\\"auto\\\"`):\\n When `\\\"auto\\\"`, halves the input to the attention heads, so attention will be computed in two steps. If\\n `\\\"max\\\"`, maxium amount of memory will be saved by running only one slice at a time. If a number is\\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\\n must be a multiple of `slice_size`.\\n \\\"\\\"\\\"\\n sliceable_head_dims = []\\n\\n def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n sliceable_head_dims.append(module.sliceable_head_dim)\\n\\n for child in module.children():\\n fn_recursive_retrieve_slicable_dims(child)\\n\\n # retrieve number of attention layers\\n for module in self.children():\\n fn_recursive_retrieve_slicable_dims(module)\\n\\n num_slicable_layers = len(sliceable_head_dims)\\n\\n if slice_size == \\\"auto\\\":\\n # half the attention head size is usually a good trade-off between\\n # speed and memory\\n slice_size = [dim // 2 for dim in sliceable_head_dims]\\n elif slice_size == \\\"max\\\":\\n # make smallest slice possible\\n slice_size = num_slicable_layers * [1]\\n\\n slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\\n\\n if len(slice_size) != len(sliceable_head_dims):\\n raise ValueError(\\n f\\\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\\\"\\n f\\\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\\\"\\n )\\n\\n for i in range(len(slice_size)):\\n size = slice_size[i]\\n dim = sliceable_head_dims[i]\\n if size is not None and size > dim:\\n raise ValueError(f\\\"size {size} has to be smaller or equal to {dim}.\\\")\\n\\n # Recursively walk through all the children.\\n # Any children which exposes the set_attention_slice method\\n # gets the message\\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n module.set_attention_slice(slice_size.pop())\\n\\n for child in module.children():\\n fn_recursive_set_attention_slice(child, slice_size)\\n\\n reversed_slice_size = list(reversed(slice_size))\\n for module in self.children():\\n fn_recursive_set_attention_slice(module, reversed_slice_size)\\n\\n def _set_gradient_checkpointing(self, module, value=False):\\n if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):\\n module.gradient_checkpointing = value\\n\\n def forward(\\n self,\\n sample: torch.FloatTensor,\\n timestep: Union[torch.Tensor, float, int],\\n encoder_hidden_states: torch.Tensor,\\n class_labels: Optional[torch.Tensor] = None,\\n attention_mask: Optional[torch.Tensor] = None,\\n # for controlnet\\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\\n mid_block_additional_residual: Optional[torch.Tensor] = None,\\n # for pose_guider\\n dwpose_conditions: Optional[torch.Tensor] = None,\\n return_dict: bool = True,\\n ) -> Union[UNet3DConditionOutput, Tuple]:\\n r\\\"\\\"\\\"\\n Args:\\n sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor\\n timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps\\n encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states\\n return_dict (`bool`, *optional*, defaults to `True`):\\n Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.\\n\\n Returns:\\n [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:\\n [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When\\n returning a tuple, the first element is the sample tensor.\\n \\\"\\\"\\\"\\n # By default samples have to be AT least a multiple of the overall upsampling factor.\\n # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).\\n # However, the upsampling interpolation output size can be forced to fit any upsampling size\\n # on the fly if necessary.\\n default_overall_up_factor = 2 ** self.num_upsamplers\\n\\n # if self.use_image_condition:\\n # # project global image to 16 tokens for cross-attention\\n # encoder_hidden_states = self.image_proj(encoder_hidden_states)\\n # encoder_hidden_states = encoder_hidden_states.reshape(-1, 16, 768)\\n # encoder_hidden_states = self.image_norm(encoder_hidden_states)\\n\\n # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`\\n forward_upsample_size = False\\n upsample_size = None\\n\\n if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\\n logger.info(\\\"Forward upsample size to force interpolation output size.\\\")\\n forward_upsample_size = True\\n\\n # prepare attention_mask\\n if attention_mask is not None:\\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\\n attention_mask = attention_mask.unsqueeze(1)\\n\\n # center input if necessary\\n if self.config.center_input_sample:\\n sample = 2 * sample - 1.0\\n\\n # time\\n timesteps = timestep\\n if not torch.is_tensor(timesteps):\\n # This would be a good case for the `match` statement (Python 3.10+)\\n is_mps = sample.device.type == \\\"mps\\\"\\n if isinstance(timestep, float):\\n dtype = torch.float32 if is_mps else torch.float64\\n else:\\n dtype = torch.int32 if is_mps else torch.int64\\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\\n elif len(timesteps.shape) == 0:\\n timesteps = timesteps[None].to(sample.device)\\n\\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\\n timesteps = timesteps.expand(sample.shape[0])\\n\\n t_emb = self.time_proj(timesteps)\\n\\n # timesteps does not contain any weights and will always return f32 tensors\\n # but time_embedding might actually be running in fp16. so we need to cast here.\\n # there might be better ways to encapsulate this.\\n t_emb = t_emb.to(dtype=self.dtype)\\n emb = self.time_embedding(t_emb)\\n\\n if self.class_embedding is not None:\\n if class_labels is None:\\n raise ValueError(\\\"class_labels should be provided when num_class_embeds > 0\\\")\\n\\n if self.config.class_embed_type == \\\"timestep\\\":\\n class_labels = self.time_proj(class_labels)\\n\\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\\n emb = emb + class_emb\\n\\n # add pose conditions\\n if dwpose_conditions is not None:\\n conditions = self.dwpose_adapter(dwpose_conditions)\\n sample += conditions\\n\\n # pre-process\\n sample = self.conv_in(sample)\\n\\n # down\\n is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None\\n\\n down_block_res_samples = (sample,)\\n for downsample_block in self.down_blocks:\\n if hasattr(downsample_block, \\\"has_cross_attention\\\") and downsample_block.has_cross_attention:\\n sample, res_samples = downsample_block(\\n hidden_states=sample,\\n temb=emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb,\\n encoder_hidden_states=encoder_hidden_states)\\n\\n down_block_res_samples += res_samples\\n\\n if is_controlnet:\\n new_down_block_res_samples = ()\\n\\n for down_block_res_sample, down_block_additional_residual in zip(\\n down_block_res_samples, down_block_additional_residuals\\n ):\\n down_block_res_sample = down_block_res_sample + down_block_additional_residual\\n new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)\\n\\n down_block_res_samples = new_down_block_res_samples\\n\\n # mid\\n sample = self.mid_block(\\n sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\\n )\\n\\n if is_controlnet:\\n sample = sample + mid_block_additional_residual\\n\\n # up\\n for i, upsample_block in enumerate(self.up_blocks):\\n is_final_block = i == len(self.up_blocks) - 1\\n\\n res_samples = down_block_res_samples[-len(upsample_block.resnets):]\\n down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]\\n\\n # if we have not reached the final block and need to forward the\\n # upsample size, we do it here\\n if not is_final_block and forward_upsample_size:\\n upsample_size = down_block_res_samples[-1].shape[2:]\\n\\n if hasattr(upsample_block, \\\"has_cross_attention\\\") and upsample_block.has_cross_attention:\\n sample = upsample_block(\\n hidden_states=sample,\\n temb=emb,\\n res_hidden_states_tuple=res_samples,\\n encoder_hidden_states=encoder_hidden_states,\\n upsample_size=upsample_size,\\n attention_mask=attention_mask,\\n )\\n else:\\n sample = upsample_block(\\n hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size,\\n encoder_hidden_states=encoder_hidden_states,\\n )\\n\\n # post-process\\n sample = self.conv_norm_out(sample)\\n sample = self.conv_act(sample)\\n sample = self.conv_out(sample)\\n\\n if not return_dict:\\n return (sample,)\\n\\n return UNet3DConditionOutput(sample=sample)\\n\\n @classmethod\\n def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):\\n if subfolder is not None:\\n pretrained_model_path = os.path.join(pretrained_model_path, subfolder)\\n print(f\\\"loaded temporal unet's pretrained weights from {pretrained_model_path} ...\\\")\\n\\n config_file = os.path.join(pretrained_model_path, 'config.json')\\n if not os.path.isfile(config_file):\\n raise RuntimeError(f\\\"{config_file} does not exist\\\")\\n with open(config_file, \\\"r\\\") as f:\\n config = json.load(f)\\n config[\\\"_class_name\\\"] = cls.__name__\\n config[\\\"down_block_types\\\"] = [\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"CrossAttnDownBlock3D\\\",\\n \\\"DownBlock3D\\\"\\n ]\\n config[\\\"up_block_types\\\"] = [\\n \\\"UpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\",\\n \\\"CrossAttnUpBlock3D\\\"\\n ]\\n # config[\\\"mid_block_type\\\"] = \\\"UNetMidBlock3DCrossAttn\\\"\\n\\n from diffusers.utils import WEIGHTS_NAME\\n model = cls.from_config(config, **unet_additional_kwargs)\\n model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)\\n if not os.path.isfile(model_file):\\n raise RuntimeError(f\\\"{model_file} does not exist\\\")\\n state_dict = torch.load(model_file, map_location=\\\"cpu\\\")\\n\\n m, u = model.load_state_dict(state_dict, strict=False)\\n print(f\\\"### missing keys: {len(m)}; \\\\n### unexpected keys: {len(u)};\\\")\\n # print(f\\\"### missing keys:\\\\n{m}\\\\n### unexpected keys:\\\\n{u}\\\\n\\\")\\n\\n params = [p.numel() if \\\"temporal\\\" in n else 0 for n, p in model.named_parameters()]\\n print(f\\\"### Temporal Module Parameters: {sum(params) / 1e6} M\\\")\\n\\n return model\"\n },\n {\n \"identifier\": \"ControlNetModel\",\n \"path\": \"animatediff/magic_animate/controlnet.py\",\n \"snippet\": \"class ControlNetModel(ModelMixin, ConfigMixin):\\n _supports_gradient_checkpointing = True\\n\\n @register_to_config\\n def __init__(\\n self,\\n in_channels: int = 4,\\n flip_sin_to_cos: bool = True,\\n freq_shift: int = 0,\\n down_block_types: Tuple[str] = (\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"CrossAttnDownBlock2D\\\",\\n \\\"DownBlock2D\\\",\\n ),\\n only_cross_attention: Union[bool, Tuple[bool]] = False,\\n block_out_channels: Tuple[int] = (320, 640, 1280, 1280),\\n layers_per_block: int = 2,\\n downsample_padding: int = 1,\\n mid_block_scale_factor: float = 1,\\n act_fn: str = \\\"silu\\\",\\n norm_num_groups: Optional[int] = 32,\\n norm_eps: float = 1e-5,\\n cross_attention_dim: int = 1280,\\n attention_head_dim: Union[int, Tuple[int]] = 8,\\n use_linear_projection: bool = False,\\n class_embed_type: Optional[str] = None,\\n num_class_embeds: Optional[int] = None,\\n upcast_attention: bool = False,\\n resnet_time_scale_shift: str = \\\"default\\\",\\n projection_class_embeddings_input_dim: Optional[int] = None,\\n controlnet_conditioning_channel_order: str = \\\"rgb\\\",\\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\\n ):\\n super().__init__()\\n\\n # Check inputs\\n if len(block_out_channels) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):\\n raise ValueError(\\n f\\\"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}.\\\"\\n )\\n\\n # input\\n conv_in_kernel = 3\\n conv_in_padding = (conv_in_kernel - 1) // 2\\n self.conv_in = nn.Conv2d(\\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\\n )\\n\\n # time\\n time_embed_dim = block_out_channels[0] * 4\\n\\n self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)\\n timestep_input_dim = block_out_channels[0]\\n\\n self.time_embedding = TimestepEmbedding(\\n timestep_input_dim,\\n time_embed_dim,\\n act_fn=act_fn,\\n )\\n\\n # class embedding\\n if class_embed_type is None and num_class_embeds is not None:\\n self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)\\n elif class_embed_type == \\\"timestep\\\":\\n self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)\\n elif class_embed_type == \\\"identity\\\":\\n self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)\\n elif class_embed_type == \\\"projection\\\":\\n if projection_class_embeddings_input_dim is None:\\n raise ValueError(\\n \\\"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set\\\"\\n )\\n # The projection `class_embed_type` is the same as the timestep `class_embed_type` except\\n # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings\\n # 2. it projects from an arbitrary input dimension.\\n #\\n # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.\\n # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.\\n # As a result, `TimestepEmbedding` can be passed arbitrary vectors.\\n self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)\\n else:\\n self.class_embedding = None\\n\\n # control net conditioning embedding\\n self.controlnet_cond_embedding = ControlNetConditioningEmbedding(\\n conditioning_embedding_channels=block_out_channels[0],\\n block_out_channels=conditioning_embedding_out_channels,\\n )\\n\\n self.down_blocks = nn.ModuleList([])\\n self.controlnet_down_blocks = nn.ModuleList([])\\n\\n if isinstance(only_cross_attention, bool):\\n only_cross_attention = [only_cross_attention] * len(down_block_types)\\n\\n if isinstance(attention_head_dim, int):\\n attention_head_dim = (attention_head_dim,) * len(down_block_types)\\n\\n # down\\n output_channel = block_out_channels[0]\\n\\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_down_blocks.append(controlnet_block)\\n\\n for i, down_block_type in enumerate(down_block_types):\\n input_channel = output_channel\\n output_channel = block_out_channels[i]\\n is_final_block = i == len(block_out_channels) - 1\\n\\n down_block = get_down_block(\\n down_block_type,\\n num_layers=layers_per_block,\\n in_channels=input_channel,\\n out_channels=output_channel,\\n temb_channels=time_embed_dim,\\n add_downsample=not is_final_block,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n resnet_groups=norm_num_groups,\\n cross_attention_dim=cross_attention_dim,\\n num_attention_heads=attention_head_dim[i],\\n downsample_padding=downsample_padding,\\n use_linear_projection=use_linear_projection,\\n only_cross_attention=only_cross_attention[i],\\n upcast_attention=upcast_attention,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n )\\n self.down_blocks.append(down_block)\\n\\n for _ in range(layers_per_block):\\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_down_blocks.append(controlnet_block)\\n\\n if not is_final_block:\\n controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_down_blocks.append(controlnet_block)\\n\\n # mid\\n mid_block_channel = block_out_channels[-1]\\n\\n controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)\\n controlnet_block = zero_module(controlnet_block)\\n self.controlnet_mid_block = controlnet_block\\n\\n self.mid_block = UNetMidBlock2DCrossAttn(\\n in_channels=mid_block_channel,\\n temb_channels=time_embed_dim,\\n resnet_eps=norm_eps,\\n resnet_act_fn=act_fn,\\n output_scale_factor=mid_block_scale_factor,\\n resnet_time_scale_shift=resnet_time_scale_shift,\\n cross_attention_dim=cross_attention_dim,\\n num_attention_heads=attention_head_dim[-1],\\n resnet_groups=norm_num_groups,\\n use_linear_projection=use_linear_projection,\\n upcast_attention=upcast_attention,\\n )\\n\\n @classmethod\\n def from_unet(\\n cls,\\n unet: UNet2DConditionModel,\\n controlnet_conditioning_channel_order: str = \\\"rgb\\\",\\n conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),\\n load_weights_from_unet: bool = True,\\n ):\\n r\\\"\\\"\\\"\\n Instantiate Controlnet class from UNet2DConditionModel.\\n\\n Parameters:\\n unet (`UNet2DConditionModel`):\\n UNet model which weights are copied to the ControlNet. Note that all configuration options are also\\n copied where applicable.\\n \\\"\\\"\\\"\\n controlnet = cls(\\n in_channels=unet.config.in_channels,\\n flip_sin_to_cos=unet.config.flip_sin_to_cos,\\n freq_shift=unet.config.freq_shift,\\n down_block_types=unet.config.down_block_types,\\n only_cross_attention=unet.config.only_cross_attention,\\n block_out_channels=unet.config.block_out_channels,\\n layers_per_block=unet.config.layers_per_block,\\n downsample_padding=unet.config.downsample_padding,\\n mid_block_scale_factor=unet.config.mid_block_scale_factor,\\n act_fn=unet.config.act_fn,\\n norm_num_groups=unet.config.norm_num_groups,\\n norm_eps=unet.config.norm_eps,\\n cross_attention_dim=unet.config.cross_attention_dim,\\n attention_head_dim=unet.config.attention_head_dim,\\n use_linear_projection=unet.config.use_linear_projection,\\n class_embed_type=unet.config.class_embed_type,\\n num_class_embeds=unet.config.num_class_embeds,\\n upcast_attention=unet.config.upcast_attention,\\n resnet_time_scale_shift=unet.config.resnet_time_scale_shift,\\n projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,\\n controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,\\n conditioning_embedding_out_channels=conditioning_embedding_out_channels,\\n )\\n\\n if load_weights_from_unet:\\n controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())\\n controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())\\n controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())\\n\\n if controlnet.class_embedding:\\n controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())\\n\\n controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())\\n controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())\\n\\n return controlnet\\n\\n # @property\\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors\\n # def attn_processors(self) -> Dict[str, AttentionProcessor]:\\n # r\\\"\\\"\\\"\\n # Returns:\\n # `dict` of attention processors: A dictionary containing all attention processors used in the model with\\n # indexed by its weight name.\\n # \\\"\\\"\\\"\\n # # set recursively\\n # processors = {}\\n\\n # def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):\\n # if hasattr(module, \\\"set_processor\\\"):\\n # processors[f\\\"{name}.processor\\\"] = module.processor\\n\\n # for sub_name, child in module.named_children():\\n # fn_recursive_add_processors(f\\\"{name}.{sub_name}\\\", child, processors)\\n\\n # return processors\\n\\n # for name, module in self.named_children():\\n # fn_recursive_add_processors(name, module, processors)\\n\\n # return processors\\n\\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor\\n # def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):\\n # r\\\"\\\"\\\"\\n # Parameters:\\n # `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):\\n # The instantiated processor class or a dictionary of processor classes that will be set as the processor\\n # of **all** `Attention` layers.\\n # In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:\\n\\n # \\\"\\\"\\\"\\n # count = len(self.attn_processors.keys())\\n\\n # if isinstance(processor, dict) and len(processor) != count:\\n # raise ValueError(\\n # f\\\"A dict of processors was passed, but the number of processors {len(processor)} does not match the\\\"\\n # f\\\" number of attention layers: {count}. Please make sure to pass {count} processor classes.\\\"\\n # )\\n\\n # def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):\\n # if hasattr(module, \\\"set_processor\\\"):\\n # if not isinstance(processor, dict):\\n # module.set_processor(processor)\\n # else:\\n # module.set_processor(processor.pop(f\\\"{name}.processor\\\"))\\n\\n # for sub_name, child in module.named_children():\\n # fn_recursive_attn_processor(f\\\"{name}.{sub_name}\\\", child, processor)\\n\\n # for name, module in self.named_children():\\n # fn_recursive_attn_processor(name, module, processor)\\n\\n # # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor\\n # def set_default_attn_processor(self):\\n # \\\"\\\"\\\"\\n # Disables custom attention processors and sets the default attention implementation.\\n # \\\"\\\"\\\"\\n # self.set_attn_processor(AttnProcessor())\\n\\n # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice\\n def set_attention_slice(self, slice_size):\\n r\\\"\\\"\\\"\\n Enable sliced attention computation.\\n\\n When this option is enabled, the attention module will split the input tensor in slices, to compute attention\\n in several steps. This is useful to save some memory in exchange for a small speed decrease.\\n\\n Args:\\n slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `\\\"auto\\\"`):\\n When `\\\"auto\\\"`, halves the input to the attention heads, so attention will be computed in two steps. If\\n `\\\"max\\\"`, maximum amount of memory will be saved by running only one slice at a time. If a number is\\n provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`\\n must be a multiple of `slice_size`.\\n \\\"\\\"\\\"\\n sliceable_head_dims = []\\n\\n def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n sliceable_head_dims.append(module.sliceable_head_dim)\\n\\n for child in module.children():\\n fn_recursive_retrieve_sliceable_dims(child)\\n\\n # retrieve number of attention layers\\n for module in self.children():\\n fn_recursive_retrieve_sliceable_dims(module)\\n\\n num_sliceable_layers = len(sliceable_head_dims)\\n\\n if slice_size == \\\"auto\\\":\\n # half the attention head size is usually a good trade-off between\\n # speed and memory\\n slice_size = [dim // 2 for dim in sliceable_head_dims]\\n elif slice_size == \\\"max\\\":\\n # make smallest slice possible\\n slice_size = num_sliceable_layers * [1]\\n\\n slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\\n\\n if len(slice_size) != len(sliceable_head_dims):\\n raise ValueError(\\n f\\\"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different\\\"\\n f\\\" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}.\\\"\\n )\\n\\n for i in range(len(slice_size)):\\n size = slice_size[i]\\n dim = sliceable_head_dims[i]\\n if size is not None and size > dim:\\n raise ValueError(f\\\"size {size} has to be smaller or equal to {dim}.\\\")\\n\\n # Recursively walk through all the children.\\n # Any children which exposes the set_attention_slice method\\n # gets the message\\n def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):\\n if hasattr(module, \\\"set_attention_slice\\\"):\\n module.set_attention_slice(slice_size.pop())\\n\\n for child in module.children():\\n fn_recursive_set_attention_slice(child, slice_size)\\n\\n reversed_slice_size = list(reversed(slice_size))\\n for module in self.children():\\n fn_recursive_set_attention_slice(module, reversed_slice_size)\\n\\n def _set_gradient_checkpointing(self, module, value=False):\\n if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):\\n module.gradient_checkpointing = value\\n\\n def forward(\\n self,\\n sample: torch.FloatTensor,\\n timestep: Union[torch.Tensor, float, int],\\n encoder_hidden_states: torch.Tensor,\\n controlnet_cond: torch.FloatTensor,\\n conditioning_scale: float = 1.0,\\n class_labels: Optional[torch.Tensor] = None,\\n timestep_cond: Optional[torch.Tensor] = None,\\n attention_mask: Optional[torch.Tensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n return_dict: bool = True,\\n ) -> Union[ControlNetOutput, Tuple]:\\n # check channel order\\n channel_order = self.config.controlnet_conditioning_channel_order\\n\\n if channel_order == \\\"rgb\\\":\\n # in rgb order by default\\n ...\\n elif channel_order == \\\"bgr\\\":\\n controlnet_cond = torch.flip(controlnet_cond, dims=[1])\\n else:\\n raise ValueError(f\\\"unknown `controlnet_conditioning_channel_order`: {channel_order}\\\")\\n\\n # prepare attention_mask\\n if attention_mask is not None:\\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\\n attention_mask = attention_mask.unsqueeze(1)\\n\\n # 1. time\\n timesteps = timestep\\n if not torch.is_tensor(timesteps):\\n # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can\\n # This would be a good case for the `match` statement (Python 3.10+)\\n is_mps = sample.device.type == \\\"mps\\\"\\n if isinstance(timestep, float):\\n dtype = torch.float32 if is_mps else torch.float64\\n else:\\n dtype = torch.int32 if is_mps else torch.int64\\n timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)\\n elif len(timesteps.shape) == 0:\\n timesteps = timesteps[None].to(sample.device)\\n\\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\\n timesteps = timesteps.expand(sample.shape[0])\\n\\n t_emb = self.time_proj(timesteps)\\n\\n # timesteps does not contain any weights and will always return f32 tensors\\n # but time_embedding might actually be running in fp16. so we need to cast here.\\n # there might be better ways to encapsulate this.\\n t_emb = t_emb.to(dtype=self.dtype)\\n\\n emb = self.time_embedding(t_emb, timestep_cond)\\n\\n if self.class_embedding is not None:\\n if class_labels is None:\\n raise ValueError(\\\"class_labels should be provided when num_class_embeds > 0\\\")\\n\\n if self.config.class_embed_type == \\\"timestep\\\":\\n class_labels = self.time_proj(class_labels)\\n\\n class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)\\n emb = emb + class_emb\\n\\n # 2. pre-process\\n sample = self.conv_in(sample)\\n\\n controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)\\n\\n sample += controlnet_cond\\n\\n # 3. down\\n down_block_res_samples = (sample,)\\n for downsample_block in self.down_blocks:\\n if hasattr(downsample_block, \\\"has_cross_attention\\\") and downsample_block.has_cross_attention:\\n sample, res_samples = downsample_block(\\n hidden_states=sample,\\n temb=emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n # cross_attention_kwargs=cross_attention_kwargs,\\n )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\\n\\n down_block_res_samples += res_samples\\n\\n # 4. mid\\n if self.mid_block is not None:\\n sample = self.mid_block(\\n sample,\\n emb,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=attention_mask,\\n # cross_attention_kwargs=cross_attention_kwargs,\\n )\\n\\n # 5. Control net blocks\\n\\n controlnet_down_block_res_samples = ()\\n\\n for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):\\n down_block_res_sample = controlnet_block(down_block_res_sample)\\n controlnet_down_block_res_samples += (down_block_res_sample,)\\n\\n down_block_res_samples = controlnet_down_block_res_samples\\n\\n mid_block_res_sample = self.controlnet_mid_block(sample)\\n\\n # 6. scaling\\n down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]\\n mid_block_res_sample *= conditioning_scale\\n\\n if not return_dict:\\n return (down_block_res_samples, mid_block_res_sample)\\n\\n return ControlNetOutput(\\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\\n )\"\n },\n {\n \"identifier\": \"ReferenceAttentionControl\",\n \"path\": \"animatediff/magic_animate/mutual_self_attention.py\",\n \"snippet\": \"class ReferenceAttentionControl():\\n \\n def __init__(self, \\n unet,\\n mode=\\\"write\\\",\\n do_classifier_free_guidance=False,\\n attention_auto_machine_weight = float('inf'),\\n gn_auto_machine_weight = 1.0,\\n style_fidelity = 1.0,\\n reference_attn=True,\\n reference_adain=False,\\n fusion_blocks=\\\"midup\\\",\\n batch_size=1,\\n clip_length=8,\\n is_image=False,\\n ) -> None:\\n # 10. Modify self attention and group norm\\n self.unet = unet\\n assert mode in [\\\"read\\\", \\\"write\\\"]\\n assert fusion_blocks in [\\\"midup\\\", \\\"full\\\"]\\n self.reference_attn = reference_attn\\n self.reference_adain = reference_adain\\n self.fusion_blocks = fusion_blocks\\n self.register_reference_hooks(\\n mode, \\n do_classifier_free_guidance,\\n clip_length,\\n attention_auto_machine_weight,\\n gn_auto_machine_weight,\\n style_fidelity,\\n reference_attn,\\n reference_adain,\\n fusion_blocks=fusion_blocks,\\n batch_size=batch_size,\\n is_image=is_image,\\n )\\n\\n def register_reference_hooks(\\n self, \\n mode, \\n do_classifier_free_guidance,\\n clip_length,\\n attention_auto_machine_weight,\\n gn_auto_machine_weight,\\n style_fidelity,\\n reference_attn,\\n reference_adain,\\n dtype=torch.float16,\\n batch_size=1, \\n num_images_per_prompt=1, \\n device=torch.device(\\\"cpu\\\"), \\n fusion_blocks='midup',\\n is_image=False,\\n ):\\n MODE = mode\\n do_classifier_free_guidance = do_classifier_free_guidance\\n attention_auto_machine_weight = attention_auto_machine_weight\\n gn_auto_machine_weight = gn_auto_machine_weight\\n style_fidelity = style_fidelity\\n reference_attn = reference_attn\\n reference_adain = reference_adain\\n fusion_blocks = fusion_blocks\\n num_images_per_prompt = num_images_per_prompt\\n dtype=dtype\\n if do_classifier_free_guidance:\\n # uc_mask = (\\n # torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)\\n # .to(device)\\n # .bool()\\n # )\\n\\n uc_mask = (\\n torch.Tensor(\\n [1] * batch_size * num_images_per_prompt * clip_length + [0] * batch_size * num_images_per_prompt * clip_length)\\n .to(device)\\n .bool()\\n )\\n\\n else:\\n uc_mask = (\\n torch.Tensor([0] * batch_size * num_images_per_prompt * 2)\\n .to(device)\\n .bool()\\n )\\n \\n def hacked_basic_transformer_inner_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n timestep: Optional[torch.LongTensor] = None,\\n cross_attention_kwargs: Dict[str, Any] = None,\\n class_labels: Optional[torch.LongTensor] = None,\\n video_length=None,\\n ):\\n if self.use_ada_layer_norm:\\n norm_hidden_states = self.norm1(hidden_states, timestep)\\n elif self.use_ada_layer_norm_zero:\\n norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(\\n hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype\\n )\\n else:\\n norm_hidden_states = self.norm1(hidden_states)\\n\\n # 1. Self-Attention\\n cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}\\n if self.only_cross_attention:\\n attn_output = self.attn1(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\\n attention_mask=attention_mask,\\n **cross_attention_kwargs,\\n )\\n else:\\n if MODE == \\\"write\\\":\\n self.bank.append(norm_hidden_states.clone())\\n attn_output = self.attn1(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,\\n attention_mask=attention_mask,\\n **cross_attention_kwargs,\\n )\\n if MODE == \\\"read\\\":\\n if not is_image:\\n self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), \\\"b t l c -> (b t) l c\\\")[:hidden_states.shape[0]] for d in self.bank]\\n\\n hidden_states_uc = self.attn1(norm_hidden_states,\\n encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),\\n attention_mask=attention_mask) + hidden_states\\n hidden_states_c = hidden_states_uc.clone()\\n _uc_mask = uc_mask.clone()\\n if do_classifier_free_guidance:\\n if hidden_states.shape[0] != _uc_mask.shape[0]:\\n _uc_mask = (\\n torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))\\n .to(device)\\n .bool()\\n )\\n hidden_states_c[_uc_mask] = self.attn1(\\n norm_hidden_states[_uc_mask],\\n encoder_hidden_states=norm_hidden_states[_uc_mask],\\n attention_mask=attention_mask,\\n ) + hidden_states[_uc_mask]\\n hidden_states = hidden_states_c.clone()\\n \\n self.bank.clear()\\n if self.attn2 is not None:\\n # Cross-Attention\\n norm_hidden_states = (\\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\\n )\\n hidden_states = (\\n self.attn2(\\n norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\\n )\\n + hidden_states\\n )\\n\\n # Feed-forward\\n hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states\\n\\n # Temporal-Attention\\n if not is_image:\\n if self.unet_use_temporal_attention:\\n d = hidden_states.shape[1]\\n hidden_states = rearrange(hidden_states, \\\"(b f) d c -> (b d) f c\\\", f=video_length)\\n norm_hidden_states = (\\n self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)\\n )\\n hidden_states = self.attn_temp(norm_hidden_states) + hidden_states\\n hidden_states = rearrange(hidden_states, \\\"(b d) f c -> (b f) d c\\\", d=d)\\n\\n return hidden_states\\n \\n if self.use_ada_layer_norm_zero:\\n attn_output = gate_msa.unsqueeze(1) * attn_output\\n hidden_states = attn_output + hidden_states\\n\\n if self.attn2 is not None:\\n norm_hidden_states = (\\n self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)\\n )\\n\\n # 2. Cross-Attention\\n attn_output = self.attn2(\\n norm_hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n attention_mask=encoder_attention_mask,\\n **cross_attention_kwargs,\\n )\\n hidden_states = attn_output + hidden_states\\n\\n # 3. Feed-forward\\n norm_hidden_states = self.norm3(hidden_states)\\n\\n if self.use_ada_layer_norm_zero:\\n norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]\\n\\n ff_output = self.ff(norm_hidden_states)\\n\\n if self.use_ada_layer_norm_zero:\\n ff_output = gate_mlp.unsqueeze(1) * ff_output\\n\\n hidden_states = ff_output + hidden_states\\n\\n return hidden_states\\n\\n def hacked_mid_forward(self, *args, **kwargs):\\n eps = 1e-6\\n x = self.original_forward(*args, **kwargs)\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append(mean)\\n self.var_bank.append(var)\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))\\n var_acc = sum(self.var_bank) / float(len(self.var_bank))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n x_uc = (((x - mean) / std) * std_acc) + mean_acc\\n x_c = x_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n x_c[uc_mask] = x[uc_mask]\\n x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc\\n self.mean_bank = []\\n self.var_bank = []\\n return x\\n\\n def hack_CrossAttnDownBlock2D_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n temb: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n ):\\n eps = 1e-6\\n\\n # TODO(Patrick, William) - attention mask is not used\\n output_states = ()\\n\\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\\n hidden_states = resnet(hidden_states, temb)\\n hidden_states = attn(\\n hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n cross_attention_kwargs=cross_attention_kwargs,\\n attention_mask=attention_mask,\\n encoder_attention_mask=encoder_attention_mask,\\n return_dict=False,\\n )[0]\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n output_states = output_states + (hidden_states,)\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.downsamplers is not None:\\n for downsampler in self.downsamplers:\\n hidden_states = downsampler(hidden_states)\\n\\n output_states = output_states + (hidden_states,)\\n\\n return hidden_states, output_states\\n\\n def hacked_DownBlock2D_forward(self, hidden_states, temb=None):\\n eps = 1e-6\\n\\n output_states = ()\\n\\n for i, resnet in enumerate(self.resnets):\\n hidden_states = resnet(hidden_states, temb)\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n output_states = output_states + (hidden_states,)\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.downsamplers is not None:\\n for downsampler in self.downsamplers:\\n hidden_states = downsampler(hidden_states)\\n\\n output_states = output_states + (hidden_states,)\\n\\n return hidden_states, output_states\\n\\n def hacked_CrossAttnUpBlock2D_forward(\\n self,\\n hidden_states: torch.FloatTensor,\\n res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],\\n temb: Optional[torch.FloatTensor] = None,\\n encoder_hidden_states: Optional[torch.FloatTensor] = None,\\n cross_attention_kwargs: Optional[Dict[str, Any]] = None,\\n upsample_size: Optional[int] = None,\\n attention_mask: Optional[torch.FloatTensor] = None,\\n encoder_attention_mask: Optional[torch.FloatTensor] = None,\\n ):\\n eps = 1e-6\\n # TODO(Patrick, William) - attention mask is not used\\n for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):\\n # pop res hidden states\\n res_hidden_states = res_hidden_states_tuple[-1]\\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\\n hidden_states = resnet(hidden_states, temb)\\n hidden_states = attn(\\n hidden_states,\\n encoder_hidden_states=encoder_hidden_states,\\n cross_attention_kwargs=cross_attention_kwargs,\\n attention_mask=attention_mask,\\n encoder_attention_mask=encoder_attention_mask,\\n return_dict=False,\\n )[0]\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.upsamplers is not None:\\n for upsampler in self.upsamplers:\\n hidden_states = upsampler(hidden_states, upsample_size)\\n\\n return hidden_states\\n\\n def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):\\n eps = 1e-6\\n for i, resnet in enumerate(self.resnets):\\n # pop res hidden states\\n res_hidden_states = res_hidden_states_tuple[-1]\\n res_hidden_states_tuple = res_hidden_states_tuple[:-1]\\n hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)\\n hidden_states = resnet(hidden_states, temb)\\n\\n if MODE == \\\"write\\\":\\n if gn_auto_machine_weight >= self.gn_weight:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n self.mean_bank.append([mean])\\n self.var_bank.append([var])\\n if MODE == \\\"read\\\":\\n if len(self.mean_bank) > 0 and len(self.var_bank) > 0:\\n var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)\\n std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5\\n mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))\\n var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))\\n std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5\\n hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc\\n hidden_states_c = hidden_states_uc.clone()\\n if do_classifier_free_guidance and style_fidelity > 0:\\n hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)\\n hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc\\n\\n if MODE == \\\"read\\\":\\n self.mean_bank = []\\n self.var_bank = []\\n\\n if self.upsamplers is not None:\\n for upsampler in self.upsamplers:\\n hidden_states = upsampler(hidden_states, upsample_size)\\n\\n return hidden_states\\n\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)] \\n attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n\\n for i, module in enumerate(attn_modules):\\n module._original_inner_forward = module.forward\\n module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)\\n module.bank = []\\n module.attn_weight = float(i) / float(len(attn_modules))\\n\\n if self.reference_adain:\\n gn_modules = [self.unet.mid_block]\\n self.unet.mid_block.gn_weight = 0\\n\\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n module.gn_weight = 1.0 - float(w) / float(len(down_blocks))\\n gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n module.gn_weight = float(w) / float(len(up_blocks))\\n gn_modules.append(module)\\n\\n for i, module in enumerate(gn_modules):\\n if getattr(module, \\\"original_forward\\\", None) is None:\\n module.original_forward = module.forward\\n if i == 0:\\n # mid_block\\n module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)\\n elif isinstance(module, CrossAttnDownBlock2D):\\n module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)\\n elif isinstance(module, DownBlock2D):\\n module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)\\n elif isinstance(module, CrossAttnUpBlock2D):\\n module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)\\n elif isinstance(module, UpBlock2D):\\n module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)\\n module.mean_bank = []\\n module.var_bank = []\\n module.gn_weight *= 2\\n \\n def update(self, writer, dtype=torch.float16):\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]\\n writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]\\n writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]\\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]) \\n writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n for r, w in zip(reader_attn_modules, writer_attn_modules):\\n r.bank = [v.clone().to(dtype) for v in w.bank]\\n # w.bank.clear()\\n if self.reference_adain:\\n reader_gn_modules = [self.unet.mid_block]\\n \\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n reader_gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n reader_gn_modules.append(module)\\n \\n writer_gn_modules = [writer.unet.mid_block]\\n \\n down_blocks = writer.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n writer_gn_modules.append(module)\\n\\n up_blocks = writer.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n writer_gn_modules.append(module)\\n \\n for r, w in zip(reader_gn_modules, writer_gn_modules):\\n if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):\\n r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]\\n r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]\\n else:\\n r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]\\n r.var_bank = [v.clone().to(dtype) for v in w.var_bank]\\n \\n def clear(self):\\n if self.reference_attn:\\n if self.fusion_blocks == \\\"midup\\\":\\n reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n elif self.fusion_blocks == \\\"full\\\":\\n reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]\\n reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])\\n for r in reader_attn_modules:\\n r.bank.clear()\\n if self.reference_adain:\\n reader_gn_modules = [self.unet.mid_block]\\n \\n down_blocks = self.unet.down_blocks\\n for w, module in enumerate(down_blocks):\\n reader_gn_modules.append(module)\\n\\n up_blocks = self.unet.up_blocks\\n for w, module in enumerate(up_blocks):\\n reader_gn_modules.append(module)\\n \\n for r in reader_gn_modules:\\n r.mean_bank.clear()\\n r.var_bank.clear()\"\n },\n {\n \"identifier\": \"get_context_scheduler\",\n \"path\": \"animatediff/magic_animate/context.py\",\n \"snippet\": \"def get_context_scheduler(name: str) -> Callable:\\n if name == \\\"uniform\\\":\\n return uniform\\n else:\\n raise ValueError(f\\\"Unknown context_overlap policy {name}\\\")\"\n },\n {\n \"identifier\": \"get_total_steps\",\n \"path\": \"animatediff/magic_animate/context.py\",\n \"snippet\": \"def get_total_steps(\\n scheduler,\\n timesteps: List[int],\\n num_steps: Optional[int] = None,\\n num_frames: int = ...,\\n context_size: Optional[int] = None,\\n context_stride: int = 3,\\n context_overlap: int = 4,\\n closed_loop: bool = True,\\n):\\n return sum(\\n len(\\n list(\\n scheduler(\\n i,\\n num_steps,\\n num_frames,\\n context_size,\\n context_stride,\\n context_overlap,\\n )\\n )\\n )\\n for i in range(len(timesteps))\\n )\"\n },\n {\n \"identifier\": \"get_tensor_interpolation_method\",\n \"path\": \"animatediff/utils/util.py\",\n \"snippet\": \"def get_tensor_interpolation_method():\\n return tensor_interpolation\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import inspect, math\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nimport einops\nfrom typing import Callable, List, Optional, Union\nfrom dataclasses import dataclass\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom diffusers.utils import is_accelerate_available\nfrom packaging import version\nfrom transformers import CLIPTextModel, CLIPTokenizer\nfrom diffusers.configuration_utils import FrozenDict\nfrom diffusers.models import AutoencoderKL\nfrom diffusers.pipeline_utils import DiffusionPipeline\nfrom diffusers.schedulers import (\n DDIMScheduler,\n DPMSolverMultistepScheduler,\n EulerAncestralDiscreteScheduler,\n EulerDiscreteScheduler,\n LMSDiscreteScheduler,\n PNDMScheduler,\n)\nfrom diffusers.utils import deprecate, logging, BaseOutput\nfrom einops import rearrange\nfrom animatediff.magic_animate.unet_controlnet import UNet3DConditionModel\nfrom animatediff.magic_animate.controlnet import ControlNetModel\nfrom animatediff.magic_animate.mutual_self_attention import ReferenceAttentionControl\nfrom animatediff.magic_animate.context import (\n get_context_scheduler,\n get_total_steps\n)\nfrom animatediff.utils.util import get_tensor_interpolation_method\n from accelerate import cpu_offload"},"token_num":{"kind":"number","value":17204,"string":"17,204"},"cropped_code":{"kind":"string","value":"# *************************************************************************\n# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-\n# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-\n# ytedance Inc..\n# *************************************************************************\n\n# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py\n\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nTODO:\n1. support multi-controlnet\n2. [DONE] support DDIM inversion\n3. support Prompt-to-prompt\n\"\"\"\n\n\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass AnimationPipelineOutput(BaseOutput):\n videos: Union[torch.Tensor, np.ndarray]\n\n\nclass AnimationPipeline(DiffusionPipeline):\n _optional_components = []\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet3DConditionModel,\n"},"all_code":{"kind":"string","value":"# *************************************************************************\n# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-\n# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-\n# ytedance Inc..\n# *************************************************************************\n\n# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py\n\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nTODO:\n1. support multi-controlnet\n2. [DONE] support DDIM inversion\n3. support Prompt-to-prompt\n\"\"\"\n\n\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\n@dataclass\nclass AnimationPipelineOutput(BaseOutput):\n videos: Union[torch.Tensor, np.ndarray]\n\n\nclass AnimationPipeline(DiffusionPipeline):\n _optional_components = []\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet3DConditionModel,"},"next_line":{"kind":"string","value":" controlnet: ControlNetModel,"},"gold_snippet_index":{"kind":"number","value":1,"string":"1"},"created_at":{"kind":"string","value":"2023-12-12 00:16:39+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5294,"cells":{"repo_name":{"kind":"string","value":"qitan/devops-backend-lite"},"file_path":{"kind":"string","value":"common/ext_fun.py"},"context":{"kind":"list like","value":[{"identifier":"generate_docu","path":"common/utils/ElasticSearchAPI.py","snippet":"def generate_docu(table, index_version=None):\n index_name = f\"{table.name}-{index_version}\" if index_version else table.name\n _tbindex = Index(index_name)\n _tbindex.analyzer(my_normalizer)\n _tbindex.settings(number_of_shards=3, number_of_replicas=1)\n _fields = Mapping().generate_data_mapping(table)\n docu = type(index_name, (CustomDocument,), _fields)\n return _tbindex.document(docu)"},{"identifier":"Search","path":"common/utils/ElasticSearchAPI.py","snippet":"class Search(BaseSearch):\n def __init__(self, prefix=False, **kwargs):\n if kwargs.get('index', None) and prefix:\n if isinstance(kwargs['index'], string_types):\n kwargs['index'] = f\"{ELASTICSEARCH_PREFIX}{kwargs['index']}\"\n elif isinstance(kwargs['index'], list):\n kwargs['index'] = [\n f\"{ELASTICSEARCH_PREFIX}{i}\" for i in kwargs['index']]\n elif isinstance(kwargs['index'], tuple):\n kwargs['index'] = tuple(\n f\"{ELASTICSEARCH_PREFIX}{i}\" for i in kwargs['index'])\n else:\n raise Exception('索引名称格式错误!')\n super(Search, self).__init__(**kwargs)"},{"identifier":"GitLabAPI","path":"common/utils/GitLabAPI.py","snippet":"class GitLabAPI(object):\n def __init__(self, url, user=None, password=None, token=None, oauth=False):\n self.__url = url\n if token:\n self.__token = token\n if oauth:\n params = {'oauth_token': self.__token}\n else:\n params = {'private_token': self.__token}\n self.__gl = gitlab.Gitlab(self.__url, **params)\n else:\n self.__gl = gitlab.Gitlab(\n self.__url, http_username=user, http_password=password)\n self.__gl.auth()\n\n def get_gl(self):\n return self.__gl\n\n def list_projects(self, get_all=False, key=None, per_page=20, page=1):\n params = {'per_page': per_page, 'page': page}\n if get_all:\n params = {'get_all': True, 'per_page': per_page}\n if key:\n params['search'] = key\n projects = self.__gl.projects.list(**params)\n return projects\n\n def get_project(self, project_id=None, project_name_with_namespace=None):\n if any([project_id, project_name_with_namespace]) is False:\n raise Exception('缺少参数，project_id或project_name_with_namespace必选其一.')\n condition = project_id or project_name_with_namespace\n try:\n project = self.__gl.projects.get(condition)\n return project\n except BaseException as e:\n logger.info(e)\n return None\n\n def create_project(self, name, namespace_id=None, initialize_with_readme=False):\n payload = {'name': name, 'path': name,\n 'initialize_with_readme': initialize_with_readme}\n if namespace_id:\n payload['namespace_id'] = namespace_id\n try:\n ret = self.__gl.projects.create(payload)\n return True, ret\n except BaseException as e:\n logger.exception(f'创建分支请求异常，原因：{e.__dict__}')\n return False, e\n\n def get_commit(self, commit_id, project_id=None, project_name_with_namespace=None):\n try:\n commit = self.get_project(\n project_id, project_name_with_namespace).get(commit_id)\n return commit\n except BaseException as e:\n logger.info(e)\n return None\n\n def list_groups(self, get_all=False, key=None, per_page=20, page=1):\n params = {'per_page': per_page, 'page': page}\n if get_all:\n params = {'get_all': True, 'all': True, 'per_page': per_page}\n if key:\n params['search'] = key\n groups = self.__gl.groups.list(**params)\n return [{'id': i.id, 'name': i.name, 'description': i.description} for i in groups if not i.parent_id]\n\n def create_group(self, name, path=None, desc=None, parent=None):\n \"\"\"\n 创建组\n \"\"\"\n payload = {'name': name, 'path': path or name,\n 'description': desc or ''}\n if parent:\n payload['parent_id'] = parent\n try:\n group = self.__gl.groups.create(payload)\n return True, group\n except BaseException as e:\n logger.info(e)\n return False, e\n\n def create_branch(self, project, src_branch, target_branch):\n payload = {'branch': target_branch,\n 'ref': src_branch}\n if isinstance(project, (int,)):\n project = self.get_project(project)\n try:\n ret = project.branches.create(payload)\n return True, ret\n except BaseException as e:\n logger.exception(f'创建分支请求异常，原因：{e.__dict__}')\n return False, e\n\n def list_branches(self, project_id=None, project_name_with_namespace=None, get_all=False, key=None, per_page=20,\n page=1, protected='0', *args, **kwargs):\n params = {'per_page': per_page, 'page': page}\n if not protected:\n protected = '0'\n if get_all:\n params = {'get_all': True, 'per_page': per_page}\n if key:\n params['search'] = key\n params.update(kwargs)\n branches = self.get_project(project_id=project_id,\n project_name_with_namespace=project_name_with_namespace).branches.list(**params)\n branches = [{'uid': f\"{G_COMMIT[0][0]}:{i.name}\", 'name': i.name, 'commit': i.commit, 'label': G_COMMIT[0][0], 'protected': i.protected}\n for i in branches]\n if protected != '0':\n # 过滤受保护分支\n _map = {'1': True, '2': False}\n branches = [i for i in branches if i['protected']\n == _map[protected]]\n return branches\n\n def list_protected_branches(self, project_id=None, project_name_with_namespace=None, get_all=False, key=None, per_page=20,\n page=1, *args, **kwargs):\n params = {'per_page': per_page, 'page': page}\n if get_all:\n params = {'get_all': True, 'per_page': per_page}\n if key:\n params['search'] = key\n params.update(kwargs)\n branches = self.get_project(project_id=project_id,\n project_name_with_namespace=project_name_with_namespace).protectedbranches.list(**params)\n branches = [{'uid': f\"{G_COMMIT[0][0]}:{i.name}\", 'name': i.name, 'commit': i.commit, 'label': G_COMMIT[0][0], 'protected': i.protected}\n for i in branches]\n return branches\n\n def list_tags(self, project_id=None, project_name_with_namespace=None, get_all=False, key=None, per_page=20,\n page=1):\n params = {'per_page': per_page, 'page': page}\n if get_all:\n params = {'get_all': True, 'per_page': per_page}\n if key:\n params['search'] = key\n tags = self.get_project(\n project_id, project_name_with_namespace).tags.list(**params)\n tags = [{'uid': f\"{G_COMMIT[1][0]}:{i.name}\", 'name': i.name, 'message': i.message, 'commit': i.commit,\n 'label': G_COMMIT[1][0]} for i in tags]\n return tags\n\n def list_commits(self, project_id=None, project_name_with_namespace=None, get_all=False, key=None, per_page=20,\n page=1, ref_name=None, since=None):\n params = {'per_page': per_page, 'page': page}\n if get_all:\n params = {'get_all': True, 'per_page': per_page}\n if key:\n params['search'] = key\n if ref_name:\n params['ref_name'] = ref_name\n if since:\n params['since'] = since\n commits = self.get_project(\n project_id, project_name_with_namespace).commits.list(**params)\n commits = [\n {'title': i.title, 'short_id': i.short_id, 'author_name': i.author_name, 'committer_name': i.committer_name,\n 'committed_date': i.committed_date, 'message': i.message, 'web_url': i.web_url} for i in commits]\n return commits\n\n def repo_checkout(self, repo):\n import subprocess\n git_url = repo.split('//')\n subprocess.call(\n ['git', 'clone', f\"{git_url[0]}//oauth2:{self.__token}@{git_url[1]}\"])\n\n def get_user_id(self, username):\n user_list = self.__gl.users.list(username=username)\n if user_list:\n return user_list[0].id\n else:\n return None\n\n def get_project_from_name(self, project_name):\n projects = self.__gl.projects.list(search=project_name)\n for p in projects:\n if p.name == project_name:\n return p\n return None\n\n def add_project_member(self, project, user_id, access_level):\n try:\n project.members.create(\n {'user_id': user_id, 'access_level': access_level})\n return True, '成功'\n except Exception as error:\n return False, error\n\n def del_project_member(self, project, user_id):\n try:\n project.members.delete(user_id)\n return True, '成功'\n except Exception as error:\n return False, error"},{"identifier":"HarborAPI","path":"common/utils/HarborAPI.py","snippet":"class HarborAPI(object):\n def __init__(self, url, username, password):\n self.__url = url.rstrip('/')\n self.__user = username\n self.__password = password\n self.__token = base64.b64encode(\n bytes('%s:%s' % (self.__user, self.__password), encoding='utf-8'))\n self.__headers = dict()\n self.__headers[\"Accept\"] = \"application/json\"\n self.__headers['authorization'] = 'Basic %s' % str(\n self.__token, encoding='utf-8')\n\n def request(self, method, obj=None, prefix='/'):\n try:\n if method == 'get':\n req = requests.request(method, '%s%s' % (self.__url, prefix), params=obj, headers=self.__headers,\n verify=False)\n if req.status_code > 399:\n return {'ecode': req.status_code, 'message': f'{req.content}\\n{req.reason}'}\n res = {'ecode': req.status_code, 'data': req.json(), 'count': req.headers.get('X-Total-Count', None),\n 'next': req.headers.get('Link', None)}\n if method == 'delete':\n req = requests.request(method, '%s%s' % (\n self.__url, prefix), headers=self.__headers, verify=False)\n if req.status_code > 399:\n return {'ecode': req.status_code, 'message': f'{req.content}\\n{req.reason}'}\n res = {'ecode': req.status_code, 'data': req.content}\n if method in ['put', 'post']:\n req = requests.request(method, '%s%s' % (self.__url, prefix), json=obj, headers=self.__headers,\n verify=False)\n if req.status_code > 399:\n return {'ecode': req.status_code, 'message': f'{req.content}\\n{req.reason}'}\n res = {'ecode': req.status_code, 'data': req.content}\n if method == 'head':\n req = requests.request(method, '%s%s' % (\n self.__url, prefix), headers=self.__headers, verify=False)\n if req.status_code > 399:\n return {'ecode': req.status_code, 'message': f'{req.content}\\n{req.reason}'}\n res = {'ecode': req.status_code, 'data': req.content}\n except BaseException as e:\n raise e\n return res\n\n def systeminfo(self):\n res = self.request('get', prefix='/systeminfo')\n return res\n\n def get_users(self):\n res = self.request('get', prefix='/users')\n return res\n\n def get_projects(self, project_name=None, page=1, page_size=20):\n \"\"\"\n :project_name: The name of project\n :page: default is 1.\n :page_size: default is 10, maximum is 100.\n \"\"\"\n params = {'page': page, 'page_size': page_size}\n if project_name:\n params['name'] = project_name\n try:\n res = self.request('get', params, prefix='/projects')\n return res\n except BaseException as e:\n return {'ecode': 500, 'message': e}\n\n def get_repositories(self, project_id, page=1, page_size=20, repo=None):\n params = {'project_id': project_id,\n 'page': page, 'page_size': page_size}\n if repo:\n params['q'] = repo\n try:\n res = self.request('get', params, '/repositories')\n return res\n except BaseException as e:\n return {'ecode': 500, 'message': e}\n\n def get_tags(self, repo):\n try:\n res = self.request('get', prefix='/repositories/%s/tags' % repo)\n tags = [\n {'name': i['name'], 'created': i['created'], 'push_time': i.get(\n 'push_time', None), 'size': i['size']}\n for i in\n res['data']]\n tags.sort(key=lambda k: (k.get('created')), reverse=True)\n return {'ecode': 200, 'data': tags, 'count': len(tags)}\n except BaseException as e:\n return {'ecode': 500, 'message': e}\n\n def fetch_project(self, project_id):\n \"\"\"\n 获取项目信息\n \"\"\"\n try:\n res = self.request(\n 'get', {'project_id': project_id}, prefix=f'/projects/{project_id}')\n return res\n except BaseException as e:\n return {'ecode': 500, 'message': e}\n\n def fetch_tag(self, repo, tag):\n \"\"\"\n 获取指定镜像标签\n \"\"\"\n try:\n res = self.request(\n 'get', prefix=f'/repositories/{repo}/tags/{tag}')\n return res\n except BaseException as e:\n return {'ecode': 500, 'message': e}\n\n def create_project(self, project_name, public=True):\n \"\"\"\n 创建仓库项目\n \"\"\"\n try:\n data = {'project_name': project_name, 'metadata': {\n 'public': 'true' if public else 'false'}}\n res = self.request('post', obj=data, prefix='/projects')\n return res\n except BaseException as e:\n return {'ecode': 500, 'message': e}\n\n def update_project(self, project_id, *args, **kwargs):\n \"\"\"\n 更新仓库项目\n \"\"\"\n try:\n res = self.request('put', obj=kwargs,\n prefix=f'/projects/{project_id}')\n return res\n except BaseException as e:\n return {'ecode': 500, 'message': e}\n\n def project_exists(self, project_name):\n \"\"\"\n 查询项目是否存在\n \"\"\"\n try:\n res = self.request(\n 'head', prefix=f'/projects?project_name={project_name}')\n return res\n except BaseException as e:\n return {'ecode': 500, 'message': e}\n\n def patch_tag(self, repo, src_image, tag_name):\n \"\"\"\n 镜像打标签\n \"\"\"\n try:\n try:\n # 创建仓库项目\n res = self.create_project(repo.split('/')[0])\n except BaseException as e:\n pass\n data = {'tag': tag_name, 'src_image': src_image, 'override': True}\n res = self.request(\n 'post', obj=data, prefix='/repositories/%s/tags' % repo)\n return res\n except BaseException as e:\n return {'ecode': 500, 'message': e}\n\n def delete_tag(self, repo, tag):\n \"\"\"\n 删除标签\n \"\"\"\n try:\n res = self.request(\n 'delete', prefix=f'/repositories/{repo}/tags/{tag}')\n return res\n except BaseException as e:\n logger.ex\n return {'ecode': 500, 'message': e}\n\n def search(self, query):\n \"\"\"\n 搜索\n \"\"\"\n try:\n res = self.request('get', {'q': query}, prefix='/search')\n return res\n except BaseException as e:\n logger.exception(e)\n return {'ecode': 500, 'message': e}"},{"identifier":"GlueJenkins","path":"common/utils/JenkinsAPI.py","snippet":"class GlueJenkins(Jenkins):\n\n def __init__(self, url=None, username=None, password=None):\n self.__url = url\n self.__username = username\n self.__password = password\n super(GlueJenkins, self).__init__(\n self.__url, self.__username, self.__password)\n\n def _get_encoded_params(self, params):\n for k, v in params.items():\n if k in [\"name\", \"msg\", \"short_name\", \"from_short_name\",\n \"to_short_name\", \"folder_url\", \"from_folder_url\", \"to_folder_url\"]:\n params[k] = quote(v.encode('utf8'))\n return params\n\n def _build_url(self, format_spec, variables=None):\n\n if variables:\n url_path = format_spec % self._get_encoded_params(variables)\n else:\n url_path = format_spec\n return str(urljoin(self.server, url_path))\n\n def assert_credential_exists(self, name, folder_name=None, domain_name='_',\n exception_message='credential[%s] does not exist.'):\n '''Raise an exception if credential does not exist in domain of folder\n\n :param name: Name of credential, ``str``\n :param folder_name: Folder name, ``str``\n :param domain_name: Domain name, default is '_', ``str``\n :param exception_message: Message to use for the exception.\n Formatted with ``name``, ``domain_name``,\n and ``folder_name``\n :throws: :class:`JenkinsException` whenever the credentail\n does not exist in domain of folder\n '''\n if not self.credential_exists(name, folder_name, domain_name):\n raise JenkinsException(exception_message\n % name)\n\n def get_credential_global_config(self, name, domain_name='_'):\n '''Get configuration of credential in domain of folder.\n :param name: Name of credentail, ``str``\n :param domain_name: Domain name, default is '_', ``str``\n :returns: Credential configuration (XML format)\n '''\n return self.jenkins_open(requests.Request(\n 'GET', self._build_url(CONFIG_CREDENTIAL_GLOBAL, locals())\n ))\n\n def get_credential_info(self, name, folder_name=None, domain_name='_'):\n '''Get credential information dictionary in domain of folder\n\n :param name: Name of credentail, ``str``\n :param folder_name: folder_name, ``str``\n :param domain_name: Domain name, default is '_', ``str``\n :returns: Dictionary of credential info, ``dict``\n '''\n try:\n response = self.jenkins_open(requests.Request(\n 'GET', self._build_url(CREDENTIAL_INFO_GLOBAL, locals())\n ))\n if response:\n return json.loads(response)\n else:\n raise JenkinsException('credential[%s] does not exist.' % name)\n except (req_exc.HTTPError, NotFoundException):\n raise JenkinsException('credential[%s] does not exist.' % name)\n except ValueError:\n raise JenkinsException(\n 'Could not parse JSON info for credential[%s].' % name\n )\n\n def credential_exists(self, name, folder_name=None, domain_name='_'):\n '''Check whether a credentail exists in domain of folder\n\n :param name: Name of credentail, ``str``\n :param folder_name: Folder name, ``str``\n :param domain_name: Domain name, default is '_', ``str``\n :returns: ``True`` if credentail exists, ``False`` otherwise\n '''\n try:\n return self.get_credential_info(name)['id'] == name\n except JenkinsException:\n return False\n\n def create_credential_global(self, name=None, user=None, password=None, secret=None, comment=None, domain_name='_'):\n '''Create credentail in domain of folder\n\n :param name: username\n :param password: password\n :param comment: comment, ``str``\n :param config_xml: New XML configuration, ``str``\n :param domain_name: Domain name, default is '_', ``str``\n '''\n st = shortuuid.ShortUUID()\n st.set_alphabet(\n f\"0123456789{''.join([chr(i) for i in range(ord('a'), ord('z') + 1)])}\")\n if name is None:\n name = '-'.join(['api', st.random(length=8),\n st.random(length=4), st.random(length=12)])\n config_xml = '''<com.cloudbees.plugins.credentials.impl.UsernamePasswordCredentialsImpl>\n <scope>GLOBAL</scope>\n <id>%s</id>\n <description>[%s] Created by DevOps Platform</description>\n <username>%s</username>\n <password>%s</password>\n</com.cloudbees.plugins.credentials.impl.UsernamePasswordCredentialsImpl>''' % (name, comment, user, password)\n if user is None:\n config_xml = '''<org.jenkinsci.plugins.plaincredentials.impl.StringCredentialsImpl>\n <scope>GLOBAL</scope>\n <id>%s</id>\n <description>[%s] Created by DevOps Platform</description>\n <secret>%s</secret>\n</org.jenkinsci.plugins.plaincredentials.impl.StringCredentialsImpl>''' % (name, comment, secret)\n if self.credential_exists(name):\n raise JenkinsException('credential[%s] already exists.' % name)\n\n self.jenkins_open(requests.Request(\n 'POST', self._build_url(CREATE_CREDENTIAL_GLOBAL, locals()),\n data=config_xml.encode('utf-8'),\n headers=DEFAULT_HEADERS\n ))\n self.assert_credential_exists(\n name, exception_message='create credential[%s] failed.')\n return {'status': 0, 'data': name}\n\n def reconfig_credential_global(self, name, user=None, password=None, secret=None, comment=None, domain_name='_'):\n \"\"\"\n Reconfig credential with new config in domain of folder\n :param name: name, ``str``\n :param user:\n :param password:\n :param secret:\n :param comment:\n :param domain_name: Domain name, default is '_', ``str``\n :return:\n \"\"\"\n reconfig_url = self._build_url(CONFIG_CREDENTIAL_GLOBAL, locals())\n config_xml = self.get_credential_global_config(name)\n xml_dict = xmltodict.parse(config_xml)\n if user is None:\n xml_dict['org.jenkinsci.plugins.plaincredentials.impl.StringCredentialsImpl']['secret'] = secret\n if comment:\n xml_dict['org.jenkinsci.plugins.plaincredentials.impl.StringCredentialsImpl']['description'] = comment\n else:\n xml_dict['com.cloudbees.plugins.credentials.impl.UsernamePasswordCredentialsImpl']['username'] = user\n xml_dict['com.cloudbees.plugins.credentials.impl.UsernamePasswordCredentialsImpl']['password'] = password\n if comment:\n xml_dict['com.cloudbees.plugins.credentials.impl.UsernamePasswordCredentialsImpl'][\n 'description'] = comment\n config_xml = xmltodict.unparse(xml_dict, pretty=True)\n self.jenkins_open(requests.Request(\n 'POST', reconfig_url,\n data=config_xml.encode('utf-8'),\n headers=DEFAULT_HEADERS\n ))\n\n def create_job(self, name, config_xml):\n '''Create a new Jenkins job\n\n :param name: Name of Jenkins job, ``str``\n :param config_xml: config file text, ``str``\n '''\n folder_url, short_name = self._get_job_folder(name)\n if self.job_exists(name):\n raise JenkinsException('job[%s] already exists' % (name))\n\n try:\n self.jenkins_open(requests.Request(\n 'POST', self._build_url(CREATE_JOB, locals()),\n data=config_xml.encode('utf-8'),\n headers=DEFAULT_HEADERS\n ))\n except NotFoundException:\n raise JenkinsException('Cannot create job[%s] because folder '\n 'for the job does not exist' % (name))\n self.assert_job_exists(name, 'create[%s] failed')\n\n def reconfig_job(self, name, config_xml):\n '''Change configuration of existing Jenkins job.\n\n To create a new job, see :meth:`Jenkins.create_job`.\n\n :param name: Name of Jenkins job, ``str``\n :param config_xml: New XML configuration, ``str``\n '''\n folder_url, short_name = self._get_job_folder(name)\n reconfig_url = self._build_url(CONFIG_JOB, locals())\n self.jenkins_open(requests.Request(\n 'POST', reconfig_url,\n data=config_xml.encode('utf-8'),\n headers=DEFAULT_HEADERS\n ))\n\n def get_stage_describe(self, name, number, node_number):\n \"\"\" 获取 单个stage 详情 \"\"\"\n folder_url, short_name = self._get_job_folder(name)\n try:\n response = self.jenkins_open(requests.Request(\n 'GET', self._build_url(STAGE_DES, locals())\n ))\n\n if response:\n return json.loads(response)\n else:\n raise JenkinsException('job[%s] number[%d] does not exist'\n % (name, number))\n except (req_exc.HTTPError, NotFoundException):\n raise JenkinsException('job[%s] number[%d] does not exist'\n % (name, number))\n except ValueError:\n raise JenkinsException(\n 'Could not parse JSON info for job[%s] number[%d]'\n % (name, number)\n )\n\n def get_stage_logs(self, name, number, node_number):\n \"\"\" 获取 stage 执行日志\"\"\"\n folder_url, short_name = self._get_job_folder(name)\n try:\n response = self.jenkins_open(requests.Request(\n 'GET', self._build_url(STAGE_LOG, locals())\n ))\n if response:\n return json.loads(response)\n else:\n raise JenkinsException('job[%s] number[%d] does not exist'\n % (name, number))\n except (req_exc.HTTPError, NotFoundException):\n raise JenkinsException('job[%s] number[%d] does not exist'\n % (name, number))\n except ValueError:\n raise JenkinsException(\n 'Could not parse JSON info for job[%s] number[%d]'\n % (name, number)\n )\n\n def get_stage_info(self, name, number, depth=0):\n\n folder_url, short_name = self._get_job_folder(name)\n try:\n response = self.jenkins_open(requests.Request(\n 'GET', self._build_url(STAGE_INFO, locals())\n ))\n if response:\n return json.loads(response)\n else:\n raise JenkinsException('job[%s] number[%d] does not exist'\n % (name, number))\n except (req_exc.HTTPError, NotFoundException):\n raise JenkinsException('job[%s] number[%d] does not exist'\n % (name, number))\n except ValueError:\n raise JenkinsException(\n 'Could not parse JSON info for job[%s] number[%d]'\n % (name, number)\n )\n\n def get_flow_detail(self, job_name, build_number):\n stage_data = self.get_stage_info(name=job_name, number=build_number)\n stages = stage_data.get('stages')\n for i in stages:\n logs = ''\n try:\n # 获取stage返回信息\n response = self.jenkins_open(requests.Request(\n 'GET', self._build_url(\n unquote(i['_links']['self']['href']), locals())\n ))\n if response:\n res = json.loads(response)\n for j in res['stageFlowNodes']:\n response = self.jenkins_open(requests.Request(\n 'GET', self._build_url(\n unquote(j['_links']['log']['href']), locals())\n ))\n res = json.loads(response)\n try:\n # 移除href html信息，保留链接文字\n import re\n pat = re.compile('<a href[^>]*>')\n logs = logs + '\\n' + \\\n pat.sub('', res['text'].replace('</a>', ''))\n except:\n pass\n else:\n raise JenkinsException('job[%s] number[%d] does not exist'\n % (job_name, build_number))\n except (req_exc.HTTPError, NotFoundException):\n raise JenkinsException('job[%s] number[%d] does not exist'\n % (job_name, build_number))\n except ValueError:\n raise JenkinsException(\n 'Could not parse JSON info for job[%s] number[%d]'\n % (job_name, build_number)\n )\n\n stage_data[\"stages\"][stages.index(i)]['logs'] = logs\n return stage_data\n\n def get_queue_item(self, number, depth=0):\n '''Get information about a queued item (to-be-created job).\n\n The returned dict will have a \"why\" key if the queued item is still\n waiting for an executor.\n\n The returned dict will have an \"executable\" key if the queued item is\n running on an executor, or has completed running. Use this to\n determine the job number / URL.\n\n :param name: queue number, ``int``\n :returns: dictionary of queued information, ``dict``\n '''\n url = self._build_url(Q_ITEM, locals())\n try:\n response = self.jenkins_open(requests.Request('GET', url))\n if response:\n return json.loads(response)\n else:\n raise JenkinsException('queue number[%d] does not exist'\n % number)\n except (req_exc.HTTPError, NotFoundException):\n raise JenkinsException('queue number[%d] does not exist' % number)\n except ValueError:\n raise JenkinsException(\n 'Could not parse JSON info for queue number[%d]' % number\n )\n\n def build_job(self, name, parameters=None, token=None):\n '''Trigger build job.\n\n This method returns a queue item number that you can pass to\n :meth:`Jenkins.get_queue_item`. Note that this queue number is only\n valid for about five minutes after the job completes, so you should\n get/poll the queue information as soon as possible to determine the\n job's URL.\n\n :param name: name of job\n :param parameters: parameters for job, or ``None``, ``dict``\n :param token: Jenkins API token\n :returns: ``int`` queue item\n '''\n response = self.jenkins_request(requests.Request(\n 'POST', self.build_job_url(name, parameters, token)))\n\n if 'Location' not in response.headers:\n raise EmptyResponseException(\n \"Header 'Location' not found in \"\n \"response from server[%s]\" % self.server)\n\n location = response.headers['Location']\n if location.endswith('/'):\n location = location[:-1]\n parts = location.split('/')\n number = int(parts[-1])\n return number\n\n def get_job_config(self, name):\n '''Get configuration of existing Jenkins job.\n\n :param name: Name of Jenkins job, ``str``\n :returns: job configuration (XML format)\n '''\n folder_url, short_name = self._get_job_folder(name)\n request = requests.Request(\n 'GET', self._build_url(CONFIG_JOB, locals()))\n return self.jenkins_open(request)\n\n def get_job_info(self, name, depth=0, fetch_all_builds=False):\n '''Get job information dictionary.\n\n :param name: Job name, ``str``\n :param depth: JSON depth, ``int``\n :param fetch_all_builds: If true, all builds will be retrieved\n from Jenkins. Otherwise, Jenkins will\n only return the most recent 100\n builds. This comes at the expense of\n an additional API call which may\n return significant amounts of\n data. ``bool``\n :returns: dictionary of job information\n '''\n folder_url, short_name = self._get_job_folder(name)\n try:\n response = self.jenkins_open(requests.Request(\n 'GET', self._build_url(JOB_INFO, locals())\n ))\n if response:\n if fetch_all_builds:\n return self._add_missing_builds(json.loads(response))\n else:\n return json.loads(response)\n else:\n raise JenkinsException('job[%s] does not exist' % name)\n except (req_exc.HTTPError, NotFoundException):\n raise JenkinsException('job[%s] does not exist' % name)\n except ValueError:\n raise JenkinsException(\n \"Could not parse JSON info for job[%s]\" % name)"},{"identifier":"convert_xml_to_str_with_pipeline","path":"common/custom_format.py","snippet":"def convert_xml_to_str_with_pipeline(xml, url, secret, desc, jenkinsfile, scm=True):\n \"\"\"\n scm\n True: jenkinsfile为指定的git地址\n False: jenkinsfile为具体的pipeline\n \"\"\"\n xml_dict = xmltodict.parse(xml)\n if scm:\n xml_dict['flow-definition']['definition']['@class'] = 'org.jenkinsci.plugins.workflow.cps.CpsScmFlowDefinition'\n xml_dict['flow-definition']['definition']['scm']['userRemoteConfigs']['hudson.plugins.git.UserRemoteConfig'][\n 'url'] = url\n xml_dict['flow-definition']['definition']['scm']['userRemoteConfigs']['hudson.plugins.git.UserRemoteConfig'][\n 'credentialsId'] = secret\n xml_dict['flow-definition']['definition']['scriptPath'] = jenkinsfile\n else:\n xml_dict['flow-definition']['definition']['@class'] = 'org.jenkinsci.plugins.workflow.cps.CpsFlowDefinition'\n xml_dict['flow-definition']['definition']['script'] = jenkinsfile\n xml_dict['flow-definition']['definition']['sandbox'] = 'true'\n xml_dict['flow-definition']['description'] = desc\n result = xmltodict.unparse(\n xml_dict, short_empty_elements=True, pretty=True)\n return result"},{"identifier":"DASHBOARD_TIME_FORMAT","path":"common/variables.py","snippet":"DASHBOARD_TIME_FORMAT = {'year_only': '%Y', 'years': '%Y-%m', 'months': '%Y-%m-%d', 'days': '%Y-%m-%d %H:00:00',\n 'hours': '%Y-%m-%d %H:%M:00', 'minutes': '%Y-%m-%d %H:%M:%S'}"},{"identifier":"DASHBOARD_TIME_FORMAT_T","path":"common/variables.py","snippet":"DASHBOARD_TIME_FORMAT_T = {'years': '%Y', 'months': '%Y-%m', 'days': '%Y-%m-%d', 'hours': \"%Y-%m-%d %H:00:00\",\n 'minutes': \"%Y-%m-%d %H:%M:00\", 'seconds': \"%Y-%m-%d %H:%M:%S\"}"},{"identifier":"DASHBOARD_TIME_FREQNAMES","path":"common/variables.py","snippet":"DASHBOARD_TIME_FREQNAMES = {'year_only': YEARLY, 'years': MONTHLY, 'months': DAILY, 'days': HOURLY, 'hours': MINUTELY,\n 'minutes': SECONDLY}"},{"identifier":"DASHBOARD_TIME_FREQNAMES_T","path":"common/variables.py","snippet":"DASHBOARD_TIME_FREQNAMES_T = {'years': YEARLY, 'months': MONTHLY, 'days': DAILY, 'hours': HOURLY, 'minutes': MINUTELY,\n 'seconds': SECONDLY}"},{"identifier":"SENSITIVE_KEYS","path":"common/variables.py","snippet":"SENSITIVE_KEYS = ['password', 'token', 'access',\n 'refresh', 'AUTHORIZATION', 'COOKIE']"},{"identifier":"JENKINS_CALLBACK_KEY","path":"common/variables.py","snippet":"JENKINS_CALLBACK_KEY = 'jenkins_callback_flag::'"},{"identifier":"JENKINS_STATUS_MAP","path":"common/variables.py","snippet":"JENKINS_STATUS_MAP = {'IN_PROGRESS': 3, 'SUCCESS': 1, 'FAILED': 2, 'ABORTED': 4, 'FAILURE': 2, 'NOT_EXECUTED': 5,\n 'NOT_EXEC_TIMEOUT': 5}"},{"identifier":"DEV_LANGUAGE_KEY","path":"common/variables.py","snippet":"DEV_LANGUAGE_KEY = 'devlanguage:'"},{"identifier":"AppInfo","path":"dbapp/models.py","snippet":""},{"identifier":"K8sAPI","path":"common/utils/K8sAPI.py","snippet":"class K8sAPI(object):\n def __init__(self, host=None, username=None, password=None, api_key=None, api_key_prefix='Bearer', verify_ssl=False,\n k8s_config=None,\n config_file=None, eks=None):\n \"\"\"\n elk: aws kubernetes\n \"\"\"\n self.__host = host\n self.__username = username\n self.__password = password\n self.__api_key = api_key\n self.__api_key_prefix = api_key_prefix\n self.__verify_ssl = verify_ssl\n if k8s_config is not None:\n config.kube_config.load_kube_config_from_dict(k8s_config)\n self.__client0 = client.CoreApi()\n self.__client = client.CoreV1Api()\n elif config_file is not None:\n config.kube_config.load_kube_config(config_file=config_file)\n self.__client0 = client.CoreApi()\n self.__client = client.CoreV1Api()\n elif self.__host:\n if self.__username and self.__password:\n self.__client = self.get_api()\n else:\n raise Exception('Please input username/password or api_key')\n else:\n raise Exception('Cannot find k8s config')\n self.client = self.__client\n\n def get_token(self):\n pass\n\n def get_api(self):\n configuration = client.Configuration()\n configuration.host = self.__host\n if self.__verify_ssl is False:\n configuration.verify_ssl = False\n configuration.username = self.__username\n configuration.password = self.__password\n basic_auth_token = configuration.get_basic_auth_token()\n api = core_v1_api.CoreV1Api(api_client.ApiClient(configuration=configuration, header_name=\"authorization\",\n header_value=basic_auth_token))\n return api\n\n def get_client(self):\n return self.__client\n\n def set_client(self, obj):\n self.__client = getattr(client, obj)()\n\n def get_apis(self):\n print(\"Supported APIs (* is preferred version):\")\n self.__client2 = client.ApisApi(self.__client0.api_client)\n for api in self.__client2.get_api_versions().groups:\n versions = []\n for v in api.versions:\n name = \"\"\n if v.version == api.preferred_version.version and len(\n api.versions) > 1:\n name += \"*\"\n name += v.version\n versions.append(name)\n\n def get_nodes(self, **kwargs):\n ret = self.__client.list_node(**kwargs)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'err': str(e)}\n\n def get_node_info(self, name):\n ret = self.__client.read_node_status(name)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'err': str(e)}\n\n def get_namespaces(self, **kwargs):\n ret = self.__client.list_namespace(**kwargs)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'err': str(e)}\n\n def create_namespace(self, name):\n payload = {\n \"apiVersion\": \"v1\",\n \"kind\": \"Namespace\",\n \"metadata\": {\n \"name\": name,\n }\n }\n ret = self.__client.create_namespace(body=payload)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n print(rs)\n return rs\n except BaseException as e:\n return {'err': str(e)}\n\n def get_services(self, namespace='default', **kwargs):\n ret = self.__client.list_namespaced_service(namespace, **kwargs)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'err': str(e)}\n\n def fetch_service(self, name, namespace='default', api_version='apps/v1'):\n try:\n ret = self.__client.read_namespaced_service(name, namespace)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n print('reason', e.reason)\n return {'ecode': e.status, 'message': e.body}\n except BaseException as e:\n print('reason', e.reason)\n return {'ecode': e.status, 'message': e.body}\n\n def create_namespace_service(self, name, app=None, targets=list, namespace='default', service_type='NodePort',\n svc_yaml=None):\n \"\"\"\n 目前只支持NodePort类型，对外服务端口随机生成（如手动生成，需配置node_port和endpoints）\n :param name: service name\n :param app: app name\n :param targets: [{port, target_port, protocol, node_port}]\n :param namespace:\n :param service_type:\n :return:\n \"\"\"\n ports = []\n if svc_yaml:\n if isinstance(svc_yaml, str):\n body = yaml.safe_load(svc_yaml)\n else:\n body = svc_yaml\n else:\n for index, target in enumerate(targets):\n port_body = {'name': f\"{name}-{index}\", 'port': target['port'], 'target_port': target['port'],\n 'protocol': target['protocol']}\n if target['node_port'] > 30000:\n port_body['node_port'] = target['node_port']\n ports.append(client.V1ServicePort(**port_body))\n body = client.V1Service(\n api_version=\"v1\",\n kind=\"Service\",\n metadata=client.V1ObjectMeta(\n name=name\n ),\n spec=client.V1ServiceSpec(\n selector={\"app\": app},\n type=service_type,\n ports=ports\n )\n )\n try:\n ret = self.__client.create_namespaced_service(namespace=namespace, body=body,\n **{'_return_http_data_only': False})\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n logger.error('reason', e)\n return {'error': True, 'message': str(e)}\n except ApiException as e:\n if e.status == 409:\n logger.error('reason', e.reason)\n return {'error': True, 'ecode': e.status, 'message': e.body}\n except BaseException as e:\n return {'error': True, 'message': str(e)}\n\n def update_namespace_service(self, name, app=None, targets=Type[list], namespace='default', service_type='NodePort',\n svc_yaml=None):\n ports = []\n if svc_yaml:\n if isinstance(svc_yaml, str):\n body = yaml.safe_load(svc_yaml)\n else:\n body = svc_yaml\n logger.debug(f'svc_yaml body == {body}')\n func = self.__client.replace_namespaced_service\n else:\n for index, target in enumerate(targets):\n port_body = {'name': target['name'], 'port': target['port'], 'target_port': target['port'],\n 'protocol': target['protocol']}\n if target['node_port'] > 30000:\n port_body['node_port'] = target['node_port']\n ports.append(client.V1ServicePort(**port_body))\n body = client.V1Service(\n api_version=\"v1\",\n kind=\"Service\",\n metadata=client.V1ObjectMeta(\n name=name\n ),\n spec=client.V1ServiceSpec(\n selector={\"app\": name},\n type=service_type,\n ports=ports\n )\n )\n func = self.__client.patch_namespaced_service\n try:\n ret = func(\n name, namespace, body=body,\n **{'_return_http_data_only': False}\n )\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n logger.error(f'ApiClient sanitize_for_serialization 异常： {e}', )\n return {'error': True, 'message': str(e)}\n except ApiException as e:\n if e.status == 409:\n logger.error(f'ApiException 异常 409 资源冲突： {e} {e.reason}', )\n return {'error': True, 'ecode': e.status, 'message': e.body}\n except BaseException as e:\n logger.error(f'patch_namespaced_service 异常： {e}', )\n return {'error': True, 'message': str(e)}\n\n def delete_namespace_service(self, name, namespace='default', api_version='apps/v1'):\n try:\n ret = self.__client.delete_namespaced_service(name, namespace)\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n return {'error': True, 'message': str(e)}\n\n def get_configmaps(self, namespace='default', **kwargs):\n ret = self.__client.list_namespaced_config_map(namespace, **kwargs)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'error': True, 'message': str(e)}\n\n def get_configmap(self, name, namespace='default', **kwargs):\n \"\"\"\n get configmap content\n \"\"\"\n try:\n ret = self.__client.read_namespaced_config_map(\n name, namespace, **kwargs)\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'error': True, 'message': str(e)}\n\n def create_namespace_configmap(self, svc_yaml, namespace='default', **kwargs):\n if isinstance(svc_yaml, str):\n body = yaml.safe_load(svc_yaml)\n else:\n body = svc_yaml\n try:\n ret = self.__client.create_namespaced_config_map(\n namespace, body, **kwargs)\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n return {'error': True, 'message': str(e)}\n\n def update_namespace_configmap(self, name, svc_yaml, namespace='default', **kwargs):\n if isinstance(svc_yaml, str):\n body = yaml.safe_load(svc_yaml)\n else:\n body = svc_yaml\n try:\n ret = self.__client.patch_namespaced_config_map(\n name, namespace, body, **kwargs)\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n return {'error': True, 'message': str(e)}\n\n def delete_namespace_configmap(self, name, namespace='default', api_version='apps/v1'):\n try:\n ret = self.__client.delete_namespaced_config_map(name, namespace)\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n return {'error': True, 'message': str(e)}\n\n def get_namespace_deployment(self, namespace='default', api_version='apps/v1', **kwargs):\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\n self.__client.api_client)(client)\n ret = self.__client2.list_namespaced_deployment(namespace, **kwargs)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'err': str(e)}\n\n def create_namespace_deployment(self, name, image=None, port=list, replicas=1, deploy_yaml=None,\n pod_type='Deployment', namespace='default'):\n \"\"\"\n\n :param name:\n :param image:\n :param port: [{containerPort: 8080, protocol: 'TCP'}]\n :param replicas:\n :param pod_type:\n :param namespace:\n :return:\n \"\"\"\n payload = {'kind': pod_type, 'spec': {'replicas': replicas, 'template': {\n 'spec': {'containers': [{'image': image, 'name': name, 'ports': port}]},\n 'metadata': {'labels': {'app': name}}},\n 'selector': {'matchLabels': {'app': name}}},\n 'apiVersion': 'apps/v1beta2',\n 'metadata': {'labels': {'app': name}, 'namespace': namespace,\n 'name': name}}\n if deploy_yaml is not None:\n payload = yaml.safe_load(deploy_yaml)\n payload['metadata'].pop('resourceVersion', None)\n self.__client2 = operator.methodcaller(\n ''.join([i.capitalize() for i in payload.get(\n 'apiVersion', 'apps/v1beta2').split('/')]) + 'Api',\n self.__client.api_client)(client)\n try:\n ret = self.__client2.create_namespaced_deployment(\n namespace=namespace, body=payload)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n return {'ecode': e.status, 'message': e.body}\n except ApiException as e:\n return {'ecode': e.status, 'message': e.body}\n\n def delete_namespace_deployment(self, name, namespace='default', api_version='apps/v1'):\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\n self.__client.api_client)(client)\n ret = self.__client2.delete_namespaced_deployment(name, namespace,\n body=client.V1DeleteOptions(grace_period_seconds=0,\n propagation_policy='Foreground'))\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'err': str(e)}\n\n def update_deployment(self, name, replicas=None, image=None, envs=None, deploy_yaml=None, namespace='default',\n api_version='apps/v1', force=False):\n \"\"\"\n force: 强制更新\n \"\"\"\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\n self.__client.api_client)(client)\n payload = {'spec': {'replicas': replicas, 'template': {}}}\n if replicas is None and image is None and deploy_yaml is None:\n return {'err': '缺少参数'}\n if replicas is not None:\n payload['spec']['replicas'] = replicas\n if image is not None:\n payload['spec']['template'] = {\n 'spec': {'containers': [{'image': image, 'name': name}]}}\n\n if envs is not None:\n payload['spec']['template'] = {\n 'spec': {'containers': [{'env': envs}]}}\n\n if deploy_yaml is not None:\n payload = yaml.safe_load(deploy_yaml)\n payload['metadata'].pop('resourceVersion', None)\n try:\n if force:\n ret = self.__client2.replace_namespaced_deployment(\n name, namespace, body=payload)\n else:\n ret = self.__client2.patch_namespaced_deployment(\n name, namespace, body=payload)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n return {'ecode': e.status, 'message': e.body}\n except ApiException as e:\n return {'ecode': e.status, 'message': e.body}\n\n def update_deployment_replica(self, name, replicas, namespace='default', api_version='apps/v1'):\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\n self.__client.api_client)(client)\n payload = {'spec': {'replicas': replicas}}\n ret = self.__client2.patch_namespaced_deployment_scale(\n name, namespace, body=payload)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'err': str(e)}\n\n def update_deployment_image(self, name, image, namespace='default', api_version='apps/v1'):\n deploy = self.fetch_deployment(name, namespace)\n if deploy.get('ecode', 200) > 399:\n return deploy\n payload = {'spec': deploy['message']['spec']}\n payload['spec']['template']['spec']['containers'][0]['image'] = image\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\n self.__client.api_client)(client)\n try:\n ret = self.__client2.patch_namespaced_deployment(name, namespace, body=payload,\n **{'_return_http_data_only': False})\n except ApiException as e:\n return {'ecode': e.status, 'message': e.body}\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n return {'ecode': e.status, 'message': e.body}\n\n def update_deployment_resource(self, name, envs, image_policy, namespace='default', api_version='apps/v1',\n **kwargs):\n payload = {'spec': {'template': {'spec': {'containers': [\n {'name': name, 'env': envs, 'imagePullPolicy': image_policy, 'resources': kwargs['resources']}]}}}}\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\n self.__client.api_client)(client)\n ret = self.__client2.patch_namespaced_deployment(\n name, namespace, body=payload)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'err': str(e)}\n\n def restart_deployment(self, name, namespace='default', api_version='apps/v1'):\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\n self.__client.api_client)(client)\n payload = {\n 'spec': {\n 'template': {\n 'spec': {\n 'containers': [\n {\n 'name': name,\n 'env': [\n {\n 'name': 'RESTART_',\n 'value': datetime.now().strftime('%Y%m%d%H%M%S')\n }\n ]\n }\n ]\n }\n }\n }\n }\n\n ret = self.__client2.patch_namespaced_deployment(\n name, namespace, body=payload)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'err': str(e)}\n\n def fetch_deployment(self, name, namespace='default', api_version='apps/v1'):\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\n self.__client.api_client)(client)\n try:\n ret = self.__client2.read_namespaced_deployment(name, namespace)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except ApiException as e:\n return {'ecode': e.status, 'message': e.body}\n except ApiException as e:\n return {'ecode': e.status, 'message': e.body}\n\n def get_replica(self, namespace='default', api_version='apps/v1', **kwargs):\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\n self.__client.api_client)(client)\n try:\n ret = self.__client2.list_namespaced_replica_set(\n namespace=namespace, **kwargs)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except ApiException as e:\n return {'ecode': e.status, 'message': e.body}\n except ApiException as e:\n return {'ecode': e.status, 'message': e.body}\n\n def get_pods(self, namespace=None, **kwargs):\n if namespace is None:\n return {}\n try:\n ret = self.__client.list_namespaced_pod(namespace, **kwargs)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n return {'ecode': e.status, 'message': e.body}\n except ApiException as e:\n return {'ecode': e.status, 'message': e.body}\n\n def fetch_pod(self, name, namespace='default'):\n try:\n ret = self.__client.read_namespaced_pod(\n name=name, namespace=namespace)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return {'ecode': 200, 'message': rs}\n except BaseException as e:\n return {'ecode': e.status, 'message': e.body}\n except BaseException as e:\n return {'ecode': e.status, 'message': e.body}\n\n def get_secrets(self, namespace='default', **kwargs):\n ret = self.__client.list_namespaced_secret(namespace, **kwargs)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'error': True, 'message': str(e)}\n\n def get_secret(self, name, namespace='default', **kwargs):\n \"\"\"\n get secret content\n \"\"\"\n ret = self.__client.read_namespaced_secret(name, namespace, **kwargs)\n try:\n ret = self.__client.read_namespaced_secret(\n name, namespace, **kwargs)\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'error': True, 'message': str(e)}\n\n def manage_secret(self, name, namespace='default', api_version='v1', **kwargs):\n payload = kwargs.pop('payload', {})\n body = kubernetes.client.V1Secret(api_version=api_version, **payload)\n ret = {}\n try:\n ret = self.__client.replace_namespaced_secret(\n name, namespace, body, **kwargs)\n except ApiException as e:\n if e.status == 404:\n ret = self.__client.create_namespaced_secret(namespace, body)\n try:\n rs = ApiClient().sanitize_for_serialization(ret)\n return rs\n except BaseException as e:\n return {'error': True, 'message': str(e)}"}],"string":"[\n {\n \"identifier\": \"generate_docu\",\n \"path\": \"common/utils/ElasticSearchAPI.py\",\n \"snippet\": \"def generate_docu(table, index_version=None):\\n index_name = f\\\"{table.name}-{index_version}\\\" if index_version else table.name\\n _tbindex = Index(index_name)\\n _tbindex.analyzer(my_normalizer)\\n _tbindex.settings(number_of_shards=3, number_of_replicas=1)\\n _fields = Mapping().generate_data_mapping(table)\\n docu = type(index_name, (CustomDocument,), _fields)\\n return _tbindex.document(docu)\"\n },\n {\n \"identifier\": \"Search\",\n \"path\": \"common/utils/ElasticSearchAPI.py\",\n \"snippet\": \"class Search(BaseSearch):\\n def __init__(self, prefix=False, **kwargs):\\n if kwargs.get('index', None) and prefix:\\n if isinstance(kwargs['index'], string_types):\\n kwargs['index'] = f\\\"{ELASTICSEARCH_PREFIX}{kwargs['index']}\\\"\\n elif isinstance(kwargs['index'], list):\\n kwargs['index'] = [\\n f\\\"{ELASTICSEARCH_PREFIX}{i}\\\" for i in kwargs['index']]\\n elif isinstance(kwargs['index'], tuple):\\n kwargs['index'] = tuple(\\n f\\\"{ELASTICSEARCH_PREFIX}{i}\\\" for i in kwargs['index'])\\n else:\\n raise Exception('索引名称格式错误!')\\n super(Search, self).__init__(**kwargs)\"\n },\n {\n \"identifier\": \"GitLabAPI\",\n \"path\": \"common/utils/GitLabAPI.py\",\n \"snippet\": \"class GitLabAPI(object):\\n def __init__(self, url, user=None, password=None, token=None, oauth=False):\\n self.__url = url\\n if token:\\n self.__token = token\\n if oauth:\\n params = {'oauth_token': self.__token}\\n else:\\n params = {'private_token': self.__token}\\n self.__gl = gitlab.Gitlab(self.__url, **params)\\n else:\\n self.__gl = gitlab.Gitlab(\\n self.__url, http_username=user, http_password=password)\\n self.__gl.auth()\\n\\n def get_gl(self):\\n return self.__gl\\n\\n def list_projects(self, get_all=False, key=None, per_page=20, page=1):\\n params = {'per_page': per_page, 'page': page}\\n if get_all:\\n params = {'get_all': True, 'per_page': per_page}\\n if key:\\n params['search'] = key\\n projects = self.__gl.projects.list(**params)\\n return projects\\n\\n def get_project(self, project_id=None, project_name_with_namespace=None):\\n if any([project_id, project_name_with_namespace]) is False:\\n raise Exception('缺少参数，project_id或project_name_with_namespace必选其一.')\\n condition = project_id or project_name_with_namespace\\n try:\\n project = self.__gl.projects.get(condition)\\n return project\\n except BaseException as e:\\n logger.info(e)\\n return None\\n\\n def create_project(self, name, namespace_id=None, initialize_with_readme=False):\\n payload = {'name': name, 'path': name,\\n 'initialize_with_readme': initialize_with_readme}\\n if namespace_id:\\n payload['namespace_id'] = namespace_id\\n try:\\n ret = self.__gl.projects.create(payload)\\n return True, ret\\n except BaseException as e:\\n logger.exception(f'创建分支请求异常，原因：{e.__dict__}')\\n return False, e\\n\\n def get_commit(self, commit_id, project_id=None, project_name_with_namespace=None):\\n try:\\n commit = self.get_project(\\n project_id, project_name_with_namespace).get(commit_id)\\n return commit\\n except BaseException as e:\\n logger.info(e)\\n return None\\n\\n def list_groups(self, get_all=False, key=None, per_page=20, page=1):\\n params = {'per_page': per_page, 'page': page}\\n if get_all:\\n params = {'get_all': True, 'all': True, 'per_page': per_page}\\n if key:\\n params['search'] = key\\n groups = self.__gl.groups.list(**params)\\n return [{'id': i.id, 'name': i.name, 'description': i.description} for i in groups if not i.parent_id]\\n\\n def create_group(self, name, path=None, desc=None, parent=None):\\n \\\"\\\"\\\"\\n 创建组\\n \\\"\\\"\\\"\\n payload = {'name': name, 'path': path or name,\\n 'description': desc or ''}\\n if parent:\\n payload['parent_id'] = parent\\n try:\\n group = self.__gl.groups.create(payload)\\n return True, group\\n except BaseException as e:\\n logger.info(e)\\n return False, e\\n\\n def create_branch(self, project, src_branch, target_branch):\\n payload = {'branch': target_branch,\\n 'ref': src_branch}\\n if isinstance(project, (int,)):\\n project = self.get_project(project)\\n try:\\n ret = project.branches.create(payload)\\n return True, ret\\n except BaseException as e:\\n logger.exception(f'创建分支请求异常，原因：{e.__dict__}')\\n return False, e\\n\\n def list_branches(self, project_id=None, project_name_with_namespace=None, get_all=False, key=None, per_page=20,\\n page=1, protected='0', *args, **kwargs):\\n params = {'per_page': per_page, 'page': page}\\n if not protected:\\n protected = '0'\\n if get_all:\\n params = {'get_all': True, 'per_page': per_page}\\n if key:\\n params['search'] = key\\n params.update(kwargs)\\n branches = self.get_project(project_id=project_id,\\n project_name_with_namespace=project_name_with_namespace).branches.list(**params)\\n branches = [{'uid': f\\\"{G_COMMIT[0][0]}:{i.name}\\\", 'name': i.name, 'commit': i.commit, 'label': G_COMMIT[0][0], 'protected': i.protected}\\n for i in branches]\\n if protected != '0':\\n # 过滤受保护分支\\n _map = {'1': True, '2': False}\\n branches = [i for i in branches if i['protected']\\n == _map[protected]]\\n return branches\\n\\n def list_protected_branches(self, project_id=None, project_name_with_namespace=None, get_all=False, key=None, per_page=20,\\n page=1, *args, **kwargs):\\n params = {'per_page': per_page, 'page': page}\\n if get_all:\\n params = {'get_all': True, 'per_page': per_page}\\n if key:\\n params['search'] = key\\n params.update(kwargs)\\n branches = self.get_project(project_id=project_id,\\n project_name_with_namespace=project_name_with_namespace).protectedbranches.list(**params)\\n branches = [{'uid': f\\\"{G_COMMIT[0][0]}:{i.name}\\\", 'name': i.name, 'commit': i.commit, 'label': G_COMMIT[0][0], 'protected': i.protected}\\n for i in branches]\\n return branches\\n\\n def list_tags(self, project_id=None, project_name_with_namespace=None, get_all=False, key=None, per_page=20,\\n page=1):\\n params = {'per_page': per_page, 'page': page}\\n if get_all:\\n params = {'get_all': True, 'per_page': per_page}\\n if key:\\n params['search'] = key\\n tags = self.get_project(\\n project_id, project_name_with_namespace).tags.list(**params)\\n tags = [{'uid': f\\\"{G_COMMIT[1][0]}:{i.name}\\\", 'name': i.name, 'message': i.message, 'commit': i.commit,\\n 'label': G_COMMIT[1][0]} for i in tags]\\n return tags\\n\\n def list_commits(self, project_id=None, project_name_with_namespace=None, get_all=False, key=None, per_page=20,\\n page=1, ref_name=None, since=None):\\n params = {'per_page': per_page, 'page': page}\\n if get_all:\\n params = {'get_all': True, 'per_page': per_page}\\n if key:\\n params['search'] = key\\n if ref_name:\\n params['ref_name'] = ref_name\\n if since:\\n params['since'] = since\\n commits = self.get_project(\\n project_id, project_name_with_namespace).commits.list(**params)\\n commits = [\\n {'title': i.title, 'short_id': i.short_id, 'author_name': i.author_name, 'committer_name': i.committer_name,\\n 'committed_date': i.committed_date, 'message': i.message, 'web_url': i.web_url} for i in commits]\\n return commits\\n\\n def repo_checkout(self, repo):\\n import subprocess\\n git_url = repo.split('//')\\n subprocess.call(\\n ['git', 'clone', f\\\"{git_url[0]}//oauth2:{self.__token}@{git_url[1]}\\\"])\\n\\n def get_user_id(self, username):\\n user_list = self.__gl.users.list(username=username)\\n if user_list:\\n return user_list[0].id\\n else:\\n return None\\n\\n def get_project_from_name(self, project_name):\\n projects = self.__gl.projects.list(search=project_name)\\n for p in projects:\\n if p.name == project_name:\\n return p\\n return None\\n\\n def add_project_member(self, project, user_id, access_level):\\n try:\\n project.members.create(\\n {'user_id': user_id, 'access_level': access_level})\\n return True, '成功'\\n except Exception as error:\\n return False, error\\n\\n def del_project_member(self, project, user_id):\\n try:\\n project.members.delete(user_id)\\n return True, '成功'\\n except Exception as error:\\n return False, error\"\n },\n {\n \"identifier\": \"HarborAPI\",\n \"path\": \"common/utils/HarborAPI.py\",\n \"snippet\": \"class HarborAPI(object):\\n def __init__(self, url, username, password):\\n self.__url = url.rstrip('/')\\n self.__user = username\\n self.__password = password\\n self.__token = base64.b64encode(\\n bytes('%s:%s' % (self.__user, self.__password), encoding='utf-8'))\\n self.__headers = dict()\\n self.__headers[\\\"Accept\\\"] = \\\"application/json\\\"\\n self.__headers['authorization'] = 'Basic %s' % str(\\n self.__token, encoding='utf-8')\\n\\n def request(self, method, obj=None, prefix='/'):\\n try:\\n if method == 'get':\\n req = requests.request(method, '%s%s' % (self.__url, prefix), params=obj, headers=self.__headers,\\n verify=False)\\n if req.status_code > 399:\\n return {'ecode': req.status_code, 'message': f'{req.content}\\\\n{req.reason}'}\\n res = {'ecode': req.status_code, 'data': req.json(), 'count': req.headers.get('X-Total-Count', None),\\n 'next': req.headers.get('Link', None)}\\n if method == 'delete':\\n req = requests.request(method, '%s%s' % (\\n self.__url, prefix), headers=self.__headers, verify=False)\\n if req.status_code > 399:\\n return {'ecode': req.status_code, 'message': f'{req.content}\\\\n{req.reason}'}\\n res = {'ecode': req.status_code, 'data': req.content}\\n if method in ['put', 'post']:\\n req = requests.request(method, '%s%s' % (self.__url, prefix), json=obj, headers=self.__headers,\\n verify=False)\\n if req.status_code > 399:\\n return {'ecode': req.status_code, 'message': f'{req.content}\\\\n{req.reason}'}\\n res = {'ecode': req.status_code, 'data': req.content}\\n if method == 'head':\\n req = requests.request(method, '%s%s' % (\\n self.__url, prefix), headers=self.__headers, verify=False)\\n if req.status_code > 399:\\n return {'ecode': req.status_code, 'message': f'{req.content}\\\\n{req.reason}'}\\n res = {'ecode': req.status_code, 'data': req.content}\\n except BaseException as e:\\n raise e\\n return res\\n\\n def systeminfo(self):\\n res = self.request('get', prefix='/systeminfo')\\n return res\\n\\n def get_users(self):\\n res = self.request('get', prefix='/users')\\n return res\\n\\n def get_projects(self, project_name=None, page=1, page_size=20):\\n \\\"\\\"\\\"\\n :project_name: The name of project\\n :page: default is 1.\\n :page_size: default is 10, maximum is 100.\\n \\\"\\\"\\\"\\n params = {'page': page, 'page_size': page_size}\\n if project_name:\\n params['name'] = project_name\\n try:\\n res = self.request('get', params, prefix='/projects')\\n return res\\n except BaseException as e:\\n return {'ecode': 500, 'message': e}\\n\\n def get_repositories(self, project_id, page=1, page_size=20, repo=None):\\n params = {'project_id': project_id,\\n 'page': page, 'page_size': page_size}\\n if repo:\\n params['q'] = repo\\n try:\\n res = self.request('get', params, '/repositories')\\n return res\\n except BaseException as e:\\n return {'ecode': 500, 'message': e}\\n\\n def get_tags(self, repo):\\n try:\\n res = self.request('get', prefix='/repositories/%s/tags' % repo)\\n tags = [\\n {'name': i['name'], 'created': i['created'], 'push_time': i.get(\\n 'push_time', None), 'size': i['size']}\\n for i in\\n res['data']]\\n tags.sort(key=lambda k: (k.get('created')), reverse=True)\\n return {'ecode': 200, 'data': tags, 'count': len(tags)}\\n except BaseException as e:\\n return {'ecode': 500, 'message': e}\\n\\n def fetch_project(self, project_id):\\n \\\"\\\"\\\"\\n 获取项目信息\\n \\\"\\\"\\\"\\n try:\\n res = self.request(\\n 'get', {'project_id': project_id}, prefix=f'/projects/{project_id}')\\n return res\\n except BaseException as e:\\n return {'ecode': 500, 'message': e}\\n\\n def fetch_tag(self, repo, tag):\\n \\\"\\\"\\\"\\n 获取指定镜像标签\\n \\\"\\\"\\\"\\n try:\\n res = self.request(\\n 'get', prefix=f'/repositories/{repo}/tags/{tag}')\\n return res\\n except BaseException as e:\\n return {'ecode': 500, 'message': e}\\n\\n def create_project(self, project_name, public=True):\\n \\\"\\\"\\\"\\n 创建仓库项目\\n \\\"\\\"\\\"\\n try:\\n data = {'project_name': project_name, 'metadata': {\\n 'public': 'true' if public else 'false'}}\\n res = self.request('post', obj=data, prefix='/projects')\\n return res\\n except BaseException as e:\\n return {'ecode': 500, 'message': e}\\n\\n def update_project(self, project_id, *args, **kwargs):\\n \\\"\\\"\\\"\\n 更新仓库项目\\n \\\"\\\"\\\"\\n try:\\n res = self.request('put', obj=kwargs,\\n prefix=f'/projects/{project_id}')\\n return res\\n except BaseException as e:\\n return {'ecode': 500, 'message': e}\\n\\n def project_exists(self, project_name):\\n \\\"\\\"\\\"\\n 查询项目是否存在\\n \\\"\\\"\\\"\\n try:\\n res = self.request(\\n 'head', prefix=f'/projects?project_name={project_name}')\\n return res\\n except BaseException as e:\\n return {'ecode': 500, 'message': e}\\n\\n def patch_tag(self, repo, src_image, tag_name):\\n \\\"\\\"\\\"\\n 镜像打标签\\n \\\"\\\"\\\"\\n try:\\n try:\\n # 创建仓库项目\\n res = self.create_project(repo.split('/')[0])\\n except BaseException as e:\\n pass\\n data = {'tag': tag_name, 'src_image': src_image, 'override': True}\\n res = self.request(\\n 'post', obj=data, prefix='/repositories/%s/tags' % repo)\\n return res\\n except BaseException as e:\\n return {'ecode': 500, 'message': e}\\n\\n def delete_tag(self, repo, tag):\\n \\\"\\\"\\\"\\n 删除标签\\n \\\"\\\"\\\"\\n try:\\n res = self.request(\\n 'delete', prefix=f'/repositories/{repo}/tags/{tag}')\\n return res\\n except BaseException as e:\\n logger.ex\\n return {'ecode': 500, 'message': e}\\n\\n def search(self, query):\\n \\\"\\\"\\\"\\n 搜索\\n \\\"\\\"\\\"\\n try:\\n res = self.request('get', {'q': query}, prefix='/search')\\n return res\\n except BaseException as e:\\n logger.exception(e)\\n return {'ecode': 500, 'message': e}\"\n },\n {\n \"identifier\": \"GlueJenkins\",\n \"path\": \"common/utils/JenkinsAPI.py\",\n \"snippet\": \"class GlueJenkins(Jenkins):\\n\\n def __init__(self, url=None, username=None, password=None):\\n self.__url = url\\n self.__username = username\\n self.__password = password\\n super(GlueJenkins, self).__init__(\\n self.__url, self.__username, self.__password)\\n\\n def _get_encoded_params(self, params):\\n for k, v in params.items():\\n if k in [\\\"name\\\", \\\"msg\\\", \\\"short_name\\\", \\\"from_short_name\\\",\\n \\\"to_short_name\\\", \\\"folder_url\\\", \\\"from_folder_url\\\", \\\"to_folder_url\\\"]:\\n params[k] = quote(v.encode('utf8'))\\n return params\\n\\n def _build_url(self, format_spec, variables=None):\\n\\n if variables:\\n url_path = format_spec % self._get_encoded_params(variables)\\n else:\\n url_path = format_spec\\n return str(urljoin(self.server, url_path))\\n\\n def assert_credential_exists(self, name, folder_name=None, domain_name='_',\\n exception_message='credential[%s] does not exist.'):\\n '''Raise an exception if credential does not exist in domain of folder\\n\\n :param name: Name of credential, ``str``\\n :param folder_name: Folder name, ``str``\\n :param domain_name: Domain name, default is '_', ``str``\\n :param exception_message: Message to use for the exception.\\n Formatted with ``name``, ``domain_name``,\\n and ``folder_name``\\n :throws: :class:`JenkinsException` whenever the credentail\\n does not exist in domain of folder\\n '''\\n if not self.credential_exists(name, folder_name, domain_name):\\n raise JenkinsException(exception_message\\n % name)\\n\\n def get_credential_global_config(self, name, domain_name='_'):\\n '''Get configuration of credential in domain of folder.\\n :param name: Name of credentail, ``str``\\n :param domain_name: Domain name, default is '_', ``str``\\n :returns: Credential configuration (XML format)\\n '''\\n return self.jenkins_open(requests.Request(\\n 'GET', self._build_url(CONFIG_CREDENTIAL_GLOBAL, locals())\\n ))\\n\\n def get_credential_info(self, name, folder_name=None, domain_name='_'):\\n '''Get credential information dictionary in domain of folder\\n\\n :param name: Name of credentail, ``str``\\n :param folder_name: folder_name, ``str``\\n :param domain_name: Domain name, default is '_', ``str``\\n :returns: Dictionary of credential info, ``dict``\\n '''\\n try:\\n response = self.jenkins_open(requests.Request(\\n 'GET', self._build_url(CREDENTIAL_INFO_GLOBAL, locals())\\n ))\\n if response:\\n return json.loads(response)\\n else:\\n raise JenkinsException('credential[%s] does not exist.' % name)\\n except (req_exc.HTTPError, NotFoundException):\\n raise JenkinsException('credential[%s] does not exist.' % name)\\n except ValueError:\\n raise JenkinsException(\\n 'Could not parse JSON info for credential[%s].' % name\\n )\\n\\n def credential_exists(self, name, folder_name=None, domain_name='_'):\\n '''Check whether a credentail exists in domain of folder\\n\\n :param name: Name of credentail, ``str``\\n :param folder_name: Folder name, ``str``\\n :param domain_name: Domain name, default is '_', ``str``\\n :returns: ``True`` if credentail exists, ``False`` otherwise\\n '''\\n try:\\n return self.get_credential_info(name)['id'] == name\\n except JenkinsException:\\n return False\\n\\n def create_credential_global(self, name=None, user=None, password=None, secret=None, comment=None, domain_name='_'):\\n '''Create credentail in domain of folder\\n\\n :param name: username\\n :param password: password\\n :param comment: comment, ``str``\\n :param config_xml: New XML configuration, ``str``\\n :param domain_name: Domain name, default is '_', ``str``\\n '''\\n st = shortuuid.ShortUUID()\\n st.set_alphabet(\\n f\\\"0123456789{''.join([chr(i) for i in range(ord('a'), ord('z') + 1)])}\\\")\\n if name is None:\\n name = '-'.join(['api', st.random(length=8),\\n st.random(length=4), st.random(length=12)])\\n config_xml = '''<com.cloudbees.plugins.credentials.impl.UsernamePasswordCredentialsImpl>\\n <scope>GLOBAL</scope>\\n <id>%s</id>\\n <description>[%s] Created by DevOps Platform</description>\\n <username>%s</username>\\n <password>%s</password>\\n</com.cloudbees.plugins.credentials.impl.UsernamePasswordCredentialsImpl>''' % (name, comment, user, password)\\n if user is None:\\n config_xml = '''<org.jenkinsci.plugins.plaincredentials.impl.StringCredentialsImpl>\\n <scope>GLOBAL</scope>\\n <id>%s</id>\\n <description>[%s] Created by DevOps Platform</description>\\n <secret>%s</secret>\\n</org.jenkinsci.plugins.plaincredentials.impl.StringCredentialsImpl>''' % (name, comment, secret)\\n if self.credential_exists(name):\\n raise JenkinsException('credential[%s] already exists.' % name)\\n\\n self.jenkins_open(requests.Request(\\n 'POST', self._build_url(CREATE_CREDENTIAL_GLOBAL, locals()),\\n data=config_xml.encode('utf-8'),\\n headers=DEFAULT_HEADERS\\n ))\\n self.assert_credential_exists(\\n name, exception_message='create credential[%s] failed.')\\n return {'status': 0, 'data': name}\\n\\n def reconfig_credential_global(self, name, user=None, password=None, secret=None, comment=None, domain_name='_'):\\n \\\"\\\"\\\"\\n Reconfig credential with new config in domain of folder\\n :param name: name, ``str``\\n :param user:\\n :param password:\\n :param secret:\\n :param comment:\\n :param domain_name: Domain name, default is '_', ``str``\\n :return:\\n \\\"\\\"\\\"\\n reconfig_url = self._build_url(CONFIG_CREDENTIAL_GLOBAL, locals())\\n config_xml = self.get_credential_global_config(name)\\n xml_dict = xmltodict.parse(config_xml)\\n if user is None:\\n xml_dict['org.jenkinsci.plugins.plaincredentials.impl.StringCredentialsImpl']['secret'] = secret\\n if comment:\\n xml_dict['org.jenkinsci.plugins.plaincredentials.impl.StringCredentialsImpl']['description'] = comment\\n else:\\n xml_dict['com.cloudbees.plugins.credentials.impl.UsernamePasswordCredentialsImpl']['username'] = user\\n xml_dict['com.cloudbees.plugins.credentials.impl.UsernamePasswordCredentialsImpl']['password'] = password\\n if comment:\\n xml_dict['com.cloudbees.plugins.credentials.impl.UsernamePasswordCredentialsImpl'][\\n 'description'] = comment\\n config_xml = xmltodict.unparse(xml_dict, pretty=True)\\n self.jenkins_open(requests.Request(\\n 'POST', reconfig_url,\\n data=config_xml.encode('utf-8'),\\n headers=DEFAULT_HEADERS\\n ))\\n\\n def create_job(self, name, config_xml):\\n '''Create a new Jenkins job\\n\\n :param name: Name of Jenkins job, ``str``\\n :param config_xml: config file text, ``str``\\n '''\\n folder_url, short_name = self._get_job_folder(name)\\n if self.job_exists(name):\\n raise JenkinsException('job[%s] already exists' % (name))\\n\\n try:\\n self.jenkins_open(requests.Request(\\n 'POST', self._build_url(CREATE_JOB, locals()),\\n data=config_xml.encode('utf-8'),\\n headers=DEFAULT_HEADERS\\n ))\\n except NotFoundException:\\n raise JenkinsException('Cannot create job[%s] because folder '\\n 'for the job does not exist' % (name))\\n self.assert_job_exists(name, 'create[%s] failed')\\n\\n def reconfig_job(self, name, config_xml):\\n '''Change configuration of existing Jenkins job.\\n\\n To create a new job, see :meth:`Jenkins.create_job`.\\n\\n :param name: Name of Jenkins job, ``str``\\n :param config_xml: New XML configuration, ``str``\\n '''\\n folder_url, short_name = self._get_job_folder(name)\\n reconfig_url = self._build_url(CONFIG_JOB, locals())\\n self.jenkins_open(requests.Request(\\n 'POST', reconfig_url,\\n data=config_xml.encode('utf-8'),\\n headers=DEFAULT_HEADERS\\n ))\\n\\n def get_stage_describe(self, name, number, node_number):\\n \\\"\\\"\\\" 获取 单个stage 详情 \\\"\\\"\\\"\\n folder_url, short_name = self._get_job_folder(name)\\n try:\\n response = self.jenkins_open(requests.Request(\\n 'GET', self._build_url(STAGE_DES, locals())\\n ))\\n\\n if response:\\n return json.loads(response)\\n else:\\n raise JenkinsException('job[%s] number[%d] does not exist'\\n % (name, number))\\n except (req_exc.HTTPError, NotFoundException):\\n raise JenkinsException('job[%s] number[%d] does not exist'\\n % (name, number))\\n except ValueError:\\n raise JenkinsException(\\n 'Could not parse JSON info for job[%s] number[%d]'\\n % (name, number)\\n )\\n\\n def get_stage_logs(self, name, number, node_number):\\n \\\"\\\"\\\" 获取 stage 执行日志\\\"\\\"\\\"\\n folder_url, short_name = self._get_job_folder(name)\\n try:\\n response = self.jenkins_open(requests.Request(\\n 'GET', self._build_url(STAGE_LOG, locals())\\n ))\\n if response:\\n return json.loads(response)\\n else:\\n raise JenkinsException('job[%s] number[%d] does not exist'\\n % (name, number))\\n except (req_exc.HTTPError, NotFoundException):\\n raise JenkinsException('job[%s] number[%d] does not exist'\\n % (name, number))\\n except ValueError:\\n raise JenkinsException(\\n 'Could not parse JSON info for job[%s] number[%d]'\\n % (name, number)\\n )\\n\\n def get_stage_info(self, name, number, depth=0):\\n\\n folder_url, short_name = self._get_job_folder(name)\\n try:\\n response = self.jenkins_open(requests.Request(\\n 'GET', self._build_url(STAGE_INFO, locals())\\n ))\\n if response:\\n return json.loads(response)\\n else:\\n raise JenkinsException('job[%s] number[%d] does not exist'\\n % (name, number))\\n except (req_exc.HTTPError, NotFoundException):\\n raise JenkinsException('job[%s] number[%d] does not exist'\\n % (name, number))\\n except ValueError:\\n raise JenkinsException(\\n 'Could not parse JSON info for job[%s] number[%d]'\\n % (name, number)\\n )\\n\\n def get_flow_detail(self, job_name, build_number):\\n stage_data = self.get_stage_info(name=job_name, number=build_number)\\n stages = stage_data.get('stages')\\n for i in stages:\\n logs = ''\\n try:\\n # 获取stage返回信息\\n response = self.jenkins_open(requests.Request(\\n 'GET', self._build_url(\\n unquote(i['_links']['self']['href']), locals())\\n ))\\n if response:\\n res = json.loads(response)\\n for j in res['stageFlowNodes']:\\n response = self.jenkins_open(requests.Request(\\n 'GET', self._build_url(\\n unquote(j['_links']['log']['href']), locals())\\n ))\\n res = json.loads(response)\\n try:\\n # 移除href html信息，保留链接文字\\n import re\\n pat = re.compile('<a href[^>]*>')\\n logs = logs + '\\\\n' + \\\\\\n pat.sub('', res['text'].replace('</a>', ''))\\n except:\\n pass\\n else:\\n raise JenkinsException('job[%s] number[%d] does not exist'\\n % (job_name, build_number))\\n except (req_exc.HTTPError, NotFoundException):\\n raise JenkinsException('job[%s] number[%d] does not exist'\\n % (job_name, build_number))\\n except ValueError:\\n raise JenkinsException(\\n 'Could not parse JSON info for job[%s] number[%d]'\\n % (job_name, build_number)\\n )\\n\\n stage_data[\\\"stages\\\"][stages.index(i)]['logs'] = logs\\n return stage_data\\n\\n def get_queue_item(self, number, depth=0):\\n '''Get information about a queued item (to-be-created job).\\n\\n The returned dict will have a \\\"why\\\" key if the queued item is still\\n waiting for an executor.\\n\\n The returned dict will have an \\\"executable\\\" key if the queued item is\\n running on an executor, or has completed running. Use this to\\n determine the job number / URL.\\n\\n :param name: queue number, ``int``\\n :returns: dictionary of queued information, ``dict``\\n '''\\n url = self._build_url(Q_ITEM, locals())\\n try:\\n response = self.jenkins_open(requests.Request('GET', url))\\n if response:\\n return json.loads(response)\\n else:\\n raise JenkinsException('queue number[%d] does not exist'\\n % number)\\n except (req_exc.HTTPError, NotFoundException):\\n raise JenkinsException('queue number[%d] does not exist' % number)\\n except ValueError:\\n raise JenkinsException(\\n 'Could not parse JSON info for queue number[%d]' % number\\n )\\n\\n def build_job(self, name, parameters=None, token=None):\\n '''Trigger build job.\\n\\n This method returns a queue item number that you can pass to\\n :meth:`Jenkins.get_queue_item`. Note that this queue number is only\\n valid for about five minutes after the job completes, so you should\\n get/poll the queue information as soon as possible to determine the\\n job's URL.\\n\\n :param name: name of job\\n :param parameters: parameters for job, or ``None``, ``dict``\\n :param token: Jenkins API token\\n :returns: ``int`` queue item\\n '''\\n response = self.jenkins_request(requests.Request(\\n 'POST', self.build_job_url(name, parameters, token)))\\n\\n if 'Location' not in response.headers:\\n raise EmptyResponseException(\\n \\\"Header 'Location' not found in \\\"\\n \\\"response from server[%s]\\\" % self.server)\\n\\n location = response.headers['Location']\\n if location.endswith('/'):\\n location = location[:-1]\\n parts = location.split('/')\\n number = int(parts[-1])\\n return number\\n\\n def get_job_config(self, name):\\n '''Get configuration of existing Jenkins job.\\n\\n :param name: Name of Jenkins job, ``str``\\n :returns: job configuration (XML format)\\n '''\\n folder_url, short_name = self._get_job_folder(name)\\n request = requests.Request(\\n 'GET', self._build_url(CONFIG_JOB, locals()))\\n return self.jenkins_open(request)\\n\\n def get_job_info(self, name, depth=0, fetch_all_builds=False):\\n '''Get job information dictionary.\\n\\n :param name: Job name, ``str``\\n :param depth: JSON depth, ``int``\\n :param fetch_all_builds: If true, all builds will be retrieved\\n from Jenkins. Otherwise, Jenkins will\\n only return the most recent 100\\n builds. This comes at the expense of\\n an additional API call which may\\n return significant amounts of\\n data. ``bool``\\n :returns: dictionary of job information\\n '''\\n folder_url, short_name = self._get_job_folder(name)\\n try:\\n response = self.jenkins_open(requests.Request(\\n 'GET', self._build_url(JOB_INFO, locals())\\n ))\\n if response:\\n if fetch_all_builds:\\n return self._add_missing_builds(json.loads(response))\\n else:\\n return json.loads(response)\\n else:\\n raise JenkinsException('job[%s] does not exist' % name)\\n except (req_exc.HTTPError, NotFoundException):\\n raise JenkinsException('job[%s] does not exist' % name)\\n except ValueError:\\n raise JenkinsException(\\n \\\"Could not parse JSON info for job[%s]\\\" % name)\"\n },\n {\n \"identifier\": \"convert_xml_to_str_with_pipeline\",\n \"path\": \"common/custom_format.py\",\n \"snippet\": \"def convert_xml_to_str_with_pipeline(xml, url, secret, desc, jenkinsfile, scm=True):\\n \\\"\\\"\\\"\\n scm\\n True: jenkinsfile为指定的git地址\\n False: jenkinsfile为具体的pipeline\\n \\\"\\\"\\\"\\n xml_dict = xmltodict.parse(xml)\\n if scm:\\n xml_dict['flow-definition']['definition']['@class'] = 'org.jenkinsci.plugins.workflow.cps.CpsScmFlowDefinition'\\n xml_dict['flow-definition']['definition']['scm']['userRemoteConfigs']['hudson.plugins.git.UserRemoteConfig'][\\n 'url'] = url\\n xml_dict['flow-definition']['definition']['scm']['userRemoteConfigs']['hudson.plugins.git.UserRemoteConfig'][\\n 'credentialsId'] = secret\\n xml_dict['flow-definition']['definition']['scriptPath'] = jenkinsfile\\n else:\\n xml_dict['flow-definition']['definition']['@class'] = 'org.jenkinsci.plugins.workflow.cps.CpsFlowDefinition'\\n xml_dict['flow-definition']['definition']['script'] = jenkinsfile\\n xml_dict['flow-definition']['definition']['sandbox'] = 'true'\\n xml_dict['flow-definition']['description'] = desc\\n result = xmltodict.unparse(\\n xml_dict, short_empty_elements=True, pretty=True)\\n return result\"\n },\n {\n \"identifier\": \"DASHBOARD_TIME_FORMAT\",\n \"path\": \"common/variables.py\",\n \"snippet\": \"DASHBOARD_TIME_FORMAT = {'year_only': '%Y', 'years': '%Y-%m', 'months': '%Y-%m-%d', 'days': '%Y-%m-%d %H:00:00',\\n 'hours': '%Y-%m-%d %H:%M:00', 'minutes': '%Y-%m-%d %H:%M:%S'}\"\n },\n {\n \"identifier\": \"DASHBOARD_TIME_FORMAT_T\",\n \"path\": \"common/variables.py\",\n \"snippet\": \"DASHBOARD_TIME_FORMAT_T = {'years': '%Y', 'months': '%Y-%m', 'days': '%Y-%m-%d', 'hours': \\\"%Y-%m-%d %H:00:00\\\",\\n 'minutes': \\\"%Y-%m-%d %H:%M:00\\\", 'seconds': \\\"%Y-%m-%d %H:%M:%S\\\"}\"\n },\n {\n \"identifier\": \"DASHBOARD_TIME_FREQNAMES\",\n \"path\": \"common/variables.py\",\n \"snippet\": \"DASHBOARD_TIME_FREQNAMES = {'year_only': YEARLY, 'years': MONTHLY, 'months': DAILY, 'days': HOURLY, 'hours': MINUTELY,\\n 'minutes': SECONDLY}\"\n },\n {\n \"identifier\": \"DASHBOARD_TIME_FREQNAMES_T\",\n \"path\": \"common/variables.py\",\n \"snippet\": \"DASHBOARD_TIME_FREQNAMES_T = {'years': YEARLY, 'months': MONTHLY, 'days': DAILY, 'hours': HOURLY, 'minutes': MINUTELY,\\n 'seconds': SECONDLY}\"\n },\n {\n \"identifier\": \"SENSITIVE_KEYS\",\n \"path\": \"common/variables.py\",\n \"snippet\": \"SENSITIVE_KEYS = ['password', 'token', 'access',\\n 'refresh', 'AUTHORIZATION', 'COOKIE']\"\n },\n {\n \"identifier\": \"JENKINS_CALLBACK_KEY\",\n \"path\": \"common/variables.py\",\n \"snippet\": \"JENKINS_CALLBACK_KEY = 'jenkins_callback_flag::'\"\n },\n {\n \"identifier\": \"JENKINS_STATUS_MAP\",\n \"path\": \"common/variables.py\",\n \"snippet\": \"JENKINS_STATUS_MAP = {'IN_PROGRESS': 3, 'SUCCESS': 1, 'FAILED': 2, 'ABORTED': 4, 'FAILURE': 2, 'NOT_EXECUTED': 5,\\n 'NOT_EXEC_TIMEOUT': 5}\"\n },\n {\n \"identifier\": \"DEV_LANGUAGE_KEY\",\n \"path\": \"common/variables.py\",\n \"snippet\": \"DEV_LANGUAGE_KEY = 'devlanguage:'\"\n },\n {\n \"identifier\": \"AppInfo\",\n \"path\": \"dbapp/models.py\",\n \"snippet\": \"\"\n },\n {\n \"identifier\": \"K8sAPI\",\n \"path\": \"common/utils/K8sAPI.py\",\n \"snippet\": \"class K8sAPI(object):\\n def __init__(self, host=None, username=None, password=None, api_key=None, api_key_prefix='Bearer', verify_ssl=False,\\n k8s_config=None,\\n config_file=None, eks=None):\\n \\\"\\\"\\\"\\n elk: aws kubernetes\\n \\\"\\\"\\\"\\n self.__host = host\\n self.__username = username\\n self.__password = password\\n self.__api_key = api_key\\n self.__api_key_prefix = api_key_prefix\\n self.__verify_ssl = verify_ssl\\n if k8s_config is not None:\\n config.kube_config.load_kube_config_from_dict(k8s_config)\\n self.__client0 = client.CoreApi()\\n self.__client = client.CoreV1Api()\\n elif config_file is not None:\\n config.kube_config.load_kube_config(config_file=config_file)\\n self.__client0 = client.CoreApi()\\n self.__client = client.CoreV1Api()\\n elif self.__host:\\n if self.__username and self.__password:\\n self.__client = self.get_api()\\n else:\\n raise Exception('Please input username/password or api_key')\\n else:\\n raise Exception('Cannot find k8s config')\\n self.client = self.__client\\n\\n def get_token(self):\\n pass\\n\\n def get_api(self):\\n configuration = client.Configuration()\\n configuration.host = self.__host\\n if self.__verify_ssl is False:\\n configuration.verify_ssl = False\\n configuration.username = self.__username\\n configuration.password = self.__password\\n basic_auth_token = configuration.get_basic_auth_token()\\n api = core_v1_api.CoreV1Api(api_client.ApiClient(configuration=configuration, header_name=\\\"authorization\\\",\\n header_value=basic_auth_token))\\n return api\\n\\n def get_client(self):\\n return self.__client\\n\\n def set_client(self, obj):\\n self.__client = getattr(client, obj)()\\n\\n def get_apis(self):\\n print(\\\"Supported APIs (* is preferred version):\\\")\\n self.__client2 = client.ApisApi(self.__client0.api_client)\\n for api in self.__client2.get_api_versions().groups:\\n versions = []\\n for v in api.versions:\\n name = \\\"\\\"\\n if v.version == api.preferred_version.version and len(\\n api.versions) > 1:\\n name += \\\"*\\\"\\n name += v.version\\n versions.append(name)\\n\\n def get_nodes(self, **kwargs):\\n ret = self.__client.list_node(**kwargs)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'err': str(e)}\\n\\n def get_node_info(self, name):\\n ret = self.__client.read_node_status(name)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'err': str(e)}\\n\\n def get_namespaces(self, **kwargs):\\n ret = self.__client.list_namespace(**kwargs)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'err': str(e)}\\n\\n def create_namespace(self, name):\\n payload = {\\n \\\"apiVersion\\\": \\\"v1\\\",\\n \\\"kind\\\": \\\"Namespace\\\",\\n \\\"metadata\\\": {\\n \\\"name\\\": name,\\n }\\n }\\n ret = self.__client.create_namespace(body=payload)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n print(rs)\\n return rs\\n except BaseException as e:\\n return {'err': str(e)}\\n\\n def get_services(self, namespace='default', **kwargs):\\n ret = self.__client.list_namespaced_service(namespace, **kwargs)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'err': str(e)}\\n\\n def fetch_service(self, name, namespace='default', api_version='apps/v1'):\\n try:\\n ret = self.__client.read_namespaced_service(name, namespace)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n print('reason', e.reason)\\n return {'ecode': e.status, 'message': e.body}\\n except BaseException as e:\\n print('reason', e.reason)\\n return {'ecode': e.status, 'message': e.body}\\n\\n def create_namespace_service(self, name, app=None, targets=list, namespace='default', service_type='NodePort',\\n svc_yaml=None):\\n \\\"\\\"\\\"\\n 目前只支持NodePort类型，对外服务端口随机生成（如手动生成，需配置node_port和endpoints）\\n :param name: service name\\n :param app: app name\\n :param targets: [{port, target_port, protocol, node_port}]\\n :param namespace:\\n :param service_type:\\n :return:\\n \\\"\\\"\\\"\\n ports = []\\n if svc_yaml:\\n if isinstance(svc_yaml, str):\\n body = yaml.safe_load(svc_yaml)\\n else:\\n body = svc_yaml\\n else:\\n for index, target in enumerate(targets):\\n port_body = {'name': f\\\"{name}-{index}\\\", 'port': target['port'], 'target_port': target['port'],\\n 'protocol': target['protocol']}\\n if target['node_port'] > 30000:\\n port_body['node_port'] = target['node_port']\\n ports.append(client.V1ServicePort(**port_body))\\n body = client.V1Service(\\n api_version=\\\"v1\\\",\\n kind=\\\"Service\\\",\\n metadata=client.V1ObjectMeta(\\n name=name\\n ),\\n spec=client.V1ServiceSpec(\\n selector={\\\"app\\\": app},\\n type=service_type,\\n ports=ports\\n )\\n )\\n try:\\n ret = self.__client.create_namespaced_service(namespace=namespace, body=body,\\n **{'_return_http_data_only': False})\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n logger.error('reason', e)\\n return {'error': True, 'message': str(e)}\\n except ApiException as e:\\n if e.status == 409:\\n logger.error('reason', e.reason)\\n return {'error': True, 'ecode': e.status, 'message': e.body}\\n except BaseException as e:\\n return {'error': True, 'message': str(e)}\\n\\n def update_namespace_service(self, name, app=None, targets=Type[list], namespace='default', service_type='NodePort',\\n svc_yaml=None):\\n ports = []\\n if svc_yaml:\\n if isinstance(svc_yaml, str):\\n body = yaml.safe_load(svc_yaml)\\n else:\\n body = svc_yaml\\n logger.debug(f'svc_yaml body == {body}')\\n func = self.__client.replace_namespaced_service\\n else:\\n for index, target in enumerate(targets):\\n port_body = {'name': target['name'], 'port': target['port'], 'target_port': target['port'],\\n 'protocol': target['protocol']}\\n if target['node_port'] > 30000:\\n port_body['node_port'] = target['node_port']\\n ports.append(client.V1ServicePort(**port_body))\\n body = client.V1Service(\\n api_version=\\\"v1\\\",\\n kind=\\\"Service\\\",\\n metadata=client.V1ObjectMeta(\\n name=name\\n ),\\n spec=client.V1ServiceSpec(\\n selector={\\\"app\\\": name},\\n type=service_type,\\n ports=ports\\n )\\n )\\n func = self.__client.patch_namespaced_service\\n try:\\n ret = func(\\n name, namespace, body=body,\\n **{'_return_http_data_only': False}\\n )\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n logger.error(f'ApiClient sanitize_for_serialization 异常： {e}', )\\n return {'error': True, 'message': str(e)}\\n except ApiException as e:\\n if e.status == 409:\\n logger.error(f'ApiException 异常 409 资源冲突： {e} {e.reason}', )\\n return {'error': True, 'ecode': e.status, 'message': e.body}\\n except BaseException as e:\\n logger.error(f'patch_namespaced_service 异常： {e}', )\\n return {'error': True, 'message': str(e)}\\n\\n def delete_namespace_service(self, name, namespace='default', api_version='apps/v1'):\\n try:\\n ret = self.__client.delete_namespaced_service(name, namespace)\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n return {'error': True, 'message': str(e)}\\n\\n def get_configmaps(self, namespace='default', **kwargs):\\n ret = self.__client.list_namespaced_config_map(namespace, **kwargs)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'error': True, 'message': str(e)}\\n\\n def get_configmap(self, name, namespace='default', **kwargs):\\n \\\"\\\"\\\"\\n get configmap content\\n \\\"\\\"\\\"\\n try:\\n ret = self.__client.read_namespaced_config_map(\\n name, namespace, **kwargs)\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'error': True, 'message': str(e)}\\n\\n def create_namespace_configmap(self, svc_yaml, namespace='default', **kwargs):\\n if isinstance(svc_yaml, str):\\n body = yaml.safe_load(svc_yaml)\\n else:\\n body = svc_yaml\\n try:\\n ret = self.__client.create_namespaced_config_map(\\n namespace, body, **kwargs)\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n return {'error': True, 'message': str(e)}\\n\\n def update_namespace_configmap(self, name, svc_yaml, namespace='default', **kwargs):\\n if isinstance(svc_yaml, str):\\n body = yaml.safe_load(svc_yaml)\\n else:\\n body = svc_yaml\\n try:\\n ret = self.__client.patch_namespaced_config_map(\\n name, namespace, body, **kwargs)\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n return {'error': True, 'message': str(e)}\\n\\n def delete_namespace_configmap(self, name, namespace='default', api_version='apps/v1'):\\n try:\\n ret = self.__client.delete_namespaced_config_map(name, namespace)\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n return {'error': True, 'message': str(e)}\\n\\n def get_namespace_deployment(self, namespace='default', api_version='apps/v1', **kwargs):\\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\\n self.__client.api_client)(client)\\n ret = self.__client2.list_namespaced_deployment(namespace, **kwargs)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'err': str(e)}\\n\\n def create_namespace_deployment(self, name, image=None, port=list, replicas=1, deploy_yaml=None,\\n pod_type='Deployment', namespace='default'):\\n \\\"\\\"\\\"\\n\\n :param name:\\n :param image:\\n :param port: [{containerPort: 8080, protocol: 'TCP'}]\\n :param replicas:\\n :param pod_type:\\n :param namespace:\\n :return:\\n \\\"\\\"\\\"\\n payload = {'kind': pod_type, 'spec': {'replicas': replicas, 'template': {\\n 'spec': {'containers': [{'image': image, 'name': name, 'ports': port}]},\\n 'metadata': {'labels': {'app': name}}},\\n 'selector': {'matchLabels': {'app': name}}},\\n 'apiVersion': 'apps/v1beta2',\\n 'metadata': {'labels': {'app': name}, 'namespace': namespace,\\n 'name': name}}\\n if deploy_yaml is not None:\\n payload = yaml.safe_load(deploy_yaml)\\n payload['metadata'].pop('resourceVersion', None)\\n self.__client2 = operator.methodcaller(\\n ''.join([i.capitalize() for i in payload.get(\\n 'apiVersion', 'apps/v1beta2').split('/')]) + 'Api',\\n self.__client.api_client)(client)\\n try:\\n ret = self.__client2.create_namespaced_deployment(\\n namespace=namespace, body=payload)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n return {'ecode': e.status, 'message': e.body}\\n except ApiException as e:\\n return {'ecode': e.status, 'message': e.body}\\n\\n def delete_namespace_deployment(self, name, namespace='default', api_version='apps/v1'):\\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\\n self.__client.api_client)(client)\\n ret = self.__client2.delete_namespaced_deployment(name, namespace,\\n body=client.V1DeleteOptions(grace_period_seconds=0,\\n propagation_policy='Foreground'))\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'err': str(e)}\\n\\n def update_deployment(self, name, replicas=None, image=None, envs=None, deploy_yaml=None, namespace='default',\\n api_version='apps/v1', force=False):\\n \\\"\\\"\\\"\\n force: 强制更新\\n \\\"\\\"\\\"\\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\\n self.__client.api_client)(client)\\n payload = {'spec': {'replicas': replicas, 'template': {}}}\\n if replicas is None and image is None and deploy_yaml is None:\\n return {'err': '缺少参数'}\\n if replicas is not None:\\n payload['spec']['replicas'] = replicas\\n if image is not None:\\n payload['spec']['template'] = {\\n 'spec': {'containers': [{'image': image, 'name': name}]}}\\n\\n if envs is not None:\\n payload['spec']['template'] = {\\n 'spec': {'containers': [{'env': envs}]}}\\n\\n if deploy_yaml is not None:\\n payload = yaml.safe_load(deploy_yaml)\\n payload['metadata'].pop('resourceVersion', None)\\n try:\\n if force:\\n ret = self.__client2.replace_namespaced_deployment(\\n name, namespace, body=payload)\\n else:\\n ret = self.__client2.patch_namespaced_deployment(\\n name, namespace, body=payload)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n return {'ecode': e.status, 'message': e.body}\\n except ApiException as e:\\n return {'ecode': e.status, 'message': e.body}\\n\\n def update_deployment_replica(self, name, replicas, namespace='default', api_version='apps/v1'):\\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\\n self.__client.api_client)(client)\\n payload = {'spec': {'replicas': replicas}}\\n ret = self.__client2.patch_namespaced_deployment_scale(\\n name, namespace, body=payload)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'err': str(e)}\\n\\n def update_deployment_image(self, name, image, namespace='default', api_version='apps/v1'):\\n deploy = self.fetch_deployment(name, namespace)\\n if deploy.get('ecode', 200) > 399:\\n return deploy\\n payload = {'spec': deploy['message']['spec']}\\n payload['spec']['template']['spec']['containers'][0]['image'] = image\\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\\n self.__client.api_client)(client)\\n try:\\n ret = self.__client2.patch_namespaced_deployment(name, namespace, body=payload,\\n **{'_return_http_data_only': False})\\n except ApiException as e:\\n return {'ecode': e.status, 'message': e.body}\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n return {'ecode': e.status, 'message': e.body}\\n\\n def update_deployment_resource(self, name, envs, image_policy, namespace='default', api_version='apps/v1',\\n **kwargs):\\n payload = {'spec': {'template': {'spec': {'containers': [\\n {'name': name, 'env': envs, 'imagePullPolicy': image_policy, 'resources': kwargs['resources']}]}}}}\\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\\n self.__client.api_client)(client)\\n ret = self.__client2.patch_namespaced_deployment(\\n name, namespace, body=payload)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'err': str(e)}\\n\\n def restart_deployment(self, name, namespace='default', api_version='apps/v1'):\\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\\n self.__client.api_client)(client)\\n payload = {\\n 'spec': {\\n 'template': {\\n 'spec': {\\n 'containers': [\\n {\\n 'name': name,\\n 'env': [\\n {\\n 'name': 'RESTART_',\\n 'value': datetime.now().strftime('%Y%m%d%H%M%S')\\n }\\n ]\\n }\\n ]\\n }\\n }\\n }\\n }\\n\\n ret = self.__client2.patch_namespaced_deployment(\\n name, namespace, body=payload)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'err': str(e)}\\n\\n def fetch_deployment(self, name, namespace='default', api_version='apps/v1'):\\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\\n self.__client.api_client)(client)\\n try:\\n ret = self.__client2.read_namespaced_deployment(name, namespace)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except ApiException as e:\\n return {'ecode': e.status, 'message': e.body}\\n except ApiException as e:\\n return {'ecode': e.status, 'message': e.body}\\n\\n def get_replica(self, namespace='default', api_version='apps/v1', **kwargs):\\n self.__client2 = operator.methodcaller(''.join([i.capitalize() for i in api_version.split('/')]) + 'Api',\\n self.__client.api_client)(client)\\n try:\\n ret = self.__client2.list_namespaced_replica_set(\\n namespace=namespace, **kwargs)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except ApiException as e:\\n return {'ecode': e.status, 'message': e.body}\\n except ApiException as e:\\n return {'ecode': e.status, 'message': e.body}\\n\\n def get_pods(self, namespace=None, **kwargs):\\n if namespace is None:\\n return {}\\n try:\\n ret = self.__client.list_namespaced_pod(namespace, **kwargs)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n return {'ecode': e.status, 'message': e.body}\\n except ApiException as e:\\n return {'ecode': e.status, 'message': e.body}\\n\\n def fetch_pod(self, name, namespace='default'):\\n try:\\n ret = self.__client.read_namespaced_pod(\\n name=name, namespace=namespace)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return {'ecode': 200, 'message': rs}\\n except BaseException as e:\\n return {'ecode': e.status, 'message': e.body}\\n except BaseException as e:\\n return {'ecode': e.status, 'message': e.body}\\n\\n def get_secrets(self, namespace='default', **kwargs):\\n ret = self.__client.list_namespaced_secret(namespace, **kwargs)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'error': True, 'message': str(e)}\\n\\n def get_secret(self, name, namespace='default', **kwargs):\\n \\\"\\\"\\\"\\n get secret content\\n \\\"\\\"\\\"\\n ret = self.__client.read_namespaced_secret(name, namespace, **kwargs)\\n try:\\n ret = self.__client.read_namespaced_secret(\\n name, namespace, **kwargs)\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'error': True, 'message': str(e)}\\n\\n def manage_secret(self, name, namespace='default', api_version='v1', **kwargs):\\n payload = kwargs.pop('payload', {})\\n body = kubernetes.client.V1Secret(api_version=api_version, **payload)\\n ret = {}\\n try:\\n ret = self.__client.replace_namespaced_secret(\\n name, namespace, body, **kwargs)\\n except ApiException as e:\\n if e.status == 404:\\n ret = self.__client.create_namespaced_secret(namespace, body)\\n try:\\n rs = ApiClient().sanitize_for_serialization(ret)\\n return rs\\n except BaseException as e:\\n return {'error': True, 'message': str(e)}\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from gitlab.exceptions import GitlabGetError\nfrom functools import reduce\nfrom common.utils.ElasticSearchAPI import generate_docu, Search\nfrom common.utils.GitLabAPI import GitLabAPI\nfrom common.utils.HarborAPI import HarborAPI\nfrom common.utils.JenkinsAPI import GlueJenkins\nfrom common.custom_format import convert_xml_to_str_with_pipeline\nfrom common.variables import DASHBOARD_TIME_FORMAT, DASHBOARD_TIME_FORMAT_T, DASHBOARD_TIME_FREQNAMES, \\\n DASHBOARD_TIME_FREQNAMES_T, SENSITIVE_KEYS, JENKINS_CALLBACK_KEY, \\\n JENKINS_STATUS_MAP, DEV_LANGUAGE_KEY\nfrom dbapp.models import AppInfo, Product, KubernetesCluster, KubernetesDeploy, MicroApp, Project, ProjectConfig, DevLanguage, BuildJob, UserProfile, SystemConfig, Role, Permission, Menu, DataDict\nfrom django.conf import settings\nfrom django.core.cache import cache\nfrom django.utils import timezone\nfrom django.db.models import Q\nfrom social_django.utils import load_strategy\nfrom rest_framework.utils.serializer_helpers import ReturnDict\nfrom config import SOCIAL_AUTH_GITLAB_API_URL, GITLAB_ADMIN_TOKEN\nfrom common.utils.K8sAPI import K8sAPI\nfrom urllib.parse import urlparse, quote_plus\nfrom dateutil.relativedelta import relativedelta\nfrom dateutil.rrule import rrule\nfrom ruamel import yaml\nfrom datetime import datetime, timedelta\n from celery import current_app\nimport copy\nimport operator\nimport re\nimport time\nimport pytz\nimport os\nimport json\nimport requests\nimport math\nimport shortuuid\nimport logging"},"token_num":{"kind":"number","value":17589,"string":"17,589"},"cropped_code":{"kind":"string","value":" 'branch': 'master',\n 'content': content,\n 'author_email': user.email,\n 'author_name': user.username,\n 'commit_message': f'Create {instance.name} {filename} by {user.username}'})\n content = project.files.raw(\n f\"{instance.name}/{filename}\", ref='master')\n return True, content\n except GitlabGetError as e:\n logger.info(f'获取异常，{e}')\n if e.response_code == 404:\n logger.info(f'{instance.name}/{filename}文件不存在')\n return True, ''\n except BaseException as e:\n logger.error(f'GitLab开发语言模板异常, 原因: {e}')\n return False, f'GitLab开发语言模板异常, 原因: {e}'\n\n\ndef snake_case(x):\n \"\"\"\n 驼峰转下划线\n \"\"\"\n\n term_exclude = ['OS', 'GPU', 'DB', 'IA', 'IP',\n 'RR', 'TTL', 'SLB', 'CPU', 'MEMORY', 'QPS']\n for i in term_exclude:\n x = x.replace(i, i.lower())\n return re.sub(r'(?P<key>[A-Z])', r'_\\g<key>', x).lower().strip('_')\n\n\ndef node_filter(node_id, data):\n \"\"\"\n 查找节点\n\n :params: node_id int 节点ID\n :params: data list 节点数组\n \"\"\"\n for i in data:\n if i['id'] == node_id:\n print('get node', i)\n return i\n else:\n if i.get('children', None):\n node = node_filter(node_id, i['children'])\n if isinstance(node, (dict,)):\n return node\n\n\ndef get_time_range(request):\n \"\"\"\n 获取时间轴\n \"\"\"\n type_range = request.query_params.get('range_type', 'static')\n if type_range == 'static':\n time_range = request.query_params.get('range', '6-months')\n else:\n time_range = request.query_params.getlist('range[]', None)\n if not time_range:\n time_range = '6-months'\n period = time_period(time_range, type_range)\n time_line = timeline_generate(period, format_type='cmdb')\n # 时间刻度, 以小时为刻度则删除年份\n time_line_x = [i.split(' ')[-1]\n for i in time_line] if period['name'] == 'hours' else time_line\n return period, time_line, time_line_x\n\n\ndef compare_dict(data, old_data):\n different_list = []\n for k1 in data:\n if k1 == 'update_time':\n continue\n v1 = data.get(k1)\n v2 = old_data.get(k1)\n if v1 != v2:\n different_list.append({\n 'key': k1,\n 'new_value': v1,\n 'old_value': v2\n })\n\n return different_list\n\n\ndef get_project_mergerequest(project: Project, cli: GitLabAPI, **params):\n \"\"\"\n 获取项目下所有应用的合并请求\n \"\"\"\n mrdata = []\n git_project = [app.repo['id']\n for app in project.microapp_set.all() if app.repo.get('id')]\n for project_id in set(git_project):\n try:\n git_project = cli.get_project(project_id)\n ok, data = cli.list_mrs(project=git_project, **params)\n if ok is False:\n continue\n mrdata.extend([i.attributes for i in data])\n except BaseException as e:\n logger.error(f'获取应用合并请求异常，原因：{e}')\n return mrdata\n\n\ndef gitlab_cli(user=None, admin=False, superadmin=False, merge=False):\n \"\"\"\n 获取GitlabAPI\n :param merge: 用于分支合并，管理员统一用配置文件里的token\n \"\"\"\n try:\n payload = {'token': GITLAB_ADMIN_TOKEN, 'oauth': False}\n cli = GitLabAPI(SOCIAL_AUTH_GITLAB_API_URL, **payload)\n return True, cli\n except BaseException as e:\n logger.warning(f'获取GitlabAPI异常，原因：{e}')\n return False, f'获取GitlabAPI异常，原因：{e}'\n\n\ndef get_deploy_image_list(app_id, appinfo_id=None, module=None, force=0):\n # 可选发布镜像\n # 获取关联应用ID\n"},"all_code":{"kind":"string","value":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n@Author : Charles Lai\n@Contact : qqing_lai@hotmail.com\n@Time : 2020/12/21 上午10:00\n@FileName: ext_fun.py\n@Blog ：https://imaojia.com\n\"\"\"\n\n\n\n\n\n\nlogger = logging.getLogger('drf')\n\n\nclass ThirdPartyUser(object):\n\n def get_user(self):\n user = UserProfile.objects.get_or_create(username='thirdparty')[0]\n self.set_permission(user, self.get_role())\n return user\n\n def get_role(self):\n return Role.objects.get_or_create(name='thirdparty')[0]\n\n def get_perm(self):\n return Permission.objects.get_or_create(name='Jenkins回调', method='jenkins_callback')[0]\n\n def set_permission(self, user, role):\n role.permissions.set([self.get_perm().id])\n user.roles.set([role.id])\n\n\ndef set_redis_data(name, config):\n cache.set(f\"system:{name}\", config, None)\n\n\ndef get_redis_data(name):\n ret = cache.get(f\"system:{name}\")\n if not ret:\n try:\n if name == 'cicd-harbor':\n qs = SystemConfig.objects.filter(type=name)[0]\n else:\n qs = SystemConfig.objects.get(name=name)\n except BaseException as e:\n return None\n ret = json.loads(qs.config)\n set_redis_data(name, ret)\n\n return ret\n\n\ndef get_datadict(name, config=0, default_value=None):\n \"\"\"\n 从数据字典获取数据\n \"\"\"\n try:\n qs = DataDict.objects.get(key=name)\n except BaseException as e:\n return default_value\n if config:\n ret = json.loads(qs.extra)\n else:\n ret = {'id': qs.id, 'key': qs.key,\n 'value': qs.value, 'desc': qs.desc}\n return ret\n\n\ndef check_pods(cluster_id, k8s_config, namespace, **kwargs):\n k8s = KubernetesCluster.objects.get(id=cluster_id)\n cli = k8s_cli(k8s, k8s_config)\n if not cli:\n return False\n count = 3\n while count:\n ret2 = cli.get_pods(namespace, **kwargs)\n count -= 1\n if len(ret2['items']) > 0:\n return True\n else:\n check_pods(k8s_config, namespace, **kwargs)\n return False\n\n\ndef template_svc_generate(appinfo_obj):\n \"\"\"\n 生成Kubernetes Svc Yaml\n\n ### 格式：\n {\n \"apiVersion\": \"v1\",\n \"kind\": \"Service\",\n \"metadata\": {\n \"name\": \"appname\",\n \"namespace\": \"env-product\",\n \"labels\": {\n \"app\": \"appname\"\n }\n },\n \"spec\": {\n \"ports\": [{\n \"port\": 8080,\n \"targetPort\": 8080,\n \"protocol\": \"TCP\",\n \"name\": \"http\"\n }],\n \"selector\": {\n \"app\": \"appname\"\n }\n }\n }\n \"\"\"\n svc_temp = DataDict.objects.filter(key='yaml.svc')\n if svc_temp.exists():\n svc_temp = json.loads(svc_temp.first().extra)\n if appinfo_obj.environment.name in svc_temp:\n svc_temp = svc_temp[appinfo_obj.environment.name]\n namespace = appinfo_obj.namespace\n svc_temp['metadata']['name'] = appinfo_obj.app.name\n svc_temp['metadata']['namespace'] = namespace\n svc_temp['metadata']['labels'] = {'app': appinfo_obj.app.name}\n\n labels = []\n labels.extend([{'name': 'app', 'value': appinfo_obj.app.name}])\n\n svc_temp['spec']['selector'] = {\n i['name']: i['value'] for i in labels}\n return True, svc_temp\n return False, None\n\n\ndef harbor_cli(namespace, **filters):\n try:\n harbor = SystemConfig.objects.filter(**filters).first()\n # 获取harbor配置\n harbor_config = json.loads(harbor.config)\n except BaseException as e:\n logger.exception(f'创建任务失败, 原因: 获取harbor仓库异常, {e}')\n return False, f\"获取harbor仓库异常：{e}\"\n # 构建前创建harbor项目\n cli = HarborAPI(url=harbor_config['url'], username=harbor_config['user'],\n password=harbor_config['password'])\n try:\n cli.create_project(\n namespace, public=harbor_config.get('public', False))\n except BaseException as e:\n pass\n return True, harbor_config\n\n\ndef k8s_cli(k8s, k8s_config):\n try:\n if k8s_config['type'] == 'basic':\n # basic auth or token auth\n k8s_config.pop('config', None)\n k8s_config.pop('type', None)\n cli = K8sAPI(**k8s_config)\n else:\n eks = None\n eks_token = None\n k8s_config = yaml.safe_load(k8s_config['config'])\n if k8s.idc.type == 1 and k8s.idc.supplier.split('.')[-1] == 'aws':\n return False, 'not support.'\n cli = K8sAPI(k8s_config=k8s_config, api_key=eks_token, eks=eks)\n return True, cli\n except BaseException as e:\n return False, str(e)\n\n\ndef template_generate(appinfo_obj: AppInfo, image=None, partial_deploy_replicas: int = 0):\n \"\"\"\n 生成Kubernetes Deployment Yaml\n \"\"\"\n\n def health_lifecycle_generate(item, enable=True):\n _c = {}\n for i in template[item]['data']:\n _x = {}\n if i.get('enable', enable):\n for j in i['items']:\n if '__' in j['name']:\n _t = j['name'].split('__')\n _value = j['value']\n if j['name'] == 'exec__command':\n _value = [\"sh\", \"-c\", j['value']]\n if _x.get(_t[0], None):\n _x[_t[0]][_t[1]] = _value\n else:\n _x[_t[0]] = {_t[1]: _value}\n else:\n _x[j['name']] = j['value']\n _c[i['name']] = _x\n return _c\n\n def container_generate(container_data):\n containers = []\n for i in container_data:\n if i.get('enable', None):\n container = get_datadict(i['key'], config=1)\n if not container:\n container = i['extra']\n containers.append(\n container)\n return containers\n language_obj = DevLanguage.objects.get(name=appinfo_obj.app.language)\n project_config = ProjectConfig.objects.filter(project_id=appinfo_obj.app.project.id,\n environment_id=appinfo_obj.environment.id)\n namespace = appinfo_obj.namespace\n harbor_config = get_redis_data('cicd-harbor')\n harbor_url = harbor_config['url'].split('://')[1]\n image = f\"{harbor_url}/{image}\"\n\n template = {}\n # 模板优先级\n # 应用模块 -> 应用 -> 项目 -> 环境\n if project_config.first():\n project_template = project_config.first().template\n for k, v in project_template.items():\n if v and isinstance(v, (dict,)):\n if v.get('custom', False) is False:\n if appinfo_obj.environment.template.get(k, None):\n template[k] = appinfo_obj.environment.template[k]\n else:\n if project_template.get(k, None):\n template[k] = project_template[k]\n\n microapp_template = appinfo_obj.app.template\n for k, v in microapp_template.items():\n if '_on' in k and v:\n _k = k.rstrip('_on')\n if microapp_template.get(_k, None):\n template[_k] = microapp_template[_k]\n use_host_network = False\n if appinfo_obj.template.get('userHostNetwork', 0):\n use_host_network = True\n for k, v in appinfo_obj.template.items():\n if v and isinstance(v, (dict,)):\n if v.get('custom', False) and appinfo_obj.template.get(k, None):\n template[k] = appinfo_obj.template[k]\n\n yaml_template = {'kind': 'Deployment', 'metadata': {}, 'spec':\n {'strategy': {}, 'template': {'metadata': {}, 'spec':\n {'containers': [{'ports': [{'containerPort': 8080}], 'resources': []}],\n 'imagePullSecrets': [{'name': 'loginharbor'}],\n 'terminationGracePeriodSeconds': 120}\n }\n }\n }\n\n try:\n tz = appinfo_obj.app.project.product.region.extra['timezone']\n except BaseException as e:\n tz = 'Asia/Shanghai'\n try:\n if template.get('strategy', None):\n for i in template['strategy']['data']:\n if i['key'] in ['maxSurge', 'maxUnavailable']:\n if yaml_template['spec']['strategy'].get('rollingUpdate', None) is None:\n yaml_template['spec']['strategy']['rollingUpdate'] = {}\n yaml_template['spec']['strategy']['rollingUpdate'][i['key']\n ] = f\"{i['value']}%\"\n else:\n yaml_template['spec'][i['key']] = i['value']\n _d = {}\n for i in template['resources']['data']:\n _t = i['key'].split('_')\n if _d.get(_t[0], None):\n _d[_t[0]][_t[1]] = f\"{i['value']}{i['slot']}\"\n else:\n _d[_t[0]] = {_t[1]: f\"{i['value']}{i['slot']}\"}\n yaml_template['spec']['template']['spec']['containers'][0]['resources'] = _d\n\n yaml_template['metadata']['name'] = appinfo_obj.app.name\n yaml_template['metadata']['namespace'] = namespace\n yaml_template['spec']['template']['spec']['containers'][0]['name'] = appinfo_obj.app.name\n yaml_template['spec']['template']['spec']['containers'][0]['image'] = image\n command = appinfo_obj.app.template.get(\n 'command', None) or language_obj.labels.get('command', None)\n if command:\n if command.startswith('./'):\n yaml_template['spec']['template']['spec']['containers'][0]['command'] = [\n command]\n else:\n yaml_template['spec']['template']['spec']['containers'][0]['command'] = [\n 'sh', '-c', command]\n\n # 优先级: 应用模块>应用>预设>开发语言\n labels = template['label']['data']\n labels.extend([{'name': 'app', 'value': appinfo_obj.app.name}])\n yaml_template['spec']['template']['metadata']['labels'] = {\n i['name']: i['value'] for i in labels}\n yaml_template['spec']['template']['metadata']['labels'][\n 'status-app-name-for-ops-platform'] = appinfo_obj.app.name\n yaml_template['spec']['selector'] = {\n 'matchLabels': {i['name']: i['value'] for i in labels}}\n\n selectors = template['selector']['data']\n yaml_template['spec']['template']['spec']['nodeSelector'] = {\n i['name']: i['value'] for i in selectors}\n\n if 'annotations' not in yaml_template['spec']['template']['metadata']:\n yaml_template['spec']['template']['metadata']['annotations'] = {}\n\n for i in template['prometheus']['data']:\n yaml_template['spec']['template']['metadata'][\n 'annotations'][f'prometheus.io/{i[\"name\"]}'] = i['value']\n if 'prometheus.io/path' in yaml_template['spec']['template']['metadata']['annotations']:\n yaml_template['spec']['template']['metadata']['annotations'][\n 'prometheus.io/app_product'] = appinfo_obj.app.project.product.name\n yaml_template['spec']['template']['metadata']['annotations'][\n 'prometheus.io/app_env'] = appinfo_obj.environment.name\n yaml_template['spec']['template']['metadata']['annotations'][\n 'prometheus.io/app_project'] = appinfo_obj.app.project.name\n\n # 环境变量\n envs = [{'name': 'TZ', 'value': tz}]\n envs.extend(template['env']['data'])\n envs.extend([\n {'name': '_RESTART', 'value': datetime.now().strftime(\n '%Y%m%d%H%M%S')}, # _RESTART变量用于强制更新deployment\n {'name': 'PRODUCT_NAME', 'value': appinfo_obj.app.project.product.name},\n {'name': 'PROJECT_NAME', 'value': appinfo_obj.app.project.name},\n {'name': 'APPNAME', 'value': appinfo_obj.app.name},\n {'name': 'APPID', 'value': appinfo_obj.app.appid},\n {'name': 'ENV', 'value': appinfo_obj.environment.name},\n {'name': 'POD_NAMESPACE', 'value': namespace}\n ])\n envs = list({i['name']: i for i in envs}.values())\n for i in envs:\n try:\n env_value = i.get('value', None)\n cmname = i.pop('cmname', None)\n cmkey = i.pop('cmkey', None)\n if env_value:\n env_value = env_value.lstrip('\"').rstrip(\n '\"').lstrip(\"'\").rstrip(\"'\")\n i.pop('value', None)\n i['name'] = i['name'].lstrip('\"').rstrip(\n '\"').lstrip(\"'\").rstrip(\"'\")\n if i.get('valueFrom', None) == 'configMapKeyRef':\n i['valueFrom'] = {'configMapKeyRef': {\n 'name': cmname, 'key': cmkey}}\n else:\n i['value'] = env_value\n i['valueFrom'] = None\n except BaseException as e:\n pass\n yaml_template['spec']['template']['spec']['containers'][0]['env'] = envs\n\n if template.get('health', False):\n _d = health_lifecycle_generate('health', True)\n for k, v in _d.items():\n yaml_template['spec']['template']['spec']['containers'][0][k] = v\n if template.get('lifecycle', False):\n yaml_template['spec']['template']['spec']['containers'][0]['lifecycle'] = {\n }\n _d = health_lifecycle_generate('lifecycle', False)\n for k, v in _d.items():\n yaml_template['spec']['template']['spec']['containers'][0]['lifecycle'][k] = v\n\n _vo_mount = [{'mountPath': '/data/logs',\n 'name': 'logs', 'readOnly': False}]\n _volumes = [{'name': 'logs', 'type': 'Directory', 'hostPath': {\n 'path': f'/data/{appinfo_obj.environment.name}-applogs/{appinfo_obj.app.project.name}/'}}]\n if template.get('storage', None):\n for k, v in template['storage']['data'].items():\n for i in v:\n _x = {}\n for m, n in i.items():\n if isinstance(n, (str,)):\n n = n.replace('${APPNAME}', appinfo_obj.app.name)\n if '_' in m:\n _t = m.split('_')\n if _x.get(_t[0], None):\n _x[_t[0]][_t[1]] = n\n else:\n _x[_t[0]] = {_t[1]: n}\n else:\n _x[m] = n\n _t = {'mountPath': _x['mount'], 'name': _x['name'],\n 'readOnly': True if _x.get('mode', None) == 'ReadOnly' else False}\n if _x.get('file', None):\n _t['subPath'] = _x['configMap']['items'][0]['key']\n _vo_mount.append(_t)\n _mode = _x.pop('mode', None)\n _x.pop('file', None)\n _x.pop('mount', None)\n if _x.get('configMap', None):\n _x['configMap']['defaultMode'] = 0o600 if _mode == 'ReadOnly' else 0o755\n _volumes.append(_x)\n yaml_template['spec']['template']['spec']['containers'][0]['volumeMounts'] = _vo_mount\n yaml_template['spec']['template']['spec']['volumes'] = _volumes\n if use_host_network:\n yaml_template['spec']['template']['spec']['hostNetwork'] = True\n partial_deploy_yaml_template = None\n\n except BaseException as e:\n logger.exception(f'generate yaml err {e.__class__} {e}')\n return {'ecode': 500, 'message': str(e)}\n\n # 多容器处理\n if appinfo_obj.template.get('containers_custom', None):\n containers = container_generate(\n appinfo_obj.template.get('containers', []))\n else:\n containers = container_generate(\n project_config.first().template.get('containers', []))\n yaml_template['spec']['template']['spec']['containers'].extend(containers)\n ret = {'ecode': 200, 'image': image, 'yaml': yaml_template}\n\n if partial_deploy_yaml_template:\n ret['partial_deploy_yaml'] = partial_deploy_yaml_template\n return ret\n\n\ndef get_members(obj):\n team_members = [j for i in obj.team_members.values() for j in i]\n return list(set(team_members))\n\n\ndef get_permission_from_role(request):\n try:\n perms = request.user.roles.values(\n 'permissions__method',\n ).distinct()\n return [p['permissions__method'] for p in perms]\n except AttributeError:\n return []\n\n\ndef get_headers(request=None):\n \"\"\"\n Function: get_headers(self, request)\n Description: To get all the headers from request\n \"\"\"\n regex = re.compile('^HTTP_')\n return dict((regex.sub('', header), value) for (header, value)\n in request.META.items() if header.startswith('HTTP_'))\n\n\ndef mask_sensitive_data(data):\n \"\"\"\n Hides sensitive keys specified in sensitive_keys settings.\n Loops recursively over nested dictionaries.\n \"\"\"\n\n if hasattr(settings, 'DRF_API_LOGGER_EXCLUDE_KEYS'):\n if type(settings.DRF_API_LOGGER_EXCLUDE_KEYS) in (list, tuple):\n SENSITIVE_KEYS.extend(settings.DRF_API_LOGGER_EXCLUDE_KEYS)\n\n if type(data) != dict and type(data) != ReturnDict:\n try:\n data = json.loads(data)\n except BaseException as e:\n return data\n\n for key, value in data.items():\n if key in SENSITIVE_KEYS:\n data[key] = \"***FILTERED***\"\n\n if type(value) == dict:\n data[key] = mask_sensitive_data(data[key])\n\n return data\n\n\ndef time_convert(target_time):\n \"\"\"\n 时间转字符串\n \"\"\"\n return target_time.astimezone(pytz.timezone('Asia/Shanghai')).strftime('%Y-%m-%d %H:%M:%S+08:00')\n\n\ndef time_comp(target_time, **kwargs):\n \"\"\"\n 时间比较/统一使用utc时间对比\n target_time: 目标时间\n kwargs: 额外参数, 时间差 如{hours: 1}, {minutes: 1}, {seconds: 1}, 数值不取负数\n \"\"\"\n ctime = timezone.now()\n if kwargs:\n # 两个时间是否在期望时间差范围\n if target_time > ctime:\n return target_time - ctime <= timedelta(**kwargs)\n else:\n return ctime - target_time <= timedelta(**kwargs)\n # 判断两个时间是否相等\n return ctime == target_time\n\n\ndef timeline_generate(time_range, format_type='dashboard'):\n \"\"\"\n 根据起始时间生成时间线\n\n : params format_type: 默认为dashboard, 用于概览报表粗略显示, 其它用于监控类的展示则使用更细粒度的格式\n \"\"\"\n TIME_FREQNAMES = DASHBOARD_TIME_FREQNAMES\n TIME_FORMAT = DASHBOARD_TIME_FORMAT\n if format_type == 'cmdb':\n TIME_FREQNAMES = DASHBOARD_TIME_FREQNAMES_T\n TIME_FORMAT = DASHBOARD_TIME_FORMAT_T\n start_time = time_range['start_time']\n end_time = time_range['end_time']\n time_line = rrule(\n freq=TIME_FREQNAMES[time_range['name']], dtstart=start_time, until=end_time)\n return [i.strftime(TIME_FORMAT[time_range['name']]) for i in time_line]\n\n\ndef time_period(time_range='6-months', type_range='static', time_zone='Asia/Shanghai', name=None):\n \"\"\"\n 根据时间范围生成起止时间\n \"\"\"\n start_time = None\n end_time = timezone.now().astimezone(pytz.timezone(time_zone))\n if type_range == 'dynamic' and name is None:\n start_time = datetime.strptime(time_range[0], '%Y-%m-%d %H:%M:%S')\n end_time = datetime.strptime(time_range[1], '%Y-%m-%d %H:%M:%S')\n if start_time > end_time:\n start_time, end_time = end_time, start_time\n if (end_time - start_time).days >= 60:\n name = 'months'\n elif (end_time - start_time).days >= 2:\n name = 'days'\n elif (end_time - start_time).days >= 1 or (end_time - start_time).seconds > 60 * 60:\n name = 'hours'\n else:\n name = 'minutes'\n return {'name': name, 'start_time': start_time, 'end_time': end_time}\n\n if type_range == 'static':\n _time = time_range.split('-')\n if _time[-1] == 'week':\n start_time = end_time - relativedelta(days=end_time.weekday(), hours=end_time.hour, minutes=end_time.minute,\n seconds=end_time.second,\n microseconds=end_time.microsecond)\n return {'name': 'days', 'start_time': start_time, 'end_time': end_time}\n if _time[-1] == 'lastweek':\n start_time = end_time - relativedelta(days=end_time.weekday() + 7, hours=end_time.hour,\n minutes=end_time.minute, seconds=end_time.second,\n microseconds=end_time.microsecond)\n end_time = end_time - relativedelta(days=end_time.weekday(), hours=end_time.hour, minutes=end_time.minute,\n seconds=end_time.second, microseconds=end_time.microsecond)\n return {'name': 'days', 'start_time': start_time, 'end_time': end_time}\n if _time[-1] in ['today', 'yesterday']:\n start_time = end_time - relativedelta(hours=end_time.hour, minutes=end_time.minute, seconds=end_time.second,\n microseconds=end_time.microsecond)\n if _time[-1] == 'yesterday':\n end_time = start_time\n start_time = end_time - relativedelta(days=1)\n return {'name': 'hours', 'start_time': start_time, 'end_time': end_time}\n name = _time[1]\n if name is None:\n if _time[1] in ['years', 'months']:\n name = 'months'\n if _time[1] == 'months' and int(_time[0]) < 2:\n name = 'days'\n if _time[1] == 'days' and int(_time[0]) < 2:\n name = 'hours'\n start_time = end_time + relativedelta(**{_time[1]: -int(_time[0])})\n return {'name': name, 'start_time': start_time, 'end_time': end_time}\n\n\ndef extend_jenkins(data, env):\n jenkins = get_redis_data('cicd-jenkins')\n app = AppInfo.objects.filter(id=data['id'])[0]\n category = DataDict.objects.get(key=app.app.category)\n job_name = app.jenkins_jobname\n jenkins_cli = GlueJenkins(jenkins.get('url', 'http://localhost'), username=jenkins.get('user', 'admin'),\n password=jenkins.get('password', None))\n try:\n view_xml_config = f'''<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<hudson.model.ListView>\n<name>{app.app.project.alias}{env.alias}</name>\n<filterExecutors>false</filterExecutors>\n<filterQueue>false</filterQueue>\n<properties class=\"hudson.model.View$PropertyList\"/>\n<jobNames>\n<comparator class=\"hudson.util.CaseInsensitiveComparator\"/>\n</jobNames>\n<jobFilters/>\n<columns>\n<hudson.views.StatusColumn/>\n<hudson.views.WeatherColumn/>\n<hudson.views.JobColumn/>\n<jenkins.branch.DescriptionColumn/>\n<hudson.views.LastSuccessColumn/>\n<hudson.views.LastFailureColumn/>\n<hudson.views.LastDurationColumn/>\n<hudson.views.BuildButtonColumn/>\n</columns>\n<includeRegex>{env.name.lower()}-.*-{app.app.project.name.lower()}-.*</includeRegex>\n</hudson.model.ListView>'''\n jenkins_cli.create_view(\n f'{app.app.project.alias}{env.alias}', view_xml_config)\n except BaseException as e:\n pass\n try:\n config_xml = convert_xml_to_str_with_pipeline(jenkins['xml'], jenkins['pipeline']['http_url_to_repo'],\n jenkins['gitlab_credit'],\n app.app.alias,\n f'{app.app.language}/Jenkinsfile')\n if not jenkins_cli.job_exists(job_name):\n jenkins_cli.create_job(name=job_name, config_xml=config_xml)\n else:\n jenkins_cli.reconfig_job(name=job_name, config_xml=config_xml)\n except Exception as e:\n logger.error(f\"创建Jenkins JOB: {job_name} 失败 ERROR: {e}\")\n\n\ndef get_celery_tasks():\n \"\"\"\n 获取celery任务\n \"\"\"\n current_app.loader.import_default_modules()\n tasks = list(\n sorted(name for name in current_app.tasks if not name.startswith('celery.')))\n return tasks\n\n\ndef is_chinese(string):\n \"\"\"\n 检查整个字符串是否包含中文\n :param string: 需要检查的字符串\n :return: bool\n \"\"\"\n for ch in string:\n if u'\\u4e00' <= ch <= u'\\u9fff':\n return True\n return False\n\n\ndef get_word_list(string):\n \"\"\"\n 切割字符串, 中文/-切割成单个字, 其它则切割成单个词\n \"\"\"\n res = re.compile(r\"([\\u4e00-\\u9fa5\\-])\")\n return [i for i in res.split(string.lower()) if len(i.strip()) > 0 and i != '-']\n\n\ndef devlanguage_template_manage(instance, filename, user=None, content=None, action='retrieve'):\n jenkins = get_redis_data('cicd-jenkins')\n ok, cli = gitlab_cli(admin=True)\n if not ok:\n return False, cli\n project_id = jenkins['pipeline'].get('id', None)\n if not project_id:\n return False, '获取流水线失败，请检查Jenkins配置.'\n project = cli.get_project(project_id)\n items = project.repository_tree(path=instance.name)\n try:\n if action == 'update':\n if filename in [i['name'] for i in items]:\n # 文件已存在则更新\n f = project.files.get(\n f\"{instance.name}/{filename}\", ref='master')\n f.content = content\n f.save(\n branch='master', commit_message=f'Update {instance.name} {filename} by {user.username}')\n else:\n # 文件不存在则创建\n logger.info(f'{instance.name}/{filename}文件不存在则创建')\n project.files.create({'file_path': f\"{instance.name}/{filename}\",\n 'branch': 'master',\n 'content': content,\n 'author_email': user.email,\n 'author_name': user.username,\n 'commit_message': f'Create {instance.name} {filename} by {user.username}'})\n content = project.files.raw(\n f\"{instance.name}/{filename}\", ref='master')\n return True, content\n except GitlabGetError as e:\n logger.info(f'获取异常，{e}')\n if e.response_code == 404:\n logger.info(f'{instance.name}/{filename}文件不存在')\n return True, ''\n except BaseException as e:\n logger.error(f'GitLab开发语言模板异常, 原因: {e}')\n return False, f'GitLab开发语言模板异常, 原因: {e}'\n\n\ndef snake_case(x):\n \"\"\"\n 驼峰转下划线\n \"\"\"\n\n term_exclude = ['OS', 'GPU', 'DB', 'IA', 'IP',\n 'RR', 'TTL', 'SLB', 'CPU', 'MEMORY', 'QPS']\n for i in term_exclude:\n x = x.replace(i, i.lower())\n return re.sub(r'(?P<key>[A-Z])', r'_\\g<key>', x).lower().strip('_')\n\n\ndef node_filter(node_id, data):\n \"\"\"\n 查找节点\n\n :params: node_id int 节点ID\n :params: data list 节点数组\n \"\"\"\n for i in data:\n if i['id'] == node_id:\n print('get node', i)\n return i\n else:\n if i.get('children', None):\n node = node_filter(node_id, i['children'])\n if isinstance(node, (dict,)):\n return node\n\n\ndef get_time_range(request):\n \"\"\"\n 获取时间轴\n \"\"\"\n type_range = request.query_params.get('range_type', 'static')\n if type_range == 'static':\n time_range = request.query_params.get('range', '6-months')\n else:\n time_range = request.query_params.getlist('range[]', None)\n if not time_range:\n time_range = '6-months'\n period = time_period(time_range, type_range)\n time_line = timeline_generate(period, format_type='cmdb')\n # 时间刻度, 以小时为刻度则删除年份\n time_line_x = [i.split(' ')[-1]\n for i in time_line] if period['name'] == 'hours' else time_line\n return period, time_line, time_line_x\n\n\ndef compare_dict(data, old_data):\n different_list = []\n for k1 in data:\n if k1 == 'update_time':\n continue\n v1 = data.get(k1)\n v2 = old_data.get(k1)\n if v1 != v2:\n different_list.append({\n 'key': k1,\n 'new_value': v1,\n 'old_value': v2\n })\n\n return different_list\n\n\ndef get_project_mergerequest(project: Project, cli: GitLabAPI, **params):\n \"\"\"\n 获取项目下所有应用的合并请求\n \"\"\"\n mrdata = []\n git_project = [app.repo['id']\n for app in project.microapp_set.all() if app.repo.get('id')]\n for project_id in set(git_project):\n try:\n git_project = cli.get_project(project_id)\n ok, data = cli.list_mrs(project=git_project, **params)\n if ok is False:\n continue\n mrdata.extend([i.attributes for i in data])\n except BaseException as e:\n logger.error(f'获取应用合并请求异常，原因：{e}')\n return mrdata\n\n\ndef gitlab_cli(user=None, admin=False, superadmin=False, merge=False):\n \"\"\"\n 获取GitlabAPI\n :param merge: 用于分支合并，管理员统一用配置文件里的token\n \"\"\"\n try:\n payload = {'token': GITLAB_ADMIN_TOKEN, 'oauth': False}\n cli = GitLabAPI(SOCIAL_AUTH_GITLAB_API_URL, **payload)\n return True, cli\n except BaseException as e:\n logger.warning(f'获取GitlabAPI异常，原因：{e}')\n return False, f'获取GitlabAPI异常，原因：{e}'\n\n\ndef get_deploy_image_list(app_id, appinfo_id=None, module=None, force=0):\n # 可选发布镜像\n # 获取关联应用ID"},"next_line":{"kind":"string","value":" app = MicroApp.objects.get(id=app_id)"},"gold_snippet_index":{"kind":"number","value":14,"string":"14"},"created_at":{"kind":"string","value":"2023-12-13 03:09:32+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5295,"cells":{"repo_name":{"kind":"string","value":"MarilynKeller/aitviewer-skel"},"file_path":{"kind":"string","value":"aitviewer/renderables/sdf.py"},"context":{"kind":"list like","value":[{"identifier":"BoundingBoxes","path":"aitviewer/renderables/bounding_boxes.py","snippet":"class BoundingBoxes(Node):\n \"\"\"\n Draw bounding boxes.\n \"\"\"\n\n def __init__(self, vertices, thickness=0.005, color=(0.0, 0.0, 1.0, 1.0), **kwargs):\n \"\"\"\n Initializer.\n :param vertices: Set of 3D coordinates as a np array of shape (N, 8, 3). The vertices will be connected in the\n following way: 0-1-2-3-0 (bottom) 4-5-6-7-4 (top) 0-4 1-5 2-6 3-7 (vertical connections between bottom\n and top).\n :param thickness: Line thickness.\n :param color: Color of the lines.\n \"\"\"\n if not isinstance(vertices, np.ndarray):\n vertices = np.array(vertices)\n if len(vertices.shape) == 2:\n vertices = vertices[np.newaxis]\n else:\n assert len(vertices.shape) == 3\n assert vertices.shape[1] == 8\n super(BoundingBoxes, self).__init__(n_frames=len(vertices), color=color, **kwargs)\n\n self.vertices = vertices\n\n self.lines = Lines(\n lines=self._get_line_coords(),\n mode=\"lines\",\n r_base=thickness,\n color=self.color,\n cast_shadow=False,\n )\n self.spheres = Spheres(positions=self.vertices, radius=thickness, color=self.color, cast_shadow=False)\n self._add_nodes(self.lines, self.spheres, show_in_hierarchy=False)\n\n @property\n def bounds(self):\n return self.get_bounds(self.vertices)\n\n @property\n def current_bounds(self):\n return self.get_bounds(self.vertices[self.current_frame_id])\n\n @staticmethod\n def from_min_max_diagonal(v_min, v_max, **kwargs):\n \"\"\"\n Create an axis-aligned bounding box from the 3D diagonal.\n :param v_min: np array of shape (N, 3).\n :param v_max: np array of shape (N, 3).\n :return: BoundingBoxes corresponding to the given diagonals.\n \"\"\"\n vertices = np.zeros((v_min.shape[0], 8, 3), dtype=v_min.dtype)\n vertices[:, 0:4] = v_min[:, np.newaxis]\n vertices[:, 1, 0] = v_max[:, 0]\n vertices[:, 2, 0:2] = v_max[:, 0:2]\n vertices[:, 3, 1] = v_max[:, 1]\n\n vertices[:, 4:] = v_max[:, np.newaxis]\n vertices[:, 4, 0:2] = v_min[:, 0:2]\n vertices[:, 7, 0] = v_min[:, 0]\n vertices[:, 5, 1] = v_min[:, 1]\n\n return BoundingBoxes(vertices, **kwargs)\n\n def _get_line_coords(self):\n lines = np.zeros((self.n_frames, 12 * 2, 3), dtype=self.vertices.dtype)\n\n # Bottom 0-1-2-3-0.\n lines[:, 0:2] = self.vertices[:, 0:2]\n lines[:, 2:4] = self.vertices[:, 1:3]\n lines[:, 4:6] = self.vertices[:, 2:4]\n lines[:, 6:8] = self.vertices[:, [3, 0]]\n\n # Top 4-5-6-7-4.\n lines[:, 8:10] = self.vertices[:, 4:6]\n lines[:, 10:12] = self.vertices[:, 5:7]\n lines[:, 12:14] = self.vertices[:, 6:8]\n lines[:, 14:16] = self.vertices[:, [7, 4]]\n\n # Vertical Connections.\n lines[:, 16:18] = self.vertices[:, [0, 4]]\n lines[:, 18:20] = self.vertices[:, [1, 5]]\n lines[:, 20:22] = self.vertices[:, [2, 6]]\n lines[:, 22:24] = self.vertices[:, [3, 7]]\n\n return lines\n\n @Node.color.setter\n def color(self, color):\n self.material.color = color\n self.lines.color = color\n self.spheres.color = color"},{"identifier":"Lines","path":"aitviewer/renderables/lines.py","snippet":"class Lines(Node):\n \"\"\"Render lines as cylinders or cones. Can render approx. 600k lines at 40 fps.\"\"\"\n\n def __init__(\n self,\n lines,\n r_base=0.01,\n r_tip=None,\n color=(0.0, 0.0, 1.0, 1.0),\n mode=\"line_strip\",\n cast_shadow=True,\n **kwargs,\n ):\n \"\"\"\n Initializer.\n :param lines: Set of 3D coordinates as a np array of shape (F, L, 3) or (L, 3).\n :param r_base: Thickness of the line.\n :param r_tip: If set, the thickness of the line will taper from r_base to r_tip. If set to 0.0 it will create\n a proper cone.\n :param color: Color of the line (4-tuple) or array of color (N_LINES, 4), one for each line.\n :param mode: 'lines' or 'line_strip'.\n 'lines': a line is drawn from point 0 to 1, from 2 to 3, and so on, number of lines is L / 2.\n 'line_strip': a line is drawn between all adjacent points, 0 to 1, 1 to 2 and so on, number of lines is L - 1.\n :param cast_shadow: If True the mesh casts a shadow on other objects.\n \"\"\"\n if len(lines.shape) == 2:\n lines = lines[np.newaxis]\n assert len(lines.shape) == 3\n assert mode == \"lines\" or mode == \"line_strip\"\n if mode == \"lines\":\n assert lines.shape[1] % 2 == 0\n\n self._lines = lines\n self.mode = mode\n self.r_base = r_base\n self.r_tip = r_tip if r_tip is not None else r_base\n\n self.vertices, self.faces = self.get_mesh()\n self.n_lines = self.lines.shape[1] // 2 if mode == \"lines\" else self.lines.shape[1] - 1\n\n # Define a default material in case there is None.\n if isinstance(color, tuple) or len(color.shape) == 1:\n kwargs[\"material\"] = kwargs.get(\"material\", Material(color=color, ambient=0.2))\n self.line_colors = kwargs[\"material\"].color\n else:\n assert (\n color.shape[1] == 4 and color.shape[0] == self.n_lines\n ), \"Color must be a tuple of 4 values or a numpy array of shape (N_LINES, 4)\"\n self.line_colors = color\n\n super(Lines, self).__init__(n_frames=self.lines.shape[0], **kwargs)\n\n self._need_upload = True\n self.draw_edges = False\n\n # Render passes.\n self.outline = True\n self.fragmap = True\n self.depth_prepass = True\n self.cast_shadow = cast_shadow\n\n @property\n def bounds(self):\n bounds = self.get_bounds(self.lines)\n r = max(self.r_base, self.r_tip)\n bounds[:, 0] -= r\n bounds[:, 1] += r\n return bounds\n\n @property\n def current_bounds(self):\n bounds = self.get_bounds(self.current_lines)\n r = max(self.r_base, self.r_tip)\n bounds[:, 0] -= r\n bounds[:, 1] += r\n return bounds\n\n @property\n def lines(self):\n return self._lines\n\n @lines.setter\n def lines(self, value):\n self._lines = value if len(value.shape) == 3 else value[np.newaxis]\n self.n_frames = self.lines.shape[0]\n self.redraw()\n\n @property\n def current_lines(self):\n idx = self.current_frame_id if self._lines.shape[0] > 1 else 0\n return self._lines[idx]\n\n @current_lines.setter\n def current_lines(self, lines):\n assert len(lines.shape) == 2\n idx = self.current_frame_id if self._lines.shape[0] > 1 else 0\n self._lines[idx] = lines\n self.redraw()\n\n @Node.color.setter\n def color(self, color):\n self.material.color = color\n self.line_colors = color\n self.redraw()\n\n @property\n def line_colors(self):\n if len(self._line_colors.shape) == 1:\n t = np.tile(np.array(self._line_colors), (self.n_lines, 1))\n return t\n else:\n return self._line_colors\n\n @line_colors.setter\n def line_colors(self, color):\n if isinstance(color, tuple):\n color = np.array(color)\n self._line_colors = color\n self.redraw()\n\n def on_frame_update(self):\n self.redraw()\n\n def redraw(self, **kwargs):\n self._need_upload = True\n\n @Node.once\n def make_renderable(self, ctx: moderngl.Context):\n self.prog = get_lines_instanced_program()\n\n vs_path = \"lines_instanced_positions.vs.glsl\"\n self.outline_program = get_outline_program(vs_path)\n self.depth_only_program = get_depth_only_program(vs_path)\n self.fragmap_program = get_fragmap_program(vs_path)\n\n self.vbo_vertices = ctx.buffer(self.vertices.astype(\"f4\").tobytes())\n self.vbo_indices = ctx.buffer(self.faces.astype(\"i4\").tobytes())\n self.vbo_instance_base = ctx.buffer(reserve=self.n_lines * 12)\n self.vbo_instance_tip = ctx.buffer(reserve=self.n_lines * 12)\n self.vbo_instance_color = ctx.buffer(reserve=self.n_lines * 16)\n\n self.vao = VAO()\n self.vao.buffer(self.vbo_vertices, \"3f4\", \"in_position\")\n self.vao.buffer(self.vbo_instance_base, \"3f4/i\", \"instance_base\")\n self.vao.buffer(self.vbo_instance_tip, \"3f4/i\", \"instance_tip\")\n self.vao.buffer(self.vbo_instance_color, \"4f4/i\", \"instance_color\")\n self.vao.index_buffer(self.vbo_indices)\n\n def _upload_buffers(self):\n if not self.is_renderable or not self._need_upload:\n return\n self._need_upload = False\n\n lines = self.current_lines\n if self.mode == \"lines\":\n v0s = lines[::2]\n v1s = lines[1::2]\n else:\n v0s = lines[:-1]\n v1s = lines[1:]\n\n self.vbo_instance_base.write(v0s.astype(\"f4\").tobytes())\n self.vbo_instance_tip.write(v1s.astype(\"f4\").tobytes())\n\n if len(self._line_colors.shape) > 1:\n self.vbo_instance_color.write(self._line_colors.astype(\"f4\").tobytes())\n\n def render(self, camera, **kwargs):\n self._upload_buffers()\n\n prog = self.prog\n prog[\"r_base\"] = self.r_base\n prog[\"r_tip\"] = self.r_tip\n if len(self._line_colors.shape) == 1:\n prog[\"use_uniform_color\"] = True\n prog[\"uniform_color\"] = tuple(self.color)\n else:\n prog[\"use_uniform_color\"] = False\n prog[\"draw_edges\"].value = 1.0 if self.draw_edges else 0.0\n prog[\"win_size\"].value = kwargs[\"window_size\"]\n prog[\"clip_control\"].value = (0, 0, 0)\n\n self.set_camera_matrices(prog, camera, **kwargs)\n set_lights_in_program(\n prog,\n kwargs[\"lights\"],\n kwargs[\"shadows_enabled\"],\n kwargs[\"ambient_strength\"],\n )\n set_material_properties(prog, self.material)\n self.receive_shadow(prog, **kwargs)\n self.vao.render(prog, moderngl.TRIANGLES, instances=self.n_lines)\n\n def render_positions(self, prog):\n if self.is_renderable:\n self._upload_buffers()\n prog[\"r_base\"] = self.r_base\n prog[\"r_tip\"] = self.r_tip\n self.vao.render(prog, moderngl.TRIANGLES, instances=self.n_lines)\n\n def get_mesh(self):\n v0s = np.array([[0, 0, 0]], np.float32)\n v1s = np.array([[0, 0, 1]], np.float32)\n\n # If r_tip is below a certain threshold, we create a proper cone, i.e. with just a single vertex at the top.\n if self.r_tip < 1e-5:\n data = _create_cone_from_to(v0s, v1s, radius=1.0)\n else:\n data = _create_cylinder_from_to(v0s, v1s, radius1=1.0, radius2=1.0)\n\n return data[\"vertices\"][0], data[\"faces\"]\n\n @hooked\n def release(self):\n if self.is_renderable:\n self.vao.release()\n\n def update_frames(self, lines, frames):\n self.lines[frames] = lines\n self.redraw()\n\n def add_frames(self, lines):\n if len(lines.shape) == 2:\n lines = lines[np.newaxis]\n self.lines = np.append(self.lines, lines, axis=0)\n\n def remove_frames(self, frames):\n self.lines = np.delete(self.lines, frames, axis=0)\n self.redraw()\n\n def export_usd(self, stage, usd_path: str, directory: str = None, verbose=False):\n name = f\"{self.name}_{self.uid:03}\".replace(\" \", \"_\")\n usd_path = f\"{usd_path}/{name}\"\n\n if self.mode == \"lines\":\n v0s = self.lines[:, ::2]\n v1s = self.lines[:, 1::2]\n else:\n v0s = self.lines[:, :-1]\n v1s = self.lines[:, 1:]\n\n print(self.lines.shape)\n print(v0s.shape)\n\n # Data is in the form of (F, N_LINES, 3), convert it to (F*N_LINES, 3)\n v0s = np.reshape(v0s, (-1, 3))\n v1s = np.reshape(v1s, (-1, 3))\n\n self.r_tip = self.r_base if self.r_tip is None else self.r_tip\n\n # If r_tip is below a certain threshold, we create a proper cone, i.e. with just a single vertex at the top.\n if self.r_tip < 10e-6:\n data = _create_cone_from_to(v0s, v1s, radius=self.r_base)\n else:\n data = _create_cylinder_from_to(v0s, v1s, radius1=self.r_base, radius2=self.r_tip)\n\n L = self.n_lines\n V = data[\"vertices\"].shape[1]\n\n vertices = data[\"vertices\"].reshape((self.n_frames, -1, 3))\n faces = data[\"faces\"]\n\n fs = faces[np.newaxis].repeat(L, 0).reshape((L, -1))\n offsets = (np.arange(L) * V).reshape((L, 1))\n faces = (fs + offsets).reshape((-1, 3))\n\n mesh = usd.add_mesh(stage, usd_path, self.name, vertices, faces, self.get_local_transform())\n usd.add_color(stage, mesh, usd_path, self.color[:3])\n\n self._export_usd_recursively(stage, usd_path, directory, verbose)"},{"identifier":"Meshes","path":"aitviewer/renderables/meshes.py","snippet":"class Meshes(Node):\n \"\"\"A sequence of triangle meshes. This assumes that the mesh topology is fixed over the sequence.\"\"\"\n\n def __init__(\n self,\n vertices,\n faces,\n vertex_normals=None,\n face_normals=None,\n vertex_colors=None,\n face_colors=None,\n uv_coords=None,\n path_to_texture=None,\n cast_shadow=True,\n pickable=True,\n flat_shading=False,\n draw_edges=False,\n draw_outline=False,\n instance_transforms=None,\n icon=\"\\u008d\",\n **kwargs,\n ):\n \"\"\"\n Initializer.\n :param vertices: A np array of shape (N, V, 3) or (V, 3).\n :param faces: A np array of shape (F, 3).\n :param vertex_normals: A np array of shape (N, V, 3). If not provided, the vertex normals will be computed,\n which incurs some overhead.\n :param face_normals: A np array of shape (N, F, 3). If not provided, the face normals will be computed, which\n incurs some overhead.\n :param vertex_colors: A np array of shape (N, V, 4) overriding the uniform color.\n :param face_colors: A np array of shape (N, F, 4) overriding the uniform or vertex colors.\n :param uv_coords: A np array of shape (V, 2) if the mesh is to be textured.\n :param path_to_texture: Path to an image file that serves as the texture.\n :param cast_shadow: If True the mesh casts a shadow on other objects.\n :param pickable: If True the mesh can be selected with a mouse click.\n :param flat_shading: If True the each face of the mesh is shaded with a constant normal.\n :param draw_edges: If True the normals the edges of the mesh is drawn on top of the mesh.\n :param draw_outline: If true an outline is drawn around the mesh.\n :instance_transforms: np array of size (N, I, 4, 4) or (I, 4, 4) or None. If not None, 'I' instances of\n the same mesh will be rendered, each with its own transformation matrix.\n \"\"\"\n if len(vertices.shape) == 2 and vertices.shape[-1] == 3:\n vertices = vertices[np.newaxis]\n assert len(vertices.shape) == 3\n assert len(faces.shape) == 2\n n_frames = vertices.shape[0]\n\n # Instancing.\n if instance_transforms is not None:\n # Check shape of transforms.\n if len(instance_transforms.shape) == 3:\n instance_transforms = instance_transforms[np.newaxis]\n assert len(instance_transforms.shape) == 4\n\n # Number of instance frames must match number of frames or be 1.\n assert n_frames == 1 or instance_transforms.shape[0] == 1 or n_frames == instance_transforms.shape[0]\n n_frames = max(n_frames, instance_transforms.shape[0])\n\n self._instance_transforms = instance_transforms\n else:\n self._instance_transforms = None\n\n super(Meshes, self).__init__(n_frames=n_frames, icon=icon, **kwargs)\n\n self._vertices = vertices\n self._faces = faces.astype(np.int32)\n\n # Create these first because other setters can call redraw() which uses this fields.\n self._face_colors = None\n self._vertex_colors = None\n self._has_transparent_vertex_or_face_colors = False\n\n def _maybe_unsqueeze(x):\n return x[np.newaxis] if x is not None and x.ndim == 2 else x\n\n self._vertex_normals = _maybe_unsqueeze(vertex_normals)\n self._face_normals = _maybe_unsqueeze(face_normals)\n self.vertex_colors = _maybe_unsqueeze(vertex_colors)\n self.face_colors = _maybe_unsqueeze(face_colors)\n\n # Texture handling.\n self.has_texture = (uv_coords is not None) and (path_to_texture is not None)\n self.uv_coords = uv_coords\n self.texture_path = path_to_texture\n\n if self.has_texture:\n self.use_pickle_texture = path_to_texture.endswith((\".pickle\", \"pkl\"))\n if self.use_pickle_texture:\n self.texture_image = pickle.load(open(path_to_texture, \"rb\"))\n else:\n self.texture_image = Image.open(path_to_texture).transpose(method=Image.FLIP_TOP_BOTTOM).convert(\"RGB\")\n else:\n self.texture_image = None\n\n # Enable rendering passes\n self.cast_shadow = cast_shadow\n self.fragmap = pickable\n self.depth_prepass = True\n self.outline = True\n\n # Misc.\n self._flat_shading = flat_shading\n self.draw_edges = draw_edges\n self.draw_outline = draw_outline\n self.show_texture = self.has_texture\n self.norm_coloring = False\n self.normals_r = None\n self.need_upload = True\n self._use_uniform_color = self._vertex_colors is None and self._face_colors is None\n self._vertex_faces_sparse = trimesh.geometry.index_sparse(self._vertices.shape[1], self._faces)\n\n self.clip_control = np.array((0, 0, 0), np.int32)\n self.clip_value = np.array((0, 0, 0), np.float32)\n\n @classmethod\n def instanced(cls, *args, positions=None, rotations=None, scales=None, **kwargs):\n \"\"\"\n Creates and returns an instanced sequence of N frames and I instances.\n Each instance will have its own position, rotation and scale.\n :param positions: np array of size (N, I, 3) or (I, 3) or None.\n :param rotations: np array of size (N, I, 3, 3) or (I, 3, 3) or None.\n :param scales: np array of size (N, I) or (I) or None.\n\n *args, and **kwargs are forwarded to the Meshes constructor.\n \"\"\"\n assert positions is not None or rotations is not None or scales is not None\n\n n_instances = 0\n n_frames = 0\n\n def check_array(a, dim):\n nonlocal n_instances, n_frames\n if a is not None:\n if len(a.shape) == dim + 1:\n a = a[np.newaxis]\n n_frames = max(n_frames, a.shape[0])\n n_instances = max(n_instances, a.shape[1])\n return a\n\n positions = check_array(positions, 1)\n rotations = check_array(rotations, 2)\n scales = check_array(scales, 0)\n\n if positions is None:\n positions = np.zeros((n_frames, n_instances, 3))\n if rotations is None:\n rotations = np.zeros((n_frames, n_instances, 3, 3))\n rotations[:, :] = np.eye(3)\n if scales is None:\n scales = np.ones((n_frames, n_instances))\n\n transforms = np.zeros((n_frames, n_instances, 4, 4))\n transforms[:, :, :3, :3] = (rotations.reshape((-1, 9)) * scales.reshape((-1, 1))).reshape(\n (n_frames, n_instances, 3, 3)\n )\n transforms[:, :, :3, 3] = positions\n transforms[:, :, 3, 3] = 1.0\n return cls(*args, **kwargs, instance_transforms=transforms)\n\n @classmethod\n def from_file(cls, file, **kwargs):\n \"\"\"\n Loads a mesh from a file that can be loaded by trimesh (e.g. \".obj\", \".ply\", ...)\n See trimesh.available_formats() for a complete list.\n \"\"\"\n mesh = trimesh.load(file)\n\n uvs = None\n vertex_colors = None\n face_colors = None\n if isinstance(mesh.visual, trimesh.visual.ColorVisuals):\n if mesh.visual.kind == \"vertex_colors\":\n vertex_colors = mesh.visual.vertex_colors\n elif mesh.visual.kind == \"face_colors\":\n face_colors = mesh.visual.vertex_colors\n elif isinstance(mesh.visual, trimesh.visual.TextureVisuals):\n uvs = mesh.visual.uv\n\n return Meshes(\n mesh.vertices,\n mesh.faces,\n vertex_normals=mesh.vertex_normals,\n face_colors=face_colors,\n vertex_colors=vertex_colors,\n uv_coords=uvs,\n **kwargs,\n )\n\n @property\n def vertices(self):\n return self._vertices\n\n @vertices.setter\n def vertices(self, vertices):\n if len(vertices.shape) == 2:\n vertices = vertices[np.newaxis]\n\n # Update vertices and redraw\n self._vertices = vertices\n self.n_frames = len(vertices)\n\n # If vertex or face normals were supplied, they are no longer valid.\n self._vertex_normals = None\n self._face_normals = None\n\n # Must clear all LRU caches where the vertices are used.\n self.compute_vertex_and_face_normals.cache_clear()\n\n self.redraw()\n\n @property\n def faces(self):\n return self._faces\n\n @faces.setter\n def faces(self, f):\n self._faces = f.astype(np.int32)\n self._vertex_faces_sparse = trimesh.geometry.index_sparse(self.vertices.shape[1], self._faces)\n\n @property\n def current_vertices(self):\n idx = self.current_frame_id if self.vertices.shape[0] > 1 else 0\n return self.vertices[idx]\n\n @current_vertices.setter\n def current_vertices(self, vertices):\n idx = self.current_frame_id if self.vertices.shape[0] > 1 else 0\n self._vertices[idx] = vertices\n self.compute_vertex_and_face_normals.cache_clear()\n self.redraw()\n\n @property\n def current_transformed_vertices(self):\n return (self.current_vertices @ self.model_matrix[:3, :3].T) + self.model_matrix[:3, 3]\n\n @property\n def transformed_vertices(self):\n return (self.vertices @ self.model_matrix[:3, :3].T) + self.model_matrix[:3, 3]\n\n @property\n def n_faces(self):\n return self.faces.shape[0]\n\n @property\n def n_vertices(self):\n return self.vertices.shape[1]\n\n @property\n def vertex_faces(self):\n # To compute the normals we need to know a mapping from vertex ID to all faces that this vertex is part of.\n # Because we are lazy we abuse trimesh to compute this for us. Not all vertices have the maximum degree, so\n # this array is padded with -1 if necessary.\n return trimesh.Trimesh(self.vertices[0], self.faces, process=False).vertex_faces\n\n @property\n def vertex_normals(self):\n \"\"\"Get or compute all vertex normals (this might take a while for long sequences).\"\"\"\n if self._vertex_normals is None:\n vertex_normals, _ = compute_vertex_and_face_normals_sparse(\n self.vertices, self.faces, self._vertex_faces_sparse, normalize=True\n )\n self._vertex_normals = vertex_normals\n return self._vertex_normals\n\n @property\n def face_normals(self):\n \"\"\"Get or compute all face normals (this might take a while for long sequences).\"\"\"\n if self._face_normals is None:\n _, face_normals = compute_vertex_and_face_normals_sparse(\n self.vertices, self.faces, self._vertex_faces_sparse, normalize=True\n )\n self._face_normals = face_normals\n return self._face_normals\n\n def vertex_normals_at(self, frame_id):\n \"\"\"Get or compute the vertex normals at the given frame.\"\"\"\n if self._vertex_normals is None:\n vn, _ = self.compute_vertex_and_face_normals(frame_id, normalize=True)\n else:\n assert len(self._vertex_normals.shape) == 3, f\"Got shape {self._vertex_normals.shape}\"\n vn = self._vertex_normals[frame_id]\n return vn\n\n def face_normals_at(self, frame_id):\n \"\"\"Get or compute the face normals at the given frame.\"\"\"\n if self._face_normals is None:\n _, fn = self.compute_vertex_and_face_normals(frame_id, normalize=True)\n else:\n assert len(self._face_normals.shape) == 3, f\"Got shape {self._face_normals.shape}\"\n fn = self._face_normals[frame_id]\n return fn\n\n @property\n def vertex_colors(self):\n if self._vertex_colors is None:\n self._vertex_colors = np.full((self.n_frames, self.n_vertices, 4), self.material.color)\n return self._vertex_colors\n\n @vertex_colors.setter\n def vertex_colors(self, vertex_colors):\n # If vertex_colors are None, we resort to the material color.\n if vertex_colors is None:\n self._vertex_colors = None\n self._use_uniform_color = True\n elif isinstance(vertex_colors, tuple) and len(vertex_colors) == 4:\n self.vertex_colors = None\n self._use_uniform_color = True\n self.material.color = vertex_colors\n else:\n if len(vertex_colors.shape) == 2:\n assert vertex_colors.shape[0] == self.n_vertices\n vertex_colors = np.repeat(vertex_colors[np.newaxis], self.n_frames, axis=0)\n assert len(vertex_colors.shape) == 3\n self._vertex_colors = vertex_colors\n self._use_uniform_color = False\n self.redraw()\n\n @property\n def current_vertex_colors(self):\n if self._use_uniform_color:\n return np.full((self.n_vertices, 4), self.material.color)\n else:\n idx = self.current_frame_id if self.vertex_colors.shape[0] > 1 else 0\n return self.vertex_colors[idx]\n\n @property\n def face_colors(self):\n return self._face_colors\n\n @face_colors.setter\n def face_colors(self, face_colors):\n if face_colors is not None:\n if len(face_colors.shape) == 2:\n face_colors = face_colors[np.newaxis]\n self._face_colors = face_colors\n self._use_uniform_color = False\n else:\n self._face_colors = None\n self.redraw()\n\n @property\n def current_face_colors(self):\n if self._use_uniform_color:\n return np.full((self.n_faces, 4), self.material.color)\n else:\n idx = self.current_frame_id if self.face_colors.shape[0] > 1 else 0\n return self.face_colors[idx]\n\n @Node.color.setter\n def color(self, color):\n self.material.color = color\n\n if self.face_colors is None:\n self.vertex_colors = color\n\n @property\n def flat_shading(self):\n return self._flat_shading\n\n @flat_shading.setter\n def flat_shading(self, flat_shading):\n if self._flat_shading != flat_shading:\n self._flat_shading = flat_shading\n self.redraw()\n\n def closest_vertex_in_triangle(self, tri_id, point):\n face_vertex_id = np.linalg.norm((self.current_vertices[self.faces[tri_id]] - point), axis=-1).argmin()\n return self.faces[tri_id][face_vertex_id]\n\n def get_bc_coords_from_points(self, tri_id, points):\n return points_to_barycentric(self.current_vertices[self.faces[[tri_id]]], points)[0]\n\n @lru_cache(2048)\n def compute_vertex_and_face_normals(self, frame_id, normalize=False):\n \"\"\"\n Compute face and vertex normals for the given frame. We use an LRU cache since this is a potentially\n expensive operation. This function exists because computing the normals on all frames can increase the\n startup time of the viewer considerably.\n\n :param frame_id: On which frame to compute the normals.\n :param normalize: Whether or not to normalize the normals. Not doing it is faster and the shaders typically\n enforce unit length of normals anyway.\n :return: The vertex and face normals as a np arrays of shape (V, 3) and (F, 3) respectively.\n \"\"\"\n vs = self.vertices[frame_id : frame_id + 1] if self.vertices.shape[0] > 1 else self.vertices\n vn, fn = compute_vertex_and_face_normals_sparse(vs, self.faces, self._vertex_faces_sparse, normalize)\n return vn.squeeze(0), fn.squeeze(0)\n\n @property\n def bounds(self):\n if self.instance_transforms is None:\n return self.get_bounds(self.vertices)\n else:\n # Get bounds in local coordinates\n bounds = self.get_local_bounds(self.vertices)\n\n # Transform bounds with instance transforms\n min = np.append(bounds[:, 0], 1.0)\n max = np.append(bounds[:, 1], 1.0)\n transforms = self.instance_transforms.reshape((-1, 4, 4))\n mins = transforms @ min\n maxs = transforms @ max\n\n # Return bounds in world coordinates\n return self.get_bounds(np.vstack((mins, maxs)))\n\n @property\n def current_bounds(self):\n if self.instance_transforms is None:\n return self.get_bounds(self.current_vertices)\n else:\n # Get bounds in local coordinates\n bounds = self.get_local_bounds(self.current_vertices)\n\n # Transform bounds with instance transforms\n min = np.append(bounds[:, 0], 1.0)\n max = np.append(bounds[:, 1], 1.0)\n transforms = self.current_instance_transforms.reshape((-1, 4, 4))\n mins = transforms @ min\n maxs = transforms @ max\n\n # Return bounds in world coordinates\n return self.get_bounds(np.vstack((mins[:, :3], maxs[:, :3])))\n\n def is_transparent(self):\n return self.color[3] < 1.0 or self._has_transparent_vertex_or_face_colors\n\n def on_frame_update(self):\n \"\"\"Called whenever a new frame must be displayed.\"\"\"\n super().on_frame_update()\n self.redraw()\n\n @property\n def current_instance_transforms(self):\n if self._instance_transforms is None:\n return None\n idx = self.current_frame_id if self._instance_transforms.shape[0] > 1 else 0\n return self._instance_transforms[idx]\n\n @property\n def instance_transforms(self):\n return self._instance_transforms\n\n @instance_transforms.setter\n def instance_transforms(self, instance_transforms):\n assert self._instance_transforms.shape == instance_transforms\n self._instance_transforms = instance_transforms\n\n @property\n def n_instances(self):\n if self._instance_transforms is None:\n return 1\n else:\n return self._instance_transforms.shape[1]\n\n def _upload_buffers(self):\n \"\"\"Upload the current frame data to the GPU for rendering.\"\"\"\n if not self.is_renderable or not self._need_upload:\n return\n\n self._need_upload = False\n\n # Write positions.\n self.vbo_vertices.write(self.current_vertices.astype(\"f4\").tobytes())\n\n # Write normals.\n if not self.flat_shading:\n vertex_normals = self.vertex_normals_at(self.current_frame_id)\n self.vbo_normals.write(vertex_normals.astype(\"f4\").tobytes())\n\n if self.face_colors is None:\n # Write vertex colors.\n self.vbo_colors.write(self.current_vertex_colors.astype(\"f4\").tobytes())\n else:\n # Write face colors.\n\n # Compute shape of 2D texture.\n shape = (min(self.faces.shape[0], 8192), (self.faces.shape[0] + 8191) // 8192)\n\n # Write texture left justifying the buffer to fill the last row of the texture.\n self.face_colors_texture.write(\n self.current_face_colors.astype(\"f4\").tobytes().ljust(shape[0] * shape[1] * 16)\n )\n\n # Write uvs.\n if self.has_texture:\n self.vbo_uvs.write(self.uv_coords.astype(\"f4\").tobytes())\n\n # Write instance transforms.\n if self.instance_transforms is not None:\n self.vbo_instance_transforms.write(\n np.transpose(self.current_instance_transforms.astype(\"f4\"), (0, 2, 1)).tobytes()\n )\n\n @hooked\n def redraw(self, **kwargs):\n self._need_upload = True\n\n transparent = False\n if self._vertex_colors is not None:\n transparent = transparent or np.any(self.vertex_colors[:, :, 3] < 1.0)\n if self._face_colors is not None:\n transparent = transparent or np.any(self.face_colors[:, :, 3] < 1.0)\n\n self._has_transparent_vertex_or_face_colors = transparent\n\n def _load_programs(self, vs, positions_vs):\n instanced = 1 if self.instance_transforms is not None else 0\n self.smooth_prog = get_smooth_lit_with_edges_program(vs, instanced)\n self.flat_prog = get_flat_lit_with_edges_program(vs, instanced)\n self.smooth_face_prog = get_smooth_lit_with_edges_face_color_program(vs, instanced)\n self.flat_face_prog = get_flat_lit_with_edges_face_color_program(vs, instanced)\n\n self.depth_only_program = get_depth_only_program(positions_vs, instanced)\n self.outline_program = get_outline_program(positions_vs, instanced)\n self.fragmap_program = get_fragmap_program(positions_vs, instanced)\n\n # noinspection PyAttributeOutsideInit\n @Node.once\n def make_renderable(self, ctx: moderngl.Context):\n \"\"\"Prepares this object for rendering. This function must be called before `render` is used.\"\"\"\n vs = \"lit_with_edges.glsl\"\n positions_vs = \"mesh_positions.vs.glsl\"\n self._load_programs(vs, positions_vs)\n\n vertices = self.current_vertices\n vertex_normals = self.vertex_normals_at(self.current_frame_id)\n vertex_colors = self.current_vertex_colors\n\n self.vbo_vertices = ctx.buffer(vertices.astype(\"f4\").tobytes())\n self.vbo_normals = ctx.buffer(vertex_normals.astype(\"f4\").tobytes())\n self.vbo_colors = ctx.buffer(vertex_colors.astype(\"f4\").tobytes())\n self.vbo_indices = ctx.buffer(self.faces.tobytes())\n\n self.vao = VAO()\n self.vao.buffer(self.vbo_vertices, \"3f4\", \"in_position\")\n self.vao.buffer(self.vbo_normals, \"3f4\", \"in_normal\")\n self.vao.buffer(self.vbo_colors, \"4f4\", \"in_color\")\n self.vao.index_buffer(self.vbo_indices)\n\n if self.instance_transforms is not None:\n self.vbo_instance_transforms = ctx.buffer(\n np.transpose(self.current_instance_transforms.astype(\"f4\"), (0, 2, 1)).tobytes()\n )\n self.vao.buffer(self.vbo_instance_transforms, \"16f4/i\", \"instance_transform\")\n\n # Compute shape of 2D texture.\n shape = (min(self.faces.shape[0], 8192), (self.faces.shape[0] + 8191) // 8192)\n self.face_colors_texture = ctx.texture(shape, 4, dtype=\"f4\")\n if self.face_colors is not None:\n # Write texture left justifying the buffer to fill the last row of the texture.\n self.face_colors_texture.write(\n self.current_face_colors.astype(\"f4\").tobytes().ljust(shape[0] * shape[1] * 16)\n )\n\n if self.has_texture:\n img = self.texture_image\n if self.use_pickle_texture:\n self.texture = ctx.texture(img.shape[:2], img.shape[2], img.tobytes())\n else:\n self.texture = ctx.texture(img.size, 3, img.tobytes())\n self.texture_prog = get_smooth_lit_texturized_program(vs)\n self.vbo_uvs = ctx.buffer(self.uv_coords.astype(\"f4\").tobytes())\n self.vao.buffer(self.vbo_uvs, \"2f4\", \"in_uv\")\n\n @hooked\n def release(self):\n if self.is_renderable:\n self.vao.release()\n if self.has_texture:\n self.texture.release()\n\n def _use_program(self, camera, **kwargs):\n if self.has_texture and self.show_texture:\n prog = self.texture_prog\n prog[\"diffuse_texture\"] = 0\n self.texture.use(0)\n else:\n if self.face_colors is None:\n if self.flat_shading:\n prog = self.flat_prog\n else:\n prog = self.smooth_prog\n else:\n if self.flat_shading:\n prog = self.flat_face_prog\n else:\n prog = self.smooth_face_prog\n self.face_colors_texture.use(0)\n prog[\"face_colors\"] = 0\n prog[\"norm_coloring\"].value = self.norm_coloring\n\n prog[\"use_uniform_color\"] = self._use_uniform_color\n prog[\"uniform_color\"] = self.material.color\n prog[\"draw_edges\"].value = 1.0 if self.draw_edges else 0.0\n prog[\"win_size\"].value = kwargs[\"window_size\"]\n\n prog[\"clip_control\"].value = tuple(self.clip_control)\n prog[\"clip_value\"].value = tuple(self.clip_value)\n\n self.set_camera_matrices(prog, camera, **kwargs)\n set_lights_in_program(\n prog,\n kwargs[\"lights\"],\n kwargs[\"shadows_enabled\"],\n kwargs[\"ambient_strength\"],\n )\n set_material_properties(prog, self.material)\n self.receive_shadow(prog, **kwargs)\n return prog\n\n def render(self, camera, **kwargs):\n self._upload_buffers()\n prog = self._use_program(camera, **kwargs)\n self.vao.render(prog, moderngl.TRIANGLES, instances=self.n_instances)\n\n def render_positions(self, prog):\n if self.is_renderable:\n self._upload_buffers()\n\n prog[\"clip_control\"].value = tuple(self.clip_control)\n prog[\"clip_value\"].value = tuple(self.clip_value)\n\n self.vao.render(prog, moderngl.TRIANGLES, instances=self.n_instances)\n\n def _show_normals(self):\n \"\"\"Create and add normals at runtime\"\"\"\n vn = self.vertex_normals\n\n bounds = self.bounds\n diag = np.linalg.norm(bounds[:, 0] - bounds[:, 1])\n\n length = 0.005 * max(diag, 1) / self.scale\n vn = vn / np.linalg.norm(vn, axis=-1, keepdims=True) * length\n\n # Must import here because if we do it at the top we create a circular dependency.\n from aitviewer.renderables.arrows import Arrows\n\n positions = self.vertices\n self.normals_r = Arrows(\n positions,\n positions + vn,\n r_base=length / 10,\n r_head=2 * length / 10,\n p=0.25,\n name=\"Normals\",\n )\n self.normals_r.current_frame_id = self.current_frame_id\n self.add(self.normals_r)\n\n def gui(self, imgui):\n super(Meshes, self).gui(imgui)\n\n _, self.show_texture = imgui.checkbox(\n \"Render Texture##render_texture{}\".format(self.unique_name),\n self.show_texture,\n )\n _, self.norm_coloring = imgui.checkbox(\n \"Norm Coloring##norm_coloring{}\".format(self.unique_name),\n self.norm_coloring,\n )\n _, self.flat_shading = imgui.checkbox(\n \"Flat shading [F]##flat_shading{}\".format(self.unique_name),\n self.flat_shading,\n )\n _, self.draw_edges = imgui.checkbox(\"Draw edges [E]##draw_edges{}\".format(self.unique_name), self.draw_edges)\n _, self.draw_outline = imgui.checkbox(\n \"Draw outline##draw_outline{}\".format(self.unique_name), self.draw_outline\n )\n\n if self.normals_r is None:\n if imgui.button(\"Show Normals ##show_normals{}\".format(self.unique_name)):\n self._show_normals()\n\n def gui_context_menu(self, imgui, x: int, y: int):\n _, self.flat_shading = imgui.menu_item(\"Flat shading\", \"F\", selected=self.flat_shading, enabled=True)\n _, self.draw_edges = imgui.menu_item(\"Draw edges\", \"E\", selected=self.draw_edges, enabled=True)\n _, self.draw_outline = imgui.menu_item(\"Draw outline\", selected=self.draw_outline)\n\n imgui.spacing()\n imgui.separator()\n imgui.spacing()\n super().gui_context_menu(imgui, x, y)\n\n def gui_io(self, imgui):\n if imgui.button(\"Export OBJ##export_{}\".format(self.unique_name)):\n mesh = trimesh.Trimesh(vertices=self.current_vertices, faces=self.faces, process=False)\n mesh.export(\"../export/\" + self.name + \".obj\")\n\n def key_event(self, key, wnd_keys):\n if key == wnd_keys.F:\n self.flat_shading = not self.flat_shading\n elif key == wnd_keys.E:\n self.draw_edges = not self.draw_edges\n\n def update_frames(self, vertices, frames):\n self.vertices[frames] = vertices\n self.redraw()\n\n def add_frames(self, vertices):\n if len(vertices.shape) == 2:\n vertices = vertices[np.newaxis]\n self.vertices = np.append(self.vertices, vertices, axis=0)\n self.n_frames = max(self.n_frames, self.vertices.shape[0])\n\n def remove_frames(self, frames):\n self.vertices = np.delete(self.vertices, frames, axis=0)\n self.redraw()\n\n def export_usd(self, stage, usd_path: str, directory: str = None, verbose=False):\n name = f\"{self.name}_{self.uid:03}\".replace(\" \", \"_\")\n usd_path = f\"{usd_path}/{name}\"\n\n mesh = usd.add_mesh(stage, usd_path, self.name, self.vertices, self.faces, self.get_local_transform())\n if self.has_texture and not self.use_pickle_texture:\n # UVs.\n a_uv = UsdGeom.PrimvarsAPI(mesh).CreatePrimvar(\n \"st\", Sdf.ValueTypeNames.TexCoord2fArray, UsdGeom.Tokens.faceVarying\n )\n a_uv.Set(time=1, value=self.uv_coords[self.faces.flatten()])\n\n if not directory:\n texture_path = os.path.abspath(self.texture_path)\n else:\n texture_path = usd.copy_texture(self.texture_path, name, directory)\n usd.add_texture(stage, mesh, usd_path, texture_path)\n else:\n # NOTE: Per vertex and per face colors using usd displayColor are not currently\n # loaded by Blender. This code path can be enabled once support is there.\n if False:\n a_colors = mesh.GetDisplayColorAttr()\n if self._face_colors is not None:\n # Per face colors.\n if self._face_colors.shape[0] == 1:\n a_colors.Set(self._face_colors[0, :, :3].astype(np.float32))\n else:\n for i in range(self.n_frames):\n a_colors.Set(time=i + 1, value=self._face_colors[i, :, :3].astype(np.float32))\n elif self._vertex_colors is not None:\n # Per vertex colors.\n if self._vertex_colors.shape[0] == 1:\n a_colors.Set(self._vertex_colors[0, :, :3].astype(np.float32))\n else:\n for i in range(self.n_frames):\n a_colors.Set(time=i + 1, value=self._vertex_colors[i, :, :3].astype(np.float32))\n else:\n # Uniform color.\n a_colors.Set(np.array(self.color, np.float32)[:3])\n else:\n usd.add_color(stage, mesh, usd_path, self.color[:3])\n\n self._export_usd_recursively(stage, usd_path, directory, verbose)"},{"identifier":"Node","path":"aitviewer/scene/node.py","snippet":"class Node(object):\n \"\"\"Interface for nodes.\"\"\"\n\n def __init__(\n self,\n name=None,\n icon=None,\n position=None,\n rotation=None,\n scale=1.0,\n color=(0.5, 0.5, 0.5, 1.0),\n material=None,\n is_selectable=True,\n gui_affine=True,\n gui_material=True,\n enabled_frames=None,\n n_frames=1,\n ):\n \"\"\"\n :param name: Name of the node\n :param icon: Custom Node Icon using custom Icon font\n :param position: Starting position in the format (X,Y,Z) or np array of positions with shape (F, 3)\n :param rotation: Starting rotation in rotation matrix representation (3,3) or np array of rotations with shape (F, 3, 3)\n :param scale: Starting scale (scalar) or np array of scale values with shape (F)\n :param color: (R,G,B,A) 0-1 formatted color value.\n :param material: Object material properties. The color specified in the material will override node color\n :param is_selectable: If True the node is selectable when clicked on, otherwise the parent node will be selected.\n :param gui_affine: If True the node will have transform controls (position, rotation, scale) in the GUI.\n :param gui_material: If True the node will have material controls in the GUI.\n :param enabled_frames: Numpy array of boolean values, the object will be enabled only in frames where the value is True,\n the number of ones in the mask must match the number of frames of the object.\n :param n_frames: How many frames this renderable has.\n \"\"\"\n # Transform & Animation\n position = np.zeros(3, dtype=np.float32) if position is None else np.array(position, dtype=np.float32)\n rotation = np.eye(3, dtype=np.float32) if rotation is None else np.array(rotation, dtype=np.float32)\n\n self._positions = position if len(position.shape) != 1 else position[np.newaxis]\n self._rotations = rotation if len(rotation.shape) != 2 else rotation[np.newaxis]\n self._scales = (scale if isinstance(scale, np.ndarray) else np.array([scale])).astype(np.float32)\n\n n_positions = self._positions.shape[0]\n n_rotations = self._rotations.shape[0]\n n_scales = self._scales.shape[0]\n\n if n_frames > 1:\n assert n_positions == 1 or n_frames == n_positions, (\n f\"Number of position frames\" f\" ({n_positions}) must be 1 or match number of Node frames {n_frames}\"\n )\n assert n_rotations == 1 or n_frames == n_rotations, (\n f\"Number of rotations frames\" f\" ({n_rotations}) must be 1 or match number of Node frames {n_frames}\"\n )\n assert n_scales == 1 or n_frames == n_scales, (\n f\"Number of scales frames\" f\" ({n_scales}) must be 1 or match number of Node frames {n_frames}\"\n )\n else:\n n_frames = max(n_positions, n_rotations, n_scales)\n assert (\n (n_positions == 1 or n_positions == n_frames)\n and (n_rotations == 1 or n_rotations == n_frames)\n and (n_scales == 1 or n_scales == n_frames)\n ), (\n f\"Number of position\"\n f\"({n_positions}), rotation ({n_rotations}) and scale ({n_scales})\"\n \"frames must be 1 or match.\"\n )\n\n # Frames\n self._n_frames = n_frames\n self._current_frame_id = 0\n self.model_matrix = self.get_local_transform()\n self._enabled_frames = enabled_frames\n if self._enabled_frames is not None:\n assert np.count_nonzero(self._enabled_frames) == n_frames, (\n f\"Number of non-zero elements in enabled_frames\"\n f\" ({np.count_nonzero(self._enabled_frames)}) must match number of frames in sequence ({n_frames})\"\n )\n # Create an array that maps from the true frame id (counting also disabled frames) to the index of the\n # first existing frame in the sequence.\n self._enabled_frame_id = np.cumsum(self._enabled_frames) - 1\n\n # Stores the true frame id (counting also disabled frames) we use this to allow going\n # through both enabled and disabled frames from the GUI.\n self._internal_frame_id = 0\n\n # Material\n self.material = Material(color=color) if material is None else material\n\n # Renderable Attributes\n self.is_renderable = False\n self.backface_culling = True\n self.backface_fragmap = False\n self.draw_outline = False\n\n # Flags to enable rendering passes\n self.cast_shadow = False\n self.depth_prepass = False\n self.fragmap = False\n self.outline = False\n\n # Programs for render passes. Subclasses are responsible for setting these.\n self.depth_only_program = None # Required for depth_prepass and cast_shadow passes\n self.fragmap_program = None # Required for fragmap pass\n self.outline_program = None # Required for outline pass\n\n # GUI\n self.name = name if name is not None else type(self).__name__\n self.uid = C.next_gui_id()\n self.unique_name = self.name + \"{}\".format(self.uid)\n self.icon = icon if icon is not None else \"\\u0082\"\n self._enabled = True\n self._expanded = False\n self.gui_controls = {\n \"affine\": {\n \"fn\": self.gui_affine,\n \"icon\": \"\\u009b\",\n \"is_visible\": gui_affine,\n },\n \"material\": {\n \"fn\": self.gui_material,\n \"icon\": \"\\u0088\",\n \"is_visible\": gui_material,\n },\n \"animation\": {\n \"fn\": self.gui_animation,\n \"icon\": \"\\u0098\",\n \"is_visible\": (lambda: self._n_frames > 1)(),\n },\n \"io\": {\n \"fn\": self.gui_io,\n \"icon\": \"\\u009a\",\n \"is_visible\": (lambda: self.gui_io.__func__ is not Node.gui_io)(),\n },\n }\n self.gui_modes = {\"view\": {\"title\": \" View\", \"fn\": self.gui_mode_view, \"icon\": \"\\u0099\"}}\n self._selected_mode = \"view\"\n self._show_in_hierarchy = True\n self.is_selectable = is_selectable\n self.export_usd_enabled = True\n self.export_usd_expanded = True\n\n self.nodes: List[Node] = []\n self.parent: Node = None\n\n # Selected Mode\n @property\n def selected_mode(self):\n return self._selected_mode\n\n @selected_mode.setter\n def selected_mode(self, selected_mode):\n self._selected_mode = selected_mode\n\n # Transform\n @property\n def position(self):\n idx = self.current_frame_id if self._positions.shape[0] > 1 else 0\n return self._positions[idx]\n\n @position.setter\n def position(self, position):\n idx = self.current_frame_id if self._positions.shape[0] > 1 else 0\n self._positions[idx] = np.array(position, dtype=np.float32).copy()\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\n\n @property\n def positions(self):\n return self._positions\n\n @positions.setter\n def positions(self, positions):\n self._positions = positions\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\n\n @property\n def rotation(self):\n idx = self.current_frame_id if self._rotations.shape[0] > 1 else 0\n return self._rotations[idx]\n\n @rotation.setter\n def rotation(self, rotation):\n idx = self.current_frame_id if self._rotations.shape[0] > 1 else 0\n self._rotations[idx] = rotation\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\n\n @property\n def rotations(self):\n return self._rotations\n\n @rotations.setter\n def rotations(self, rotations):\n self._rotations = rotations\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\n\n @property\n def scale(self):\n idx = self.current_frame_id if self._scales.shape[0] > 1 else 0\n return self._scales[idx]\n\n @scale.setter\n def scale(self, scale):\n idx = self.current_frame_id if self._scales.shape[0] > 1 else 0\n self._scales[idx] = scale\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\n\n @property\n def scales(self):\n return self._scales\n\n @scales.setter\n def scales(self, scales):\n self._scales = scales\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\n\n @staticmethod\n @lru_cache()\n def _compute_transform(pos, rot, scale):\n rotation = np.eye(4)\n rotation[:3, :3] = np.array(rot)\n\n trans = np.eye(4)\n trans[:3, 3] = np.array(pos)\n\n scale = np.diag([scale, scale, scale, 1])\n\n return (trans @ rotation @ scale).astype(\"f4\")\n\n def get_local_transform(self):\n \"\"\"Construct local transform as a 4x4 matrix from this node's position, orientation and scale.\"\"\"\n return self._compute_transform(tuple(self.position), tuple(map(tuple, self.rotation)), self.scale)\n\n def update_transform(self, parent_transform=None):\n \"\"\"Update the model matrix of this node and all of its descendants.\"\"\"\n if parent_transform is None:\n self.model_matrix = self.get_local_transform()\n else:\n self.model_matrix = parent_transform.astype(\"f4\") @ self.get_local_transform()\n\n for n in self.nodes:\n n.update_transform(self.model_matrix)\n\n @property\n def color(self):\n return self.material.color\n\n @color.setter\n def color(self, color):\n self.material.color = color\n\n @property\n def bounds(self):\n \"\"\"The bounds in the format ((x_min, x_max), (y_min, y_max), (z_min, z_max))\"\"\"\n return np.array([[0, 0], [0, 0], [0, 0]])\n\n @property\n def current_bounds(self):\n return np.array([[0, 0], [0, 0], [0, 0]])\n\n @property\n def current_center(self):\n return self.current_bounds.mean(-1)\n\n @property\n def center(self):\n return self.bounds.mean(-1)\n\n def get_local_bounds(self, points):\n if len(points.shape) == 2 and points.shape[-1] == 3:\n points = points[np.newaxis]\n assert len(points.shape) == 3\n\n # Compute min and max coordinates of the bounding box ignoring NaNs.\n val = np.array(\n [\n [np.nanmin(points[:, :, 0]), np.nanmax(points[:, :, 0])],\n [np.nanmin(points[:, :, 1]), np.nanmax(points[:, :, 1])],\n [np.nanmin(points[:, :, 2]), np.nanmax(points[:, :, 2])],\n ]\n )\n\n # If any of the elements is NaN return an empty bounding box.\n if np.isnan(val).any():\n return np.array([[0, 0], [0, 0], [0, 0]])\n else:\n return val\n\n def get_bounds(self, points):\n val = self.get_local_bounds(points)\n\n # Transform bounding box with the model matrix.\n val = (self.model_matrix @ np.vstack((val, np.array([1.0, 1.0]))))[:3]\n\n # If any of the elements is NaN return an empty bounding box.\n if np.isnan(val).any():\n return np.array([[0, 0], [0, 0], [0, 0]])\n else:\n return val\n\n @property\n def n_frames(self):\n return self._n_frames\n\n @n_frames.setter\n def n_frames(self, n_frames):\n self._n_frames = n_frames\n\n def __len__(self):\n return self.n_frames\n\n @property\n def current_frame_id(self):\n return self._current_frame_id\n\n @current_frame_id.setter\n def current_frame_id(self, frame_id):\n # Check if the frame changed.\n last_frame_id = self._current_frame_id if self._enabled_frames is None else self._internal_frame_id\n if self.n_frames == 1 or frame_id == last_frame_id:\n return\n\n self.on_before_frame_update()\n if self._enabled_frames is None:\n if frame_id < 0:\n self._current_frame_id = 0\n elif frame_id >= len(self):\n self._current_frame_id = len(self) - 1\n else:\n self._current_frame_id = frame_id\n else:\n # If an enabled_frames is present use it to get the current frame.\n if frame_id < 0:\n self._internal_frame_id = 0\n elif frame_id >= self._enabled_frames.shape[0]:\n self._internal_frame_id = self._enabled_frames.shape[0] - 1\n else:\n self._internal_frame_id = frame_id\n self._current_frame_id = self._enabled_frame_id[self._internal_frame_id]\n # Update enabled using the mask.\n self.enabled = self._enabled_frames[self._internal_frame_id]\n\n # Update frame id of all children nodes.\n for n in self.nodes:\n n.current_frame_id = self._current_frame_id\n\n self.on_frame_update()\n if self.parent and (self._positions.shape[0] > 1 or self._rotations.shape[0] > 1 or self._scales.shape[0] > 1):\n self.update_transform(self.parent.model_matrix)\n\n def next_frame(self):\n self.current_frame_id = self.current_frame_id + 1 if self.current_frame_id < len(self) - 1 else 0\n\n def previous_frame(self):\n self.current_frame_id = self.current_frame_id - 1 if self.current_frame_id > 0 else len(self) - 1\n\n def on_before_frame_update(self):\n \"\"\"Called when the current frame is about to change, 'self.current_frame_id' still has the id of the\n previous frame.\"\"\"\n pass\n\n def on_frame_update(self):\n \"\"\"Called when the current frame is changed.\"\"\"\n pass\n\n def add(self, *nodes, **kwargs):\n self._add_nodes(*nodes, **kwargs)\n\n def _add_node(self, n: \"Node\", show_in_hierarchy=True, expanded=False, enabled=True):\n \"\"\"\n Add a single node\n :param show_in_hierarchy: Whether to show the node in the scene hierarchy.\n :param expanded: Whether the node is initially expanded in the GUI.\n \"\"\"\n if n is None:\n return\n n._show_in_hierarchy = show_in_hierarchy\n n._expanded = expanded\n n._enabled = enabled if n._enabled_frames is None else n._enabled_frames[n.current_frame_id]\n self.nodes.append(n)\n n.parent = self\n n.update_transform(self.model_matrix)\n\n def _add_nodes(self, *nodes, **kwargs):\n \"\"\"Add multiple nodes\"\"\"\n for n in nodes:\n self._add_node(n, **kwargs)\n\n def remove(self, *nodes):\n for n in nodes:\n n.release()\n try:\n self.nodes.remove(n)\n except:\n pass\n\n @property\n def show_in_hierarchy(self):\n return self._show_in_hierarchy\n\n @property\n def enabled(self):\n return self._enabled\n\n @enabled.setter\n def enabled(self, enabled):\n self._enabled = enabled\n\n @property\n def expanded(self):\n return self._expanded\n\n @expanded.setter\n def expanded(self, expanded):\n self._expanded = expanded\n\n def is_transparent(self):\n \"\"\"\n Returns true if the object is transparent and should thus be sorted when rendering.\n Subclassess that use a different color should implement this method to be rendered correctly when transparent.\n \"\"\"\n return self.material.color[3] < 1.0\n\n def gui(self, imgui):\n \"\"\"\n Render GUI for custom node properties and controls. Implementation optional.\n Elements rendered here will show up in the scene hierarchy\n :param imgui: imgui context.\n See https://pyimgui.readthedocs.io/en/latest/reference/imgui.core.html for available elements to render\n \"\"\"\n pass\n\n def gui_modes(self, imgui):\n \"\"\"Render GUI with toolbar (tools) for this particular node\"\"\"\n\n def gui_animation(self, imgui):\n \"\"\"Render GUI for animation related settings\"\"\"\n\n if self._enabled_frames is None:\n if self.n_frames > 1:\n u, fid = imgui.slider_int(\n \"Frame##r_{}\".format(self.unique_name),\n self.current_frame_id,\n min_value=0,\n max_value=self.n_frames - 1,\n )\n if u:\n self.current_frame_id = fid\n else:\n u, fid = imgui.slider_int(\n \"Frame##r_{}\".format(self.unique_name),\n self._internal_frame_id,\n min_value=0,\n max_value=self._enabled_frames.shape[0] - 1,\n )\n if u:\n self.current_frame_id = fid\n\n def gui_affine(self, imgui):\n \"\"\"Render GUI for affine transformations\"\"\"\n # Position controls\n up, pos = imgui.drag_float3(\n \"Position##pos{}\".format(self.unique_name),\n *self.position,\n 1e-2,\n format=\"%.2f\",\n )\n if up:\n self.position = pos\n\n # Rotation controls\n euler_angles = rot2euler_numpy(self.rotation[np.newaxis], degrees=True)[0]\n ur, euler_angles = imgui.drag_float3(\n \"Rotation##pos{}\".format(self.unique_name),\n *euler_angles,\n 1e-2,\n format=\"%.2f\",\n )\n if ur:\n self.rotation = euler2rot_numpy(np.array(euler_angles)[np.newaxis], degrees=True)[0]\n\n # Scale controls\n us, scale = imgui.drag_float(\n \"Scale##scale{}\".format(self.unique_name),\n self.scale,\n 1e-2,\n min_value=0.001,\n max_value=100.0,\n format=\"%.3f\",\n )\n if us:\n self.scale = scale\n\n def gui_material(self, imgui):\n \"\"\"Render GUI with material properties\"\"\"\n\n # Color Control\n uc, color = imgui.color_edit4(\"Color##color{}'\".format(self.unique_name), *self.material.color)\n if uc:\n self.color = color\n\n # Diffuse\n ud, diffuse = imgui.slider_float(\n \"Diffuse##diffuse{}\".format(self.unique_name),\n self.material.diffuse,\n 0.0,\n 1.0,\n \"%.2f\",\n )\n if ud:\n self.material.diffuse = diffuse\n\n # Ambient\n ua, ambient = imgui.slider_float(\n \"Ambient##ambient{}\".format(self.unique_name),\n self.material.ambient,\n 0.0,\n 1.0,\n \"%.2f\",\n )\n if ua:\n self.material.ambient = ambient\n\n def gui_io(self, imgui):\n \"\"\"Render GUI for import/export\"\"\"\n pass\n\n def gui_mode_view(self, imgui):\n \"\"\"Render custom GUI for view mode\"\"\"\n pass\n\n def gui_context_menu(self, imgui, x: int, y: int):\n _, self.enabled = imgui.checkbox(\"Enabled\", self.enabled)\n if any([n._show_in_hierarchy for n in self.nodes]):\n imgui.spacing()\n imgui.separator()\n imgui.spacing()\n for n in self.nodes:\n if not n._show_in_hierarchy:\n continue\n if imgui.begin_menu(f\"{n.name}##{n.uid}\"):\n n.gui_context_menu(imgui, x, y)\n imgui.end_menu()\n\n # Renderable\n @staticmethod\n def once(func):\n def _decorator(self, *args, **kwargs):\n if self.is_renderable:\n return\n else:\n func(self, *args, **kwargs)\n self.is_renderable = True\n\n return _decorator\n\n def make_renderable(self, ctx):\n \"\"\"\n Prepares this object for rendering. This function must be called before `render` is used.\n :param ctx: The moderngl context.\n \"\"\"\n pass\n\n def render(self, camera, position=None, rotation=None, **kwargs):\n \"\"\"Render the current frame in this sequence.\"\"\"\n pass\n\n def render_positions(self, prog):\n \"\"\"\n Render with a VAO with only positions bound, used for shadow mapping, fragmap and depth prepass.\n \"\"\"\n pass\n\n def redraw(self, **kwargs):\n \"\"\"Perform update and redraw operations. Push to the GPU when finished. Recursively redraw child nodes\"\"\"\n for n in self.nodes:\n n.redraw(**kwargs)\n\n def set_camera_matrices(self, prog, camera, **kwargs):\n \"\"\"Set the model view projection matrix in the given program.\"\"\"\n # Transpose because np is row-major but OpenGL expects column-major.\n prog[\"model_matrix\"].write(self.model_matrix.T.astype(\"f4\").tobytes())\n prog[\"view_projection_matrix\"].write(camera.get_view_projection_matrix().T.astype(\"f4\").tobytes())\n\n def receive_shadow(self, program, **kwargs):\n \"\"\"\n Call this function if the renderable is to receive shadows.\n :param program: The shader program that can shade with shadows.\n :param kwargs: The render kwargs.\n \"\"\"\n if kwargs.get(\"shadows_enabled\", False):\n lights = kwargs[\"lights\"]\n\n for i, light in enumerate(lights):\n if light.shadow_enabled and light.shadow_map:\n light_matrix = light.mvp() @ self.model_matrix\n program[f\"dirLights[{i}].matrix\"].write(light_matrix.T.tobytes())\n\n # Bind shadowmap to slot i + 1, we reserve slot 0 for the mesh texture\n # and use slots 1 to (#lights + 1) for shadow maps\n light.shadow_map.use(location=i + 1)\n\n # Set sampler uniforms\n uniform = program[f\"shadow_maps\"]\n uniform.value = 1 if uniform.array_length == 1 else [*range(1, len(lights) + 1)]\n\n def render_shadowmap(self, light_matrix):\n if not self.cast_shadow or self.depth_only_program is None or self.color[3] == 0.0:\n return\n\n prog = self.depth_only_program\n prog[\"model_matrix\"].write(self.model_matrix.T.tobytes())\n prog[\"view_projection_matrix\"].write(light_matrix.T.tobytes())\n\n self.render_positions(prog)\n\n def render_fragmap(self, ctx, camera, uid=None):\n if not self.fragmap or self.fragmap_program is None:\n return\n\n # Transpose because np is row-major but OpenGL expects column-major.\n prog = self.fragmap_program\n self.set_camera_matrices(prog, camera)\n\n # Render with the specified object uid, if None use the node uid instead.\n prog[\"obj_id\"] = uid or self.uid\n\n if self.backface_culling or self.backface_fragmap:\n ctx.enable(moderngl.CULL_FACE)\n else:\n ctx.disable(moderngl.CULL_FACE)\n\n # If backface_fragmap is enabled for this node only render backfaces\n if self.backface_fragmap:\n ctx.cull_face = \"front\"\n\n self.render_positions(prog)\n\n # Restore cull face to back\n if self.backface_fragmap:\n ctx.cull_face = \"back\"\n\n def render_depth_prepass(self, camera, **kwargs):\n if not self.depth_prepass or self.depth_only_program is None:\n return\n\n prog = self.depth_only_program\n self.set_camera_matrices(prog, camera)\n self.render_positions(prog)\n\n def render_outline(self, ctx, camera):\n if self.outline and self.outline_program is not None:\n prog = self.outline_program\n self.set_camera_matrices(prog, camera)\n\n if self.backface_culling:\n ctx.enable(moderngl.CULL_FACE)\n else:\n ctx.disable(moderngl.CULL_FACE)\n self.render_positions(prog)\n\n # Render children node recursively.\n for n in self.nodes:\n n.render_outline(ctx, camera)\n\n def release(self):\n \"\"\"\n Release all OpenGL resources used by this node and any of its children. Subclasses that instantiate OpenGL\n objects should implement this method with '@hooked' to avoid leaking resources.\n \"\"\"\n for n in self.nodes:\n n.release()\n\n def on_selection(self, node, instance_id, tri_id):\n \"\"\"\n Called when the node is selected\n\n :param node: the node which was clicked (can be None if the selection wasn't a mouse event)\n :param instance_id: the id of the instance that was clicked, 0 if the object is not instanced\n (can be None if the selection wasn't a mouse event)\n :param tri_id: the id of the triangle that was clicked from the 'node' mesh\n (can be None if the selection wasn't a mouse event)\n \"\"\"\n pass\n\n def key_event(self, key, wnd_keys):\n \"\"\"\n Handle shortcut key presses (if you are the selected object)\n \"\"\"\n pass\n\n def update_frames(self, *args, **kwargs):\n pass\n\n def add_frames(self, *args, **kwargs):\n pass\n\n def remove_frames(self, *args, **kwargs):\n pass\n\n def _export_usd_recursively(self, stage, usd_path, directory, verbose):\n if verbose:\n print(usd_path)\n for n in self.nodes:\n if n.export_usd_enabled:\n n.export_usd(stage, usd_path, directory, verbose)\n\n def export_usd(self, stage, usd_path: str, directory: str = None, verbose=False):\n \"\"\"\n Export the node into an USD file. Nodes that implement this method should use\n recursively call this for every children that should also be exported.\n\n :param stage: an object of type Usd.Stage into which to export the node\n :param usd_path: the path of the parent object in the USD file scene hierarchy.\n \"\"\"\n from pxr import Gf, UsdGeom\n\n usd_path = f\"{usd_path}/{self.name.replace(' ', '_')}_{self.uid:03}\"\n\n # Transform.\n xform = UsdGeom.Xform.Define(stage, usd_path)\n a_xform = xform.AddTransformOp()\n a_xform.Set(Gf.Matrix4d(self.get_local_transform().astype(np.float64).T))\n\n self._export_usd_recursively(stage, usd_path, directory, verbose)"},{"identifier":"hooked","path":"aitviewer/utils/decorators.py","snippet":"class hooked:\n def __init__(self, fn):\n self.fn = fn\n\n def __set_name__(self, owner, name):\n func = self.fn\n\n def _decorator(self, *args, **kwargs):\n super_obj = super(owner, self)\n super_fn = getattr(super_obj, func.__name__)\n super_fn(*args, **kwargs)\n return func(self, *args, **kwargs)\n\n setattr(owner, name, _decorator)\n\n def __call__(self):\n assert (\n False\n ), \"@hooked decorator object should never be called directly. This can happen if you apply this decorator to a function that is not a method.\""}],"string":"[\n {\n \"identifier\": \"BoundingBoxes\",\n \"path\": \"aitviewer/renderables/bounding_boxes.py\",\n \"snippet\": \"class BoundingBoxes(Node):\\n \\\"\\\"\\\"\\n Draw bounding boxes.\\n \\\"\\\"\\\"\\n\\n def __init__(self, vertices, thickness=0.005, color=(0.0, 0.0, 1.0, 1.0), **kwargs):\\n \\\"\\\"\\\"\\n Initializer.\\n :param vertices: Set of 3D coordinates as a np array of shape (N, 8, 3). The vertices will be connected in the\\n following way: 0-1-2-3-0 (bottom) 4-5-6-7-4 (top) 0-4 1-5 2-6 3-7 (vertical connections between bottom\\n and top).\\n :param thickness: Line thickness.\\n :param color: Color of the lines.\\n \\\"\\\"\\\"\\n if not isinstance(vertices, np.ndarray):\\n vertices = np.array(vertices)\\n if len(vertices.shape) == 2:\\n vertices = vertices[np.newaxis]\\n else:\\n assert len(vertices.shape) == 3\\n assert vertices.shape[1] == 8\\n super(BoundingBoxes, self).__init__(n_frames=len(vertices), color=color, **kwargs)\\n\\n self.vertices = vertices\\n\\n self.lines = Lines(\\n lines=self._get_line_coords(),\\n mode=\\\"lines\\\",\\n r_base=thickness,\\n color=self.color,\\n cast_shadow=False,\\n )\\n self.spheres = Spheres(positions=self.vertices, radius=thickness, color=self.color, cast_shadow=False)\\n self._add_nodes(self.lines, self.spheres, show_in_hierarchy=False)\\n\\n @property\\n def bounds(self):\\n return self.get_bounds(self.vertices)\\n\\n @property\\n def current_bounds(self):\\n return self.get_bounds(self.vertices[self.current_frame_id])\\n\\n @staticmethod\\n def from_min_max_diagonal(v_min, v_max, **kwargs):\\n \\\"\\\"\\\"\\n Create an axis-aligned bounding box from the 3D diagonal.\\n :param v_min: np array of shape (N, 3).\\n :param v_max: np array of shape (N, 3).\\n :return: BoundingBoxes corresponding to the given diagonals.\\n \\\"\\\"\\\"\\n vertices = np.zeros((v_min.shape[0], 8, 3), dtype=v_min.dtype)\\n vertices[:, 0:4] = v_min[:, np.newaxis]\\n vertices[:, 1, 0] = v_max[:, 0]\\n vertices[:, 2, 0:2] = v_max[:, 0:2]\\n vertices[:, 3, 1] = v_max[:, 1]\\n\\n vertices[:, 4:] = v_max[:, np.newaxis]\\n vertices[:, 4, 0:2] = v_min[:, 0:2]\\n vertices[:, 7, 0] = v_min[:, 0]\\n vertices[:, 5, 1] = v_min[:, 1]\\n\\n return BoundingBoxes(vertices, **kwargs)\\n\\n def _get_line_coords(self):\\n lines = np.zeros((self.n_frames, 12 * 2, 3), dtype=self.vertices.dtype)\\n\\n # Bottom 0-1-2-3-0.\\n lines[:, 0:2] = self.vertices[:, 0:2]\\n lines[:, 2:4] = self.vertices[:, 1:3]\\n lines[:, 4:6] = self.vertices[:, 2:4]\\n lines[:, 6:8] = self.vertices[:, [3, 0]]\\n\\n # Top 4-5-6-7-4.\\n lines[:, 8:10] = self.vertices[:, 4:6]\\n lines[:, 10:12] = self.vertices[:, 5:7]\\n lines[:, 12:14] = self.vertices[:, 6:8]\\n lines[:, 14:16] = self.vertices[:, [7, 4]]\\n\\n # Vertical Connections.\\n lines[:, 16:18] = self.vertices[:, [0, 4]]\\n lines[:, 18:20] = self.vertices[:, [1, 5]]\\n lines[:, 20:22] = self.vertices[:, [2, 6]]\\n lines[:, 22:24] = self.vertices[:, [3, 7]]\\n\\n return lines\\n\\n @Node.color.setter\\n def color(self, color):\\n self.material.color = color\\n self.lines.color = color\\n self.spheres.color = color\"\n },\n {\n \"identifier\": \"Lines\",\n \"path\": \"aitviewer/renderables/lines.py\",\n \"snippet\": \"class Lines(Node):\\n \\\"\\\"\\\"Render lines as cylinders or cones. Can render approx. 600k lines at 40 fps.\\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n lines,\\n r_base=0.01,\\n r_tip=None,\\n color=(0.0, 0.0, 1.0, 1.0),\\n mode=\\\"line_strip\\\",\\n cast_shadow=True,\\n **kwargs,\\n ):\\n \\\"\\\"\\\"\\n Initializer.\\n :param lines: Set of 3D coordinates as a np array of shape (F, L, 3) or (L, 3).\\n :param r_base: Thickness of the line.\\n :param r_tip: If set, the thickness of the line will taper from r_base to r_tip. If set to 0.0 it will create\\n a proper cone.\\n :param color: Color of the line (4-tuple) or array of color (N_LINES, 4), one for each line.\\n :param mode: 'lines' or 'line_strip'.\\n 'lines': a line is drawn from point 0 to 1, from 2 to 3, and so on, number of lines is L / 2.\\n 'line_strip': a line is drawn between all adjacent points, 0 to 1, 1 to 2 and so on, number of lines is L - 1.\\n :param cast_shadow: If True the mesh casts a shadow on other objects.\\n \\\"\\\"\\\"\\n if len(lines.shape) == 2:\\n lines = lines[np.newaxis]\\n assert len(lines.shape) == 3\\n assert mode == \\\"lines\\\" or mode == \\\"line_strip\\\"\\n if mode == \\\"lines\\\":\\n assert lines.shape[1] % 2 == 0\\n\\n self._lines = lines\\n self.mode = mode\\n self.r_base = r_base\\n self.r_tip = r_tip if r_tip is not None else r_base\\n\\n self.vertices, self.faces = self.get_mesh()\\n self.n_lines = self.lines.shape[1] // 2 if mode == \\\"lines\\\" else self.lines.shape[1] - 1\\n\\n # Define a default material in case there is None.\\n if isinstance(color, tuple) or len(color.shape) == 1:\\n kwargs[\\\"material\\\"] = kwargs.get(\\\"material\\\", Material(color=color, ambient=0.2))\\n self.line_colors = kwargs[\\\"material\\\"].color\\n else:\\n assert (\\n color.shape[1] == 4 and color.shape[0] == self.n_lines\\n ), \\\"Color must be a tuple of 4 values or a numpy array of shape (N_LINES, 4)\\\"\\n self.line_colors = color\\n\\n super(Lines, self).__init__(n_frames=self.lines.shape[0], **kwargs)\\n\\n self._need_upload = True\\n self.draw_edges = False\\n\\n # Render passes.\\n self.outline = True\\n self.fragmap = True\\n self.depth_prepass = True\\n self.cast_shadow = cast_shadow\\n\\n @property\\n def bounds(self):\\n bounds = self.get_bounds(self.lines)\\n r = max(self.r_base, self.r_tip)\\n bounds[:, 0] -= r\\n bounds[:, 1] += r\\n return bounds\\n\\n @property\\n def current_bounds(self):\\n bounds = self.get_bounds(self.current_lines)\\n r = max(self.r_base, self.r_tip)\\n bounds[:, 0] -= r\\n bounds[:, 1] += r\\n return bounds\\n\\n @property\\n def lines(self):\\n return self._lines\\n\\n @lines.setter\\n def lines(self, value):\\n self._lines = value if len(value.shape) == 3 else value[np.newaxis]\\n self.n_frames = self.lines.shape[0]\\n self.redraw()\\n\\n @property\\n def current_lines(self):\\n idx = self.current_frame_id if self._lines.shape[0] > 1 else 0\\n return self._lines[idx]\\n\\n @current_lines.setter\\n def current_lines(self, lines):\\n assert len(lines.shape) == 2\\n idx = self.current_frame_id if self._lines.shape[0] > 1 else 0\\n self._lines[idx] = lines\\n self.redraw()\\n\\n @Node.color.setter\\n def color(self, color):\\n self.material.color = color\\n self.line_colors = color\\n self.redraw()\\n\\n @property\\n def line_colors(self):\\n if len(self._line_colors.shape) == 1:\\n t = np.tile(np.array(self._line_colors), (self.n_lines, 1))\\n return t\\n else:\\n return self._line_colors\\n\\n @line_colors.setter\\n def line_colors(self, color):\\n if isinstance(color, tuple):\\n color = np.array(color)\\n self._line_colors = color\\n self.redraw()\\n\\n def on_frame_update(self):\\n self.redraw()\\n\\n def redraw(self, **kwargs):\\n self._need_upload = True\\n\\n @Node.once\\n def make_renderable(self, ctx: moderngl.Context):\\n self.prog = get_lines_instanced_program()\\n\\n vs_path = \\\"lines_instanced_positions.vs.glsl\\\"\\n self.outline_program = get_outline_program(vs_path)\\n self.depth_only_program = get_depth_only_program(vs_path)\\n self.fragmap_program = get_fragmap_program(vs_path)\\n\\n self.vbo_vertices = ctx.buffer(self.vertices.astype(\\\"f4\\\").tobytes())\\n self.vbo_indices = ctx.buffer(self.faces.astype(\\\"i4\\\").tobytes())\\n self.vbo_instance_base = ctx.buffer(reserve=self.n_lines * 12)\\n self.vbo_instance_tip = ctx.buffer(reserve=self.n_lines * 12)\\n self.vbo_instance_color = ctx.buffer(reserve=self.n_lines * 16)\\n\\n self.vao = VAO()\\n self.vao.buffer(self.vbo_vertices, \\\"3f4\\\", \\\"in_position\\\")\\n self.vao.buffer(self.vbo_instance_base, \\\"3f4/i\\\", \\\"instance_base\\\")\\n self.vao.buffer(self.vbo_instance_tip, \\\"3f4/i\\\", \\\"instance_tip\\\")\\n self.vao.buffer(self.vbo_instance_color, \\\"4f4/i\\\", \\\"instance_color\\\")\\n self.vao.index_buffer(self.vbo_indices)\\n\\n def _upload_buffers(self):\\n if not self.is_renderable or not self._need_upload:\\n return\\n self._need_upload = False\\n\\n lines = self.current_lines\\n if self.mode == \\\"lines\\\":\\n v0s = lines[::2]\\n v1s = lines[1::2]\\n else:\\n v0s = lines[:-1]\\n v1s = lines[1:]\\n\\n self.vbo_instance_base.write(v0s.astype(\\\"f4\\\").tobytes())\\n self.vbo_instance_tip.write(v1s.astype(\\\"f4\\\").tobytes())\\n\\n if len(self._line_colors.shape) > 1:\\n self.vbo_instance_color.write(self._line_colors.astype(\\\"f4\\\").tobytes())\\n\\n def render(self, camera, **kwargs):\\n self._upload_buffers()\\n\\n prog = self.prog\\n prog[\\\"r_base\\\"] = self.r_base\\n prog[\\\"r_tip\\\"] = self.r_tip\\n if len(self._line_colors.shape) == 1:\\n prog[\\\"use_uniform_color\\\"] = True\\n prog[\\\"uniform_color\\\"] = tuple(self.color)\\n else:\\n prog[\\\"use_uniform_color\\\"] = False\\n prog[\\\"draw_edges\\\"].value = 1.0 if self.draw_edges else 0.0\\n prog[\\\"win_size\\\"].value = kwargs[\\\"window_size\\\"]\\n prog[\\\"clip_control\\\"].value = (0, 0, 0)\\n\\n self.set_camera_matrices(prog, camera, **kwargs)\\n set_lights_in_program(\\n prog,\\n kwargs[\\\"lights\\\"],\\n kwargs[\\\"shadows_enabled\\\"],\\n kwargs[\\\"ambient_strength\\\"],\\n )\\n set_material_properties(prog, self.material)\\n self.receive_shadow(prog, **kwargs)\\n self.vao.render(prog, moderngl.TRIANGLES, instances=self.n_lines)\\n\\n def render_positions(self, prog):\\n if self.is_renderable:\\n self._upload_buffers()\\n prog[\\\"r_base\\\"] = self.r_base\\n prog[\\\"r_tip\\\"] = self.r_tip\\n self.vao.render(prog, moderngl.TRIANGLES, instances=self.n_lines)\\n\\n def get_mesh(self):\\n v0s = np.array([[0, 0, 0]], np.float32)\\n v1s = np.array([[0, 0, 1]], np.float32)\\n\\n # If r_tip is below a certain threshold, we create a proper cone, i.e. with just a single vertex at the top.\\n if self.r_tip < 1e-5:\\n data = _create_cone_from_to(v0s, v1s, radius=1.0)\\n else:\\n data = _create_cylinder_from_to(v0s, v1s, radius1=1.0, radius2=1.0)\\n\\n return data[\\\"vertices\\\"][0], data[\\\"faces\\\"]\\n\\n @hooked\\n def release(self):\\n if self.is_renderable:\\n self.vao.release()\\n\\n def update_frames(self, lines, frames):\\n self.lines[frames] = lines\\n self.redraw()\\n\\n def add_frames(self, lines):\\n if len(lines.shape) == 2:\\n lines = lines[np.newaxis]\\n self.lines = np.append(self.lines, lines, axis=0)\\n\\n def remove_frames(self, frames):\\n self.lines = np.delete(self.lines, frames, axis=0)\\n self.redraw()\\n\\n def export_usd(self, stage, usd_path: str, directory: str = None, verbose=False):\\n name = f\\\"{self.name}_{self.uid:03}\\\".replace(\\\" \\\", \\\"_\\\")\\n usd_path = f\\\"{usd_path}/{name}\\\"\\n\\n if self.mode == \\\"lines\\\":\\n v0s = self.lines[:, ::2]\\n v1s = self.lines[:, 1::2]\\n else:\\n v0s = self.lines[:, :-1]\\n v1s = self.lines[:, 1:]\\n\\n print(self.lines.shape)\\n print(v0s.shape)\\n\\n # Data is in the form of (F, N_LINES, 3), convert it to (F*N_LINES, 3)\\n v0s = np.reshape(v0s, (-1, 3))\\n v1s = np.reshape(v1s, (-1, 3))\\n\\n self.r_tip = self.r_base if self.r_tip is None else self.r_tip\\n\\n # If r_tip is below a certain threshold, we create a proper cone, i.e. with just a single vertex at the top.\\n if self.r_tip < 10e-6:\\n data = _create_cone_from_to(v0s, v1s, radius=self.r_base)\\n else:\\n data = _create_cylinder_from_to(v0s, v1s, radius1=self.r_base, radius2=self.r_tip)\\n\\n L = self.n_lines\\n V = data[\\\"vertices\\\"].shape[1]\\n\\n vertices = data[\\\"vertices\\\"].reshape((self.n_frames, -1, 3))\\n faces = data[\\\"faces\\\"]\\n\\n fs = faces[np.newaxis].repeat(L, 0).reshape((L, -1))\\n offsets = (np.arange(L) * V).reshape((L, 1))\\n faces = (fs + offsets).reshape((-1, 3))\\n\\n mesh = usd.add_mesh(stage, usd_path, self.name, vertices, faces, self.get_local_transform())\\n usd.add_color(stage, mesh, usd_path, self.color[:3])\\n\\n self._export_usd_recursively(stage, usd_path, directory, verbose)\"\n },\n {\n \"identifier\": \"Meshes\",\n \"path\": \"aitviewer/renderables/meshes.py\",\n \"snippet\": \"class Meshes(Node):\\n \\\"\\\"\\\"A sequence of triangle meshes. This assumes that the mesh topology is fixed over the sequence.\\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n vertices,\\n faces,\\n vertex_normals=None,\\n face_normals=None,\\n vertex_colors=None,\\n face_colors=None,\\n uv_coords=None,\\n path_to_texture=None,\\n cast_shadow=True,\\n pickable=True,\\n flat_shading=False,\\n draw_edges=False,\\n draw_outline=False,\\n instance_transforms=None,\\n icon=\\\"\\\\u008d\\\",\\n **kwargs,\\n ):\\n \\\"\\\"\\\"\\n Initializer.\\n :param vertices: A np array of shape (N, V, 3) or (V, 3).\\n :param faces: A np array of shape (F, 3).\\n :param vertex_normals: A np array of shape (N, V, 3). If not provided, the vertex normals will be computed,\\n which incurs some overhead.\\n :param face_normals: A np array of shape (N, F, 3). If not provided, the face normals will be computed, which\\n incurs some overhead.\\n :param vertex_colors: A np array of shape (N, V, 4) overriding the uniform color.\\n :param face_colors: A np array of shape (N, F, 4) overriding the uniform or vertex colors.\\n :param uv_coords: A np array of shape (V, 2) if the mesh is to be textured.\\n :param path_to_texture: Path to an image file that serves as the texture.\\n :param cast_shadow: If True the mesh casts a shadow on other objects.\\n :param pickable: If True the mesh can be selected with a mouse click.\\n :param flat_shading: If True the each face of the mesh is shaded with a constant normal.\\n :param draw_edges: If True the normals the edges of the mesh is drawn on top of the mesh.\\n :param draw_outline: If true an outline is drawn around the mesh.\\n :instance_transforms: np array of size (N, I, 4, 4) or (I, 4, 4) or None. If not None, 'I' instances of\\n the same mesh will be rendered, each with its own transformation matrix.\\n \\\"\\\"\\\"\\n if len(vertices.shape) == 2 and vertices.shape[-1] == 3:\\n vertices = vertices[np.newaxis]\\n assert len(vertices.shape) == 3\\n assert len(faces.shape) == 2\\n n_frames = vertices.shape[0]\\n\\n # Instancing.\\n if instance_transforms is not None:\\n # Check shape of transforms.\\n if len(instance_transforms.shape) == 3:\\n instance_transforms = instance_transforms[np.newaxis]\\n assert len(instance_transforms.shape) == 4\\n\\n # Number of instance frames must match number of frames or be 1.\\n assert n_frames == 1 or instance_transforms.shape[0] == 1 or n_frames == instance_transforms.shape[0]\\n n_frames = max(n_frames, instance_transforms.shape[0])\\n\\n self._instance_transforms = instance_transforms\\n else:\\n self._instance_transforms = None\\n\\n super(Meshes, self).__init__(n_frames=n_frames, icon=icon, **kwargs)\\n\\n self._vertices = vertices\\n self._faces = faces.astype(np.int32)\\n\\n # Create these first because other setters can call redraw() which uses this fields.\\n self._face_colors = None\\n self._vertex_colors = None\\n self._has_transparent_vertex_or_face_colors = False\\n\\n def _maybe_unsqueeze(x):\\n return x[np.newaxis] if x is not None and x.ndim == 2 else x\\n\\n self._vertex_normals = _maybe_unsqueeze(vertex_normals)\\n self._face_normals = _maybe_unsqueeze(face_normals)\\n self.vertex_colors = _maybe_unsqueeze(vertex_colors)\\n self.face_colors = _maybe_unsqueeze(face_colors)\\n\\n # Texture handling.\\n self.has_texture = (uv_coords is not None) and (path_to_texture is not None)\\n self.uv_coords = uv_coords\\n self.texture_path = path_to_texture\\n\\n if self.has_texture:\\n self.use_pickle_texture = path_to_texture.endswith((\\\".pickle\\\", \\\"pkl\\\"))\\n if self.use_pickle_texture:\\n self.texture_image = pickle.load(open(path_to_texture, \\\"rb\\\"))\\n else:\\n self.texture_image = Image.open(path_to_texture).transpose(method=Image.FLIP_TOP_BOTTOM).convert(\\\"RGB\\\")\\n else:\\n self.texture_image = None\\n\\n # Enable rendering passes\\n self.cast_shadow = cast_shadow\\n self.fragmap = pickable\\n self.depth_prepass = True\\n self.outline = True\\n\\n # Misc.\\n self._flat_shading = flat_shading\\n self.draw_edges = draw_edges\\n self.draw_outline = draw_outline\\n self.show_texture = self.has_texture\\n self.norm_coloring = False\\n self.normals_r = None\\n self.need_upload = True\\n self._use_uniform_color = self._vertex_colors is None and self._face_colors is None\\n self._vertex_faces_sparse = trimesh.geometry.index_sparse(self._vertices.shape[1], self._faces)\\n\\n self.clip_control = np.array((0, 0, 0), np.int32)\\n self.clip_value = np.array((0, 0, 0), np.float32)\\n\\n @classmethod\\n def instanced(cls, *args, positions=None, rotations=None, scales=None, **kwargs):\\n \\\"\\\"\\\"\\n Creates and returns an instanced sequence of N frames and I instances.\\n Each instance will have its own position, rotation and scale.\\n :param positions: np array of size (N, I, 3) or (I, 3) or None.\\n :param rotations: np array of size (N, I, 3, 3) or (I, 3, 3) or None.\\n :param scales: np array of size (N, I) or (I) or None.\\n\\n *args, and **kwargs are forwarded to the Meshes constructor.\\n \\\"\\\"\\\"\\n assert positions is not None or rotations is not None or scales is not None\\n\\n n_instances = 0\\n n_frames = 0\\n\\n def check_array(a, dim):\\n nonlocal n_instances, n_frames\\n if a is not None:\\n if len(a.shape) == dim + 1:\\n a = a[np.newaxis]\\n n_frames = max(n_frames, a.shape[0])\\n n_instances = max(n_instances, a.shape[1])\\n return a\\n\\n positions = check_array(positions, 1)\\n rotations = check_array(rotations, 2)\\n scales = check_array(scales, 0)\\n\\n if positions is None:\\n positions = np.zeros((n_frames, n_instances, 3))\\n if rotations is None:\\n rotations = np.zeros((n_frames, n_instances, 3, 3))\\n rotations[:, :] = np.eye(3)\\n if scales is None:\\n scales = np.ones((n_frames, n_instances))\\n\\n transforms = np.zeros((n_frames, n_instances, 4, 4))\\n transforms[:, :, :3, :3] = (rotations.reshape((-1, 9)) * scales.reshape((-1, 1))).reshape(\\n (n_frames, n_instances, 3, 3)\\n )\\n transforms[:, :, :3, 3] = positions\\n transforms[:, :, 3, 3] = 1.0\\n return cls(*args, **kwargs, instance_transforms=transforms)\\n\\n @classmethod\\n def from_file(cls, file, **kwargs):\\n \\\"\\\"\\\"\\n Loads a mesh from a file that can be loaded by trimesh (e.g. \\\".obj\\\", \\\".ply\\\", ...)\\n See trimesh.available_formats() for a complete list.\\n \\\"\\\"\\\"\\n mesh = trimesh.load(file)\\n\\n uvs = None\\n vertex_colors = None\\n face_colors = None\\n if isinstance(mesh.visual, trimesh.visual.ColorVisuals):\\n if mesh.visual.kind == \\\"vertex_colors\\\":\\n vertex_colors = mesh.visual.vertex_colors\\n elif mesh.visual.kind == \\\"face_colors\\\":\\n face_colors = mesh.visual.vertex_colors\\n elif isinstance(mesh.visual, trimesh.visual.TextureVisuals):\\n uvs = mesh.visual.uv\\n\\n return Meshes(\\n mesh.vertices,\\n mesh.faces,\\n vertex_normals=mesh.vertex_normals,\\n face_colors=face_colors,\\n vertex_colors=vertex_colors,\\n uv_coords=uvs,\\n **kwargs,\\n )\\n\\n @property\\n def vertices(self):\\n return self._vertices\\n\\n @vertices.setter\\n def vertices(self, vertices):\\n if len(vertices.shape) == 2:\\n vertices = vertices[np.newaxis]\\n\\n # Update vertices and redraw\\n self._vertices = vertices\\n self.n_frames = len(vertices)\\n\\n # If vertex or face normals were supplied, they are no longer valid.\\n self._vertex_normals = None\\n self._face_normals = None\\n\\n # Must clear all LRU caches where the vertices are used.\\n self.compute_vertex_and_face_normals.cache_clear()\\n\\n self.redraw()\\n\\n @property\\n def faces(self):\\n return self._faces\\n\\n @faces.setter\\n def faces(self, f):\\n self._faces = f.astype(np.int32)\\n self._vertex_faces_sparse = trimesh.geometry.index_sparse(self.vertices.shape[1], self._faces)\\n\\n @property\\n def current_vertices(self):\\n idx = self.current_frame_id if self.vertices.shape[0] > 1 else 0\\n return self.vertices[idx]\\n\\n @current_vertices.setter\\n def current_vertices(self, vertices):\\n idx = self.current_frame_id if self.vertices.shape[0] > 1 else 0\\n self._vertices[idx] = vertices\\n self.compute_vertex_and_face_normals.cache_clear()\\n self.redraw()\\n\\n @property\\n def current_transformed_vertices(self):\\n return (self.current_vertices @ self.model_matrix[:3, :3].T) + self.model_matrix[:3, 3]\\n\\n @property\\n def transformed_vertices(self):\\n return (self.vertices @ self.model_matrix[:3, :3].T) + self.model_matrix[:3, 3]\\n\\n @property\\n def n_faces(self):\\n return self.faces.shape[0]\\n\\n @property\\n def n_vertices(self):\\n return self.vertices.shape[1]\\n\\n @property\\n def vertex_faces(self):\\n # To compute the normals we need to know a mapping from vertex ID to all faces that this vertex is part of.\\n # Because we are lazy we abuse trimesh to compute this for us. Not all vertices have the maximum degree, so\\n # this array is padded with -1 if necessary.\\n return trimesh.Trimesh(self.vertices[0], self.faces, process=False).vertex_faces\\n\\n @property\\n def vertex_normals(self):\\n \\\"\\\"\\\"Get or compute all vertex normals (this might take a while for long sequences).\\\"\\\"\\\"\\n if self._vertex_normals is None:\\n vertex_normals, _ = compute_vertex_and_face_normals_sparse(\\n self.vertices, self.faces, self._vertex_faces_sparse, normalize=True\\n )\\n self._vertex_normals = vertex_normals\\n return self._vertex_normals\\n\\n @property\\n def face_normals(self):\\n \\\"\\\"\\\"Get or compute all face normals (this might take a while for long sequences).\\\"\\\"\\\"\\n if self._face_normals is None:\\n _, face_normals = compute_vertex_and_face_normals_sparse(\\n self.vertices, self.faces, self._vertex_faces_sparse, normalize=True\\n )\\n self._face_normals = face_normals\\n return self._face_normals\\n\\n def vertex_normals_at(self, frame_id):\\n \\\"\\\"\\\"Get or compute the vertex normals at the given frame.\\\"\\\"\\\"\\n if self._vertex_normals is None:\\n vn, _ = self.compute_vertex_and_face_normals(frame_id, normalize=True)\\n else:\\n assert len(self._vertex_normals.shape) == 3, f\\\"Got shape {self._vertex_normals.shape}\\\"\\n vn = self._vertex_normals[frame_id]\\n return vn\\n\\n def face_normals_at(self, frame_id):\\n \\\"\\\"\\\"Get or compute the face normals at the given frame.\\\"\\\"\\\"\\n if self._face_normals is None:\\n _, fn = self.compute_vertex_and_face_normals(frame_id, normalize=True)\\n else:\\n assert len(self._face_normals.shape) == 3, f\\\"Got shape {self._face_normals.shape}\\\"\\n fn = self._face_normals[frame_id]\\n return fn\\n\\n @property\\n def vertex_colors(self):\\n if self._vertex_colors is None:\\n self._vertex_colors = np.full((self.n_frames, self.n_vertices, 4), self.material.color)\\n return self._vertex_colors\\n\\n @vertex_colors.setter\\n def vertex_colors(self, vertex_colors):\\n # If vertex_colors are None, we resort to the material color.\\n if vertex_colors is None:\\n self._vertex_colors = None\\n self._use_uniform_color = True\\n elif isinstance(vertex_colors, tuple) and len(vertex_colors) == 4:\\n self.vertex_colors = None\\n self._use_uniform_color = True\\n self.material.color = vertex_colors\\n else:\\n if len(vertex_colors.shape) == 2:\\n assert vertex_colors.shape[0] == self.n_vertices\\n vertex_colors = np.repeat(vertex_colors[np.newaxis], self.n_frames, axis=0)\\n assert len(vertex_colors.shape) == 3\\n self._vertex_colors = vertex_colors\\n self._use_uniform_color = False\\n self.redraw()\\n\\n @property\\n def current_vertex_colors(self):\\n if self._use_uniform_color:\\n return np.full((self.n_vertices, 4), self.material.color)\\n else:\\n idx = self.current_frame_id if self.vertex_colors.shape[0] > 1 else 0\\n return self.vertex_colors[idx]\\n\\n @property\\n def face_colors(self):\\n return self._face_colors\\n\\n @face_colors.setter\\n def face_colors(self, face_colors):\\n if face_colors is not None:\\n if len(face_colors.shape) == 2:\\n face_colors = face_colors[np.newaxis]\\n self._face_colors = face_colors\\n self._use_uniform_color = False\\n else:\\n self._face_colors = None\\n self.redraw()\\n\\n @property\\n def current_face_colors(self):\\n if self._use_uniform_color:\\n return np.full((self.n_faces, 4), self.material.color)\\n else:\\n idx = self.current_frame_id if self.face_colors.shape[0] > 1 else 0\\n return self.face_colors[idx]\\n\\n @Node.color.setter\\n def color(self, color):\\n self.material.color = color\\n\\n if self.face_colors is None:\\n self.vertex_colors = color\\n\\n @property\\n def flat_shading(self):\\n return self._flat_shading\\n\\n @flat_shading.setter\\n def flat_shading(self, flat_shading):\\n if self._flat_shading != flat_shading:\\n self._flat_shading = flat_shading\\n self.redraw()\\n\\n def closest_vertex_in_triangle(self, tri_id, point):\\n face_vertex_id = np.linalg.norm((self.current_vertices[self.faces[tri_id]] - point), axis=-1).argmin()\\n return self.faces[tri_id][face_vertex_id]\\n\\n def get_bc_coords_from_points(self, tri_id, points):\\n return points_to_barycentric(self.current_vertices[self.faces[[tri_id]]], points)[0]\\n\\n @lru_cache(2048)\\n def compute_vertex_and_face_normals(self, frame_id, normalize=False):\\n \\\"\\\"\\\"\\n Compute face and vertex normals for the given frame. We use an LRU cache since this is a potentially\\n expensive operation. This function exists because computing the normals on all frames can increase the\\n startup time of the viewer considerably.\\n\\n :param frame_id: On which frame to compute the normals.\\n :param normalize: Whether or not to normalize the normals. Not doing it is faster and the shaders typically\\n enforce unit length of normals anyway.\\n :return: The vertex and face normals as a np arrays of shape (V, 3) and (F, 3) respectively.\\n \\\"\\\"\\\"\\n vs = self.vertices[frame_id : frame_id + 1] if self.vertices.shape[0] > 1 else self.vertices\\n vn, fn = compute_vertex_and_face_normals_sparse(vs, self.faces, self._vertex_faces_sparse, normalize)\\n return vn.squeeze(0), fn.squeeze(0)\\n\\n @property\\n def bounds(self):\\n if self.instance_transforms is None:\\n return self.get_bounds(self.vertices)\\n else:\\n # Get bounds in local coordinates\\n bounds = self.get_local_bounds(self.vertices)\\n\\n # Transform bounds with instance transforms\\n min = np.append(bounds[:, 0], 1.0)\\n max = np.append(bounds[:, 1], 1.0)\\n transforms = self.instance_transforms.reshape((-1, 4, 4))\\n mins = transforms @ min\\n maxs = transforms @ max\\n\\n # Return bounds in world coordinates\\n return self.get_bounds(np.vstack((mins, maxs)))\\n\\n @property\\n def current_bounds(self):\\n if self.instance_transforms is None:\\n return self.get_bounds(self.current_vertices)\\n else:\\n # Get bounds in local coordinates\\n bounds = self.get_local_bounds(self.current_vertices)\\n\\n # Transform bounds with instance transforms\\n min = np.append(bounds[:, 0], 1.0)\\n max = np.append(bounds[:, 1], 1.0)\\n transforms = self.current_instance_transforms.reshape((-1, 4, 4))\\n mins = transforms @ min\\n maxs = transforms @ max\\n\\n # Return bounds in world coordinates\\n return self.get_bounds(np.vstack((mins[:, :3], maxs[:, :3])))\\n\\n def is_transparent(self):\\n return self.color[3] < 1.0 or self._has_transparent_vertex_or_face_colors\\n\\n def on_frame_update(self):\\n \\\"\\\"\\\"Called whenever a new frame must be displayed.\\\"\\\"\\\"\\n super().on_frame_update()\\n self.redraw()\\n\\n @property\\n def current_instance_transforms(self):\\n if self._instance_transforms is None:\\n return None\\n idx = self.current_frame_id if self._instance_transforms.shape[0] > 1 else 0\\n return self._instance_transforms[idx]\\n\\n @property\\n def instance_transforms(self):\\n return self._instance_transforms\\n\\n @instance_transforms.setter\\n def instance_transforms(self, instance_transforms):\\n assert self._instance_transforms.shape == instance_transforms\\n self._instance_transforms = instance_transforms\\n\\n @property\\n def n_instances(self):\\n if self._instance_transforms is None:\\n return 1\\n else:\\n return self._instance_transforms.shape[1]\\n\\n def _upload_buffers(self):\\n \\\"\\\"\\\"Upload the current frame data to the GPU for rendering.\\\"\\\"\\\"\\n if not self.is_renderable or not self._need_upload:\\n return\\n\\n self._need_upload = False\\n\\n # Write positions.\\n self.vbo_vertices.write(self.current_vertices.astype(\\\"f4\\\").tobytes())\\n\\n # Write normals.\\n if not self.flat_shading:\\n vertex_normals = self.vertex_normals_at(self.current_frame_id)\\n self.vbo_normals.write(vertex_normals.astype(\\\"f4\\\").tobytes())\\n\\n if self.face_colors is None:\\n # Write vertex colors.\\n self.vbo_colors.write(self.current_vertex_colors.astype(\\\"f4\\\").tobytes())\\n else:\\n # Write face colors.\\n\\n # Compute shape of 2D texture.\\n shape = (min(self.faces.shape[0], 8192), (self.faces.shape[0] + 8191) // 8192)\\n\\n # Write texture left justifying the buffer to fill the last row of the texture.\\n self.face_colors_texture.write(\\n self.current_face_colors.astype(\\\"f4\\\").tobytes().ljust(shape[0] * shape[1] * 16)\\n )\\n\\n # Write uvs.\\n if self.has_texture:\\n self.vbo_uvs.write(self.uv_coords.astype(\\\"f4\\\").tobytes())\\n\\n # Write instance transforms.\\n if self.instance_transforms is not None:\\n self.vbo_instance_transforms.write(\\n np.transpose(self.current_instance_transforms.astype(\\\"f4\\\"), (0, 2, 1)).tobytes()\\n )\\n\\n @hooked\\n def redraw(self, **kwargs):\\n self._need_upload = True\\n\\n transparent = False\\n if self._vertex_colors is not None:\\n transparent = transparent or np.any(self.vertex_colors[:, :, 3] < 1.0)\\n if self._face_colors is not None:\\n transparent = transparent or np.any(self.face_colors[:, :, 3] < 1.0)\\n\\n self._has_transparent_vertex_or_face_colors = transparent\\n\\n def _load_programs(self, vs, positions_vs):\\n instanced = 1 if self.instance_transforms is not None else 0\\n self.smooth_prog = get_smooth_lit_with_edges_program(vs, instanced)\\n self.flat_prog = get_flat_lit_with_edges_program(vs, instanced)\\n self.smooth_face_prog = get_smooth_lit_with_edges_face_color_program(vs, instanced)\\n self.flat_face_prog = get_flat_lit_with_edges_face_color_program(vs, instanced)\\n\\n self.depth_only_program = get_depth_only_program(positions_vs, instanced)\\n self.outline_program = get_outline_program(positions_vs, instanced)\\n self.fragmap_program = get_fragmap_program(positions_vs, instanced)\\n\\n # noinspection PyAttributeOutsideInit\\n @Node.once\\n def make_renderable(self, ctx: moderngl.Context):\\n \\\"\\\"\\\"Prepares this object for rendering. This function must be called before `render` is used.\\\"\\\"\\\"\\n vs = \\\"lit_with_edges.glsl\\\"\\n positions_vs = \\\"mesh_positions.vs.glsl\\\"\\n self._load_programs(vs, positions_vs)\\n\\n vertices = self.current_vertices\\n vertex_normals = self.vertex_normals_at(self.current_frame_id)\\n vertex_colors = self.current_vertex_colors\\n\\n self.vbo_vertices = ctx.buffer(vertices.astype(\\\"f4\\\").tobytes())\\n self.vbo_normals = ctx.buffer(vertex_normals.astype(\\\"f4\\\").tobytes())\\n self.vbo_colors = ctx.buffer(vertex_colors.astype(\\\"f4\\\").tobytes())\\n self.vbo_indices = ctx.buffer(self.faces.tobytes())\\n\\n self.vao = VAO()\\n self.vao.buffer(self.vbo_vertices, \\\"3f4\\\", \\\"in_position\\\")\\n self.vao.buffer(self.vbo_normals, \\\"3f4\\\", \\\"in_normal\\\")\\n self.vao.buffer(self.vbo_colors, \\\"4f4\\\", \\\"in_color\\\")\\n self.vao.index_buffer(self.vbo_indices)\\n\\n if self.instance_transforms is not None:\\n self.vbo_instance_transforms = ctx.buffer(\\n np.transpose(self.current_instance_transforms.astype(\\\"f4\\\"), (0, 2, 1)).tobytes()\\n )\\n self.vao.buffer(self.vbo_instance_transforms, \\\"16f4/i\\\", \\\"instance_transform\\\")\\n\\n # Compute shape of 2D texture.\\n shape = (min(self.faces.shape[0], 8192), (self.faces.shape[0] + 8191) // 8192)\\n self.face_colors_texture = ctx.texture(shape, 4, dtype=\\\"f4\\\")\\n if self.face_colors is not None:\\n # Write texture left justifying the buffer to fill the last row of the texture.\\n self.face_colors_texture.write(\\n self.current_face_colors.astype(\\\"f4\\\").tobytes().ljust(shape[0] * shape[1] * 16)\\n )\\n\\n if self.has_texture:\\n img = self.texture_image\\n if self.use_pickle_texture:\\n self.texture = ctx.texture(img.shape[:2], img.shape[2], img.tobytes())\\n else:\\n self.texture = ctx.texture(img.size, 3, img.tobytes())\\n self.texture_prog = get_smooth_lit_texturized_program(vs)\\n self.vbo_uvs = ctx.buffer(self.uv_coords.astype(\\\"f4\\\").tobytes())\\n self.vao.buffer(self.vbo_uvs, \\\"2f4\\\", \\\"in_uv\\\")\\n\\n @hooked\\n def release(self):\\n if self.is_renderable:\\n self.vao.release()\\n if self.has_texture:\\n self.texture.release()\\n\\n def _use_program(self, camera, **kwargs):\\n if self.has_texture and self.show_texture:\\n prog = self.texture_prog\\n prog[\\\"diffuse_texture\\\"] = 0\\n self.texture.use(0)\\n else:\\n if self.face_colors is None:\\n if self.flat_shading:\\n prog = self.flat_prog\\n else:\\n prog = self.smooth_prog\\n else:\\n if self.flat_shading:\\n prog = self.flat_face_prog\\n else:\\n prog = self.smooth_face_prog\\n self.face_colors_texture.use(0)\\n prog[\\\"face_colors\\\"] = 0\\n prog[\\\"norm_coloring\\\"].value = self.norm_coloring\\n\\n prog[\\\"use_uniform_color\\\"] = self._use_uniform_color\\n prog[\\\"uniform_color\\\"] = self.material.color\\n prog[\\\"draw_edges\\\"].value = 1.0 if self.draw_edges else 0.0\\n prog[\\\"win_size\\\"].value = kwargs[\\\"window_size\\\"]\\n\\n prog[\\\"clip_control\\\"].value = tuple(self.clip_control)\\n prog[\\\"clip_value\\\"].value = tuple(self.clip_value)\\n\\n self.set_camera_matrices(prog, camera, **kwargs)\\n set_lights_in_program(\\n prog,\\n kwargs[\\\"lights\\\"],\\n kwargs[\\\"shadows_enabled\\\"],\\n kwargs[\\\"ambient_strength\\\"],\\n )\\n set_material_properties(prog, self.material)\\n self.receive_shadow(prog, **kwargs)\\n return prog\\n\\n def render(self, camera, **kwargs):\\n self._upload_buffers()\\n prog = self._use_program(camera, **kwargs)\\n self.vao.render(prog, moderngl.TRIANGLES, instances=self.n_instances)\\n\\n def render_positions(self, prog):\\n if self.is_renderable:\\n self._upload_buffers()\\n\\n prog[\\\"clip_control\\\"].value = tuple(self.clip_control)\\n prog[\\\"clip_value\\\"].value = tuple(self.clip_value)\\n\\n self.vao.render(prog, moderngl.TRIANGLES, instances=self.n_instances)\\n\\n def _show_normals(self):\\n \\\"\\\"\\\"Create and add normals at runtime\\\"\\\"\\\"\\n vn = self.vertex_normals\\n\\n bounds = self.bounds\\n diag = np.linalg.norm(bounds[:, 0] - bounds[:, 1])\\n\\n length = 0.005 * max(diag, 1) / self.scale\\n vn = vn / np.linalg.norm(vn, axis=-1, keepdims=True) * length\\n\\n # Must import here because if we do it at the top we create a circular dependency.\\n from aitviewer.renderables.arrows import Arrows\\n\\n positions = self.vertices\\n self.normals_r = Arrows(\\n positions,\\n positions + vn,\\n r_base=length / 10,\\n r_head=2 * length / 10,\\n p=0.25,\\n name=\\\"Normals\\\",\\n )\\n self.normals_r.current_frame_id = self.current_frame_id\\n self.add(self.normals_r)\\n\\n def gui(self, imgui):\\n super(Meshes, self).gui(imgui)\\n\\n _, self.show_texture = imgui.checkbox(\\n \\\"Render Texture##render_texture{}\\\".format(self.unique_name),\\n self.show_texture,\\n )\\n _, self.norm_coloring = imgui.checkbox(\\n \\\"Norm Coloring##norm_coloring{}\\\".format(self.unique_name),\\n self.norm_coloring,\\n )\\n _, self.flat_shading = imgui.checkbox(\\n \\\"Flat shading [F]##flat_shading{}\\\".format(self.unique_name),\\n self.flat_shading,\\n )\\n _, self.draw_edges = imgui.checkbox(\\\"Draw edges [E]##draw_edges{}\\\".format(self.unique_name), self.draw_edges)\\n _, self.draw_outline = imgui.checkbox(\\n \\\"Draw outline##draw_outline{}\\\".format(self.unique_name), self.draw_outline\\n )\\n\\n if self.normals_r is None:\\n if imgui.button(\\\"Show Normals ##show_normals{}\\\".format(self.unique_name)):\\n self._show_normals()\\n\\n def gui_context_menu(self, imgui, x: int, y: int):\\n _, self.flat_shading = imgui.menu_item(\\\"Flat shading\\\", \\\"F\\\", selected=self.flat_shading, enabled=True)\\n _, self.draw_edges = imgui.menu_item(\\\"Draw edges\\\", \\\"E\\\", selected=self.draw_edges, enabled=True)\\n _, self.draw_outline = imgui.menu_item(\\\"Draw outline\\\", selected=self.draw_outline)\\n\\n imgui.spacing()\\n imgui.separator()\\n imgui.spacing()\\n super().gui_context_menu(imgui, x, y)\\n\\n def gui_io(self, imgui):\\n if imgui.button(\\\"Export OBJ##export_{}\\\".format(self.unique_name)):\\n mesh = trimesh.Trimesh(vertices=self.current_vertices, faces=self.faces, process=False)\\n mesh.export(\\\"../export/\\\" + self.name + \\\".obj\\\")\\n\\n def key_event(self, key, wnd_keys):\\n if key == wnd_keys.F:\\n self.flat_shading = not self.flat_shading\\n elif key == wnd_keys.E:\\n self.draw_edges = not self.draw_edges\\n\\n def update_frames(self, vertices, frames):\\n self.vertices[frames] = vertices\\n self.redraw()\\n\\n def add_frames(self, vertices):\\n if len(vertices.shape) == 2:\\n vertices = vertices[np.newaxis]\\n self.vertices = np.append(self.vertices, vertices, axis=0)\\n self.n_frames = max(self.n_frames, self.vertices.shape[0])\\n\\n def remove_frames(self, frames):\\n self.vertices = np.delete(self.vertices, frames, axis=0)\\n self.redraw()\\n\\n def export_usd(self, stage, usd_path: str, directory: str = None, verbose=False):\\n name = f\\\"{self.name}_{self.uid:03}\\\".replace(\\\" \\\", \\\"_\\\")\\n usd_path = f\\\"{usd_path}/{name}\\\"\\n\\n mesh = usd.add_mesh(stage, usd_path, self.name, self.vertices, self.faces, self.get_local_transform())\\n if self.has_texture and not self.use_pickle_texture:\\n # UVs.\\n a_uv = UsdGeom.PrimvarsAPI(mesh).CreatePrimvar(\\n \\\"st\\\", Sdf.ValueTypeNames.TexCoord2fArray, UsdGeom.Tokens.faceVarying\\n )\\n a_uv.Set(time=1, value=self.uv_coords[self.faces.flatten()])\\n\\n if not directory:\\n texture_path = os.path.abspath(self.texture_path)\\n else:\\n texture_path = usd.copy_texture(self.texture_path, name, directory)\\n usd.add_texture(stage, mesh, usd_path, texture_path)\\n else:\\n # NOTE: Per vertex and per face colors using usd displayColor are not currently\\n # loaded by Blender. This code path can be enabled once support is there.\\n if False:\\n a_colors = mesh.GetDisplayColorAttr()\\n if self._face_colors is not None:\\n # Per face colors.\\n if self._face_colors.shape[0] == 1:\\n a_colors.Set(self._face_colors[0, :, :3].astype(np.float32))\\n else:\\n for i in range(self.n_frames):\\n a_colors.Set(time=i + 1, value=self._face_colors[i, :, :3].astype(np.float32))\\n elif self._vertex_colors is not None:\\n # Per vertex colors.\\n if self._vertex_colors.shape[0] == 1:\\n a_colors.Set(self._vertex_colors[0, :, :3].astype(np.float32))\\n else:\\n for i in range(self.n_frames):\\n a_colors.Set(time=i + 1, value=self._vertex_colors[i, :, :3].astype(np.float32))\\n else:\\n # Uniform color.\\n a_colors.Set(np.array(self.color, np.float32)[:3])\\n else:\\n usd.add_color(stage, mesh, usd_path, self.color[:3])\\n\\n self._export_usd_recursively(stage, usd_path, directory, verbose)\"\n },\n {\n \"identifier\": \"Node\",\n \"path\": \"aitviewer/scene/node.py\",\n \"snippet\": \"class Node(object):\\n \\\"\\\"\\\"Interface for nodes.\\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n name=None,\\n icon=None,\\n position=None,\\n rotation=None,\\n scale=1.0,\\n color=(0.5, 0.5, 0.5, 1.0),\\n material=None,\\n is_selectable=True,\\n gui_affine=True,\\n gui_material=True,\\n enabled_frames=None,\\n n_frames=1,\\n ):\\n \\\"\\\"\\\"\\n :param name: Name of the node\\n :param icon: Custom Node Icon using custom Icon font\\n :param position: Starting position in the format (X,Y,Z) or np array of positions with shape (F, 3)\\n :param rotation: Starting rotation in rotation matrix representation (3,3) or np array of rotations with shape (F, 3, 3)\\n :param scale: Starting scale (scalar) or np array of scale values with shape (F)\\n :param color: (R,G,B,A) 0-1 formatted color value.\\n :param material: Object material properties. The color specified in the material will override node color\\n :param is_selectable: If True the node is selectable when clicked on, otherwise the parent node will be selected.\\n :param gui_affine: If True the node will have transform controls (position, rotation, scale) in the GUI.\\n :param gui_material: If True the node will have material controls in the GUI.\\n :param enabled_frames: Numpy array of boolean values, the object will be enabled only in frames where the value is True,\\n the number of ones in the mask must match the number of frames of the object.\\n :param n_frames: How many frames this renderable has.\\n \\\"\\\"\\\"\\n # Transform & Animation\\n position = np.zeros(3, dtype=np.float32) if position is None else np.array(position, dtype=np.float32)\\n rotation = np.eye(3, dtype=np.float32) if rotation is None else np.array(rotation, dtype=np.float32)\\n\\n self._positions = position if len(position.shape) != 1 else position[np.newaxis]\\n self._rotations = rotation if len(rotation.shape) != 2 else rotation[np.newaxis]\\n self._scales = (scale if isinstance(scale, np.ndarray) else np.array([scale])).astype(np.float32)\\n\\n n_positions = self._positions.shape[0]\\n n_rotations = self._rotations.shape[0]\\n n_scales = self._scales.shape[0]\\n\\n if n_frames > 1:\\n assert n_positions == 1 or n_frames == n_positions, (\\n f\\\"Number of position frames\\\" f\\\" ({n_positions}) must be 1 or match number of Node frames {n_frames}\\\"\\n )\\n assert n_rotations == 1 or n_frames == n_rotations, (\\n f\\\"Number of rotations frames\\\" f\\\" ({n_rotations}) must be 1 or match number of Node frames {n_frames}\\\"\\n )\\n assert n_scales == 1 or n_frames == n_scales, (\\n f\\\"Number of scales frames\\\" f\\\" ({n_scales}) must be 1 or match number of Node frames {n_frames}\\\"\\n )\\n else:\\n n_frames = max(n_positions, n_rotations, n_scales)\\n assert (\\n (n_positions == 1 or n_positions == n_frames)\\n and (n_rotations == 1 or n_rotations == n_frames)\\n and (n_scales == 1 or n_scales == n_frames)\\n ), (\\n f\\\"Number of position\\\"\\n f\\\"({n_positions}), rotation ({n_rotations}) and scale ({n_scales})\\\"\\n \\\"frames must be 1 or match.\\\"\\n )\\n\\n # Frames\\n self._n_frames = n_frames\\n self._current_frame_id = 0\\n self.model_matrix = self.get_local_transform()\\n self._enabled_frames = enabled_frames\\n if self._enabled_frames is not None:\\n assert np.count_nonzero(self._enabled_frames) == n_frames, (\\n f\\\"Number of non-zero elements in enabled_frames\\\"\\n f\\\" ({np.count_nonzero(self._enabled_frames)}) must match number of frames in sequence ({n_frames})\\\"\\n )\\n # Create an array that maps from the true frame id (counting also disabled frames) to the index of the\\n # first existing frame in the sequence.\\n self._enabled_frame_id = np.cumsum(self._enabled_frames) - 1\\n\\n # Stores the true frame id (counting also disabled frames) we use this to allow going\\n # through both enabled and disabled frames from the GUI.\\n self._internal_frame_id = 0\\n\\n # Material\\n self.material = Material(color=color) if material is None else material\\n\\n # Renderable Attributes\\n self.is_renderable = False\\n self.backface_culling = True\\n self.backface_fragmap = False\\n self.draw_outline = False\\n\\n # Flags to enable rendering passes\\n self.cast_shadow = False\\n self.depth_prepass = False\\n self.fragmap = False\\n self.outline = False\\n\\n # Programs for render passes. Subclasses are responsible for setting these.\\n self.depth_only_program = None # Required for depth_prepass and cast_shadow passes\\n self.fragmap_program = None # Required for fragmap pass\\n self.outline_program = None # Required for outline pass\\n\\n # GUI\\n self.name = name if name is not None else type(self).__name__\\n self.uid = C.next_gui_id()\\n self.unique_name = self.name + \\\"{}\\\".format(self.uid)\\n self.icon = icon if icon is not None else \\\"\\\\u0082\\\"\\n self._enabled = True\\n self._expanded = False\\n self.gui_controls = {\\n \\\"affine\\\": {\\n \\\"fn\\\": self.gui_affine,\\n \\\"icon\\\": \\\"\\\\u009b\\\",\\n \\\"is_visible\\\": gui_affine,\\n },\\n \\\"material\\\": {\\n \\\"fn\\\": self.gui_material,\\n \\\"icon\\\": \\\"\\\\u0088\\\",\\n \\\"is_visible\\\": gui_material,\\n },\\n \\\"animation\\\": {\\n \\\"fn\\\": self.gui_animation,\\n \\\"icon\\\": \\\"\\\\u0098\\\",\\n \\\"is_visible\\\": (lambda: self._n_frames > 1)(),\\n },\\n \\\"io\\\": {\\n \\\"fn\\\": self.gui_io,\\n \\\"icon\\\": \\\"\\\\u009a\\\",\\n \\\"is_visible\\\": (lambda: self.gui_io.__func__ is not Node.gui_io)(),\\n },\\n }\\n self.gui_modes = {\\\"view\\\": {\\\"title\\\": \\\" View\\\", \\\"fn\\\": self.gui_mode_view, \\\"icon\\\": \\\"\\\\u0099\\\"}}\\n self._selected_mode = \\\"view\\\"\\n self._show_in_hierarchy = True\\n self.is_selectable = is_selectable\\n self.export_usd_enabled = True\\n self.export_usd_expanded = True\\n\\n self.nodes: List[Node] = []\\n self.parent: Node = None\\n\\n # Selected Mode\\n @property\\n def selected_mode(self):\\n return self._selected_mode\\n\\n @selected_mode.setter\\n def selected_mode(self, selected_mode):\\n self._selected_mode = selected_mode\\n\\n # Transform\\n @property\\n def position(self):\\n idx = self.current_frame_id if self._positions.shape[0] > 1 else 0\\n return self._positions[idx]\\n\\n @position.setter\\n def position(self, position):\\n idx = self.current_frame_id if self._positions.shape[0] > 1 else 0\\n self._positions[idx] = np.array(position, dtype=np.float32).copy()\\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\\n\\n @property\\n def positions(self):\\n return self._positions\\n\\n @positions.setter\\n def positions(self, positions):\\n self._positions = positions\\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\\n\\n @property\\n def rotation(self):\\n idx = self.current_frame_id if self._rotations.shape[0] > 1 else 0\\n return self._rotations[idx]\\n\\n @rotation.setter\\n def rotation(self, rotation):\\n idx = self.current_frame_id if self._rotations.shape[0] > 1 else 0\\n self._rotations[idx] = rotation\\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\\n\\n @property\\n def rotations(self):\\n return self._rotations\\n\\n @rotations.setter\\n def rotations(self, rotations):\\n self._rotations = rotations\\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\\n\\n @property\\n def scale(self):\\n idx = self.current_frame_id if self._scales.shape[0] > 1 else 0\\n return self._scales[idx]\\n\\n @scale.setter\\n def scale(self, scale):\\n idx = self.current_frame_id if self._scales.shape[0] > 1 else 0\\n self._scales[idx] = scale\\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\\n\\n @property\\n def scales(self):\\n return self._scales\\n\\n @scales.setter\\n def scales(self, scales):\\n self._scales = scales\\n self.update_transform(None if self.parent is None else self.parent.model_matrix)\\n\\n @staticmethod\\n @lru_cache()\\n def _compute_transform(pos, rot, scale):\\n rotation = np.eye(4)\\n rotation[:3, :3] = np.array(rot)\\n\\n trans = np.eye(4)\\n trans[:3, 3] = np.array(pos)\\n\\n scale = np.diag([scale, scale, scale, 1])\\n\\n return (trans @ rotation @ scale).astype(\\\"f4\\\")\\n\\n def get_local_transform(self):\\n \\\"\\\"\\\"Construct local transform as a 4x4 matrix from this node's position, orientation and scale.\\\"\\\"\\\"\\n return self._compute_transform(tuple(self.position), tuple(map(tuple, self.rotation)), self.scale)\\n\\n def update_transform(self, parent_transform=None):\\n \\\"\\\"\\\"Update the model matrix of this node and all of its descendants.\\\"\\\"\\\"\\n if parent_transform is None:\\n self.model_matrix = self.get_local_transform()\\n else:\\n self.model_matrix = parent_transform.astype(\\\"f4\\\") @ self.get_local_transform()\\n\\n for n in self.nodes:\\n n.update_transform(self.model_matrix)\\n\\n @property\\n def color(self):\\n return self.material.color\\n\\n @color.setter\\n def color(self, color):\\n self.material.color = color\\n\\n @property\\n def bounds(self):\\n \\\"\\\"\\\"The bounds in the format ((x_min, x_max), (y_min, y_max), (z_min, z_max))\\\"\\\"\\\"\\n return np.array([[0, 0], [0, 0], [0, 0]])\\n\\n @property\\n def current_bounds(self):\\n return np.array([[0, 0], [0, 0], [0, 0]])\\n\\n @property\\n def current_center(self):\\n return self.current_bounds.mean(-1)\\n\\n @property\\n def center(self):\\n return self.bounds.mean(-1)\\n\\n def get_local_bounds(self, points):\\n if len(points.shape) == 2 and points.shape[-1] == 3:\\n points = points[np.newaxis]\\n assert len(points.shape) == 3\\n\\n # Compute min and max coordinates of the bounding box ignoring NaNs.\\n val = np.array(\\n [\\n [np.nanmin(points[:, :, 0]), np.nanmax(points[:, :, 0])],\\n [np.nanmin(points[:, :, 1]), np.nanmax(points[:, :, 1])],\\n [np.nanmin(points[:, :, 2]), np.nanmax(points[:, :, 2])],\\n ]\\n )\\n\\n # If any of the elements is NaN return an empty bounding box.\\n if np.isnan(val).any():\\n return np.array([[0, 0], [0, 0], [0, 0]])\\n else:\\n return val\\n\\n def get_bounds(self, points):\\n val = self.get_local_bounds(points)\\n\\n # Transform bounding box with the model matrix.\\n val = (self.model_matrix @ np.vstack((val, np.array([1.0, 1.0]))))[:3]\\n\\n # If any of the elements is NaN return an empty bounding box.\\n if np.isnan(val).any():\\n return np.array([[0, 0], [0, 0], [0, 0]])\\n else:\\n return val\\n\\n @property\\n def n_frames(self):\\n return self._n_frames\\n\\n @n_frames.setter\\n def n_frames(self, n_frames):\\n self._n_frames = n_frames\\n\\n def __len__(self):\\n return self.n_frames\\n\\n @property\\n def current_frame_id(self):\\n return self._current_frame_id\\n\\n @current_frame_id.setter\\n def current_frame_id(self, frame_id):\\n # Check if the frame changed.\\n last_frame_id = self._current_frame_id if self._enabled_frames is None else self._internal_frame_id\\n if self.n_frames == 1 or frame_id == last_frame_id:\\n return\\n\\n self.on_before_frame_update()\\n if self._enabled_frames is None:\\n if frame_id < 0:\\n self._current_frame_id = 0\\n elif frame_id >= len(self):\\n self._current_frame_id = len(self) - 1\\n else:\\n self._current_frame_id = frame_id\\n else:\\n # If an enabled_frames is present use it to get the current frame.\\n if frame_id < 0:\\n self._internal_frame_id = 0\\n elif frame_id >= self._enabled_frames.shape[0]:\\n self._internal_frame_id = self._enabled_frames.shape[0] - 1\\n else:\\n self._internal_frame_id = frame_id\\n self._current_frame_id = self._enabled_frame_id[self._internal_frame_id]\\n # Update enabled using the mask.\\n self.enabled = self._enabled_frames[self._internal_frame_id]\\n\\n # Update frame id of all children nodes.\\n for n in self.nodes:\\n n.current_frame_id = self._current_frame_id\\n\\n self.on_frame_update()\\n if self.parent and (self._positions.shape[0] > 1 or self._rotations.shape[0] > 1 or self._scales.shape[0] > 1):\\n self.update_transform(self.parent.model_matrix)\\n\\n def next_frame(self):\\n self.current_frame_id = self.current_frame_id + 1 if self.current_frame_id < len(self) - 1 else 0\\n\\n def previous_frame(self):\\n self.current_frame_id = self.current_frame_id - 1 if self.current_frame_id > 0 else len(self) - 1\\n\\n def on_before_frame_update(self):\\n \\\"\\\"\\\"Called when the current frame is about to change, 'self.current_frame_id' still has the id of the\\n previous frame.\\\"\\\"\\\"\\n pass\\n\\n def on_frame_update(self):\\n \\\"\\\"\\\"Called when the current frame is changed.\\\"\\\"\\\"\\n pass\\n\\n def add(self, *nodes, **kwargs):\\n self._add_nodes(*nodes, **kwargs)\\n\\n def _add_node(self, n: \\\"Node\\\", show_in_hierarchy=True, expanded=False, enabled=True):\\n \\\"\\\"\\\"\\n Add a single node\\n :param show_in_hierarchy: Whether to show the node in the scene hierarchy.\\n :param expanded: Whether the node is initially expanded in the GUI.\\n \\\"\\\"\\\"\\n if n is None:\\n return\\n n._show_in_hierarchy = show_in_hierarchy\\n n._expanded = expanded\\n n._enabled = enabled if n._enabled_frames is None else n._enabled_frames[n.current_frame_id]\\n self.nodes.append(n)\\n n.parent = self\\n n.update_transform(self.model_matrix)\\n\\n def _add_nodes(self, *nodes, **kwargs):\\n \\\"\\\"\\\"Add multiple nodes\\\"\\\"\\\"\\n for n in nodes:\\n self._add_node(n, **kwargs)\\n\\n def remove(self, *nodes):\\n for n in nodes:\\n n.release()\\n try:\\n self.nodes.remove(n)\\n except:\\n pass\\n\\n @property\\n def show_in_hierarchy(self):\\n return self._show_in_hierarchy\\n\\n @property\\n def enabled(self):\\n return self._enabled\\n\\n @enabled.setter\\n def enabled(self, enabled):\\n self._enabled = enabled\\n\\n @property\\n def expanded(self):\\n return self._expanded\\n\\n @expanded.setter\\n def expanded(self, expanded):\\n self._expanded = expanded\\n\\n def is_transparent(self):\\n \\\"\\\"\\\"\\n Returns true if the object is transparent and should thus be sorted when rendering.\\n Subclassess that use a different color should implement this method to be rendered correctly when transparent.\\n \\\"\\\"\\\"\\n return self.material.color[3] < 1.0\\n\\n def gui(self, imgui):\\n \\\"\\\"\\\"\\n Render GUI for custom node properties and controls. Implementation optional.\\n Elements rendered here will show up in the scene hierarchy\\n :param imgui: imgui context.\\n See https://pyimgui.readthedocs.io/en/latest/reference/imgui.core.html for available elements to render\\n \\\"\\\"\\\"\\n pass\\n\\n def gui_modes(self, imgui):\\n \\\"\\\"\\\"Render GUI with toolbar (tools) for this particular node\\\"\\\"\\\"\\n\\n def gui_animation(self, imgui):\\n \\\"\\\"\\\"Render GUI for animation related settings\\\"\\\"\\\"\\n\\n if self._enabled_frames is None:\\n if self.n_frames > 1:\\n u, fid = imgui.slider_int(\\n \\\"Frame##r_{}\\\".format(self.unique_name),\\n self.current_frame_id,\\n min_value=0,\\n max_value=self.n_frames - 1,\\n )\\n if u:\\n self.current_frame_id = fid\\n else:\\n u, fid = imgui.slider_int(\\n \\\"Frame##r_{}\\\".format(self.unique_name),\\n self._internal_frame_id,\\n min_value=0,\\n max_value=self._enabled_frames.shape[0] - 1,\\n )\\n if u:\\n self.current_frame_id = fid\\n\\n def gui_affine(self, imgui):\\n \\\"\\\"\\\"Render GUI for affine transformations\\\"\\\"\\\"\\n # Position controls\\n up, pos = imgui.drag_float3(\\n \\\"Position##pos{}\\\".format(self.unique_name),\\n *self.position,\\n 1e-2,\\n format=\\\"%.2f\\\",\\n )\\n if up:\\n self.position = pos\\n\\n # Rotation controls\\n euler_angles = rot2euler_numpy(self.rotation[np.newaxis], degrees=True)[0]\\n ur, euler_angles = imgui.drag_float3(\\n \\\"Rotation##pos{}\\\".format(self.unique_name),\\n *euler_angles,\\n 1e-2,\\n format=\\\"%.2f\\\",\\n )\\n if ur:\\n self.rotation = euler2rot_numpy(np.array(euler_angles)[np.newaxis], degrees=True)[0]\\n\\n # Scale controls\\n us, scale = imgui.drag_float(\\n \\\"Scale##scale{}\\\".format(self.unique_name),\\n self.scale,\\n 1e-2,\\n min_value=0.001,\\n max_value=100.0,\\n format=\\\"%.3f\\\",\\n )\\n if us:\\n self.scale = scale\\n\\n def gui_material(self, imgui):\\n \\\"\\\"\\\"Render GUI with material properties\\\"\\\"\\\"\\n\\n # Color Control\\n uc, color = imgui.color_edit4(\\\"Color##color{}'\\\".format(self.unique_name), *self.material.color)\\n if uc:\\n self.color = color\\n\\n # Diffuse\\n ud, diffuse = imgui.slider_float(\\n \\\"Diffuse##diffuse{}\\\".format(self.unique_name),\\n self.material.diffuse,\\n 0.0,\\n 1.0,\\n \\\"%.2f\\\",\\n )\\n if ud:\\n self.material.diffuse = diffuse\\n\\n # Ambient\\n ua, ambient = imgui.slider_float(\\n \\\"Ambient##ambient{}\\\".format(self.unique_name),\\n self.material.ambient,\\n 0.0,\\n 1.0,\\n \\\"%.2f\\\",\\n )\\n if ua:\\n self.material.ambient = ambient\\n\\n def gui_io(self, imgui):\\n \\\"\\\"\\\"Render GUI for import/export\\\"\\\"\\\"\\n pass\\n\\n def gui_mode_view(self, imgui):\\n \\\"\\\"\\\"Render custom GUI for view mode\\\"\\\"\\\"\\n pass\\n\\n def gui_context_menu(self, imgui, x: int, y: int):\\n _, self.enabled = imgui.checkbox(\\\"Enabled\\\", self.enabled)\\n if any([n._show_in_hierarchy for n in self.nodes]):\\n imgui.spacing()\\n imgui.separator()\\n imgui.spacing()\\n for n in self.nodes:\\n if not n._show_in_hierarchy:\\n continue\\n if imgui.begin_menu(f\\\"{n.name}##{n.uid}\\\"):\\n n.gui_context_menu(imgui, x, y)\\n imgui.end_menu()\\n\\n # Renderable\\n @staticmethod\\n def once(func):\\n def _decorator(self, *args, **kwargs):\\n if self.is_renderable:\\n return\\n else:\\n func(self, *args, **kwargs)\\n self.is_renderable = True\\n\\n return _decorator\\n\\n def make_renderable(self, ctx):\\n \\\"\\\"\\\"\\n Prepares this object for rendering. This function must be called before `render` is used.\\n :param ctx: The moderngl context.\\n \\\"\\\"\\\"\\n pass\\n\\n def render(self, camera, position=None, rotation=None, **kwargs):\\n \\\"\\\"\\\"Render the current frame in this sequence.\\\"\\\"\\\"\\n pass\\n\\n def render_positions(self, prog):\\n \\\"\\\"\\\"\\n Render with a VAO with only positions bound, used for shadow mapping, fragmap and depth prepass.\\n \\\"\\\"\\\"\\n pass\\n\\n def redraw(self, **kwargs):\\n \\\"\\\"\\\"Perform update and redraw operations. Push to the GPU when finished. Recursively redraw child nodes\\\"\\\"\\\"\\n for n in self.nodes:\\n n.redraw(**kwargs)\\n\\n def set_camera_matrices(self, prog, camera, **kwargs):\\n \\\"\\\"\\\"Set the model view projection matrix in the given program.\\\"\\\"\\\"\\n # Transpose because np is row-major but OpenGL expects column-major.\\n prog[\\\"model_matrix\\\"].write(self.model_matrix.T.astype(\\\"f4\\\").tobytes())\\n prog[\\\"view_projection_matrix\\\"].write(camera.get_view_projection_matrix().T.astype(\\\"f4\\\").tobytes())\\n\\n def receive_shadow(self, program, **kwargs):\\n \\\"\\\"\\\"\\n Call this function if the renderable is to receive shadows.\\n :param program: The shader program that can shade with shadows.\\n :param kwargs: The render kwargs.\\n \\\"\\\"\\\"\\n if kwargs.get(\\\"shadows_enabled\\\", False):\\n lights = kwargs[\\\"lights\\\"]\\n\\n for i, light in enumerate(lights):\\n if light.shadow_enabled and light.shadow_map:\\n light_matrix = light.mvp() @ self.model_matrix\\n program[f\\\"dirLights[{i}].matrix\\\"].write(light_matrix.T.tobytes())\\n\\n # Bind shadowmap to slot i + 1, we reserve slot 0 for the mesh texture\\n # and use slots 1 to (#lights + 1) for shadow maps\\n light.shadow_map.use(location=i + 1)\\n\\n # Set sampler uniforms\\n uniform = program[f\\\"shadow_maps\\\"]\\n uniform.value = 1 if uniform.array_length == 1 else [*range(1, len(lights) + 1)]\\n\\n def render_shadowmap(self, light_matrix):\\n if not self.cast_shadow or self.depth_only_program is None or self.color[3] == 0.0:\\n return\\n\\n prog = self.depth_only_program\\n prog[\\\"model_matrix\\\"].write(self.model_matrix.T.tobytes())\\n prog[\\\"view_projection_matrix\\\"].write(light_matrix.T.tobytes())\\n\\n self.render_positions(prog)\\n\\n def render_fragmap(self, ctx, camera, uid=None):\\n if not self.fragmap or self.fragmap_program is None:\\n return\\n\\n # Transpose because np is row-major but OpenGL expects column-major.\\n prog = self.fragmap_program\\n self.set_camera_matrices(prog, camera)\\n\\n # Render with the specified object uid, if None use the node uid instead.\\n prog[\\\"obj_id\\\"] = uid or self.uid\\n\\n if self.backface_culling or self.backface_fragmap:\\n ctx.enable(moderngl.CULL_FACE)\\n else:\\n ctx.disable(moderngl.CULL_FACE)\\n\\n # If backface_fragmap is enabled for this node only render backfaces\\n if self.backface_fragmap:\\n ctx.cull_face = \\\"front\\\"\\n\\n self.render_positions(prog)\\n\\n # Restore cull face to back\\n if self.backface_fragmap:\\n ctx.cull_face = \\\"back\\\"\\n\\n def render_depth_prepass(self, camera, **kwargs):\\n if not self.depth_prepass or self.depth_only_program is None:\\n return\\n\\n prog = self.depth_only_program\\n self.set_camera_matrices(prog, camera)\\n self.render_positions(prog)\\n\\n def render_outline(self, ctx, camera):\\n if self.outline and self.outline_program is not None:\\n prog = self.outline_program\\n self.set_camera_matrices(prog, camera)\\n\\n if self.backface_culling:\\n ctx.enable(moderngl.CULL_FACE)\\n else:\\n ctx.disable(moderngl.CULL_FACE)\\n self.render_positions(prog)\\n\\n # Render children node recursively.\\n for n in self.nodes:\\n n.render_outline(ctx, camera)\\n\\n def release(self):\\n \\\"\\\"\\\"\\n Release all OpenGL resources used by this node and any of its children. Subclasses that instantiate OpenGL\\n objects should implement this method with '@hooked' to avoid leaking resources.\\n \\\"\\\"\\\"\\n for n in self.nodes:\\n n.release()\\n\\n def on_selection(self, node, instance_id, tri_id):\\n \\\"\\\"\\\"\\n Called when the node is selected\\n\\n :param node: the node which was clicked (can be None if the selection wasn't a mouse event)\\n :param instance_id: the id of the instance that was clicked, 0 if the object is not instanced\\n (can be None if the selection wasn't a mouse event)\\n :param tri_id: the id of the triangle that was clicked from the 'node' mesh\\n (can be None if the selection wasn't a mouse event)\\n \\\"\\\"\\\"\\n pass\\n\\n def key_event(self, key, wnd_keys):\\n \\\"\\\"\\\"\\n Handle shortcut key presses (if you are the selected object)\\n \\\"\\\"\\\"\\n pass\\n\\n def update_frames(self, *args, **kwargs):\\n pass\\n\\n def add_frames(self, *args, **kwargs):\\n pass\\n\\n def remove_frames(self, *args, **kwargs):\\n pass\\n\\n def _export_usd_recursively(self, stage, usd_path, directory, verbose):\\n if verbose:\\n print(usd_path)\\n for n in self.nodes:\\n if n.export_usd_enabled:\\n n.export_usd(stage, usd_path, directory, verbose)\\n\\n def export_usd(self, stage, usd_path: str, directory: str = None, verbose=False):\\n \\\"\\\"\\\"\\n Export the node into an USD file. Nodes that implement this method should use\\n recursively call this for every children that should also be exported.\\n\\n :param stage: an object of type Usd.Stage into which to export the node\\n :param usd_path: the path of the parent object in the USD file scene hierarchy.\\n \\\"\\\"\\\"\\n from pxr import Gf, UsdGeom\\n\\n usd_path = f\\\"{usd_path}/{self.name.replace(' ', '_')}_{self.uid:03}\\\"\\n\\n # Transform.\\n xform = UsdGeom.Xform.Define(stage, usd_path)\\n a_xform = xform.AddTransformOp()\\n a_xform.Set(Gf.Matrix4d(self.get_local_transform().astype(np.float64).T))\\n\\n self._export_usd_recursively(stage, usd_path, directory, verbose)\"\n },\n {\n \"identifier\": \"hooked\",\n \"path\": \"aitviewer/utils/decorators.py\",\n \"snippet\": \"class hooked:\\n def __init__(self, fn):\\n self.fn = fn\\n\\n def __set_name__(self, owner, name):\\n func = self.fn\\n\\n def _decorator(self, *args, **kwargs):\\n super_obj = super(owner, self)\\n super_fn = getattr(super_obj, func.__name__)\\n super_fn(*args, **kwargs)\\n return func(self, *args, **kwargs)\\n\\n setattr(owner, name, _decorator)\\n\\n def __call__(self):\\n assert (\\n False\\n ), \\\"@hooked decorator object should never be called directly. This can happen if you apply this decorator to a function that is not a method.\\\"\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import numpy as np\nfrom skimage import measure\nfrom aitviewer.renderables.bounding_boxes import BoundingBoxes\nfrom aitviewer.renderables.lines import Lines\nfrom aitviewer.renderables.meshes import Meshes\nfrom aitviewer.scene.node import Node\nfrom aitviewer.utils.decorators import hooked"},"token_num":{"kind":"number","value":19602,"string":"19,602"},"cropped_code":{"kind":"string","value":"# Copyright (C) 2023 ETH Zurich, Manuel Kaufmann, Velko Vechev, Dario Mylonopoulos\n\n\n\nclass SDF(Node):\n \"\"\"\n Renderable that can be used to draw level sets of a dense SDF volume meshed using marching cubes.\n\n This renderable internally uses the marching cubes algorithm from skimage.\n For a faster marching cubes implementation see the Volume renderable.\n \"\"\"\n\n def __init__(\n self,\n volume,\n size=(1, 1, 1),\n level=0.0,\n color=(0.7, 0.7, 0.7, 1.0),\n level_sets=None,\n level_set_colors=None,\n mc_step_size=1,\n **kwargs,\n ):\n \"\"\"Initializer.\n :param volume: np array of shape (X, Y, Z) of signed distance values\n :param size: size of the volume in local units.\n :param level: the level set used for the main mesh.\n :param color: color of the main mesh.\n :param level_sets: a list or array of additional level set values to display.\n :param level_set_colors: a list or array of shape (L, 4) of the same length as\n the level_set parameter with colors to use for the additional level sets.\n :param mc_step_size: step size used for marching cubes.\n :param **kwargs: arguments forwarded to the Node constructor.\n \"\"\"\n assert len(volume.shape) == 3 and len(size) == 3\n kwargs[\"gui_material\"] = False\n super().__init__(**kwargs)\n\n self.volume = volume\n self.size = np.array((size), np.float32)\n\n # Mesh.\n verts, faces, normals, _ = measure.marching_cubes(\n volume, level, spacing=self.size / (np.array(self.volume.shape) - 1.0), step_size=mc_step_size\n )\n self.mesh = Meshes(verts, faces, vertex_normals=-normals, color=color, name=\"Mesh\")\n\n # Level sets.\n self.level_sets: list[Meshes] = []\n if level_sets is not None:\n if level_set_colors is not None:\n assert len(level_sets) == len(level_set_colors)\n\n for i, s in enumerate(level_sets):\n verts, faces, normals, _ = measure.marching_cubes(\n volume, s, spacing=self.size / (np.array(self.volume.shape) - 1.0), step_size=mc_step_size\n )\n shell = Meshes(verts, faces, vertex_normals=-normals, name=f\"Level {s:.03f}\", cast_shadow=False)\n if level_set_colors is not None:\n shell.color = tuple(level_set_colors[i])\n\n shell.clip_control = np.array((1, 1, 1))\n shell.clip_value = self.size.copy()\n shell.backface_culling = False\n\n self.level_sets.append(shell)\n\n # Bounding box.\n self.bounding_box = BoundingBoxes.from_min_max_diagonal(\n np.array([[0.0, 0.0, 0.0]]),\n np.array([self.size], dtype=np.float32),\n color=(0, 0, 0, 1),\n name=\"Bounding Box\",\n gui_affine=False,\n )\n\n # Clip plane lines.\n self.clip_lines = []\n for i, axis in enumerate([\"X\", \"Y\", \"Z\"]):\n s0 = self.size[(i + 0) % 3]\n s1 = self.size[(i + 1) % 3]\n s2 = self.size[(i + 2) % 3]\n lines = np.array(\n (\n [s0, 0, 0],\n [s0, s1, 0],\n [s0, s1, s2],\n [s0, 0, s2],\n [s0, 0, 0],\n ),\n dtype=np.float32,\n )\n lines = np.roll(lines, axis=1, shift=(0, i))\n color = np.array([0, 0, 0, 1])\n color[i] = 1\n"},"all_code":{"kind":"string","value":"# Copyright (C) 2023 ETH Zurich, Manuel Kaufmann, Velko Vechev, Dario Mylonopoulos\n\n\n\nclass SDF(Node):\n \"\"\"\n Renderable that can be used to draw level sets of a dense SDF volume meshed using marching cubes.\n\n This renderable internally uses the marching cubes algorithm from skimage.\n For a faster marching cubes implementation see the Volume renderable.\n \"\"\"\n\n def __init__(\n self,\n volume,\n size=(1, 1, 1),\n level=0.0,\n color=(0.7, 0.7, 0.7, 1.0),\n level_sets=None,\n level_set_colors=None,\n mc_step_size=1,\n **kwargs,\n ):\n \"\"\"Initializer.\n :param volume: np array of shape (X, Y, Z) of signed distance values\n :param size: size of the volume in local units.\n :param level: the level set used for the main mesh.\n :param color: color of the main mesh.\n :param level_sets: a list or array of additional level set values to display.\n :param level_set_colors: a list or array of shape (L, 4) of the same length as\n the level_set parameter with colors to use for the additional level sets.\n :param mc_step_size: step size used for marching cubes.\n :param **kwargs: arguments forwarded to the Node constructor.\n \"\"\"\n assert len(volume.shape) == 3 and len(size) == 3\n kwargs[\"gui_material\"] = False\n super().__init__(**kwargs)\n\n self.volume = volume\n self.size = np.array((size), np.float32)\n\n # Mesh.\n verts, faces, normals, _ = measure.marching_cubes(\n volume, level, spacing=self.size / (np.array(self.volume.shape) - 1.0), step_size=mc_step_size\n )\n self.mesh = Meshes(verts, faces, vertex_normals=-normals, color=color, name=\"Mesh\")\n\n # Level sets.\n self.level_sets: list[Meshes] = []\n if level_sets is not None:\n if level_set_colors is not None:\n assert len(level_sets) == len(level_set_colors)\n\n for i, s in enumerate(level_sets):\n verts, faces, normals, _ = measure.marching_cubes(\n volume, s, spacing=self.size / (np.array(self.volume.shape) - 1.0), step_size=mc_step_size\n )\n shell = Meshes(verts, faces, vertex_normals=-normals, name=f\"Level {s:.03f}\", cast_shadow=False)\n if level_set_colors is not None:\n shell.color = tuple(level_set_colors[i])\n\n shell.clip_control = np.array((1, 1, 1))\n shell.clip_value = self.size.copy()\n shell.backface_culling = False\n\n self.level_sets.append(shell)\n\n # Bounding box.\n self.bounding_box = BoundingBoxes.from_min_max_diagonal(\n np.array([[0.0, 0.0, 0.0]]),\n np.array([self.size], dtype=np.float32),\n color=(0, 0, 0, 1),\n name=\"Bounding Box\",\n gui_affine=False,\n )\n\n # Clip plane lines.\n self.clip_lines = []\n for i, axis in enumerate([\"X\", \"Y\", \"Z\"]):\n s0 = self.size[(i + 0) % 3]\n s1 = self.size[(i + 1) % 3]\n s2 = self.size[(i + 2) % 3]\n lines = np.array(\n (\n [s0, 0, 0],\n [s0, s1, 0],\n [s0, s1, s2],\n [s0, 0, s2],\n [s0, 0, 0],\n ),\n dtype=np.float32,\n )\n lines = np.roll(lines, axis=1, shift=(0, i))\n color = np.array([0, 0, 0, 1])\n color[i] = 1"},"next_line":{"kind":"string","value":" self.clip_lines.append(Lines(lines, cast_shadow=False, color=color, name=f\"Clip {axis}\", gui_affine=False))"},"gold_snippet_index":{"kind":"number","value":1,"string":"1"},"created_at":{"kind":"string","value":"2023-12-07 16:13:50+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5296,"cells":{"repo_name":{"kind":"string","value":"nexB/dejacode"},"file_path":{"kind":"string","value":"dje/admin.py"},"context":{"kind":"list like","value":[{"identifier":"IS_FILTER_LOOKUP_VAR","path":"dje/filters.py","snippet":"IS_FILTER_LOOKUP_VAR = \"_filter_lookup\""},{"identifier":"CreatedByListFilter","path":"dje/filters.py","snippet":"class CreatedByListFilter(filters.SimpleListFilter):\n \"\"\"\n Filter by the user who created an object according to the objects's\n associated History. The value from LimitToDataspaceListFilter is\n used for the Dataspace if it exists.\n \"\"\"\n\n title = _(\"created by\")\n parameter_name = \"created_by\"\n\n def __init__(self, request, params, model, model_admin):\n self.model_content_type = ContentType.objects.get_for_model(model)\n super().__init__(request, params, model, model_admin)\n\n def lookups(self, request, model_admin):\n dataspace_id = request.GET.get(DataspaceFilter.parameter_name, request.user.dataspace.id)\n # Find users of the selected Dataspace\n users = get_user_model().objects.scope_by_id(dataspace_id)\n\n # Find users who have created at least 1 object\n users = (\n users.filter(\n history__action_flag=History.ADDITION,\n history__content_type=self.model_content_type,\n history__object_dataspace__id=dataspace_id,\n )\n .distinct()\n .order_by(\"last_name\")\n )\n return [(user.pk, user.get_full_name()) for user in users]\n\n def queryset(self, request, queryset):\n if self.value() is not None:\n user_pk = self.value()\n history_entries = History.objects.filter(\n content_type=self.model_content_type, action_flag=History.ADDITION, user__pk=user_pk\n )\n pks = list(history_entries.values_list(\"object_id\", flat=True))\n return queryset.filter(pk__in=pks)"},{"identifier":"DataspaceFilter","path":"dje/filters.py","snippet":"class DataspaceFilter(ChoicesOnlyListFilterMixin, BaseDataspaceLookupsFilter):\n \"\"\"\n Scope the ChangeList results by a Dataspace.\n Default is the current User Dataspace.\n Anyone can look into reference Dataspace.\n Only Reference User can look into other Dataspaces.\n \"\"\"\n\n title = _(\"dataspace\")\n parameter_name = \"dataspace__id__exact\"\n\n def lookups(self, request, model_admin):\n \"\"\"Set the lookup value for the current user dataspace choice to None.\"\"\"\n lookups = super().lookups(request, model_admin)\n return [(None if name == request.user.dataspace.name else pk, name) for pk, name in lookups]\n\n def queryset(self, request, queryset):\n if self.value():\n return queryset.scope_by_id(self.value())\n return queryset.scope(request.user.dataspace)"},{"identifier":"HistoryCreatedActionTimeListFilter","path":"dje/filters.py","snippet":"class HistoryCreatedActionTimeListFilter(HistoryActionTimeListFilter):\n title = _(\"created date\")\n parameter_name = \"created_date\"\n action_flag = History.ADDITION"},{"identifier":"HistoryModifiedActionTimeListFilter","path":"dje/filters.py","snippet":"class HistoryModifiedActionTimeListFilter(HistoryActionTimeListFilter):\n title = _(\"modified date\")\n parameter_name = \"modified_date\"\n action_flag = History.CHANGE"},{"identifier":"LimitToDataspaceListFilter","path":"dje/filters.py","snippet":"class LimitToDataspaceListFilter(filters.RelatedFieldListFilter):\n \"\"\"\n Limit the choices of a filter on a FK to the currently \"filtered\" Dataspace.\n The limit_choices_to declared on the model field will be applied too.\n \"\"\"\n\n def __init__(self, field, request, params, model, model_admin, field_path):\n super().__init__(field, request, params, model, model_admin, field_path)\n\n # The get_limit_choices_to_from_path is broken in 1.7, see code in 1.6\n # limit_choices_to = get_limit_choices_to_from_path(model, field_path)\n limit_choices_to = models.Q(**field.get_limit_choices_to())\n\n dataspace_id = request.GET.get(DataspaceFilter.parameter_name, request.user.dataspace_id)\n queryset = field.related_model.objects.scope_by_id(dataspace_id).filter(limit_choices_to)\n\n if field.related_model.__name__ == \"UsagePolicy\":\n content_type = ContentType.objects.get_for_model(model)\n queryset = queryset.filter(content_type=content_type)\n\n self.lookup_choices = [(x._get_pk_val(), str(x)) for x in queryset]"},{"identifier":"MissingInFilter","path":"dje/filters.py","snippet":"class MissingInFilter(BaseDataspaceLookupsFilter):\n \"\"\"\n Filter by objects missing in the given dataspace, compared with the\n current `DataspaceFilter.parameter_name` or user dataspace.\n Both values for reference and target Dataspace are validated against the\n self.lookup_choices to make sure the user has the proper access permissions.\n This filter is only available to superusers, this is enforced in\n DataspacedAdmin.get_list_filter()\n \"\"\"\n\n title = _(\"missing in\")\n parameter_name = \"missing_in\"\n\n def queryset(self, request, queryset):\n if not self.value():\n return\n\n valid_choices = [str(choice) for choice, _ in self.lookup_choices]\n if str(self.value()) not in valid_choices:\n raise IncorrectLookupParameters()\n\n return queryset.exclude(uuid__in=get_uuids_list_sorted(self.value(), queryset.model))"},{"identifier":"DataspaceAdminForm","path":"dje/forms.py","snippet":"class DataspaceAdminForm(forms.ModelForm):\n def clean(self):\n \"\"\"Add validation for `update_packages_from_scan` field.\"\"\"\n cleaned_data = super().clean()\n enable_package_scanning = cleaned_data.get(\"enable_package_scanning\")\n update_packages_from_scan = cleaned_data.get(\"update_packages_from_scan\")\n\n if update_packages_from_scan and not enable_package_scanning:\n msg = \"Package scanning needs to be enabled to use the automatic updates.\"\n self.add_error(\"update_packages_from_scan\", msg)"},{"identifier":"DataspacedAdminForm","path":"dje/forms.py","snippet":"class DataspacedAdminForm(forms.ModelForm):\n \"\"\"\n Use as the base ModelForm for every Model declared as DataspacedModel.\n This is usually not required for inline Forms.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Injects the dataspace in the instance right after the form\n initialization as a value for dataspace is required for the\n built-in unique_together validation.\n \"\"\"\n super().__init__(*args, **kwargs)\n # request is not injected in inline forms\n request = getattr(self, \"request\", None)\n\n add = not kwargs.get(\"instance\")\n if add and request:\n self.instance.dataspace = request.user.dataspace\n\n def _get_validation_exclusions(self):\n \"\"\"\n Remove the `dataspace` and `uuid` fields from the exclusion (ie: validate on those)\n so the unique_together validation within dataspace is enforced.\n \"\"\"\n exclude = super()._get_validation_exclusions()\n return {field for field in exclude if field not in (\"dataspace\", \"uuid\")}"},{"identifier":"DejaCodeAuthenticationForm","path":"dje/forms.py","snippet":"class DejaCodeAuthenticationForm(AuthenticationForm):\n \"\"\"Login form.\"\"\"\n\n use_required_attribute = False\n\n @property\n def helper(self):\n helper = FormHelper()\n helper.form_id = \"sign-in\"\n helper.form_action = \"login\"\n helper.form_method = \"post\"\n helper.form_tag = False\n\n fields = [\n Field(\"username\", css_class=\"input-block-level mb-3\", placeholder=_(\"Username\")),\n Field(\"password\", css_class=\"input-block-level mb-3\", placeholder=_(\"Password\")),\n Div(\n StrictSubmit(\"submit\", _(\"Sign in\"), css_class=\"btn-warning\"),\n css_class=\"d-grid\",\n ),\n ]\n\n helper.add_layout(Layout(Fieldset(\"\", *fields)))\n return helper\n\n def get_invalid_login_error(self):\n username = self.cleaned_data.get(\"username\")\n if \"@\" in username:\n return ValidationError(\n \"Be sure to enter your DejaCode username rather than your email \"\n \"address to sign in to DejaCode.\"\n )\n return super().get_invalid_login_error()"},{"identifier":"import_view","path":"dje/importers.py","snippet":"@login_required()\ndef import_view(request, importer_class):\n user = request.user\n importer = importer_class(user)\n upload_form_class = ImportableUploadFileForm\n if importer_class.__name__ == \"PackageImporter\":\n upload_form_class = PackageImportableUploadFileForm\n\n opts = importer.model_form._meta.model._meta\n perm_codename = get_permission_codename(\"add\", opts)\n if not user.has_perm(f\"{opts.app_label}.{perm_codename}\"):\n return HttpResponseRedirect(resolve_url(settings.LOGIN_REDIRECT_URL))\n\n if request.GET.get(\"get_template\"): # ?get_template=1\n header = \",\".join(importer.required_fields + importer.supported_fields)\n filename = \"{}_import_template.csv\".format(importer.verbose_name.replace(\" \", \"_\"))\n response = HttpResponse(header, content_type=\"application/csv\")\n response[\"Content-Disposition\"] = f'attachment; filename=\"{filename}\"'\n return response\n\n file_form = None\n if request.method == \"POST\":\n if \"form-TOTAL_FORMS\" in request.POST:\n importer = importer_class(user, formset_data=request.POST)\n importer.save_all()\n else:\n # Every uploaded file are stored in a temp location as we removed\n # the MemoryFileUploadHandler in the FILE_UPLOAD_HANDLERS settings.\n # We do not keep track of this location once the file has been\n # processed.\n file_form = upload_form_class(request.POST, request.FILES)\n if file_form.is_valid():\n uploaded_file = request.FILES[\"file\"]\n file_location = uploaded_file.temporary_file_path()\n importer = importer_class(user, file_location=file_location)\n else:\n file_form = upload_form_class()\n\n return render(\n request,\n \"admin/object_import.html\",\n {\n \"file_form\": file_form,\n \"importer\": importer,\n \"add_to_product_form\": importer.get_add_to_product_form(request),\n },\n )"},{"identifier":"AsURL","path":"dje/list_display.py","snippet":"class AsURL(ListDisplayItem):\n def to_representation(self, value):\n return urlize_target_blank(value)"},{"identifier":"mass_update_action","path":"dje/mass_update.py","snippet":"def mass_update_action(modeladmin, request, queryset):\n if not queryset:\n return\n\n # Dataspace is required for scoping, we trust the queryset for security purpose\n # over the values provided in the request data.\n dataspace = queryset.first().dataspace\n opts = modeladmin.model._meta\n preserved_filters = modeladmin.get_preserved_filters(request)\n\n # Allows to specified a custom mass update Form in the ModelAdmin\n mass_update_form = getattr(modeladmin, \"mass_update_form\", BaseMassUpdateForm)\n MassUpdateForm = modelform_factory(\n modeladmin.model,\n form=mass_update_form,\n exclude=get_protected_fields(modeladmin.model, request.user),\n formfield_callback=not_required,\n )\n MassUpdateForm.admin_site = modeladmin.admin_site # required by the ForeignKeyRawIdWidget\n\n if \"apply\" in request.POST:\n form = MassUpdateForm(request.POST, dataspace=dataspace)\n if form.is_valid():\n changelist_url = reverse(f\"admin:{opts.app_label}_{opts.model_name}_changelist\")\n redirect_url = add_preserved_filters(\n {\"preserved_filters\": preserved_filters, \"opts\": opts}, changelist_url\n )\n\n updated = 0\n errors = []\n for record in queryset:\n for field_name, value in form.cleaned_data.items():\n try:\n field_object = record._meta.get_field(field_name)\n except FieldDoesNotExist:\n continue # field_name is not part of the model\n\n # auto_created is only True on implicit m2m model\n if (\n field_object.many_to_many\n and not field_object.remote_field.through._meta.auto_created\n ):\n set_intermediate_explicit_m2m(record, field_object, value)\n else:\n setattr(record, field_name, value)\n # This have no impact if the model does not declare this field.\n record.last_modified_by = request.user\n # Some validation errors cannot be caught during the form validation as only\n # the \"new\" value of the selected fields are available.\n # We cannot validate those changes against the existing value of others fields on\n # the instance so the error can be raised at save() time.\n try:\n record.save()\n except ValidationError as e:\n errors.append(e.message)\n else:\n updated += 1\n\n if updated:\n messages.info(request, _(f\"Updated {updated} records\"))\n\n if errors:\n messages.error(request, _(f'{len(errors)} error(s): {\", \".join(errors)}'))\n\n action_end.send(\n sender=modeladmin.model,\n action=\"mass_update\",\n request=request,\n queryset=queryset,\n modeladmin=modeladmin,\n form=form,\n )\n return redirect(redirect_url)\n else:\n initial = {\n ACTION_CHECKBOX_NAME: request.POST.getlist(ACTION_CHECKBOX_NAME),\n \"select_across\": request.POST.get(\"select_across\") == \"1\",\n }\n form = MassUpdateForm(initial=initial, dataspace=dataspace)\n\n adminform = AdminForm(form, modeladmin.get_fieldsets(request), {}, [], model_admin=modeladmin)\n\n with suppress(AttributeError):\n form.extra_init(request, modeladmin)\n\n context = {\n \"adminform\": adminform,\n \"form\": form,\n \"opts\": opts,\n \"queryset\": queryset,\n \"preserved_filters\": preserved_filters,\n \"media\": modeladmin.media,\n }\n return render(request, \"admin/mass_update.html\", context)"},{"identifier":"Dataspace","path":"dje/models.py","snippet":"class Dataspace(models.Model):\n \"\"\"\n The Dataspace is a way to keep data for each organization data\n separated and still store them in the same database, schema or table.\n Therefore the Dataspace is part of the primary key of most models\n and it part of a unicity constraint for these models.\n For a given installation there can be several Owner Org defined, but only\n one reference.\n\n This is an important concept used throughout DejaCode to\n separate the reference data provided by nexB from the data used in a given\n installation of DJE.\n\n It is essentially a notion of tenant in a DJE installation and is used to\n segregate org-specific and/or org-private records enabling both\n multi-tenancy as well as nexB-provided reference data and org-specific or\n customized data.\n\n This separation has several purposes such as allowing:\n * orderly and simpler data update from the nexB reference data and inter\n Dataspace data exchange\n * Dataspace specific data customizations (for instance license\n tags configurations or some preferences)\n * multi-tenancy where different organizations can share the same DJE\n instance\n \"\"\"\n\n uuid = models.UUIDField(\n _(\"UUID\"),\n default=uuid.uuid4,\n editable=False,\n unique=True,\n )\n\n name = models.SlugField(\n unique=True,\n max_length=20,\n help_text=_(\n 'Unique name of a Dataspace. The name \"nexB\" is reserved for '\n \"the creators/maintainers of the system software. Dataspace name \"\n \"only allows letters, numbers, underscores and hyphens.\"\n ),\n )\n\n homepage_url = models.URLField(\n _(\"Homepage URL\"),\n max_length=1024,\n blank=True,\n help_text=_(\"The homepage URL of the Dataspace owner.\"),\n )\n\n contact_info = models.CharField(\n _(\"Contact information\"),\n max_length=500,\n blank=True,\n help_text=_(\n \"A dedicated email address or URL for contacting the owner of \"\n \"the Dataspace. Can be used for Attribution Package generation.\"\n ),\n )\n\n notes = models.TextField(\n blank=True,\n help_text=_(\"Extended Notes about a Dataspace.\"),\n )\n\n show_license_profile_in_license_list_view = models.BooleanField(\n default=False,\n verbose_name=format_lazy(\n \"Show {license_profile} in license list view\",\n license_profile=_(\"license profile\"),\n ),\n help_text=format_lazy(\n \"When true (checked), include the {license_profile} column in the license list view.\",\n license_profile=_(\"license profile\"),\n ),\n )\n\n show_license_type_in_license_list_view = models.BooleanField(\n default=True,\n help_text=_(\n \"When true (checked), include the license type column in the license list view.\",\n ),\n )\n\n show_spdx_short_identifier_in_license_list_view = models.BooleanField(\n verbose_name=_(\"show SPDX short identifier in license list view\"),\n default=False,\n help_text=_(\n \"When true (checked), include the SPDX short identifier in the license list view.\",\n ),\n )\n\n show_usage_policy_in_user_views = models.BooleanField(\n default=True,\n help_text=_(\n \"When true (checked), include the usage policy in user views that \"\n \"show licenses or components.\",\n ),\n )\n\n show_type_in_component_list_view = models.BooleanField(\n default=False,\n help_text=_(\n \"When true (checked), include the type column in the component list view.\",\n ),\n )\n\n hide_empty_fields_in_component_details_view = models.BooleanField(\n default=False,\n help_text=_(\"When true (checked), hide empty fields in the component details view.\"),\n )\n\n set_usage_policy_on_new_component_from_licenses = models.BooleanField(\n _(\"set usage policy on component or package from license policy\"),\n default=False,\n help_text=_(\n \"When true (checked), the application will automatically assign a usage \"\n \"policy to a component or package when its license expression is set or \"\n \"updated when you create, import, edit, or copy that component or package, \"\n \"based on the associated policies that you have defined on the license policy.\"\n ),\n )\n\n logo_url = models.URLField(\n _(\"Logo URL\"),\n max_length=1024,\n blank=True,\n help_text=_(\"URL to a Dataspace Logo. If set, it will be included in reports.\"),\n )\n\n full_name = models.CharField(\n max_length=100,\n blank=True,\n help_text=_(\n \"The full name of the Dataspace organization. \"\n \"Can be used for Attribution Package generation.\"\n ),\n )\n\n address = models.TextField(\n blank=True,\n help_text=(\n \"The address of the Dataspace organization. \"\n \"Can be used for Attribution Package generation.\"\n ),\n )\n\n open_source_information_url = models.URLField(\n _(\"Open Source Information URL\"),\n max_length=1024,\n blank=True,\n help_text=_(\n \"A public URL where you publish information about the Dataspace \"\n \"organization's Open Source policies and procedures. \"\n \"Can be used for Attribution Package generation.\"\n ),\n )\n\n open_source_download_url = models.URLField(\n _(\"Open Source Download URL\"),\n max_length=1024,\n blank=True,\n help_text=_(\n \"A public URL where you provide copies of Open Source software that \"\n \"require Redistribution when you use them in your products. Can be \"\n \"used for Attribution Package generation.\"\n ),\n )\n\n home_page_announcements = models.TextField(\n blank=True,\n help_text=_(\n \"Use this field to enter text to appear on the DejaCode home page, \"\n \"normally for the purpose of providing your user community with \"\n \"general-purpose announcements about using DejaCode. \"\n \"Note that you can include URL's in the text if you want to direct \"\n \"users to detailed instructions and announcements.\"\n ),\n )\n\n enable_package_scanning = models.BooleanField(\n default=False,\n help_text=_(\n 'When true (checked), allows a user to click the \"Scan Package\" button when viewing '\n \"a Package, initiating a call to ScanCode.io to scan the Package based on its URL. \"\n \"This setting also activates a DejaCode feature to submit any Package created using \"\n 'the \"Add Package\" button to ScanCode.io for scanning, and it activates the Scans '\n \"choice from the DejaCode Tools dropdown menu.\"\n ),\n )\n\n update_packages_from_scan = models.BooleanField(\n _(\"Update packages automatically from scan\"),\n default=False,\n help_text=_(\n \"When true (checked), enables an automatic DejaCode process to update \"\n \"selected Package fields (such as license expression, primary language, \"\n \"copyright, etc.) when a package scan is completed, depending on the \"\n \"quality of the scan results.\"\n ),\n )\n\n enable_purldb_access = models.BooleanField(\n _(\"Enable PurlDB access\"),\n default=False,\n help_text=_(\n \"When true (checked), enables user access to the PurlDB option from the Tools menu, \"\n \"which presents a list of PurlDB data mined and scanned automatically from multiple \"\n \"public sources. Users can view PurlDB details and can create DejaCode Package \"\n \"definitions using those details, and DejaCode also presents a new PurlDB tab when \"\n \"viewing the details of a Package with matching key values. This option also enhances \"\n \"the Global Search feature to extend the search scope beyond the standard DejaCode \"\n \"objects (Packages, Components, Licenses, Owners) and perform an asynchronous query of \"\n \"the PurlDB to find relevant data.\"\n ),\n )\n\n enable_vulnerablecodedb_access = models.BooleanField(\n _(\"Enable VulnerableCodeDB access\"),\n default=False,\n help_text=_(\n \"When true (checked), authorizes DejaCode to access the VulnerableCodeDB \"\n \"using a Package URL (purl) to determine if there are any reported \"\n \"vulnerabilities for a specific Package and return the Vulnerability ID \"\n \"and related URLs to a Vulnerabilities tab in the Package details user \"\n \"view.\"\n ),\n )\n\n objects = DataspaceManager()\n\n class Meta:\n ordering = [\"name\"]\n\n def __str__(self):\n return self.name\n\n def get_admin_url(self):\n opts = self._meta\n viewname = f\"admin:{opts.app_label}_{opts.model_name}_change\"\n return reverse(viewname, args=[self.pk])\n\n def natural_key(self):\n return (self.name,)\n\n @cached_property\n def is_reference(self):\n \"\"\"Return True if this Dataspace is the reference.\"\"\"\n reference = self.__class__._default_manager.get_reference()\n return True if reference and self == reference else False\n\n def get_configuration(self, field_name=None):\n \"\"\"\n Return the associated DataspaceConfiguration.\n If a `field_name` is provided, Return the value for that field from\n the `DataspaceConfiguration`.\n \"\"\"\n try:\n configuration = self.configuration\n except ObjectDoesNotExist:\n return\n\n if field_name:\n return getattr(configuration, field_name, None)\n return configuration\n\n @property\n def has_configuration(self):\n \"\"\"Return True if an associated DataspaceConfiguration instance exists.\"\"\"\n return bool(self.get_configuration())\n\n @property\n def tab_permissions_enabled(self):\n return bool(self.get_configuration(\"tab_permissions\"))"},{"identifier":"DataspaceConfiguration","path":"dje/models.py","snippet":"class DataspaceConfiguration(models.Model):\n dataspace = models.OneToOneField(\n to=\"dje.Dataspace\",\n on_delete=models.CASCADE,\n related_name=\"configuration\",\n )\n\n tab_permissions = models.JSONField(\n blank=True,\n default=dict,\n )\n\n copy_defaults = models.JSONField(\n blank=True,\n null=True,\n )\n\n homepage_layout = models.ForeignKey(\n to=\"reporting.CardLayout\",\n null=True,\n blank=True,\n related_name=\"+\",\n on_delete=models.CASCADE,\n serialize=False,\n help_text=_(\n \"Select a general purpose Card layout that provides timely query \"\n \"results on the DejaCode homepage to your application users.\"\n ),\n )\n\n scancodeio_url = models.URLField(\n _(\"ScanCode.io URL\"),\n max_length=1024,\n blank=True,\n help_text=_(\n \"Enter the URL of your organization's private ScanCode.io instance, \"\n \"if available. If not, DejaCode will use the public ScanCode.io instance \"\n \"to scan your Packages.\"\n ),\n )\n\n scancodeio_api_key = models.CharField(\n _(\"ScanCode.io API key\"),\n max_length=40,\n blank=True,\n help_text=_(\n \"If your organization's private ScanCode.io instance requires an API key \"\n \"for access, provide it here. Otherwise, you can leave this field empty.\"\n ),\n )\n\n vulnerablecode_url = models.URLField(\n _(\"VulnerableCode URL\"),\n max_length=1024,\n blank=True,\n help_text=_(\n \"If your organization has a private VulnerableCode instance, enter its URL \"\n \"here. Otherwise, DejaCode will use the public VulnerableCode to check for \"\n \"vulnerabilities\"\n ),\n )\n\n vulnerablecode_api_key = models.CharField(\n _(\"VulnerableCode API key\"),\n max_length=40,\n blank=True,\n help_text=_(\n \"If your private VulnerableCode instance requires an API key for access, \"\n \"input it here. If not, you can leave this field blank.\"\n ),\n )\n\n purldb_url = models.URLField(\n _(\"PurlDB URL\"),\n max_length=1024,\n blank=True,\n help_text=_(\n \"Enter the URL of your organization's private PurlDB instance, if \"\n \"applicable. If not, DejaCode will utilize the public PurlDB to offer a \"\n \"database of Packages collected from public sources.\"\n ),\n )\n\n purldb_api_key = models.CharField(\n _(\"PurlDB API key\"),\n max_length=40,\n blank=True,\n help_text=_(\n \"If your organization's private PurlDB instance requires an API key for \"\n \"access, provide it here. If not, you can leave this field empty.\"\n ),\n )\n\n def __str__(self):\n return f\"Configuration for {self.dataspace}\""},{"identifier":"DejacodeUser","path":"dje/models.py","snippet":"class DejacodeUser(AbstractUser):\n uuid = models.UUIDField(\n _(\"UUID\"),\n default=uuid.uuid4,\n editable=False,\n unique=True,\n )\n\n dataspace = models.ForeignKey(\n to=\"dje.Dataspace\",\n on_delete=models.PROTECT,\n help_text=DATASPACE_FIELD_HELP_TEXT,\n )\n\n data_email_notification = models.BooleanField(\n default=False,\n help_text=_(\n \"Check this to send email notifications to the user regarding DejaCode data updates; \"\n \"note that the volume could be very large, so this option is normally enabled only \"\n \"during system setup and rollout to monitor application activity.\"\n ),\n )\n\n workflow_email_notification = models.BooleanField(\n default=False,\n help_text=_(\n \"Check this to send email notifications to the user for associated workflow requests; \"\n \"otherwise, request notifications alerts will appear in the DejaCode notifications \"\n \"form.\"\n ),\n )\n\n updates_email_notification = models.BooleanField(\n default=False,\n help_text=_(\n \"Check this to receive email notifications with updates on DejaCode \"\n \"features and news.\"\n ),\n )\n\n company = models.CharField(\n max_length=30,\n blank=True,\n help_text=_(\n \"The company the user is associated with. \"\n \"This can be submitted during the signup process.\"\n ),\n )\n\n last_api_access = models.DateTimeField(\n verbose_name=_(\"last API access\"),\n blank=True,\n null=True,\n )\n\n homepage_layout = models.ForeignKey(\n to=\"reporting.CardLayout\",\n null=True,\n blank=True,\n related_name=\"+\",\n on_delete=models.CASCADE,\n serialize=False,\n help_text=_(\n \"Select a Card layout that provides the query results on the \"\n \"DejaCode homepage that are useful and interesting to you.\"\n ),\n )\n\n objects = DejacodeUserManager()\n\n class Meta:\n ordering = [\"username\"]\n\n def save(self, *args, **kwargs):\n \"\"\"\n Send an email to the user when his password has been changed.\n\n The password can be changed from those locations:\n - Change password user view : /account/password_change/\n - Password reset user view: /account/password_reset/\n - Management command: ./manage.py changepassword\n - Model instance: set_password() + save()\n \"\"\"\n from dje.notification import send_password_changed_email\n\n # self._password will be set to None during a hasher upgrade as we do not\n # want to notify in that case, as it's not considered a password changes.\n # See AbstractBaseUser.check_password.setter\n password_changed = self._password is not None\n user_exists = bool(self.pk)\n\n super().save(*args, **kwargs)\n\n # Do not notify users that are setting their initial password during registration\n if password_changed and user_exists and self.last_login:\n send_password_changed_email(self)\n\n @property\n def last_active(self):\n activity_date_fields = [\n self.date_joined,\n self.last_login,\n self.last_api_access,\n ]\n return max([field for field in activity_date_fields if field])\n\n def get_group_names(self):\n \"\"\"\n Return the group names assigned to the User through the DB and LDAP\n authentication (when enabled).\n \"\"\"\n group_names_from_db = list(self.groups.values_list(\"name\", flat=True))\n\n ldap_user = getattr(self, \"ldap_user\", None)\n if ldap_user:\n return list(set(group_names_from_db).union(ldap_user.group_names))\n\n return group_names_from_db\n\n def get_homepage_layout(self):\n \"\"\"\n Return the User `homepage_layout`, from the this instance first,\n or fallback on the Dataspace layout, if set.\n \"\"\"\n return self.homepage_layout or self.dataspace.get_configuration(\"homepage_layout\")\n\n def email_user(self, subject, message, from_email=None, **kwargs):\n \"\"\"Wrap the method in a task.\"\"\"\n send_mail_task.delay(subject, message, from_email, [self.email], **kwargs)\n\n def regenerate_api_key(self):\n \"\"\"\n Regenerate the user API key.\n Since the `key` value is the primary key on the Token `model`,\n the old key needs to be deleted first, a new one is then created.\n \"\"\"\n self.auth_token.delete()\n Token.objects.create(user=self)\n\n def serialize_user_data(self):\n fields = [\n \"email\",\n \"first_name\",\n \"last_name\",\n \"username\",\n \"company\",\n \"last_login\",\n \"date_joined\",\n \"last_api_access\",\n \"last_active\",\n \"is_superuser\",\n \"is_staff\",\n \"is_active\",\n \"updates_email_notification\",\n \"dataspace\",\n ]\n\n return {\n field: str(value) for field in fields if (value := getattr(self, field)) is not None\n }\n\n def serialize_hook(self, hook):\n return {\n \"hook\": hook.dict(),\n **self.serialize_user_data(),\n }"},{"identifier":"ExternalReference","path":"dje/models.py","snippet":"class ExternalReference(HistoryFieldsMixin, DataspacedModel):\n \"\"\"\n Maps DJE objects to external resources.\n One DJE object may have several ExternalReference when it's referenced on\n multiple sources.\n Also, there is no unicity collision possible as we use the object_id.\n\n The copy for GenericForeignKey field is not supported yet.\n \"\"\"\n\n # The following models should always inherit from ExternalReferenceMixin\n # for the proper deletion in CASCADE behavior.\n CT_LIMIT = (\n models.Q(app_label=\"organization\", model=\"owner\")\n | models.Q(app_label=\"license_library\", model=\"license\")\n | models.Q(app_label=\"component_catalog\", model=\"component\")\n | models.Q(app_label=\"component_catalog\", model=\"package\")\n )\n\n content_type = models.ForeignKey(\n to=ContentType,\n limit_choices_to=CT_LIMIT,\n on_delete=models.PROTECT,\n )\n\n object_id = models.PositiveIntegerField()\n\n content_object = GenericForeignKey(\"content_type\", \"object_id\")\n\n external_source = models.ForeignKey(\n to=\"dje.ExternalSource\",\n on_delete=models.PROTECT,\n )\n\n external_id = models.CharField(\n max_length=500,\n blank=True,\n help_text=_(\"Value of the identifier used on the source to reference the object.\"),\n )\n\n external_url = models.URLField(\n max_length=1024,\n blank=True,\n help_text=_(\"A URL to the component, or component metadata, in the external source.\"),\n )\n\n objects = ExternalReferenceManager()\n\n class Meta:\n unique_together = (\"dataspace\", \"uuid\")\n ordering = [\"external_source\", \"external_id\"]\n\n def __str__(self):\n return f\"{self.external_source}: {self.external_id}\"\n\n def save(self, *args, **kwargs):\n self.dataspace = self.content_object.dataspace\n super().save(*args, **kwargs)"},{"identifier":"ExternalSource","path":"dje/models.py","snippet":"class ExternalSource(DataspacedModel):\n label = models.CharField(\n max_length=50,\n help_text=_(\"A Label is a concise name of the external source as it \" \"is commonly known.\"),\n )\n\n notes = models.TextField(\n blank=True,\n help_text=_(\n \"Notes describe the purpose and special characteristics \" \"of the external source.\"\n ),\n )\n\n homepage_url = models.URLField(\n max_length=1024,\n blank=True,\n help_text=_(\"Main homepage URL of the external source.\"),\n )\n\n class Meta:\n ordering = [\"label\"]\n unique_together = ((\"dataspace\", \"label\"), (\"dataspace\", \"uuid\"))\n\n def __str__(self):\n return self.label"},{"identifier":"History","path":"dje/models.py","snippet":"class History(models.Model):\n ADDITION = ADDITION\n CHANGE = CHANGE\n DELETION = DELETION\n\n ACTION_FLAG_CHOICES = (\n (ADDITION, _(\"Addition\")),\n (CHANGE, _(\"Change\")),\n (DELETION, _(\"Deletion\")),\n )\n\n object_dataspace = models.ForeignKey(\n to=\"dje.Dataspace\",\n on_delete=models.CASCADE,\n null=True,\n blank=True,\n editable=False,\n )\n\n serialized_data = models.TextField(\n null=True,\n blank=True,\n editable=False,\n help_text=_(\"Serialized data of the instance just before this change.\"),\n )\n\n # The following fields are directly taken from django.contrib.admin.models.LogEntry\n # Since the LogEntry is not abstract we cannot properly inherit from it.\n\n action_time = models.DateTimeField(\n _(\"action time\"),\n default=timezone.now,\n editable=False,\n )\n\n user = models.ForeignKey(\n settings.AUTH_USER_MODEL,\n models.CASCADE,\n verbose_name=_(\"user\"),\n )\n\n content_type = models.ForeignKey(\n ContentType,\n models.SET_NULL,\n verbose_name=_(\"content type\"),\n blank=True,\n null=True,\n )\n\n object_id = models.TextField(\n _(\"object id\"),\n blank=True,\n null=True,\n )\n\n object_repr = models.CharField(\n _(\"object repr\"),\n max_length=200,\n )\n\n action_flag = models.PositiveSmallIntegerField(\n _(\"action flag\"),\n choices=ACTION_FLAG_CHOICES,\n )\n\n # change_message is either a string or a JSON structure\n change_message = models.TextField(\n _(\"change message\"),\n blank=True,\n )\n\n objects = HistoryManager()\n\n class Meta:\n verbose_name = _(\"history entry\")\n verbose_name_plural = _(\"history entries\")\n ordering = (\"-action_time\",)\n\n # Clone the method from Django's LogEntry model.\n __repr__ = LogEntry.__repr__\n __str__ = LogEntry.__str__\n is_addition = LogEntry.is_addition\n is_change = LogEntry.is_change\n is_deletion = LogEntry.is_deletion\n get_change_message = LogEntry.get_change_message\n get_edited_object = LogEntry.get_edited_object\n get_admin_url = LogEntry.get_edited_object\n\n @classmethod\n def log_addition(cls, user, obj, message=None):\n \"\"\"Create History entry on Addition with the proper `change_message`.\"\"\"\n if not message:\n message = [{\"added\": {}}]\n\n return cls.objects.log_action(user, obj, cls.ADDITION, message)\n\n @classmethod\n def log_change(cls, user, obj, message, serialized_data=None):\n \"\"\"Create History entry on Change.\"\"\"\n return cls.objects.log_action(user, obj, cls.CHANGE, message, serialized_data)\n\n @classmethod\n def log_deletion(cls, user, obj):\n \"\"\"\n Create History entry on Deletion.\n Include the serialized_data if `as_json()` is available on the model class.\n \"\"\"\n serialized_data = None\n with suppress(AttributeError):\n serialized_data = obj.as_json()\n\n return cls.objects.log_action(user, obj, cls.DELETION, serialized_data=serialized_data)"},{"identifier":"HistoryFieldsMixin","path":"dje/models.py","snippet":"class HistoryFieldsMixin(HistoryUserFieldsMixin, HistoryDateFieldsMixin):\n \"\"\"Add the created_date, last_modified_date, created_by, last_modified_by fields.\"\"\"\n\n class Meta:\n abstract = True"},{"identifier":"is_dataspace_related","path":"dje/models.py","snippet":"def is_dataspace_related(model_class):\n \"\"\"\n Return True if the given model_class has a ForeignKey field related to\n the Dataspace model.\n \"\"\"\n return any(\n 1\n for f in model_class._meta.get_fields()\n if f.many_to_one and (f.related_model == Dataspace or f.related_model == \"dje.Dataspace\")\n )"},{"identifier":"send_notification_email","path":"dje/notification.py","snippet":"def send_notification_email(user, instance, action, message=\"\"):\n if not has_email_settings():\n return\n\n if not hasattr(instance, \"dataspace\"):\n return\n\n recipients = get_user_model().objects.get_data_update_recipients(instance.dataspace)\n if not recipients:\n return\n\n verbose_name = instance._meta.verbose_name.capitalize()\n verbose_action = VERBOSE_ACTION[action]\n subject = f'{verbose_action} {verbose_name}: \"{instance}\"'\n body = (\n f'{verbose_name} \"{instance}\" in dataspace \"{instance.dataspace.name}\" '\n f\"{verbose_action.lower()} by: {user.first_name} {user.last_name} ({user.username})\"\n )\n\n if action is History.CHANGE and message:\n if message == \"No fields changed.\":\n return\n body += f\"\\n\\n{message}\"\n\n if action is not History.DELETION and settings.SITE_URL:\n site_url = settings.SITE_URL.rstrip(\"/\")\n body += f\"\\n\\n{site_url}{instance.get_admin_url()}\"\n\n send_mail_task.delay(subject, body, settings.DEFAULT_FROM_EMAIL, recipients)"},{"identifier":"send_notification_email_on_queryset","path":"dje/notification.py","snippet":"def send_notification_email_on_queryset(user, queryset, action, message=\"\"):\n if not has_email_settings():\n return\n\n if not queryset:\n return\n\n if len(queryset) == 1:\n return send_notification_email(user, queryset[0], action, message)\n\n first = queryset[0]\n if not hasattr(first, \"dataspace\"):\n return\n\n recipients = get_user_model().objects.get_data_update_recipients(first.dataspace)\n if not recipients:\n return\n\n verbose_name_plural = first._meta.verbose_name_plural.capitalize()\n verbose_action = VERBOSE_ACTION[action]\n\n subject = f\"Multiple {verbose_name_plural} {verbose_action.lower()}\"\n body = (\n f'{verbose_name_plural} in dataspace \"{first.dataspace.name}\" '\n f\"{verbose_action.lower()} by {user.first_name} {user.last_name} ({user.username}):\"\n )\n\n for instance in queryset:\n body += f\"\\n- {instance}\"\n\n if action is not History.DELETION and settings.SITE_URL:\n site_url = settings.SITE_URL.rstrip(\"/\")\n body += f\" {site_url}{instance.get_admin_url()}\"\n\n if message:\n body += f\"\\n\\n{message}\"\n\n send_mail_task.delay(subject, body, settings.DEFAULT_FROM_EMAIL, recipients)"},{"identifier":"get_protected_fields","path":"dje/permissions.py","snippet":"def get_protected_fields(model_class, user):\n \"\"\"Return the list of protected fields names for the given `user`.\"\"\"\n protected_fields = getattr(model_class(), \"permission_protected_fields\", {})\n\n return [\n field_name\n for field_name, perm_codename in protected_fields.items()\n if not user.has_perm(f\"{model_class._meta.app_label}.{perm_codename}\")\n ]"},{"identifier":"advanced_search","path":"dje/search.py","snippet":"def advanced_search(search_terms, search_fields):\n lookup_types = {\n \":\": \"icontains\",\n \"=\": \"iexact\",\n \"^\": \"istartswith\",\n }\n or_queries = []\n\n for term in split_search_terms(search_terms):\n lookup_type = \"icontains\" # default\n lookup_fields = search_fields\n lookup_operators = [force_str(key) for key in lookup_types.keys()]\n\n # 'apache', '^apache', '=apache', , ':apache'\n if term.startswith(tuple(lookup_operators)):\n term, lookup_type = term[1:], lookup_types.get(term[0])\n\n # 'name:apache', 'name^apache', 'name=apache'\n else:\n for field_name in lookup_fields:\n missing_operator = term == field_name\n if not term.startswith(field_name) or missing_operator:\n continue\n\n operator = term[len(field_name)]\n if operator in lookup_operators:\n lookup_type = lookup_types.get(operator)\n lookup_fields = [field_name]\n _, term = term.split(f\"{field_name}{operator}\", 1)\n break\n\n if not term:\n continue\n\n orm_lookups = [f\"{field}__{lookup_type}\" for field in lookup_fields]\n or_queries.extend([models.Q(**{orm_lookup: term}) for orm_lookup in orm_lookups])\n\n if or_queries:\n return reduce(or_, or_queries)"},{"identifier":"CHANGELIST_LINK_TEMPLATE","path":"dje/utils.py","snippet":"CHANGELIST_LINK_TEMPLATE = (\n '<strong>See the <a href=\"{}\" target=\"_blank\">{} {}</a> in changelist</strong>'\n)"},{"identifier":"class_wrap","path":"dje/utils.py","snippet":"def class_wrap(value, class_):\n \"\"\"Return the given HTML wrapped in a div with the given class set.\"\"\"\n return format_html('<div class=\"{}\">{}</div>', class_, mark_safe(value))"},{"identifier":"construct_changes_details_message","path":"dje/utils.py","snippet":"def construct_changes_details_message(changes_details):\n msg = []\n header = '\\n\\n\\nChanges details for {model_class} \"{instance}\"'\n change_line = \"\\n\\n* {field}\\nOld value: {old}\\nNew value: {new}\"\n\n for instance, data in changes_details.items():\n msg.append(header.format(model_class=instance.__class__.__name__, instance=instance))\n for field, old, new in data:\n msg.append(change_line.format(field=field, old=old, new=new))\n return \"\".join(msg)"},{"identifier":"get_previous_next","path":"dje/utils.py","snippet":"def get_previous_next(ids, current_id):\n \"\"\"\n Return the previous and next entries from a given `ids` list\n and the current id value.\n\n >>> get_previous_next([1, 2, 3, 4], 3)\n (2, 4)\n >>> get_previous_next(['a', 'b', 'c'], 'b')\n ('a', 'c')\n \"\"\"\n previous, next = None, None\n\n try:\n index = ids.index(current_id)\n except ValueError:\n return None, None\n\n if index > 0:\n previous = ids[index - 1]\n if index < len(ids) - 1:\n next = ids[index + 1]\n\n return previous, next"},{"identifier":"group_by","path":"dje/utils.py","snippet":"def group_by(queryset, field_name, values=None, count_on=None, distinct=False):\n from django.db.models import Count\n\n values = values or [field_name]\n count_on = count_on or field_name\n\n return queryset.values(*values).order_by().annotate(count=Count(count_on, distinct=distinct))"},{"identifier":"has_permission","path":"dje/utils.py","snippet":"def has_permission(model, user, action):\n \"\"\"Return True is the `user` has the Permission for the given action of the model.\"\"\"\n opts = model._meta\n codename = get_permission_codename(action, opts)\n return user.has_perm(f\"{opts.app_label}.{codename}\")"},{"identifier":"queryset_to_changelist_href","path":"dje/utils.py","snippet":"def queryset_to_changelist_href(queryset, params=None):\n \"\"\"Return an URL to a changelist based on the given queryset.\"\"\"\n if not queryset:\n return\n\n if params is None:\n params = {}\n\n opts = queryset.model._meta\n url = reverse(f\"admin:{opts.app_label}_{opts.model_name}_changelist\")\n\n ids = queryset.values_list(\"id\", flat=True)\n params.update({\"id__in\": \",\".join(str(id_) for id_ in ids)})\n\n return f\"{url}?{urlencode(params)}\""},{"identifier":"ActivityLog","path":"dje/views.py","snippet":"class ActivityLog(\n LoginRequiredMixin,\n BootstrapCSSMixin,\n DownloadableMixin,\n TemplateView,\n):\n template_name = \"activity_log.html\"\n model = None\n action_flag_map = {\n History.ADDITION: \"Addition\",\n History.CHANGE: \"Change\",\n History.DELETION: \"Deletion\",\n }\n\n def get_days(self):\n days = self.request.GET.get(\"days\", None)\n try:\n return int(days)\n except (TypeError, ValueError):\n return 90 # Default value\n\n def get_history_entries(self, days):\n \"\"\"\n Return all the History entries, filtered by the number of days and\n scoped using the current user Dataspace.\n \"\"\"\n user_dataspace_id = self.request.user.dataspace_id\n content_type = ContentType.objects.get_for_model(self.model)\n start = datetime.datetime.now() - datetime.timedelta(days=days)\n\n history_entries = History.objects.filter(\n object_dataspace_id=user_dataspace_id,\n content_type=content_type,\n action_time__gt=start,\n )\n\n has_history_fields = issubclass(self.model, HistoryFieldsMixin)\n if has_history_fields:\n # Use the history fields from the model for the Addition entries\n history_entries = history_entries.exclude(action_flag=History.ADDITION)\n\n objects = self.model.objects.scope_by_id(user_dataspace_id).filter(\n created_date__gt=start\n )\n\n addition_entries = []\n for obj in objects:\n addition_entries.append(\n History(\n action_time=obj.created_date,\n user=obj.created_by,\n object_dataspace=obj.dataspace,\n content_type=content_type,\n object_id=obj.id,\n action_flag=History.ADDITION,\n change_message=\"Added.\",\n )\n )\n\n history_entries = list(history_entries) + addition_entries\n\n return history_entries\n\n @staticmethod\n def get_object_or_repr(history_entry):\n try:\n return history_entry.get_edited_object()\n except ObjectDoesNotExist:\n return history_entry.object_repr\n\n def get_objects(self, days):\n history_entries = self.get_history_entries(days)\n\n objects = []\n for history_entry in history_entries:\n objects.append(\n {\n \"history\": history_entry,\n \"obj\": self.get_object_or_repr(history_entry),\n \"action\": self.action_flag_map[history_entry.action_flag],\n }\n )\n\n return objects\n\n def get_format(self):\n return \"html\"\n\n def get_root_filename(self):\n model_name = self.model._meta.model_name\n return f\"{model_name}_activity_log\"\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n days = self.get_days()\n context.update(\n {\n \"days\": days,\n \"objects\": self.get_objects(days),\n \"verbose_name\": self.model._meta.verbose_name,\n \"dataspace\": self.request.user.dataspace,\n \"now\": datetime.datetime.now(),\n }\n )\n return context"},{"identifier":"clone_dataset_view","path":"dje/views.py","snippet":"@login_required\ndef clone_dataset_view(request, pk):\n \"\"\"Call the clonedataset management command as a celery task.\"\"\"\n changelist_url = reverse(\"admin:dje_dataspace_changelist\")\n user = request.user\n template_dataspace = settings.TEMPLATE_DATASPACE\n\n if not all(\n [\n template_dataspace,\n user.is_superuser,\n user.dataspace.is_reference,\n ]\n ):\n return redirect(changelist_url)\n\n try:\n reference = Dataspace.objects.get(name=template_dataspace)\n target = Dataspace.objects.get(pk=pk)\n except Dataspace.DoesNotExist:\n return redirect(changelist_url)\n\n call_management_command.delay(\n \"clonedataset\",\n reference.name,\n target.name,\n user.username,\n user_id=user.id,\n product_portfolio=True,\n )\n\n msg = \"Cloning task in progress.\"\n if user.email:\n msg += f' An email will be sent to \"{user.email}\" on completion.'\n messages.success(request, msg)\n\n return redirect(changelist_url)"},{"identifier":"docs_models_view","path":"dje/views.py","snippet":"@login_required\ndef docs_models_view(request):\n from dje.admin import dejacode_site\n\n apps_white_list = [\n \"organization\",\n \"license_library\",\n \"component_catalog\",\n \"product_portfolio\",\n \"workflow\",\n \"reporting\",\n \"policy\",\n ]\n\n model_classes = [\n model for model in apps.get_models() if model._meta.app_label in apps_white_list\n ]\n\n def get_limited_fields(model):\n return [\n f\n for f in model._meta.get_fields()\n if not f.auto_created and f.name not in [\"dataspace\", \"uuid\"]\n ]\n\n help_data = defaultdict(list)\n for model in model_classes:\n opts = model._meta\n app_verbose_name = apps.app_configs.get(opts.app_label).verbose_name\n model_admin = dejacode_site._registry.get(model)\n help_data[app_verbose_name].append(\n {\n \"verbose_name\": opts.verbose_name,\n \"short_description\": getattr(model_admin, \"short_description\", \"\"),\n \"long_description\": getattr(model_admin, \"long_description\", \"\"),\n \"fields\": get_limited_fields(model),\n }\n )\n\n context = {\n \"help_data\": dict(help_data),\n \"document\": {\"title\": \"Models documentation\"},\n }\n\n return render(request, \"admin/docs/models.html\", context)"},{"identifier":"manage_copy_defaults_view","path":"dje/views.py","snippet":"@login_required\ndef manage_copy_defaults_view(request, pk):\n changelist_url = reverse(\"admin:dje_dataspace_changelist\")\n\n supported_apps = [\n \"dje\",\n \"organization\",\n \"license_library\",\n \"component_catalog\",\n \"product_portfolio\",\n \"workflow\",\n \"reporting\",\n \"policy\",\n \"notification\",\n ]\n\n try:\n dataspace = Dataspace.objects.get(pk=unquote_plus(pk))\n except Dataspace.DoesNotExist:\n return redirect(changelist_url)\n\n if not request.user.is_superuser or dataspace != request.user.dataspace:\n return redirect(changelist_url)\n\n if request.method == \"POST\":\n formset = CopyDefaultsFormSet(request.POST)\n if formset.is_valid():\n formset.save(dataspace)\n formset.log_change(request, dataspace, \"Changed copy defaults configuration.\")\n messages.success(request, _(\"Copy defaults updated.\"))\n else:\n messages.error(request, _(\"Error, please refresh the page and try again.\"))\n else:\n initial = [\n {\"app_name\": str(apps.get_app_config(app_label).verbose_name)}\n for app_label in supported_apps\n ]\n formset = CopyDefaultsFormSet(initial=initial)\n formset.load(dataspace, default=COPY_DEFAULT_EXCLUDE)\n\n context = {\n \"formset\": formset,\n \"object\": dataspace,\n \"opts\": dataspace._meta,\n }\n\n return render(request, \"admin/dje/dataspace/copy_defaults_form.html\", context)"},{"identifier":"manage_tab_permissions_view","path":"dje/views.py","snippet":"@login_required\ndef manage_tab_permissions_view(request, pk):\n changelist_url = reverse(\"admin:dje_dataspace_changelist\")\n\n try:\n dataspace = Dataspace.objects.get(pk=unquote_plus(pk))\n except Dataspace.DoesNotExist:\n return redirect(changelist_url)\n\n if not request.user.is_superuser or dataspace != request.user.dataspace:\n return redirect(changelist_url)\n\n if request.method == \"POST\":\n formset = TabPermissionsFormSet(request.POST)\n if formset.is_valid():\n formset.save(dataspace)\n formset.log_change(request, dataspace, \"Changed tab permissions configuration.\")\n messages.success(request, _(\"Tab permissions updated.\"))\n else:\n messages.error(request, _(\"Error, please refresh the page and try again.\"))\n else:\n initial = [{\"group_name\": group.name} for group in Group.objects.all()]\n formset = TabPermissionsFormSet(initial=initial)\n formset.load(dataspace)\n\n context = {\n \"formset\": formset,\n \"object\": dataspace,\n \"opts\": dataspace._meta,\n }\n\n return render(request, \"admin/dje/dataspace/tab_permissions_form.html\", context)"},{"identifier":"object_compare_view","path":"dje/views.py","snippet":"@login_required\ndef object_compare_view(request):\n target_dataspace_id = request.GET.get(\"target\", \"\")\n ids = request.GET.get(\"ids\", \"\").split(\",\")\n\n model_class = get_model_class_from_path(request.path)\n opts = model_class._meta\n preserved_filters = get_preserved_filters(\n request, model_class, parameter_name=\"_changelist_filters\"\n )\n changelist_url = reverse(f\"admin:{opts.app_label}_{opts.model_name}_changelist\")\n redirect_url = add_preserved_filters(\n {\"preserved_filters\": preserved_filters, \"opts\": opts}, changelist_url\n )\n\n if len(ids) != 1:\n messages.warning(\n request, \"Compare allows 1 object only. Please select 1 object to compare.\"\n )\n return redirect(redirect_url)\n\n if request.user.dataspace.is_reference:\n if not target_dataspace_id:\n return dataspace_choice_for_compare_view(request)\n try:\n target_dataspace = Dataspace.objects.get(pk=target_dataspace_id)\n except ObjectDoesNotExist:\n return redirect(redirect_url)\n else:\n target_dataspace = request.user.dataspace\n\n try:\n source_object = model_class.objects.get(id=ids[0])\n except ObjectDoesNotExist:\n return redirect(redirect_url)\n\n target_object = get_object_in(source_object, target_dataspace)\n if not target_object:\n error = f'No related object in the Dataspace \"{target_dataspace}\"'\n messages.warning(request, error)\n return redirect(redirect_url)\n\n # First section of the view, presenting the per filed diff.\n if request.method == \"GET\":\n excluded = [\n \"last_modified_date\",\n \"created_date\",\n \"completion_level\",\n \"usage_policy\",\n \"guidance\",\n \"guidance_url\",\n ]\n\n compare_diff, compare_diff_m2m = get_object_compare_diff(\n source_object, target_object, excluded\n )\n\n return render(\n request,\n \"admin/object_compare.html\",\n {\n \"source_object\": source_object,\n \"target_object\": target_object,\n \"compare_diff\": compare_diff,\n \"compare_diff_m2m\": compare_diff_m2m,\n \"opts\": opts,\n \"preserved_filters\": preserved_filters,\n },\n )\n\n # POST section of the view\n updated_fields = []\n\n for field_name in request.POST.getlist(\"checkbox_select\"):\n field = source_object._meta.get_field(field_name)\n field_value = getattr(source_object, field_name)\n\n if isinstance(field, models.ForeignKey):\n if field_value is not None:\n fk_in_target = get_or_create_in(field_value, target_dataspace, request.user)\n if fk_in_target:\n setattr(target_object, field_name, fk_in_target)\n updated_fields.append(field_name)\n else:\n setattr(target_object, field_name, None)\n updated_fields.append(field_name)\n else:\n setattr(target_object, field_name, field_value)\n updated_fields.append(field_name)\n\n # Saving only at the end of the process, if at least one field was modified\n if updated_fields:\n target_object.save()\n message = 'Changed {}, values updated from \"{}\".'.format(\n \", \".join(updated_fields), source_object.dataspace.name\n )\n History.log_addition(request.user, target_object, message)\n messages.success(request, message)\n\n return redirect(redirect_url)"},{"identifier":"object_copy_view","path":"dje/views.py","snippet":"@login_required\ndef object_copy_view(request):\n \"\"\"\n Copy objects across Dataspaces.\n This is the view called by the \"copy objects\" admin action.\n The first step is to present to the user the list of Object to be copied\n into his Dataspace and the list of Object that already exists\n in the target and give the choice to update those.\n If the User is a member of the Reference Dataspace, he's allowed\n to copy from any source Dataspace and to select any destination.\n This result as an extra step of presenting the target Dataspace list of\n choices.\n \"\"\"\n user_dataspace = request.user.dataspace\n # Declared here as it required in GET and POST cases.\n M2MConfigurationFormSet = formset_factory(\n wraps(M2MCopyConfigurationForm)(partial(M2MCopyConfigurationForm, user=request.user)),\n extra=0,\n )\n\n model_class = get_model_class_from_path(request.path)\n\n # Default entry point of the view, requested using a GET\n # At that stage, we are only looking at what the User requested,\n # making sure everything is in order, present him what is going to\n # happens and ask for his confirmation.\n if request.method == \"GET\":\n requested_ids = request.GET.get(\"ids\", \"\")\n\n # In case the view is not requested with the proper parameters\n if not requested_ids:\n raise Http404\n\n opts = model_class._meta\n preserved_filters = get_preserved_filters(\n request, model_class, parameter_name=\"_changelist_filters\"\n )\n changelist_url = reverse(f\"admin:{opts.app_label}_{opts.model_name}_changelist\")\n redirect_url = add_preserved_filters(\n {\"preserved_filters\": preserved_filters, \"opts\": opts}, changelist_url\n )\n\n # Ids of objects to be copied\n ids = requested_ids.split(\",\")\n\n # Limit the copy to 100 Objects at the time, as it's the number of\n # Objects we display per page, default value for the list_per_page\n # of the ModelAdmin\n COPY_NB_OBJECT_LIMIT = 100\n if len(ids) > COPY_NB_OBJECT_LIMIT:\n msg = (\n f\"Maximum of objects that can be copied at once is \"\n f\"limited to {COPY_NB_OBJECT_LIMIT} (by system-wide settings)\"\n )\n messages.warning(request, msg)\n return redirect(redirect_url)\n\n # Let's find the Source Dataspace using the first id\n # This block will redirect the user to the list if the\n # first id of the list do not exist\n try:\n source_object = model_class.objects.get(id=ids[0])\n except ObjectDoesNotExist:\n return redirect(redirect_url)\n\n # No custom permission for 'copy', we use the 'add' one\n if not has_permission(source_object, request.user, \"add\"):\n messages.error(request, _(\"Sorry you do not have rights to execute this action\"))\n return redirect(redirect_url)\n\n source = source_object.dataspace\n # As a non-Reference Dataspace User, I can only use the Reference\n # data as the source and my Dataspace as the target\n # As a Reference User, I can choose both, source and target.\n if user_dataspace.is_reference:\n # The following is only used when the User is in the Reference\n targets_from_request = request.GET.getlist(\"target\")\n # If the target has been set, then we can continue\n if targets_from_request:\n data = {\"target\": targets_from_request}\n choice_form = MultiDataspaceChoiceForm(source, request.user, data=data)\n if not choice_form.is_valid():\n return redirect(redirect_url)\n targets = choice_form.cleaned_data[\"target\"]\n # else, we build a form to offer the choice to the user,\n # choices do not include the current source\n else:\n initial = {\n \"ids\": requested_ids,\n \"_changelist_filters\": dict(parse_qsl(preserved_filters)).get(\n \"_changelist_filters\"\n ),\n }\n is_popup = request.GET.get(IS_POPUP_VAR, False)\n if is_popup:\n initial[\"_popup\"] = is_popup\n choice_form = MultiDataspaceChoiceForm(source, request.user, initial=initial)\n return render(\n request,\n \"admin/object_copy_dataspace_form.html\",\n {\n \"form\": choice_form,\n \"opts\": opts,\n \"is_popup\": is_popup,\n \"preserved_filters\": preserved_filters,\n },\n )\n elif not source.is_reference:\n # As a non-Reference User my only \"external\" source of data allowed\n # is the Reference Dataspace\n return redirect(redirect_url)\n else:\n targets = [user_dataspace]\n\n # At this stage, we have the Source and Target Dataspaces\n # Let's see which objects are eligible for copy, or offer the update\n copy_candidates = []\n update_candidates = []\n\n # Building a QuerySet based on the given ids, if an non-authorized or\n # non-existing id was injected it will be ignored thanks to the\n # id__in and the dataspace scoping.\n queryset = model_class.objects.scope(source).filter(id__in=ids)\n\n for target in targets:\n for source_instance in queryset:\n matched_object = get_object_in(source_instance, target)\n if matched_object:\n # Inject the source_instance for future usage in the template\n update_candidates.append((matched_object, source_instance))\n else:\n copy_candidates.append((source_instance, target))\n\n initial = {\n \"source\": source,\n \"targets\": targets,\n \"ct\": ContentType.objects.get_for_model(model_class).id,\n }\n form = CopyConfigurationForm(request.user, initial=initial)\n\n # Many2Many exclude on copy/update\n m2m_initial = [\n {\"ct\": ContentType.objects.get_for_model(m2m_field.remote_field.through).id}\n for m2m_field in model_class._meta.many_to_many\n ]\n\n # Also handle relational fields if explicitly declared on the Model using the\n # get_extra_relational_fields method.\n for field_name in model_class.get_extra_relational_fields():\n related_model = model_class._meta.get_field(field_name).related_model\n if related_model().get_exclude_candidates_fields():\n ct = ContentType.objects.get_for_model(related_model)\n m2m_initial.append({\"ct\": ct.id})\n\n m2m_formset = M2MConfigurationFormSet(initial=m2m_initial)\n\n return render(\n request,\n \"admin/object_copy.html\",\n {\n \"copy_candidates\": copy_candidates,\n \"update_candidates\": update_candidates,\n \"form\": form,\n \"m2m_formset\": m2m_formset,\n \"opts\": source_object._meta,\n \"preserved_filters\": preserved_filters,\n },\n )\n\n # Second section of the view, following the POST\n if request.method == \"POST\":\n config_form = CopyConfigurationForm(request.user, request.POST)\n\n if not config_form.is_valid():\n raise Http404\n\n model_class = config_form.model_class\n opts = model_class._meta\n preserved_filters = get_preserved_filters(\n request, model_class, parameter_name=\"_changelist_filters\"\n )\n\n # We use False rather than empty list to keep track of the non-selection\n # vs unknown in lower level copy method.\n exclude_copy = {model_class: config_form.cleaned_data.get(\"exclude_copy\")}\n exclude_update = {model_class: config_form.cleaned_data.get(\"exclude_update\")}\n\n # Append the m2m copy configuration\n for m2m_form in M2MConfigurationFormSet(request.POST):\n if not m2m_form.is_valid():\n continue\n m2m_model_class = m2m_form.model_class\n cleaned_data = m2m_form.cleaned_data\n skip_on_copy = cleaned_data.get(\"skip_on_copy\")\n skip_on_update = cleaned_data.get(\"skip_on_update\")\n exclude_copy.update(\n {m2m_model_class: SKIP if skip_on_copy else cleaned_data.get(\"exclude_copy\")}\n )\n exclude_update.update(\n {m2m_model_class: SKIP if skip_on_update else cleaned_data.get(\"exclude_update\")}\n )\n\n copy_candidates = request.POST.get(\"copy_candidates\", \"\")\n selected_for_update = request.POST.getlist(\"select_for_update\")\n\n source, copied, updated, errors = config_form.submit(\n copy_candidates, selected_for_update, exclude_copy, exclude_update\n )\n\n if copied or updated:\n msg = \"Copied/updated from {} dataspace.\".format(\n source.name if source.is_reference else \"another\"\n )\n\n if errors:\n errors_count = len(errors)\n msg = \"{} object w{} not copied/updated.\".format(\n errors_count, pluralize(errors_count, \"as,ere\")\n )\n messages.error(request, msg)\n\n object_for_raw_id_lookup = None\n if request.GET.get(IS_POPUP_VAR, 0):\n object_for_raw_id_lookup = copied + updated\n if len(object_for_raw_id_lookup) == 1:\n object_for_raw_id_lookup = object_for_raw_id_lookup[0][1]\n else:\n object_for_raw_id_lookup = None\n\n return render(\n request,\n \"admin/object_copy_results.html\",\n {\n \"copied\": copied,\n \"updated\": updated,\n \"errors\": errors,\n \"opts\": opts,\n \"preserved_filters\": preserved_filters,\n \"object_for_raw_id_lookup\": object_for_raw_id_lookup,\n },\n )"}],"string":"[\n {\n \"identifier\": \"IS_FILTER_LOOKUP_VAR\",\n \"path\": \"dje/filters.py\",\n \"snippet\": \"IS_FILTER_LOOKUP_VAR = \\\"_filter_lookup\\\"\"\n },\n {\n \"identifier\": \"CreatedByListFilter\",\n \"path\": \"dje/filters.py\",\n \"snippet\": \"class CreatedByListFilter(filters.SimpleListFilter):\\n \\\"\\\"\\\"\\n Filter by the user who created an object according to the objects's\\n associated History. The value from LimitToDataspaceListFilter is\\n used for the Dataspace if it exists.\\n \\\"\\\"\\\"\\n\\n title = _(\\\"created by\\\")\\n parameter_name = \\\"created_by\\\"\\n\\n def __init__(self, request, params, model, model_admin):\\n self.model_content_type = ContentType.objects.get_for_model(model)\\n super().__init__(request, params, model, model_admin)\\n\\n def lookups(self, request, model_admin):\\n dataspace_id = request.GET.get(DataspaceFilter.parameter_name, request.user.dataspace.id)\\n # Find users of the selected Dataspace\\n users = get_user_model().objects.scope_by_id(dataspace_id)\\n\\n # Find users who have created at least 1 object\\n users = (\\n users.filter(\\n history__action_flag=History.ADDITION,\\n history__content_type=self.model_content_type,\\n history__object_dataspace__id=dataspace_id,\\n )\\n .distinct()\\n .order_by(\\\"last_name\\\")\\n )\\n return [(user.pk, user.get_full_name()) for user in users]\\n\\n def queryset(self, request, queryset):\\n if self.value() is not None:\\n user_pk = self.value()\\n history_entries = History.objects.filter(\\n content_type=self.model_content_type, action_flag=History.ADDITION, user__pk=user_pk\\n )\\n pks = list(history_entries.values_list(\\\"object_id\\\", flat=True))\\n return queryset.filter(pk__in=pks)\"\n },\n {\n \"identifier\": \"DataspaceFilter\",\n \"path\": \"dje/filters.py\",\n \"snippet\": \"class DataspaceFilter(ChoicesOnlyListFilterMixin, BaseDataspaceLookupsFilter):\\n \\\"\\\"\\\"\\n Scope the ChangeList results by a Dataspace.\\n Default is the current User Dataspace.\\n Anyone can look into reference Dataspace.\\n Only Reference User can look into other Dataspaces.\\n \\\"\\\"\\\"\\n\\n title = _(\\\"dataspace\\\")\\n parameter_name = \\\"dataspace__id__exact\\\"\\n\\n def lookups(self, request, model_admin):\\n \\\"\\\"\\\"Set the lookup value for the current user dataspace choice to None.\\\"\\\"\\\"\\n lookups = super().lookups(request, model_admin)\\n return [(None if name == request.user.dataspace.name else pk, name) for pk, name in lookups]\\n\\n def queryset(self, request, queryset):\\n if self.value():\\n return queryset.scope_by_id(self.value())\\n return queryset.scope(request.user.dataspace)\"\n },\n {\n \"identifier\": \"HistoryCreatedActionTimeListFilter\",\n \"path\": \"dje/filters.py\",\n \"snippet\": \"class HistoryCreatedActionTimeListFilter(HistoryActionTimeListFilter):\\n title = _(\\\"created date\\\")\\n parameter_name = \\\"created_date\\\"\\n action_flag = History.ADDITION\"\n },\n {\n \"identifier\": \"HistoryModifiedActionTimeListFilter\",\n \"path\": \"dje/filters.py\",\n \"snippet\": \"class HistoryModifiedActionTimeListFilter(HistoryActionTimeListFilter):\\n title = _(\\\"modified date\\\")\\n parameter_name = \\\"modified_date\\\"\\n action_flag = History.CHANGE\"\n },\n {\n \"identifier\": \"LimitToDataspaceListFilter\",\n \"path\": \"dje/filters.py\",\n \"snippet\": \"class LimitToDataspaceListFilter(filters.RelatedFieldListFilter):\\n \\\"\\\"\\\"\\n Limit the choices of a filter on a FK to the currently \\\"filtered\\\" Dataspace.\\n The limit_choices_to declared on the model field will be applied too.\\n \\\"\\\"\\\"\\n\\n def __init__(self, field, request, params, model, model_admin, field_path):\\n super().__init__(field, request, params, model, model_admin, field_path)\\n\\n # The get_limit_choices_to_from_path is broken in 1.7, see code in 1.6\\n # limit_choices_to = get_limit_choices_to_from_path(model, field_path)\\n limit_choices_to = models.Q(**field.get_limit_choices_to())\\n\\n dataspace_id = request.GET.get(DataspaceFilter.parameter_name, request.user.dataspace_id)\\n queryset = field.related_model.objects.scope_by_id(dataspace_id).filter(limit_choices_to)\\n\\n if field.related_model.__name__ == \\\"UsagePolicy\\\":\\n content_type = ContentType.objects.get_for_model(model)\\n queryset = queryset.filter(content_type=content_type)\\n\\n self.lookup_choices = [(x._get_pk_val(), str(x)) for x in queryset]\"\n },\n {\n \"identifier\": \"MissingInFilter\",\n \"path\": \"dje/filters.py\",\n \"snippet\": \"class MissingInFilter(BaseDataspaceLookupsFilter):\\n \\\"\\\"\\\"\\n Filter by objects missing in the given dataspace, compared with the\\n current `DataspaceFilter.parameter_name` or user dataspace.\\n Both values for reference and target Dataspace are validated against the\\n self.lookup_choices to make sure the user has the proper access permissions.\\n This filter is only available to superusers, this is enforced in\\n DataspacedAdmin.get_list_filter()\\n \\\"\\\"\\\"\\n\\n title = _(\\\"missing in\\\")\\n parameter_name = \\\"missing_in\\\"\\n\\n def queryset(self, request, queryset):\\n if not self.value():\\n return\\n\\n valid_choices = [str(choice) for choice, _ in self.lookup_choices]\\n if str(self.value()) not in valid_choices:\\n raise IncorrectLookupParameters()\\n\\n return queryset.exclude(uuid__in=get_uuids_list_sorted(self.value(), queryset.model))\"\n },\n {\n \"identifier\": \"DataspaceAdminForm\",\n \"path\": \"dje/forms.py\",\n \"snippet\": \"class DataspaceAdminForm(forms.ModelForm):\\n def clean(self):\\n \\\"\\\"\\\"Add validation for `update_packages_from_scan` field.\\\"\\\"\\\"\\n cleaned_data = super().clean()\\n enable_package_scanning = cleaned_data.get(\\\"enable_package_scanning\\\")\\n update_packages_from_scan = cleaned_data.get(\\\"update_packages_from_scan\\\")\\n\\n if update_packages_from_scan and not enable_package_scanning:\\n msg = \\\"Package scanning needs to be enabled to use the automatic updates.\\\"\\n self.add_error(\\\"update_packages_from_scan\\\", msg)\"\n },\n {\n \"identifier\": \"DataspacedAdminForm\",\n \"path\": \"dje/forms.py\",\n \"snippet\": \"class DataspacedAdminForm(forms.ModelForm):\\n \\\"\\\"\\\"\\n Use as the base ModelForm for every Model declared as DataspacedModel.\\n This is usually not required for inline Forms.\\n \\\"\\\"\\\"\\n\\n def __init__(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Injects the dataspace in the instance right after the form\\n initialization as a value for dataspace is required for the\\n built-in unique_together validation.\\n \\\"\\\"\\\"\\n super().__init__(*args, **kwargs)\\n # request is not injected in inline forms\\n request = getattr(self, \\\"request\\\", None)\\n\\n add = not kwargs.get(\\\"instance\\\")\\n if add and request:\\n self.instance.dataspace = request.user.dataspace\\n\\n def _get_validation_exclusions(self):\\n \\\"\\\"\\\"\\n Remove the `dataspace` and `uuid` fields from the exclusion (ie: validate on those)\\n so the unique_together validation within dataspace is enforced.\\n \\\"\\\"\\\"\\n exclude = super()._get_validation_exclusions()\\n return {field for field in exclude if field not in (\\\"dataspace\\\", \\\"uuid\\\")}\"\n },\n {\n \"identifier\": \"DejaCodeAuthenticationForm\",\n \"path\": \"dje/forms.py\",\n \"snippet\": \"class DejaCodeAuthenticationForm(AuthenticationForm):\\n \\\"\\\"\\\"Login form.\\\"\\\"\\\"\\n\\n use_required_attribute = False\\n\\n @property\\n def helper(self):\\n helper = FormHelper()\\n helper.form_id = \\\"sign-in\\\"\\n helper.form_action = \\\"login\\\"\\n helper.form_method = \\\"post\\\"\\n helper.form_tag = False\\n\\n fields = [\\n Field(\\\"username\\\", css_class=\\\"input-block-level mb-3\\\", placeholder=_(\\\"Username\\\")),\\n Field(\\\"password\\\", css_class=\\\"input-block-level mb-3\\\", placeholder=_(\\\"Password\\\")),\\n Div(\\n StrictSubmit(\\\"submit\\\", _(\\\"Sign in\\\"), css_class=\\\"btn-warning\\\"),\\n css_class=\\\"d-grid\\\",\\n ),\\n ]\\n\\n helper.add_layout(Layout(Fieldset(\\\"\\\", *fields)))\\n return helper\\n\\n def get_invalid_login_error(self):\\n username = self.cleaned_data.get(\\\"username\\\")\\n if \\\"@\\\" in username:\\n return ValidationError(\\n \\\"Be sure to enter your DejaCode username rather than your email \\\"\\n \\\"address to sign in to DejaCode.\\\"\\n )\\n return super().get_invalid_login_error()\"\n },\n {\n \"identifier\": \"import_view\",\n \"path\": \"dje/importers.py\",\n \"snippet\": \"@login_required()\\ndef import_view(request, importer_class):\\n user = request.user\\n importer = importer_class(user)\\n upload_form_class = ImportableUploadFileForm\\n if importer_class.__name__ == \\\"PackageImporter\\\":\\n upload_form_class = PackageImportableUploadFileForm\\n\\n opts = importer.model_form._meta.model._meta\\n perm_codename = get_permission_codename(\\\"add\\\", opts)\\n if not user.has_perm(f\\\"{opts.app_label}.{perm_codename}\\\"):\\n return HttpResponseRedirect(resolve_url(settings.LOGIN_REDIRECT_URL))\\n\\n if request.GET.get(\\\"get_template\\\"): # ?get_template=1\\n header = \\\",\\\".join(importer.required_fields + importer.supported_fields)\\n filename = \\\"{}_import_template.csv\\\".format(importer.verbose_name.replace(\\\" \\\", \\\"_\\\"))\\n response = HttpResponse(header, content_type=\\\"application/csv\\\")\\n response[\\\"Content-Disposition\\\"] = f'attachment; filename=\\\"{filename}\\\"'\\n return response\\n\\n file_form = None\\n if request.method == \\\"POST\\\":\\n if \\\"form-TOTAL_FORMS\\\" in request.POST:\\n importer = importer_class(user, formset_data=request.POST)\\n importer.save_all()\\n else:\\n # Every uploaded file are stored in a temp location as we removed\\n # the MemoryFileUploadHandler in the FILE_UPLOAD_HANDLERS settings.\\n # We do not keep track of this location once the file has been\\n # processed.\\n file_form = upload_form_class(request.POST, request.FILES)\\n if file_form.is_valid():\\n uploaded_file = request.FILES[\\\"file\\\"]\\n file_location = uploaded_file.temporary_file_path()\\n importer = importer_class(user, file_location=file_location)\\n else:\\n file_form = upload_form_class()\\n\\n return render(\\n request,\\n \\\"admin/object_import.html\\\",\\n {\\n \\\"file_form\\\": file_form,\\n \\\"importer\\\": importer,\\n \\\"add_to_product_form\\\": importer.get_add_to_product_form(request),\\n },\\n )\"\n },\n {\n \"identifier\": \"AsURL\",\n \"path\": \"dje/list_display.py\",\n \"snippet\": \"class AsURL(ListDisplayItem):\\n def to_representation(self, value):\\n return urlize_target_blank(value)\"\n },\n {\n \"identifier\": \"mass_update_action\",\n \"path\": \"dje/mass_update.py\",\n \"snippet\": \"def mass_update_action(modeladmin, request, queryset):\\n if not queryset:\\n return\\n\\n # Dataspace is required for scoping, we trust the queryset for security purpose\\n # over the values provided in the request data.\\n dataspace = queryset.first().dataspace\\n opts = modeladmin.model._meta\\n preserved_filters = modeladmin.get_preserved_filters(request)\\n\\n # Allows to specified a custom mass update Form in the ModelAdmin\\n mass_update_form = getattr(modeladmin, \\\"mass_update_form\\\", BaseMassUpdateForm)\\n MassUpdateForm = modelform_factory(\\n modeladmin.model,\\n form=mass_update_form,\\n exclude=get_protected_fields(modeladmin.model, request.user),\\n formfield_callback=not_required,\\n )\\n MassUpdateForm.admin_site = modeladmin.admin_site # required by the ForeignKeyRawIdWidget\\n\\n if \\\"apply\\\" in request.POST:\\n form = MassUpdateForm(request.POST, dataspace=dataspace)\\n if form.is_valid():\\n changelist_url = reverse(f\\\"admin:{opts.app_label}_{opts.model_name}_changelist\\\")\\n redirect_url = add_preserved_filters(\\n {\\\"preserved_filters\\\": preserved_filters, \\\"opts\\\": opts}, changelist_url\\n )\\n\\n updated = 0\\n errors = []\\n for record in queryset:\\n for field_name, value in form.cleaned_data.items():\\n try:\\n field_object = record._meta.get_field(field_name)\\n except FieldDoesNotExist:\\n continue # field_name is not part of the model\\n\\n # auto_created is only True on implicit m2m model\\n if (\\n field_object.many_to_many\\n and not field_object.remote_field.through._meta.auto_created\\n ):\\n set_intermediate_explicit_m2m(record, field_object, value)\\n else:\\n setattr(record, field_name, value)\\n # This have no impact if the model does not declare this field.\\n record.last_modified_by = request.user\\n # Some validation errors cannot be caught during the form validation as only\\n # the \\\"new\\\" value of the selected fields are available.\\n # We cannot validate those changes against the existing value of others fields on\\n # the instance so the error can be raised at save() time.\\n try:\\n record.save()\\n except ValidationError as e:\\n errors.append(e.message)\\n else:\\n updated += 1\\n\\n if updated:\\n messages.info(request, _(f\\\"Updated {updated} records\\\"))\\n\\n if errors:\\n messages.error(request, _(f'{len(errors)} error(s): {\\\", \\\".join(errors)}'))\\n\\n action_end.send(\\n sender=modeladmin.model,\\n action=\\\"mass_update\\\",\\n request=request,\\n queryset=queryset,\\n modeladmin=modeladmin,\\n form=form,\\n )\\n return redirect(redirect_url)\\n else:\\n initial = {\\n ACTION_CHECKBOX_NAME: request.POST.getlist(ACTION_CHECKBOX_NAME),\\n \\\"select_across\\\": request.POST.get(\\\"select_across\\\") == \\\"1\\\",\\n }\\n form = MassUpdateForm(initial=initial, dataspace=dataspace)\\n\\n adminform = AdminForm(form, modeladmin.get_fieldsets(request), {}, [], model_admin=modeladmin)\\n\\n with suppress(AttributeError):\\n form.extra_init(request, modeladmin)\\n\\n context = {\\n \\\"adminform\\\": adminform,\\n \\\"form\\\": form,\\n \\\"opts\\\": opts,\\n \\\"queryset\\\": queryset,\\n \\\"preserved_filters\\\": preserved_filters,\\n \\\"media\\\": modeladmin.media,\\n }\\n return render(request, \\\"admin/mass_update.html\\\", context)\"\n },\n {\n \"identifier\": \"Dataspace\",\n \"path\": \"dje/models.py\",\n \"snippet\": \"class Dataspace(models.Model):\\n \\\"\\\"\\\"\\n The Dataspace is a way to keep data for each organization data\\n separated and still store them in the same database, schema or table.\\n Therefore the Dataspace is part of the primary key of most models\\n and it part of a unicity constraint for these models.\\n For a given installation there can be several Owner Org defined, but only\\n one reference.\\n\\n This is an important concept used throughout DejaCode to\\n separate the reference data provided by nexB from the data used in a given\\n installation of DJE.\\n\\n It is essentially a notion of tenant in a DJE installation and is used to\\n segregate org-specific and/or org-private records enabling both\\n multi-tenancy as well as nexB-provided reference data and org-specific or\\n customized data.\\n\\n This separation has several purposes such as allowing:\\n * orderly and simpler data update from the nexB reference data and inter\\n Dataspace data exchange\\n * Dataspace specific data customizations (for instance license\\n tags configurations or some preferences)\\n * multi-tenancy where different organizations can share the same DJE\\n instance\\n \\\"\\\"\\\"\\n\\n uuid = models.UUIDField(\\n _(\\\"UUID\\\"),\\n default=uuid.uuid4,\\n editable=False,\\n unique=True,\\n )\\n\\n name = models.SlugField(\\n unique=True,\\n max_length=20,\\n help_text=_(\\n 'Unique name of a Dataspace. The name \\\"nexB\\\" is reserved for '\\n \\\"the creators/maintainers of the system software. Dataspace name \\\"\\n \\\"only allows letters, numbers, underscores and hyphens.\\\"\\n ),\\n )\\n\\n homepage_url = models.URLField(\\n _(\\\"Homepage URL\\\"),\\n max_length=1024,\\n blank=True,\\n help_text=_(\\\"The homepage URL of the Dataspace owner.\\\"),\\n )\\n\\n contact_info = models.CharField(\\n _(\\\"Contact information\\\"),\\n max_length=500,\\n blank=True,\\n help_text=_(\\n \\\"A dedicated email address or URL for contacting the owner of \\\"\\n \\\"the Dataspace. Can be used for Attribution Package generation.\\\"\\n ),\\n )\\n\\n notes = models.TextField(\\n blank=True,\\n help_text=_(\\\"Extended Notes about a Dataspace.\\\"),\\n )\\n\\n show_license_profile_in_license_list_view = models.BooleanField(\\n default=False,\\n verbose_name=format_lazy(\\n \\\"Show {license_profile} in license list view\\\",\\n license_profile=_(\\\"license profile\\\"),\\n ),\\n help_text=format_lazy(\\n \\\"When true (checked), include the {license_profile} column in the license list view.\\\",\\n license_profile=_(\\\"license profile\\\"),\\n ),\\n )\\n\\n show_license_type_in_license_list_view = models.BooleanField(\\n default=True,\\n help_text=_(\\n \\\"When true (checked), include the license type column in the license list view.\\\",\\n ),\\n )\\n\\n show_spdx_short_identifier_in_license_list_view = models.BooleanField(\\n verbose_name=_(\\\"show SPDX short identifier in license list view\\\"),\\n default=False,\\n help_text=_(\\n \\\"When true (checked), include the SPDX short identifier in the license list view.\\\",\\n ),\\n )\\n\\n show_usage_policy_in_user_views = models.BooleanField(\\n default=True,\\n help_text=_(\\n \\\"When true (checked), include the usage policy in user views that \\\"\\n \\\"show licenses or components.\\\",\\n ),\\n )\\n\\n show_type_in_component_list_view = models.BooleanField(\\n default=False,\\n help_text=_(\\n \\\"When true (checked), include the type column in the component list view.\\\",\\n ),\\n )\\n\\n hide_empty_fields_in_component_details_view = models.BooleanField(\\n default=False,\\n help_text=_(\\\"When true (checked), hide empty fields in the component details view.\\\"),\\n )\\n\\n set_usage_policy_on_new_component_from_licenses = models.BooleanField(\\n _(\\\"set usage policy on component or package from license policy\\\"),\\n default=False,\\n help_text=_(\\n \\\"When true (checked), the application will automatically assign a usage \\\"\\n \\\"policy to a component or package when its license expression is set or \\\"\\n \\\"updated when you create, import, edit, or copy that component or package, \\\"\\n \\\"based on the associated policies that you have defined on the license policy.\\\"\\n ),\\n )\\n\\n logo_url = models.URLField(\\n _(\\\"Logo URL\\\"),\\n max_length=1024,\\n blank=True,\\n help_text=_(\\\"URL to a Dataspace Logo. If set, it will be included in reports.\\\"),\\n )\\n\\n full_name = models.CharField(\\n max_length=100,\\n blank=True,\\n help_text=_(\\n \\\"The full name of the Dataspace organization. \\\"\\n \\\"Can be used for Attribution Package generation.\\\"\\n ),\\n )\\n\\n address = models.TextField(\\n blank=True,\\n help_text=(\\n \\\"The address of the Dataspace organization. \\\"\\n \\\"Can be used for Attribution Package generation.\\\"\\n ),\\n )\\n\\n open_source_information_url = models.URLField(\\n _(\\\"Open Source Information URL\\\"),\\n max_length=1024,\\n blank=True,\\n help_text=_(\\n \\\"A public URL where you publish information about the Dataspace \\\"\\n \\\"organization's Open Source policies and procedures. \\\"\\n \\\"Can be used for Attribution Package generation.\\\"\\n ),\\n )\\n\\n open_source_download_url = models.URLField(\\n _(\\\"Open Source Download URL\\\"),\\n max_length=1024,\\n blank=True,\\n help_text=_(\\n \\\"A public URL where you provide copies of Open Source software that \\\"\\n \\\"require Redistribution when you use them in your products. Can be \\\"\\n \\\"used for Attribution Package generation.\\\"\\n ),\\n )\\n\\n home_page_announcements = models.TextField(\\n blank=True,\\n help_text=_(\\n \\\"Use this field to enter text to appear on the DejaCode home page, \\\"\\n \\\"normally for the purpose of providing your user community with \\\"\\n \\\"general-purpose announcements about using DejaCode. \\\"\\n \\\"Note that you can include URL's in the text if you want to direct \\\"\\n \\\"users to detailed instructions and announcements.\\\"\\n ),\\n )\\n\\n enable_package_scanning = models.BooleanField(\\n default=False,\\n help_text=_(\\n 'When true (checked), allows a user to click the \\\"Scan Package\\\" button when viewing '\\n \\\"a Package, initiating a call to ScanCode.io to scan the Package based on its URL. \\\"\\n \\\"This setting also activates a DejaCode feature to submit any Package created using \\\"\\n 'the \\\"Add Package\\\" button to ScanCode.io for scanning, and it activates the Scans '\\n \\\"choice from the DejaCode Tools dropdown menu.\\\"\\n ),\\n )\\n\\n update_packages_from_scan = models.BooleanField(\\n _(\\\"Update packages automatically from scan\\\"),\\n default=False,\\n help_text=_(\\n \\\"When true (checked), enables an automatic DejaCode process to update \\\"\\n \\\"selected Package fields (such as license expression, primary language, \\\"\\n \\\"copyright, etc.) when a package scan is completed, depending on the \\\"\\n \\\"quality of the scan results.\\\"\\n ),\\n )\\n\\n enable_purldb_access = models.BooleanField(\\n _(\\\"Enable PurlDB access\\\"),\\n default=False,\\n help_text=_(\\n \\\"When true (checked), enables user access to the PurlDB option from the Tools menu, \\\"\\n \\\"which presents a list of PurlDB data mined and scanned automatically from multiple \\\"\\n \\\"public sources. Users can view PurlDB details and can create DejaCode Package \\\"\\n \\\"definitions using those details, and DejaCode also presents a new PurlDB tab when \\\"\\n \\\"viewing the details of a Package with matching key values. This option also enhances \\\"\\n \\\"the Global Search feature to extend the search scope beyond the standard DejaCode \\\"\\n \\\"objects (Packages, Components, Licenses, Owners) and perform an asynchronous query of \\\"\\n \\\"the PurlDB to find relevant data.\\\"\\n ),\\n )\\n\\n enable_vulnerablecodedb_access = models.BooleanField(\\n _(\\\"Enable VulnerableCodeDB access\\\"),\\n default=False,\\n help_text=_(\\n \\\"When true (checked), authorizes DejaCode to access the VulnerableCodeDB \\\"\\n \\\"using a Package URL (purl) to determine if there are any reported \\\"\\n \\\"vulnerabilities for a specific Package and return the Vulnerability ID \\\"\\n \\\"and related URLs to a Vulnerabilities tab in the Package details user \\\"\\n \\\"view.\\\"\\n ),\\n )\\n\\n objects = DataspaceManager()\\n\\n class Meta:\\n ordering = [\\\"name\\\"]\\n\\n def __str__(self):\\n return self.name\\n\\n def get_admin_url(self):\\n opts = self._meta\\n viewname = f\\\"admin:{opts.app_label}_{opts.model_name}_change\\\"\\n return reverse(viewname, args=[self.pk])\\n\\n def natural_key(self):\\n return (self.name,)\\n\\n @cached_property\\n def is_reference(self):\\n \\\"\\\"\\\"Return True if this Dataspace is the reference.\\\"\\\"\\\"\\n reference = self.__class__._default_manager.get_reference()\\n return True if reference and self == reference else False\\n\\n def get_configuration(self, field_name=None):\\n \\\"\\\"\\\"\\n Return the associated DataspaceConfiguration.\\n If a `field_name` is provided, Return the value for that field from\\n the `DataspaceConfiguration`.\\n \\\"\\\"\\\"\\n try:\\n configuration = self.configuration\\n except ObjectDoesNotExist:\\n return\\n\\n if field_name:\\n return getattr(configuration, field_name, None)\\n return configuration\\n\\n @property\\n def has_configuration(self):\\n \\\"\\\"\\\"Return True if an associated DataspaceConfiguration instance exists.\\\"\\\"\\\"\\n return bool(self.get_configuration())\\n\\n @property\\n def tab_permissions_enabled(self):\\n return bool(self.get_configuration(\\\"tab_permissions\\\"))\"\n },\n {\n \"identifier\": \"DataspaceConfiguration\",\n \"path\": \"dje/models.py\",\n \"snippet\": \"class DataspaceConfiguration(models.Model):\\n dataspace = models.OneToOneField(\\n to=\\\"dje.Dataspace\\\",\\n on_delete=models.CASCADE,\\n related_name=\\\"configuration\\\",\\n )\\n\\n tab_permissions = models.JSONField(\\n blank=True,\\n default=dict,\\n )\\n\\n copy_defaults = models.JSONField(\\n blank=True,\\n null=True,\\n )\\n\\n homepage_layout = models.ForeignKey(\\n to=\\\"reporting.CardLayout\\\",\\n null=True,\\n blank=True,\\n related_name=\\\"+\\\",\\n on_delete=models.CASCADE,\\n serialize=False,\\n help_text=_(\\n \\\"Select a general purpose Card layout that provides timely query \\\"\\n \\\"results on the DejaCode homepage to your application users.\\\"\\n ),\\n )\\n\\n scancodeio_url = models.URLField(\\n _(\\\"ScanCode.io URL\\\"),\\n max_length=1024,\\n blank=True,\\n help_text=_(\\n \\\"Enter the URL of your organization's private ScanCode.io instance, \\\"\\n \\\"if available. If not, DejaCode will use the public ScanCode.io instance \\\"\\n \\\"to scan your Packages.\\\"\\n ),\\n )\\n\\n scancodeio_api_key = models.CharField(\\n _(\\\"ScanCode.io API key\\\"),\\n max_length=40,\\n blank=True,\\n help_text=_(\\n \\\"If your organization's private ScanCode.io instance requires an API key \\\"\\n \\\"for access, provide it here. Otherwise, you can leave this field empty.\\\"\\n ),\\n )\\n\\n vulnerablecode_url = models.URLField(\\n _(\\\"VulnerableCode URL\\\"),\\n max_length=1024,\\n blank=True,\\n help_text=_(\\n \\\"If your organization has a private VulnerableCode instance, enter its URL \\\"\\n \\\"here. Otherwise, DejaCode will use the public VulnerableCode to check for \\\"\\n \\\"vulnerabilities\\\"\\n ),\\n )\\n\\n vulnerablecode_api_key = models.CharField(\\n _(\\\"VulnerableCode API key\\\"),\\n max_length=40,\\n blank=True,\\n help_text=_(\\n \\\"If your private VulnerableCode instance requires an API key for access, \\\"\\n \\\"input it here. If not, you can leave this field blank.\\\"\\n ),\\n )\\n\\n purldb_url = models.URLField(\\n _(\\\"PurlDB URL\\\"),\\n max_length=1024,\\n blank=True,\\n help_text=_(\\n \\\"Enter the URL of your organization's private PurlDB instance, if \\\"\\n \\\"applicable. If not, DejaCode will utilize the public PurlDB to offer a \\\"\\n \\\"database of Packages collected from public sources.\\\"\\n ),\\n )\\n\\n purldb_api_key = models.CharField(\\n _(\\\"PurlDB API key\\\"),\\n max_length=40,\\n blank=True,\\n help_text=_(\\n \\\"If your organization's private PurlDB instance requires an API key for \\\"\\n \\\"access, provide it here. If not, you can leave this field empty.\\\"\\n ),\\n )\\n\\n def __str__(self):\\n return f\\\"Configuration for {self.dataspace}\\\"\"\n },\n {\n \"identifier\": \"DejacodeUser\",\n \"path\": \"dje/models.py\",\n \"snippet\": \"class DejacodeUser(AbstractUser):\\n uuid = models.UUIDField(\\n _(\\\"UUID\\\"),\\n default=uuid.uuid4,\\n editable=False,\\n unique=True,\\n )\\n\\n dataspace = models.ForeignKey(\\n to=\\\"dje.Dataspace\\\",\\n on_delete=models.PROTECT,\\n help_text=DATASPACE_FIELD_HELP_TEXT,\\n )\\n\\n data_email_notification = models.BooleanField(\\n default=False,\\n help_text=_(\\n \\\"Check this to send email notifications to the user regarding DejaCode data updates; \\\"\\n \\\"note that the volume could be very large, so this option is normally enabled only \\\"\\n \\\"during system setup and rollout to monitor application activity.\\\"\\n ),\\n )\\n\\n workflow_email_notification = models.BooleanField(\\n default=False,\\n help_text=_(\\n \\\"Check this to send email notifications to the user for associated workflow requests; \\\"\\n \\\"otherwise, request notifications alerts will appear in the DejaCode notifications \\\"\\n \\\"form.\\\"\\n ),\\n )\\n\\n updates_email_notification = models.BooleanField(\\n default=False,\\n help_text=_(\\n \\\"Check this to receive email notifications with updates on DejaCode \\\"\\n \\\"features and news.\\\"\\n ),\\n )\\n\\n company = models.CharField(\\n max_length=30,\\n blank=True,\\n help_text=_(\\n \\\"The company the user is associated with. \\\"\\n \\\"This can be submitted during the signup process.\\\"\\n ),\\n )\\n\\n last_api_access = models.DateTimeField(\\n verbose_name=_(\\\"last API access\\\"),\\n blank=True,\\n null=True,\\n )\\n\\n homepage_layout = models.ForeignKey(\\n to=\\\"reporting.CardLayout\\\",\\n null=True,\\n blank=True,\\n related_name=\\\"+\\\",\\n on_delete=models.CASCADE,\\n serialize=False,\\n help_text=_(\\n \\\"Select a Card layout that provides the query results on the \\\"\\n \\\"DejaCode homepage that are useful and interesting to you.\\\"\\n ),\\n )\\n\\n objects = DejacodeUserManager()\\n\\n class Meta:\\n ordering = [\\\"username\\\"]\\n\\n def save(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Send an email to the user when his password has been changed.\\n\\n The password can be changed from those locations:\\n - Change password user view : /account/password_change/\\n - Password reset user view: /account/password_reset/\\n - Management command: ./manage.py changepassword\\n - Model instance: set_password() + save()\\n \\\"\\\"\\\"\\n from dje.notification import send_password_changed_email\\n\\n # self._password will be set to None during a hasher upgrade as we do not\\n # want to notify in that case, as it's not considered a password changes.\\n # See AbstractBaseUser.check_password.setter\\n password_changed = self._password is not None\\n user_exists = bool(self.pk)\\n\\n super().save(*args, **kwargs)\\n\\n # Do not notify users that are setting their initial password during registration\\n if password_changed and user_exists and self.last_login:\\n send_password_changed_email(self)\\n\\n @property\\n def last_active(self):\\n activity_date_fields = [\\n self.date_joined,\\n self.last_login,\\n self.last_api_access,\\n ]\\n return max([field for field in activity_date_fields if field])\\n\\n def get_group_names(self):\\n \\\"\\\"\\\"\\n Return the group names assigned to the User through the DB and LDAP\\n authentication (when enabled).\\n \\\"\\\"\\\"\\n group_names_from_db = list(self.groups.values_list(\\\"name\\\", flat=True))\\n\\n ldap_user = getattr(self, \\\"ldap_user\\\", None)\\n if ldap_user:\\n return list(set(group_names_from_db).union(ldap_user.group_names))\\n\\n return group_names_from_db\\n\\n def get_homepage_layout(self):\\n \\\"\\\"\\\"\\n Return the User `homepage_layout`, from the this instance first,\\n or fallback on the Dataspace layout, if set.\\n \\\"\\\"\\\"\\n return self.homepage_layout or self.dataspace.get_configuration(\\\"homepage_layout\\\")\\n\\n def email_user(self, subject, message, from_email=None, **kwargs):\\n \\\"\\\"\\\"Wrap the method in a task.\\\"\\\"\\\"\\n send_mail_task.delay(subject, message, from_email, [self.email], **kwargs)\\n\\n def regenerate_api_key(self):\\n \\\"\\\"\\\"\\n Regenerate the user API key.\\n Since the `key` value is the primary key on the Token `model`,\\n the old key needs to be deleted first, a new one is then created.\\n \\\"\\\"\\\"\\n self.auth_token.delete()\\n Token.objects.create(user=self)\\n\\n def serialize_user_data(self):\\n fields = [\\n \\\"email\\\",\\n \\\"first_name\\\",\\n \\\"last_name\\\",\\n \\\"username\\\",\\n \\\"company\\\",\\n \\\"last_login\\\",\\n \\\"date_joined\\\",\\n \\\"last_api_access\\\",\\n \\\"last_active\\\",\\n \\\"is_superuser\\\",\\n \\\"is_staff\\\",\\n \\\"is_active\\\",\\n \\\"updates_email_notification\\\",\\n \\\"dataspace\\\",\\n ]\\n\\n return {\\n field: str(value) for field in fields if (value := getattr(self, field)) is not None\\n }\\n\\n def serialize_hook(self, hook):\\n return {\\n \\\"hook\\\": hook.dict(),\\n **self.serialize_user_data(),\\n }\"\n },\n {\n \"identifier\": \"ExternalReference\",\n \"path\": \"dje/models.py\",\n \"snippet\": \"class ExternalReference(HistoryFieldsMixin, DataspacedModel):\\n \\\"\\\"\\\"\\n Maps DJE objects to external resources.\\n One DJE object may have several ExternalReference when it's referenced on\\n multiple sources.\\n Also, there is no unicity collision possible as we use the object_id.\\n\\n The copy for GenericForeignKey field is not supported yet.\\n \\\"\\\"\\\"\\n\\n # The following models should always inherit from ExternalReferenceMixin\\n # for the proper deletion in CASCADE behavior.\\n CT_LIMIT = (\\n models.Q(app_label=\\\"organization\\\", model=\\\"owner\\\")\\n | models.Q(app_label=\\\"license_library\\\", model=\\\"license\\\")\\n | models.Q(app_label=\\\"component_catalog\\\", model=\\\"component\\\")\\n | models.Q(app_label=\\\"component_catalog\\\", model=\\\"package\\\")\\n )\\n\\n content_type = models.ForeignKey(\\n to=ContentType,\\n limit_choices_to=CT_LIMIT,\\n on_delete=models.PROTECT,\\n )\\n\\n object_id = models.PositiveIntegerField()\\n\\n content_object = GenericForeignKey(\\\"content_type\\\", \\\"object_id\\\")\\n\\n external_source = models.ForeignKey(\\n to=\\\"dje.ExternalSource\\\",\\n on_delete=models.PROTECT,\\n )\\n\\n external_id = models.CharField(\\n max_length=500,\\n blank=True,\\n help_text=_(\\\"Value of the identifier used on the source to reference the object.\\\"),\\n )\\n\\n external_url = models.URLField(\\n max_length=1024,\\n blank=True,\\n help_text=_(\\\"A URL to the component, or component metadata, in the external source.\\\"),\\n )\\n\\n objects = ExternalReferenceManager()\\n\\n class Meta:\\n unique_together = (\\\"dataspace\\\", \\\"uuid\\\")\\n ordering = [\\\"external_source\\\", \\\"external_id\\\"]\\n\\n def __str__(self):\\n return f\\\"{self.external_source}: {self.external_id}\\\"\\n\\n def save(self, *args, **kwargs):\\n self.dataspace = self.content_object.dataspace\\n super().save(*args, **kwargs)\"\n },\n {\n \"identifier\": \"ExternalSource\",\n \"path\": \"dje/models.py\",\n \"snippet\": \"class ExternalSource(DataspacedModel):\\n label = models.CharField(\\n max_length=50,\\n help_text=_(\\\"A Label is a concise name of the external source as it \\\" \\\"is commonly known.\\\"),\\n )\\n\\n notes = models.TextField(\\n blank=True,\\n help_text=_(\\n \\\"Notes describe the purpose and special characteristics \\\" \\\"of the external source.\\\"\\n ),\\n )\\n\\n homepage_url = models.URLField(\\n max_length=1024,\\n blank=True,\\n help_text=_(\\\"Main homepage URL of the external source.\\\"),\\n )\\n\\n class Meta:\\n ordering = [\\\"label\\\"]\\n unique_together = ((\\\"dataspace\\\", \\\"label\\\"), (\\\"dataspace\\\", \\\"uuid\\\"))\\n\\n def __str__(self):\\n return self.label\"\n },\n {\n \"identifier\": \"History\",\n \"path\": \"dje/models.py\",\n \"snippet\": \"class History(models.Model):\\n ADDITION = ADDITION\\n CHANGE = CHANGE\\n DELETION = DELETION\\n\\n ACTION_FLAG_CHOICES = (\\n (ADDITION, _(\\\"Addition\\\")),\\n (CHANGE, _(\\\"Change\\\")),\\n (DELETION, _(\\\"Deletion\\\")),\\n )\\n\\n object_dataspace = models.ForeignKey(\\n to=\\\"dje.Dataspace\\\",\\n on_delete=models.CASCADE,\\n null=True,\\n blank=True,\\n editable=False,\\n )\\n\\n serialized_data = models.TextField(\\n null=True,\\n blank=True,\\n editable=False,\\n help_text=_(\\\"Serialized data of the instance just before this change.\\\"),\\n )\\n\\n # The following fields are directly taken from django.contrib.admin.models.LogEntry\\n # Since the LogEntry is not abstract we cannot properly inherit from it.\\n\\n action_time = models.DateTimeField(\\n _(\\\"action time\\\"),\\n default=timezone.now,\\n editable=False,\\n )\\n\\n user = models.ForeignKey(\\n settings.AUTH_USER_MODEL,\\n models.CASCADE,\\n verbose_name=_(\\\"user\\\"),\\n )\\n\\n content_type = models.ForeignKey(\\n ContentType,\\n models.SET_NULL,\\n verbose_name=_(\\\"content type\\\"),\\n blank=True,\\n null=True,\\n )\\n\\n object_id = models.TextField(\\n _(\\\"object id\\\"),\\n blank=True,\\n null=True,\\n )\\n\\n object_repr = models.CharField(\\n _(\\\"object repr\\\"),\\n max_length=200,\\n )\\n\\n action_flag = models.PositiveSmallIntegerField(\\n _(\\\"action flag\\\"),\\n choices=ACTION_FLAG_CHOICES,\\n )\\n\\n # change_message is either a string or a JSON structure\\n change_message = models.TextField(\\n _(\\\"change message\\\"),\\n blank=True,\\n )\\n\\n objects = HistoryManager()\\n\\n class Meta:\\n verbose_name = _(\\\"history entry\\\")\\n verbose_name_plural = _(\\\"history entries\\\")\\n ordering = (\\\"-action_time\\\",)\\n\\n # Clone the method from Django's LogEntry model.\\n __repr__ = LogEntry.__repr__\\n __str__ = LogEntry.__str__\\n is_addition = LogEntry.is_addition\\n is_change = LogEntry.is_change\\n is_deletion = LogEntry.is_deletion\\n get_change_message = LogEntry.get_change_message\\n get_edited_object = LogEntry.get_edited_object\\n get_admin_url = LogEntry.get_edited_object\\n\\n @classmethod\\n def log_addition(cls, user, obj, message=None):\\n \\\"\\\"\\\"Create History entry on Addition with the proper `change_message`.\\\"\\\"\\\"\\n if not message:\\n message = [{\\\"added\\\": {}}]\\n\\n return cls.objects.log_action(user, obj, cls.ADDITION, message)\\n\\n @classmethod\\n def log_change(cls, user, obj, message, serialized_data=None):\\n \\\"\\\"\\\"Create History entry on Change.\\\"\\\"\\\"\\n return cls.objects.log_action(user, obj, cls.CHANGE, message, serialized_data)\\n\\n @classmethod\\n def log_deletion(cls, user, obj):\\n \\\"\\\"\\\"\\n Create History entry on Deletion.\\n Include the serialized_data if `as_json()` is available on the model class.\\n \\\"\\\"\\\"\\n serialized_data = None\\n with suppress(AttributeError):\\n serialized_data = obj.as_json()\\n\\n return cls.objects.log_action(user, obj, cls.DELETION, serialized_data=serialized_data)\"\n },\n {\n \"identifier\": \"HistoryFieldsMixin\",\n \"path\": \"dje/models.py\",\n \"snippet\": \"class HistoryFieldsMixin(HistoryUserFieldsMixin, HistoryDateFieldsMixin):\\n \\\"\\\"\\\"Add the created_date, last_modified_date, created_by, last_modified_by fields.\\\"\\\"\\\"\\n\\n class Meta:\\n abstract = True\"\n },\n {\n \"identifier\": \"is_dataspace_related\",\n \"path\": \"dje/models.py\",\n \"snippet\": \"def is_dataspace_related(model_class):\\n \\\"\\\"\\\"\\n Return True if the given model_class has a ForeignKey field related to\\n the Dataspace model.\\n \\\"\\\"\\\"\\n return any(\\n 1\\n for f in model_class._meta.get_fields()\\n if f.many_to_one and (f.related_model == Dataspace or f.related_model == \\\"dje.Dataspace\\\")\\n )\"\n },\n {\n \"identifier\": \"send_notification_email\",\n \"path\": \"dje/notification.py\",\n \"snippet\": \"def send_notification_email(user, instance, action, message=\\\"\\\"):\\n if not has_email_settings():\\n return\\n\\n if not hasattr(instance, \\\"dataspace\\\"):\\n return\\n\\n recipients = get_user_model().objects.get_data_update_recipients(instance.dataspace)\\n if not recipients:\\n return\\n\\n verbose_name = instance._meta.verbose_name.capitalize()\\n verbose_action = VERBOSE_ACTION[action]\\n subject = f'{verbose_action} {verbose_name}: \\\"{instance}\\\"'\\n body = (\\n f'{verbose_name} \\\"{instance}\\\" in dataspace \\\"{instance.dataspace.name}\\\" '\\n f\\\"{verbose_action.lower()} by: {user.first_name} {user.last_name} ({user.username})\\\"\\n )\\n\\n if action is History.CHANGE and message:\\n if message == \\\"No fields changed.\\\":\\n return\\n body += f\\\"\\\\n\\\\n{message}\\\"\\n\\n if action is not History.DELETION and settings.SITE_URL:\\n site_url = settings.SITE_URL.rstrip(\\\"/\\\")\\n body += f\\\"\\\\n\\\\n{site_url}{instance.get_admin_url()}\\\"\\n\\n send_mail_task.delay(subject, body, settings.DEFAULT_FROM_EMAIL, recipients)\"\n },\n {\n \"identifier\": \"send_notification_email_on_queryset\",\n \"path\": \"dje/notification.py\",\n \"snippet\": \"def send_notification_email_on_queryset(user, queryset, action, message=\\\"\\\"):\\n if not has_email_settings():\\n return\\n\\n if not queryset:\\n return\\n\\n if len(queryset) == 1:\\n return send_notification_email(user, queryset[0], action, message)\\n\\n first = queryset[0]\\n if not hasattr(first, \\\"dataspace\\\"):\\n return\\n\\n recipients = get_user_model().objects.get_data_update_recipients(first.dataspace)\\n if not recipients:\\n return\\n\\n verbose_name_plural = first._meta.verbose_name_plural.capitalize()\\n verbose_action = VERBOSE_ACTION[action]\\n\\n subject = f\\\"Multiple {verbose_name_plural} {verbose_action.lower()}\\\"\\n body = (\\n f'{verbose_name_plural} in dataspace \\\"{first.dataspace.name}\\\" '\\n f\\\"{verbose_action.lower()} by {user.first_name} {user.last_name} ({user.username}):\\\"\\n )\\n\\n for instance in queryset:\\n body += f\\\"\\\\n- {instance}\\\"\\n\\n if action is not History.DELETION and settings.SITE_URL:\\n site_url = settings.SITE_URL.rstrip(\\\"/\\\")\\n body += f\\\" {site_url}{instance.get_admin_url()}\\\"\\n\\n if message:\\n body += f\\\"\\\\n\\\\n{message}\\\"\\n\\n send_mail_task.delay(subject, body, settings.DEFAULT_FROM_EMAIL, recipients)\"\n },\n {\n \"identifier\": \"get_protected_fields\",\n \"path\": \"dje/permissions.py\",\n \"snippet\": \"def get_protected_fields(model_class, user):\\n \\\"\\\"\\\"Return the list of protected fields names for the given `user`.\\\"\\\"\\\"\\n protected_fields = getattr(model_class(), \\\"permission_protected_fields\\\", {})\\n\\n return [\\n field_name\\n for field_name, perm_codename in protected_fields.items()\\n if not user.has_perm(f\\\"{model_class._meta.app_label}.{perm_codename}\\\")\\n ]\"\n },\n {\n \"identifier\": \"advanced_search\",\n \"path\": \"dje/search.py\",\n \"snippet\": \"def advanced_search(search_terms, search_fields):\\n lookup_types = {\\n \\\":\\\": \\\"icontains\\\",\\n \\\"=\\\": \\\"iexact\\\",\\n \\\"^\\\": \\\"istartswith\\\",\\n }\\n or_queries = []\\n\\n for term in split_search_terms(search_terms):\\n lookup_type = \\\"icontains\\\" # default\\n lookup_fields = search_fields\\n lookup_operators = [force_str(key) for key in lookup_types.keys()]\\n\\n # 'apache', '^apache', '=apache', , ':apache'\\n if term.startswith(tuple(lookup_operators)):\\n term, lookup_type = term[1:], lookup_types.get(term[0])\\n\\n # 'name:apache', 'name^apache', 'name=apache'\\n else:\\n for field_name in lookup_fields:\\n missing_operator = term == field_name\\n if not term.startswith(field_name) or missing_operator:\\n continue\\n\\n operator = term[len(field_name)]\\n if operator in lookup_operators:\\n lookup_type = lookup_types.get(operator)\\n lookup_fields = [field_name]\\n _, term = term.split(f\\\"{field_name}{operator}\\\", 1)\\n break\\n\\n if not term:\\n continue\\n\\n orm_lookups = [f\\\"{field}__{lookup_type}\\\" for field in lookup_fields]\\n or_queries.extend([models.Q(**{orm_lookup: term}) for orm_lookup in orm_lookups])\\n\\n if or_queries:\\n return reduce(or_, or_queries)\"\n },\n {\n \"identifier\": \"CHANGELIST_LINK_TEMPLATE\",\n \"path\": \"dje/utils.py\",\n \"snippet\": \"CHANGELIST_LINK_TEMPLATE = (\\n '<strong>See the <a href=\\\"{}\\\" target=\\\"_blank\\\">{} {}</a> in changelist</strong>'\\n)\"\n },\n {\n \"identifier\": \"class_wrap\",\n \"path\": \"dje/utils.py\",\n \"snippet\": \"def class_wrap(value, class_):\\n \\\"\\\"\\\"Return the given HTML wrapped in a div with the given class set.\\\"\\\"\\\"\\n return format_html('<div class=\\\"{}\\\">{}</div>', class_, mark_safe(value))\"\n },\n {\n \"identifier\": \"construct_changes_details_message\",\n \"path\": \"dje/utils.py\",\n \"snippet\": \"def construct_changes_details_message(changes_details):\\n msg = []\\n header = '\\\\n\\\\n\\\\nChanges details for {model_class} \\\"{instance}\\\"'\\n change_line = \\\"\\\\n\\\\n* {field}\\\\nOld value: {old}\\\\nNew value: {new}\\\"\\n\\n for instance, data in changes_details.items():\\n msg.append(header.format(model_class=instance.__class__.__name__, instance=instance))\\n for field, old, new in data:\\n msg.append(change_line.format(field=field, old=old, new=new))\\n return \\\"\\\".join(msg)\"\n },\n {\n \"identifier\": \"get_previous_next\",\n \"path\": \"dje/utils.py\",\n \"snippet\": \"def get_previous_next(ids, current_id):\\n \\\"\\\"\\\"\\n Return the previous and next entries from a given `ids` list\\n and the current id value.\\n\\n >>> get_previous_next([1, 2, 3, 4], 3)\\n (2, 4)\\n >>> get_previous_next(['a', 'b', 'c'], 'b')\\n ('a', 'c')\\n \\\"\\\"\\\"\\n previous, next = None, None\\n\\n try:\\n index = ids.index(current_id)\\n except ValueError:\\n return None, None\\n\\n if index > 0:\\n previous = ids[index - 1]\\n if index < len(ids) - 1:\\n next = ids[index + 1]\\n\\n return previous, next\"\n },\n {\n \"identifier\": \"group_by\",\n \"path\": \"dje/utils.py\",\n \"snippet\": \"def group_by(queryset, field_name, values=None, count_on=None, distinct=False):\\n from django.db.models import Count\\n\\n values = values or [field_name]\\n count_on = count_on or field_name\\n\\n return queryset.values(*values).order_by().annotate(count=Count(count_on, distinct=distinct))\"\n },\n {\n \"identifier\": \"has_permission\",\n \"path\": \"dje/utils.py\",\n \"snippet\": \"def has_permission(model, user, action):\\n \\\"\\\"\\\"Return True is the `user` has the Permission for the given action of the model.\\\"\\\"\\\"\\n opts = model._meta\\n codename = get_permission_codename(action, opts)\\n return user.has_perm(f\\\"{opts.app_label}.{codename}\\\")\"\n },\n {\n \"identifier\": \"queryset_to_changelist_href\",\n \"path\": \"dje/utils.py\",\n \"snippet\": \"def queryset_to_changelist_href(queryset, params=None):\\n \\\"\\\"\\\"Return an URL to a changelist based on the given queryset.\\\"\\\"\\\"\\n if not queryset:\\n return\\n\\n if params is None:\\n params = {}\\n\\n opts = queryset.model._meta\\n url = reverse(f\\\"admin:{opts.app_label}_{opts.model_name}_changelist\\\")\\n\\n ids = queryset.values_list(\\\"id\\\", flat=True)\\n params.update({\\\"id__in\\\": \\\",\\\".join(str(id_) for id_ in ids)})\\n\\n return f\\\"{url}?{urlencode(params)}\\\"\"\n },\n {\n \"identifier\": \"ActivityLog\",\n \"path\": \"dje/views.py\",\n \"snippet\": \"class ActivityLog(\\n LoginRequiredMixin,\\n BootstrapCSSMixin,\\n DownloadableMixin,\\n TemplateView,\\n):\\n template_name = \\\"activity_log.html\\\"\\n model = None\\n action_flag_map = {\\n History.ADDITION: \\\"Addition\\\",\\n History.CHANGE: \\\"Change\\\",\\n History.DELETION: \\\"Deletion\\\",\\n }\\n\\n def get_days(self):\\n days = self.request.GET.get(\\\"days\\\", None)\\n try:\\n return int(days)\\n except (TypeError, ValueError):\\n return 90 # Default value\\n\\n def get_history_entries(self, days):\\n \\\"\\\"\\\"\\n Return all the History entries, filtered by the number of days and\\n scoped using the current user Dataspace.\\n \\\"\\\"\\\"\\n user_dataspace_id = self.request.user.dataspace_id\\n content_type = ContentType.objects.get_for_model(self.model)\\n start = datetime.datetime.now() - datetime.timedelta(days=days)\\n\\n history_entries = History.objects.filter(\\n object_dataspace_id=user_dataspace_id,\\n content_type=content_type,\\n action_time__gt=start,\\n )\\n\\n has_history_fields = issubclass(self.model, HistoryFieldsMixin)\\n if has_history_fields:\\n # Use the history fields from the model for the Addition entries\\n history_entries = history_entries.exclude(action_flag=History.ADDITION)\\n\\n objects = self.model.objects.scope_by_id(user_dataspace_id).filter(\\n created_date__gt=start\\n )\\n\\n addition_entries = []\\n for obj in objects:\\n addition_entries.append(\\n History(\\n action_time=obj.created_date,\\n user=obj.created_by,\\n object_dataspace=obj.dataspace,\\n content_type=content_type,\\n object_id=obj.id,\\n action_flag=History.ADDITION,\\n change_message=\\\"Added.\\\",\\n )\\n )\\n\\n history_entries = list(history_entries) + addition_entries\\n\\n return history_entries\\n\\n @staticmethod\\n def get_object_or_repr(history_entry):\\n try:\\n return history_entry.get_edited_object()\\n except ObjectDoesNotExist:\\n return history_entry.object_repr\\n\\n def get_objects(self, days):\\n history_entries = self.get_history_entries(days)\\n\\n objects = []\\n for history_entry in history_entries:\\n objects.append(\\n {\\n \\\"history\\\": history_entry,\\n \\\"obj\\\": self.get_object_or_repr(history_entry),\\n \\\"action\\\": self.action_flag_map[history_entry.action_flag],\\n }\\n )\\n\\n return objects\\n\\n def get_format(self):\\n return \\\"html\\\"\\n\\n def get_root_filename(self):\\n model_name = self.model._meta.model_name\\n return f\\\"{model_name}_activity_log\\\"\\n\\n def get_context_data(self, **kwargs):\\n context = super().get_context_data(**kwargs)\\n days = self.get_days()\\n context.update(\\n {\\n \\\"days\\\": days,\\n \\\"objects\\\": self.get_objects(days),\\n \\\"verbose_name\\\": self.model._meta.verbose_name,\\n \\\"dataspace\\\": self.request.user.dataspace,\\n \\\"now\\\": datetime.datetime.now(),\\n }\\n )\\n return context\"\n },\n {\n \"identifier\": \"clone_dataset_view\",\n \"path\": \"dje/views.py\",\n \"snippet\": \"@login_required\\ndef clone_dataset_view(request, pk):\\n \\\"\\\"\\\"Call the clonedataset management command as a celery task.\\\"\\\"\\\"\\n changelist_url = reverse(\\\"admin:dje_dataspace_changelist\\\")\\n user = request.user\\n template_dataspace = settings.TEMPLATE_DATASPACE\\n\\n if not all(\\n [\\n template_dataspace,\\n user.is_superuser,\\n user.dataspace.is_reference,\\n ]\\n ):\\n return redirect(changelist_url)\\n\\n try:\\n reference = Dataspace.objects.get(name=template_dataspace)\\n target = Dataspace.objects.get(pk=pk)\\n except Dataspace.DoesNotExist:\\n return redirect(changelist_url)\\n\\n call_management_command.delay(\\n \\\"clonedataset\\\",\\n reference.name,\\n target.name,\\n user.username,\\n user_id=user.id,\\n product_portfolio=True,\\n )\\n\\n msg = \\\"Cloning task in progress.\\\"\\n if user.email:\\n msg += f' An email will be sent to \\\"{user.email}\\\" on completion.'\\n messages.success(request, msg)\\n\\n return redirect(changelist_url)\"\n },\n {\n \"identifier\": \"docs_models_view\",\n \"path\": \"dje/views.py\",\n \"snippet\": \"@login_required\\ndef docs_models_view(request):\\n from dje.admin import dejacode_site\\n\\n apps_white_list = [\\n \\\"organization\\\",\\n \\\"license_library\\\",\\n \\\"component_catalog\\\",\\n \\\"product_portfolio\\\",\\n \\\"workflow\\\",\\n \\\"reporting\\\",\\n \\\"policy\\\",\\n ]\\n\\n model_classes = [\\n model for model in apps.get_models() if model._meta.app_label in apps_white_list\\n ]\\n\\n def get_limited_fields(model):\\n return [\\n f\\n for f in model._meta.get_fields()\\n if not f.auto_created and f.name not in [\\\"dataspace\\\", \\\"uuid\\\"]\\n ]\\n\\n help_data = defaultdict(list)\\n for model in model_classes:\\n opts = model._meta\\n app_verbose_name = apps.app_configs.get(opts.app_label).verbose_name\\n model_admin = dejacode_site._registry.get(model)\\n help_data[app_verbose_name].append(\\n {\\n \\\"verbose_name\\\": opts.verbose_name,\\n \\\"short_description\\\": getattr(model_admin, \\\"short_description\\\", \\\"\\\"),\\n \\\"long_description\\\": getattr(model_admin, \\\"long_description\\\", \\\"\\\"),\\n \\\"fields\\\": get_limited_fields(model),\\n }\\n )\\n\\n context = {\\n \\\"help_data\\\": dict(help_data),\\n \\\"document\\\": {\\\"title\\\": \\\"Models documentation\\\"},\\n }\\n\\n return render(request, \\\"admin/docs/models.html\\\", context)\"\n },\n {\n \"identifier\": \"manage_copy_defaults_view\",\n \"path\": \"dje/views.py\",\n \"snippet\": \"@login_required\\ndef manage_copy_defaults_view(request, pk):\\n changelist_url = reverse(\\\"admin:dje_dataspace_changelist\\\")\\n\\n supported_apps = [\\n \\\"dje\\\",\\n \\\"organization\\\",\\n \\\"license_library\\\",\\n \\\"component_catalog\\\",\\n \\\"product_portfolio\\\",\\n \\\"workflow\\\",\\n \\\"reporting\\\",\\n \\\"policy\\\",\\n \\\"notification\\\",\\n ]\\n\\n try:\\n dataspace = Dataspace.objects.get(pk=unquote_plus(pk))\\n except Dataspace.DoesNotExist:\\n return redirect(changelist_url)\\n\\n if not request.user.is_superuser or dataspace != request.user.dataspace:\\n return redirect(changelist_url)\\n\\n if request.method == \\\"POST\\\":\\n formset = CopyDefaultsFormSet(request.POST)\\n if formset.is_valid():\\n formset.save(dataspace)\\n formset.log_change(request, dataspace, \\\"Changed copy defaults configuration.\\\")\\n messages.success(request, _(\\\"Copy defaults updated.\\\"))\\n else:\\n messages.error(request, _(\\\"Error, please refresh the page and try again.\\\"))\\n else:\\n initial = [\\n {\\\"app_name\\\": str(apps.get_app_config(app_label).verbose_name)}\\n for app_label in supported_apps\\n ]\\n formset = CopyDefaultsFormSet(initial=initial)\\n formset.load(dataspace, default=COPY_DEFAULT_EXCLUDE)\\n\\n context = {\\n \\\"formset\\\": formset,\\n \\\"object\\\": dataspace,\\n \\\"opts\\\": dataspace._meta,\\n }\\n\\n return render(request, \\\"admin/dje/dataspace/copy_defaults_form.html\\\", context)\"\n },\n {\n \"identifier\": \"manage_tab_permissions_view\",\n \"path\": \"dje/views.py\",\n \"snippet\": \"@login_required\\ndef manage_tab_permissions_view(request, pk):\\n changelist_url = reverse(\\\"admin:dje_dataspace_changelist\\\")\\n\\n try:\\n dataspace = Dataspace.objects.get(pk=unquote_plus(pk))\\n except Dataspace.DoesNotExist:\\n return redirect(changelist_url)\\n\\n if not request.user.is_superuser or dataspace != request.user.dataspace:\\n return redirect(changelist_url)\\n\\n if request.method == \\\"POST\\\":\\n formset = TabPermissionsFormSet(request.POST)\\n if formset.is_valid():\\n formset.save(dataspace)\\n formset.log_change(request, dataspace, \\\"Changed tab permissions configuration.\\\")\\n messages.success(request, _(\\\"Tab permissions updated.\\\"))\\n else:\\n messages.error(request, _(\\\"Error, please refresh the page and try again.\\\"))\\n else:\\n initial = [{\\\"group_name\\\": group.name} for group in Group.objects.all()]\\n formset = TabPermissionsFormSet(initial=initial)\\n formset.load(dataspace)\\n\\n context = {\\n \\\"formset\\\": formset,\\n \\\"object\\\": dataspace,\\n \\\"opts\\\": dataspace._meta,\\n }\\n\\n return render(request, \\\"admin/dje/dataspace/tab_permissions_form.html\\\", context)\"\n },\n {\n \"identifier\": \"object_compare_view\",\n \"path\": \"dje/views.py\",\n \"snippet\": \"@login_required\\ndef object_compare_view(request):\\n target_dataspace_id = request.GET.get(\\\"target\\\", \\\"\\\")\\n ids = request.GET.get(\\\"ids\\\", \\\"\\\").split(\\\",\\\")\\n\\n model_class = get_model_class_from_path(request.path)\\n opts = model_class._meta\\n preserved_filters = get_preserved_filters(\\n request, model_class, parameter_name=\\\"_changelist_filters\\\"\\n )\\n changelist_url = reverse(f\\\"admin:{opts.app_label}_{opts.model_name}_changelist\\\")\\n redirect_url = add_preserved_filters(\\n {\\\"preserved_filters\\\": preserved_filters, \\\"opts\\\": opts}, changelist_url\\n )\\n\\n if len(ids) != 1:\\n messages.warning(\\n request, \\\"Compare allows 1 object only. Please select 1 object to compare.\\\"\\n )\\n return redirect(redirect_url)\\n\\n if request.user.dataspace.is_reference:\\n if not target_dataspace_id:\\n return dataspace_choice_for_compare_view(request)\\n try:\\n target_dataspace = Dataspace.objects.get(pk=target_dataspace_id)\\n except ObjectDoesNotExist:\\n return redirect(redirect_url)\\n else:\\n target_dataspace = request.user.dataspace\\n\\n try:\\n source_object = model_class.objects.get(id=ids[0])\\n except ObjectDoesNotExist:\\n return redirect(redirect_url)\\n\\n target_object = get_object_in(source_object, target_dataspace)\\n if not target_object:\\n error = f'No related object in the Dataspace \\\"{target_dataspace}\\\"'\\n messages.warning(request, error)\\n return redirect(redirect_url)\\n\\n # First section of the view, presenting the per filed diff.\\n if request.method == \\\"GET\\\":\\n excluded = [\\n \\\"last_modified_date\\\",\\n \\\"created_date\\\",\\n \\\"completion_level\\\",\\n \\\"usage_policy\\\",\\n \\\"guidance\\\",\\n \\\"guidance_url\\\",\\n ]\\n\\n compare_diff, compare_diff_m2m = get_object_compare_diff(\\n source_object, target_object, excluded\\n )\\n\\n return render(\\n request,\\n \\\"admin/object_compare.html\\\",\\n {\\n \\\"source_object\\\": source_object,\\n \\\"target_object\\\": target_object,\\n \\\"compare_diff\\\": compare_diff,\\n \\\"compare_diff_m2m\\\": compare_diff_m2m,\\n \\\"opts\\\": opts,\\n \\\"preserved_filters\\\": preserved_filters,\\n },\\n )\\n\\n # POST section of the view\\n updated_fields = []\\n\\n for field_name in request.POST.getlist(\\\"checkbox_select\\\"):\\n field = source_object._meta.get_field(field_name)\\n field_value = getattr(source_object, field_name)\\n\\n if isinstance(field, models.ForeignKey):\\n if field_value is not None:\\n fk_in_target = get_or_create_in(field_value, target_dataspace, request.user)\\n if fk_in_target:\\n setattr(target_object, field_name, fk_in_target)\\n updated_fields.append(field_name)\\n else:\\n setattr(target_object, field_name, None)\\n updated_fields.append(field_name)\\n else:\\n setattr(target_object, field_name, field_value)\\n updated_fields.append(field_name)\\n\\n # Saving only at the end of the process, if at least one field was modified\\n if updated_fields:\\n target_object.save()\\n message = 'Changed {}, values updated from \\\"{}\\\".'.format(\\n \\\", \\\".join(updated_fields), source_object.dataspace.name\\n )\\n History.log_addition(request.user, target_object, message)\\n messages.success(request, message)\\n\\n return redirect(redirect_url)\"\n },\n {\n \"identifier\": \"object_copy_view\",\n \"path\": \"dje/views.py\",\n \"snippet\": \"@login_required\\ndef object_copy_view(request):\\n \\\"\\\"\\\"\\n Copy objects across Dataspaces.\\n This is the view called by the \\\"copy objects\\\" admin action.\\n The first step is to present to the user the list of Object to be copied\\n into his Dataspace and the list of Object that already exists\\n in the target and give the choice to update those.\\n If the User is a member of the Reference Dataspace, he's allowed\\n to copy from any source Dataspace and to select any destination.\\n This result as an extra step of presenting the target Dataspace list of\\n choices.\\n \\\"\\\"\\\"\\n user_dataspace = request.user.dataspace\\n # Declared here as it required in GET and POST cases.\\n M2MConfigurationFormSet = formset_factory(\\n wraps(M2MCopyConfigurationForm)(partial(M2MCopyConfigurationForm, user=request.user)),\\n extra=0,\\n )\\n\\n model_class = get_model_class_from_path(request.path)\\n\\n # Default entry point of the view, requested using a GET\\n # At that stage, we are only looking at what the User requested,\\n # making sure everything is in order, present him what is going to\\n # happens and ask for his confirmation.\\n if request.method == \\\"GET\\\":\\n requested_ids = request.GET.get(\\\"ids\\\", \\\"\\\")\\n\\n # In case the view is not requested with the proper parameters\\n if not requested_ids:\\n raise Http404\\n\\n opts = model_class._meta\\n preserved_filters = get_preserved_filters(\\n request, model_class, parameter_name=\\\"_changelist_filters\\\"\\n )\\n changelist_url = reverse(f\\\"admin:{opts.app_label}_{opts.model_name}_changelist\\\")\\n redirect_url = add_preserved_filters(\\n {\\\"preserved_filters\\\": preserved_filters, \\\"opts\\\": opts}, changelist_url\\n )\\n\\n # Ids of objects to be copied\\n ids = requested_ids.split(\\\",\\\")\\n\\n # Limit the copy to 100 Objects at the time, as it's the number of\\n # Objects we display per page, default value for the list_per_page\\n # of the ModelAdmin\\n COPY_NB_OBJECT_LIMIT = 100\\n if len(ids) > COPY_NB_OBJECT_LIMIT:\\n msg = (\\n f\\\"Maximum of objects that can be copied at once is \\\"\\n f\\\"limited to {COPY_NB_OBJECT_LIMIT} (by system-wide settings)\\\"\\n )\\n messages.warning(request, msg)\\n return redirect(redirect_url)\\n\\n # Let's find the Source Dataspace using the first id\\n # This block will redirect the user to the list if the\\n # first id of the list do not exist\\n try:\\n source_object = model_class.objects.get(id=ids[0])\\n except ObjectDoesNotExist:\\n return redirect(redirect_url)\\n\\n # No custom permission for 'copy', we use the 'add' one\\n if not has_permission(source_object, request.user, \\\"add\\\"):\\n messages.error(request, _(\\\"Sorry you do not have rights to execute this action\\\"))\\n return redirect(redirect_url)\\n\\n source = source_object.dataspace\\n # As a non-Reference Dataspace User, I can only use the Reference\\n # data as the source and my Dataspace as the target\\n # As a Reference User, I can choose both, source and target.\\n if user_dataspace.is_reference:\\n # The following is only used when the User is in the Reference\\n targets_from_request = request.GET.getlist(\\\"target\\\")\\n # If the target has been set, then we can continue\\n if targets_from_request:\\n data = {\\\"target\\\": targets_from_request}\\n choice_form = MultiDataspaceChoiceForm(source, request.user, data=data)\\n if not choice_form.is_valid():\\n return redirect(redirect_url)\\n targets = choice_form.cleaned_data[\\\"target\\\"]\\n # else, we build a form to offer the choice to the user,\\n # choices do not include the current source\\n else:\\n initial = {\\n \\\"ids\\\": requested_ids,\\n \\\"_changelist_filters\\\": dict(parse_qsl(preserved_filters)).get(\\n \\\"_changelist_filters\\\"\\n ),\\n }\\n is_popup = request.GET.get(IS_POPUP_VAR, False)\\n if is_popup:\\n initial[\\\"_popup\\\"] = is_popup\\n choice_form = MultiDataspaceChoiceForm(source, request.user, initial=initial)\\n return render(\\n request,\\n \\\"admin/object_copy_dataspace_form.html\\\",\\n {\\n \\\"form\\\": choice_form,\\n \\\"opts\\\": opts,\\n \\\"is_popup\\\": is_popup,\\n \\\"preserved_filters\\\": preserved_filters,\\n },\\n )\\n elif not source.is_reference:\\n # As a non-Reference User my only \\\"external\\\" source of data allowed\\n # is the Reference Dataspace\\n return redirect(redirect_url)\\n else:\\n targets = [user_dataspace]\\n\\n # At this stage, we have the Source and Target Dataspaces\\n # Let's see which objects are eligible for copy, or offer the update\\n copy_candidates = []\\n update_candidates = []\\n\\n # Building a QuerySet based on the given ids, if an non-authorized or\\n # non-existing id was injected it will be ignored thanks to the\\n # id__in and the dataspace scoping.\\n queryset = model_class.objects.scope(source).filter(id__in=ids)\\n\\n for target in targets:\\n for source_instance in queryset:\\n matched_object = get_object_in(source_instance, target)\\n if matched_object:\\n # Inject the source_instance for future usage in the template\\n update_candidates.append((matched_object, source_instance))\\n else:\\n copy_candidates.append((source_instance, target))\\n\\n initial = {\\n \\\"source\\\": source,\\n \\\"targets\\\": targets,\\n \\\"ct\\\": ContentType.objects.get_for_model(model_class).id,\\n }\\n form = CopyConfigurationForm(request.user, initial=initial)\\n\\n # Many2Many exclude on copy/update\\n m2m_initial = [\\n {\\\"ct\\\": ContentType.objects.get_for_model(m2m_field.remote_field.through).id}\\n for m2m_field in model_class._meta.many_to_many\\n ]\\n\\n # Also handle relational fields if explicitly declared on the Model using the\\n # get_extra_relational_fields method.\\n for field_name in model_class.get_extra_relational_fields():\\n related_model = model_class._meta.get_field(field_name).related_model\\n if related_model().get_exclude_candidates_fields():\\n ct = ContentType.objects.get_for_model(related_model)\\n m2m_initial.append({\\\"ct\\\": ct.id})\\n\\n m2m_formset = M2MConfigurationFormSet(initial=m2m_initial)\\n\\n return render(\\n request,\\n \\\"admin/object_copy.html\\\",\\n {\\n \\\"copy_candidates\\\": copy_candidates,\\n \\\"update_candidates\\\": update_candidates,\\n \\\"form\\\": form,\\n \\\"m2m_formset\\\": m2m_formset,\\n \\\"opts\\\": source_object._meta,\\n \\\"preserved_filters\\\": preserved_filters,\\n },\\n )\\n\\n # Second section of the view, following the POST\\n if request.method == \\\"POST\\\":\\n config_form = CopyConfigurationForm(request.user, request.POST)\\n\\n if not config_form.is_valid():\\n raise Http404\\n\\n model_class = config_form.model_class\\n opts = model_class._meta\\n preserved_filters = get_preserved_filters(\\n request, model_class, parameter_name=\\\"_changelist_filters\\\"\\n )\\n\\n # We use False rather than empty list to keep track of the non-selection\\n # vs unknown in lower level copy method.\\n exclude_copy = {model_class: config_form.cleaned_data.get(\\\"exclude_copy\\\")}\\n exclude_update = {model_class: config_form.cleaned_data.get(\\\"exclude_update\\\")}\\n\\n # Append the m2m copy configuration\\n for m2m_form in M2MConfigurationFormSet(request.POST):\\n if not m2m_form.is_valid():\\n continue\\n m2m_model_class = m2m_form.model_class\\n cleaned_data = m2m_form.cleaned_data\\n skip_on_copy = cleaned_data.get(\\\"skip_on_copy\\\")\\n skip_on_update = cleaned_data.get(\\\"skip_on_update\\\")\\n exclude_copy.update(\\n {m2m_model_class: SKIP if skip_on_copy else cleaned_data.get(\\\"exclude_copy\\\")}\\n )\\n exclude_update.update(\\n {m2m_model_class: SKIP if skip_on_update else cleaned_data.get(\\\"exclude_update\\\")}\\n )\\n\\n copy_candidates = request.POST.get(\\\"copy_candidates\\\", \\\"\\\")\\n selected_for_update = request.POST.getlist(\\\"select_for_update\\\")\\n\\n source, copied, updated, errors = config_form.submit(\\n copy_candidates, selected_for_update, exclude_copy, exclude_update\\n )\\n\\n if copied or updated:\\n msg = \\\"Copied/updated from {} dataspace.\\\".format(\\n source.name if source.is_reference else \\\"another\\\"\\n )\\n\\n if errors:\\n errors_count = len(errors)\\n msg = \\\"{} object w{} not copied/updated.\\\".format(\\n errors_count, pluralize(errors_count, \\\"as,ere\\\")\\n )\\n messages.error(request, msg)\\n\\n object_for_raw_id_lookup = None\\n if request.GET.get(IS_POPUP_VAR, 0):\\n object_for_raw_id_lookup = copied + updated\\n if len(object_for_raw_id_lookup) == 1:\\n object_for_raw_id_lookup = object_for_raw_id_lookup[0][1]\\n else:\\n object_for_raw_id_lookup = None\\n\\n return render(\\n request,\\n \\\"admin/object_copy_results.html\\\",\\n {\\n \\\"copied\\\": copied,\\n \\\"updated\\\": updated,\\n \\\"errors\\\": errors,\\n \\\"opts\\\": opts,\\n \\\"preserved_filters\\\": preserved_filters,\\n \\\"object_for_raw_id_lookup\\\": object_for_raw_id_lookup,\\n },\\n )\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import csv\nimport operator\nfrom collections import OrderedDict\nfrom copy import copy\nfrom functools import reduce\nfrom django import forms\nfrom django.conf import settings\nfrom django.contrib import admin\nfrom django.contrib import messages\nfrom django.contrib.admin.helpers import ACTION_CHECKBOX_NAME\nfrom django.contrib.admin.options import IS_POPUP_VAR\nfrom django.contrib.admin.sites import AdminSite\nfrom django.contrib.admin.templatetags.admin_urls import add_preserved_filters\nfrom django.contrib.admin.utils import lookup_spawns_duplicates\nfrom django.contrib.admin.utils import unquote\nfrom django.contrib.admin.views.main import ChangeList\nfrom django.contrib.admin.widgets import AdminDateWidget\nfrom django.contrib.auth.admin import GroupAdmin\nfrom django.contrib.auth.admin import UserAdmin\nfrom django.contrib.auth.models import Group\nfrom django.contrib.auth.models import Permission\nfrom django.contrib.auth.views import LogoutView\nfrom django.contrib.contenttypes.admin import GenericTabularInline\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.core import checks\nfrom django.core.exceptions import FieldDoesNotExist\nfrom django.core.exceptions import FieldError\nfrom django.core.exceptions import PermissionDenied\nfrom django.db import models\nfrom django.forms.formsets import DELETION_FIELD_NAME\nfrom django.http import HttpResponse\nfrom django.http.request import QueryDict\nfrom django.shortcuts import get_object_or_404\nfrom django.shortcuts import redirect\nfrom django.template.loader import render_to_string\nfrom django.template.response import TemplateResponse\nfrom django.urls import path\nfrom django.urls import reverse\nfrom django.utils.encoding import force_str\nfrom django.utils.html import format_html\nfrom django.utils.html import format_html_join\nfrom django.utils.http import urlencode\nfrom django.utils.translation import gettext as _\nfrom django.views.generic import RedirectView\nfrom django_registration.backends.activation.views import RegistrationView\nfrom dje.filters import IS_FILTER_LOOKUP_VAR\nfrom dje.filters import CreatedByListFilter\nfrom dje.filters import DataspaceFilter\nfrom dje.filters import HistoryCreatedActionTimeListFilter\nfrom dje.filters import HistoryModifiedActionTimeListFilter\nfrom dje.filters import LimitToDataspaceListFilter\nfrom dje.filters import MissingInFilter\nfrom dje.forms import DataspaceAdminForm\nfrom dje.forms import DataspacedAdminForm\nfrom dje.forms import DejaCodeAuthenticationForm\nfrom dje.importers import import_view\nfrom dje.list_display import AsURL\nfrom dje.mass_update import mass_update_action\nfrom dje.models import Dataspace\nfrom dje.models import DataspaceConfiguration\nfrom dje.models import DejacodeUser\nfrom dje.models import ExternalReference\nfrom dje.models import ExternalSource\nfrom dje.models import History\nfrom dje.models import HistoryFieldsMixin\nfrom dje.models import is_dataspace_related\nfrom dje.notification import send_notification_email\nfrom dje.notification import send_notification_email_on_queryset\nfrom dje.permissions import get_protected_fields\nfrom dje.search import advanced_search\nfrom dje.utils import CHANGELIST_LINK_TEMPLATE\nfrom dje.utils import class_wrap\nfrom dje.utils import construct_changes_details_message\nfrom dje.utils import get_previous_next\nfrom dje.utils import group_by\nfrom dje.utils import has_permission\nfrom dje.utils import queryset_to_changelist_href\nfrom dje.views import ActivityLog\nfrom dje.views import clone_dataset_view\nfrom dje.views import docs_models_view\nfrom dje.views import manage_copy_defaults_view\nfrom dje.views import manage_tab_permissions_view\nfrom dje.views import object_compare_view\nfrom dje.views import object_copy_view\n from axes.admin import AccessAttemptAdmin\n from axes.models import AccessAttempt"},"token_num":{"kind":"number","value":19188,"string":"19,188"},"cropped_code":{"kind":"string","value":" (\n \"Application Process Settings\",\n {\n \"fields\": (\n \"set_usage_policy_on_new_component_from_licenses\",\n \"enable_package_scanning\",\n \"update_packages_from_scan\",\n \"enable_purldb_access\",\n \"enable_vulnerablecodedb_access\",\n )\n },\n ),\n )\n search_fields = (\"name\",)\n inlines = [DataspaceConfigurationInline]\n form = DataspaceAdminForm\n change_form_template = \"admin/dje/dataspace/change_form.html\"\n change_list_template = \"admin/change_list_extended.html\"\n\n def has_change_permission(self, request, obj=None):\n \"\"\"\n Bypass the ReferenceOnlyPermissions to allow regular Dataspace admins,\n with the right permission, to edit their own Dataspace.\n \"\"\"\n return super(admin.ModelAdmin, self).has_change_permission(request, obj)\n\n def get_readonly_fields(self, request, obj=None):\n \"\"\"Make Dataspace.name field readonly on edit except for reference Dataspace superusers.\"\"\"\n readonly_fields = super().get_readonly_fields(request, obj)\n\n user = request.user\n if obj and not (user.dataspace.is_reference and user.is_superuser):\n readonly_fields += (\"name\",)\n\n return readonly_fields\n\n def get_urls(self):\n info = self.model._meta.app_label, self.model._meta.model_name\n\n urls = [\n path(\n \"<pk>/clonedataset/\",\n self.admin_site.admin_view(clone_dataset_view),\n name=\"{}_{}_clonedataset\".format(*info),\n ),\n path(\n \"<pk>/tab_permissions/\",\n self.admin_site.admin_view(manage_tab_permissions_view),\n name=\"{}_{}_tab_permissions\".format(*info),\n ),\n path(\n \"<pk>/copy_defaults/\",\n self.admin_site.admin_view(manage_copy_defaults_view),\n name=\"{}_{}_copy_defaults\".format(*info),\n ),\n ]\n\n return urls + super().get_urls()\n\n def get_queryset(self, request):\n \"\"\"\n Limit the QuerySet to the current user Dataspace. + the Reference one.\n If the user Dataspace is the Reference then show all.\n \"\"\"\n qs = super().get_queryset(request)\n if not request.user.dataspace.is_reference:\n qs = qs.filter(id=request.user.dataspace_id)\n return qs\n\n def get_actions(self, request):\n \"\"\"Remove the bulk delete action, it does not make sense for Dataspace.\"\"\"\n actions = super().get_actions(request)\n if \"delete_selected\" in actions:\n del actions[\"delete_selected\"]\n return actions\n\n def changeform_view(self, request, object_id=None, form_url=\"\", extra_context=None):\n extra_context = extra_context or {}\n extra_context[\"template_dataspace\"] = settings.TEMPLATE_DATASPACE\n return super().changeform_view(request, object_id, form_url, extra_context)\n\n\nclass ChildRelationshipInline(DataspacedFKMixin, admin.TabularInline):\n fk_name = \"parent\"\n extra = 0\n classes = (\"grp-collapse grp-open\",)\n raw_id_fields = (\"child\",)\n autocomplete_lookup_fields = {\"fk\": [\"child\"]}\n verbose_name = _(\"Child\")\n\n\nclass ExternalReferenceInline(DataspacedFKMixin, GenericTabularInline):\n model = ExternalReference\n extra = 0\n classes = (\"grp-collapse grp-open\",)\n\n\n@admin.register(ExternalSource, site=dejacode_site)\nclass ExternalSourceAdmin(DataspacedAdmin):\n def references(self, obj):\n \"\"\"\n Return links to the content_object changelist of ExternalReference instances,\n for the given ExternalSource instance, grouped per ContentType.\n \"\"\"\n changelist_links = []\n queryset = obj.externalreference_set\n grouped = group_by(queryset, \"content_type\", count_on=\"object_id\", distinct=True)\n\n for value in grouped:\n model_class = ContentType.objects.get(id=value[\"content_type\"]).model_class()\n opts = model_class._meta\n url = reverse(f\"admin:{opts.app_label}_{opts.model_name}_changelist\")\n params = {EXTERNAL_SOURCE_LOOKUP: obj.id}\n href = f\"{url}?{urlencode(params)}\"\n changelist_link = format_html(\n CHANGELIST_LINK_TEMPLATE, href, value[\"count\"], opts.verbose_name_plural\n )\n changelist_links.append([changelist_link])\n\n html_list = \"<ul>{}</ul>\".format(format_html_join(\"\", \"<li>{}</li>\", changelist_links))\n"},"all_code":{"kind":"string","value":"#\n# Copyright (c) nexB Inc. and others. All rights reserved.\n# DejaCode is a trademark of nexB Inc.\n# SPDX-License-Identifier: AGPL-3.0-only\n# See https://github.com/nexB/dejacode for support or download.\n# See https://aboutcode.org for more information about AboutCode FOSS projects.\n#\n\n\n\n\n\nEXTERNAL_SOURCE_LOOKUP = \"external_references__external_source_id\"\n\nADDITION = History.ADDITION\nCHANGE = History.CHANGE\nDELETION = History.DELETION\n\n\nclass DejaCodeAdminSite(AdminSite):\n login_template = \"registration/login.html\"\n login_form = DejaCodeAuthenticationForm\n site_title = _(\"DejaCode Administration\")\n site_header = _(\"DejaCode Administration\")\n index_title = _(\"DejaCode Administration\")\n empty_value_display = \"\"\n\n def get_urls(self):\n \"\"\"Override the admin:logout and admin:password_change views to the default ones.\"\"\"\n urls = [\n path(\"logout/\", LogoutView.as_view(next_page=\"login\"), name=\"logout\"),\n path(\n \"password_change/\",\n RedirectView.as_view(url=\"/account/password_change/\", permanent=True),\n name=\"password_change\",\n ),\n path(\"docs/models/\", docs_models_view, name=\"docs_models\"),\n ]\n\n return urls + super().get_urls()\n\n\ndejacode_site = DejaCodeAdminSite()\n\n\n@admin.display(description=\"\")\ndef get_hierarchy_link(obj):\n \"\"\"Return a link to the Hierarchy view if the obj has at least 1 parent or 1 child.\"\"\"\n if obj.has_parent_or_child():\n return format_html(\n '<a href=\"{}#hierarchy\" target=\"_blank\" class=\"hierarchy-icon\"'\n ' title=\"Hierarchy\"> </a>',\n obj.get_absolute_url(),\n )\n\n\ndef get_additional_information_fieldset(pre_fields=None):\n fields = (\n \"dataspace\",\n \"uuid\",\n \"created_date\",\n \"created_by\",\n \"last_modified_date\",\n \"last_modified_by\",\n )\n\n if pre_fields:\n fields = pre_fields + fields\n\n return (\"Additional Information\", {\"classes\": (\"grp-collapse grp-closed\",), \"fields\": fields})\n\n\nclass ReferenceOnlyPermissions:\n def has_add_permission(self, request):\n \"\"\"Limits the addition to Reference dataspace users.\"\"\"\n perm = super().has_add_permission(request)\n return perm and request.user.dataspace.is_reference\n\n def has_change_permission(self, request, obj=None):\n \"\"\"Limits the change to Reference dataspace users.\"\"\"\n perm = super().has_change_permission(request, obj)\n return perm and request.user.dataspace.is_reference\n\n def has_delete_permission(self, request, obj=None):\n \"\"\"Limits the deletion to Reference dataspace users.\"\"\"\n perm = super().has_delete_permission(request, obj)\n return perm and request.user.dataspace.is_reference\n\n def has_view_permission(self, request, obj=None):\n perm = super().has_view_permission(request, obj)\n return perm and request.user.dataspace.is_reference\n\n\nclass DataspacedFKMixin:\n \"\"\"\n Limit the QuerySet of ForeignKeys to the current Dataspace,\n or to the parent object in case of Inlines.\n On ADDITION, the Dataspace is taken from the User\n On MODIFICATION, it's taken on the current object instance or parent\n instance in case of Inlines.\n This class can be applied to ModelAdmins and Inlines.\n The declared limit_choices_to on the model field will be respected.\n \"\"\"\n\n # The QuerySet for the fields in this list will be scoped by the Model content_type\n content_type_scope_fields = []\n\n def formfield_for_foreignkey(self, db_field, request=None, **kwargs):\n # If a QuerySet was given in the kwargs of the calling method, we then\n # assume that the filtering was done and we skip further processing.\n qs = kwargs.get(\"queryset\", None)\n if qs is not None:\n return super().formfield_for_foreignkey(db_field, request, **kwargs)\n\n related_model = db_field.related_model\n if is_dataspace_related(related_model):\n # No instance, ADDITION, get dataspace from user\n if not getattr(request, \"_object\", None):\n dataspace = request.user.dataspace\n # Parent instance, MODIFICATION, dataspace from instance\n else:\n dataspace = request._object.dataspace\n\n kwargs[\"queryset\"] = db_field.related_model.objects.scope(dataspace).complex_filter(\n db_field.remote_field.limit_choices_to\n )\n\n if db_field.name in self.content_type_scope_fields:\n kwargs[\"queryset\"] = kwargs[\"queryset\"].filter(\n content_type=ContentType.objects.get_for_model(self.model)\n )\n\n return super().formfield_for_foreignkey(db_field, request, **kwargs)\n\n\nclass ProtectedFieldsMixin:\n def get_readonly_fields(self, request, obj=None):\n \"\"\"Add field level permissions.\"\"\"\n readonly_fields = super().get_readonly_fields(request, obj)\n\n protected_fields = get_protected_fields(self.model, request.user)\n if protected_fields:\n readonly_fields += tuple(protected_fields)\n\n return readonly_fields\n\n\nclass ChangelistPopupPermissionMixin:\n \"\"\"\n Allow the changelist view access in popup mode for users without change permission.\n In the case of raw_id_fields feature, this view need be be available to select the related\n object.\n This mixin bypass the limitation in Django: https://code.djangoproject.com/ticket/11561\n Only the changelist is available, the form is never accessible.\n \"\"\"\n\n def has_change_permission(self, request, obj=None):\n if obj is None and IS_POPUP_VAR in request.GET:\n return True\n return super().has_change_permission(request, obj)\n\n\nclass ProhibitDataspaceLookupMixin:\n \"\"\"\n Prohibit all `dataspace` related lookups.\n Remove the possibility to look into other Dataspaces.\n \"\"\"\n\n def lookup_allowed(self, lookup, value):\n if lookup.startswith(\"dataspace\"):\n return False\n return super().lookup_allowed(lookup, value)\n\n def check(self, **kwargs):\n errors = super().check(**kwargs)\n has_dataspace_filter = DataspaceFilter in self.list_filter\n\n if has_dataspace_filter:\n errors.append(\n checks.Error(f\"Remove {DataspaceFilter} from {self}.list_filter\", obj=self)\n )\n\n return errors\n\n def get_queryset(self, request):\n return super().get_queryset(request).scope_for_user(request.user)\n\n\nclass AdvancedSearchAdminMixin:\n def get_search_results(self, request, queryset, search_term):\n \"\"\"Replace default search with advanced system.\"\"\"\n use_distinct = False\n search_fields = self.get_search_fields(request)\n\n if search_fields and search_term:\n filters = []\n try:\n filters = advanced_search(search_term, search_fields)\n except FieldError as e:\n messages.error(request, e)\n except ValueError as e:\n messages.error(request, f\"Search terms error: {e}\")\n if filters:\n queryset = queryset.filter(filters)\n if not use_distinct:\n for search_spec, __ in filters.children:\n if lookup_spawns_duplicates(self.opts, search_spec):\n use_distinct = True\n break\n\n return queryset, use_distinct\n\n\nclass HistoryAdminMixin:\n def log_addition(self, request, object, change_message=None):\n history_entry = History.log_addition(request.user, object)\n\n if ADDITION in getattr(self, \"email_notification_on\", []):\n send_notification_email(request.user, object, ADDITION)\n\n return history_entry\n\n def log_change(self, request, object, message):\n \"\"\"\n Add notification on object update.\n The notification system can be disabled by setting _disable_notification\n to True on the request.\n \"\"\"\n serialized_data = getattr(request, \"_serialized_data\", None)\n history_entry = History.log_change(request.user, object, message, serialized_data)\n\n message = history_entry.get_change_message()\n disable_notification = getattr(request, \"_disable_notification\", False)\n if CHANGE in getattr(self, \"email_notification_on\", []) and not disable_notification:\n # Expending the base message with details\n changes_details = getattr(request, \"changes_details\", {})\n message += construct_changes_details_message(changes_details)\n send_notification_email(request.user, object, CHANGE, message)\n\n return history_entry\n\n def log_deletion(self, request, object, object_repr):\n \"\"\"\n Log that an object will be deleted.\n Note that this method must be called before the deletion.\n \"\"\"\n return History.log_deletion(request.user, object)\n\n def history_view(self, request, object_id, extra_context=None):\n response = super().history_view(request, object_id, extra_context)\n\n context_data = getattr(response, \"context_data\", None)\n if context_data: # In case response is a HttpResponseRedirect\n obj = context_data[\"object\"]\n\n history_qs = History.objects\n if is_dataspace_related(self.model):\n history_qs = history_qs.filter(object_dataspace__id=obj.dataspace_id)\n\n history_entries = (\n history_qs.filter(\n object_id=unquote(object_id),\n content_type=ContentType.objects.get_for_model(self.model),\n )\n .select_related()\n .order_by(\"-action_time\")\n )\n\n obj_has_history_fields = isinstance(obj, HistoryFieldsMixin)\n if obj_has_history_fields:\n # Use the history fields from the model for the Addition entry.\n addition_entry = History(\n action_time=obj.created_date,\n user=obj.created_by,\n change_message=\"Added.\",\n )\n history_entries = history_entries.exclude(action_flag=History.ADDITION)\n history_entries = list(history_entries) + [addition_entry]\n\n response.context_data[\"action_list\"] = history_entries\n\n return response\n\n\nclass ColoredIconAdminMixin:\n class Media:\n js = [\n \"fontawesomefree/js/all.min.js\",\n ]\n\n def colored_icon(self, obj):\n if obj.icon and obj.color_code:\n return obj.get_icon_as_html()\n\n\nclass DataspacedChangeList(ChangeList):\n def get_results(self, request):\n \"\"\"\n Store the result_list ids in the session for the Previous and Next navigation button\n and \"Save and go to next\" feature.\n The session values are then used in the change_view() method of the ModelAdmin.\n\n Injects the preserved_filters on each object of the result_list to be used in\n list_display callable, as the request is not available there.\n Hierarchy links on ComponentAdmin and OwnerAdmin, as well as the annotation link\n on the LicenseAdmin requires this.\n This workaround could be removed once if the the following gets solved in Django:\n https://code.djangoproject.com/ticket/13659\n \"\"\"\n super().get_results(request)\n for obj in self.result_list:\n obj._preserved_filters = self.preserved_filters\n self.set_reference_link(request)\n\n @property\n def has_filters_activated(self):\n return bool(self.get_filters_params())\n\n def get_filters_params(self, params=None):\n lookup_params = super().get_filters_params(params)\n if IS_FILTER_LOOKUP_VAR in lookup_params:\n del lookup_params[IS_FILTER_LOOKUP_VAR]\n return lookup_params\n\n def set_reference_link(self, request):\n \"\"\"Add a 'View Reference Data' or 'View My Data 'link in the changelist header.\"\"\"\n do_set_link = all(\n [\n DataspaceFilter in self.model_admin.list_filter,\n self.model_admin.lookup_allowed(DataspaceFilter.parameter_name, None),\n not self.is_popup,\n ]\n )\n\n if not do_set_link:\n return\n\n reference_dataspace = Dataspace.objects.get_reference()\n if reference_dataspace and reference_dataspace != request.user.dataspace:\n dataspace_id = request.GET.get(DataspaceFilter.parameter_name)\n if dataspace_id and dataspace_id != request.user.dataspace_id:\n self.my_dataspace_link = True\n else:\n params = f\"?{DataspaceFilter.parameter_name}={reference_dataspace.id}\"\n self.reference_params = params\n\n\nclass DataspacedAdmin(\n DataspacedFKMixin,\n ProtectedFieldsMixin,\n AdvancedSearchAdminMixin,\n HistoryAdminMixin,\n admin.ModelAdmin,\n):\n formfield_overrides = {\n models.DateField: {\"widget\": AdminDateWidget(attrs={\"placeholder\": \"YYYY-MM-DD\"})},\n }\n list_filter = (DataspaceFilter,)\n readonly_fields = (\n \"dataspace\",\n \"uuid\",\n )\n actions = [\"copy_to\", \"compare_with\"]\n actions_to_remove = []\n email_notification_on = [ADDITION, CHANGE, DELETION]\n save_as = True\n\n # Display only the current count\n show_full_result_count = False\n\n # Display a warning if any of the identifier_fields has changed\n identifier_fields_warning = True\n\n # Default form, customized form should always extend DataspacedAdminForm\n form = DataspacedAdminForm\n\n # Using extended version of base templates to avoid code duplication\n change_form_template = \"admin/change_form_extended.html\"\n change_list_template = \"admin/change_list_extended.html\"\n\n # Set this to a BaseImporter extension of the Model to enable the import\n importer_class = None\n\n # Set this to a DejacodeMassUpdateForm to enable the mass update action\n mass_update_form = None\n\n # Set this to False to disable the Activity Log feature\n activity_log = True\n\n # Set this to True to enable the Previous and Next buttons in change view\n navigation_buttons = False\n\n preserve_filters = True\n\n # Do not display the View on site links by default\n # Set: view_on_site = DataspacedAdmin.changeform_view_on_site\n # for the default obj.get_absolute_url()\n view_on_site = False\n\n def __init__(self, model, admin_site):\n self.form.admin_site = admin_site\n super().__init__(model, admin_site)\n\n def check(self, **kwargs):\n errors = super().check(**kwargs)\n\n has_wrong_form_subclass = all(\n [\n not issubclass(self.form, DataspacedAdminForm),\n self.model._meta.unique_together != ((\"dataspace\", \"uuid\"),),\n ]\n )\n\n if has_wrong_form_subclass:\n errors.extend(\n [checks.Error(f\"{self.form} is not a subclass of {DataspacedAdminForm}\", obj=self)]\n )\n\n return errors\n\n def changeform_view_on_site(self, obj):\n return obj.get_absolute_url()\n\n @admin.display(description=_(\"View\"))\n def changelist_view_on_site(self, obj):\n return format_html('<a href=\"{}\" target=\"_blank\">View</a>', obj.get_absolute_url())\n\n @admin.display(description=_(\"URN\"))\n def urn_link(self, instance):\n \"\"\"Attach the URN link if URN is supported on the Model.\"\"\"\n if instance.pk:\n return instance.urn_link\n return f\"URN will be available once the {instance._meta.verbose_name} is saved.\"\n\n def get_queryset(self, request):\n qs = super().get_queryset(request)\n return qs.scope_for_user_in_admin(request.user)\n\n def get_changelist(self, request, **kwargs):\n return DataspacedChangeList\n\n def get_list_filter(self, request):\n \"\"\"Limit the availability of `MissingInFilter` to superusers.\"\"\"\n list_filter = list(super().get_list_filter(request))\n\n if not request.user.is_superuser and MissingInFilter in list_filter:\n del list_filter[list_filter.index(MissingInFilter)]\n\n # Custom LogEntry-based filters when field not available on the model\n history_filters = {\n \"created_by\": CreatedByListFilter,\n \"last_modified_by\": None,\n \"created_date\": HistoryCreatedActionTimeListFilter,\n \"last_modified_date\": HistoryModifiedActionTimeListFilter,\n }\n\n for field_name, default_filter in history_filters.items():\n try:\n field = self.model._meta.get_field(field_name)\n except FieldDoesNotExist:\n if default_filter:\n list_filter.append(default_filter)\n continue\n\n filtr = field_name\n if isinstance(field, models.ForeignKey):\n filtr = (field_name, LimitToDataspaceListFilter)\n list_filter.append(filtr)\n\n return list_filter\n\n def get_readonly_fields(self, request, obj=None):\n readonly_fields = super().get_readonly_fields(request, obj)\n\n if issubclass(self.model, HistoryFieldsMixin):\n readonly_fields += (\n \"created_date\",\n \"created_by\",\n \"last_modified_date\",\n \"last_modified_by\",\n )\n\n return readonly_fields\n\n def change_view(self, request, object_id, form_url=\"\", extra_context=None):\n \"\"\"\n Render the changelist using the current preserved filters to gather the\n previous and next id.\n \"\"\"\n context = extra_context or {}\n\n # WARNING: request.GET is important as a condition since we do not want to run this\n # expensive operation in case no given filter/search/sort is applied.\n # For example when the admin form is reached directly from the user details view.\n if self.navigation_buttons and request.method == \"GET\" and request.GET:\n fake_request = copy(request)\n query_dict = fake_request.GET.copy()\n preserved_filters = query_dict.pop(\"_changelist_filters\", \"\")\n if preserved_filters:\n preserved_filters = force_str(preserved_filters[0])\n fake_request.GET = QueryDict(preserved_filters)\n changelist_view = self.changelist_view(fake_request)\n\n # Sanity check, if the _changelist_filters were manually changed for example.\n if hasattr(changelist_view, \"context_data\"):\n # Do not use \".values_list('id', flat=True)\" to avoid an extra query\n ids_list = [str(obj.id) for obj in changelist_view.context_data[\"cl\"].result_list]\n previous_id, next_id = get_previous_next(ids_list, str(object_id))\n context.update(\n {\n \"previous_id\": previous_id,\n \"next_id\": next_id,\n }\n )\n\n if self.save_as and self.identifier_fields_warning:\n identifier_fields = self.model.get_identifier_fields()\n context[\"identifier_fields\"] = identifier_fields\n\n return super().change_view(request, object_id, form_url, context)\n\n def render_change_form(self, request, context, add=False, change=False, form_url=\"\", obj=None):\n response = super().render_change_form(request, context, add, change, form_url, obj)\n # De-activating the 'Save as' option if the user is editing an Object\n # belonging to another Dataspace.\n # We are able to update the context_data at that point as the\n # TemplateResponse as not been rendered yet.\n if obj and obj.dataspace != request.user.dataspace:\n response.context_data[\"save_as\"] = False\n return response\n\n @staticmethod\n def get_selected_ids_from_request(request, queryset):\n select_across = request.POST.get(\"select_across\", 0)\n if int(select_across):\n # This is the \"Selected all\" case, we are using the all queryset\n object_ids = list(queryset.values_list(\"id\", flat=True))\n else:\n object_ids = request.POST.getlist(ACTION_CHECKBOX_NAME)\n # Converting the ids list in a comma separated string, to be used\n # in the GET parameters\n return \",\".join(str(id_) for id_ in object_ids)\n\n def base_action_with_redirect(self, request, queryset, viewname):\n ids = self.get_selected_ids_from_request(request, queryset)\n opts = self.model._meta\n preserved_filters = self.get_preserved_filters(request)\n view_url = reverse(f\"admin:{opts.app_label}_{opts.model_name}_{viewname}\")\n url_with_params = \"{}?{}\".format(view_url, urlencode({\"ids\": ids}))\n redirect_url = add_preserved_filters(\n {\"preserved_filters\": preserved_filters, \"opts\": opts}, url_with_params\n )\n return redirect(redirect_url)\n\n @admin.display(description=_(\"Copy the selected objects\"))\n def copy_to(self, request, queryset):\n \"\"\"Copy the selected objects to another Dataspace.\"\"\"\n return self.base_action_with_redirect(request, queryset, \"copy\")\n\n @admin.display(description=_(\"Compare the selected object\"))\n def compare_with(self, request, queryset):\n \"\"\"Compare one selected object with a matching object in another Dataspace.\"\"\"\n return self.base_action_with_redirect(request, queryset, \"compare\")\n\n @admin.display(description=_(\"Check for updates in reference data\"))\n def check_updates_in_reference(self, request, queryset):\n values = queryset.values_list(\"uuid\", \"last_modified_date\")\n\n orm_lookups = [\n models.Q(**{\"uuid\": uuid, \"last_modified_date__gt\": last_modified_date})\n for uuid, last_modified_date in values\n ]\n\n return self.base_check_in_reference_action(request, self.model, orm_lookups)\n\n @admin.display(description=_(\"Check for newer versions in reference data\"))\n def check_newer_version_in_reference(self, request, queryset):\n values = queryset.values_list(\"name\", \"version\")\n\n orm_lookups = [\n models.Q(**{\"name\": name, \"version__gt\": version}) for name, version in values\n ]\n\n return self.base_check_in_reference_action(request, self.model, orm_lookups)\n\n @staticmethod\n def base_check_in_reference_action(request, model_class, orm_lookups):\n reference_dataspace = Dataspace.objects.get_reference()\n if not reference_dataspace or not orm_lookups:\n return\n\n updated_qs = model_class.objects.scope(reference_dataspace).filter(\n reduce(operator.or_, orm_lookups)\n )\n\n params = {DataspaceFilter.parameter_name: reference_dataspace.pk}\n changelist_href = queryset_to_changelist_href(updated_qs, params)\n if changelist_href:\n return redirect(changelist_href)\n\n messages.warning(request, \"No updates available in the reference dataspace.\")\n\n @staticmethod\n def get_changes_details(form):\n \"\"\"\n Introspect a given form to collect the changes details.\n Original values are collected on the DB instance (pre-save value)\n and New values are collected on the form (post-save value)\n \"\"\"\n if not form.instance.pk:\n return {}\n\n model_class = form.instance.__class__\n original_instance = model_class.objects.get(pk=form.instance.pk)\n\n changes_details = []\n # Using form.changed_data to only iterate on updated fields\n for field in form.changed_data:\n original_value = getattr(original_instance, field)\n new_value = getattr(form.instance, field)\n changes_details.append((field, original_value, new_value))\n\n return {form.instance: changes_details}\n\n def save_model(self, request, obj, form, change):\n \"\"\"Set the created_by and last_modified_by fields at save time.\"\"\"\n # This have no impact on save() if the model does not declare those fields.\n obj.last_modified_by = request.user\n if not change:\n obj.created_by = request.user\n\n # Injecting the results in the request for future use in the\n # log_change() method. This content will be used to add the changes\n # details into the notification message.\n # Using an OrderedDict to keep the main instance details first\n # The related objects changes (inlines) are gathered in\n # self.save_formset()\n request.changes_details = OrderedDict()\n if change and CHANGE in self.email_notification_on:\n request.changes_details.update(self.get_changes_details(form))\n\n super().save_model(request, obj, form, change)\n\n def save_formset(self, request, form, formset, change):\n \"\"\"\n Set the Dataspace on the Inline instance before it's saved. Using the\n Dataspace of the Model instance of this ModelAdmin.\n Also craft the change details of the Inlines.\n \"\"\"\n for f in formset.forms:\n # Skip if nothing has changed in the current inline form\n if not f.changed_data:\n continue\n\n # Set the Dataspace on the Inline instance in case of addition of\n # the current inline.\n # The Dataspace is taken from the main form instance.\n if not f.instance.dataspace_id:\n f.instance.dataspace = form.instance.dataspace\n\n # Only in case of a 'change' on the main instance\n if change and CHANGE in self.email_notification_on:\n # As the `change` param is only about the main instance, we use\n # the pk of the inline instance to make sure we are in a\n # MODIFICATION case.\n #\n # If the pk of the inline instance is None, this is an ADDITION,\n # so skip the details creation. Also, if DELETION_FIELD_NAME is\n # in changed_data, we are in an inline deletion case, skipping\n # too.\n if f.instance.pk and DELETION_FIELD_NAME not in f.changed_data:\n # request.changes_details is created in self.save_model()\n request.changes_details.update(self.get_changes_details(f))\n\n super().save_formset(request, form, formset, change)\n\n def delete_model(self, request, obj):\n # We are using this rather than self.log_deletion because it's not called\n # Here, History.log_deletion is called for each object in the bulk.\n History.log_deletion(request.user, obj)\n super().delete_model(request, obj)\n if DELETION in self.email_notification_on:\n send_notification_email(request.user, obj, DELETION)\n\n def delete_queryset(self, request, queryset):\n \"\"\"\n Add the email notification on bulk deletion through the default django\n 'delete_selected' action.\n \"\"\"\n send_notification_email_on_queryset(request.user, queryset, DELETION)\n super().delete_queryset(request, queryset)\n\n def get_urls(self):\n info = self.model._meta.app_label, self.model._meta.model_name\n urls = []\n\n if self.activity_log:\n urls += [\n path(\n \"activity_log/\",\n self.admin_site.admin_view(ActivityLog.as_view(model=self.model)),\n name=\"{}_{}_activity_log\".format(*info),\n )\n ]\n\n actions = getattr(self, \"actions\", [])\n actions_to_remove = getattr(self, \"actions_to_remove\", [])\n\n if \"copy_to\" in actions and \"copy_to\" not in actions_to_remove:\n urls += [\n path(\n \"copy/\",\n self.admin_site.admin_view(object_copy_view),\n name=\"{}_{}_copy\".format(*info),\n ),\n ]\n\n if \"compare_with\" in actions and \"compare_with\" not in actions_to_remove:\n urls += [\n path(\n \"compare/\",\n self.admin_site.admin_view(object_compare_view),\n name=\"{}_{}_compare\".format(*info),\n ),\n ]\n\n if self.importer_class:\n urls += [\n path(\n \"import/\",\n self.admin_site.admin_view(import_view),\n {\"importer_class\": self.importer_class},\n name=\"{}_{}_import\".format(*info),\n ),\n ]\n\n return urls + super().get_urls()\n\n def get_form(self, request, obj=None, **kwargs):\n \"\"\"\n Set the `obj` instance on the `request` for future processing.\n Set the serialized_data of the object on the `request` to be used in the\n `log_change` method.\n Set the `request` on the `form_class`.\n \"\"\"\n if obj:\n request._object = obj\n request._serialized_data = obj.as_json()\n\n form_class = super().get_form(request, obj, **kwargs)\n form_class.request = request\n return form_class\n\n def get_fieldsets(self, request, obj=None):\n \"\"\"Exclude form fields from the ADMIN_FORMS_CONFIGURATION settings.\"\"\"\n fieldsets = super().get_fieldsets(request, obj)\n\n forms_config = settings.ADMIN_FORMS_CONFIGURATION\n if not forms_config:\n return fieldsets\n\n model_config = forms_config.get(self.model._meta.model_name, {})\n exclude = model_config.get(\"exclude\", [])\n if not exclude:\n return fieldsets\n\n fieldsets_with_exclude = []\n for label, entry in fieldsets:\n fields = entry.get(\"fields\")\n if fields:\n entry[\"fields\"] = [field for field in fields if field not in exclude]\n fieldsets_with_exclude.append((label, entry))\n\n return fieldsets_with_exclude\n\n def get_inline_instances(self, request, obj=None):\n \"\"\"Injects the ``request`` in each inline form to be used in validation.\"\"\"\n base_instances = super().get_inline_instances(request, obj)\n\n instances = []\n request._object = obj\n for inline in base_instances:\n inline.form.request = request\n instances.append(inline)\n\n return instances\n\n def get_actions(self, request):\n \"\"\"Limit the available actions based on who you are and what you are looking at.\"\"\"\n if IS_POPUP_VAR in request.GET:\n return OrderedDict()\n\n actions = super().get_actions(request)\n is_user_dataspace = DataspaceFilter.parameter_name not in request.GET\n\n can_mass_update = all(\n [\n self.mass_update_form,\n has_permission(self.model, request.user, \"change\"),\n is_user_dataspace or request.user.dataspace.is_reference,\n ]\n )\n\n if can_mass_update:\n actions[\"mass_update\"] = (mass_update_action, \"mass_update\", \"Mass update\")\n\n if not has_permission(self.model, request.user, \"add\") and \"copy_to\" in actions:\n del actions[\"copy_to\"]\n\n if not request.user.dataspace.is_reference:\n if is_user_dataspace: # The user is looking at his own Dataspace\n if \"copy_to\" in actions:\n del actions[\"copy_to\"]\n if \"compare_with\" in actions:\n del actions[\"compare_with\"]\n else: # The user is looking at another Dataspace\n if \"delete_selected\" in actions:\n del actions[\"delete_selected\"]\n\n if request.user.dataspace.is_reference or not is_user_dataspace:\n if \"check_updates_in_reference\" in actions:\n del actions[\"check_updates_in_reference\"]\n if \"check_newer_version_in_reference\" in actions:\n del actions[\"check_newer_version_in_reference\"]\n\n for action in self.actions_to_remove:\n if action in actions:\n del actions[action]\n\n return actions\n\n @admin.display(description=_(\"Copy to my Dataspace\"))\n def copy_link(self, obj):\n return format_html(\n '<strong><a href=\"{}&{}=1\">{}</a></strong>',\n obj.get_copy_url(),\n IS_POPUP_VAR,\n _(\"Copy to my Dataspace\"),\n )\n\n @staticmethod\n def hide_display_links(request):\n return all(\n [\n DataspaceFilter.parameter_name in request.GET,\n request.GET.get(DataspaceFilter.parameter_name) != str(request.user.dataspace_id),\n ]\n )\n\n def get_list_display(self, request):\n \"\"\"\n Remove the view_on_site and hierarchy links in popup mode.\n Also insert the copy link when looking at another dataspace.\n \"\"\"\n list_display = super().get_list_display(request)\n\n if IS_POPUP_VAR in request.GET:\n list_display = list(list_display)\n if \"changelist_view_on_site\" in list_display:\n list_display.remove(\"changelist_view_on_site\")\n if get_hierarchy_link in list_display:\n list_display.remove(get_hierarchy_link)\n\n if self.hide_display_links(request):\n list_display = list(list_display)\n if \"copy_to\" not in self.actions_to_remove:\n list_display.insert(0, \"copy_link\")\n if get_hierarchy_link in list_display:\n list_display.remove(get_hierarchy_link)\n\n return list_display\n\n def get_list_display_links(self, request, list_display):\n \"\"\"Remove all the display_links when looking at another dataspace.\"\"\"\n if not self.hide_display_links(request):\n return super().get_list_display_links(request, list_display)\n\n def response_change(self, request, obj):\n \"\"\"Add the logic for the \"Save and go to next\" feature.\"\"\"\n next_id = request.POST.get(\"next_id\")\n if \"_next\" in request.POST and next_id:\n opts = self.model._meta\n preserved_filters = self.get_preserved_filters(request)\n\n msg_dict = {\n \"name\": str(opts.verbose_name),\n \"obj\": str(obj),\n }\n msg = 'The {name} \"{obj}\" was changed successfully.'.format(**msg_dict)\n self.message_user(request, msg, messages.SUCCESS)\n\n viewname = f\"admin:{opts.app_label}_{opts.model_name}_change\"\n next_url = reverse(viewname, args=[next_id], current_app=self.admin_site.name)\n redirect_url = add_preserved_filters(\n {\"preserved_filters\": preserved_filters, \"opts\": opts}, next_url\n )\n return redirect(redirect_url)\n\n return super().response_change(request, obj)\n\n def lookup_allowed(self, lookup, value):\n if lookup in [EXTERNAL_SOURCE_LOOKUP]:\n return True\n return super().lookup_allowed(lookup, value)\n\n @staticmethod\n def _limited_permission(request, obj, has_perm):\n # Model permission\n if not has_perm:\n return False\n\n # Object instance permission\n if obj and obj.dataspace_id != request.user.dataspace_id:\n return request.user.dataspace.is_reference\n return True\n\n def has_add_permission(self, request):\n has_perm = super().has_add_permission(request)\n # Do not display the \"Add\" link in filter lookup popup mode\n if IS_FILTER_LOOKUP_VAR in request.GET:\n return False\n return has_perm\n\n def has_change_permission(self, request, obj=None):\n has_perm = super().has_change_permission(request, obj)\n return self._limited_permission(request, obj, has_perm)\n\n def has_delete_permission(self, request, obj=None):\n has_perm = super().has_delete_permission(request, obj)\n return self._limited_permission(request, obj, has_perm)\n\n def has_view_permission(self, request, obj=None):\n has_perm = super().has_view_permission(request, obj)\n return self._limited_permission(request, obj, has_perm)\n\n def has_importer(self):\n \"\"\"Return True if the importer_class has been set.\"\"\"\n if self.importer_class:\n return True\n\n def has_activity_log(self):\n \"\"\"Return True if the activity_log has been set.\"\"\"\n if self.activity_log:\n return True\n\n\nclass HiddenValueWidget(forms.TextInput):\n \"\"\"Render a hidden value in the UI.\"\"\"\n\n HIDDEN_VALUE = \"*******\"\n\n def render(self, name, value, attrs=None, renderer=None):\n value = self.HIDDEN_VALUE if value else None\n return super().render(name, value, attrs, renderer)\n\n\nclass DataspaceConfigurationForm(forms.ModelForm):\n \"\"\"\n Configure Dataspace settings.\n\n This form includes fields for various API keys, with sensitive values\n hidden in the UI using the HiddenValueWidget.\n \"\"\"\n\n hidden_value_fields = [\n \"scancodeio_api_key\",\n \"vulnerablecode_api_key\",\n \"purldb_api_key\",\n ]\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initialize the form and set HiddenValueWidget for specified fields.\"\"\"\n super().__init__(*args, **kwargs)\n for field_name in self.hidden_value_fields:\n self.fields[field_name].widget = HiddenValueWidget()\n\n def clean(self):\n \"\"\"Clean the form data, excluding hidden values from cleaned_data.\"\"\"\n for field_name in self.hidden_value_fields:\n value = self.cleaned_data.get(field_name)\n if value == HiddenValueWidget.HIDDEN_VALUE:\n del self.cleaned_data[field_name]\n\n\nclass DataspaceConfigurationInline(DataspacedFKMixin, admin.StackedInline):\n model = DataspaceConfiguration\n form = DataspaceConfigurationForm\n verbose_name_plural = _(\"Configuration\")\n verbose_name = _(\"Dataspace configuration\")\n fields = [\n \"homepage_layout\",\n \"scancodeio_url\",\n \"scancodeio_api_key\",\n \"vulnerablecode_url\",\n \"vulnerablecode_api_key\",\n \"purldb_url\",\n \"purldb_api_key\",\n ]\n can_delete = False\n\n\n@admin.register(Dataspace, site=dejacode_site)\nclass DataspaceAdmin(\n ReferenceOnlyPermissions,\n HistoryAdminMixin,\n admin.ModelAdmin,\n):\n short_description = (\n \"A Dataspace is an independent, exclusive set of DejaCode data, \"\n \"which can be either nexB reference data or installation-specific data.\"\n )\n\n long_description = (\n \"Each DJE application User is associated with exactly one Dataspace, \"\n \"and the data owned by that Dataspace is presented to the user when \"\n \"accessing the application. \"\n \"An installation of DejaCode typically contains the following Dataspaces:\"\n \"nexB: Reference reference data from nexB\"\n \"{{mySite}}: Production data for a specific DejaCode installation\"\n \"{{sandbox}}: Data for testing, training, or staging activities\"\n )\n\n list_display = (\n \"name\",\n \"full_name\",\n AsURL(\"homepage_url\", short_description=\"Homepage URL\"),\n AsURL(\"contact_info\", short_description=\"Contact information\"),\n )\n fieldsets = (\n (\n \"\",\n {\n \"fields\": (\n \"name\",\n \"homepage_url\",\n \"notes\",\n \"home_page_announcements\",\n \"logo_url\",\n )\n },\n ),\n (\n \"Attribution Package Information\",\n {\n \"fields\": (\n \"full_name\",\n \"address\",\n \"contact_info\",\n \"open_source_information_url\",\n \"open_source_download_url\",\n )\n },\n ),\n (\n \"User Interface Settings\",\n {\n \"fields\": (\n \"show_license_profile_in_license_list_view\",\n \"show_license_type_in_license_list_view\",\n \"show_spdx_short_identifier_in_license_list_view\",\n \"show_usage_policy_in_user_views\",\n \"show_type_in_component_list_view\",\n \"hide_empty_fields_in_component_details_view\",\n )\n },\n ),\n (\n \"Application Process Settings\",\n {\n \"fields\": (\n \"set_usage_policy_on_new_component_from_licenses\",\n \"enable_package_scanning\",\n \"update_packages_from_scan\",\n \"enable_purldb_access\",\n \"enable_vulnerablecodedb_access\",\n )\n },\n ),\n )\n search_fields = (\"name\",)\n inlines = [DataspaceConfigurationInline]\n form = DataspaceAdminForm\n change_form_template = \"admin/dje/dataspace/change_form.html\"\n change_list_template = \"admin/change_list_extended.html\"\n\n def has_change_permission(self, request, obj=None):\n \"\"\"\n Bypass the ReferenceOnlyPermissions to allow regular Dataspace admins,\n with the right permission, to edit their own Dataspace.\n \"\"\"\n return super(admin.ModelAdmin, self).has_change_permission(request, obj)\n\n def get_readonly_fields(self, request, obj=None):\n \"\"\"Make Dataspace.name field readonly on edit except for reference Dataspace superusers.\"\"\"\n readonly_fields = super().get_readonly_fields(request, obj)\n\n user = request.user\n if obj and not (user.dataspace.is_reference and user.is_superuser):\n readonly_fields += (\"name\",)\n\n return readonly_fields\n\n def get_urls(self):\n info = self.model._meta.app_label, self.model._meta.model_name\n\n urls = [\n path(\n \"<pk>/clonedataset/\",\n self.admin_site.admin_view(clone_dataset_view),\n name=\"{}_{}_clonedataset\".format(*info),\n ),\n path(\n \"<pk>/tab_permissions/\",\n self.admin_site.admin_view(manage_tab_permissions_view),\n name=\"{}_{}_tab_permissions\".format(*info),\n ),\n path(\n \"<pk>/copy_defaults/\",\n self.admin_site.admin_view(manage_copy_defaults_view),\n name=\"{}_{}_copy_defaults\".format(*info),\n ),\n ]\n\n return urls + super().get_urls()\n\n def get_queryset(self, request):\n \"\"\"\n Limit the QuerySet to the current user Dataspace. + the Reference one.\n If the user Dataspace is the Reference then show all.\n \"\"\"\n qs = super().get_queryset(request)\n if not request.user.dataspace.is_reference:\n qs = qs.filter(id=request.user.dataspace_id)\n return qs\n\n def get_actions(self, request):\n \"\"\"Remove the bulk delete action, it does not make sense for Dataspace.\"\"\"\n actions = super().get_actions(request)\n if \"delete_selected\" in actions:\n del actions[\"delete_selected\"]\n return actions\n\n def changeform_view(self, request, object_id=None, form_url=\"\", extra_context=None):\n extra_context = extra_context or {}\n extra_context[\"template_dataspace\"] = settings.TEMPLATE_DATASPACE\n return super().changeform_view(request, object_id, form_url, extra_context)\n\n\nclass ChildRelationshipInline(DataspacedFKMixin, admin.TabularInline):\n fk_name = \"parent\"\n extra = 0\n classes = (\"grp-collapse grp-open\",)\n raw_id_fields = (\"child\",)\n autocomplete_lookup_fields = {\"fk\": [\"child\"]}\n verbose_name = _(\"Child\")\n\n\nclass ExternalReferenceInline(DataspacedFKMixin, GenericTabularInline):\n model = ExternalReference\n extra = 0\n classes = (\"grp-collapse grp-open\",)\n\n\n@admin.register(ExternalSource, site=dejacode_site)\nclass ExternalSourceAdmin(DataspacedAdmin):\n def references(self, obj):\n \"\"\"\n Return links to the content_object changelist of ExternalReference instances,\n for the given ExternalSource instance, grouped per ContentType.\n \"\"\"\n changelist_links = []\n queryset = obj.externalreference_set\n grouped = group_by(queryset, \"content_type\", count_on=\"object_id\", distinct=True)\n\n for value in grouped:\n model_class = ContentType.objects.get(id=value[\"content_type\"]).model_class()\n opts = model_class._meta\n url = reverse(f\"admin:{opts.app_label}_{opts.model_name}_changelist\")\n params = {EXTERNAL_SOURCE_LOOKUP: obj.id}\n href = f\"{url}?{urlencode(params)}\"\n changelist_link = format_html(\n CHANGELIST_LINK_TEMPLATE, href, value[\"count\"], opts.verbose_name_plural\n )\n changelist_links.append([changelist_link])\n\n html_list = \"<ul>{}</ul>\".format(format_html_join(\"\", \"<li>{}</li>\", changelist_links))"},"next_line":{"kind":"string","value":" return class_wrap(html_list, \"width200\")"},"gold_snippet_index":{"kind":"number","value":26,"string":"26"},"created_at":{"kind":"string","value":"2023-12-07 16:57:42+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5297,"cells":{"repo_name":{"kind":"string","value":"wusize/CLIM"},"file_path":{"kind":"string","value":"src/open_clip/model.py"},"context":{"kind":"list like","value":[{"identifier":"HFTextEncoder","path":"src/open_clip/hf_model.py","snippet":"class HFTextEncoder(nn.Module):\n \"\"\"HuggingFace model adapter\"\"\"\n output_tokens: torch.jit.Final[bool]\n\n def __init__(\n self,\n model_name_or_path: str,\n output_dim: int,\n config: PretrainedConfig = None,\n pooler_type: str = None,\n proj: str = None,\n pretrained: bool = True,\n output_tokens: bool = False,\n ):\n super().__init__()\n self.output_tokens = output_tokens\n self.output_dim = output_dim\n\n # TODO: find better way to get this information\n uses_transformer_pooler = (pooler_type == \"cls_pooler\")\n\n if transformers is None:\n raise RuntimeError(\"Please `pip install transformers` to use pre-trained HuggingFace models\")\n if config is None:\n self.config = AutoConfig.from_pretrained(model_name_or_path)\n create_func, model_args = (AutoModel.from_pretrained, model_name_or_path) if pretrained else (\n AutoModel.from_config, self.config)\n # TODO: do all model configs have this attribute? PretrainedConfig does so yes??\n if hasattr(self.config, \"is_encoder_decoder\") and self.config.is_encoder_decoder:\n self.transformer = create_func(model_args)\n self.transformer = self.transformer.encoder\n else:\n self.transformer = create_func(model_args, add_pooling_layer=uses_transformer_pooler)\n else:\n self.config = config\n self.transformer = AutoModel.from_config(config)\n if pooler_type is None: # get default arch pooler\n pooler_type = (arch_dict[self.config.model_type][\"pooler\"])\n \n self.pooler = _POOLERS[pooler_type]()\n\n d_model = getattr(self.config, arch_dict[self.config.model_type][\"config_names\"][\"width\"])\n if (d_model == output_dim) and (proj is None): # do we always need a proj?\n self.proj = nn.Identity()\n elif proj == 'linear':\n self.proj = nn.Linear(d_model, output_dim, bias=False)\n elif proj == 'mlp':\n hidden_size = (d_model + output_dim) // 2\n self.proj = nn.Sequential(\n nn.Linear(d_model, hidden_size, bias=False),\n nn.GELU(),\n nn.Linear(hidden_size, output_dim, bias=False),\n )\n\n def forward(self, x: TensorType):\n attn_mask = (x != self.config.pad_token_id).long()\n out = self.transformer(input_ids=x, attention_mask=attn_mask)\n pooled_out = self.pooler(out, attn_mask)\n projected = self.proj(pooled_out)\n\n seq_len = out.last_hidden_state.shape[1]\n tokens = (\n out.last_hidden_state[:, torch.arange(seq_len) != self.pooler.cls_token_position, :] \n if type(self.pooler) == ClsPooler \n else out.last_hidden_state\n )\n \n if self.output_tokens:\n return projected, tokens\n return projected\n\n def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):\n if not unlocked_layers: # full freezing\n for n, p in self.transformer.named_parameters():\n p.requires_grad = (not freeze_layer_norm) if \"LayerNorm\" in n.split(\".\") else False\n return\n\n encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer\n layer_list = getattr(encoder, arch_dict[self.config.model_type][\"config_names\"][\"layer_attr\"])\n print(f\"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model\")\n embeddings = getattr(\n self.transformer, arch_dict[self.config.model_type][\"config_names\"][\"token_embeddings_attr\"])\n modules = [embeddings, *layer_list][:-unlocked_layers]\n # freeze layers\n for module in modules:\n for n, p in module.named_parameters():\n p.requires_grad = (not freeze_layer_norm) if \"LayerNorm\" in n.split(\".\") else False\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n self.transformer.gradient_checkpointing_enable()\n\n def init_parameters(self):\n pass"},{"identifier":"ModifiedResNet","path":"src/open_clip/modified_resnet.py","snippet":"class ModifiedResNet(nn.Module):\n \"\"\"\n A ResNet class that is similar to torchvision's but contains the following changes:\n - There are now 3 \"stem\" convolutions as opposed to 1, with an average pool instead of a max pool.\n - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1\n - The final pooling layer is a QKV attention instead of an average pool\n \"\"\"\n\n def __init__(self, layers, output_dim, heads, image_size=224, width=64,\n freeze_output=True,\n freeze_all_bns=True):\n super().__init__()\n self.output_dim = output_dim\n self.image_size = image_size\n self.freeze_output = freeze_output\n self.freeze_all_bns = freeze_all_bns\n # the 3-layer stem\n self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(width // 2)\n self.act1 = nn.ReLU(inplace=True)\n self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(width // 2)\n self.act2 = nn.ReLU(inplace=True)\n self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)\n self.bn3 = nn.BatchNorm2d(width)\n self.act3 = nn.ReLU(inplace=True)\n self.avgpool = nn.AvgPool2d(2)\n\n # residual layers\n self._inplanes = width # this is a *mutable* variable used during construction\n self.layer1 = self._make_layer(width, layers[0])\n self.layer2 = self._make_layer(width * 2, layers[1], stride=2)\n self.layer3 = self._make_layer(width * 4, layers[2], stride=2)\n self.layer4 = self._make_layer(width * 8, layers[3], stride=2)\n\n embed_dim = width * 32 # the ResNet feature dimension\n self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim, freeze_output)\n self.attnpool_input_size = image_size // 32\n\n def _make_layer(self, planes, blocks, stride=1):\n layers = [Bottleneck(self._inplanes, planes, stride)]\n\n self._inplanes = planes * Bottleneck.expansion\n for _ in range(1, blocks):\n layers.append(Bottleneck(self._inplanes, planes))\n\n return nn.Sequential(*layers)\n\n def lock(self, unlocked_groups=0, freeze_bn_stats=True):\n assert freeze_bn_stats\n def _lock(module):\n for param in module.parameters():\n param.requires_grad = False\n if freeze_bn_stats:\n freeze_batch_norm_2d(module)\n module.eval()\n\n freeze_at = 5 - unlocked_groups\n print(f'Freeze the resnet at {freeze_at}', flush=True)\n\n if freeze_at >= 1: # stem\n _lock(self.conv1)\n _lock(self.bn1)\n _lock(self.conv2)\n _lock(self.bn2)\n _lock(self.conv3)\n _lock(self.bn3)\n # each stage is a torch.nn.modules.container.Sequential\n for idx, stage in enumerate([self.layer1, self.layer2, self.layer3, self.layer4], start=2):\n if freeze_at >= idx:\n for block in stage.children(): # each block is a Bottleneck\n _lock(block)\n if self.freeze_all_bns:\n print(f'Freeze all bn layers', flush=True) # TODO: study if this is necessary\n freeze_batch_norm_2d(self)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n # FIXME support for non-transformer\n pass\n\n def stem(self, x):\n x = self.act1(self.bn1(self.conv1(x)))\n x = self.act2(self.bn2(self.conv2(x)))\n x = self.act3(self.bn3(self.conv3(x)))\n x = self.avgpool(x)\n return x\n\n def forward(self, x):\n with torch.no_grad():\n x = self.stem(x)\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n x = self.attnpool(x)\n\n return x\n\n @staticmethod\n def _denormalize_boxes(normed_boxes, x):\n h, w = x.shape[-2:]\n denormed_boxes = []\n for boxes in normed_boxes:\n new_boxes = boxes.clone() # FIXME: do not change the value in normed_boxes!\n new_boxes[:, [0, 2]] *= w\n new_boxes[:, [1, 3]] *= h\n denormed_boxes.append(new_boxes)\n return denormed_boxes\n\n def extract_roi_features(self, x, normed_boxes, extract_type='v2'):\n if extract_type == 'v1':\n return self._extract_roi_features_v1(x, normed_boxes)\n else:\n assert extract_type == 'v2'\n return self._extract_roi_features_v2(x, normed_boxes)\n\n def mask_attn_pool(self, image, masks):\n return self.mask_pool(image, masks)\n\n def mask_pool(self, image, masks):\n x = self.stem(image)\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n\n feature_map = self.attnpool.forward_dense(x)\n feature_map = F.normalize(feature_map, dim=1) # remember to normalize!\n\n feature_map = feature_map.flatten(-2, -1) # bs, c, h*w\n num_masks_per_image = [len(masks_per_image) for masks_per_image in masks]\n masks = torch.cat(masks).float().flatten(-2, -1) # bs, h*w\n feature_map = torch.repeat_interleave(\n feature_map, torch.tensor(num_masks_per_image, device=feature_map.device), dim=0)\n features = (feature_map * masks[:, None]).sum(-1) / (masks.sum(1, keepdim=True) + 1e-12)\n\n return features\n\n def _extract_roi_features_v1(self, x, normed_boxes, **kwargs):\n with torch.no_grad():\n x = self.stem(x)\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n\n x = self.attnpool.forward_dense(x)\n x = F.normalize(x, dim=1) # remember to normalize!\n # TODO: debug\n roi_feats = roi_align(x, self._denormalize_boxes(normed_boxes, x),\n (1, 1), 1.0, -1, True)[:, :, 0, 0]\n return roi_feats\n\n def _extract_roi_features_v2(self, x, normed_boxes, **kwargs):\n with torch.no_grad():\n x = self.stem(x)\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x) # only the last layer is finetuned in our implementation\n\n tar_size = self.attnpool_input_size\n # TODO: debug\n roi_feats = roi_align(x, self._denormalize_boxes(normed_boxes, x),\n (tar_size, tar_size), 1.0, -1, True)\n\n roi_feats = self.attnpool(roi_feats)\n\n return roi_feats\n\n def encode_dense(self, x, keep_shape=True):\n x = self.stem(x)\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n\n feature_map = self.attnpool.forward_dense(x)\n feature_map = F.normalize(feature_map, dim=1) # remember to normalize!\n\n return feature_map"},{"identifier":"TimmModel","path":"src/open_clip/timm_model.py","snippet":"class TimmModel(nn.Module):\n \"\"\" timm model adapter\n \"\"\"\n\n def __init__(\n self,\n model_name,\n embed_dim,\n image_size=224,\n pool='avg',\n proj='linear',\n proj_bias=False,\n drop=0.,\n drop_path=None,\n patch_drop=None,\n pretrained=False,\n ):\n super().__init__()\n if timm is None:\n raise RuntimeError(\"Please `pip install timm` to use timm models.\")\n self.image_size = to_2tuple(image_size)\n\n # setup kwargs that may not be common across all models\n timm_kwargs = {}\n if drop_path is not None:\n timm_kwargs['drop_path_rate'] = drop_path\n if patch_drop is not None:\n timm_kwargs['patch_drop_rate'] = patch_drop\n\n custom_pool = pool in ('abs_attn', 'rot_attn')\n if not proj and not custom_pool:\n # use network classifier head as projection if no proj specified and no custom pooling used\n self.trunk = timm.create_model(\n model_name,\n num_classes=embed_dim,\n global_pool=pool,\n pretrained=pretrained,\n **timm_kwargs,\n )\n prev_chs = embed_dim\n else:\n self.trunk = timm.create_model(\n model_name,\n pretrained=pretrained,\n **timm_kwargs,\n )\n feat_size = self.trunk.default_cfg.get('pool_size', None)\n feature_ndim = 1 if not feat_size else 2\n if custom_pool:\n assert feature_ndim == 2\n # if attn pooling used, remove both classifier and default pool\n self.trunk.reset_classifier(0, global_pool='')\n else:\n # reset global pool if pool config set, otherwise leave as network default\n reset_kwargs = dict(global_pool=pool) if pool else {}\n self.trunk.reset_classifier(0, **reset_kwargs)\n prev_chs = self.trunk.num_features\n\n head_layers = OrderedDict()\n\n # Add custom pooling to head\n if pool == 'abs_attn':\n head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim)\n prev_chs = embed_dim\n elif pool == 'rot_attn':\n head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim)\n prev_chs = embed_dim\n\n # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used\n if proj == 'linear':\n head_layers['drop'] = nn.Dropout(drop)\n head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias)\n elif proj == 'mlp':\n head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=(drop, 0), bias=(True, proj_bias))\n else:\n assert not proj, f'Unknown projection type {proj}.'\n\n self.head = nn.Sequential(head_layers)\n\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\n \"\"\" lock modules\n Args:\n unlocked_groups (int): leave last n layer groups unlocked (default: 0)\n \"\"\"\n if not unlocked_groups:\n # lock full model\n for param in self.trunk.parameters():\n param.requires_grad = False\n if freeze_bn_stats:\n freeze_batch_norm_2d(self.trunk)\n else:\n # NOTE: partial freeze requires latest timm (master) branch and is subject to change\n try:\n # FIXME import here until API stable and in an official release\n from timm.models.helpers import group_parameters, group_modules\n except ImportError:\n raise RuntimeError(\n 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`')\n matcher = self.trunk.group_matcher()\n gparams = group_parameters(self.trunk, matcher)\n max_layer_id = max(gparams.keys())\n max_layer_id = max_layer_id - unlocked_groups\n for group_idx in range(max_layer_id + 1):\n group = gparams[group_idx]\n for param in group:\n self.trunk.get_parameter(param).requires_grad = False\n if freeze_bn_stats:\n gmodules = group_modules(self.trunk, matcher, reverse=True)\n gmodules = {k for k, v in gmodules.items() if v <= max_layer_id}\n freeze_batch_norm_2d(self.trunk, gmodules)\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n try:\n self.trunk.set_grad_checkpointing(enable)\n except Exception as e:\n logging.warning('grad checkpointing not supported for this timm image tower, continuing without...')\n\n def forward(self, x):\n x = self.trunk(x)\n x = self.head(x)\n return x\n\n @staticmethod\n def _denormalize_boxes(normed_boxes, x):\n h, w = x.shape[-2:]\n denormed_boxes = []\n for boxes in normed_boxes:\n new_boxes = boxes.clone() # FIXME: do not change the value in normed_boxes!\n new_boxes[:, [0, 2]] *= w\n new_boxes[:, [1, 3]] *= h\n denormed_boxes.append(new_boxes)\n return denormed_boxes\n\n def _extract_roi_features_v1(self, x, normed_boxes, **kwargs):\n h, w = x.shape[-2:]\n x = self.trunk.forward_features(x)\n h_f, w_f = x.shape[-2:]\n tar_h = (self.image_size[0] * h_f) // h\n tar_w = (self.image_size[1] * w_f) // w\n x = roi_align(x, self._denormalize_boxes(normed_boxes, x), (tar_h, tar_w),\n 1.0, -1, True)\n\n x = self.trunk.forward_head(x)\n x = self.head(x)\n\n return x\n\n def encode_dense(self, x, **kwargs):\n x = self.trunk.forward_features(x)\n x = self.dense_trunk_head(x)\n x = self.head(x)\n x = x.permute(0, 3, 1, 2)\n\n return x\n\n def dense_trunk_head(self, x):\n x = self.trunk.head.norm(x)\n x = x.permute(0, 2, 3, 1)\n x = self.trunk.head.drop(x)\n # x = x.permute(0, 3, 1, 2)\n\n return x\n\n def mask_pool(self, image, masks):\n feature_map = self.encode_dense(image)\n feature_map = F.normalize(feature_map, dim=1) # remember to normalize!\n feature_map = feature_map.flatten(-2, -1) # bs, c, h*w\n num_masks_per_image = [len(masks_per_image) for masks_per_image in masks]\n masks = torch.cat(masks).float().flatten(-2, -1) # bs, h*w\n feature_map = torch.repeat_interleave(\n feature_map, torch.tensor(num_masks_per_image, device=feature_map.device), dim=0)\n features = (feature_map * masks[:, None]).sum(-1) / (masks.sum(1, keepdim=True) + 1e-12)\n\n return features\n\n def extract_roi_features(self, x, normed_boxes, extract_type='v1'):\n assert extract_type == \"v1\"\n if extract_type == 'v1':\n return self._extract_roi_features_v1(x, normed_boxes)\n else:\n assert extract_type == 'v2'\n return self._extract_roi_features_v2(x, normed_boxes)\n\n def _extract_roi_features_v2(self, x, normed_boxes, **kwargs):\n x = self.encode_dense(x)\n x = F.normalize(x, dim=1) # remember to normalize!\n\n roi_feats = roi_align(x, self._denormalize_boxes(normed_boxes, x), (1, 1),\n 1.0, -1, True)[..., 0, 0]\n return roi_feats\n\n def encode_rois_and_image(self, x, normed_boxes, **kwargs):\n h, w = x.shape[-2:]\n x = self.trunk.forward_features(x)\n h_f, w_f = x.shape[-2:]\n tar_h = (self.image_size[0] * h_f) // h\n tar_w = (self.image_size[1] * w_f) // w\n x_image = x\n x_rois = roi_align(x, self._denormalize_boxes(normed_boxes, x), (tar_h, tar_w),\n 1.0, -1, True)\n\n x_rois = self.trunk.forward_head(x_rois)\n x_rois = self.head(x_rois)\n x_rois = F.normalize(x_rois, dim=-1)\n\n x_image = self.trunk.forward_head(x_image)\n x_image = self.head(x_image)\n x_image = F.normalize(x_image, dim=-1)\n\n return x_rois, x_image"},{"identifier":"LayerNormFp32","path":"src/open_clip/transformer.py","snippet":"class LayerNormFp32(nn.LayerNorm):\n \"\"\"Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).\"\"\"\n\n def forward(self, x: torch.Tensor):\n orig_type = x.dtype\n x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps)\n return x.to(orig_type)"},{"identifier":"LayerNorm","path":"src/open_clip/transformer.py","snippet":"class LayerNorm(nn.LayerNorm):\n \"\"\"Subclass torch's LayerNorm (with cast back to input dtype).\"\"\"\n\n def forward(self, x: torch.Tensor):\n orig_type = x.dtype\n x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)\n return x.to(orig_type)"},{"identifier":"QuickGELU","path":"src/open_clip/transformer.py","snippet":"class QuickGELU(nn.Module):\n # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory\n def forward(self, x: torch.Tensor):\n return x * torch.sigmoid(1.702 * x)"},{"identifier":"Attention","path":"src/open_clip/transformer.py","snippet":"class Attention(nn.Module):\n def __init__(\n self,\n dim,\n num_heads=8,\n qkv_bias=True,\n scaled_cosine=False,\n scale_heads=False,\n logit_scale_max=math.log(1. / 0.01),\n attn_drop=0.,\n proj_drop=0.\n ):\n super().__init__()\n self.scaled_cosine = scaled_cosine\n self.scale_heads = scale_heads\n assert dim % num_heads == 0, 'dim should be divisible by num_heads'\n self.num_heads = num_heads\n self.head_dim = dim // num_heads\n self.scale = self.head_dim ** -0.5\n self.logit_scale_max = logit_scale_max\n\n # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original\n self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale)\n if qkv_bias:\n self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3))\n else:\n self.in_proj_bias = None\n\n if self.scaled_cosine:\n self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))\n else:\n self.logit_scale = None\n self.attn_drop = nn.Dropout(attn_drop)\n if self.scale_heads:\n self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1)))\n else:\n self.head_scale = None\n self.out_proj = nn.Linear(dim, dim)\n self.out_drop = nn.Dropout(proj_drop)\n\n def forward(self, x, attn_mask: Optional[torch.Tensor] = None):\n L, N, C = x.shape\n q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1)\n q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)\n k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)\n v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)\n\n if self.logit_scale is not None:\n attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2))\n logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()\n attn = attn.view(N, self.num_heads, L, L) * logit_scale\n attn = attn.view(-1, L, L)\n else:\n q = q * self.scale\n attn = torch.bmm(q, k.transpose(-1, -2))\n\n if attn_mask is not None:\n if attn_mask.dtype == torch.bool:\n new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)\n new_attn_mask.masked_fill_(attn_mask, float(\"-inf\"))\n attn_mask = new_attn_mask\n attn += attn_mask\n\n attn = attn.softmax(dim=-1)\n attn = self.attn_drop(attn)\n\n x = torch.bmm(attn, v)\n if self.head_scale is not None:\n x = x.view(N, self.num_heads, L, C) * self.head_scale\n x = x.view(-1, L, C)\n x = x.transpose(0, 1).reshape(L, N, C)\n x = self.out_proj(x)\n x = self.out_drop(x)\n return x"},{"identifier":"VisionTransformer","path":"src/open_clip/transformer.py","snippet":"class VisionTransformer(nn.Module):\n output_tokens: torch.jit.Final[bool]\n\n def __init__(\n self,\n image_size: int,\n patch_size: int,\n width: int,\n layers: int,\n heads: int,\n mlp_ratio: float,\n ls_init_value: float = None,\n global_average_pool: bool = False,\n attentional_pool: bool = False,\n n_queries: int = 256,\n attn_pooler_heads: int = 8,\n output_dim: int = 512,\n patch_dropout: float = 0.,\n input_patchnorm: bool = False,\n act_layer: Callable = nn.GELU,\n norm_layer: Callable = LayerNorm,\n output_tokens: bool = False\n ):\n super().__init__()\n self.output_tokens = output_tokens\n image_height, image_width = self.image_size = to_2tuple(image_size)\n patch_height, patch_width = self.patch_size = to_2tuple(patch_size)\n self.grid_size = (image_height // patch_height, image_width // patch_width)\n self.output_dim = output_dim\n\n # whether to layernorm each patch, as done in dual patchnorm paper - https://arxiv.org/abs/2302.01327v1\n self.input_patchnorm = input_patchnorm\n assert not input_patchnorm\n if input_patchnorm:\n patch_input_dim = patch_height * patch_width * 3\n self.patchnorm_pre_ln = LayerNorm(patch_input_dim)\n self.conv1 = nn.Linear(patch_input_dim, width)\n else:\n self.patchnorm_pre_ln = nn.Identity()\n self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)\n\n # class embeddings and positional embeddings\n scale = width ** -0.5\n self.class_embedding = nn.Parameter(scale * torch.randn(width))\n self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width))\n\n # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn\n self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()\n\n self.ln_pre = norm_layer(width)\n self.transformer = Transformer(\n width,\n layers,\n heads,\n mlp_ratio,\n ls_init_value=ls_init_value,\n act_layer=act_layer,\n norm_layer=norm_layer,\n )\n self.num_heads = heads\n\n self.global_average_pool = global_average_pool\n if attentional_pool:\n self.attn_pool = AttentionalPooler(output_dim, width, n_head=attn_pooler_heads, n_queries=n_queries)\n self.ln_post = norm_layer(output_dim)\n self.proj = nn.Parameter(scale * torch.randn(output_dim, output_dim))\n else:\n self.attn_pool = None\n self.ln_post = norm_layer(width)\n self.proj = nn.Parameter(scale * torch.randn(width, output_dim))\n\n self.init_parameters()\n\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\n for param in self.parameters():\n param.requires_grad = False\n\n if unlocked_groups != 0:\n groups = [\n [\n self.conv1,\n self.class_embedding,\n self.ln_pre,\n ],\n self.positional_embedding,\n *self.transformer.resblocks[:-1],\n [\n self.transformer.resblocks[-1],\n # self.ln_post, # fix layer norm\n ],\n # self.proj, # fix output layers\n ]\n\n def _unlock(x):\n if isinstance(x, Sequence):\n for g in x:\n _unlock(g)\n else:\n if isinstance(x, torch.nn.Parameter):\n x.requires_grad = True\n else:\n for p in x.parameters():\n p.requires_grad = True\n\n _unlock(groups[-unlocked_groups:])\n\n def attention_lock(self, **kwargs):\n for name, params in self.named_parameters():\n params.requires_grad = True if \"attn\" in name or \"position\" in name else False\n\n def init_parameters(self):\n # FIXME OpenAI CLIP did not define an init for the VisualTransformer\n # TODO experiment if default PyTorch init, below, or alternate init is best.\n pass\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n self.transformer.grad_checkpointing = enable\n\n def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n if self.global_average_pool:\n return x.mean(dim=1), x\n else:\n return x[:, 0], x[:, 1:]\n\n def forward(self, x: torch.Tensor):\n\n # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1\n # if self.input_patchnorm:\n # # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')\n # x = x.reshape(x.shape[0], x.shape[1], self.grid_size[0], self.patch_size[0], self.grid_size[1], self.patch_size[1])\n # x = x.permute(0, 2, 4, 1, 3, 5)\n # x = x.reshape(x.shape[0], self.grid_size[0] * self.grid_size[1], -1)\n # x = self.patchnorm_pre_ln(x)\n # x = self.conv1(x)\n # else:\n x = self.conv1(x) # shape = [*, width, grid, grid]\n bs, _, h, w = x.shape\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\n\n # class embeddings and positional embeddings\n x = torch.cat(\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\n # TODO: Allow interpolating the positional embeddings\n\n if (h, w) == self.grid_size:\n pe = self.positional_embedding.to(x.dtype)\n else:\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\n\n x = x + pe\n\n # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in\n x = self.patch_dropout(x)\n x = self.ln_pre(x)\n\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x)\n x = x.permute(1, 0, 2) # LND -> NLD\n\n if self.attn_pool is not None:\n x = self.attn_pool(x)\n x = self.ln_post(x)\n pooled, tokens = self._global_pool(x)\n else:\n pooled, tokens = self._global_pool(x)\n pooled = self.ln_post(pooled)\n\n if self.proj is not None:\n pooled = pooled @ self.proj\n\n if self.output_tokens:\n return pooled, tokens\n \n return pooled\n\n def post_attention(self, x):\n x = x.permute(1, 0, 2) # LND -> NLD\n\n if self.attn_pool is not None:\n x = self.attn_pool(x)\n x = self.ln_post(x)\n pooled, tokens = self._global_pool(x)\n else:\n pooled, tokens = self._global_pool(x)\n pooled = self.ln_post(pooled)\n\n if self.proj is not None:\n pooled = pooled @ self.proj\n\n if self.output_tokens:\n return pooled, tokens\n\n return pooled\n\n def extract_roi_features(self, x, normed_boxes, extract_type='v2'):\n if extract_type == 'v1':\n return self._extract_roi_features_v1(x, normed_boxes)\n elif extract_type == 'v2':\n return self._extract_roi_features_v2(x, normed_boxes)\n else:\n raise NotImplementedError\n # assert extract_type == 'v3'\n # return self._extract_roi_features_v3(x, normed_boxes)\n\n def mask_pool(self, x, masks):\n feature_map = self.encode_dense(x)\n feature_map = F.normalize(feature_map, dim=-1)\n\n num_masks_per_image = [len(masks_per_image) for masks_per_image in masks]\n masks = torch.cat(masks).float().flatten(-2, -1) # bs, h*w\n feature_map = torch.repeat_interleave(\n feature_map, torch.tensor(num_masks_per_image, device=feature_map.device), dim=0)\n features = (feature_map * masks.unsqueeze(-1)).sum(1) / (masks.sum(1, keepdim=True) + 1e-12)\n\n return features\n\n def mask_features(self, x, masks):\n feature_map = self.encode_dense(x)\n feature_map = F.normalize(feature_map, dim=-1)\n\n num_masks_per_image = [len(masks_per_image) for masks_per_image in masks]\n masks = torch.cat(masks).flatten(-2, -1) > 0 # bs, h*w\n feature_map = torch.repeat_interleave(\n feature_map, torch.tensor(num_masks_per_image, device=feature_map.device), dim=0)\n\n mask_features = [f[m] for m, f in zip(masks, feature_map)]\n\n return mask_features\n\n def encode_dense(self, x, keep_shape=False):\n x = self.conv1(x) # shape = [*, width, grid, grid]\n bs, _, h, w = x.shape\n # assert h == w # TODO: support input of any shape, need to change the normed boxes to real boxes\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\n x = torch.cat(\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\n if (h, w) == self.grid_size:\n pe = self.positional_embedding.to(x.dtype)\n else:\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\n\n x = x + pe\n\n # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in\n x = self.patch_dropout(x)\n x = self.ln_pre(x)\n\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer.extract_feature_map(x)\n x = x.permute(1, 0, 2) # LND -> NLD\n\n if self.attn_pool is not None:\n x = self.attn_pool(x)\n x = self.ln_post(x)\n _, tokens = self._global_pool(x)\n else:\n _, tokens = self._global_pool(x)\n tokens = self.ln_post(tokens)\n\n if self.proj is not None:\n tokens = tokens @ self.proj\n\n feature_map = tokens.view(bs, h * w, -1) # .permute(0, 3, 1, 2)\n feature_map = F.normalize(feature_map, dim=-1) # normalize at the last dimension\n if keep_shape:\n feature_map = feature_map.view(bs, h, w, -1).permute(0, 3, 1, 2)\n return feature_map\n\n def mask_crop(self, x, masks):\n x = self.conv1(x) # shape = [*, width, grid, grid]\n num_masks_per_image = [len(masks_per_image) for masks_per_image in masks]\n masks = torch.cat(masks).to(x) # bs, h, w\n x = torch.repeat_interleave(\n x, torch.tensor(num_masks_per_image, device=x.device), dim=0)\n x = x * masks[:, None]\n bs, _, h, w = x.shape\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\n\n # class embeddings and positional embeddings\n x = torch.cat(\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\n # TODO: Allow interpolating the positional embeddings\n\n if (h, w) == self.grid_size:\n pe = self.positional_embedding.to(x.dtype)\n else:\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\n\n x = x + pe\n\n x = self.patch_dropout(x)\n x = self.ln_pre(x)\n\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x)\n x = x.permute(1, 0, 2) # LND -> NLD\n\n if self.attn_pool is not None:\n x = self.attn_pool(x)\n x = self.ln_post(x)\n pooled, tokens = self._global_pool(x)\n else:\n pooled, tokens = self._global_pool(x)\n pooled = self.ln_post(pooled)\n\n if self.proj is not None:\n pooled = pooled @ self.proj\n\n return pooled\n\n @staticmethod\n def _generate_masks_per_image(normed_boxes, mask_h, mask_w):\n num_boxes = len(normed_boxes)\n boxes = normed_boxes * torch.tensor(\n [[mask_w, mask_h, mask_w, mask_h]], device=normed_boxes.device)\n masks = torch.zeros(num_boxes, mask_h, mask_w,\n dtype=torch.bool, device=normed_boxes.device)\n for i, box in enumerate(boxes):\n x0, y0, x1, y1 = box.long().tolist()\n masks[i, y0:y1, x0:x1] = True\n\n return masks\n \n @staticmethod\n def _denormalize_boxes(normed_boxes, x):\n h, w = x.shape[-2:]\n denormed_boxes = []\n for boxes in normed_boxes:\n new_boxes = boxes.clone() # FIXME: do not change the value in normed_boxes!\n new_boxes[:, [0, 2]] *= w\n new_boxes[:, [1, 3]] *= h\n denormed_boxes.append(new_boxes)\n return denormed_boxes\n\n def _extract_roi_features_v1(self, x, normed_boxes):\n # used masks\n bs, _, h, w = x.shape\n patch_height, patch_width = self.patch_size\n mask_h, mask_w = h // patch_height, w // patch_width\n masks = [self._generate_masks_per_image(normed_boxes_, mask_h, mask_w)\n for normed_boxes_ in normed_boxes]\n\n return self.mask_attn_pool(x, masks)\n\n def _extract_roi_features_v3(self, x, normed_boxes): # v3 for extract two types\n # used masks\n bs, _, h, w = x.shape\n patch_height, patch_width = self.patch_size\n mask_h, mask_w = h // patch_height, w // patch_width\n masks = [self._generate_masks_per_image(normed_boxes_, mask_h, mask_w)\n for normed_boxes_ in normed_boxes]\n\n roi_features_v1, dense_x = self.mask_attn_pool(x, masks, return_dense=True)\n dense_x = F.normalize(dense_x, dim=-1) # normalize along last dimension\n dense_x = dense_x.permute(0, 3, 1, 2)\n roi_features_v2 = roi_align(dense_x, self._denormalize_boxes(normed_boxes, dense_x), \n (1, 1), 1.0, -1, True)[..., 0, 0]\n\n return roi_features_v1, roi_features_v2\n\n def _extract_roi_features_v2(self, x, normed_boxes):\n x = self.conv1(x) # shape = [*, width, grid, grid]\n bs, _, h, w = x.shape\n # assert h == w # TODO: support input of any shape, need to change the normed boxes to real boxes\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\n x = torch.cat(\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\n if (h, w) == self.grid_size:\n pe = self.positional_embedding.to(x.dtype)\n else:\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\n\n x = x + pe\n\n # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in\n x = self.patch_dropout(x)\n x = self.ln_pre(x)\n\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer.extract_feature_map(x)\n x = x.permute(1, 0, 2) # LND -> NLD\n\n if self.attn_pool is not None:\n x = self.attn_pool(x)\n x = self.ln_post(x)\n _, tokens = self._global_pool(x)\n else:\n _, tokens = self._global_pool(x)\n tokens = self.ln_post(tokens)\n\n if self.proj is not None:\n tokens = tokens @ self.proj\n tokens = F.normalize(tokens, dim=-1) # normalize along last dimension\n tokens = tokens.view(bs, h, w, -1).permute(0, 3, 1, 2)\n return roi_align(tokens, self._denormalize_boxes(normed_boxes, tokens),\n (1, 1), 1.0, -1, True)[..., 0, 0]\n\n def rescale_positional_embedding(self, out_size, dtype):\n h, w = out_size\n rescaled_positional_embedding = \\\n self.positional_embedding.new_zeros(1 + h*w, self.positional_embedding.shape[1])\n rescaled_positional_embedding[0] = self.positional_embedding[0]\n pe_2d = self.positional_embedding[1:].T.contiguous().view(\n 1, -1, *self.grid_size)\n pe_2d = F.interpolate(pe_2d, out_size, mode='bicubic', align_corners=False).view(-1, h*w)\n rescaled_positional_embedding[1:] = pe_2d.T.contiguous()\n\n return rescaled_positional_embedding.to(dtype=dtype)\n\n def _mask_attn_pool(self, x: torch.Tensor, attn_mask: torch.Tensor, num_mask_tokens: int, return_dense=False):\n x = self.conv1(x) # shape = [*, width, grid, grid]\n bs, _, h, w = x.shape\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\n x = torch.cat(\n [\n self.class_embedding.to(x.dtype)\n + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\n x,\n ],\n dim=1,\n ) # shape = [*, grid ** 2 + 1, width]\n if (h, w) == self.grid_size:\n pe = self.positional_embedding.to(x.dtype)\n else:\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\n\n x = x + pe\n x = self.ln_pre(x)\n\n x = x.permute(1, 0, 2) # NLD -> LND\n cls_embed = x[0:1]\n cls_embed = cls_embed.expand(num_mask_tokens, -1, -1)\n x = torch.cat([cls_embed, x], dim=0)\n if return_dense:\n x, x_dense = self.transformer.forward_image_dense(x, attn_mask)\n x_dense = x_dense.permute(1, 0, 2) # LND -> NLD\n x_dense = x_dense[:, num_mask_tokens + 1:]\n\n x_dense = self.ln_post(x_dense)\n\n if self.proj is not None:\n x_dense = x_dense @ self.proj\n x_dense = F.normalize(x_dense, dim=-1) # normalize along last dimension\n x_dense = x_dense.view(bs, h, w, -1)\n else:\n x = self.transformer(x, attn_mask)\n x_dense = None\n x = x.permute(1, 0, 2) # LND -> NLD\n\n # [N, L, D]\n x = self.ln_post(x[:, :num_mask_tokens, :])\n\n if self.proj is not None:\n x = torch.einsum(\"nld,dc->nlc\", x, self.proj)\n\n return x, x_dense\n\n def mask_attn_pool(self, image, masks, return_dense=False):\n assert hasattr(self, \"positional_embedding\")\n batch_size = image.shape[0]\n assert batch_size == len(masks)\n num_masks_per_image = [mask.shape[0] for mask in masks]\n num_queries = max(num_masks_per_image)\n mask_h, mask_w = masks[0].shape[1:]\n\n batch_masks = torch.ones(batch_size, num_queries, mask_h, mask_w, dtype=torch.bool).to(image.device)\n for batch_id, mask in enumerate(masks):\n batch_masks[batch_id, :mask.shape[0]] = mask\n\n mask_token_attn_mask = torch.logical_not(batch_masks)\n # [B, Q, H//P x W//P]\n mask_token_attn_mask = mask_token_attn_mask.reshape(batch_size, num_queries, -1)\n\n num_mask_token = num_queries\n num_image_cls_token = (mask_h * mask_w + 1)\n num_image_token = num_image_cls_token - 1\n num_all_token = num_mask_token + num_image_cls_token\n\n # we start with no mask out\n attn_mask = torch.zeros(\n (num_all_token, num_all_token), dtype=torch.bool, device=image.device\n )\n\n # mask+cls+image token to mask token attention is masked out\n attn_mask[:, :num_mask_token] = True\n\n attn_mask = attn_mask.unsqueeze(0).repeat_interleave(batch_size, dim=0)\n attn_mask[:, :num_mask_token, -num_image_token:] = mask_token_attn_mask\n num_heads = self.num_heads # head width 64\n attn_mask = attn_mask.unsqueeze(1).expand(-1, num_heads, -1, -1)\n attn_mask = attn_mask.reshape(batch_size * num_heads, num_all_token, num_all_token)\n\n batch_mask_features, x_dense = self._mask_attn_pool(image, attn_mask, num_mask_token,\n return_dense=return_dense)\n\n mask_features = [batch_mask_features[batch_id, :num_masks]\n for batch_id, num_masks, in enumerate(num_masks_per_image)]\n if return_dense:\n # x_dense = F.normalize(x_dense, dim=-1).flatten(1, 2) # bs, h*w, c\n # masks = torch.cat(masks).float().flatten(-2, -1) # bs, h*w\n # x_dense = torch.repeat_interleave(\n # x_dense, torch.tensor(num_masks_per_image, device=x_dense.device), dim=0)\n # x_dense = (x_dense * masks.unsqueeze(-1)).sum(1) / masks.sum(1, keepdim=True)\n\n return torch.cat(mask_features), x_dense\n else:\n return torch.cat(mask_features)\n\n def encode_rois_and_image(self, x, normed_boxes):\n x = self.conv1(x) # shape = [*, width, grid, grid]\n bs, _, h, w = x.shape\n # assert h == w # TODO: support input of any shape, need to change the normed boxes to real boxes\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\n x = torch.cat(\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\n if (h, w) == self.grid_size:\n pe = self.positional_embedding.to(x.dtype)\n else:\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\n\n x = x + pe\n\n # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in\n x = self.patch_dropout(x)\n x = self.ln_pre(x)\n\n x = x.permute(1, 0, 2) # NLD -> LND\n x, x_image = self.transformer.extract_feature_map(x, return_forward=True)\n x = x.permute(1, 0, 2) # LND -> NLD\n\n if self.attn_pool is not None:\n x = self.attn_pool(x)\n x = self.ln_post(x)\n _, tokens = self._global_pool(x)\n else:\n _, tokens = self._global_pool(x)\n tokens = self.ln_post(tokens)\n\n if self.proj is not None:\n tokens = tokens @ self.proj\n\n feature_map = tokens.view(bs, h * w, -1) # .permute(0, 3, 1, 2)\n feature_map = F.normalize(feature_map, dim=-1)\n feature_map = feature_map.view(bs, h, w, -1).permute(0, 3, 1, 2)\n x_rois = roi_align(feature_map, self._denormalize_boxes(normed_boxes, feature_map),\n (1, 1), 1.0, -1, True)[..., 0, 0]\n x_rois = F.normalize(x_rois, dim=-1)\n\n x_image = self.post_attention(x_image)\n x_image = F.normalize(x_image, dim=-1)\n\n return x_rois, x_image"},{"identifier":"TextTransformer","path":"src/open_clip/transformer.py","snippet":"class TextTransformer(nn.Module):\n output_tokens: torch.jit.Final[bool]\n\n def __init__(\n self,\n context_length: int = 77,\n vocab_size: int = 49408,\n width: int = 512,\n heads: int = 8,\n layers: int = 12,\n ls_init_value: float = None,\n output_dim: int = 512,\n act_layer: Callable = nn.GELU,\n norm_layer: Callable = LayerNorm,\n embed_cls: bool = False,\n pad_id: int = 0,\n output_tokens: bool = False,\n ):\n super().__init__()\n self.output_tokens = output_tokens\n self.num_pos = self.context_length = context_length\n self.vocab_size = vocab_size\n self.width = width\n self.output_dim = output_dim\n self.heads = heads\n self.pad_id = pad_id\n\n self.text_projection = nn.Parameter(torch.empty(width, output_dim))\n\n if embed_cls:\n self.cls_emb = nn.Parameter(torch.empty(width))\n self.num_pos += 1\n else:\n self.cls_emb = None\n\n self.token_embedding = nn.Embedding(vocab_size, width)\n self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width))\n self.transformer = Transformer(\n width=width,\n layers=layers,\n heads=heads,\n ls_init_value=ls_init_value,\n act_layer=act_layer,\n norm_layer=norm_layer,\n )\n self.ln_final = norm_layer(width)\n\n self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)\n\n self.init_parameters()\n\n def init_parameters(self):\n nn.init.normal_(self.token_embedding.weight, std=0.02)\n nn.init.normal_(self.positional_embedding, std=0.01)\n if self.cls_emb is not None:\n nn.init.normal_(self.cls_emb, std=0.01)\n\n proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)\n attn_std = self.transformer.width ** -0.5\n fc_std = (2 * self.transformer.width) ** -0.5\n for block in self.transformer.resblocks:\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\n\n if self.text_projection is not None:\n nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)\n\n def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):\n assert unlocked_layers == 0 and freeze_layer_norm\n print(f'Freeze the text encoder', flush=True)\n for p in self.parameters():\n p.requires_grad = False\n\n @torch.jit.ignore\n def set_grad_checkpointing(self, enable=True):\n self.transformer.grad_checkpointing = enable\n\n def build_attention_mask(self):\n # lazily create causal attention mask, with full attention between the tokens\n # pytorch uses additive attention mask; fill with -inf\n mask = torch.empty(self.num_pos, self.num_pos)\n mask.fill_(float(\"-inf\"))\n mask.triu_(1) # zero out the lower diagonal\n return mask\n\n def build_cls_mask(self, text, cast_dtype: torch.dtype):\n cls_mask = (text != self.pad_id).unsqueeze(1)\n cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0)\n additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device)\n additive_mask.fill_(0)\n additive_mask.masked_fill_(~cls_mask, float(\"-inf\"))\n additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0)\n return additive_mask\n\n def _repeat(self, t, N: int):\n return t.reshape(1, 1, -1).repeat(N, 1, 1)\n\n def forward(self, text):\n cast_dtype = self.transformer.get_cast_dtype()\n seq_len = text.shape[1]\n\n x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model]\n attn_mask = self.attn_mask\n if self.cls_emb is not None:\n seq_len += 1\n x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1)\n cls_mask = self.build_cls_mask(text, cast_dtype)\n attn_mask = attn_mask[None, :seq_len, :seq_len] + cls_mask[:, :seq_len, :seq_len]\n\n x = x + self.positional_embedding[:seq_len].to(cast_dtype)\n x = x.permute(1, 0, 2) # NLD -> LND\n x = self.transformer(x, attn_mask=attn_mask)\n x = x.permute(1, 0, 2) # LND -> NLD\n\n # x.shape = [batch_size, n_ctx, transformer.width]\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n if self.cls_emb is not None:\n pooled, tokens = x[:, -1], x[:, :-1]\n pooled = self.ln_final(pooled)\n else:\n x = self.ln_final(x)\n pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x\n\n if self.text_projection is not None:\n pooled = pooled @ self.text_projection\n\n if self.output_tokens:\n return pooled, tokens\n\n return pooled"},{"identifier":"to_2tuple","path":"src/open_clip/utils.py","snippet":"def freeze_batch_norm_2d(module, module_match={}, name=''):\ndef _ntuple(n):\n def parse(x):"}],"string":"[\n {\n \"identifier\": \"HFTextEncoder\",\n \"path\": \"src/open_clip/hf_model.py\",\n \"snippet\": \"class HFTextEncoder(nn.Module):\\n \\\"\\\"\\\"HuggingFace model adapter\\\"\\\"\\\"\\n output_tokens: torch.jit.Final[bool]\\n\\n def __init__(\\n self,\\n model_name_or_path: str,\\n output_dim: int,\\n config: PretrainedConfig = None,\\n pooler_type: str = None,\\n proj: str = None,\\n pretrained: bool = True,\\n output_tokens: bool = False,\\n ):\\n super().__init__()\\n self.output_tokens = output_tokens\\n self.output_dim = output_dim\\n\\n # TODO: find better way to get this information\\n uses_transformer_pooler = (pooler_type == \\\"cls_pooler\\\")\\n\\n if transformers is None:\\n raise RuntimeError(\\\"Please `pip install transformers` to use pre-trained HuggingFace models\\\")\\n if config is None:\\n self.config = AutoConfig.from_pretrained(model_name_or_path)\\n create_func, model_args = (AutoModel.from_pretrained, model_name_or_path) if pretrained else (\\n AutoModel.from_config, self.config)\\n # TODO: do all model configs have this attribute? PretrainedConfig does so yes??\\n if hasattr(self.config, \\\"is_encoder_decoder\\\") and self.config.is_encoder_decoder:\\n self.transformer = create_func(model_args)\\n self.transformer = self.transformer.encoder\\n else:\\n self.transformer = create_func(model_args, add_pooling_layer=uses_transformer_pooler)\\n else:\\n self.config = config\\n self.transformer = AutoModel.from_config(config)\\n if pooler_type is None: # get default arch pooler\\n pooler_type = (arch_dict[self.config.model_type][\\\"pooler\\\"])\\n \\n self.pooler = _POOLERS[pooler_type]()\\n\\n d_model = getattr(self.config, arch_dict[self.config.model_type][\\\"config_names\\\"][\\\"width\\\"])\\n if (d_model == output_dim) and (proj is None): # do we always need a proj?\\n self.proj = nn.Identity()\\n elif proj == 'linear':\\n self.proj = nn.Linear(d_model, output_dim, bias=False)\\n elif proj == 'mlp':\\n hidden_size = (d_model + output_dim) // 2\\n self.proj = nn.Sequential(\\n nn.Linear(d_model, hidden_size, bias=False),\\n nn.GELU(),\\n nn.Linear(hidden_size, output_dim, bias=False),\\n )\\n\\n def forward(self, x: TensorType):\\n attn_mask = (x != self.config.pad_token_id).long()\\n out = self.transformer(input_ids=x, attention_mask=attn_mask)\\n pooled_out = self.pooler(out, attn_mask)\\n projected = self.proj(pooled_out)\\n\\n seq_len = out.last_hidden_state.shape[1]\\n tokens = (\\n out.last_hidden_state[:, torch.arange(seq_len) != self.pooler.cls_token_position, :] \\n if type(self.pooler) == ClsPooler \\n else out.last_hidden_state\\n )\\n \\n if self.output_tokens:\\n return projected, tokens\\n return projected\\n\\n def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):\\n if not unlocked_layers: # full freezing\\n for n, p in self.transformer.named_parameters():\\n p.requires_grad = (not freeze_layer_norm) if \\\"LayerNorm\\\" in n.split(\\\".\\\") else False\\n return\\n\\n encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer\\n layer_list = getattr(encoder, arch_dict[self.config.model_type][\\\"config_names\\\"][\\\"layer_attr\\\"])\\n print(f\\\"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model\\\")\\n embeddings = getattr(\\n self.transformer, arch_dict[self.config.model_type][\\\"config_names\\\"][\\\"token_embeddings_attr\\\"])\\n modules = [embeddings, *layer_list][:-unlocked_layers]\\n # freeze layers\\n for module in modules:\\n for n, p in module.named_parameters():\\n p.requires_grad = (not freeze_layer_norm) if \\\"LayerNorm\\\" in n.split(\\\".\\\") else False\\n\\n @torch.jit.ignore\\n def set_grad_checkpointing(self, enable=True):\\n self.transformer.gradient_checkpointing_enable()\\n\\n def init_parameters(self):\\n pass\"\n },\n {\n \"identifier\": \"ModifiedResNet\",\n \"path\": \"src/open_clip/modified_resnet.py\",\n \"snippet\": \"class ModifiedResNet(nn.Module):\\n \\\"\\\"\\\"\\n A ResNet class that is similar to torchvision's but contains the following changes:\\n - There are now 3 \\\"stem\\\" convolutions as opposed to 1, with an average pool instead of a max pool.\\n - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1\\n - The final pooling layer is a QKV attention instead of an average pool\\n \\\"\\\"\\\"\\n\\n def __init__(self, layers, output_dim, heads, image_size=224, width=64,\\n freeze_output=True,\\n freeze_all_bns=True):\\n super().__init__()\\n self.output_dim = output_dim\\n self.image_size = image_size\\n self.freeze_output = freeze_output\\n self.freeze_all_bns = freeze_all_bns\\n # the 3-layer stem\\n self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)\\n self.bn1 = nn.BatchNorm2d(width // 2)\\n self.act1 = nn.ReLU(inplace=True)\\n self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)\\n self.bn2 = nn.BatchNorm2d(width // 2)\\n self.act2 = nn.ReLU(inplace=True)\\n self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)\\n self.bn3 = nn.BatchNorm2d(width)\\n self.act3 = nn.ReLU(inplace=True)\\n self.avgpool = nn.AvgPool2d(2)\\n\\n # residual layers\\n self._inplanes = width # this is a *mutable* variable used during construction\\n self.layer1 = self._make_layer(width, layers[0])\\n self.layer2 = self._make_layer(width * 2, layers[1], stride=2)\\n self.layer3 = self._make_layer(width * 4, layers[2], stride=2)\\n self.layer4 = self._make_layer(width * 8, layers[3], stride=2)\\n\\n embed_dim = width * 32 # the ResNet feature dimension\\n self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim, freeze_output)\\n self.attnpool_input_size = image_size // 32\\n\\n def _make_layer(self, planes, blocks, stride=1):\\n layers = [Bottleneck(self._inplanes, planes, stride)]\\n\\n self._inplanes = planes * Bottleneck.expansion\\n for _ in range(1, blocks):\\n layers.append(Bottleneck(self._inplanes, planes))\\n\\n return nn.Sequential(*layers)\\n\\n def lock(self, unlocked_groups=0, freeze_bn_stats=True):\\n assert freeze_bn_stats\\n def _lock(module):\\n for param in module.parameters():\\n param.requires_grad = False\\n if freeze_bn_stats:\\n freeze_batch_norm_2d(module)\\n module.eval()\\n\\n freeze_at = 5 - unlocked_groups\\n print(f'Freeze the resnet at {freeze_at}', flush=True)\\n\\n if freeze_at >= 1: # stem\\n _lock(self.conv1)\\n _lock(self.bn1)\\n _lock(self.conv2)\\n _lock(self.bn2)\\n _lock(self.conv3)\\n _lock(self.bn3)\\n # each stage is a torch.nn.modules.container.Sequential\\n for idx, stage in enumerate([self.layer1, self.layer2, self.layer3, self.layer4], start=2):\\n if freeze_at >= idx:\\n for block in stage.children(): # each block is a Bottleneck\\n _lock(block)\\n if self.freeze_all_bns:\\n print(f'Freeze all bn layers', flush=True) # TODO: study if this is necessary\\n freeze_batch_norm_2d(self)\\n\\n @torch.jit.ignore\\n def set_grad_checkpointing(self, enable=True):\\n # FIXME support for non-transformer\\n pass\\n\\n def stem(self, x):\\n x = self.act1(self.bn1(self.conv1(x)))\\n x = self.act2(self.bn2(self.conv2(x)))\\n x = self.act3(self.bn3(self.conv3(x)))\\n x = self.avgpool(x)\\n return x\\n\\n def forward(self, x):\\n with torch.no_grad():\\n x = self.stem(x)\\n x = self.layer1(x)\\n x = self.layer2(x)\\n x = self.layer3(x)\\n x = self.layer4(x)\\n x = self.attnpool(x)\\n\\n return x\\n\\n @staticmethod\\n def _denormalize_boxes(normed_boxes, x):\\n h, w = x.shape[-2:]\\n denormed_boxes = []\\n for boxes in normed_boxes:\\n new_boxes = boxes.clone() # FIXME: do not change the value in normed_boxes!\\n new_boxes[:, [0, 2]] *= w\\n new_boxes[:, [1, 3]] *= h\\n denormed_boxes.append(new_boxes)\\n return denormed_boxes\\n\\n def extract_roi_features(self, x, normed_boxes, extract_type='v2'):\\n if extract_type == 'v1':\\n return self._extract_roi_features_v1(x, normed_boxes)\\n else:\\n assert extract_type == 'v2'\\n return self._extract_roi_features_v2(x, normed_boxes)\\n\\n def mask_attn_pool(self, image, masks):\\n return self.mask_pool(image, masks)\\n\\n def mask_pool(self, image, masks):\\n x = self.stem(image)\\n x = self.layer1(x)\\n x = self.layer2(x)\\n x = self.layer3(x)\\n x = self.layer4(x)\\n\\n feature_map = self.attnpool.forward_dense(x)\\n feature_map = F.normalize(feature_map, dim=1) # remember to normalize!\\n\\n feature_map = feature_map.flatten(-2, -1) # bs, c, h*w\\n num_masks_per_image = [len(masks_per_image) for masks_per_image in masks]\\n masks = torch.cat(masks).float().flatten(-2, -1) # bs, h*w\\n feature_map = torch.repeat_interleave(\\n feature_map, torch.tensor(num_masks_per_image, device=feature_map.device), dim=0)\\n features = (feature_map * masks[:, None]).sum(-1) / (masks.sum(1, keepdim=True) + 1e-12)\\n\\n return features\\n\\n def _extract_roi_features_v1(self, x, normed_boxes, **kwargs):\\n with torch.no_grad():\\n x = self.stem(x)\\n x = self.layer1(x)\\n x = self.layer2(x)\\n x = self.layer3(x)\\n x = self.layer4(x)\\n\\n x = self.attnpool.forward_dense(x)\\n x = F.normalize(x, dim=1) # remember to normalize!\\n # TODO: debug\\n roi_feats = roi_align(x, self._denormalize_boxes(normed_boxes, x),\\n (1, 1), 1.0, -1, True)[:, :, 0, 0]\\n return roi_feats\\n\\n def _extract_roi_features_v2(self, x, normed_boxes, **kwargs):\\n with torch.no_grad():\\n x = self.stem(x)\\n x = self.layer1(x)\\n x = self.layer2(x)\\n x = self.layer3(x)\\n x = self.layer4(x) # only the last layer is finetuned in our implementation\\n\\n tar_size = self.attnpool_input_size\\n # TODO: debug\\n roi_feats = roi_align(x, self._denormalize_boxes(normed_boxes, x),\\n (tar_size, tar_size), 1.0, -1, True)\\n\\n roi_feats = self.attnpool(roi_feats)\\n\\n return roi_feats\\n\\n def encode_dense(self, x, keep_shape=True):\\n x = self.stem(x)\\n x = self.layer1(x)\\n x = self.layer2(x)\\n x = self.layer3(x)\\n x = self.layer4(x)\\n\\n feature_map = self.attnpool.forward_dense(x)\\n feature_map = F.normalize(feature_map, dim=1) # remember to normalize!\\n\\n return feature_map\"\n },\n {\n \"identifier\": \"TimmModel\",\n \"path\": \"src/open_clip/timm_model.py\",\n \"snippet\": \"class TimmModel(nn.Module):\\n \\\"\\\"\\\" timm model adapter\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n model_name,\\n embed_dim,\\n image_size=224,\\n pool='avg',\\n proj='linear',\\n proj_bias=False,\\n drop=0.,\\n drop_path=None,\\n patch_drop=None,\\n pretrained=False,\\n ):\\n super().__init__()\\n if timm is None:\\n raise RuntimeError(\\\"Please `pip install timm` to use timm models.\\\")\\n self.image_size = to_2tuple(image_size)\\n\\n # setup kwargs that may not be common across all models\\n timm_kwargs = {}\\n if drop_path is not None:\\n timm_kwargs['drop_path_rate'] = drop_path\\n if patch_drop is not None:\\n timm_kwargs['patch_drop_rate'] = patch_drop\\n\\n custom_pool = pool in ('abs_attn', 'rot_attn')\\n if not proj and not custom_pool:\\n # use network classifier head as projection if no proj specified and no custom pooling used\\n self.trunk = timm.create_model(\\n model_name,\\n num_classes=embed_dim,\\n global_pool=pool,\\n pretrained=pretrained,\\n **timm_kwargs,\\n )\\n prev_chs = embed_dim\\n else:\\n self.trunk = timm.create_model(\\n model_name,\\n pretrained=pretrained,\\n **timm_kwargs,\\n )\\n feat_size = self.trunk.default_cfg.get('pool_size', None)\\n feature_ndim = 1 if not feat_size else 2\\n if custom_pool:\\n assert feature_ndim == 2\\n # if attn pooling used, remove both classifier and default pool\\n self.trunk.reset_classifier(0, global_pool='')\\n else:\\n # reset global pool if pool config set, otherwise leave as network default\\n reset_kwargs = dict(global_pool=pool) if pool else {}\\n self.trunk.reset_classifier(0, **reset_kwargs)\\n prev_chs = self.trunk.num_features\\n\\n head_layers = OrderedDict()\\n\\n # Add custom pooling to head\\n if pool == 'abs_attn':\\n head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim)\\n prev_chs = embed_dim\\n elif pool == 'rot_attn':\\n head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim)\\n prev_chs = embed_dim\\n\\n # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used\\n if proj == 'linear':\\n head_layers['drop'] = nn.Dropout(drop)\\n head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias)\\n elif proj == 'mlp':\\n head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=(drop, 0), bias=(True, proj_bias))\\n else:\\n assert not proj, f'Unknown projection type {proj}.'\\n\\n self.head = nn.Sequential(head_layers)\\n\\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\\n \\\"\\\"\\\" lock modules\\n Args:\\n unlocked_groups (int): leave last n layer groups unlocked (default: 0)\\n \\\"\\\"\\\"\\n if not unlocked_groups:\\n # lock full model\\n for param in self.trunk.parameters():\\n param.requires_grad = False\\n if freeze_bn_stats:\\n freeze_batch_norm_2d(self.trunk)\\n else:\\n # NOTE: partial freeze requires latest timm (master) branch and is subject to change\\n try:\\n # FIXME import here until API stable and in an official release\\n from timm.models.helpers import group_parameters, group_modules\\n except ImportError:\\n raise RuntimeError(\\n 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`')\\n matcher = self.trunk.group_matcher()\\n gparams = group_parameters(self.trunk, matcher)\\n max_layer_id = max(gparams.keys())\\n max_layer_id = max_layer_id - unlocked_groups\\n for group_idx in range(max_layer_id + 1):\\n group = gparams[group_idx]\\n for param in group:\\n self.trunk.get_parameter(param).requires_grad = False\\n if freeze_bn_stats:\\n gmodules = group_modules(self.trunk, matcher, reverse=True)\\n gmodules = {k for k, v in gmodules.items() if v <= max_layer_id}\\n freeze_batch_norm_2d(self.trunk, gmodules)\\n\\n @torch.jit.ignore\\n def set_grad_checkpointing(self, enable=True):\\n try:\\n self.trunk.set_grad_checkpointing(enable)\\n except Exception as e:\\n logging.warning('grad checkpointing not supported for this timm image tower, continuing without...')\\n\\n def forward(self, x):\\n x = self.trunk(x)\\n x = self.head(x)\\n return x\\n\\n @staticmethod\\n def _denormalize_boxes(normed_boxes, x):\\n h, w = x.shape[-2:]\\n denormed_boxes = []\\n for boxes in normed_boxes:\\n new_boxes = boxes.clone() # FIXME: do not change the value in normed_boxes!\\n new_boxes[:, [0, 2]] *= w\\n new_boxes[:, [1, 3]] *= h\\n denormed_boxes.append(new_boxes)\\n return denormed_boxes\\n\\n def _extract_roi_features_v1(self, x, normed_boxes, **kwargs):\\n h, w = x.shape[-2:]\\n x = self.trunk.forward_features(x)\\n h_f, w_f = x.shape[-2:]\\n tar_h = (self.image_size[0] * h_f) // h\\n tar_w = (self.image_size[1] * w_f) // w\\n x = roi_align(x, self._denormalize_boxes(normed_boxes, x), (tar_h, tar_w),\\n 1.0, -1, True)\\n\\n x = self.trunk.forward_head(x)\\n x = self.head(x)\\n\\n return x\\n\\n def encode_dense(self, x, **kwargs):\\n x = self.trunk.forward_features(x)\\n x = self.dense_trunk_head(x)\\n x = self.head(x)\\n x = x.permute(0, 3, 1, 2)\\n\\n return x\\n\\n def dense_trunk_head(self, x):\\n x = self.trunk.head.norm(x)\\n x = x.permute(0, 2, 3, 1)\\n x = self.trunk.head.drop(x)\\n # x = x.permute(0, 3, 1, 2)\\n\\n return x\\n\\n def mask_pool(self, image, masks):\\n feature_map = self.encode_dense(image)\\n feature_map = F.normalize(feature_map, dim=1) # remember to normalize!\\n feature_map = feature_map.flatten(-2, -1) # bs, c, h*w\\n num_masks_per_image = [len(masks_per_image) for masks_per_image in masks]\\n masks = torch.cat(masks).float().flatten(-2, -1) # bs, h*w\\n feature_map = torch.repeat_interleave(\\n feature_map, torch.tensor(num_masks_per_image, device=feature_map.device), dim=0)\\n features = (feature_map * masks[:, None]).sum(-1) / (masks.sum(1, keepdim=True) + 1e-12)\\n\\n return features\\n\\n def extract_roi_features(self, x, normed_boxes, extract_type='v1'):\\n assert extract_type == \\\"v1\\\"\\n if extract_type == 'v1':\\n return self._extract_roi_features_v1(x, normed_boxes)\\n else:\\n assert extract_type == 'v2'\\n return self._extract_roi_features_v2(x, normed_boxes)\\n\\n def _extract_roi_features_v2(self, x, normed_boxes, **kwargs):\\n x = self.encode_dense(x)\\n x = F.normalize(x, dim=1) # remember to normalize!\\n\\n roi_feats = roi_align(x, self._denormalize_boxes(normed_boxes, x), (1, 1),\\n 1.0, -1, True)[..., 0, 0]\\n return roi_feats\\n\\n def encode_rois_and_image(self, x, normed_boxes, **kwargs):\\n h, w = x.shape[-2:]\\n x = self.trunk.forward_features(x)\\n h_f, w_f = x.shape[-2:]\\n tar_h = (self.image_size[0] * h_f) // h\\n tar_w = (self.image_size[1] * w_f) // w\\n x_image = x\\n x_rois = roi_align(x, self._denormalize_boxes(normed_boxes, x), (tar_h, tar_w),\\n 1.0, -1, True)\\n\\n x_rois = self.trunk.forward_head(x_rois)\\n x_rois = self.head(x_rois)\\n x_rois = F.normalize(x_rois, dim=-1)\\n\\n x_image = self.trunk.forward_head(x_image)\\n x_image = self.head(x_image)\\n x_image = F.normalize(x_image, dim=-1)\\n\\n return x_rois, x_image\"\n },\n {\n \"identifier\": \"LayerNormFp32\",\n \"path\": \"src/open_clip/transformer.py\",\n \"snippet\": \"class LayerNormFp32(nn.LayerNorm):\\n \\\"\\\"\\\"Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).\\\"\\\"\\\"\\n\\n def forward(self, x: torch.Tensor):\\n orig_type = x.dtype\\n x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps)\\n return x.to(orig_type)\"\n },\n {\n \"identifier\": \"LayerNorm\",\n \"path\": \"src/open_clip/transformer.py\",\n \"snippet\": \"class LayerNorm(nn.LayerNorm):\\n \\\"\\\"\\\"Subclass torch's LayerNorm (with cast back to input dtype).\\\"\\\"\\\"\\n\\n def forward(self, x: torch.Tensor):\\n orig_type = x.dtype\\n x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)\\n return x.to(orig_type)\"\n },\n {\n \"identifier\": \"QuickGELU\",\n \"path\": \"src/open_clip/transformer.py\",\n \"snippet\": \"class QuickGELU(nn.Module):\\n # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory\\n def forward(self, x: torch.Tensor):\\n return x * torch.sigmoid(1.702 * x)\"\n },\n {\n \"identifier\": \"Attention\",\n \"path\": \"src/open_clip/transformer.py\",\n \"snippet\": \"class Attention(nn.Module):\\n def __init__(\\n self,\\n dim,\\n num_heads=8,\\n qkv_bias=True,\\n scaled_cosine=False,\\n scale_heads=False,\\n logit_scale_max=math.log(1. / 0.01),\\n attn_drop=0.,\\n proj_drop=0.\\n ):\\n super().__init__()\\n self.scaled_cosine = scaled_cosine\\n self.scale_heads = scale_heads\\n assert dim % num_heads == 0, 'dim should be divisible by num_heads'\\n self.num_heads = num_heads\\n self.head_dim = dim // num_heads\\n self.scale = self.head_dim ** -0.5\\n self.logit_scale_max = logit_scale_max\\n\\n # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original\\n self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale)\\n if qkv_bias:\\n self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3))\\n else:\\n self.in_proj_bias = None\\n\\n if self.scaled_cosine:\\n self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))\\n else:\\n self.logit_scale = None\\n self.attn_drop = nn.Dropout(attn_drop)\\n if self.scale_heads:\\n self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1)))\\n else:\\n self.head_scale = None\\n self.out_proj = nn.Linear(dim, dim)\\n self.out_drop = nn.Dropout(proj_drop)\\n\\n def forward(self, x, attn_mask: Optional[torch.Tensor] = None):\\n L, N, C = x.shape\\n q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1)\\n q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)\\n k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)\\n v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)\\n\\n if self.logit_scale is not None:\\n attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2))\\n logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()\\n attn = attn.view(N, self.num_heads, L, L) * logit_scale\\n attn = attn.view(-1, L, L)\\n else:\\n q = q * self.scale\\n attn = torch.bmm(q, k.transpose(-1, -2))\\n\\n if attn_mask is not None:\\n if attn_mask.dtype == torch.bool:\\n new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)\\n new_attn_mask.masked_fill_(attn_mask, float(\\\"-inf\\\"))\\n attn_mask = new_attn_mask\\n attn += attn_mask\\n\\n attn = attn.softmax(dim=-1)\\n attn = self.attn_drop(attn)\\n\\n x = torch.bmm(attn, v)\\n if self.head_scale is not None:\\n x = x.view(N, self.num_heads, L, C) * self.head_scale\\n x = x.view(-1, L, C)\\n x = x.transpose(0, 1).reshape(L, N, C)\\n x = self.out_proj(x)\\n x = self.out_drop(x)\\n return x\"\n },\n {\n \"identifier\": \"VisionTransformer\",\n \"path\": \"src/open_clip/transformer.py\",\n \"snippet\": \"class VisionTransformer(nn.Module):\\n output_tokens: torch.jit.Final[bool]\\n\\n def __init__(\\n self,\\n image_size: int,\\n patch_size: int,\\n width: int,\\n layers: int,\\n heads: int,\\n mlp_ratio: float,\\n ls_init_value: float = None,\\n global_average_pool: bool = False,\\n attentional_pool: bool = False,\\n n_queries: int = 256,\\n attn_pooler_heads: int = 8,\\n output_dim: int = 512,\\n patch_dropout: float = 0.,\\n input_patchnorm: bool = False,\\n act_layer: Callable = nn.GELU,\\n norm_layer: Callable = LayerNorm,\\n output_tokens: bool = False\\n ):\\n super().__init__()\\n self.output_tokens = output_tokens\\n image_height, image_width = self.image_size = to_2tuple(image_size)\\n patch_height, patch_width = self.patch_size = to_2tuple(patch_size)\\n self.grid_size = (image_height // patch_height, image_width // patch_width)\\n self.output_dim = output_dim\\n\\n # whether to layernorm each patch, as done in dual patchnorm paper - https://arxiv.org/abs/2302.01327v1\\n self.input_patchnorm = input_patchnorm\\n assert not input_patchnorm\\n if input_patchnorm:\\n patch_input_dim = patch_height * patch_width * 3\\n self.patchnorm_pre_ln = LayerNorm(patch_input_dim)\\n self.conv1 = nn.Linear(patch_input_dim, width)\\n else:\\n self.patchnorm_pre_ln = nn.Identity()\\n self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)\\n\\n # class embeddings and positional embeddings\\n scale = width ** -0.5\\n self.class_embedding = nn.Parameter(scale * torch.randn(width))\\n self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width))\\n\\n # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn\\n self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()\\n\\n self.ln_pre = norm_layer(width)\\n self.transformer = Transformer(\\n width,\\n layers,\\n heads,\\n mlp_ratio,\\n ls_init_value=ls_init_value,\\n act_layer=act_layer,\\n norm_layer=norm_layer,\\n )\\n self.num_heads = heads\\n\\n self.global_average_pool = global_average_pool\\n if attentional_pool:\\n self.attn_pool = AttentionalPooler(output_dim, width, n_head=attn_pooler_heads, n_queries=n_queries)\\n self.ln_post = norm_layer(output_dim)\\n self.proj = nn.Parameter(scale * torch.randn(output_dim, output_dim))\\n else:\\n self.attn_pool = None\\n self.ln_post = norm_layer(width)\\n self.proj = nn.Parameter(scale * torch.randn(width, output_dim))\\n\\n self.init_parameters()\\n\\n def lock(self, unlocked_groups=0, freeze_bn_stats=False):\\n for param in self.parameters():\\n param.requires_grad = False\\n\\n if unlocked_groups != 0:\\n groups = [\\n [\\n self.conv1,\\n self.class_embedding,\\n self.ln_pre,\\n ],\\n self.positional_embedding,\\n *self.transformer.resblocks[:-1],\\n [\\n self.transformer.resblocks[-1],\\n # self.ln_post, # fix layer norm\\n ],\\n # self.proj, # fix output layers\\n ]\\n\\n def _unlock(x):\\n if isinstance(x, Sequence):\\n for g in x:\\n _unlock(g)\\n else:\\n if isinstance(x, torch.nn.Parameter):\\n x.requires_grad = True\\n else:\\n for p in x.parameters():\\n p.requires_grad = True\\n\\n _unlock(groups[-unlocked_groups:])\\n\\n def attention_lock(self, **kwargs):\\n for name, params in self.named_parameters():\\n params.requires_grad = True if \\\"attn\\\" in name or \\\"position\\\" in name else False\\n\\n def init_parameters(self):\\n # FIXME OpenAI CLIP did not define an init for the VisualTransformer\\n # TODO experiment if default PyTorch init, below, or alternate init is best.\\n pass\\n\\n @torch.jit.ignore\\n def set_grad_checkpointing(self, enable=True):\\n self.transformer.grad_checkpointing = enable\\n\\n def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\\n if self.global_average_pool:\\n return x.mean(dim=1), x\\n else:\\n return x[:, 0], x[:, 1:]\\n\\n def forward(self, x: torch.Tensor):\\n\\n # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1\\n # if self.input_patchnorm:\\n # # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')\\n # x = x.reshape(x.shape[0], x.shape[1], self.grid_size[0], self.patch_size[0], self.grid_size[1], self.patch_size[1])\\n # x = x.permute(0, 2, 4, 1, 3, 5)\\n # x = x.reshape(x.shape[0], self.grid_size[0] * self.grid_size[1], -1)\\n # x = self.patchnorm_pre_ln(x)\\n # x = self.conv1(x)\\n # else:\\n x = self.conv1(x) # shape = [*, width, grid, grid]\\n bs, _, h, w = x.shape\\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\\n\\n # class embeddings and positional embeddings\\n x = torch.cat(\\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\\n # TODO: Allow interpolating the positional embeddings\\n\\n if (h, w) == self.grid_size:\\n pe = self.positional_embedding.to(x.dtype)\\n else:\\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\\n\\n x = x + pe\\n\\n # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in\\n x = self.patch_dropout(x)\\n x = self.ln_pre(x)\\n\\n x = x.permute(1, 0, 2) # NLD -> LND\\n x = self.transformer(x)\\n x = x.permute(1, 0, 2) # LND -> NLD\\n\\n if self.attn_pool is not None:\\n x = self.attn_pool(x)\\n x = self.ln_post(x)\\n pooled, tokens = self._global_pool(x)\\n else:\\n pooled, tokens = self._global_pool(x)\\n pooled = self.ln_post(pooled)\\n\\n if self.proj is not None:\\n pooled = pooled @ self.proj\\n\\n if self.output_tokens:\\n return pooled, tokens\\n \\n return pooled\\n\\n def post_attention(self, x):\\n x = x.permute(1, 0, 2) # LND -> NLD\\n\\n if self.attn_pool is not None:\\n x = self.attn_pool(x)\\n x = self.ln_post(x)\\n pooled, tokens = self._global_pool(x)\\n else:\\n pooled, tokens = self._global_pool(x)\\n pooled = self.ln_post(pooled)\\n\\n if self.proj is not None:\\n pooled = pooled @ self.proj\\n\\n if self.output_tokens:\\n return pooled, tokens\\n\\n return pooled\\n\\n def extract_roi_features(self, x, normed_boxes, extract_type='v2'):\\n if extract_type == 'v1':\\n return self._extract_roi_features_v1(x, normed_boxes)\\n elif extract_type == 'v2':\\n return self._extract_roi_features_v2(x, normed_boxes)\\n else:\\n raise NotImplementedError\\n # assert extract_type == 'v3'\\n # return self._extract_roi_features_v3(x, normed_boxes)\\n\\n def mask_pool(self, x, masks):\\n feature_map = self.encode_dense(x)\\n feature_map = F.normalize(feature_map, dim=-1)\\n\\n num_masks_per_image = [len(masks_per_image) for masks_per_image in masks]\\n masks = torch.cat(masks).float().flatten(-2, -1) # bs, h*w\\n feature_map = torch.repeat_interleave(\\n feature_map, torch.tensor(num_masks_per_image, device=feature_map.device), dim=0)\\n features = (feature_map * masks.unsqueeze(-1)).sum(1) / (masks.sum(1, keepdim=True) + 1e-12)\\n\\n return features\\n\\n def mask_features(self, x, masks):\\n feature_map = self.encode_dense(x)\\n feature_map = F.normalize(feature_map, dim=-1)\\n\\n num_masks_per_image = [len(masks_per_image) for masks_per_image in masks]\\n masks = torch.cat(masks).flatten(-2, -1) > 0 # bs, h*w\\n feature_map = torch.repeat_interleave(\\n feature_map, torch.tensor(num_masks_per_image, device=feature_map.device), dim=0)\\n\\n mask_features = [f[m] for m, f in zip(masks, feature_map)]\\n\\n return mask_features\\n\\n def encode_dense(self, x, keep_shape=False):\\n x = self.conv1(x) # shape = [*, width, grid, grid]\\n bs, _, h, w = x.shape\\n # assert h == w # TODO: support input of any shape, need to change the normed boxes to real boxes\\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\\n x = torch.cat(\\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\\n if (h, w) == self.grid_size:\\n pe = self.positional_embedding.to(x.dtype)\\n else:\\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\\n\\n x = x + pe\\n\\n # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in\\n x = self.patch_dropout(x)\\n x = self.ln_pre(x)\\n\\n x = x.permute(1, 0, 2) # NLD -> LND\\n x = self.transformer.extract_feature_map(x)\\n x = x.permute(1, 0, 2) # LND -> NLD\\n\\n if self.attn_pool is not None:\\n x = self.attn_pool(x)\\n x = self.ln_post(x)\\n _, tokens = self._global_pool(x)\\n else:\\n _, tokens = self._global_pool(x)\\n tokens = self.ln_post(tokens)\\n\\n if self.proj is not None:\\n tokens = tokens @ self.proj\\n\\n feature_map = tokens.view(bs, h * w, -1) # .permute(0, 3, 1, 2)\\n feature_map = F.normalize(feature_map, dim=-1) # normalize at the last dimension\\n if keep_shape:\\n feature_map = feature_map.view(bs, h, w, -1).permute(0, 3, 1, 2)\\n return feature_map\\n\\n def mask_crop(self, x, masks):\\n x = self.conv1(x) # shape = [*, width, grid, grid]\\n num_masks_per_image = [len(masks_per_image) for masks_per_image in masks]\\n masks = torch.cat(masks).to(x) # bs, h, w\\n x = torch.repeat_interleave(\\n x, torch.tensor(num_masks_per_image, device=x.device), dim=0)\\n x = x * masks[:, None]\\n bs, _, h, w = x.shape\\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\\n\\n # class embeddings and positional embeddings\\n x = torch.cat(\\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\\n # TODO: Allow interpolating the positional embeddings\\n\\n if (h, w) == self.grid_size:\\n pe = self.positional_embedding.to(x.dtype)\\n else:\\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\\n\\n x = x + pe\\n\\n x = self.patch_dropout(x)\\n x = self.ln_pre(x)\\n\\n x = x.permute(1, 0, 2) # NLD -> LND\\n x = self.transformer(x)\\n x = x.permute(1, 0, 2) # LND -> NLD\\n\\n if self.attn_pool is not None:\\n x = self.attn_pool(x)\\n x = self.ln_post(x)\\n pooled, tokens = self._global_pool(x)\\n else:\\n pooled, tokens = self._global_pool(x)\\n pooled = self.ln_post(pooled)\\n\\n if self.proj is not None:\\n pooled = pooled @ self.proj\\n\\n return pooled\\n\\n @staticmethod\\n def _generate_masks_per_image(normed_boxes, mask_h, mask_w):\\n num_boxes = len(normed_boxes)\\n boxes = normed_boxes * torch.tensor(\\n [[mask_w, mask_h, mask_w, mask_h]], device=normed_boxes.device)\\n masks = torch.zeros(num_boxes, mask_h, mask_w,\\n dtype=torch.bool, device=normed_boxes.device)\\n for i, box in enumerate(boxes):\\n x0, y0, x1, y1 = box.long().tolist()\\n masks[i, y0:y1, x0:x1] = True\\n\\n return masks\\n \\n @staticmethod\\n def _denormalize_boxes(normed_boxes, x):\\n h, w = x.shape[-2:]\\n denormed_boxes = []\\n for boxes in normed_boxes:\\n new_boxes = boxes.clone() # FIXME: do not change the value in normed_boxes!\\n new_boxes[:, [0, 2]] *= w\\n new_boxes[:, [1, 3]] *= h\\n denormed_boxes.append(new_boxes)\\n return denormed_boxes\\n\\n def _extract_roi_features_v1(self, x, normed_boxes):\\n # used masks\\n bs, _, h, w = x.shape\\n patch_height, patch_width = self.patch_size\\n mask_h, mask_w = h // patch_height, w // patch_width\\n masks = [self._generate_masks_per_image(normed_boxes_, mask_h, mask_w)\\n for normed_boxes_ in normed_boxes]\\n\\n return self.mask_attn_pool(x, masks)\\n\\n def _extract_roi_features_v3(self, x, normed_boxes): # v3 for extract two types\\n # used masks\\n bs, _, h, w = x.shape\\n patch_height, patch_width = self.patch_size\\n mask_h, mask_w = h // patch_height, w // patch_width\\n masks = [self._generate_masks_per_image(normed_boxes_, mask_h, mask_w)\\n for normed_boxes_ in normed_boxes]\\n\\n roi_features_v1, dense_x = self.mask_attn_pool(x, masks, return_dense=True)\\n dense_x = F.normalize(dense_x, dim=-1) # normalize along last dimension\\n dense_x = dense_x.permute(0, 3, 1, 2)\\n roi_features_v2 = roi_align(dense_x, self._denormalize_boxes(normed_boxes, dense_x), \\n (1, 1), 1.0, -1, True)[..., 0, 0]\\n\\n return roi_features_v1, roi_features_v2\\n\\n def _extract_roi_features_v2(self, x, normed_boxes):\\n x = self.conv1(x) # shape = [*, width, grid, grid]\\n bs, _, h, w = x.shape\\n # assert h == w # TODO: support input of any shape, need to change the normed boxes to real boxes\\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\\n x = torch.cat(\\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\\n if (h, w) == self.grid_size:\\n pe = self.positional_embedding.to(x.dtype)\\n else:\\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\\n\\n x = x + pe\\n\\n # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in\\n x = self.patch_dropout(x)\\n x = self.ln_pre(x)\\n\\n x = x.permute(1, 0, 2) # NLD -> LND\\n x = self.transformer.extract_feature_map(x)\\n x = x.permute(1, 0, 2) # LND -> NLD\\n\\n if self.attn_pool is not None:\\n x = self.attn_pool(x)\\n x = self.ln_post(x)\\n _, tokens = self._global_pool(x)\\n else:\\n _, tokens = self._global_pool(x)\\n tokens = self.ln_post(tokens)\\n\\n if self.proj is not None:\\n tokens = tokens @ self.proj\\n tokens = F.normalize(tokens, dim=-1) # normalize along last dimension\\n tokens = tokens.view(bs, h, w, -1).permute(0, 3, 1, 2)\\n return roi_align(tokens, self._denormalize_boxes(normed_boxes, tokens),\\n (1, 1), 1.0, -1, True)[..., 0, 0]\\n\\n def rescale_positional_embedding(self, out_size, dtype):\\n h, w = out_size\\n rescaled_positional_embedding = \\\\\\n self.positional_embedding.new_zeros(1 + h*w, self.positional_embedding.shape[1])\\n rescaled_positional_embedding[0] = self.positional_embedding[0]\\n pe_2d = self.positional_embedding[1:].T.contiguous().view(\\n 1, -1, *self.grid_size)\\n pe_2d = F.interpolate(pe_2d, out_size, mode='bicubic', align_corners=False).view(-1, h*w)\\n rescaled_positional_embedding[1:] = pe_2d.T.contiguous()\\n\\n return rescaled_positional_embedding.to(dtype=dtype)\\n\\n def _mask_attn_pool(self, x: torch.Tensor, attn_mask: torch.Tensor, num_mask_tokens: int, return_dense=False):\\n x = self.conv1(x) # shape = [*, width, grid, grid]\\n bs, _, h, w = x.shape\\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\\n x = torch.cat(\\n [\\n self.class_embedding.to(x.dtype)\\n + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\\n x,\\n ],\\n dim=1,\\n ) # shape = [*, grid ** 2 + 1, width]\\n if (h, w) == self.grid_size:\\n pe = self.positional_embedding.to(x.dtype)\\n else:\\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\\n\\n x = x + pe\\n x = self.ln_pre(x)\\n\\n x = x.permute(1, 0, 2) # NLD -> LND\\n cls_embed = x[0:1]\\n cls_embed = cls_embed.expand(num_mask_tokens, -1, -1)\\n x = torch.cat([cls_embed, x], dim=0)\\n if return_dense:\\n x, x_dense = self.transformer.forward_image_dense(x, attn_mask)\\n x_dense = x_dense.permute(1, 0, 2) # LND -> NLD\\n x_dense = x_dense[:, num_mask_tokens + 1:]\\n\\n x_dense = self.ln_post(x_dense)\\n\\n if self.proj is not None:\\n x_dense = x_dense @ self.proj\\n x_dense = F.normalize(x_dense, dim=-1) # normalize along last dimension\\n x_dense = x_dense.view(bs, h, w, -1)\\n else:\\n x = self.transformer(x, attn_mask)\\n x_dense = None\\n x = x.permute(1, 0, 2) # LND -> NLD\\n\\n # [N, L, D]\\n x = self.ln_post(x[:, :num_mask_tokens, :])\\n\\n if self.proj is not None:\\n x = torch.einsum(\\\"nld,dc->nlc\\\", x, self.proj)\\n\\n return x, x_dense\\n\\n def mask_attn_pool(self, image, masks, return_dense=False):\\n assert hasattr(self, \\\"positional_embedding\\\")\\n batch_size = image.shape[0]\\n assert batch_size == len(masks)\\n num_masks_per_image = [mask.shape[0] for mask in masks]\\n num_queries = max(num_masks_per_image)\\n mask_h, mask_w = masks[0].shape[1:]\\n\\n batch_masks = torch.ones(batch_size, num_queries, mask_h, mask_w, dtype=torch.bool).to(image.device)\\n for batch_id, mask in enumerate(masks):\\n batch_masks[batch_id, :mask.shape[0]] = mask\\n\\n mask_token_attn_mask = torch.logical_not(batch_masks)\\n # [B, Q, H//P x W//P]\\n mask_token_attn_mask = mask_token_attn_mask.reshape(batch_size, num_queries, -1)\\n\\n num_mask_token = num_queries\\n num_image_cls_token = (mask_h * mask_w + 1)\\n num_image_token = num_image_cls_token - 1\\n num_all_token = num_mask_token + num_image_cls_token\\n\\n # we start with no mask out\\n attn_mask = torch.zeros(\\n (num_all_token, num_all_token), dtype=torch.bool, device=image.device\\n )\\n\\n # mask+cls+image token to mask token attention is masked out\\n attn_mask[:, :num_mask_token] = True\\n\\n attn_mask = attn_mask.unsqueeze(0).repeat_interleave(batch_size, dim=0)\\n attn_mask[:, :num_mask_token, -num_image_token:] = mask_token_attn_mask\\n num_heads = self.num_heads # head width 64\\n attn_mask = attn_mask.unsqueeze(1).expand(-1, num_heads, -1, -1)\\n attn_mask = attn_mask.reshape(batch_size * num_heads, num_all_token, num_all_token)\\n\\n batch_mask_features, x_dense = self._mask_attn_pool(image, attn_mask, num_mask_token,\\n return_dense=return_dense)\\n\\n mask_features = [batch_mask_features[batch_id, :num_masks]\\n for batch_id, num_masks, in enumerate(num_masks_per_image)]\\n if return_dense:\\n # x_dense = F.normalize(x_dense, dim=-1).flatten(1, 2) # bs, h*w, c\\n # masks = torch.cat(masks).float().flatten(-2, -1) # bs, h*w\\n # x_dense = torch.repeat_interleave(\\n # x_dense, torch.tensor(num_masks_per_image, device=x_dense.device), dim=0)\\n # x_dense = (x_dense * masks.unsqueeze(-1)).sum(1) / masks.sum(1, keepdim=True)\\n\\n return torch.cat(mask_features), x_dense\\n else:\\n return torch.cat(mask_features)\\n\\n def encode_rois_and_image(self, x, normed_boxes):\\n x = self.conv1(x) # shape = [*, width, grid, grid]\\n bs, _, h, w = x.shape\\n # assert h == w # TODO: support input of any shape, need to change the normed boxes to real boxes\\n x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]\\n x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]\\n x = torch.cat(\\n [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),\\n x], dim=1) # shape = [*, grid ** 2 + 1, width]\\n if (h, w) == self.grid_size:\\n pe = self.positional_embedding.to(x.dtype)\\n else:\\n pe = self.rescale_positional_embedding(out_size=(h, w), dtype=x.dtype)\\n\\n x = x + pe\\n\\n # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in\\n x = self.patch_dropout(x)\\n x = self.ln_pre(x)\\n\\n x = x.permute(1, 0, 2) # NLD -> LND\\n x, x_image = self.transformer.extract_feature_map(x, return_forward=True)\\n x = x.permute(1, 0, 2) # LND -> NLD\\n\\n if self.attn_pool is not None:\\n x = self.attn_pool(x)\\n x = self.ln_post(x)\\n _, tokens = self._global_pool(x)\\n else:\\n _, tokens = self._global_pool(x)\\n tokens = self.ln_post(tokens)\\n\\n if self.proj is not None:\\n tokens = tokens @ self.proj\\n\\n feature_map = tokens.view(bs, h * w, -1) # .permute(0, 3, 1, 2)\\n feature_map = F.normalize(feature_map, dim=-1)\\n feature_map = feature_map.view(bs, h, w, -1).permute(0, 3, 1, 2)\\n x_rois = roi_align(feature_map, self._denormalize_boxes(normed_boxes, feature_map),\\n (1, 1), 1.0, -1, True)[..., 0, 0]\\n x_rois = F.normalize(x_rois, dim=-1)\\n\\n x_image = self.post_attention(x_image)\\n x_image = F.normalize(x_image, dim=-1)\\n\\n return x_rois, x_image\"\n },\n {\n \"identifier\": \"TextTransformer\",\n \"path\": \"src/open_clip/transformer.py\",\n \"snippet\": \"class TextTransformer(nn.Module):\\n output_tokens: torch.jit.Final[bool]\\n\\n def __init__(\\n self,\\n context_length: int = 77,\\n vocab_size: int = 49408,\\n width: int = 512,\\n heads: int = 8,\\n layers: int = 12,\\n ls_init_value: float = None,\\n output_dim: int = 512,\\n act_layer: Callable = nn.GELU,\\n norm_layer: Callable = LayerNorm,\\n embed_cls: bool = False,\\n pad_id: int = 0,\\n output_tokens: bool = False,\\n ):\\n super().__init__()\\n self.output_tokens = output_tokens\\n self.num_pos = self.context_length = context_length\\n self.vocab_size = vocab_size\\n self.width = width\\n self.output_dim = output_dim\\n self.heads = heads\\n self.pad_id = pad_id\\n\\n self.text_projection = nn.Parameter(torch.empty(width, output_dim))\\n\\n if embed_cls:\\n self.cls_emb = nn.Parameter(torch.empty(width))\\n self.num_pos += 1\\n else:\\n self.cls_emb = None\\n\\n self.token_embedding = nn.Embedding(vocab_size, width)\\n self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width))\\n self.transformer = Transformer(\\n width=width,\\n layers=layers,\\n heads=heads,\\n ls_init_value=ls_init_value,\\n act_layer=act_layer,\\n norm_layer=norm_layer,\\n )\\n self.ln_final = norm_layer(width)\\n\\n self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)\\n\\n self.init_parameters()\\n\\n def init_parameters(self):\\n nn.init.normal_(self.token_embedding.weight, std=0.02)\\n nn.init.normal_(self.positional_embedding, std=0.01)\\n if self.cls_emb is not None:\\n nn.init.normal_(self.cls_emb, std=0.01)\\n\\n proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)\\n attn_std = self.transformer.width ** -0.5\\n fc_std = (2 * self.transformer.width) ** -0.5\\n for block in self.transformer.resblocks:\\n nn.init.normal_(block.attn.in_proj_weight, std=attn_std)\\n nn.init.normal_(block.attn.out_proj.weight, std=proj_std)\\n nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)\\n nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)\\n\\n if self.text_projection is not None:\\n nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)\\n\\n def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):\\n assert unlocked_layers == 0 and freeze_layer_norm\\n print(f'Freeze the text encoder', flush=True)\\n for p in self.parameters():\\n p.requires_grad = False\\n\\n @torch.jit.ignore\\n def set_grad_checkpointing(self, enable=True):\\n self.transformer.grad_checkpointing = enable\\n\\n def build_attention_mask(self):\\n # lazily create causal attention mask, with full attention between the tokens\\n # pytorch uses additive attention mask; fill with -inf\\n mask = torch.empty(self.num_pos, self.num_pos)\\n mask.fill_(float(\\\"-inf\\\"))\\n mask.triu_(1) # zero out the lower diagonal\\n return mask\\n\\n def build_cls_mask(self, text, cast_dtype: torch.dtype):\\n cls_mask = (text != self.pad_id).unsqueeze(1)\\n cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0)\\n additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device)\\n additive_mask.fill_(0)\\n additive_mask.masked_fill_(~cls_mask, float(\\\"-inf\\\"))\\n additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0)\\n return additive_mask\\n\\n def _repeat(self, t, N: int):\\n return t.reshape(1, 1, -1).repeat(N, 1, 1)\\n\\n def forward(self, text):\\n cast_dtype = self.transformer.get_cast_dtype()\\n seq_len = text.shape[1]\\n\\n x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model]\\n attn_mask = self.attn_mask\\n if self.cls_emb is not None:\\n seq_len += 1\\n x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1)\\n cls_mask = self.build_cls_mask(text, cast_dtype)\\n attn_mask = attn_mask[None, :seq_len, :seq_len] + cls_mask[:, :seq_len, :seq_len]\\n\\n x = x + self.positional_embedding[:seq_len].to(cast_dtype)\\n x = x.permute(1, 0, 2) # NLD -> LND\\n x = self.transformer(x, attn_mask=attn_mask)\\n x = x.permute(1, 0, 2) # LND -> NLD\\n\\n # x.shape = [batch_size, n_ctx, transformer.width]\\n # take features from the eot embedding (eot_token is the highest number in each sequence)\\n if self.cls_emb is not None:\\n pooled, tokens = x[:, -1], x[:, :-1]\\n pooled = self.ln_final(pooled)\\n else:\\n x = self.ln_final(x)\\n pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x\\n\\n if self.text_projection is not None:\\n pooled = pooled @ self.text_projection\\n\\n if self.output_tokens:\\n return pooled, tokens\\n\\n return pooled\"\n },\n {\n \"identifier\": \"to_2tuple\",\n \"path\": \"src/open_clip/utils.py\",\n \"snippet\": \"def freeze_batch_norm_2d(module, module_match={}, name=''):\\ndef _ntuple(n):\\n def parse(x):\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from dataclasses import dataclass\nfrom typing import Optional, Tuple, Union\nfrom torch import nn\nfrom torch.utils.checkpoint import checkpoint\nfrom .hf_model import HFTextEncoder\nfrom .modified_resnet import ModifiedResNet\nfrom .timm_model import TimmModel\nfrom .transformer import LayerNormFp32, LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer\nfrom .utils import to_2tuple\nimport logging\nimport math\nimport numpy as np\nimport torch\nimport torch.nn.functional as F"},"token_num":{"kind":"number","value":15880,"string":"15,880"},"cropped_code":{"kind":"string","value":"\"\"\" CLIP Model\n\nAdapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.\n\"\"\"\n\n\n\n\n@dataclass\nclass CLIPVisionCfg:\n layers: Union[Tuple[int, int, int, int], int] = 12\n width: int = 768\n head_width: int = 64\n mlp_ratio: float = 4.0\n patch_size: int = 16\n image_size: Union[Tuple[int, int], int] = 224\n ls_init_value: Optional[float] = None # layer scale initial value\n patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results\n input_patchnorm: bool = False # whether to use dual patchnorm - would only apply the input layernorm on each patch, as post-layernorm already exist in original clip vit design\n global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580)\n attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer\n n_queries: int = 256 # n_queries for attentional pooler\n attn_pooler_heads: int = 8 # n heads for attentional_pooling\n timm_model_name: str = None # a valid model name overrides layers, width, patch_size\n timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model\n timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')\n timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '')\n timm_proj_bias: bool = False # enable bias final projection\n timm_drop: float = 0. # head dropout\n timm_drop_path: Optional[float] = None # backbone stochastic depth\n output_tokens: bool = False\n freeze_output = True\n freeze_all_bns = True\n\n\n@dataclass\nclass CLIPTextCfg:\n context_length: int = 77\n vocab_size: int = 49408\n width: int = 512\n heads: int = 8\n layers: int = 12\n ls_init_value: Optional[float] = None # layer scale initial value\n hf_model_name: str = None\n hf_tokenizer_name: str = None\n hf_model_pretrained: bool = True\n proj: str = 'mlp'\n pooler_type: str = 'mean_pooler'\n embed_cls: bool = False\n pad_id: int = 0\n output_tokens: bool = False\n\n\ndef get_cast_dtype(precision: str):\n cast_dtype = None\n if precision == 'bf16':\n cast_dtype = torch.bfloat16\n elif precision == 'fp16':\n cast_dtype = torch.float16\n return cast_dtype\n\n\ndef _build_vision_tower(\n embed_dim: int,\n vision_cfg: CLIPVisionCfg,\n quick_gelu: bool = False,\n cast_dtype: Optional[torch.dtype] = None\n):\n if isinstance(vision_cfg, dict):\n vision_cfg = CLIPVisionCfg(**vision_cfg)\n\n # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more\n # memory efficient in recent PyTorch releases (>= 1.10).\n # NOTE: timm models always use native GELU regardless of quick_gelu flag.\n act_layer = QuickGELU if quick_gelu else nn.GELU\n\n if vision_cfg.timm_model_name:\n visual = TimmModel(\n vision_cfg.timm_model_name,\n pretrained=vision_cfg.timm_model_pretrained,\n pool=vision_cfg.timm_pool,\n proj=vision_cfg.timm_proj,\n proj_bias=vision_cfg.timm_proj_bias,\n drop=vision_cfg.timm_drop,\n drop_path=vision_cfg.timm_drop_path,\n patch_drop=vision_cfg.patch_dropout if vision_cfg.patch_dropout > 0 else None,\n embed_dim=embed_dim,\n image_size=vision_cfg.image_size,\n )\n act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models\n elif isinstance(vision_cfg.layers, (tuple, list)):\n vision_heads = vision_cfg.width * 32 // vision_cfg.head_width\n visual = ModifiedResNet(\n layers=vision_cfg.layers,\n output_dim=embed_dim,\n heads=vision_heads,\n image_size=vision_cfg.image_size,\n width=vision_cfg.width,\n freeze_output=vision_cfg.freeze_output,\n freeze_all_bns=vision_cfg.freeze_all_bns\n )\n else:\n vision_heads = vision_cfg.width // vision_cfg.head_width\n"},"all_code":{"kind":"string","value":"\"\"\" CLIP Model\n\nAdapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.\n\"\"\"\n\n\n\n\n@dataclass\nclass CLIPVisionCfg:\n layers: Union[Tuple[int, int, int, int], int] = 12\n width: int = 768\n head_width: int = 64\n mlp_ratio: float = 4.0\n patch_size: int = 16\n image_size: Union[Tuple[int, int], int] = 224\n ls_init_value: Optional[float] = None # layer scale initial value\n patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results\n input_patchnorm: bool = False # whether to use dual patchnorm - would only apply the input layernorm on each patch, as post-layernorm already exist in original clip vit design\n global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580)\n attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer\n n_queries: int = 256 # n_queries for attentional pooler\n attn_pooler_heads: int = 8 # n heads for attentional_pooling\n timm_model_name: str = None # a valid model name overrides layers, width, patch_size\n timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model\n timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')\n timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '')\n timm_proj_bias: bool = False # enable bias final projection\n timm_drop: float = 0. # head dropout\n timm_drop_path: Optional[float] = None # backbone stochastic depth\n output_tokens: bool = False\n freeze_output = True\n freeze_all_bns = True\n\n\n@dataclass\nclass CLIPTextCfg:\n context_length: int = 77\n vocab_size: int = 49408\n width: int = 512\n heads: int = 8\n layers: int = 12\n ls_init_value: Optional[float] = None # layer scale initial value\n hf_model_name: str = None\n hf_tokenizer_name: str = None\n hf_model_pretrained: bool = True\n proj: str = 'mlp'\n pooler_type: str = 'mean_pooler'\n embed_cls: bool = False\n pad_id: int = 0\n output_tokens: bool = False\n\n\ndef get_cast_dtype(precision: str):\n cast_dtype = None\n if precision == 'bf16':\n cast_dtype = torch.bfloat16\n elif precision == 'fp16':\n cast_dtype = torch.float16\n return cast_dtype\n\n\ndef _build_vision_tower(\n embed_dim: int,\n vision_cfg: CLIPVisionCfg,\n quick_gelu: bool = False,\n cast_dtype: Optional[torch.dtype] = None\n):\n if isinstance(vision_cfg, dict):\n vision_cfg = CLIPVisionCfg(**vision_cfg)\n\n # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more\n # memory efficient in recent PyTorch releases (>= 1.10).\n # NOTE: timm models always use native GELU regardless of quick_gelu flag.\n act_layer = QuickGELU if quick_gelu else nn.GELU\n\n if vision_cfg.timm_model_name:\n visual = TimmModel(\n vision_cfg.timm_model_name,\n pretrained=vision_cfg.timm_model_pretrained,\n pool=vision_cfg.timm_pool,\n proj=vision_cfg.timm_proj,\n proj_bias=vision_cfg.timm_proj_bias,\n drop=vision_cfg.timm_drop,\n drop_path=vision_cfg.timm_drop_path,\n patch_drop=vision_cfg.patch_dropout if vision_cfg.patch_dropout > 0 else None,\n embed_dim=embed_dim,\n image_size=vision_cfg.image_size,\n )\n act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models\n elif isinstance(vision_cfg.layers, (tuple, list)):\n vision_heads = vision_cfg.width * 32 // vision_cfg.head_width\n visual = ModifiedResNet(\n layers=vision_cfg.layers,\n output_dim=embed_dim,\n heads=vision_heads,\n image_size=vision_cfg.image_size,\n width=vision_cfg.width,\n freeze_output=vision_cfg.freeze_output,\n freeze_all_bns=vision_cfg.freeze_all_bns\n )\n else:\n vision_heads = vision_cfg.width // vision_cfg.head_width"},"next_line":{"kind":"string","value":" norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm"},"gold_snippet_index":{"kind":"number","value":3,"string":"3"},"created_at":{"kind":"string","value":"2023-12-09 05:43:08+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5298,"cells":{"repo_name":{"kind":"string","value":"LkPrtctrd/BSL-V53"},"file_path":{"kind":"string","value":"Heart/Logic/LogicLaserMessageFactory.py"},"context":{"kind":"list like","value":[{"identifier":"ClientHelloMessage","path":"Heart/Packets/Client/Authentification/ClientHelloMessage.py","snippet":"class ClientHelloMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n pass\n\n def decode(self):\n fields = {}\n fields[\"Protocol\"] = self.readInt()\n fields[\"KeyVersion\"] = self.readInt()\n fields[\"MajorVersion\"] = self.readInt()\n fields[\"MinorVersion\"] = self.readInt()\n fields[\"Build\"] = self.readInt()\n fields[\"ContentHash\"] = self.readString()\n fields[\"DeviceType\"] = self.readInt()\n fields[\"AppStore\"] = self.readInt()\n super().decode(fields)\n return fields\n\n def execute(message, calling_instance, fields, cryptoInit):\n fields[\"Socket\"] = calling_instance.client\n Messaging.sendMessage(20100, fields, cryptoInit)\n\n def getMessageType(self):\n return 10100\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"LoginMessage","path":"Heart/Packets/Client/Authentification/LoginMessage.py","snippet":"class LoginMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n pass\n\n def decode(self):\n fields = {}\n fields[\"AccountID\"] = self.readLong()\n fields[\"PassToken\"] = self.readString()\n fields[\"ClientMajor\"] = self.readInt()\n fields[\"ClientMinor\"] = self.readInt()\n fields[\"ClientBuild\"] = self.readInt()\n fields[\"ResourceSha\"] = self.readString()\n fields[\"Device\"] = self.readString()\n fields[\"PreferredLanguage\"] = self.readDataReference()\n fields[\"PreferredDeviceLanguage\"] = self.readString()\n fields[\"OSVersion\"] = self.readString()\n fields[\"isAndroid\"] = self.readBoolean()\n fields[\"IMEI\"] = self.readString()\n fields[\"AndroidID\"] = self.readString()\n fields[\"isAdvertisingEnabled\"] = self.readBoolean()\n fields[\"AppleIFV\"] = self.readString()\n fields[\"RndKey\"] = self.readInt()\n fields[\"AppStore\"] = self.readVInt()\n fields[\"ClientVersion\"] = self.readString()\n fields[\"TencentOpenId\"] = self.readString()\n fields[\"TencentToken\"] = self.readString()\n fields[\"TencentPlatform\"] = self.readVInt()\n fields[\"DeviceVerifierResponse\"] = self.readString()\n fields[\"AppLicensingSignature\"] = self.readString()\n fields[\"DeviceVerifierResponse\"] = self.readString()\n super().decode(fields)\n return fields\n\n def execute(message, calling_instance, fields, cryptoInit):\n if fields[\"ClientMajor\"]==53:\n calling_instance.player.ClientVersion = f'{str(fields[\"ClientMajor\"])}.{str(fields[\"ClientBuild\"])}.{str(fields[\"ClientMinor\"])}'\n fields[\"Socket\"] = calling_instance.client\n db_instance = DatabaseHandler()\n if db_instance.playerExist(fields[\"PassToken\"], fields[\"AccountID\"]):\n player_data = json.loads(db_instance.getPlayerEntry(fields[\"AccountID\"])[2])\n db_instance.loadAccount(calling_instance.player, fields[\"AccountID\"])\n else:\n db_instance.createAccount(calling_instance.player.getDataTemplate(fields[\"AccountID\"][0], fields[\"AccountID\"][1], fields[\"PassToken\"]))\n ClientsManager.AddPlayer(calling_instance.player.ID, calling_instance.client)\n Messaging.sendMessage(20104, fields, cryptoInit, calling_instance.player)\n Messaging.sendMessage(24101, fields, cryptoInit, calling_instance.player)\n Messaging.sendMessage(24399, fields, cryptoInit, calling_instance.player)\n\n def getMessageType(self):\n return 10101\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"AskForBattleEndMessage","path":"Heart/Packets/Client/Battle/AskForBattleEndMessage.py","snippet":"class AskForBattleEndMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n pass\n\n def decode(self):\n fields = {}\n fields[\"Unk1\"] = self.readVInt()\n fields[\"Result\"] = self.readVInt()\n fields[\"Rank\"] = self.readVInt()\n fields[\"MapID\"] = self.readDataReference()\n fields[\"HeroesCount\"] = self.readVInt()\n fields[\"Heroes\"] = []\n for i in range(fields[\"HeroesCount\"]): fields[\"Heroes\"].append({\"Brawler\": {\"ID\": self.readDataReference(), \"SkinID\": self.readDataReference()}, \"Team\": self.readVInt(), \"IsPlayer\": self.readBoolean(), \"PlayerName\": self.readString()})\n super().decode(fields)\n return fields\n\n def execute(message, calling_instance, fields, cryptoInit):\n fields[\"Socket\"] = calling_instance.client\n Messaging.sendMessage(23456, fields, cryptoInit, calling_instance.player)\n\n def getMessageType(self):\n return 14110\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"ChangeAvatarNameMessage","path":"Heart/Packets/Client/Home/ChangeAvatarNameMessage.py","snippet":"class ChangeAvatarNameMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n self.writeString(fields[\"Name\"])\n self.writeBoolean(fields[\"NameSetByUser\"])\n\n def decode(self):\n fields = {}\n fields[\"Name\"] = self.readString()\n fields[\"NameSetByUser\"] = self.readBoolean()\n super().decode(fields)\n return fields\n\n def execute(message, calling_instance, fields, cryptoInit):\n db_instance = DatabaseHandler()\n playerData = db_instance.getPlayer(calling_instance.player.ID)\n playerData[\"Name\"] = fields[\"Name\"]\n playerData[\"Registered\"] = True\n db_instance.updatePlayerData(playerData, calling_instance)\n fields[\"Socket\"] = calling_instance.client\n fields[\"Command\"] = {\"ID\": 201}\n Messaging.sendMessage(24111, fields, cryptoInit, calling_instance.player)\n\n def getMessageType(self):\n return 10212\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"EndClientTurnMessage","path":"Heart/Packets/Client/Home/EndClientTurnMessage.py","snippet":"class EndClientTurnMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n pass\n\n def decode(self):\n fields = {}\n self.readBoolean()\n fields[\"Tick\"] = self.readVInt()\n fields[\"Checksum\"] = self.readVInt()\n fields[\"CommandsCount\"] = self.readVInt()\n super().decode(fields)\n fields[\"Commands\"] = []\n for i in range(fields[\"CommandsCount\"]):\n fields[\"Commands\"].append({\"ID\": self.readVInt()})\n if LogicCommandManager.commandExist(fields[\"Commands\"][i][\"ID\"]):\n command = LogicCommandManager.createCommand(fields[\"Commands\"][i][\"ID\"])\n print(\"Command\", LogicCommandManager.getCommandsName(fields[\"Commands\"][i][\"ID\"]))\n if command is not None:\n fields[\"Commands\"][i][\"Fields\"] = command.decode(self)\n fields[\"Commands\"][i][\"Instance\"] = command\n return fields\n\n def execute(message, calling_instance, fields, cryptoInit):\n fields[\"Socket\"] = calling_instance.client\n for command in fields[\"Commands\"]:\n if \"Instance\" not in command.keys():\n return\n\n if hasattr(command[\"Instance\"], 'execute'):\n command[\"Instance\"].execute(calling_instance, command[\"Fields\"], cryptoInit)\n if command[\"ID\"] == 519:\n Messaging.sendMessage(24104, {\"Socket\": calling_instance.client, \"ServerChecksum\": 0, \"ClientChecksum\": 0, \"Tick\": 0}, cryptoInit)\n\n def getMessageType(self):\n return 14102\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"GoHomeFromOfflinePractiseMessage","path":"Heart/Packets/Client/Home/GoHomeFromOfflinePractiseMessage.py","snippet":"class GoHomeFromOfflinePractiseMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n pass\n\n def decode(self):\n fields = {}\n self.readBoolean()\n return fields\n\n def execute(message, calling_instance, fields, cryptoInit):\n fields[\"Socket\"] = calling_instance.client\n Messaging.sendMessage(24101, fields, cryptoInit, calling_instance.player)\n\n def getMessageType(self):\n return 14109\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"GoHomeMessage","path":"Heart/Packets/Client/Home/GoHomeMessage.py","snippet":"class GoHomeMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n pass\n\n def decode(self):\n fields = {}\n self.readBoolean()\n return fields\n\n def execute(message, calling_instance, fields, cryptoInit):\n fields[\"Socket\"] = calling_instance.client\n Messaging.sendMessage(24101, fields, cryptoInit, calling_instance.player)\n\n def getMessageType(self):\n return 17750\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"GetPlayerProfileMessage","path":"Heart/Packets/Client/Home/GetPlayerProfileMessage.py","snippet":"class GetPlayerProfileMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n pass\n\n def decode(self):\n fields = {}\n fields[\"BattleInfoBoolean\"] = self.readBoolean()\n if fields[\"BattleInfoBoolean\"]:\n fields[\"unk1\"] = self.readVInt()\n fields[\"AnotherID\"] = self.readLong()\n fields[\"unk2\"] = self.readVInt()\n for i in self.readVInt():\n fields[\"CsvID\"] = self.readDataReference()\n fields[\"unk3\"] = self.readVInt()\n fields[\"unk4\"] = self.readVInt()\n fields[\"unk5\"] = self.readVInt()\n fields[\"unk6\"] = self.readVInt()\n fields[\"PlayerName\"] = self.readString()\n fields[\"unk7\"] = self.readVInt()\n fields[\"Thumbnail\"] = self.readVInt()\n fields[\"NameColor\"] = self.readVInt()\n fields[\"unk10\"] = self.readVInt()\n fields[\"unk11\"] = self.readVInt()\n fields[\"PlayerHighID\"] = self.readInt()\n fields[\"PlayerLowID\"] = self.readInt()\n super().decode(fields)\n\n\n return fields\n\n def execute(message, calling_instance, fields, cryptoInit):\n fields[\"Socket\"] = calling_instance.client\n Messaging.sendMessage(24113, fields, cryptoInit, calling_instance.player)\n\n def getMessageType(self):\n return 15081\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"AskForAllianceDataMessage","path":"Heart/Packets/Client/Home/AskForAllianceDataMessage.py","snippet":"class AskForAllianceDataMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n pass\n\n def decode(self):\n fields = {}\n fields[\"id\"] = self.readVLong()\n fields[\"isInAlliance\"] = self.readBoolean()\n if fields[\"isInAlliance\"] == True:\n fields[\"anotherIDHigh\"] = self.readVInt()\n fields[\"anotherIDLow\"] = self.readVInt()\n super().decode(fields)\n\n return fields\n\n def execute(message, calling_instance, fields, cryptoInit):\n fields[\"Socket\"] = calling_instance.client\n Messaging.sendMessage(24301, fields, cryptoInit, calling_instance.player)\n\n def getMessageType(self):\n return 14302\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"KeepAliveMessage","path":"Heart/Packets/Client/Socket/KeepAliveMessage.py","snippet":"class KeepAliveMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n pass\n\n def decode(self):\n return {}\n\n def execute(message, calling_instance, fields, cryptoInit):\n fields[\"Socket\"] = calling_instance.client\n Messaging.sendMessage(20108, fields, cryptoInit)\n\n def getMessageType(self):\n return 10108\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"LoginFailedMessage","path":"Heart/Packets/Server/Authentification/LoginFailedMessage.py","snippet":"class LoginFailedMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n self.writeInt(fields['ErrorID'])\n self.writeString(fields['FingerprintData'])\n self.writeString()\n self.writeString(fields['ContentURL'])\n self.writeString()\n self.writeString(fields['Message'])\n self.writeInt(0)\n self.writeBoolean(False)\n self.writeInt(0)\n self.writeInt(0)\n self.writeInt(0)\n self.writeInt(0)\n self.writeString()\n self.writeInt(0)\n self.writeBoolean(True)\n self.writeBoolean(True)\n self.writeString()\n self.writeVInt(0)\n self.writeString()\n self.writeBoolean(False)\n\n def decode(self):\n fields = {}\n fields[\"ErrorCode\"] = self.readInt()\n fields[\"ResourceFingerprintData\"] = self.readString()\n fields[\"RedirectDomain\"] = self.readString()\n fields[\"ContentURL\"] = self.readString()\n fields[\"UpdateURL\"] = self.readString()\n fields[\"Reason\"] = self.readString()\n fields[\"SecondsUntilMaintenanceEnd\"] = self.readInt()\n fields[\"ShowContactSupportForBan\"] = self.readBoolean()\n fields[\"CompressedFingerprintData\"] = self.readBytesWithoutLength()\n fields[\"ContentURLListCount\"] = self.readInt()\n fields[\"ContentURLList\"] = []\n for i in range(fields[\"ContentURLListCount\"]):\n fields[\"ContentURLList\"].append(self.readString())\n fields[\"KunlunAppStore\"] = self.readInt()\n fields[\"MaintenanceType\"] = self.readInt()\n fields[\"HelpshiftFaqId\"] = self.readString()\n fields[\"Tier\"] = self.readInt()\n fields[\"Unk1\"] = self.readBoolean()\n fields[\"Unk2\"] = self.readBoolean()\n fields[\"Unk3\"] = self.readString()\n fields[\"Unk4\"] = self.readVInt()\n fields[\"Unk5\"] = self.readString()\n fields[\"OptionalTargetedAccountIdState\"] = self.readBoolean()\n if fields[\"OptionalTargetedAccountIdState\"] == True:\n fields[\"OptionalTargetedAccountId\"] = self.readLong()\n super().decode(fields)\n return fields\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 20103\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"LoginOkMessage","path":"Heart/Packets/Server/Authentification/LoginOkMessage.py","snippet":"class LoginOkMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 1\n\n def encode(self, fields, player):\n self.writeLong(player.ID[0], player.ID[1])\n self.writeLong(player.ID[0], player.ID[1])\n self.writeString(player.Token)\n self.writeString()\n self.writeString()\n self.writeInt(53)\n self.writeInt(176)\n self.writeInt(1)\n self.writeString(\"dev\")\n self.writeInt(0)\n self.writeInt(0)\n self.writeInt(0)\n self.writeString()\n self.writeString()\n self.writeString()\n self.writeInt(0)\n self.writeString()\n self.writeString(\"RU\")\n self.writeString()\n self.writeInt(0)\n self.writeString()\n self.writeInt(2)\n self.writeString('https://game-assets.brawlstarsgame.com')\n self.writeString('http://a678dbc1c015a893c9fd-4e8cc3b1ad3a3c940c504815caefa967.r87.cf2.rackcdn.com')\n self.writeInt(2)\n self.writeString('https://event-assets.brawlstars.com')\n self.writeString('https://24b999e6da07674e22b0-8209975788a0f2469e68e84405ae4fcf.ssl.cf2.rackcdn.com/event-assets')\n self.writeVInt(0)\n self.writeCompressedString(b'')\n self.writeBoolean(True)\n self.writeBoolean(False)\n self.writeString()\n self.writeString()\n self.writeString()\n self.writeString('https://play.google.com/store/apps/details?id=com.supercell.brawlstars')\n self.writeString()\n self.writeBoolean(False)\n\n self.writeBoolean(False)\n if False:\n self.writeString()\n\n self.writeBoolean(False)\n if False:\n self.writeString()\n\n self.writeBoolean(False)\n if False:\n self.writeString()\n\n self.writeBoolean(False)\n if False:\n self.writeString()\n\n\n def decode(self):\n fields = {}\n fields[\"AccountID\"] = self.readLong()\n fields[\"HomeID\"] = self.readLong()\n fields[\"PassToken\"] = self.readString()\n fields[\"FacebookID\"] = self.readString()\n fields[\"GamecenterID\"] = self.readString()\n fields[\"ServerMajorVersion\"] = self.readInt()\n fields[\"ContentVersion\"] = self.readInt()\n fields[\"ServerBuild\"] = self.readInt()\n fields[\"ServerEnvironment\"] = self.readString()\n fields[\"SessionCount\"] = self.readInt()\n fields[\"PlayTimeSeconds\"] = self.readInt()\n fields[\"DaysSinceStartedPlaying\"] = self.readInt()\n fields[\"FacebookAppID\"] = self.readString()\n fields[\"ServerTime\"] = self.readString()\n fields[\"AccountCreatedDate\"] = self.readString()\n fields[\"StartupCooldownSeconds\"] = self.readInt()\n fields[\"GoogleServiceID\"] = self.readString()\n fields[\"LoginCountry\"] = self.readString()\n fields[\"KunlunID\"] = self.readString()\n fields[\"Tier\"] = self.readInt()\n fields[\"TencentID\"] = self.readString()\n\n ContentUrlCount = self.readInt()\n fields[\"GameAssetsUrls\"] = []\n for i in range(ContentUrlCount):\n fields[\"GameAssetsUrls\"].append(self.readString())\n\n EventUrlCount = self.readInt()\n fields[\"EventAssetsUrls\"] = []\n for i in range(EventUrlCount):\n fields[\"EventAssetsUrls\"].append(self.readString())\n\n fields[\"SecondsUntilAccountDeletion\"] = self.readVInt()\n fields[\"SupercellIDToken\"] = self.readCompressedString()\n fields[\"IsSupercellIDLogoutAllDevicesAllowed\"] = self.readBoolean()\n fields[\"isSupercellIDEligible\"] = self.readBoolean()\n fields[\"LineID\"] = self.readString()\n fields[\"SessionID\"] = self.readString()\n fields[\"KakaoID\"] = self.readString()\n fields[\"UpdateURL\"] = self.readString()\n fields[\"YoozooPayNotifyUrl\"] = self.readString()\n fields[\"UnbotifyEnabled\"] = self.readBoolean()\n\n Unknown1 = self.readBoolean()\n fields[\"Unknown1\"] = Unknown1\n if Unknown1:\n fields[\"Unknown2\"] = self.readString()\n\n Unknown3 = self.readBoolean()\n fields[\"Unknown3\"] = Unknown1\n if Unknown3:\n fields[\"Unknown4\"] = self.readString()\n\n Unknown5 = self.readBoolean()\n fields[\"Unknown5\"] = Unknown1\n if Unknown5:\n fields[\"Unknown6\"] = self.readString()\n\n Unknown7 = self.readBoolean()\n fields[\"Unknown7\"] = Unknown1\n if Unknown7:\n fields[\"Unknown8\"] = self.readString()\n super().decode(fields)\n return fields\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 20104\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"OutOfSyncMessage","path":"Heart/Packets/Server/Authentification/OutOfSyncMessage.py","snippet":"class OutOfSyncMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n self.writeVInt(fields[\"ServerChecksum\"])\n self.writeVInt(fields[\"ClientChecksum\"])\n self.writeVInt(fields[\"Tick\"])\n\n def decode(self):\n fields = {}\n fields[\"ServerChecksum\"] = self.readVInt()\n fields[\"ClientChecksum\"] = self.readVInt()\n fields[\"Tick\"] = self.readVInt()\n super().decode(fields)\n return fields\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 24104\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"ServerHelloMessage","path":"Heart/Packets/Server/Authentification/ServerHelloMessage.py","snippet":"class ServerHelloMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n self.writeBytes(urandom(24), 24)\n\n def decode(self):\n fields = {}\n fields[\"Random\"] = self.readBytesWithoutLength()\n super().decode(fields)\n return fields\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 20100\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"BattleEndMessage","path":"Heart/Packets/Server/Battle/BattleEndMessage.py","snippet":"class BattleEndMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields, player):\n self.writeLong(0, 0) # Battle UUID High\n self.writeLong(0, 0) # Battle UUID Low\n self.writeVInt(2) # Battle End Game Mode (gametype)\n self.writeVInt(fields[\"Rank\"]) # Result (Victory/Defeat/Draw/Rank Score)\n self.writeVInt(0) # Tokens Gained (Gained Keys)\n self.writeVInt(0) # Trophies Result (Metascore change)\n self.writeVInt(0) # Power Play Points Gained (Pro League Points)\n self.writeVInt(0) # Doubled Tokens (Double Keys)\n self.writeVInt(0) # Double Token Event (Double Event Keys)\n self.writeVInt(0) # Token Doubler Remaining (Double Keys Remaining)\n self.writeVInt(0) # game Lenght In Seconds\n self.writeVInt(0) # Epic Win Power Play Points Gained (op Win Points)\n self.writeVInt(0) # Championship Level Reached (CC Wins)\n self.writeBoolean(False)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeBoolean(True)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeVInt(-1)\n self.writeBoolean(False)\n\n self.writeVInt(fields[\"HeroesCount\"])\n for heroEntry in fields[\"Heroes\"]:\n self.writeBoolean(heroEntry[\"IsPlayer\"])\n self.writeBoolean(bool(heroEntry[\"Team\"]))\n self.writeBoolean(bool(heroEntry[\"Team\"]))\n self.writeByte(1)\n for i in range(1):\n self.writeDataReference(heroEntry[\"Brawler\"][\"ID\"][0], heroEntry[\"Brawler\"][\"ID\"][1])\n self.writeByte(1)\n for i in range(1):\n if (heroEntry[\"Brawler\"][\"SkinID\"] is None):\n self.writeVInt(0)\n else:\n self.writeDataReference(heroEntry[\"Brawler\"][\"SkinID\"][0], heroEntry[\"Brawler\"][\"SkinID\"][1])\n self.writeByte(1)\n for i in range(1):\n self.writeVInt(1250)\n self.writeByte(1)\n for i in range(1):\n self.writeVInt(11)\n self.writeByte(1)\n for i in range(1):\n self.writeVInt(0)\n\n self.writeVInt(0)\n self.writeVInt(0)\n\n self.writeBoolean(heroEntry[\"IsPlayer\"])\n if heroEntry[\"IsPlayer\"]:\n self.writeLong(player.ID[0], player.ID[1])\n self.writeString(heroEntry[\"PlayerName\"])\n self.writeVInt(100)\n self.writeVInt(28000000)\n self.writeVInt(43000000)\n self.writeVInt(-2)\n if heroEntry[\"IsPlayer\"]:\n self.writeBoolean(True)\n self.writeVLong(5, 4181497)\n self.writeString('haccer club')\n self.writeDataReference(8, 16)\n else:\n self.writeBoolean(False)\n\n self.writeInt8(1)\n self.writeVInt(5978)\n self.writeInt8(1)\n self.writeVInt(0)\n\n self.writeInt16(5)\n self.writeInt16(3)\n self.writeInt(27328)\n self.writeInt(25659)\n\n self.writeDataReference(0)\n\n self.writeVInt(0)\n self.writeVInt(1)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False) # 0x0\n self.writeBoolean(False) # 0x0\n self.writeBoolean(False) # 0x0\n self.writeBoolean(False) # 0x0\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False) # 0x0\n self.writeVInt(0)\n self.writeBoolean(False) # 0x0\n self.writeVInt(0)\n self.writeBoolean(False) # 0x0\n self.writeBoolean(False) # 0x0\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False) # 0x0\n self.writeBoolean(False) # 0x0\n self.writeBoolean(False) # 0x0\n\n def decode(self):\n fields = {}\n return {}\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 23456\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"AvailableServerCommandMessage","path":"Heart/Packets/Server/Home/AvailableServerCommandMessage.py","snippet":"class AvailableServerCommandMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields, player):\n self.writeVInt(fields[\"Command\"][\"ID\"])\n command = LogicCommandManager.createCommand(fields[\"Command\"][\"ID\"], self.messagePayload)\n self.messagePayload = command.encode(fields)\n\n def decode(self):\n return {}\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 24111\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"LobbyInfoMessage","path":"Heart/Packets/Server/Home/LobbyInfoMessage.py","snippet":"class LobbyInfoMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields, player):\n self.writeVInt(ClientsManager.GetCount())\n self.writeString(f\"\"\"Version: {player.ClientVersion}\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\"\"\")\n self.writeVInt(0) # count event\n self.writeVInt(0) # new timer in v51\n\n def decode(self):\n fields = {}\n fields[\"PlayerCount\"] = self.readVInt()\n fields[\"Text\"] = self.readString()\n fields[\"Unk1\"] = self.readVInt()\n super().decode(fields)\n return {}\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 23457\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"OwnHomeDataMessage","path":"Heart/Packets/Server/Home/OwnHomeDataMessage.py","snippet":"class OwnHomeDataMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields, player):\n self.writeVInt(1688816070)\n self.writeVInt(1191532375)\n self.writeVInt(2023189)\n self.writeVInt(73530)\n\n self.writeVInt(player.Trophies)\n self.writeVInt(player.HighestTrophies)\n self.writeVInt(player.HighestTrophies) \n self.writeVInt(player.TrophyRoadTier)\n self.writeVInt(player.Experience)\n self.writeDataReference(28, player.Thumbnail)\n self.writeDataReference(43, player.Namecolor)\n\n self.writeVInt(26)\n for x in range(26):\n self.writeVInt(x)\n\n self.writeVInt(0)\n\n self.writeVInt(0)\n\n self.writeVInt(0)\n \n self.writeVInt(len(player.OwnedSkins))\n for x in player.OwnedSkins:\n self.writeDataReference(29, x)\n\n self.writeVInt(0)\n\n self.writeVInt(0)\n\n self.writeVInt(0)\n self.writeVInt(player.HighestTrophies)\n self.writeVInt(0)\n self.writeVInt(2)\n self.writeBoolean(True)\n self.writeVInt(0)\n self.writeVInt(115)\n self.writeVInt(335442)\n self.writeVInt(1001442)\n self.writeVInt(5778642) \n\n self.writeVInt(120)\n self.writeVInt(200)\n self.writeVInt(0)\n\n self.writeBoolean(True)\n self.writeVInt(2)\n self.writeVInt(2)\n self.writeVInt(2)\n self.writeVInt(0)\n self.writeVInt(0)\n\n self.writeVInt(1) # Shop Offers\n\n self.writeVInt(1) # RewardCount\n\n self.writeVInt(38) # ItemType\n self.writeVInt(1337) # Amount\n self.writeDataReference(0) # CsvID\n self.writeVInt(0) # SkinID\n\n self.writeVInt(0) # Currency(0-Gems, 1-Gold, 3-StarpoInts)\n self.writeVInt(0) # Cost\n self.writeVInt(0) # Time\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False) # Daily Offer\n self.writeVInt(0) # Old price\n self.writeString('Offer') # Text\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeString(\"offer_bgr_xmas23\") # Background\n self.writeVInt(0)\n self.writeBoolean(False) # This purchase is already being processed\n self.writeVInt(0) # Type Benefit\n self.writeVInt(0) # Benefit\n self.writeString()\n self.writeBoolean(False) # One time offer\n self.writeBoolean(False) # Claimed\n self.writeDataReference(0)\n self.writeDataReference(0)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n \n self.writeVInt(20)\n self.writeVInt(1428)\n\n self.writeVInt(0)\n\n self.writeVInt(1)\n self.writeVInt(30)\n\n self.writeByte(1) # count brawlers selected\n self.writeDataReference(16, player.SelectedBrawlers[0]) # selected brawler\n self.writeString(player.Region) # location\n self.writeString(player.ContentCreator) # supported creator\n\n self.writeVInt(6) \n self.writeVInt(1) \n self.writeVInt(9) \n self.writeVInt(1) \n self.writeVInt(22) \n self.writeVInt(3) \n self.writeVInt(25) \n self.writeVInt(1) \n self.writeVInt(24) \n self.writeVInt(0)\n self.writeVInt(15)\n self.writeVInt(32447)\n self.writeVInt(28)\n\n\n self.writeVInt(0)\n\n self.writeVInt(1)\n for season in range(1):\n self.writeVInt(22-1)\n self.writeVInt(40000)\n self.writeBoolean(True)\n self.writeVInt(0)\n self.writeBoolean(False)\n self.writeBoolean(True)\n self.writeInt(0)\n self.writeInt(0)\n self.writeInt(0)\n self.writeInt(0)\n self.writeBoolean(True)\n self.writeInt(0)\n self.writeInt(0)\n self.writeInt(0)\n self.writeInt(0)\n self.writeBoolean(True)\n self.writeBoolean(True)\n self.writeInt(0)\n self.writeInt(0)\n self.writeInt(0)\n self.writeInt(0)\n\n self.writeVInt(0)\n\n self.writeBoolean(True)\n self.writeVInt(0)\n self.writeVInt(1)\n self.writeVInt(2)\n self.writeVInt(0) \n\n self.writeBoolean(True) # Vanity items\n self.writeVInt(len(player.OwnedThumbnails)+len(player.OwnedPins))\n for x in player.OwnedThumbnails:\n self.writeVInt(28)\n self.writeVInt(x)\n self.writeVInt(0)\n for x in player.OwnedPins:\n self.writeVInt(52)\n self.writeVInt(x)\n self.writeVInt(0)\n\n\n self.writeBoolean(False) # Power league season data\n\n self.writeInt(0)\n self.writeVInt(0)\n self.writeVInt(16)\n self.writeVInt(76)\n self.writeBoolean(False)\n self.writeVInt(0)\n self.writeVInt(0)\n\n self.writeVInt(2023189)\n\n self.writeVInt(35) # event slot id\n self.writeVInt(1)\n self.writeVInt(2)\n self.writeVInt(3)\n self.writeVInt(4)\n self.writeVInt(5)\n self.writeVInt(6)\n self.writeVInt(7)\n self.writeVInt(8)\n self.writeVInt(9)\n self.writeVInt(10)\n self.writeVInt(11)\n self.writeVInt(12)\n self.writeVInt(13) \n self.writeVInt(14)\n self.writeVInt(15)\n self.writeVInt(16)\n self.writeVInt(17)\n self.writeVInt(18) \n self.writeVInt(19)\n self.writeVInt(20)\n self.writeVInt(21) \n self.writeVInt(22)\n self.writeVInt(23)\n self.writeVInt(24)\n self.writeVInt(25)\n self.writeVInt(26)\n self.writeVInt(27)\n self.writeVInt(28)\n self.writeVInt(29)\n self.writeVInt(30)\n self.writeVInt(31)\n self.writeVInt(32)\n self.writeVInt(33)\n self.writeVInt(34)\n self.writeVInt(35)\n\n self.writeVInt(1)\n\n self.writeVInt(4)\n self.writeVInt(7)\n self.writeVInt(1)\n self.writeVInt(0)\n self.writeVInt(72292)\n self.writeVInt(10) \n self.writeDataReference(15, 21) # map id\n self.writeVInt(-1)\n self.writeVInt(2)\n self.writeString(\"\")\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False) # MapMaker map structure array\n self.writeVInt(0)\n self.writeBoolean(False) # Power League array entry\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeVInt(-1)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeVInt(-1)\n self.writeVInt(0) \n self.writeVInt(0) \n self.writeVInt(0) \n self.writeBoolean(False) \n\n self.writeVInt(0)\n \n ByteStreamHelper.encodeIntList(self, [20, 35, 75, 140, 290, 480, 800, 1250, 1875, 2800])\n ByteStreamHelper.encodeIntList(self, [30, 80, 170, 360]) # Shop Coins Price\n ByteStreamHelper.encodeIntList(self, [300, 880, 2040, 4680]) # Shop Coins Amount\n\n self.writeVInt(0) \n\n self.writeVInt(1)\n self.writeVInt(41000086) # theme\n self.writeVInt(1)\n\n self.writeVInt(0) \n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n\n self.writeVInt(2)\n self.writeVInt(1)\n self.writeVInt(2)\n self.writeVInt(2)\n self.writeVInt(1)\n self.writeVInt(-1)\n self.writeVInt(2)\n self.writeVInt(1)\n self.writeVInt(4)\n\n ByteStreamHelper.encodeIntList(self, [0, 29, 79, 169, 349, 699])\n ByteStreamHelper.encodeIntList(self, [0, 160, 450, 500, 1250, 2500])\n\n self.writeLong(0, 1) # Player ID\n\n self.writeVInt(0) # Notification factory\n \n self.writeVInt(1)\n self.writeBoolean(False)\n self.writeVInt(0)\n self.writeVInt(0) \n self.writeVInt(0)\n self.writeBoolean(False) # Login Calendar\n self.writeVInt(0)\n self.writeBoolean(True) # Starr Road\n for i in range(7):\n self.writeVInt(0)\n\n self.writeVInt(0) # Mastery\n\n #BattleCard\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeBoolean(False)\n self.writeBoolean(False)\n\n self.writeVInt(0) #Brawler's BattleCards\n\n self.writeVInt(5)\n for i in range(5):\n self.writeDataReference(80, i)\n self.writeVInt(-1)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeInt(0)\n self.writeVInt(0) \n self.writeVInt(0)\n self.writeVInt(86400*24)\n self.writeVInt(0)\n self.writeVInt(0)\n\n self.writeBoolean(False)\n\n # end LogicClientHome\n\n self.writeVLong(player.ID[0], player.ID[1])\n self.writeVLong(player.ID[0], player.ID[1])\n self.writeVLong(player.ID[0], player.ID[1])\n self.writeStringReference(player.Name)\n self.writeBoolean(player.Registered)\n self.writeInt(-1)\n\n self.writeVInt(17)\n unlocked_brawler = [i['CardID'] for x,i in player.OwnedBrawlers.items()]\n self.writeVInt(len(unlocked_brawler) + 2)\n for x in unlocked_brawler:\n self.writeDataReference(23, x)\n self.writeVInt(-1)\n self.writeVInt(1)\n\n self.writeDataReference(5, 8)\n self.writeVInt(-1)\n self.writeVInt(player.Coins)\n\n self.writeDataReference(5, 23)\n self.writeVInt(-1)\n self.writeVInt(player.Blings)\n\n self.writeVInt(len(player.OwnedBrawlers)) # HeroScore\n for x,i in player.OwnedBrawlers.items():\n self.writeDataReference(16, x)\n self.writeVInt(-1)\n self.writeVInt(i[\"Trophies\"])\n\n self.writeVInt(len(player.OwnedBrawlers)) # HeroHighScore\n for x,i in player.OwnedBrawlers.items():\n self.writeDataReference(16, x)\n self.writeVInt(-1)\n self.writeVInt(i[\"HighestTrophies\"])\n\n self.writeVInt(0) # Array\n\n self.writeVInt(0) # HeroPower\n \n self.writeVInt(len(player.OwnedBrawlers)) # HeroLevel\n for x,i in player.OwnedBrawlers.items():\n self.writeDataReference(16, x)\n self.writeVInt(-1)\n self.writeVInt(i[\"PowerLevel\"]-1)\n\n self.writeVInt(0) # hero star power gadget and hypercharge\n\n self.writeVInt(len(player.OwnedBrawlers)) # HeroSeenState\n for x,i in player.OwnedBrawlers.items():\n self.writeDataReference(16, x)\n self.writeVInt(-1)\n self.writeVInt(2)\n\n self.writeVInt(0) # Array\n self.writeVInt(0) # Array\n self.writeVInt(0) # Array\n self.writeVInt(0) # Array\n self.writeVInt(0) # Array\n self.writeVInt(0) # Array\n self.writeVInt(0) # Array\n self.writeVInt(0) # Array\n self.writeVInt(0) # Array\n\n self.writeVInt(player.Gems) # Diamonds\n self.writeVInt(player.Gems) # Free Diamonds\n self.writeVInt(10) # Player Level\n self.writeVInt(100)\n self.writeVInt(0) # CumulativePurchasedDiamonds or Avatar User Level Tier | 10000 < Level Tier = 3 | 1000 < Level Tier = 2 | 0 < Level Tier = 1\n self.writeVInt(100) # Battle Count\n self.writeVInt(10) # WinCount\n self.writeVInt(80) # LoseCount\n self.writeVInt(50) # WinLooseStreak\n self.writeVInt(20) # NpcWinCount\n self.writeVInt(0) # NpcLoseCount\n self.writeVInt(2) # TutorialState | shouldGoToFirstTutorialBattle = State == 0\n self.writeVInt(12)\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeString()\n self.writeVInt(0)\n self.writeVInt(0)\n self.writeVInt(1)\n\n def decode(self):\n fields = {}\n return fields\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 24101\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"KeepAliveServerMessage","path":"Heart/Packets/Server/Socket/KeepAliveServerMessage.py","snippet":"class KeepAliveServerMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields):\n pass\n\n def decode(self):\n return {}\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 20108\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"PlayerProfileMessage","path":"Heart/Packets/Server/Home/PlayerProfileMessage.py","snippet":"class PlayerProfileMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields, player):\n self.writeVLong(fields[\"PlayerHighID\"], fields[\"PlayerLowID\"])\n self.writeDataReference(16,11) # \n self.writeVInt(70)\n for i in range(70):\n self.writeDataReference(16, i)\n self.writeDataReference(0)\n self.writeVInt(500) # trophies\n self.writeVInt(1250) # highestTrophies\n self.writeVInt(11) #power level\n \n self.writeVInt(18)\n\n self.writeVInt(1) \n self.writeVInt(1) # 3v3 victories\n\n self.writeVInt(2)\n self.writeVInt(528859) # total exp\n\n self.writeVInt(3)\n self.writeVInt(3) # current trophies\n\n self.writeVInt(4)\n self.writeVInt(4) # highest trophies\n\n self.writeVInt(5) \n self.writeVInt(5) # unlocked brawler?\n\n self.writeVInt(8)\n self.writeVInt(6) # solo victories\n\n self.writeVInt(11) \n self.writeVInt(7) # duo victories\n\n self.writeVInt(9) \n self.writeVInt(8) # highest level robo rumble\n\n self.writeVInt(12) \n self.writeVInt(9) # highest level boss fight\n\n self.writeVInt(13)\n self.writeVInt(10) # highest power league points\n\n self.writeVInt(14)\n self.writeVInt(11) # some power league stuff\n\n self.writeVInt(15)\n self.writeVInt(12) # most challenge win\n\n self.writeVInt(16) #highest level city rampage\n self.writeVInt(13)\n\n self.writeVInt(18) #highest solo power league rank\n self.writeVInt(14)\n\n self.writeVInt(17) #highest team power league rank\n self.writeVInt(15)\n\n self.writeVInt(19) # highest Club league rank\n self.writeVInt(16)\n\n self.writeVInt(20) # number fame\n self.writeVInt(1000)\n\n self.writeVInt(21)\n self.writeVInt(502052) #v50\n\n self.writeString(player.Name) #PlayerInfo\n self.writeVInt(100)\n self.writeVInt(28000000 + player.Thumbnail)\n self.writeVInt(43000000 + player.Namecolor)\n self.writeVInt(14)\n\n self.writeBoolean(True)\n self.writeVInt(300)\n\n self.writeString(\"hello world\")\n self.writeVInt(100)\n self.writeVInt(200)\n self.writeDataReference(29, 558)\n self.writeDataReference(0)\n self.writeDataReference(0)\n self.writeDataReference(0)\n self.writeDataReference(0)\n\n self.writeBoolean(True) #alliance\n self.writeLong(0,1) #alliance ID\n self.writeString(\"haccers\") #alliance name\n self.writeDataReference(8,1) # alliance icon\n self.writeVInt(1) # type\n self.writeVInt(1) # member count\n self.writeVInt(10000) # total trophies\n self.writeVInt(1) # minimum trophies to enter\n self.writeDataReference(0)\n self.writeString(\"RU\") #location\n self.writeVInt(4) # unknown\n self.writeBoolean(True) #is Family friendly\n self.writeVInt(0)\n \n\n self.writeDataReference(25, 1) #alliance role\n self.writeVInt(16)\n\n def decode(self):\n pass\n # fields = {}\n # fields[\"PlayerCount\"] = self.readVInt()\n # fields[\"Text\"] = self.readString()\n # fields[\"Unk1\"] = self.readVInt()\n # super().decode(fields)\n return {}\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 24113\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"MyAllianceMessage","path":"Heart/Packets/Server/Home/MyAllianceMessage.py","snippet":"class MyAllianceMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields, player):\n self.writeVInt(1) # Online people in alliance\n self.writeBoolean(True) # isInAlliance\n self.writeDataReference(25, 4)\n self.writeLong(0, 1) # alliance ID\n self.writeString(player.ContentCreator) # alliance name\n self.writeDataReference(8, 37) # alliance icon\n self.writeVInt(3) # type\n self.writeVInt(1) # member count\n self.writeVInt(9500) # total trophies\n self.writeVInt(1) # minimum trophies to enter\n self.writeVInt(0) # 0\n self.writeString('RU') # location\n self.writeVInt(3) # unknown\n self.writeBoolean(True) # isFamilyFriendly\n self.writeVInt(0)\n\n def decode(self):\n fields = {}\n super().decode(fields)\n return {}\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 24399\n\n def getMessageVersion(self):\n return self.messageVersion"},{"identifier":"AllianceDataMessage","path":"Heart/Packets/Server/Home/AllianceDataMessage.py","snippet":"class AllianceDataMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields, player):\n self.writeBoolean(True)\n\n self.writeLong(0, 1) # alliance ID\n self.writeString(player.ContentCreator) # alliance name\n self.writeDataReference(8, 37) # alliance icon\n self.writeVInt(1) # type\n self.writeVInt(1) # member count\n self.writeVInt(player.Trophies) # total trophies\n self.writeVInt(0) # minimum trophies to enter\n self.writeVInt(0) # 0\n self.writeString('RU') # location\n self.writeVInt(1) # people online\n self.writeBoolean(True) # isFamilyFriendly\n self.writeVInt(0)\n\n self.writeString(\"this is the hacciest club in the world\")\n\n self.writeVInt(1) # member count\n self.writeLong(player.ID[0], player.ID[1]) # player ID\n self.writeVInt(2) # role\n self.writeVInt(player.Trophies) # trophies\n self.writeVInt(0) # status: 0=offline 2=online\n self.writeVInt(1) # last connected time seconds ?\n highestPowerLeagueRank = 2\n self.writeVInt(highestPowerLeagueRank)\n if highestPowerLeagueRank != 0:\n self.writeVInt(2) #solo\n self.writeVInt(1) #duo\n self.writeBoolean(False) # boolean always false?\n\n self.writeString(player.Name) # player name\n self.writeVInt(100) # VInt always 100\n self.writeVInt(28000000 + player.Thumbnail) # thumbnail\n self.writeVInt(43000000 + player.Namecolor) # name color\n self.writeVInt(46000000 + player.Namecolor)\n\n self.writeVInt(-1) # most people have it -1 but some with something\n self.writeBoolean(False) # whats this ? only 2/30 people have it true in my club\n week = 58 # week 58 of club league as of 2023/07/05, this number is 0 if you just arrived in the club\n self.writeVInt(week)\n if week != 0: # club league week number?\n self.writeVInt(3) # day\n self.writeVInt(18) # total club trophies earned\n self.writeVInt(0) # event day club trophies earned\n self.writeVInt(8) # total tickets used\n self.writeVInt(0) # event day tickets used\n self.writeVInt(6) # event day max tickets\n self.writeVInt(6) # event day tickets left\n self.writeVInt(0) # event day player ranking\n self.writeBoolean(True) # everyone have it to true\n self.writeVInt(200) # player experience lvl but why tf it doesn't show for some people\n\n def decode(self):\n fields = {}\n super().decode(fields)\n return {}\n\n def execute(message, calling_instance, fields):\n pass\n\n def getMessageType(self):\n return 24301\n\n def getMessageVersion(self):\n return self.messageVersion"}],"string":"[\n {\n \"identifier\": \"ClientHelloMessage\",\n \"path\": \"Heart/Packets/Client/Authentification/ClientHelloMessage.py\",\n \"snippet\": \"class ClientHelloMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n pass\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"Protocol\\\"] = self.readInt()\\n fields[\\\"KeyVersion\\\"] = self.readInt()\\n fields[\\\"MajorVersion\\\"] = self.readInt()\\n fields[\\\"MinorVersion\\\"] = self.readInt()\\n fields[\\\"Build\\\"] = self.readInt()\\n fields[\\\"ContentHash\\\"] = self.readString()\\n fields[\\\"DeviceType\\\"] = self.readInt()\\n fields[\\\"AppStore\\\"] = self.readInt()\\n super().decode(fields)\\n return fields\\n\\n def execute(message, calling_instance, fields, cryptoInit):\\n fields[\\\"Socket\\\"] = calling_instance.client\\n Messaging.sendMessage(20100, fields, cryptoInit)\\n\\n def getMessageType(self):\\n return 10100\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"LoginMessage\",\n \"path\": \"Heart/Packets/Client/Authentification/LoginMessage.py\",\n \"snippet\": \"class LoginMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n pass\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"AccountID\\\"] = self.readLong()\\n fields[\\\"PassToken\\\"] = self.readString()\\n fields[\\\"ClientMajor\\\"] = self.readInt()\\n fields[\\\"ClientMinor\\\"] = self.readInt()\\n fields[\\\"ClientBuild\\\"] = self.readInt()\\n fields[\\\"ResourceSha\\\"] = self.readString()\\n fields[\\\"Device\\\"] = self.readString()\\n fields[\\\"PreferredLanguage\\\"] = self.readDataReference()\\n fields[\\\"PreferredDeviceLanguage\\\"] = self.readString()\\n fields[\\\"OSVersion\\\"] = self.readString()\\n fields[\\\"isAndroid\\\"] = self.readBoolean()\\n fields[\\\"IMEI\\\"] = self.readString()\\n fields[\\\"AndroidID\\\"] = self.readString()\\n fields[\\\"isAdvertisingEnabled\\\"] = self.readBoolean()\\n fields[\\\"AppleIFV\\\"] = self.readString()\\n fields[\\\"RndKey\\\"] = self.readInt()\\n fields[\\\"AppStore\\\"] = self.readVInt()\\n fields[\\\"ClientVersion\\\"] = self.readString()\\n fields[\\\"TencentOpenId\\\"] = self.readString()\\n fields[\\\"TencentToken\\\"] = self.readString()\\n fields[\\\"TencentPlatform\\\"] = self.readVInt()\\n fields[\\\"DeviceVerifierResponse\\\"] = self.readString()\\n fields[\\\"AppLicensingSignature\\\"] = self.readString()\\n fields[\\\"DeviceVerifierResponse\\\"] = self.readString()\\n super().decode(fields)\\n return fields\\n\\n def execute(message, calling_instance, fields, cryptoInit):\\n if fields[\\\"ClientMajor\\\"]==53:\\n calling_instance.player.ClientVersion = f'{str(fields[\\\"ClientMajor\\\"])}.{str(fields[\\\"ClientBuild\\\"])}.{str(fields[\\\"ClientMinor\\\"])}'\\n fields[\\\"Socket\\\"] = calling_instance.client\\n db_instance = DatabaseHandler()\\n if db_instance.playerExist(fields[\\\"PassToken\\\"], fields[\\\"AccountID\\\"]):\\n player_data = json.loads(db_instance.getPlayerEntry(fields[\\\"AccountID\\\"])[2])\\n db_instance.loadAccount(calling_instance.player, fields[\\\"AccountID\\\"])\\n else:\\n db_instance.createAccount(calling_instance.player.getDataTemplate(fields[\\\"AccountID\\\"][0], fields[\\\"AccountID\\\"][1], fields[\\\"PassToken\\\"]))\\n ClientsManager.AddPlayer(calling_instance.player.ID, calling_instance.client)\\n Messaging.sendMessage(20104, fields, cryptoInit, calling_instance.player)\\n Messaging.sendMessage(24101, fields, cryptoInit, calling_instance.player)\\n Messaging.sendMessage(24399, fields, cryptoInit, calling_instance.player)\\n\\n def getMessageType(self):\\n return 10101\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"AskForBattleEndMessage\",\n \"path\": \"Heart/Packets/Client/Battle/AskForBattleEndMessage.py\",\n \"snippet\": \"class AskForBattleEndMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n pass\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"Unk1\\\"] = self.readVInt()\\n fields[\\\"Result\\\"] = self.readVInt()\\n fields[\\\"Rank\\\"] = self.readVInt()\\n fields[\\\"MapID\\\"] = self.readDataReference()\\n fields[\\\"HeroesCount\\\"] = self.readVInt()\\n fields[\\\"Heroes\\\"] = []\\n for i in range(fields[\\\"HeroesCount\\\"]): fields[\\\"Heroes\\\"].append({\\\"Brawler\\\": {\\\"ID\\\": self.readDataReference(), \\\"SkinID\\\": self.readDataReference()}, \\\"Team\\\": self.readVInt(), \\\"IsPlayer\\\": self.readBoolean(), \\\"PlayerName\\\": self.readString()})\\n super().decode(fields)\\n return fields\\n\\n def execute(message, calling_instance, fields, cryptoInit):\\n fields[\\\"Socket\\\"] = calling_instance.client\\n Messaging.sendMessage(23456, fields, cryptoInit, calling_instance.player)\\n\\n def getMessageType(self):\\n return 14110\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"ChangeAvatarNameMessage\",\n \"path\": \"Heart/Packets/Client/Home/ChangeAvatarNameMessage.py\",\n \"snippet\": \"class ChangeAvatarNameMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n self.writeString(fields[\\\"Name\\\"])\\n self.writeBoolean(fields[\\\"NameSetByUser\\\"])\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"Name\\\"] = self.readString()\\n fields[\\\"NameSetByUser\\\"] = self.readBoolean()\\n super().decode(fields)\\n return fields\\n\\n def execute(message, calling_instance, fields, cryptoInit):\\n db_instance = DatabaseHandler()\\n playerData = db_instance.getPlayer(calling_instance.player.ID)\\n playerData[\\\"Name\\\"] = fields[\\\"Name\\\"]\\n playerData[\\\"Registered\\\"] = True\\n db_instance.updatePlayerData(playerData, calling_instance)\\n fields[\\\"Socket\\\"] = calling_instance.client\\n fields[\\\"Command\\\"] = {\\\"ID\\\": 201}\\n Messaging.sendMessage(24111, fields, cryptoInit, calling_instance.player)\\n\\n def getMessageType(self):\\n return 10212\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"EndClientTurnMessage\",\n \"path\": \"Heart/Packets/Client/Home/EndClientTurnMessage.py\",\n \"snippet\": \"class EndClientTurnMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n pass\\n\\n def decode(self):\\n fields = {}\\n self.readBoolean()\\n fields[\\\"Tick\\\"] = self.readVInt()\\n fields[\\\"Checksum\\\"] = self.readVInt()\\n fields[\\\"CommandsCount\\\"] = self.readVInt()\\n super().decode(fields)\\n fields[\\\"Commands\\\"] = []\\n for i in range(fields[\\\"CommandsCount\\\"]):\\n fields[\\\"Commands\\\"].append({\\\"ID\\\": self.readVInt()})\\n if LogicCommandManager.commandExist(fields[\\\"Commands\\\"][i][\\\"ID\\\"]):\\n command = LogicCommandManager.createCommand(fields[\\\"Commands\\\"][i][\\\"ID\\\"])\\n print(\\\"Command\\\", LogicCommandManager.getCommandsName(fields[\\\"Commands\\\"][i][\\\"ID\\\"]))\\n if command is not None:\\n fields[\\\"Commands\\\"][i][\\\"Fields\\\"] = command.decode(self)\\n fields[\\\"Commands\\\"][i][\\\"Instance\\\"] = command\\n return fields\\n\\n def execute(message, calling_instance, fields, cryptoInit):\\n fields[\\\"Socket\\\"] = calling_instance.client\\n for command in fields[\\\"Commands\\\"]:\\n if \\\"Instance\\\" not in command.keys():\\n return\\n\\n if hasattr(command[\\\"Instance\\\"], 'execute'):\\n command[\\\"Instance\\\"].execute(calling_instance, command[\\\"Fields\\\"], cryptoInit)\\n if command[\\\"ID\\\"] == 519:\\n Messaging.sendMessage(24104, {\\\"Socket\\\": calling_instance.client, \\\"ServerChecksum\\\": 0, \\\"ClientChecksum\\\": 0, \\\"Tick\\\": 0}, cryptoInit)\\n\\n def getMessageType(self):\\n return 14102\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"GoHomeFromOfflinePractiseMessage\",\n \"path\": \"Heart/Packets/Client/Home/GoHomeFromOfflinePractiseMessage.py\",\n \"snippet\": \"class GoHomeFromOfflinePractiseMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n pass\\n\\n def decode(self):\\n fields = {}\\n self.readBoolean()\\n return fields\\n\\n def execute(message, calling_instance, fields, cryptoInit):\\n fields[\\\"Socket\\\"] = calling_instance.client\\n Messaging.sendMessage(24101, fields, cryptoInit, calling_instance.player)\\n\\n def getMessageType(self):\\n return 14109\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"GoHomeMessage\",\n \"path\": \"Heart/Packets/Client/Home/GoHomeMessage.py\",\n \"snippet\": \"class GoHomeMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n pass\\n\\n def decode(self):\\n fields = {}\\n self.readBoolean()\\n return fields\\n\\n def execute(message, calling_instance, fields, cryptoInit):\\n fields[\\\"Socket\\\"] = calling_instance.client\\n Messaging.sendMessage(24101, fields, cryptoInit, calling_instance.player)\\n\\n def getMessageType(self):\\n return 17750\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"GetPlayerProfileMessage\",\n \"path\": \"Heart/Packets/Client/Home/GetPlayerProfileMessage.py\",\n \"snippet\": \"class GetPlayerProfileMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n pass\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"BattleInfoBoolean\\\"] = self.readBoolean()\\n if fields[\\\"BattleInfoBoolean\\\"]:\\n fields[\\\"unk1\\\"] = self.readVInt()\\n fields[\\\"AnotherID\\\"] = self.readLong()\\n fields[\\\"unk2\\\"] = self.readVInt()\\n for i in self.readVInt():\\n fields[\\\"CsvID\\\"] = self.readDataReference()\\n fields[\\\"unk3\\\"] = self.readVInt()\\n fields[\\\"unk4\\\"] = self.readVInt()\\n fields[\\\"unk5\\\"] = self.readVInt()\\n fields[\\\"unk6\\\"] = self.readVInt()\\n fields[\\\"PlayerName\\\"] = self.readString()\\n fields[\\\"unk7\\\"] = self.readVInt()\\n fields[\\\"Thumbnail\\\"] = self.readVInt()\\n fields[\\\"NameColor\\\"] = self.readVInt()\\n fields[\\\"unk10\\\"] = self.readVInt()\\n fields[\\\"unk11\\\"] = self.readVInt()\\n fields[\\\"PlayerHighID\\\"] = self.readInt()\\n fields[\\\"PlayerLowID\\\"] = self.readInt()\\n super().decode(fields)\\n\\n\\n return fields\\n\\n def execute(message, calling_instance, fields, cryptoInit):\\n fields[\\\"Socket\\\"] = calling_instance.client\\n Messaging.sendMessage(24113, fields, cryptoInit, calling_instance.player)\\n\\n def getMessageType(self):\\n return 15081\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"AskForAllianceDataMessage\",\n \"path\": \"Heart/Packets/Client/Home/AskForAllianceDataMessage.py\",\n \"snippet\": \"class AskForAllianceDataMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n pass\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"id\\\"] = self.readVLong()\\n fields[\\\"isInAlliance\\\"] = self.readBoolean()\\n if fields[\\\"isInAlliance\\\"] == True:\\n fields[\\\"anotherIDHigh\\\"] = self.readVInt()\\n fields[\\\"anotherIDLow\\\"] = self.readVInt()\\n super().decode(fields)\\n\\n return fields\\n\\n def execute(message, calling_instance, fields, cryptoInit):\\n fields[\\\"Socket\\\"] = calling_instance.client\\n Messaging.sendMessage(24301, fields, cryptoInit, calling_instance.player)\\n\\n def getMessageType(self):\\n return 14302\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"KeepAliveMessage\",\n \"path\": \"Heart/Packets/Client/Socket/KeepAliveMessage.py\",\n \"snippet\": \"class KeepAliveMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n pass\\n\\n def decode(self):\\n return {}\\n\\n def execute(message, calling_instance, fields, cryptoInit):\\n fields[\\\"Socket\\\"] = calling_instance.client\\n Messaging.sendMessage(20108, fields, cryptoInit)\\n\\n def getMessageType(self):\\n return 10108\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"LoginFailedMessage\",\n \"path\": \"Heart/Packets/Server/Authentification/LoginFailedMessage.py\",\n \"snippet\": \"class LoginFailedMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n self.writeInt(fields['ErrorID'])\\n self.writeString(fields['FingerprintData'])\\n self.writeString()\\n self.writeString(fields['ContentURL'])\\n self.writeString()\\n self.writeString(fields['Message'])\\n self.writeInt(0)\\n self.writeBoolean(False)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeString()\\n self.writeInt(0)\\n self.writeBoolean(True)\\n self.writeBoolean(True)\\n self.writeString()\\n self.writeVInt(0)\\n self.writeString()\\n self.writeBoolean(False)\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"ErrorCode\\\"] = self.readInt()\\n fields[\\\"ResourceFingerprintData\\\"] = self.readString()\\n fields[\\\"RedirectDomain\\\"] = self.readString()\\n fields[\\\"ContentURL\\\"] = self.readString()\\n fields[\\\"UpdateURL\\\"] = self.readString()\\n fields[\\\"Reason\\\"] = self.readString()\\n fields[\\\"SecondsUntilMaintenanceEnd\\\"] = self.readInt()\\n fields[\\\"ShowContactSupportForBan\\\"] = self.readBoolean()\\n fields[\\\"CompressedFingerprintData\\\"] = self.readBytesWithoutLength()\\n fields[\\\"ContentURLListCount\\\"] = self.readInt()\\n fields[\\\"ContentURLList\\\"] = []\\n for i in range(fields[\\\"ContentURLListCount\\\"]):\\n fields[\\\"ContentURLList\\\"].append(self.readString())\\n fields[\\\"KunlunAppStore\\\"] = self.readInt()\\n fields[\\\"MaintenanceType\\\"] = self.readInt()\\n fields[\\\"HelpshiftFaqId\\\"] = self.readString()\\n fields[\\\"Tier\\\"] = self.readInt()\\n fields[\\\"Unk1\\\"] = self.readBoolean()\\n fields[\\\"Unk2\\\"] = self.readBoolean()\\n fields[\\\"Unk3\\\"] = self.readString()\\n fields[\\\"Unk4\\\"] = self.readVInt()\\n fields[\\\"Unk5\\\"] = self.readString()\\n fields[\\\"OptionalTargetedAccountIdState\\\"] = self.readBoolean()\\n if fields[\\\"OptionalTargetedAccountIdState\\\"] == True:\\n fields[\\\"OptionalTargetedAccountId\\\"] = self.readLong()\\n super().decode(fields)\\n return fields\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 20103\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"LoginOkMessage\",\n \"path\": \"Heart/Packets/Server/Authentification/LoginOkMessage.py\",\n \"snippet\": \"class LoginOkMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 1\\n\\n def encode(self, fields, player):\\n self.writeLong(player.ID[0], player.ID[1])\\n self.writeLong(player.ID[0], player.ID[1])\\n self.writeString(player.Token)\\n self.writeString()\\n self.writeString()\\n self.writeInt(53)\\n self.writeInt(176)\\n self.writeInt(1)\\n self.writeString(\\\"dev\\\")\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeString()\\n self.writeString()\\n self.writeString()\\n self.writeInt(0)\\n self.writeString()\\n self.writeString(\\\"RU\\\")\\n self.writeString()\\n self.writeInt(0)\\n self.writeString()\\n self.writeInt(2)\\n self.writeString('https://game-assets.brawlstarsgame.com')\\n self.writeString('http://a678dbc1c015a893c9fd-4e8cc3b1ad3a3c940c504815caefa967.r87.cf2.rackcdn.com')\\n self.writeInt(2)\\n self.writeString('https://event-assets.brawlstars.com')\\n self.writeString('https://24b999e6da07674e22b0-8209975788a0f2469e68e84405ae4fcf.ssl.cf2.rackcdn.com/event-assets')\\n self.writeVInt(0)\\n self.writeCompressedString(b'')\\n self.writeBoolean(True)\\n self.writeBoolean(False)\\n self.writeString()\\n self.writeString()\\n self.writeString()\\n self.writeString('https://play.google.com/store/apps/details?id=com.supercell.brawlstars')\\n self.writeString()\\n self.writeBoolean(False)\\n\\n self.writeBoolean(False)\\n if False:\\n self.writeString()\\n\\n self.writeBoolean(False)\\n if False:\\n self.writeString()\\n\\n self.writeBoolean(False)\\n if False:\\n self.writeString()\\n\\n self.writeBoolean(False)\\n if False:\\n self.writeString()\\n\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"AccountID\\\"] = self.readLong()\\n fields[\\\"HomeID\\\"] = self.readLong()\\n fields[\\\"PassToken\\\"] = self.readString()\\n fields[\\\"FacebookID\\\"] = self.readString()\\n fields[\\\"GamecenterID\\\"] = self.readString()\\n fields[\\\"ServerMajorVersion\\\"] = self.readInt()\\n fields[\\\"ContentVersion\\\"] = self.readInt()\\n fields[\\\"ServerBuild\\\"] = self.readInt()\\n fields[\\\"ServerEnvironment\\\"] = self.readString()\\n fields[\\\"SessionCount\\\"] = self.readInt()\\n fields[\\\"PlayTimeSeconds\\\"] = self.readInt()\\n fields[\\\"DaysSinceStartedPlaying\\\"] = self.readInt()\\n fields[\\\"FacebookAppID\\\"] = self.readString()\\n fields[\\\"ServerTime\\\"] = self.readString()\\n fields[\\\"AccountCreatedDate\\\"] = self.readString()\\n fields[\\\"StartupCooldownSeconds\\\"] = self.readInt()\\n fields[\\\"GoogleServiceID\\\"] = self.readString()\\n fields[\\\"LoginCountry\\\"] = self.readString()\\n fields[\\\"KunlunID\\\"] = self.readString()\\n fields[\\\"Tier\\\"] = self.readInt()\\n fields[\\\"TencentID\\\"] = self.readString()\\n\\n ContentUrlCount = self.readInt()\\n fields[\\\"GameAssetsUrls\\\"] = []\\n for i in range(ContentUrlCount):\\n fields[\\\"GameAssetsUrls\\\"].append(self.readString())\\n\\n EventUrlCount = self.readInt()\\n fields[\\\"EventAssetsUrls\\\"] = []\\n for i in range(EventUrlCount):\\n fields[\\\"EventAssetsUrls\\\"].append(self.readString())\\n\\n fields[\\\"SecondsUntilAccountDeletion\\\"] = self.readVInt()\\n fields[\\\"SupercellIDToken\\\"] = self.readCompressedString()\\n fields[\\\"IsSupercellIDLogoutAllDevicesAllowed\\\"] = self.readBoolean()\\n fields[\\\"isSupercellIDEligible\\\"] = self.readBoolean()\\n fields[\\\"LineID\\\"] = self.readString()\\n fields[\\\"SessionID\\\"] = self.readString()\\n fields[\\\"KakaoID\\\"] = self.readString()\\n fields[\\\"UpdateURL\\\"] = self.readString()\\n fields[\\\"YoozooPayNotifyUrl\\\"] = self.readString()\\n fields[\\\"UnbotifyEnabled\\\"] = self.readBoolean()\\n\\n Unknown1 = self.readBoolean()\\n fields[\\\"Unknown1\\\"] = Unknown1\\n if Unknown1:\\n fields[\\\"Unknown2\\\"] = self.readString()\\n\\n Unknown3 = self.readBoolean()\\n fields[\\\"Unknown3\\\"] = Unknown1\\n if Unknown3:\\n fields[\\\"Unknown4\\\"] = self.readString()\\n\\n Unknown5 = self.readBoolean()\\n fields[\\\"Unknown5\\\"] = Unknown1\\n if Unknown5:\\n fields[\\\"Unknown6\\\"] = self.readString()\\n\\n Unknown7 = self.readBoolean()\\n fields[\\\"Unknown7\\\"] = Unknown1\\n if Unknown7:\\n fields[\\\"Unknown8\\\"] = self.readString()\\n super().decode(fields)\\n return fields\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 20104\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"OutOfSyncMessage\",\n \"path\": \"Heart/Packets/Server/Authentification/OutOfSyncMessage.py\",\n \"snippet\": \"class OutOfSyncMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n self.writeVInt(fields[\\\"ServerChecksum\\\"])\\n self.writeVInt(fields[\\\"ClientChecksum\\\"])\\n self.writeVInt(fields[\\\"Tick\\\"])\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"ServerChecksum\\\"] = self.readVInt()\\n fields[\\\"ClientChecksum\\\"] = self.readVInt()\\n fields[\\\"Tick\\\"] = self.readVInt()\\n super().decode(fields)\\n return fields\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 24104\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"ServerHelloMessage\",\n \"path\": \"Heart/Packets/Server/Authentification/ServerHelloMessage.py\",\n \"snippet\": \"class ServerHelloMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n self.writeBytes(urandom(24), 24)\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"Random\\\"] = self.readBytesWithoutLength()\\n super().decode(fields)\\n return fields\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 20100\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"BattleEndMessage\",\n \"path\": \"Heart/Packets/Server/Battle/BattleEndMessage.py\",\n \"snippet\": \"class BattleEndMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields, player):\\n self.writeLong(0, 0) # Battle UUID High\\n self.writeLong(0, 0) # Battle UUID Low\\n self.writeVInt(2) # Battle End Game Mode (gametype)\\n self.writeVInt(fields[\\\"Rank\\\"]) # Result (Victory/Defeat/Draw/Rank Score)\\n self.writeVInt(0) # Tokens Gained (Gained Keys)\\n self.writeVInt(0) # Trophies Result (Metascore change)\\n self.writeVInt(0) # Power Play Points Gained (Pro League Points)\\n self.writeVInt(0) # Doubled Tokens (Double Keys)\\n self.writeVInt(0) # Double Token Event (Double Event Keys)\\n self.writeVInt(0) # Token Doubler Remaining (Double Keys Remaining)\\n self.writeVInt(0) # game Lenght In Seconds\\n self.writeVInt(0) # Epic Win Power Play Points Gained (op Win Points)\\n self.writeVInt(0) # Championship Level Reached (CC Wins)\\n self.writeBoolean(False)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeBoolean(True)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeVInt(-1)\\n self.writeBoolean(False)\\n\\n self.writeVInt(fields[\\\"HeroesCount\\\"])\\n for heroEntry in fields[\\\"Heroes\\\"]:\\n self.writeBoolean(heroEntry[\\\"IsPlayer\\\"])\\n self.writeBoolean(bool(heroEntry[\\\"Team\\\"]))\\n self.writeBoolean(bool(heroEntry[\\\"Team\\\"]))\\n self.writeByte(1)\\n for i in range(1):\\n self.writeDataReference(heroEntry[\\\"Brawler\\\"][\\\"ID\\\"][0], heroEntry[\\\"Brawler\\\"][\\\"ID\\\"][1])\\n self.writeByte(1)\\n for i in range(1):\\n if (heroEntry[\\\"Brawler\\\"][\\\"SkinID\\\"] is None):\\n self.writeVInt(0)\\n else:\\n self.writeDataReference(heroEntry[\\\"Brawler\\\"][\\\"SkinID\\\"][0], heroEntry[\\\"Brawler\\\"][\\\"SkinID\\\"][1])\\n self.writeByte(1)\\n for i in range(1):\\n self.writeVInt(1250)\\n self.writeByte(1)\\n for i in range(1):\\n self.writeVInt(11)\\n self.writeByte(1)\\n for i in range(1):\\n self.writeVInt(0)\\n\\n self.writeVInt(0)\\n self.writeVInt(0)\\n\\n self.writeBoolean(heroEntry[\\\"IsPlayer\\\"])\\n if heroEntry[\\\"IsPlayer\\\"]:\\n self.writeLong(player.ID[0], player.ID[1])\\n self.writeString(heroEntry[\\\"PlayerName\\\"])\\n self.writeVInt(100)\\n self.writeVInt(28000000)\\n self.writeVInt(43000000)\\n self.writeVInt(-2)\\n if heroEntry[\\\"IsPlayer\\\"]:\\n self.writeBoolean(True)\\n self.writeVLong(5, 4181497)\\n self.writeString('haccer club')\\n self.writeDataReference(8, 16)\\n else:\\n self.writeBoolean(False)\\n\\n self.writeInt8(1)\\n self.writeVInt(5978)\\n self.writeInt8(1)\\n self.writeVInt(0)\\n\\n self.writeInt16(5)\\n self.writeInt16(3)\\n self.writeInt(27328)\\n self.writeInt(25659)\\n\\n self.writeDataReference(0)\\n\\n self.writeVInt(0)\\n self.writeVInt(1)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False) # 0x0\\n self.writeBoolean(False) # 0x0\\n self.writeBoolean(False) # 0x0\\n self.writeBoolean(False) # 0x0\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False) # 0x0\\n self.writeVInt(0)\\n self.writeBoolean(False) # 0x0\\n self.writeVInt(0)\\n self.writeBoolean(False) # 0x0\\n self.writeBoolean(False) # 0x0\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False) # 0x0\\n self.writeBoolean(False) # 0x0\\n self.writeBoolean(False) # 0x0\\n\\n def decode(self):\\n fields = {}\\n return {}\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 23456\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"AvailableServerCommandMessage\",\n \"path\": \"Heart/Packets/Server/Home/AvailableServerCommandMessage.py\",\n \"snippet\": \"class AvailableServerCommandMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields, player):\\n self.writeVInt(fields[\\\"Command\\\"][\\\"ID\\\"])\\n command = LogicCommandManager.createCommand(fields[\\\"Command\\\"][\\\"ID\\\"], self.messagePayload)\\n self.messagePayload = command.encode(fields)\\n\\n def decode(self):\\n return {}\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 24111\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"LobbyInfoMessage\",\n \"path\": \"Heart/Packets/Server/Home/LobbyInfoMessage.py\",\n \"snippet\": \"class LobbyInfoMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields, player):\\n self.writeVInt(ClientsManager.GetCount())\\n self.writeString(f\\\"\\\"\\\"Version: {player.ClientVersion}\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\"\\\"\\\")\\n self.writeVInt(0) # count event\\n self.writeVInt(0) # new timer in v51\\n\\n def decode(self):\\n fields = {}\\n fields[\\\"PlayerCount\\\"] = self.readVInt()\\n fields[\\\"Text\\\"] = self.readString()\\n fields[\\\"Unk1\\\"] = self.readVInt()\\n super().decode(fields)\\n return {}\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 23457\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"OwnHomeDataMessage\",\n \"path\": \"Heart/Packets/Server/Home/OwnHomeDataMessage.py\",\n \"snippet\": \"class OwnHomeDataMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields, player):\\n self.writeVInt(1688816070)\\n self.writeVInt(1191532375)\\n self.writeVInt(2023189)\\n self.writeVInt(73530)\\n\\n self.writeVInt(player.Trophies)\\n self.writeVInt(player.HighestTrophies)\\n self.writeVInt(player.HighestTrophies) \\n self.writeVInt(player.TrophyRoadTier)\\n self.writeVInt(player.Experience)\\n self.writeDataReference(28, player.Thumbnail)\\n self.writeDataReference(43, player.Namecolor)\\n\\n self.writeVInt(26)\\n for x in range(26):\\n self.writeVInt(x)\\n\\n self.writeVInt(0)\\n\\n self.writeVInt(0)\\n\\n self.writeVInt(0)\\n \\n self.writeVInt(len(player.OwnedSkins))\\n for x in player.OwnedSkins:\\n self.writeDataReference(29, x)\\n\\n self.writeVInt(0)\\n\\n self.writeVInt(0)\\n\\n self.writeVInt(0)\\n self.writeVInt(player.HighestTrophies)\\n self.writeVInt(0)\\n self.writeVInt(2)\\n self.writeBoolean(True)\\n self.writeVInt(0)\\n self.writeVInt(115)\\n self.writeVInt(335442)\\n self.writeVInt(1001442)\\n self.writeVInt(5778642) \\n\\n self.writeVInt(120)\\n self.writeVInt(200)\\n self.writeVInt(0)\\n\\n self.writeBoolean(True)\\n self.writeVInt(2)\\n self.writeVInt(2)\\n self.writeVInt(2)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n\\n self.writeVInt(1) # Shop Offers\\n\\n self.writeVInt(1) # RewardCount\\n\\n self.writeVInt(38) # ItemType\\n self.writeVInt(1337) # Amount\\n self.writeDataReference(0) # CsvID\\n self.writeVInt(0) # SkinID\\n\\n self.writeVInt(0) # Currency(0-Gems, 1-Gold, 3-StarpoInts)\\n self.writeVInt(0) # Cost\\n self.writeVInt(0) # Time\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False) # Daily Offer\\n self.writeVInt(0) # Old price\\n self.writeString('Offer') # Text\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeString(\\\"offer_bgr_xmas23\\\") # Background\\n self.writeVInt(0)\\n self.writeBoolean(False) # This purchase is already being processed\\n self.writeVInt(0) # Type Benefit\\n self.writeVInt(0) # Benefit\\n self.writeString()\\n self.writeBoolean(False) # One time offer\\n self.writeBoolean(False) # Claimed\\n self.writeDataReference(0)\\n self.writeDataReference(0)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n \\n self.writeVInt(20)\\n self.writeVInt(1428)\\n\\n self.writeVInt(0)\\n\\n self.writeVInt(1)\\n self.writeVInt(30)\\n\\n self.writeByte(1) # count brawlers selected\\n self.writeDataReference(16, player.SelectedBrawlers[0]) # selected brawler\\n self.writeString(player.Region) # location\\n self.writeString(player.ContentCreator) # supported creator\\n\\n self.writeVInt(6) \\n self.writeVInt(1) \\n self.writeVInt(9) \\n self.writeVInt(1) \\n self.writeVInt(22) \\n self.writeVInt(3) \\n self.writeVInt(25) \\n self.writeVInt(1) \\n self.writeVInt(24) \\n self.writeVInt(0)\\n self.writeVInt(15)\\n self.writeVInt(32447)\\n self.writeVInt(28)\\n\\n\\n self.writeVInt(0)\\n\\n self.writeVInt(1)\\n for season in range(1):\\n self.writeVInt(22-1)\\n self.writeVInt(40000)\\n self.writeBoolean(True)\\n self.writeVInt(0)\\n self.writeBoolean(False)\\n self.writeBoolean(True)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeBoolean(True)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeBoolean(True)\\n self.writeBoolean(True)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeInt(0)\\n self.writeInt(0)\\n\\n self.writeVInt(0)\\n\\n self.writeBoolean(True)\\n self.writeVInt(0)\\n self.writeVInt(1)\\n self.writeVInt(2)\\n self.writeVInt(0) \\n\\n self.writeBoolean(True) # Vanity items\\n self.writeVInt(len(player.OwnedThumbnails)+len(player.OwnedPins))\\n for x in player.OwnedThumbnails:\\n self.writeVInt(28)\\n self.writeVInt(x)\\n self.writeVInt(0)\\n for x in player.OwnedPins:\\n self.writeVInt(52)\\n self.writeVInt(x)\\n self.writeVInt(0)\\n\\n\\n self.writeBoolean(False) # Power league season data\\n\\n self.writeInt(0)\\n self.writeVInt(0)\\n self.writeVInt(16)\\n self.writeVInt(76)\\n self.writeBoolean(False)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n\\n self.writeVInt(2023189)\\n\\n self.writeVInt(35) # event slot id\\n self.writeVInt(1)\\n self.writeVInt(2)\\n self.writeVInt(3)\\n self.writeVInt(4)\\n self.writeVInt(5)\\n self.writeVInt(6)\\n self.writeVInt(7)\\n self.writeVInt(8)\\n self.writeVInt(9)\\n self.writeVInt(10)\\n self.writeVInt(11)\\n self.writeVInt(12)\\n self.writeVInt(13) \\n self.writeVInt(14)\\n self.writeVInt(15)\\n self.writeVInt(16)\\n self.writeVInt(17)\\n self.writeVInt(18) \\n self.writeVInt(19)\\n self.writeVInt(20)\\n self.writeVInt(21) \\n self.writeVInt(22)\\n self.writeVInt(23)\\n self.writeVInt(24)\\n self.writeVInt(25)\\n self.writeVInt(26)\\n self.writeVInt(27)\\n self.writeVInt(28)\\n self.writeVInt(29)\\n self.writeVInt(30)\\n self.writeVInt(31)\\n self.writeVInt(32)\\n self.writeVInt(33)\\n self.writeVInt(34)\\n self.writeVInt(35)\\n\\n self.writeVInt(1)\\n\\n self.writeVInt(4)\\n self.writeVInt(7)\\n self.writeVInt(1)\\n self.writeVInt(0)\\n self.writeVInt(72292)\\n self.writeVInt(10) \\n self.writeDataReference(15, 21) # map id\\n self.writeVInt(-1)\\n self.writeVInt(2)\\n self.writeString(\\\"\\\")\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False) # MapMaker map structure array\\n self.writeVInt(0)\\n self.writeBoolean(False) # Power League array entry\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeVInt(-1)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeVInt(-1)\\n self.writeVInt(0) \\n self.writeVInt(0) \\n self.writeVInt(0) \\n self.writeBoolean(False) \\n\\n self.writeVInt(0)\\n \\n ByteStreamHelper.encodeIntList(self, [20, 35, 75, 140, 290, 480, 800, 1250, 1875, 2800])\\n ByteStreamHelper.encodeIntList(self, [30, 80, 170, 360]) # Shop Coins Price\\n ByteStreamHelper.encodeIntList(self, [300, 880, 2040, 4680]) # Shop Coins Amount\\n\\n self.writeVInt(0) \\n\\n self.writeVInt(1)\\n self.writeVInt(41000086) # theme\\n self.writeVInt(1)\\n\\n self.writeVInt(0) \\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n\\n self.writeVInt(2)\\n self.writeVInt(1)\\n self.writeVInt(2)\\n self.writeVInt(2)\\n self.writeVInt(1)\\n self.writeVInt(-1)\\n self.writeVInt(2)\\n self.writeVInt(1)\\n self.writeVInt(4)\\n\\n ByteStreamHelper.encodeIntList(self, [0, 29, 79, 169, 349, 699])\\n ByteStreamHelper.encodeIntList(self, [0, 160, 450, 500, 1250, 2500])\\n\\n self.writeLong(0, 1) # Player ID\\n\\n self.writeVInt(0) # Notification factory\\n \\n self.writeVInt(1)\\n self.writeBoolean(False)\\n self.writeVInt(0)\\n self.writeVInt(0) \\n self.writeVInt(0)\\n self.writeBoolean(False) # Login Calendar\\n self.writeVInt(0)\\n self.writeBoolean(True) # Starr Road\\n for i in range(7):\\n self.writeVInt(0)\\n\\n self.writeVInt(0) # Mastery\\n\\n #BattleCard\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n self.writeBoolean(False)\\n\\n self.writeVInt(0) #Brawler's BattleCards\\n\\n self.writeVInt(5)\\n for i in range(5):\\n self.writeDataReference(80, i)\\n self.writeVInt(-1)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeInt(0)\\n self.writeVInt(0) \\n self.writeVInt(0)\\n self.writeVInt(86400*24)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n\\n self.writeBoolean(False)\\n\\n # end LogicClientHome\\n\\n self.writeVLong(player.ID[0], player.ID[1])\\n self.writeVLong(player.ID[0], player.ID[1])\\n self.writeVLong(player.ID[0], player.ID[1])\\n self.writeStringReference(player.Name)\\n self.writeBoolean(player.Registered)\\n self.writeInt(-1)\\n\\n self.writeVInt(17)\\n unlocked_brawler = [i['CardID'] for x,i in player.OwnedBrawlers.items()]\\n self.writeVInt(len(unlocked_brawler) + 2)\\n for x in unlocked_brawler:\\n self.writeDataReference(23, x)\\n self.writeVInt(-1)\\n self.writeVInt(1)\\n\\n self.writeDataReference(5, 8)\\n self.writeVInt(-1)\\n self.writeVInt(player.Coins)\\n\\n self.writeDataReference(5, 23)\\n self.writeVInt(-1)\\n self.writeVInt(player.Blings)\\n\\n self.writeVInt(len(player.OwnedBrawlers)) # HeroScore\\n for x,i in player.OwnedBrawlers.items():\\n self.writeDataReference(16, x)\\n self.writeVInt(-1)\\n self.writeVInt(i[\\\"Trophies\\\"])\\n\\n self.writeVInt(len(player.OwnedBrawlers)) # HeroHighScore\\n for x,i in player.OwnedBrawlers.items():\\n self.writeDataReference(16, x)\\n self.writeVInt(-1)\\n self.writeVInt(i[\\\"HighestTrophies\\\"])\\n\\n self.writeVInt(0) # Array\\n\\n self.writeVInt(0) # HeroPower\\n \\n self.writeVInt(len(player.OwnedBrawlers)) # HeroLevel\\n for x,i in player.OwnedBrawlers.items():\\n self.writeDataReference(16, x)\\n self.writeVInt(-1)\\n self.writeVInt(i[\\\"PowerLevel\\\"]-1)\\n\\n self.writeVInt(0) # hero star power gadget and hypercharge\\n\\n self.writeVInt(len(player.OwnedBrawlers)) # HeroSeenState\\n for x,i in player.OwnedBrawlers.items():\\n self.writeDataReference(16, x)\\n self.writeVInt(-1)\\n self.writeVInt(2)\\n\\n self.writeVInt(0) # Array\\n self.writeVInt(0) # Array\\n self.writeVInt(0) # Array\\n self.writeVInt(0) # Array\\n self.writeVInt(0) # Array\\n self.writeVInt(0) # Array\\n self.writeVInt(0) # Array\\n self.writeVInt(0) # Array\\n self.writeVInt(0) # Array\\n\\n self.writeVInt(player.Gems) # Diamonds\\n self.writeVInt(player.Gems) # Free Diamonds\\n self.writeVInt(10) # Player Level\\n self.writeVInt(100)\\n self.writeVInt(0) # CumulativePurchasedDiamonds or Avatar User Level Tier | 10000 < Level Tier = 3 | 1000 < Level Tier = 2 | 0 < Level Tier = 1\\n self.writeVInt(100) # Battle Count\\n self.writeVInt(10) # WinCount\\n self.writeVInt(80) # LoseCount\\n self.writeVInt(50) # WinLooseStreak\\n self.writeVInt(20) # NpcWinCount\\n self.writeVInt(0) # NpcLoseCount\\n self.writeVInt(2) # TutorialState | shouldGoToFirstTutorialBattle = State == 0\\n self.writeVInt(12)\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeString()\\n self.writeVInt(0)\\n self.writeVInt(0)\\n self.writeVInt(1)\\n\\n def decode(self):\\n fields = {}\\n return fields\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 24101\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"KeepAliveServerMessage\",\n \"path\": \"Heart/Packets/Server/Socket/KeepAliveServerMessage.py\",\n \"snippet\": \"class KeepAliveServerMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields):\\n pass\\n\\n def decode(self):\\n return {}\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 20108\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"PlayerProfileMessage\",\n \"path\": \"Heart/Packets/Server/Home/PlayerProfileMessage.py\",\n \"snippet\": \"class PlayerProfileMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields, player):\\n self.writeVLong(fields[\\\"PlayerHighID\\\"], fields[\\\"PlayerLowID\\\"])\\n self.writeDataReference(16,11) # \\n self.writeVInt(70)\\n for i in range(70):\\n self.writeDataReference(16, i)\\n self.writeDataReference(0)\\n self.writeVInt(500) # trophies\\n self.writeVInt(1250) # highestTrophies\\n self.writeVInt(11) #power level\\n \\n self.writeVInt(18)\\n\\n self.writeVInt(1) \\n self.writeVInt(1) # 3v3 victories\\n\\n self.writeVInt(2)\\n self.writeVInt(528859) # total exp\\n\\n self.writeVInt(3)\\n self.writeVInt(3) # current trophies\\n\\n self.writeVInt(4)\\n self.writeVInt(4) # highest trophies\\n\\n self.writeVInt(5) \\n self.writeVInt(5) # unlocked brawler?\\n\\n self.writeVInt(8)\\n self.writeVInt(6) # solo victories\\n\\n self.writeVInt(11) \\n self.writeVInt(7) # duo victories\\n\\n self.writeVInt(9) \\n self.writeVInt(8) # highest level robo rumble\\n\\n self.writeVInt(12) \\n self.writeVInt(9) # highest level boss fight\\n\\n self.writeVInt(13)\\n self.writeVInt(10) # highest power league points\\n\\n self.writeVInt(14)\\n self.writeVInt(11) # some power league stuff\\n\\n self.writeVInt(15)\\n self.writeVInt(12) # most challenge win\\n\\n self.writeVInt(16) #highest level city rampage\\n self.writeVInt(13)\\n\\n self.writeVInt(18) #highest solo power league rank\\n self.writeVInt(14)\\n\\n self.writeVInt(17) #highest team power league rank\\n self.writeVInt(15)\\n\\n self.writeVInt(19) # highest Club league rank\\n self.writeVInt(16)\\n\\n self.writeVInt(20) # number fame\\n self.writeVInt(1000)\\n\\n self.writeVInt(21)\\n self.writeVInt(502052) #v50\\n\\n self.writeString(player.Name) #PlayerInfo\\n self.writeVInt(100)\\n self.writeVInt(28000000 + player.Thumbnail)\\n self.writeVInt(43000000 + player.Namecolor)\\n self.writeVInt(14)\\n\\n self.writeBoolean(True)\\n self.writeVInt(300)\\n\\n self.writeString(\\\"hello world\\\")\\n self.writeVInt(100)\\n self.writeVInt(200)\\n self.writeDataReference(29, 558)\\n self.writeDataReference(0)\\n self.writeDataReference(0)\\n self.writeDataReference(0)\\n self.writeDataReference(0)\\n\\n self.writeBoolean(True) #alliance\\n self.writeLong(0,1) #alliance ID\\n self.writeString(\\\"haccers\\\") #alliance name\\n self.writeDataReference(8,1) # alliance icon\\n self.writeVInt(1) # type\\n self.writeVInt(1) # member count\\n self.writeVInt(10000) # total trophies\\n self.writeVInt(1) # minimum trophies to enter\\n self.writeDataReference(0)\\n self.writeString(\\\"RU\\\") #location\\n self.writeVInt(4) # unknown\\n self.writeBoolean(True) #is Family friendly\\n self.writeVInt(0)\\n \\n\\n self.writeDataReference(25, 1) #alliance role\\n self.writeVInt(16)\\n\\n def decode(self):\\n pass\\n # fields = {}\\n # fields[\\\"PlayerCount\\\"] = self.readVInt()\\n # fields[\\\"Text\\\"] = self.readString()\\n # fields[\\\"Unk1\\\"] = self.readVInt()\\n # super().decode(fields)\\n return {}\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 24113\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"MyAllianceMessage\",\n \"path\": \"Heart/Packets/Server/Home/MyAllianceMessage.py\",\n \"snippet\": \"class MyAllianceMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields, player):\\n self.writeVInt(1) # Online people in alliance\\n self.writeBoolean(True) # isInAlliance\\n self.writeDataReference(25, 4)\\n self.writeLong(0, 1) # alliance ID\\n self.writeString(player.ContentCreator) # alliance name\\n self.writeDataReference(8, 37) # alliance icon\\n self.writeVInt(3) # type\\n self.writeVInt(1) # member count\\n self.writeVInt(9500) # total trophies\\n self.writeVInt(1) # minimum trophies to enter\\n self.writeVInt(0) # 0\\n self.writeString('RU') # location\\n self.writeVInt(3) # unknown\\n self.writeBoolean(True) # isFamilyFriendly\\n self.writeVInt(0)\\n\\n def decode(self):\\n fields = {}\\n super().decode(fields)\\n return {}\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 24399\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n },\n {\n \"identifier\": \"AllianceDataMessage\",\n \"path\": \"Heart/Packets/Server/Home/AllianceDataMessage.py\",\n \"snippet\": \"class AllianceDataMessage(PiranhaMessage):\\n def __init__(self, messageData):\\n super().__init__(messageData)\\n self.messageVersion = 0\\n\\n def encode(self, fields, player):\\n self.writeBoolean(True)\\n\\n self.writeLong(0, 1) # alliance ID\\n self.writeString(player.ContentCreator) # alliance name\\n self.writeDataReference(8, 37) # alliance icon\\n self.writeVInt(1) # type\\n self.writeVInt(1) # member count\\n self.writeVInt(player.Trophies) # total trophies\\n self.writeVInt(0) # minimum trophies to enter\\n self.writeVInt(0) # 0\\n self.writeString('RU') # location\\n self.writeVInt(1) # people online\\n self.writeBoolean(True) # isFamilyFriendly\\n self.writeVInt(0)\\n\\n self.writeString(\\\"this is the hacciest club in the world\\\")\\n\\n self.writeVInt(1) # member count\\n self.writeLong(player.ID[0], player.ID[1]) # player ID\\n self.writeVInt(2) # role\\n self.writeVInt(player.Trophies) # trophies\\n self.writeVInt(0) # status: 0=offline 2=online\\n self.writeVInt(1) # last connected time seconds ?\\n highestPowerLeagueRank = 2\\n self.writeVInt(highestPowerLeagueRank)\\n if highestPowerLeagueRank != 0:\\n self.writeVInt(2) #solo\\n self.writeVInt(1) #duo\\n self.writeBoolean(False) # boolean always false?\\n\\n self.writeString(player.Name) # player name\\n self.writeVInt(100) # VInt always 100\\n self.writeVInt(28000000 + player.Thumbnail) # thumbnail\\n self.writeVInt(43000000 + player.Namecolor) # name color\\n self.writeVInt(46000000 + player.Namecolor)\\n\\n self.writeVInt(-1) # most people have it -1 but some with something\\n self.writeBoolean(False) # whats this ? only 2/30 people have it true in my club\\n week = 58 # week 58 of club league as of 2023/07/05, this number is 0 if you just arrived in the club\\n self.writeVInt(week)\\n if week != 0: # club league week number?\\n self.writeVInt(3) # day\\n self.writeVInt(18) # total club trophies earned\\n self.writeVInt(0) # event day club trophies earned\\n self.writeVInt(8) # total tickets used\\n self.writeVInt(0) # event day tickets used\\n self.writeVInt(6) # event day max tickets\\n self.writeVInt(6) # event day tickets left\\n self.writeVInt(0) # event day player ranking\\n self.writeBoolean(True) # everyone have it to true\\n self.writeVInt(200) # player experience lvl but why tf it doesn't show for some people\\n\\n def decode(self):\\n fields = {}\\n super().decode(fields)\\n return {}\\n\\n def execute(message, calling_instance, fields):\\n pass\\n\\n def getMessageType(self):\\n return 24301\\n\\n def getMessageVersion(self):\\n return self.messageVersion\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"from Heart.Packets.Client.Authentification.ClientHelloMessage import ClientHelloMessage\nfrom Heart.Packets.Client.Authentification.LoginMessage import LoginMessage\nfrom Heart.Packets.Client.Battle.AskForBattleEndMessage import AskForBattleEndMessage\nfrom Heart.Packets.Client.Home.ChangeAvatarNameMessage import ChangeAvatarNameMessage\nfrom Heart.Packets.Client.Home.EndClientTurnMessage import EndClientTurnMessage\nfrom Heart.Packets.Client.Home.GoHomeFromOfflinePractiseMessage import GoHomeFromOfflinePractiseMessage\nfrom Heart.Packets.Client.Home.GoHomeMessage import GoHomeMessage\nfrom Heart.Packets.Client.Home.GetPlayerProfileMessage import GetPlayerProfileMessage\nfrom Heart.Packets.Client.Home.AskForAllianceDataMessage import AskForAllianceDataMessage\nfrom Heart.Packets.Client.Socket.KeepAliveMessage import KeepAliveMessage\nfrom Heart.Packets.Server.Authentification.LoginFailedMessage import LoginFailedMessage\nfrom Heart.Packets.Server.Authentification.LoginOkMessage import LoginOkMessage\nfrom Heart.Packets.Server.Authentification.OutOfSyncMessage import OutOfSyncMessage\nfrom Heart.Packets.Server.Authentification.ServerHelloMessage import ServerHelloMessage\nfrom Heart.Packets.Server.Battle.BattleEndMessage import BattleEndMessage\nfrom Heart.Packets.Server.Home.AvailableServerCommandMessage import AvailableServerCommandMessage\nfrom Heart.Packets.Server.Home.LobbyInfoMessage import LobbyInfoMessage\nfrom Heart.Packets.Server.Home.OwnHomeDataMessage import OwnHomeDataMessage\nfrom Heart.Packets.Server.Socket.KeepAliveServerMessage import KeepAliveServerMessage\nfrom Heart.Packets.Server.Home.PlayerProfileMessage import PlayerProfileMessage\nfrom Heart.Packets.Server.Home.MyAllianceMessage import MyAllianceMessage\nfrom Heart.Packets.Server.Home.AllianceDataMessage import AllianceDataMessage"},"token_num":{"kind":"number","value":16878,"string":"16,878"},"cropped_code":{"kind":"string","value":" 14362: 'TeamSetEventMessage',\n 14363: 'TeamSetLocationMessage',\n 14364: 'TeamReportChatMessage',\n 14365: 'TeamInviteMessage',\n 14366: 'PlayerStatusMessage',\n 14367: 'TeamClearInviteMessage',\n 14368: 'TeamInviteResponseMessage',\n 14369: 'TeamPremadeChatMessage',\n 14370: 'TeamAllianceMemberInviteMessage',\n 14371: 'TeamJoinOrCreateGameRoomMessage',\n 14372: 'TeamToggleSettingsMessage',\n 14373: 'TeamBotSlotDisableMessage',\n 14403: 'GetLeaderboardMessage',\n 14405: 'AskForAvatarStreamMessage',\n 14406: 'AskForBattleReplayStreamMessage',\n 14418: 'RemoveAvatarStreamEntryMessage',\n 14469: 'AlliancePremadeChatMessage',\n 14479: 'TeamInvitationResponseMessage',\n 14600: 'AvatarNameCheckRequestMessage',\n 14700: 'ListBrawlTvChannelsMessage',\n 14701: 'TuneBrawlTvChannelMessage',\n 14715: 'SendGlobalChatLineMessage',\n 14777: 'SetInvitesBlockedMessage',\n 14778: 'SetTeamChatMutedMessage',\n 14867: 'SetRegionMessage',\n 14880: 'TeamRequestJoinCancelMessage',\n 14881: 'TeamRequestJoinMessage',\n 14882: 'TeamRequestJoinApproveMessage',\n 15081: GetPlayerProfileMessage, #v50\n 15793: 'GetTokenFriendMessage',\n 16000: 'LogicDeviceLinkCodeRequestMessage',\n 16001: 'LogicDeviceLinkMenuClosedMessage',\n 16002: 'LogicDeviceLinkEnterCodeMessage',\n 16003: 'LogicDeviceLinkConfirmYesMessage',\n 16939: 'AskApiTokenMessage',\n 17000: 'LogicAccountTransferCodeRequestMessage',\n 17190: 'JoinAllianceUsingTokenMessage',\n 17337: 'UnbotifyReportMessage',\n 17338: 'AdjustPackageMessage',\n 17750: GoHomeFromOfflinePractiseMessage, #v50\n 18686: 'SetSupportedCreatorMessage',\n 19001: 'LatencyTestResultMessage',\n 19002: 'UdpLatencyTestRequestMessage',\n 19003: 'TriggerStartLatencyTestMessage',\n 19004: 'RequestLatencyTestStatusMessage',\n 20000: 'SetEncryptionMessage',\n 20100: ServerHelloMessage,\n 20101: 'CreateAccountOkMessage',\n 20103: LoginFailedMessage,\n 20104: LoginOkMessage,\n 20105: 'FriendListMessage',\n 20106: 'FriendListUpdateMessage',\n 20107: 'AddableFriendsMessage',\n 20108: KeepAliveServerMessage,\n 20109: 'FriendOnlineStatusMessage',\n 20110: 'FriendLoggedInMessage',\n 20111: 'FriendLoggedOutMessage',\n 20112: 'AddFriendFailedMessage',\n 20117: 'ReportUserStatusMessage',\n 20118: 'ChatAccountBanStatusMessage',\n 20121: 'BillingRequestFailedMessage',\n 20132: 'UnlockAccountOkMessage',\n 20133: 'UnlockAccountFailedMessage',\n 20151: 'AppleBillingProcessedByServerMessage',\n 20152: 'GoogleBillingProcessedByServerMessage',\n 20153: 'TencentBillingProcessedByServerMessage',\n 20154: 'CafeBazaarBillingProcessedByServerMessage',\n 20156: 'KunlunBillingProcessedByServerMessage',\n 20161: 'ShutdownStartedMessage',\n 20171: 'PersonalBreakStartedMessage',\n 20173: 'YoozooBillingProcessedByServerMessage',\n 20199: 'FriendSuggestionsMessage',\n 20205: 'AvatarNameChangeFailedMessage',\n 20206: 'AvatarOnlineStatusUpdated',\n 20207: 'AllianceOnlineStatusUpdatedMessage',\n 20300: 'AvatarNameCheckResponseMessage',\n 20402: 'CreateGameFailedMessage',\n 20405: 'MatchMakingStatusMessage',\n 20406: 'MatchMakingCancelledMessage',\n 20501: 'AcceptFriendFailedMessage',\n 20523: 'YoozooOrderAvailableMessage',\n 20545: 'YoozooOrderDeliveryFailedMessage',\n 20559: 'StartLoadingMessage',\n 20801: 'NotificationMessage',\n 20802: 'OpponentRejoinsMatchNotificationMessage',\n 20931: 'AntiAddictionDataUpdatedMessage',\n 22089: 'GetTokenFriendResultMessage',\n 22100: 'CreatePlayerMapResponseMessage',\n 22101: 'DeletePlayerMapResponseMessage',\n 22102: 'PlayerMapsMessage',\n 22103: 'UpdatePlayerMapResponseMessage',\n 22104: 'SubmitPlayerMapResponseMessage',\n 22105: 'PublishPlayerMapResponseMessage',\n 22106: 'ChangePlayerMapNameMResponseMessage',\n 22107: 'PlayerMapInfoUpdatedMessage',\n 22109: 'DebugPlayerMapReviewResultOverrideSetMessage',\n 22111: 'PlayerMapGreenlightedMessage',\n 22125: 'ReportPlayerMapResponseMessage',\n 22150: 'RankedMatchStartMessage',\n 22151: 'RankedMatchBanStartedMessage',\n 22152: 'RankedMatchBanHeroResponseMessage',\n 22153: 'RankedMatchBanEndedMessage',\n 22154: 'RankedMatchPickStartedMessage',\n 22155: 'RankedMatchPickHeroFailedMessage',\n 22156: 'RankedMatchHeroPickedMessage',\n 22157: 'RankedMatchHeroDataUpdatedMessage',\n 22158: 'RankedMatchFinalPreparationStartedMessage',\n 22159: 'RankedMatchTerminatedMessage',\n 22202: 'MapPreviewMessage',\n 22377: 'GoogleServiceAccountBoundMessage',\n 22687: 'GamecenterAccountAlreadyBoundMessage',\n 22957: 'PvpMatchmakeNotificationMessage',\n 23067: 'SCIDLogoutAllDevicesResultMessage',\n 23302: 'GetAllianceInviteTokenResultMessage',\n 23456: BattleEndMessage,\n 23457: LobbyInfoMessage,\n 23458: 'BattleLogMessage',\n 23459: 'BattleLogReplayAvailableMessage',\n 23494: 'GoogleServiceAccountAlreadyBoundMessage',\n 23774: 'PlayerJWTokenMessage',\n"},"all_code":{"kind":"string","value":"\n\nclass LogicLaserMessageFactory:\n messagesList = {\n 10055: 'AskPlayerJWTokenMessage',\n 10099: 'ClientCryptoErrorMessage',\n 10100: ClientHelloMessage,\n 10101: LoginMessage,\n 10102: 'LoginUsingSessionMessage',\n 10103: 'CreateAccountMessage',\n 10107: 'ClientCapabilitiesMessage',\n 10108: KeepAliveMessage,\n 10109: 'UdpCheckConnectionMessage',\n 10110: 'AnalyticEventMessage',\n 10111: 'AccountIdentifiersMessage',\n 10112: 'AuthenticationCheckMessage',\n 10113: 'SetDeviceTokenMessage',\n 10116: 'ResetAccountMessage',\n 10117: 'ReportUserMessage',\n 10118: 'AccountSwitchedMessage',\n 10119: 'ReportAllianceStreamMessage',\n 10121: 'UnlockAccountMessage',\n 10150: 'AppleBillingRequestMessage',\n 10151: 'GoogleBillingRequestMessage',\n 10152: 'TencentBillingRequestMessage',\n 10153: 'CafeBazaarBillingRequestMessage',\n 10159: 'KunlunBillingRequestMessage',\n 10160: 'BillingCancelledByClientMessage',\n 10177: 'ClientInfoMessage',\n 10212: ChangeAvatarNameMessage,\n 10309: 'GetAllianceInviteTokenMessage',\n 10321: 'AttributionEventMessage',\n 10401: 'CreateGameMessage',\n 10501: 'AcceptFriendMessage',\n 10502: 'AddFriendMessage',\n 10503: 'AskForAddableFriendsMessage',\n 10504: 'AskForFriendListMessage',\n 10506: 'RemoveFriendMessage',\n 10507: 'AddFriendByEmailMessage',\n 10509: 'AddFriendByAvatarNameAndCodeMessage',\n 10512: 'AskForPlayingGamecenterFriendsMessage',\n 10513: 'AskForPlayingFacebookFriendsMessage',\n 10514: 'AskForPlayingKakaoFriendsMessage',\n 10515: 'AskForPlayingTencentFriendsMessage',\n 10516: 'AskForPlayingLineFriendsMessage',\n 10517: 'AskForPlayingSupercellFriendsMessage',\n 10523: 'YoozooBillingRequestMessage',\n 10555: 'ClientInputMessage',\n 10576: 'SetBlockFriendRequestsMessage',\n 10599: 'AskForFriendSuggestionsMessage',\n 10636: 'SCIDBindAccountMessage',\n 11736: 'SCIDLogoutAllDevicesMessage',\n 12100: 'CreatePlayerMapMessage',\n 12101: 'DeletePlayerMapMessage',\n 12102: 'GetPlayerMapsMessage',\n 12103: 'UpdatePlayerMapMessage',\n 12104: 'SubmitPlayerMapMessage',\n 12105: 'PublishPlayerMapMessage',\n 12106: 'ChangePlayerMapNameMessage',\n 12107: 'EnterMapEditorMessage',\n 12108: 'GoHomeFromMapEditorMessage',\n 12110: 'TeamSetPlayerMapMessage',\n 12111: 'SignoffPlayerMapMessage',\n 12125: 'ReportPlayerMapMessage',\n 12152: 'RankedMatchBanHeroMessage',\n 12155: 'RankedMatchPickHeroMessage',\n 12157: 'RankedMatchUpdateHeroDataMessage',\n 12905: 'GetCurrentBattleReplayDataMessage',\n 12998: 'SetCountryMessage',\n 13922: 'AcceptTokenFriendMessage',\n 14101: GoHomeMessage,\n 14102: EndClientTurnMessage,\n 14103: 'StartGameMessage',\n 14104: 'StartSpectateMessage',\n 14105: 'HomeLogicStoppedMessage',\n 14106: 'CancelMatchmakingMessage',\n 14107: 'StopSpectateMessage',\n 14108: 'GoHomeFromSpectateMessage',\n #14109: GoHomeFromOfflinePractiseMessage, //before v50\n 14110: AskForBattleEndMessage,\n #14113: GetPlayerProfileMessage, //before v50\n 14114: 'GetBattleLogMessage',\n 14115: 'BattleLogViewReplayMessage',\n 14116: 'ViewReplayByStringMessage',\n 14117: 'RequestMatchCancelMessage',\n 14118: 'SinglePlayerMatchRequestMessage',\n 14166: 'ChronosEventSeenMessage',\n 14167: 'ChronosEventSeenMessage',\n 14177: 'PlayAgainMessage',\n 14178: 'DebugCommandMessage',\n 14199: 'LookForGameRoomRequestMessage',\n 14211: 'UnbindFacebookAccountMessage',\n 14201: 'BindFacebookAccountMessage',\n 14202: 'BindKakaoAccountMessage',\n 14203: 'BingLineAccountMessage',\n 14212: 'BindGamecenterAccountMessage',\n 14213: 'UnbindKakaoAccountMessage',\n 14214: 'UnbindLineAccountMessage',\n 14262: 'BindGoogleServiceAccountMessage',\n 14266: 'BindTencentAccountMessage',\n 14268: 'TencentCheckCanPayMessage',\n 14276: 'TencentAntiAddictionInstructionExecutedMessage',\n 14277: 'GetSeasonRewardsMessage',\n 14299: 'SetAllianceCountryMessage',\n 14301: 'CreateAllianceMessage',\n 14302: AskForAllianceDataMessage,\n 14303: 'AskForJoinableAlliancesListMessage',\n 14304: 'AskForAllianceStreamMessage',\n 14305: 'JoinAllianceMessage',\n 14306: 'ChangeAllianceMemberRoleMessage',\n 14307: 'KickAllianceMemberMessage',\n 14308: 'LeaveAllianceMessage',\n 14315: 'ChatToAllianceStreamMessage',\n 14316: 'ChangeAllianceSettingsMessage',\n 14317: 'RequestJoinAllianceMessage',\n 14321: 'RespondToAllianceJoinRequestMessage',\n 14322: 'SendAllianceInvitationMessage',\n 14323: 'JoinAllianceUsingInvitationMessage',\n 14324: 'SearchAlliancesMessage',\n 14326: 'SendAllianceInvitationToFriendMessage',\n 14330: 'SendAllianceMailMessage',\n 14350: 'TeamCreateMessage',\n 14351: 'TeamJoinMessage',\n 14352: 'TeamKickMessage',\n 14353: 'TeamLeaveMessage',\n 14354: 'TeamChangeMemberSettingsMessage',\n 14355: 'TeamSetMemberReadyMessage',\n 14356: 'TeamTogglePractiseMessage',\n 14357: 'TeamToggleMemberSideMessage',\n 14358: 'TeamSpectateMessage',\n 14359: 'TeamChatMessage',\n 14360: 'TeamPostAdMessage',\n 14361: 'TeamMemberStatusMessage',\n 14362: 'TeamSetEventMessage',\n 14363: 'TeamSetLocationMessage',\n 14364: 'TeamReportChatMessage',\n 14365: 'TeamInviteMessage',\n 14366: 'PlayerStatusMessage',\n 14367: 'TeamClearInviteMessage',\n 14368: 'TeamInviteResponseMessage',\n 14369: 'TeamPremadeChatMessage',\n 14370: 'TeamAllianceMemberInviteMessage',\n 14371: 'TeamJoinOrCreateGameRoomMessage',\n 14372: 'TeamToggleSettingsMessage',\n 14373: 'TeamBotSlotDisableMessage',\n 14403: 'GetLeaderboardMessage',\n 14405: 'AskForAvatarStreamMessage',\n 14406: 'AskForBattleReplayStreamMessage',\n 14418: 'RemoveAvatarStreamEntryMessage',\n 14469: 'AlliancePremadeChatMessage',\n 14479: 'TeamInvitationResponseMessage',\n 14600: 'AvatarNameCheckRequestMessage',\n 14700: 'ListBrawlTvChannelsMessage',\n 14701: 'TuneBrawlTvChannelMessage',\n 14715: 'SendGlobalChatLineMessage',\n 14777: 'SetInvitesBlockedMessage',\n 14778: 'SetTeamChatMutedMessage',\n 14867: 'SetRegionMessage',\n 14880: 'TeamRequestJoinCancelMessage',\n 14881: 'TeamRequestJoinMessage',\n 14882: 'TeamRequestJoinApproveMessage',\n 15081: GetPlayerProfileMessage, #v50\n 15793: 'GetTokenFriendMessage',\n 16000: 'LogicDeviceLinkCodeRequestMessage',\n 16001: 'LogicDeviceLinkMenuClosedMessage',\n 16002: 'LogicDeviceLinkEnterCodeMessage',\n 16003: 'LogicDeviceLinkConfirmYesMessage',\n 16939: 'AskApiTokenMessage',\n 17000: 'LogicAccountTransferCodeRequestMessage',\n 17190: 'JoinAllianceUsingTokenMessage',\n 17337: 'UnbotifyReportMessage',\n 17338: 'AdjustPackageMessage',\n 17750: GoHomeFromOfflinePractiseMessage, #v50\n 18686: 'SetSupportedCreatorMessage',\n 19001: 'LatencyTestResultMessage',\n 19002: 'UdpLatencyTestRequestMessage',\n 19003: 'TriggerStartLatencyTestMessage',\n 19004: 'RequestLatencyTestStatusMessage',\n 20000: 'SetEncryptionMessage',\n 20100: ServerHelloMessage,\n 20101: 'CreateAccountOkMessage',\n 20103: LoginFailedMessage,\n 20104: LoginOkMessage,\n 20105: 'FriendListMessage',\n 20106: 'FriendListUpdateMessage',\n 20107: 'AddableFriendsMessage',\n 20108: KeepAliveServerMessage,\n 20109: 'FriendOnlineStatusMessage',\n 20110: 'FriendLoggedInMessage',\n 20111: 'FriendLoggedOutMessage',\n 20112: 'AddFriendFailedMessage',\n 20117: 'ReportUserStatusMessage',\n 20118: 'ChatAccountBanStatusMessage',\n 20121: 'BillingRequestFailedMessage',\n 20132: 'UnlockAccountOkMessage',\n 20133: 'UnlockAccountFailedMessage',\n 20151: 'AppleBillingProcessedByServerMessage',\n 20152: 'GoogleBillingProcessedByServerMessage',\n 20153: 'TencentBillingProcessedByServerMessage',\n 20154: 'CafeBazaarBillingProcessedByServerMessage',\n 20156: 'KunlunBillingProcessedByServerMessage',\n 20161: 'ShutdownStartedMessage',\n 20171: 'PersonalBreakStartedMessage',\n 20173: 'YoozooBillingProcessedByServerMessage',\n 20199: 'FriendSuggestionsMessage',\n 20205: 'AvatarNameChangeFailedMessage',\n 20206: 'AvatarOnlineStatusUpdated',\n 20207: 'AllianceOnlineStatusUpdatedMessage',\n 20300: 'AvatarNameCheckResponseMessage',\n 20402: 'CreateGameFailedMessage',\n 20405: 'MatchMakingStatusMessage',\n 20406: 'MatchMakingCancelledMessage',\n 20501: 'AcceptFriendFailedMessage',\n 20523: 'YoozooOrderAvailableMessage',\n 20545: 'YoozooOrderDeliveryFailedMessage',\n 20559: 'StartLoadingMessage',\n 20801: 'NotificationMessage',\n 20802: 'OpponentRejoinsMatchNotificationMessage',\n 20931: 'AntiAddictionDataUpdatedMessage',\n 22089: 'GetTokenFriendResultMessage',\n 22100: 'CreatePlayerMapResponseMessage',\n 22101: 'DeletePlayerMapResponseMessage',\n 22102: 'PlayerMapsMessage',\n 22103: 'UpdatePlayerMapResponseMessage',\n 22104: 'SubmitPlayerMapResponseMessage',\n 22105: 'PublishPlayerMapResponseMessage',\n 22106: 'ChangePlayerMapNameMResponseMessage',\n 22107: 'PlayerMapInfoUpdatedMessage',\n 22109: 'DebugPlayerMapReviewResultOverrideSetMessage',\n 22111: 'PlayerMapGreenlightedMessage',\n 22125: 'ReportPlayerMapResponseMessage',\n 22150: 'RankedMatchStartMessage',\n 22151: 'RankedMatchBanStartedMessage',\n 22152: 'RankedMatchBanHeroResponseMessage',\n 22153: 'RankedMatchBanEndedMessage',\n 22154: 'RankedMatchPickStartedMessage',\n 22155: 'RankedMatchPickHeroFailedMessage',\n 22156: 'RankedMatchHeroPickedMessage',\n 22157: 'RankedMatchHeroDataUpdatedMessage',\n 22158: 'RankedMatchFinalPreparationStartedMessage',\n 22159: 'RankedMatchTerminatedMessage',\n 22202: 'MapPreviewMessage',\n 22377: 'GoogleServiceAccountBoundMessage',\n 22687: 'GamecenterAccountAlreadyBoundMessage',\n 22957: 'PvpMatchmakeNotificationMessage',\n 23067: 'SCIDLogoutAllDevicesResultMessage',\n 23302: 'GetAllianceInviteTokenResultMessage',\n 23456: BattleEndMessage,\n 23457: LobbyInfoMessage,\n 23458: 'BattleLogMessage',\n 23459: 'BattleLogReplayAvailableMessage',\n 23494: 'GoogleServiceAccountAlreadyBoundMessage',\n 23774: 'PlayerJWTokenMessage',"},"next_line":{"kind":"string","value":" 24101: OwnHomeDataMessage,"},"gold_snippet_index":{"kind":"number","value":17,"string":"17"},"created_at":{"kind":"string","value":"2023-12-14 18:57:56+00:00"},"level":{"kind":"string","value":"24k"}}},{"rowIdx":5299,"cells":{"repo_name":{"kind":"string","value":"GXNU-ZhongLab/ODTrack"},"file_path":{"kind":"string","value":"lib/train/base_functions.py"},"context":{"kind":"list like","value":[{"identifier":"Lasot","path":"lib/train/dataset/lasot.py","snippet":"class Lasot(BaseVideoDataset):\n \"\"\" LaSOT dataset.\n\n Publication:\n LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking\n Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling\n CVPR, 2019\n https://arxiv.org/pdf/1809.07845.pdf\n\n Download the dataset from https://cis.temple.edu/lasot/download.html\n \"\"\"\n\n def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None):\n \"\"\"\n args:\n root - path to the lasot dataset.\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\n is used by default.\n vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the\n videos with subscripts -1, -3, and -5 from each class will be used for training.\n split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of\n vid_ids or split option can be used at a time.\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\n \"\"\"\n root = env_settings().lasot_dir if root is None else root\n super().__init__('LaSOT', root, image_loader)\n\n # Keep a list of all classes\n self.class_list = [f for f in os.listdir(self.root)]\n self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)}\n\n self.sequence_list = self._build_sequence_list(vid_ids, split)\n\n if data_fraction is not None:\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\n\n self.seq_per_class = self._build_class_list()\n\n def _build_sequence_list(self, vid_ids=None, split=None):\n if split is not None:\n if vid_ids is not None:\n raise ValueError('Cannot set both split_name and vid_ids.')\n ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\n if split == 'train':\n file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt')\n else:\n raise ValueError('Unknown split name.')\n # sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist()\n sequence_list = pandas.read_csv(file_path, header=None).squeeze(\"columns\").values.tolist()\n elif vid_ids is not None:\n sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids]\n else:\n raise ValueError('Set either split_name or vid_ids.')\n\n return sequence_list\n\n def _build_class_list(self):\n seq_per_class = {}\n for seq_id, seq_name in enumerate(self.sequence_list):\n class_name = seq_name.split('-')[0]\n if class_name in seq_per_class:\n seq_per_class[class_name].append(seq_id)\n else:\n seq_per_class[class_name] = [seq_id]\n\n return seq_per_class\n\n def get_name(self):\n return 'lasot'\n\n def has_class_info(self):\n return True\n\n def has_occlusion_info(self):\n return True\n\n def get_num_sequences(self):\n return len(self.sequence_list)\n\n def get_num_classes(self):\n return len(self.class_list)\n\n def get_sequences_in_class(self, class_name):\n return self.seq_per_class[class_name]\n\n def _read_bb_anno(self, seq_path):\n bb_anno_file = os.path.join(seq_path, \"groundtruth.txt\")\n gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values\n return torch.tensor(gt)\n\n def _read_target_visible(self, seq_path):\n # Read full occlusion and out_of_view\n occlusion_file = os.path.join(seq_path, \"full_occlusion.txt\")\n out_of_view_file = os.path.join(seq_path, \"out_of_view.txt\")\n\n with open(occlusion_file, 'r', newline='') as f:\n occlusion = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]])\n with open(out_of_view_file, 'r') as f:\n out_of_view = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]])\n\n target_visible = ~occlusion & ~out_of_view\n\n return target_visible\n\n def _get_sequence_path(self, seq_id):\n seq_name = self.sequence_list[seq_id]\n class_name = seq_name.split('-')[0]\n vid_id = seq_name.split('-')[1]\n\n return os.path.join(self.root, class_name, class_name + '-' + vid_id)\n\n def get_sequence_info(self, seq_id):\n seq_path = self._get_sequence_path(seq_id)\n bbox = self._read_bb_anno(seq_path)\n\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\n visible = self._read_target_visible(seq_path) & valid.byte()\n\n return {'bbox': bbox, 'valid': valid, 'visible': visible}\n\n def _get_frame_path(self, seq_path, frame_id):\n return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1\n\n def _get_frame(self, seq_path, frame_id):\n return self.image_loader(self._get_frame_path(seq_path, frame_id))\n\n def _get_class(self, seq_path):\n raw_class = seq_path.split('/')[-2]\n return raw_class\n\n def get_class_name(self, seq_id):\n seq_path = self._get_sequence_path(seq_id)\n obj_class = self._get_class(seq_path)\n\n return obj_class\n\n def get_frames(self, seq_id, frame_ids, anno=None):\n seq_path = self._get_sequence_path(seq_id)\n\n obj_class = self._get_class(seq_path)\n frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\n\n if anno is None:\n anno = self.get_sequence_info(seq_id)\n\n anno_frames = {}\n for key, value in anno.items():\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n object_meta = OrderedDict({'object_class_name': obj_class,\n 'motion_class': None,\n 'major_class': None,\n 'root_class': None,\n 'motion_adverb': None})\n\n return frame_list, anno_frames, object_meta"},{"identifier":"Got10k","path":"lib/train/dataset/got10k.py","snippet":"class Got10k(BaseVideoDataset):\n \"\"\" GOT-10k dataset.\n\n Publication:\n GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild\n Lianghua Huang, Xin Zhao, and Kaiqi Huang\n arXiv:1810.11981, 2018\n https://arxiv.org/pdf/1810.11981.pdf\n\n Download dataset from http://got-10k.aitestunion.com/downloads\n \"\"\"\n\n def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None):\n \"\"\"\n args:\n root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\n is used by default.\n split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split,\n not NOT the official got-10k validation split. To use the official validation split, provide that as\n the root folder instead.\n seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids'\n options can be used at the same time.\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\n \"\"\"\n root = env_settings().got10k_dir if root is None else root\n super().__init__('GOT10k', root, image_loader)\n\n # all folders inside the root\n self.sequence_list = self._get_sequence_list()\n\n # seq_id is the index of the folder inside the got10k root path\n if split is not None:\n if seq_ids is not None:\n raise ValueError('Cannot set both split_name and seq_ids.')\n ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\n if split == 'train':\n file_path = os.path.join(ltr_path, 'data_specs', 'got10k_train_split.txt')\n elif split == 'val':\n file_path = os.path.join(ltr_path, 'data_specs', 'got10k_val_split.txt')\n elif split == 'train_full':\n file_path = os.path.join(ltr_path, 'data_specs', 'got10k_train_full_split.txt')\n elif split == 'vottrain':\n file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_train_split.txt')\n elif split == 'votval':\n file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_val_split.txt')\n else:\n raise ValueError('Unknown split name.')\n # seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist()\n seq_ids = pandas.read_csv(file_path, header=None, dtype=np.int64).squeeze(\"columns\").values.tolist()\n elif seq_ids is None:\n seq_ids = list(range(0, len(self.sequence_list)))\n\n self.sequence_list = [self.sequence_list[i] for i in seq_ids]\n\n if data_fraction is not None:\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\n\n self.sequence_meta_info = self._load_meta_info()\n self.seq_per_class = self._build_seq_per_class()\n\n self.class_list = list(self.seq_per_class.keys())\n self.class_list.sort()\n\n def get_name(self):\n return 'got10k'\n\n def has_class_info(self):\n return True\n\n def has_occlusion_info(self):\n return True\n\n def _load_meta_info(self):\n sequence_meta_info = {s: self._read_meta(os.path.join(self.root, s)) for s in self.sequence_list}\n return sequence_meta_info\n\n def _read_meta(self, seq_path):\n try:\n with open(os.path.join(seq_path, 'meta_info.ini')) as f:\n meta_info = f.readlines()\n object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1][:-1],\n 'motion_class': meta_info[6].split(': ')[-1][:-1],\n 'major_class': meta_info[7].split(': ')[-1][:-1],\n 'root_class': meta_info[8].split(': ')[-1][:-1],\n 'motion_adverb': meta_info[9].split(': ')[-1][:-1]})\n except:\n object_meta = OrderedDict({'object_class_name': None,\n 'motion_class': None,\n 'major_class': None,\n 'root_class': None,\n 'motion_adverb': None})\n return object_meta\n\n def _build_seq_per_class(self):\n seq_per_class = {}\n\n for i, s in enumerate(self.sequence_list):\n object_class = self.sequence_meta_info[s]['object_class_name']\n if object_class in seq_per_class:\n seq_per_class[object_class].append(i)\n else:\n seq_per_class[object_class] = [i]\n\n return seq_per_class\n\n def get_sequences_in_class(self, class_name):\n return self.seq_per_class[class_name]\n\n def _get_sequence_list(self):\n with open(os.path.join(self.root, 'list.txt')) as f:\n dir_list = list(csv.reader(f))\n dir_list = [dir_name[0] for dir_name in dir_list]\n return dir_list\n\n def _read_bb_anno(self, seq_path):\n bb_anno_file = os.path.join(seq_path, \"groundtruth.txt\")\n gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values\n return torch.tensor(gt)\n\n def _read_target_visible(self, seq_path):\n # Read full occlusion and out_of_view\n occlusion_file = os.path.join(seq_path, \"absence.label\")\n cover_file = os.path.join(seq_path, \"cover.label\")\n\n with open(occlusion_file, 'r', newline='') as f:\n occlusion = torch.ByteTensor([int(v[0]) for v in csv.reader(f)])\n with open(cover_file, 'r', newline='') as f:\n cover = torch.ByteTensor([int(v[0]) for v in csv.reader(f)])\n\n target_visible = ~occlusion & (cover>0).byte()\n\n visible_ratio = cover.float() / 8\n return target_visible, visible_ratio\n\n def _get_sequence_path(self, seq_id):\n return os.path.join(self.root, self.sequence_list[seq_id])\n\n def get_sequence_info(self, seq_id):\n seq_path = self._get_sequence_path(seq_id)\n bbox = self._read_bb_anno(seq_path)\n\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\n visible, visible_ratio = self._read_target_visible(seq_path)\n visible = visible & valid.byte()\n\n return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio}\n\n def _get_frame_path(self, seq_path, frame_id):\n return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1\n\n def _get_frame(self, seq_path, frame_id):\n return self.image_loader(self._get_frame_path(seq_path, frame_id))\n\n def get_class_name(self, seq_id):\n obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]\n\n return obj_meta['object_class_name']\n\n def get_frames(self, seq_id, frame_ids, anno=None):\n seq_path = self._get_sequence_path(seq_id)\n obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]\n\n frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\n\n if anno is None:\n anno = self.get_sequence_info(seq_id)\n\n anno_frames = {}\n for key, value in anno.items():\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n return frame_list, anno_frames, obj_meta"},{"identifier":"TrackingNet","path":"lib/train/dataset/tracking_net.py","snippet":"class TrackingNet(BaseVideoDataset):\n \"\"\" TrackingNet dataset.\n\n Publication:\n TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild.\n Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem\n ECCV, 2018\n https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf\n\n Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit.\n \"\"\"\n def __init__(self, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None):\n \"\"\"\n args:\n root - The path to the TrackingNet folder, containing the training sets.\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\n is used by default.\n set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the\n sets (0 - 11) will be used.\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\n \"\"\"\n root = env_settings().trackingnet_dir if root is None else root\n super().__init__('TrackingNet', root, image_loader)\n\n if set_ids is None:\n set_ids = [i for i in range(12)]\n\n self.set_ids = set_ids\n\n # Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and\n # video_name for each sequence\n self.sequence_list = list_sequences(self.root, self.set_ids)\n\n if data_fraction is not None:\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction))\n\n self.seq_to_class_map, self.seq_per_class = self._load_class_info()\n\n # we do not have the class_lists for the tracking net\n self.class_list = list(self.seq_per_class.keys())\n self.class_list.sort()\n\n def _load_class_info(self):\n ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\n class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt')\n\n with open(class_map_path, 'r') as f:\n seq_to_class_map = {seq_class.split('\\t')[0]: seq_class.rstrip().split('\\t')[1] for seq_class in f}\n\n seq_per_class = {}\n for i, seq in enumerate(self.sequence_list):\n class_name = seq_to_class_map.get(seq[1], 'Unknown')\n if class_name not in seq_per_class:\n seq_per_class[class_name] = [i]\n else:\n seq_per_class[class_name].append(i)\n\n return seq_to_class_map, seq_per_class\n\n def get_name(self):\n return 'trackingnet'\n\n def has_class_info(self):\n return True\n\n def get_sequences_in_class(self, class_name):\n return self.seq_per_class[class_name]\n\n def _read_bb_anno(self, seq_id):\n set_id = self.sequence_list[seq_id][0]\n vid_name = self.sequence_list[seq_id][1]\n bb_anno_file = os.path.join(self.root, \"TRAIN_\" + str(set_id), \"anno\", vid_name + \".txt\")\n gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False,\n low_memory=False).values\n return torch.tensor(gt)\n\n def get_sequence_info(self, seq_id):\n bbox = self._read_bb_anno(seq_id)\n\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\n visible = valid.clone().byte()\n return {'bbox': bbox, 'valid': valid, 'visible': visible}\n\n def _get_frame(self, seq_id, frame_id):\n set_id = self.sequence_list[seq_id][0]\n vid_name = self.sequence_list[seq_id][1]\n frame_path = os.path.join(self.root, \"TRAIN_\" + str(set_id), \"frames\", vid_name, str(frame_id) + \".jpg\")\n return self.image_loader(frame_path)\n\n def _get_class(self, seq_id):\n seq_name = self.sequence_list[seq_id][1]\n return self.seq_to_class_map[seq_name]\n\n def get_class_name(self, seq_id):\n obj_class = self._get_class(seq_id)\n\n return obj_class\n\n def get_frames(self, seq_id, frame_ids, anno=None):\n frame_list = [self._get_frame(seq_id, f) for f in frame_ids]\n\n if anno is None:\n anno = self.get_sequence_info(seq_id)\n\n anno_frames = {}\n for key, value in anno.items():\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n obj_class = self._get_class(seq_id)\n\n object_meta = OrderedDict({'object_class_name': obj_class,\n 'motion_class': None,\n 'major_class': None,\n 'root_class': None,\n 'motion_adverb': None})\n\n return frame_list, anno_frames, object_meta"},{"identifier":"ImagenetVID","path":"lib/train/dataset/imagenetvid.py","snippet":"class ImagenetVID(BaseVideoDataset):\n \"\"\" Imagenet VID dataset.\n\n Publication:\n ImageNet Large Scale Visual Recognition Challenge\n Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy,\n Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei\n IJCV, 2015\n https://arxiv.org/pdf/1409.0575.pdf\n\n Download the dataset from http://image-net.org/\n \"\"\"\n def __init__(self, root=None, image_loader=jpeg4py_loader, min_length=0, max_target_area=1):\n \"\"\"\n args:\n root - path to the imagenet vid dataset.\n image_loader (default_image_loader) - The function to read the images. If installed,\n jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else,\n opencv's imread is used.\n min_length - Minimum allowed sequence length.\n max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets\n which cover complete image.\n \"\"\"\n root = env_settings().imagenet_dir if root is None else root\n super().__init__(\"imagenetvid\", root, image_loader)\n\n cache_file = os.path.join(root, 'cache.json')\n if os.path.isfile(cache_file):\n # If available, load the pre-processed cache file containing meta-info for each sequence\n with open(cache_file, 'r') as f:\n sequence_list_dict = json.load(f)\n\n self.sequence_list = sequence_list_dict\n else:\n # Else process the imagenet annotations and generate the cache file\n self.sequence_list = self._process_anno(root)\n\n with open(cache_file, 'w') as f:\n json.dump(self.sequence_list, f)\n\n # Filter the sequences based on min_length and max_target_area in the first frame\n self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and\n get_target_to_image_ratio(x) < max_target_area]\n\n def get_name(self):\n return 'imagenetvid'\n\n def get_num_sequences(self):\n return len(self.sequence_list)\n\n def get_sequence_info(self, seq_id):\n bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno'])\n valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0)\n visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte()\n return {'bbox': bb_anno, 'valid': valid, 'visible': visible}\n\n def _get_frame(self, sequence, frame_id):\n set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id'])\n vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id'])\n frame_number = frame_id + sequence['start_frame']\n frame_path = os.path.join(self.root, 'Data', 'VID', 'train', set_name, vid_name,\n '{:06d}.JPEG'.format(frame_number))\n return self.image_loader(frame_path)\n\n def get_frames(self, seq_id, frame_ids, anno=None):\n sequence = self.sequence_list[seq_id]\n\n frame_list = [self._get_frame(sequence, f) for f in frame_ids]\n\n if anno is None:\n anno = self.get_sequence_info(seq_id)\n\n # Create anno dict\n anno_frames = {}\n for key, value in anno.items():\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n # added the class info to the meta info\n object_meta = OrderedDict({'object_class': sequence['class_name'],\n 'motion_class': None,\n 'major_class': None,\n 'root_class': None,\n 'motion_adverb': None})\n\n return frame_list, anno_frames, object_meta\n\n def _process_anno(self, root):\n # Builds individual tracklets\n base_vid_anno_path = os.path.join(root, 'Annotations', 'VID', 'train')\n\n all_sequences = []\n for set in sorted(os.listdir(base_vid_anno_path)):\n set_id = int(set.split('_')[-1])\n for vid in sorted(os.listdir(os.path.join(base_vid_anno_path, set))):\n\n vid_id = int(vid.split('_')[-1])\n anno_files = sorted(os.listdir(os.path.join(base_vid_anno_path, set, vid)))\n\n frame1_anno = ET.parse(os.path.join(base_vid_anno_path, set, vid, anno_files[0]))\n image_size = [int(frame1_anno.find('size/width').text), int(frame1_anno.find('size/height').text)]\n\n objects = [ET.ElementTree(file=os.path.join(base_vid_anno_path, set, vid, f)).findall('object')\n for f in anno_files]\n\n tracklets = {}\n\n # Find all tracklets along with start frame\n for f_id, all_targets in enumerate(objects):\n for target in all_targets:\n tracklet_id = target.find('trackid').text\n if tracklet_id not in tracklets:\n tracklets[tracklet_id] = f_id\n\n for tracklet_id, tracklet_start in tracklets.items():\n tracklet_anno = []\n target_visible = []\n class_name_id = None\n\n for f_id in range(tracklet_start, len(objects)):\n found = False\n for target in objects[f_id]:\n if target.find('trackid').text == tracklet_id:\n if not class_name_id:\n class_name_id = target.find('name').text\n x1 = int(target.find('bndbox/xmin').text)\n y1 = int(target.find('bndbox/ymin').text)\n x2 = int(target.find('bndbox/xmax').text)\n y2 = int(target.find('bndbox/ymax').text)\n\n tracklet_anno.append([x1, y1, x2 - x1, y2 - y1])\n target_visible.append(target.find('occluded').text == '0')\n\n found = True\n break\n if not found:\n break\n\n new_sequence = {'set_id': set_id, 'vid_id': vid_id, 'class_name': class_name_id,\n 'start_frame': tracklet_start, 'anno': tracklet_anno,\n 'target_visible': target_visible, 'image_size': image_size}\n all_sequences.append(new_sequence)\n\n return all_sequences"},{"identifier":"MSCOCOSeq","path":"lib/train/dataset/coco_seq.py","snippet":"class MSCOCOSeq(BaseVideoDataset):\n \"\"\" The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1.\n\n Publication:\n Microsoft COCO: Common Objects in Context.\n Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona,\n Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick\n ECCV, 2014\n https://arxiv.org/pdf/1405.0312.pdf\n\n Download the images along with annotations from http://cocodataset.org/#download. The root folder should be\n organized as follows.\n - coco_root\n - annotations\n - instances_train2014.json\n - instances_train2017.json\n - images\n - train2014\n - train2017\n\n Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi.\n \"\"\"\n\n def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split=\"train\", version=\"2014\"):\n \"\"\"\n args:\n root - path to the coco dataset.\n image_loader (default_image_loader) - The function to read the images. If installed,\n jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else,\n opencv's imread is used.\n data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the\n images will be used\n split - 'train' or 'val'.\n version - version of coco dataset (2014 or 2017)\n \"\"\"\n root = env_settings().coco_dir if root is None else root\n super().__init__('COCO', root, image_loader)\n\n self.img_pth = os.path.join(root, 'images/{}{}/'.format(split, version))\n self.anno_path = os.path.join(root, 'annotations/instances_{}{}.json'.format(split, version))\n\n # Load the COCO set.\n self.coco_set = COCO(self.anno_path)\n\n self.cats = self.coco_set.cats\n\n self.class_list = self.get_class_list()\n\n self.sequence_list = self._get_sequence_list()\n\n if data_fraction is not None:\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\n self.seq_per_class = self._build_seq_per_class()\n\n def _get_sequence_list(self):\n ann_list = list(self.coco_set.anns.keys())\n seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0]\n\n return seq_list\n\n def is_video_sequence(self):\n return False\n\n def get_num_classes(self):\n return len(self.class_list)\n\n def get_name(self):\n return 'coco'\n\n def has_class_info(self):\n return True\n\n def get_class_list(self):\n class_list = []\n for cat_id in self.cats.keys():\n class_list.append(self.cats[cat_id]['name'])\n return class_list\n\n def has_segmentation_info(self):\n return True\n\n def get_num_sequences(self):\n return len(self.sequence_list)\n\n def _build_seq_per_class(self):\n seq_per_class = {}\n for i, seq in enumerate(self.sequence_list):\n class_name = self.cats[self.coco_set.anns[seq]['category_id']]['name']\n if class_name not in seq_per_class:\n seq_per_class[class_name] = [i]\n else:\n seq_per_class[class_name].append(i)\n\n return seq_per_class\n\n def get_sequences_in_class(self, class_name):\n return self.seq_per_class[class_name]\n\n def get_sequence_info(self, seq_id):\n anno = self._get_anno(seq_id)\n\n bbox = torch.Tensor(anno['bbox']).view(1, 4)\n\n mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0)\n\n '''2021.1.3 To avoid too small bounding boxes. Here we change the threshold to 50 pixels'''\n valid = (bbox[:, 2] > 50) & (bbox[:, 3] > 50)\n\n visible = valid.clone().byte()\n\n return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible}\n\n def _get_anno(self, seq_id):\n anno = self.coco_set.anns[self.sequence_list[seq_id]]\n\n return anno\n\n def _get_frames(self, seq_id):\n path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name']\n img = self.image_loader(os.path.join(self.img_pth, path))\n return img\n\n def get_meta_info(self, seq_id):\n try:\n cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']]\n object_meta = OrderedDict({'object_class_name': cat_dict_current['name'],\n 'motion_class': None,\n 'major_class': cat_dict_current['supercategory'],\n 'root_class': None,\n 'motion_adverb': None})\n except:\n object_meta = OrderedDict({'object_class_name': None,\n 'motion_class': None,\n 'major_class': None,\n 'root_class': None,\n 'motion_adverb': None})\n return object_meta\n\n\n def get_class_name(self, seq_id):\n cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']]\n return cat_dict_current['name']\n\n def get_frames(self, seq_id=None, frame_ids=None, anno=None):\n # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a\n # list containing these replicated images.\n frame = self._get_frames(seq_id)\n\n frame_list = [frame.copy() for _ in frame_ids]\n\n if anno is None:\n anno = self.get_sequence_info(seq_id)\n\n anno_frames = {}\n for key, value in anno.items():\n anno_frames[key] = [value[0, ...] for _ in frame_ids]\n\n object_meta = self.get_meta_info(seq_id)\n\n return frame_list, anno_frames, object_meta"},{"identifier":"Got10k_lmdb","path":"lib/train/dataset/got10k_lmdb.py","snippet":"class Got10k_lmdb(BaseVideoDataset):\n\n def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None):\n \"\"\"\n args:\n root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\n is used by default.\n split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split,\n not NOT the official got-10k validation split. To use the official validation split, provide that as\n the root folder instead.\n seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids'\n options can be used at the same time.\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\n use_lmdb - whether the dataset is stored in lmdb format\n \"\"\"\n root = env_settings().got10k_lmdb_dir if root is None else root\n super().__init__('GOT10k_lmdb', root, image_loader)\n\n # all folders inside the root\n self.sequence_list = self._get_sequence_list()\n\n # seq_id is the index of the folder inside the got10k root path\n if split is not None:\n if seq_ids is not None:\n raise ValueError('Cannot set both split_name and seq_ids.')\n train_lib_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\n if split == 'train':\n file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_split.txt')\n elif split == 'val':\n file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_val_split.txt')\n elif split == 'train_full':\n file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_full_split.txt')\n elif split == 'vottrain':\n file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_train_split.txt')\n elif split == 'votval':\n file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_val_split.txt')\n else:\n raise ValueError('Unknown split name.')\n seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist()\n elif seq_ids is None:\n seq_ids = list(range(0, len(self.sequence_list)))\n\n self.sequence_list = [self.sequence_list[i] for i in seq_ids]\n\n if data_fraction is not None:\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\n\n self.sequence_meta_info = self._load_meta_info()\n self.seq_per_class = self._build_seq_per_class()\n\n self.class_list = list(self.seq_per_class.keys())\n self.class_list.sort()\n\n def get_name(self):\n return 'got10k_lmdb'\n\n def has_class_info(self):\n return True\n\n def has_occlusion_info(self):\n return True\n\n def _load_meta_info(self):\n def _read_meta(meta_info):\n\n object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1],\n 'motion_class': meta_info[6].split(': ')[-1],\n 'major_class': meta_info[7].split(': ')[-1],\n 'root_class': meta_info[8].split(': ')[-1],\n 'motion_adverb': meta_info[9].split(': ')[-1]})\n\n return object_meta\n sequence_meta_info = {}\n for s in self.sequence_list:\n try:\n meta_str = decode_str(self.root, \"train/%s/meta_info.ini\" %s)\n sequence_meta_info[s] = _read_meta(meta_str.split('\\n'))\n except:\n sequence_meta_info[s] = OrderedDict({'object_class_name': None,\n 'motion_class': None,\n 'major_class': None,\n 'root_class': None,\n 'motion_adverb': None})\n return sequence_meta_info\n\n def _build_seq_per_class(self):\n seq_per_class = {}\n\n for i, s in enumerate(self.sequence_list):\n object_class = self.sequence_meta_info[s]['object_class_name']\n if object_class in seq_per_class:\n seq_per_class[object_class].append(i)\n else:\n seq_per_class[object_class] = [i]\n\n return seq_per_class\n\n def get_sequences_in_class(self, class_name):\n return self.seq_per_class[class_name]\n\n def _get_sequence_list(self):\n dir_str = decode_str(self.root, 'train/list.txt')\n dir_list = dir_str.split('\\n')\n return dir_list\n\n def _read_bb_anno(self, seq_path):\n bb_anno_file = os.path.join(seq_path, \"groundtruth.txt\")\n gt_str_list = decode_str(self.root, bb_anno_file).split('\\n')[:-1] # the last line in got10k is empty\n gt_list = [list(map(float, line.split(','))) for line in gt_str_list]\n gt_arr = np.array(gt_list).astype(np.float32)\n\n return torch.tensor(gt_arr)\n\n def _read_target_visible(self, seq_path):\n # full occlusion and out_of_view files\n occlusion_file = os.path.join(seq_path, \"absence.label\")\n cover_file = os.path.join(seq_path, \"cover.label\")\n # Read these files\n occ_list = list(map(int, decode_str(self.root, occlusion_file).split('\\n')[:-1])) # the last line in got10k is empty\n occlusion = torch.ByteTensor(occ_list)\n cover_list = list(map(int, decode_str(self.root, cover_file).split('\\n')[:-1])) # the last line in got10k is empty\n cover = torch.ByteTensor(cover_list)\n\n target_visible = ~occlusion & (cover>0).byte()\n\n visible_ratio = cover.float() / 8\n return target_visible, visible_ratio\n\n def _get_sequence_path(self, seq_id):\n return os.path.join(\"train\", self.sequence_list[seq_id])\n\n def get_sequence_info(self, seq_id):\n seq_path = self._get_sequence_path(seq_id)\n bbox = self._read_bb_anno(seq_path)\n\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\n visible, visible_ratio = self._read_target_visible(seq_path)\n visible = visible & valid.byte()\n\n return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio}\n\n def _get_frame_path(self, seq_path, frame_id):\n return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1\n\n def _get_frame(self, seq_path, frame_id):\n return decode_img(self.root, self._get_frame_path(seq_path, frame_id))\n\n def get_class_name(self, seq_id):\n obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]\n\n return obj_meta['object_class_name']\n\n def get_frames(self, seq_id, frame_ids, anno=None):\n seq_path = self._get_sequence_path(seq_id)\n obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]\n\n frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\n\n if anno is None:\n anno = self.get_sequence_info(seq_id)\n\n anno_frames = {}\n for key, value in anno.items():\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n return frame_list, anno_frames, obj_meta"},{"identifier":"Lasot_lmdb","path":"lib/train/dataset/lasot_lmdb.py","snippet":"class Lasot_lmdb(BaseVideoDataset):\n\n def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None):\n \"\"\"\n args:\n root - path to the lasot dataset.\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\n is used by default.\n vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the\n videos with subscripts -1, -3, and -5 from each class will be used for training.\n split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of\n vid_ids or split option can be used at a time.\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\n \"\"\"\n root = env_settings().lasot_lmdb_dir if root is None else root\n super().__init__('LaSOT_lmdb', root, image_loader)\n\n self.sequence_list = self._build_sequence_list(vid_ids, split)\n class_list = [seq_name.split('-')[0] for seq_name in self.sequence_list]\n self.class_list = []\n for ele in class_list:\n if ele not in self.class_list:\n self.class_list.append(ele)\n # Keep a list of all classes\n self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)}\n\n if data_fraction is not None:\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\n\n self.seq_per_class = self._build_class_list()\n\n def _build_sequence_list(self, vid_ids=None, split=None):\n if split is not None:\n if vid_ids is not None:\n raise ValueError('Cannot set both split_name and vid_ids.')\n ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\n if split == 'train':\n file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt')\n else:\n raise ValueError('Unknown split name.')\n sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist()\n elif vid_ids is not None:\n sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids]\n else:\n raise ValueError('Set either split_name or vid_ids.')\n\n return sequence_list\n\n def _build_class_list(self):\n seq_per_class = {}\n for seq_id, seq_name in enumerate(self.sequence_list):\n class_name = seq_name.split('-')[0]\n if class_name in seq_per_class:\n seq_per_class[class_name].append(seq_id)\n else:\n seq_per_class[class_name] = [seq_id]\n\n return seq_per_class\n\n def get_name(self):\n return 'lasot_lmdb'\n\n def has_class_info(self):\n return True\n\n def has_occlusion_info(self):\n return True\n\n def get_num_sequences(self):\n return len(self.sequence_list)\n\n def get_num_classes(self):\n return len(self.class_list)\n\n def get_sequences_in_class(self, class_name):\n return self.seq_per_class[class_name]\n\n def _read_bb_anno(self, seq_path):\n bb_anno_file = os.path.join(seq_path, \"groundtruth.txt\")\n gt_str_list = decode_str(self.root, bb_anno_file).split('\\n')[:-1] # the last line is empty\n gt_list = [list(map(float, line.split(','))) for line in gt_str_list]\n gt_arr = np.array(gt_list).astype(np.float32)\n return torch.tensor(gt_arr)\n\n def _read_target_visible(self, seq_path):\n # Read full occlusion and out_of_view\n occlusion_file = os.path.join(seq_path, \"full_occlusion.txt\")\n out_of_view_file = os.path.join(seq_path, \"out_of_view.txt\")\n\n occ_list = list(map(int, decode_str(self.root, occlusion_file).split(',')))\n occlusion = torch.ByteTensor(occ_list)\n out_view_list = list(map(int, decode_str(self.root, out_of_view_file).split(',')))\n out_of_view = torch.ByteTensor(out_view_list)\n\n target_visible = ~occlusion & ~out_of_view\n\n return target_visible\n\n def _get_sequence_path(self, seq_id):\n seq_name = self.sequence_list[seq_id]\n class_name = seq_name.split('-')[0]\n vid_id = seq_name.split('-')[1]\n\n return os.path.join(class_name, class_name + '-' + vid_id)\n\n def get_sequence_info(self, seq_id):\n seq_path = self._get_sequence_path(seq_id)\n bbox = self._read_bb_anno(seq_path)\n\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\n visible = self._read_target_visible(seq_path) & valid.byte()\n\n return {'bbox': bbox, 'valid': valid, 'visible': visible}\n\n def _get_frame_path(self, seq_path, frame_id):\n return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1\n\n def _get_frame(self, seq_path, frame_id):\n return decode_img(self.root, self._get_frame_path(seq_path, frame_id))\n\n def _get_class(self, seq_path):\n raw_class = seq_path.split('/')[-2]\n return raw_class\n\n def get_class_name(self, seq_id):\n seq_path = self._get_sequence_path(seq_id)\n obj_class = self._get_class(seq_path)\n\n return obj_class\n\n def get_frames(self, seq_id, frame_ids, anno=None):\n seq_path = self._get_sequence_path(seq_id)\n\n obj_class = self._get_class(seq_path)\n frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\n\n if anno is None:\n anno = self.get_sequence_info(seq_id)\n\n anno_frames = {}\n for key, value in anno.items():\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n object_meta = OrderedDict({'object_class_name': obj_class,\n 'motion_class': None,\n 'major_class': None,\n 'root_class': None,\n 'motion_adverb': None})\n\n return frame_list, anno_frames, object_meta"},{"identifier":"ImagenetVID_lmdb","path":"lib/train/dataset/imagenetvid_lmdb.py","snippet":"class ImagenetVID_lmdb(BaseVideoDataset):\n \"\"\" Imagenet VID dataset.\n\n Publication:\n ImageNet Large Scale Visual Recognition Challenge\n Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy,\n Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei\n IJCV, 2015\n https://arxiv.org/pdf/1409.0575.pdf\n\n Download the dataset from http://image-net.org/\n \"\"\"\n def __init__(self, root=None, image_loader=jpeg4py_loader, min_length=0, max_target_area=1):\n \"\"\"\n args:\n root - path to the imagenet vid dataset.\n image_loader (default_image_loader) - The function to read the images. If installed,\n jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else,\n opencv's imread is used.\n min_length - Minimum allowed sequence length.\n max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets\n which cover complete image.\n \"\"\"\n root = env_settings().imagenet_dir if root is None else root\n super().__init__(\"imagenetvid_lmdb\", root, image_loader)\n\n sequence_list_dict = decode_json(root, \"cache.json\")\n self.sequence_list = sequence_list_dict\n\n # Filter the sequences based on min_length and max_target_area in the first frame\n self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and\n get_target_to_image_ratio(x) < max_target_area]\n\n def get_name(self):\n return 'imagenetvid_lmdb'\n\n def get_num_sequences(self):\n return len(self.sequence_list)\n\n def get_sequence_info(self, seq_id):\n bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno'])\n valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0)\n visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte()\n return {'bbox': bb_anno, 'valid': valid, 'visible': visible}\n\n def _get_frame(self, sequence, frame_id):\n set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id'])\n vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id'])\n frame_number = frame_id + sequence['start_frame']\n frame_path = os.path.join('Data', 'VID', 'train', set_name, vid_name,\n '{:06d}.JPEG'.format(frame_number))\n return decode_img(self.root, frame_path)\n\n def get_frames(self, seq_id, frame_ids, anno=None):\n sequence = self.sequence_list[seq_id]\n\n frame_list = [self._get_frame(sequence, f) for f in frame_ids]\n\n if anno is None:\n anno = self.get_sequence_info(seq_id)\n\n # Create anno dict\n anno_frames = {}\n for key, value in anno.items():\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n # added the class info to the meta info\n object_meta = OrderedDict({'object_class': sequence['class_name'],\n 'motion_class': None,\n 'major_class': None,\n 'root_class': None,\n 'motion_adverb': None})\n\n return frame_list, anno_frames, object_meta"},{"identifier":"MSCOCOSeq_lmdb","path":"lib/train/dataset/coco_seq_lmdb.py","snippet":"class MSCOCOSeq_lmdb(BaseVideoDataset):\n \"\"\" The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1.\n\n Publication:\n Microsoft COCO: Common Objects in Context.\n Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona,\n Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick\n ECCV, 2014\n https://arxiv.org/pdf/1405.0312.pdf\n\n Download the images along with annotations from http://cocodataset.org/#download. The root folder should be\n organized as follows.\n - coco_root\n - annotations\n - instances_train2014.json\n - instances_train2017.json\n - images\n - train2014\n - train2017\n\n Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi.\n \"\"\"\n\n def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split=\"train\", version=\"2014\"):\n \"\"\"\n args:\n root - path to the coco dataset.\n image_loader (default_image_loader) - The function to read the images. If installed,\n jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else,\n opencv's imread is used.\n data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the\n images will be used\n split - 'train' or 'val'.\n version - version of coco dataset (2014 or 2017)\n \"\"\"\n root = env_settings().coco_dir if root is None else root\n super().__init__('COCO_lmdb', root, image_loader)\n self.root = root\n self.img_pth = 'images/{}{}/'.format(split, version)\n self.anno_path = 'annotations/instances_{}{}.json'.format(split, version)\n\n # Load the COCO set.\n print('loading annotations into memory...')\n tic = time.time()\n coco_json = decode_json(root, self.anno_path)\n print('Done (t={:0.2f}s)'.format(time.time() - tic))\n\n self.coco_set = COCO(coco_json)\n\n self.cats = self.coco_set.cats\n\n self.class_list = self.get_class_list()\n\n self.sequence_list = self._get_sequence_list()\n\n if data_fraction is not None:\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\n self.seq_per_class = self._build_seq_per_class()\n\n def _get_sequence_list(self):\n ann_list = list(self.coco_set.anns.keys())\n seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0]\n\n return seq_list\n\n def is_video_sequence(self):\n return False\n\n def get_num_classes(self):\n return len(self.class_list)\n\n def get_name(self):\n return 'coco_lmdb'\n\n def has_class_info(self):\n return True\n\n def get_class_list(self):\n class_list = []\n for cat_id in self.cats.keys():\n class_list.append(self.cats[cat_id]['name'])\n return class_list\n\n def has_segmentation_info(self):\n return True\n\n def get_num_sequences(self):\n return len(self.sequence_list)\n\n def _build_seq_per_class(self):\n seq_per_class = {}\n for i, seq in enumerate(self.sequence_list):\n class_name = self.cats[self.coco_set.anns[seq]['category_id']]['name']\n if class_name not in seq_per_class:\n seq_per_class[class_name] = [i]\n else:\n seq_per_class[class_name].append(i)\n\n return seq_per_class\n\n def get_sequences_in_class(self, class_name):\n return self.seq_per_class[class_name]\n\n def get_sequence_info(self, seq_id):\n anno = self._get_anno(seq_id)\n\n bbox = torch.Tensor(anno['bbox']).view(1, 4)\n\n mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0)\n\n '''2021.1.3 To avoid too small bounding boxes. Here we change the threshold to 50 pixels'''\n valid = (bbox[:, 2] > 50) & (bbox[:, 3] > 50)\n\n visible = valid.clone().byte()\n\n return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible}\n\n def _get_anno(self, seq_id):\n anno = self.coco_set.anns[self.sequence_list[seq_id]]\n\n return anno\n\n def _get_frames(self, seq_id):\n path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name']\n # img = self.image_loader(os.path.join(self.img_pth, path))\n img = decode_img(self.root, os.path.join(self.img_pth, path))\n return img\n\n def get_meta_info(self, seq_id):\n try:\n cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']]\n object_meta = OrderedDict({'object_class_name': cat_dict_current['name'],\n 'motion_class': None,\n 'major_class': cat_dict_current['supercategory'],\n 'root_class': None,\n 'motion_adverb': None})\n except:\n object_meta = OrderedDict({'object_class_name': None,\n 'motion_class': None,\n 'major_class': None,\n 'root_class': None,\n 'motion_adverb': None})\n return object_meta\n\n\n def get_class_name(self, seq_id):\n cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']]\n return cat_dict_current['name']\n\n def get_frames(self, seq_id=None, frame_ids=None, anno=None):\n # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a\n # list containing these replicated images.\n frame = self._get_frames(seq_id)\n\n frame_list = [frame.copy() for _ in frame_ids]\n\n if anno is None:\n anno = self.get_sequence_info(seq_id)\n\n anno_frames = {}\n for key, value in anno.items():\n anno_frames[key] = [value[0, ...] for _ in frame_ids]\n\n object_meta = self.get_meta_info(seq_id)\n\n return frame_list, anno_frames, object_meta"},{"identifier":"TrackingNet_lmdb","path":"lib/train/dataset/tracking_net_lmdb.py","snippet":"class TrackingNet_lmdb(BaseVideoDataset):\n \"\"\" TrackingNet dataset.\n\n Publication:\n TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild.\n Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem\n ECCV, 2018\n https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf\n\n Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit.\n \"\"\"\n def __init__(self, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None):\n \"\"\"\n args:\n root - The path to the TrackingNet folder, containing the training sets.\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\n is used by default.\n set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the\n sets (0 - 11) will be used.\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\n \"\"\"\n root = env_settings().trackingnet_lmdb_dir if root is None else root\n super().__init__('TrackingNet_lmdb', root, image_loader)\n\n if set_ids is None:\n set_ids = [i for i in range(12)]\n\n self.set_ids = set_ids\n\n # Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and\n # video_name for each sequence\n self.sequence_list = list_sequences(self.root)\n\n if data_fraction is not None:\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction))\n\n self.seq_to_class_map, self.seq_per_class = self._load_class_info()\n\n # we do not have the class_lists for the tracking net\n self.class_list = list(self.seq_per_class.keys())\n self.class_list.sort()\n\n def _load_class_info(self):\n ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\n class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt')\n\n with open(class_map_path, 'r') as f:\n seq_to_class_map = {seq_class.split('\\t')[0]: seq_class.rstrip().split('\\t')[1] for seq_class in f}\n\n seq_per_class = {}\n for i, seq in enumerate(self.sequence_list):\n class_name = seq_to_class_map.get(seq[1], 'Unknown')\n if class_name not in seq_per_class:\n seq_per_class[class_name] = [i]\n else:\n seq_per_class[class_name].append(i)\n\n return seq_to_class_map, seq_per_class\n\n def get_name(self):\n return 'trackingnet_lmdb'\n\n def has_class_info(self):\n return True\n\n def get_sequences_in_class(self, class_name):\n return self.seq_per_class[class_name]\n\n def _read_bb_anno(self, seq_id):\n set_id = self.sequence_list[seq_id][0]\n vid_name = self.sequence_list[seq_id][1]\n gt_str_list = decode_str(os.path.join(self.root, \"TRAIN_%d_lmdb\" % set_id),\n os.path.join(\"anno\", vid_name + \".txt\")).split('\\n')[:-1]\n gt_list = [list(map(float, line.split(','))) for line in gt_str_list]\n gt_arr = np.array(gt_list).astype(np.float32)\n return torch.tensor(gt_arr)\n\n def get_sequence_info(self, seq_id):\n bbox = self._read_bb_anno(seq_id)\n\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\n visible = valid.clone().byte()\n return {'bbox': bbox, 'valid': valid, 'visible': visible}\n\n def _get_frame(self, seq_id, frame_id):\n set_id = self.sequence_list[seq_id][0]\n vid_name = self.sequence_list[seq_id][1]\n return decode_img(os.path.join(self.root, \"TRAIN_%d_lmdb\" % set_id),\n os.path.join(\"frames\", vid_name, str(frame_id) + \".jpg\"))\n\n def _get_class(self, seq_id):\n seq_name = self.sequence_list[seq_id][1]\n return self.seq_to_class_map[seq_name]\n\n def get_class_name(self, seq_id):\n obj_class = self._get_class(seq_id)\n\n return obj_class\n\n def get_frames(self, seq_id, frame_ids, anno=None):\n frame_list = [self._get_frame(seq_id, f) for f in frame_ids]\n\n if anno is None:\n anno = self.get_sequence_info(seq_id)\n\n anno_frames = {}\n for key, value in anno.items():\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n obj_class = self._get_class(seq_id)\n\n object_meta = OrderedDict({'object_class_name': obj_class,\n 'motion_class': None,\n 'major_class': None,\n 'root_class': None,\n 'motion_adverb': None})\n\n return frame_list, anno_frames, object_meta"},{"identifier":"sampler","path":"lib/train/data/sampler.py","snippet":"def no_processing(data):\n def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap,\n num_search_frames, num_template_frames=1, processing=no_processing, frame_sample_mode='causal',\n train_cls=False, pos_prob=0.5):\n def __len__(self):\n def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None,\n allow_invisible=False, force_invisible=False):\n def __getitem__(self, index):\n def getitem(self):\n def getitem_cls(self):\n def get_center_box(self, H, W, ratio=1/8):\n def sample_seq_from_dataset(self, dataset, is_video_dataset):\n def get_one_search(self):\n def get_frame_ids_trident(self, visible):\n def get_frame_ids_stark(self, visible, valid):\nclass TrackingSampler(torch.utils.data.Dataset):\n H, W, _ = template_frames[0].shape\n H, W, _ = template_frames[0].shape\n H, W, _ = search_frames[0].shape"},{"identifier":"processing","path":"lib/train/data/processing.py","snippet":"def stack_tensors(x):\n def __init__(self, transform=transforms.ToTensor(), template_transform=None, search_transform=None, joint_transform=None):\n def __call__(self, data: TensorDict):\n def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor,\n mode='pair', settings=None, *args, **kwargs):\n def _get_jittered_box(self, box, mode):\n def __call__(self, data: TensorDict):\nclass BaseProcessing:\nclass STARKProcessing(BaseProcessing):"},{"identifier":"LTRLoader","path":"lib/train/data/loader.py","snippet":"class LTRLoader(torch.utils.data.dataloader.DataLoader):\n \"\"\"\n Data loader. Combines a dataset and a sampler, and provides\n single- or multi-process iterators over the dataset.\n\n Note: The only difference with default pytorch DataLoader is that an additional option stack_dim is available to\n select along which dimension the data should be stacked to form a batch.\n\n Arguments:\n dataset (Dataset): dataset from which to load the data.\n batch_size (int, optional): how many samples per batch to load\n (default: 1).\n shuffle (bool, optional): set to ``True`` to have the data reshuffled\n at every epoch (default: False).\n sampler (Sampler, optional): defines the strategy to draw samples from\n the dataset. If specified, ``shuffle`` must be False.\n batch_sampler (Sampler, optional): like sampler, but returns a batch of\n indices at a time. Mutually exclusive with batch_size, shuffle,\n sampler, and drop_last.\n num_workers (int, optional): how many subprocesses to use for data\n loading. 0 means that the data will be loaded in the main process.\n (default: 0)\n collate_fn (callable, optional): merges a list of samples to form a mini-batch.\n stack_dim (int): Dimension along which to stack to form the batch. (default: 0)\n pin_memory (bool, optional): If ``True``, the data loader will copy tensors\n into CUDA pinned memory before returning them.\n drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,\n if the dataset size is not divisible by the batch size. If ``False`` and\n the size of dataset is not divisible by the batch size, then the last batch\n will be smaller. (default: False)\n timeout (numeric, optional): if positive, the timeout value for collecting a batch\n from workers. Should always be non-negative. (default: 0)\n worker_init_fn (callable, optional): If not None, this will be called on each\n worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as\n input, after seeding and before data loading. (default: None)\n\n .. note:: By default, each worker will have its PyTorch seed set to\n ``base_seed + worker_id``, where ``base_seed`` is a long generated\n by main process using its RNG. However, seeds for other libraries\n may be duplicated upon initializing workers (w.g., NumPy), causing\n each worker to return identical random numbers. (See\n :ref:`dataloader-workers-random-seed` section in FAQ.) You may\n use ``torch.initial_seed()`` to access the PyTorch seed for each\n worker in :attr:`worker_init_fn`, and use it to set other seeds\n before data loading.\n\n .. warning:: If ``spawn`` start method is used, :attr:`worker_init_fn` cannot be an\n unpicklable object, e.g., a lambda function.\n \"\"\"\n\n __initialized = False\n\n def __init__(self, name, dataset, training=True, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,\n num_workers=0, epoch_interval=1, collate_fn=None, stack_dim=0, pin_memory=False, drop_last=False,\n timeout=0, worker_init_fn=None):\n if collate_fn is None:\n if stack_dim == 0:\n collate_fn = ltr_collate\n elif stack_dim == 1:\n collate_fn = ltr_collate_stack1\n else:\n raise ValueError('Stack dim no supported. Must be 0 or 1.')\n\n super(LTRLoader, self).__init__(dataset, batch_size, shuffle, sampler, batch_sampler,\n num_workers, collate_fn, pin_memory, drop_last,\n timeout, worker_init_fn)\n\n self.name = name\n self.training = training\n self.epoch_interval = epoch_interval\n self.stack_dim = stack_dim"},{"identifier":"opencv_loader","path":"lib/train/data/image_loader.py","snippet":"def opencv_loader(path):\n \"\"\" Read image using opencv's imread function and returns it in rgb format\"\"\"\n try:\n im = cv.imread(path, cv.IMREAD_COLOR)\n\n # convert to rgb and return\n return cv.cvtColor(im, cv.COLOR_BGR2RGB)\n except Exception as e:\n print('ERROR: Could not read image \"{}\"'.format(path))\n print(e)\n return None"},{"identifier":"is_main_process","path":"lib/utils/misc.py","snippet":"def is_main_process():\n return get_rank() == 0"}],"string":"[\n {\n \"identifier\": \"Lasot\",\n \"path\": \"lib/train/dataset/lasot.py\",\n \"snippet\": \"class Lasot(BaseVideoDataset):\\n \\\"\\\"\\\" LaSOT dataset.\\n\\n Publication:\\n LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking\\n Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling\\n CVPR, 2019\\n https://arxiv.org/pdf/1809.07845.pdf\\n\\n Download the dataset from https://cis.temple.edu/lasot/download.html\\n \\\"\\\"\\\"\\n\\n def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None):\\n \\\"\\\"\\\"\\n args:\\n root - path to the lasot dataset.\\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\\n is used by default.\\n vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the\\n videos with subscripts -1, -3, and -5 from each class will be used for training.\\n split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of\\n vid_ids or split option can be used at a time.\\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\\n \\\"\\\"\\\"\\n root = env_settings().lasot_dir if root is None else root\\n super().__init__('LaSOT', root, image_loader)\\n\\n # Keep a list of all classes\\n self.class_list = [f for f in os.listdir(self.root)]\\n self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)}\\n\\n self.sequence_list = self._build_sequence_list(vid_ids, split)\\n\\n if data_fraction is not None:\\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\\n\\n self.seq_per_class = self._build_class_list()\\n\\n def _build_sequence_list(self, vid_ids=None, split=None):\\n if split is not None:\\n if vid_ids is not None:\\n raise ValueError('Cannot set both split_name and vid_ids.')\\n ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\\n if split == 'train':\\n file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt')\\n else:\\n raise ValueError('Unknown split name.')\\n # sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist()\\n sequence_list = pandas.read_csv(file_path, header=None).squeeze(\\\"columns\\\").values.tolist()\\n elif vid_ids is not None:\\n sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids]\\n else:\\n raise ValueError('Set either split_name or vid_ids.')\\n\\n return sequence_list\\n\\n def _build_class_list(self):\\n seq_per_class = {}\\n for seq_id, seq_name in enumerate(self.sequence_list):\\n class_name = seq_name.split('-')[0]\\n if class_name in seq_per_class:\\n seq_per_class[class_name].append(seq_id)\\n else:\\n seq_per_class[class_name] = [seq_id]\\n\\n return seq_per_class\\n\\n def get_name(self):\\n return 'lasot'\\n\\n def has_class_info(self):\\n return True\\n\\n def has_occlusion_info(self):\\n return True\\n\\n def get_num_sequences(self):\\n return len(self.sequence_list)\\n\\n def get_num_classes(self):\\n return len(self.class_list)\\n\\n def get_sequences_in_class(self, class_name):\\n return self.seq_per_class[class_name]\\n\\n def _read_bb_anno(self, seq_path):\\n bb_anno_file = os.path.join(seq_path, \\\"groundtruth.txt\\\")\\n gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values\\n return torch.tensor(gt)\\n\\n def _read_target_visible(self, seq_path):\\n # Read full occlusion and out_of_view\\n occlusion_file = os.path.join(seq_path, \\\"full_occlusion.txt\\\")\\n out_of_view_file = os.path.join(seq_path, \\\"out_of_view.txt\\\")\\n\\n with open(occlusion_file, 'r', newline='') as f:\\n occlusion = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]])\\n with open(out_of_view_file, 'r') as f:\\n out_of_view = torch.ByteTensor([int(v) for v in list(csv.reader(f))[0]])\\n\\n target_visible = ~occlusion & ~out_of_view\\n\\n return target_visible\\n\\n def _get_sequence_path(self, seq_id):\\n seq_name = self.sequence_list[seq_id]\\n class_name = seq_name.split('-')[0]\\n vid_id = seq_name.split('-')[1]\\n\\n return os.path.join(self.root, class_name, class_name + '-' + vid_id)\\n\\n def get_sequence_info(self, seq_id):\\n seq_path = self._get_sequence_path(seq_id)\\n bbox = self._read_bb_anno(seq_path)\\n\\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\\n visible = self._read_target_visible(seq_path) & valid.byte()\\n\\n return {'bbox': bbox, 'valid': valid, 'visible': visible}\\n\\n def _get_frame_path(self, seq_path, frame_id):\\n return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1\\n\\n def _get_frame(self, seq_path, frame_id):\\n return self.image_loader(self._get_frame_path(seq_path, frame_id))\\n\\n def _get_class(self, seq_path):\\n raw_class = seq_path.split('/')[-2]\\n return raw_class\\n\\n def get_class_name(self, seq_id):\\n seq_path = self._get_sequence_path(seq_id)\\n obj_class = self._get_class(seq_path)\\n\\n return obj_class\\n\\n def get_frames(self, seq_id, frame_ids, anno=None):\\n seq_path = self._get_sequence_path(seq_id)\\n\\n obj_class = self._get_class(seq_path)\\n frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\\n\\n if anno is None:\\n anno = self.get_sequence_info(seq_id)\\n\\n anno_frames = {}\\n for key, value in anno.items():\\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\\n\\n object_meta = OrderedDict({'object_class_name': obj_class,\\n 'motion_class': None,\\n 'major_class': None,\\n 'root_class': None,\\n 'motion_adverb': None})\\n\\n return frame_list, anno_frames, object_meta\"\n },\n {\n \"identifier\": \"Got10k\",\n \"path\": \"lib/train/dataset/got10k.py\",\n \"snippet\": \"class Got10k(BaseVideoDataset):\\n \\\"\\\"\\\" GOT-10k dataset.\\n\\n Publication:\\n GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild\\n Lianghua Huang, Xin Zhao, and Kaiqi Huang\\n arXiv:1810.11981, 2018\\n https://arxiv.org/pdf/1810.11981.pdf\\n\\n Download dataset from http://got-10k.aitestunion.com/downloads\\n \\\"\\\"\\\"\\n\\n def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None):\\n \\\"\\\"\\\"\\n args:\\n root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k\\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\\n is used by default.\\n split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split,\\n not NOT the official got-10k validation split. To use the official validation split, provide that as\\n the root folder instead.\\n seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids'\\n options can be used at the same time.\\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\\n \\\"\\\"\\\"\\n root = env_settings().got10k_dir if root is None else root\\n super().__init__('GOT10k', root, image_loader)\\n\\n # all folders inside the root\\n self.sequence_list = self._get_sequence_list()\\n\\n # seq_id is the index of the folder inside the got10k root path\\n if split is not None:\\n if seq_ids is not None:\\n raise ValueError('Cannot set both split_name and seq_ids.')\\n ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\\n if split == 'train':\\n file_path = os.path.join(ltr_path, 'data_specs', 'got10k_train_split.txt')\\n elif split == 'val':\\n file_path = os.path.join(ltr_path, 'data_specs', 'got10k_val_split.txt')\\n elif split == 'train_full':\\n file_path = os.path.join(ltr_path, 'data_specs', 'got10k_train_full_split.txt')\\n elif split == 'vottrain':\\n file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_train_split.txt')\\n elif split == 'votval':\\n file_path = os.path.join(ltr_path, 'data_specs', 'got10k_vot_val_split.txt')\\n else:\\n raise ValueError('Unknown split name.')\\n # seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist()\\n seq_ids = pandas.read_csv(file_path, header=None, dtype=np.int64).squeeze(\\\"columns\\\").values.tolist()\\n elif seq_ids is None:\\n seq_ids = list(range(0, len(self.sequence_list)))\\n\\n self.sequence_list = [self.sequence_list[i] for i in seq_ids]\\n\\n if data_fraction is not None:\\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\\n\\n self.sequence_meta_info = self._load_meta_info()\\n self.seq_per_class = self._build_seq_per_class()\\n\\n self.class_list = list(self.seq_per_class.keys())\\n self.class_list.sort()\\n\\n def get_name(self):\\n return 'got10k'\\n\\n def has_class_info(self):\\n return True\\n\\n def has_occlusion_info(self):\\n return True\\n\\n def _load_meta_info(self):\\n sequence_meta_info = {s: self._read_meta(os.path.join(self.root, s)) for s in self.sequence_list}\\n return sequence_meta_info\\n\\n def _read_meta(self, seq_path):\\n try:\\n with open(os.path.join(seq_path, 'meta_info.ini')) as f:\\n meta_info = f.readlines()\\n object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1][:-1],\\n 'motion_class': meta_info[6].split(': ')[-1][:-1],\\n 'major_class': meta_info[7].split(': ')[-1][:-1],\\n 'root_class': meta_info[8].split(': ')[-1][:-1],\\n 'motion_adverb': meta_info[9].split(': ')[-1][:-1]})\\n except:\\n object_meta = OrderedDict({'object_class_name': None,\\n 'motion_class': None,\\n 'major_class': None,\\n 'root_class': None,\\n 'motion_adverb': None})\\n return object_meta\\n\\n def _build_seq_per_class(self):\\n seq_per_class = {}\\n\\n for i, s in enumerate(self.sequence_list):\\n object_class = self.sequence_meta_info[s]['object_class_name']\\n if object_class in seq_per_class:\\n seq_per_class[object_class].append(i)\\n else:\\n seq_per_class[object_class] = [i]\\n\\n return seq_per_class\\n\\n def get_sequences_in_class(self, class_name):\\n return self.seq_per_class[class_name]\\n\\n def _get_sequence_list(self):\\n with open(os.path.join(self.root, 'list.txt')) as f:\\n dir_list = list(csv.reader(f))\\n dir_list = [dir_name[0] for dir_name in dir_list]\\n return dir_list\\n\\n def _read_bb_anno(self, seq_path):\\n bb_anno_file = os.path.join(seq_path, \\\"groundtruth.txt\\\")\\n gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values\\n return torch.tensor(gt)\\n\\n def _read_target_visible(self, seq_path):\\n # Read full occlusion and out_of_view\\n occlusion_file = os.path.join(seq_path, \\\"absence.label\\\")\\n cover_file = os.path.join(seq_path, \\\"cover.label\\\")\\n\\n with open(occlusion_file, 'r', newline='') as f:\\n occlusion = torch.ByteTensor([int(v[0]) for v in csv.reader(f)])\\n with open(cover_file, 'r', newline='') as f:\\n cover = torch.ByteTensor([int(v[0]) for v in csv.reader(f)])\\n\\n target_visible = ~occlusion & (cover>0).byte()\\n\\n visible_ratio = cover.float() / 8\\n return target_visible, visible_ratio\\n\\n def _get_sequence_path(self, seq_id):\\n return os.path.join(self.root, self.sequence_list[seq_id])\\n\\n def get_sequence_info(self, seq_id):\\n seq_path = self._get_sequence_path(seq_id)\\n bbox = self._read_bb_anno(seq_path)\\n\\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\\n visible, visible_ratio = self._read_target_visible(seq_path)\\n visible = visible & valid.byte()\\n\\n return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio}\\n\\n def _get_frame_path(self, seq_path, frame_id):\\n return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1\\n\\n def _get_frame(self, seq_path, frame_id):\\n return self.image_loader(self._get_frame_path(seq_path, frame_id))\\n\\n def get_class_name(self, seq_id):\\n obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]\\n\\n return obj_meta['object_class_name']\\n\\n def get_frames(self, seq_id, frame_ids, anno=None):\\n seq_path = self._get_sequence_path(seq_id)\\n obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]\\n\\n frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\\n\\n if anno is None:\\n anno = self.get_sequence_info(seq_id)\\n\\n anno_frames = {}\\n for key, value in anno.items():\\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\\n\\n return frame_list, anno_frames, obj_meta\"\n },\n {\n \"identifier\": \"TrackingNet\",\n \"path\": \"lib/train/dataset/tracking_net.py\",\n \"snippet\": \"class TrackingNet(BaseVideoDataset):\\n \\\"\\\"\\\" TrackingNet dataset.\\n\\n Publication:\\n TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild.\\n Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem\\n ECCV, 2018\\n https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf\\n\\n Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit.\\n \\\"\\\"\\\"\\n def __init__(self, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None):\\n \\\"\\\"\\\"\\n args:\\n root - The path to the TrackingNet folder, containing the training sets.\\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\\n is used by default.\\n set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the\\n sets (0 - 11) will be used.\\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\\n \\\"\\\"\\\"\\n root = env_settings().trackingnet_dir if root is None else root\\n super().__init__('TrackingNet', root, image_loader)\\n\\n if set_ids is None:\\n set_ids = [i for i in range(12)]\\n\\n self.set_ids = set_ids\\n\\n # Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and\\n # video_name for each sequence\\n self.sequence_list = list_sequences(self.root, self.set_ids)\\n\\n if data_fraction is not None:\\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction))\\n\\n self.seq_to_class_map, self.seq_per_class = self._load_class_info()\\n\\n # we do not have the class_lists for the tracking net\\n self.class_list = list(self.seq_per_class.keys())\\n self.class_list.sort()\\n\\n def _load_class_info(self):\\n ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\\n class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt')\\n\\n with open(class_map_path, 'r') as f:\\n seq_to_class_map = {seq_class.split('\\\\t')[0]: seq_class.rstrip().split('\\\\t')[1] for seq_class in f}\\n\\n seq_per_class = {}\\n for i, seq in enumerate(self.sequence_list):\\n class_name = seq_to_class_map.get(seq[1], 'Unknown')\\n if class_name not in seq_per_class:\\n seq_per_class[class_name] = [i]\\n else:\\n seq_per_class[class_name].append(i)\\n\\n return seq_to_class_map, seq_per_class\\n\\n def get_name(self):\\n return 'trackingnet'\\n\\n def has_class_info(self):\\n return True\\n\\n def get_sequences_in_class(self, class_name):\\n return self.seq_per_class[class_name]\\n\\n def _read_bb_anno(self, seq_id):\\n set_id = self.sequence_list[seq_id][0]\\n vid_name = self.sequence_list[seq_id][1]\\n bb_anno_file = os.path.join(self.root, \\\"TRAIN_\\\" + str(set_id), \\\"anno\\\", vid_name + \\\".txt\\\")\\n gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False,\\n low_memory=False).values\\n return torch.tensor(gt)\\n\\n def get_sequence_info(self, seq_id):\\n bbox = self._read_bb_anno(seq_id)\\n\\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\\n visible = valid.clone().byte()\\n return {'bbox': bbox, 'valid': valid, 'visible': visible}\\n\\n def _get_frame(self, seq_id, frame_id):\\n set_id = self.sequence_list[seq_id][0]\\n vid_name = self.sequence_list[seq_id][1]\\n frame_path = os.path.join(self.root, \\\"TRAIN_\\\" + str(set_id), \\\"frames\\\", vid_name, str(frame_id) + \\\".jpg\\\")\\n return self.image_loader(frame_path)\\n\\n def _get_class(self, seq_id):\\n seq_name = self.sequence_list[seq_id][1]\\n return self.seq_to_class_map[seq_name]\\n\\n def get_class_name(self, seq_id):\\n obj_class = self._get_class(seq_id)\\n\\n return obj_class\\n\\n def get_frames(self, seq_id, frame_ids, anno=None):\\n frame_list = [self._get_frame(seq_id, f) for f in frame_ids]\\n\\n if anno is None:\\n anno = self.get_sequence_info(seq_id)\\n\\n anno_frames = {}\\n for key, value in anno.items():\\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\\n\\n obj_class = self._get_class(seq_id)\\n\\n object_meta = OrderedDict({'object_class_name': obj_class,\\n 'motion_class': None,\\n 'major_class': None,\\n 'root_class': None,\\n 'motion_adverb': None})\\n\\n return frame_list, anno_frames, object_meta\"\n },\n {\n \"identifier\": \"ImagenetVID\",\n \"path\": \"lib/train/dataset/imagenetvid.py\",\n \"snippet\": \"class ImagenetVID(BaseVideoDataset):\\n \\\"\\\"\\\" Imagenet VID dataset.\\n\\n Publication:\\n ImageNet Large Scale Visual Recognition Challenge\\n Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy,\\n Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei\\n IJCV, 2015\\n https://arxiv.org/pdf/1409.0575.pdf\\n\\n Download the dataset from http://image-net.org/\\n \\\"\\\"\\\"\\n def __init__(self, root=None, image_loader=jpeg4py_loader, min_length=0, max_target_area=1):\\n \\\"\\\"\\\"\\n args:\\n root - path to the imagenet vid dataset.\\n image_loader (default_image_loader) - The function to read the images. If installed,\\n jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else,\\n opencv's imread is used.\\n min_length - Minimum allowed sequence length.\\n max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets\\n which cover complete image.\\n \\\"\\\"\\\"\\n root = env_settings().imagenet_dir if root is None else root\\n super().__init__(\\\"imagenetvid\\\", root, image_loader)\\n\\n cache_file = os.path.join(root, 'cache.json')\\n if os.path.isfile(cache_file):\\n # If available, load the pre-processed cache file containing meta-info for each sequence\\n with open(cache_file, 'r') as f:\\n sequence_list_dict = json.load(f)\\n\\n self.sequence_list = sequence_list_dict\\n else:\\n # Else process the imagenet annotations and generate the cache file\\n self.sequence_list = self._process_anno(root)\\n\\n with open(cache_file, 'w') as f:\\n json.dump(self.sequence_list, f)\\n\\n # Filter the sequences based on min_length and max_target_area in the first frame\\n self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and\\n get_target_to_image_ratio(x) < max_target_area]\\n\\n def get_name(self):\\n return 'imagenetvid'\\n\\n def get_num_sequences(self):\\n return len(self.sequence_list)\\n\\n def get_sequence_info(self, seq_id):\\n bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno'])\\n valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0)\\n visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte()\\n return {'bbox': bb_anno, 'valid': valid, 'visible': visible}\\n\\n def _get_frame(self, sequence, frame_id):\\n set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id'])\\n vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id'])\\n frame_number = frame_id + sequence['start_frame']\\n frame_path = os.path.join(self.root, 'Data', 'VID', 'train', set_name, vid_name,\\n '{:06d}.JPEG'.format(frame_number))\\n return self.image_loader(frame_path)\\n\\n def get_frames(self, seq_id, frame_ids, anno=None):\\n sequence = self.sequence_list[seq_id]\\n\\n frame_list = [self._get_frame(sequence, f) for f in frame_ids]\\n\\n if anno is None:\\n anno = self.get_sequence_info(seq_id)\\n\\n # Create anno dict\\n anno_frames = {}\\n for key, value in anno.items():\\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\\n\\n # added the class info to the meta info\\n object_meta = OrderedDict({'object_class': sequence['class_name'],\\n 'motion_class': None,\\n 'major_class': None,\\n 'root_class': None,\\n 'motion_adverb': None})\\n\\n return frame_list, anno_frames, object_meta\\n\\n def _process_anno(self, root):\\n # Builds individual tracklets\\n base_vid_anno_path = os.path.join(root, 'Annotations', 'VID', 'train')\\n\\n all_sequences = []\\n for set in sorted(os.listdir(base_vid_anno_path)):\\n set_id = int(set.split('_')[-1])\\n for vid in sorted(os.listdir(os.path.join(base_vid_anno_path, set))):\\n\\n vid_id = int(vid.split('_')[-1])\\n anno_files = sorted(os.listdir(os.path.join(base_vid_anno_path, set, vid)))\\n\\n frame1_anno = ET.parse(os.path.join(base_vid_anno_path, set, vid, anno_files[0]))\\n image_size = [int(frame1_anno.find('size/width').text), int(frame1_anno.find('size/height').text)]\\n\\n objects = [ET.ElementTree(file=os.path.join(base_vid_anno_path, set, vid, f)).findall('object')\\n for f in anno_files]\\n\\n tracklets = {}\\n\\n # Find all tracklets along with start frame\\n for f_id, all_targets in enumerate(objects):\\n for target in all_targets:\\n tracklet_id = target.find('trackid').text\\n if tracklet_id not in tracklets:\\n tracklets[tracklet_id] = f_id\\n\\n for tracklet_id, tracklet_start in tracklets.items():\\n tracklet_anno = []\\n target_visible = []\\n class_name_id = None\\n\\n for f_id in range(tracklet_start, len(objects)):\\n found = False\\n for target in objects[f_id]:\\n if target.find('trackid').text == tracklet_id:\\n if not class_name_id:\\n class_name_id = target.find('name').text\\n x1 = int(target.find('bndbox/xmin').text)\\n y1 = int(target.find('bndbox/ymin').text)\\n x2 = int(target.find('bndbox/xmax').text)\\n y2 = int(target.find('bndbox/ymax').text)\\n\\n tracklet_anno.append([x1, y1, x2 - x1, y2 - y1])\\n target_visible.append(target.find('occluded').text == '0')\\n\\n found = True\\n break\\n if not found:\\n break\\n\\n new_sequence = {'set_id': set_id, 'vid_id': vid_id, 'class_name': class_name_id,\\n 'start_frame': tracklet_start, 'anno': tracklet_anno,\\n 'target_visible': target_visible, 'image_size': image_size}\\n all_sequences.append(new_sequence)\\n\\n return all_sequences\"\n },\n {\n \"identifier\": \"MSCOCOSeq\",\n \"path\": \"lib/train/dataset/coco_seq.py\",\n \"snippet\": \"class MSCOCOSeq(BaseVideoDataset):\\n \\\"\\\"\\\" The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1.\\n\\n Publication:\\n Microsoft COCO: Common Objects in Context.\\n Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona,\\n Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick\\n ECCV, 2014\\n https://arxiv.org/pdf/1405.0312.pdf\\n\\n Download the images along with annotations from http://cocodataset.org/#download. The root folder should be\\n organized as follows.\\n - coco_root\\n - annotations\\n - instances_train2014.json\\n - instances_train2017.json\\n - images\\n - train2014\\n - train2017\\n\\n Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi.\\n \\\"\\\"\\\"\\n\\n def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split=\\\"train\\\", version=\\\"2014\\\"):\\n \\\"\\\"\\\"\\n args:\\n root - path to the coco dataset.\\n image_loader (default_image_loader) - The function to read the images. If installed,\\n jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else,\\n opencv's imread is used.\\n data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the\\n images will be used\\n split - 'train' or 'val'.\\n version - version of coco dataset (2014 or 2017)\\n \\\"\\\"\\\"\\n root = env_settings().coco_dir if root is None else root\\n super().__init__('COCO', root, image_loader)\\n\\n self.img_pth = os.path.join(root, 'images/{}{}/'.format(split, version))\\n self.anno_path = os.path.join(root, 'annotations/instances_{}{}.json'.format(split, version))\\n\\n # Load the COCO set.\\n self.coco_set = COCO(self.anno_path)\\n\\n self.cats = self.coco_set.cats\\n\\n self.class_list = self.get_class_list()\\n\\n self.sequence_list = self._get_sequence_list()\\n\\n if data_fraction is not None:\\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\\n self.seq_per_class = self._build_seq_per_class()\\n\\n def _get_sequence_list(self):\\n ann_list = list(self.coco_set.anns.keys())\\n seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0]\\n\\n return seq_list\\n\\n def is_video_sequence(self):\\n return False\\n\\n def get_num_classes(self):\\n return len(self.class_list)\\n\\n def get_name(self):\\n return 'coco'\\n\\n def has_class_info(self):\\n return True\\n\\n def get_class_list(self):\\n class_list = []\\n for cat_id in self.cats.keys():\\n class_list.append(self.cats[cat_id]['name'])\\n return class_list\\n\\n def has_segmentation_info(self):\\n return True\\n\\n def get_num_sequences(self):\\n return len(self.sequence_list)\\n\\n def _build_seq_per_class(self):\\n seq_per_class = {}\\n for i, seq in enumerate(self.sequence_list):\\n class_name = self.cats[self.coco_set.anns[seq]['category_id']]['name']\\n if class_name not in seq_per_class:\\n seq_per_class[class_name] = [i]\\n else:\\n seq_per_class[class_name].append(i)\\n\\n return seq_per_class\\n\\n def get_sequences_in_class(self, class_name):\\n return self.seq_per_class[class_name]\\n\\n def get_sequence_info(self, seq_id):\\n anno = self._get_anno(seq_id)\\n\\n bbox = torch.Tensor(anno['bbox']).view(1, 4)\\n\\n mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0)\\n\\n '''2021.1.3 To avoid too small bounding boxes. Here we change the threshold to 50 pixels'''\\n valid = (bbox[:, 2] > 50) & (bbox[:, 3] > 50)\\n\\n visible = valid.clone().byte()\\n\\n return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible}\\n\\n def _get_anno(self, seq_id):\\n anno = self.coco_set.anns[self.sequence_list[seq_id]]\\n\\n return anno\\n\\n def _get_frames(self, seq_id):\\n path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name']\\n img = self.image_loader(os.path.join(self.img_pth, path))\\n return img\\n\\n def get_meta_info(self, seq_id):\\n try:\\n cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']]\\n object_meta = OrderedDict({'object_class_name': cat_dict_current['name'],\\n 'motion_class': None,\\n 'major_class': cat_dict_current['supercategory'],\\n 'root_class': None,\\n 'motion_adverb': None})\\n except:\\n object_meta = OrderedDict({'object_class_name': None,\\n 'motion_class': None,\\n 'major_class': None,\\n 'root_class': None,\\n 'motion_adverb': None})\\n return object_meta\\n\\n\\n def get_class_name(self, seq_id):\\n cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']]\\n return cat_dict_current['name']\\n\\n def get_frames(self, seq_id=None, frame_ids=None, anno=None):\\n # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a\\n # list containing these replicated images.\\n frame = self._get_frames(seq_id)\\n\\n frame_list = [frame.copy() for _ in frame_ids]\\n\\n if anno is None:\\n anno = self.get_sequence_info(seq_id)\\n\\n anno_frames = {}\\n for key, value in anno.items():\\n anno_frames[key] = [value[0, ...] for _ in frame_ids]\\n\\n object_meta = self.get_meta_info(seq_id)\\n\\n return frame_list, anno_frames, object_meta\"\n },\n {\n \"identifier\": \"Got10k_lmdb\",\n \"path\": \"lib/train/dataset/got10k_lmdb.py\",\n \"snippet\": \"class Got10k_lmdb(BaseVideoDataset):\\n\\n def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None):\\n \\\"\\\"\\\"\\n args:\\n root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k\\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\\n is used by default.\\n split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split,\\n not NOT the official got-10k validation split. To use the official validation split, provide that as\\n the root folder instead.\\n seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids'\\n options can be used at the same time.\\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\\n use_lmdb - whether the dataset is stored in lmdb format\\n \\\"\\\"\\\"\\n root = env_settings().got10k_lmdb_dir if root is None else root\\n super().__init__('GOT10k_lmdb', root, image_loader)\\n\\n # all folders inside the root\\n self.sequence_list = self._get_sequence_list()\\n\\n # seq_id is the index of the folder inside the got10k root path\\n if split is not None:\\n if seq_ids is not None:\\n raise ValueError('Cannot set both split_name and seq_ids.')\\n train_lib_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\\n if split == 'train':\\n file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_split.txt')\\n elif split == 'val':\\n file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_val_split.txt')\\n elif split == 'train_full':\\n file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_train_full_split.txt')\\n elif split == 'vottrain':\\n file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_train_split.txt')\\n elif split == 'votval':\\n file_path = os.path.join(train_lib_path, 'data_specs', 'got10k_vot_val_split.txt')\\n else:\\n raise ValueError('Unknown split name.')\\n seq_ids = pandas.read_csv(file_path, header=None, squeeze=True, dtype=np.int64).values.tolist()\\n elif seq_ids is None:\\n seq_ids = list(range(0, len(self.sequence_list)))\\n\\n self.sequence_list = [self.sequence_list[i] for i in seq_ids]\\n\\n if data_fraction is not None:\\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\\n\\n self.sequence_meta_info = self._load_meta_info()\\n self.seq_per_class = self._build_seq_per_class()\\n\\n self.class_list = list(self.seq_per_class.keys())\\n self.class_list.sort()\\n\\n def get_name(self):\\n return 'got10k_lmdb'\\n\\n def has_class_info(self):\\n return True\\n\\n def has_occlusion_info(self):\\n return True\\n\\n def _load_meta_info(self):\\n def _read_meta(meta_info):\\n\\n object_meta = OrderedDict({'object_class_name': meta_info[5].split(': ')[-1],\\n 'motion_class': meta_info[6].split(': ')[-1],\\n 'major_class': meta_info[7].split(': ')[-1],\\n 'root_class': meta_info[8].split(': ')[-1],\\n 'motion_adverb': meta_info[9].split(': ')[-1]})\\n\\n return object_meta\\n sequence_meta_info = {}\\n for s in self.sequence_list:\\n try:\\n meta_str = decode_str(self.root, \\\"train/%s/meta_info.ini\\\" %s)\\n sequence_meta_info[s] = _read_meta(meta_str.split('\\\\n'))\\n except:\\n sequence_meta_info[s] = OrderedDict({'object_class_name': None,\\n 'motion_class': None,\\n 'major_class': None,\\n 'root_class': None,\\n 'motion_adverb': None})\\n return sequence_meta_info\\n\\n def _build_seq_per_class(self):\\n seq_per_class = {}\\n\\n for i, s in enumerate(self.sequence_list):\\n object_class = self.sequence_meta_info[s]['object_class_name']\\n if object_class in seq_per_class:\\n seq_per_class[object_class].append(i)\\n else:\\n seq_per_class[object_class] = [i]\\n\\n return seq_per_class\\n\\n def get_sequences_in_class(self, class_name):\\n return self.seq_per_class[class_name]\\n\\n def _get_sequence_list(self):\\n dir_str = decode_str(self.root, 'train/list.txt')\\n dir_list = dir_str.split('\\\\n')\\n return dir_list\\n\\n def _read_bb_anno(self, seq_path):\\n bb_anno_file = os.path.join(seq_path, \\\"groundtruth.txt\\\")\\n gt_str_list = decode_str(self.root, bb_anno_file).split('\\\\n')[:-1] # the last line in got10k is empty\\n gt_list = [list(map(float, line.split(','))) for line in gt_str_list]\\n gt_arr = np.array(gt_list).astype(np.float32)\\n\\n return torch.tensor(gt_arr)\\n\\n def _read_target_visible(self, seq_path):\\n # full occlusion and out_of_view files\\n occlusion_file = os.path.join(seq_path, \\\"absence.label\\\")\\n cover_file = os.path.join(seq_path, \\\"cover.label\\\")\\n # Read these files\\n occ_list = list(map(int, decode_str(self.root, occlusion_file).split('\\\\n')[:-1])) # the last line in got10k is empty\\n occlusion = torch.ByteTensor(occ_list)\\n cover_list = list(map(int, decode_str(self.root, cover_file).split('\\\\n')[:-1])) # the last line in got10k is empty\\n cover = torch.ByteTensor(cover_list)\\n\\n target_visible = ~occlusion & (cover>0).byte()\\n\\n visible_ratio = cover.float() / 8\\n return target_visible, visible_ratio\\n\\n def _get_sequence_path(self, seq_id):\\n return os.path.join(\\\"train\\\", self.sequence_list[seq_id])\\n\\n def get_sequence_info(self, seq_id):\\n seq_path = self._get_sequence_path(seq_id)\\n bbox = self._read_bb_anno(seq_path)\\n\\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\\n visible, visible_ratio = self._read_target_visible(seq_path)\\n visible = visible & valid.byte()\\n\\n return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio}\\n\\n def _get_frame_path(self, seq_path, frame_id):\\n return os.path.join(seq_path, '{:08}.jpg'.format(frame_id+1)) # frames start from 1\\n\\n def _get_frame(self, seq_path, frame_id):\\n return decode_img(self.root, self._get_frame_path(seq_path, frame_id))\\n\\n def get_class_name(self, seq_id):\\n obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]\\n\\n return obj_meta['object_class_name']\\n\\n def get_frames(self, seq_id, frame_ids, anno=None):\\n seq_path = self._get_sequence_path(seq_id)\\n obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]\\n\\n frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\\n\\n if anno is None:\\n anno = self.get_sequence_info(seq_id)\\n\\n anno_frames = {}\\n for key, value in anno.items():\\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\\n\\n return frame_list, anno_frames, obj_meta\"\n },\n {\n \"identifier\": \"Lasot_lmdb\",\n \"path\": \"lib/train/dataset/lasot_lmdb.py\",\n \"snippet\": \"class Lasot_lmdb(BaseVideoDataset):\\n\\n def __init__(self, root=None, image_loader=jpeg4py_loader, vid_ids=None, split=None, data_fraction=None):\\n \\\"\\\"\\\"\\n args:\\n root - path to the lasot dataset.\\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\\n is used by default.\\n vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the\\n videos with subscripts -1, -3, and -5 from each class will be used for training.\\n split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of\\n vid_ids or split option can be used at a time.\\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\\n \\\"\\\"\\\"\\n root = env_settings().lasot_lmdb_dir if root is None else root\\n super().__init__('LaSOT_lmdb', root, image_loader)\\n\\n self.sequence_list = self._build_sequence_list(vid_ids, split)\\n class_list = [seq_name.split('-')[0] for seq_name in self.sequence_list]\\n self.class_list = []\\n for ele in class_list:\\n if ele not in self.class_list:\\n self.class_list.append(ele)\\n # Keep a list of all classes\\n self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)}\\n\\n if data_fraction is not None:\\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\\n\\n self.seq_per_class = self._build_class_list()\\n\\n def _build_sequence_list(self, vid_ids=None, split=None):\\n if split is not None:\\n if vid_ids is not None:\\n raise ValueError('Cannot set both split_name and vid_ids.')\\n ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\\n if split == 'train':\\n file_path = os.path.join(ltr_path, 'data_specs', 'lasot_train_split.txt')\\n else:\\n raise ValueError('Unknown split name.')\\n sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist()\\n elif vid_ids is not None:\\n sequence_list = [c+'-'+str(v) for c in self.class_list for v in vid_ids]\\n else:\\n raise ValueError('Set either split_name or vid_ids.')\\n\\n return sequence_list\\n\\n def _build_class_list(self):\\n seq_per_class = {}\\n for seq_id, seq_name in enumerate(self.sequence_list):\\n class_name = seq_name.split('-')[0]\\n if class_name in seq_per_class:\\n seq_per_class[class_name].append(seq_id)\\n else:\\n seq_per_class[class_name] = [seq_id]\\n\\n return seq_per_class\\n\\n def get_name(self):\\n return 'lasot_lmdb'\\n\\n def has_class_info(self):\\n return True\\n\\n def has_occlusion_info(self):\\n return True\\n\\n def get_num_sequences(self):\\n return len(self.sequence_list)\\n\\n def get_num_classes(self):\\n return len(self.class_list)\\n\\n def get_sequences_in_class(self, class_name):\\n return self.seq_per_class[class_name]\\n\\n def _read_bb_anno(self, seq_path):\\n bb_anno_file = os.path.join(seq_path, \\\"groundtruth.txt\\\")\\n gt_str_list = decode_str(self.root, bb_anno_file).split('\\\\n')[:-1] # the last line is empty\\n gt_list = [list(map(float, line.split(','))) for line in gt_str_list]\\n gt_arr = np.array(gt_list).astype(np.float32)\\n return torch.tensor(gt_arr)\\n\\n def _read_target_visible(self, seq_path):\\n # Read full occlusion and out_of_view\\n occlusion_file = os.path.join(seq_path, \\\"full_occlusion.txt\\\")\\n out_of_view_file = os.path.join(seq_path, \\\"out_of_view.txt\\\")\\n\\n occ_list = list(map(int, decode_str(self.root, occlusion_file).split(',')))\\n occlusion = torch.ByteTensor(occ_list)\\n out_view_list = list(map(int, decode_str(self.root, out_of_view_file).split(',')))\\n out_of_view = torch.ByteTensor(out_view_list)\\n\\n target_visible = ~occlusion & ~out_of_view\\n\\n return target_visible\\n\\n def _get_sequence_path(self, seq_id):\\n seq_name = self.sequence_list[seq_id]\\n class_name = seq_name.split('-')[0]\\n vid_id = seq_name.split('-')[1]\\n\\n return os.path.join(class_name, class_name + '-' + vid_id)\\n\\n def get_sequence_info(self, seq_id):\\n seq_path = self._get_sequence_path(seq_id)\\n bbox = self._read_bb_anno(seq_path)\\n\\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\\n visible = self._read_target_visible(seq_path) & valid.byte()\\n\\n return {'bbox': bbox, 'valid': valid, 'visible': visible}\\n\\n def _get_frame_path(self, seq_path, frame_id):\\n return os.path.join(seq_path, 'img', '{:08}.jpg'.format(frame_id+1)) # frames start from 1\\n\\n def _get_frame(self, seq_path, frame_id):\\n return decode_img(self.root, self._get_frame_path(seq_path, frame_id))\\n\\n def _get_class(self, seq_path):\\n raw_class = seq_path.split('/')[-2]\\n return raw_class\\n\\n def get_class_name(self, seq_id):\\n seq_path = self._get_sequence_path(seq_id)\\n obj_class = self._get_class(seq_path)\\n\\n return obj_class\\n\\n def get_frames(self, seq_id, frame_ids, anno=None):\\n seq_path = self._get_sequence_path(seq_id)\\n\\n obj_class = self._get_class(seq_path)\\n frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\\n\\n if anno is None:\\n anno = self.get_sequence_info(seq_id)\\n\\n anno_frames = {}\\n for key, value in anno.items():\\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\\n\\n object_meta = OrderedDict({'object_class_name': obj_class,\\n 'motion_class': None,\\n 'major_class': None,\\n 'root_class': None,\\n 'motion_adverb': None})\\n\\n return frame_list, anno_frames, object_meta\"\n },\n {\n \"identifier\": \"ImagenetVID_lmdb\",\n \"path\": \"lib/train/dataset/imagenetvid_lmdb.py\",\n \"snippet\": \"class ImagenetVID_lmdb(BaseVideoDataset):\\n \\\"\\\"\\\" Imagenet VID dataset.\\n\\n Publication:\\n ImageNet Large Scale Visual Recognition Challenge\\n Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy,\\n Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei\\n IJCV, 2015\\n https://arxiv.org/pdf/1409.0575.pdf\\n\\n Download the dataset from http://image-net.org/\\n \\\"\\\"\\\"\\n def __init__(self, root=None, image_loader=jpeg4py_loader, min_length=0, max_target_area=1):\\n \\\"\\\"\\\"\\n args:\\n root - path to the imagenet vid dataset.\\n image_loader (default_image_loader) - The function to read the images. If installed,\\n jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else,\\n opencv's imread is used.\\n min_length - Minimum allowed sequence length.\\n max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets\\n which cover complete image.\\n \\\"\\\"\\\"\\n root = env_settings().imagenet_dir if root is None else root\\n super().__init__(\\\"imagenetvid_lmdb\\\", root, image_loader)\\n\\n sequence_list_dict = decode_json(root, \\\"cache.json\\\")\\n self.sequence_list = sequence_list_dict\\n\\n # Filter the sequences based on min_length and max_target_area in the first frame\\n self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and\\n get_target_to_image_ratio(x) < max_target_area]\\n\\n def get_name(self):\\n return 'imagenetvid_lmdb'\\n\\n def get_num_sequences(self):\\n return len(self.sequence_list)\\n\\n def get_sequence_info(self, seq_id):\\n bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno'])\\n valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0)\\n visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte()\\n return {'bbox': bb_anno, 'valid': valid, 'visible': visible}\\n\\n def _get_frame(self, sequence, frame_id):\\n set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id'])\\n vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id'])\\n frame_number = frame_id + sequence['start_frame']\\n frame_path = os.path.join('Data', 'VID', 'train', set_name, vid_name,\\n '{:06d}.JPEG'.format(frame_number))\\n return decode_img(self.root, frame_path)\\n\\n def get_frames(self, seq_id, frame_ids, anno=None):\\n sequence = self.sequence_list[seq_id]\\n\\n frame_list = [self._get_frame(sequence, f) for f in frame_ids]\\n\\n if anno is None:\\n anno = self.get_sequence_info(seq_id)\\n\\n # Create anno dict\\n anno_frames = {}\\n for key, value in anno.items():\\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\\n\\n # added the class info to the meta info\\n object_meta = OrderedDict({'object_class': sequence['class_name'],\\n 'motion_class': None,\\n 'major_class': None,\\n 'root_class': None,\\n 'motion_adverb': None})\\n\\n return frame_list, anno_frames, object_meta\"\n },\n {\n \"identifier\": \"MSCOCOSeq_lmdb\",\n \"path\": \"lib/train/dataset/coco_seq_lmdb.py\",\n \"snippet\": \"class MSCOCOSeq_lmdb(BaseVideoDataset):\\n \\\"\\\"\\\" The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1.\\n\\n Publication:\\n Microsoft COCO: Common Objects in Context.\\n Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona,\\n Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick\\n ECCV, 2014\\n https://arxiv.org/pdf/1405.0312.pdf\\n\\n Download the images along with annotations from http://cocodataset.org/#download. The root folder should be\\n organized as follows.\\n - coco_root\\n - annotations\\n - instances_train2014.json\\n - instances_train2017.json\\n - images\\n - train2014\\n - train2017\\n\\n Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi.\\n \\\"\\\"\\\"\\n\\n def __init__(self, root=None, image_loader=jpeg4py_loader, data_fraction=None, split=\\\"train\\\", version=\\\"2014\\\"):\\n \\\"\\\"\\\"\\n args:\\n root - path to the coco dataset.\\n image_loader (default_image_loader) - The function to read the images. If installed,\\n jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else,\\n opencv's imread is used.\\n data_fraction (None) - Fraction of images to be used. The images are selected randomly. If None, all the\\n images will be used\\n split - 'train' or 'val'.\\n version - version of coco dataset (2014 or 2017)\\n \\\"\\\"\\\"\\n root = env_settings().coco_dir if root is None else root\\n super().__init__('COCO_lmdb', root, image_loader)\\n self.root = root\\n self.img_pth = 'images/{}{}/'.format(split, version)\\n self.anno_path = 'annotations/instances_{}{}.json'.format(split, version)\\n\\n # Load the COCO set.\\n print('loading annotations into memory...')\\n tic = time.time()\\n coco_json = decode_json(root, self.anno_path)\\n print('Done (t={:0.2f}s)'.format(time.time() - tic))\\n\\n self.coco_set = COCO(coco_json)\\n\\n self.cats = self.coco_set.cats\\n\\n self.class_list = self.get_class_list()\\n\\n self.sequence_list = self._get_sequence_list()\\n\\n if data_fraction is not None:\\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list)*data_fraction))\\n self.seq_per_class = self._build_seq_per_class()\\n\\n def _get_sequence_list(self):\\n ann_list = list(self.coco_set.anns.keys())\\n seq_list = [a for a in ann_list if self.coco_set.anns[a]['iscrowd'] == 0]\\n\\n return seq_list\\n\\n def is_video_sequence(self):\\n return False\\n\\n def get_num_classes(self):\\n return len(self.class_list)\\n\\n def get_name(self):\\n return 'coco_lmdb'\\n\\n def has_class_info(self):\\n return True\\n\\n def get_class_list(self):\\n class_list = []\\n for cat_id in self.cats.keys():\\n class_list.append(self.cats[cat_id]['name'])\\n return class_list\\n\\n def has_segmentation_info(self):\\n return True\\n\\n def get_num_sequences(self):\\n return len(self.sequence_list)\\n\\n def _build_seq_per_class(self):\\n seq_per_class = {}\\n for i, seq in enumerate(self.sequence_list):\\n class_name = self.cats[self.coco_set.anns[seq]['category_id']]['name']\\n if class_name not in seq_per_class:\\n seq_per_class[class_name] = [i]\\n else:\\n seq_per_class[class_name].append(i)\\n\\n return seq_per_class\\n\\n def get_sequences_in_class(self, class_name):\\n return self.seq_per_class[class_name]\\n\\n def get_sequence_info(self, seq_id):\\n anno = self._get_anno(seq_id)\\n\\n bbox = torch.Tensor(anno['bbox']).view(1, 4)\\n\\n mask = torch.Tensor(self.coco_set.annToMask(anno)).unsqueeze(dim=0)\\n\\n '''2021.1.3 To avoid too small bounding boxes. Here we change the threshold to 50 pixels'''\\n valid = (bbox[:, 2] > 50) & (bbox[:, 3] > 50)\\n\\n visible = valid.clone().byte()\\n\\n return {'bbox': bbox, 'mask': mask, 'valid': valid, 'visible': visible}\\n\\n def _get_anno(self, seq_id):\\n anno = self.coco_set.anns[self.sequence_list[seq_id]]\\n\\n return anno\\n\\n def _get_frames(self, seq_id):\\n path = self.coco_set.loadImgs([self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0]['file_name']\\n # img = self.image_loader(os.path.join(self.img_pth, path))\\n img = decode_img(self.root, os.path.join(self.img_pth, path))\\n return img\\n\\n def get_meta_info(self, seq_id):\\n try:\\n cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']]\\n object_meta = OrderedDict({'object_class_name': cat_dict_current['name'],\\n 'motion_class': None,\\n 'major_class': cat_dict_current['supercategory'],\\n 'root_class': None,\\n 'motion_adverb': None})\\n except:\\n object_meta = OrderedDict({'object_class_name': None,\\n 'motion_class': None,\\n 'major_class': None,\\n 'root_class': None,\\n 'motion_adverb': None})\\n return object_meta\\n\\n\\n def get_class_name(self, seq_id):\\n cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[seq_id]]['category_id']]\\n return cat_dict_current['name']\\n\\n def get_frames(self, seq_id=None, frame_ids=None, anno=None):\\n # COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a\\n # list containing these replicated images.\\n frame = self._get_frames(seq_id)\\n\\n frame_list = [frame.copy() for _ in frame_ids]\\n\\n if anno is None:\\n anno = self.get_sequence_info(seq_id)\\n\\n anno_frames = {}\\n for key, value in anno.items():\\n anno_frames[key] = [value[0, ...] for _ in frame_ids]\\n\\n object_meta = self.get_meta_info(seq_id)\\n\\n return frame_list, anno_frames, object_meta\"\n },\n {\n \"identifier\": \"TrackingNet_lmdb\",\n \"path\": \"lib/train/dataset/tracking_net_lmdb.py\",\n \"snippet\": \"class TrackingNet_lmdb(BaseVideoDataset):\\n \\\"\\\"\\\" TrackingNet dataset.\\n\\n Publication:\\n TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild.\\n Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem\\n ECCV, 2018\\n https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf\\n\\n Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit.\\n \\\"\\\"\\\"\\n def __init__(self, root=None, image_loader=jpeg4py_loader, set_ids=None, data_fraction=None):\\n \\\"\\\"\\\"\\n args:\\n root - The path to the TrackingNet folder, containing the training sets.\\n image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)\\n is used by default.\\n set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the\\n sets (0 - 11) will be used.\\n data_fraction - Fraction of dataset to be used. The complete dataset is used by default\\n \\\"\\\"\\\"\\n root = env_settings().trackingnet_lmdb_dir if root is None else root\\n super().__init__('TrackingNet_lmdb', root, image_loader)\\n\\n if set_ids is None:\\n set_ids = [i for i in range(12)]\\n\\n self.set_ids = set_ids\\n\\n # Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and\\n # video_name for each sequence\\n self.sequence_list = list_sequences(self.root)\\n\\n if data_fraction is not None:\\n self.sequence_list = random.sample(self.sequence_list, int(len(self.sequence_list) * data_fraction))\\n\\n self.seq_to_class_map, self.seq_per_class = self._load_class_info()\\n\\n # we do not have the class_lists for the tracking net\\n self.class_list = list(self.seq_per_class.keys())\\n self.class_list.sort()\\n\\n def _load_class_info(self):\\n ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')\\n class_map_path = os.path.join(ltr_path, 'data_specs', 'trackingnet_classmap.txt')\\n\\n with open(class_map_path, 'r') as f:\\n seq_to_class_map = {seq_class.split('\\\\t')[0]: seq_class.rstrip().split('\\\\t')[1] for seq_class in f}\\n\\n seq_per_class = {}\\n for i, seq in enumerate(self.sequence_list):\\n class_name = seq_to_class_map.get(seq[1], 'Unknown')\\n if class_name not in seq_per_class:\\n seq_per_class[class_name] = [i]\\n else:\\n seq_per_class[class_name].append(i)\\n\\n return seq_to_class_map, seq_per_class\\n\\n def get_name(self):\\n return 'trackingnet_lmdb'\\n\\n def has_class_info(self):\\n return True\\n\\n def get_sequences_in_class(self, class_name):\\n return self.seq_per_class[class_name]\\n\\n def _read_bb_anno(self, seq_id):\\n set_id = self.sequence_list[seq_id][0]\\n vid_name = self.sequence_list[seq_id][1]\\n gt_str_list = decode_str(os.path.join(self.root, \\\"TRAIN_%d_lmdb\\\" % set_id),\\n os.path.join(\\\"anno\\\", vid_name + \\\".txt\\\")).split('\\\\n')[:-1]\\n gt_list = [list(map(float, line.split(','))) for line in gt_str_list]\\n gt_arr = np.array(gt_list).astype(np.float32)\\n return torch.tensor(gt_arr)\\n\\n def get_sequence_info(self, seq_id):\\n bbox = self._read_bb_anno(seq_id)\\n\\n valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\\n visible = valid.clone().byte()\\n return {'bbox': bbox, 'valid': valid, 'visible': visible}\\n\\n def _get_frame(self, seq_id, frame_id):\\n set_id = self.sequence_list[seq_id][0]\\n vid_name = self.sequence_list[seq_id][1]\\n return decode_img(os.path.join(self.root, \\\"TRAIN_%d_lmdb\\\" % set_id),\\n os.path.join(\\\"frames\\\", vid_name, str(frame_id) + \\\".jpg\\\"))\\n\\n def _get_class(self, seq_id):\\n seq_name = self.sequence_list[seq_id][1]\\n return self.seq_to_class_map[seq_name]\\n\\n def get_class_name(self, seq_id):\\n obj_class = self._get_class(seq_id)\\n\\n return obj_class\\n\\n def get_frames(self, seq_id, frame_ids, anno=None):\\n frame_list = [self._get_frame(seq_id, f) for f in frame_ids]\\n\\n if anno is None:\\n anno = self.get_sequence_info(seq_id)\\n\\n anno_frames = {}\\n for key, value in anno.items():\\n anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\\n\\n obj_class = self._get_class(seq_id)\\n\\n object_meta = OrderedDict({'object_class_name': obj_class,\\n 'motion_class': None,\\n 'major_class': None,\\n 'root_class': None,\\n 'motion_adverb': None})\\n\\n return frame_list, anno_frames, object_meta\"\n },\n {\n \"identifier\": \"sampler\",\n \"path\": \"lib/train/data/sampler.py\",\n \"snippet\": \"def no_processing(data):\\n def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap,\\n num_search_frames, num_template_frames=1, processing=no_processing, frame_sample_mode='causal',\\n train_cls=False, pos_prob=0.5):\\n def __len__(self):\\n def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None,\\n allow_invisible=False, force_invisible=False):\\n def __getitem__(self, index):\\n def getitem(self):\\n def getitem_cls(self):\\n def get_center_box(self, H, W, ratio=1/8):\\n def sample_seq_from_dataset(self, dataset, is_video_dataset):\\n def get_one_search(self):\\n def get_frame_ids_trident(self, visible):\\n def get_frame_ids_stark(self, visible, valid):\\nclass TrackingSampler(torch.utils.data.Dataset):\\n H, W, _ = template_frames[0].shape\\n H, W, _ = template_frames[0].shape\\n H, W, _ = search_frames[0].shape\"\n },\n {\n \"identifier\": \"processing\",\n \"path\": \"lib/train/data/processing.py\",\n \"snippet\": \"def stack_tensors(x):\\n def __init__(self, transform=transforms.ToTensor(), template_transform=None, search_transform=None, joint_transform=None):\\n def __call__(self, data: TensorDict):\\n def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor,\\n mode='pair', settings=None, *args, **kwargs):\\n def _get_jittered_box(self, box, mode):\\n def __call__(self, data: TensorDict):\\nclass BaseProcessing:\\nclass STARKProcessing(BaseProcessing):\"\n },\n {\n \"identifier\": \"LTRLoader\",\n \"path\": \"lib/train/data/loader.py\",\n \"snippet\": \"class LTRLoader(torch.utils.data.dataloader.DataLoader):\\n \\\"\\\"\\\"\\n Data loader. Combines a dataset and a sampler, and provides\\n single- or multi-process iterators over the dataset.\\n\\n Note: The only difference with default pytorch DataLoader is that an additional option stack_dim is available to\\n select along which dimension the data should be stacked to form a batch.\\n\\n Arguments:\\n dataset (Dataset): dataset from which to load the data.\\n batch_size (int, optional): how many samples per batch to load\\n (default: 1).\\n shuffle (bool, optional): set to ``True`` to have the data reshuffled\\n at every epoch (default: False).\\n sampler (Sampler, optional): defines the strategy to draw samples from\\n the dataset. If specified, ``shuffle`` must be False.\\n batch_sampler (Sampler, optional): like sampler, but returns a batch of\\n indices at a time. Mutually exclusive with batch_size, shuffle,\\n sampler, and drop_last.\\n num_workers (int, optional): how many subprocesses to use for data\\n loading. 0 means that the data will be loaded in the main process.\\n (default: 0)\\n collate_fn (callable, optional): merges a list of samples to form a mini-batch.\\n stack_dim (int): Dimension along which to stack to form the batch. (default: 0)\\n pin_memory (bool, optional): If ``True``, the data loader will copy tensors\\n into CUDA pinned memory before returning them.\\n drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,\\n if the dataset size is not divisible by the batch size. If ``False`` and\\n the size of dataset is not divisible by the batch size, then the last batch\\n will be smaller. (default: False)\\n timeout (numeric, optional): if positive, the timeout value for collecting a batch\\n from workers. Should always be non-negative. (default: 0)\\n worker_init_fn (callable, optional): If not None, this will be called on each\\n worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as\\n input, after seeding and before data loading. (default: None)\\n\\n .. note:: By default, each worker will have its PyTorch seed set to\\n ``base_seed + worker_id``, where ``base_seed`` is a long generated\\n by main process using its RNG. However, seeds for other libraries\\n may be duplicated upon initializing workers (w.g., NumPy), causing\\n each worker to return identical random numbers. (See\\n :ref:`dataloader-workers-random-seed` section in FAQ.) You may\\n use ``torch.initial_seed()`` to access the PyTorch seed for each\\n worker in :attr:`worker_init_fn`, and use it to set other seeds\\n before data loading.\\n\\n .. warning:: If ``spawn`` start method is used, :attr:`worker_init_fn` cannot be an\\n unpicklable object, e.g., a lambda function.\\n \\\"\\\"\\\"\\n\\n __initialized = False\\n\\n def __init__(self, name, dataset, training=True, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,\\n num_workers=0, epoch_interval=1, collate_fn=None, stack_dim=0, pin_memory=False, drop_last=False,\\n timeout=0, worker_init_fn=None):\\n if collate_fn is None:\\n if stack_dim == 0:\\n collate_fn = ltr_collate\\n elif stack_dim == 1:\\n collate_fn = ltr_collate_stack1\\n else:\\n raise ValueError('Stack dim no supported. Must be 0 or 1.')\\n\\n super(LTRLoader, self).__init__(dataset, batch_size, shuffle, sampler, batch_sampler,\\n num_workers, collate_fn, pin_memory, drop_last,\\n timeout, worker_init_fn)\\n\\n self.name = name\\n self.training = training\\n self.epoch_interval = epoch_interval\\n self.stack_dim = stack_dim\"\n },\n {\n \"identifier\": \"opencv_loader\",\n \"path\": \"lib/train/data/image_loader.py\",\n \"snippet\": \"def opencv_loader(path):\\n \\\"\\\"\\\" Read image using opencv's imread function and returns it in rgb format\\\"\\\"\\\"\\n try:\\n im = cv.imread(path, cv.IMREAD_COLOR)\\n\\n # convert to rgb and return\\n return cv.cvtColor(im, cv.COLOR_BGR2RGB)\\n except Exception as e:\\n print('ERROR: Could not read image \\\"{}\\\"'.format(path))\\n print(e)\\n return None\"\n },\n {\n \"identifier\": \"is_main_process\",\n \"path\": \"lib/utils/misc.py\",\n \"snippet\": \"def is_main_process():\\n return get_rank() == 0\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import os\nimport torch\nimport lib.train.data.transforms as tfm\nfrom torch.utils.data.distributed import DistributedSampler\nfrom lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet\nfrom lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb\nfrom lib.train.data import sampler, opencv_loader, processing, LTRLoader\nfrom lib.utils.misc import is_main_process"},"token_num":{"kind":"number","value":18653,"string":"18,653"},"cropped_code":{"kind":"string","value":"# datasets related\n\n\ndef update_settings(settings, cfg):\n settings.print_interval = cfg.TRAIN.PRINT_INTERVAL\n settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR,\n 'search': cfg.DATA.SEARCH.FACTOR}\n settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE,\n 'search': cfg.DATA.SEARCH.SIZE}\n settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER,\n 'search': cfg.DATA.SEARCH.CENTER_JITTER}\n settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER,\n 'search': cfg.DATA.SEARCH.SCALE_JITTER}\n settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM\n settings.print_stats = None\n settings.batchsize = cfg.TRAIN.BATCH_SIZE\n settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE\n\n\ndef names2datasets(name_list: list, settings, image_loader):\n assert isinstance(name_list, list)\n datasets = []\n for name in name_list:\n assert name in [\"LASOT\", \"GOT10K_vottrain\", \"GOT10K_votval\", \"GOT10K_train_full\", \"GOT10K_official_val\",\n \"COCO17\", \"VID\", \"TRACKINGNET\",\n ]\n # Tracking Task\n if name == \"LASOT\":\n if settings.use_lmdb:\n print(\"Building lasot dataset from lmdb\")\n datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader))\n else:\n datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader))\n if name == \"GOT10K_vottrain\":\n if settings.use_lmdb:\n print(\"Building got10k from lmdb\")\n datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader))\n else:\n datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader))\n if name == \"GOT10K_train_full\":\n if settings.use_lmdb:\n print(\"Building got10k_train_full from lmdb\")\n datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader))\n else:\n datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader))\n if name == \"GOT10K_votval\":\n if settings.use_lmdb:\n print(\"Building got10k from lmdb\")\n datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader))\n else:\n datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader))\n if name == \"GOT10K_official_val\":\n if settings.use_lmdb:\n raise ValueError(\"Not implement\")\n else:\n datasets.append(Got10k(settings.env.got10k_val_dir, split=None, image_loader=image_loader))\n if name == \"COCO17\":\n if settings.use_lmdb:\n print(\"Building COCO2017 from lmdb\")\n datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version=\"2017\", image_loader=image_loader))\n else:\n datasets.append(MSCOCOSeq(settings.env.coco_dir, version=\"2017\", image_loader=image_loader))\n if name == \"VID\":\n if settings.use_lmdb:\n print(\"Building VID from lmdb\")\n datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader))\n else:\n datasets.append(ImagenetVID(settings.env.imagenet_dir, image_loader=image_loader))\n if name == \"TRACKINGNET\":\n if settings.use_lmdb:\n print(\"Building TrackingNet from lmdb\")\n datasets.append(TrackingNet_lmdb(settings.env.trackingnet_lmdb_dir, image_loader=image_loader))\n else:\n # raise ValueError(\"NOW WE CAN ONLY USE TRACKINGNET FROM LMDB\")\n datasets.append(TrackingNet(settings.env.trackingnet_dir, image_loader=image_loader))\n \n return datasets\n\n\ndef build_dataloaders(cfg, settings):\n # Data transform\n transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05),\n tfm.RandomHorizontalFlip(probability=0.5))\n\n transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2),\n tfm.RandomHorizontalFlip_Norm(probability=0.5),\n # tfm.RandomHorizontalFlip(probability=0.5),\n tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))\n\n transform_val = tfm.Transform(tfm.ToTensor(),\n tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))\n\n # The tracking pairs processing module\n output_sz = settings.output_sz\n search_area_factor = settings.search_area_factor\n\n data_processing_train = processing.STARKProcessing(search_area_factor=search_area_factor,\n output_sz=output_sz,\n center_jitter_factor=settings.center_jitter_factor,\n scale_jitter_factor=settings.scale_jitter_factor,\n mode='sequence',\n transform=transform_train,\n joint_transform=transform_joint,\n settings=settings)\n\n data_processing_val = processing.STARKProcessing(search_area_factor=search_area_factor,\n output_sz=output_sz,\n center_jitter_factor=settings.center_jitter_factor,\n scale_jitter_factor=settings.scale_jitter_factor,\n mode='sequence',\n transform=transform_val,\n joint_transform=transform_joint,\n settings=settings)\n\n # Train sampler and loader\n settings.num_template = getattr(cfg.DATA.TEMPLATE, \"NUMBER\", 1)\n settings.num_search = getattr(cfg.DATA.SEARCH, \"NUMBER\", 1)\n sampler_mode = getattr(cfg.DATA, \"SAMPLER_MODE\", \"causal\")\n train_cls = getattr(cfg.TRAIN, \"TRAIN_CLS\", False)\n print(\"sampler_mode: \", sampler_mode)\n"},"all_code":{"kind":"string","value":"# datasets related\n\n\ndef update_settings(settings, cfg):\n settings.print_interval = cfg.TRAIN.PRINT_INTERVAL\n settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR,\n 'search': cfg.DATA.SEARCH.FACTOR}\n settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE,\n 'search': cfg.DATA.SEARCH.SIZE}\n settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER,\n 'search': cfg.DATA.SEARCH.CENTER_JITTER}\n settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER,\n 'search': cfg.DATA.SEARCH.SCALE_JITTER}\n settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM\n settings.print_stats = None\n settings.batchsize = cfg.TRAIN.BATCH_SIZE\n settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE\n\n\ndef names2datasets(name_list: list, settings, image_loader):\n assert isinstance(name_list, list)\n datasets = []\n for name in name_list:\n assert name in [\"LASOT\", \"GOT10K_vottrain\", \"GOT10K_votval\", \"GOT10K_train_full\", \"GOT10K_official_val\",\n \"COCO17\", \"VID\", \"TRACKINGNET\",\n ]\n # Tracking Task\n if name == \"LASOT\":\n if settings.use_lmdb:\n print(\"Building lasot dataset from lmdb\")\n datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader))\n else:\n datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader))\n if name == \"GOT10K_vottrain\":\n if settings.use_lmdb:\n print(\"Building got10k from lmdb\")\n datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader))\n else:\n datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader))\n if name == \"GOT10K_train_full\":\n if settings.use_lmdb:\n print(\"Building got10k_train_full from lmdb\")\n datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader))\n else:\n datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader))\n if name == \"GOT10K_votval\":\n if settings.use_lmdb:\n print(\"Building got10k from lmdb\")\n datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader))\n else:\n datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader))\n if name == \"GOT10K_official_val\":\n if settings.use_lmdb:\n raise ValueError(\"Not implement\")\n else:\n datasets.append(Got10k(settings.env.got10k_val_dir, split=None, image_loader=image_loader))\n if name == \"COCO17\":\n if settings.use_lmdb:\n print(\"Building COCO2017 from lmdb\")\n datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version=\"2017\", image_loader=image_loader))\n else:\n datasets.append(MSCOCOSeq(settings.env.coco_dir, version=\"2017\", image_loader=image_loader))\n if name == \"VID\":\n if settings.use_lmdb:\n print(\"Building VID from lmdb\")\n datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader))\n else:\n datasets.append(ImagenetVID(settings.env.imagenet_dir, image_loader=image_loader))\n if name == \"TRACKINGNET\":\n if settings.use_lmdb:\n print(\"Building TrackingNet from lmdb\")\n datasets.append(TrackingNet_lmdb(settings.env.trackingnet_lmdb_dir, image_loader=image_loader))\n else:\n # raise ValueError(\"NOW WE CAN ONLY USE TRACKINGNET FROM LMDB\")\n datasets.append(TrackingNet(settings.env.trackingnet_dir, image_loader=image_loader))\n \n return datasets\n\n\ndef build_dataloaders(cfg, settings):\n # Data transform\n transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05),\n tfm.RandomHorizontalFlip(probability=0.5))\n\n transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2),\n tfm.RandomHorizontalFlip_Norm(probability=0.5),\n # tfm.RandomHorizontalFlip(probability=0.5),\n tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))\n\n transform_val = tfm.Transform(tfm.ToTensor(),\n tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))\n\n # The tracking pairs processing module\n output_sz = settings.output_sz\n search_area_factor = settings.search_area_factor\n\n data_processing_train = processing.STARKProcessing(search_area_factor=search_area_factor,\n output_sz=output_sz,\n center_jitter_factor=settings.center_jitter_factor,\n scale_jitter_factor=settings.scale_jitter_factor,\n mode='sequence',\n transform=transform_train,\n joint_transform=transform_joint,\n settings=settings)\n\n data_processing_val = processing.STARKProcessing(search_area_factor=search_area_factor,\n output_sz=output_sz,\n center_jitter_factor=settings.center_jitter_factor,\n scale_jitter_factor=settings.scale_jitter_factor,\n mode='sequence',\n transform=transform_val,\n joint_transform=transform_joint,\n settings=settings)\n\n # Train sampler and loader\n settings.num_template = getattr(cfg.DATA.TEMPLATE, \"NUMBER\", 1)\n settings.num_search = getattr(cfg.DATA.SEARCH, \"NUMBER\", 1)\n sampler_mode = getattr(cfg.DATA, \"SAMPLER_MODE\", \"causal\")\n train_cls = getattr(cfg.TRAIN, \"TRAIN_CLS\", False)\n print(\"sampler_mode: \", sampler_mode)"},"next_line":{"kind":"string","value":" dataset_train = sampler.TrackingSampler(datasets=names2datasets(cfg.DATA.TRAIN.DATASETS_NAME, settings, opencv_loader),"},"gold_snippet_index":{"kind":"number","value":13,"string":"13"},"created_at":{"kind":"string","value":"2023-12-10 03:57:19+00:00"},"level":{"kind":"string","value":"24k"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":52,"numItemsPerPage":100,"numTotalItems":8033,"offset":5200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDA5OTcyOCwic3ViIjoiL2RhdGFzZXRzL3RpYW55YW5nL3JlcG9iZW5jaF9weXRob25fdjEuMSIsImV4cCI6MTc3NDEwMzMyOCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.gT0sVDcHBxv30HCnnzdmjPoz2zL2M_3SWLXmrGxFD4ZgX6Wztm6yO1u5olln34v0i0kp_C_MmoouNb8pbA6_DQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"cross_file_first","numRows":8033,"numBytesParquet":163954346},{"config":"default","split":"cross_file_random","numRows":7618,"numBytesParquet":153067110},{"config":"default","split":"in_file","numRows":7910,"numBytesParquet":155972843}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[{"views":[{"key":"default/cross_file_first","displayName":"cross_file_first","viewName":"cross_file_first"},{"key":"default/cross_file_random","displayName":"cross_file_random","viewName":"cross_file_random"},{"key":"default/in_file","displayName":"in_file","viewName":"in_file"}],"sql":"WITH matched AS (\n SELECT cff_repo\n FROM same_split\n WHERE cff_level IN ('2k','4k','8k','12k')\n GROUP BY cff_repo\n HAVING COUNT(DISTINCT cff_level) = 4\n)\nSELECT scff.cff_repo, scff.cff_level, scff.cff_next_line, scff.cff_file_path\nFROM same_split scff\nJOIN matched m\n ON scff.cff_repo = m.cff_repo\nWHERE scff.cff_level IN ('2k','4k','8k','12k')\nORDER BY scff.cff_level, scff.cff_repo, scff.cff_next_line;","title":"SQL Console for tianyang/repobench_python_v1.1","createdAt":"2026-02-16T12:58:32.763Z","slug":"GYS17Jn","private":false,"justification":"Identifies repositories that have consistent code formatting levels across multiple scales (2k, 4k, 8k, 12k) and reveals the structured formatting patterns within these repositories.","viewNames":["cross_file_first","cross_file_random","in_file"]},{"views":[{"key":"default/cross_file_first","displayName":"cross_file_first","viewName":"cross_file_first"},{"key":"default/cross_file_random","displayName":"cross_file_random","viewName":"cross_file_random"},{"key":"default/in_file","displayName":"in_file","viewName":"in_file"}],"sql":" SELECT\n cff.repo_name as cff_repo,\n cff.file_path as cff_file_path,\n cff.level AS cff_level,\n cff.next_line AS cff_next_line,\n cfr.repo_name as cfr_repo,\n cfr.file_path as cfr_file_path,\n cfr.level AS cfr_level,\n cfr.next_line AS cfr_next_line,\n iff.repo_name as iff_repo,\n iff.file_path as iff_file_path,\n iff.level AS iff_level,\n iff.next_line AS iff_next_line,\n FROM cross_file_first cff\n JOIN cross_file_random cfr\n ON cff.repo_name = cfr.repo_name and cff.file_path = cfr.file_path\n JOIN in_file iff\n ON cff.repo_name = iff.repo_name and cff.file_path = iff.file_path\n WHERE cff.level IN ('2k','4k','8k','12k','16k','24k','32k')\nORDER BY cff_level;","title":"SQL Console for tianyang/repobench_python_v1.1","createdAt":"2026-02-16T12:18:39.032Z","slug":"QYIEEK-","private":false,"justification":"Compares cross-file and in-file code structure patterns across different complexity levels, revealing how file organization strategies vary with code size and potentially informing better code architecture decisions.","viewNames":["cross_file_first","cross_file_random","in_file"]},{"views":[{"key":"default/cross_file_first","displayName":"cross_file_first","viewName":"cross_file_first"},{"key":"default/cross_file_random","displayName":"cross_file_random","viewName":"cross_file_random"},{"key":"default/in_file","displayName":"in_file","viewName":"in_file"}],"sql":"WITH matched AS (\n SELECT repo_name\n FROM cross_file_first\n WHERE level IN ('2k','4k','8k','12k','16k','24k','32k')\n GROUP BY repo_name\n HAVING COUNT(DISTINCT level) = 7\n)\nSELECT cff.repo_name, cff.level, cff.next_line\nFROM cross_file_first cff\nJOIN matched m\n ON cff.repo_name = m.repo_name\nWHERE cff.level IN ('2k','4k','8k','12k','16k','24k','32k')\nORDER BY cff.repo_name, cff.next_line, cff.level;","title":"SQL Console for tianyang/repobench_python_v1.1","createdAt":"2026-02-16T11:10:16.565Z","slug":"KP5rZyR","private":false,"justification":"Identifies repositories that have complete performance data across all seven code complexity levels, revealing consistent benchmarking patterns across different code sizes.","viewNames":["cross_file_first","cross_file_random","in_file"]},{"views":[{"key":"default/cross_file_first","displayName":"cross_file_first","viewName":"cross_file_first"},{"key":"default/cross_file_random","displayName":"cross_file_random","viewName":"cross_file_random"},{"key":"default/in_file","displayName":"in_file","viewName":"in_file"}],"sql":"SELECT repo_name, COUNT(DISTINCT level) AS levels_present\nFROM cross_file_first\nWHERE level IN ('2k','4k','8k','12k','16k','24k','32k')\nGROUP BY repo_name\nHAVING COUNT(DISTINCT level) = 7;\n","title":"SQL Console for tianyang/repobench_python_v1.1","createdAt":"2026-02-16T11:07:20.043Z","slug":"jD2BZzH","private":false,"justification":"Identifies repositories that contain all 7 distinct quality levels (2k through 32k), revealing complete datasets that might be useful for comprehensive analysis.","viewNames":["cross_file_first","cross_file_random","in_file"]}],"user":[]}}">
repo_name stringlengths 7 71 | file_path stringlengths 5 118 | context list | import_statement stringlengths 45 12.5k | token_num int64 641 99.4k | cropped_code stringlengths 44 17k | all_code stringlengths 43 754k | next_line stringlengths 2 330 | gold_snippet_index int64 0 68 | created_at stringlengths 25 25 | level stringclasses 9
values |
|---|---|---|---|---|---|---|---|---|---|---|
alexzhou907/DreamPropeller | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
import trimesh
from dataclasses import dataclass, field
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.misc import broadcast
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
from pysdf import SDF | 14,740 | self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
shape_init_mesh_up: str = "+z"
shape_init_mesh_front: str = "+x"
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self,*args, **kwargs) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters(): | broadcast(param, src=0) | 9 | 2023-11-27 23:39:49+00:00 | 24k |
abdulhaim/LMRL-Gym | llm_rl_scripts/wordle/ppo/train_ppo.py | [
{
"identifier": "train_loop",
"path": "LLM_RL/algorithms/ppo/train.py",
"snippet": "def train_loop(\n trainer: PPOTrain, \n inference: PPOInference, \n policy: PPOPolicy, \n load_dataset: Callable[[PPOInference, PPOPolicy], Union[PPODataset, PPOIterableDataset]], \n evaluator: Optional[Ca... | from typing import Optional
from JaxSeq.bucket_manager import open_with_bucket as open
from transformers import AutoTokenizer
from JaxSeq.utils import jsonl_stream, convert_path, load_mesh, setup_experiment_save
from JaxSeq.utils import BlockingStrategy, Padding, Truncation, get_weight_decay_mask, create_path, get_enabled_save_path, MapIterable, FileOpenIterable
from JaxSeq.models.gptj.interface import GPTJInference
from JaxSeq.models.gptj.load import load_train_state, ModelLoadMode
from LLM_RL.algorithms.ppo.train import train_loop
from LLM_RL.algorithms.ppo.base_interface import ppo_loss_fn, FixedKLController, AdaptiveKLController
from transformers.generation import GenerationConfig
from jaxtyping import PyTree
from LLM_RL.environment import text_env_eval, TextTrajectory, TextTrajectoryChain, TokenTrajectory, text_history_to_str
from LLM_RL.algorithms.ppo.gptj.interface import GPTJPPOPolicy, GPTJPPOInference, GPTJPPOTrain
from LLM_RL.heads.linear_head import load_train_state_from_config as load_head_train_state_from_config
from LLM_RL.heads.linear_head import LinearHeadConfig
from JaxSeq.shard_model import shard_params_from_params
from LLM_RL.algorithms.ppo.data import PPODataset
from LLM_RL.utils import get_tensor_stats_np
from functools import partial
from JaxSeq.logs import label_logs, log, pull_logs
from JaxSeq.utils import multihost_device_get
from JaxSeq.data import MaskIterableDataset
from llm_rl_scripts.wordle.env.env import ReformatWordleEnvironment, WordleEnvironment
from llm_rl_scripts.wordle.env.game import Vocabulary
from dataclasses import replace
from JaxSeq.models.gptj.interface import loss_fn_mask
import tyro
import jax
import jax.numpy as jnp
import os
import optax
import pickle as pkl
import re
import numpy as np
import json | 16,580 | force_pad_embeddings=force_pad_embeddings,
params_dtype=jnp.float32,
)
policy_model.config.gradient_checkpointing = gradient_checkpointing
policy_model.config.gradient_checkpointing_policy = gradient_checkpointing_policy
policy_model.config.resid_pdrop = 0.0
policy_model.config.embd_pdrop = 0.0
policy_model.config.attn_pdrop = 0.0
with jax.default_device(jax.devices('cpu')[0]):
initital_policy_params = jax.tree_util.tree_map(
lambda x: multihost_device_get(x, mesh=mesh).copy(),
policy_train_state.params,
)
initital_policy_params = shard_params_from_params(
model=policy_model,
params=initital_policy_params,
)
loop_state = dict()
if should_restore_loop_state and (model_load_mode in {ModelLoadMode.TRAIN_STATE,
ModelLoadMode.TRAIN_STATE_PARAMS,
ModelLoadMode.PARAMS}):
with open(os.path.join(convert_path(model_load_path), 'loop_state.pkl'), 'rb') as f:
loop_state = pkl.load(f)
policy_inference = GPTJInference.load_inference(
params=policy_train_state.params,
model=policy_model,
tokenizer=tokenizer,
)
vocab = Vocabulary.from_file(
vocab_file=vocab_file,
fill_cache=False,
)
env = ReformatWordleEnvironment(WordleEnvironment(vocab, require_words_in_vocab=True, bad_word_reward=-10.0))
policy_prng = jax.random.PRNGKey(0)
policy = GPTJPPOPolicy(
inference=policy_inference,
prng_key=policy_prng,
generation_config=GenerationConfig(
do_sample=policy_do_sample,
num_beams=policy_num_beams,
temperature=policy_temperature,
top_p=policy_top_p,
top_k=policy_top_k,
eos_token_id=tokenizer.encode('\n')[0],
pad_token_id=tokenizer.pad_token_id,
max_new_tokens=max_output_length,
),
blocking_strategy=BlockingStrategy(
padding=Padding.LEFT,
truncation=Truncation.LEFT,
max_length=max_input_length,
),
out_str_process=lambda x: x.removesuffix('\n')+'\n',
)
def value_head_optim_getter(params: PyTree):
mask = get_weight_decay_mask(("bias",))(params)
optim = optax.adamw(
learning_rate=lr,
b1=0.9,
b2=0.95,
eps=1e-8,
weight_decay=weight_decay,
mask=mask,
)
if grad_accum_steps is not None:
optim = optax.MultiSteps(
optim,
every_k_schedule=grad_accum_steps,
)
return optim
head_prng_key = jax.random.PRNGKey(3)
value_head_train_state, value_head = load_head_train_state_from_config(
model_config=LinearHeadConfig(
input_dim=policy_model.config.n_embd,
output_dim=1,
use_bias=True,
initializer_range=0.0,
bias_init=-4.1,
),
model_dtype=jnp.bfloat16 if bf16_activations else jnp.float32,
optim_getter=value_head_optim_getter,
mesh=mesh,
prng_key=head_prng_key,
pad_to_output_dim=None,
params_dtype=jnp.float32,
)
loss_f = partial(ppo_loss_fn, cliprange_value=cliprange_value, cliprange=cliprange, value_loss_coef=value_loss_coef)
ppo_inference = GPTJPPOInference.load_inference(
initial_policy_params=initital_policy_params,
policy_params=policy_train_state.params,
value_head_params=value_head_train_state.params,
initial_policy_model=policy_model,
policy_model=policy_model,
value_head_model=value_head,
tokenizer=tokenizer,
loss_fn=loss_f,
bc_loss_fn=loss_fn_mask,
bc_loss_weight=bc_loss_weight,
)
ppo_trainer = GPTJPPOTrain.load_train(
policy_train_state=policy_train_state,
value_head_train_state=value_head_train_state,
policy_model=policy_model,
value_head_model=value_head,
tokenizer=tokenizer,
loss_fn=loss_f,
bc_loss_fn=loss_fn_mask,
bc_loss_weight=bc_loss_weight,
)
if use_adaptive_kl:
|
def main(
model_load_mode: ModelLoadMode,
model_load_path: str,
bc_data_path: str,
vocab_file: str,
/, # Mark the end of positional arguments.
exp_name: Optional[str]=None,
outputs_path: Optional[str]=None,
data_mesh_shape: int=1,
fsdp_mesh_shape: int=1,
model_mesh_shape: int=-1,
use_wandb: bool=False,
wandb_project: Optional[str]=None,
n_rounds: int=1,
epochs: int=1,
max_steps: Optional[int]=None,
lr: float=1e-5,
weight_decay: float=0.0,
train_bsize: int=32,
train_bc_bsize: Optional[int]=None,
grad_accum_steps: Optional[int]=None,
rollout_bsize: int=32,
n_rollouts: int=128,
ppo_data_bsize: int=32,
bf16_activations: bool=False,
gradient_checkpointing: bool=False,
gradient_checkpointing_policy: str='nothing_saveable',
max_input_length: int=512,
max_output_length: int=512,
log_every: int=256,
eval_every_steps: Optional[int]=None,
eval_every_epochs: Optional[int]=None,
eval_every_rounds: Optional[int]=None,
eval_at_beginning: bool=False,
eval_at_end: bool=True,
save_every_steps: Optional[int]=None,
save_every_epochs: Optional[int]=None,
save_every_rounds: Optional[int]=None,
save_at_beginning: bool=False,
save_at_end: bool=False,
save_best: bool=True,
max_checkpoints: Optional[int]=None,
save_train_state: bool=True,
save_ppo_dataset: bool=True,
save_bf16: bool=True,
policy_do_sample: bool=True,
policy_num_beams: int=1,
policy_temperature: Optional[float]=None,
policy_top_p: Optional[float]=None,
policy_top_k: Optional[int]=None,
gamma: float=1.0,
lam: float=0.95,
use_advantage_whitening: bool=True,
init_kl_coef: float=0.001,
kl_target: Optional[float]=None,
kl_horizon: Optional[int]=None,
cliprange_value: float=0.2,
cliprange: float=0.2,
value_loss_coef: float=1.0,
bc_loss_weight: float=1.0,
force_pad_embeddings: bool=False,
should_restore_loop_state: bool=False,
):
input_args = locals()
print(input_args)
use_adaptive_kl = (kl_target is not None and kl_horizon is not None)
if not use_adaptive_kl:
assert kl_target is None and kl_horizon is None
tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B')
tokenizer.add_special_tokens({'pad_token': '<|pad|>'})
mesh = load_mesh((data_mesh_shape, fsdp_mesh_shape, model_mesh_shape), ('dp', 'fsdp', 'mp'))
is_main_process = jax.process_index() == 0
print(f"Mesh: {mesh}")
print(f"Is main process: {is_main_process}")
# load data
bc_data = MaskIterableDataset.blocked_from_str_segments_iterable(
MapIterable(lambda x: [(tokenizer.bos_token, 0.0)]+x['sequence']+[(tokenizer.eos_token, 1.0)], FileOpenIterable(convert_path(bc_data_path), 'r', pipe=jsonl_stream)),
tokenizer,
blocking_strategy=BlockingStrategy(
padding=Padding.RIGHT,
truncation=Truncation.LEFT,
max_length=max_input_length+max_output_length,
),
)
def policy_optim_getter(params: PyTree):
mask = get_weight_decay_mask((
"".join([r"\['ln_[0-9]+'\]", re.escape("['bias']")]),
"".join([r"\['ln_[0-9]+'\]", re.escape("['scale']")]),
re.escape("['ln_f']['bias']"),
re.escape("['ln_f']['scale']"),
"bias",
))(params)
optim = optax.adamw(
learning_rate=lr,
b1=0.9,
b2=0.95,
eps=1e-8,
weight_decay=weight_decay,
mask=mask,
)
if grad_accum_steps is not None:
optim = optax.MultiSteps(
optim,
every_k_schedule=grad_accum_steps,
)
return optim
model_prng_key = jax.random.PRNGKey(2)
policy_train_state, policy_model = load_train_state(
model_load_mode=model_load_mode,
model_load_path=convert_path(model_load_path) if model_load_mode != ModelLoadMode.HF else model_load_path,
model_dtype=jnp.bfloat16 if bf16_activations else jnp.float32,
optim_getter=policy_optim_getter,
tokenizer=tokenizer,
mesh=mesh,
prng_key=model_prng_key,
force_pad_embeddings=force_pad_embeddings,
params_dtype=jnp.float32,
)
policy_model.config.gradient_checkpointing = gradient_checkpointing
policy_model.config.gradient_checkpointing_policy = gradient_checkpointing_policy
policy_model.config.resid_pdrop = 0.0
policy_model.config.embd_pdrop = 0.0
policy_model.config.attn_pdrop = 0.0
with jax.default_device(jax.devices('cpu')[0]):
initital_policy_params = jax.tree_util.tree_map(
lambda x: multihost_device_get(x, mesh=mesh).copy(),
policy_train_state.params,
)
initital_policy_params = shard_params_from_params(
model=policy_model,
params=initital_policy_params,
)
loop_state = dict()
if should_restore_loop_state and (model_load_mode in {ModelLoadMode.TRAIN_STATE,
ModelLoadMode.TRAIN_STATE_PARAMS,
ModelLoadMode.PARAMS}):
with open(os.path.join(convert_path(model_load_path), 'loop_state.pkl'), 'rb') as f:
loop_state = pkl.load(f)
policy_inference = GPTJInference.load_inference(
params=policy_train_state.params,
model=policy_model,
tokenizer=tokenizer,
)
vocab = Vocabulary.from_file(
vocab_file=vocab_file,
fill_cache=False,
)
env = ReformatWordleEnvironment(WordleEnvironment(vocab, require_words_in_vocab=True, bad_word_reward=-10.0))
policy_prng = jax.random.PRNGKey(0)
policy = GPTJPPOPolicy(
inference=policy_inference,
prng_key=policy_prng,
generation_config=GenerationConfig(
do_sample=policy_do_sample,
num_beams=policy_num_beams,
temperature=policy_temperature,
top_p=policy_top_p,
top_k=policy_top_k,
eos_token_id=tokenizer.encode('\n')[0],
pad_token_id=tokenizer.pad_token_id,
max_new_tokens=max_output_length,
),
blocking_strategy=BlockingStrategy(
padding=Padding.LEFT,
truncation=Truncation.LEFT,
max_length=max_input_length,
),
out_str_process=lambda x: x.removesuffix('\n')+'\n',
)
def value_head_optim_getter(params: PyTree):
mask = get_weight_decay_mask(("bias",))(params)
optim = optax.adamw(
learning_rate=lr,
b1=0.9,
b2=0.95,
eps=1e-8,
weight_decay=weight_decay,
mask=mask,
)
if grad_accum_steps is not None:
optim = optax.MultiSteps(
optim,
every_k_schedule=grad_accum_steps,
)
return optim
head_prng_key = jax.random.PRNGKey(3)
value_head_train_state, value_head = load_head_train_state_from_config(
model_config=LinearHeadConfig(
input_dim=policy_model.config.n_embd,
output_dim=1,
use_bias=True,
initializer_range=0.0,
bias_init=-4.1,
),
model_dtype=jnp.bfloat16 if bf16_activations else jnp.float32,
optim_getter=value_head_optim_getter,
mesh=mesh,
prng_key=head_prng_key,
pad_to_output_dim=None,
params_dtype=jnp.float32,
)
loss_f = partial(ppo_loss_fn, cliprange_value=cliprange_value, cliprange=cliprange, value_loss_coef=value_loss_coef)
ppo_inference = GPTJPPOInference.load_inference(
initial_policy_params=initital_policy_params,
policy_params=policy_train_state.params,
value_head_params=value_head_train_state.params,
initial_policy_model=policy_model,
policy_model=policy_model,
value_head_model=value_head,
tokenizer=tokenizer,
loss_fn=loss_f,
bc_loss_fn=loss_fn_mask,
bc_loss_weight=bc_loss_weight,
)
ppo_trainer = GPTJPPOTrain.load_train(
policy_train_state=policy_train_state,
value_head_train_state=value_head_train_state,
policy_model=policy_model,
value_head_model=value_head,
tokenizer=tokenizer,
loss_fn=loss_f,
bc_loss_fn=loss_fn_mask,
bc_loss_weight=bc_loss_weight,
)
if use_adaptive_kl: | kl_controller = AdaptiveKLController(init_kl_coef=init_kl_coef, target=kl_target, horizon=kl_horizon) | 3 | 2023-11-21 00:16:42+00:00 | 24k |
jzmzhong/Automatic-Prosody-Annotator-with-SSWP-CLAP | src/clap_module/conformer/encoder.py | [
{
"identifier": "ConvolutionModule",
"path": "src/clap_module/conformer/convolution.py",
"snippet": "class ConvolutionModule(nn.Module):\r\n \"\"\"ConvolutionModule in Conformer model.\r\n\r\n Args:\r\n channels (int): The number of channels of conv layers.\r\n kernel_size (int): Ker... | import logging
import torch
import math
from .convolution import ConvolutionModule
from .encoder_layer import EncoderLayer
from .modules import get_activation
from .modules import VGG2L
from .modules import (
LegacyRelPositionMultiHeadedAttention,
MultiHeadedAttention,
RelPositionMultiHeadedAttention,
)
from .embedding import (
LegacyRelPositionalEncoding,
PositionalEncoding,
RelPositionalEncoding,
ScaledPositionalEncoding,
)
from .modules import LayerNorm
from .multi_layer_conv import (
Conv1dLinear,
MultiLayeredConv1d,
)
from .modules import (
PositionwiseFeedForward,
)
from .modules import repeat
from .sub_sampling import Conv2dSubsampling
from ..feature_fusion import AttentionPool1d, DAF, AFF, iAFF
| 14,564 | attention_dim (int): Dimension of attention.
attention_heads (int): The number of heads of multi head attention.
linear_units (int): The number of units of position-wise feed forward.
num_blocks (int): The number of decoder blocks.
dropout_rate (float): Dropout rate.
positional_dropout_rate (float): Dropout rate after adding positional encoding.
attention_dropout_rate (float): Dropout rate in attention.
input_layer (Union[str, torch.nn.Module]): Input layer type.
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool): Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
macaron_style (bool): Whether to use macaron style for positionwise layer.
pos_enc_layer_type (str): Encoder positional encoding layer type.
selfattention_layer_type (str): Encoder attention layer type.
activation_type (str): Encoder activation function type.
use_cnn_module (bool): Whether to use convolution module.
zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
cnn_module_kernel (int): Kernerl size of convolution module.
padding_idx (int): Padding idx for input_layer=embed.
stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
intermediate_layers (Union[List[int], None]): indices of intermediate CTC layer.
indices start from 1.
if not None, intermediate outputs are returned (which changes return type
signature.)
"""
def __init__(
self,
idim,
attention_dim=256,
attention_heads=4,
linear_units=2048,
num_blocks=6,
dropout_rate=0.1,
positional_dropout_rate=0.1,
attention_dropout_rate=0.0,
input_layer="conv2d",
normalize_before=True,
concat_after=False,
ffn_layer_type="linear",
ffn_conv_kernel_size=1,
macaron_style=False,
pos_enc_layer_type="abs_pos",
selfattention_layer_type="selfattn",
activation_type="relu",
use_cnn_module=True,
zero_triu=False,
cnn_module_kernel=31,
padding_idx=-1,
stochastic_depth_rate=0.0,
intermediate_layers=None,
ctc_softmax=None,
conditioning_layer_dim=None,
max_seq_len=100,
enable_fusion=False,
fusion_type="",
):
"""Construct an Encoder object."""
super(Encoder, self).__init__()
self.max_seq_len = max_seq_len
activation = get_activation(activation_type)
if pos_enc_layer_type == "abs_pos":
pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "scaled_abs_pos":
pos_enc_class = ScaledPositionalEncoding
elif pos_enc_layer_type == "rel_pos":
assert selfattention_layer_type == "rel_selfattn"
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "legacy_rel_pos":
assert selfattention_layer_type == "legacy_rel_selfattn"
pos_enc_class = LegacyRelPositionalEncoding
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
self.conv_subsampling_factor = 1
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(idim, attention_dim),
torch.nn.LayerNorm(attention_dim),
torch.nn.Dropout(dropout_rate),
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif input_layer == "conv2d":
self.embed = Conv2dSubsampling(
idim,
attention_dim,
dropout_rate,
pos_enc_class(attention_dim, positional_dropout_rate),
)
self.conv_subsampling_factor = 4
elif input_layer == "vgg2l":
self.embed = VGG2L(idim, attention_dim)
self.conv_subsampling_factor = 4
elif input_layer == "embed":
self.embed = torch.nn.Sequential(
torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif isinstance(input_layer, torch.nn.Module):
self.embed = torch.nn.Sequential(
input_layer,
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif input_layer is None:
self.embed = torch.nn.Sequential(
pos_enc_class(attention_dim, positional_dropout_rate)
)
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
# self-attention module definition
if selfattention_layer_type == "selfattn":
logging.info("encoder self-attention layer type = self-attention")
| # Copyright 2020 Johns Hopkins University (Shinji Watanabe)
# Northwestern Polytechnical University (Pengcheng Guo)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Encoder definition."""
class Encoder(torch.nn.Module):
"""Conformer encoder module.
Args:
idim (int): Input dimension.
attention_dim (int): Dimension of attention.
attention_heads (int): The number of heads of multi head attention.
linear_units (int): The number of units of position-wise feed forward.
num_blocks (int): The number of decoder blocks.
dropout_rate (float): Dropout rate.
positional_dropout_rate (float): Dropout rate after adding positional encoding.
attention_dropout_rate (float): Dropout rate in attention.
input_layer (Union[str, torch.nn.Module]): Input layer type.
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool): Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
macaron_style (bool): Whether to use macaron style for positionwise layer.
pos_enc_layer_type (str): Encoder positional encoding layer type.
selfattention_layer_type (str): Encoder attention layer type.
activation_type (str): Encoder activation function type.
use_cnn_module (bool): Whether to use convolution module.
zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
cnn_module_kernel (int): Kernerl size of convolution module.
padding_idx (int): Padding idx for input_layer=embed.
stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
intermediate_layers (Union[List[int], None]): indices of intermediate CTC layer.
indices start from 1.
if not None, intermediate outputs are returned (which changes return type
signature.)
"""
def __init__(
self,
idim,
attention_dim=256,
attention_heads=4,
linear_units=2048,
num_blocks=6,
dropout_rate=0.1,
positional_dropout_rate=0.1,
attention_dropout_rate=0.0,
input_layer="conv2d",
normalize_before=True,
concat_after=False,
ffn_layer_type="linear",
ffn_conv_kernel_size=1,
macaron_style=False,
pos_enc_layer_type="abs_pos",
selfattention_layer_type="selfattn",
activation_type="relu",
use_cnn_module=True,
zero_triu=False,
cnn_module_kernel=31,
padding_idx=-1,
stochastic_depth_rate=0.0,
intermediate_layers=None,
ctc_softmax=None,
conditioning_layer_dim=None,
max_seq_len=100,
enable_fusion=False,
fusion_type="",
):
"""Construct an Encoder object."""
super(Encoder, self).__init__()
self.max_seq_len = max_seq_len
activation = get_activation(activation_type)
if pos_enc_layer_type == "abs_pos":
pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "scaled_abs_pos":
pos_enc_class = ScaledPositionalEncoding
elif pos_enc_layer_type == "rel_pos":
assert selfattention_layer_type == "rel_selfattn"
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "legacy_rel_pos":
assert selfattention_layer_type == "legacy_rel_selfattn"
pos_enc_class = LegacyRelPositionalEncoding
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
self.conv_subsampling_factor = 1
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(idim, attention_dim),
torch.nn.LayerNorm(attention_dim),
torch.nn.Dropout(dropout_rate),
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif input_layer == "conv2d":
self.embed = Conv2dSubsampling(
idim,
attention_dim,
dropout_rate,
pos_enc_class(attention_dim, positional_dropout_rate),
)
self.conv_subsampling_factor = 4
elif input_layer == "vgg2l":
self.embed = VGG2L(idim, attention_dim)
self.conv_subsampling_factor = 4
elif input_layer == "embed":
self.embed = torch.nn.Sequential(
torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif isinstance(input_layer, torch.nn.Module):
self.embed = torch.nn.Sequential(
input_layer,
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif input_layer is None:
self.embed = torch.nn.Sequential(
pos_enc_class(attention_dim, positional_dropout_rate)
)
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
# self-attention module definition
if selfattention_layer_type == "selfattn":
logging.info("encoder self-attention layer type = self-attention")
| encoder_selfattn_layer = MultiHeadedAttention
| 5 | 2023-11-25 02:38:32+00:00 | 24k |
Luo-Z13/pointobb | PointOBB/mmdet/models/roi_heads/PointOBB_head.py | [
{
"identifier": "HEADS",
"path": "PointOBB/mmdet/models/builder.py",
"snippet": "HEADS = MODELS"
},
{
"identifier": "MODELS",
"path": "PointOBB/mmdet/models/builder.py",
"snippet": "MODELS = Registry('models', parent=MMCV_MODELS)"
},
{
"identifier": "build_head",
"path": "Poi... | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
import copy
import numpy as np
import cv2
from mmdet.core import bbox2result, bbox2roi, rbbox2roi, build_assigner, build_sampler, multi_apply
from ..builder import HEADS, MODELS, build_head, build_roi_extractor, build_loss
from .standard_roi_head import StandardRoIHead
from .cascade_roi_head import CascadeRoIHead
from mmdet.core.bbox.iou_calculators import bbox_overlaps
from .test_mixins import BBoxTestMixin, MaskTestMixin
from mmdet.core.bbox import bbox_xyxy_to_cxcywh
from mmdet.core.bbox.transforms import rbbox2result
from mmcv.cnn import Scale, ConvModule
from mmcv.ops import box_iou_rotated
from typing import Any, List, Sequence, Tuple, Union
from torch import Tensor
from mmdet.models.utils.base_bbox_coder import BaseBBoxCoder
from ..detectors.utils import obb2xyxy, regularize_boxes, reduce_mean, obb2poly_np | 16,504 | bbox_results = self._bbox_forward(x, rois)
outs = outs + (bbox_results['cls_score'],
bbox_results['bbox_pred'])
return outs
def grid_priors(self,
featmap_sizes: List[Tuple],
dtype: torch.dtype = torch.float32,
device = 'cuda',
with_stride: bool = False):
num_levels = len(self.featmap_strides)
assert num_levels == len(featmap_sizes)
multi_level_priors = []
for i in range(num_levels):
priors = self.single_level_grid_priors(
featmap_sizes[i],
level_idx=i,
dtype=dtype,
device=device,
with_stride=with_stride)
multi_level_priors.append(priors)
return multi_level_priors
def single_level_grid_priors(self,
featmap_size: Tuple[int],
level_idx: int,
dtype: torch.dtype = torch.float32,
device = 'cuda',
offset = 0.5,
with_stride: bool = False) -> Tensor:
feat_h, feat_w = featmap_size
stride_w = self.featmap_strides[level_idx]
stride_h = stride_w
shift_x = ((torch.arange(0, feat_w, device=device) + offset) * stride_w).to(dtype)
shift_y = ((torch.arange(0, feat_h, device=device) + offset) * stride_h).to(dtype)
shift_xx, shift_yy = meshgrid(shift_x, shift_y)
if not with_stride:
shifts = torch.stack([shift_xx, shift_yy], dim=-1)
else:
stride_w = shift_xx.new_full((shift_xx.shape[0], ),
stride_w).to(dtype)
stride_h = shift_xx.new_full((shift_yy.shape[0], ),
stride_h).to(dtype)
shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h],
dim=-1)
all_points = shifts.to(device)
return all_points
def get_targets(self, x, points, gt_points, proposals, gt_labels, img_metas):
self.norm_on_bbox = True
num_levels = len(x)
concat_points = torch.cat(points, dim=0)
# the number of points per img, per lvl
num_points = [center.size(0) for center in points]
labels_list, angle_targets_list, id_targets_list = multi_apply(
self._get_targets_single,
gt_points,
proposals,
gt_labels,
img_metas,
points=concat_points,
num_points_per_lvl=num_points)
# split to per img, per level
labels_list = [labels.split(num_points, 0) for labels in labels_list]
angle_targets_list = [
angle_targets.split(num_points, 0)
for angle_targets in angle_targets_list
]
id_targets_list = [
id_targets.split(num_points, 0) for id_targets in id_targets_list
]
# concat per level image
concat_lvl_labels = []
concat_lvl_angle_targets = []
concat_lvl_id_targets = []
for i in range(num_levels):
concat_lvl_labels.append(
torch.cat([labels[i] for labels in labels_list]))
# bbox_targets = torch.cat(
# [bbox_targets[i] for bbox_targets in bbox_targets_list])
angle_targets = torch.cat(
[angle_targets[i] for angle_targets in angle_targets_list])
id_targets = torch.cat(
[id_targets[i] for id_targets in id_targets_list])
concat_lvl_angle_targets.append(angle_targets)
concat_lvl_id_targets.append(id_targets)
return (concat_lvl_labels, concat_lvl_angle_targets, concat_lvl_id_targets)
def _get_targets_single(
self, gt_points, proposals, gt_label, img_meta, points,
num_points_per_lvl: List[int]) -> Tuple[Tensor, Tensor, Tensor]:
"""Compute regression and classification targets for a single image."""
self.center_sampling = True
self.center_sample_radius = 1.5
self.pseudow = 3
self.pseudoh = 2
num_points = points.size(0)
num_gts = len(gt_points)
gt_labels = gt_label
gt_bid = img_meta['gt_bid']
gen_proposals = proposals.reshape(len(gt_points), -1, proposals.size(-1))
if gt_points.size(-1) == 2:
extra_tensor = torch.tensor([self.pseudow, self.pseudoh, gen_proposals[0,0,-1]],
device=gt_points.device, dtype=gt_points.dtype).repeat(len(gt_points), 1)
gt_bboxes = torch.cat((gt_points, extra_tensor), dim=1)
else:
gt_bboxes = gt_points.clone()
if num_gts == 0:
return gt_labels.new_full((num_points,), self.num_classes), \
gt_bboxes.new_zeros((num_points, 1)), \
gt_bboxes.new_zeros((num_points,))
areas = (gt_bboxes[:,2] * gt_bboxes[:,3]).squeeze()
|
RangeType = Sequence[Tuple[int, int]]
INF = 1e8
def meshgrid(x: Tensor,
y: Tensor,
row_major: bool = True) -> Tuple[Tensor, Tensor]:
yy, xx = torch.meshgrid(y, x)
if row_major:
# warning .flatten() would cause error in ONNX exportingF
# have to use reshape here
return xx.reshape(-1), yy.reshape(-1)
else:
return yy.reshape(-1), xx.reshape(-1)
def obb2cxcywh_le90(obboxes):
"""Convert oriented bounding boxes to horizontal bounding boxes.
Args:
obbs (torch.Tensor): [x_ctr,y_ctr,w,h,angle]
Returns:
hbbs (torch.Tensor): [x_lt,y_lt,x_rb,y_rb]
"""
center, w, h, theta = torch.split(obboxes, [2, 1, 1, 1], dim=-1)
Cos, Sin = torch.cos(theta), torch.sin(theta)
x_bias = torch.abs(w / 2 * Cos) + torch.abs(h / 2 * Sin)
y_bias = torch.abs(w / 2 * Sin) + torch.abs(h / 2 * Cos)
bias = torch.cat([x_bias, y_bias], dim=-1)
wh = bias * 2
return torch.cat([center, wh, torch.zeros_like(theta)], dim=-1)
@HEADS.register_module()
class PSCCoder(BaseBBoxCoder):
"""Phase-Shifting Coder.
`Phase-Shifting Coder (PSC)
<https://arxiv.org/abs/2211.06368>`.
Args:
angle_version (str): Angle definition.
Only 'le90' is supported at present.
dual_freq (bool, optional): Use dual frequency. Default: True.
num_step (int, optional): Number of phase steps. Default: 3.
thr_mod (float): Threshold of modulation. Default: 0.47.
"""
def __init__(self,
angle_version: str,
dual_freq: bool = True,
num_step: int = 3,
thr_mod: float = 0.47):
super().__init__()
self.angle_version = angle_version
assert angle_version in ['le90']
self.dual_freq = dual_freq
self.num_step = num_step
self.thr_mod = thr_mod
if self.dual_freq:
self.encode_size = 2 * self.num_step
else:
self.encode_size = self.num_step
self.coef_sin = torch.tensor(
tuple(
torch.sin(torch.tensor(2 * k * math.pi / self.num_step))
for k in range(self.num_step)))
self.coef_cos = torch.tensor(
tuple(
torch.cos(torch.tensor(2 * k * math.pi / self.num_step))
for k in range(self.num_step)))
def encode(self, angle_targets: Tensor) -> Tensor:
"""Phase-Shifting Encoder.
Args:
angle_targets (Tensor): Angle offset for each scale level.
Has shape (num_anchors * H * W, 1)
Returns:
list[Tensor]: The psc coded data (phase-shifting patterns)
for each scale level.
Has shape (num_anchors * H * W, encode_size)
"""
phase_targets = angle_targets * 2
phase_shift_targets = tuple(
torch.cos(phase_targets + 2 * math.pi * x / self.num_step)
for x in range(self.num_step))
# Dual-freq PSC for square-like problem
if self.dual_freq:
phase_targets = angle_targets * 4
phase_shift_targets += tuple(
torch.cos(phase_targets + 2 * math.pi * x / self.num_step)
for x in range(self.num_step))
return torch.cat(phase_shift_targets, axis=-1)
def decode(self, angle_preds: Tensor, keepdim: bool = False) -> Tensor:
"""Phase-Shifting Decoder.
Args:
angle_preds (Tensor): The psc coded data (phase-shifting patterns)
for each scale level.
Has shape (num_anchors * H * W, encode_size)
keepdim (bool): Whether the output tensor has dim retained or not.
Returns:
list[Tensor]: Angle offset for each scale level.
Has shape (num_anchors * H * W, 1) when keepdim is true,
(num_anchors * H * W) otherwise
"""
self.coef_sin = self.coef_sin.to(angle_preds)
self.coef_cos = self.coef_cos.to(angle_preds)
phase_sin = torch.sum(
angle_preds[:, 0:self.num_step] * self.coef_sin,
dim=-1,
keepdim=keepdim)
phase_cos = torch.sum(
angle_preds[:, 0:self.num_step] * self.coef_cos,
dim=-1,
keepdim=keepdim)
phase_mod = phase_cos**2 + phase_sin**2
phase = -torch.atan2(phase_sin, phase_cos) # In range [-pi,pi)
if self.dual_freq:
phase_sin = torch.sum(
angle_preds[:, self.num_step:(2 * self.num_step)] *
self.coef_sin,
dim=-1,
keepdim=keepdim)
phase_cos = torch.sum(
angle_preds[:, self.num_step:(2 * self.num_step)] *
self.coef_cos,
dim=-1,
keepdim=keepdim)
phase_mod = phase_cos**2 + phase_sin**2
phase2 = -torch.atan2(phase_sin, phase_cos) / 2
# Phase unwarpping, dual freq mixing
# Angle between phase and phase2 is obtuse angle
idx = torch.cos(phase) * torch.cos(phase2) + torch.sin(
phase) * torch.sin(phase2) < 0
# Add pi to phase2 and keep it in range [-pi,pi)
phase2[idx] = phase2[idx] % (2 * math.pi) - math.pi
phase = phase2
# Set the angle of isotropic objects to zero
phase[phase_mod < self.thr_mod] *= 0
angle_pred = phase / 2
return angle_pred
@HEADS.register_module()
class PointOBBHead(StandardRoIHead):
"""Simplest base roi head including one bbox head and one mask head."""
def __init__(self,
bbox_roi_extractor,
num_stages,
bbox_head,
top_k=7,
with_atten=None,
conv_cfg=None,
norm_cfg=None,
scale_angle: bool = True,
stacked_convs = 4,
loss_symmetry_ss=dict(
type='SmoothL1Loss', loss_weight=1.0, beta=0.1),
angle_coder=dict(
type='PSCCoder',
angle_version='le90',
dual_freq=False,
num_step=3,
thr_mod=0),
angle_version = 'le90',
use_angle_loss = True,
add_angle_pred_begin = False,
not_use_rot_mil = False,
detach_angle_head = False,
rotation_agnostic_classes = None,
agnostic_resize_classes = None,
cls_scores_weight = 1.0,
ins_scores_weight = 1.0,
**kwargs):
super(PointOBBHead, self).__init__(bbox_roi_extractor=bbox_roi_extractor, bbox_head=bbox_head, **kwargs)
self.threshold = 0.3
self.merge_mode = 'weighted_clsins'
self.test_mean_iou = False
# self.test_mean_iou = True
self.sum_iou = 0
self.sum_num = 0
self.num_stages = num_stages
self.topk1 = top_k # 7
self.topk2 = top_k # 7
self.featmap_strides = bbox_roi_extractor.featmap_strides
self.with_atten = with_atten
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.in_channels=256
self.feat_channels=256
self.stacked_convs=stacked_convs
self.is_scale_angle = scale_angle
self.angle_coder = HEADS.build(angle_coder)
self.loss_symmetry_ss = build_loss(loss_symmetry_ss)
self.angle_version = angle_version
self.rotation_agnostic_classes = rotation_agnostic_classes
self.agnostic_resize_classes = agnostic_resize_classes
self.add_angle_pred_begin = add_angle_pred_begin
self.use_angle_loss = use_angle_loss
self.not_use_rot_mil = not_use_rot_mil
self.detach_angle_head = detach_angle_head
self.cls_scores_weight = cls_scores_weight
self.ins_scores_weight = ins_scores_weight
self.num_classes = self.bbox_head.num_classes
self._init_layers()
def _init_layers(self):
"""Initialize layers of the head."""
self.relu = nn.ReLU(inplace=True)
self.cls_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
ConvModule(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.conv_angle = nn.Conv2d(
self.feat_channels, self.angle_coder.encode_size, 3, padding=1)
if self.is_scale_angle:
self.scale_angle = Scale(1.0)
def angle_forward(self, feats: Tuple[Tensor]) -> Tuple[List[Tensor], List[Tensor]]:
angle_results = []
for feat in feats:
if self.detach_angle_head:
feat_detach = feat.clone().detach()
single_angle_pred = self.angle_forward_single(feat_detach)
else:
single_angle_pred = self.angle_forward_single(feat)
angle_results.append(single_angle_pred)
return tuple(angle_results)
def angle_forward_single(self, x: Tensor):
cls_feat = x
for cls_layer in self.cls_convs:
cls_feat = cls_layer(cls_feat)
# cls_score = self.conv_cls(cls_feat)
angle_pred = self.conv_angle(cls_feat)
if self.is_scale_angle:
angle_pred = self.scale_angle(angle_pred).float()
return angle_pred
def init_assigner_sampler(self):
"""Initialize assigner and sampler."""
self.bbox_assigner = None
self.bbox_sampler = None
if self.train_cfg:
self.bbox_assigner = build_assigner(self.train_cfg.assigner)
self.bbox_sampler = build_sampler(
self.train_cfg.sampler, context=self)
def init_bbox_head(self, bbox_roi_extractor, bbox_head):
"""Initialize ``bbox_head``"""
self.bbox_roi_extractor = build_roi_extractor(bbox_roi_extractor)
# self.cdb = build_head(dict(type='ConvConcreteDB', cfg=None, planes=256))
self.bbox_head = build_head(bbox_head)
def init_mask_head(self, mask_roi_extractor, mask_head):
"""Initialize ``mask_head``"""
if mask_roi_extractor is not None:
self.mask_roi_extractor = build_roi_extractor(mask_roi_extractor)
self.share_roi_extractor = False
else:
self.share_roi_extractor = True
self.mask_roi_extractor = self.bbox_roi_extractor
self.mask_head = build_head(mask_head)
def forward_dummy(self, x, proposals):
"""Dummy forward function."""
# bbox head
outs = ()
rois = bbox2roi([proposals])
if self.with_bbox:
bbox_results = self._bbox_forward(x, rois)
outs = outs + (bbox_results['cls_score'],
bbox_results['bbox_pred'])
return outs
def grid_priors(self,
featmap_sizes: List[Tuple],
dtype: torch.dtype = torch.float32,
device = 'cuda',
with_stride: bool = False):
num_levels = len(self.featmap_strides)
assert num_levels == len(featmap_sizes)
multi_level_priors = []
for i in range(num_levels):
priors = self.single_level_grid_priors(
featmap_sizes[i],
level_idx=i,
dtype=dtype,
device=device,
with_stride=with_stride)
multi_level_priors.append(priors)
return multi_level_priors
def single_level_grid_priors(self,
featmap_size: Tuple[int],
level_idx: int,
dtype: torch.dtype = torch.float32,
device = 'cuda',
offset = 0.5,
with_stride: bool = False) -> Tensor:
feat_h, feat_w = featmap_size
stride_w = self.featmap_strides[level_idx]
stride_h = stride_w
shift_x = ((torch.arange(0, feat_w, device=device) + offset) * stride_w).to(dtype)
shift_y = ((torch.arange(0, feat_h, device=device) + offset) * stride_h).to(dtype)
shift_xx, shift_yy = meshgrid(shift_x, shift_y)
if not with_stride:
shifts = torch.stack([shift_xx, shift_yy], dim=-1)
else:
stride_w = shift_xx.new_full((shift_xx.shape[0], ),
stride_w).to(dtype)
stride_h = shift_xx.new_full((shift_yy.shape[0], ),
stride_h).to(dtype)
shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h],
dim=-1)
all_points = shifts.to(device)
return all_points
def get_targets(self, x, points, gt_points, proposals, gt_labels, img_metas):
self.norm_on_bbox = True
num_levels = len(x)
concat_points = torch.cat(points, dim=0)
# the number of points per img, per lvl
num_points = [center.size(0) for center in points]
labels_list, angle_targets_list, id_targets_list = multi_apply(
self._get_targets_single,
gt_points,
proposals,
gt_labels,
img_metas,
points=concat_points,
num_points_per_lvl=num_points)
# split to per img, per level
labels_list = [labels.split(num_points, 0) for labels in labels_list]
angle_targets_list = [
angle_targets.split(num_points, 0)
for angle_targets in angle_targets_list
]
id_targets_list = [
id_targets.split(num_points, 0) for id_targets in id_targets_list
]
# concat per level image
concat_lvl_labels = []
concat_lvl_angle_targets = []
concat_lvl_id_targets = []
for i in range(num_levels):
concat_lvl_labels.append(
torch.cat([labels[i] for labels in labels_list]))
# bbox_targets = torch.cat(
# [bbox_targets[i] for bbox_targets in bbox_targets_list])
angle_targets = torch.cat(
[angle_targets[i] for angle_targets in angle_targets_list])
id_targets = torch.cat(
[id_targets[i] for id_targets in id_targets_list])
concat_lvl_angle_targets.append(angle_targets)
concat_lvl_id_targets.append(id_targets)
return (concat_lvl_labels, concat_lvl_angle_targets, concat_lvl_id_targets)
def _get_targets_single(
self, gt_points, proposals, gt_label, img_meta, points,
num_points_per_lvl: List[int]) -> Tuple[Tensor, Tensor, Tensor]:
"""Compute regression and classification targets for a single image."""
self.center_sampling = True
self.center_sample_radius = 1.5
self.pseudow = 3
self.pseudoh = 2
num_points = points.size(0)
num_gts = len(gt_points)
gt_labels = gt_label
gt_bid = img_meta['gt_bid']
gen_proposals = proposals.reshape(len(gt_points), -1, proposals.size(-1))
if gt_points.size(-1) == 2:
extra_tensor = torch.tensor([self.pseudow, self.pseudoh, gen_proposals[0,0,-1]],
device=gt_points.device, dtype=gt_points.dtype).repeat(len(gt_points), 1)
gt_bboxes = torch.cat((gt_points, extra_tensor), dim=1)
else:
gt_bboxes = gt_points.clone()
if num_gts == 0:
return gt_labels.new_full((num_points,), self.num_classes), \
gt_bboxes.new_zeros((num_points, 1)), \
gt_bboxes.new_zeros((num_points,))
areas = (gt_bboxes[:,2] * gt_bboxes[:,3]).squeeze() | gt_bboxes = regularize_boxes(gt_bboxes, pattern=self.angle_version) | 10 | 2023-11-20 07:50:12+00:00 | 24k |
ModelTC/EasyLLM | llm/models/hf_models/qwen_vl/modeling_qwen.py | [
{
"identifier": "QWenConfig",
"path": "llm/models/hf_models/qwen_vl/configuration_qwen.py",
"snippet": "class QWenConfig(PretrainedConfig):\n model_type = \"qwen\"\n keys_to_ignore_at_inference = [\"past_key_values\"]\n\n def __init__(\n self,\n vocab_size=151936,\n hidden_... | import importlib
import math
import torch # noqa
import torch.nn.functional as F # noqa
import torch.utils.checkpoint # noqa
from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union, Callable, List, Any, Generator # noqa
from torch.cuda.amp import autocast # noqa
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList # noqa
from transformers.generation.logits_process import LogitsProcessorList # noqa
from transformers.generation.streamers import BaseStreamer # noqa
from transformers.generation.utils import GenerateOutput # noqa
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel # noqa
from transformers.utils import logging
from einops import rearrange
from torch import nn
from .configuration_qwen import QWenConfig # noqa
from .qwen_generation_utils import (
make_context,
) # noqa
from llm.models.hf_models.qwen.qwen_generation_utils import (
HistoryType,
decode_tokens,
get_stop_words_ids,
)
from .visual import VisionTransformer
from llm.models.hf_models.qwen.modeling_qwen import RMSNorm, apply_rotary_pos_emb, QWenMLP
from llm.models.hf_models.qwen.modeling_qwen import QWenAttention as QWenAttention_chat
from llm.models.hf_models.qwen.modeling_qwen import QWenModel as QWenModel_chat
from llm.models.hf_models.qwen.modeling_qwen import QWenLMHeadModel as QWenLMHeadModel_chat
from einops import rearrange | 15,993 | input_shape[-1] + past_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
encoder_attention_mask = None
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, inputs_embeds, past_length
)
hidden_states = inputs_embeds
kv_seq_len = hidden_states.size()[1]
if past_key_values[0] is not None:
# past key values[0][0] shape: bs * seq_len * head_num * dim
kv_seq_len += past_key_values[0][0].shape[1]
if (
self.use_dynamic_ntk
and kv_seq_len == hidden_states.size()[1]
and not self.training
):
context_value = math.log(kv_seq_len / self.seq_length, 2) + 1
ntk_alpha = 2 ** math.ceil(context_value) - 1
ntk_alpha = max(ntk_alpha, 1)
else:
ntk_alpha = self.rotary_emb._ntk_alpha_cached
rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha)
for idx in range(len(rotary_pos_emb)):
rotary_pos_emb[idx] = rotary_pos_emb[idx].to(hidden_states.device)
hidden_states = self.drop(hidden_states).clone()
if fake_images is not None:
hidden_states = hidden_states + images.mean() * 0
elif images is not None:
for idx, (i, a, b) in enumerate(img_pos):
hidden_states[i][a + 1 : b] = images[idx]
output_shape = input_shape + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, use_cache, output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
rotary_pos_emb,
self.registered_causal_mask,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
rotary_pos_emb=rotary_pos_emb,
registered_causal_mask=self.registered_causal_mask,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v for v in [hidden_states, presents, all_hidden_states] if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
| # Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
if TYPE_CHECKING:
try:
except ImportError:
rearrange = None
SUPPORT_CUDA = torch.cuda.is_available()
SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported()
SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "qwen"
_CONFIG_FOR_DOC = "QWenConfig"
QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"]
_ERROR_BAD_CHAT_FORMAT = """\
We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml".
If you are directly using the model downloaded from Huggingface,
please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat().
我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。
如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。
"""
_SENTINEL = object()
_ERROR_STREAM_IN_CHAT = """\
Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True).
向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。
"""
apply_rotary_emb_func = None
rms_norm = None
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
class QWenAttention(QWenAttention_chat):
def __init__(self, config):
super().__init__(config)
def _attn(self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None):
attn_weights = torch.matmul(query, key.transpose(-1, -2))
if self.scale_attn_weights:
attn_weights = attn_weights / torch.full(
[],
value.size(-1) ** 0.5,
dtype=attn_weights.dtype,
device=attn_weights.device,
)
attn_weights = attn_weights + attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
attn_output = attn_output.transpose(1, 2)
return attn_output, attn_weights
def forward(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
rotary_pos_emb: Optional[List[torch.Tensor]] = None,
registered_causal_mask: Optional[torch.Tensor] = None,
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
):
mixed_x_layer = self.c_attn(hidden_states)
query, key, value = mixed_x_layer.split(self.split_size, dim=2)
query = self._split_heads(query, self.num_heads, self.head_dim)
key = self._split_heads(key, self.num_heads, self.head_dim)
value = self._split_heads(value, self.num_heads, self.head_dim)
if rotary_pos_emb is not None:
cur_len = query.shape[1]
rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
rotary_pos_emb = (rotary_pos_emb,) * 2
q_pos_emb, k_pos_emb = rotary_pos_emb
# Slice the pos emb for current inference
query = apply_rotary_pos_emb(query, q_pos_emb)
key = apply_rotary_pos_emb(key, k_pos_emb)
if layer_past is not None:
past_key, past_value = layer_past[0], layer_past[1]
key = torch.cat((past_key, key), dim=1)
value = torch.cat((past_value, value), dim=1)
if use_cache:
present = (key, value)
else:
present = None
if self.use_logn_attn and not self.training:
if self.logn_tensor.device != query.device or self.logn_tensor.dtype != query.dtype:
self.logn_tensor = self.logn_tensor.to(query.device).type_as(query)
seq_start = key.size(1) - query.size(1)
seq_end = key.size(1)
logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :]
query = query * logn_tensor.expand_as(query)
query = query.permute(0, 2, 1, 3)
key = key.permute(0, 2, 1, 3)
value = value.permute(0, 2, 1, 3)
attn_output, attn_weight = self._attn(
query, key, value, registered_causal_mask, attention_mask, head_mask
)
context_layer = self._merge_heads(
attn_output, self.num_heads, self.head_dim
)
attn_output = self.c_proj(context_layer)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weight,)
return outputs
class QWenBlock(nn.Module):
def __init__(self, config):
super().__init__()
hidden_size = config.hidden_size
self.bf16 = config.bf16
self.ln_1 = RMSNorm(
hidden_size,
eps=config.layer_norm_epsilon,
)
self.attn = QWenAttention(config)
self.ln_2 = RMSNorm(
hidden_size,
eps=config.layer_norm_epsilon,
)
self.mlp = QWenMLP(config)
def forward(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
rotary_pos_emb: Optional[List[torch.Tensor]] = None,
registered_causal_mask: Optional[torch.Tensor] = None,
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
):
layernorm_output = self.ln_1(hidden_states)
attn_outputs = self.attn(
layernorm_output,
rotary_pos_emb,
registered_causal_mask=registered_causal_mask,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0]
outputs = attn_outputs[1:]
residual = hidden_states
layernorm_input = attn_output + residual
layernorm_output = self.ln_2(layernorm_input)
residual = layernorm_input
mlp_output = self.mlp(layernorm_output)
hidden_states = residual + mlp_output
if use_cache:
outputs = (hidden_states,) + outputs
else:
outputs = (hidden_states,) + outputs[1:]
return outputs
class QWenModel(QWenModel_chat):
_keys_to_ignore_on_load_missing = ["attn.masked_bias"]
def __init__(self, config):
super().__init__(config)
dim = (
self.rotary_ndims
if self.rotary_ndims is not None
else config.kv_channels
)
self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base)
self.registered_causal_mask = None
self.h = nn.ModuleList(
[
QWenBlock(
config
)
for i in range(config.num_hidden_layers)
]
)
self.visual = VisionTransformer(**config.visual)
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
if past_key_values is None and torch.any(input_ids == self.config.visual['image_start_id']):
bos_pos = torch.where(input_ids == self.config.visual['image_start_id'])
eos_pos = torch.where(input_ids == self.config.visual['image_start_id'] + 1)
assert (bos_pos[0] == eos_pos[0]).all()
img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1)
images = []
for i, a, b in img_pos:
image = input_ids[i][a + 1 : b - 1].tolist()
image = image[: image.index(self.config.visual['image_start_id'] + 2)]
images.append(bytes(image).decode('utf-8'))
images = self.visual.encode(images)
assert images.shape[0] == len(images)
fake_images = None
elif self.training:
fake_images = torch.zeros(1, 3, 224, 224).to(
dtype=self.visual.conv1.weight.dtype, device=self.visual.conv1.weight.device)
images = self.visual(fake_images)
else:
fake_images = None
images = None
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(
past_length,
input_shape[-1] + past_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
encoder_attention_mask = None
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, inputs_embeds, past_length
)
hidden_states = inputs_embeds
kv_seq_len = hidden_states.size()[1]
if past_key_values[0] is not None:
# past key values[0][0] shape: bs * seq_len * head_num * dim
kv_seq_len += past_key_values[0][0].shape[1]
if (
self.use_dynamic_ntk
and kv_seq_len == hidden_states.size()[1]
and not self.training
):
context_value = math.log(kv_seq_len / self.seq_length, 2) + 1
ntk_alpha = 2 ** math.ceil(context_value) - 1
ntk_alpha = max(ntk_alpha, 1)
else:
ntk_alpha = self.rotary_emb._ntk_alpha_cached
rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha)
for idx in range(len(rotary_pos_emb)):
rotary_pos_emb[idx] = rotary_pos_emb[idx].to(hidden_states.device)
hidden_states = self.drop(hidden_states).clone()
if fake_images is not None:
hidden_states = hidden_states + images.mean() * 0
elif images is not None:
for idx, (i, a, b) in enumerate(img_pos):
hidden_states[i][a + 1 : b] = images[idx]
output_shape = input_shape + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, use_cache, output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
rotary_pos_emb,
self.registered_causal_mask,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
rotary_pos_emb=rotary_pos_emb,
registered_causal_mask=self.registered_causal_mask,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v for v in [hidden_states, presents, all_hidden_states] if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
| class QWenLMHeadModel(QWenLMHeadModel_chat): | 6 | 2023-11-26 10:12:52+00:00 | 24k |
wangermeng2021/llm-webui | main.py | [
{
"identifier": "login_huggingface",
"path": "src/utils/common.py",
"snippet": "def login_huggingface(token,base_model_name_dropdown):\n if base_model_name_dropdown.lower().find(\"llama\") >= 0:\n if token:\n HUGGINGFACE_HUB_TOKEN = token\n print(\"d1:\",HUGGINGFACE_HUB_T... | import pandas as pd
import math
import numpy as np
import gc
import os,requests
import subprocess,threading
import time
import gradio as gr
import os
import traceback
import numpy as np
import glob
import shutil
import torch
import socket
from src.utils.common import login_huggingface
from src.finetune.huggingface_inference import HuggingfaceInference
from src.finetune.llama_cpp_inference import LlamaCppInference
from src.rag.qa_with_rag import QAWithRAG
from src.utils.common import read_yaml,get_first_row_from_dataset,\
get_runs_model_names_from_dir,get_hg_model_names_from_dir,get_hg_model_names_and_gguf_from_dir,validate_model_path,get_runs_models
from src.utils.chat_prompts import get_model_type,get_chat_history_prompt,get_model_prompt_template
from transformers.training_args import OptimizerNames
from huggingface_hub import hf_hub_download
from src.utils import download_model
from pathlib import Path
from src.finetune.qlora_trainer import QloraTrainer
from src.finetune.qlora_trainer import TRAINING_STATUS
from src.utils.download_huggingface_repo import download_model_wrapper,download_dataset_wrapper | 14,501 | gr.Markdown("## Setting", elem_classes="white_background")
with gr.Group():
with gr.Group():
gr.Markdown("### 1.Chunking", elem_classes="white_background")
with gr.Row():
text_splitter_dropdown = gr.Dropdown(["RecursiveCharacterTextSplitter"],
label=f"Text Splitter",
value="RecursiveCharacterTextSplitter",
interactive=True, scale=1, min_width=1)
with gr.Row():
chunk_size_slider = gr.Slider(32, 1024, value=256, step=32, label="Chunk Size",
interactive=True, scale=1)
chunk_overlap_slider = gr.Slider(0, 500, value=20, step=10, label="Chunk Overlap",
interactive=True)
Separators_textbox = gr.Textbox(label="Separators",
value='''["\n\n", "\n", ".", " ", ""]''',
interactive=True,visible=False)
with gr.Group():
gr.Markdown("### 2.Vector Store Retriever", elem_classes="white_background") #
local_embedding_model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),"rag","embedding_models")
local_embedding_model_names = get_hg_model_names_from_dir(local_embedding_model_dir,"embedding_models")
embedding_model_source_radio_choices = ["Download From Huggingface Hub",
f"From Local Dir(hg format:{local_embedding_model_dir})"]
embedding_model_source_radio = gr.Radio(embedding_model_source_radio_choices,
label="Embedding Model Source",
value=embedding_model_source_radio_choices[0],
interactive=True)
with gr.Row():
hub_embedding_model_names_dropdown = gr.Dropdown(embedding_model_names,
label=f"",show_label=False,
value=embedding_model_names[0] if embedding_model_names else None,
interactive=True, scale=4, min_width=1)
download_hub_embedding_model_names_btn = gr.Button("Download", scale=1)
stop_download_hub_embedding_model_names_btn = gr.Button("Stop", scale=1, visible=False)
local_embedding_model_names_dropdown = gr.Dropdown(local_embedding_model_names,
label=f"Embedding Model",show_label=False,
value=local_embedding_model_names[0] if local_embedding_model_names else None,
interactive=True, scale=4, min_width=1,visible=False)
refresh_local_embedding_model_names_btn = gr.Button("Refresh", scale=1,visible=False)
# model_config_path1 = os.path.join(local_embedding_model_dir,
# embedding_model_names[0], "pytorch_model.bin")
# model_config_path2 = os.path.join(local_embedding_model_dir,
# embedding_model_names[0], "model.safetensors")
model_config_path = os.path.join(local_embedding_model_dir,
embedding_model_names[0], "config.json")
if os.path.exists(model_config_path):
download_hub_embedding_model_status_markdown = gr.Markdown(
'<span style="color:green"> This model has already been downloaded to local.</span>')
else:
download_hub_embedding_model_status_markdown = gr.Markdown(
'<span style="color:red"> This model has not been downloaded.</span>')
with gr.Row():
search_top_k_slider = gr.Slider(1, 10, value=3, step=1, label="Search Top K", interactive=True)
search_score_threshold_slider = gr.Slider(0, 1, value=0.5, step=0.1, label="Search Score Threshold",interactive=True)
with gr.Group():
gr.Markdown("### 3.Chat Model", elem_classes="white_background")
local_chat_model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),"models")
runs_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "runs")
# local_chat_model_names = get_hg_model_names_from_dir(local_chat_model_dir)
local_chat_model_names = get_hg_model_names_and_gguf_from_dir(local_chat_model_dir,runs_model_root_dir)
chat_model_source_radio_choices = ["Download From Huggingface Hub",
f"From Local Dir(hg format:{local_chat_model_dir})"]
chat_model_source_radio = gr.Radio(chat_model_source_radio_choices,
label="Chat Model source",show_label=False,
value=chat_model_source_radio_choices[0],
interactive=True)
with gr.Row():
hub_chat_model_names_dropdown = gr.Dropdown(base_model_names,
label=f"Chat Model",show_label=False,allow_custom_value=True,
value=base_model_names[0] if base_model_names else None,
interactive=True, scale=4, min_width=1)
download_hub_chat_model_names_btn = gr.Button("Download", scale=1)
stop_download_hub_chat_model_names_btn = gr.Button("Stop", scale=1, visible=False)
local_chat_model_names_dropdown = gr.Dropdown(local_chat_model_names,
label=f"Chat Model",show_label=False,
value=local_chat_model_names[0] if local_chat_model_names else None,
interactive=True, scale=4, min_width=1,visible=False)
refresh_local_chat_model_names_btn = gr.Button("Refresh", scale=1,visible=False)
rag_using_4bit_quantization_checkbox = gr.Checkbox(True, label="Using 4-bit quantization",
interactive=True, visible=True,
info="Less memory but slower", scale=1
)
if validate_model_path(base_model_names[0])[0]:
download_hub_chat_model_status_markdown = gr.Markdown(
'<span style="color:green"> This model has already been downloaded to local.</span>')
else:
download_hub_chat_model_status_markdown = gr.Markdown(
'<span style="color:red"> This model has not been downloaded.</span>')
with gr.Tab("Setting"):
# with gr.Column(scale=4, min_width=1):
with gr.Group():
gr.Markdown("## Setting", elem_classes="white_background")
with gr.Group():
with gr.Row():
max_new_tokens_slider = gr.Slider(1, 4096, value=256, step=0.1, label="Max New Tokens",
interactive=True)
temperature_slider = gr.Slider(0, 5, value=1, step=0.1, label="Temperature",
interactive=True)
with gr.Row():
top_k_slider = gr.Slider(1, 100, value=50, step=1, label="Top_k",
interactive=True)
top_p_slider = gr.Slider(0, 1, value=1, step=0.1, label="Top_p",
interactive=True)
with gr.Row():
repeat_penalty_slider = gr.Slider(1, 5, value=1, step=0.1, label="Repeat Penalty", interactive=True)
with gr.Row():
chat_history_window_slider = gr.Slider(1, 20, value=3, step=1, label="Chat History Window",
interactive=True)
low_cpu_mem_usage_checkbox = gr.Checkbox(False, label="Low Cpu Mem Usage",interactive=True,visible=False)
Huggingface_hub_token = gr.Textbox(label="Huggingface Hub Token", value="")
def check_local_model_or_dataset_is_empty1(base_model_name_dropdown,Huggingface_hub_token):
if len(base_model_name_dropdown.strip()) == 0:
raise gr.Error("Name is empty!")
try:
|
# os.environ['HTTP_PROXY'] = 'http://127.0.0.1:8889'
# os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:8889'
LOCAL_HOST_IP = "0.0.0.0"
TENSORBOARD_URL = "http://" + LOCAL_HOST_IP + ":6006/"
INIT_DATASET_NAME = "test_python_code_instructions_5000_rows"
RAG_DATA_LIST_DROPDOWN = ""
TEXT_SPLITTER_DROPDOWN = ""
CHUNK_SIZE_SLIDER = 0
CHUNK_OVERLAP_SLIDER = -1
SEPARATORS_TEXTBOX = ""
EMBEDDING_MODEL_SOURCE_RADIO = ""
HUB_EMBEDDING_MODEL_NAMES_DROPDOWN = ""
LOCAL_EMBEDDING_MODEL_NAMES_DROPDOWN = ""
CHAT_MODEL_SOURCE_RADIO = ""
HUB_CHAT_MODEL_NAMES_DROPDOWN = ""
LOCAL_CHAT_MODEL_NAMES_DROPDOWN = ""
SEARCH_TOP_K_SLIDER = ""
SEARCH_SCORE_THRESHOLD_SLIDER = ""
training_ret_val = -1
error_msg = ""
current_running_model_name = ""
infer_model = None
stop_generation_status = False
chatbot_history=[]
chatbot_height = 500
rag_chatbot_history=[]
rag_stop_generation_status = False
qa_with_rag = QAWithRAG()
train_param_config = {}
train_param_config["dataset"]={}
train_param_config["model"]={}
train_param_config["training"]={}
model_zoo_config = {}
transformer_optimizer_list = []
model_context_window = 0
init_train_file_path = None
init_val_file_path = None
INIT_PREFIX1 = ""
INIT_PREFIX2 = ""
INIT_PREFIX3 = ""
INIT_PREFIX4 = ""
INIT_COL1_TEXT = ""
INIT_COL2_TEXT = ""
INIT_COL3_TEXT = ""
INIT_COL4_TEXT = ""
col_names = []
DATASET_FIRST_ROW = None
local_model_list = ""
local_model_root_dir = ""
base_model_names = []
training_base_model_names = []
embedding_model_names = []
base_model_context_window = []
local_dataset_list = []
local_dataset_root_dir = ""
def get_local_embedding_model_list():
local_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "rag", "embedding_models")
local_model_root_files = os.listdir(local_model_root_dir)
local_model_list = []
for model_dir in local_model_root_files:
if os.path.isdir(os.path.join(local_model_root_dir, model_dir)):
local_model_list.append(model_dir)
return local_model_list,local_model_root_dir
def get_local_model_list():
local_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "models")
local_model_root_files = os.listdir(local_model_root_dir)
local_model_list = []
for model_dir in local_model_root_files:
if os.path.isdir(os.path.join(local_model_root_dir, model_dir)):
local_model_list.append(model_dir)
return local_model_list,local_model_root_dir
def get_local_dataset_list():
local_dataset_list = []
local_dataset_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "datasets")
matched_dataset_file_path_list = glob.glob(os.path.join(local_dataset_root_dir,"**","dataset_infos.json"),recursive=False)
for matched_file_path in matched_dataset_file_path_list:
matched_pos1 = matched_file_path.rfind("datasets")
matched_pos2 = matched_file_path.rfind("dataset_infos.json")
local_dataset_list.append(matched_file_path[matched_pos1 + 9:matched_pos2-1])
matched_dataset_file_path_list = glob.glob(os.path.join(local_dataset_root_dir,"**","dataset_dict.json"),recursive=False)
for matched_file_path in matched_dataset_file_path_list:
matched_pos1 = matched_file_path.rfind("datasets")
matched_pos2 = matched_file_path.rfind("dataset_dict.json")
local_dataset_list.append(matched_file_path[matched_pos1 + 9:matched_pos2-1])
return local_dataset_list,local_dataset_root_dir
def start_tensorboard_server():
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((LOCAL_HOST_IP, 6006))
s.close()
except Exception as e:
tensorboard_cmd = f"tensorboard --logdir {os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs')} --reload_multifile True"
tensorboard_proc = subprocess.Popen(tensorboard_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
shell=True, close_fds=True) # bufsize=0, close_fds=True
def init():
global config_dict,transformer_optimizer_list,model_context_window,init_train_file_path,init_val_file_path
global INIT_PREFIX1,INIT_COL1_TEXT,INIT_PREFIX2,INIT_COL2_TEXT,INIT_PREFIX3,INIT_COL3_TEXT,INIT_PREFIX4,INIT_COL4_TEXT,col_names,DATASET_FIRST_ROW
global local_model_list,local_model_root_dir
global base_model_names,base_model_context_window,embedding_model_names,training_base_model_names
global local_dataset_list, local_dataset_root_dir
start_tensorboard_server()
model_zoo_config = read_yaml(os.path.join(os.path.dirname(os.path.abspath(__file__)),"config","model_zoo.yaml"))
transformer_optimizer_list = list(vars(OptimizerNames)["_value2member_map_"].keys())
#get dynamic context window from selected model
model_context_window = [2048,1024,512]
init_train_file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "datasets", INIT_DATASET_NAME)
DATASET_FIRST_ROW,split_list = get_first_row_from_dataset(init_train_file_path)
col_names = list(DATASET_FIRST_ROW)
col_names.insert(0,"")
INIT_PREFIX1 = "<s>[INST] "
INIT_PREFIX2 = "here are the inputs "
INIT_PREFIX3 = " [/INST]"
INIT_PREFIX4 = "</s>"
INIT_COL1_TEXT = str(DATASET_FIRST_ROW[col_names[1]])
INIT_COL2_TEXT = str(DATASET_FIRST_ROW[col_names[2]])
INIT_COL3_TEXT = str(DATASET_FIRST_ROW[col_names[3]])
INIT_COL4_TEXT = ""
local_model_list,local_model_root_dir = get_local_model_list()
base_model_names = [model_name for model_name in model_zoo_config["model_list"]]
training_base_model_names = [model_name for model_name in base_model_names if not model_name.endswith(".gguf")]
# base_model_context_window = [model_name[1] for model_name in model_zoo_config["model_list"]]
embedding_model_names = [model_name for model_name in model_zoo_config["embedding_model_list"]]
local_dataset_list, local_dataset_root_dir = get_local_dataset_list()
with gr.Blocks(title="FINETUNE",css="#vertical_center_align_markdown { position:absolute; top:30%;background-color:white;} .white_background {background-color: #ffffff} .none_border {border: none;border-collapse:collapse;}") as demo:
init()
local_model_root_dir_textbox = gr.Textbox(label="", value=local_model_root_dir, visible=False)
local_dataset_root_dir_textbox = gr.Textbox(label="",value=local_dataset_root_dir, visible=False)
local_embedding_model_root_dir_textbox = gr.Textbox(label="", value=os.path.join(os.path.dirname(os.path.abspath(__file__)), "rag", "embedding_models"), visible=False)
local_chat_model_root_dir_textbox = gr.Textbox(label="", value=local_model_root_dir, visible=False)
local_home_chat_model_root_dir_textbox = gr.Textbox(label="", value=local_model_root_dir, visible=False)
session_state = gr.State(value={})
# html = gr.HTML("<p align='center';>llm-web-ui</p>",elem_id="header")
with gr.Tab("Home"):
with gr.Row():
# with gr.Column(scale=4, min_width=1):
with gr.Group():
gr.Markdown("## ChatBot", elem_classes="white_background")
with gr.Group():
gr.Markdown("### Chat Model", elem_classes="white_background")
local_home_chat_model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "models")
runs_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "runs")
local_home_chat_model_names = get_hg_model_names_and_gguf_from_dir(local_home_chat_model_dir,
runs_model_root_dir)
home_chat_model_source_radio_choices = ["Download From Huggingface Hub",
f"From Local Dir(hg format:{local_home_chat_model_dir})"]
home_chat_model_source_radio = gr.Radio(home_chat_model_source_radio_choices,
label="Chat Model source", show_label=False,
value=home_chat_model_source_radio_choices[0],
interactive=True)
with gr.Row():
hub_home_chat_model_names_dropdown = gr.Dropdown(base_model_names,
label=f"Chat Model", show_label=False,
allow_custom_value=True,
value=base_model_names[
0] if base_model_names else None,
interactive=True, scale=4, min_width=1)
local_home_chat_model_names_dropdown = gr.Dropdown(local_home_chat_model_names,
label=f"Chat Model", show_label=False,
value=local_home_chat_model_names[
0] if local_home_chat_model_names else None,
interactive=True, scale=4, min_width=1,
visible=False)
download_hub_home_chat_model_names_btn = gr.Button("Download", scale=1)
stop_download_hub_home_chat_model_names_btn = gr.Button("Stop", scale=1, visible=False)
refresh_local_home_chat_model_names_btn = gr.Button("Refresh", scale=1, visible=False)
load_home_chat_model_btn = gr.Button("Load Model", scale=1, visible=True)
using_4bit_quantization_checkbox = gr.Checkbox(True, label="Using 4-bit quantization",
interactive=True, visible=True,
info="Less memory but slower", scale=1
)
if validate_model_path(base_model_names[0])[0]:
download_hub_home_chat_model_status_markdown = gr.Markdown(
'<span style="color:green"> This model has already been downloaded to local,click load model to run.</span>')
else:
download_hub_home_chat_model_status_markdown = gr.Markdown(
'<span style="color:red"> This model has not been downloaded.</span>')
# home_chat_model_running_status_markdown = gr.Markdown(
# '<span style="color:red"> This model has not been downloaded.</span>')
with gr.Row():
chatbot = gr.Chatbot(value=[],bubble_full_width=False,rtl=False,layout="panel",height=chatbot_height,
avatar_images=((os.path.join(os.path.abspath(''),"pics", "user1.png")), (os.path.join(os.path.abspath(''),"pics", "bot4.png"))),
)
with gr.Row():
input_txtbox = gr.Textbox(
show_label=False,autofocus=True,
placeholder="Enter text and press enter",scale=3
)
generate_btn = gr.Button("Generate", scale=1)
stop_btn = gr.Button("Stop", scale=1)
# clear_btn = gr.Button("Clear",scale=1)
with gr.Tab("Fine-Tuning"):
with gr.Tabs() as tensorboard_tab:
with gr.TabItem("Training", id=0):
with gr.Row():
with gr.Column(scale=1, min_width=1):
with gr.Group():
gr.Markdown("## 1.Training", elem_classes="white_background")
with gr.Group():
gr.Markdown("### 1).Model", elem_classes="white_background")
with gr.Group():
# gr.Markdown("<br> Base Model")
base_model_source_radio_choices = ["Download From Huggingface Hub",
f"From Local Dir(hg format:{local_model_root_dir})"]
base_model_source_radio = gr.Radio(base_model_source_radio_choices,
label="Base Model",
value=base_model_source_radio_choices[0],
interactive=True)
with gr.Row(elem_classes="white_background"):
base_model_name_dropdown = gr.Dropdown(training_base_model_names,
label="Model Name", value=training_base_model_names[0] if training_base_model_names else None,
interactive=True, visible=True, scale=5,
allow_custom_value=True)
download_local_model_btn = gr.Button("Download", scale=1, visible=True)
stop_download_local_model_btn = gr.Button("Stop", scale=1, visible=False)
# model_download_status = gr.Markdown("<div id='vertical_center_align_markdown'><p style='text-align: center;'>Not downloaded</p></div>", elem_classes="white_background",scale=1,full_width=True,visible=False)
if validate_model_path(training_base_model_names[0])[0]:
download_model_status_markdown = gr.Markdown('<span style="color:green"> This model has already been downloaded to local.</span>')
else:
download_model_status_markdown = gr.Markdown('<span style="color:red"> This model has not been downloaded.</span>')
with gr.Row():
# local_home_chat_model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "models")
# runs_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "runs")
# local_model_list = get_hg_model_names_and_gguf_from_dir(local_home_chat_model_dir,runs_model_root_dir)
local_model_list = get_hg_model_names_from_dir(os.path.dirname(os.path.abspath(__file__)), "models")
local_model_dropdown = gr.Dropdown(local_model_list, label="Local Model",
info="",
value=local_model_list[0] if len(local_model_list) > 0 else None,
interactive=True,
elem_classes="white_background", scale=5,
visible=False)
refresh_local_model_list_btn = gr.Button("Refresh", scale=1, visible=False)
fine_tuning_type_dropdown = gr.Dropdown(["QLoRA", "LoRA"],
label="Fine-Tuning Type", info="",
value="QLoRA", interactive=True)
with gr.Group():
with gr.Row(elem_classes="white_background"):
# gr.Markdown("### LoRA Config", elem_classes="white_background")
lora_r_list = [str(ri) for ri in range(8, 65, 8)]
lora_r_slider = gr.Slider(8, 64, value=8, step=8, label="lora_r",
interactive=True)
# lora_r_dropdown = gr.Dropdown(lora_r_list,label="lora_r", value=lora_r_list[0],interactive=True,allow_custom_value=True)
lora_alpha_slider = gr.Slider(8, 96, value=32, step=8, label="lora_alpha",
interactive=True)
# lora_alpha_list = [str(ri) for ri in range(8, 97, 8)]
# lora_alpha_dropdown = gr.Dropdown(lora_alpha_list,label="lora_alpha", value=lora_alpha_list[3],interactive=True,allow_custom_value=True)
with gr.Row(elem_classes="white_background"):
lora_dropout_slider = gr.Slider(0, 1, value=0.05, step=0.01,
label="lora_dropout", interactive=True)
lora_bias_dropdown = gr.Dropdown(["none", "all", "lora_only"],
label="lora_bias", info="", value="none",
interactive=True)
with gr.Group():
gr.Markdown("### 2).Dataset",elem_classes="white_background")
dataset_source_radio_choices = ["Download From Huggingface Hub",
f"From Local HG Dataset In {local_dataset_root_dir})"]
dataset_source_radio = gr.Radio(dataset_source_radio_choices, label="Dataset Source",
value=dataset_source_radio_choices[1], interactive=True)
with gr.Row(equal_height=True):
hg_dataset_path_textbox = gr.Textbox(label="Dataset Name:",elem_classes="none_border",visible=False, interactive=True, scale=4,
value="iamtarun/python_code_instructions_18k_alpaca")
download_local_dataset_btn = gr.Button("Download", scale=1, visible=False)
stop_download_local_dataset_btn = gr.Button("Stop", scale=1, visible=False)
download_dataset_status_markdown = gr.Markdown('')
with gr.Row():
hg_train_dataset_dropdown = gr.Dropdown(["train"], label="Train set", info="", interactive=False,visible=False,
elem_classes="white_background", scale=1,value="train")
hg_val_dataset_dropdown = gr.Dropdown([], label="Val set", info="", interactive=False,visible=False,
elem_classes="white_background", scale=1)
with gr.Row():
local_dataset_list.pop(
local_dataset_list.index(INIT_DATASET_NAME))
local_dataset_list.insert(0, INIT_DATASET_NAME)
local_train_path_dataset_dropdown = gr.Dropdown(local_dataset_list, label="Train Dataset", info="",
value=local_dataset_list[0] if len(local_dataset_list)>0 else None, interactive=True,
elem_classes="white_background", scale=5, visible=True)
refresh_local_train_path_dataset_list_btn = gr.Button("Refresh", scale=1, visible=True)
with gr.Row():
local_train_dataset_dropdown = gr.Dropdown(["train"], label="Train set", info="", interactive=True,
elem_classes="white_background", scale=1,value="train",visible=True)
local_val_dataset_dropdown = gr.Dropdown([], label="Val set", info="", interactive=True,
elem_classes="white_background", scale=1,visible=True)
with gr.Group(elem_classes="white_background"):
# gr.Markdown("<h4><br> Prompt Template: (Prefix1 + ColumnName1 + Prefix2 + ColumnName2)</h4>",elem_classes="white_background")
gr.Markdown("<br> **Prompt Template: (Prefix1+ColumnName1+Prefix2+ColumnName2+Prefix3+ColumnName3+Prefix4+ColumnName4)**",elem_classes="white_background")
gr.Markdown(
"<span> **Note**: Llama2/Mistral Chat Template:<s\>[INST] instruction+input [/INST] output</s\> </span>",elem_classes="white_background")
# using_llama2_chat_template_checkbox = gr.Checkbox(True, label="Using Llama2/Mistral chat template",interactive=True,visible=False)
with gr.Row(elem_classes="white_background"):
# prompt_template
prefix1_textbox = gr.Textbox(label="Prefix1:",value=INIT_PREFIX1,lines=2,interactive=True,elem_classes="white_background")
datatset_col1_dropdown = gr.Dropdown(col_names, label="ColumnName1:", info="",value=col_names[1],interactive=True,elem_classes="white_background")
prefix2_textbox = gr.Textbox(label="Prefix2:",value=INIT_PREFIX2,lines=2,interactive=True,elem_classes="white_background")
datatset_col2_dropdown = gr.Dropdown(col_names, label="ColumnName2:", info="",value=col_names[2],interactive=True,elem_classes="white_background")
with gr.Row(elem_classes="white_background"):
prefix3_textbox = gr.Textbox(label="Prefix3:",value=INIT_PREFIX3,lines=2,interactive=True,elem_classes="white_background")
datatset_col3_dropdown = gr.Dropdown(col_names, label="ColumnName3:", info="",value=col_names[3],interactive=True,elem_classes="white_background")
prefix4_textbox = gr.Textbox(label="Prefix4:",value=INIT_PREFIX4,lines=2,interactive=True,elem_classes="white_background")
datatset_col4_dropdown = gr.Dropdown(col_names, label="ColumnName4:", info="",value=col_names[0],interactive=True,elem_classes="white_background")
# print("")
prompt_sample = INIT_PREFIX1 + INIT_COL1_TEXT + INIT_PREFIX2 + INIT_COL2_TEXT + INIT_PREFIX3 + INIT_COL3_TEXT + INIT_PREFIX4 + INIT_COL4_TEXT
prompt_sample_textbox = gr.Textbox(label="Prompt Sample:",interactive=False,value=prompt_sample,lines=4)
max_length_dropdown = gr.Dropdown(["Model Max Length"]+model_context_window, label="Max Length",value="Model Max Length", interactive=True,allow_custom_value=True)
with gr.Group():
gr.Markdown("### 3).Training Arguments",elem_classes="white_background")
with gr.Row(elem_classes="white_background"):
epochs_slider = gr.Slider(1, 100, value=10, step=1, label="Epochs", interactive=True)
# epochs_dropdown = gr.Dropdown([1]+[bi for bi in range(10,101,10)], label="Epochs",value=1, interactive=True,allow_custom_value=True)
batch_size_list = [1,2,3]+[bi for bi in range(4,32+1,4)]
batch_size_slider = gr.Slider(1, 100, value=1, step=1, label="Batch Size", interactive=True)
# batch_size_dropdown = gr.Dropdown(batch_size_list,label="Batch Size", info="",value=batch_size_list[0],interactive=True,allow_custom_value=True)
# learning_rate_textbox = gr.Textbox(label="Learning Rate", value=2e-4,interactive=True)
with gr.Row(elem_classes="white_background"):
learning_rate_slider = gr.Slider(0, 0.01, value=2e-4, step=0.0001, label="Learning Rate", interactive=True)
warmup_steps_slider = gr.Slider(0, 400, value=100, step=10, label="Warmup Steps",
interactive=True)
with gr.Row(elem_classes="white_background"):
optimizer_dropdown = gr.Dropdown(transformer_optimizer_list, label="Optimizer", info="",
value=transformer_optimizer_list[1], interactive=True)
lr_scheduler_list = ["linear","cosine","cosine_with_hard_restarts","polynomial_decay","constant","constant_with_warmup","inverse_sqrt","reduce_on_plateau"]
lr_scheduler_type_dropdown = gr.Dropdown(lr_scheduler_list, label="LR Scheduler Type", info="",
value=lr_scheduler_list[0], interactive=True)
with gr.Row(elem_classes="white_background"):
early_stopping_patience_slider = gr.Slider(0, 50+1, value=0, step=5, label="Early Stopping Patience",
interactive=True)
gradient_accumulation_steps_slider = gr.Slider(1, 50, value=1, step=1,
label="Gradient Accumulation Steps")
with gr.Row(elem_classes="white_background"):
eval_steps_slider = gr.Slider(0, 1000, value=100, step=100, label="eval_steps", interactive=True)
gradient_checkpointing_checkbox = gr.Checkbox(True,label="Gradient Checkpointing",interactive=True)
train_btn = gr.Button("Start Training")
with gr.Column(scale=1, min_width=1):
with gr.Group():
gr.Markdown("## 2.Test",elem_classes="white_background")
training_runs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs')
run_names = os.listdir(training_runs_dir)
run_names.sort(key=lambda file:os.path.getmtime(os.path.join(training_runs_dir,file)))
runs_output_model = []
for run_name in run_names:
run_name_dir = os.path.join(training_runs_dir,run_name)
run_output_model = os.path.join(run_name_dir,"output_model")
if os.path.exists(run_output_model):
run_output_model_names = os.listdir(run_output_model)
for run_output_model_name in run_output_model_names:
if run_output_model_name.find("merged_")>=0:
runs_output_model.append(os.path.join(run_name,"output_model",run_output_model_name, "ori"))
runs_output_model = runs_output_model[::-1]
runs_output_model_dropdown = gr.Dropdown(runs_output_model, label="runs_output_model",
value=runs_output_model[0] if runs_output_model else None, interactive=True)
gr.Markdown("")
gr.Markdown(
"<span> **Note**: Llama2/Mistral Chat Template:<s\>[INST] instruction+input [/INST] output</s\> </span>",
elem_classes="white_background")
with gr.Row():
test_input_textbox = gr.Textbox(label="Input:", interactive=True, value="", lines=4,
scale=4)
generate_text_btn = gr.Button("Generate",scale=1)
finetune_test_using_4bit_quantization_checkbox = gr.Checkbox(True, label="Using 4-bit quantization",
interactive=True, visible=True,
info="Less memory but slower", scale=1
)
# test_prompt = gr.Textbox(label="Prompt:", interactive=False, lines=2, scale=1)
test_output = gr.Textbox(label="Output:", interactive=False,lines=4, scale=1)
# def change_test_input_textbox(test_prefix1_textbox,test_input_textbox,test_prefix2_textbox):
# return gr.update(value=test_prefix1_textbox+test_input_textbox+test_prefix2_textbox)
# test_input_textbox.change(change_test_input_textbox,[test_prefix1_textbox,test_input_textbox,test_prefix2_textbox],test_prompt)
with gr.Group():
gr.Markdown("## 3.Quantization",elem_classes="white_background")
with gr.Row():
quantization_type_list = ["gguf"]
quantization_type_dropdown = gr.Dropdown(quantization_type_list, label="Quantization Type",value=quantization_type_list[0], interactive=True,scale=3)
local_quantization_dataset_dropdown = gr.Dropdown(local_dataset_list, label="Dataset for quantization",
value=local_dataset_list[0] if len(
local_dataset_list) > 0 else None,
interactive=True,
elem_classes="white_background", scale=7,
visible=False)
refresh_local_quantization_dataset_btn = gr.Button("Refresh", scale=2, visible=False)
def click_refresh_local_quantization_dataset_btn():
local_dataset_list, _ = get_local_dataset_list()
return gr.update(choices=local_dataset_list,
value=local_dataset_list[0] if len(local_dataset_list) > 0 else "")
refresh_local_quantization_dataset_btn.click(click_refresh_local_quantization_dataset_btn,[],local_quantization_dataset_dropdown)
with gr.Row():
training_runs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs')
run_names = os.listdir(training_runs_dir)
run_names.sort(key=lambda file: os.path.getmtime(os.path.join(training_runs_dir, file)))
runs_output_model = []
for run_name in run_names:
run_name_dir = os.path.join(training_runs_dir, run_name)
run_output_model = os.path.join(run_name_dir, "output_model")
if os.path.exists(run_output_model):
run_output_model_names = os.listdir(run_output_model)
for run_output_model_name in run_output_model_names:
if run_output_model_name.find("merged_") >= 0:
runs_output_model.append(
os.path.join(run_name, "output_model", run_output_model_name,
"ori"))
runs_output_model = runs_output_model[::-1]
quantization_runs_output_model_dropdown = gr.Dropdown(runs_output_model,
label="runs_output_model",
value=runs_output_model[
0] if runs_output_model else None,
interactive=True, scale=6)
quantize_btn = gr.Button("Quantize", scale=1,visible=False)
if runs_output_model:
model_name = runs_output_model[0].split(os.sep)[-2].split('_')[-1]
quantized_model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs',
os.sep.join(runs_output_model[0].split(os.sep)[0:-1]),
"quantized_" + quantization_type_list[0] + "_" + model_name)
if not os.path.exists(quantized_model_dir):
os.makedirs(quantized_model_dir)
quantization_logging_markdown = gr.Markdown("")
gguf_quantization_markdown0 = gr.Markdown("### GGUF Quantization Instruction:", elem_classes="white_background", visible=True)
gguf_quantization_markdown1 = gr.Markdown(''' 1.Follow the instructions in the llama.cpp to generate a GGUF:[https://github.com/ggerganov/llama.cpp#prepare-data--run](https://github.com/ggerganov/llama.cpp#prepare-data--run),<span style="color:red"> Q4_K_M is recommend</span>''',visible=True)
if runs_output_model:
gguf_quantization_markdown2 = gr.Markdown(f" 2.Convert {runs_output_model[0]} to gguf model",visible=True)
else:
gguf_quantization_markdown2 = gr.Markdown(
f"", visible=True)
gguf_quantization_markdown3 = gr.Markdown(f" 3.Deploy gguf model", visible=False)
else:
quantization_logging_markdown = gr.Markdown("")
gguf_quantization_markdown0 = gr.Markdown("### GGUF Quantization Instruction:", elem_classes="white_background", visible=True)
gguf_quantization_markdown1 = gr.Markdown('''''',visible=True)
gguf_quantization_markdown2 = gr.Markdown(f"",visible=True)
gguf_quantization_markdown3 = gr.Markdown(f"", visible=True)
with gr.Group(visible=False):
gr.Markdown("## 4.Deploy",elem_classes="white_background")
with gr.Row():
deployment_framework_dropdown = gr.Dropdown(["TGI","llama-cpp-python"], label="Deployment Framework",value="TGI", interactive=True)
with gr.Row():
training_runs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs')
run_names = os.listdir(training_runs_dir)
run_names.sort(key=lambda file: os.path.getmtime(os.path.join(training_runs_dir, file)))
# ori_model_runs_output_model = []
tgi_model_format_runs_output_model = []
gguf_model_format_runs_output_model = []
for run_name in run_names:
run_name_dir = os.path.join(training_runs_dir, run_name)
run_output_model = os.path.join(run_name_dir, "output_model")
if os.path.exists(run_output_model):
run_output_model_names = os.listdir(run_output_model)
for run_output_model_name in run_output_model_names:
model_bin_path = os.path.exists(
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs',
run_name, "output_model", run_output_model_name, "ori",
"pytorch_model.bin"))
if run_output_model_name.find("merged_") >= 0 and model_bin_path:
tgi_model_format_runs_output_model.append(
os.path.join(run_name, "output_model", run_output_model_name, "ori"))
gptq_model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs',run_name, "output_model", run_output_model_name, "quantized_gptq_"+run_output_model_name.split('_')[-1],
"pytorch_model.bin")
if os.path.exists(gptq_model_path):
tgi_model_format_runs_output_model.append(os.path.join(run_name, "output_model", run_output_model_name, "quantized_gptq_"+run_output_model_name.split('_')[-1]))
gguf_model_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'runs', run_name,
"output_model", run_output_model_name,
"quantized_gguf_" + run_output_model_name.split('_')[-1])
if os.path.exists(gguf_model_dir):
gguf_model_names = os.listdir(gguf_model_dir)
for gguf_model_name in gguf_model_names:
if gguf_model_name.split('.')[-1] == "gguf":
gguf_model_format_runs_output_model.append(
os.path.join(run_name, "output_model",
run_output_model_name, "quantized_gguf_" +
run_output_model_name.split('_')[-1],
gguf_model_name))
tgi_model_format_runs_output_model = tgi_model_format_runs_output_model[::-1]
gguf_model_format_runs_output_model = gguf_model_format_runs_output_model[::-1]
deployment_runs_output_model_dropdown = gr.Dropdown(tgi_model_format_runs_output_model, label="runs_output_model",
value=tgi_model_format_runs_output_model[
0] if tgi_model_format_runs_output_model else None,
interactive=True,scale=6)
refresh_deployment_runs_output_model_btn = gr.Button("Refresh", scale=1, visible=True)
if tgi_model_format_runs_output_model:
model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs',
os.path.dirname(tgi_model_format_runs_output_model[0]))
model_name = os.path.basename(tgi_model_format_runs_output_model[0])
if model_name.rfind("quantized_gptq_") >= 0:
run_server_value = f'''docker run --gpus all --shm-size 1g -p 8080:80 -v {model_dir}:/data ghcr.io/huggingface/text-generation-inference:latest --model-id /data/{model_name} --quantize gptq'''
else:
run_server_value = f'''docker run --gpus all --shm-size 1g -p 8080:80 -v {model_dir}:/data ghcr.io/huggingface/text-generation-inference:latest --model-id /data/{model_name}'''
run_server_script_textbox = gr.Textbox(label="Run Server:", interactive=False,lines=2, scale=1,value=run_server_value)
run_client_value = '''Command-Line Interface(CLI):\ncurl 127.0.0.1:8080/generate -X POST -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' -H 'Content-Type: application/json'\n\nPython:\nfrom huggingface_hub import InferenceClient \nclient = InferenceClient(model="http://127.0.0.1:8080")\noutput = client.text_generation(prompt="What is Deep Learning?",max_new_tokens=512)
'''
run_client_script_textbox = gr.Textbox(label="Run Client:", interactive=False, lines=6,scale=1,value=run_client_value)
else:
run_server_script_textbox = gr.Textbox(label="Run Server:", interactive=False,lines=2, scale=1,value="")
run_client_script_textbox = gr.Textbox(label="Run Client:", interactive=False, lines=6,
scale=1, value="")
# deploy_llm_code = gr.Code(code_str, language="shell", lines=5, label="Install Requirements:")
install_requirements_value = '''
### 1.install docker
### 2.Install NVIDIA Container Toolkit
<h4> 2.1 Configure the repository: </h4>
<p> curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \
&& \
sudo apt-get update </p>
<h4> 2.2 Install the NVIDIA Container Toolkit packages: </h4>
<p> sudo apt-get install -y nvidia-container-toolkit </p>
'''
with gr.Accordion("Install Requirements",open=False) as install_requirements_accordion:
install_requirements_markdown = gr.Markdown(install_requirements_value)
run_llama_cpp_python_code = gr.Code("", language="python", lines=10, label="run_model_using_llama_cpp_python.py",visible=False)
# run_script_textbox = gr.Textbox(label="Install Requirements:", interactive=False, scale=1,value=install_requirements_value)
#dependencies
with gr.TabItem("Tensorboard", id=1) as fdddd:
# training_log_markdown = gr.Markdown('',every=mytestfun)
with gr.Row():
# training_log_textbox = gr.Textbox(label="logging:",value="", interactive=True, lines=2, scale=1)
with gr.Group():
training_log_markdown = gr.Markdown('')
stop_training_btn = gr.Button("Stop Training")
training_runs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'runs')
run_names = os.listdir(training_runs_dir)
run_names = [run_name for run_name in run_names if os.path.isdir(os.path.join(training_runs_dir,run_name))]
run_names.sort(key=lambda f: os.path.getmtime(os.path.join(training_runs_dir, f)))
# print("dddddddd:",run_names)
with gr.Group():
# with gr.Row():
training_runs_dropdown = gr.Dropdown(run_names, label="Training Runs",value=run_names[0] if run_names else None, interactive=True, scale=1)
delete_text_btn = gr.Button("Delete Run", scale=1)
iframe = f'<iframe src={TENSORBOARD_URL} style="border:none;height:1024px;width:100%">'
tensorboard_html = gr.HTML(iframe)
with gr.Tab("RAG"):
with gr.Row():
with gr.Column(scale=4, min_width=1):
with gr.Group():
gr.Markdown("## ChatBot", elem_classes="white_background")
rag_data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'rag', 'data')
matched_file_list = []
supported_doc_type = ["*.pdf","*.txt","*.docx"]
for doc_type in supported_doc_type:
matched_file_list += glob.glob(os.path.join(rag_data_dir, doc_type), recursive=False)
matched_file_list.sort(key=lambda file: os.path.getmtime(file),reverse=True)
matched_file_name_list = []
for matched_file in matched_file_list:
matched_file_name_list.append(os.path.basename(matched_file))
# chat_data_source_radio_choices = ["Chat With Document",
# f"Chat With Image"]
gr.Markdown("### Chat With Document", elem_classes="white_background")
# chat_data_source_radio = gr.Radio(chat_data_source_radio_choices,
# label="",
# value=chat_data_source_radio_choices[0],
# interactive=True)
with gr.Row():
rag_data_list_dropdown = gr.Dropdown(matched_file_name_list, label=f"Local Documents In {rag_data_dir}",
value=matched_file_name_list[0] if matched_file_name_list else None,
interactive=True,scale=4, min_width=1)
refresh_rag_data_list_btn = gr.Button("Refresh", scale=1, min_width=1)
# if not current_running_model_name:
# model_running_status_markdown = gr.Markdown(f"<span style='color:red'> No modelis running!</span>")
# else:
# model_running_status_markdown = gr.Markdown(f"<span style='color:green'> Model is runing:{current_running_model_name}.</span>")
def click_refresh_rag_data_list_btn():
rag_data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'rag', 'data')
matched_file_list = []
supported_doc_type = ["*.pdf", "*.txt", "*.docx"]
for doc_type in supported_doc_type:
matched_file_list += glob.glob(os.path.join(rag_data_dir, doc_type), recursive=False)
matched_file_list.sort(key=lambda file: os.path.getmtime(file), reverse=True)
matched_file_name_list = []
for matched_file in matched_file_list:
matched_file_name_list.append(os.path.basename(matched_file))
return gr.update(choices=matched_file_name_list,value=matched_file_name_list[0] if matched_file_name_list else None)
refresh_rag_data_list_btn.click(click_refresh_rag_data_list_btn,[],rag_data_list_dropdown)
# def update_model_running_status():
# return gr.update(value=f"<span style='color:red'> {current_running_model_name} is runing!.</span>")
#
# load_model_btn.click(click_load_model_btn,model_list_dropdown,[model_list_dropdown]).success(update_model_running_status,[],model_running_status_markdown)
with gr.Row():
rag_chatbot = gr.Chatbot(value=[],bubble_full_width=False,rtl=False,layout="panel",height=chatbot_height,
avatar_images=((os.path.join(os.path.abspath(''),"pics", "user1.png")), (os.path.join(os.path.abspath(''),"pics", "bot4.png"))),
)
with gr.Row():
rag_input_txtbox = gr.Textbox(
show_label=False,autofocus=True,
placeholder="Enter text and press enter",scale=6)
rag_generate_btn = gr.Button("Generate", scale=1)
rag_stop_btn = gr.Button("Stop", scale=1)
# rag_clear_btn = gr.Button("Clear", scale=1)
rag_model_running_status_markdown = gr.Markdown(
f"### Retrieved Document Chunks",visible=True)
# retrieved_document_chunks_markdown = gr.Markdown(
# f"### Retrieved Document Chunks",visible=True)
retrieved_document_chunks_dataframe = gr.Dataframe(
headers=["ID", "Chunk"],
datatype=["str", "str"],
show_label=False,
value=None
)
with gr.Column(scale=4, min_width=1):
with gr.Group():
gr.Markdown("## Setting", elem_classes="white_background")
with gr.Group():
with gr.Group():
gr.Markdown("### 1.Chunking", elem_classes="white_background")
with gr.Row():
text_splitter_dropdown = gr.Dropdown(["RecursiveCharacterTextSplitter"],
label=f"Text Splitter",
value="RecursiveCharacterTextSplitter",
interactive=True, scale=1, min_width=1)
with gr.Row():
chunk_size_slider = gr.Slider(32, 1024, value=256, step=32, label="Chunk Size",
interactive=True, scale=1)
chunk_overlap_slider = gr.Slider(0, 500, value=20, step=10, label="Chunk Overlap",
interactive=True)
Separators_textbox = gr.Textbox(label="Separators",
value='''["\n\n", "\n", ".", " ", ""]''',
interactive=True,visible=False)
with gr.Group():
gr.Markdown("### 2.Vector Store Retriever", elem_classes="white_background") #
local_embedding_model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),"rag","embedding_models")
local_embedding_model_names = get_hg_model_names_from_dir(local_embedding_model_dir,"embedding_models")
embedding_model_source_radio_choices = ["Download From Huggingface Hub",
f"From Local Dir(hg format:{local_embedding_model_dir})"]
embedding_model_source_radio = gr.Radio(embedding_model_source_radio_choices,
label="Embedding Model Source",
value=embedding_model_source_radio_choices[0],
interactive=True)
with gr.Row():
hub_embedding_model_names_dropdown = gr.Dropdown(embedding_model_names,
label=f"",show_label=False,
value=embedding_model_names[0] if embedding_model_names else None,
interactive=True, scale=4, min_width=1)
download_hub_embedding_model_names_btn = gr.Button("Download", scale=1)
stop_download_hub_embedding_model_names_btn = gr.Button("Stop", scale=1, visible=False)
local_embedding_model_names_dropdown = gr.Dropdown(local_embedding_model_names,
label=f"Embedding Model",show_label=False,
value=local_embedding_model_names[0] if local_embedding_model_names else None,
interactive=True, scale=4, min_width=1,visible=False)
refresh_local_embedding_model_names_btn = gr.Button("Refresh", scale=1,visible=False)
# model_config_path1 = os.path.join(local_embedding_model_dir,
# embedding_model_names[0], "pytorch_model.bin")
# model_config_path2 = os.path.join(local_embedding_model_dir,
# embedding_model_names[0], "model.safetensors")
model_config_path = os.path.join(local_embedding_model_dir,
embedding_model_names[0], "config.json")
if os.path.exists(model_config_path):
download_hub_embedding_model_status_markdown = gr.Markdown(
'<span style="color:green"> This model has already been downloaded to local.</span>')
else:
download_hub_embedding_model_status_markdown = gr.Markdown(
'<span style="color:red"> This model has not been downloaded.</span>')
with gr.Row():
search_top_k_slider = gr.Slider(1, 10, value=3, step=1, label="Search Top K", interactive=True)
search_score_threshold_slider = gr.Slider(0, 1, value=0.5, step=0.1, label="Search Score Threshold",interactive=True)
with gr.Group():
gr.Markdown("### 3.Chat Model", elem_classes="white_background")
local_chat_model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),"models")
runs_model_root_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "runs")
# local_chat_model_names = get_hg_model_names_from_dir(local_chat_model_dir)
local_chat_model_names = get_hg_model_names_and_gguf_from_dir(local_chat_model_dir,runs_model_root_dir)
chat_model_source_radio_choices = ["Download From Huggingface Hub",
f"From Local Dir(hg format:{local_chat_model_dir})"]
chat_model_source_radio = gr.Radio(chat_model_source_radio_choices,
label="Chat Model source",show_label=False,
value=chat_model_source_radio_choices[0],
interactive=True)
with gr.Row():
hub_chat_model_names_dropdown = gr.Dropdown(base_model_names,
label=f"Chat Model",show_label=False,allow_custom_value=True,
value=base_model_names[0] if base_model_names else None,
interactive=True, scale=4, min_width=1)
download_hub_chat_model_names_btn = gr.Button("Download", scale=1)
stop_download_hub_chat_model_names_btn = gr.Button("Stop", scale=1, visible=False)
local_chat_model_names_dropdown = gr.Dropdown(local_chat_model_names,
label=f"Chat Model",show_label=False,
value=local_chat_model_names[0] if local_chat_model_names else None,
interactive=True, scale=4, min_width=1,visible=False)
refresh_local_chat_model_names_btn = gr.Button("Refresh", scale=1,visible=False)
rag_using_4bit_quantization_checkbox = gr.Checkbox(True, label="Using 4-bit quantization",
interactive=True, visible=True,
info="Less memory but slower", scale=1
)
if validate_model_path(base_model_names[0])[0]:
download_hub_chat_model_status_markdown = gr.Markdown(
'<span style="color:green"> This model has already been downloaded to local.</span>')
else:
download_hub_chat_model_status_markdown = gr.Markdown(
'<span style="color:red"> This model has not been downloaded.</span>')
with gr.Tab("Setting"):
# with gr.Column(scale=4, min_width=1):
with gr.Group():
gr.Markdown("## Setting", elem_classes="white_background")
with gr.Group():
with gr.Row():
max_new_tokens_slider = gr.Slider(1, 4096, value=256, step=0.1, label="Max New Tokens",
interactive=True)
temperature_slider = gr.Slider(0, 5, value=1, step=0.1, label="Temperature",
interactive=True)
with gr.Row():
top_k_slider = gr.Slider(1, 100, value=50, step=1, label="Top_k",
interactive=True)
top_p_slider = gr.Slider(0, 1, value=1, step=0.1, label="Top_p",
interactive=True)
with gr.Row():
repeat_penalty_slider = gr.Slider(1, 5, value=1, step=0.1, label="Repeat Penalty", interactive=True)
with gr.Row():
chat_history_window_slider = gr.Slider(1, 20, value=3, step=1, label="Chat History Window",
interactive=True)
low_cpu_mem_usage_checkbox = gr.Checkbox(False, label="Low Cpu Mem Usage",interactive=True,visible=False)
Huggingface_hub_token = gr.Textbox(label="Huggingface Hub Token", value="")
def check_local_model_or_dataset_is_empty1(base_model_name_dropdown,Huggingface_hub_token):
if len(base_model_name_dropdown.strip()) == 0:
raise gr.Error("Name is empty!")
try: | login_huggingface(Huggingface_hub_token,base_model_name_dropdown) | 0 | 2023-11-25 12:37:21+00:00 | 24k |
danilonumeroso/conar | models/tsp_reasoner.py | [
{
"identifier": "vmapped_beam_search_rollout",
"path": "baselines/beam_search.py",
"snippet": "BEAM_WIDTH = 128\ndef expand_single(beam_vis, beam_last, beam_cost, beam_par, W):\ndef beam_search_rollout_step(W, beam_width, i, tpl):\ndef beam_search_rollout(start_route, W, num_nodes, beam_width):\ndef bea... | from collections import defaultdict
from pprint import pprint
from torch_geometric.loader import DataLoader
from pytorch_lightning.trainer.supporters import CombinedLoader
from baselines.beam_search import vmapped_beam_search_rollout, BEAM_WIDTH
from models.algorithm_reasoner import AlgorithmReasoner, LitAlgorithmReasoner
from hyperparameters import get_hyperparameters
from torch_geometric.utils import k_hop_subgraph
from datasets._configs import CONFIGS
from utils_execution import cross_entropy, check_edge_index_sorted, prepare_constants, edge_one_hot_encode_pointers, get_number_of_nodes
from clrs import Type, Location, Stage
import copy
import itertools
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_scatter
import torch_geometric
import pytorch_lightning as pl | 15,275 | u1, v1 = batch.edge_index[:, i]
u2, v2 = edges[:, j]
if u1 == u2 and v1 == v2:
mask[i] = 1
j += 1
if j == edges.shape[1]:
break
assert j == edges.shape[1]
return mask
def get_mask_v2(edges):
dense_edges = torch_geometric.utils.to_dense_adj(edges, batch=batch.batch).bool()
dense_edges_batch = torch_geometric.utils.to_dense_adj(batch.edge_index, batch=batch.batch).bool()
edge_index, mask = torch_geometric.utils.dense_to_sparse(((dense_edges & dense_edges_batch).float()+1))
mask = mask - 1
return mask
acc = None
# st = time.time()
outputs = type(self.algorithm_module).convert_logits_to_outputs(
self.dataset.spec,
output_logits,
batch.edge_index[0],
batch.edge_index[1],
batch.num_nodes,
batch.batch,
include_probabilities=False)['output']
for name in outputs:
pred = outputs[name]
pred_gt = getattr(batch, name)
stage, loc, data_type = self.dataset.spec[name]
if loc == Location.NODE:
if name == 'predecessor_index':
tours = torch.stack([torch.arange(pred.shape[0]).to(pred), pred])
mask = get_mask_v2(tours).bool()
st = time.time()
mattr = batch.edge_attr[mask]
mbatch = batch.edge_index_batch[mask]
msrc, mdst = batch.edge_index[:, mask]
tour_len = torch_scatter.scatter_sum(mattr, mbatch)
tour_correctness = torch_scatter.scatter_sum((msrc == mdst.sort().values), mbatch)
assert sum(tour_correctness)/len(tour_correctness) == 1
return dict(tour_len=tour_len.mean(),
tour_len_gt=batch.optimal_value.mean().item(),
tour_correctness=sum(tour_correctness)/len(tour_correctness),
tour_relative_error=((tour_len-batch.optimal_value)/batch.optimal_value).mean())
def process_TSP_tour_greedy(self, batch, output_logits):
mask_active_nodes = torch.tensor(batch.start_route).bool()
mask_edges_to_nodes_in_tour = torch.zeros_like(batch.edge_index[0]).bool()
max_nodes_per_graph = batch.batch.unique(return_counts=True)[1].max()
num_nodes_per_graph = batch.num_nodes // batch.num_graphs
for _ in range(max_nodes_per_graph - 1):
mask_active_edges = mask_active_nodes[batch.edge_index[0]] & ~mask_edges_to_nodes_in_tour # Any edge outwards of active nodes and not pointing to previously used node
mask_edges_to_nodes_in_tour |= mask_active_nodes[batch.edge_index[1]] # any edge towards the active nodes should not be used in future iterations
sloops = (batch.edge_index[0] == batch.edge_index[1])
preds = output_logits['output']['predecessor_index'].clone()
preds = preds.masked_fill(~mask_active_edges | sloops, -1e6)
# nudge the max value to ensure there is a unique maximum
max_idxs = preds.reshape(-1, num_nodes_per_graph).argmax(-1)
max_idxs = F.one_hot(max_idxs, num_nodes_per_graph)
preds[max_idxs.bool().flatten()] = (preds.reshape(-1, num_nodes_per_graph)[max_idxs.bool()] + 1e-4).flatten()
output_logits['output']['predecessor_index'][mask_active_nodes[batch.edge_index[0]]] = preds[mask_active_nodes[batch.edge_index[0]]]
new_active_nodes = preds.reshape(-1, num_nodes_per_graph).argmax(-1)[mask_active_nodes.bool()].unsqueeze(-1) # NOTE the reshape/flatten mechanic may not work if graphs in the same batch are of different sizes (consider using torch_scatter.scatter_max)
mask_active_nodes = F.one_hot(new_active_nodes, num_nodes_per_graph).flatten().bool()
final_pred_mask = mask_active_nodes[batch.edge_index[0]] & batch.start_route.bool()[batch.edge_index[1]]
output_logits['output']['predecessor_index'] = output_logits['output']['predecessor_index'].masked_fill(final_pred_mask, 1e8)
return output_logits
def process_TSP_tour_BS(self, batch, output_logits):
start_route = torch_geometric.utils.to_dense_batch(batch.start_route, batch=batch.batch)[0]
dens_logits = torch_geometric.utils.to_dense_adj(batch.edge_index, batch=batch.batch, edge_attr=output_logits['output']['predecessor_index'])
num_nodes = start_route.shape[1]
# st = time.time()
tours = torch.tensor(np.array(vmapped_beam_search_rollout(
start_route.cpu().detach().numpy(),
-dens_logits.cpu().detach().numpy(),
num_nodes, BEAM_WIDTH)), device=start_route.device)
# print('tours took', time.time()-st)
# st = time.time()
dens_logits_o = torch.full_like(dens_logits, -1e9)
arranged = torch.arange(dens_logits_o.shape[0], device=dens_logits.device)
fr = tours[arranged, 0]
to = tours[arranged, 1]
batch_id = arranged.unsqueeze(1).expand_as(fr)
fr = fr.flatten()
to = to.flatten()
batch_id = batch_id.flatten()
dens_logits_o[batch_id, fr, to] = 1e9
edge_index, sparse_logits = torch_geometric.utils.dense_to_sparse(dens_logits_o)
sparse_logits = sparse_logits.to(batch.edge_index.device)
assert (edge_index == batch.edge_index).all()
output_logits['output']['predecessor_index'] = sparse_logits
# print('rest took', time.time()-st)
return output_logits
def process_TSP_tour(self, batch, output_logits):
if self.ensure_permutation == "greedy":
return self.process_TSP_tour_greedy(batch, output_logits)
return self.process_TSP_tour_BS(batch, output_logits)
def get_metrics(self,
batch,
all_hint_logits,
output_logits,
all_masks_graph):
output_logits = self.process_TSP_tour(batch, output_logits)
accs_dict = super().get_metrics(batch, all_hint_logits, output_logits,
all_masks_graph)
accs_dict.update(**self.get_tour_metrics(output_logits,
batch))
return accs_dict
def load_dataset(self, split, suffix=''):
split = split+suffix
|
class TSPReasoner(AlgorithmReasoner):
def __init__(self,
spec,
data,
latent_features,
algo_processor,
bias=True,
use_TF=False,
L1_loss=False,
global_termination_pool='max', #'predinet',
get_attention=False,
use_batch_norm=False,
transferring=False,
timeit=True,
double_process=False,
**algo_reasoner_kwargs):
super().__init__(
spec,
data,
latent_features,
algo_processor,
use_TF=use_TF,
timeit=timeit,
L1_loss=L1_loss,
global_termination_pool=global_termination_pool,
get_attention=get_attention,
use_batch_norm=use_batch_norm,
transferring=transferring,
**algo_reasoner_kwargs,
)
self.step_idx = 0
self.assert_checks = False
self.debug = False
self.debug_epoch_threshold = 1e9
self.next_step_pool = True
self.double_process = double_process
self.lambda_mul = 1# 0.0001
self.transferring = transferring
def get_input_output_hints(self, batch):
hint_inp_curr = dict()
hint_out_curr = dict()
return hint_inp_curr, hint_out_curr
def process(
self,
*args,
**kwargs):
self.all_hint_logits, self.last_logits, self.all_masks_graph = super().process(
*args,
first_n_processors=1000 if not self.double_process else 1,
**kwargs)
if self.double_process:
self.all_hint_logits, self.last_logits, self.all_masks_graph = super().process(
*args,
init_last_latent=self.last_latent,
**kwargs)
return self.all_hint_logits, self.last_logits, self.all_masks_graph
class LitTSPReasoner(LitAlgorithmReasoner):
def __init__(self,
hidden_dim,
algo_processor,
dataset_class,
dataset_root,
dataset_kwargs,
bias=True,
use_TF=False,
ensure_permutation='greedy',
transferring=False,
learning_rate=get_hyperparameters()['lr'],
double_process=False,
**algo_reasoner_kwargs):
super().__init__(hidden_dim,
algo_processor,
dataset_class,
dataset_root,
dataset_kwargs,
bias=bias,
use_TF=use_TF,
transferring=transferring,
learning_rate=learning_rate,
**algo_reasoner_kwargs)
self.algorithm_module = TSPReasoner(self.dataset.spec,
self.dataset[0],
hidden_dim,
algo_processor,
bias=bias,
use_TF=use_TF,
transferring=transferring,
timeit=self.timeit,
double_process=double_process,
**algo_reasoner_kwargs)
self.ensure_permutation = ensure_permutation
self.double_process = double_process
self.save_hyperparameters(ignore=['algo_processor'])
def training_step(self, batch, batch_idx):
ret = {'loss': 0, 'losses_dict': defaultdict(list), 'accuracies': defaultdict(list)}
for bb in batch:
ans = super().training_step(bb, batch_idx)
ret['loss'] += ans['loss']
for name in ['losses_dict', 'accuracies']:
for k, v in ans[name].items():
ret[name][k].append(v)
ret['loss'] /= len(batch)
for name in ['losses_dict', 'accuracies']:
for k, v in ans[name].items():
ret[name][k] = torch.tensor(v).mean()
return ret
def get_tour_metrics(self, output_logits, batch):
def get_mask(edges):
mask = torch.zeros_like(batch.edge_index[0])
j = 0
for i in range(batch.edge_index.shape[1]):
u1, v1 = batch.edge_index[:, i]
u2, v2 = edges[:, j]
if u1 == u2 and v1 == v2:
mask[i] = 1
j += 1
if j == edges.shape[1]:
break
assert j == edges.shape[1]
return mask
def get_mask_v2(edges):
dense_edges = torch_geometric.utils.to_dense_adj(edges, batch=batch.batch).bool()
dense_edges_batch = torch_geometric.utils.to_dense_adj(batch.edge_index, batch=batch.batch).bool()
edge_index, mask = torch_geometric.utils.dense_to_sparse(((dense_edges & dense_edges_batch).float()+1))
mask = mask - 1
return mask
acc = None
# st = time.time()
outputs = type(self.algorithm_module).convert_logits_to_outputs(
self.dataset.spec,
output_logits,
batch.edge_index[0],
batch.edge_index[1],
batch.num_nodes,
batch.batch,
include_probabilities=False)['output']
for name in outputs:
pred = outputs[name]
pred_gt = getattr(batch, name)
stage, loc, data_type = self.dataset.spec[name]
if loc == Location.NODE:
if name == 'predecessor_index':
tours = torch.stack([torch.arange(pred.shape[0]).to(pred), pred])
mask = get_mask_v2(tours).bool()
st = time.time()
mattr = batch.edge_attr[mask]
mbatch = batch.edge_index_batch[mask]
msrc, mdst = batch.edge_index[:, mask]
tour_len = torch_scatter.scatter_sum(mattr, mbatch)
tour_correctness = torch_scatter.scatter_sum((msrc == mdst.sort().values), mbatch)
assert sum(tour_correctness)/len(tour_correctness) == 1
return dict(tour_len=tour_len.mean(),
tour_len_gt=batch.optimal_value.mean().item(),
tour_correctness=sum(tour_correctness)/len(tour_correctness),
tour_relative_error=((tour_len-batch.optimal_value)/batch.optimal_value).mean())
def process_TSP_tour_greedy(self, batch, output_logits):
mask_active_nodes = torch.tensor(batch.start_route).bool()
mask_edges_to_nodes_in_tour = torch.zeros_like(batch.edge_index[0]).bool()
max_nodes_per_graph = batch.batch.unique(return_counts=True)[1].max()
num_nodes_per_graph = batch.num_nodes // batch.num_graphs
for _ in range(max_nodes_per_graph - 1):
mask_active_edges = mask_active_nodes[batch.edge_index[0]] & ~mask_edges_to_nodes_in_tour # Any edge outwards of active nodes and not pointing to previously used node
mask_edges_to_nodes_in_tour |= mask_active_nodes[batch.edge_index[1]] # any edge towards the active nodes should not be used in future iterations
sloops = (batch.edge_index[0] == batch.edge_index[1])
preds = output_logits['output']['predecessor_index'].clone()
preds = preds.masked_fill(~mask_active_edges | sloops, -1e6)
# nudge the max value to ensure there is a unique maximum
max_idxs = preds.reshape(-1, num_nodes_per_graph).argmax(-1)
max_idxs = F.one_hot(max_idxs, num_nodes_per_graph)
preds[max_idxs.bool().flatten()] = (preds.reshape(-1, num_nodes_per_graph)[max_idxs.bool()] + 1e-4).flatten()
output_logits['output']['predecessor_index'][mask_active_nodes[batch.edge_index[0]]] = preds[mask_active_nodes[batch.edge_index[0]]]
new_active_nodes = preds.reshape(-1, num_nodes_per_graph).argmax(-1)[mask_active_nodes.bool()].unsqueeze(-1) # NOTE the reshape/flatten mechanic may not work if graphs in the same batch are of different sizes (consider using torch_scatter.scatter_max)
mask_active_nodes = F.one_hot(new_active_nodes, num_nodes_per_graph).flatten().bool()
final_pred_mask = mask_active_nodes[batch.edge_index[0]] & batch.start_route.bool()[batch.edge_index[1]]
output_logits['output']['predecessor_index'] = output_logits['output']['predecessor_index'].masked_fill(final_pred_mask, 1e8)
return output_logits
def process_TSP_tour_BS(self, batch, output_logits):
start_route = torch_geometric.utils.to_dense_batch(batch.start_route, batch=batch.batch)[0]
dens_logits = torch_geometric.utils.to_dense_adj(batch.edge_index, batch=batch.batch, edge_attr=output_logits['output']['predecessor_index'])
num_nodes = start_route.shape[1]
# st = time.time()
tours = torch.tensor(np.array(vmapped_beam_search_rollout(
start_route.cpu().detach().numpy(),
-dens_logits.cpu().detach().numpy(),
num_nodes, BEAM_WIDTH)), device=start_route.device)
# print('tours took', time.time()-st)
# st = time.time()
dens_logits_o = torch.full_like(dens_logits, -1e9)
arranged = torch.arange(dens_logits_o.shape[0], device=dens_logits.device)
fr = tours[arranged, 0]
to = tours[arranged, 1]
batch_id = arranged.unsqueeze(1).expand_as(fr)
fr = fr.flatten()
to = to.flatten()
batch_id = batch_id.flatten()
dens_logits_o[batch_id, fr, to] = 1e9
edge_index, sparse_logits = torch_geometric.utils.dense_to_sparse(dens_logits_o)
sparse_logits = sparse_logits.to(batch.edge_index.device)
assert (edge_index == batch.edge_index).all()
output_logits['output']['predecessor_index'] = sparse_logits
# print('rest took', time.time()-st)
return output_logits
def process_TSP_tour(self, batch, output_logits):
if self.ensure_permutation == "greedy":
return self.process_TSP_tour_greedy(batch, output_logits)
return self.process_TSP_tour_BS(batch, output_logits)
def get_metrics(self,
batch,
all_hint_logits,
output_logits,
all_masks_graph):
output_logits = self.process_TSP_tour(batch, output_logits)
accs_dict = super().get_metrics(batch, all_hint_logits, output_logits,
all_masks_graph)
accs_dict.update(**self.get_tour_metrics(output_logits,
batch))
return accs_dict
def load_dataset(self, split, suffix=''):
split = split+suffix | nns = get_number_of_nodes(self.algorithm, split) | 9 | 2023-11-20 15:32:43+00:00 | 24k |
bearyi26/DCPT | lib/train/base_functions.py | [
{
"identifier": "Lasot",
"path": "lib/train/dataset/lasot.py",
"snippet": "class Lasot(BaseVideoDataset):\n \"\"\" LaSOT dataset.\n\n Publication:\n LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking\n Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, H... | import torch
import lib.train.data.transforms as tfm
from torch.utils.data.distributed import DistributedSampler
from lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet, BDD100K_Night, SHIFT_Night, ExDark
from lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb
from lib.train.data import sampler, opencv_loader, processing, LTRLoader
from lib.utils.misc import is_main_process | 21,081 | # datasets related
def update_settings(settings, cfg):
settings.print_interval = cfg.TRAIN.PRINT_INTERVAL
settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR,
'search': cfg.DATA.SEARCH.FACTOR}
settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE,
'search': cfg.DATA.SEARCH.SIZE}
settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER,
'search': cfg.DATA.SEARCH.CENTER_JITTER}
settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER,
'search': cfg.DATA.SEARCH.SCALE_JITTER}
settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM
settings.print_stats = None
settings.batchsize = cfg.TRAIN.BATCH_SIZE
settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE
def names2datasets(name_list: list, settings, image_loader):
assert isinstance(name_list, list)
datasets = []
for name in name_list:
assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "GOT10K_official_val",
"COCO17", "VID", "TRACKINGNET", "BDD100K_NIGHT", "SHIFT_NIGHT", "ExDark"]
if name == "LASOT":
if settings.use_lmdb:
print("Building lasot dataset from lmdb")
datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader))
else:
datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader))
if name == "GOT10K_vottrain":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader))
else:
| # datasets related
def update_settings(settings, cfg):
settings.print_interval = cfg.TRAIN.PRINT_INTERVAL
settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR,
'search': cfg.DATA.SEARCH.FACTOR}
settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE,
'search': cfg.DATA.SEARCH.SIZE}
settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER,
'search': cfg.DATA.SEARCH.CENTER_JITTER}
settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER,
'search': cfg.DATA.SEARCH.SCALE_JITTER}
settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM
settings.print_stats = None
settings.batchsize = cfg.TRAIN.BATCH_SIZE
settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE
def names2datasets(name_list: list, settings, image_loader):
assert isinstance(name_list, list)
datasets = []
for name in name_list:
assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "GOT10K_official_val",
"COCO17", "VID", "TRACKINGNET", "BDD100K_NIGHT", "SHIFT_NIGHT", "ExDark"]
if name == "LASOT":
if settings.use_lmdb:
print("Building lasot dataset from lmdb")
datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader))
else:
datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader))
if name == "GOT10K_vottrain":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader))
else: | datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader)) | 1 | 2023-11-20 06:41:15+00:00 | 24k |
shercoo/RGDiffSR | ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "ldm/util.py",
"snippet": "def log_txt_as_img(wh, xc, size=10):\n # wh a tuple of (width, height)\n # xc a list of captions to plot\n b = len(xc)\n txts = list()\n for bi in range(b):\n txt = Image.new(\"RGB\", wh, color=\"white\")\n ... | import datetime
import math
import cv2
import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import pygame
from collections import OrderedDict
from matplotlib import pyplot as plt
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from torchvision import transforms
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from text_super_resolution.model.VisionLAN.utils import Attention_AR_counter
from text_super_resolution.model.tps_spatial_transformer import TPSSpatialTransformer
from text_super_resolution.model.stn_head import STNHead
from text_super_resolution.model.VisionLAN.VisionLAN import VisionLAN
from utils.render_standard_text import *
from text_super_resolution.loss.semantic_loss import SemanticLoss
from text_super_resolution.utils import ssim_psnr
from pygame import freetype
from utils.metrics import * | 14,521 | return img, intermediates
@torch.no_grad()
def p_sample_loop(self, cond, shape, return_intermediates=False,
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
range(0, timesteps))
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
verbose=True, timesteps=None, quantize_denoised=False,
mask=None, x0=None, shape=None, **kwargs):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
shape,
return_intermediates=return_intermediates, x_T=x_T,
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
mask=mask, x0=x0)
@torch.no_grad()
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
# print(cond.shape)
if self.text_prior_enable:
if isinstance(cond, dict):
shape = (self.channels, cond['c_concat'][0].shape[2], cond['c_concat'][0].shape[3])
elif isinstance(cond, list):
shape = (self.channels, cond[0].shape[2], cond[0].shape[3])
else:
shape = (self.channels, cond.shape[2], cond.shape[3])
else:
shape = (self.channels, cond.shape[2], cond.shape[3])
# shape = (self.channels, self.image_size, self.image_size)
samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
shape, cond, verbose=False, **kwargs)
else:
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
return_intermediates=True, **kwargs)
return samples, intermediates
@torch.no_grad()
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
plot_diffusion_rows=True, **kwargs):
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=N)
# print('**********************c shape',c.shape)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption"]:
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
__conditioning_keys__ = {'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'}
sem_loss = SemanticLoss()
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(self,
unet_config,
timesteps=1000,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor="val/loss",
use_ema=True,
first_stage_key="image",
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
):
super().__init__()
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if exists(given_betas):
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_torch(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_torch(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_torch(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
@contextmanager
def ema_scope(self, context=None):
if self.use_ema:
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
print(f"{context}: Switched to EMA weights")
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
print(f"{context}: Restored training weights")
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
print(sd.keys())
print(sd['epoch'])
print(sd['global_step'])
print(sd['callbacks'])
# print(sd['optimizer_states'])
# print(sd['lr_schedulers'])
# print(sd['state_dict'].keys())
# exit(0)
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
if clip_denoised:
x_recon.clamp_(-1., 1.)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
device = self.betas.device
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
clip_denoised=self.clip_denoised)
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
return self.p_sample_loop((batch_size, channels, image_size, image_size),
return_intermediates=return_intermediates)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def get_loss(self, pred, target, mean=True):
if self.loss_type == 'l1':
loss = (target - pred).abs()
if mean:
loss = loss.mean()
elif self.loss_type == 'l2':
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
return loss
def p_losses(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_out = self.model(x_noisy, t)
loss_dict = {}
if self.parameterization == "eps":
target = noise
elif self.parameterization == "x0":
target = x_start
else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
log_prefix = 'train' if self.training else 'val'
loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
loss_simple = loss.mean() * self.l_simple_weight
loss_vlb = (self.lvlb_weights[t] * loss).mean()
loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
loss = loss_simple + self.original_elbo_weight * loss_vlb
loss_dict.update({f'{log_prefix}/loss': loss})
return loss, loss_dict
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
# print('************************fuck',k)
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = rearrange(x, 'b h w c -> b c h w')
x = x.to(memory_format=torch.contiguous_format).float()
return x
def shared_step(self, batch):
x = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x)
return loss, loss_dict
def training_step(self, batch, batch_idx):
loss, loss_dict = self.shared_step(batch)
self.log_dict(loss_dict, prog_bar=True,
logger=True, on_step=True, on_epoch=True)
self.log("global_step", self.global_step,
prog_bar=True, logger=True, on_step=True, on_epoch=False)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
return loss
@torch.no_grad()
def validation_step(self, batch, batch_idx):
# print('******************************in validation')
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
self.model_ema(self.model)
def _get_rows_from_list(self, samples):
n_imgs_per_row = len(samples)
denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
@torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
log["inputs"] = x
# get diffusion row
diffusion_row = list()
x_start = x[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(x_start)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
log["samples"] = samples
log["denoise_row"] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
return log
else:
return {key: log[key] for key in return_keys}
return log
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.model.parameters())
if self.learn_logvar:
params = params + [self.logvar]
opt = torch.optim.AdamW(params, lr=lr)
return opt
class LatentDiffusion(DDPM):
"""main class"""
def __init__(self,
first_stage_config,
cond_stage_config,
num_timesteps_cond=None,
cond_stage_key="image",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
text_prior_enable=False,
image_height=32,
image_width=128,
STN_enable=False,
standard_text=False,
VL_pretrained_path=None,
fid_eval=False,
visualize=False,
down_sample_rate=2,
recog_loss_enable=False,
font_path=None,
*args, **kwargs):
self.fid_eval = fid_eval
self.visualize = visualize
self.text_prior_enable = text_prior_enable
self.recog_loss_enable = recog_loss_enable
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
assert self.num_timesteps_cond <= kwargs['timesteps']
# for backwards compatibility after implementation of DiffusionWrapper
if conditioning_key is None:
conditioning_key = 'concat' if concat_mode else 'crossattn'
if cond_stage_config == '__is_unconditional__':
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
ignore_keys = kwargs.pop("ignore_keys", [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
try:
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
except:
self.num_downs = 0
if not scale_by_std:
self.scale_factor = scale_factor
else:
self.register_buffer('scale_factor', torch.tensor(scale_factor))
self.instantiate_first_stage(first_stage_config)
self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False
self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys)
self.restarted_from_ckpt = True
self.image_height = image_height
self.image_width = image_width
self.stn = STN_enable
if self.stn:
self.tps_inputsize = [image_height // down_sample_rate, image_width // down_sample_rate]
tps_outputsize = [image_height // down_sample_rate, image_width // down_sample_rate]
num_control_points = 20
tps_margins = [0.05, 0.05]
self.tps = TPSSpatialTransformer(
output_image_size=tuple(tps_outputsize),
num_control_points=num_control_points,
margins=tuple(tps_margins))
self.stn_head = STNHead(
in_planes=3,
num_ctrlpoints=num_control_points,
activation='none',
input_size=self.tps_inputsize)
self.standard_text = standard_text
if self.standard_text:
# self.VL_model = self.VisionLAN_init(VL_pretrained_path)
# self.test_acc_counter = Attention_AR_counter('\ntest accuracy: ',
# '/home/zhouyuxuan/latent-diffusion/dic_36.txt', False)
self.font_path = font_path
pygame.init()
freetype.init()
self.cal_psnr = ssim_psnr.calculate_psnr
self.cal_ssim = ssim_psnr.SSIM()
def VisionLAN_init(self, path=None):
cfg = {'args': {
'strides': [(1, 1), (2, 2), (2, 2), (2, 2), (1, 1), (1, 1)],
'input_shape': [3, 64, 256], # C x H x W
},
'init_state_dict': '/home/zhouyuxuan/latent-diffusion/visionlan.pth', }
model_VL = VisionLAN(**cfg['args'])
model_path = cfg['init_state_dict'] if path is None else path
print('load pre_trained VisionLAN model from %s' % model_path)
model_VL = model_VL.to(self.device)
model_VL = nn.DataParallel(model_VL)
if cfg['init_state_dict'] != None:
fe_state_dict_ori = torch.load(model_path)
fe_state_dict = OrderedDict()
for k, v in fe_state_dict_ori.items():
if 'module' not in k:
k = 'module.' + k
else:
k = k.replace('features.module.', 'module.features.')
fe_state_dict[k] = v
model_dict_fe = model_VL.state_dict()
state_dict_fe = {k: v for k, v in fe_state_dict.items() if k in model_dict_fe.keys()}
model_dict_fe.update(state_dict_fe)
model_VL.load_state_dict(model_dict_fe)
return model_VL
def parse_visionlan_data(self, imgs_input):
imgs_input = transforms.ToPILImage()(imgs_input).convert('RGB')
imgs_input = cv2.resize(np.array(imgs_input), (256, 64))
imgs_input = transforms.ToTensor()(imgs_input).unsqueeze(0)
imgs_input = imgs_input.to(self.device)
return imgs_input
def make_cond_schedule(self, ):
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
self.cond_ids[:self.num_timesteps_cond] = ids
def on_save_checkpoint(self, checkpoint):
if not isinstance(self.cond_stage_model, torch.nn.Identity):
self.cond_stage_model.save_state_dict(
'/home/zhouyuxuan/latent-diffusion/crnn_ckpt/', self.current_epoch)
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
self.register_buffer('scale_factor', 1. / z.flatten().std())
print(f"setting self.scale_factor to {self.scale_factor}")
print("### USING STD-RESCALING ###")
def register_schedule(self,
given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
self.make_cond_schedule()
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
if config == "__is_first_stage__":
print("Using first stage also as cond stage.")
self.cond_stage_model = self.first_stage_model
elif config == "__is_unconditional__":
print(f"Training {self.__class__.__name__} as an unconditional model.")
self.cond_stage_model = None
# self.be_unconditional = True
else:
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
self.cond_stage_model.train = disabled_train
for param in self.cond_stage_model.parameters():
param.requires_grad = False
else:
assert config != '__is_first_stage__'
assert config != '__is_unconditional__'
model = instantiate_from_config(config)
self.cond_stage_model = model
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
denoise_row = []
for zd in tqdm(samples, desc=desc):
denoise_row.append(self.decode_first_stage(zd.to(self.device),
force_not_quantize=force_no_decoder_quantization))
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
return denoise_grid
def get_first_stage_encoding(self, encoder_posterior):
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
z = encoder_posterior.sample()
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
c = self.cond_stage_model.encode(c)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
c = self.cond_stage_model(c)
else:
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
return c
def meshgrid(self, h, w):
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
arr = torch.cat([y, x], dim=-1)
return arr
def delta_border(self, h, w):
"""
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
self.split_input_params["clip_max_weight"], )
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
if self.split_input_params["tie_braker"]:
L_weighting = self.delta_border(Ly, Lx)
L_weighting = torch.clip(L_weighting,
self.split_input_params["clip_min_tie_weight"],
self.split_input_params["clip_max_tie_weight"])
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
"""
bs, nc, h, w = x.shape
# print(x.shape)
# number of crops in image
Ly = (h - kernel_size[0]) // stride[0] + 1
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
elif uf > 1 and df == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[1] * uf),
dilation=1, padding=0,
stride=(stride[0] * uf, stride[1] * uf))
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
# print('weighting',weighting.shape,Ly,Lx)
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
elif df > 1 and uf == 1:
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
unfold = torch.nn.Unfold(**fold_params)
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[1] // df),
dilation=1, padding=0,
stride=(stride[0] // df, stride[1] // df))
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
else:
raise NotImplementedError
return fold, unfold, normalization, weighting
@torch.no_grad()
def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
cond_key=None, return_original_cond=False, bs=None):
x = super().get_input(batch, k)
if bs is not None:
x = x[:bs]
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
if self.model.conditioning_key is not None:
if cond_key is None:
cond_key = self.cond_stage_key
if cond_key != self.first_stage_key:
if cond_key in ['caption', 'coordinates_bbox']:
xc = batch[cond_key]
elif cond_key == 'class_label':
xc = batch
else:
xc = super().get_input(batch, cond_key).to(self.device)
else:
xc = x
if not self.cond_stage_trainable or force_c_encode:
# if not self.cond_stage_trainable or force_c_encode:
if isinstance(xc, dict) or isinstance(xc, list):
# import pudb; pudb.set_trace()
c = self.get_learned_conditioning(xc)
else:
c = self.get_learned_conditioning(xc.to(self.device))
if self.text_prior_enable:
c = self.get_additional_cond(xc, c)
# c = {'c_concat': [xc], 'c_crossattn': [c]}
else:
c = xc
if bs is not None:
if isinstance(c, dict):
for k, v in c.items():
c[k] = [v[0][:bs]]
else:
c = c[:bs]
if self.use_positional_encodings:
pos_x, pos_y = self.compute_latent_shifts(batch)
ckey = __conditioning_keys__[self.model.conditioning_key]
c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
else:
c = None
xc = None
if self.use_positional_encodings:
pos_x, pos_y = self.compute_latent_shifts(batch)
c = {'pos_x': pos_x, 'pos_y': pos_y}
out = [z, c]
# print('fuck',c.shape)
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_original_cond:
out.append(xc)
return out
@torch.no_grad()
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
print('decode z shape', z.shape)
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
print(ks, stride, uf)
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
# same as above but without decorator
def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
z = rearrange(z, 'b h w c -> b c h w').contiguous()
z = 1. / self.scale_factor * z
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
uf = self.split_input_params["vqf"]
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
force_not_quantize=predict_cids or force_not_quantize)
for i in range(z.shape[-1])]
else:
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
else:
return self.first_stage_model.decode(z)
@torch.no_grad()
def encode_first_stage(self, x):
if hasattr(self, "split_input_params"):
if self.split_input_params["patch_distributed_vq"]:
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
df = self.split_input_params["vqf"]
self.split_input_params['original_image_size'] = x.shape[-2:]
bs, nc, h, w = x.shape
print('encode x shape', x.shape)
print('ks', ks, 'stride', stride, 'df', df)
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
print("reducing stride")
fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
z = unfold(x) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
print('encode z shape', z.shape)
output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
for i in range(z.shape[-1])]
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization
return decoded
else:
return self.first_stage_model.encode(x)
else:
return self.first_stage_model.encode(x)
def on_validation_start(self) -> None:
print(f'******************************in validation {self.current_epoch}')
def validation_step(self, batch, batch_idx):
# print('******************************in validation')
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
if self.fid_eval and self.current_epoch % 10 == 0:
results = self.recognize_sample(batch, N=114514, inpaint=False)
rec_image = results['samples']
target = batch[self.first_stage_key]
target = rearrange(target, 'b h w c -> b c h w')
cond = batch[self.cond_stage_key]
cond = rearrange(cond, 'b h w c -> b c h w')
if self.visualize:
batchlen = rec_image.shape[0]
rc = int(math.sqrt(batchlen))
f, axs = plt.subplots(rc, rc, figsize=(16, 4), sharex=True, sharey=True)
plt.subplots_adjust(wspace=0, hspace=0)
print(len(axs), batchlen, int(math.sqrt(batchlen)))
assert len(axs) ** 2 == batchlen
for i in range(batchlen):
axs[i // rc, i % rc].set_xticklabels([])
axs[i // rc, i % rc].set_yticklabels([])
axs[i // rc, i % rc].set_aspect('equal')
axs[i // rc, i % rc].imshow(rec_image[i, :3, :, :].cpu().numpy().transpose(1, 2, 0))
axs[i // rc, i % rc].axis('off')
plt.savefig(f'/home/zhouyuxuan/res/sample_{batch_idx}.jpg')
plt.cla()
f, axs = plt.subplots(rc, rc, figsize=(16, 4), sharex=True, sharey=True)
plt.subplots_adjust(wspace=0, hspace=0)
for i in range(batchlen):
axs[i // rc, i % rc].imshow(target[i, :3, :, :].cpu().numpy().transpose(1, 2, 0))
axs[i // rc, i % rc].axis('off')
plt.savefig(f'/home/zhouyuxuan/res/target_{batch_idx}.jpg')
plt.cla()
f, axs = plt.subplots(rc, rc, figsize=(16, 4), sharex=True, sharey=True)
plt.subplots_adjust(wspace=0, hspace=0)
for i in range(batchlen):
axs[i // rc, i % rc].imshow(cond[i, :3, :, :].cpu().numpy().transpose(1, 2, 0))
axs[i // rc, i % rc].axis('off')
plt.savefig(f'/home/zhouyuxuan/res/input_{batch_idx}.jpg')
PSNR = self.cal_psnr(rec_image[:, :3], target[:, :3])
SSIM = self.cal_ssim(rec_image[:, :3], target[:, :3])
self.log_dict({'PSNR': PSNR, 'SSIM': SSIM}, prog_bar=False, logger=True, on_step=False, on_epoch=True)
def shared_step(self, batch, **kwargs):
# print('*******************************************************batch',batch['image'].shape)
# print('*******************************************************batch',batch['image'].shape)
# if hasattr(self, "split_input_params"):
# print(self.split_input_params)
# else:
# print('fuck')
x, c = self.get_input(batch, self.first_stage_key)
loss, loss_dict = self(x, c)
if self.recog_loss_enable:
HR = batch['image']
HR = rearrange(HR, 'b h w c -> b c h w')
HR = HR.to(memory_format=torch.contiguous_format).float()
LR = c
label_vecs = self.get_learned_conditioning(c).permute(1, 0, 2)
label_vecs_hr = self.get_learned_conditioning(HR).permute(1, 0, 2)
loss_recog_distill = sem_loss(label_vecs, label_vecs_hr) * 100 # 100
loss = loss + loss_recog_distill
loss_dict.update({f'loss_recog': loss_recog_distill})
# return loss + loss_recog_distill, loss_dict
#
# else:
return loss, loss_dict
def get_additional_cond(self, c, tp):
if self.stn:
_, ctrl_points_c = self.stn_head(c)
c, _ = self.tps(c, ctrl_points_c)
if self.standard_text:
x_q = torch.empty(1, 2, c.shape[2], c.shape[3])
# prob_lr = torch.empty(1, 25, 37)
rec_results = get_string_crnn(tp.permute(1, 0, 2), False)
for i in range(c.shape[0]):
# visionlan_dict_lr = self.parse_visionlan_data(c[i, :3, :, :])
# target = ''
# label_lr, label_length = self.VL_model(visionlan_dict_lr, target, '', False)
# pred_str_lr, pred_prob = self.test_acc_counter.convert(label_lr, label_length)
# s = pred_str_lr[0]
# prob_lr = torch.cat([prob_lr, pred_prob], dim=0)
s = rec_results[i]
if s == "" or type(s) == torch.Tensor:
s = "\t"
lower_case = s.lower()
upper_case = s.upper()
i_t_lower = make_standard_text(self.font_path, lower_case,
(c.shape[2], c.shape[3]))
i_t_lower_tensor = torch.from_numpy(i_t_lower).unsqueeze(0).unsqueeze(0)
i_t_upper = make_standard_text(self.font_path, upper_case,
(c.shape[2], c.shape[3]))
i_t_upper_tensor = torch.from_numpy(i_t_upper).unsqueeze(0).unsqueeze(0)
i_t_tensor = torch.cat([i_t_lower_tensor, i_t_upper_tensor], dim=1)
x_q = torch.cat([x_q, i_t_tensor], dim=0)
x_q = x_q[1:]
# prob_lr = prob_lr[1:]
x_q = x_q.to(self.device)
# prob_lr = prob_lr.to(self.device)
c = torch.cat([c, x_q], dim=1)
return {'c_concat': [c], 'c_crossattn': [tp]}
def forward(self, x, c, *args, **kwargs):
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
if self.model.conditioning_key is not None:
assert c is not None
if self.text_prior_enable and self.model.conditioning_key == 'hybrid':
tp = self.get_learned_conditioning(c)
c = self.get_additional_cond(c, tp)
else:
if self.cond_stage_trainable:
c = self.get_learned_conditioning(c)
if self.shorten_cond_schedule: # TODO: drop this option
tc = self.cond_ids[t].to(self.device)
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
return self.p_losses(x, c, t, *args, **kwargs)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
return x0, y0, w, h
return [rescale_bbox(b) for b in bboxes]
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
if not isinstance(cond, list):
cond = [cond]
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
if hasattr(self, "split_input_params"):
assert len(cond) == 1 # todo can only deal with one conditioning atm
assert not return_ids
ks = self.split_input_params["ks"] # eg. (128, 128)
stride = self.split_input_params["stride"] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
# print("reducing Kernel")
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
# print("reducing stride")
# print('ddpm','x_noisy shape',x_noisy.shape,'ks',ks,'stride',stride)
fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
if self.cond_stage_key in ["image", "LR_image", "segmentation",
'bbox_img'] and self.model.conditioning_key: # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
assert (len(c) == 1) # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
elif self.cond_stage_key == 'coordinates_bbox':
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
full_img_h, full_img_w = self.split_input_params['original_image_size']
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
rescale_latent = 2 ** (num_downs)
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
for patch_nr in range(z.shape[-1])]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
patch_limits = [(x_tl, y_tl,
rescale_latent * ks[0] / full_img_w,
rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
print(patch_limits_tknzd[0].shape)
# cut tknzd crop position from conditioning
assert isinstance(cond, dict), 'cond must be dict to be fed into model'
cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
print(cut_cond.shape)
adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
print(adapted_cond.shape)
adapted_cond = self.get_learned_conditioning(adapted_cond)
print(adapted_cond.shape)
adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
print(adapted_cond.shape)
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
else:
cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
# apply model by loop over crops
output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
assert not isinstance(output_list[0],
tuple) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
else:
x_recon = self.model(x_noisy, t, **cond)
if isinstance(x_recon, tuple) and not return_ids:
return x_recon[0]
else:
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
return mean_flat(kl_prior) / np.log(2.0)
def p_losses(self, x_start, cond, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
model_output = self.apply_model(x_noisy, t, cond)
loss_dict = {}
prefix = 'train' if self.training else 'val'
if self.parameterization == "x0":
target = x_start
elif self.parameterization == "eps":
target = noise
else:
raise NotImplementedError()
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
self.logvar = self.logvar.to(self.device)
logvar_t = self.logvar[t].to(self.device)
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
if self.learn_logvar:
loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
loss_dict.update({'logvar': self.logvar.data.mean()})
loss = self.l_simple_weight * loss.mean()
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
loss += (self.original_elbo_weight * loss_vlb)
loss_dict.update({f'{prefix}/loss': loss})
return loss, loss_dict
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
return_x0=False, score_corrector=None, corrector_kwargs=None):
t_in = t
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
if score_corrector is not None:
assert self.parameterization == "eps"
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
if return_codebook_ids:
model_out, logits = model_out
if self.parameterization == "eps":
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
elif self.parameterization == "x0":
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
x_recon.clamp_(-1., 1.)
if quantize_denoised:
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
if return_codebook_ids:
return model_mean, posterior_variance, posterior_log_variance, logits
elif return_x0:
return model_mean, posterior_variance, posterior_log_variance, x_recon
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
return_codebook_ids=False, quantize_denoised=False, return_x0=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
b, *_, device = *x.shape, x.device
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
return_codebook_ids=return_codebook_ids,
quantize_denoised=quantize_denoised,
return_x0=return_x0,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if return_codebook_ids:
raise DeprecationWarning("Support dropped.")
model_mean, _, model_log_variance, logits = outputs
elif return_x0:
model_mean, _, model_log_variance, x0 = outputs
else:
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
if return_codebook_ids:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
if return_x0:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
else:
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
@torch.no_grad()
def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
timesteps = self.num_timesteps
if batch_size is not None:
b = batch_size if batch_size is not None else shape[0]
shape = [batch_size] + list(shape)
else:
b = batch_size = shape[0]
if x_T is None:
img = torch.randn(shape, device=self.device)
else:
img = x_T
intermediates = []
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
total=timesteps) if verbose else reversed(
range(0, timesteps))
if type(temperature) == float:
temperature = [temperature] * timesteps
for i in iterator:
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img, x0_partial = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised, return_x0=True,
temperature=temperature[i], noise_dropout=noise_dropout,
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
if callback: callback(i)
if img_callback: img_callback(img, i)
return img, intermediates
@torch.no_grad()
def p_sample_loop(self, cond, shape, return_intermediates=False,
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, start_T=None,
log_every_t=None):
if not log_every_t:
log_every_t = self.log_every_t
device = self.betas.device
b = shape[0]
if x_T is None:
img = torch.randn(shape, device=device)
else:
img = x_T
intermediates = [img]
if timesteps is None:
timesteps = self.num_timesteps
if start_T is not None:
timesteps = min(timesteps, start_T)
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
range(0, timesteps))
if mask is not None:
assert x0 is not None
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
img = self.p_sample(img, cond, ts,
clip_denoised=self.clip_denoised,
quantize_denoised=quantize_denoised)
if mask is not None:
img_orig = self.q_sample(x0, ts)
img = img_orig * mask + (1. - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
if callback: callback(i)
if img_callback: img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
verbose=True, timesteps=None, quantize_denoised=False,
mask=None, x0=None, shape=None, **kwargs):
if shape is None:
shape = (batch_size, self.channels, self.image_size, self.image_size)
if cond is not None:
if isinstance(cond, dict):
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
else:
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
return self.p_sample_loop(cond,
shape,
return_intermediates=return_intermediates, x_T=x_T,
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
mask=mask, x0=x0)
@torch.no_grad()
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
# print(cond.shape)
if self.text_prior_enable:
if isinstance(cond, dict):
shape = (self.channels, cond['c_concat'][0].shape[2], cond['c_concat'][0].shape[3])
elif isinstance(cond, list):
shape = (self.channels, cond[0].shape[2], cond[0].shape[3])
else:
shape = (self.channels, cond.shape[2], cond.shape[3])
else:
shape = (self.channels, cond.shape[2], cond.shape[3])
# shape = (self.channels, self.image_size, self.image_size)
samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
shape, cond, verbose=False, **kwargs)
else:
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
return_intermediates=True, **kwargs)
return samples, intermediates
@torch.no_grad()
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
plot_diffusion_rows=True, **kwargs):
use_ddim = ddim_steps is not None
log = dict()
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=True,
return_original_cond=True,
bs=N)
# print('**********************c shape',c.shape)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
log["inputs"] = x
log["reconstruction"] = xrec
if self.model.conditioning_key is not None:
if hasattr(self.cond_stage_model, "decode"):
xc = self.cond_stage_model.decode(c)
log["conditioning"] = xc
elif self.cond_stage_key in ["caption"]: | xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) | 0 | 2023-11-20 06:34:21+00:00 | 24k |
microsoft/Project-BayesDAG | src/causica/models/visl.py | [
{
"identifier": "Dataset",
"path": "src/causica/datasets/dataset.py",
"snippet": "class Dataset(BaseDataset):\n \"\"\"\n Class to store dense train/val/test data and masks and variables metadata.\n Note that the data and masks provided by this class are read only.\n \"\"\"\n\n def __init_... | import json
import math
import os
import warnings
import numpy as np # type: ignore
import torch
import torch.distributions as tdist
import torch.nn.functional as F
from typing import Callable, Dict, List, Optional, Tuple
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from ..datasets.dataset import Dataset
from ..datasets.variables import Variables
from ..models.imodel import IModelForCausalInference
from ..utils.helper_functions import to_tensors
from ..utils.io_utils import save_json
from ..utils.nri_utils import compute_dag_loss, get_feature_indices_per_node, kl_categorical, piecewise_linear
from ..utils.training_objectives import get_input_and_scoring_masks, kl_divergence, negative_log_likelihood
from .pvae_base_model import PVAEBaseModel | 18,661 | # 1. filling the missing values before applying the GNN-based VAE,
# 2. processing the output of the GNN-based VAE (i.e. use torch.sigmoid in the binary case)
types = np.array([v.type_ for v in self.variables._variables])
if (types == "binary").all():
self.var_types = "binary"
elif (types == "continuous").all():
self.var_types = "continuous"
elif (types == "categorical").all():
self.var_types = "categorical"
else:
raise ValueError("Right now all the variables need to have the same type")
def _train( # type: ignore
self,
dataset: Dataset,
report_progress_callback: Optional[Callable[[str, int, int], None]],
learning_rate: float,
batch_size: int,
epochs: int,
max_p_train_dropout: float,
use_dag_loss: bool,
output_variance: float,
hard: bool,
two_steps: bool,
lambda_nll: float,
lambda_kl_z: float,
lambda_kl_A: float,
lambda_dagloss: float,
sample_count: int,
) -> Dict[str, List[float]]:
"""
Train the model using the given data.
Args:
dataset: Dataset with data and masks in processed form.
train_output_dir: Path to save any training information to.
report_progress_callback: Function to report model progress for API.
learning_rate: Learning rate for Adam optimiser.
batch_size: Size of minibatches to use.
epochs: Number of epochs to train for.
max_p_train_dropout: Maximum fraction of extra training features to drop for each row. 0 is none, 1 is all.
use_dag_loss: Whether to use the DAG loss regularisation.
output_variance: The variance for the output of the GNN based VAE.
hard: Whether to use hard or soft samples for the distribution over edges (if hard=True, the edge weights are just 0/1).
two_steps: Whether to use the two-step variant of VISL. That is, the first half of training uses only
the forward MLP and the second half fixes the distribution over edges and only optimizes the forward and backward MLPs.
lambda_nll: Lambda coefficient for the ELBO term negative-log-likelihood
lambda_kl_z: Lambda coefficient for the ELBO term lambda*KL(q(z|x) || p(z))
lambda_kl_A: Lambda coefficient for the ELBO term lambda*KL(q(A) || p(A))
lambda_dagloss: Lambda coefficient for the dagloss term of the ELBO.
sample_count: Number of samples to reconstruct.
Returns:
train_results (dictionary): training_loss, KL divergence, NLL, dag_loss, training_loss_complete
"""
# Put PyTorch into train mode.
self.train()
# Setting the hard attribute which will be used for training and testing
self.hard = hard
# Loading data and mask, creating results_dict
data, mask = dataset.train_data_and_mask
results_dict: Dict[str, List] = {
metric: []
for metric in [
"training_loss",
"kl_z_term",
"kl_A_term",
"nll_term",
"dag_loss_term",
"training_loss_complete",
]
}
# Creating the optimizer
# If two_steps is True, we create a different optimizer for the second half. This optimizer does not optimize over the adjacency matrix.
optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate)
if two_steps:
named_parameters = list(self.named_parameters())
all_but_adj_matrix = []
for a in named_parameters:
if a[0] != "Z_edges_logits":
all_but_adj_matrix.append(a[1])
optimizer_second_half = torch.optim.Adam(all_but_adj_matrix, lr=learning_rate)
# Creating the dataloader
tensor_dataset = TensorDataset(*to_tensors(data, mask, device=self.device))
dataloader = DataLoader(tensor_dataset, batch_size=batch_size, shuffle=True)
# If DAG loss is used, it appears after 'epochs_without_dagloss' epochs and its coefficient (lambda) grows linearly
# during 10% of the total number of epochs until 1. This scheme is used for lambda because of empirical
# reasons (DAG loss might take over the training if it is used with coefficient 1.0 from the beginning).
if use_dag_loss:
epochs_without_dagloss = 5
if (epochs_without_dagloss + 0.1 * epochs) > epochs:
warnings.warn("max lambda will not be achieved")
best_train_loss = np.nan
for epoch in range(epochs):
training_loss_full = 0.0
nll_term_full = 0.0
kl_z_term_full = 0.0
kl_A_term_full = 0.0
dag_loss_term_full = 0.0
training_loss_complete_full = 0.0
# Set the optimizer_to_use depending on whether we are using the two-steps variant or not.
if not two_steps:
optimizer_to_use = optimizer
only_forward = False
elif epoch < epochs // 2:
optimizer_to_use = optimizer
only_forward = True
else:
optimizer_to_use = optimizer_second_half
only_forward = False
for x, mask_train_batch in dataloader:
# Drop additional values (same procedure as PVAE)
| # This is required in python 3 to allow return types of the same class.
from __future__ import annotations
class VISL(PVAEBaseModel, IModelForCausalInference):
"""
Subclass of `models.pvae_base_model.PVAEBaseModel` representing the algorithm VISL (missing value imputation with causal discovery).
Requires file <data_dir>/<dataset_name>/adj_matrix.csv to evaluate causal discovery against ground truth.
"""
def __init__(
self,
model_id: str,
variables: Variables,
save_dir: str,
device: torch.device,
gnn_iters: int,
shared_init_and_final_mappings: bool,
embedding_dim: int,
init_prob: float,
simpler: str = None,
**_,
):
"""
Args:
model_id: Unique model ID for referencing this model instance.
variables: Information about variables/features used by this model.
save_dir: Location to save any information about this model, including training data.
device: Device to load model to.
gnn_iters: Number of message passing iterations for the GNN.
shared_init_and_final_mappings: Whether all the nodes should use the same MLPs for the initial and final mappings.
embedding_dim: Dimensionality of the nodes embedding.
init_prob: Initial probability of having edge.
simpler: Choose what MLP should be simpler (options are 'forward', 'backward', or None). Specifically,
'simpler' means to divide by 10 the dimensionality of the hidden layer of the corresponding MLP (with a minimum of 10 units).
"""
super().__init__(model_id, variables, save_dir, device)
# Define some useful attributes
feature_indices_per_node, ordered_nodes = get_feature_indices_per_node(variables)
with open(os.path.join(self.save_dir, "ordered_nodes.json"), "w", encoding="utf-8") as f:
json.dump(ordered_nodes, f, indent=4)
self.num_nodes = len(feature_indices_per_node)
self.num_edges = self.num_nodes * (self.num_nodes - 1)
self.input_dim = variables.num_processed_cols
# Define and initialize Z_edges
# The learnable parameter is Z_edges_logits. Z_edges is F.softmax(Z_edges_logits, dim=1).
self.Z_edges_logits = torch.nn.Parameter(
torch.stack(
[
torch.full([self.num_edges], math.log(1 - init_prob)),
torch.full([self.num_edges], math.log(init_prob)),
],
dim=1,
).to(device)
)
# Define the GNN-based VAE
self.gnn_vae = GNN_based_VAE(
embedding_dim=embedding_dim,
skip_first=True,
device=device,
n_iters=gnn_iters,
num_nodes=self.num_nodes,
shared_init_and_final_mappings=shared_init_and_final_mappings,
simpler=simpler,
feature_indices_per_node=feature_indices_per_node,
)
# Create rel_rec and rel_send, which codify the receiving and sending node for each edge
# Shape of rel_rec and rel_send: [num_edges, num_nodes]
# The second dimension is a one-hot encoding of the receiver or sender node
off_diag = np.ones([self.num_nodes, self.num_nodes]) - np.eye(self.num_nodes)
rel_rec = F.one_hot(torch.tensor(np.where(off_diag)[0], dtype=torch.long))
rel_send = F.one_hot(torch.tensor(np.where(off_diag)[1], dtype=torch.long))
self.rel_rec = rel_rec.type(torch.float).to(device)
self.rel_send = rel_send.type(torch.float).to(device)
# Define the prior over edge types (favors sparse graphs)
self.log_prior = torch.log(
torch.tensor([0.95, 0.05], device=device)
) # The no-edge type is the first one (recall the skip_first argument of GNN_based_VAE __init__)
# Save type of variables. Used in reconstruct method for
# 1. filling the missing values before applying the GNN-based VAE,
# 2. processing the output of the GNN-based VAE (i.e. use torch.sigmoid in the binary case)
types = np.array([v.type_ for v in self.variables._variables])
if (types == "binary").all():
self.var_types = "binary"
elif (types == "continuous").all():
self.var_types = "continuous"
elif (types == "categorical").all():
self.var_types = "categorical"
else:
raise ValueError("Right now all the variables need to have the same type")
def _train( # type: ignore
self,
dataset: Dataset,
report_progress_callback: Optional[Callable[[str, int, int], None]],
learning_rate: float,
batch_size: int,
epochs: int,
max_p_train_dropout: float,
use_dag_loss: bool,
output_variance: float,
hard: bool,
two_steps: bool,
lambda_nll: float,
lambda_kl_z: float,
lambda_kl_A: float,
lambda_dagloss: float,
sample_count: int,
) -> Dict[str, List[float]]:
"""
Train the model using the given data.
Args:
dataset: Dataset with data and masks in processed form.
train_output_dir: Path to save any training information to.
report_progress_callback: Function to report model progress for API.
learning_rate: Learning rate for Adam optimiser.
batch_size: Size of minibatches to use.
epochs: Number of epochs to train for.
max_p_train_dropout: Maximum fraction of extra training features to drop for each row. 0 is none, 1 is all.
use_dag_loss: Whether to use the DAG loss regularisation.
output_variance: The variance for the output of the GNN based VAE.
hard: Whether to use hard or soft samples for the distribution over edges (if hard=True, the edge weights are just 0/1).
two_steps: Whether to use the two-step variant of VISL. That is, the first half of training uses only
the forward MLP and the second half fixes the distribution over edges and only optimizes the forward and backward MLPs.
lambda_nll: Lambda coefficient for the ELBO term negative-log-likelihood
lambda_kl_z: Lambda coefficient for the ELBO term lambda*KL(q(z|x) || p(z))
lambda_kl_A: Lambda coefficient for the ELBO term lambda*KL(q(A) || p(A))
lambda_dagloss: Lambda coefficient for the dagloss term of the ELBO.
sample_count: Number of samples to reconstruct.
Returns:
train_results (dictionary): training_loss, KL divergence, NLL, dag_loss, training_loss_complete
"""
# Put PyTorch into train mode.
self.train()
# Setting the hard attribute which will be used for training and testing
self.hard = hard
# Loading data and mask, creating results_dict
data, mask = dataset.train_data_and_mask
results_dict: Dict[str, List] = {
metric: []
for metric in [
"training_loss",
"kl_z_term",
"kl_A_term",
"nll_term",
"dag_loss_term",
"training_loss_complete",
]
}
# Creating the optimizer
# If two_steps is True, we create a different optimizer for the second half. This optimizer does not optimize over the adjacency matrix.
optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate)
if two_steps:
named_parameters = list(self.named_parameters())
all_but_adj_matrix = []
for a in named_parameters:
if a[0] != "Z_edges_logits":
all_but_adj_matrix.append(a[1])
optimizer_second_half = torch.optim.Adam(all_but_adj_matrix, lr=learning_rate)
# Creating the dataloader
tensor_dataset = TensorDataset(*to_tensors(data, mask, device=self.device))
dataloader = DataLoader(tensor_dataset, batch_size=batch_size, shuffle=True)
# If DAG loss is used, it appears after 'epochs_without_dagloss' epochs and its coefficient (lambda) grows linearly
# during 10% of the total number of epochs until 1. This scheme is used for lambda because of empirical
# reasons (DAG loss might take over the training if it is used with coefficient 1.0 from the beginning).
if use_dag_loss:
epochs_without_dagloss = 5
if (epochs_without_dagloss + 0.1 * epochs) > epochs:
warnings.warn("max lambda will not be achieved")
best_train_loss = np.nan
for epoch in range(epochs):
training_loss_full = 0.0
nll_term_full = 0.0
kl_z_term_full = 0.0
kl_A_term_full = 0.0
dag_loss_term_full = 0.0
training_loss_complete_full = 0.0
# Set the optimizer_to_use depending on whether we are using the two-steps variant or not.
if not two_steps:
optimizer_to_use = optimizer
only_forward = False
elif epoch < epochs // 2:
optimizer_to_use = optimizer
only_forward = True
else:
optimizer_to_use = optimizer_second_half
only_forward = False
for x, mask_train_batch in dataloader:
# Drop additional values (same procedure as PVAE) | input_mask, scoring_mask = get_input_and_scoring_masks( | 9 | 2023-11-21 12:55:08+00:00 | 24k |
KU-Leuven-Geomatics/geomapi | geomapi/nodes/panonode.py | [
{
"identifier": "Node",
"path": "geomapi/nodes/node.py",
"snippet": "class Node:\n def __init__(self, subject: URIRef = None,\n graph: Graph = None,\n graphPath: str = None, \n name:str=None,\n ... | from ast import Raise
from distutils import extension
from pathlib import Path
from typing import Tuple
from rdflib import Graph, URIRef
from scipy.spatial.transform import Rotation as R
from geomapi.nodes import Node
from geomapi.nodes import ImageNode
import xml.etree.ElementTree as ET
import cv2
import PIL
import numpy as np
import os
import open3d as o3d
import math
import uuid
import matplotlib.pyplot as plt
import geomapi.utils as ut
import geomapi.utils.geometryutils as gmu
import geomapi.utils.imageutils as it | 15,659 | """
Panonode is a Python Class to govern the data and metadata of panoramic data (OpenCV, PIL).
This node builds upon the OpenCV and PIL API for the image definitions.
It directly inherits from Node.
Be sure to check the properties defined in the above classes to initialise the Node.
"""
#IMPORT PACKAGES
#IMPORT MODULES
| """
Panonode is a Python Class to govern the data and metadata of panoramic data (OpenCV, PIL).
This node builds upon the OpenCV and PIL API for the image definitions.
It directly inherits from Node.
Be sure to check the properties defined in the above classes to initialise the Node.
"""
#IMPORT PACKAGES
#IMPORT MODULES
| class PanoNode(ImageNode): | 1 | 2023-11-23 08:15:01+00:00 | 24k |
Yifei-Y/Openset-RCNN | openset_rcnn/evaluation/os_coco_evaluation.py | [
{
"identifier": "GRASPNET_KNOWN_IDS",
"path": "openset_rcnn/data/graspnet_meta.py",
"snippet": "GRASPNET_KNOWN_IDS = [graspnet_known_name_id_dic[name_cat] for name_cat in GRASPNET_KNOWN_CATEGORIES]"
},
{
"identifier": "GRASPNET_KNOWN_CATEGORIES",
"path": "openset_rcnn/data/graspnet_meta.py",... | import contextlib
import copy
import io
import itertools
import json
import logging
import numpy as np
import os
import pickle
import pycocotools.mask as mask_util
import torch
import detectron2.utils.comm as comm
from collections import OrderedDict
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from tabulate import tabulate
from detectron2.config import CfgNode
from detectron2.data import MetadataCatalog
from detectron2.data.datasets.coco import convert_to_coco_json
from detectron2.structures import Boxes, BoxMode, pairwise_iou
from detectron2.utils.file_io import PathManager
from detectron2.utils.logger import create_small_table
from detectron2.evaluation.evaluator import DatasetEvaluator
from detectron2.evaluation.coco_evaluation import instances_to_coco_json
from openset_rcnn.data.graspnet_meta import GRASPNET_KNOWN_IDS, GRASPNET_KNOWN_CATEGORIES
from .os_cocoeval import OpensetCOCOEval | 14,921 | # Copyright (c) Facebook, Inc. and its affiliates.
class OpensetCOCOEvaluator(DatasetEvaluator):
"""
Evaluate AR for object proposals, AP for instance detection/segmentation, AP
for keypoint detection outputs using COCO's metrics.
See http://cocodataset.org/#detection-eval and
http://cocodataset.org/#keypoints-eval to understand its metrics.
The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
the metric cannot be computed (e.g. due to no predictions made).
In addition to COCO, this evaluator is able to support any bounding box detection,
instance segmentation, or keypoint detection dataset.
"""
def __init__(
self,
dataset_name,
eval_type,
tasks=None,
distributed=True,
output_dir=None,
*,
max_dets_per_image=None,
use_fast_impl=True,
kpt_oks_sigmas=(),
):
"""
Args:
dataset_name (str): name of the dataset to be evaluated.
It must have either the following corresponding metadata:
"json_file": the path to the COCO format annotation
Or it must be in detectron2's standard dataset format
so it can be converted to COCO format automatically.
tasks (tuple[str]): tasks that can be evaluated under the given
configuration. A task is one of "bbox", "segm", "keypoints".
By default, will infer this automatically from predictions.
distributed (True): if True, will collect results from all ranks and run evaluation
in the main process.
Otherwise, will only evaluate the results in the current process.
output_dir (str): optional, an output directory to dump all
results predicted on the dataset. The dump contains two files:
1. "instances_predictions.pth" a file that can be loaded with `torch.load` and
contains all the results in the format they are produced by the model.
2. "coco_instances_results.json" a json file in COCO's result format.
max_dets_per_image (list[int]): limit on the maximum number of detections per image.
use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
Although the results should be very close to the official implementation in COCO
API, it is still recommended to compute results with the official API for use in
papers. The faster implementation also uses more RAM.
"""
self._logger = logging.getLogger(__name__)
self._distributed = distributed
self._output_dir = output_dir
self._use_fast_impl = use_fast_impl
self._max_dets_per_image = max_dets_per_image
if tasks is not None and isinstance(tasks, CfgNode):
kpt_oks_sigmas = (
tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas
)
self._logger.warn(
"COCO Evaluator instantiated using config, this is deprecated behavior."
" Please pass in explicit arguments instead."
)
self._tasks = None # Infering it from predictions should be better
else:
self._tasks = tasks
self._cpu_device = torch.device("cpu")
self.known_names = GRASPNET_KNOWN_CATEGORIES
| # Copyright (c) Facebook, Inc. and its affiliates.
class OpensetCOCOEvaluator(DatasetEvaluator):
"""
Evaluate AR for object proposals, AP for instance detection/segmentation, AP
for keypoint detection outputs using COCO's metrics.
See http://cocodataset.org/#detection-eval and
http://cocodataset.org/#keypoints-eval to understand its metrics.
The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
the metric cannot be computed (e.g. due to no predictions made).
In addition to COCO, this evaluator is able to support any bounding box detection,
instance segmentation, or keypoint detection dataset.
"""
def __init__(
self,
dataset_name,
eval_type,
tasks=None,
distributed=True,
output_dir=None,
*,
max_dets_per_image=None,
use_fast_impl=True,
kpt_oks_sigmas=(),
):
"""
Args:
dataset_name (str): name of the dataset to be evaluated.
It must have either the following corresponding metadata:
"json_file": the path to the COCO format annotation
Or it must be in detectron2's standard dataset format
so it can be converted to COCO format automatically.
tasks (tuple[str]): tasks that can be evaluated under the given
configuration. A task is one of "bbox", "segm", "keypoints".
By default, will infer this automatically from predictions.
distributed (True): if True, will collect results from all ranks and run evaluation
in the main process.
Otherwise, will only evaluate the results in the current process.
output_dir (str): optional, an output directory to dump all
results predicted on the dataset. The dump contains two files:
1. "instances_predictions.pth" a file that can be loaded with `torch.load` and
contains all the results in the format they are produced by the model.
2. "coco_instances_results.json" a json file in COCO's result format.
max_dets_per_image (list[int]): limit on the maximum number of detections per image.
use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
Although the results should be very close to the official implementation in COCO
API, it is still recommended to compute results with the official API for use in
papers. The faster implementation also uses more RAM.
"""
self._logger = logging.getLogger(__name__)
self._distributed = distributed
self._output_dir = output_dir
self._use_fast_impl = use_fast_impl
self._max_dets_per_image = max_dets_per_image
if tasks is not None and isinstance(tasks, CfgNode):
kpt_oks_sigmas = (
tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas
)
self._logger.warn(
"COCO Evaluator instantiated using config, this is deprecated behavior."
" Please pass in explicit arguments instead."
)
self._tasks = None # Infering it from predictions should be better
else:
self._tasks = tasks
self._cpu_device = torch.device("cpu")
self.known_names = GRASPNET_KNOWN_CATEGORIES | self.known_ids = GRASPNET_KNOWN_IDS | 0 | 2023-11-21 01:47:01+00:00 | 24k |
jiawei-ren/dreamgaussian4d | diffusers/src/diffusers/models/unet_3d_blocks.py | [
{
"identifier": "is_torch_version",
"path": "diffusers/src/diffusers/utils/import_utils.py",
"snippet": "def is_torch_version(operation: str, version: str):\n \"\"\"\n Args:\n Compares the current PyTorch version to a given reference with an operation.\n operation (`str`):\n A... | from typing import Any, Dict, Optional, Tuple, Union
from torch import nn
from ..utils import is_torch_version
from ..utils.torch_utils import apply_freeu
from .attention import Attention
from .dual_transformer_2d import DualTransformer2DModel
from .resnet import (
Downsample2D,
ResnetBlock2D,
SpatioTemporalResBlock,
TemporalConvLayer,
Upsample2D,
)
from .transformer_2d import Transformer2DModel
from .transformer_temporal import (
TransformerSpatioTemporalModel,
TransformerTemporalModel,
)
import torch | 19,280 | name="op",
)
]
)
else:
self.downsamplers = None
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
num_frames: int = 1,
) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
output_states = ()
for resnet, temp_conv in zip(self.resnets, self.temp_convs):
hidden_states = resnet(hidden_states, temb)
hidden_states = temp_conv(hidden_states, num_frames=num_frames)
output_states += (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states
class CrossAttnUpBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
num_attention_heads: int = 1,
cross_attention_dim: int = 1280,
output_scale_factor: float = 1.0,
add_upsample: bool = True,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
resolution_idx: Optional[int] = None,
):
super().__init__()
resnets = []
temp_convs = []
attentions = []
temp_attentions = []
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
temp_convs.append(
TemporalConvLayer(
out_channels,
out_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
)
attentions.append(
Transformer2DModel(
out_channels // num_attention_heads,
num_attention_heads,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
)
)
temp_attentions.append(
TransformerTemporalModel(
out_channels // num_attention_heads,
num_attention_heads,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
self.resnets = nn.ModuleList(resnets)
self.temp_convs = nn.ModuleList(temp_convs)
self.attentions = nn.ModuleList(attentions)
self.temp_attentions = nn.ModuleList(temp_attentions)
if add_upsample:
| # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
def get_down_block(
down_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
temb_channels: int,
add_downsample: bool,
resnet_eps: float,
resnet_act_fn: str,
num_attention_heads: int,
resnet_groups: Optional[int] = None,
cross_attention_dim: Optional[int] = None,
downsample_padding: Optional[int] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = True,
only_cross_attention: bool = False,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
temporal_num_attention_heads: int = 8,
temporal_max_seq_length: int = 32,
transformer_layers_per_block: int = 1,
) -> Union[
"DownBlock3D",
"CrossAttnDownBlock3D",
"DownBlockMotion",
"CrossAttnDownBlockMotion",
"DownBlockSpatioTemporal",
"CrossAttnDownBlockSpatioTemporal",
]:
if down_block_type == "DownBlock3D":
return DownBlock3D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
resnet_time_scale_shift=resnet_time_scale_shift,
)
elif down_block_type == "CrossAttnDownBlock3D":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
return CrossAttnDownBlock3D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
)
if down_block_type == "DownBlockMotion":
return DownBlockMotion(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
resnet_time_scale_shift=resnet_time_scale_shift,
temporal_num_attention_heads=temporal_num_attention_heads,
temporal_max_seq_length=temporal_max_seq_length,
)
elif down_block_type == "CrossAttnDownBlockMotion":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockMotion")
return CrossAttnDownBlockMotion(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
downsample_padding=downsample_padding,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
temporal_num_attention_heads=temporal_num_attention_heads,
temporal_max_seq_length=temporal_max_seq_length,
)
elif down_block_type == "DownBlockSpatioTemporal":
# added for SDV
return DownBlockSpatioTemporal(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
)
elif down_block_type == "CrossAttnDownBlockSpatioTemporal":
# added for SDV
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockSpatioTemporal")
return CrossAttnDownBlockSpatioTemporal(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
num_layers=num_layers,
transformer_layers_per_block=transformer_layers_per_block,
add_downsample=add_downsample,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
)
raise ValueError(f"{down_block_type} does not exist.")
def get_up_block(
up_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
add_upsample: bool,
resnet_eps: float,
resnet_act_fn: str,
num_attention_heads: int,
resolution_idx: Optional[int] = None,
resnet_groups: Optional[int] = None,
cross_attention_dim: Optional[int] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = True,
only_cross_attention: bool = False,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
temporal_num_attention_heads: int = 8,
temporal_cross_attention_dim: Optional[int] = None,
temporal_max_seq_length: int = 32,
transformer_layers_per_block: int = 1,
dropout: float = 0.0,
) -> Union[
"UpBlock3D",
"CrossAttnUpBlock3D",
"UpBlockMotion",
"CrossAttnUpBlockMotion",
"UpBlockSpatioTemporal",
"CrossAttnUpBlockSpatioTemporal",
]:
if up_block_type == "UpBlock3D":
return UpBlock3D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
resolution_idx=resolution_idx,
)
elif up_block_type == "CrossAttnUpBlock3D":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
return CrossAttnUpBlock3D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
resolution_idx=resolution_idx,
)
if up_block_type == "UpBlockMotion":
return UpBlockMotion(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
resnet_time_scale_shift=resnet_time_scale_shift,
resolution_idx=resolution_idx,
temporal_num_attention_heads=temporal_num_attention_heads,
temporal_max_seq_length=temporal_max_seq_length,
)
elif up_block_type == "CrossAttnUpBlockMotion":
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockMotion")
return CrossAttnUpBlockMotion(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
add_upsample=add_upsample,
resnet_eps=resnet_eps,
resnet_act_fn=resnet_act_fn,
resnet_groups=resnet_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
dual_cross_attention=dual_cross_attention,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
resnet_time_scale_shift=resnet_time_scale_shift,
resolution_idx=resolution_idx,
temporal_num_attention_heads=temporal_num_attention_heads,
temporal_max_seq_length=temporal_max_seq_length,
)
elif up_block_type == "UpBlockSpatioTemporal":
# added for SDV
return UpBlockSpatioTemporal(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
resolution_idx=resolution_idx,
add_upsample=add_upsample,
)
elif up_block_type == "CrossAttnUpBlockSpatioTemporal":
# added for SDV
if cross_attention_dim is None:
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockSpatioTemporal")
return CrossAttnUpBlockSpatioTemporal(
in_channels=in_channels,
out_channels=out_channels,
prev_output_channel=prev_output_channel,
temb_channels=temb_channels,
num_layers=num_layers,
transformer_layers_per_block=transformer_layers_per_block,
add_upsample=add_upsample,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads,
resolution_idx=resolution_idx,
)
raise ValueError(f"{up_block_type} does not exist.")
class UNetMidBlock3DCrossAttn(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
num_attention_heads: int = 1,
output_scale_factor: float = 1.0,
cross_attention_dim: int = 1280,
dual_cross_attention: bool = False,
use_linear_projection: bool = True,
upcast_attention: bool = False,
):
super().__init__()
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
# there is always at least one resnet
resnets = [
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
]
temp_convs = [
TemporalConvLayer(
in_channels,
in_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
]
attentions = []
temp_attentions = []
for _ in range(num_layers):
attentions.append(
Transformer2DModel(
in_channels // num_attention_heads,
num_attention_heads,
in_channels=in_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
upcast_attention=upcast_attention,
)
)
temp_attentions.append(
TransformerTemporalModel(
in_channels // num_attention_heads,
num_attention_heads,
in_channels=in_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
temp_convs.append(
TemporalConvLayer(
in_channels,
in_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
)
self.resnets = nn.ModuleList(resnets)
self.temp_convs = nn.ModuleList(temp_convs)
self.attentions = nn.ModuleList(attentions)
self.temp_attentions = nn.ModuleList(temp_attentions)
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
num_frames: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
) -> torch.FloatTensor:
hidden_states = self.resnets[0](hidden_states, temb)
hidden_states = self.temp_convs[0](hidden_states, num_frames=num_frames)
for attn, temp_attn, resnet, temp_conv in zip(
self.attentions, self.temp_attentions, self.resnets[1:], self.temp_convs[1:]
):
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
hidden_states = temp_attn(
hidden_states,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
hidden_states = resnet(hidden_states, temb)
hidden_states = temp_conv(hidden_states, num_frames=num_frames)
return hidden_states
class CrossAttnDownBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
num_attention_heads: int = 1,
cross_attention_dim: int = 1280,
output_scale_factor: float = 1.0,
downsample_padding: int = 1,
add_downsample: bool = True,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
):
super().__init__()
resnets = []
attentions = []
temp_attentions = []
temp_convs = []
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
temp_convs.append(
TemporalConvLayer(
out_channels,
out_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
)
attentions.append(
Transformer2DModel(
out_channels // num_attention_heads,
num_attention_heads,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
)
)
temp_attentions.append(
TransformerTemporalModel(
out_channels // num_attention_heads,
num_attention_heads,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
self.resnets = nn.ModuleList(resnets)
self.temp_convs = nn.ModuleList(temp_convs)
self.attentions = nn.ModuleList(attentions)
self.temp_attentions = nn.ModuleList(temp_attentions)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels,
use_conv=True,
out_channels=out_channels,
padding=downsample_padding,
name="op",
)
]
)
else:
self.downsamplers = None
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
num_frames: int = 1,
cross_attention_kwargs: Dict[str, Any] = None,
) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
# TODO(Patrick, William) - attention mask is not used
output_states = ()
for resnet, temp_conv, attn, temp_attn in zip(
self.resnets, self.temp_convs, self.attentions, self.temp_attentions
):
hidden_states = resnet(hidden_states, temb)
hidden_states = temp_conv(hidden_states, num_frames=num_frames)
hidden_states = attn(
hidden_states,
encoder_hidden_states=encoder_hidden_states,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
hidden_states = temp_attn(
hidden_states,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
output_states += (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states
class DownBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor: float = 1.0,
add_downsample: bool = True,
downsample_padding: int = 1,
):
super().__init__()
resnets = []
temp_convs = []
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
temp_convs.append(
TemporalConvLayer(
out_channels,
out_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
)
self.resnets = nn.ModuleList(resnets)
self.temp_convs = nn.ModuleList(temp_convs)
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels,
use_conv=True,
out_channels=out_channels,
padding=downsample_padding,
name="op",
)
]
)
else:
self.downsamplers = None
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.FloatTensor,
temb: Optional[torch.FloatTensor] = None,
num_frames: int = 1,
) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
output_states = ()
for resnet, temp_conv in zip(self.resnets, self.temp_convs):
hidden_states = resnet(hidden_states, temb)
hidden_states = temp_conv(hidden_states, num_frames=num_frames)
output_states += (hidden_states,)
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states
class CrossAttnUpBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
prev_output_channel: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
num_attention_heads: int = 1,
cross_attention_dim: int = 1280,
output_scale_factor: float = 1.0,
add_upsample: bool = True,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
only_cross_attention: bool = False,
upcast_attention: bool = False,
resolution_idx: Optional[int] = None,
):
super().__init__()
resnets = []
temp_convs = []
attentions = []
temp_attentions = []
self.has_cross_attention = True
self.num_attention_heads = num_attention_heads
for i in range(num_layers):
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
resnet_in_channels = prev_output_channel if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=resnet_in_channels + res_skip_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
temp_convs.append(
TemporalConvLayer(
out_channels,
out_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
)
attentions.append(
Transformer2DModel(
out_channels // num_attention_heads,
num_attention_heads,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
use_linear_projection=use_linear_projection,
only_cross_attention=only_cross_attention,
upcast_attention=upcast_attention,
)
)
temp_attentions.append(
TransformerTemporalModel(
out_channels // num_attention_heads,
num_attention_heads,
in_channels=out_channels,
num_layers=1,
cross_attention_dim=cross_attention_dim,
norm_num_groups=resnet_groups,
)
)
self.resnets = nn.ModuleList(resnets)
self.temp_convs = nn.ModuleList(temp_convs)
self.attentions = nn.ModuleList(attentions)
self.temp_attentions = nn.ModuleList(temp_attentions)
if add_upsample: | self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) | 8 | 2023-12-28 08:17:40+00:00 | 24k |
FoundationVision/UniRef | detectron2/utils/visualizer.py | [
{
"identifier": "MetadataCatalog",
"path": "detectron2/data/catalog.py",
"snippet": "class _DatasetCatalog(UserDict):\nclass Metadata(types.SimpleNamespace):\nclass _MetadataCatalog(UserDict):\n def register(self, name, func):\n def get(self, name):\n def list(self) -> List[str]:\n def remov... | import colorsys
import logging
import math
import cv2
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import pycocotools.mask as mask_util
import torch
from enum import Enum, unique
from detectron2.data import MetadataCatalog
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Keypoints,
PolygonMasks,
RotatedBoxes,
)
from detectron2.utils.file_io import PathManager
from matplotlib.backends.backend_agg import FigureCanvasAgg
from PIL import Image
from .colormap import random_color
from panopticapi.utils import rgb2id | 16,641 | dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.
Returns:
output (VisImage): image object with visualizations.
"""
annos = dic.get("annotations", None)
if annos:
if "segmentation" in annos[0]:
masks = [x["segmentation"] for x in annos]
else:
masks = None
if "keypoints" in annos[0]:
keypts = [x["keypoints"] for x in annos]
keypts = np.array(keypts).reshape(len(annos), -1, 3)
else:
keypts = None
boxes = [
BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS)
if len(x["bbox"]) == 4
else x["bbox"]
for x in annos
]
colors = None
category_ids = [x["category_id"] for x in annos]
if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
colors = [
self._jitter([x / 255 for x in self.metadata.thing_colors[c]])
for c in category_ids
]
names = self.metadata.get("thing_classes", None)
labels = _create_text_labels(
category_ids,
scores=None,
class_names=names,
is_crowd=[x.get("iscrowd", 0) for x in annos],
)
self.overlay_instances(
labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors
)
sem_seg = dic.get("sem_seg", None)
if sem_seg is None and "sem_seg_file_name" in dic:
with PathManager.open(dic["sem_seg_file_name"], "rb") as f:
sem_seg = Image.open(f)
sem_seg = np.asarray(sem_seg, dtype="uint8")
if sem_seg is not None:
self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)
pan_seg = dic.get("pan_seg", None)
if pan_seg is None and "pan_seg_file_name" in dic:
with PathManager.open(dic["pan_seg_file_name"], "rb") as f:
pan_seg = Image.open(f)
pan_seg = np.asarray(pan_seg)
pan_seg = rgb2id(pan_seg)
if pan_seg is not None:
segments_info = dic["segments_info"]
pan_seg = torch.tensor(pan_seg)
self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)
return self.output
def overlay_instances(
self,
*,
boxes=None,
labels=None,
masks=None,
keypoints=None,
assigned_colors=None,
alpha=0.5,
):
"""
Args:
boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
or a :class:`RotatedBoxes`,
or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
for the N objects in a single image,
labels (list[str]): the text to be displayed for each instance.
masks (masks-like object): Supported types are:
* :class:`detectron2.structures.PolygonMasks`,
:class:`detectron2.structures.BitMasks`.
* list[list[ndarray]]: contains the segmentation masks for all objects in one image.
The first level of the list corresponds to individual instances. The second
level to all the polygon that compose the instance, and the third level
to the polygon coordinates. The third level should have the format of
[x0, y0, x1, y1, ..., xn, yn] (n >= 3).
* list[ndarray]: each ndarray is a binary mask of shape (H, W).
* list[dict]: each dict is a COCO-style RLE.
keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),
where the N is the number of instances and K is the number of keypoints.
The last dimension corresponds to (x, y, visibility or score).
assigned_colors (list[matplotlib.colors]): a list of colors, where each color
corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
for full list of formats that the colors are accepted in.
Returns:
output (VisImage): image object with visualizations.
"""
num_instances = 0
if boxes is not None:
boxes = self._convert_boxes(boxes)
num_instances = len(boxes)
if masks is not None:
masks = self._convert_masks(masks)
if num_instances:
assert len(masks) == num_instances
else:
num_instances = len(masks)
if keypoints is not None:
if num_instances:
assert len(keypoints) == num_instances
else:
num_instances = len(keypoints)
keypoints = self._convert_keypoints(keypoints)
if labels is not None:
assert len(labels) == num_instances
if assigned_colors is None:
| # Copyright (c) Facebook, Inc. and its affiliates.
logger = logging.getLogger(__name__)
__all__ = ["ColorMode", "VisImage", "Visualizer"]
_SMALL_OBJECT_AREA_THRESH = 1000
_LARGE_MASK_AREA_THRESH = 120000
_OFF_WHITE = (1.0, 1.0, 240.0 / 255)
_BLACK = (0, 0, 0)
_RED = (1.0, 0, 0)
_KEYPOINT_THRESHOLD = 0.05
@unique
class ColorMode(Enum):
"""
Enum of different color modes to use for instance visualizations.
"""
IMAGE = 0
"""
Picks a random color for every instance and overlay segmentations with low opacity.
"""
SEGMENTATION = 1
"""
Let instances of the same category have similar colors
(from metadata.thing_colors), and overlay them with
high opacity. This provides more attention on the quality of segmentation.
"""
IMAGE_BW = 2
"""
Same as IMAGE, but convert all areas without masks to gray-scale.
Only available for drawing per-instance mask predictions.
"""
class GenericMask:
"""
Attribute:
polygons (list[ndarray]): list[ndarray]: polygons for this mask.
Each ndarray has format [x, y, x, y, ...]
mask (ndarray): a binary mask
"""
def __init__(self, mask_or_polygons, height, width):
self._mask = self._polygons = self._has_holes = None
self.height = height
self.width = width
m = mask_or_polygons
if isinstance(m, dict):
# RLEs
assert "counts" in m and "size" in m
if isinstance(m["counts"], list): # uncompressed RLEs
h, w = m["size"]
assert h == height and w == width
m = mask_util.frPyObjects(m, h, w)
self._mask = mask_util.decode(m)[:, :]
return
if isinstance(m, list): # list[ndarray]
self._polygons = [np.asarray(x).reshape(-1) for x in m]
return
if isinstance(m, np.ndarray): # assumed to be a binary mask
assert m.shape[1] != 2, m.shape
assert m.shape == (
height,
width,
), f"mask shape: {m.shape}, target dims: {height}, {width}"
self._mask = m.astype("uint8")
return
raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m)))
@property
def mask(self):
if self._mask is None:
self._mask = self.polygons_to_mask(self._polygons)
return self._mask
@property
def polygons(self):
if self._polygons is None:
self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
return self._polygons
@property
def has_holes(self):
if self._has_holes is None:
if self._mask is not None:
self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
else:
self._has_holes = False # if original format is polygon, does not have holes
return self._has_holes
def mask_to_polygons(self, mask):
# cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level
# hierarchy. External contours (boundary) of the object are placed in hierarchy-1.
# Internal contours (holes) are placed in hierarchy-2.
# cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.
mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr
res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
hierarchy = res[-1]
if hierarchy is None: # empty mask
return [], False
has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0
res = res[-2]
res = [x.flatten() for x in res]
# These coordinates from OpenCV are integers in range [0, W-1 or H-1].
# We add 0.5 to turn them into real-value coordinate space. A better solution
# would be to first +0.5 and then dilate the returned polygon by 0.5.
res = [x + 0.5 for x in res if len(x) >= 6]
return res, has_holes
def polygons_to_mask(self, polygons):
rle = mask_util.frPyObjects(polygons, self.height, self.width)
rle = mask_util.merge(rle)
return mask_util.decode(rle)[:, :]
def area(self):
return self.mask.sum()
def bbox(self):
p = mask_util.frPyObjects(self.polygons, self.height, self.width)
p = mask_util.merge(p)
bbox = mask_util.toBbox(p)
bbox[2] += bbox[0]
bbox[3] += bbox[1]
return bbox
class _PanopticPrediction:
"""
Unify different panoptic annotation/prediction formats
"""
def __init__(self, panoptic_seg, segments_info, metadata=None):
if segments_info is None:
assert metadata is not None
# If "segments_info" is None, we assume "panoptic_img" is a
# H*W int32 image storing the panoptic_id in the format of
# category_id * label_divisor + instance_id. We reserve -1 for
# VOID label.
label_divisor = metadata.label_divisor
segments_info = []
for panoptic_label in np.unique(panoptic_seg.numpy()):
if panoptic_label == -1:
# VOID region.
continue
pred_class = panoptic_label // label_divisor
isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()
segments_info.append(
{
"id": int(panoptic_label),
"category_id": int(pred_class),
"isthing": bool(isthing),
}
)
del metadata
self._seg = panoptic_seg
self._sinfo = {s["id"]: s for s in segments_info} # seg id -> seg info
segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)
areas = areas.numpy()
sorted_idxs = np.argsort(-areas)
self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]
self._seg_ids = self._seg_ids.tolist()
for sid, area in zip(self._seg_ids, self._seg_areas):
if sid in self._sinfo:
self._sinfo[sid]["area"] = float(area)
def non_empty_mask(self):
"""
Returns:
(H, W) array, a mask for all pixels that have a prediction
"""
empty_ids = []
for id in self._seg_ids:
if id not in self._sinfo:
empty_ids.append(id)
if len(empty_ids) == 0:
return np.zeros(self._seg.shape, dtype=np.uint8)
assert (
len(empty_ids) == 1
), ">1 ids corresponds to no labels. This is currently not supported"
return (self._seg != empty_ids[0]).numpy().astype(np.bool)
def semantic_masks(self):
for sid in self._seg_ids:
sinfo = self._sinfo.get(sid)
if sinfo is None or sinfo["isthing"]:
# Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.
continue
yield (self._seg == sid).numpy().astype(np.bool), sinfo
def instance_masks(self):
for sid in self._seg_ids:
sinfo = self._sinfo.get(sid)
if sinfo is None or not sinfo["isthing"]:
continue
mask = (self._seg == sid).numpy().astype(np.bool)
if mask.sum() > 0:
yield mask, sinfo
def _create_text_labels(classes, scores, class_names, is_crowd=None):
"""
Args:
classes (list[int] or None):
scores (list[float] or None):
class_names (list[str] or None):
is_crowd (list[bool] or None):
Returns:
list[str] or None
"""
labels = None
if classes is not None:
if class_names is not None and len(class_names) > 0:
labels = [class_names[i] for i in classes]
else:
labels = [str(i) for i in classes]
if scores is not None:
if labels is None:
labels = ["{:.0f}%".format(s * 100) for s in scores]
else:
labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)]
if labels is not None and is_crowd is not None:
labels = [l + ("|crowd" if crowd else "") for l, crowd in zip(labels, is_crowd)]
return labels
class VisImage:
def __init__(self, img, scale=1.0):
"""
Args:
img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].
scale (float): scale the input image
"""
self.img = img
self.scale = scale
self.width, self.height = img.shape[1], img.shape[0]
self._setup_figure(img)
def _setup_figure(self, img):
"""
Args:
Same as in :meth:`__init__()`.
Returns:
fig (matplotlib.pyplot.figure): top level container for all the image plot elements.
ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.
"""
fig = mplfigure.Figure(frameon=False)
self.dpi = fig.get_dpi()
# add a small 1e-2 to avoid precision lost due to matplotlib's truncation
# (https://github.com/matplotlib/matplotlib/issues/15363)
fig.set_size_inches(
(self.width * self.scale + 1e-2) / self.dpi,
(self.height * self.scale + 1e-2) / self.dpi,
)
self.canvas = FigureCanvasAgg(fig)
# self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
ax.axis("off")
self.fig = fig
self.ax = ax
self.reset_image(img)
def reset_image(self, img):
"""
Args:
img: same as in __init__
"""
img = img.astype("uint8")
self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")
def save(self, filepath):
"""
Args:
filepath (str): a string that contains the absolute path, including the file name, where
the visualized image will be saved.
"""
self.fig.savefig(filepath)
def get_image(self):
"""
Returns:
ndarray:
the visualized image of shape (H, W, 3) (RGB) in uint8 type.
The shape is scaled w.r.t the input image using the given `scale` argument.
"""
canvas = self.canvas
s, (width, height) = canvas.print_to_buffer()
# buf = io.BytesIO() # works for cairo backend
# canvas.print_rgba(buf)
# width, height = self.width, self.height
# s = buf.getvalue()
buffer = np.frombuffer(s, dtype="uint8")
img_rgba = buffer.reshape(height, width, 4)
rgb, alpha = np.split(img_rgba, [3], axis=2)
return rgb.astype("uint8")
class Visualizer:
"""
Visualizer that draws data about detection/segmentation on images.
It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`
that draw primitive objects to images, as well as high-level wrappers like
`draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`
that draw composite data in some pre-defined style.
Note that the exact visualization style for the high-level wrappers are subject to change.
Style such as color, opacity, label contents, visibility of labels, or even the visibility
of objects themselves (e.g. when the object is too small) may change according
to different heuristics, as long as the results still look visually reasonable.
To obtain a consistent style, you can implement custom drawing functions with the
abovementioned primitive methods instead. If you need more customized visualization
styles, you can process the data yourself following their format documented in
tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not
intend to satisfy everyone's preference on drawing styles.
This visualizer focuses on high rendering quality rather than performance. It is not
designed to be used for real-time applications.
"""
# TODO implement a fast, rasterized version using OpenCV
def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):
"""
Args:
img_rgb: a numpy array of shape (H, W, C), where H and W correspond to
the height and width of the image respectively. C is the number of
color channels. The image is required to be in RGB format since that
is a requirement of the Matplotlib library. The image is also expected
to be in the range [0, 255].
metadata (Metadata): dataset metadata (e.g. class names and colors)
instance_mode (ColorMode): defines one of the pre-defined style for drawing
instances on an image.
"""
self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
if metadata is None:
metadata = MetadataCatalog.get("__nonexist__")
self.metadata = metadata
self.output = VisImage(self.img, scale=scale)
self.cpu_device = torch.device("cpu")
# too small texts are useless, therefore clamp to 9
self._default_font_size = max(
np.sqrt(self.output.height * self.output.width) // 90, 10 // scale
)
self._instance_mode = instance_mode
self.keypoint_threshold = _KEYPOINT_THRESHOLD
def draw_instance_predictions(self, predictions):
"""
Draw instance-level prediction results on an image.
Args:
predictions (Instances): the output of an instance detection/segmentation
model. Following fields will be used to draw:
"pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
Returns:
output (VisImage): image object with visualizations.
"""
boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None
scores = predictions.scores if predictions.has("scores") else None
classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None
labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None))
keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None
if predictions.has("pred_masks"):
masks = np.asarray(predictions.pred_masks)
masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]
else:
masks = None
if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
colors = [
self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes
]
alpha = 0.8
else:
colors = None
alpha = 0.5
if self._instance_mode == ColorMode.IMAGE_BW:
self.output.reset_image(
self._create_grayscale_image(
(predictions.pred_masks.any(dim=0) > 0).numpy()
if predictions.has("pred_masks")
else None
)
)
alpha = 0.3
self.overlay_instances(
masks=masks,
boxes=boxes,
labels=labels,
keypoints=keypoints,
assigned_colors=colors,
alpha=alpha,
)
return self.output
def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):
"""
Draw semantic segmentation predictions/labels.
Args:
sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
Each value is the integer label of the pixel.
area_threshold (int): segments with less than `area_threshold` are not drawn.
alpha (float): the larger it is, the more opaque the segmentations are.
Returns:
output (VisImage): image object with visualizations.
"""
if isinstance(sem_seg, torch.Tensor):
sem_seg = sem_seg.numpy()
labels, areas = np.unique(sem_seg, return_counts=True)
sorted_idxs = np.argsort(-areas).tolist()
labels = labels[sorted_idxs]
for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):
try:
mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]
except (AttributeError, IndexError):
mask_color = None
binary_mask = (sem_seg == label).astype(np.uint8)
text = self.metadata.stuff_classes[label]
self.draw_binary_mask(
binary_mask,
color=mask_color,
edge_color=_OFF_WHITE,
text=text,
alpha=alpha,
area_threshold=area_threshold,
)
return self.output
def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7):
"""
Draw panoptic prediction annotations or results.
Args:
panoptic_seg (Tensor): of shape (height, width) where the values are ids for each
segment.
segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.
If it is a ``list[dict]``, each dict contains keys "id", "category_id".
If None, category id of each pixel is computed by
``pixel // metadata.label_divisor``.
area_threshold (int): stuff segments with less than `area_threshold` are not drawn.
Returns:
output (VisImage): image object with visualizations.
"""
pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)
if self._instance_mode == ColorMode.IMAGE_BW:
self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))
# draw mask for all semantic segments first i.e. "stuff"
for mask, sinfo in pred.semantic_masks():
category_idx = sinfo["category_id"]
try:
mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]
except AttributeError:
mask_color = None
text = self.metadata.stuff_classes[category_idx]
self.draw_binary_mask(
mask,
color=mask_color,
edge_color=_OFF_WHITE,
text=text,
alpha=alpha,
area_threshold=area_threshold,
)
# draw mask for all instances second
all_instances = list(pred.instance_masks())
if len(all_instances) == 0:
return self.output
masks, sinfo = list(zip(*all_instances))
category_ids = [x["category_id"] for x in sinfo]
try:
scores = [x["score"] for x in sinfo]
except KeyError:
scores = None
labels = _create_text_labels(
category_ids, scores, self.metadata.thing_classes, [x.get("iscrowd", 0) for x in sinfo]
)
try:
colors = [
self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in category_ids
]
except AttributeError:
colors = None
self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha)
return self.output
draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility
def draw_dataset_dict(self, dic):
"""
Draw annotations/segmentaions in Detectron2 Dataset format.
Args:
dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.
Returns:
output (VisImage): image object with visualizations.
"""
annos = dic.get("annotations", None)
if annos:
if "segmentation" in annos[0]:
masks = [x["segmentation"] for x in annos]
else:
masks = None
if "keypoints" in annos[0]:
keypts = [x["keypoints"] for x in annos]
keypts = np.array(keypts).reshape(len(annos), -1, 3)
else:
keypts = None
boxes = [
BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS)
if len(x["bbox"]) == 4
else x["bbox"]
for x in annos
]
colors = None
category_ids = [x["category_id"] for x in annos]
if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
colors = [
self._jitter([x / 255 for x in self.metadata.thing_colors[c]])
for c in category_ids
]
names = self.metadata.get("thing_classes", None)
labels = _create_text_labels(
category_ids,
scores=None,
class_names=names,
is_crowd=[x.get("iscrowd", 0) for x in annos],
)
self.overlay_instances(
labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors
)
sem_seg = dic.get("sem_seg", None)
if sem_seg is None and "sem_seg_file_name" in dic:
with PathManager.open(dic["sem_seg_file_name"], "rb") as f:
sem_seg = Image.open(f)
sem_seg = np.asarray(sem_seg, dtype="uint8")
if sem_seg is not None:
self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)
pan_seg = dic.get("pan_seg", None)
if pan_seg is None and "pan_seg_file_name" in dic:
with PathManager.open(dic["pan_seg_file_name"], "rb") as f:
pan_seg = Image.open(f)
pan_seg = np.asarray(pan_seg)
pan_seg = rgb2id(pan_seg)
if pan_seg is not None:
segments_info = dic["segments_info"]
pan_seg = torch.tensor(pan_seg)
self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)
return self.output
def overlay_instances(
self,
*,
boxes=None,
labels=None,
masks=None,
keypoints=None,
assigned_colors=None,
alpha=0.5,
):
"""
Args:
boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
or a :class:`RotatedBoxes`,
or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
for the N objects in a single image,
labels (list[str]): the text to be displayed for each instance.
masks (masks-like object): Supported types are:
* :class:`detectron2.structures.PolygonMasks`,
:class:`detectron2.structures.BitMasks`.
* list[list[ndarray]]: contains the segmentation masks for all objects in one image.
The first level of the list corresponds to individual instances. The second
level to all the polygon that compose the instance, and the third level
to the polygon coordinates. The third level should have the format of
[x0, y0, x1, y1, ..., xn, yn] (n >= 3).
* list[ndarray]: each ndarray is a binary mask of shape (H, W).
* list[dict]: each dict is a COCO-style RLE.
keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),
where the N is the number of instances and K is the number of keypoints.
The last dimension corresponds to (x, y, visibility or score).
assigned_colors (list[matplotlib.colors]): a list of colors, where each color
corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
for full list of formats that the colors are accepted in.
Returns:
output (VisImage): image object with visualizations.
"""
num_instances = 0
if boxes is not None:
boxes = self._convert_boxes(boxes)
num_instances = len(boxes)
if masks is not None:
masks = self._convert_masks(masks)
if num_instances:
assert len(masks) == num_instances
else:
num_instances = len(masks)
if keypoints is not None:
if num_instances:
assert len(keypoints) == num_instances
else:
num_instances = len(keypoints)
keypoints = self._convert_keypoints(keypoints)
if labels is not None:
assert len(labels) == num_instances
if assigned_colors is None: | assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] | 8 | 2023-12-22 13:31:33+00:00 | 24k |
xhuangcv/humannorm | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | from dataclasses import dataclass, field
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
from pysdf import SDF
from tqdm import tqdm
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
import trimesh | 16,107 |
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
# Initialize SDF to a given shape when no weights are provided or force_shape_init is True
optim = torch.optim.Adam(self.parameters(), lr=1e-3)
for _ in tqdm(
range(1000),
desc=f"Initializing SDF to a(n) {self.cfg.shape_init}:",
disable=get_rank() != 0,
):
points_rand = (
torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0
)
sdf_gt = get_gt_sdf(points_rand)
sdf_pred = self.forward_sdf(points_rand)
loss = F.mse_loss(sdf_pred, sdf_gt)
optim.zero_grad()
loss.backward()
optim.step()
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh, _ = other.isosurface()
instance.isosurface_bbox = mesh.extras["bbox"]
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
if self.cfg.sdf_bias != 0.0:
threestudio.warn(
"shape_init and sdf_bias are both specified, which may lead to unexpected results."
)
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
scene = trimesh.load(mesh_path)
if isinstance(scene, trimesh.Trimesh):
mesh = scene
elif isinstance(scene, trimesh.scene.Scene):
mesh = trimesh.Trimesh()
for obj in scene.geometry.values():
mesh = trimesh.util.concatenate([mesh, obj])
else:
raise ValueError(f"Unknown mesh type at {mesh_path}.")
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
# Initialize SDF to a given shape when no weights are provided or force_shape_init is True
optim = torch.optim.Adam(self.parameters(), lr=1e-3)
for _ in tqdm(
range(1000),
desc=f"Initializing SDF to a(n) {self.cfg.shape_init}:",
disable=get_rank() != 0,
):
points_rand = (
torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0
)
sdf_gt = get_gt_sdf(points_rand)
sdf_pred = self.forward_sdf(points_rand)
loss = F.mse_loss(sdf_pred, sdf_gt)
optim.zero_grad()
loss.backward()
optim.step()
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh, _ = other.isosurface()
instance.isosurface_bbox = mesh.extras["bbox"]
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance | elif isinstance(other, ImplicitSDF): | 3 | 2023-12-23 12:37:48+00:00 | 24k |
dakpinaroglu/Frame2seq | frame2seq/openfold/model/structure_module.py | [
{
"identifier": "Linear",
"path": "frame2seq/openfold/model/primitives.py",
"snippet": "class Linear(nn.Linear):\n \"\"\"\n A Linear layer with built-in nonstandard initializations. Called just\n like torch.nn.Linear.\n\n Implements the initializers in 1.11.4, plus some additional ones found... | from functools import reduce
from operator import mul
from typing import Optional, Tuple, Sequence
from frame2seq.openfold.model.primitives import Linear, LayerNorm, ipa_point_weights_init_
from frame2seq.openfold.np.residue_constants import (
restype_rigid_group_default_frame,
restype_atom14_to_rigid_group,
restype_atom14_mask,
restype_atom14_rigid_group_positions,
)
from frame2seq.openfold.utils.feats import (
frames_and_literature_positions_to_atom14_pos,
torsion_angles_to_frames,
)
from frame2seq.openfold.utils.precision_utils import is_fp16_enabled
from frame2seq.openfold.utils.rigid_utils import Rotation, Rigid
from frame2seq.openfold.utils.tensor_utils import (
dict_multimap,
permute_final_dims,
flatten_final_dims,
)
import importlib
import math
import sys
import torch
import torch.nn as nn | 14,779 | # [*, N_res, H * C_hidden]
o = flatten_final_dims(o, 2)
# [*, H, 3, N_res, P_v]
if(inplace_safe):
v_pts = permute_final_dims(v_pts, (1, 3, 0, 2))
o_pt = [
torch.matmul(a, v.to(a.dtype))
for v in torch.unbind(v_pts, dim=-3)
]
o_pt = torch.stack(o_pt, dim=-3)
else:
o_pt = torch.sum(
(
a[..., None, :, :, None]
* permute_final_dims(v_pts, (1, 3, 0, 2))[..., None, :, :]
),
dim=-2,
)
# [*, N_res, H, P_v, 3]
o_pt = permute_final_dims(o_pt, (2, 0, 3, 1))
o_pt = r[..., None, None].invert_apply(o_pt)
# [*, N_res, H * P_v]
o_pt_norm = flatten_final_dims(
torch.sqrt(torch.sum(o_pt ** 2, dim=-1) + self.eps), 2
)
# [*, N_res, H * P_v, 3]
o_pt = o_pt.reshape(*o_pt.shape[:-3], -1, 3)
if(_offload_inference):
z[0] = z[0].to(o_pt.device)
# [*, N_res, H, C_z]
o_pair = torch.matmul(a.transpose(-2, -3), z[0].to(dtype=a.dtype))
# [*, N_res, H * C_z]
o_pair = flatten_final_dims(o_pair, 2)
# [*, N_res, C_s]
s = self.linear_out(
torch.cat(
(o, *torch.unbind(o_pt, dim=-1), o_pt_norm, o_pair), dim=-1
).to(dtype=z[0].dtype)
)
return s
class BackboneUpdate(nn.Module):
"""
Implements part of Algorithm 23.
"""
def __init__(self, c_s):
"""
Args:
c_s:
Single representation channel dimension
"""
super(BackboneUpdate, self).__init__()
self.c_s = c_s
self.linear = Linear(self.c_s, 6, init="final")
def forward(self, s: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
[*, N_res, C_s] single representation
Returns:
[*, N_res, 6] update vector
"""
# [*, 6]
update = self.linear(s)
return update
class StructureModuleTransitionLayer(nn.Module):
def __init__(self, c):
super(StructureModuleTransitionLayer, self).__init__()
self.c = c
self.linear_1 = Linear(self.c, self.c, init="relu")
self.linear_2 = Linear(self.c, self.c, init="relu")
self.linear_3 = Linear(self.c, self.c, init="final")
self.relu = nn.ReLU()
def forward(self, s):
s_initial = s
s = self.linear_1(s)
s = self.relu(s)
s = self.linear_2(s)
s = self.relu(s)
s = self.linear_3(s)
s = s + s_initial
return s
class StructureModuleTransition(nn.Module):
def __init__(self, c, num_layers, dropout_rate):
super(StructureModuleTransition, self).__init__()
self.c = c
self.num_layers = num_layers
self.dropout_rate = dropout_rate
self.layers = nn.ModuleList()
for _ in range(self.num_layers):
l = StructureModuleTransitionLayer(self.c)
self.layers.append(l)
self.dropout = nn.Dropout(self.dropout_rate)
| # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
attn_core_inplace_cuda = False
class AngleResnetBlock(nn.Module):
def __init__(self, c_hidden):
"""
Args:
c_hidden:
Hidden channel dimension
"""
super(AngleResnetBlock, self).__init__()
self.c_hidden = c_hidden
self.linear_1 = Linear(self.c_hidden, self.c_hidden, init="relu")
self.linear_2 = Linear(self.c_hidden, self.c_hidden, init="final")
self.relu = nn.ReLU()
def forward(self, a: torch.Tensor) -> torch.Tensor:
s_initial = a
a = self.relu(a)
a = self.linear_1(a)
a = self.relu(a)
a = self.linear_2(a)
return a + s_initial
class AngleResnet(nn.Module):
"""
Implements Algorithm 20, lines 11-14
"""
def __init__(self, c_in, c_hidden, no_blocks, no_angles, epsilon):
"""
Args:
c_in:
Input channel dimension
c_hidden:
Hidden channel dimension
no_blocks:
Number of resnet blocks
no_angles:
Number of torsion angles to generate
epsilon:
Small constant for normalization
"""
super(AngleResnet, self).__init__()
self.c_in = c_in
self.c_hidden = c_hidden
self.no_blocks = no_blocks
self.no_angles = no_angles
self.eps = epsilon
self.linear_in = Linear(self.c_in, self.c_hidden)
self.linear_initial = Linear(self.c_in, self.c_hidden)
self.layers = nn.ModuleList()
for _ in range(self.no_blocks):
layer = AngleResnetBlock(c_hidden=self.c_hidden)
self.layers.append(layer)
self.linear_out = Linear(self.c_hidden, self.no_angles * 2)
self.relu = nn.ReLU()
def forward(
self, s: torch.Tensor, s_initial: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
s:
[*, C_hidden] single embedding
s_initial:
[*, C_hidden] single embedding as of the start of the
StructureModule
Returns:
[*, no_angles, 2] predicted angles
"""
# NOTE: The ReLU's applied to the inputs are absent from the supplement
# pseudocode but present in the source. For maximal compatibility with
# the pretrained weights, I'm going with the source.
# [*, C_hidden]
s_initial = self.relu(s_initial)
s_initial = self.linear_initial(s_initial)
s = self.relu(s)
s = self.linear_in(s)
s = s + s_initial
for l in self.layers:
s = l(s)
s = self.relu(s)
# [*, no_angles * 2]
s = self.linear_out(s)
# [*, no_angles, 2]
s = s.view(s.shape[:-1] + (-1, 2))
unnormalized_s = s
norm_denom = torch.sqrt(
torch.clamp(
torch.sum(s ** 2, dim=-1, keepdim=True),
min=self.eps,
)
)
s = s / norm_denom
return unnormalized_s, s
class InvariantPointAttention(nn.Module):
"""
Implements Algorithm 22.
"""
def __init__(
self,
c_s: int,
c_z: int,
c_hidden: int,
no_heads: int,
no_qk_points: int,
no_v_points: int,
inf: float = 1e5,
eps: float = 1e-8,
):
"""
Args:
c_s:
Single representation channel dimension
c_z:
Pair representation channel dimension
c_hidden:
Hidden channel dimension
no_heads:
Number of attention heads
no_qk_points:
Number of query/key points to generate
no_v_points:
Number of value points to generate
"""
super(InvariantPointAttention, self).__init__()
self.c_s = c_s
self.c_z = c_z
self.c_hidden = c_hidden
self.no_heads = no_heads
self.no_qk_points = no_qk_points
self.no_v_points = no_v_points
self.inf = inf
self.eps = eps
# These linear layers differ from their specifications in the
# supplement. There, they lack bias and use Glorot initialization.
# Here as in the official source, they have bias and use the default
# Lecun initialization.
hc = self.c_hidden * self.no_heads
self.linear_q = Linear(self.c_s, hc)
self.linear_kv = Linear(self.c_s, 2 * hc)
hpq = self.no_heads * self.no_qk_points * 3
self.linear_q_points = Linear(self.c_s, hpq)
hpkv = self.no_heads * (self.no_qk_points + self.no_v_points) * 3
self.linear_kv_points = Linear(self.c_s, hpkv)
hpv = self.no_heads * self.no_v_points * 3
self.linear_b = Linear(self.c_z, self.no_heads)
self.head_weights = nn.Parameter(torch.zeros((no_heads)))
ipa_point_weights_init_(self.head_weights)
concat_out_dim = self.no_heads * (
self.c_z + self.c_hidden + self.no_v_points * 4
)
self.linear_out = Linear(concat_out_dim, self.c_s, init="final")
self.softmax = nn.Softmax(dim=-1)
self.softplus = nn.Softplus()
def forward(
self,
s: torch.Tensor,
z: Optional[torch.Tensor],
r: Rigid,
mask: torch.Tensor,
inplace_safe: bool = False,
_offload_inference: bool = False,
_z_reference_list: Optional[Sequence[torch.Tensor]] = None,
attn_drop_rate = 0.0,
) -> torch.Tensor:
"""
Args:
s:
[*, N_res, C_s] single representation
z:
[*, N_res, N_res, C_z] pair representation
r:
[*, N_res] transformation object
mask:
[*, N_res] mask
Returns:
[*, N_res, C_s] single representation update
"""
if(_offload_inference and inplace_safe):
z = _z_reference_list
else:
z = [z]
#######################################
# Generate scalar and point activations
#######################################
# [*, N_res, H * C_hidden]
q = self.linear_q(s)
kv = self.linear_kv(s)
# [*, N_res, H, C_hidden]
q = q.view(q.shape[:-1] + (self.no_heads, -1))
# [*, N_res, H, 2 * C_hidden]
kv = kv.view(kv.shape[:-1] + (self.no_heads, -1))
# [*, N_res, H, C_hidden]
k, v = torch.split(kv, self.c_hidden, dim=-1)
# [*, N_res, H * P_q * 3]
q_pts = self.linear_q_points(s)
# This is kind of clunky, but it's how the original does it
# [*, N_res, H * P_q, 3]
q_pts = torch.split(q_pts, q_pts.shape[-1] // 3, dim=-1)
q_pts = torch.stack(q_pts, dim=-1)
q_pts = r[..., None].apply(q_pts)
# [*, N_res, H, P_q, 3]
q_pts = q_pts.view(
q_pts.shape[:-2] + (self.no_heads, self.no_qk_points, 3)
)
# [*, N_res, H * (P_q + P_v) * 3]
kv_pts = self.linear_kv_points(s)
# [*, N_res, H * (P_q + P_v), 3]
kv_pts = torch.split(kv_pts, kv_pts.shape[-1] // 3, dim=-1)
kv_pts = torch.stack(kv_pts, dim=-1)
kv_pts = r[..., None].apply(kv_pts)
# [*, N_res, H, (P_q + P_v), 3]
kv_pts = kv_pts.view(kv_pts.shape[:-2] + (self.no_heads, -1, 3))
# [*, N_res, H, P_q/P_v, 3]
k_pts, v_pts = torch.split(
kv_pts, [self.no_qk_points, self.no_v_points], dim=-2
)
##########################
# Compute attention scores
##########################
# [*, N_res, N_res, H]
b = self.linear_b(z[0])
if(_offload_inference):
assert(sys.getrefcount(z[0]) == 2)
z[0] = z[0].cpu()
# [*, H, N_res, N_res]
if(is_fp16_enabled()):
with torch.cuda.amp.autocast(enabled=False):
a = torch.matmul(
permute_final_dims(q.float(), (1, 0, 2)), # [*, H, N_res, C_hidden]
permute_final_dims(k.float(), (1, 2, 0)), # [*, H, C_hidden, N_res]
)
else:
a = torch.matmul(
permute_final_dims(q, (1, 0, 2)), # [*, H, N_res, C_hidden]
permute_final_dims(k, (1, 2, 0)), # [*, H, C_hidden, N_res]
)
a *= math.sqrt(1.0 / (3 * self.c_hidden))
a += (math.sqrt(1.0 / 3) * permute_final_dims(b, (2, 0, 1)))
# [*, N_res, N_res, H, P_q, 3]
pt_att = q_pts.unsqueeze(-4) - k_pts.unsqueeze(-5)
if(inplace_safe):
pt_att *= pt_att
else:
pt_att = pt_att ** 2
# [*, N_res, N_res, H, P_q]
pt_att = sum(torch.unbind(pt_att, dim=-1))
head_weights = self.softplus(self.head_weights).view(
*((1,) * len(pt_att.shape[:-2]) + (-1, 1))
)
head_weights = head_weights * math.sqrt(
1.0 / (3 * (self.no_qk_points * 9.0 / 2))
)
if(inplace_safe):
pt_att *= head_weights
else:
pt_att = pt_att * head_weights
# [*, N_res, N_res, H]
pt_att = torch.sum(pt_att, dim=-1) * (-0.5)
# [*, N_res, N_res]
square_mask = mask.unsqueeze(-1) * mask.unsqueeze(-2)
square_mask = self.inf * (square_mask - 1)
"""
Frame2seq implementation of IPA regularization via attention dropout
"""
if attn_drop_rate > 0.0:
random_square_mask = torch.rand(square_mask.shape, device=square_mask.device)
random_square_mask = self.inf * -1 * (random_square_mask < attn_drop_rate)
square_mask += random_square_mask
# [*, H, N_res, N_res]
pt_att = permute_final_dims(pt_att, (2, 0, 1))
if(inplace_safe):
a += pt_att
del pt_att
a += square_mask.unsqueeze(-3)
# in-place softmax
attn_core_inplace_cuda.forward_(
a,
reduce(mul, a.shape[:-1]),
a.shape[-1],
)
else:
a = a + pt_att
a = a + square_mask.unsqueeze(-3)
a = self.softmax(a)
################
# Compute output
################
# [*, N_res, H, C_hidden]
o = torch.matmul(
a, v.transpose(-2, -3).to(dtype=a.dtype)
).transpose(-2, -3)
# [*, N_res, H * C_hidden]
o = flatten_final_dims(o, 2)
# [*, H, 3, N_res, P_v]
if(inplace_safe):
v_pts = permute_final_dims(v_pts, (1, 3, 0, 2))
o_pt = [
torch.matmul(a, v.to(a.dtype))
for v in torch.unbind(v_pts, dim=-3)
]
o_pt = torch.stack(o_pt, dim=-3)
else:
o_pt = torch.sum(
(
a[..., None, :, :, None]
* permute_final_dims(v_pts, (1, 3, 0, 2))[..., None, :, :]
),
dim=-2,
)
# [*, N_res, H, P_v, 3]
o_pt = permute_final_dims(o_pt, (2, 0, 3, 1))
o_pt = r[..., None, None].invert_apply(o_pt)
# [*, N_res, H * P_v]
o_pt_norm = flatten_final_dims(
torch.sqrt(torch.sum(o_pt ** 2, dim=-1) + self.eps), 2
)
# [*, N_res, H * P_v, 3]
o_pt = o_pt.reshape(*o_pt.shape[:-3], -1, 3)
if(_offload_inference):
z[0] = z[0].to(o_pt.device)
# [*, N_res, H, C_z]
o_pair = torch.matmul(a.transpose(-2, -3), z[0].to(dtype=a.dtype))
# [*, N_res, H * C_z]
o_pair = flatten_final_dims(o_pair, 2)
# [*, N_res, C_s]
s = self.linear_out(
torch.cat(
(o, *torch.unbind(o_pt, dim=-1), o_pt_norm, o_pair), dim=-1
).to(dtype=z[0].dtype)
)
return s
class BackboneUpdate(nn.Module):
"""
Implements part of Algorithm 23.
"""
def __init__(self, c_s):
"""
Args:
c_s:
Single representation channel dimension
"""
super(BackboneUpdate, self).__init__()
self.c_s = c_s
self.linear = Linear(self.c_s, 6, init="final")
def forward(self, s: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
[*, N_res, C_s] single representation
Returns:
[*, N_res, 6] update vector
"""
# [*, 6]
update = self.linear(s)
return update
class StructureModuleTransitionLayer(nn.Module):
def __init__(self, c):
super(StructureModuleTransitionLayer, self).__init__()
self.c = c
self.linear_1 = Linear(self.c, self.c, init="relu")
self.linear_2 = Linear(self.c, self.c, init="relu")
self.linear_3 = Linear(self.c, self.c, init="final")
self.relu = nn.ReLU()
def forward(self, s):
s_initial = s
s = self.linear_1(s)
s = self.relu(s)
s = self.linear_2(s)
s = self.relu(s)
s = self.linear_3(s)
s = s + s_initial
return s
class StructureModuleTransition(nn.Module):
def __init__(self, c, num_layers, dropout_rate):
super(StructureModuleTransition, self).__init__()
self.c = c
self.num_layers = num_layers
self.dropout_rate = dropout_rate
self.layers = nn.ModuleList()
for _ in range(self.num_layers):
l = StructureModuleTransitionLayer(self.c)
self.layers.append(l)
self.dropout = nn.Dropout(self.dropout_rate) | self.layer_norm = LayerNorm(self.c) | 1 | 2023-12-25 09:29:36+00:00 | 24k |
iKala/ievals | ievals/cli/ieval.py | [
{
"identifier": "TGI_Evaluator",
"path": "ievals/modules/qa_evaluators/tgi.py",
"snippet": "class TGI_Evaluator(Evaluator):\n def __init__(\n self,\n choices,\n k,\n ip_addr,\n model_name,\n systemMessageToken=\"<|im_start|>system\\n\",\n messageEndTok... | import os
import logging
import argparse
import pandas as pd
from datasets import load_dataset
from ievals.modules.qa_evaluators.tgi import TGI_Evaluator
from ievals.modules.qa_evaluators.gemini import Gemini_Evaluator
from ievals.modules.qa_evaluators.claude import Claude_Evaluator
from ievals.modules.qa_evaluators.azure import Azure_Evaluator
from ievals.modules.qa_evaluators.oai_complete import GPT_Evaluator
from ievals.modules.qa_evaluators.chatgpt import ChatGPT_Evaluator
from ievals.modules.qa_evaluators.hf_chat import HF_Chat_Evaluator
from ievals.modules.qa_evaluators.hf_base import (
Qwen_Evaluator,
) # we only use this for qwen base model
from ievals.modules.qa_evaluators.ali_dashscope import DashScope_Evaluator
from ievals.exp_executer import run_exp | 19,443 | """
CLI for all models
Support mode:
if tgi service was used you must pass in IP and hostname
if the service was found in model_config.csv you could skip providing the 4 tokens (user, assistant, system, eos)
else you need to pass in the four token in args
"""
try:
except ImportError as e:
logging.error("huggingface and qwen models are not supported due to " + str(e))
def get_model_config():
current_dir = os.path.dirname(os.path.abspath(__file__))
up_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
df = pd.read_csv(os.path.join(up_dir, "model_config.csv"))
df.fillna("", inplace=True)
valid_model_names = df["model_name"].tolist()
return valid_model_names, df
def get_tgi_prompt_config(model_name):
valid_model_names, df = get_model_config()
if model_name not in valid_model_names:
return None, None
prompt_config = df[df["model_name"] == model_name].iloc[0]
prompt_config.pop("model_name")
return prompt_config
def get_evaluator(model_name, series=""):
if len(series):
if series == "azure":
return Azure_Evaluator
elif series == "openai_chat":
return ChatGPT_Evaluator
elif series == "openai_complete":
| """
CLI for all models
Support mode:
if tgi service was used you must pass in IP and hostname
if the service was found in model_config.csv you could skip providing the 4 tokens (user, assistant, system, eos)
else you need to pass in the four token in args
"""
try:
except ImportError as e:
logging.error("huggingface and qwen models are not supported due to " + str(e))
def get_model_config():
current_dir = os.path.dirname(os.path.abspath(__file__))
up_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
df = pd.read_csv(os.path.join(up_dir, "model_config.csv"))
df.fillna("", inplace=True)
valid_model_names = df["model_name"].tolist()
return valid_model_names, df
def get_tgi_prompt_config(model_name):
valid_model_names, df = get_model_config()
if model_name not in valid_model_names:
return None, None
prompt_config = df[df["model_name"] == model_name].iloc[0]
prompt_config.pop("model_name")
return prompt_config
def get_evaluator(model_name, series=""):
if len(series):
if series == "azure":
return Azure_Evaluator
elif series == "openai_chat":
return ChatGPT_Evaluator
elif series == "openai_complete": | return GPT_Evaluator | 4 | 2023-12-24 08:00:38+00:00 | 24k |
kraina-ai/quackosm | quackosm/functions.py | [
{
"identifier": "GroupedOsmTagsFilter",
"path": "quackosm/_osm_tags_filters.py",
"snippet": "def merge_osm_tags_filter(osm_tags_filter: OsmTagsFilter) -> OsmTagsFilter: ...\ndef merge_osm_tags_filter(osm_tags_filter: GroupedOsmTagsFilter) -> OsmTagsFilter: ...\ndef merge_osm_tags_filter(osm_tags_filter:... | from collections.abc import Iterable
from pathlib import Path
from typing import Any, Optional, Union
from shapely.geometry.base import BaseGeometry
from quackosm._osm_tags_filters import GroupedOsmTagsFilter, OsmTagsFilter
from quackosm._osm_way_polygon_features import OsmWayPolygonConfig
from quackosm.pbf_file_reader import PbfFileReader
import geopandas as gpd | 21,439 | │ node/10025656392 │ {name=Direction de… │ POINT (7.4270392 43.7365262) │
│ node/10025656393 │ {name=IQOS, openin… │ POINT (7.4275175 43.7373195) │
│ node/10025656394 │ {artist_name=Anna … │ POINT (7.4293446 43.737448) │
│ · │ · │ · │
│ · │ · │ · │
│ · │ · │ · │
│ way/986864693 │ {natural=bare_rock} │ POLYGON ((7.4340482 43.745598, 7.4340263 4… │
│ way/986864694 │ {barrier=wall} │ LINESTRING (7.4327547 43.7445382, 7.432808… │
│ way/986864695 │ {natural=bare_rock} │ POLYGON ((7.4332994 43.7449315, 7.4332912 … │
│ way/986864696 │ {barrier=wall} │ LINESTRING (7.4356006 43.7464325, 7.435574… │
│ way/986864697 │ {natural=bare_rock} │ POLYGON ((7.4362767 43.74697, 7.4362983 43… │
│ way/990669427 │ {amenity=shelter, … │ POLYGON ((7.4146087 43.733883, 7.4146192 4… │
│ way/990669428 │ {highway=secondary… │ LINESTRING (7.4136598 43.7334433, 7.413640… │
│ way/990669429 │ {highway=secondary… │ LINESTRING (7.4137621 43.7334251, 7.413746… │
│ way/990848785 │ {addr:city=Monaco,… │ POLYGON ((7.4142551 43.7339622, 7.4143113 … │
│ way/993121275 │ {building=yes, nam… │ POLYGON ((7.4321416 43.7481309, 7.4321638 … │
├──────────────────┴──────────────────────┴──────────────────────────────────────────────┤
│ 7906 rows (20 shown) 3 columns │
└────────────────────────────────────────────────────────────────────────────────────────┘
Get only buildings, amenities and highways from a PBF file.
Tags will be split into separate columns because of applying the filter.
>>> gpq_path = qosm.convert_pbf_to_gpq(
... monaco_pbf_path,
... tags_filter={"building": True, "amenity": True, "highway": True}
... )
>>> gpq_path.as_posix()
'files/monaco_6593ca69098459d039054bc5fe0a87c56681e29a5f59d38ce3485c03cb0e9374_noclip_exploded.geoparquet'
Inspect the file with duckdb
>>> import duckdb
>>> duckdb.load_extension('spatial')
>>> duckdb.read_parquet(str(gpq_path)).project(
... "* REPLACE (ST_GeomFromWKB(geometry) AS geometry)"
... ).order("feature_id") # doctest: +SKIP
┌──────────────────┬──────────┬────────────┬─────────────┬───────────────────────────────┐
│ feature_id │ building │ amenity │ highway │ geometry │
│ varchar │ varchar │ varchar │ varchar │ geometry │
├──────────────────┼──────────┼────────────┼─────────────┼───────────────────────────────┤
│ node/10025656390 │ NULL │ restaurant │ NULL │ POINT (7.4269287 43.7368818) │
│ node/10025843517 │ NULL │ restaurant │ NULL │ POINT (7.4219362 43.7367446) │
│ node/10025852089 │ NULL │ bar │ NULL │ POINT (7.4227543 43.7369926) │
│ node/10025852090 │ NULL │ restaurant │ NULL │ POINT (7.4225093 43.7369627) │
│ node/10068880332 │ NULL │ NULL │ platform │ POINT (7.4380849 43.7493273) │
│ node/10068880335 │ NULL │ bench │ NULL │ POINT (7.4186855 43.7321515) │
│ node/10127713363 │ NULL │ cafe │ NULL │ POINT (7.4266367 43.7420755) │
│ node/10601158089 │ NULL │ restaurant │ NULL │ POINT (7.4213086 43.7336187) │
│ node/10671507005 │ NULL │ bar │ NULL │ POINT (7.4296915 43.7423307) │
│ node/10674256605 │ NULL │ bar │ NULL │ POINT (7.4213558 43.7336317) │
│ · │ · │ · │ · │ · │
│ · │ · │ · │ · │ · │
│ · │ · │ · │ · │ · │
│ way/981971425 │ NULL │ NULL │ residential │ LINESTRING (7.4321217 43.74… │
│ way/982061461 │ NULL │ NULL │ secondary │ LINESTRING (7.4246341 43.74… │
│ way/982081599 │ NULL │ NULL │ tertiary │ LINESTRING (7.4225202 43.73… │
│ way/982081600 │ NULL │ NULL │ service │ LINESTRING (7.4225202 43.73… │
│ way/986029035 │ NULL │ NULL │ path │ LINESTRING (7.4189462 43.73… │
│ way/990669427 │ NULL │ shelter │ NULL │ POLYGON ((7.4146087 43.7338… │
│ way/990669428 │ NULL │ NULL │ secondary │ LINESTRING (7.4136598 43.73… │
│ way/990669429 │ NULL │ NULL │ secondary │ LINESTRING (7.4137621 43.73… │
│ way/990848785 │ yes │ NULL │ NULL │ POLYGON ((7.4142551 43.7339… │
│ way/993121275 │ yes │ NULL │ NULL │ POLYGON ((7.4321416 43.7481… │
├──────────────────┴──────────┴────────────┴─────────────┴───────────────────────────────┤
│ 5772 rows (20 shown) 5 columns │
└────────────────────────────────────────────────────────────────────────────────────────┘
Get features for Malé - the capital city of Maldives
Tags will be kept in a single column.
>>> from shapely.geometry import box
>>> gpq_path = qosm.convert_pbf_to_gpq(
... maldives_pbf_path,
... geometry_filter=box(
... minx=73.4975872,
... miny=4.1663240,
... maxx=73.5215528,
... maxy=4.1818121
... )
... )
>>> gpq_path.as_posix()
'files/maldives_nofilter_35532d32333a47a057265be0d7903ce27f6aa6ca3df31fe45f4ce67e4dbb3fb5_compact.geoparquet'
Inspect the file with duckdb
>>> import duckdb
>>> duckdb.load_extension('spatial')
>>> duckdb.read_parquet(str(gpq_path)).project(
... "* REPLACE (ST_GeomFromWKB(geometry) AS geometry)"
... ).order("feature_id") # doctest: +SKIP
┌──────────────────┬──────────────────────┬──────────────────────────────────────────────┐
│ feature_id │ tags │ geometry │
│ varchar │ map(varchar, varch… │ geometry │
├──────────────────┼──────────────────────┼──────────────────────────────────────────────┤
│ node/10010180778 │ {brand=Ooredoo, br… │ POINT (73.5179039 4.1752105) │
│ node/10062500171 │ {contact:facebook=… │ POINT (73.509583 4.1724485) │
│ node/10078084764 │ {addr:city=Male', … │ POINT (73.5047972 4.1726734) │
│ node/10078086040 │ {addr:city=Malé, a… │ POINT (73.5031714 4.1759622) │
│ node/10158825718 │ {addr:postcode=201… │ POINT (73.5083189 4.1730108) │
│ node/10289176711 │ {addr:street=Dhona… │ POINT (73.5133902 4.1725724) │
│ node/10294045310 │ {amenity=restauran… │ POINT (73.5091277 4.1735378) │
│ node/10294045311 │ {amenity=restauran… │ POINT (73.5055534 4.1759515) │
│ node/10294045411 │ {amenity=restauran… │ POINT (73.5037257 4.1717866) │
│ node/10294045412 │ {amenity=restauran… │ POINT (73.5024147 4.1761633) │
│ · │ · │ · │
│ · │ · │ · │
│ · │ · │ · │
│ way/91986244 │ {highway=residenti… │ LINESTRING (73.5069785 4.1704686, 73.50759… │
│ way/91986245 │ {highway=residenti… │ LINESTRING (73.5135834 4.1740562, 73.51383… │
│ way/91986249 │ {highway=residenti… │ LINESTRING (73.5153971 4.1735146, 73.51601… │
│ way/91986251 │ {highway=residenti… │ LINESTRING (73.5082522 4.1709887, 73.50823… │
│ way/91986254 │ {highway=residenti… │ LINESTRING (73.508114 4.1693477, 73.508154… │
│ way/91986255 │ {landuse=cemetery,… │ POLYGON ((73.507509 4.1731064, 73.5078884 … │
│ way/91986256 │ {highway=residenti… │ LINESTRING (73.5106692 4.1744828, 73.51082… │
│ way/935784864 │ {layer=-1, locatio… │ LINESTRING (73.4875382 4.1703263, 73.50074… │
│ way/935784867 │ {layer=-1, locatio… │ LINESTRING (73.446172 4.1856738, 73.460937… │
│ way/959150179 │ {amenity=place_of_… │ POLYGON ((73.5184052 4.1755282, 73.5184863… │
├──────────────────┴──────────────────────┴──────────────────────────────────────────────┤
│ 2140 rows (20 shown) 3 columns │
└────────────────────────────────────────────────────────────────────────────────────────┘
"""
| """
Functions.
This module contains helper functions to simplify the usage.
"""
def convert_pbf_to_gpq(
pbf_path: Union[str, Path],
tags_filter: Optional[Union[OsmTagsFilter, GroupedOsmTagsFilter]] = None,
geometry_filter: Optional[BaseGeometry] = None,
result_file_path: Optional[Union[str, Path]] = None,
explode_tags: Optional[bool] = None,
ignore_cache: bool = False,
filter_osm_ids: Optional[list[str]] = None,
working_directory: Union[str, Path] = "files",
osm_way_polygon_features_config: Optional[Union[OsmWayPolygonConfig, dict[str, Any]]] = None,
) -> Path:
"""
Convert PBF file to GeoParquet file.
Args:
pbf_path (Union[str, Path]): Pbf file to be parsed to GeoParquet.
tags_filter (Union[OsmTagsFilter, GroupedOsmTagsFilter], optional): A dictionary
specifying which tags to download.
The keys should be OSM tags (e.g. `building`, `amenity`).
The values should either be `True` for retrieving all objects with the tag,
string for retrieving a single tag-value pair
or list of strings for retrieving all values specified in the list.
`tags={'leisure': 'park}` would return parks from the area.
`tags={'leisure': 'park, 'amenity': True, 'shop': ['bakery', 'bicycle']}`
would return parks, all amenity types, bakeries and bicycle shops.
If `None`, handler will allow all of the tags to be parsed. Defaults to `None`.
geometry_filter (BaseGeometry, optional): Region which can be used to filter only
intersecting OSM objects. Defaults to `None`.
result_file_path (Union[str, Path], optional): Where to save
the geoparquet file. If not provided, will be generated based on hashes
from provided tags filter and geometry filter. Defaults to `None`.
explode_tags (bool, optional): Whether to split tags into columns based on OSM tag keys.
If `None`, will be set based on `tags_filter` parameter. If no tags filter is provided,
then `explode_tags` will set to `False`, if there is tags filter it will set to `True`.
Defaults to `None`.
ignore_cache (bool, optional): Whether to ignore precalculated geoparquet files or not.
Defaults to False.
filter_osm_ids: (list[str], optional): List of OSM features ids to read from the file.
Have to be in the form of 'node/<id>', 'way/<id>' or 'relation/<id>'.
Defaults to an empty list.
working_directory (Union[str, Path], optional): Directory where to save
the parsed `*.parquet` files. Defaults to "files".
osm_way_polygon_features_config (Union[OsmWayPolygonConfig, dict[str, Any]], optional):
Config used to determine which closed way features are polygons.
Modifications to this config left are left for experienced OSM users.
Defaults to predefined "osm_way_polygon_features.json".
Returns:
Path: Path to the generated GeoParquet file.
Examples:
Get OSM data from a PBF file.
Tags will be kept in a single column.
>>> import quackosm as qosm
>>> gpq_path = qosm.convert_pbf_to_gpq(monaco_pbf_path)
>>> gpq_path.as_posix()
'files/monaco_nofilter_noclip_compact.geoparquet'
Inspect the file with duckdb
>>> import duckdb
>>> duckdb.load_extension('spatial')
>>> duckdb.read_parquet(str(gpq_path)).project(
... "* REPLACE (ST_GeomFromWKB(geometry) AS geometry)"
... ).order("feature_id") # doctest: +SKIP
┌──────────────────┬──────────────────────┬──────────────────────────────────────────────┐
│ feature_id │ tags │ geometry │
│ varchar │ map(varchar, varch… │ geometry │
├──────────────────┼──────────────────────┼──────────────────────────────────────────────┤
│ node/10005045289 │ {shop=bakery} │ POINT (7.4224498 43.7310532) │
│ node/10020887517 │ {leisure=swimming_… │ POINT (7.4131561 43.7338391) │
│ node/10021298117 │ {leisure=swimming_… │ POINT (7.4277743 43.7427669) │
│ node/10021298717 │ {leisure=swimming_… │ POINT (7.4263029 43.7409734) │
│ node/10025656383 │ {ferry=yes, name=Q… │ POINT (7.4254971 43.7369002) │
│ node/10025656390 │ {amenity=restauran… │ POINT (7.4269287 43.7368818) │
│ node/10025656391 │ {name=Capitainerie… │ POINT (7.4272127 43.7359593) │
│ node/10025656392 │ {name=Direction de… │ POINT (7.4270392 43.7365262) │
│ node/10025656393 │ {name=IQOS, openin… │ POINT (7.4275175 43.7373195) │
│ node/10025656394 │ {artist_name=Anna … │ POINT (7.4293446 43.737448) │
│ · │ · │ · │
│ · │ · │ · │
│ · │ · │ · │
│ way/986864693 │ {natural=bare_rock} │ POLYGON ((7.4340482 43.745598, 7.4340263 4… │
│ way/986864694 │ {barrier=wall} │ LINESTRING (7.4327547 43.7445382, 7.432808… │
│ way/986864695 │ {natural=bare_rock} │ POLYGON ((7.4332994 43.7449315, 7.4332912 … │
│ way/986864696 │ {barrier=wall} │ LINESTRING (7.4356006 43.7464325, 7.435574… │
│ way/986864697 │ {natural=bare_rock} │ POLYGON ((7.4362767 43.74697, 7.4362983 43… │
│ way/990669427 │ {amenity=shelter, … │ POLYGON ((7.4146087 43.733883, 7.4146192 4… │
│ way/990669428 │ {highway=secondary… │ LINESTRING (7.4136598 43.7334433, 7.413640… │
│ way/990669429 │ {highway=secondary… │ LINESTRING (7.4137621 43.7334251, 7.413746… │
│ way/990848785 │ {addr:city=Monaco,… │ POLYGON ((7.4142551 43.7339622, 7.4143113 … │
│ way/993121275 │ {building=yes, nam… │ POLYGON ((7.4321416 43.7481309, 7.4321638 … │
├──────────────────┴──────────────────────┴──────────────────────────────────────────────┤
│ 7906 rows (20 shown) 3 columns │
└────────────────────────────────────────────────────────────────────────────────────────┘
Get only buildings, amenities and highways from a PBF file.
Tags will be split into separate columns because of applying the filter.
>>> gpq_path = qosm.convert_pbf_to_gpq(
... monaco_pbf_path,
... tags_filter={"building": True, "amenity": True, "highway": True}
... )
>>> gpq_path.as_posix()
'files/monaco_6593ca69098459d039054bc5fe0a87c56681e29a5f59d38ce3485c03cb0e9374_noclip_exploded.geoparquet'
Inspect the file with duckdb
>>> import duckdb
>>> duckdb.load_extension('spatial')
>>> duckdb.read_parquet(str(gpq_path)).project(
... "* REPLACE (ST_GeomFromWKB(geometry) AS geometry)"
... ).order("feature_id") # doctest: +SKIP
┌──────────────────┬──────────┬────────────┬─────────────┬───────────────────────────────┐
│ feature_id │ building │ amenity │ highway │ geometry │
│ varchar │ varchar │ varchar │ varchar │ geometry │
├──────────────────┼──────────┼────────────┼─────────────┼───────────────────────────────┤
│ node/10025656390 │ NULL │ restaurant │ NULL │ POINT (7.4269287 43.7368818) │
│ node/10025843517 │ NULL │ restaurant │ NULL │ POINT (7.4219362 43.7367446) │
│ node/10025852089 │ NULL │ bar │ NULL │ POINT (7.4227543 43.7369926) │
│ node/10025852090 │ NULL │ restaurant │ NULL │ POINT (7.4225093 43.7369627) │
│ node/10068880332 │ NULL │ NULL │ platform │ POINT (7.4380849 43.7493273) │
│ node/10068880335 │ NULL │ bench │ NULL │ POINT (7.4186855 43.7321515) │
│ node/10127713363 │ NULL │ cafe │ NULL │ POINT (7.4266367 43.7420755) │
│ node/10601158089 │ NULL │ restaurant │ NULL │ POINT (7.4213086 43.7336187) │
│ node/10671507005 │ NULL │ bar │ NULL │ POINT (7.4296915 43.7423307) │
│ node/10674256605 │ NULL │ bar │ NULL │ POINT (7.4213558 43.7336317) │
│ · │ · │ · │ · │ · │
│ · │ · │ · │ · │ · │
│ · │ · │ · │ · │ · │
│ way/981971425 │ NULL │ NULL │ residential │ LINESTRING (7.4321217 43.74… │
│ way/982061461 │ NULL │ NULL │ secondary │ LINESTRING (7.4246341 43.74… │
│ way/982081599 │ NULL │ NULL │ tertiary │ LINESTRING (7.4225202 43.73… │
│ way/982081600 │ NULL │ NULL │ service │ LINESTRING (7.4225202 43.73… │
│ way/986029035 │ NULL │ NULL │ path │ LINESTRING (7.4189462 43.73… │
│ way/990669427 │ NULL │ shelter │ NULL │ POLYGON ((7.4146087 43.7338… │
│ way/990669428 │ NULL │ NULL │ secondary │ LINESTRING (7.4136598 43.73… │
│ way/990669429 │ NULL │ NULL │ secondary │ LINESTRING (7.4137621 43.73… │
│ way/990848785 │ yes │ NULL │ NULL │ POLYGON ((7.4142551 43.7339… │
│ way/993121275 │ yes │ NULL │ NULL │ POLYGON ((7.4321416 43.7481… │
├──────────────────┴──────────┴────────────┴─────────────┴───────────────────────────────┤
│ 5772 rows (20 shown) 5 columns │
└────────────────────────────────────────────────────────────────────────────────────────┘
Get features for Malé - the capital city of Maldives
Tags will be kept in a single column.
>>> from shapely.geometry import box
>>> gpq_path = qosm.convert_pbf_to_gpq(
... maldives_pbf_path,
... geometry_filter=box(
... minx=73.4975872,
... miny=4.1663240,
... maxx=73.5215528,
... maxy=4.1818121
... )
... )
>>> gpq_path.as_posix()
'files/maldives_nofilter_35532d32333a47a057265be0d7903ce27f6aa6ca3df31fe45f4ce67e4dbb3fb5_compact.geoparquet'
Inspect the file with duckdb
>>> import duckdb
>>> duckdb.load_extension('spatial')
>>> duckdb.read_parquet(str(gpq_path)).project(
... "* REPLACE (ST_GeomFromWKB(geometry) AS geometry)"
... ).order("feature_id") # doctest: +SKIP
┌──────────────────┬──────────────────────┬──────────────────────────────────────────────┐
│ feature_id │ tags │ geometry │
│ varchar │ map(varchar, varch… │ geometry │
├──────────────────┼──────────────────────┼──────────────────────────────────────────────┤
│ node/10010180778 │ {brand=Ooredoo, br… │ POINT (73.5179039 4.1752105) │
│ node/10062500171 │ {contact:facebook=… │ POINT (73.509583 4.1724485) │
│ node/10078084764 │ {addr:city=Male', … │ POINT (73.5047972 4.1726734) │
│ node/10078086040 │ {addr:city=Malé, a… │ POINT (73.5031714 4.1759622) │
│ node/10158825718 │ {addr:postcode=201… │ POINT (73.5083189 4.1730108) │
│ node/10289176711 │ {addr:street=Dhona… │ POINT (73.5133902 4.1725724) │
│ node/10294045310 │ {amenity=restauran… │ POINT (73.5091277 4.1735378) │
│ node/10294045311 │ {amenity=restauran… │ POINT (73.5055534 4.1759515) │
│ node/10294045411 │ {amenity=restauran… │ POINT (73.5037257 4.1717866) │
│ node/10294045412 │ {amenity=restauran… │ POINT (73.5024147 4.1761633) │
│ · │ · │ · │
│ · │ · │ · │
│ · │ · │ · │
│ way/91986244 │ {highway=residenti… │ LINESTRING (73.5069785 4.1704686, 73.50759… │
│ way/91986245 │ {highway=residenti… │ LINESTRING (73.5135834 4.1740562, 73.51383… │
│ way/91986249 │ {highway=residenti… │ LINESTRING (73.5153971 4.1735146, 73.51601… │
│ way/91986251 │ {highway=residenti… │ LINESTRING (73.5082522 4.1709887, 73.50823… │
│ way/91986254 │ {highway=residenti… │ LINESTRING (73.508114 4.1693477, 73.508154… │
│ way/91986255 │ {landuse=cemetery,… │ POLYGON ((73.507509 4.1731064, 73.5078884 … │
│ way/91986256 │ {highway=residenti… │ LINESTRING (73.5106692 4.1744828, 73.51082… │
│ way/935784864 │ {layer=-1, locatio… │ LINESTRING (73.4875382 4.1703263, 73.50074… │
│ way/935784867 │ {layer=-1, locatio… │ LINESTRING (73.446172 4.1856738, 73.460937… │
│ way/959150179 │ {amenity=place_of_… │ POLYGON ((73.5184052 4.1755282, 73.5184863… │
├──────────────────┴──────────────────────┴──────────────────────────────────────────────┤
│ 2140 rows (20 shown) 3 columns │
└────────────────────────────────────────────────────────────────────────────────────────┘
""" | return PbfFileReader( | 2 | 2023-12-28 11:26:41+00:00 | 24k |
KyanChen/TTP | mmdet/models/roi_heads/bbox_heads/multi_instance_bbox_head.py | [
{
"identifier": "BBoxHead",
"path": "mmdet/models/roi_heads/bbox_heads/bbox_head.py",
"snippet": "class BBoxHead(BaseModule):\n \"\"\"Simplest RoI head, with only two fc layers for classification and\n regression respectively.\"\"\"\n\n def __init__(self,\n with_avg_pool: bool =... | from typing import List, Optional, Tuple, Union
from mmcv.cnn import ConvModule
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from torch import Tensor, nn
from mmdet.models.roi_heads.bbox_heads.bbox_head import BBoxHead
from mmdet.models.task_modules.samplers import SamplingResult
from mmdet.models.utils import empty_instances
from mmdet.registry import MODELS
from mmdet.structures.bbox import bbox_overlaps
import numpy as np
import torch
import torch.nn.functional as F | 15,729 | bbox_pred: Tensor,
img_meta: dict,
rescale: bool = False,
rcnn_test_cfg: Optional[ConfigDict] = None) -> InstanceData:
"""Transform a single image's features extracted from the head into
bbox results.
Args:
roi (Tensor): Boxes to be transformed. Has shape (num_boxes, 5).
last dimension 5 arrange as (batch_index, x1, y1, x2, y2).
cls_score (Tensor): Box scores, has shape
(num_boxes, num_classes + 1).
bbox_pred (Tensor): Box energies / deltas. has shape
(num_boxes, num_classes * 4).
img_meta (dict): image information.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head.
Defaults to None
Returns:
:obj:`InstanceData`: Detection results of each image.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
"""
cls_score = cls_score.reshape(-1, self.num_classes + 1)
bbox_pred = bbox_pred.reshape(-1, 4)
roi = roi.repeat_interleave(self.num_instance, dim=0)
results = InstanceData()
if roi.shape[0] == 0:
return empty_instances([img_meta],
roi.device,
task_type='bbox',
instance_results=[results])[0]
scores = cls_score.softmax(dim=-1) if cls_score is not None else None
img_shape = img_meta['img_shape']
bboxes = self.bbox_coder.decode(
roi[..., 1:], bbox_pred, max_shape=img_shape)
if rescale and bboxes.size(0) > 0:
assert img_meta.get('scale_factor') is not None
scale_factor = bboxes.new_tensor(img_meta['scale_factor']).repeat(
(1, 2))
bboxes = (bboxes.view(bboxes.size(0), -1, 4) / scale_factor).view(
bboxes.size()[0], -1)
if rcnn_test_cfg is None:
# This means that it is aug test.
# It needs to return the raw results without nms.
results.bboxes = bboxes
results.scores = scores
else:
roi_idx = np.tile(
np.arange(bboxes.shape[0] / self.num_instance)[:, None],
(1, self.num_instance)).reshape(-1, 1)[:, 0]
roi_idx = torch.from_numpy(roi_idx).to(bboxes.device).reshape(
-1, 1)
bboxes = torch.cat([bboxes, roi_idx], dim=1)
det_bboxes, det_scores = self.set_nms(
bboxes, scores[:, 1], rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms['iou_threshold'], rcnn_test_cfg.max_per_img)
results.bboxes = det_bboxes[:, :-1]
results.scores = det_scores
results.labels = torch.zeros_like(det_scores)
return results
@staticmethod
def set_nms(bboxes: Tensor,
scores: Tensor,
score_thr: float,
iou_threshold: float,
max_num: int = -1) -> Tuple[Tensor, Tensor]:
"""NMS for multi-instance prediction. Please refer to
https://github.com/Purkialo/CrowdDet for more details.
Args:
bboxes (Tensor): predict bboxes.
scores (Tensor): The score of each predict bbox.
score_thr (float): bbox threshold, bboxes with scores lower than it
will not be considered.
iou_threshold (float): IoU threshold to be considered as
conflicted.
max_num (int, optional): if there are more than max_num bboxes
after NMS, only top max_num will be kept. Default to -1.
Returns:
Tuple[Tensor, Tensor]: (bboxes, scores).
"""
bboxes = bboxes[scores > score_thr]
scores = scores[scores > score_thr]
ordered_scores, order = scores.sort(descending=True)
ordered_bboxes = bboxes[order]
roi_idx = ordered_bboxes[:, -1]
keep = torch.ones(len(ordered_bboxes)) == 1
ruler = torch.arange(len(ordered_bboxes))
keep = keep.to(bboxes.device)
ruler = ruler.to(bboxes.device)
while ruler.shape[0] > 0:
basement = ruler[0]
ruler = ruler[1:]
idx = roi_idx[basement]
# calculate the body overlap
basement_bbox = ordered_bboxes[:, :4][basement].reshape(-1, 4)
ruler_bbox = ordered_bboxes[:, :4][ruler].reshape(-1, 4)
| # Copyright (c) OpenMMLab. All rights reserved.
@MODELS.register_module()
class MultiInstanceBBoxHead(BBoxHead):
r"""Bbox head used in CrowdDet.
.. code-block:: none
/-> cls convs_1 -> cls fcs_1 -> cls_1
|--
| \-> reg convs_1 -> reg fcs_1 -> reg_1
|
| /-> cls convs_2 -> cls fcs_2 -> cls_2
shared convs -> shared fcs |--
| \-> reg convs_2 -> reg fcs_2 -> reg_2
|
| ...
|
| /-> cls convs_k -> cls fcs_k -> cls_k
|--
\-> reg convs_k -> reg fcs_k -> reg_k
Args:
num_instance (int): The number of branches after shared fcs.
Defaults to 2.
with_refine (bool): Whether to use refine module. Defaults to False.
num_shared_convs (int): The number of shared convs. Defaults to 0.
num_shared_fcs (int): The number of shared fcs. Defaults to 2.
num_cls_convs (int): The number of cls convs. Defaults to 0.
num_cls_fcs (int): The number of cls fcs. Defaults to 0.
num_reg_convs (int): The number of reg convs. Defaults to 0.
num_reg_fcs (int): The number of reg fcs. Defaults to 0.
conv_out_channels (int): The number of conv out channels.
Defaults to 256.
fc_out_channels (int): The number of fc out channels. Defaults to 1024.
init_cfg (dict or list[dict], optional): Initialization config dict.
Defaults to None.
""" # noqa: W605
def __init__(self,
num_instance: int = 2,
with_refine: bool = False,
num_shared_convs: int = 0,
num_shared_fcs: int = 2,
num_cls_convs: int = 0,
num_cls_fcs: int = 0,
num_reg_convs: int = 0,
num_reg_fcs: int = 0,
conv_out_channels: int = 256,
fc_out_channels: int = 1024,
init_cfg: Optional[Union[dict, ConfigDict]] = None,
*args,
**kwargs) -> None:
super().__init__(*args, init_cfg=init_cfg, **kwargs)
assert (num_shared_convs + num_shared_fcs + num_cls_convs +
num_cls_fcs + num_reg_convs + num_reg_fcs > 0)
assert num_instance == 2, 'Currently only 2 instances are supported'
if num_cls_convs > 0 or num_reg_convs > 0:
assert num_shared_fcs == 0
if not self.with_cls:
assert num_cls_convs == 0 and num_cls_fcs == 0
if not self.with_reg:
assert num_reg_convs == 0 and num_reg_fcs == 0
self.num_instance = num_instance
self.num_shared_convs = num_shared_convs
self.num_shared_fcs = num_shared_fcs
self.num_cls_convs = num_cls_convs
self.num_cls_fcs = num_cls_fcs
self.num_reg_convs = num_reg_convs
self.num_reg_fcs = num_reg_fcs
self.conv_out_channels = conv_out_channels
self.fc_out_channels = fc_out_channels
self.with_refine = with_refine
# add shared convs and fcs
self.shared_convs, self.shared_fcs, last_layer_dim = \
self._add_conv_fc_branch(
self.num_shared_convs, self.num_shared_fcs, self.in_channels,
True)
self.shared_out_channels = last_layer_dim
self.relu = nn.ReLU(inplace=True)
if self.with_refine:
refine_model_cfg = {
'type': 'Linear',
'in_features': self.shared_out_channels + 20,
'out_features': self.shared_out_channels
}
self.shared_fcs_ref = MODELS.build(refine_model_cfg)
self.fc_cls_ref = nn.ModuleList()
self.fc_reg_ref = nn.ModuleList()
self.cls_convs = nn.ModuleList()
self.cls_fcs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
self.reg_fcs = nn.ModuleList()
self.cls_last_dim = list()
self.reg_last_dim = list()
self.fc_cls = nn.ModuleList()
self.fc_reg = nn.ModuleList()
for k in range(self.num_instance):
# add cls specific branch
cls_convs, cls_fcs, cls_last_dim = self._add_conv_fc_branch(
self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels)
self.cls_convs.append(cls_convs)
self.cls_fcs.append(cls_fcs)
self.cls_last_dim.append(cls_last_dim)
# add reg specific branch
reg_convs, reg_fcs, reg_last_dim = self._add_conv_fc_branch(
self.num_reg_convs, self.num_reg_fcs, self.shared_out_channels)
self.reg_convs.append(reg_convs)
self.reg_fcs.append(reg_fcs)
self.reg_last_dim.append(reg_last_dim)
if self.num_shared_fcs == 0 and not self.with_avg_pool:
if self.num_cls_fcs == 0:
self.cls_last_dim *= self.roi_feat_area
if self.num_reg_fcs == 0:
self.reg_last_dim *= self.roi_feat_area
if self.with_cls:
if self.custom_cls_channels:
cls_channels = self.loss_cls.get_cls_channels(
self.num_classes)
else:
cls_channels = self.num_classes + 1
cls_predictor_cfg_ = self.cls_predictor_cfg.copy() # deepcopy
cls_predictor_cfg_.update(
in_features=self.cls_last_dim[k],
out_features=cls_channels)
self.fc_cls.append(MODELS.build(cls_predictor_cfg_))
if self.with_refine:
self.fc_cls_ref.append(MODELS.build(cls_predictor_cfg_))
if self.with_reg:
out_dim_reg = (4 if self.reg_class_agnostic else 4 *
self.num_classes)
reg_predictor_cfg_ = self.reg_predictor_cfg.copy()
reg_predictor_cfg_.update(
in_features=self.reg_last_dim[k], out_features=out_dim_reg)
self.fc_reg.append(MODELS.build(reg_predictor_cfg_))
if self.with_refine:
self.fc_reg_ref.append(MODELS.build(reg_predictor_cfg_))
if init_cfg is None:
# when init_cfg is None,
# It has been set to
# [[dict(type='Normal', std=0.01, override=dict(name='fc_cls'))],
# [dict(type='Normal', std=0.001, override=dict(name='fc_reg'))]
# after `super(ConvFCBBoxHead, self).__init__()`
# we only need to append additional configuration
# for `shared_fcs`, `cls_fcs` and `reg_fcs`
self.init_cfg += [
dict(
type='Xavier',
distribution='uniform',
override=[
dict(name='shared_fcs'),
dict(name='cls_fcs'),
dict(name='reg_fcs')
])
]
def _add_conv_fc_branch(self,
num_branch_convs: int,
num_branch_fcs: int,
in_channels: int,
is_shared: bool = False) -> tuple:
"""Add shared or separable branch.
convs -> avg pool (optional) -> fcs
"""
last_layer_dim = in_channels
# add branch specific conv layers
branch_convs = nn.ModuleList()
if num_branch_convs > 0:
for i in range(num_branch_convs):
conv_in_channels = (
last_layer_dim if i == 0 else self.conv_out_channels)
branch_convs.append(
ConvModule(
conv_in_channels, self.conv_out_channels, 3,
padding=1))
last_layer_dim = self.conv_out_channels
# add branch specific fc layers
branch_fcs = nn.ModuleList()
if num_branch_fcs > 0:
# for shared branch, only consider self.with_avg_pool
# for separated branches, also consider self.num_shared_fcs
if (is_shared
or self.num_shared_fcs == 0) and not self.with_avg_pool:
last_layer_dim *= self.roi_feat_area
for i in range(num_branch_fcs):
fc_in_channels = (
last_layer_dim if i == 0 else self.fc_out_channels)
branch_fcs.append(
nn.Linear(fc_in_channels, self.fc_out_channels))
last_layer_dim = self.fc_out_channels
return branch_convs, branch_fcs, last_layer_dim
def forward(self, x: Tuple[Tensor]) -> tuple:
"""Forward features from the upstream network.
Args:
x (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
tuple: A tuple of classification scores and bbox prediction.
- cls_score (Tensor): Classification scores for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * num_classes.
- bbox_pred (Tensor): Box energies / deltas for all scale
levels, each is a 4D-tensor, the channels number is
num_base_priors * 4.
- cls_score_ref (Tensor): The cls_score after refine model.
- bbox_pred_ref (Tensor): The bbox_pred after refine model.
"""
# shared part
if self.num_shared_convs > 0:
for conv in self.shared_convs:
x = conv(x)
if self.num_shared_fcs > 0:
if self.with_avg_pool:
x = self.avg_pool(x)
x = x.flatten(1)
for fc in self.shared_fcs:
x = self.relu(fc(x))
x_cls = x
x_reg = x
# separate branches
cls_score = list()
bbox_pred = list()
for k in range(self.num_instance):
for conv in self.cls_convs[k]:
x_cls = conv(x_cls)
if x_cls.dim() > 2:
if self.with_avg_pool:
x_cls = self.avg_pool(x_cls)
x_cls = x_cls.flatten(1)
for fc in self.cls_fcs[k]:
x_cls = self.relu(fc(x_cls))
for conv in self.reg_convs[k]:
x_reg = conv(x_reg)
if x_reg.dim() > 2:
if self.with_avg_pool:
x_reg = self.avg_pool(x_reg)
x_reg = x_reg.flatten(1)
for fc in self.reg_fcs[k]:
x_reg = self.relu(fc(x_reg))
cls_score.append(self.fc_cls[k](x_cls) if self.with_cls else None)
bbox_pred.append(self.fc_reg[k](x_reg) if self.with_reg else None)
if self.with_refine:
x_ref = x
cls_score_ref = list()
bbox_pred_ref = list()
for k in range(self.num_instance):
feat_ref = cls_score[k].softmax(dim=-1)
feat_ref = torch.cat((bbox_pred[k], feat_ref[:, 1][:, None]),
dim=1).repeat(1, 4)
feat_ref = torch.cat((x_ref, feat_ref), dim=1)
feat_ref = F.relu_(self.shared_fcs_ref(feat_ref))
cls_score_ref.append(self.fc_cls_ref[k](feat_ref))
bbox_pred_ref.append(self.fc_reg_ref[k](feat_ref))
cls_score = torch.cat(cls_score, dim=1)
bbox_pred = torch.cat(bbox_pred, dim=1)
cls_score_ref = torch.cat(cls_score_ref, dim=1)
bbox_pred_ref = torch.cat(bbox_pred_ref, dim=1)
return cls_score, bbox_pred, cls_score_ref, bbox_pred_ref
cls_score = torch.cat(cls_score, dim=1)
bbox_pred = torch.cat(bbox_pred, dim=1)
return cls_score, bbox_pred
def get_targets(self,
sampling_results: List[SamplingResult],
rcnn_train_cfg: ConfigDict,
concat: bool = True) -> tuple:
"""Calculate the ground truth for all samples in a batch according to
the sampling_results.
Almost the same as the implementation in bbox_head, we passed
additional parameters pos_inds_list and neg_inds_list to
`_get_targets_single` function.
Args:
sampling_results (List[obj:SamplingResult]): Assign results of
all images in a batch after sampling.
rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
concat (bool): Whether to concatenate the results of all
the images in a single batch.
Returns:
Tuple[Tensor]: Ground truth for proposals in a single image.
Containing the following list of Tensors:
- labels (list[Tensor],Tensor): Gt_labels for all proposals in a
batch, each tensor in list has shape (num_proposals,) when
`concat=False`, otherwise just a single tensor has shape
(num_all_proposals,).
- label_weights (list[Tensor]): Labels_weights for
all proposals in a batch, each tensor in list has shape
(num_proposals,) when `concat=False`, otherwise just a single
tensor has shape (num_all_proposals,).
- bbox_targets (list[Tensor],Tensor): Regression target for all
proposals in a batch, each tensor in list has shape
(num_proposals, 4) when `concat=False`, otherwise just a single
tensor has shape (num_all_proposals, 4), the last dimension 4
represents [tl_x, tl_y, br_x, br_y].
- bbox_weights (list[tensor],Tensor): Regression weights for
all proposals in a batch, each tensor in list has shape
(num_proposals, 4) when `concat=False`, otherwise just a
single tensor has shape (num_all_proposals, 4).
"""
labels = []
bbox_targets = []
bbox_weights = []
label_weights = []
for i in range(len(sampling_results)):
sample_bboxes = torch.cat([
sampling_results[i].pos_gt_bboxes,
sampling_results[i].neg_gt_bboxes
])
sample_priors = sampling_results[i].priors
sample_priors = sample_priors.repeat(1, self.num_instance).reshape(
-1, 4)
sample_bboxes = sample_bboxes.reshape(-1, 4)
if not self.reg_decoded_bbox:
_bbox_targets = self.bbox_coder.encode(sample_priors,
sample_bboxes)
else:
_bbox_targets = sample_priors
_bbox_targets = _bbox_targets.reshape(-1, self.num_instance * 4)
_bbox_weights = torch.ones(_bbox_targets.shape)
_labels = torch.cat([
sampling_results[i].pos_gt_labels,
sampling_results[i].neg_gt_labels
])
_labels_weights = torch.ones(_labels.shape)
bbox_targets.append(_bbox_targets)
bbox_weights.append(_bbox_weights)
labels.append(_labels)
label_weights.append(_labels_weights)
if concat:
labels = torch.cat(labels, 0)
label_weights = torch.cat(label_weights, 0)
bbox_targets = torch.cat(bbox_targets, 0)
bbox_weights = torch.cat(bbox_weights, 0)
return labels, label_weights, bbox_targets, bbox_weights
def loss(self, cls_score: Tensor, bbox_pred: Tensor, rois: Tensor,
labels: Tensor, label_weights: Tensor, bbox_targets: Tensor,
bbox_weights: Tensor, **kwargs) -> dict:
"""Calculate the loss based on the network predictions and targets.
Args:
cls_score (Tensor): Classification prediction results of all class,
has shape (batch_size * num_proposals_single_image,
(num_classes + 1) * k), k represents the number of prediction
boxes generated by each proposal box.
bbox_pred (Tensor): Regression prediction results, has shape
(batch_size * num_proposals_single_image, 4 * k), the last
dimension 4 represents [tl_x, tl_y, br_x, br_y].
rois (Tensor): RoIs with the shape
(batch_size * num_proposals_single_image, 5) where the first
column indicates batch id of each RoI.
labels (Tensor): Gt_labels for all proposals in a batch, has
shape (batch_size * num_proposals_single_image, k).
label_weights (Tensor): Labels_weights for all proposals in a
batch, has shape (batch_size * num_proposals_single_image, k).
bbox_targets (Tensor): Regression target for all proposals in a
batch, has shape (batch_size * num_proposals_single_image,
4 * k), the last dimension 4 represents [tl_x, tl_y, br_x,
br_y].
bbox_weights (Tensor): Regression weights for all proposals in a
batch, has shape (batch_size * num_proposals_single_image,
4 * k).
Returns:
dict: A dictionary of loss.
"""
losses = dict()
if bbox_pred.numel():
loss_0 = self.emd_loss(bbox_pred[:, 0:4], cls_score[:, 0:2],
bbox_pred[:, 4:8], cls_score[:, 2:4],
bbox_targets, labels)
loss_1 = self.emd_loss(bbox_pred[:, 4:8], cls_score[:, 2:4],
bbox_pred[:, 0:4], cls_score[:, 0:2],
bbox_targets, labels)
loss = torch.cat([loss_0, loss_1], dim=1)
_, min_indices = loss.min(dim=1)
loss_emd = loss[torch.arange(loss.shape[0]), min_indices]
loss_emd = loss_emd.mean()
else:
loss_emd = bbox_pred.sum()
losses['loss_rcnn_emd'] = loss_emd
return losses
def emd_loss(self, bbox_pred_0: Tensor, cls_score_0: Tensor,
bbox_pred_1: Tensor, cls_score_1: Tensor, targets: Tensor,
labels: Tensor) -> Tensor:
"""Calculate the emd loss.
Note:
This implementation is modified from https://github.com/Purkialo/
CrowdDet/blob/master/lib/det_oprs/loss_opr.py
Args:
bbox_pred_0 (Tensor): Part of regression prediction results, has
shape (batch_size * num_proposals_single_image, 4), the last
dimension 4 represents [tl_x, tl_y, br_x, br_y].
cls_score_0 (Tensor): Part of classification prediction results,
has shape (batch_size * num_proposals_single_image,
(num_classes + 1)), where 1 represents the background.
bbox_pred_1 (Tensor): The other part of regression prediction
results, has shape (batch_size*num_proposals_single_image, 4).
cls_score_1 (Tensor):The other part of classification prediction
results, has shape (batch_size * num_proposals_single_image,
(num_classes + 1)).
targets (Tensor):Regression target for all proposals in a
batch, has shape (batch_size * num_proposals_single_image,
4 * k), the last dimension 4 represents [tl_x, tl_y, br_x,
br_y], k represents the number of prediction boxes generated
by each proposal box.
labels (Tensor): Gt_labels for all proposals in a batch, has
shape (batch_size * num_proposals_single_image, k).
Returns:
torch.Tensor: The calculated loss.
"""
bbox_pred = torch.cat([bbox_pred_0, bbox_pred_1],
dim=1).reshape(-1, bbox_pred_0.shape[-1])
cls_score = torch.cat([cls_score_0, cls_score_1],
dim=1).reshape(-1, cls_score_0.shape[-1])
targets = targets.reshape(-1, 4)
labels = labels.long().flatten()
# masks
valid_masks = labels >= 0
fg_masks = labels > 0
# multiple class
bbox_pred = bbox_pred.reshape(-1, self.num_classes, 4)
fg_gt_classes = labels[fg_masks]
bbox_pred = bbox_pred[fg_masks, fg_gt_classes - 1, :]
# loss for regression
loss_bbox = self.loss_bbox(bbox_pred, targets[fg_masks])
loss_bbox = loss_bbox.sum(dim=1)
# loss for classification
labels = labels * valid_masks
loss_cls = self.loss_cls(cls_score, labels)
loss_cls[fg_masks] = loss_cls[fg_masks] + loss_bbox
loss = loss_cls.reshape(-1, 2).sum(dim=1)
return loss.reshape(-1, 1)
def _predict_by_feat_single(
self,
roi: Tensor,
cls_score: Tensor,
bbox_pred: Tensor,
img_meta: dict,
rescale: bool = False,
rcnn_test_cfg: Optional[ConfigDict] = None) -> InstanceData:
"""Transform a single image's features extracted from the head into
bbox results.
Args:
roi (Tensor): Boxes to be transformed. Has shape (num_boxes, 5).
last dimension 5 arrange as (batch_index, x1, y1, x2, y2).
cls_score (Tensor): Box scores, has shape
(num_boxes, num_classes + 1).
bbox_pred (Tensor): Box energies / deltas. has shape
(num_boxes, num_classes * 4).
img_meta (dict): image information.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head.
Defaults to None
Returns:
:obj:`InstanceData`: Detection results of each image.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
"""
cls_score = cls_score.reshape(-1, self.num_classes + 1)
bbox_pred = bbox_pred.reshape(-1, 4)
roi = roi.repeat_interleave(self.num_instance, dim=0)
results = InstanceData()
if roi.shape[0] == 0:
return empty_instances([img_meta],
roi.device,
task_type='bbox',
instance_results=[results])[0]
scores = cls_score.softmax(dim=-1) if cls_score is not None else None
img_shape = img_meta['img_shape']
bboxes = self.bbox_coder.decode(
roi[..., 1:], bbox_pred, max_shape=img_shape)
if rescale and bboxes.size(0) > 0:
assert img_meta.get('scale_factor') is not None
scale_factor = bboxes.new_tensor(img_meta['scale_factor']).repeat(
(1, 2))
bboxes = (bboxes.view(bboxes.size(0), -1, 4) / scale_factor).view(
bboxes.size()[0], -1)
if rcnn_test_cfg is None:
# This means that it is aug test.
# It needs to return the raw results without nms.
results.bboxes = bboxes
results.scores = scores
else:
roi_idx = np.tile(
np.arange(bboxes.shape[0] / self.num_instance)[:, None],
(1, self.num_instance)).reshape(-1, 1)[:, 0]
roi_idx = torch.from_numpy(roi_idx).to(bboxes.device).reshape(
-1, 1)
bboxes = torch.cat([bboxes, roi_idx], dim=1)
det_bboxes, det_scores = self.set_nms(
bboxes, scores[:, 1], rcnn_test_cfg.score_thr,
rcnn_test_cfg.nms['iou_threshold'], rcnn_test_cfg.max_per_img)
results.bboxes = det_bboxes[:, :-1]
results.scores = det_scores
results.labels = torch.zeros_like(det_scores)
return results
@staticmethod
def set_nms(bboxes: Tensor,
scores: Tensor,
score_thr: float,
iou_threshold: float,
max_num: int = -1) -> Tuple[Tensor, Tensor]:
"""NMS for multi-instance prediction. Please refer to
https://github.com/Purkialo/CrowdDet for more details.
Args:
bboxes (Tensor): predict bboxes.
scores (Tensor): The score of each predict bbox.
score_thr (float): bbox threshold, bboxes with scores lower than it
will not be considered.
iou_threshold (float): IoU threshold to be considered as
conflicted.
max_num (int, optional): if there are more than max_num bboxes
after NMS, only top max_num will be kept. Default to -1.
Returns:
Tuple[Tensor, Tensor]: (bboxes, scores).
"""
bboxes = bboxes[scores > score_thr]
scores = scores[scores > score_thr]
ordered_scores, order = scores.sort(descending=True)
ordered_bboxes = bboxes[order]
roi_idx = ordered_bboxes[:, -1]
keep = torch.ones(len(ordered_bboxes)) == 1
ruler = torch.arange(len(ordered_bboxes))
keep = keep.to(bboxes.device)
ruler = ruler.to(bboxes.device)
while ruler.shape[0] > 0:
basement = ruler[0]
ruler = ruler[1:]
idx = roi_idx[basement]
# calculate the body overlap
basement_bbox = ordered_bboxes[:, :4][basement].reshape(-1, 4)
ruler_bbox = ordered_bboxes[:, :4][ruler].reshape(-1, 4) | overlap = bbox_overlaps(basement_bbox, ruler_bbox) | 4 | 2023-12-23 08:36:47+00:00 | 24k |
see2023/Bert-VITS2-ext | train_ms.py | [
{
"identifier": "config",
"path": "config.py",
"snippet": "class Resample_config:\nclass Preprocess_text_config:\nclass Bert_gen_config:\nclass Emo_gen_config:\nclass Train_ms_config:\nclass Webui_config:\nclass Server_config:\nclass Translate_config:\nclass Config:\n def __init__(self, in_dir: str, ... | import platform
import os
import torch
import torch.distributed as dist
import logging
import argparse
import datetime
import gc
import commons
import utils
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
from tqdm import tqdm
from config import config
from data_utils import (
TextAudioSpeakerLoader,
TextAudioSpeakerCollate,
DistributedBucketSampler,
AudioVisemesLoader,
)
from models import (
SynthesizerTrn,
MultiPeriodDiscriminator,
DurationDiscriminator,
WavLMDiscriminator,
VisemesNet,
)
from losses import (
generator_loss,
discriminator_loss,
feature_loss,
kl_loss,
WavLMLoss,
)
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from text.symbols import symbols | 15,278 | )
else:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc, net_wd, wl],
[optim_g, optim_d, optim_dur_disc, optim_wd],
[scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd],
scaler,
[train_loader, None],
None,
None,
)
scheduler_g.step()
scheduler_d.step()
scheduler_wd.step()
if net_dur_disc is not None:
scheduler_dur_disc.step()
def train_and_evaluate(
rank,
local_rank,
epoch,
hps,
nets,
optims,
schedulers,
scaler,
loaders,
logger,
writers,
):
net_g, net_d, net_dur_disc, net_wd, wl = nets
optim_g, optim_d, optim_dur_disc, optim_wd = optims
scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd = schedulers
train_loader, eval_loader = loaders
if writers is not None:
writer, writer_eval = writers
train_loader.batch_sampler.set_epoch(epoch)
global global_step
net_g.train()
net_d.train()
net_wd.train()
if net_dur_disc is not None:
net_dur_disc.train()
for batch_idx, (
x,
x_lengths,
spec,
spec_lengths,
y,
y_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
) in enumerate(tqdm(train_loader)):
if net_g.module.use_noise_scaled_mas:
current_mas_noise_scale = (
net_g.module.mas_noise_scale_initial
- net_g.module.noise_scale_delta * global_step
)
net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
local_rank, non_blocking=True
)
spec, spec_lengths = spec.cuda(
local_rank, non_blocking=True
), spec_lengths.cuda(local_rank, non_blocking=True)
y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
local_rank, non_blocking=True
)
speakers = speakers.cuda(local_rank, non_blocking=True)
tone = tone.cuda(local_rank, non_blocking=True)
language = language.cuda(local_rank, non_blocking=True)
bert = bert.cuda(local_rank, non_blocking=True)
ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
en_bert = en_bert.cuda(local_rank, non_blocking=True)
with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
(
y_hat,
l_length,
attn,
ids_slice,
x_mask,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
(hidden_x, logw, logw_, logw_sdp),
g,
) = net_g(
x,
x_lengths,
spec,
spec_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
)
mel = spec_to_mel_torch(
spec,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y_mel = commons.slice_segments(
mel, ids_slice, hps.train.segment_size // hps.data.hop_length
)
| # flake8: noqa: E402
logging.getLogger("numba").setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = (
True # If encontered training problem,please try to disable TF32.
)
torch.set_float32_matmul_precision("medium")
torch.backends.cuda.sdp_kernel("flash")
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(
True
) # Not available if torch version is lower than 2.0
global_step = 0
global_visemes_step = 0
def run_only_visemes(hps):
# 使用最简单的单机模式，仅训练隐变量z到表情(visemes)的全连接 VisemesFCNet 的参数
global global_visemes_step
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(0)
train_dataset = AudioVisemesLoader(hps.data.training_visemes_files, hps.data)
train_loader = DataLoader(train_dataset, num_workers=0, shuffle=False, pin_memory=True,
batch_size=1, drop_last=True)
eval_dataset = AudioVisemesLoader(hps.data.validation_visemes_files, hps.data)
eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
batch_size=1, pin_memory=True,
drop_last=False)
net_v = VisemesNet(hps.model.hidden_channels).cuda()
latest_model_path = utils.latest_checkpoint_path(hps.model_dir, "V_*.pth")
if latest_model_path is not None:
_, optim_d, _, epoch_str = utils.load_checkpoint(latest_model_path, net_v, None, skip_optimizer=False)
else :
epoch_str = 1
global_visemes_step = 0
net_v.init_weights()
optim_v = torch.optim.AdamW(
net_v.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps)
optim_v.param_groups[0]['initial_lr'] = hps.train.learning_rate
scheduler_v = torch.optim.lr_scheduler.ExponentialLR(optim_v, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2, )
scaler = GradScaler(enabled=hps.train.bf16_run)
for epoch in range(epoch_str, hps.train.epochs + 1):
train_visemes_only(epoch, hps, net_v, train_loader, optim_v, scaler)
scheduler_v.step()
if epoch % hps.train.eval_interval == 0:
eval_visemes_only(epoch, hps, net_v, eval_loader)
utils.save_checkpoint(net_v, optim_v,hps.train.learning_rate , epoch, os.path.join(hps.model_dir, "V_{}.pth".format(epoch)))
def train_visemes_only(epoch, hps, net_v, train_loader, optim_v, scaler):
for batch_idx, (spec, visemes) in tqdm(enumerate(train_loader)):
spec, visemes = spec.cuda(), visemes.cuda()
with autocast(enabled=hps.train.bf16_run):
# 通过VisemesNet从z生成visemes_hat，和均方差
visemes_hat = net_v(spec)
visemes_hat_mse = get_visemes_mse(visemes, visemes_hat)
optim_v.zero_grad()
scaler.scale(visemes_hat_mse).backward()
scaler.unscale_(optim_v)
grad_norm_v = commons.clip_grad_value_(net_v.parameters(), None)
scaler.step(optim_v)
global global_visemes_step
global_visemes_step += 1
if batch_idx % hps.train.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tvisemes_hat_mse: {:.6f}\tgrad_norm_v: {:.6f}'.format(
epoch, batch_idx * len(spec), len(train_loader.dataset),
100. * batch_idx / len(train_loader), visemes_hat_mse.item(), grad_norm_v))
def get_visemes_mse(visemes, visemes_hat):
if visemes.shape[-1] != visemes_hat.shape[-1]: # 如果y和x的最低维度不一样
visemes_hat = F.interpolate(visemes_hat, size=visemes.shape[-1], mode='linear', align_corners=True) # 对x进行线性插值，使其形状与y一致
visemes_hat_mse = torch.mean(torch.pow(visemes_hat - visemes, 2))
return visemes_hat_mse
def eval_visemes_only(epoch, hps, net_v, eval_loader):
net_v.eval()
with torch.no_grad():
visemes_hat_mse_sum = 0.0
for batch_idx, (spec, visemes) in tqdm(enumerate(eval_loader)):
spec, visemes = spec.cuda(), visemes.cuda()
# 通过VisemesFCNet从z生成visemes_hat，和均方差
visemes_hat = net_v(spec)
visemes_hat_mse = get_visemes_mse(visemes, visemes_hat)
visemes_hat_mse_sum += visemes_hat_mse
# print('visemes_hat_mse', visemes_hat_mse)
break
visemes_hat_mse_avg = visemes_hat_mse_sum / (batch_idx + 1)
log_str = '------------------ eval epoch: {} visemes_hat_mse_avg: {:.6f}'.format(epoch, visemes_hat_mse_avg)
print(log_str)
logger.warning(log_str)
net_v.train()
def run():
# 环境变量解析
envs = config.train_ms_config.env
for env_name, env_value in envs.items():
if env_name not in os.environ.keys():
print("加载config中的配置{}".format(str(env_value)))
os.environ[env_name] = str(env_value)
print(
"加载环境变量 \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
os.environ["MASTER_ADDR"],
os.environ["MASTER_PORT"],
os.environ["WORLD_SIZE"],
os.environ["RANK"],
os.environ["LOCAL_RANK"],
)
)
backend = "nccl"
if platform.system() == "Windows":
backend = "gloo" # If Windows,switch to gloo backend.
dist.init_process_group(
backend=backend,
init_method="env://",
timeout=datetime.timedelta(seconds=300),
) # Use torchrun instead of mp.spawn
rank = dist.get_rank()
local_rank = int(os.environ["LOCAL_RANK"])
n_gpus = dist.get_world_size()
# 命令行/config.yml配置解析
# hps = utils.get_hparams()
parser = argparse.ArgumentParser()
# 非必要不建议使用命令行配置，请使用config.yml文件
parser.add_argument(
"-c",
"--config",
type=str,
default=config.train_ms_config.config_path,
help="JSON file for configuration",
)
parser.add_argument(
"-m",
"--model",
type=str,
help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
default=config.dataset_path,
)
parser.add_argument('--visemes', dest='visemes', action="store_true", default=False, help="train visemes only, lock the encoder and decoder")
args = parser.parse_args()
model_dir = os.path.join(args.model, config.train_ms_config.model)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
hps = utils.get_hparams_from_file(args.config)
hps.model_dir = model_dir
set_logger(hps)
if args.visemes:
run_only_visemes(hps)
# 比较路径是否相同
if os.path.realpath(args.config) != os.path.realpath(
config.train_ms_config.config_path
):
with open(args.config, "r", encoding="utf-8") as f:
data = f.read()
with open(config.train_ms_config.config_path, "w", encoding="utf-8") as f:
f.write(data)
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(local_rank)
global global_step
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
utils.check_git_hash(hps.model_dir)
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size,
[32, 300, 400, 500, 600, 700, 800, 900, 1000],
num_replicas=n_gpus,
rank=rank,
shuffle=True,
)
collate_fn = TextAudioSpeakerCollate()
train_loader = DataLoader(
train_dataset,
num_workers=min(config.train_ms_config.num_workers, os.cpu_count() - 1),
shuffle=False,
pin_memory=True,
collate_fn=collate_fn,
batch_sampler=train_sampler,
persistent_workers=True,
prefetch_factor=4,
) # DataLoader config could be adjusted.
if rank == 0:
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
eval_loader = DataLoader(
eval_dataset,
num_workers=0,
shuffle=False,
batch_size=1,
pin_memory=True,
drop_last=False,
collate_fn=collate_fn,
)
if (
"use_noise_scaled_mas" in hps.model.keys()
and hps.model.use_noise_scaled_mas is True
):
print("Using noise scaled MAS for VITS2")
mas_noise_scale_initial = 0.01
noise_scale_delta = 2e-6
else:
print("Using normal MAS for VITS1")
mas_noise_scale_initial = 0.0
noise_scale_delta = 0.0
if (
"use_duration_discriminator" in hps.model.keys()
and hps.model.use_duration_discriminator is True
):
print("Using duration discriminator for VITS2")
net_dur_disc = DurationDiscriminator(
hps.model.hidden_channels,
hps.model.hidden_channels,
3,
0.1,
gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
).cuda(local_rank)
else:
net_dur_disc = None
if (
"use_spk_conditioned_encoder" in hps.model.keys()
and hps.model.use_spk_conditioned_encoder is True
):
if hps.data.n_speakers == 0:
raise ValueError(
"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
)
else:
print("Using normal encoder for VITS1")
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
mas_noise_scale_initial=mas_noise_scale_initial,
noise_scale_delta=noise_scale_delta,
**hps.model,
).cuda(local_rank)
if getattr(hps.train, "freeze_ZH_bert", False):
print("Freezing ZH bert encoder !!!")
for param in net_g.enc_p.bert_proj.parameters():
param.requires_grad = False
if getattr(hps.train, "freeze_EN_bert", False):
print("Freezing EN bert encoder !!!")
for param in net_g.enc_p.en_bert_proj.parameters():
param.requires_grad = False
if getattr(hps.train, "freeze_JP_bert", False):
print("Freezing JP bert encoder !!!")
for param in net_g.enc_p.ja_bert_proj.parameters():
param.requires_grad = False
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank)
net_wd = WavLMDiscriminator(
hps.model.slm.hidden, hps.model.slm.nlayers, hps.model.slm.initial_channel
).cuda(local_rank)
optim_g = torch.optim.AdamW(
filter(lambda p: p.requires_grad, net_g.parameters()),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
optim_d = torch.optim.AdamW(
net_d.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
optim_wd = torch.optim.AdamW(
net_wd.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
if net_dur_disc is not None:
optim_dur_disc = torch.optim.AdamW(
net_dur_disc.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps,
)
else:
optim_dur_disc = None
net_g = DDP(net_g, device_ids=[local_rank], bucket_cap_mb=512)
net_d = DDP(net_d, device_ids=[local_rank], bucket_cap_mb=512)
net_wd = DDP(net_wd, device_ids=[local_rank], bucket_cap_mb=512)
if net_dur_disc is not None:
net_dur_disc = DDP(
net_dur_disc,
device_ids=[local_rank],
bucket_cap_mb=512,
)
# 下载底模
if config.train_ms_config.base["use_base_model"]:
utils.download_checkpoint(
hps.model_dir,
config.train_ms_config.base,
token=config.openi_token,
mirror=config.mirror,
)
dur_resume_lr = hps.train.learning_rate
wd_resume_lr = hps.train.learning_rate
if net_dur_disc is not None:
try:
_, _, dur_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"),
net_dur_disc,
optim_dur_disc,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
if not optim_dur_disc.param_groups[0].get("initial_lr"):
optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
except:
print("Initialize dur_disc")
try:
_, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"),
net_g,
optim_g,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
_, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"),
net_d,
optim_d,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
if not optim_g.param_groups[0].get("initial_lr"):
optim_g.param_groups[0]["initial_lr"] = g_resume_lr
if not optim_d.param_groups[0].get("initial_lr"):
optim_d.param_groups[0]["initial_lr"] = d_resume_lr
epoch_str = max(epoch_str, 1)
# global_step = (epoch_str - 1) * len(train_loader)
global_step = int(
utils.get_steps(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"))
)
print(
f"******************检测到模型存在，epoch为 {epoch_str}，gloabl step为 {global_step}*********************"
)
except Exception as e:
print(e)
epoch_str = 1
global_step = 0
try:
_, optim_wd, wd_resume_lr, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "WD_*.pth"),
net_wd,
optim_wd,
skip_optimizer=hps.train.skip_optimizer
if "skip_optimizer" in hps.train
else True,
)
if not optim_wd.param_groups[0].get("initial_lr"):
optim_wd.param_groups[0]["initial_lr"] = wd_resume_lr
except Exception as e:
print(e)
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
scheduler_wd = torch.optim.lr_scheduler.ExponentialLR(
optim_wd, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
if net_dur_disc is not None:
scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(
optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
)
else:
scheduler_dur_disc = None
scaler = GradScaler(enabled=hps.train.bf16_run)
wl = WavLMLoss(
hps.model.slm.model,
net_wd,
hps.data.sampling_rate,
hps.model.slm.sr,
).to(local_rank)
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank == 0:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc, net_wd, wl],
[optim_g, optim_d, optim_dur_disc, optim_wd],
[scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd],
scaler,
[train_loader, eval_loader],
logger,
[writer, writer_eval],
)
else:
train_and_evaluate(
rank,
local_rank,
epoch,
hps,
[net_g, net_d, net_dur_disc, net_wd, wl],
[optim_g, optim_d, optim_dur_disc, optim_wd],
[scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd],
scaler,
[train_loader, None],
None,
None,
)
scheduler_g.step()
scheduler_d.step()
scheduler_wd.step()
if net_dur_disc is not None:
scheduler_dur_disc.step()
def train_and_evaluate(
rank,
local_rank,
epoch,
hps,
nets,
optims,
schedulers,
scaler,
loaders,
logger,
writers,
):
net_g, net_d, net_dur_disc, net_wd, wl = nets
optim_g, optim_d, optim_dur_disc, optim_wd = optims
scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd = schedulers
train_loader, eval_loader = loaders
if writers is not None:
writer, writer_eval = writers
train_loader.batch_sampler.set_epoch(epoch)
global global_step
net_g.train()
net_d.train()
net_wd.train()
if net_dur_disc is not None:
net_dur_disc.train()
for batch_idx, (
x,
x_lengths,
spec,
spec_lengths,
y,
y_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
) in enumerate(tqdm(train_loader)):
if net_g.module.use_noise_scaled_mas:
current_mas_noise_scale = (
net_g.module.mas_noise_scale_initial
- net_g.module.noise_scale_delta * global_step
)
net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
local_rank, non_blocking=True
)
spec, spec_lengths = spec.cuda(
local_rank, non_blocking=True
), spec_lengths.cuda(local_rank, non_blocking=True)
y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
local_rank, non_blocking=True
)
speakers = speakers.cuda(local_rank, non_blocking=True)
tone = tone.cuda(local_rank, non_blocking=True)
language = language.cuda(local_rank, non_blocking=True)
bert = bert.cuda(local_rank, non_blocking=True)
ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
en_bert = en_bert.cuda(local_rank, non_blocking=True)
with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
(
y_hat,
l_length,
attn,
ids_slice,
x_mask,
z_mask,
(z, z_p, m_p, logs_p, m_q, logs_q),
(hidden_x, logw, logw_, logw_sdp),
g,
) = net_g(
x,
x_lengths,
spec,
spec_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
)
mel = spec_to_mel_torch(
spec,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y_mel = commons.slice_segments(
mel, ids_slice, hps.train.segment_size // hps.data.hop_length
) | y_hat_mel = mel_spectrogram_torch( | 15 | 2023-12-27 03:09:11+00:00 | 24k |
chinhsuanwu/ifusion-threestudio | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
import trimesh
from dataclasses import dataclass, field
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.misc import broadcast
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
from pysdf import SDF | 15,498 | "+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
shape_init_mesh_up: str = "+z"
shape_init_mesh_front: str = "+x"
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance | elif isinstance(other, ImplicitVolume): | 4 | 2023-12-27 20:30:33+00:00 | 24k |
open-mmlab/Amphion | modules/wenet_extractor/transformer/encoder.py | [
{
"identifier": "MultiHeadedAttention",
"path": "modules/wenet_extractor/transformer/attention.py",
"snippet": "class MultiHeadedAttention(nn.Module):\n \"\"\"Multi-Head Attention layer.\n\n Args:\n n_head (int): The number of heads.\n n_feat (int): The number of features.\n d... | from typing import Tuple
from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
from modules.wenet_extractor.transformer.attention import (
RelPositionMultiHeadedAttention,
)
from modules.wenet_extractor.transformer.convolution import ConvolutionModule
from modules.wenet_extractor.transformer.embedding import PositionalEncoding
from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding
from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding
from modules.wenet_extractor.transformer.encoder_layer import TransformerEncoderLayer
from modules.wenet_extractor.transformer.encoder_layer import ConformerEncoderLayer
from modules.wenet_extractor.transformer.positionwise_feed_forward import (
PositionwiseFeedForward,
)
from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling4
from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling6
from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling8
from modules.wenet_extractor.transformer.subsampling import LinearNoSubsampling
from modules.wenet_extractor.utils.common import get_activation
from modules.wenet_extractor.utils.mask import make_pad_mask
from modules.wenet_extractor.utils.mask import add_optional_chunk_mask
import torch | 15,865 | if self.normalize_before:
xs = self.after_norm(xs)
# NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),
# ? may be larger than cache_t1, it depends on required_cache_size
r_att_cache = torch.cat(r_att_cache, dim=0)
# NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
r_cnn_cache = torch.cat(r_cnn_cache, dim=0)
return (xs, r_att_cache, r_cnn_cache)
def forward_chunk_by_chunk(
self,
xs: torch.Tensor,
decoding_chunk_size: int,
num_decoding_left_chunks: int = -1,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Forward input chunk by chunk with chunk_size like a streaming
fashion
Here we should pay special attention to computation cache in the
streaming style forward chunk by chunk. Three things should be taken
into account for computation in the current network:
1. transformer/conformer encoder layers output cache
2. convolution in conformer
3. convolution in subsampling
However, we don't implement subsampling cache for:
1. We can control subsampling module to output the right result by
overlapping input instead of cache left context, even though it
wastes some computation, but subsampling only takes a very
small fraction of computation in the whole model.
2. Typically, there are several covolution layers with subsampling
in subsampling module, it is tricky and complicated to do cache
with different convolution layers with different subsampling
rate.
3. Currently, nn.Sequential is used to stack all the convolution
layers in subsampling, we need to rewrite it to make it work
with cache, which is not prefered.
Args:
xs (torch.Tensor): (1, max_len, dim)
chunk_size (int): decoding chunk size
"""
assert decoding_chunk_size > 0
# The model is trained by static or dynamic chunk
assert self.static_chunk_size > 0 or self.use_dynamic_chunk
subsampling = self.embed.subsampling_rate
context = self.embed.right_context + 1 # Add current frame
stride = subsampling * decoding_chunk_size
decoding_window = (decoding_chunk_size - 1) * subsampling + context
num_frames = xs.size(1)
att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
outputs = []
offset = 0
required_cache_size = decoding_chunk_size * num_decoding_left_chunks
# Feed forward overlap input step by step
for cur in range(0, num_frames - context + 1, stride):
end = min(cur + decoding_window, num_frames)
chunk_xs = xs[:, cur:end, :]
(y, att_cache, cnn_cache) = self.forward_chunk(
chunk_xs, offset, required_cache_size, att_cache, cnn_cache
)
outputs.append(y)
offset += y.size(1)
ys = torch.cat(outputs, 1)
masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool)
return ys, masks
class TransformerEncoder(BaseEncoder):
"""Transformer encoder module."""
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
input_layer: str = "conv2d",
pos_enc_layer_type: str = "abs_pos",
normalize_before: bool = True,
static_chunk_size: int = 0,
use_dynamic_chunk: bool = False,
global_cmvn: torch.nn.Module = None,
use_dynamic_left_chunk: bool = False,
):
"""Construct TransformerEncoder
See Encoder for the meaning of each parameter.
"""
super().__init__(
input_size,
output_size,
attention_heads,
linear_units,
num_blocks,
dropout_rate,
positional_dropout_rate,
attention_dropout_rate,
input_layer,
pos_enc_layer_type,
normalize_before,
static_chunk_size,
use_dynamic_chunk,
global_cmvn,
use_dynamic_left_chunk,
)
self.encoders = torch.nn.ModuleList(
[
TransformerEncoderLayer(
output_size,
MultiHeadedAttention(
attention_heads, output_size, attention_dropout_rate
),
| # This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
"""Encoder definition."""
class BaseEncoder(torch.nn.Module):
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
input_layer: str = "conv2d",
pos_enc_layer_type: str = "abs_pos",
normalize_before: bool = True,
static_chunk_size: int = 0,
use_dynamic_chunk: bool = False,
global_cmvn: torch.nn.Module = None,
use_dynamic_left_chunk: bool = False,
):
"""
Args:
input_size (int): input dim
output_size (int): dimension of attention
attention_heads (int): the number of heads of multi head attention
linear_units (int): the hidden units number of position-wise feed
forward
num_blocks (int): the number of decoder blocks
dropout_rate (float): dropout rate
attention_dropout_rate (float): dropout rate in attention
positional_dropout_rate (float): dropout rate after adding
positional encoding
input_layer (str): input layer type.
optional [linear, conv2d, conv2d6, conv2d8]
pos_enc_layer_type (str): Encoder positional encoding layer type.
opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
normalize_before (bool):
True: use layer_norm before each sub-block of a layer.
False: use layer_norm after each sub-block of a layer.
static_chunk_size (int): chunk size for static chunk training and
decoding
use_dynamic_chunk (bool): whether use dynamic chunk size for
training or not, You can only use fixed chunk(chunk_size > 0)
or dyanmic chunk size(use_dynamic_chunk = True)
global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
use_dynamic_left_chunk (bool): whether use dynamic left chunk in
dynamic chunk training
"""
super().__init__()
self._output_size = output_size
if pos_enc_layer_type == "abs_pos":
pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "rel_pos":
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "no_pos":
pos_enc_class = NoPositionalEncoding
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
if input_layer == "linear":
subsampling_class = LinearNoSubsampling
elif input_layer == "conv2d":
subsampling_class = Conv2dSubsampling4
elif input_layer == "conv2d6":
subsampling_class = Conv2dSubsampling6
elif input_layer == "conv2d8":
subsampling_class = Conv2dSubsampling8
else:
raise ValueError("unknown input_layer: " + input_layer)
self.global_cmvn = global_cmvn
self.embed = subsampling_class(
input_size,
output_size,
dropout_rate,
pos_enc_class(output_size, positional_dropout_rate),
)
self.normalize_before = normalize_before
self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
self.static_chunk_size = static_chunk_size
self.use_dynamic_chunk = use_dynamic_chunk
self.use_dynamic_left_chunk = use_dynamic_left_chunk
def output_size(self) -> int:
return self._output_size
def forward(
self,
xs: torch.Tensor,
xs_lens: torch.Tensor,
decoding_chunk_size: int = 0,
num_decoding_left_chunks: int = -1,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Embed positions in tensor.
Args:
xs: padded input tensor (B, T, D)
xs_lens: input length (B)
decoding_chunk_size: decoding chunk size for dynamic chunk
0: default for training, use random dynamic chunk.
<0: for decoding, use full chunk.
>0: for decoding, use fixed chunk size as set.
num_decoding_left_chunks: number of left chunks, this is for decoding,
the chunk size is decoding_chunk_size.
>=0: use num_decoding_left_chunks
<0: use all left chunks
Returns:
encoder output tensor xs, and subsampled masks
xs: padded output tensor (B, T' ~= T/subsample_rate, D)
masks: torch.Tensor batch padding mask after subsample
(B, 1, T' ~= T/subsample_rate)
"""
T = xs.size(1)
masks = ~make_pad_mask(xs_lens, T).unsqueeze(1) # (B, 1, T)
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
xs, pos_emb, masks = self.embed(xs, masks)
mask_pad = masks # (B, 1, T/subsample_rate)
chunk_masks = add_optional_chunk_mask(
xs,
masks,
self.use_dynamic_chunk,
self.use_dynamic_left_chunk,
decoding_chunk_size,
self.static_chunk_size,
num_decoding_left_chunks,
)
for layer in self.encoders:
xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
if self.normalize_before:
xs = self.after_norm(xs)
# Here we assume the mask is not changed in encoder layers, so just
# return the masks before encoder layers, and the masks will be used
# for cross attention with decoder later
return xs, masks
def forward_chunk(
self,
xs: torch.Tensor,
offset: int,
required_cache_size: int,
att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
""" Forward just one chunk
Args:
xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim),
where `time == (chunk_size - 1) * subsample_rate + \
subsample.right_context + 1`
offset (int): current offset in encoder output time stamp
required_cache_size (int): cache size required for next chunk
compuation
>=0: actual cache size
<0: means all history cache is required
att_cache (torch.Tensor): cache tensor for KEY & VALUE in
transformer/conformer attention, with shape
(elayers, head, cache_t1, d_k * 2), where
`head * d_k == hidden-dim` and
`cache_t1 == chunk_size * num_decoding_left_chunks`.
cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer,
(elayers, b=1, hidden-dim, cache_t2), where
`cache_t2 == cnn.lorder - 1`
Returns:
torch.Tensor: output of current input xs,
with shape (b=1, chunk_size, hidden-dim).
torch.Tensor: new attention cache required for next chunk, with
dynamic shape (elayers, head, ?, d_k * 2)
depending on required_cache_size.
torch.Tensor: new conformer cnn cache required for next chunk, with
same shape as the original cnn_cache.
"""
assert xs.size(0) == 1
# tmp_masks is just for interface compatibility
tmp_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
tmp_masks = tmp_masks.unsqueeze(1)
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
# NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)
xs, pos_emb, _ = self.embed(xs, tmp_masks, offset)
# NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim)
elayers, cache_t1 = att_cache.size(0), att_cache.size(2)
chunk_size = xs.size(1)
attention_key_size = cache_t1 + chunk_size
pos_emb = self.embed.position_encoding(
offset=offset - cache_t1, size=attention_key_size
)
if required_cache_size < 0:
next_cache_start = 0
elif required_cache_size == 0:
next_cache_start = attention_key_size
else:
next_cache_start = max(attention_key_size - required_cache_size, 0)
r_att_cache = []
r_cnn_cache = []
for i, layer in enumerate(self.encoders):
# NOTE(xcsong): Before layer.forward
# shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2),
# shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2)
xs, _, new_att_cache, new_cnn_cache = layer(
xs,
att_mask,
pos_emb,
att_cache=att_cache[i : i + 1] if elayers > 0 else att_cache,
cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache,
)
# NOTE(xcsong): After layer.forward
# shape(new_att_cache) is (1, head, attention_key_size, d_k * 2),
# shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2)
r_att_cache.append(new_att_cache[:, :, next_cache_start:, :])
r_cnn_cache.append(new_cnn_cache.unsqueeze(0))
if self.normalize_before:
xs = self.after_norm(xs)
# NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),
# ? may be larger than cache_t1, it depends on required_cache_size
r_att_cache = torch.cat(r_att_cache, dim=0)
# NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
r_cnn_cache = torch.cat(r_cnn_cache, dim=0)
return (xs, r_att_cache, r_cnn_cache)
def forward_chunk_by_chunk(
self,
xs: torch.Tensor,
decoding_chunk_size: int,
num_decoding_left_chunks: int = -1,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Forward input chunk by chunk with chunk_size like a streaming
fashion
Here we should pay special attention to computation cache in the
streaming style forward chunk by chunk. Three things should be taken
into account for computation in the current network:
1. transformer/conformer encoder layers output cache
2. convolution in conformer
3. convolution in subsampling
However, we don't implement subsampling cache for:
1. We can control subsampling module to output the right result by
overlapping input instead of cache left context, even though it
wastes some computation, but subsampling only takes a very
small fraction of computation in the whole model.
2. Typically, there are several covolution layers with subsampling
in subsampling module, it is tricky and complicated to do cache
with different convolution layers with different subsampling
rate.
3. Currently, nn.Sequential is used to stack all the convolution
layers in subsampling, we need to rewrite it to make it work
with cache, which is not prefered.
Args:
xs (torch.Tensor): (1, max_len, dim)
chunk_size (int): decoding chunk size
"""
assert decoding_chunk_size > 0
# The model is trained by static or dynamic chunk
assert self.static_chunk_size > 0 or self.use_dynamic_chunk
subsampling = self.embed.subsampling_rate
context = self.embed.right_context + 1 # Add current frame
stride = subsampling * decoding_chunk_size
decoding_window = (decoding_chunk_size - 1) * subsampling + context
num_frames = xs.size(1)
att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
outputs = []
offset = 0
required_cache_size = decoding_chunk_size * num_decoding_left_chunks
# Feed forward overlap input step by step
for cur in range(0, num_frames - context + 1, stride):
end = min(cur + decoding_window, num_frames)
chunk_xs = xs[:, cur:end, :]
(y, att_cache, cnn_cache) = self.forward_chunk(
chunk_xs, offset, required_cache_size, att_cache, cnn_cache
)
outputs.append(y)
offset += y.size(1)
ys = torch.cat(outputs, 1)
masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool)
return ys, masks
class TransformerEncoder(BaseEncoder):
"""Transformer encoder module."""
def __init__(
self,
input_size: int,
output_size: int = 256,
attention_heads: int = 4,
linear_units: int = 2048,
num_blocks: int = 6,
dropout_rate: float = 0.1,
positional_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.0,
input_layer: str = "conv2d",
pos_enc_layer_type: str = "abs_pos",
normalize_before: bool = True,
static_chunk_size: int = 0,
use_dynamic_chunk: bool = False,
global_cmvn: torch.nn.Module = None,
use_dynamic_left_chunk: bool = False,
):
"""Construct TransformerEncoder
See Encoder for the meaning of each parameter.
"""
super().__init__(
input_size,
output_size,
attention_heads,
linear_units,
num_blocks,
dropout_rate,
positional_dropout_rate,
attention_dropout_rate,
input_layer,
pos_enc_layer_type,
normalize_before,
static_chunk_size,
use_dynamic_chunk,
global_cmvn,
use_dynamic_left_chunk,
)
self.encoders = torch.nn.ModuleList(
[
TransformerEncoderLayer(
output_size,
MultiHeadedAttention(
attention_heads, output_size, attention_dropout_rate
), | PositionwiseFeedForward(output_size, linear_units, dropout_rate), | 8 | 2023-11-15 09:19:27+00:00 | 24k |
BobaZooba/xllm | tests/unit/core/test_dependencies.py | [
{
"identifier": "LMCollator",
"path": "src/xllm/collators/lm.py",
"snippet": "class LMCollator(BaseCollator):\n \"\"\"\n `LMCollator` is a data collator class specifically designed to prepare batches of data for language modeling tasks.\n Extending the `BaseCollator`, it adapts the general data... | import pytest
from peft import PeftModel
from pytest import MonkeyPatch
from torch import Tensor
from transformers import (
BitsAndBytesConfig,
GPTQConfig,
PreTrainedTokenizer,
TrainingArguments,
)
from src.xllm.collators.lm import LMCollator
from src.xllm.collators.registry import collators_registry
from src.xllm.core.config import Config
from src.xllm.core.dependencies import (
build_collator,
build_dataset,
build_model,
build_quantization_config,
build_tokenizer,
build_trainer,
build_training_arguments,
)
from src.xllm.datasets.registry import datasets_registry
from src.xllm.datasets.soda import SodaDataset
from src.xllm.trainers.registry import trainers_registry
from tests.helpers.constants import LLAMA_TOKENIZER_DIR
from tests.helpers.dummy_data import DATA, DummyDataset
from tests.helpers.patches import patch_from_pretrained_auto_causal_lm | 19,271 |
def test_build_training_arguments(config: Config):
arguments = build_training_arguments(config=config)
assert arguments.per_device_train_batch_size == config.per_device_train_batch_size
assert arguments.deepspeed is None
def test_build_dataset_train(path_to_train_dummy_data: str):
datasets_registry.add(key="dummy", value=DummyDataset)
config = Config(dataset_key="dummy", train_local_path_to_data=path_to_train_dummy_data)
dataset = build_dataset(config=config, is_train=True)
assert dataset[0] is not None
def test_build_dataset_eval(path_to_train_dummy_data: str):
datasets_registry.add(key="dummy1", value=DummyDataset)
config = Config(dataset_key="dummy1", eval_local_path_to_data=path_to_train_dummy_data)
dataset = build_dataset(config=config, is_train=False)
assert dataset[0] is not None
def test_build_dataset_eval_none(path_to_train_dummy_data: str):
datasets_registry.add(key="dummy2", value=DummyDataset)
config = Config(
dataset_key="dummy2",
train_local_path_to_data=path_to_train_dummy_data,
eval_local_path_to_data=None,
)
dataset = build_dataset(config=config, is_train=False)
assert dataset is None
def test_build_dataset_exception(path_to_train_dummy_data: str):
datasets_registry.add(key="exc", value=Config)
config = Config(dataset_key="exc", train_local_path_to_data=path_to_train_dummy_data)
with pytest.raises(ValueError):
build_dataset(config=config, is_train=True)
def test_build_tokenizer():
config = Config(tokenizer_name_or_path=LLAMA_TOKENIZER_DIR)
tokenizer = build_tokenizer(config=config)
tokenizer("hello")
def test_build_tokenizer_use_fast():
config = Config(tokenizer_name_or_path=LLAMA_TOKENIZER_DIR)
tokenizer = build_tokenizer(config=config, use_fast=False)
tokenizer("hello")
def test_build_tokenizer_padding_size():
config = Config(tokenizer_name_or_path=LLAMA_TOKENIZER_DIR, tokenizer_padding_side="right")
tokenizer = build_tokenizer(config=config)
tokenizer("hello")
def test_build_collator(config: Config, llama_tokenizer: PreTrainedTokenizer):
collator = build_collator(config=config, tokenizer=llama_tokenizer)
batch = collator(DATA)
for value in batch.values():
assert isinstance(value, Tensor)
def test_build_collator_exception(llama_tokenizer: PreTrainedTokenizer):
collators_registry.add(key="exc", value=Config)
config = Config(collator_key="exc")
with pytest.raises(ValueError):
_ = build_collator(config=config, tokenizer=llama_tokenizer)
def test_build_quantization_config_bnb():
config = Config(load_in_8bit=True)
quantization_config = build_quantization_config(config=config)
assert isinstance(quantization_config, BitsAndBytesConfig)
assert quantization_config.load_in_8bit
def test_build_quantization_config_gptq():
config = Config(gptq_bits=4, gptq_group_size=128, from_gptq=True)
quantization_config = build_quantization_config(config=config)
assert isinstance(quantization_config, GPTQConfig)
assert quantization_config.bits == 4
assert quantization_config.group_size == 128
def test_build_quantization_config_none():
config = Config(from_gptq=False, load_in_4bit=False, load_in_8bit=False)
quantization_config = build_quantization_config(config=config)
assert quantization_config is None
@pytest.mark.parametrize("apply_lora", [False, True])
def test_build_model(monkeypatch: MonkeyPatch, apply_lora: bool):
config = Config(apply_lora=apply_lora)
with patch_from_pretrained_auto_causal_lm(monkeypatch=monkeypatch):
_ = build_model(
config=config,
quantization_config=None,
)
def test_build_model_bnb_after_init(monkeypatch: MonkeyPatch):
config = Config(bnb_quantize_after_model_init=True)
with patch_from_pretrained_auto_causal_lm(monkeypatch=monkeypatch):
_ = build_model(
config=config,
quantization_config=None,
)
def test_build_trainer(
config: Config,
training_arguments: TrainingArguments,
llama_lora_model: PeftModel,
soda_dataset: SodaDataset,
llama_lm_collator: LMCollator,
):
| # Copyright 2023 Boris Zubarev. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
def test_build_training_arguments(config: Config):
arguments = build_training_arguments(config=config)
assert arguments.per_device_train_batch_size == config.per_device_train_batch_size
assert arguments.deepspeed is None
def test_build_dataset_train(path_to_train_dummy_data: str):
datasets_registry.add(key="dummy", value=DummyDataset)
config = Config(dataset_key="dummy", train_local_path_to_data=path_to_train_dummy_data)
dataset = build_dataset(config=config, is_train=True)
assert dataset[0] is not None
def test_build_dataset_eval(path_to_train_dummy_data: str):
datasets_registry.add(key="dummy1", value=DummyDataset)
config = Config(dataset_key="dummy1", eval_local_path_to_data=path_to_train_dummy_data)
dataset = build_dataset(config=config, is_train=False)
assert dataset[0] is not None
def test_build_dataset_eval_none(path_to_train_dummy_data: str):
datasets_registry.add(key="dummy2", value=DummyDataset)
config = Config(
dataset_key="dummy2",
train_local_path_to_data=path_to_train_dummy_data,
eval_local_path_to_data=None,
)
dataset = build_dataset(config=config, is_train=False)
assert dataset is None
def test_build_dataset_exception(path_to_train_dummy_data: str):
datasets_registry.add(key="exc", value=Config)
config = Config(dataset_key="exc", train_local_path_to_data=path_to_train_dummy_data)
with pytest.raises(ValueError):
build_dataset(config=config, is_train=True)
def test_build_tokenizer():
config = Config(tokenizer_name_or_path=LLAMA_TOKENIZER_DIR)
tokenizer = build_tokenizer(config=config)
tokenizer("hello")
def test_build_tokenizer_use_fast():
config = Config(tokenizer_name_or_path=LLAMA_TOKENIZER_DIR)
tokenizer = build_tokenizer(config=config, use_fast=False)
tokenizer("hello")
def test_build_tokenizer_padding_size():
config = Config(tokenizer_name_or_path=LLAMA_TOKENIZER_DIR, tokenizer_padding_side="right")
tokenizer = build_tokenizer(config=config)
tokenizer("hello")
def test_build_collator(config: Config, llama_tokenizer: PreTrainedTokenizer):
collator = build_collator(config=config, tokenizer=llama_tokenizer)
batch = collator(DATA)
for value in batch.values():
assert isinstance(value, Tensor)
def test_build_collator_exception(llama_tokenizer: PreTrainedTokenizer):
collators_registry.add(key="exc", value=Config)
config = Config(collator_key="exc")
with pytest.raises(ValueError):
_ = build_collator(config=config, tokenizer=llama_tokenizer)
def test_build_quantization_config_bnb():
config = Config(load_in_8bit=True)
quantization_config = build_quantization_config(config=config)
assert isinstance(quantization_config, BitsAndBytesConfig)
assert quantization_config.load_in_8bit
def test_build_quantization_config_gptq():
config = Config(gptq_bits=4, gptq_group_size=128, from_gptq=True)
quantization_config = build_quantization_config(config=config)
assert isinstance(quantization_config, GPTQConfig)
assert quantization_config.bits == 4
assert quantization_config.group_size == 128
def test_build_quantization_config_none():
config = Config(from_gptq=False, load_in_4bit=False, load_in_8bit=False)
quantization_config = build_quantization_config(config=config)
assert quantization_config is None
@pytest.mark.parametrize("apply_lora", [False, True])
def test_build_model(monkeypatch: MonkeyPatch, apply_lora: bool):
config = Config(apply_lora=apply_lora)
with patch_from_pretrained_auto_causal_lm(monkeypatch=monkeypatch):
_ = build_model(
config=config,
quantization_config=None,
)
def test_build_model_bnb_after_init(monkeypatch: MonkeyPatch):
config = Config(bnb_quantize_after_model_init=True)
with patch_from_pretrained_auto_causal_lm(monkeypatch=monkeypatch):
_ = build_model(
config=config,
quantization_config=None,
)
def test_build_trainer(
config: Config,
training_arguments: TrainingArguments,
llama_lora_model: PeftModel,
soda_dataset: SodaDataset,
llama_lm_collator: LMCollator,
): | trainer = build_trainer( | 8 | 2023-11-10 17:55:03+00:00 | 24k |
AMAAI-Lab/mustango | diffusers/src/diffusers/models/unet_2d_condition_flax.py | [
{
"identifier": "ConfigMixin",
"path": "diffusers/src/diffusers/configuration_utils.py",
"snippet": "class ConfigMixin:\n r\"\"\"\n Base class for all configuration classes. Stores all configuration parameters under `self.config` Also handles all\n methods for loading/downloading/saving classes... | from typing import Tuple, Union
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_2d_blocks_flax import (
FlaxCrossAttnDownBlock2D,
FlaxCrossAttnUpBlock2D,
FlaxDownBlock2D,
FlaxUNetMidBlock2DCrossAttn,
FlaxUpBlock2D,
)
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp | 17,317 | # http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
@flax.struct.dataclass
class FlaxUNet2DConditionOutput(BaseOutput):
"""
Args:
sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
sample: jnp.ndarray
@flax_register_to_config
class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
r"""
FlaxUNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a
timestep and returns sample shaped output.
This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the models (such as downloading or saving, etc.)
Also, this model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
sample_size (`int`, *optional*):
The size of the input sample.
in_channels (`int`, *optional*, defaults to 4):
The number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4):
The number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use. The corresponding class names will be: "FlaxCrossAttnDownBlock2D",
"FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D"
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
The tuple of upsample blocks to use. The corresponding class names will be: "FlaxUpBlock2D",
"FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D"
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2):
The number of layers per block.
attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8):
The dimension of the attention heads.
cross_attention_dim (`int`, *optional*, defaults to 768):
The dimension of the cross attention features.
dropout (`float`, *optional*, defaults to 0):
Dropout probability for down, up and bottleneck blocks.
flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
"""
sample_size: int = 32
in_channels: int = 4
out_channels: int = 4
down_block_types: Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
only_cross_attention: Union[bool, Tuple[bool]] = False
block_out_channels: Tuple[int] = (320, 640, 1280, 1280)
layers_per_block: int = 2
attention_head_dim: Union[int, Tuple[int]] = 8
cross_attention_dim: int = 1280
dropout: float = 0.0
use_linear_projection: bool = False
dtype: jnp.dtype = jnp.float32
flip_sin_to_cos: bool = True
freq_shift: int = 0
def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict:
# init input tensors
sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
sample = jnp.zeros(sample_shape, dtype=jnp.float32)
timesteps = jnp.ones((1,), dtype=jnp.int32)
encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
return self.init(rngs, sample, timesteps, encoder_hidden_states)["params"]
def setup(self):
block_out_channels = self.block_out_channels
time_embed_dim = block_out_channels[0] * 4
# input
self.conv_in = nn.Conv(
block_out_channels[0],
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
# time
self.time_proj = FlaxTimesteps(
block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift
)
| # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
@flax.struct.dataclass
class FlaxUNet2DConditionOutput(BaseOutput):
"""
Args:
sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
sample: jnp.ndarray
@flax_register_to_config
class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
r"""
FlaxUNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a
timestep and returns sample shaped output.
This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the models (such as downloading or saving, etc.)
Also, this model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
sample_size (`int`, *optional*):
The size of the input sample.
in_channels (`int`, *optional*, defaults to 4):
The number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4):
The number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use. The corresponding class names will be: "FlaxCrossAttnDownBlock2D",
"FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D"
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
The tuple of upsample blocks to use. The corresponding class names will be: "FlaxUpBlock2D",
"FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D"
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2):
The number of layers per block.
attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8):
The dimension of the attention heads.
cross_attention_dim (`int`, *optional*, defaults to 768):
The dimension of the cross attention features.
dropout (`float`, *optional*, defaults to 0):
Dropout probability for down, up and bottleneck blocks.
flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
Whether to flip the sin to cos in the time embedding.
freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
"""
sample_size: int = 32
in_channels: int = 4
out_channels: int = 4
down_block_types: Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
only_cross_attention: Union[bool, Tuple[bool]] = False
block_out_channels: Tuple[int] = (320, 640, 1280, 1280)
layers_per_block: int = 2
attention_head_dim: Union[int, Tuple[int]] = 8
cross_attention_dim: int = 1280
dropout: float = 0.0
use_linear_projection: bool = False
dtype: jnp.dtype = jnp.float32
flip_sin_to_cos: bool = True
freq_shift: int = 0
def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict:
# init input tensors
sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
sample = jnp.zeros(sample_shape, dtype=jnp.float32)
timesteps = jnp.ones((1,), dtype=jnp.int32)
encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=jnp.float32)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
return self.init(rngs, sample, timesteps, encoder_hidden_states)["params"]
def setup(self):
block_out_channels = self.block_out_channels
time_embed_dim = block_out_channels[0] * 4
# input
self.conv_in = nn.Conv(
block_out_channels[0],
kernel_size=(3, 3),
strides=(1, 1),
padding=((1, 1), (1, 1)),
dtype=self.dtype,
)
# time
self.time_proj = FlaxTimesteps(
block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift
) | self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype) | 3 | 2023-11-14 23:29:31+00:00 | 24k |
BraveGroup/Drive-WM | src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py | [
{
"identifier": "logging",
"path": "src/diffusers/utils/logging.py",
"snippet": "def _get_default_logging_level() -> int:\ndef _get_library_name() -> str:\ndef _get_library_root_logger() -> logging.Logger:\ndef _configure_library_root_logger() -> None:\ndef _reset_library_root_logger() -> None:\ndef get... | from typing import List, Optional, Tuple, Union
from ...utils import logging
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
import torch | 20,873 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class DanceDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for audio generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
unet ([`UNet1DModel`]):
A `UNet1DModel` to denoise the encoded audio.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
[`IPNDMScheduler`].
"""
model_cpu_offload_seq = "unet"
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
num_inference_steps: int = 100,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
audio_length_in_s: Optional[float] = None,
return_dict: bool = True,
) -> Union[AudioPipelineOutput, Tuple]:
r"""
The call function to the pipeline for generation.
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of audio samples to generate.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher-quality audio sample at
the expense of slower inference.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
audio_length_in_s (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`):
The length of the generated audio sample in seconds.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
Example:
```py
from diffusers import DiffusionPipeline
from scipy.io.wavfile import write
model_id = "harmonai/maestro-150k"
pipe = DiffusionPipeline.from_pretrained(model_id)
pipe = pipe.to("cuda")
audios = pipe(audio_length_in_s=4.0).audios
# To save locally
for i, audio in enumerate(audios):
write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose())
# To dislay in google colab
import IPython.display as ipd
for audio in audios:
display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
```
Returns:
[`~pipelines.AudioPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated audio.
"""
if audio_length_in_s is None:
audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate
sample_size = audio_length_in_s * self.unet.config.sample_rate
down_scale_factor = 2 ** len(self.unet.up_blocks)
if sample_size < 3 * down_scale_factor:
raise ValueError(
f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
f" {3 * down_scale_factor / self.unet.config.sample_rate}."
)
original_sample_size = int(sample_size)
if sample_size % down_scale_factor != 0:
sample_size = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
" process."
)
sample_size = int(sample_size)
dtype = next(self.unet.parameters()).dtype
shape = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
| # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class DanceDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for audio generation.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Parameters:
unet ([`UNet1DModel`]):
A `UNet1DModel` to denoise the encoded audio.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
[`IPNDMScheduler`].
"""
model_cpu_offload_seq = "unet"
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
batch_size: int = 1,
num_inference_steps: int = 100,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
audio_length_in_s: Optional[float] = None,
return_dict: bool = True,
) -> Union[AudioPipelineOutput, Tuple]:
r"""
The call function to the pipeline for generation.
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of audio samples to generate.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher-quality audio sample at
the expense of slower inference.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
audio_length_in_s (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`):
The length of the generated audio sample in seconds.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple.
Example:
```py
from diffusers import DiffusionPipeline
from scipy.io.wavfile import write
model_id = "harmonai/maestro-150k"
pipe = DiffusionPipeline.from_pretrained(model_id)
pipe = pipe.to("cuda")
audios = pipe(audio_length_in_s=4.0).audios
# To save locally
for i, audio in enumerate(audios):
write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose())
# To dislay in google colab
import IPython.display as ipd
for audio in audios:
display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
```
Returns:
[`~pipelines.AudioPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated audio.
"""
if audio_length_in_s is None:
audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate
sample_size = audio_length_in_s * self.unet.config.sample_rate
down_scale_factor = 2 ** len(self.unet.up_blocks)
if sample_size < 3 * down_scale_factor:
raise ValueError(
f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
f" {3 * down_scale_factor / self.unet.config.sample_rate}."
)
original_sample_size = int(sample_size)
if sample_size % down_scale_factor != 0:
sample_size = (
(audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
) * down_scale_factor
logger.info(
f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
" process."
)
sample_size = int(sample_size)
dtype = next(self.unet.parameters()).dtype
shape = (batch_size, self.unet.config.in_channels, sample_size)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
| audio = randn_tensor(shape, generator=generator, device=self._execution_device, dtype=dtype) | 1 | 2023-11-18 01:40:55+00:00 | 24k |
wjun0830/CGDETR | cg_detr/train.py | [
{
"identifier": "BaseOptions",
"path": "cg_detr/config.py",
"snippet": "class BaseOptions(object):\n saved_option_filename = \"opt.json\"\n ckpt_filename = \"model.ckpt\"\n tensorboard_log_dir = \"tensorboard_log\"\n train_log_filename = \"train.log.txt\"\n eval_log_filename = \"eval.log.... | import os
import time
import json
import pprint
import random
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import logging
import sys
from tqdm import tqdm, trange
from collections import defaultdict
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from cg_detr.config import BaseOptions
from cg_detr.start_end_dataset import \
StartEndDataset, start_end_collate, prepare_batch_inputs
from cg_detr.inference import eval_epoch, start_inference, setup_model
from utils.basic_utils import AverageMeter, dict_to_markdown
from utils.model_utils import count_parameters | 14,870 | es_cnt = 0
prev_best_score = stop_score
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"epoch": epoch_i,
"opt": opt
}
torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
for src, tgt in zip(latest_file_paths, best_file_paths):
os.renames(src, tgt)
logger.info("The checkpoint file has been updated.")
else:
es_cnt += 1
if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
with open(opt.train_log_filepath, "a") as f:
f.write(f"Early Stop at epoch {epoch_i}")
logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
break
# save ckpt
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"epoch": epoch_i,
"opt": opt
}
torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
save_interval = 10 if "subs_train" in opt.train_path else 50 # smaller for pretrain
if (epoch_i + 1) % save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"epoch": epoch_i,
"opt": opt
}
torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
if opt.debug:
break
tb_writer.close()
def start_training():
logger.info("Setup config, data and model...")
opt = BaseOptions().parse()
set_seed(opt.seed)
if opt.debug: # keep the model run deterministically
# 'cudnn.benchmark = True' enabled auto finding the best algorithm for a specific input/net config.
# Enable this only when input size is fixed.
cudnn.benchmark = False
cudnn.deterministic = True
dataset_config = dict(
dset_name=opt.dset_name,
data_path=opt.train_path,
v_feat_dirs=opt.v_feat_dirs,
q_feat_dir=opt.t_feat_dir,
q_feat_type="last_hidden_state",
max_q_l=opt.max_q_l,
max_v_l=opt.max_v_l,
ctx_mode=opt.ctx_mode,
data_ratio=opt.data_ratio,
normalize_v=not opt.no_norm_vfeat,
normalize_t=not opt.no_norm_tfeat,
clip_len=opt.clip_length,
max_windows=opt.max_windows,
span_loss_type=opt.span_loss_type,
txt_drop_ratio=opt.txt_drop_ratio,
dset_domain=opt.dset_domain,
)
dataset_config["data_path"] = opt.train_path
train_dataset = StartEndDataset(**dataset_config)
if opt.eval_path is not None:
dataset_config["data_path"] = opt.eval_path
dataset_config["txt_drop_ratio"] = 0
dataset_config["q_feat_dir"] = opt.t_feat_dir.replace("sub_features", "text_features") # for pretraining
# dataset_config["load_labels"] = False # uncomment to calculate eval loss
eval_dataset = StartEndDataset(**dataset_config)
else:
eval_dataset = None
model, criterion, optimizer, lr_scheduler = setup_model(opt)
logger.info(f"Model {model}")
count_parameters(model)
logger.info("Start Training...")
# For tvsum dataset, use train_hl function
if opt.dset_name in ['tvsum', 'youtube_uni']:
train_hl(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
else:
train(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
return opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"), opt.eval_split_name, opt.eval_path, opt.debug, opt
if __name__ == '__main__':
best_ckpt_path, eval_split_name, eval_path, debug, opt = start_training()
if not debug:
input_args = ["--resume", best_ckpt_path,
"--eval_split_name", eval_split_name,
"--eval_path", eval_path]
sys.argv[1:] = input_args
logger.info("\n\n\nFINISHED TRAINING!!!")
logger.info("Evaluating model at {}".format(best_ckpt_path))
logger.info("Input args {}".format(sys.argv[1:]))
|
logger = logging.getLogger(__name__)
logging.basicConfig(format="%(asctime)s.%(msecs)03d:%(levelname)s:%(name)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=logging.INFO)
def set_seed(seed, use_cuda=True):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed_all(seed)
def train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer):
logger.info(f"[Epoch {epoch_i+1}]")
model.train()
criterion.train()
# init meters
time_meters = defaultdict(AverageMeter)
loss_meters = defaultdict(AverageMeter)
num_training_examples = len(train_loader)
timer_dataloading = time.time()
for batch_idx, batch in tqdm(enumerate(train_loader),
desc="Training Iteration",
total=num_training_examples):
time_meters["dataloading_time"].update(time.time() - timer_dataloading)
timer_start = time.time()
model_inputs, targets = prepare_batch_inputs(batch[1], opt.device, non_blocking=opt.pin_memory)
time_meters["prepare_inputs_time"].update(time.time() - timer_start)
timer_start = time.time()
outputs = model(**model_inputs, targets=targets)
loss_dict = criterion(outputs, targets)
weight_dict = criterion.weight_dict
losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
time_meters["model_forward_time"].update(time.time() - timer_start)
timer_start = time.time()
optimizer.zero_grad()
losses.backward()
if opt.grad_clip > 0:
nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
optimizer.step()
time_meters["model_backward_time"].update(time.time() - timer_start)
loss_dict["loss_overall"] = float(losses) # for logging only
for k, v in loss_dict.items():
loss_meters[k].update(float(v) * weight_dict[k] if k in weight_dict else float(v))
timer_dataloading = time.time()
if opt.debug and batch_idx == 3:
break
# print/add logs
tb_writer.add_scalar("Train/lr", float(optimizer.param_groups[0]["lr"]), epoch_i+1)
for k, v in loss_meters.items():
tb_writer.add_scalar("Train/{}".format(k), v.avg, epoch_i+1)
to_write = opt.train_log_txt_formatter.format(
time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
epoch=epoch_i+1,
loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in loss_meters.items()]))
with open(opt.train_log_filepath, "a") as f:
f.write(to_write)
logger.info("Epoch time stats:")
for name, meter in time_meters.items():
d = {k: f"{getattr(meter, k):.4f}" for k in ["max", "min", "avg"]}
logger.info(f"{name} ==> {d}")
def train(model, criterion, optimizer, lr_scheduler, train_dataset, val_dataset, opt):
if opt.device.type == "cuda":
logger.info("CUDA enabled.")
model.to(opt.device)
tb_writer = SummaryWriter(opt.tensorboard_log_dir)
tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
train_loader = DataLoader(
train_dataset,
collate_fn=start_end_collate,
batch_size=opt.bsz,
num_workers=opt.num_workers,
shuffle=True,
pin_memory=opt.pin_memory
)
prev_best_score = 0.
es_cnt = 0
# start_epoch = 0
if opt.start_epoch is None:
start_epoch = -1 if opt.eval_untrained else 0
else:
start_epoch = opt.start_epoch
save_submission_filename = "latest_{}_{}_preds.jsonl".format(opt.dset_name, opt.eval_split_name)
for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
if epoch_i > -1:
train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer)
lr_scheduler.step()
eval_epoch_interval = opt.eval_epoch
if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
with torch.no_grad():
metrics_no_nms, metrics_nms, eval_loss_meters, latest_file_paths = \
eval_epoch(model, val_dataset, opt, save_submission_filename, epoch_i, criterion, tb_writer)
# log
to_write = opt.eval_log_txt_formatter.format(
time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
epoch=epoch_i,
loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in eval_loss_meters.items()]),
eval_metrics_str=json.dumps(metrics_no_nms))
with open(opt.eval_log_filepath, "a") as f:
f.write(to_write)
logger.info("metrics_no_nms {}".format(pprint.pformat(metrics_no_nms["brief"], indent=4)))
if metrics_nms is not None:
logger.info("metrics_nms {}".format(pprint.pformat(metrics_nms["brief"], indent=4)))
metrics = metrics_no_nms
for k, v in metrics["brief"].items():
tb_writer.add_scalar(f"Eval/{k}", float(v), epoch_i+1)
if opt.dset_name in ['hl']:
stop_score = metrics["brief"]["MR-full-mAP"]
else:
stop_score = (metrics["brief"]["MR-full-R1@0.7"] + metrics["brief"]["MR-full-R1@0.5"]) / 2
if stop_score > prev_best_score:
es_cnt = 0
prev_best_score = stop_score
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"epoch": epoch_i,
"opt": opt
}
torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
for src, tgt in zip(latest_file_paths, best_file_paths):
os.renames(src, tgt)
logger.info("The checkpoint file has been updated.")
else:
es_cnt += 1
if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
with open(opt.train_log_filepath, "a") as f:
f.write(f"Early Stop at epoch {epoch_i}")
logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
break
# save ckpt
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"epoch": epoch_i,
"opt": opt
}
torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
# save_interval = 10 if "subs_train" in opt.train_path else 50 # smaller for pretrain
# if (epoch_i + 1) % save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
# checkpoint = {
# "model": model.state_dict(),
# "optimizer": optimizer.state_dict(),
# "epoch": epoch_i,
# "opt": opt
# }
# torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
if opt.debug:
break
tb_writer.close()
def train_hl(model, criterion, optimizer, lr_scheduler, train_dataset, val_dataset, opt):
if opt.device.type == "cuda":
logger.info("CUDA enabled.")
model.to(opt.device)
tb_writer = SummaryWriter(opt.tensorboard_log_dir)
tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
train_loader = DataLoader(
train_dataset,
collate_fn=start_end_collate,
batch_size=opt.bsz,
num_workers=opt.num_workers,
shuffle=True,
pin_memory=opt.pin_memory
)
prev_best_score = 0.
es_cnt = 0
# start_epoch = 0
if opt.start_epoch is None:
start_epoch = -1 if opt.eval_untrained else 0
else:
start_epoch = opt.start_epoch
save_submission_filename = "latest_{}_{}_preds.jsonl".format(opt.dset_name, opt.eval_split_name)
for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
if epoch_i > -1:
train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer)
lr_scheduler.step()
eval_epoch_interval = 5
if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
with torch.no_grad():
metrics_no_nms, metrics_nms, eval_loss_meters, latest_file_paths = \
eval_epoch(model, val_dataset, opt, save_submission_filename, epoch_i, criterion, tb_writer)
# log
to_write = opt.eval_log_txt_formatter.format(
time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
epoch=epoch_i,
loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in eval_loss_meters.items()]),
eval_metrics_str=json.dumps(metrics_no_nms))
with open(opt.eval_log_filepath, "a") as f:
f.write(to_write)
logger.info("metrics_no_nms {}".format(pprint.pformat(metrics_no_nms["brief"], indent=4)))
if metrics_nms is not None:
logger.info("metrics_nms {}".format(pprint.pformat(metrics_nms["brief"], indent=4)))
metrics = metrics_no_nms
for k, v in metrics["brief"].items():
tb_writer.add_scalar(f"Eval/{k}", float(v), epoch_i+1)
# stop_score = metrics["brief"]["MR-full-mAP"]
stop_score = metrics["brief"]["mAP"]
if stop_score > prev_best_score:
es_cnt = 0
prev_best_score = stop_score
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"epoch": epoch_i,
"opt": opt
}
torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
for src, tgt in zip(latest_file_paths, best_file_paths):
os.renames(src, tgt)
logger.info("The checkpoint file has been updated.")
else:
es_cnt += 1
if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
with open(opt.train_log_filepath, "a") as f:
f.write(f"Early Stop at epoch {epoch_i}")
logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
break
# save ckpt
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"epoch": epoch_i,
"opt": opt
}
torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
save_interval = 10 if "subs_train" in opt.train_path else 50 # smaller for pretrain
if (epoch_i + 1) % save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
checkpoint = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"epoch": epoch_i,
"opt": opt
}
torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
if opt.debug:
break
tb_writer.close()
def start_training():
logger.info("Setup config, data and model...")
opt = BaseOptions().parse()
set_seed(opt.seed)
if opt.debug: # keep the model run deterministically
# 'cudnn.benchmark = True' enabled auto finding the best algorithm for a specific input/net config.
# Enable this only when input size is fixed.
cudnn.benchmark = False
cudnn.deterministic = True
dataset_config = dict(
dset_name=opt.dset_name,
data_path=opt.train_path,
v_feat_dirs=opt.v_feat_dirs,
q_feat_dir=opt.t_feat_dir,
q_feat_type="last_hidden_state",
max_q_l=opt.max_q_l,
max_v_l=opt.max_v_l,
ctx_mode=opt.ctx_mode,
data_ratio=opt.data_ratio,
normalize_v=not opt.no_norm_vfeat,
normalize_t=not opt.no_norm_tfeat,
clip_len=opt.clip_length,
max_windows=opt.max_windows,
span_loss_type=opt.span_loss_type,
txt_drop_ratio=opt.txt_drop_ratio,
dset_domain=opt.dset_domain,
)
dataset_config["data_path"] = opt.train_path
train_dataset = StartEndDataset(**dataset_config)
if opt.eval_path is not None:
dataset_config["data_path"] = opt.eval_path
dataset_config["txt_drop_ratio"] = 0
dataset_config["q_feat_dir"] = opt.t_feat_dir.replace("sub_features", "text_features") # for pretraining
# dataset_config["load_labels"] = False # uncomment to calculate eval loss
eval_dataset = StartEndDataset(**dataset_config)
else:
eval_dataset = None
model, criterion, optimizer, lr_scheduler = setup_model(opt)
logger.info(f"Model {model}")
count_parameters(model)
logger.info("Start Training...")
# For tvsum dataset, use train_hl function
if opt.dset_name in ['tvsum', 'youtube_uni']:
train_hl(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
else:
train(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
return opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"), opt.eval_split_name, opt.eval_path, opt.debug, opt
if __name__ == '__main__':
best_ckpt_path, eval_split_name, eval_path, debug, opt = start_training()
if not debug:
input_args = ["--resume", best_ckpt_path,
"--eval_split_name", eval_split_name,
"--eval_path", eval_path]
sys.argv[1:] = input_args
logger.info("\n\n\nFINISHED TRAINING!!!")
logger.info("Evaluating model at {}".format(best_ckpt_path))
logger.info("Input args {}".format(sys.argv[1:])) | start_inference(opt) | 5 | 2023-11-10 12:45:25+00:00 | 24k |
ej0cl6/TextEE | TextEE/models/OneIE/E2Etrainer.py | [
{
"identifier": "BasicTrainer",
"path": "TextEE/models/trainer.py",
"snippet": "class BasicTrainer(object):\n def __init__(self, config, type_set=None):\n self.config = config\n self.type_set = type_set\n \n @classmethod\n def add_extra_info_fn(cls, instances, raw_data, con... | import os, sys, logging, tqdm, pprint, copy
import torch
import numpy as np
import ipdb
from transformers import (BertTokenizer, RobertaTokenizer, XLMRobertaTokenizer,
AutoTokenizer, AdamW, get_linear_schedule_with_warmup)
from torch.utils.data import DataLoader
from torch.optim import AdamW
from ..trainer import BasicTrainer
from .E2Emodel import OneIEE2EModel
from .data import IEDataset
from .util import generate_vocabs, load_valid_patterns, save_result, best_score_by_task
from .scorer import score_graphs
from scorer import compute_f1, print_scores | 16,322 | def load_model_(self, checkpoint=None):
assert self.tokenizer
if checkpoint:
logger.info(f"Loading model from {checkpoint}")
state = torch.load(os.path.join(checkpoint, "best_model.state"), map_location=f'cuda:{self.config.gpu_device}')
self.vocabs = state["vocabs"]
self.type_set = state["type_set"]
self.valid_patterns = state["valid_patterns"]
self.model = OneIEE2EModel(self.config, self.vocabs, self.valid_patterns)
self.model.load_state_dict(state['model'])
self.model.cuda(device=self.config.gpu_device)
else:
self.valid_patterns = load_valid_patterns(self.config.valid_pattern_path, self.vocabs)
self.model = OneIEE2EModel(self.config, self.vocabs, self.valid_patterns)
self.model.cuda(device=self.config.gpu_device)
def load_model(self, checkpoint=None):
self.load_tokenizer_(checkpoint=checkpoint)
self.load_model_(checkpoint=checkpoint)
def train(self, train_data, dev_data, **kwargs):
self.load_tokenizer_()
train_set = IEDataset(train_data, self.tokenizer,
max_length=self.config.max_length,
gpu=True,
relation_mask_self=self.config.relation_mask_self,
relation_directional=self.config.relation_directional,
symmetric_relations=self.config.symmetric_relations,
test=False
)
dev_set = IEDataset(dev_data, self.tokenizer,
max_length=self.config.max_length,
gpu=True,
relation_mask_self=self.config.relation_mask_self,
relation_directional=self.config.relation_directional,
symmetric_relations=self.config.symmetric_relations,
test=False
)
self.vocabs = generate_vocabs([train_set, dev_set])
train_set.numberize(self.tokenizer, self.vocabs)
dev_set.numberize(self.tokenizer, self.vocabs)
self.load_model_()
batch_num = len(train_set) // self.config.batch_size + (len(train_set) % self.config.batch_size != 0)
dev_batch_num = len(dev_set) // self.config.eval_batch_size + (len(dev_set) % self.config.eval_batch_size != 0)
param_groups = [
{
'params': [p for n, p in self.model.named_parameters() if n.startswith('bert')],
'lr': self.config.bert_learning_rate, 'weight_decay': self.config.bert_weight_decay
},
{
'params': [p for n, p in self.model.named_parameters() if not n.startswith('bert')
and 'crf' not in n and 'global_feature' not in n],
'lr': self.config.learning_rate, 'weight_decay': self.config.weight_decay
},
{
'params': [p for n, p in self.model.named_parameters() if not n.startswith('bert')
and ('crf' in n or 'global_feature' in n)],
'lr': self.config.learning_rate, 'weight_decay': 0
}
]
optimizer = AdamW(params=param_groups)
schedule = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=batch_num*self.config.warmup_epoch,
num_training_steps=batch_num*self.config.max_epoch)
best_scores = {self.config.target_task: {"f": 0.0}}
best_epoch = -1
target_task = self.config.target_task
logger.info('================Start Training================')
for epoch in range(self.config.max_epoch):
logger.info('Epoch: {}'.format(epoch))
# training step
progress = tqdm.tqdm(total=batch_num, ncols=75,
desc='Train {}'.format(epoch))
optimizer.zero_grad()
cummulate_loss = 0.
for batch_idx, batch in enumerate(DataLoader(
train_set, batch_size=self.config.batch_size // self.config.accumulate_step,
shuffle=True, drop_last=False, collate_fn=train_set.collate_fn)):
loss = self.model(batch)
loss = loss * (1 / self.config.accumulate_step)
cummulate_loss += loss
loss.backward()
if (batch_idx + 1) % self.config.accumulate_step == 0:
progress.update(1)
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.config.grad_clipping)
optimizer.step()
schedule.step()
optimizer.zero_grad()
progress.close()
logger.info({"average training loss": (cummulate_loss / batch_idx).data})
# dev set
progress = tqdm.tqdm(total=dev_batch_num, ncols=75,
desc='Dev {}'.format(epoch))
best_dev_role_model = False
dev_gold_graphs, dev_pred_graphs, dev_tokens, dev_wnd_ids = [], [], [], []
for batch in DataLoader(dev_set, batch_size=self.config.eval_batch_size,
shuffle=False, collate_fn=dev_set.collate_fn):
progress.update(1)
graphs = self.model.predict(batch, gold_tri=False)
gold_graph = copy.deepcopy(batch.graphs)
for graph in graphs:
graph.clean(relation_directional=self.config.relation_directional,
symmetric_relations=self.config.symmetric_relations)
dev_gold_graphs.extend(gold_graph)
dev_pred_graphs.extend(graphs)
dev_tokens.extend(batch.tokens)
dev_wnd_ids.extend(batch.wnd_ids)
progress.close()
|
logger = logging.getLogger(__name__)
class OneIEE2ETrainer(BasicTrainer):
def __init__(self, config, type_set=None):
super().__init__(config, type_set)
self.tokenizer = None
self.model = None
self.valid_patterns = None
@classmethod
def add_extra_info_fn(cls, instances, raw_data, config):
extra_info_map = {}
for dt in raw_data:
extra_info = {
"entity_mentions": dt["entity_mentions"] if "entity_mentions" in dt else [],
"relation_mentions": dt["relation_mentions"] if "relation_mentions" in dt else [],
"event_mentions": dt["event_mentions"] if "event_mentions" in dt else [],
}
extra_info_map[(dt["doc_id"], dt["wnd_id"])] = extra_info
for instance in instances:
instance["extra_info"] = extra_info_map[(instance["doc_id"], instance["wnd_id"])]
return instances
def load_tokenizer_(self, checkpoint=None):
if checkpoint:
logger.info(f"Loading tokenizer from {checkpoint}")
state = torch.load(os.path.join(checkpoint, "best_model.tokenizer"))
self.tokenizer = state["tokenizer"]
else:
logger.info(f"Loading tokenizer from {self.config.pretrained_model_name}")
if self.config.pretrained_model_name.startswith('bert-'):
self.tokenizer = BertTokenizer.from_pretrained(self.config.pretrained_model_name, cache_dir=self.config.cache_dir)
elif self.config.pretrained_model_name.startswith('roberta-'):
self.tokenizer = RobertaTokenizer.from_pretrained(self.config.pretrained_model_name, cache_dir=self.config.cache_dir)
elif self.config.pretrained_model_name.startswith('xlm-roberta-'):
self.tokenizer = XLMRobertaTokenizer.from_pretrained(self.config.pretrained_model_name, cache_dir=self.config.cache_dir)
else:
self.tokenizer = AutoTokenizer.from_pretrained(self.config.pretrained_model_name, cache_dir=self.config.cache_dir, do_lower_case=False)
def load_model_(self, checkpoint=None):
assert self.tokenizer
if checkpoint:
logger.info(f"Loading model from {checkpoint}")
state = torch.load(os.path.join(checkpoint, "best_model.state"), map_location=f'cuda:{self.config.gpu_device}')
self.vocabs = state["vocabs"]
self.type_set = state["type_set"]
self.valid_patterns = state["valid_patterns"]
self.model = OneIEE2EModel(self.config, self.vocabs, self.valid_patterns)
self.model.load_state_dict(state['model'])
self.model.cuda(device=self.config.gpu_device)
else:
self.valid_patterns = load_valid_patterns(self.config.valid_pattern_path, self.vocabs)
self.model = OneIEE2EModel(self.config, self.vocabs, self.valid_patterns)
self.model.cuda(device=self.config.gpu_device)
def load_model(self, checkpoint=None):
self.load_tokenizer_(checkpoint=checkpoint)
self.load_model_(checkpoint=checkpoint)
def train(self, train_data, dev_data, **kwargs):
self.load_tokenizer_()
train_set = IEDataset(train_data, self.tokenizer,
max_length=self.config.max_length,
gpu=True,
relation_mask_self=self.config.relation_mask_self,
relation_directional=self.config.relation_directional,
symmetric_relations=self.config.symmetric_relations,
test=False
)
dev_set = IEDataset(dev_data, self.tokenizer,
max_length=self.config.max_length,
gpu=True,
relation_mask_self=self.config.relation_mask_self,
relation_directional=self.config.relation_directional,
symmetric_relations=self.config.symmetric_relations,
test=False
)
self.vocabs = generate_vocabs([train_set, dev_set])
train_set.numberize(self.tokenizer, self.vocabs)
dev_set.numberize(self.tokenizer, self.vocabs)
self.load_model_()
batch_num = len(train_set) // self.config.batch_size + (len(train_set) % self.config.batch_size != 0)
dev_batch_num = len(dev_set) // self.config.eval_batch_size + (len(dev_set) % self.config.eval_batch_size != 0)
param_groups = [
{
'params': [p for n, p in self.model.named_parameters() if n.startswith('bert')],
'lr': self.config.bert_learning_rate, 'weight_decay': self.config.bert_weight_decay
},
{
'params': [p for n, p in self.model.named_parameters() if not n.startswith('bert')
and 'crf' not in n and 'global_feature' not in n],
'lr': self.config.learning_rate, 'weight_decay': self.config.weight_decay
},
{
'params': [p for n, p in self.model.named_parameters() if not n.startswith('bert')
and ('crf' in n or 'global_feature' in n)],
'lr': self.config.learning_rate, 'weight_decay': 0
}
]
optimizer = AdamW(params=param_groups)
schedule = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=batch_num*self.config.warmup_epoch,
num_training_steps=batch_num*self.config.max_epoch)
best_scores = {self.config.target_task: {"f": 0.0}}
best_epoch = -1
target_task = self.config.target_task
logger.info('================Start Training================')
for epoch in range(self.config.max_epoch):
logger.info('Epoch: {}'.format(epoch))
# training step
progress = tqdm.tqdm(total=batch_num, ncols=75,
desc='Train {}'.format(epoch))
optimizer.zero_grad()
cummulate_loss = 0.
for batch_idx, batch in enumerate(DataLoader(
train_set, batch_size=self.config.batch_size // self.config.accumulate_step,
shuffle=True, drop_last=False, collate_fn=train_set.collate_fn)):
loss = self.model(batch)
loss = loss * (1 / self.config.accumulate_step)
cummulate_loss += loss
loss.backward()
if (batch_idx + 1) % self.config.accumulate_step == 0:
progress.update(1)
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.config.grad_clipping)
optimizer.step()
schedule.step()
optimizer.zero_grad()
progress.close()
logger.info({"average training loss": (cummulate_loss / batch_idx).data})
# dev set
progress = tqdm.tqdm(total=dev_batch_num, ncols=75,
desc='Dev {}'.format(epoch))
best_dev_role_model = False
dev_gold_graphs, dev_pred_graphs, dev_tokens, dev_wnd_ids = [], [], [], []
for batch in DataLoader(dev_set, batch_size=self.config.eval_batch_size,
shuffle=False, collate_fn=dev_set.collate_fn):
progress.update(1)
graphs = self.model.predict(batch, gold_tri=False)
gold_graph = copy.deepcopy(batch.graphs)
for graph in graphs:
graph.clean(relation_directional=self.config.relation_directional,
symmetric_relations=self.config.symmetric_relations)
dev_gold_graphs.extend(gold_graph)
dev_pred_graphs.extend(graphs)
dev_tokens.extend(batch.tokens)
dev_wnd_ids.extend(batch.wnd_ids)
progress.close() | dev_scores = score_graphs(dev_gold_graphs, dev_pred_graphs, self.vocabs['event_type']) | 7 | 2023-11-15 21:32:56+00:00 | 24k |
ahayler/s4c | models/bts/trainer_overfit.py | [
{
"identifier": "make_datasets",
"path": "datasets/data_util.py",
"snippet": "def make_datasets(config):\n type = config.get(\"type\", \"KITTI_Raw\")\n if type == \"KITTI_Odometry\":\n train_dataset = KittiOdometryDataset(\n base_path=config[\"data_path\"],\n frame_cou... | import math
import ignite.distributed as idist
import torch
import numpy as np
from copy import copy
from typing import Optional, Union, Iterable, Sequence
from ignite.contrib.handlers import TensorboardLogger
from ignite.engine import Engine
from matplotlib import pyplot as plt
from torch import optim, nn
from torch.utils.data import DataLoader, Dataset, Sampler
from torch.utils.data.dataloader import T_co, _collate_fn_t, _worker_init_fn_t
from torchvision.utils import make_grid
from datasets.data_util import make_datasets
from models.common.model.scheduler import make_scheduler
from models.common.render import NeRFRenderer
from models.bts.model.loss import ReconstructionLoss
from models.bts.trainer import get_metrics, BTSWrapper, BTSNet
from scripts.inference_setup import render_profile
from utils.array_operations import map_fn, unsqueezer, to
from utils.base_trainer import base_training
from utils.plotting import color_tensor, color_segmentation_tensor | 15,653 |
class EncoderDummy(nn.Module):
def __init__(self, size, feat_dim, num_views=1) -> None:
super().__init__()
self.feats = nn.Parameter(torch.randn(num_views, feat_dim, *size))
self.latent_size = feat_dim
def forward(self, x):
n = x.shape[0]
return [self.feats.expand(n, -1, -1, -1)]
class DataloaderDummy(DataLoader):
def __init__(self, dataset: Dataset[T_co], batch_size: Optional[int] = 1, shuffle: Optional[bool] = None,
sampler: Union[Sampler, Iterable, None] = None,
batch_sampler: Union[Sampler[Sequence], Iterable[Sequence], None] = None, num_workers: int = 0,
collate_fn: Optional[_collate_fn_t] = None, pin_memory: bool = False, drop_last: bool = False,
timeout: float = 0, worker_init_fn: Optional[_worker_init_fn_t] = None, multiprocessing_context=None,
generator=None, *, prefetch_factor: int = 2, persistent_workers: bool = False,
pin_memory_device: str = ""):
super().__init__(dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory,
drop_last, timeout, worker_init_fn, multiprocessing_context, generator,
prefetch_factor=prefetch_factor, persistent_workers=persistent_workers,
pin_memory_device=pin_memory_device)
self.element = to(map_fn(map_fn(dataset.__getitem__(0), torch.tensor), unsqueezer), "cuda:0")
def _get_iterator(self):
return iter([self.element])
def __iter__(self):
return super().__iter__()
def __len__(self) -> int:
return 1
|
class EncoderDummy(nn.Module):
def __init__(self, size, feat_dim, num_views=1) -> None:
super().__init__()
self.feats = nn.Parameter(torch.randn(num_views, feat_dim, *size))
self.latent_size = feat_dim
def forward(self, x):
n = x.shape[0]
return [self.feats.expand(n, -1, -1, -1)]
class DataloaderDummy(DataLoader):
def __init__(self, dataset: Dataset[T_co], batch_size: Optional[int] = 1, shuffle: Optional[bool] = None,
sampler: Union[Sampler, Iterable, None] = None,
batch_sampler: Union[Sampler[Sequence], Iterable[Sequence], None] = None, num_workers: int = 0,
collate_fn: Optional[_collate_fn_t] = None, pin_memory: bool = False, drop_last: bool = False,
timeout: float = 0, worker_init_fn: Optional[_worker_init_fn_t] = None, multiprocessing_context=None,
generator=None, *, prefetch_factor: int = 2, persistent_workers: bool = False,
pin_memory_device: str = ""):
super().__init__(dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory,
drop_last, timeout, worker_init_fn, multiprocessing_context, generator,
prefetch_factor=prefetch_factor, persistent_workers=persistent_workers,
pin_memory_device=pin_memory_device)
self.element = to(map_fn(map_fn(dataset.__getitem__(0), torch.tensor), unsqueezer), "cuda:0")
def _get_iterator(self):
return iter([self.element])
def __iter__(self):
return super().__iter__()
def __len__(self) -> int:
return 1
| class BTSWrapperOverfit(BTSWrapper): | 4 | 2023-11-12 21:53:27+00:00 | 24k |
newcastleuniversity/DISPEL | dispel/providers/generic/sensor.py | [
{
"identifier": "Reading",
"path": "dispel/data/core.py",
"snippet": "class Reading(FlagMixIn):\n \"\"\"A data capture from an experiment.\n\n Attributes\n ----------\n evaluation\n The evaluation information for this reading\n session\n The session information for this read... | from functools import partial
from typing import Iterable, List, Optional, Tuple, Union
from dispel.data.core import Reading
from dispel.data.levels import Level
from dispel.data.measures import MeasureValueDefinitionPrototype
from dispel.data.raw import (
ACCELEROMETER_COLUMNS,
DEFAULT_COLUMNS,
GRAVITY_COLUMNS,
RawDataValueDefinition,
)
from dispel.data.values import AbbreviatedValue as AV
from dispel.processing.assertions import NotEmptyDataSetAssertionMixin
from dispel.processing.data_set import transformation
from dispel.processing.extract import ExtractMultipleStep, ExtractStep
from dispel.processing.level import LevelFilterType
from dispel.processing.modalities import SensorModality
from dispel.processing.transform import Apply, TransformStep
from dispel.providers.bdh.data import BDHReading
from dispel.signal.accelerometer import (
GRAVITY_CONSTANT,
apply_rotation_matrices,
compute_rotation_matrices_quaternion,
remove_gravity_component,
remove_gravity_component_ori,
)
from dispel.signal.core import (
amplitude,
discretize_sampling_frequency,
energy,
entropy,
euclidean_norm,
peak,
)
from dispel.signal.sensor import SENSOR_UNIT, find_zero_crossings
import numpy as np
import pandas as pd | 16,999 | for axis in "XYZ"
]
+ [
RawDataValueDefinition(
f"gravity{axis}",
f"gravity component along the {axis} axis.",
data_type="float",
)
for axis in "XYZ"
]
+ [RawDataValueDefinition("ts", "time index")]
)
@staticmethod
def add_gravity(
accelerometer: pd.DataFrame,
level: Level,
gravity: Optional[pd.DataFrame] = None,
) -> pd.DataFrame:
"""Format gravity data to ADS format."""
if gravity is None:
cols = ["x", "y", "z"]
raw_acc = level.get_raw_data_set("raw_accelerometer").data
accelerometer = raw_acc
if level.has_raw_data_set("attitude"):
ori = level.get_raw_data_set("attitude").data
ori_cols = ["w", "x", "y", "z"]
lin_accelerometer, gravity = remove_gravity_component_ori(
accelerometer[cols].values, ori[ori_cols].values
)
lin_accelerometer = pd.DataFrame(lin_accelerometer, columns=cols)
gravity = pd.DataFrame(gravity, columns=cols)
else:
lin_accelerometer, gravity = remove_gravity_component(
accelerometer[cols]
)
res = pd.DataFrame(
{
"userAccelerationX": lin_accelerometer["x"],
"userAccelerationY": lin_accelerometer["y"],
"userAccelerationZ": lin_accelerometer["z"],
}
)
res["gravityX"] = gravity["x"]
res["gravityY"] = gravity["y"]
res["gravityZ"] = gravity["z"]
res["ts"] = accelerometer["ts"]
else:
# Merging on the timestamps vs. on the indexes
acc_renamed = accelerometer.rename(
mapper={
"x": "userAccelerationX",
"y": "userAccelerationY",
"z": "userAccelerationZ",
},
axis=1,
)
gravity_renamed = gravity.rename(
mapper={"x": "gravityX", "y": "gravityY", "z": "gravityZ"}, axis=1
)
merged = acc_renamed.merge(gravity_renamed, how="outer")
merged = merged.set_index("ts")
merged_sorted = merged.sort_index()
merged_sorted_interpolated = merged_sorted.interpolate(
method="nearest", limit_direction="both"
)
res = merged_sorted_interpolated.loc[acc_renamed.ts].reset_index()
return res.dropna()
@staticmethod
@transformation
def _reformat(accelerometer: pd.DataFrame, level: Level) -> pd.DataFrame:
target_cols = {
f"{sensor}{axis}"
for sensor in ("userAcceleration", "gravity")
for axis in "XYZ"
}
if not target_cols.issubset(accelerometer.columns):
try:
return TransformUserAcceleration.add_gravity(
accelerometer, level, level.get_raw_data_set("gravity").data
)
except ValueError:
# Happens in BDH pinch
return TransformUserAcceleration.add_gravity(accelerometer, level)
return accelerometer
class TransformGyroscope(TransformStep):
r"""Format gyroscope data to ADS format if not already the case.
On ADS format, the gyroscope is synchronized with the accelerometer. Here
we make sure gyroscope is synchronized with the acc data set.
Parameters
----------
level_filter
An optional :class:`dispel.processing.level.LevelFilter` to determine the
levels to be transformed. If no filter is provided, all levels will be
transformed. The ``level_filter`` also accepts :class:`str`,
:class:`~dispel.data.core.LevelId`\ s and lists of either and passes them
to a :class:`~dispel.processing.level.LevelFilter` for convenience.
"""
data_set_ids = ["acc", "gyroscope"]
new_data_set_id = "gyroscope"
definitions = [
RawDataValueDefinition(
axis, f"Rotation speed along the {axis} axis.", data_type="float"
)
for axis in "xyz"
] + [RawDataValueDefinition("ts", "time index")]
@staticmethod
@transformation
def _synchronize_gyroscope(
accelerometer: pd.DataFrame, gyroscope: pd.DataFrame, reading: Reading
) -> pd.DataFrame:
| """Generic functionality for signal processing steps."""
# Define expected sampling frequencies
FREQ_20HZ = 20
FREQ_50HZ = 50
FREQ_60HZ = 60
FREQ_100HZ = 100 # SensorLog can sample at 100Hz
FREQ_128HZ = 128 # APDM files are sampled at 128Hz
VALID_FREQ_LIST = [FREQ_20HZ, FREQ_50HZ, FREQ_100HZ, FREQ_128HZ]
class RenameColumns(TransformStep):
r"""Rename and select columns of a raw data set.
Parameters
----------
data_set_id
The data set id of the time series to be renamed.
level_filter
An optional :class:`~dispel.processing.level.LevelFilter` to determine
the levels to be transformed. If no filter is provided, all levels
will be transformed. The ``level_filter`` also accepts :class:`str`,
:class:`~dispel.data.core.LevelId`\ s and lists of either and passes them
to a :class:`~dispel.processing.level.LevelFilter` for convenience.
kwargs
All arguments passed into this class will serve as a renaming mapping
for the raw data set.
"""
def __init__(
self, data_set_id: str, level_filter: Optional[LevelFilterType] = None, **kwargs
):
def _transform_function(data: pd.DataFrame) -> pd.DataFrame:
data_ = data.rename(columns=kwargs)
return data_[kwargs.values()]
super().__init__(
data_set_id,
_transform_function,
f"{data_set_id}_renamed",
[RawDataValueDefinition(column, column) for column in kwargs.values()],
level_filter=level_filter,
)
class SetTimestampIndex(TransformStep):
r"""Create a new time series based on a date time or time delta column.
Parameters
----------
data_set_id
The data set id of the time series to be transformed.
columns
The columns to consider in the new raw data set.
time_stamp_column
The time series column name to use as index.
level_filter
An optional :class:`dispel.processing.level.LevelFilter` to determine the
levels to be transformed. If no filter is provided, all levels will be
transformed. The ``level_filter`` also accepts :class:`str`,
:class:`~dispel.data.core.LevelId`\ s and lists of either and passes them
to a :class:`~dispel.processing.level.LevelFilter` for convenience.
duplicates
The strategy used to handle duplicates.
Has to be one of ``ignore``, ``raise``, ``first``, ``last``.
"""
def __init__(
self,
data_set_id: str,
columns: List[str],
time_stamp_column: str = "ts",
level_filter: Optional[LevelFilterType] = None,
duplicates: Optional[str] = None,
):
def _transform_function(
data: pd.DataFrame, rm_duplicate: Optional[str]
) -> pd.DataFrame:
if rm_duplicate is None:
return data.set_index(time_stamp_column)[columns].copy()
res = data.set_index(time_stamp_column)[columns].copy()
return res[~res.index.duplicated(keep=duplicates)]
super().__init__(
data_set_id,
lambda x: _transform_function(x, duplicates),
f"{data_set_id}_ts",
[RawDataValueDefinition(column, column) for column in columns],
level_filter=level_filter,
)
class Trim(TransformStep):
"""Trim a sensor signal at the beginning and/or end.
Parameters
----------
trim_left
The amount of data to trim from the left side of the sensor readings.
trim_right
The amount of data to trim from the right side of the sensor readings.
ts_column
The column id to be used in the provided raw data set through
``data_set_ids``. If no column is provided, the data set is expected
to have a time-based index that is used to trim the data set.
"""
trim_left = pd.Timedelta(0)
trim_right = pd.Timedelta(0)
ts_column: Optional[str] = None
def __init__(self, *args, **kwargs):
if (left := kwargs.pop("trim_left", None)) is not None:
self.trim_left = left
if (right := kwargs.pop("trim_right", None)) is not None:
self.trim_right = right
if (column := kwargs.pop("ts_column", None)) is not None:
self.ts_column = column
super().__init__(*args, **kwargs)
@transformation
def _trim(self, data: pd.DataFrame) -> pd.DataFrame:
ts_col = data.index if self.ts_column is None else data[self.ts_column]
if self.trim_left > pd.Timedelta(0):
data = data[ts_col > ts_col.min() + self.trim_left]
if self.trim_right > pd.Timedelta(0):
data = data[ts_col < ts_col.max() - self.trim_right]
return data.copy()
class Resample(NotEmptyDataSetAssertionMixin, TransformStep):
r"""Resample a time-based raw data set to a specific sampling frequency.
The resampling creates a new raw data set which is accessible via the
data set comprised of the original one concatenated with ``_resampled``.
Parameters
----------
data_set_id
The data set to be resampled. This has to be a data set that uses a
time-based index. You might first have to apply the
:class:`SetTimestampIndex` processing step before you can apply
this step.
aggregations
A list of resampling methods to be applied in order. Each can be any
method that is also accepted by :meth:`pandas.DataFrame.agg`.
columns
The columns to be considered during the resampling.
freq
The frequency to resample to. See also
:meth:`pandas.DataFrame.resample` for details. If freq is not provided
the frequency is estimated automatically taking the median frequency.
max_frequency_distance
An optional integer specifying the maximum accepted
distance between the expected frequency and the estimated frequency
above which we raise an error.
level_filter
An optional :class:`dispel.processing.level.LevelFilter` to determine the
levels to be transformed. If no filter is provided, all levels will be
transformed. The ``level_filter`` also accepts :class:`str`,
:class:`~dispel.data.core.LevelId`\ s and lists of either and passes them
to a :class:`~dispel.processing.level.LevelFilter` for convenience.
"""
def __init__(
self,
data_set_id: str,
aggregations: Iterable[str],
columns: Iterable[str],
freq: Optional[Union[float, str]] = None,
max_frequency_distance: Optional[int] = None,
level_filter: Optional[LevelFilterType] = None,
):
def _resample(
data: pd.DataFrame, sampling_frequency: Optional[Union[float, str]] = None
) -> pd.DataFrame:
# Check if a sampling frequency is provided
# If not, we discretized the sampling frequency
if sampling_frequency is None:
discretize_args = [data, VALID_FREQ_LIST]
if max_frequency_distance:
discretize_args.append(max_frequency_distance)
sampling_frequency = discretize_sampling_frequency(*discretize_args)
# Convert the float sampling frequency to a Timedelta format
if not isinstance(sampling_frequency, str):
sampling_frequency = pd.Timedelta(1 / sampling_frequency, unit="s")
resample_obj = data[columns].resample(sampling_frequency)
for method in aggregations:
resample_obj = resample_obj.agg(method)
return resample_obj
def _definition_factory(column: str) -> RawDataValueDefinition:
return RawDataValueDefinition(
column, f"{column} resampled with {aggregations}"
)
super().__init__(
data_set_id,
partial(_resample, sampling_frequency=freq),
f"{data_set_id}_resampled",
[_definition_factory(column) for column in columns],
level_filter=level_filter,
)
class Upsample(Apply):
r"""Upsample a time-based raw data set to a specific sampling frequency.
The upsampling creates a new raw data set which is an upsampled version
of the original data set identified by data_set_id. The upsampled data
set is accessible via the new_data_set_id which is a concatenation of the
original data_set_id and a suffix ``_upsampled``.
Parameters
----------
interpolation_method
Interpolation technique to use to fill NaN values. It should be a
method that is also accepted by :meth:`pandas.DataFrame.interpolate`.
freq
The frequency to upsample to. See also
:meth:`pandas.DataFrame.resample` for details.
"""
def get_new_data_set_id(self) -> str:
"""Overwrite new_data_set_id."""
return f"{self.get_data_set_ids()[0]}_upsampled" # type: ignore
def __init__(self, interpolation_method: str, freq: Union[float, str], **kwargs):
def _upsample(
data: pd.DataFrame, sampling_frequency: Union[float, str]
) -> pd.DataFrame:
"""Upsample a dataframe to a given sampling frequency."""
# Convert the float sampling frequency to a Timedelta format
if not isinstance(sampling_frequency, str):
sampling_frequency = pd.Timedelta(1 / sampling_frequency, unit="s")
resample_obj = data.resample(sampling_frequency)
return resample_obj.interpolate(interpolation_method)
super().__init__(
method=_upsample, method_kwargs={"sampling_frequency": freq}, **kwargs
)
class ExtractAverageSignalEnergy(NotEmptyDataSetAssertionMixin, ExtractStep):
r"""An average signal energy extraction step.
Parameters
----------
sensor
The type of sensor on which the extraction is to be performed.
data_set_id
The data set id on which the extraction is to be performed.
columns
The columns onto which the signal energy is to be computed.
level_filter
An optional :class:`~dispel.processing.level.LevelFilter` to determine the
levels to be transformed. If no filter is provided, all levels will be
transformed. The ``level_filter`` also accepts :class:`str`,
:class:`~dispel.data.core.LevelId`\ s and lists of either and passes them
to a :class:`~dispel.processing.level.LevelFilter` for convenience.
"""
def __init__(
self,
sensor: SensorModality,
data_set_id: str,
columns: List[str],
level_filter: Optional[LevelFilterType] = None,
):
def _average_signal(data: pd.DataFrame):
return np.linalg.norm(data[columns], ord=2)
super().__init__(
data_set_id,
_average_signal,
definition=MeasureValueDefinitionPrototype(
measure_name=AV(f"average {sensor} energy", f"{sensor.abbr}_sig_ene"),
data_type="float64",
description=f"The average {sensor} energy of the "
f'{"".join(columns)} columns of the signal.',
unit=SENSOR_UNIT[sensor.abbr],
),
level_filter=level_filter,
)
class ExtractPowerSpectrumMeasures(NotEmptyDataSetAssertionMixin, ExtractMultipleStep):
r"""A measure extraction processing step for power spectrum measures.
Parameters
----------
sensor
The type of sensor on which the extraction is to be performed.
data_set_id
The data set id on which the extraction is to be performed.
columns
The columns onto which the power spectrum measures are to be extracted.
lower_bound
The lower bound of frequencies below which the signal is filtered.
upper_bound
The higher bound of frequencies above which the signal is filtered.
level_filter
An optional :class:`~dispel.processing.level.LevelFilter` to determine the
levels to be transformed. If no filter is provided, all levels will be
transformed. The ``level_filter`` also accepts :class:`str`,
:class:`~dispel.data.core.LevelId`\ s and lists of either and passes them
to a :class:`~dispel.processing.level.LevelFilter` for convenience.
"""
def __init__(
self,
sensor: SensorModality,
data_set_id: str,
columns: List[str],
lower_bound: Optional[float] = None,
upper_bound: Optional[float] = None,
level_filter: Optional[LevelFilterType] = None,
):
unit = sensor.unit(order=2)
atomic_functions = [
{
"func": partial(energy, lowcut=lower_bound, highcut=upper_bound),
"name": AV("energy", "ene"),
"description": "The power spectrum energy summed between the "
f"frequencies ({lower_bound}, {upper_bound}) "
f"of the {{axis}} axis for the {sensor} "
f"signal.",
"unit": unit,
"outcome_uuid": "99ef9a8d-a925-4eb0-9e80-be58cd4a9ac9",
},
{
"func": peak,
"name": AV("peak", "peak"),
"description": f"The frequency at which the power spectrum of "
"the {axis} axis reaches its maximum value for "
f"the {sensor} signal.",
"unit": "Hz",
"outcome_uuid": "87512c93-3a5b-4c9e-9575-fd9ed19649ca",
},
{
"func": entropy,
"name": AV("entropy", "ent"),
"description": "The power spectrum entropy of the {axis} axis "
f"for the {sensor} signal.",
"unit": unit,
"outcome_uuid": "6726bb5a-8084-49f5-a53e-6a28a8f27695",
},
{
"func": amplitude,
"name": AV("amplitude", "amp"),
"description": "The power spectrum amplitude (i.e. the maximum"
" value) of the {axis} axis for the "
f"{sensor} signal.",
"unit": unit,
"outcome_uuid": "bde2c1f9-abf7-41e7-91f8-e0ddddf34a5c",
},
]
def _function_factory(atomic_function, axis):
return dict(
func=lambda x: atomic_function["func"](x[axis]),
description=atomic_function["description"].format(axis=axis),
unit=atomic_function["unit"],
measure_name=AV(
f'{sensor} power spectrum {atomic_function["name"]} {axis}'
f" axis",
f'{sensor.abbr}_ps_{atomic_function["name"].abbr}_{axis}',
),
)
functions = [
_function_factory(atomic_function, axis)
for atomic_function in atomic_functions
for axis in columns
]
super().__init__(
data_set_id,
functions,
definition=MeasureValueDefinitionPrototype(data_type="float64"),
level_filter=level_filter,
)
class ComputeGravityRotationMatrices(TransformStep):
r"""Compute a series of rotation matrices to align sensors to gravity.
This transformation step creates a series of rotation matrices based on the
gravity information contained in the accelerometer sensor. This allows to
rotate other sensors on a desired orientation related to gravity. This is
in particular of interest if we want to measure physical interactions with
devices around the plane perpendicular to gravity.
Parameters
----------
target_gravity
The target gravity vector, e.g. ``(-1, 0, 0)`` to create rotation
matrices that rotate the x-axis of a device onto gravity.
level_filter
An optional :class:`~dispel.processing.level.LevelFilter` to determine the
levels to be transformed. If no filter is provided, all levels will be
transformed. The ``level_filter`` also accepts :class:`str`,
:class:`~dispel.data.core.LevelId`\ s and lists of either and passes them
to a :class:`~dispel.processing.level.LevelFilter` for convenience.
"""
def __init__(
self, data_set_id: str, target_gravity: Tuple[float, float, float], **kwargs
):
def _transform_function(data: pd.DataFrame) -> pd.Series:
return compute_rotation_matrices_quaternion(
data[GRAVITY_COLUMNS], target_gravity
)
super().__init__(
data_set_id,
_transform_function,
"gravity_rotation_matrices",
[RawDataValueDefinition("rotation_matrix", "Rotation Matrix")],
**kwargs,
)
class RotateSensorWithGravityRotationMatrices(TransformStep):
r"""Apply a series of rotation matrices to a sensor.
This is a complementary step to :class:`ComputeGravityRotationMatrices` and
applies the rotation matrices to the specified sensor.
Parameters
----------
data_set_id
The id of the sensor data set to be rotated.
columns
The columns of the sensor data set to be considered in the rotation.
level_filter
An optional :class:`~dispel.processing.level.LevelFilter` to determine the
levels to be transformed. If no filter is provided, all levels will be
transformed. The ``level_filter`` also accepts :class:`str`,
:class:`~dispel.data.core.LevelId`\ s and lists of either and passes them
to a :class:`~dispel.processing.level.LevelFilter` for convenience.
Examples
--------
Assuming you want to rotate the gyroscope vector onto gravity you can
achieve this by chaining the following steps:
.. doctest:: processing
>>> from dispel.data.raw import DEFAULT_COLUMNS
>>> from dispel.processing import process
>>> from dispel.providers.generic.sensor import (
... ComputeGravityRotationMatrices,
... RotateSensorWithGravityRotationMatrices
... )
>>> cols = DEFAULT_COLUMNS
>>> steps = [
... ComputeGravityRotationMatrices('accelerometer', (-1, 0, 0)),
... RotateSensorWithGravityRotationMatrices('gyroscope', cols)
... ]
>>> _ = process(reading, steps) # doctest: +SKIP
The results of the roation are available in the raw data set with the id
``<data_set_id>_rotated``:
.. doctest:: processing
:options: +NORMALIZE_WHITESPACE
>>> level = reading.get_level(level_id) # doctest: +SKIP
>>> level.get_raw_data_set('gyroscope').data.head() # doctest: +SKIP
x y z ts
0 0.035728 -0.021515 0.014879 2020-05-04 17:31:38.574
1 -0.012046 0.005010 -0.009029 2020-05-04 17:31:38.625
2 0.006779 0.000761 -0.003253 2020-05-04 17:31:38.680
3 0.032636 -0.020272 -0.021915 2020-05-04 17:31:38.729
4 0.007495 -0.014061 0.012886 2020-05-04 17:31:38.779
>>> level.get_raw_data_set(
... 'gyroscope_rotated'
... ).data.head() # doctest: +SKIP
x y z
0 -0.002309 -0.042509 -0.012182
1 -0.003754 0.014983 0.003624
2 -0.002237 -0.002116 -0.006901
3 -0.030461 -0.021654 -0.023656
4 0.001203 -0.019580 0.005924
"""
def __init__(
self,
data_set_id: str,
columns: Iterable[str],
level_filter: Optional[LevelFilterType] = None,
):
def _transform_function(
sensor_df: pd.DataFrame, matrices: pd.DataFrame
) -> pd.DataFrame:
return apply_rotation_matrices(
matrices["rotation_matrix"], sensor_df[columns]
)
def _definition_factory(column: str) -> RawDataValueDefinition:
return RawDataValueDefinition(column, f"{column} rotated")
super().__init__(
[data_set_id, "gravity_rotation_matrices"],
_transform_function,
f"{data_set_id}_rotated",
[_definition_factory(column) for column in columns],
level_filter=level_filter,
)
class TransformUserAcceleration(TransformStep):
r"""Format accelerometer data to ADS format if not already the case.
Prior to formatting, linear acceleration and gravity are decoupled
from acceleration.
Parameters
----------
level_filter
An optional :class:`dispel.processing.level.LevelFilter` to determine the
levels to be transformed. If no filter is provided, all levels will be
transformed. The ``level_filter`` also accepts :class:`str`,
:class:`~dispel.data.core.LevelId`\ s and lists of either and passes them
to a :class:`~dispel.processing.level.LevelFilter` for convenience.
"""
data_set_ids = "accelerometer"
new_data_set_id = "acc"
definitions = (
[
RawDataValueDefinition(
f"userAcceleration{axis}",
f"Linear Acceleration along the {axis} axis.",
data_type="float",
)
for axis in "XYZ"
]
+ [
RawDataValueDefinition(
f"gravity{axis}",
f"gravity component along the {axis} axis.",
data_type="float",
)
for axis in "XYZ"
]
+ [RawDataValueDefinition("ts", "time index")]
)
@staticmethod
def add_gravity(
accelerometer: pd.DataFrame,
level: Level,
gravity: Optional[pd.DataFrame] = None,
) -> pd.DataFrame:
"""Format gravity data to ADS format."""
if gravity is None:
cols = ["x", "y", "z"]
raw_acc = level.get_raw_data_set("raw_accelerometer").data
accelerometer = raw_acc
if level.has_raw_data_set("attitude"):
ori = level.get_raw_data_set("attitude").data
ori_cols = ["w", "x", "y", "z"]
lin_accelerometer, gravity = remove_gravity_component_ori(
accelerometer[cols].values, ori[ori_cols].values
)
lin_accelerometer = pd.DataFrame(lin_accelerometer, columns=cols)
gravity = pd.DataFrame(gravity, columns=cols)
else:
lin_accelerometer, gravity = remove_gravity_component(
accelerometer[cols]
)
res = pd.DataFrame(
{
"userAccelerationX": lin_accelerometer["x"],
"userAccelerationY": lin_accelerometer["y"],
"userAccelerationZ": lin_accelerometer["z"],
}
)
res["gravityX"] = gravity["x"]
res["gravityY"] = gravity["y"]
res["gravityZ"] = gravity["z"]
res["ts"] = accelerometer["ts"]
else:
# Merging on the timestamps vs. on the indexes
acc_renamed = accelerometer.rename(
mapper={
"x": "userAccelerationX",
"y": "userAccelerationY",
"z": "userAccelerationZ",
},
axis=1,
)
gravity_renamed = gravity.rename(
mapper={"x": "gravityX", "y": "gravityY", "z": "gravityZ"}, axis=1
)
merged = acc_renamed.merge(gravity_renamed, how="outer")
merged = merged.set_index("ts")
merged_sorted = merged.sort_index()
merged_sorted_interpolated = merged_sorted.interpolate(
method="nearest", limit_direction="both"
)
res = merged_sorted_interpolated.loc[acc_renamed.ts].reset_index()
return res.dropna()
@staticmethod
@transformation
def _reformat(accelerometer: pd.DataFrame, level: Level) -> pd.DataFrame:
target_cols = {
f"{sensor}{axis}"
for sensor in ("userAcceleration", "gravity")
for axis in "XYZ"
}
if not target_cols.issubset(accelerometer.columns):
try:
return TransformUserAcceleration.add_gravity(
accelerometer, level, level.get_raw_data_set("gravity").data
)
except ValueError:
# Happens in BDH pinch
return TransformUserAcceleration.add_gravity(accelerometer, level)
return accelerometer
class TransformGyroscope(TransformStep):
r"""Format gyroscope data to ADS format if not already the case.
On ADS format, the gyroscope is synchronized with the accelerometer. Here
we make sure gyroscope is synchronized with the acc data set.
Parameters
----------
level_filter
An optional :class:`dispel.processing.level.LevelFilter` to determine the
levels to be transformed. If no filter is provided, all levels will be
transformed. The ``level_filter`` also accepts :class:`str`,
:class:`~dispel.data.core.LevelId`\ s and lists of either and passes them
to a :class:`~dispel.processing.level.LevelFilter` for convenience.
"""
data_set_ids = ["acc", "gyroscope"]
new_data_set_id = "gyroscope"
definitions = [
RawDataValueDefinition(
axis, f"Rotation speed along the {axis} axis.", data_type="float"
)
for axis in "xyz"
] + [RawDataValueDefinition("ts", "time index")]
@staticmethod
@transformation
def _synchronize_gyroscope(
accelerometer: pd.DataFrame, gyroscope: pd.DataFrame, reading: Reading
) -> pd.DataFrame: | if isinstance(reading, BDHReading): | 16 | 2023-11-14 10:06:46+00:00 | 24k |
Jisencc/yolov5_dual_weighting | segment/val.py | [
{
"identifier": "DetectMultiBackend",
"path": "models/common.py",
"snippet": "class DetectMultiBackend(nn.Module):\n # YOLOv5 MultiBackend class for python inference on various backends\n def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=Tr... | import argparse
import json
import os
import subprocess
import sys
import numpy as np
import torch
import torch.nn.functional as F
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tqdm import tqdm
from models.common import DetectMultiBackend
from models.yolo import SegmentationModel
from utils.callbacks import Callbacks
from utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size,
check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path,
non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
from utils.metrics import ConfusionMatrix, box_iou
from utils.plots import output_to_target, plot_val_study
from utils.segment.dataloaders import create_dataloader
from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image
from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
from utils.segment.plots import plot_images_and_masks
from utils.torch_utils import de_parallel, select_device, smart_inference_mode
from pycocotools.mask import encode
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval | 16,967 |
# Load model
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
half = model.fp16 # FP16 supported on limited backends with CUDA
nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks
if engine:
batch_size = model.batch_size
else:
device = model.device
if not (pt or jit):
batch_size = 1 # export.py models default to batch-size 1
LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
# Data
data = check_dataset(data) # check
# Configure
model.eval()
cuda = device.type != 'cpu'
is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset
nc = 1 if single_cls else int(data['nc']) # number of classes
iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
niou = iouv.numel()
# Dataloader
if not training:
if pt and not single_cls: # check --weights are trained on --data
ncm = model.model.nc
assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
f'classes). Pass correct combination of --weights and --data that are trained together.'
model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks
task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task],
imgsz,
batch_size,
stride,
single_cls,
pad=pad,
rect=rect,
workers=workers,
prefix=colorstr(f'{task}: '),
overlap_mask=overlap,
mask_downsample_ratio=mask_downsample_ratio)[0]
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
names = model.names if hasattr(model, 'names') else model.module.names # get class names
if isinstance(names, (list, tuple)): # old format
names = dict(enumerate(names))
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P', 'R',
'mAP50', 'mAP50-95)')
dt = Profile(), Profile(), Profile()
metrics = Metrics()
loss = torch.zeros(4, device=device)
jdict, stats = [], []
# callbacks.run('on_val_start')
pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar):
# callbacks.run('on_val_batch_start')
with dt[0]:
if cuda:
im = im.to(device, non_blocking=True)
targets = targets.to(device)
masks = masks.to(device)
masks = masks.float()
im = im.half() if half else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
nb, _, height, width = im.shape # batch size, channels, height, width
# Inference
with dt[1]:
preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None)
# Loss
if compute_loss:
loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls
# NMS
targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
with dt[2]:
preds = non_max_suppression(preds,
conf_thres,
iou_thres,
labels=lb,
multi_label=True,
agnostic=single_cls,
max_det=max_det,
nm=nm)
# Metrics
plot_masks = [] # masks for plotting
for si, (pred, proto) in enumerate(zip(preds, protos)):
labels = targets[targets[:, 0] == si, 1:]
nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
path, shape = Path(paths[si]), shapes[si][0]
correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
seen += 1
if npr == 0:
if nl:
stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0]))
if plots:
confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
continue
# Masks
midx = [si] if overlap else targets[:, 0] == si
gt_masks = masks[midx]
pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:])
# Predictions
if single_cls:
pred[:, 5] = 0
predn = pred.clone()
| # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
Validate a trained YOLOv5 segment model on a segment dataset
Usage:
$ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images)
$ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments
Usage - formats:
$ python segment/val.py --weights yolov5s-seg.pt # PyTorch
yolov5s-seg.torchscript # TorchScript
yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s-seg_openvino_label # OpenVINO
yolov5s-seg.engine # TensorRT
yolov5s-seg.mlmodel # CoreML (macOS-only)
yolov5s-seg_saved_model # TensorFlow SavedModel
yolov5s-seg.pb # TensorFlow GraphDef
yolov5s-seg.tflite # TensorFlow Lite
yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
yolov5s-seg_paddle_model # PaddlePaddle
"""
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
def save_one_txt(predn, save_conf, shape, file):
# Save one txt result
gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(file, 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
def save_one_json(predn, jdict, path, class_map, pred_masks):
# Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
def single_encode(x):
rle = encode(np.asarray(x[:, :, None], order='F', dtype='uint8'))[0]
rle['counts'] = rle['counts'].decode('utf-8')
return rle
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
pred_masks = np.transpose(pred_masks, (2, 0, 1))
with ThreadPool(NUM_THREADS) as pool:
rles = pool.map(single_encode, pred_masks)
for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())):
jdict.append({
'image_id': image_id,
'category_id': class_map[int(p[5])],
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5),
'segmentation': rles[i]})
def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):
"""
Return correct prediction matrix
Arguments:
detections (array[N, 6]), x1, y1, x2, y2, conf, class
labels (array[M, 5]), class, x1, y1, x2, y2
Returns:
correct (array[N, 10]), for 10 IoU levels
"""
if masks:
if overlap:
nl = len(labels)
index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
if gt_masks.shape[1:] != pred_masks.shape[1:]:
gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode='bilinear', align_corners=False)[0]
gt_masks = gt_masks.gt_(0.5)
iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
else: # boxes
iou = box_iou(labels[:, 1:], detections[:, :4])
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(iouv)):
x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
@smart_inference_mode()
def run(
data,
weights=None, # model.pt path(s)
batch_size=32, # batch size
imgsz=640, # inference size (pixels)
conf_thres=0.001, # confidence threshold
iou_thres=0.6, # NMS IoU threshold
max_det=300, # maximum detections per image
task='val', # train, val, test, speed or study
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
workers=8, # max dataloader workers (per RANK in DDP mode)
single_cls=False, # treat as single-class dataset
augment=False, # augmented inference
verbose=False, # verbose output
save_txt=False, # save results to *.txt
save_hybrid=False, # save label+prediction hybrid results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_json=False, # save a COCO-JSON results file
project=ROOT / 'runs/val-seg', # save to project/name
name='exp', # save to project/name
exist_ok=False, # existing project/name ok, do not increment
half=True, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
model=None,
dataloader=None,
save_dir=Path(''),
plots=True,
overlap=False,
mask_downsample_ratio=1,
compute_loss=None,
callbacks=Callbacks(),
):
if save_json:
check_requirements('pycocotools>=2.0.6')
process = process_mask_native # more accurate
else:
process = process_mask # faster
# Initialize/load model and set device
training = model is not None
if training: # called by train.py
device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
half &= device.type != 'cpu' # half precision only supported on CUDA
model.half() if half else model.float()
nm = de_parallel(model).model[-1].nm # number of masks
else: # called directly
device = select_device(device, batch_size=batch_size)
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
half = model.fp16 # FP16 supported on limited backends with CUDA
nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks
if engine:
batch_size = model.batch_size
else:
device = model.device
if not (pt or jit):
batch_size = 1 # export.py models default to batch-size 1
LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
# Data
data = check_dataset(data) # check
# Configure
model.eval()
cuda = device.type != 'cpu'
is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset
nc = 1 if single_cls else int(data['nc']) # number of classes
iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
niou = iouv.numel()
# Dataloader
if not training:
if pt and not single_cls: # check --weights are trained on --data
ncm = model.model.nc
assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
f'classes). Pass correct combination of --weights and --data that are trained together.'
model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks
task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task],
imgsz,
batch_size,
stride,
single_cls,
pad=pad,
rect=rect,
workers=workers,
prefix=colorstr(f'{task}: '),
overlap_mask=overlap,
mask_downsample_ratio=mask_downsample_ratio)[0]
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
names = model.names if hasattr(model, 'names') else model.module.names # get class names
if isinstance(names, (list, tuple)): # old format
names = dict(enumerate(names))
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P', 'R',
'mAP50', 'mAP50-95)')
dt = Profile(), Profile(), Profile()
metrics = Metrics()
loss = torch.zeros(4, device=device)
jdict, stats = [], []
# callbacks.run('on_val_start')
pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar):
# callbacks.run('on_val_batch_start')
with dt[0]:
if cuda:
im = im.to(device, non_blocking=True)
targets = targets.to(device)
masks = masks.to(device)
masks = masks.float()
im = im.half() if half else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
nb, _, height, width = im.shape # batch size, channels, height, width
# Inference
with dt[1]:
preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None)
# Loss
if compute_loss:
loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls
# NMS
targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
with dt[2]:
preds = non_max_suppression(preds,
conf_thres,
iou_thres,
labels=lb,
multi_label=True,
agnostic=single_cls,
max_det=max_det,
nm=nm)
# Metrics
plot_masks = [] # masks for plotting
for si, (pred, proto) in enumerate(zip(preds, protos)):
labels = targets[targets[:, 0] == si, 1:]
nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
path, shape = Path(paths[si]), shapes[si][0]
correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
seen += 1
if npr == 0:
if nl:
stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0]))
if plots:
confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
continue
# Masks
midx = [si] if overlap else targets[:, 0] == si
gt_masks = masks[midx]
pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:])
# Predictions
if single_cls:
pred[:, 5] = 0
predn = pred.clone() | scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred | 3 | 2023-11-12 13:28:26+00:00 | 24k |
cyberark/ark-sdk-python | ark_sdk_python/cli_services/dpa/vm/ark_dpa_vm_policies_editor_service.py | [
{
"identifier": "ArkInquirerRender",
"path": "ark_sdk_python/args/ark_args_formatter.py",
"snippet": "class ArkInquirerRender(ConsoleRender):\n # pylint: disable=keyword-arg-before-vararg,protected-access\n def __init__(self, event_generator=None, *args, **kwargs):\n super().__init__(event_... | from datetime import date, timedelta
from typing import Dict, Final, List, Optional
from overrides import overrides
from ark_sdk_python.args.ark_args_formatter import ArkInquirerRender
from ark_sdk_python.auth.ark_isp_auth import ArkISPAuth
from ark_sdk_python.cli_services.dpa.common.ark_dpa_base_policies_editor_service import ArkDPABasePoliciesEditorService
from ark_sdk_python.models.ark_profile import ArkProfile
from ark_sdk_python.models.cli_services.dpa.policies_editor.vm import ArkDPAVMGeneratePolicy
from ark_sdk_python.models.common import ArkProtocolType, ArkWorkspaceType
from ark_sdk_python.models.services import ArkServiceConfig
from ark_sdk_python.models.services.dpa.policies.common import ArkDPADeletePolicy, ArkDPAGetPolicy, ArkDPARuleStatus, ArkDPAUserData
from ark_sdk_python.models.services.dpa.policies.vm import (
ArkDPAVMAddPolicy,
ArkDPAVMAuthorizationRule,
ArkDPAVMAWSProviderData,
ArkDPAVMAzureProviderData,
ArkDPAVMConnectionDataType,
ArkDPAVMConnectionInformation,
ArkDPAVMFQDNOperator,
ArkDPAVMFQDNRule,
ArkDPAVMFQDNRulesConjunction,
ArkDPAVMGCPProviderData,
ArkDPAVMLocalEphemeralUserConnectionMethodData,
ArkDPAVMOnPremProviderData,
ArkDPAVMPolicy,
ArkDPAVMPolicyListItem,
ArkDPAVMProvider,
ArkDPAVMRDPLocalEphemeralUserConnectionData,
ArkDPAVMUpdatePolicy,
)
from ark_sdk_python.services.dpa.policies.vm.ark_dpa_vm_policies_service import ArkDPAVMPoliciesService
import inquirer | 14,503 |
SERVICE_CONFIG: Final[ArkServiceConfig] = ArkServiceConfig(
service_name='dpa-policies-vm-editor', required_authenticator_names=['isp'], optional_authenticator_names=[]
)
DEFAULT_GENERATED_POLICY: Final[ArkDPAVMPolicy] = ArkDPAVMPolicy(
policy_name='Default VM Policy',
status=ArkDPARuleStatus.Draft,
description='Auto generated vm policy',
providers_data={},
start_date=date.today().strftime('%Y-%m-%d'),
end_date=(date.today() + timedelta(days=7)).strftime('%Y-%m-%d'),
user_access_rules=[],
)
DEFAULT_GENERATED_AUTHORIZATION_RULE: Final[ArkDPAVMAuthorizationRule] = ArkDPAVMAuthorizationRule(
rule_name='Default VM Rule',
user_data=ArkDPAUserData(roles=['DpaAdmin'], groups=[], users=[]),
connection_information=ArkDPAVMConnectionInformation(
connect_as={},
grant_access=2,
idle_time=10,
days_of_week=[],
full_days=True,
hours_from='07:00',
hours_to='17:00',
time_zone='Asia/Jerusalem',
),
)
DEFAULT_GENERATED_PROVIDERS: Final[Dict[ArkWorkspaceType, ArkDPAVMProvider]] = {
ArkWorkspaceType.AWS: ArkDPAVMAWSProviderData(regions=[], tags=[{'key': 'value'}], vpc_ids=[], account_ids=[]),
ArkWorkspaceType.AZURE: ArkDPAVMAzureProviderData(
regions=[], tags=[{'key': 'value'}], resource_groups=[], vnet_ids=[], subscriptions=[]
),
ArkWorkspaceType.GCP: ArkDPAVMGCPProviderData(regions=[], tags=[{'key': 'value'}], network_ids=[], projects=[]),
ArkWorkspaceType.ONPREM: ArkDPAVMOnPremProviderData(
fqdn_rules_conjunction=ArkDPAVMFQDNRulesConjunction.OR,
fqdn_rules=[ArkDPAVMFQDNRule(operator=ArkDPAVMFQDNOperator.WILDCARD, computername_pattern='*', domain='default.com')],
),
}
DEFAULT_GENERATED_PROTOCOLS: Final[Dict[ArkProtocolType, ArkDPAVMConnectionDataType]] = {
ArkProtocolType.SSH: 'root',
ArkProtocolType.RDP: ArkDPAVMRDPLocalEphemeralUserConnectionData(
local_ephemeral_user=ArkDPAVMLocalEphemeralUserConnectionMethodData(assign_groups={'Administrators'})
),
}
SUPPORTED_SSH_PROTOCOL_PROVIDERS: Final[ArkWorkspaceType] = [
ArkWorkspaceType.AWS,
ArkWorkspaceType.AZURE,
ArkWorkspaceType.GCP,
ArkWorkspaceType.ONPREM,
]
SUPPORTED_RDP_PROTOCOL_PROVIDERS: Final[ArkWorkspaceType] = [ArkWorkspaceType.AWS, ArkWorkspaceType.AZURE, ArkWorkspaceType.ONPREM]
class ArkDPAVMPoliciesEditorService(
ArkDPABasePoliciesEditorService[ArkDPAVMPolicy, ArkDPAVMPolicyListItem, ArkDPAVMAddPolicy, ArkDPAVMUpdatePolicy, ArkDPAVMGeneratePolicy]
):
|
SERVICE_CONFIG: Final[ArkServiceConfig] = ArkServiceConfig(
service_name='dpa-policies-vm-editor', required_authenticator_names=['isp'], optional_authenticator_names=[]
)
DEFAULT_GENERATED_POLICY: Final[ArkDPAVMPolicy] = ArkDPAVMPolicy(
policy_name='Default VM Policy',
status=ArkDPARuleStatus.Draft,
description='Auto generated vm policy',
providers_data={},
start_date=date.today().strftime('%Y-%m-%d'),
end_date=(date.today() + timedelta(days=7)).strftime('%Y-%m-%d'),
user_access_rules=[],
)
DEFAULT_GENERATED_AUTHORIZATION_RULE: Final[ArkDPAVMAuthorizationRule] = ArkDPAVMAuthorizationRule(
rule_name='Default VM Rule',
user_data=ArkDPAUserData(roles=['DpaAdmin'], groups=[], users=[]),
connection_information=ArkDPAVMConnectionInformation(
connect_as={},
grant_access=2,
idle_time=10,
days_of_week=[],
full_days=True,
hours_from='07:00',
hours_to='17:00',
time_zone='Asia/Jerusalem',
),
)
DEFAULT_GENERATED_PROVIDERS: Final[Dict[ArkWorkspaceType, ArkDPAVMProvider]] = {
ArkWorkspaceType.AWS: ArkDPAVMAWSProviderData(regions=[], tags=[{'key': 'value'}], vpc_ids=[], account_ids=[]),
ArkWorkspaceType.AZURE: ArkDPAVMAzureProviderData(
regions=[], tags=[{'key': 'value'}], resource_groups=[], vnet_ids=[], subscriptions=[]
),
ArkWorkspaceType.GCP: ArkDPAVMGCPProviderData(regions=[], tags=[{'key': 'value'}], network_ids=[], projects=[]),
ArkWorkspaceType.ONPREM: ArkDPAVMOnPremProviderData(
fqdn_rules_conjunction=ArkDPAVMFQDNRulesConjunction.OR,
fqdn_rules=[ArkDPAVMFQDNRule(operator=ArkDPAVMFQDNOperator.WILDCARD, computername_pattern='*', domain='default.com')],
),
}
DEFAULT_GENERATED_PROTOCOLS: Final[Dict[ArkProtocolType, ArkDPAVMConnectionDataType]] = {
ArkProtocolType.SSH: 'root',
ArkProtocolType.RDP: ArkDPAVMRDPLocalEphemeralUserConnectionData(
local_ephemeral_user=ArkDPAVMLocalEphemeralUserConnectionMethodData(assign_groups={'Administrators'})
),
}
SUPPORTED_SSH_PROTOCOL_PROVIDERS: Final[ArkWorkspaceType] = [
ArkWorkspaceType.AWS,
ArkWorkspaceType.AZURE,
ArkWorkspaceType.GCP,
ArkWorkspaceType.ONPREM,
]
SUPPORTED_RDP_PROTOCOL_PROVIDERS: Final[ArkWorkspaceType] = [ArkWorkspaceType.AWS, ArkWorkspaceType.AZURE, ArkWorkspaceType.ONPREM]
class ArkDPAVMPoliciesEditorService(
ArkDPABasePoliciesEditorService[ArkDPAVMPolicy, ArkDPAVMPolicyListItem, ArkDPAVMAddPolicy, ArkDPAVMUpdatePolicy, ArkDPAVMGeneratePolicy]
): | def __init__(self, isp_auth: ArkISPAuth, policies_cache_dir: Optional[str] = None, profile: Optional[ArkProfile] = None) -> None: | 1 | 2023-11-13 09:24:31+00:00 | 24k |
i-super/Saleor | saleor/graphql/plugins/dataloaders.py | [
{
"identifier": "PluginsManager",
"path": "saleor/plugins/manager.py",
"snippet": "class PluginsManager(PaymentInterface):\n \"\"\"Base manager for handling plugins logic.\"\"\"\n\n plugins_per_channel: dict[str, list[\"BasePlugin\"]] = {}\n global_plugins: list[\"BasePlugin\"] = []\n all_pl... | from collections import defaultdict
from functools import partial, wraps
from promise import Promise
from ...plugins.manager import PluginsManager, get_plugins_manager
from ...plugins.models import EmailTemplate
from ..app.dataloaders import get_app_promise
from ..core import SaleorContext
from ..core.dataloaders import DataLoader | 17,359 |
class EmailTemplatesByPluginConfigurationLoader(DataLoader):
"""Loads email templates by plugin configuration ID."""
context_key = "email_template_by_plugin_configuration"
def batch_load(self, keys):
email_templates = EmailTemplate.objects.using(
self.database_connection_name
).filter(plugin_configuration_id__in=keys)
config_to_template = defaultdict(list)
for et in email_templates:
config_to_template[et.plugin_configuration_id].append(et)
return [config_to_template[key] for key in keys]
class PluginManagerByRequestorDataloader(DataLoader):
context_key = "plugin_manager_by_requestor"
def batch_load(self, keys):
allow_replica = getattr(self.context, "allow_replica", True)
|
class EmailTemplatesByPluginConfigurationLoader(DataLoader):
"""Loads email templates by plugin configuration ID."""
context_key = "email_template_by_plugin_configuration"
def batch_load(self, keys):
email_templates = EmailTemplate.objects.using(
self.database_connection_name
).filter(plugin_configuration_id__in=keys)
config_to_template = defaultdict(list)
for et in email_templates:
config_to_template[et.plugin_configuration_id].append(et)
return [config_to_template[key] for key in keys]
class PluginManagerByRequestorDataloader(DataLoader):
context_key = "plugin_manager_by_requestor"
def batch_load(self, keys):
allow_replica = getattr(self.context, "allow_replica", True) | return [get_plugins_manager(lambda: key, allow_replica) for key in keys] | 1 | 2023-11-13 05:00:35+00:00 | 24k |
kampta/asic | train.py | [
{
"identifier": "Logger",
"path": "commons/logger.py",
"snippet": "class Logger(SummaryWriter):\n\n def __init__(self, results_path, log_to_tb=False, log_to_wandb=True):\n super().__init__(results_path)\n self.results_path = results_path\n self.log_to_tb = log_to_tb\n self... | import argparse
import torch
import numpy as np
import json
import os
import torch.nn.functional as F
import wandb
from torch import nn, optim
from tqdm import tqdm
from pathlib import Path
from commons.logger import Logger, log_visuals
from commons.distributed import get_rank, setup_distributed, reduce_loss_dict,\
get_world_size, primary
from commons.utils import sample_tuples
from datasets.cub import CUBDataset
from datasets.in_memory import InMemoryDataset
from datasets.spair import SpairDataset
from datasets.utils import Augmentor
from models.utils import accumulate, requires_grad
from models.canonical import Canonical, CanonicalMLP
from models.asic import Asic
from losses.reg_losses import total_variation_loss
from thirdparty.lpips.lpips import get_perceptual_loss
from losses.matching_losses import LossCorrsSparse
from thirdparty.gangealing.annealing import DecayingCosineAnnealingWarmRestarts,\
lr_cycle_iters | 17,457 | N = args.batch
pairs = sample_tuples(len(train_dset), count=N // 2)
src_idx, trg_idx = pairs[:, 0], pairs[:, 1]
all_idx = np.concatenate([src_idx, trg_idx])
batch_imgs = all_imgs[all_idx]
batch_parts = all_parts[all_idx]
if args.use_nbb_parts:
batch_masks = (batch_parts != num_parts).unsqueeze(1).float()
batch_masks_resized = resize_fn(batch_masks)
else:
batch_masks = all_masks[all_idx]
batch_masks_resized = resize_fn(batch_masks)
kp1 = pseudo_kps[src_idx, trg_idx][:, :loss_topk] # (N/2, K, 4)
kp2 = pseudo_kps[trg_idx, src_idx][:, :loss_topk] # (N/2, K, 4)
batch_kps_vis = kp1[..., 2] > 0 # (N/2, K)
batch_kps_wt = torch.ones_like(batch_kps_vis).float() # (N/2, K)
batch_kps = torch.cat([kp1, kp2])[..., :2] # (N, K, 2)
if args.use_nbb_parts:
nbb_parts_vis = (kp1[..., 3] != args.num_parts) * (kp2[..., 3] != args.num_parts)
batch_kps_wt *= nbb_parts_vis
# Map the images to the canonical space
flow, delta_flow = stn(batch_imgs)
unwarped = canon.unwarp(flow, args.unwarp_size)
# NBB weight
if args.nbb_weight > 0.:
nbb_loss = nbb_loss_fn(flow[:N//2], flow[N//2:],
batch_kps[:N//2], batch_kps[N//2:],
batch_kps_vis, batch_kps_wt)
if args.equi_weight > 0.:
# Apply tps transformations
if args.disable_tps:
batch_imgs_t = aug.forward_geom(aug.forward_color(batch_imgs))
batch_masks_t = aug.forward_geom(batch_masks, fixed=True)
# Apply tps to flow
flow_tf = aug.forward_geom(flow.permute(0, 3, 1, 2), fixed=True).permute(0, 2, 3, 1)
else:
batch_imgs_t = aug.forward_tps(aug.forward_color(batch_imgs))
batch_masks_t = aug.forward_tps(batch_masks, fixed=True)
# Apply tps to flow
flow_tf = aug.forward_tps(flow.permute(0, 3, 1, 2), fixed=True).permute(0, 2, 3, 1)
batch_masks_t = torch.where(batch_masks_t > 0.5, 1., 0.)
batch_masks_t_resized = resize_fn(batch_masks_t)
vis = batch_masks_t * batch_masks
# Flow of tps image
flow_ft, _ = stn(batch_imgs_t)
unwarped_ft = canon.unwarp(flow_ft, args.unwarp_size)
equi_loss = F.l1_loss(flow_ft, flow_tf.detach(), reduction='none') \
+ F.l1_loss(flow_tf, flow_ft.detach(), reduction='none')
equi_loss = (equi_loss * vis.squeeze(1).unsqueeze(-1)).mean()
if args.mask_weight > 0:
unwarped_mask = unwarped[:, [3]]
mask_loss = F.binary_cross_entropy_with_logits(unwarped_mask, batch_masks_resized)
if args.equi_weight > 0.:
unwarped_ft_mask = unwarped_ft[:, [3]]
mask_loss = 0.5 * mask_loss + \
0.5 * F.binary_cross_entropy_with_logits(
unwarped_ft_mask, batch_masks_t_resized)
# Get Total Variation Loss on flow
if args.flow_tv_weight > 0:
flow_tv_loss = total_variation_loss(delta_flow)
# Reconstruction loss
if args.rec_weight > 0:
unwarped = unwarped * batch_masks_resized
resized_img = resize_fn(batch_imgs) * batch_masks_resized
rec_loss = loss_fn(unwarped[:, :3], resized_img).mean()
if args.equi_weight > 0.:
unwarped_ft = unwarped_ft * batch_masks_t_resized
resized_img = resize_fn(batch_imgs_t) * batch_masks_t_resized
rec_loss = 0.5*rec_loss + 0.5 * loss_fn(unwarped_ft[:, :3], resized_img).mean()
# Parts Loss
if args.parts_weight > 0.:
# Calculate the centroid of each part
part_centroids = torch.zeros(num_parts+1, 2, dtype=torch.float,
device=device)
part_centroids.index_add_(0, batch_parts.reshape(-1),
flow.reshape(-1, 2))
part_counts = torch.bincount(batch_parts.reshape(-1)).float()
part_centroids = (part_centroids/part_counts.unsqueeze(-1)).detach()
# Compute the loss as the distance of the centroid from the flows
parts_loss = F.l1_loss(flow, part_centroids[batch_parts],
reduction='none')
parts_loss = (parts_loss * batch_masks.squeeze(1).unsqueeze(-1)).mean()
loss_dict = {"p": rec_loss, "ftv": flow_tv_loss,
"nbb": nbb_loss, "equi": equi_loss, "mask": mask_loss,
'parts': parts_loss}
canon.zero_grad()
stn.zero_grad()
full_stn_loss = args.rec_weight * rec_loss + \
args.flow_tv_weight * flow_tv_loss + \
args.nbb_weight * nbb_loss + args.equi_weight * equi_loss + \
args.mask_weight * mask_loss + args.parts_weight * parts_loss
full_stn_loss.backward()
t_optim.step()
epoch = max(0, i / args.period)
t_sched.step(epoch)
if args.canon_lr > 0:
canon_optim.step()
canon_sched.step(epoch)
if args.stn_ema:
|
def save_state_dict(ckpt_name, c_module, t_module, c_ema, t_ema, canon_optim,
canon_sched, t_optim, t_sched, args, step,
add_step_to_name=False):
ckpt_dict = {
"canon": c_module.state_dict(),
"t": t_module.state_dict(),
"c_ema": c_ema.state_dict(),
"t_ema": t_ema.state_dict(),
"t_optim": t_optim.state_dict(),
"t_sched": t_sched.state_dict(),
"canon_optim": canon_optim.state_dict()
if canon_optim is not None else None,
"canon_sched": canon_sched.state_dict()
if canon_sched is not None else None,
"args": args,
"iter": step
}
torch.save(ckpt_dict, f'{results_path}/{ckpt_name}.pt')
if add_step_to_name:
torch.save(ckpt_dict, f'{results_path}/{ckpt_name}_{step:07d}.pt')
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def base_training_argparse():
parser = argparse.ArgumentParser(description="Training")
# Main training arguments:
parser.add_argument("--exp-name", type=str, required=True,
help="Name for experiment run (used for logging)")
parser.add_argument("--results", type=str, default='logs',
help='path to the results directory')
parser.add_argument("--seed", default=0, type=int,
help='Random seed for this experiment')
parser.add_argument("--dset", type=str, default='cub',
choices=["cub", "spair"])
parser.add_argument("--img_dir", type=str, required=True,
help="Path to real data")
parser.add_argument("--flow_dir", type=str, default='processed_data',
help="Path to preprocessed flows")
parser.add_argument("--mask_threshold", type=int, default=1,
help="Threshold for masking")
parser.add_argument("--mask_bbox_pad", type=int, default=4,
help="Crop with some padding")
parser.add_argument("--img_size", default=256, type=int,
help='resolution of real images')
parser.add_argument("--iter", type=int, default=20000,
help="total training iterations")
parser.add_argument("--batch", type=int, default=20,
help="batch size per-GPU")
parser.add_argument("--num_workers", type=int, default=2,
help="num workers for dataloader")
# Dataset hyperparameters:
parser.add_argument("--cub_idx", type=int, default=1, help="cub category")
parser.add_argument("--split", default='test',
choices=['test', 'val'],
help='splits for training and validation')
parser.add_argument("--use_coseg_masks", action='store_true')
parser.add_argument("--num_parts", default=4, type=int)
parser.add_argument("--spair_cat", default='cat', help="cub category")
# Loss hyperparameters:
parser.add_argument("--loss_fn", type=str, default='vgg_ssl',
choices=['lpips', 'vgg_ssl'],
help="The perceptual loss to use.")
parser.add_argument("--rec_weight", type=float, default=1.,
help='weight for reconstruction loss')
parser.add_argument("--nbb_weight", type=float, default=30.,
help='weight for nbb loss')
parser.add_argument("--flow_tv_weight", default=15000.0, type=float,
help="""Loss weighting of the Total Variation
smoothness regularizer on the residual flow""")
parser.add_argument("--equi_weight", default=1.0, type=float,
help='Loss weighting for equivariance')
parser.add_argument("--sparse_topk", type=int, default=None,
help='number of sparse correspondences for loss')
parser.add_argument("--sparse_temp", type=float, default=1,
help='temperature for sparse loss')
parser.add_argument("--mask_weight", default=0.1, type=float,
help="""Loss weighting of the mask""")
parser.add_argument("--parts_weight", default=10.0, type=float,
help="""Loss weighting of the Parts Mask""")
parser.add_argument("--use_nbb_parts", action='store_true')
# Augmentation hyperparameters
parser.add_argument("--jitter", default=[0.4, 0.4, 0.2, 0.1], type=float,
nargs='+', help='augmentation mode')
parser.add_argument("--jitter_prob", default=0.8, type=float)
parser.add_argument("--gray_prob", default=0.2, type=float)
parser.add_argument("--solar_prob", default=0.2, type=float)
parser.add_argument("--tps_scale", default=0.4, type=float)
# Canonical space
parser.add_argument("--unwarp_size", type=int, default=128,
help="resolution for unwarping")
# Learned Grid hyperparameters
parser.add_argument("--canon_size", type=int, default=256,
help="resolution of canonical space")
parser.add_argument("--clamp", action='store_true',
help="clamp values of canonical space (-1, 1)")
# MLP Hyperparams
parser.add_argument("--use_mlp", action='store_true')
parser.add_argument("--mlp_hidden_dim", type=int, default=256,
help="number of hidden units per layer")
parser.add_argument("--mlp_num_layers", type=int, default=8,
help="number of layers")
parser.add_argument("--mlp_skip_layers", type=int, nargs='+',
default=[4, 7], help="skip layers")
# Model hyperparameters:
parser.add_argument("--canon_lr", type=float, default=0.003,
help="base learning rate of canonical space")
parser.add_argument("--canon_ema", action='store_true',
help='Enable ema for canonical space')
parser.add_argument("--stn_ema", action='store_true',
help='Enable ema for canonical space')
parser.add_argument("--stn_lr", type=float, default=0.003,
help="base learning rate of SpatialTransformer")
parser.add_argument("--flow_ssl", action='store_true',
help="""If specified, apply STN on SSL features)""")
parser.add_argument("--channel_multiplier", default=0.5, type=float,
help='channel multiplier for smaller models')
parser.add_argument("--bilinear", action='store_true',
help='Apply bilinear upsample/downsample')
parser.add_argument("--padding_mode", default='border',
choices=['border', 'zeros', 'reflection'], type=str,
help="""Padding algorithm for when the STN samples
beyond image boundaries""")
parser.add_argument("--use_tanh", action='store_true',
help='Use tanh activation at the flow output')
parser.add_argument("--disable_tps", action='store_true',
help='disable tps transformations')
# Backbone parameters
parser.add_argument("--bb", default='dino_vits8',
choices=['dino_vits8', 'dino_vits16', 'dino_vitb8',
'dino_vitb16', 'vit_small_patch8_224',
'vit_small_patch16_224',
'vit_base_patch16_224'],
help='backbone models')
parser.add_argument('--bb_stride', default=2, type=int,
help="stride.")
# Visualization hyperparameters:
parser.add_argument("--vis_every", type=int, default=500,
help="""frequency with which visualizations are
generated during training""")
parser.add_argument("--vis_denseres", type=int, default=32,
help='number of sparse correspondences to visualize')
parser.add_argument("--ckpt_every", type=int, default=10000,
help='frequency of checkpointing during training')
parser.add_argument("--log_every", default=25, type=int,
help='How frequently to log data to TensorBoard')
parser.add_argument("--n_sample", type=int, default=4,
help="""number of images (real and fake) to
generate visuals for""")
parser.add_argument("--disable_wandb", action='store_true',
help='Disable wandb for debugging')
# Learning Rate scheduler hyperparameters:
parser.add_argument("--period", default=10000, type=float,
help="""Period for cosine learning rate scheduler
(measured in gradient steps)""")
parser.add_argument("--decay", default=0.9, type=float,
help="""Decay factor for the cosine learning rate
scheduler""")
parser.add_argument("--tm", default=2, type=int,
help="""Period multiplier for the cosine learning
rate scheduler""")
return parser
def train(args, train_dset, canon, stn, c_ema, t_ema, canon_optim,
canon_sched, t_optim, t_sched, loss_fn, nbb_loss_fn, device, writer):
# Record modules to make saving checkpoints easier:
if args.distributed:
t_module = stn.module
c_module = canon.module
else:
t_module = stn
c_module = canon
# Initialize Spatial Transformation Generator (Thin Plate Spline)
aug = Augmentor(jitter=args.jitter, jitter_prob=args.jitter_prob,
gray_prob=args.gray_prob, solar_prob=args.solar_prob,
tps_scale=args.tps_scale).to(device)
# A model checkpoint will be saved whenever the learning rate is zero:
zero_lr_iters = lr_cycle_iters(0, args.period, args.iter, args.tm)
early_ckpt_iters = set(zero_lr_iters)
early_vis_iters = {100}
early_vis_iters.update(early_ckpt_iters)
# Initialize various training variables and constants:
rec_loss = torch.tensor(0.0, device='cuda')
flow_tv_loss = torch.tensor(0.0, device='cuda')
nbb_loss = torch.tensor(0.0, device='cuda')
equi_loss = torch.tensor(0.0, device='cuda')
mask_loss = torch.tensor(0.0, device='cuda')
parts_loss = torch.tensor(0.0, device='cuda')
accum = 0.5 ** (32 / (10 * 1000))
# Resize function for perceptual loss
if args.unwarp_size != args.img_size:
scale_factor = args.unwarp_size / args.img_size
resize_fn = nn.Upsample(scale_factor=scale_factor, mode='bilinear',
align_corners=True)
else:
resize_fn = nn.Identity()
# Pre-load on GPU
# Assuming ~30 images of size 256x256, takes up ~23 MB device memory
has_gt_kp = train_dset.kps is not None
all_imgs = train_dset.imgs = train_dset.imgs.to(device) # / 127.5 - 1.0
all_masks = train_dset.masks = train_dset.masks.unsqueeze(1).to(device)
all_parts = train_dset.parts = train_dset.parts.to(device)
if has_gt_kp:
all_kps = train_dset.kps = train_dset.kps.to(device)
# Pseudo GT
pseudo_kps = train_dset.pseudo_kps = torch.from_numpy(train_dset.pseudo_kps).to(device)
num_parts = train_dset.num_parts
loss_topk = pseudo_kps.shape[2] if args.sparse_topk is None else min(args.sparse_topk, pseudo_kps.shape[2])
# Progress bar for monitoring training:
pbar = range(args.start_iter, args.iter)
if primary():
pbar = tqdm(pbar, initial=args.start_iter, dynamic_ncols=True,
smoothing=0.2)
pck_pairs, pck_cycles = log_visuals(
c_ema, t_ema, train_dset, 0, writer, vis_sample=args.n_sample,
vis_denseres=args.vis_denseres)
best_pck_pairs = pck_pairs
best_pck_cycles = pck_cycles
requires_grad(stn, True)
requires_grad(canon, True)
for idx in pbar: # main training loop
i = idx + args.start_iter + 1
####################################
# TRAIN STN and CANON #
####################################
N = args.batch
pairs = sample_tuples(len(train_dset), count=N // 2)
src_idx, trg_idx = pairs[:, 0], pairs[:, 1]
all_idx = np.concatenate([src_idx, trg_idx])
batch_imgs = all_imgs[all_idx]
batch_parts = all_parts[all_idx]
if args.use_nbb_parts:
batch_masks = (batch_parts != num_parts).unsqueeze(1).float()
batch_masks_resized = resize_fn(batch_masks)
else:
batch_masks = all_masks[all_idx]
batch_masks_resized = resize_fn(batch_masks)
kp1 = pseudo_kps[src_idx, trg_idx][:, :loss_topk] # (N/2, K, 4)
kp2 = pseudo_kps[trg_idx, src_idx][:, :loss_topk] # (N/2, K, 4)
batch_kps_vis = kp1[..., 2] > 0 # (N/2, K)
batch_kps_wt = torch.ones_like(batch_kps_vis).float() # (N/2, K)
batch_kps = torch.cat([kp1, kp2])[..., :2] # (N, K, 2)
if args.use_nbb_parts:
nbb_parts_vis = (kp1[..., 3] != args.num_parts) * (kp2[..., 3] != args.num_parts)
batch_kps_wt *= nbb_parts_vis
# Map the images to the canonical space
flow, delta_flow = stn(batch_imgs)
unwarped = canon.unwarp(flow, args.unwarp_size)
# NBB weight
if args.nbb_weight > 0.:
nbb_loss = nbb_loss_fn(flow[:N//2], flow[N//2:],
batch_kps[:N//2], batch_kps[N//2:],
batch_kps_vis, batch_kps_wt)
if args.equi_weight > 0.:
# Apply tps transformations
if args.disable_tps:
batch_imgs_t = aug.forward_geom(aug.forward_color(batch_imgs))
batch_masks_t = aug.forward_geom(batch_masks, fixed=True)
# Apply tps to flow
flow_tf = aug.forward_geom(flow.permute(0, 3, 1, 2), fixed=True).permute(0, 2, 3, 1)
else:
batch_imgs_t = aug.forward_tps(aug.forward_color(batch_imgs))
batch_masks_t = aug.forward_tps(batch_masks, fixed=True)
# Apply tps to flow
flow_tf = aug.forward_tps(flow.permute(0, 3, 1, 2), fixed=True).permute(0, 2, 3, 1)
batch_masks_t = torch.where(batch_masks_t > 0.5, 1., 0.)
batch_masks_t_resized = resize_fn(batch_masks_t)
vis = batch_masks_t * batch_masks
# Flow of tps image
flow_ft, _ = stn(batch_imgs_t)
unwarped_ft = canon.unwarp(flow_ft, args.unwarp_size)
equi_loss = F.l1_loss(flow_ft, flow_tf.detach(), reduction='none') \
+ F.l1_loss(flow_tf, flow_ft.detach(), reduction='none')
equi_loss = (equi_loss * vis.squeeze(1).unsqueeze(-1)).mean()
if args.mask_weight > 0:
unwarped_mask = unwarped[:, [3]]
mask_loss = F.binary_cross_entropy_with_logits(unwarped_mask, batch_masks_resized)
if args.equi_weight > 0.:
unwarped_ft_mask = unwarped_ft[:, [3]]
mask_loss = 0.5 * mask_loss + \
0.5 * F.binary_cross_entropy_with_logits(
unwarped_ft_mask, batch_masks_t_resized)
# Get Total Variation Loss on flow
if args.flow_tv_weight > 0:
flow_tv_loss = total_variation_loss(delta_flow)
# Reconstruction loss
if args.rec_weight > 0:
unwarped = unwarped * batch_masks_resized
resized_img = resize_fn(batch_imgs) * batch_masks_resized
rec_loss = loss_fn(unwarped[:, :3], resized_img).mean()
if args.equi_weight > 0.:
unwarped_ft = unwarped_ft * batch_masks_t_resized
resized_img = resize_fn(batch_imgs_t) * batch_masks_t_resized
rec_loss = 0.5*rec_loss + 0.5 * loss_fn(unwarped_ft[:, :3], resized_img).mean()
# Parts Loss
if args.parts_weight > 0.:
# Calculate the centroid of each part
part_centroids = torch.zeros(num_parts+1, 2, dtype=torch.float,
device=device)
part_centroids.index_add_(0, batch_parts.reshape(-1),
flow.reshape(-1, 2))
part_counts = torch.bincount(batch_parts.reshape(-1)).float()
part_centroids = (part_centroids/part_counts.unsqueeze(-1)).detach()
# Compute the loss as the distance of the centroid from the flows
parts_loss = F.l1_loss(flow, part_centroids[batch_parts],
reduction='none')
parts_loss = (parts_loss * batch_masks.squeeze(1).unsqueeze(-1)).mean()
loss_dict = {"p": rec_loss, "ftv": flow_tv_loss,
"nbb": nbb_loss, "equi": equi_loss, "mask": mask_loss,
'parts': parts_loss}
canon.zero_grad()
stn.zero_grad()
full_stn_loss = args.rec_weight * rec_loss + \
args.flow_tv_weight * flow_tv_loss + \
args.nbb_weight * nbb_loss + args.equi_weight * equi_loss + \
args.mask_weight * mask_loss + args.parts_weight * parts_loss
full_stn_loss.backward()
t_optim.step()
epoch = max(0, i / args.period)
t_sched.step(epoch)
if args.canon_lr > 0:
canon_optim.step()
canon_sched.step(epoch)
if args.stn_ema: | accumulate(t_ema, t_module, accum) | 12 | 2023-11-14 16:43:16+00:00 | 24k |
tyang816/ProtSSN | src/data.py | [
{
"identifier": "CathDataset",
"path": "src/dataset/cath_dataset.py",
"snippet": "class CathDataset(InMemoryDataset):\n r\"\"\"\n Args:\n root (string): Root directory where the dataset should be saved.\n name (string): The name of the dataset.\n raw_dir (string, optional): Ro... | import os, sys
import argparse
from src.dataset.cath_dataset import CathDataset
from src.dataset.mutant_dataset import MutantDataset
from src.utils.dataset_utils import NormalizeProtein | 17,167 | # set path
current_dir = os.getcwd()
sys.path.append(current_dir)
def build_cath_dataset(args, split):
| # set path
current_dir = os.getcwd()
sys.path.append(current_dir)
def build_cath_dataset(args, split): | dataset = CathDataset( | 0 | 2023-11-10 07:21:37+00:00 | 24k |
atlantic-quantum/Shipyard | tests/printers/visualizer/test_visualize_pulse_sequences.py | [
{
"identifier": "CoreType",
"path": "shipyard/awg_core/awg_core.py",
"snippet": "class CoreType(Enum):\n \"\"\"Enumeration of AWG Core types\"\"\"\n\n HD = \"HD\"\n QA = \"QA\"\n SG = \"SG\""
},
{
"identifier": "ActivationRecord",
"path": "shipyard/call_stack.py",
"snippet": ... | import codecs
import json
import numpy as np
import pytest
from pathlib import Path
from shipyard.awg_core.awg_core import CoreType
from shipyard.call_stack import ActivationRecord, ARType
from shipyard.compiler import Compiler
from shipyard.duration import Duration, TimeUnits
from shipyard.passes.duration_transformer import DurationTransformer
from shipyard.passes.resolve_io_declaration import ResolveIODeclaration
from shipyard.passes.semantic_analysis.semantic_analyzer import SemanticAnalyzer
from shipyard.printers.visualizer.visualize_pulse_sequence import PulseVisualizer
from shipyard.printers.zi import waveform_functions
from shipyard.setup.internal import Frame, Instrument, Port, SetupInternal | 17,463 |
final_call_stack = {
"nested_subroutines": {"dummy": 16},
"complex_arrays": {
"dummy": 4,
"two_d": [[1, 2], [3, 4], [5, 6]],
"my_arr": [complex(1, 0), complex(0, 1), complex(0.8, 0.6)],
"second": [1, 2, 3, 4],
},
}
def files() -> list[str]:
base_path = Path(__file__).parent.parent.parent / "qasm/visualize_pulse"
plen = len(base_path.parts)
FILES = list(base_path.glob("**/*.qasm"))
return [str(Path(*path.parts[plen:])) for path in FILES]
QASM_FILES = files()
def common_files() -> list[str]:
files = []
cut = -5
for q_file in QASM_FILES:
files.append(q_file[:cut])
return files
COMMON_FILES = common_files()
@pytest.fixture(name="basic_setup")
def fixture_basic_setup() -> SetupInternal:
json_path = Path(__file__).parent.parent.parent / "setups/interpreter.json"
return SetupInternal.from_json(json_path)
def test_visit_ClassicalDeclaration():
setup_path = Path(__file__).parent.parent.parent / "setups/complex.json"
qasm_path = Path(__file__).parent.parent.parent / "qasm/interpreter/phase_freq.qasm"
compiler = Compiler(qasm_path, setup_path)
qasm_ast = compiler.load_program(qasm_path)
ResolveIODeclaration().visit(qasm_ast)
SemanticAnalyzer().visit(qasm_ast)
DurationTransformer().visit(qasm_ast)
|
final_call_stack = {
"nested_subroutines": {"dummy": 16},
"complex_arrays": {
"dummy": 4,
"two_d": [[1, 2], [3, 4], [5, 6]],
"my_arr": [complex(1, 0), complex(0, 1), complex(0.8, 0.6)],
"second": [1, 2, 3, 4],
},
}
def files() -> list[str]:
base_path = Path(__file__).parent.parent.parent / "qasm/visualize_pulse"
plen = len(base_path.parts)
FILES = list(base_path.glob("**/*.qasm"))
return [str(Path(*path.parts[plen:])) for path in FILES]
QASM_FILES = files()
def common_files() -> list[str]:
files = []
cut = -5
for q_file in QASM_FILES:
files.append(q_file[:cut])
return files
COMMON_FILES = common_files()
@pytest.fixture(name="basic_setup")
def fixture_basic_setup() -> SetupInternal:
json_path = Path(__file__).parent.parent.parent / "setups/interpreter.json"
return SetupInternal.from_json(json_path)
def test_visit_ClassicalDeclaration():
setup_path = Path(__file__).parent.parent.parent / "setups/complex.json"
qasm_path = Path(__file__).parent.parent.parent / "qasm/interpreter/phase_freq.qasm"
compiler = Compiler(qasm_path, setup_path)
qasm_ast = compiler.load_program(qasm_path)
ResolveIODeclaration().visit(qasm_ast)
SemanticAnalyzer().visit(qasm_ast)
DurationTransformer().visit(qasm_ast) | pv = PulseVisualizer( | 9 | 2023-11-16 17:37:29+00:00 | 24k |
quantuminterface/qiclib | src/qiclib/code/qi_jobs.py | [
{
"identifier": "TaskRunner",
"path": "src/qiclib/hardware/taskrunner.py",
"snippet": "class TaskRunner(PlatformComponent):\n \"\"\"Driver to control the Taskrunner on the Hardware Platform.\"\"\"\n\n def __init__(\n self,\n name: str,\n connection,\n controller,\n ... | import os
import json
import functools
import warnings
import numpy as np
import qiclib.packages.utility as util
from abc import abstractmethod
from typing import Dict, List, Callable, Optional, Union, Set, Any, Type
from ..hardware.taskrunner import TaskRunner
from ..experiment.qicode.data_provider import DataProvider
from ..experiment.qicode.data_handler import DataHandler
from .qi_seq_instructions import SequencerInstruction
from .qi_var_definitions import (
_QiVariableBase,
_QiCalcBase,
_QiConstValue,
QiCellProperty,
QiExpression,
QiVariableSet,
QiCondition,
)
from .qi_pulse import QiPulse
from .qi_visitor import (
QiCMContainedCellVisitor,
QiResultCollector,
QiVarInForRange,
)
from .qi_prog_builder import QiProgramBuilder
from .qi_types import (
QiType,
QiPostTypecheckVisitor,
QiTypeFallbackVisitor,
_TypeDefiningUse,
)
from .qi_types import _TypeDefiningUse
from .qi_types import _TypeDefiningUse
from .qi_types import (
_TypeConstraintReasonQiCommand,
_IllegalTypeReason,
_add_equal_constraints,
)
from .qi_types import (
_TypeConstraintReasonQiCommand,
_IllegalTypeReason,
_add_equal_constraints,
)
from .analysis.qi_insert_mem_parameters import (
insert_recording_offset_store_commands,
insert_manipulation_pulse_frequency_store_commands,
insert_readout_pulse_frequency_store_commands,
)
from .qi_simulate import Simulator
from ..experiment.qicode.base import QiCodeExperiment
from qiclib.experiment.qicode.base import _TaskrunnerSettings
from .qi_visitor import QiStringifyJob | 16,846 | raise RuntimeError("Can not use command outside QiJob context manager.")
_QiJobReference._add_command(cmd)
def _set_job_reference(job):
"""Used for testing purposes"""
# pylint: disable=global-statement
global _QiJobReference
_QiJobReference = job
def _delete_job_reference():
"""Used for testing purposes"""
# pylint: disable=global-statement
global _QiJobReference
_QiJobReference = None
class QiCell:
"""A QiCell is an abstract representation of the qubit/cell the program is run on.
Usually, a single :python:`QiCell` is not instantiated, but instead a :class:`QiCells` object.
For a single :python:`QiCell`, use instead :python:`QiCells(1)`
A :python:`QiCell` must be instantiated inside within a :class:`QiJob` context.
The :python:`QiCell` object can be used to get properties that are defined on :class:`QiSamples <QiSample>`.
For this, index the :python:`QiCell` object using the name of the property:
.. code-block:: python
q: QiCell = ...
t1_time = q["t1"]
The actual value for the accessed property (in the example above, the T1 time) is filled in when executing a
:class:`QiJob` and providing the actual sample.
**Tasks of the QiCell**:
- Saves the pulses needed for program execution.
- Provides a dictionary functionality to define commonly used durations/properties.
- Implements a Sequencer object, which contains the assembler program after compilation.
:param cellID: A unique ID
:raises RuntimeError: When the :python:`QiCell` is instantiated outside a `QiJob`
"""
def __init__(self, cellID: int):
if not isinstance(_QiJobReference, QiJob):
raise RuntimeError("QiCell can't be used outside of QiJob.")
self.cellID = cellID
self.manipulation_pulses: List[QiPulse] = []
self.flux_pulses: List[QiPulse] = []
self.readout_pulses: List[QiPulse] = []
self._result_container: Dict[str, QiResult] = {}
# The order in which recorded values are assigned to which result container
self._result_recording_order: List[QiResult] = []
self._unresolved_property: Set[QiCellProperty] = set()
self._job_ref = _QiJobReference
self._relevant_vars: Set[_QiVariableBase] = set()
# These attributes are determined by dataflow analyses
self._initial_manip_freq: float = None
self._initial_readout_freq: float = None
self._initial_rec_offset: float = None
self._rec_length: Union[int, float, QiCellProperty] = None
self._properties: Dict[QiCellProperty, Any] = {}
def __getitem__(self, key):
if _QiJobReference != self._job_ref:
raise RuntimeError(
"Tried getting values for cells registered to other QiJob"
)
prop = self._properties.get(key, QiCellProperty(self, key))
if isinstance(prop, QiCellProperty):
self._unresolved_property.add(key)
return prop
def __setitem__(self, key, value):
if _QiJobReference != self._job_ref:
raise RuntimeError(
"Tried setting values for cells registered to other QiJob"
)
self._properties[key] = value
def __call__(self, qic):
return qic.cell[self.qic_cell]
def get_properties(self):
return self._properties.copy()
def add_pulse(self, pulse: QiPulse):
if pulse not in self.manipulation_pulses:
self.manipulation_pulses.append(pulse)
if len(self.manipulation_pulses) > 13:
raise RuntimeError("Too many pulses in use")
return self.manipulation_pulses.index(pulse) + 1 # index 0 and 15 are reserved
@property
def initial_manipulation_frequency(self):
if self._initial_manip_freq is None:
if len(self.manipulation_pulses) > 0:
warnings.warn(
"Manipulation pulses without frequency given, using 90 MHz."
)
return 90e6 # Default frequency
freq = self._initial_manip_freq
return freq() if isinstance(freq, QiCellProperty) else freq
def add_recording_length(self, length):
if self._rec_length is None:
self._rec_length = length
elif (
not self._rec_length._equal_syntax(length)
| # Copyright © 2017-2023 Quantum Interface (quantuminterface@ipe.kit.edu)
# Richard Gebauer, IPE, Karlsruhe Institute of Technology
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
This is the main module of QiCode.
Here, all important commands write QiPrograms are defined.
"""
class QiResult:
"""Result of an experiment. Can be accessed via :python:`job.cells[cell_index].data("result name")`.
Where :python:`cells` denotes a :class:`QiCells` object and :python:`cell_index` an integer.
The actual data can be retrieved as a numpy array using the :meth:`get` Method
Example
-------
.. code-block:: python
qic: QiController = ...
sample: QiSample = ...
with QiJob() as job:
q = QiCells(1)
Readout(q[0], save_to="result")
job.run(qic, sample, averages=1000)
data = job.cells[0].data("result")
:param name: The name of the variable, by default None
"""
def __init__(self, name: Optional[str] = None) -> None:
self._cell = None
self.data = None
self.recording_count = 0
self.name: str = "" if name is None else name
def get(self) -> np.ndarray:
"""gets the data of the result as a numpy array
:return: The data of the experiment
"""
return np.array(self.data)
def __str__(self) -> str:
return f'QiResult("{self.name}")'
class QiCommand:
"""Base class of every Job command.
Provides _relevant_cells, containing every cell used for the execution of the command.
Provides _associated_variable_set, containing every variable needed for the execution of the command.
"""
def __init__(self) -> None:
self._associated_variable_set = QiVariableSet()
self._relevant_cells: Set[QiCell] = set()
@abstractmethod
def accept(self, visitor, *input):
raise RuntimeError(
f"{self.__class__} doesn't implement `accept`. This is a bug."
)
def is_variable_relevant(self, variable: _QiVariableBase) -> bool:
return variable in self._associated_variable_set
def add_associated_variable(self, x):
if isinstance(x, _QiVariableBase):
self._associated_variable_set.add(x)
def __str__(self) -> str:
return "cQiCommand"
def _stringify(self) -> str:
raise NotImplementedError(f"_stringify not implemented for {repr(self)}")
_QiJobReference = None
def _add_cmd_to_job(cmd: QiCommand):
if _QiJobReference is None:
raise RuntimeError("Can not use command outside QiJob context manager.")
_QiJobReference._add_command(cmd)
def _set_job_reference(job):
"""Used for testing purposes"""
# pylint: disable=global-statement
global _QiJobReference
_QiJobReference = job
def _delete_job_reference():
"""Used for testing purposes"""
# pylint: disable=global-statement
global _QiJobReference
_QiJobReference = None
class QiCell:
"""A QiCell is an abstract representation of the qubit/cell the program is run on.
Usually, a single :python:`QiCell` is not instantiated, but instead a :class:`QiCells` object.
For a single :python:`QiCell`, use instead :python:`QiCells(1)`
A :python:`QiCell` must be instantiated inside within a :class:`QiJob` context.
The :python:`QiCell` object can be used to get properties that are defined on :class:`QiSamples <QiSample>`.
For this, index the :python:`QiCell` object using the name of the property:
.. code-block:: python
q: QiCell = ...
t1_time = q["t1"]
The actual value for the accessed property (in the example above, the T1 time) is filled in when executing a
:class:`QiJob` and providing the actual sample.
**Tasks of the QiCell**:
- Saves the pulses needed for program execution.
- Provides a dictionary functionality to define commonly used durations/properties.
- Implements a Sequencer object, which contains the assembler program after compilation.
:param cellID: A unique ID
:raises RuntimeError: When the :python:`QiCell` is instantiated outside a `QiJob`
"""
def __init__(self, cellID: int):
if not isinstance(_QiJobReference, QiJob):
raise RuntimeError("QiCell can't be used outside of QiJob.")
self.cellID = cellID
self.manipulation_pulses: List[QiPulse] = []
self.flux_pulses: List[QiPulse] = []
self.readout_pulses: List[QiPulse] = []
self._result_container: Dict[str, QiResult] = {}
# The order in which recorded values are assigned to which result container
self._result_recording_order: List[QiResult] = []
self._unresolved_property: Set[QiCellProperty] = set()
self._job_ref = _QiJobReference
self._relevant_vars: Set[_QiVariableBase] = set()
# These attributes are determined by dataflow analyses
self._initial_manip_freq: float = None
self._initial_readout_freq: float = None
self._initial_rec_offset: float = None
self._rec_length: Union[int, float, QiCellProperty] = None
self._properties: Dict[QiCellProperty, Any] = {}
def __getitem__(self, key):
if _QiJobReference != self._job_ref:
raise RuntimeError(
"Tried getting values for cells registered to other QiJob"
)
prop = self._properties.get(key, QiCellProperty(self, key))
if isinstance(prop, QiCellProperty):
self._unresolved_property.add(key)
return prop
def __setitem__(self, key, value):
if _QiJobReference != self._job_ref:
raise RuntimeError(
"Tried setting values for cells registered to other QiJob"
)
self._properties[key] = value
def __call__(self, qic):
return qic.cell[self.qic_cell]
def get_properties(self):
return self._properties.copy()
def add_pulse(self, pulse: QiPulse):
if pulse not in self.manipulation_pulses:
self.manipulation_pulses.append(pulse)
if len(self.manipulation_pulses) > 13:
raise RuntimeError("Too many pulses in use")
return self.manipulation_pulses.index(pulse) + 1 # index 0 and 15 are reserved
@property
def initial_manipulation_frequency(self):
if self._initial_manip_freq is None:
if len(self.manipulation_pulses) > 0:
warnings.warn(
"Manipulation pulses without frequency given, using 90 MHz."
)
return 90e6 # Default frequency
freq = self._initial_manip_freq
return freq() if isinstance(freq, QiCellProperty) else freq
def add_recording_length(self, length):
if self._rec_length is None:
self._rec_length = length
elif (
not self._rec_length._equal_syntax(length) | if isinstance(self._rec_length, QiExpression) | 8 | 2023-11-10 10:26:10+00:00 | 24k |
jpcadena/fastapi-boilerplate | app/api/api_v1/router/auth.py | [
{
"identifier": "get_redis_dep",
"path": "app/api/deps.py",
"snippet": "async def get_redis_dep(\n redis_dependency: Annotated[RedisDependency, Depends()]\n) -> AsyncGenerator[Redis, None]: # type: ignore\n \"\"\"\n Lazy generation of Redis dependency\n :param redis_dependency: The dependen... | import logging
from typing import Annotated, Any, Optional
from fastapi import (
APIRouter,
Body,
Depends,
Header,
HTTPException,
Path,
Request,
status,
)
from fastapi.security import OAuth2PasswordRequestForm
from pydantic import EmailStr
from redis.asyncio import Redis
from starlette.datastructures import Address
from app.api.deps import get_redis_dep
from app.api.oauth2_validation import get_current_user, get_refresh_current_user
from app.config.config import (
get_auth_settings,
get_init_settings,
get_settings,
init_setting,
)
from app.config.db.auth_settings import AuthSettings
from app.config.init_settings import InitSettings
from app.config.settings import Settings
from app.core.security.password import verify_password
from app.exceptions.exceptions import NotFoundException, ServiceException
from app.models.sql.user import User as UserDB
from app.schemas.external.msg import Msg
from app.schemas.external.token import TokenResetPassword, TokenResponse
from app.schemas.external.user import (
UserResponse,
UserUpdate,
UserUpdateResponse,
)
from app.schemas.infrastructure.user import UserAuth
from app.services.infrastructure.auth import common_auth_procedure
from app.services.infrastructure.token import TokenService
from app.services.infrastructure.user import UserService, get_user_service
from app.tasks.email_tasks.email_tasks import (
send_password_changed_confirmation_email,
send_reset_password_email,
)
from app.utils.security.password import (
generate_password_reset_token,
verify_password_reset_token,
) | 14,722 | return current_user
@router.post("/recover-password/{email}", response_model=Msg)
async def recover_password(
settings: Annotated[Settings, Depends(get_settings)],
auth_settings: Annotated[AuthSettings, Depends(get_auth_settings)],
email: Annotated[
EmailStr,
Path(
...,
title="Email",
description="The email used to recover the password",
example={"email": "someone@example.com"},
openapi_examples=init_setting.EMAIL_BODY_EXAMPLES,
),
],
user_service: Annotated[UserService, Depends(get_user_service)],
init_settings: Annotated[InitSettings, Depends(get_init_settings)],
) -> Msg:
"""
Endpoint to handle password recovery.
## Parameter:
- `email:` **Path parameter that references the email used to recover
the password**
- `type:` **EmailStr**
## Response:
- `return:` **Message object**
- `rtype:` **Msg**
\f
:param user_service: Dependency method for User service object
:type user_service: UserService
:param settings: Dependency method for cached setting object
:type settings: config.Settings
:param auth_settings: Dependency method for cached setting object
:type auth_settings: AuthSettings
:param init_settings: Dependency method for cached init setting object
:type init_settings: InitSettings
"""
try:
user: Optional[UserResponse] = await user_service.get_user_by_email(
email
)
except ServiceException as exc:
logger.error(exc)
user = None
if user:
password_reset_token: str = generate_password_reset_token(
email, auth_settings
)
await send_reset_password_email(
user.email,
user.username,
password_reset_token,
settings,
init_settings,
auth_settings,
)
return Msg(msg="If the email is registered, a reset link will be sent.")
@router.post("/reset-password", response_model=Msg)
async def reset_password(
settings: Annotated[Settings, Depends(get_settings)],
auth_settings: Annotated[AuthSettings, Depends(get_auth_settings)],
user_service: Annotated[UserService, Depends(get_user_service)],
token_reset_password: Annotated[
TokenResetPassword,
Body(
...,
title="Body object",
description="Object with access token and new password",
openapi_examples=init_setting.TOKEN_PAYLOAD_EXAMPLES,
),
],
init_settings: Annotated[InitSettings, Depends(get_init_settings)],
) -> Msg:
"""
Endpoint to handle password reset.
## Parameter:
- `token_reset_password:` **Body Object with token and new password**
- `type:` **TokenResetPassword**
## Response:
- `return:` **Message object**
- `rtype:` **Msg**
\f
:param settings: Dependency method for cached setting object
:type settings: config.Settings
:param user_service: Dependency method for User service object
:type user_service: UserService
:param auth_settings: Dependency method for cached setting object
:type auth_settings: AuthSettings
:param init_settings: Dependency method for cached init setting object
:type init_settings: InitSettings
"""
email: Optional[EmailStr] = verify_password_reset_token(
token_reset_password.token, auth_settings
)
if not email:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Invalid or expired token",
)
try:
found_user: Optional[
UserResponse
] = await user_service.get_user_by_email(email)
except ServiceException as exc:
logger.error(exc)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="There was an issue with the request",
) from exc
if not found_user:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND, detail="User not found"
)
user_data: dict[str, Any] = found_user.model_dump()
user_data["password"] = token_reset_password.password
user_update: UserUpdate = UserUpdate(**user_data)
| """
Authentication API Router.
This module provides login and password recovery functionality.
"""
logger: logging.Logger = logging.getLogger(__name__)
router: APIRouter = APIRouter(prefix="/auth", tags=["auth"])
@router.post("/login", response_model=TokenResponse)
async def login(
request: Request,
auth_settings: Annotated[AuthSettings, Depends(get_auth_settings)],
user: Annotated[OAuth2PasswordRequestForm, Depends()],
user_service: Annotated[UserService, Depends(get_user_service)],
redis: Annotated[Redis, Depends(get_redis_dep)], # type: ignore
) -> TokenResponse:
"""
Endpoint to handle user login with OAuth2 authentication using
request form.
## Parameter:
- `user:` **Request body with username and password**
- `type:` **OAuth2PasswordRequestForm**
## Response:
- `return:` **Token information with access token, its type and
refresh token**
- `rtype:` **TokenResponse**
\f
:param request: Request object for client host information
:type request: Request
:param user_service: Dependency method for User Service
:type user_service: UserService
:param auth_settings: Dependency method for cached setting object
:type auth_settings: AuthSettings
:param redis: Dependency method for async Redis connection
:type redis: Redis
"""
client: Optional[Address] = request.client
if not client:
raise NotFoundException(auth_settings.NO_CLIENT_FOUND)
client_ip: str = client.host
try:
found_user: UserDB = await user_service.get_login_user(user.username)
except ServiceException as exc:
logger.error(exc)
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND, detail="Invalid credentials"
) from exc
if not verify_password(found_user.password, user.password):
detail: str = "Incorrect password"
logger.warning(detail)
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND, detail=detail
)
if not found_user.is_active:
user_detail: str = "Inactive user"
logger.warning(user_detail)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST, detail=user_detail
)
return await common_auth_procedure(
found_user, client_ip, redis, auth_settings
)
@router.post(
"/refresh",
response_model=TokenResponse,
status_code=status.HTTP_201_CREATED,
)
async def refresh_token(
request: Request,
user_service: Annotated[UserService, Depends(get_user_service)],
auth_settings: Annotated[AuthSettings, Depends(get_auth_settings)],
refresh_current_user: Annotated[
UserAuth, Depends(get_refresh_current_user)
],
redis: Annotated[Redis, Depends(get_redis_dep)], # type: ignore
) -> TokenResponse:
"""
Generates a refresh token for the current user and saves it to the
database
## Response:
- `return:` **Token information with access token, its type and
refresh token**
- `rtype:` **TokenResponse**
\f
:param request: The HTTP request on the server
:type request: Request
:param user_service: Dependency method for User Service
:type user_service: UserService
:param auth_settings: Dependency method for cached setting object
:type auth_settings: AuthSettings
:param refresh_current_user: The current user dependency for refresh token
:type refresh_current_user: UserAuth
:param redis: Dependency method for async Redis connection
:type redis: Redis
"""
client: Optional[Address]
if not (client := request.client):
raise NotFoundException(auth_settings.NO_CLIENT_FOUND)
client_ip: str = client.host
try:
user: UserDB = await user_service.get_login_user(
refresh_current_user.username
)
except ServiceException as exc:
detail: str = "Can not found user information."
logger.error(detail)
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND, detail=detail
) from exc
return await common_auth_procedure(user, client_ip, redis, auth_settings)
@router.post("/validate-token", response_model=UserAuth)
async def validate_token(
current_user: Annotated[UserAuth, Depends(get_current_user)]
) -> UserAuth:
"""
Endpoint to validate an access token.
## Response:
- `return:` **The authenticated user instance**
- `rtype:` **UserAuth**
\f
:param current_user: The current user
:type current_user: UserAuth
"""
return current_user
@router.post("/recover-password/{email}", response_model=Msg)
async def recover_password(
settings: Annotated[Settings, Depends(get_settings)],
auth_settings: Annotated[AuthSettings, Depends(get_auth_settings)],
email: Annotated[
EmailStr,
Path(
...,
title="Email",
description="The email used to recover the password",
example={"email": "someone@example.com"},
openapi_examples=init_setting.EMAIL_BODY_EXAMPLES,
),
],
user_service: Annotated[UserService, Depends(get_user_service)],
init_settings: Annotated[InitSettings, Depends(get_init_settings)],
) -> Msg:
"""
Endpoint to handle password recovery.
## Parameter:
- `email:` **Path parameter that references the email used to recover
the password**
- `type:` **EmailStr**
## Response:
- `return:` **Message object**
- `rtype:` **Msg**
\f
:param user_service: Dependency method for User service object
:type user_service: UserService
:param settings: Dependency method for cached setting object
:type settings: config.Settings
:param auth_settings: Dependency method for cached setting object
:type auth_settings: AuthSettings
:param init_settings: Dependency method for cached init setting object
:type init_settings: InitSettings
"""
try:
user: Optional[UserResponse] = await user_service.get_user_by_email(
email
)
except ServiceException as exc:
logger.error(exc)
user = None
if user:
password_reset_token: str = generate_password_reset_token(
email, auth_settings
)
await send_reset_password_email(
user.email,
user.username,
password_reset_token,
settings,
init_settings,
auth_settings,
)
return Msg(msg="If the email is registered, a reset link will be sent.")
@router.post("/reset-password", response_model=Msg)
async def reset_password(
settings: Annotated[Settings, Depends(get_settings)],
auth_settings: Annotated[AuthSettings, Depends(get_auth_settings)],
user_service: Annotated[UserService, Depends(get_user_service)],
token_reset_password: Annotated[
TokenResetPassword,
Body(
...,
title="Body object",
description="Object with access token and new password",
openapi_examples=init_setting.TOKEN_PAYLOAD_EXAMPLES,
),
],
init_settings: Annotated[InitSettings, Depends(get_init_settings)],
) -> Msg:
"""
Endpoint to handle password reset.
## Parameter:
- `token_reset_password:` **Body Object with token and new password**
- `type:` **TokenResetPassword**
## Response:
- `return:` **Message object**
- `rtype:` **Msg**
\f
:param settings: Dependency method for cached setting object
:type settings: config.Settings
:param user_service: Dependency method for User service object
:type user_service: UserService
:param auth_settings: Dependency method for cached setting object
:type auth_settings: AuthSettings
:param init_settings: Dependency method for cached init setting object
:type init_settings: InitSettings
"""
email: Optional[EmailStr] = verify_password_reset_token(
token_reset_password.token, auth_settings
)
if not email:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Invalid or expired token",
)
try:
found_user: Optional[
UserResponse
] = await user_service.get_user_by_email(email)
except ServiceException as exc:
logger.error(exc)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="There was an issue with the request",
) from exc
if not found_user:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND, detail="User not found"
)
user_data: dict[str, Any] = found_user.model_dump()
user_data["password"] = token_reset_password.password
user_update: UserUpdate = UserUpdate(**user_data) | user: UserUpdateResponse = await user_service.update_user( # type: ignore | 16 | 2023-11-17 00:32:32+00:00 | 24k |
fg320/DEASC | examples/12C_5x1_farm_dyn_tuning_wso_grouping_looping.py | [
{
"identifier": "WfModel",
"path": "deasc/wf_model.py",
"snippet": "class WfModel:\n \"\"\"\n Class for wind farm modelling (Interface setup but not limited to FLORIS\n framework).\n \"\"\"\n\n def __init__(self, input_file, path):\n \"\"\"\n Initialise wind farm object by p... | import numpy as np
from deasc import WfModel
from deasc import WSOpt
from deasc import Tuning
from deasc import GPWrap
from deasc import TuningDyn_Grouping
from deasc import TuningDyn_Looping_Turbine
from deasc.utils_floris import (
floris_extract_object_dict,
floris_extract_parameter,
floris_param_change_object_dict,
floris_param_change_object
) | 15,511 |
"""
This example shows wake steering optimisation on a 5x1 wind farm of NREL 5 MW turbines.
Dynamic parameter tuning with the looping approach is implemented to refine the results
achieved with grouping. Tuning is introduced in the optimisation for the wake expansion
parameter k of the Jensen wake model. The tuning variables are the yaw angles of all wind
turbines in the farm, excluding the most downstream one.
"""
# %% Initial wake steering optimisation - Grouping approach for dynamic parameter tuning
# Initialise and set layout for wind farm model
path = "./inputs/"
input_file = "jensen.yaml"
wf_model = WfModel(input_file, path)
wf_model.set_aligned_layout(5, 1, 7, 5)
# Set kd deflection parameter
wf_model_dict = floris_extract_object_dict(wf_model)
wf_model_dict = floris_param_change_object_dict(wf_model_dict,
'wake_deflection_parameters',
'kd',
0.3)
wf_model = floris_param_change_object(wf_model, wf_model_dict)
# Specify atmopheric conditions
ws = 8.0
wd = 270
ti = 0.05
shear = 0.0
# Wake steering optimisation inputs
yaw_initial = np.full(shape=(5), fill_value=0)
inflow = (yaw_initial, wd, ws, ti, shear)
variables = [1, 2, 3, 4]
var_bounds = (-25, 25)
var_initial = np.full(shape=(len(variables)), fill_value=0)
# Dynamic tuning object
# Parameter info
parameter_class = 'wake_velocity_parameters'
parameter_name = 'we'
# Import optimal parameter dataset and extract GP input
dataset_path = "./optimal_parameter_datasets/"
dataset_import = np.load(dataset_path+'we_5x1_2dim_grouping.npy', allow_pickle=True)
optimal_parameter_dataset = dataset_import.item()
yaw_data = []
param_data = []
for key in optimal_parameter_dataset.keys():
yaw_data.append([key[0], key[2]]) # Extract group yaw
param_data.append([optimal_parameter_dataset[key]])
# Construct Gaussian Process (GP)
GP_obj = GPWrap(parameter_class=parameter_class,
parameter_name=parameter_name,
dimensions=2)
GP_model = GP_obj.GP_so(yaw_data, param_data, num_restarts=100, noise=0.05)
# Tuning object initialisation
|
"""
This example shows wake steering optimisation on a 5x1 wind farm of NREL 5 MW turbines.
Dynamic parameter tuning with the looping approach is implemented to refine the results
achieved with grouping. Tuning is introduced in the optimisation for the wake expansion
parameter k of the Jensen wake model. The tuning variables are the yaw angles of all wind
turbines in the farm, excluding the most downstream one.
"""
# %% Initial wake steering optimisation - Grouping approach for dynamic parameter tuning
# Initialise and set layout for wind farm model
path = "./inputs/"
input_file = "jensen.yaml"
wf_model = WfModel(input_file, path)
wf_model.set_aligned_layout(5, 1, 7, 5)
# Set kd deflection parameter
wf_model_dict = floris_extract_object_dict(wf_model)
wf_model_dict = floris_param_change_object_dict(wf_model_dict,
'wake_deflection_parameters',
'kd',
0.3)
wf_model = floris_param_change_object(wf_model, wf_model_dict)
# Specify atmopheric conditions
ws = 8.0
wd = 270
ti = 0.05
shear = 0.0
# Wake steering optimisation inputs
yaw_initial = np.full(shape=(5), fill_value=0)
inflow = (yaw_initial, wd, ws, ti, shear)
variables = [1, 2, 3, 4]
var_bounds = (-25, 25)
var_initial = np.full(shape=(len(variables)), fill_value=0)
# Dynamic tuning object
# Parameter info
parameter_class = 'wake_velocity_parameters'
parameter_name = 'we'
# Import optimal parameter dataset and extract GP input
dataset_path = "./optimal_parameter_datasets/"
dataset_import = np.load(dataset_path+'we_5x1_2dim_grouping.npy', allow_pickle=True)
optimal_parameter_dataset = dataset_import.item()
yaw_data = []
param_data = []
for key in optimal_parameter_dataset.keys():
yaw_data.append([key[0], key[2]]) # Extract group yaw
param_data.append([optimal_parameter_dataset[key]])
# Construct Gaussian Process (GP)
GP_obj = GPWrap(parameter_class=parameter_class,
parameter_name=parameter_name,
dimensions=2)
GP_model = GP_obj.GP_so(yaw_data, param_data, num_restarts=100, noise=0.05)
# Tuning object initialisation | tuning_dyn_obj = TuningDyn_Grouping(param_class=parameter_class, | 4 | 2023-11-10 18:13:27+00:00 | 24k |
PlaxtonFlarion/NexaFlow | nexaflow/skills/alynex.py | [
{
"identifier": "toolbox",
"path": "nexaflow/toolbox.py",
"snippet": "def video_capture(video_path: str):\ndef video_jump(video_cap: cv2.VideoCapture, frame_id: int):\ndef compare_ssim(pic1: np.ndarray, pic2: np.ndarray) -> float:\ndef multi_compare_ssim(\n pic1_list: typing.List, pic2_list: typing.L... | import os
import cv2
import time
import random
import asyncio
from loguru import logger
from typing import List, Union, Optional
from concurrent.futures import ThreadPoolExecutor
from nexaflow import toolbox
from nexaflow.skills.report import Report
from nexaflow.skills.record import Record
from nexaflow.skills.player import Player
from nexaflow.skills.switch import Switch
from nexaflow.cutter.cutter import VideoCutter
from nexaflow.video import VideoObject, Frame
from nexaflow.classifier.keras_classifier import KerasClassifier
from nexaflow.hook import BaseHook, CropHook, OmitHook, FrameSaveHook
from nexaflow.classifier.base import ClassifierResult, SingleClassifierResult | 16,146 |
class Alynex(object):
target_size: tuple = (350, 700)
fps: int = 60
step: int = 1
block: int = 6
threshold: Union[int | float] = 0.97
offset: int = 3
compress_rate: float = 0.5
window_size: int = 1
window_coefficient: int = 2
kc: KerasClassifier = KerasClassifier(
target_size=target_size, data_size=target_size
)
def __init__(self):
self.__report: Optional[Report] = None
self.__record: Optional[Record] = Record()
|
class Alynex(object):
target_size: tuple = (350, 700)
fps: int = 60
step: int = 1
block: int = 6
threshold: Union[int | float] = 0.97
offset: int = 3
compress_rate: float = 0.5
window_size: int = 1
window_coefficient: int = 2
kc: KerasClassifier = KerasClassifier(
target_size=target_size, data_size=target_size
)
def __init__(self):
self.__report: Optional[Report] = None
self.__record: Optional[Record] = Record() | self.__player: Optional[Player] = Player() | 3 | 2023-11-13 05:27:34+00:00 | 24k |
deepseek-ai/DreamCraft3D | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
import trimesh
from dataclasses import dataclass, field
from threestudio.models.geometry.base import (
BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,
)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.misc import broadcast
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
from pysdf import SDF | 16,118 | mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh = other.isosurface()
instance.isosurface_bbox = mesh.extras["bbox"]
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
shape_init_mesh_up: str = "+z"
shape_init_mesh_front: str = "+x"
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh = other.isosurface()
instance.isosurface_bbox = mesh.extras["bbox"]
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance | elif isinstance(other, ImplicitSDF): | 3 | 2023-10-23 07:40:20+00:00 | 24k |
microsoft/SoM | task_adapter/semantic_sam/tasks/inference_semsam_m2m_auto.py | [
{
"identifier": "Visualizer",
"path": "task_adapter/utils/visualizer.py",
"snippet": "class Visualizer:\n \"\"\"\n Visualizer that draws data about detection/segmentation on images.\n\n It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`\n that draw primitive objects t... | import torch
import numpy as np
import matplotlib.pyplot as plt
import cv2
import io
import cv2 # type: ignore
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
from .automatic_mask_generator import SemanticSamAutomaticMaskGenerator
from task_adapter.utils.visualizer import Visualizer | 15,527 | # --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang (hzhangcx@connect.ust.hk)
# --------------------------------------------------------
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
def inference_semsam_m2m_auto(model, image, level, all_classes, all_parts, thresh, text_size, hole_scale, island_scale, semantic, refimg=None, reftxt=None, audio_pth=None, video_pth=None, label_mode='1', alpha=0.1, anno_mode=['Mask']):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image)
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
| # --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang (hzhangcx@connect.ust.hk)
# --------------------------------------------------------
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
def inference_semsam_m2m_auto(model, image, level, all_classes, all_parts, thresh, text_size, hole_scale, island_scale, semantic, refimg=None, reftxt=None, audio_pth=None, video_pth=None, label_mode='1', alpha=0.1, anno_mode=['Mask']):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image)
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
| mask_generator = SemanticSamAutomaticMaskGenerator(model,points_per_side=32, | 1 | 2023-10-16 03:39:26+00:00 | 24k |
hkchengrex/Cutie | gui/main_controller.py | [
{
"identifier": "CUTIE",
"path": "cutie/model/cutie.py",
"snippet": "class CUTIE(nn.Module):\n def __init__(self, cfg: DictConfig, *, single_object=False):\n super().__init__()\n model_cfg = cfg.model\n self.ms_dims = model_cfg.pixel_encoder.ms_dims\n self.key_dim = model_... | import os
import logging
import cv2
import torch
import numpy as np
from os import path
from typing import Literal
from torch import mps
from torch import autocast
from torchvision.transforms.functional import to_tensor
from omegaconf import DictConfig, open_dict
from cutie.model.cutie import CUTIE
from cutie.inference.inference_core import InferenceCore
from gui.interaction import *
from gui.interactive_utils import *
from gui.resource_manager import ResourceManager
from gui.gui import GUI
from gui.click_controller import ClickController
from gui.reader import PropagationReader, get_data_loader
from gui.exporter import convert_frames_to_video, convert_mask_to_binary
from scripts.download_models import download_models_if_needed | 17,325 | if loaded_mask is None:
self.curr_mask.fill(0)
else:
self.curr_mask = loaded_mask.copy()
self.curr_prob = None
def convert_current_image_mask_torch(self, no_mask: bool = False):
if self.curr_image_torch is None:
self.curr_image_torch = to_tensor(self.curr_image_np).to(self.device, non_blocking=True)
if self.curr_prob is None and not no_mask:
self.curr_prob = index_numpy_to_one_hot_torch(self.curr_mask, self.num_objects + 1).to(
self.device, non_blocking=True)
def compose_current_im(self):
self.vis_image = get_visualization(self.vis_mode, self.curr_image_np, self.curr_mask,
self.overlay_layer, self.vis_target_objects)
def update_canvas(self):
self.gui.set_canvas(self.vis_image)
def update_current_image_fast(self):
# fast path, uses gpu. Changes the image in-place to avoid copying
# thus current_image_torch must be voided afterwards
self.vis_image = get_visualization_torch(self.vis_mode, self.curr_image_torch,
self.curr_prob, self.overlay_layer_torch,
self.vis_target_objects)
self.curr_image_torch = None
self.vis_image = np.ascontiguousarray(self.vis_image)
if self.save_visualization:
self.res_man.save_visualization(self.curr_ti, self.vis_mode, self.vis_image)
if self.save_soft_mask:
self.res_man.save_soft_mask(self.curr_ti, self.curr_prob.cpu().numpy())
self.gui.set_canvas(self.vis_image)
def show_current_frame(self, fast: bool = False):
# Re-compute overlay and show the image
if fast:
self.update_current_image_fast()
else:
self.compose_current_im()
if self.save_visualization:
self.res_man.save_visualization(self.curr_ti, self.vis_mode, self.vis_image)
self.update_canvas()
self.gui.update_slider(self.curr_ti)
self.gui.frame_name.setText(self.res_man.names[self.curr_ti] + '.jpg')
def set_vis_mode(self):
self.vis_mode = self.gui.combo.currentText()
self.show_current_frame()
def save_current_mask(self):
# save mask to hard disk
self.res_man.save_mask(self.curr_ti, self.curr_mask)
def on_slider_update(self):
# if we are propagating, the on_run function will take care of everything
# don't do duplicate work here
self.curr_ti = self.gui.tl_slider.value()
if not self.propagating:
# with self.vis_cond:
# self.vis_cond.notify()
if self.curr_frame_dirty:
self.save_current_mask()
self.curr_frame_dirty = False
self.reset_this_interaction()
self.curr_ti = self.gui.tl_slider.value()
self.load_current_image_mask()
self.show_current_frame()
def on_forward_propagation(self):
if self.propagating:
# acts as a pause button
self.propagating = False
self.propagate_direction = 'none'
else:
self.propagate_fn = self.on_next_frame
self.gui.forward_propagation_start()
self.propagate_direction = 'forward'
self.on_propagate()
def on_backward_propagation(self):
if self.propagating:
# acts as a pause button
self.propagating = False
self.propagate_direction = 'none'
else:
self.propagate_fn = self.on_prev_frame
self.gui.backward_propagation_start()
self.propagate_direction = 'backward'
self.on_propagate()
def on_pause(self):
self.propagating = False
self.gui.text(f'Propagation stopped at t={self.curr_ti}.')
self.gui.pause_propagation()
def on_propagate(self):
# start to propagate
with autocast(self.device, enabled=(self.amp and self.device == 'cuda')):
self.convert_current_image_mask_torch()
self.gui.text(f'Propagation started at t={self.curr_ti}.')
self.processor.clear_sensory_memory()
self.curr_prob = self.processor.step(self.curr_image_torch,
self.curr_prob[1:],
idx_mask=False)
self.curr_mask = torch_prob_to_numpy_mask(self.curr_prob)
# clear
self.interacted_prob = None
self.reset_this_interaction()
self.show_current_frame(fast=True)
self.propagating = True
self.gui.clear_all_mem_button.setEnabled(False)
self.gui.clear_non_perm_mem_button.setEnabled(False)
self.gui.tl_slider.setEnabled(False)
|
# fix conflicts between qt5 and cv2
os.environ.pop("QT_QPA_PLATFORM_PLUGIN_PATH")
try:
except:
print('torch.MPS not available.')
log = logging.getLogger()
class MainController():
def __init__(self, cfg: DictConfig) -> None:
super().__init__()
self.initialized = False
# setting up the workspace
if cfg["workspace"] is None:
if cfg["images"] is not None:
basename = path.basename(cfg["images"])
elif cfg["video"] is not None:
basename = path.basename(cfg["video"])[:-4]
else:
raise NotImplementedError('Either images, video, or workspace has to be specified')
cfg["workspace"] = path.join(cfg['workspace_root'], basename)
# reading arguments
self.cfg = cfg
self.num_objects = cfg['num_objects']
self.device = cfg['device']
self.amp = cfg['amp']
# initializing the network(s)
self.initialize_networks()
# main components
self.res_man = ResourceManager(cfg)
self.processor = InferenceCore(self.cutie, self.cfg)
self.gui = GUI(self, self.cfg)
# initialize control info
self.length: int = self.res_man.length
self.interaction: Interaction = None
self.interaction_type: str = 'Click'
self.curr_ti: int = 0
self.curr_object: int = 1
self.propagating: bool = False
self.propagate_direction: Literal['forward', 'backward', 'none'] = 'none'
self.last_ex = self.last_ey = 0
# current frame info
self.curr_frame_dirty: bool = False
self.curr_image_np: np.ndarray = np.zeros((self.h, self.w, 3), dtype=np.uint8)
self.curr_image_torch: torch.Tensor = None
self.curr_mask: np.ndarray = np.zeros((self.h, self.w), dtype=np.uint8)
self.curr_prob: torch.Tensor = torch.zeros((self.num_objects + 1, self.h, self.w),
dtype=torch.float).to(self.device)
self.curr_prob[0] = 1
# visualization info
self.vis_mode: str = 'davis'
self.vis_image: np.ndarray = None
self.save_visualization: bool = False
self.save_soft_mask: bool = False
self.interacted_prob: torch.Tensor = None
self.overlay_layer: np.ndarray = None
self.overlay_layer_torch: torch.Tensor = None
# the object id used for popup/layer overlay
self.vis_target_objects = list(range(1, self.num_objects + 1))
self.load_current_image_mask()
self.show_current_frame()
# initialize stuff
self.update_memory_gauges()
self.update_gpu_gauges()
self.gui.work_mem_min.setValue(self.processor.memory.min_mem_frames)
self.gui.work_mem_max.setValue(self.processor.memory.max_mem_frames)
self.gui.long_mem_max.setValue(self.processor.memory.max_long_tokens)
self.gui.mem_every_box.setValue(self.processor.mem_every)
# for exporting videos
self.output_fps = cfg['output_fps']
self.output_bitrate = cfg['output_bitrate']
# set callbacks
self.gui.on_mouse_motion_xy = self.on_mouse_motion_xy
self.gui.click_fn = self.click_fn
self.gui.show()
self.gui.text('Initialized.')
self.initialized = True
# try to load the default overlay
self._try_load_layer('./docs/uiuc.png')
self.gui.set_object_color(self.curr_object)
self.update_config()
def initialize_networks(self) -> None:
download_models_if_needed()
self.cutie = CUTIE(self.cfg).eval().to(self.device)
model_weights = torch.load(self.cfg.weights, map_location=self.device)
self.cutie.load_weights(model_weights)
self.click_ctrl = ClickController(self.cfg.ritm_weights, device=self.device)
def hit_number_key(self, number: int):
if number == self.curr_object:
return
self.curr_object = number
self.gui.object_dial.setValue(number)
if self.click_ctrl is not None:
self.click_ctrl.unanchor()
self.gui.text(f'Current object changed to {number}.')
self.gui.set_object_color(number)
self.show_current_frame()
def click_fn(self, action: Literal['left', 'right', 'middle'], x: int, y: int):
if self.propagating:
return
last_interaction = self.interaction
new_interaction = None
with autocast(self.device, enabled=(self.amp and self.device == 'cuda')):
if action in ['left', 'right']:
# left: positive click
# right: negative click
self.convert_current_image_mask_torch()
image = self.curr_image_torch
if (last_interaction is None or last_interaction.tar_obj != self.curr_object):
# create new interaction is needed
self.complete_interaction()
self.click_ctrl.unanchor()
new_interaction = ClickInteraction(image, self.curr_prob, (self.h, self.w),
self.click_ctrl, self.curr_object)
if new_interaction is not None:
self.interaction = new_interaction
self.interaction.push_point(x, y, is_neg=(action == 'right'))
self.interacted_prob = self.interaction.predict().to(self.device, non_blocking=True)
self.update_interacted_mask()
self.update_gpu_gauges()
elif action == 'middle':
# middle: select a new visualization object
target_object = self.curr_mask[int(y), int(x)]
if target_object in self.vis_target_objects:
self.vis_target_objects.remove(target_object)
else:
self.vis_target_objects.append(target_object)
self.gui.text(f'Overlay target(s) changed to {self.vis_target_objects}')
self.show_current_frame()
return
else:
raise NotImplementedError
def load_current_image_mask(self, no_mask: bool = False):
self.curr_image_np = self.res_man.get_image(self.curr_ti)
self.curr_image_torch = None
if not no_mask:
loaded_mask = self.res_man.get_mask(self.curr_ti)
if loaded_mask is None:
self.curr_mask.fill(0)
else:
self.curr_mask = loaded_mask.copy()
self.curr_prob = None
def convert_current_image_mask_torch(self, no_mask: bool = False):
if self.curr_image_torch is None:
self.curr_image_torch = to_tensor(self.curr_image_np).to(self.device, non_blocking=True)
if self.curr_prob is None and not no_mask:
self.curr_prob = index_numpy_to_one_hot_torch(self.curr_mask, self.num_objects + 1).to(
self.device, non_blocking=True)
def compose_current_im(self):
self.vis_image = get_visualization(self.vis_mode, self.curr_image_np, self.curr_mask,
self.overlay_layer, self.vis_target_objects)
def update_canvas(self):
self.gui.set_canvas(self.vis_image)
def update_current_image_fast(self):
# fast path, uses gpu. Changes the image in-place to avoid copying
# thus current_image_torch must be voided afterwards
self.vis_image = get_visualization_torch(self.vis_mode, self.curr_image_torch,
self.curr_prob, self.overlay_layer_torch,
self.vis_target_objects)
self.curr_image_torch = None
self.vis_image = np.ascontiguousarray(self.vis_image)
if self.save_visualization:
self.res_man.save_visualization(self.curr_ti, self.vis_mode, self.vis_image)
if self.save_soft_mask:
self.res_man.save_soft_mask(self.curr_ti, self.curr_prob.cpu().numpy())
self.gui.set_canvas(self.vis_image)
def show_current_frame(self, fast: bool = False):
# Re-compute overlay and show the image
if fast:
self.update_current_image_fast()
else:
self.compose_current_im()
if self.save_visualization:
self.res_man.save_visualization(self.curr_ti, self.vis_mode, self.vis_image)
self.update_canvas()
self.gui.update_slider(self.curr_ti)
self.gui.frame_name.setText(self.res_man.names[self.curr_ti] + '.jpg')
def set_vis_mode(self):
self.vis_mode = self.gui.combo.currentText()
self.show_current_frame()
def save_current_mask(self):
# save mask to hard disk
self.res_man.save_mask(self.curr_ti, self.curr_mask)
def on_slider_update(self):
# if we are propagating, the on_run function will take care of everything
# don't do duplicate work here
self.curr_ti = self.gui.tl_slider.value()
if not self.propagating:
# with self.vis_cond:
# self.vis_cond.notify()
if self.curr_frame_dirty:
self.save_current_mask()
self.curr_frame_dirty = False
self.reset_this_interaction()
self.curr_ti = self.gui.tl_slider.value()
self.load_current_image_mask()
self.show_current_frame()
def on_forward_propagation(self):
if self.propagating:
# acts as a pause button
self.propagating = False
self.propagate_direction = 'none'
else:
self.propagate_fn = self.on_next_frame
self.gui.forward_propagation_start()
self.propagate_direction = 'forward'
self.on_propagate()
def on_backward_propagation(self):
if self.propagating:
# acts as a pause button
self.propagating = False
self.propagate_direction = 'none'
else:
self.propagate_fn = self.on_prev_frame
self.gui.backward_propagation_start()
self.propagate_direction = 'backward'
self.on_propagate()
def on_pause(self):
self.propagating = False
self.gui.text(f'Propagation stopped at t={self.curr_ti}.')
self.gui.pause_propagation()
def on_propagate(self):
# start to propagate
with autocast(self.device, enabled=(self.amp and self.device == 'cuda')):
self.convert_current_image_mask_torch()
self.gui.text(f'Propagation started at t={self.curr_ti}.')
self.processor.clear_sensory_memory()
self.curr_prob = self.processor.step(self.curr_image_torch,
self.curr_prob[1:],
idx_mask=False)
self.curr_mask = torch_prob_to_numpy_mask(self.curr_prob)
# clear
self.interacted_prob = None
self.reset_this_interaction()
self.show_current_frame(fast=True)
self.propagating = True
self.gui.clear_all_mem_button.setEnabled(False)
self.gui.clear_non_perm_mem_button.setEnabled(False)
self.gui.tl_slider.setEnabled(False)
| dataset = PropagationReader(self.res_man, self.curr_ti, self.propagate_direction) | 5 | 2023-10-19 17:49:24+00:00 | 24k |
ZhengyiLuo/PerpetualHumanoidControl | scripts/vis/vis_smpl_o3d_multi.py | [
{
"identifier": "SMPL_Parser",
"path": "uhc/smpllib/smpl_parser.py",
"snippet": "class SMPL_Parser(_SMPL):\n\n def __init__(self, create_transl=False, *args, **kwargs):\n \"\"\"SMPL model constructor\n Parameters\n ----------\n model_path: str\n The path to the ... | import glob
import os
import sys
import pdb
import os.path as osp
import open3d as o3d
import open3d.visualization.rendering as rendering
import imageio
import joblib
import numpy as np
import torch
import random
import matplotlib.pyplot as plt
import cv2
import matplotlib as mpl
from tqdm import tqdm
from uhc.smpllib.smpl_parser import (
SMPL_Parser,
SMPLH_Parser,
SMPLX_Parser,
)
from uhc.smpllib.smpl_mujoco import SMPL_BONE_ORDER_NAMES as joint_names
from poselib.poselib.skeleton.skeleton3d import SkeletonTree, SkeletonMotion, SkeletonState
from scipy.spatial.transform import Rotation as sRot
from tqdm import tqdm | 19,852 |
sys.path.append(os.getcwd())
paused, reset, recording, image_list, writer, control, curr_zoom = False, False, False, [], None, None, 0.01
def pause_func(action):
global paused
paused = not paused
print(f"Paused: {paused}")
return True
def reset_func(action):
global reset
reset = not reset
print(f"Reset: {reset}")
return True
def record_func(action):
global recording, writer
if not recording:
fps = 30
curr_video_file_name = "test.mp4"
writer = imageio.get_writer(curr_video_file_name, fps=fps, macro_block_size=None)
elif not writer is None:
writer.close()
writer = None
recording = not recording
print(f"Recording: {recording}")
return True
def capture_func(action):
global capture
capture = not capture
return True
def zoom_func(action):
global control, curr_zoom
curr_zoom = curr_zoom * 0.9
control.set_zoom(curr_zoom)
print(f"Reset: {reset}")
return True
mujoco_joint_names = ['Pelvis', 'L_Hip', 'L_Knee', 'L_Ankle', 'L_Toe', 'R_Hip', 'R_Knee', 'R_Ankle', 'R_Toe', 'Torso', 'Spine', 'Chest', 'Neck', 'Head', 'L_Thorax', 'L_Shoulder', 'L_Elbow', 'L_Wrist', 'L_Hand', 'R_Thorax', 'R_Shoulder', 'R_Elbow', 'R_Wrist', 'R_Hand']
Name = "getting_started"
Title = "Getting Started"
data_dir = "data/smpl"
smpl_parser_n = SMPL_Parser(model_path=data_dir, gender="neutral")
smpl_parser_m = SMPL_Parser(model_path=data_dir, gender="male")
smpl_parser_f = SMPL_Parser(model_path=data_dir, gender="female")
# pkl_dir = "output/renderings/smpl_ego_long_8-2023-01-20-11:28:00.pkl"
# pkl_dir = "output/renderings/smpl_im_comp_8-2023-02-05-15:36:14.pkl"
pkl_dir = "output/renderings/smpl_im_comp_pnn_3-2023-03-07-14:31:50.pkl"
Name = pkl_dir.split("/")[-1].split(".")[0]
pkl_data = joblib.load(pkl_dir)
|
sys.path.append(os.getcwd())
paused, reset, recording, image_list, writer, control, curr_zoom = False, False, False, [], None, None, 0.01
def pause_func(action):
global paused
paused = not paused
print(f"Paused: {paused}")
return True
def reset_func(action):
global reset
reset = not reset
print(f"Reset: {reset}")
return True
def record_func(action):
global recording, writer
if not recording:
fps = 30
curr_video_file_name = "test.mp4"
writer = imageio.get_writer(curr_video_file_name, fps=fps, macro_block_size=None)
elif not writer is None:
writer.close()
writer = None
recording = not recording
print(f"Recording: {recording}")
return True
def capture_func(action):
global capture
capture = not capture
return True
def zoom_func(action):
global control, curr_zoom
curr_zoom = curr_zoom * 0.9
control.set_zoom(curr_zoom)
print(f"Reset: {reset}")
return True
mujoco_joint_names = ['Pelvis', 'L_Hip', 'L_Knee', 'L_Ankle', 'L_Toe', 'R_Hip', 'R_Knee', 'R_Ankle', 'R_Toe', 'Torso', 'Spine', 'Chest', 'Neck', 'Head', 'L_Thorax', 'L_Shoulder', 'L_Elbow', 'L_Wrist', 'L_Hand', 'R_Thorax', 'R_Shoulder', 'R_Elbow', 'R_Wrist', 'R_Hand']
Name = "getting_started"
Title = "Getting Started"
data_dir = "data/smpl"
smpl_parser_n = SMPL_Parser(model_path=data_dir, gender="neutral")
smpl_parser_m = SMPL_Parser(model_path=data_dir, gender="male")
smpl_parser_f = SMPL_Parser(model_path=data_dir, gender="female")
# pkl_dir = "output/renderings/smpl_ego_long_8-2023-01-20-11:28:00.pkl"
# pkl_dir = "output/renderings/smpl_im_comp_8-2023-02-05-15:36:14.pkl"
pkl_dir = "output/renderings/smpl_im_comp_pnn_3-2023-03-07-14:31:50.pkl"
Name = pkl_dir.split("/")[-1].split(".")[0]
pkl_data = joblib.load(pkl_dir) | mujoco_2_smpl = [mujoco_joint_names.index(q) for q in joint_names if q in mujoco_joint_names] | 0 | 2023-10-15 19:05:47+00:00 | 24k |
e4s2023/E4S2023 | run_UI_seg19.py | [
{
"identifier": "UIOptions",
"path": "options/ui_options.py",
"snippet": "class UIOptions:\n\n\tdef __init__(self):\n\t\tself.parser = ArgumentParser()\n\t\tself.initialize()\n\n\tdef initialize(self):\n\t\tself.parser.add_argument('--exp_dir', type=str, default=\"/apdcephfs/share_1290939/zhianliu/runni... | from options.ui_options import UIOptions
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.QtPrintSupport import QPrintDialog, QPrinter
from ui_run.ui import Ui_Form
from ui_run.mouse_event import GraphicsScene
from ui_run.util import number_color, color_pred,celebAHQ_masks_to_faceParser_mask_detailed, my_number_object, COMPS
from PIL import Image
from PyQt5 import QtGui
from models.networks import Net3
from glob import glob
from utils import torch_utils
from datasets.dataset import CelebAHQDataset, get_transforms, TO_TENSOR, NORMALIZE
import sys
import cv2
import skimage.io
import qdarkstyle
import qdarkgraystyle
import os
import numpy as np
import skimage.io
import os
import torch
import copy
import torchvision.transforms as transforms | 18,980 | @pyqtSlot()
def change_alpha_value(self):
self.alpha = self.alphaSlider.value() / 20
self.alphaLabel.setText('Alpha: %.2f' % self.alpha)
@pyqtSlot()
def switch_labels(self, label): # 换了一种label颜色按钮
self.scene.label = label
self.scene.color = number_color[label]
self.color_Button.setStyleSheet("background-color: %s;" % self.scene.color)
@pyqtSlot()
def undo(self):
self.scene.undo()
def __init__(self, opt):
super().__init__()
self.init_deep_model(opt)
self.setupUi(self)
self.show()
# 下面都是一些默认值
self.modes = 0
self.alpha = 1 # 插值的alpha
self.ref_style_img_path = None
self.mouse_clicked = False
self.scene = GraphicsScene(self.modes, self) # 用来编辑的 scene
self.scene.setSceneRect(0, 0, 512, 512)
self.graphicsView.setScene(self.scene)
self.graphicsView.setAlignment(Qt.AlignCenter)
self.graphicsView.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.graphicsView.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.result_scene = QGraphicsScene()
self.graphicsView_2.setScene(self.result_scene)
self.graphicsView_2.setAlignment(Qt.AlignCenter)
self.graphicsView_2.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.graphicsView_2.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.GT_scene = QGraphicsScene()
self.graphicsView_GT.setScene(self.GT_scene)
self.graphicsView_GT.setAlignment(Qt.AlignCenter)
self.graphicsView_GT.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.graphicsView_GT.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.dlg = QColorDialog(self.graphicsView)
self.init_screen() # 初始化screen
def init_screen(self):
#self.image = QPixmap(self.graphicsView.size())
self.image = QPixmap(QSize(512, 512)) # 这张是待编辑的mask可视化图片
self.image.fill(QColor('#FFFFFF'))
self.mat_img = np.zeros([512, 512, 3], np.uint8) # mask图片, [0-12], 3通道
self.mat_img_org = self.mat_img.copy()
self.GT_img_path = None
GT_img = np.ones([512, 512, 3], np.uint8)*255
self.GT_img = Image.fromarray(GT_img)
self.GT_img = self.GT_img.convert('RGB')
#################### add GT image
self.update_GT_image(GT_img)
#####################
self.scene.reset()
if len(self.scene.items()) > 0:
self.scene.reset_items()
self.scene.addPixmap(self.image)
############### load average features
# TODO: 把这两行注释打开
# self.load_average_feature()
# self.run_deep_model()
self.recorded_img_names = []
self.clean_snapshots()
self.clean_generated_result()
def init_deep_model(self, opt): # 初始化模型
self.opt = opt
assert self.opt.checkpoint_path is not None, "please specify the pre-trained weights!"
print("Loading model and weights, please wait a few seconds...")
self.net = Net3(self.opt).eval().to(self.opt.device)
ckpt_dict=torch.load(self.opt.checkpoint_path)
self.net.latent_avg = ckpt_dict['latent_avg'].to(self.opt.device) if self.opt.start_from_latent_avg else None
self.net.load_state_dict(torch_utils.remove_module_prefix(ckpt_dict["state_dict"],prefix="module."))
print("Loading Done!")
# 固定noise
channels = {
4: 512,
8: 512,
16: 512,
32: 512,
64: 256 * 2,
128: 128 * 2,
256: 64 * 2,
512: 32 * 2,
1024: 16 * 2,
}
self.noise = [torch.randn(1,512,4,4).to(self.opt.device)]
for i in [8,16,32,64,128,256,512,1024]:
self.noise.append(torch.randn(1,channels[i],i,i).to(self.opt.device))
self.noise.append(torch.randn(1,channels[i],i,i).to(self.opt.device))
# ===================================================
def editing(self): # 生成编辑的结果
|
class ExWindow(QMainWindow):
def __init__(self, opt):
super().__init__()
self.EX = Ex(opt)
self.setWindowIcon(QtGui.QIcon('ui_run/icons/edit_icon.svg'))
class Ex(QWidget, Ui_Form):
@pyqtSlot()
def change_brush_size(self): # 改变画刷的 粗细
self.scene.brush_size = self.brushSlider.value()
self.brushsizeLabel.setText('Brush size: %d' % self.scene.brush_size)
@pyqtSlot()
def change_alpha_value(self):
self.alpha = self.alphaSlider.value() / 20
self.alphaLabel.setText('Alpha: %.2f' % self.alpha)
@pyqtSlot()
def switch_labels(self, label): # 换了一种label颜色按钮
self.scene.label = label
self.scene.color = number_color[label]
self.color_Button.setStyleSheet("background-color: %s;" % self.scene.color)
@pyqtSlot()
def undo(self):
self.scene.undo()
def __init__(self, opt):
super().__init__()
self.init_deep_model(opt)
self.setupUi(self)
self.show()
# 下面都是一些默认值
self.modes = 0
self.alpha = 1 # 插值的alpha
self.ref_style_img_path = None
self.mouse_clicked = False
self.scene = GraphicsScene(self.modes, self) # 用来编辑的 scene
self.scene.setSceneRect(0, 0, 512, 512)
self.graphicsView.setScene(self.scene)
self.graphicsView.setAlignment(Qt.AlignCenter)
self.graphicsView.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.graphicsView.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.result_scene = QGraphicsScene()
self.graphicsView_2.setScene(self.result_scene)
self.graphicsView_2.setAlignment(Qt.AlignCenter)
self.graphicsView_2.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.graphicsView_2.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.GT_scene = QGraphicsScene()
self.graphicsView_GT.setScene(self.GT_scene)
self.graphicsView_GT.setAlignment(Qt.AlignCenter)
self.graphicsView_GT.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.graphicsView_GT.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.dlg = QColorDialog(self.graphicsView)
self.init_screen() # 初始化screen
def init_screen(self):
#self.image = QPixmap(self.graphicsView.size())
self.image = QPixmap(QSize(512, 512)) # 这张是待编辑的mask可视化图片
self.image.fill(QColor('#FFFFFF'))
self.mat_img = np.zeros([512, 512, 3], np.uint8) # mask图片, [0-12], 3通道
self.mat_img_org = self.mat_img.copy()
self.GT_img_path = None
GT_img = np.ones([512, 512, 3], np.uint8)*255
self.GT_img = Image.fromarray(GT_img)
self.GT_img = self.GT_img.convert('RGB')
#################### add GT image
self.update_GT_image(GT_img)
#####################
self.scene.reset()
if len(self.scene.items()) > 0:
self.scene.reset_items()
self.scene.addPixmap(self.image)
############### load average features
# TODO: 把这两行注释打开
# self.load_average_feature()
# self.run_deep_model()
self.recorded_img_names = []
self.clean_snapshots()
self.clean_generated_result()
def init_deep_model(self, opt): # 初始化模型
self.opt = opt
assert self.opt.checkpoint_path is not None, "please specify the pre-trained weights!"
print("Loading model and weights, please wait a few seconds...")
self.net = Net3(self.opt).eval().to(self.opt.device)
ckpt_dict=torch.load(self.opt.checkpoint_path)
self.net.latent_avg = ckpt_dict['latent_avg'].to(self.opt.device) if self.opt.start_from_latent_avg else None
self.net.load_state_dict(torch_utils.remove_module_prefix(ckpt_dict["state_dict"],prefix="module."))
print("Loading Done!")
# 固定noise
channels = {
4: 512,
8: 512,
16: 512,
32: 512,
64: 256 * 2,
128: 128 * 2,
256: 64 * 2,
512: 32 * 2,
1024: 16 * 2,
}
self.noise = [torch.randn(1,512,4,4).to(self.opt.device)]
for i in [8,16,32,64,128,256,512,1024]:
self.noise.append(torch.randn(1,channels[i],i,i).to(self.opt.device))
self.noise.append(torch.randn(1,channels[i],i,i).to(self.opt.device))
# ===================================================
def editing(self): # 生成编辑的结果 | mat_img_seg12 = celebAHQ_masks_to_faceParser_mask_detailed(self.mat_img[:,:,0]) | 3 | 2023-10-15 12:15:01+00:00 | 24k |
sotopia-lab/sotopia | sotopia/server.py | [
{
"identifier": "Agents",
"path": "sotopia/agents/llm_agent.py",
"snippet": "class Agents(dict[str, BaseAgent[Observation, AgentAction]]):\n def reset(self) -> None:\n for agent in self.values():\n agent.reset()\n\n def act(self, obs: dict[str, Observation]) -> dict[str, AgentAct... | import asyncio
import functools
import itertools
import logging
import gin
import rich
from typing import Callable, Literal, Sequence, Type, cast
from beartype import beartype
from tqdm.asyncio import tqdm_asyncio
from sotopia.agents import (
Agents,
HumanAgent,
LLMAgent,
RedisAgent,
ScriptWritingAgent,
SpeakAgent,
)
from sotopia.agents.base_agent import BaseAgent
from sotopia.database import EpisodeLog
from sotopia.database.persistent_profile import (
AgentProfile,
EnvironmentProfile,
)
from sotopia.envs import ParallelSotopiaEnv
from sotopia.envs.evaluators import (
ReachGoalLLMEvaluator,
RuleBasedTerminatedEvaluator,
unweighted_aggregate_evaluate,
)
from sotopia.generation_utils.generate import LLM_Name, agenerate_script
from sotopia.messages import AgentAction, Message, Observation
from sotopia.messages.message_classes import (
ScriptBackground,
ScriptEnvironmentResponse,
ScriptInteraction,
)
from sotopia.samplers import (
BaseSampler,
ConstraintBasedSampler,
EnvAgentCombo,
UniformSampler,
) | 18,742 |
@beartype
def run_sync_server(
model_name_dict: dict[str, LLM_Name],
action_order: Literal["simutaneous", "round-robin", "random"],
agents_info: dict[str, dict[str, str]] | None = None,
partial_background_file: str | None = None,
full_background_file: str | None = None,
mode: str | None = None,
) -> list[tuple[str, str, Message]]:
# Create Environment and agents
# This step will be moved to outside this function
env = ParallelSotopiaEnv(
model_name=model_name_dict["env"],
action_order=action_order,
evaluators=[
RuleBasedTerminatedEvaluator(),
],
)
if partial_background_file:
environment_messages = env.reset(
options={"partial_background_file": partial_background_file}
)
elif full_background_file:
environment_messages = env.reset(
options={"full_background_file": full_background_file}
)
else:
environment_messages = env.reset()
agents = Agents()
agents_model_names = [model_name_dict["agent1"], model_name_dict["agent2"]]
for agent_name, agent_model in zip(env.agents, agents_model_names):
if agent_model == "human":
agents[agent_name] = HumanAgent(agent_name)
elif mode == "speak":
agents[agent_name] = SpeakAgent(agent_name, model_name=agent_model)
else:
agents[agent_name] = LLMAgent(agent_name, model_name=agent_model)
agents.reset()
messages: list[tuple[str, str, Message]] = []
# Main Event Loop
done = False
for agent_name in env.agents:
messages.append(
("Environment", agent_name, environment_messages[agent_name])
)
while not done:
# gather agent messages
agent_messages: dict[str, AgentAction] = dict()
for agent_name in env.agents:
if agents_info is not None:
agents[agent_name].goal = agents_info[agent_name]["goal"]
agent_messages[agent_name] = agents[agent_name].act(
environment_messages[agent_name]
)
messages.append(
(agent_name, "Environment", agent_messages[agent_name])
)
# send agent messages to environment
environment_messages, _, terminated, ___, ____ = env.step(
agent_messages
)
for agent_name in env.agents:
messages.append(
("Environment", agent_name, environment_messages[agent_name])
)
done = all(terminated.values())
return messages
@gin.configurable
async def arun_one_episode(
env: ParallelSotopiaEnv,
agent_list: Sequence[BaseAgent[Observation, AgentAction]],
model_dict: dict[str, LLM_Name],
omniscient: bool = False,
script_like: bool = False,
json_in_script: bool = False,
tag: str | None = None,
push_to_db: bool = False,
) -> list[tuple[str, str, Message]]:
agents = Agents({agent.agent_name: agent for agent in agent_list})
environment_messages = env.reset(agents=agents, omniscient=omniscient)
agents_model_names = [model_dict["agent1"], model_dict["agent2"]]
for agent_name, agent_model in zip(env.agents, agents_model_names):
if agent_model == "human":
agents[agent_name] = HumanAgent(agent_name)
elif agent_model == "redis":
|
@beartype
def run_sync_server(
model_name_dict: dict[str, LLM_Name],
action_order: Literal["simutaneous", "round-robin", "random"],
agents_info: dict[str, dict[str, str]] | None = None,
partial_background_file: str | None = None,
full_background_file: str | None = None,
mode: str | None = None,
) -> list[tuple[str, str, Message]]:
# Create Environment and agents
# This step will be moved to outside this function
env = ParallelSotopiaEnv(
model_name=model_name_dict["env"],
action_order=action_order,
evaluators=[
RuleBasedTerminatedEvaluator(),
],
)
if partial_background_file:
environment_messages = env.reset(
options={"partial_background_file": partial_background_file}
)
elif full_background_file:
environment_messages = env.reset(
options={"full_background_file": full_background_file}
)
else:
environment_messages = env.reset()
agents = Agents()
agents_model_names = [model_name_dict["agent1"], model_name_dict["agent2"]]
for agent_name, agent_model in zip(env.agents, agents_model_names):
if agent_model == "human":
agents[agent_name] = HumanAgent(agent_name)
elif mode == "speak":
agents[agent_name] = SpeakAgent(agent_name, model_name=agent_model)
else:
agents[agent_name] = LLMAgent(agent_name, model_name=agent_model)
agents.reset()
messages: list[tuple[str, str, Message]] = []
# Main Event Loop
done = False
for agent_name in env.agents:
messages.append(
("Environment", agent_name, environment_messages[agent_name])
)
while not done:
# gather agent messages
agent_messages: dict[str, AgentAction] = dict()
for agent_name in env.agents:
if agents_info is not None:
agents[agent_name].goal = agents_info[agent_name]["goal"]
agent_messages[agent_name] = agents[agent_name].act(
environment_messages[agent_name]
)
messages.append(
(agent_name, "Environment", agent_messages[agent_name])
)
# send agent messages to environment
environment_messages, _, terminated, ___, ____ = env.step(
agent_messages
)
for agent_name in env.agents:
messages.append(
("Environment", agent_name, environment_messages[agent_name])
)
done = all(terminated.values())
return messages
@gin.configurable
async def arun_one_episode(
env: ParallelSotopiaEnv,
agent_list: Sequence[BaseAgent[Observation, AgentAction]],
model_dict: dict[str, LLM_Name],
omniscient: bool = False,
script_like: bool = False,
json_in_script: bool = False,
tag: str | None = None,
push_to_db: bool = False,
) -> list[tuple[str, str, Message]]:
agents = Agents({agent.agent_name: agent for agent in agent_list})
environment_messages = env.reset(agents=agents, omniscient=omniscient)
agents_model_names = [model_dict["agent1"], model_dict["agent2"]]
for agent_name, agent_model in zip(env.agents, agents_model_names):
if agent_model == "human":
agents[agent_name] = HumanAgent(agent_name)
elif agent_model == "redis": | agents[agent_name] = RedisAgent(agent_name) | 5 | 2023-10-23 19:47:26+00:00 | 24k |
shrimo/SLAMBox | node_graph.py | [
{
"identifier": "NodeGraph",
"path": "NodeGraphQt/NodeGraphQt/base/graph.py",
"snippet": "class NodeGraph(QtCore.QObject):\n \"\"\"\n The ``NodeGraph`` class is the main controller for managing all nodes\n and the node graph.\n\n Inherited from: :class:`PySide2.QtCore.QObject`\n\n .. imag... | import sys
import socket
import pickle
import json
import requests
import config as cfg
import plugins_ui as plugins
from Qt import QtCore, QtWidgets
from PySide2.QtGui import QPixmap
from NodeGraphQt import NodeGraph, PropertiesBinWidget | 16,903 | #!/usr/bin/python3.10
"""
GUI for video or image analysis and processing
Based on the NodeGraphQt a node graph UI framework
written in python that can be implemented and re-purposed
into applications supporting PySide2.
"""
# Loading configuration data
if cfg.nodegraphqt not in sys.path:
sys.path.append(cfg.nodegraphqt)
PLUGINS = plugins.PluginRegistration()
| #!/usr/bin/python3.10
"""
GUI for video or image analysis and processing
Based on the NodeGraphQt a node graph UI framework
written in python that can be implemented and re-purposed
into applications supporting PySide2.
"""
# Loading configuration data
if cfg.nodegraphqt not in sys.path:
sys.path.append(cfg.nodegraphqt)
PLUGINS = plugins.PluginRegistration()
| class NodeBased(NodeGraph): | 0 | 2023-10-18 14:11:43+00:00 | 24k |
f0uriest/interpax | tests/test_interpolate.py | [
{
"identifier": "fft_interp1d",
"path": "interpax/_fourier.py",
"snippet": "@partial(jit, static_argnames=\"n\")\ndef fft_interp1d(f: jax.Array, n: int, sx: jax.Array = None, dx: float = 1.0):\n \"\"\"Interpolation of a 1d periodic function via FFT.\n\n Parameters\n ----------\n f : ndarray,... | import jax
import jax.numpy as jnp
import numpy as np
import pytest
from jax import config as jax_config
from interpax import (
Interpolator1D,
Interpolator2D,
Interpolator3D,
fft_interp1d,
fft_interp2d,
interp1d,
interp2d,
interp3d,
) | 15,785 | """Ensure monotonic interpolation is actually monotonic."""
# true function is just linear with a jump discontinuity at x=1.5
x = np.linspace(-4, 5, 10)
f = np.heaviside(x - 1.5, 0) + 0.1 * x
xq = np.linspace(-4, 5, 1000)
dfc = interp1d(xq, x, f, derivative=1, method="cubic")
dfm = interp1d(xq, x, f, derivative=1, method="monotonic")
dfm0 = interp1d(xq, x, f, derivative=1, method="monotonic-0")
assert dfc.min() < 0 # cubic interpolation undershoots, giving negative slope
assert dfm.min() > 0 # monotonic interpolation doesn't
assert dfm0.min() >= 0 # monotonic-0 doesn't overshoot either
# ensure monotonic-0 has 0 slope at end points
np.testing.assert_allclose(dfm0[np.array([0, -1])], 0, atol=1e-12)
class TestInterp2D:
"""Tests for interp2d function."""
@pytest.mark.unit
@pytest.mark.parametrize(
"x, y",
[
(np.linspace(0, 3 * np.pi, 1000), np.linspace(0, 2 * np.pi, 1000)),
(0.0, 0.0),
],
)
def test_interp2d(self, x, y):
"""Test accuracy of different 2d interpolation methods."""
xp = np.linspace(0, 3 * np.pi, 99)
yp = np.linspace(0, 2 * np.pi, 40)
xxp, yyp = np.meshgrid(xp, yp, indexing="ij")
f = lambda x, y: np.sin(x) * np.cos(y)
fp = f(xxp, yyp)
interp1 = lambda xq, yq, *args, **kwargs: interp2d(xq, yq, *args, **kwargs)
interp2 = lambda xq, yq, *args, **kwargs: Interpolator2D(*args, **kwargs)(
xq, yq
)
for interp in [interp1, interp2]:
fq = interp(
x, y, xp, yp, fp, method="nearest", period=(2 * np.pi, 2 * np.pi)
)
np.testing.assert_allclose(fq, f(x, y), rtol=1e-2, atol=1)
fq = interp(
x, y, xp, yp, fp, method="linear", period=(2 * np.pi, 2 * np.pi)
)
np.testing.assert_allclose(fq, f(x, y), rtol=1e-4, atol=1e-2)
atol = 2e-3
rtol = 1e-5
fq = interp(x, y, xp, yp, fp, method="cubic", period=(2 * np.pi, 2 * np.pi))
np.testing.assert_allclose(fq, f(x, y), rtol=rtol, atol=atol)
fq = interp(
x, y, xp, yp, fp, method="cubic2", period=(2 * np.pi, 2 * np.pi)
)
np.testing.assert_allclose(fq, f(x, y), rtol=rtol, atol=atol)
fq = interp(
x, y, xp, yp, fp, method="catmull-rom", period=(2 * np.pi, 2 * np.pi)
)
np.testing.assert_allclose(fq, f(x, y), rtol=rtol, atol=atol)
fq = interp(
x, y, xp, yp, fp, method="cardinal", period=(2 * np.pi, 2 * np.pi)
)
np.testing.assert_allclose(fq, f(x, y), rtol=rtol, atol=atol)
@pytest.mark.unit
def test_interp2d_vector_valued(self):
"""Test for interpolating vector valued function."""
xp = np.linspace(0, 3 * np.pi, 99)
yp = np.linspace(0, 2 * np.pi, 40)
x = np.linspace(0, 3 * np.pi, 200)
y = np.linspace(0, 2 * np.pi, 200)
xxp, yyp = np.meshgrid(xp, yp, indexing="ij")
f = lambda x, y: np.array([np.sin(x) * np.cos(y), np.sin(x) + np.cos(y)])
fp = f(xxp.T, yyp.T).T
fq = interp2d(x, y, xp, yp, fp, method="nearest")
np.testing.assert_allclose(fq, f(x, y).T, rtol=1e-2, atol=1.2e-1)
fq = interp2d(x, y, xp, yp, fp, method="linear")
np.testing.assert_allclose(fq, f(x, y).T, rtol=1e-3, atol=1e-2)
fq = interp2d(x, y, xp, yp, fp, method="cubic")
np.testing.assert_allclose(fq, f(x, y).T, rtol=1e-5, atol=2e-3)
class TestInterp3D:
"""Tests for interp3d function."""
@pytest.mark.unit
@pytest.mark.parametrize(
"x, y, z",
[
(
np.linspace(0, np.pi, 1000),
np.linspace(0, 2 * np.pi, 1000),
np.linspace(0, 3, 1000),
),
(0.0, 0.0, 0.0),
],
)
def test_interp3d(self, x, y, z):
"""Test accuracy of different 3d interpolation methods."""
xp = np.linspace(0, np.pi, 20)
yp = np.linspace(0, 2 * np.pi, 30)
zp = np.linspace(0, 3, 25)
xxp, yyp, zzp = np.meshgrid(xp, yp, zp, indexing="ij")
f = lambda x, y, z: np.sin(x) * np.cos(y) * z**2
fp = f(xxp, yyp, zzp)
interp1 = lambda xq, yq, zq, *args, **kwargs: interp3d(
xq, yq, zq, *args, **kwargs
)
| """Tests for interpolation functions."""
jax_config.update("jax_enable_x64", True)
class TestInterp1D:
"""Tests for interp1d function."""
@pytest.mark.unit
@pytest.mark.parametrize(
"x",
[
np.linspace(0, 2 * np.pi, 10000),
0.0,
],
)
def test_interp1d(self, x):
"""Test accuracy of different 1d interpolation methods."""
xp = np.linspace(0, 2 * np.pi, 100)
f = lambda x: np.sin(x)
fp = f(xp)
interp1 = lambda xq, *args, **kwargs: interp1d(xq, *args, **kwargs)
interp2 = lambda xq, *args, **kwargs: Interpolator1D(*args, **kwargs)(xq)
for interp in [interp1, interp2]:
fq = interp(x, xp, fp, method="nearest")
np.testing.assert_allclose(fq, f(x), rtol=1e-2, atol=1e-1)
fq = interp(x, xp, fp, method="linear")
np.testing.assert_allclose(fq, f(x), rtol=1e-4, atol=1e-3)
fq = interp(x, xp, fp, method="cubic")
np.testing.assert_allclose(fq, f(x), rtol=1e-6, atol=1e-5)
fq = interp(x, xp, fp, method="cubic2")
np.testing.assert_allclose(fq, f(x), rtol=1e-6, atol=1e-5)
fq = interp(x, xp, fp, method="cardinal")
np.testing.assert_allclose(fq, f(x), rtol=1e-6, atol=1e-5)
fq = interp(x, xp, fp, method="catmull-rom")
np.testing.assert_allclose(fq, f(x), rtol=1e-6, atol=1e-5)
fq = interp(x, xp, fp, method="monotonic")
np.testing.assert_allclose(fq, f(x), rtol=1e-4, atol=1e-3)
fq = interp(x, xp, fp, method="monotonic-0")
np.testing.assert_allclose(fq, f(x), rtol=1e-4, atol=1e-2)
@pytest.mark.unit
def test_interp1d_vector_valued(self):
"""Test for interpolating vector valued function."""
xp = np.linspace(0, 2 * np.pi, 100)
x = np.linspace(0, 2 * np.pi, 300)[10:-10]
f = lambda x: np.array([np.sin(x), np.cos(x)])
fp = f(xp).T
fq = interp1d(x, xp, fp, method="nearest")
np.testing.assert_allclose(fq, f(x).T, rtol=1e-2, atol=1e-1)
fq = interp1d(x, xp, fp, method="linear")
np.testing.assert_allclose(fq, f(x).T, rtol=1e-4, atol=1e-3)
fq = interp1d(x, xp, fp, method="cubic")
np.testing.assert_allclose(fq, f(x).T, rtol=1e-6, atol=1e-5)
fq = interp1d(x, xp, fp, method="cubic2")
np.testing.assert_allclose(fq, f(x).T, rtol=1e-6, atol=1e-5)
fq = interp1d(x, xp, fp, method="cardinal")
np.testing.assert_allclose(fq, f(x).T, rtol=1e-6, atol=1e-5)
fq = interp1d(x, xp, fp, method="catmull-rom")
np.testing.assert_allclose(fq, f(x).T, rtol=1e-6, atol=1e-5)
fq = interp1d(x, xp, fp, method="monotonic")
np.testing.assert_allclose(fq, f(x).T, rtol=1e-4, atol=1e-3)
fq = interp1d(x, xp, fp, method="monotonic-0")
np.testing.assert_allclose(fq, f(x).T, rtol=1e-4, atol=1e-2)
@pytest.mark.unit
def test_interp1d_extrap_periodic(self):
"""Test extrapolation and periodic BC of 1d interpolation."""
xp = np.linspace(0, 2 * np.pi, 200)
x = np.linspace(-1, 2 * np.pi + 1, 10000)
f = lambda x: np.sin(x)
fp = f(xp)
fq = interp1d(x, xp, fp, method="cubic", extrap=False)
assert np.isnan(fq[0])
assert np.isnan(fq[-1])
fq = interp1d(x, xp, fp, method="cubic", extrap=True)
assert not np.isnan(fq[0])
assert not np.isnan(fq[-1])
fq = interp1d(x, xp, fp, method="cubic", period=2 * np.pi)
np.testing.assert_allclose(fq, f(x), rtol=1e-6, atol=1e-2)
@pytest.mark.unit
def test_interp1d_monotonic(self):
"""Ensure monotonic interpolation is actually monotonic."""
# true function is just linear with a jump discontinuity at x=1.5
x = np.linspace(-4, 5, 10)
f = np.heaviside(x - 1.5, 0) + 0.1 * x
xq = np.linspace(-4, 5, 1000)
dfc = interp1d(xq, x, f, derivative=1, method="cubic")
dfm = interp1d(xq, x, f, derivative=1, method="monotonic")
dfm0 = interp1d(xq, x, f, derivative=1, method="monotonic-0")
assert dfc.min() < 0 # cubic interpolation undershoots, giving negative slope
assert dfm.min() > 0 # monotonic interpolation doesn't
assert dfm0.min() >= 0 # monotonic-0 doesn't overshoot either
# ensure monotonic-0 has 0 slope at end points
np.testing.assert_allclose(dfm0[np.array([0, -1])], 0, atol=1e-12)
class TestInterp2D:
"""Tests for interp2d function."""
@pytest.mark.unit
@pytest.mark.parametrize(
"x, y",
[
(np.linspace(0, 3 * np.pi, 1000), np.linspace(0, 2 * np.pi, 1000)),
(0.0, 0.0),
],
)
def test_interp2d(self, x, y):
"""Test accuracy of different 2d interpolation methods."""
xp = np.linspace(0, 3 * np.pi, 99)
yp = np.linspace(0, 2 * np.pi, 40)
xxp, yyp = np.meshgrid(xp, yp, indexing="ij")
f = lambda x, y: np.sin(x) * np.cos(y)
fp = f(xxp, yyp)
interp1 = lambda xq, yq, *args, **kwargs: interp2d(xq, yq, *args, **kwargs)
interp2 = lambda xq, yq, *args, **kwargs: Interpolator2D(*args, **kwargs)(
xq, yq
)
for interp in [interp1, interp2]:
fq = interp(
x, y, xp, yp, fp, method="nearest", period=(2 * np.pi, 2 * np.pi)
)
np.testing.assert_allclose(fq, f(x, y), rtol=1e-2, atol=1)
fq = interp(
x, y, xp, yp, fp, method="linear", period=(2 * np.pi, 2 * np.pi)
)
np.testing.assert_allclose(fq, f(x, y), rtol=1e-4, atol=1e-2)
atol = 2e-3
rtol = 1e-5
fq = interp(x, y, xp, yp, fp, method="cubic", period=(2 * np.pi, 2 * np.pi))
np.testing.assert_allclose(fq, f(x, y), rtol=rtol, atol=atol)
fq = interp(
x, y, xp, yp, fp, method="cubic2", period=(2 * np.pi, 2 * np.pi)
)
np.testing.assert_allclose(fq, f(x, y), rtol=rtol, atol=atol)
fq = interp(
x, y, xp, yp, fp, method="catmull-rom", period=(2 * np.pi, 2 * np.pi)
)
np.testing.assert_allclose(fq, f(x, y), rtol=rtol, atol=atol)
fq = interp(
x, y, xp, yp, fp, method="cardinal", period=(2 * np.pi, 2 * np.pi)
)
np.testing.assert_allclose(fq, f(x, y), rtol=rtol, atol=atol)
@pytest.mark.unit
def test_interp2d_vector_valued(self):
"""Test for interpolating vector valued function."""
xp = np.linspace(0, 3 * np.pi, 99)
yp = np.linspace(0, 2 * np.pi, 40)
x = np.linspace(0, 3 * np.pi, 200)
y = np.linspace(0, 2 * np.pi, 200)
xxp, yyp = np.meshgrid(xp, yp, indexing="ij")
f = lambda x, y: np.array([np.sin(x) * np.cos(y), np.sin(x) + np.cos(y)])
fp = f(xxp.T, yyp.T).T
fq = interp2d(x, y, xp, yp, fp, method="nearest")
np.testing.assert_allclose(fq, f(x, y).T, rtol=1e-2, atol=1.2e-1)
fq = interp2d(x, y, xp, yp, fp, method="linear")
np.testing.assert_allclose(fq, f(x, y).T, rtol=1e-3, atol=1e-2)
fq = interp2d(x, y, xp, yp, fp, method="cubic")
np.testing.assert_allclose(fq, f(x, y).T, rtol=1e-5, atol=2e-3)
class TestInterp3D:
"""Tests for interp3d function."""
@pytest.mark.unit
@pytest.mark.parametrize(
"x, y, z",
[
(
np.linspace(0, np.pi, 1000),
np.linspace(0, 2 * np.pi, 1000),
np.linspace(0, 3, 1000),
),
(0.0, 0.0, 0.0),
],
)
def test_interp3d(self, x, y, z):
"""Test accuracy of different 3d interpolation methods."""
xp = np.linspace(0, np.pi, 20)
yp = np.linspace(0, 2 * np.pi, 30)
zp = np.linspace(0, 3, 25)
xxp, yyp, zzp = np.meshgrid(xp, yp, zp, indexing="ij")
f = lambda x, y, z: np.sin(x) * np.cos(y) * z**2
fp = f(xxp, yyp, zzp)
interp1 = lambda xq, yq, zq, *args, **kwargs: interp3d(
xq, yq, zq, *args, **kwargs
) | interp2 = lambda xq, yq, zq, *args, **kwargs: Interpolator3D(*args, **kwargs)( | 4 | 2023-10-18 13:12:20+00:00 | 24k |
city96/ComfyUI_ExtraModels | PixArt/sampler.py | [
{
"identifier": "gaussian_diffusion",
"path": "PixArt/sampling/gaussian_diffusion.py",
"snippet": "def mean_flat(tensor):\n def is_vb(self):\ndef _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac):\ndef get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_time... | import torch
import comfy.utils
import latent_preview
from .sampling import gaussian_diffusion as gd
from .sampling.dpm_solver import model_wrapper, DPM_Solver, NoiseScheduleVP
from comfy.sample import prepare_sampling, prepare_noise, cleanup_additional_models, get_models_from_cond
| 20,107 |
def sample_pixart(model, seed, steps, cfg, noise_schedule, noise_schedule_vp, positive, negative, latent_image):
"""
Mostly just a wrapper around the reference code.
"""
# prepare model
noise = prepare_noise(latent_image, seed)
real_model, _, _, _, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask=None)
# negative cond
cond = positive[0][0]
raw_uncond = negative[0][0]
# Sampler seems to want the same dim for cond and uncond
# truncate uncond to the length of cond
# if shorter, pad uncond with y_null
null_y = real_model.diffusion_model.y_embedder.y_embedding[None].repeat(latent_image.shape[0], 1, 1)
uncond = null_y[:, :cond.shape[1], :]
uncond[:, :raw_uncond.shape[1], :] = raw_uncond[:, :cond.shape[1], :]
if raw_uncond.shape[1] > cond.shape[1]:
print("PixArt: Warning. Your negative prompt is too long.")
uncond[:, -1, :] = raw_uncond[:, -1, :] # add back EOS token
# Move inputs
cond = cond.to(model.load_device).to(real_model.diffusion_model.dtype)
uncond = uncond.to(model.load_device).to(real_model.diffusion_model.dtype)
noise = noise.to(model.load_device).to(real_model.diffusion_model.dtype)
# preview
pbar = comfy.utils.ProgressBar(steps)
previewer = latent_preview.get_previewer(model.load_device, model.model.latent_format)
## Noise schedule.
betas = torch.tensor(gd.get_named_beta_schedule(noise_schedule, 1000))
noise_schedule = NoiseScheduleVP(schedule=noise_schedule_vp, betas=betas)
## Convert your discrete-time `model` to the continuous-time
## noise prediction model. Here is an example for a diffusion model
## `model` with the noise prediction type ("noise") .
|
def sample_pixart(model, seed, steps, cfg, noise_schedule, noise_schedule_vp, positive, negative, latent_image):
"""
Mostly just a wrapper around the reference code.
"""
# prepare model
noise = prepare_noise(latent_image, seed)
real_model, _, _, _, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask=None)
# negative cond
cond = positive[0][0]
raw_uncond = negative[0][0]
# Sampler seems to want the same dim for cond and uncond
# truncate uncond to the length of cond
# if shorter, pad uncond with y_null
null_y = real_model.diffusion_model.y_embedder.y_embedding[None].repeat(latent_image.shape[0], 1, 1)
uncond = null_y[:, :cond.shape[1], :]
uncond[:, :raw_uncond.shape[1], :] = raw_uncond[:, :cond.shape[1], :]
if raw_uncond.shape[1] > cond.shape[1]:
print("PixArt: Warning. Your negative prompt is too long.")
uncond[:, -1, :] = raw_uncond[:, -1, :] # add back EOS token
# Move inputs
cond = cond.to(model.load_device).to(real_model.diffusion_model.dtype)
uncond = uncond.to(model.load_device).to(real_model.diffusion_model.dtype)
noise = noise.to(model.load_device).to(real_model.diffusion_model.dtype)
# preview
pbar = comfy.utils.ProgressBar(steps)
previewer = latent_preview.get_previewer(model.load_device, model.model.latent_format)
## Noise schedule.
betas = torch.tensor(gd.get_named_beta_schedule(noise_schedule, 1000))
noise_schedule = NoiseScheduleVP(schedule=noise_schedule_vp, betas=betas)
## Convert your discrete-time `model` to the continuous-time
## noise prediction model. Here is an example for a diffusion model
## `model` with the noise prediction type ("noise") .
| model_fn = model_wrapper(
| 1 | 2023-10-20 21:19:44+00:00 | 24k |
amitfin/oref_alert | custom_components/oref_alert/config_flow.py | [
{
"identifier": "CONF_AREAS",
"path": "custom_components/oref_alert/const.py",
"snippet": "CONF_AREAS: Final = \"areas\""
},
{
"identifier": "CONF_ALERT_MAX_AGE",
"path": "custom_components/oref_alert/const.py",
"snippet": "CONF_ALERT_MAX_AGE: Final = \"alert_max_age\""
},
{
"ide... | import contextlib
import voluptuous as vol
import homeassistant.helpers.config_validation as cv
from typing import Any
from homeassistant.config_entries import ConfigEntry, ConfigFlow, OptionsFlow
from homeassistant.core import async_get_hass, callback
from homeassistant.data_entry_flow import FlowResult
from homeassistant.exceptions import HomeAssistantError
from homeassistant.helpers import selector
from .const import (
CONF_AREAS,
CONF_ALERT_MAX_AGE,
CONF_OFF_ICON,
CONF_ON_ICON,
CONF_POLL_INTERVAL,
DEFAULT_ALERT_MAX_AGE,
DOMAIN,
DEFAULT_OFF_ICON,
DEFAULT_ON_ICON,
DEFAULT_POLL_INTERVAL,
TITLE,
)
from .metadata.area_to_polygon import find_area
from .metadata.areas_and_groups import AREAS_AND_GROUPS | 18,888 | """Config flow for oref_alert integration."""
from __future__ import annotations
AREAS_CONFIG = selector.SelectSelectorConfig(
options=AREAS_AND_GROUPS,
mode=selector.SelectSelectorMode.DROPDOWN,
multiple=True,
custom_value=False,
)
CONFIG_SCHEMA = vol.Schema(
{vol.Required(CONF_AREAS, default=[]): selector.SelectSelector(AREAS_CONFIG)}
)
class OrefAlertConfigFlow(ConfigFlow, domain=DOMAIN):
"""Config flow."""
def __init__(self) -> None:
"""Initialize object with defaults."""
self._auto_detected_area: str | None = None
async def async_step_user(
self, user_input: dict[str, Any] | None = None
) -> FlowResult:
"""Handle a flow initialized by the user."""
if self._async_current_entries():
return self.async_abort(reason="single_instance_allowed")
if user_input is not None:
return await self.async_step_confirm(user_input)
hass = None
with contextlib.suppress(HomeAssistantError):
hass = async_get_hass()
if hass:
self._auto_detected_area = find_area(
hass.config.latitude, hass.config.longitude
)
if not self._auto_detected_area:
return self.async_show_form(step_id="user", data_schema=CONFIG_SCHEMA)
return await self.async_step_confirm(None)
async def async_step_confirm(
self, user_input: dict[str, Any] | None = None
) -> FlowResult:
"""Confirm the setup."""
if user_input is not None:
return self.async_create_entry(
title=TITLE,
data={},
options={
CONF_AREAS: user_input.get(CONF_AREAS, [self._auto_detected_area]),
CONF_ALERT_MAX_AGE: DEFAULT_ALERT_MAX_AGE,
CONF_POLL_INTERVAL: DEFAULT_POLL_INTERVAL,
| """Config flow for oref_alert integration."""
from __future__ import annotations
AREAS_CONFIG = selector.SelectSelectorConfig(
options=AREAS_AND_GROUPS,
mode=selector.SelectSelectorMode.DROPDOWN,
multiple=True,
custom_value=False,
)
CONFIG_SCHEMA = vol.Schema(
{vol.Required(CONF_AREAS, default=[]): selector.SelectSelector(AREAS_CONFIG)}
)
class OrefAlertConfigFlow(ConfigFlow, domain=DOMAIN):
"""Config flow."""
def __init__(self) -> None:
"""Initialize object with defaults."""
self._auto_detected_area: str | None = None
async def async_step_user(
self, user_input: dict[str, Any] | None = None
) -> FlowResult:
"""Handle a flow initialized by the user."""
if self._async_current_entries():
return self.async_abort(reason="single_instance_allowed")
if user_input is not None:
return await self.async_step_confirm(user_input)
hass = None
with contextlib.suppress(HomeAssistantError):
hass = async_get_hass()
if hass:
self._auto_detected_area = find_area(
hass.config.latitude, hass.config.longitude
)
if not self._auto_detected_area:
return self.async_show_form(step_id="user", data_schema=CONFIG_SCHEMA)
return await self.async_step_confirm(None)
async def async_step_confirm(
self, user_input: dict[str, Any] | None = None
) -> FlowResult:
"""Confirm the setup."""
if user_input is not None:
return self.async_create_entry(
title=TITLE,
data={},
options={
CONF_AREAS: user_input.get(CONF_AREAS, [self._auto_detected_area]),
CONF_ALERT_MAX_AGE: DEFAULT_ALERT_MAX_AGE,
CONF_POLL_INTERVAL: DEFAULT_POLL_INTERVAL, | CONF_ON_ICON: DEFAULT_ON_ICON, | 8 | 2023-10-18 11:16:41+00:00 | 24k |
RobertCsordas/moe | tasks/simple/language_model/transformer_lm_mixin.py | [
{
"identifier": "TransformerLanguageModel",
"path": "models/transformer_language_model.py",
"snippet": "class TransformerLanguageModel(LoggingLayer, torch.nn.Module):\n def __init__(self, voc_size: int, embedding_size: Optional[int], state_size: int, dropout: float,\n tied_embedding: ... | import framework
import torch
import torch.nn
import torch.nn.functional as F
import torch.utils.data
import math
from typing import List, Tuple, Dict, Any
from models import TransformerLanguageModel
from ... import task, args
from layers.transformer import RelativeTransformerEncoderLayer, PrelnRelativeTransformerEncoderLayer
from layers.transformer.relative_preln_kvmem_transformer import PrelnRelativeKVMemTransformerEncoderLayer
from layers.transformer.relative_moe_transformer import RelativeMoeTransformerEncoderLayer
from layers.transformer.topk_transformer import TopkTransformer
from layers.moe_layer import MoE
from interfaces import Result | 18,811 | parser.add_argument("-moe.n_random", default=0)
parser.add_argument("-moe.std_correction", default=False)
parser.add_argument("-moe.topk_mode", default="full", choice=["full", "l1_approx", "approx"])
parser.add_argument("-moe.activation_after_topk", default=False)
parser.add_argument("-moe.weight_grouping", default="none", choice=["none", "keys_only", "keys_and_experts"])
parser.add_argument("-moe.drop_parallel", default=True)
parser.add_argument("-moe.mlp_selection", default=False)
parser.add_argument("-moe.block_expert_sel_in_grad", default=False)
parser.add_argument("-moe.classification_target", default="sum", choice=["sum", "max"])
parser.add_argument("-moe.recluster_steps", default="", parser=parser.int_list_parser)
parser.add_argument("-moe.norm_key_init", default=False)
parser.add_argument("-moe.norm_value_init", default=False)
parser.add_argument("-moe.norm_expert_sel_init", default=False)
parser.add_argument("-moe.norm_standard_parallel_values", default=False)
parser.add_argument("-moe.identical_init", default=False)
parser.add_argument("-moe.topological_sel_reg", default=0.0)
parser.add_argument("-moe.topological_expert_reg", default=0.0)
parser.add_argument("-moe.sel_lr_multipler", default=1.0)
parser.add_argument("-moe.expert_lr_multipler", default=1.0)
parser.add_argument("-moe.gumbel_select_only", default=False)
parser.add_argument("-moe.topk_value_norm_compensation", default=False)
parser.add_argument("-moe.norm_expert_scores", default=False)
parser.add_argument("-moe.sel_input_cluster_init", default=False)
parser.add_argument("-moe.init_norm_mode", default="full")
parser.add_argument("-moe.bias", default=False)
parser.add_argument("-moe.sel_bias", default=False)
parser.add_argument("-moe.rescale_normed", default=False)
parser.add_argument("-moe.sel_norm", default="none", choice=["none", "cos", "input", "weights"])
parser.add_argument("-moe.rescale_grads", default=False)
parser.add_argument("-moe.gumbel_decay", default=0)
parser.add_argument("-moe.sinkhorn_local", default=False)
parser.add_argument("-moe.sinkhron_n_iters", default=3)
parser.add_argument("-moe.dropout_factor", default=1.0)
parser.add_argument("-moe.drop_expert", default=0.0)
parser.add_argument("-moe.expert_size_init", default=False)
parser.add_argument("-moe.sync_distributed", default=True)
parser.add_argument("-moe.modulation_amplitude", default=0.5)
parser.add_argument("-moe.invisible_selection", default=False)
parser.add_argument("-moe.slope_multiplier", default=1.0)
parser.add_argument("-moe.init_scale", default=1.0)
parser.add_argument("-kvmem.linproj", default=False)
parser.add_argument("-kvmem.head_merge_topk", default=False)
parser.add_argument("-kvmem.load_balance", default=False)
parser.add_argument("-kvmem.dropout", default="none", choice=["none", "early", "late", "weight", "score"])
parser.add_argument("-kvmem.randomize_indices", default=False)
parser.add_argument("-kvmem.standard_parallel", default=False)
parser.add_argument("-kvmem.query_bias", default=False)
parser.add_argument("-kvmem.approx_topk", default=False)
parser.add_argument("-kvmem.norm_values", default=False)
parser.add_argument("-kvmem.factorize", default=False)
parser.add_argument("-kvmem.full_key", default=False)
parser.add_argument("-kvmem.key_redundancy_factor", default=1)
parser.add_argument("-kvmem.two_stage", default=False)
parser.add_argument("-kvmem.head_exclusive", default=False)
parser.add_argument("-transformer.topk_value", default=32)
parser.add_argument("-transformer.universal.nonshared", default=0)
parser.add_argument("-transformer.topk_use_norm", default=True)
parser.add_argument("-transformer.activation", default="relu", choice=["relu", "topk", "gelu", "identity", "sigmoid", "softmax"])
parser.add_argument("-transformer.p_drop_layer", default=0.0)
parser.add_argument("-transformer.head_projection_size", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-transformer.ln_affine", default=True)
parser.add_argument("-transformer.ln_after_attention", default=True)
parser.add_argument("-transformer.output_mode", default="normal", choice=["normal", "sum", "geometric", "sigmoid"])
@task()
class TransformerLMMixin:
helper: framework.helpers.TrainingHelper
def is_preln(self) -> bool:
return "preln" in self.helper.args.transformer.variant
def topk_activation(self, x: torch.Tensor) -> torch.Tensor:
nx = -x
return torch.masked_fill(x, nx <= nx.kthvalue(self.helper.args.transformer.topk_value, keepdim=True)[0], 0)
def get_layers(self) -> List[torch.nn.Module]:
# pyright: reportOptionalMemberAccess=false
if self.helper.args.transformer.activation == "relu":
activation = F.relu
elif self.helper.args.transformer.activation == "topk":
activation = self.topk_activation
elif self.helper.args.transformer.activation == "identity":
activation = lambda x: x
elif self.helper.args.transformer.activation == "sigmoid":
activation = torch.sigmoid
elif self.helper.args.transformer.activation == "gelu":
activation = F.gelu
elif self.helper.args.transformer.activation == "softmax":
activation = lambda x: F.softmax(x, dim=-1)
else:
raise ValueError(f"Invalid activation: {self.helper.args.transformer.activation}")
base_args = dict(
d_model=self.helper.args.state_size,
nhead=self.helper.args.transformer.n_heads,
dim_feedforward=int(self.helper.args.state_size * self.helper.args.transformer.ff_multiplier),
dropout=self.helper.args.dropout,
activation=activation
)
extra_args = {} if not self.helper.args.transformer.variant.endswith("_gelu") else {
"activation": F.gelu,
"drop_expand": False
}
if self.helper.args.transformer.variant in {"preln_relative"}:
mklayer = lambda: PrelnRelativeTransformerEncoderLayer(
**base_args, **extra_args, test_pos_clamp=self.helper.args.lm.trafo.test_pos_clamp,
n_layers=self.helper.args.transformer.encoder_n_layers,
head_projection_size=self.helper.args.transformer.head_projection_size,)
elif self.helper.args.transformer.variant in {"preln_topk"}:
mklayer = lambda: TopkTransformer(
**base_args, **extra_args, test_pos_clamp=self.helper.args.lm.trafo.test_pos_clamp,
n_layers=self.helper.args.transformer.encoder_n_layers, k=self.helper.args.transformer.topk_value,
use_norm=self.helper.args.transformer.topk_use_norm,
head_projection_size=self.helper.args.transformer.head_projection_size,)
elif self.helper.args.transformer.variant in {"preln_kvmem"}:
|
@args
def a(parser: framework.helpers.ArgumentParser):
parser.add_argument("-lm.trafo.context_blocks", default=1)
parser.add_argument("-lm.trafo.test_context_blocks", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-lm.trafo.test_pos_clamp", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-lm.trafo.same_length_eval", default=False)
parser.add_argument("-lm.trafo.same_length", default=False)
parser.add_argument("-lm.trafo.last_layer_context", default=False)
parser.add_argument("-lm.trafo.xl_init", default=False)
parser.add_argument("-lm.trafo.embedding_mode_init", default="default", choice=["default", "scale_to_sqrt_dmodel", "init_to_sqrt_dmodel", "one_and_scale_to_sqrt_dmodel", "like_preln"])
parser.add_argument("-pkm.n_keys", default="128", parser=parser.int_list_parser)
parser.add_argument("-pkm.n_heads", default=1)
parser.add_argument("-pkm.knn", default=32)
parser.add_argument("-pkm.stochastic", default=False)
parser.add_argument("-pkm.query_batchnorm", default=False)
parser.add_argument("-pkm.custom_init", default=0)
parser.add_argument("-pkm.slice_values", default=False)
parser.add_argument("-pkm.slice_proj", default=False)
parser.add_argument("-pkm.sample_smallest", default=False)
parser.add_argument("-moe.n_experts", default=128)
parser.add_argument("-moe.expert_size", default=128)
parser.add_argument("-moe.selection_mode", default="add", choice=["add", "gate", "sigmoid", "gumbel", "hard_gumbel", "predict", "predict_mlp", "classify", "gumbel_sigmoid", "sinkhorn", "sinkhorn2", "sinkmoid", "sinkmax", "moe", "mul", "random", "sinkmoid2", "sinkmax2", "modulate"])
parser.add_argument("-moe.perplexity_reg", default=0.0)
parser.add_argument("-moe.perplexity_reg_mode", default="step", choice=["step", "global", "time", "global_time"])
parser.add_argument("-moe.reg_type", default="entropy", choice=["perplexity", "variance", "entropy", "l2", "switch", "normal"])
parser.add_argument("-moe.key_mode", default="moe", choice=["moe", "both", "shared"])
parser.add_argument("-moe.half_key", default=False)
parser.add_argument("-moe.norm_keys", default=False)
parser.add_argument("-moe.kmeans_distance", default='cosine', choice=['cosine', 'euclidean'])
parser.add_argument("-moe.n_random", default=0)
parser.add_argument("-moe.std_correction", default=False)
parser.add_argument("-moe.topk_mode", default="full", choice=["full", "l1_approx", "approx"])
parser.add_argument("-moe.activation_after_topk", default=False)
parser.add_argument("-moe.weight_grouping", default="none", choice=["none", "keys_only", "keys_and_experts"])
parser.add_argument("-moe.drop_parallel", default=True)
parser.add_argument("-moe.mlp_selection", default=False)
parser.add_argument("-moe.block_expert_sel_in_grad", default=False)
parser.add_argument("-moe.classification_target", default="sum", choice=["sum", "max"])
parser.add_argument("-moe.recluster_steps", default="", parser=parser.int_list_parser)
parser.add_argument("-moe.norm_key_init", default=False)
parser.add_argument("-moe.norm_value_init", default=False)
parser.add_argument("-moe.norm_expert_sel_init", default=False)
parser.add_argument("-moe.norm_standard_parallel_values", default=False)
parser.add_argument("-moe.identical_init", default=False)
parser.add_argument("-moe.topological_sel_reg", default=0.0)
parser.add_argument("-moe.topological_expert_reg", default=0.0)
parser.add_argument("-moe.sel_lr_multipler", default=1.0)
parser.add_argument("-moe.expert_lr_multipler", default=1.0)
parser.add_argument("-moe.gumbel_select_only", default=False)
parser.add_argument("-moe.topk_value_norm_compensation", default=False)
parser.add_argument("-moe.norm_expert_scores", default=False)
parser.add_argument("-moe.sel_input_cluster_init", default=False)
parser.add_argument("-moe.init_norm_mode", default="full")
parser.add_argument("-moe.bias", default=False)
parser.add_argument("-moe.sel_bias", default=False)
parser.add_argument("-moe.rescale_normed", default=False)
parser.add_argument("-moe.sel_norm", default="none", choice=["none", "cos", "input", "weights"])
parser.add_argument("-moe.rescale_grads", default=False)
parser.add_argument("-moe.gumbel_decay", default=0)
parser.add_argument("-moe.sinkhorn_local", default=False)
parser.add_argument("-moe.sinkhron_n_iters", default=3)
parser.add_argument("-moe.dropout_factor", default=1.0)
parser.add_argument("-moe.drop_expert", default=0.0)
parser.add_argument("-moe.expert_size_init", default=False)
parser.add_argument("-moe.sync_distributed", default=True)
parser.add_argument("-moe.modulation_amplitude", default=0.5)
parser.add_argument("-moe.invisible_selection", default=False)
parser.add_argument("-moe.slope_multiplier", default=1.0)
parser.add_argument("-moe.init_scale", default=1.0)
parser.add_argument("-kvmem.linproj", default=False)
parser.add_argument("-kvmem.head_merge_topk", default=False)
parser.add_argument("-kvmem.load_balance", default=False)
parser.add_argument("-kvmem.dropout", default="none", choice=["none", "early", "late", "weight", "score"])
parser.add_argument("-kvmem.randomize_indices", default=False)
parser.add_argument("-kvmem.standard_parallel", default=False)
parser.add_argument("-kvmem.query_bias", default=False)
parser.add_argument("-kvmem.approx_topk", default=False)
parser.add_argument("-kvmem.norm_values", default=False)
parser.add_argument("-kvmem.factorize", default=False)
parser.add_argument("-kvmem.full_key", default=False)
parser.add_argument("-kvmem.key_redundancy_factor", default=1)
parser.add_argument("-kvmem.two_stage", default=False)
parser.add_argument("-kvmem.head_exclusive", default=False)
parser.add_argument("-transformer.topk_value", default=32)
parser.add_argument("-transformer.universal.nonshared", default=0)
parser.add_argument("-transformer.topk_use_norm", default=True)
parser.add_argument("-transformer.activation", default="relu", choice=["relu", "topk", "gelu", "identity", "sigmoid", "softmax"])
parser.add_argument("-transformer.p_drop_layer", default=0.0)
parser.add_argument("-transformer.head_projection_size", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-transformer.ln_affine", default=True)
parser.add_argument("-transformer.ln_after_attention", default=True)
parser.add_argument("-transformer.output_mode", default="normal", choice=["normal", "sum", "geometric", "sigmoid"])
@task()
class TransformerLMMixin:
helper: framework.helpers.TrainingHelper
def is_preln(self) -> bool:
return "preln" in self.helper.args.transformer.variant
def topk_activation(self, x: torch.Tensor) -> torch.Tensor:
nx = -x
return torch.masked_fill(x, nx <= nx.kthvalue(self.helper.args.transformer.topk_value, keepdim=True)[0], 0)
def get_layers(self) -> List[torch.nn.Module]:
# pyright: reportOptionalMemberAccess=false
if self.helper.args.transformer.activation == "relu":
activation = F.relu
elif self.helper.args.transformer.activation == "topk":
activation = self.topk_activation
elif self.helper.args.transformer.activation == "identity":
activation = lambda x: x
elif self.helper.args.transformer.activation == "sigmoid":
activation = torch.sigmoid
elif self.helper.args.transformer.activation == "gelu":
activation = F.gelu
elif self.helper.args.transformer.activation == "softmax":
activation = lambda x: F.softmax(x, dim=-1)
else:
raise ValueError(f"Invalid activation: {self.helper.args.transformer.activation}")
base_args = dict(
d_model=self.helper.args.state_size,
nhead=self.helper.args.transformer.n_heads,
dim_feedforward=int(self.helper.args.state_size * self.helper.args.transformer.ff_multiplier),
dropout=self.helper.args.dropout,
activation=activation
)
extra_args = {} if not self.helper.args.transformer.variant.endswith("_gelu") else {
"activation": F.gelu,
"drop_expand": False
}
if self.helper.args.transformer.variant in {"preln_relative"}:
mklayer = lambda: PrelnRelativeTransformerEncoderLayer(
**base_args, **extra_args, test_pos_clamp=self.helper.args.lm.trafo.test_pos_clamp,
n_layers=self.helper.args.transformer.encoder_n_layers,
head_projection_size=self.helper.args.transformer.head_projection_size,)
elif self.helper.args.transformer.variant in {"preln_topk"}:
mklayer = lambda: TopkTransformer(
**base_args, **extra_args, test_pos_clamp=self.helper.args.lm.trafo.test_pos_clamp,
n_layers=self.helper.args.transformer.encoder_n_layers, k=self.helper.args.transformer.topk_value,
use_norm=self.helper.args.transformer.topk_use_norm,
head_projection_size=self.helper.args.transformer.head_projection_size,)
elif self.helper.args.transformer.variant in {"preln_kvmem"}: | mklayer = lambda: PrelnRelativeKVMemTransformerEncoderLayer( | 5 | 2023-10-16 11:26:45+00:00 | 24k |
Jacob-Zhou/gecdi | gec/parser.py | [
{
"identifier": "Dataset",
"path": "gec/data.py",
"snippet": "class Dataset(torch.utils.data.Dataset):\n r\"\"\"\n Dataset that is compatible with :class:`torch.utils.data.Dataset`, serving as a wrapper for manipulating all data fields\n with the operating behaviours defined in :class:`~supar.u... | import os
import shutil
import tempfile
import math
import dill
import torch
import torch.distributed as dist
from datetime import datetime, timedelta
from typing import Iterable, Union
from gec.data import Dataset
from gec.fn import map_token_ids
from supar.parser import Parser
from supar.utils import Config
from supar.utils.common import MIN, NUL, UNK
from supar.utils.field import RawField
from supar.utils.fn import set_rng_state
from supar.utils.logging import get_logger, init_logger, progress_bar
from supar.utils.metric import Metric
from supar.utils.optim import PolynomialLR
from supar.utils.parallel import DistributedDataParallel as DDP, gather, is_dist
from supar.utils.parallel import is_master
from supar.utils.tokenizer import TransformerTokenizer
from supar.utils.transform import AttachJuxtaposeTree, Batch
from torch.cuda.amp import GradScaler
from torch.optim import AdamW
from torch.optim.lr_scheduler import ExponentialLR
from torch.nn.functional import embedding
from .metric import PerplexityMetric, SpanMetric
from .model import Seq2SeqDetectModel, Seq2SeqModel
from .transform import Field, Text, Tree
from torch.distributed.algorithms.ddp_comm_hooks.default_hooks import fp16_compress_hook
from torch.distributed.algorithms.ddp_comm_hooks.default_hooks import fp16_compress_hook
from transformers import AutoTokenizer, GPT2LMHeadModel | 14,429 | self.checkpoint_state_dict.pop('optimizer_state_dict'))
self.scheduler.load_state_dict(
self.checkpoint_state_dict.pop('scheduler_state_dict'))
self.scaler.load_state_dict(
self.checkpoint_state_dict.pop('scaler_state_dict'))
set_rng_state(self.checkpoint_state_dict.pop('rng_state'))
for k, v in self.checkpoint_state_dict.items():
setattr(self, k, v)
train.loader.batch_sampler.epoch = self.epoch
except AttributeError:
logger.warning(
"No checkpoint found. Try re-launching the traing procedure instead"
)
for epoch in range(self.epoch, args.epochs + 1):
start = datetime.now()
bar, metric = progress_bar(train.loader), Metric()
logger.info(f"Epoch {epoch} / {args.epochs}:")
self.model.train()
if self.epoch == 1:
torch.cuda.empty_cache()
with self.join():
# we should zero `step` as the number of batches in different processes is not necessarily equal
self.step = 0
for batch in bar:
with self.sync():
with torch.autocast(self.device,
enabled=self.args.amp):
loss = self.train_step(batch)
self.backward(loss)
if self.sync_grad:
self.clip_grad_norm_(self.model.parameters(),
self.args.clip)
self.scaler.step(self.optimizer)
self.scaler.update()
self.scheduler.step()
self.optimizer.zero_grad(True)
bar.set_postfix_str(
f"lr: {self.scheduler.get_last_lr()[0]:.4e} - loss: {loss:.4f}"
)
self.step += 1
logger.info(f"{bar.postfix}")
self.model.eval()
with self.join(), torch.autocast(self.device,
enabled=self.args.amp):
metric = self.reduce(
sum([self.eval_step(i) for i in progress_bar(dev.loader)],
Metric()))
logger.info(f"{'dev:':5} {metric}")
if args.test:
test_metric = sum(
[self.eval_step(i) for i in progress_bar(test.loader)],
Metric())
logger.info(f"{'test:':5} {self.reduce(test_metric)}")
t = datetime.now() - start
self.epoch += 1
self.patience -= 1
self.elapsed += t
if metric > self.best_metric:
self.best_e, self.patience, self.best_metric = epoch, patience, metric
if is_master():
self.save_checkpoint(args.path)
logger.info(f"{t}s elapsed (saved)\n")
else:
logger.info(f"{t}s elapsed\n")
if self.patience < 1:
break
if dist.is_initialized():
dist.barrier()
best = self.load(**args)
# only allow the master device to save models
if is_master():
best.save(args.path)
logger.info(f"Epoch {self.best_e} saved")
logger.info(f"{'dev:':5} {self.best_metric}")
if args.test:
best.model.eval()
with best.join():
test_metric = sum(
[best.eval_step(i) for i in progress_bar(test.loader)],
Metric())
logger.info(f"{'test:':5} {best.reduce(test_metric)}")
logger.info(f"{self.elapsed}s elapsed, {self.elapsed / epoch}s/epoch")
def train_step(self, batch: Batch) -> torch.Tensor:
src, tgt, _, src_error, _, tgt_error = batch
src_mask, tgt_mask = src.ne(self.args.pad_index), tgt.ne(
self.args.pad_index)
x = self.model(src)
loss = self.model.loss(x, tgt, src_error, tgt_error, src_mask,
tgt_mask)
return loss
@torch.no_grad()
def eval_step(self, batch: Batch) -> PerplexityMetric:
src, tgt, _, src_error, tgt_error_raw, tgt_error = batch
src_mask, tgt_mask = src.ne(self.args.pad_index), tgt.ne(
self.args.pad_index)
x = self.model(src)
loss = self.model.loss(x, tgt, src_error, tgt_error, src_mask,
tgt_mask)
def error_label_factorize(errors):
return sum(
[[(i, i + 1, e) for e in eb.split("::")]
for i, eb in enumerate(errors) if eb not in {'CORRECT', NUL}],
[])
ged_golds = [error_label_factorize(e) for e in tgt_error_raw]
ged_preds = [
error_label_factorize(
[self.TGT_ERROR.vocab[i] for i in e if i >= 0])
for e in self.model.decode(x, tgt, src_mask, tgt_mask).tolist()
]
| # -*- coding: utf-8 -*-
logger = get_logger(__name__)
class Seq2SeqParser(Parser):
NAME = 'seq2seq'
MODEL = Seq2SeqModel
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.SRC = self.transform.SRC
self.TGT = self.transform.TGT
def train(self,
train: Union[str, Iterable],
dev: Union[str, Iterable],
test: Union[str, Iterable],
epochs: int,
patience: int,
batch_size: int = 5000,
update_steps: int = 1,
buckets: int = 32,
workers: int = 0,
clip: float = 5.0,
amp: bool = False,
cache: bool = False,
verbose: bool = True,
**kwargs) -> None:
args = self.args.update(locals())
init_logger(logger, verbose=args.verbose)
self.transform.train()
batch_size = batch_size // update_steps
if dist.is_initialized():
batch_size = batch_size // dist.get_world_size()
logger.info("Loading the data")
if args.cache:
args.bin = os.path.join(os.path.dirname(args.path), 'bin')
train = Dataset(self.transform, args.train,
**args).build(batch_size,
buckets,
True,
dist.is_initialized(),
workers,
chunk_size=args.chunk_size,
seed=args.seed)
dev = Dataset(self.transform, args.dev,
**args).build(batch_size, buckets, False,
dist.is_initialized(), workers)
logger.info(f"{'train:':6} {train}")
if not args.test:
logger.info(f"{'dev:':6} {dev}\n")
else:
test = Dataset(self.transform, args.test,
**args).build(batch_size, buckets, False,
dist.is_initialized(), workers)
logger.info(f"{'dev:':6} {dev}")
logger.info(f"{'test:':6} {test}\n")
self.optimizer = AdamW(self.model.parameters(), args.lr,
(args.mu, args.nu), args.eps, args.weight_decay)
steps = len(train.loader) * epochs // args.update_steps
self.scheduler = PolynomialLR(self.optimizer,
warmup_steps=self.args.warmup_steps,
steps=steps)
self.scaler = GradScaler(enabled=args.amp)
if dist.is_initialized():
self.model = DDP(self.model,
device_ids=[args.local_rank],
find_unused_parameters=args.get(
'find_unused_parameters', True))
if args.amp:
self.model.register_comm_hook(dist.group.WORLD,
fp16_compress_hook)
self.step, self.epoch, self.best_e, self.patience, self.n_batches = 1, 1, 1, patience, len(
train.loader)
self.best_metric, self.elapsed = Metric(), timedelta()
if self.args.checkpoint:
try:
self.optimizer.load_state_dict(
self.checkpoint_state_dict.pop('optimizer_state_dict'))
self.scheduler.load_state_dict(
self.checkpoint_state_dict.pop('scheduler_state_dict'))
self.scaler.load_state_dict(
self.checkpoint_state_dict.pop('scaler_state_dict'))
set_rng_state(self.checkpoint_state_dict.pop('rng_state'))
for k, v in self.checkpoint_state_dict.items():
setattr(self, k, v)
train.loader.batch_sampler.epoch = self.epoch
except AttributeError:
logger.warning(
"No checkpoint found. Try re-launching the traing procedure instead"
)
for epoch in range(self.epoch, args.epochs + 1):
start = datetime.now()
bar, metric = progress_bar(train.loader), Metric()
logger.info(f"Epoch {epoch} / {args.epochs}:")
self.model.train()
if self.epoch == 1:
torch.cuda.empty_cache()
with self.join():
# we should zero `step` as the number of batches in different processes is not necessarily equal
self.step = 0
for batch in bar:
with self.sync():
with torch.autocast(self.device,
enabled=self.args.amp):
loss = self.train_step(batch)
self.backward(loss)
if self.sync_grad:
self.clip_grad_norm_(self.model.parameters(),
self.args.clip)
self.scaler.step(self.optimizer)
self.scaler.update()
self.scheduler.step()
self.optimizer.zero_grad(True)
bar.set_postfix_str(
f"lr: {self.scheduler.get_last_lr()[0]:.4e} - loss: {loss:.4f}"
)
self.step += 1
logger.info(f"{bar.postfix}")
self.model.eval()
with self.join(), torch.autocast(self.device,
enabled=self.args.amp):
metric = self.reduce(
sum([self.eval_step(i) for i in progress_bar(dev.loader)],
Metric()))
logger.info(f"{'dev:':5} {metric}")
if args.test:
test_metric = sum(
[self.eval_step(i) for i in progress_bar(test.loader)],
Metric())
logger.info(f"{'test:':5} {self.reduce(test_metric)}")
t = datetime.now() - start
self.epoch += 1
self.patience -= 1
self.elapsed += t
if metric > self.best_metric:
self.best_e, self.patience, self.best_metric = epoch, patience, metric
if is_master():
self.save_checkpoint(args.path)
logger.info(f"{t}s elapsed (saved)\n")
else:
logger.info(f"{t}s elapsed\n")
if self.patience < 1:
break
if dist.is_initialized():
dist.barrier()
best = self.load(**args)
# only allow the master device to save models
if is_master():
best.save(args.path)
logger.info(f"Epoch {self.best_e} saved")
logger.info(f"{'dev:':5} {self.best_metric}")
if args.test:
best.model.eval()
with best.join():
test_metric = sum(
[best.eval_step(i) for i in progress_bar(test.loader)],
Metric())
logger.info(f"{'test:':5} {best.reduce(test_metric)}")
logger.info(f"{self.elapsed}s elapsed, {self.elapsed / epoch}s/epoch")
def evaluate(self,
data: Union[str, Iterable],
batch_size: int = 5000,
buckets: int = 8,
workers: int = 0,
amp: bool = False,
cache: bool = False,
punct: bool = False,
tree: bool = True,
proj: bool = False,
partial: bool = False,
verbose: bool = True,
**kwargs):
return super().evaluate(**Config().update(locals()))
def predict(self,
data: Union[str, Iterable],
pred: str = None,
lang: str = None,
prob: bool = False,
batch_size: int = 5000,
buckets: int = 8,
workers: int = 0,
amp: bool = False,
cache: bool = False,
tree: bool = True,
proj: bool = False,
verbose: bool = True,
**kwargs):
return super().predict(**Config().update(locals()))
def train_step(self, batch: Batch) -> torch.Tensor:
src, tgt = batch
src_mask, tgt_mask = batch.mask, tgt.ne(self.args.pad_index)
x = self.model(src)
loss = self.model.loss(x, tgt, src_mask, tgt_mask)
return loss
@torch.no_grad()
def eval_step(self, batch: Batch) -> PerplexityMetric:
src, tgt = batch
src_mask, tgt_mask = batch.mask, tgt.ne(self.args.pad_index)
x = self.model(src)
loss = self.model.loss(x, tgt, src_mask, tgt_mask)
preds = golds = None
if self.args.eval_tgt:
golds = [(s.values[0], s.values[1]) for s in batch.sentences]
preds = [(s.values[0], self.TGT.tokenize.decode(i[0]))
for s, i in zip(batch.sentences,
self.model.decode(x, batch.mask).tolist())
]
return PerplexityMetric(loss, preds, golds, tgt_mask,
not self.args.eval_tgt)
@torch.no_grad()
def pred_step(self, batch: Batch) -> Batch:
src, = batch
x = self.model(src)
tgt = self.model.decode(x, batch.mask)
batch.tgt = [[self.TGT.tokenize.decode(cand) for cand in i]
for i in tgt.tolist()]
return batch
@classmethod
def build(cls, path, min_freq=2, fix_len=20, **kwargs):
r"""
Build a brand-new Parser, including initialization of all data fields and model parameters.
Args:
path (str):
The path of the model to be saved.
min_freq (str):
The minimum frequency needed to include a token in the vocabulary. Default: 2.
fix_len (int):
The max length of all subword pieces. The excess part of each piece will be truncated.
Required if using CharLSTM/BERT.
Default: 20.
kwargs (dict):
A dict holding the unconsumed arguments.
"""
args = Config(**locals())
os.makedirs(os.path.dirname(path) or './', exist_ok=True)
if os.path.exists(path) and not args.build:
return cls.load(**args)
logger.info("Building the fields")
t = TransformerTokenizer(name=args.bart)
SRC = Field('src',
pad=t.pad,
unk=t.unk,
bos=t.bos,
eos=t.eos,
tokenize=t)
TGT = Field('tgt',
pad=t.pad,
unk=t.unk,
bos=t.bos,
eos=t.eos,
tokenize=t)
transform = Text(SRC=SRC, TGT=TGT)
# share the vocab
SRC.vocab = TGT.vocab = t.vocab
args.update({
'n_words': len(SRC.vocab),
'pad_index': SRC.pad_index,
'unk_index': SRC.unk_index,
'bos_index': SRC.bos_index,
'eos_index': SRC.eos_index
})
logger.info(f"{transform}")
logger.info("Building the model")
model = cls.MODEL(**args)
logger.info(f"{model}\n")
parser = cls(args, model, transform)
parser.model.to(parser.device)
return parser
class Seq2SeqDetector(Seq2SeqParser):
NAME = 'seq2seq'
MODEL = Seq2SeqDetectModel
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.SRC = self.transform.SRC
self.TGT = self.transform.TGT
(_, self.TGT_ERROR) = self.transform.TGTERROR
def train(self,
train: Union[str, Iterable],
dev: Union[str, Iterable],
test: Union[str, Iterable],
epochs: int,
patience: int,
batch_size: int = 5000,
update_steps: int = 1,
buckets: int = 32,
workers: int = 0,
clip: float = 5.0,
amp: bool = False,
cache: bool = False,
verbose: bool = True,
**kwargs) -> None:
args = self.args.update(locals())
init_logger(logger, verbose=args.verbose)
self.transform.train()
batch_size = batch_size // update_steps
if dist.is_initialized():
batch_size = batch_size // dist.get_world_size()
logger.info("Loading the data")
if args.cache:
if args.bin_path is None:
args.bin = os.path.join(os.path.dirname(args.path), 'bin')
else:
args.bin = args.bin_path
train = Dataset(self.transform, args.train,
**args).build(batch_size,
buckets,
True,
dist.is_initialized(),
workers,
chunk_size=args.chunk_size,
seed=args.seed)
dev = Dataset(self.transform, args.dev,
**args).build(batch_size, buckets, False,
dist.is_initialized(), workers)
logger.info(f"{'train:':6} {train}")
if not args.test:
logger.info(f"{'dev:':6} {dev}\n")
else:
test = Dataset(self.transform, args.test,
**args).build(batch_size, buckets, False,
dist.is_initialized(), workers)
logger.info(f"{'dev:':6} {dev}")
logger.info(f"{'test:':6} {test}\n")
def ged_param(name):
if name.startswith("encoder."):
return False
elif name.startswith("decoder."):
return False
else:
return True
no_decay = []
self.optimizer = AdamW([{
'params':
p,
'lr':
args.lr * (1 if not ged_param(n) else args.lr_rate),
"weight_decay":
args.weight_decay if not any(nd in n for nd in no_decay) else 0.0,
} for n, p in self.model.named_parameters()], args.lr,
(args.mu, args.nu), args.eps, args.weight_decay)
self.scheduler = ExponentialLR(self.optimizer,
args.decay**(1 / args.decay_steps))
self.scaler = GradScaler(enabled=args.amp)
if dist.is_initialized():
self.model = DDP(self.model,
device_ids=[args.local_rank],
find_unused_parameters=args.get(
'find_unused_parameters', True))
if args.amp:
self.model.register_comm_hook(dist.group.WORLD,
fp16_compress_hook)
self.step, self.epoch, self.best_e, self.patience, self.n_batches = 1, 1, 1, patience, len(
train.loader)
self.best_metric, self.elapsed = Metric(), timedelta()
if self.args.checkpoint:
try:
self.optimizer.load_state_dict(
self.checkpoint_state_dict.pop('optimizer_state_dict'))
self.scheduler.load_state_dict(
self.checkpoint_state_dict.pop('scheduler_state_dict'))
self.scaler.load_state_dict(
self.checkpoint_state_dict.pop('scaler_state_dict'))
set_rng_state(self.checkpoint_state_dict.pop('rng_state'))
for k, v in self.checkpoint_state_dict.items():
setattr(self, k, v)
train.loader.batch_sampler.epoch = self.epoch
except AttributeError:
logger.warning(
"No checkpoint found. Try re-launching the traing procedure instead"
)
for epoch in range(self.epoch, args.epochs + 1):
start = datetime.now()
bar, metric = progress_bar(train.loader), Metric()
logger.info(f"Epoch {epoch} / {args.epochs}:")
self.model.train()
if self.epoch == 1:
torch.cuda.empty_cache()
with self.join():
# we should zero `step` as the number of batches in different processes is not necessarily equal
self.step = 0
for batch in bar:
with self.sync():
with torch.autocast(self.device,
enabled=self.args.amp):
loss = self.train_step(batch)
self.backward(loss)
if self.sync_grad:
self.clip_grad_norm_(self.model.parameters(),
self.args.clip)
self.scaler.step(self.optimizer)
self.scaler.update()
self.scheduler.step()
self.optimizer.zero_grad(True)
bar.set_postfix_str(
f"lr: {self.scheduler.get_last_lr()[0]:.4e} - loss: {loss:.4f}"
)
self.step += 1
logger.info(f"{bar.postfix}")
self.model.eval()
with self.join(), torch.autocast(self.device,
enabled=self.args.amp):
metric = self.reduce(
sum([self.eval_step(i) for i in progress_bar(dev.loader)],
Metric()))
logger.info(f"{'dev:':5} {metric}")
if args.test:
test_metric = sum(
[self.eval_step(i) for i in progress_bar(test.loader)],
Metric())
logger.info(f"{'test:':5} {self.reduce(test_metric)}")
t = datetime.now() - start
self.epoch += 1
self.patience -= 1
self.elapsed += t
if metric > self.best_metric:
self.best_e, self.patience, self.best_metric = epoch, patience, metric
if is_master():
self.save_checkpoint(args.path)
logger.info(f"{t}s elapsed (saved)\n")
else:
logger.info(f"{t}s elapsed\n")
if self.patience < 1:
break
if dist.is_initialized():
dist.barrier()
best = self.load(**args)
# only allow the master device to save models
if is_master():
best.save(args.path)
logger.info(f"Epoch {self.best_e} saved")
logger.info(f"{'dev:':5} {self.best_metric}")
if args.test:
best.model.eval()
with best.join():
test_metric = sum(
[best.eval_step(i) for i in progress_bar(test.loader)],
Metric())
logger.info(f"{'test:':5} {best.reduce(test_metric)}")
logger.info(f"{self.elapsed}s elapsed, {self.elapsed / epoch}s/epoch")
def train_step(self, batch: Batch) -> torch.Tensor:
src, tgt, _, src_error, _, tgt_error = batch
src_mask, tgt_mask = src.ne(self.args.pad_index), tgt.ne(
self.args.pad_index)
x = self.model(src)
loss = self.model.loss(x, tgt, src_error, tgt_error, src_mask,
tgt_mask)
return loss
@torch.no_grad()
def eval_step(self, batch: Batch) -> PerplexityMetric:
src, tgt, _, src_error, tgt_error_raw, tgt_error = batch
src_mask, tgt_mask = src.ne(self.args.pad_index), tgt.ne(
self.args.pad_index)
x = self.model(src)
loss = self.model.loss(x, tgt, src_error, tgt_error, src_mask,
tgt_mask)
def error_label_factorize(errors):
return sum(
[[(i, i + 1, e) for e in eb.split("::")]
for i, eb in enumerate(errors) if eb not in {'CORRECT', NUL}],
[])
ged_golds = [error_label_factorize(e) for e in tgt_error_raw]
ged_preds = [
error_label_factorize(
[self.TGT_ERROR.vocab[i] for i in e if i >= 0])
for e in self.model.decode(x, tgt, src_mask, tgt_mask).tolist()
]
| return SpanMetric(loss, ged_preds, ged_golds) | 3 | 2023-10-18 10:55:33+00:00 | 24k |
boppreh/hello_tls | src/hello_tls/protocol.py | [
{
"identifier": "Protocol",
"path": "src/hello_tls/names_and_numbers.py",
"snippet": "class Protocol(Enum):\n # Keep protocols in order of preference.\n TLS1_3 = b\"\\x03\\x04\"\n TLS1_2 = b\"\\x03\\x03\"\n TLS1_1 = b\"\\x03\\x02\"\n TLS1_0 = b\"\\x03\\x01\"\n SSLv3 = b\"\\x03\\x00\"\n... | from typing import Iterator, List, Sequence, Optional, Iterable, Callable, Tuple
from contextlib import contextmanager
from dataclasses import dataclass
from .names_and_numbers import Protocol, RecordType, HandshakeType, CompressionMethod, CipherSuite, ExtensionType, Group, AlertLevel, AlertDescription, PskKeyExchangeMode
import logging | 14,587 |
logger = logging.getLogger(__name__)
class ScanError(Exception):
""" Base error class for errors that occur during scanning. """
pass
class ServerAlertError(ScanError):
|
logger = logging.getLogger(__name__)
class ScanError(Exception):
""" Base error class for errors that occur during scanning. """
pass
class ServerAlertError(ScanError): | def __init__(self, level: AlertLevel, description: AlertDescription): | 8 | 2023-10-21 02:00:13+00:00 | 24k |
zhaojw1998/AccoMontage-3 | arrangement_utils.py | [
{
"identifier": "split_phrases",
"path": "piano_arranger/acc_utils.py",
"snippet": "def split_phrases(segmentation):\n \"\"\"Split a phrase label string into individual phrase meta info\"\"\"\n if '\\n' not in segmentation:\n segmentation += '\\n'\n phrases = []\n lengths = []\n cu... | import os
import pretty_midi as pyd
import numpy as np
import torch
import piano_arranger.format_converter as cvt
from torch.utils.data import DataLoader
from scipy.interpolate import interp1d
from tqdm import tqdm
from piano_arranger.acc_utils import split_phrases
from piano_arranger.models import DisentangleVAE
from piano_arranger.AccoMontage import find_by_length, dp_search, re_harmonization, get_texture_filter, ref_spotlight
from orchestrator import Slakh2100_Pop909_Dataset, collate_fn, compute_pr_feat, EMBED_PROGRAM_MAPPING, Prior
from orchestrator.QA_dataset import SLAKH_CLASS_PROGRAMS
from orchestrator.utils import grid2pr, pr2grid, matrix2midi, midi2matrix
from orchestrator.prior_dataset import TOTAL_LEN_BIN, ABS_POS_BIN, REL_POS_BIN | 18,858 |
SLAKH_CLASS_MAPPING = {v: k for k, v in EMBED_PROGRAM_MAPPING.items()}
def load_premise(DATA_FILE_ROOT, DEVICE):
"""Load AccoMontage Search Space"""
print('Loading AccoMontage piano texture search space. This may take 1 or 2 minutes ...')
data = np.load(os.path.join(DATA_FILE_ROOT, 'phrase_data.npz'), allow_pickle=True)
melody = data['melody']
acc = data['acc']
chord = data['chord']
vel = data['velocity']
cc = data['cc']
acc_pool = {}
for LEN in tqdm(range(2, 13)):
(mel, acc_, chord_, vel_, cc_, song_reference) = find_by_length(melody, acc, chord, vel, cc, LEN)
acc_pool[LEN] = (mel, acc_, chord_, vel_, cc_, song_reference)
texture_filter = get_texture_filter(acc_pool)
edge_weights=np.load(os.path.join(DATA_FILE_ROOT, 'edge_weights.npz'), allow_pickle=True)
"""Load Q&A Prompt Search Space"""
print('loading orchestration prompt search space ...')
slakh_dir = os.path.join(DATA_FILE_ROOT, 'Slakh2100_inference_set')
dataset = Slakh2100_Pop909_Dataset(slakh_dir=slakh_dir, pop909_dir=None, debug_mode=False, split='validation', mode='train')
loader = DataLoader(dataset, batch_size=1, shuffle=True, collate_fn=lambda b:collate_fn(b, DEVICE))
REF = []
REF_PROG = []
REF_MIX = []
for (_, prog, function, _, _, _) in loader:
prog = prog[0, :]
REF.extend([batch for batch in function])
REF_PROG.extend([prog for _ in range(len(function))])
REF_MIX.append(torch.sum(function, dim=1))
REF_MIX = torch.cat(REF_MIX, dim=0)
"""Initialize orchestration model (Prior + Q&A)"""
print('Initialize model ...')
prior_model_path = os.path.join(DATA_FILE_ROOT, 'params_prior.pt')
QaA_model_path = os.path.join(DATA_FILE_ROOT, 'params_qa.pt')
orchestrator = Prior.init_inference_model(prior_model_path, QaA_model_path, DEVICE=DEVICE)
orchestrator.to(DEVICE)
orchestrator.eval()
piano_arranger = DisentangleVAE.init_model(torch.device('cuda')).cuda()
piano_arranger.load_state_dict(torch.load(os.path.join(DATA_FILE_ROOT, 'params_reharmonizer.pt')))
print('Finished.')
return piano_arranger, orchestrator, (acc_pool, edge_weights, texture_filter), (REF, REF_PROG, REF_MIX)
def read_lead_sheet(DEMO_ROOT, SONG_NAME, SEGMENTATION, NOTE_SHIFT, melody_track_ID=0):
melody_roll, chord_roll = cvt.leadsheet2matrix(os.path.join(DEMO_ROOT, SONG_NAME, 'lead sheet.mid'), melody_track_ID)
assert(len(melody_roll == len(chord_roll)))
if NOTE_SHIFT != 0:
melody_roll = melody_roll[int(NOTE_SHIFT*4):, :]
chord_roll = chord_roll[int(NOTE_SHIFT*4):, :]
if len(melody_roll) % 16 != 0:
pad_len = (len(melody_roll)//16+1)*16-len(melody_roll)
melody_roll = np.pad(melody_roll, ((0, pad_len), (0, 0)))
melody_roll[-pad_len:, -1] = 1
chord_roll = np.pad(chord_roll, ((0, pad_len), (0, 0)))
chord_roll[-pad_len:, 0] = -1
chord_roll[-pad_len:, -1] = -1
CHORD_TABLE = np.stack([cvt.expand_chord(chord) for chord in chord_roll[::4]], axis=0)
LEADSHEET = np.concatenate((melody_roll, chord_roll[:, 1: -1]), axis=-1) #T*142, quantized at 16th
query_phrases = split_phrases(SEGMENTATION) #[('A', 8, 0), ('A', 8, 8), ('B', 8, 16), ('B', 8, 24)]
midi_len = len(LEADSHEET)//16
anno_len = sum([item[1] for item in query_phrases])
if midi_len > anno_len:
LEADSHEET = LEADSHEET[: anno_len*16]
CHORD_TABLE = CHORD_TABLE[: anno_len*4]
print(f'Mismatch warning: Detect {midi_len} bars in the lead sheet (MIDI) and {anno_len} bars in the provided phrase annotation. The lead sheet is truncated to {anno_len} bars.')
elif midi_len < anno_len:
pad_len = (anno_len - midi_len)*16
LEADSHEET = np.pad(LEADSHEET, ((0, pad_len), (0, 0)))
LEADSHEET[-pad_len:, 129] = 1
CHORD_TABLE = np.pad(CHORD_TABLE, ((0, pad_len//4), (0, 0)))
CHORD_TABLE[-pad_len//4:, 11] = -1
CHORD_TABLE[-pad_len//4:, -1] = -1
print(f'Mismatch warning: Detect {midi_len} bars in the lead sheet (MIDI) and {anno_len} bars in the provided phrase annotation. The lead sheet is padded to {anno_len} bars.')
melody_queries = []
for item in query_phrases:
start_bar = item[-1]
length = item[-2]
segment = LEADSHEET[start_bar*16: (start_bar+length)*16]
melody_queries.append(segment) #melody queries: list of T16*142, segmented by phrases
return (LEADSHEET, CHORD_TABLE, melody_queries, query_phrases)
def piano_arrangement(pianoRoll, chord_table, melody_queries, query_phrases, acc_pool, edge_weights, texture_filter, piano_arranger, PREFILTER, tempo=100):
print('Phrasal Unit selection begins:\n\t', f'{len(query_phrases)} phrases in the lead sheet;\n\t', f'set note density filter: {PREFILTER}.')
phrase_indice, chord_shift = dp_search( melody_queries,
query_phrases,
acc_pool,
edge_weights,
texture_filter,
filter_id=PREFILTER)
path = phrase_indice[0]
shift = chord_shift[0]
print('Re-harmonization begins ...')
midi_recon, acc = re_harmonization(pianoRoll, chord_table, query_phrases, path, shift, acc_pool, model=piano_arranger, get_est=True, tempo=tempo)
|
SLAKH_CLASS_MAPPING = {v: k for k, v in EMBED_PROGRAM_MAPPING.items()}
def load_premise(DATA_FILE_ROOT, DEVICE):
"""Load AccoMontage Search Space"""
print('Loading AccoMontage piano texture search space. This may take 1 or 2 minutes ...')
data = np.load(os.path.join(DATA_FILE_ROOT, 'phrase_data.npz'), allow_pickle=True)
melody = data['melody']
acc = data['acc']
chord = data['chord']
vel = data['velocity']
cc = data['cc']
acc_pool = {}
for LEN in tqdm(range(2, 13)):
(mel, acc_, chord_, vel_, cc_, song_reference) = find_by_length(melody, acc, chord, vel, cc, LEN)
acc_pool[LEN] = (mel, acc_, chord_, vel_, cc_, song_reference)
texture_filter = get_texture_filter(acc_pool)
edge_weights=np.load(os.path.join(DATA_FILE_ROOT, 'edge_weights.npz'), allow_pickle=True)
"""Load Q&A Prompt Search Space"""
print('loading orchestration prompt search space ...')
slakh_dir = os.path.join(DATA_FILE_ROOT, 'Slakh2100_inference_set')
dataset = Slakh2100_Pop909_Dataset(slakh_dir=slakh_dir, pop909_dir=None, debug_mode=False, split='validation', mode='train')
loader = DataLoader(dataset, batch_size=1, shuffle=True, collate_fn=lambda b:collate_fn(b, DEVICE))
REF = []
REF_PROG = []
REF_MIX = []
for (_, prog, function, _, _, _) in loader:
prog = prog[0, :]
REF.extend([batch for batch in function])
REF_PROG.extend([prog for _ in range(len(function))])
REF_MIX.append(torch.sum(function, dim=1))
REF_MIX = torch.cat(REF_MIX, dim=0)
"""Initialize orchestration model (Prior + Q&A)"""
print('Initialize model ...')
prior_model_path = os.path.join(DATA_FILE_ROOT, 'params_prior.pt')
QaA_model_path = os.path.join(DATA_FILE_ROOT, 'params_qa.pt')
orchestrator = Prior.init_inference_model(prior_model_path, QaA_model_path, DEVICE=DEVICE)
orchestrator.to(DEVICE)
orchestrator.eval()
piano_arranger = DisentangleVAE.init_model(torch.device('cuda')).cuda()
piano_arranger.load_state_dict(torch.load(os.path.join(DATA_FILE_ROOT, 'params_reharmonizer.pt')))
print('Finished.')
return piano_arranger, orchestrator, (acc_pool, edge_weights, texture_filter), (REF, REF_PROG, REF_MIX)
def read_lead_sheet(DEMO_ROOT, SONG_NAME, SEGMENTATION, NOTE_SHIFT, melody_track_ID=0):
melody_roll, chord_roll = cvt.leadsheet2matrix(os.path.join(DEMO_ROOT, SONG_NAME, 'lead sheet.mid'), melody_track_ID)
assert(len(melody_roll == len(chord_roll)))
if NOTE_SHIFT != 0:
melody_roll = melody_roll[int(NOTE_SHIFT*4):, :]
chord_roll = chord_roll[int(NOTE_SHIFT*4):, :]
if len(melody_roll) % 16 != 0:
pad_len = (len(melody_roll)//16+1)*16-len(melody_roll)
melody_roll = np.pad(melody_roll, ((0, pad_len), (0, 0)))
melody_roll[-pad_len:, -1] = 1
chord_roll = np.pad(chord_roll, ((0, pad_len), (0, 0)))
chord_roll[-pad_len:, 0] = -1
chord_roll[-pad_len:, -1] = -1
CHORD_TABLE = np.stack([cvt.expand_chord(chord) for chord in chord_roll[::4]], axis=0)
LEADSHEET = np.concatenate((melody_roll, chord_roll[:, 1: -1]), axis=-1) #T*142, quantized at 16th
query_phrases = split_phrases(SEGMENTATION) #[('A', 8, 0), ('A', 8, 8), ('B', 8, 16), ('B', 8, 24)]
midi_len = len(LEADSHEET)//16
anno_len = sum([item[1] for item in query_phrases])
if midi_len > anno_len:
LEADSHEET = LEADSHEET[: anno_len*16]
CHORD_TABLE = CHORD_TABLE[: anno_len*4]
print(f'Mismatch warning: Detect {midi_len} bars in the lead sheet (MIDI) and {anno_len} bars in the provided phrase annotation. The lead sheet is truncated to {anno_len} bars.')
elif midi_len < anno_len:
pad_len = (anno_len - midi_len)*16
LEADSHEET = np.pad(LEADSHEET, ((0, pad_len), (0, 0)))
LEADSHEET[-pad_len:, 129] = 1
CHORD_TABLE = np.pad(CHORD_TABLE, ((0, pad_len//4), (0, 0)))
CHORD_TABLE[-pad_len//4:, 11] = -1
CHORD_TABLE[-pad_len//4:, -1] = -1
print(f'Mismatch warning: Detect {midi_len} bars in the lead sheet (MIDI) and {anno_len} bars in the provided phrase annotation. The lead sheet is padded to {anno_len} bars.')
melody_queries = []
for item in query_phrases:
start_bar = item[-1]
length = item[-2]
segment = LEADSHEET[start_bar*16: (start_bar+length)*16]
melody_queries.append(segment) #melody queries: list of T16*142, segmented by phrases
return (LEADSHEET, CHORD_TABLE, melody_queries, query_phrases)
def piano_arrangement(pianoRoll, chord_table, melody_queries, query_phrases, acc_pool, edge_weights, texture_filter, piano_arranger, PREFILTER, tempo=100):
print('Phrasal Unit selection begins:\n\t', f'{len(query_phrases)} phrases in the lead sheet;\n\t', f'set note density filter: {PREFILTER}.')
phrase_indice, chord_shift = dp_search( melody_queries,
query_phrases,
acc_pool,
edge_weights,
texture_filter,
filter_id=PREFILTER)
path = phrase_indice[0]
shift = chord_shift[0]
print('Re-harmonization begins ...')
midi_recon, acc = re_harmonization(pianoRoll, chord_table, query_phrases, path, shift, acc_pool, model=piano_arranger, get_est=True, tempo=tempo) | acc = np.array([grid2pr(matrix) for matrix in acc]) | 13 | 2023-10-23 12:36:57+00:00 | 24k |
liuqidong07/MOELoRA-peft | src/MLoRA/peft/peft_model.py | [
{
"identifier": "PeftConfig",
"path": "src/MLoRA/peft/utils/config.py",
"snippet": "class PeftConfig(PeftConfigMixin):\n \"\"\"\n This is the base configuration class to store the configuration of a [`PeftModel`].\n\n Args:\n peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The... | import inspect
import os
import warnings
import torch
import torch.nn as nn
from contextlib import contextmanager
from accelerate import dispatch_model, infer_auto_device_map
from accelerate.hooks import AlignDevicesHook, add_hook_to_module, remove_hook_from_submodules
from accelerate.utils import get_balanced_memory
from huggingface_hub import hf_hub_download
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers import PreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput, TokenClassifierOutput
from transformers.utils import PushToHubMixin
from .utils import PeftConfig
from .shared import Gate, GateN
from .tuners import (
AdaLoraModel,
AdaptionPromptModel,
LoraModel,
PrefixEncoder,
PromptEmbedding,
PromptEncoder,
MMOELoraModelS,
)
from .utils import (
TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING,
WEIGHTS_NAME,
PeftConfig,
PeftType,
PromptLearningConfig,
TaskType,
_set_adapter,
_set_trainable,
get_peft_model_state_dict,
set_peft_model_state_dict,
shift_tokens_right,
)
from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING
from .mapping import PEFT_TYPE_TO_CONFIG_MAPPING | 16,973 | )
model_kwargs["past_key_values"] = past_key_values
else:
if model_kwargs["past_key_values"] is None:
inputs_embeds = self.word_embeddings(model_kwargs["input_ids"])
prompts = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0])
prompts = prompts.to(inputs_embeds.dtype)
model_kwargs["inputs_embeds"] = torch.cat((prompts, inputs_embeds), dim=1)
model_kwargs["input_ids"] = None
return model_kwargs
class PeftModelForSeq2SeqLM(PeftModel):
"""
Peft model for sequence-to-sequence language modeling.
Args:
model ([`~transformers.PreTrainedModel`]): Base transformer model.
peft_config ([`PeftConfig`]): Peft config.
Example:
```py
>>> from transformers import AutoModelForSeq2SeqLM
>>> from peft import PeftModelForSeq2SeqLM, get_peft_config
>>> config = {
... "peft_type": "LORA",
... "task_type": "SEQ_2_SEQ_LM",
... "inference_mode": False,
... "r": 8,
... "target_modules": ["q", "v"],
... "lora_alpha": 32,
... "lora_dropout": 0.1,
... "merge_weights": False,
... "fan_in_fan_out": False,
... "enable_lora": None,
... "bias": "none",
... }
>>> peft_config = get_peft_config(config)
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> peft_model = PeftModelForSeq2SeqLM(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 884736 || all params: 223843584 || trainable%: 0.3952474242013566
```
"""
def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
super().__init__(model, peft_config, adapter_name)
self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
self.base_model_prepare_encoder_decoder_kwargs_for_generation = (
self.base_model._prepare_encoder_decoder_kwargs_for_generation
)
def forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
decoder_input_ids=None,
decoder_attention_mask=None,
decoder_inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
peft_config = self.active_peft_config
if not isinstance(peft_config, PromptLearningConfig):
return self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_inputs_embeds=decoder_inputs_embeds,
labels=labels,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
batch_size = input_ids.shape[0]
if decoder_attention_mask is not None:
# concat prompt attention mask
prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(self.device)
decoder_attention_mask = torch.cat((prefix_attention_mask, decoder_attention_mask), dim=1)
if kwargs.get("position_ids", None) is not None:
warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
kwargs["position_ids"] = None
if kwargs.get("token_type_ids", None) is not None:
warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
kwargs["token_type_ids"] = None
kwargs.update(
{
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"labels": labels,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
}
)
if peft_config.peft_type == PeftType.PREFIX_TUNING:
past_key_values = self.get_prompt(batch_size)
return self.base_model(
input_ids=input_ids, decoder_input_ids=decoder_input_ids, past_key_values=past_key_values, **kwargs
)
else:
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if decoder_inputs_embeds is None and decoder_input_ids is None:
| # coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
PEFT_TYPE_TO_MODEL_MAPPING = {
PeftType.LORA: LoraModel,
PeftType.PROMPT_TUNING: PromptEmbedding,
PeftType.P_TUNING: PromptEncoder,
PeftType.PREFIX_TUNING: PrefixEncoder,
PeftType.ADALORA: AdaLoraModel,
PeftType.ADAPTION_PROMPT: AdaptionPromptModel,
PeftType.MMOELORAS: MMOELoraModelS,
}
class PeftModel(PushToHubMixin, torch.nn.Module):
"""
Base model encompassing various Peft methods.
Args:
model ([`~transformers.PreTrainedModel`]): The base transformer model used for Peft.
peft_config ([`PeftConfig`]): The configuration of the Peft model.
**Attributes**:
- **base_model** ([`~transformers.PreTrainedModel`]) -- The base transformer model used for Peft.
- **peft_config** ([`PeftConfig`]) -- The configuration of the Peft model.
- **modules_to_save** (`list` of `str`) -- The list of sub-module names to save when
saving the model.
- **prompt_encoder** ([`PromptEncoder`]) -- The prompt encoder used for Peft if
using [`PromptLearningConfig`].
- **prompt_tokens** (`torch.Tensor`) -- The virtual prompt tokens used for Peft if
using [`PromptLearningConfig`].
- **transformer_backbone_name** (`str`) -- The name of the transformer
backbone in the base model if using [`PromptLearningConfig`].
- **word_embeddings** (`torch.nn.Embedding`) -- The word embeddings of the transformer backbone
in the base model if using [`PromptLearningConfig`].
"""
def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
super().__init__()
self.base_model = model
self.config = self.base_model.config
self.modules_to_save = None
self.peft_config = {}
self.active_adapter = adapter_name
self.peft_type = peft_config.peft_type
self.base_model_torch_dtype = getattr(model, "dtype", None)
if not isinstance(peft_config, PromptLearningConfig):
self.peft_config[adapter_name] = peft_config
self.base_model = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type](
self.base_model, self.peft_config, adapter_name
)
self.set_additional_trainable_modules(peft_config, adapter_name)
else:
self.add_adapter(adapter_name, peft_config)
def save_pretrained(self, save_directory, **kwargs):
r"""
This function saves the adapter model and the adapter configuration files to a directory, so that it can be
reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`]
method.
Args:
save_directory (`str`):
Directory where the adapter model and configuration files will be saved (will be created if it does not
exist).
kwargs (additional keyword arguments, *optional*):
Additional keyword arguments passed along to the `push_to_hub` method.
"""
if os.path.isfile(save_directory):
raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
for adapter_name, peft_config in self.peft_config.items():
# save only the trainable weights
output_state_dict = get_peft_model_state_dict(
self, state_dict=kwargs.get("state_dict", None), adapter_name=adapter_name
) # save the weights based on the adapter name
output_dir = os.path.join(save_directory, adapter_name) if adapter_name != "default" else save_directory
os.makedirs(output_dir, exist_ok=True)
torch.save(output_state_dict, os.path.join(output_dir, WEIGHTS_NAME))
# save the config and change the inference mode to `True`
if peft_config.base_model_name_or_path is None:
peft_config.base_model_name_or_path = (
self.base_model.__dict__.get("name_or_path", None)
if isinstance(peft_config, PromptLearningConfig)
else self.base_model.model.__dict__.get("name_or_path", None)
)
inference_mode = peft_config.inference_mode
peft_config.inference_mode = True
peft_config.save_pretrained(output_dir) # save the config to file
peft_config.inference_mode = inference_mode
@classmethod
def from_pretrained(cls, model, model_id, adapter_name="default", is_trainable=False, **kwargs):
r"""
Instantiate a [`LoraModel`] from a pretrained Lora configuration and weights.
Args:
model ([`~transformers.PreTrainedModel`]):
The model to be adapted. The model should be initialized with the
[`~transformers.PreTrainedModel.from_pretrained`] method from the 🤗 Transformers library.
model_id (`str` or `os.PathLike`):
The name of the Lora configuration to use. Can be either:
- A string, the `model id` of a Lora configuration hosted inside a model repo on the Hugging Face
Hub.
- A path to a directory containing a Lora configuration file saved using the `save_pretrained`
method (`./my_lora_config_directory/`).
"""
# load the config
config = PEFT_TYPE_TO_CONFIG_MAPPING[
PeftConfig.from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)).peft_type
].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None))
if (getattr(model, "hf_device_map", None) is not None) and len(
set(model.hf_device_map.values()).intersection({"cpu", "disk"})
) > 0:
remove_hook_from_submodules(model)
if isinstance(config, PromptLearningConfig) and is_trainable:
raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
else:
config.inference_mode = not is_trainable
if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys():
model = cls(model, config, adapter_name)
else:
model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config, adapter_name) # New a PeftModel
model.load_adapter(model_id, adapter_name, **kwargs)
return model
def _setup_prompt_encoder(self, adapter_name):
config = self.peft_config[adapter_name]
self.prompt_encoder = torch.nn.ModuleDict({})
self.prompt_tokens = {}
transformer_backbone = None
for name, module in self.base_model.named_children():
for param in module.parameters():
param.requires_grad = False
if isinstance(module, PreTrainedModel):
# Make sure to freeze Tranformers model
if transformer_backbone is None:
transformer_backbone = module
self.transformer_backbone_name = name
if config.num_transformer_submodules is None:
config.num_transformer_submodules = 2 if config.task_type == TaskType.SEQ_2_SEQ_LM else 1
for named_param, value in list(transformer_backbone.named_parameters()):
if value.shape[0] == self.base_model.config.vocab_size:
self.word_embeddings = transformer_backbone.get_submodule(named_param.replace(".weight", ""))
break
if config.peft_type == PeftType.PROMPT_TUNING:
prompt_encoder = PromptEmbedding(config, self.word_embeddings)
elif config.peft_type == PeftType.P_TUNING:
prompt_encoder = PromptEncoder(config)
elif config.peft_type == PeftType.PREFIX_TUNING:
prompt_encoder = PrefixEncoder(config)
else:
raise ValueError("Not supported")
self.prompt_encoder.update(torch.nn.ModuleDict({adapter_name: prompt_encoder}))
self.prompt_tokens[adapter_name] = torch.arange(
config.num_virtual_tokens * config.num_transformer_submodules
).long()
def get_prompt_embedding_to_save(self, adapter_name):
"""
Returns the prompt embedding to save when saving the model. Only applicable when `peft_config.peft_type !=
PeftType.LORA`.
"""
prompt_tokens = self.prompt_tokens[adapter_name].unsqueeze(0).expand(1, -1).to(self.device)
if self.peft_config[adapter_name].peft_type == PeftType.PREFIX_TUNING:
prompt_tokens = prompt_tokens[:, : self.peft_config[adapter_name].num_virtual_tokens]
prompt_embeddings = self.prompt_encoder[adapter_name](prompt_tokens)
return prompt_embeddings[0].detach().cpu()
def get_prompt(self, batch_size):
"""
Returns the virtual prompts to use for Peft. Only applicable when `peft_config.peft_type != PeftType.LORA`.
"""
peft_config = self.active_peft_config
prompt_encoder = self.prompt_encoder[self.active_adapter]
prompt_tokens = self.prompt_tokens[self.active_adapter].unsqueeze(0).expand(batch_size, -1).to(self.device)
if peft_config.peft_type == PeftType.PREFIX_TUNING:
prompt_tokens = prompt_tokens[:, : peft_config.num_virtual_tokens]
if peft_config.inference_mode:
past_key_values = prompt_encoder.embedding.weight.repeat(batch_size, 1, 1)
else:
past_key_values = prompt_encoder(prompt_tokens)
past_key_values = past_key_values.view(
batch_size,
peft_config.num_virtual_tokens,
peft_config.num_layers * 2,
peft_config.num_attention_heads,
peft_config.token_dim // peft_config.num_attention_heads,
)
if peft_config.num_transformer_submodules == 2:
past_key_values = torch.cat([past_key_values, past_key_values], dim=2)
past_key_values = past_key_values.permute([2, 0, 3, 1, 4]).split(
peft_config.num_transformer_submodules * 2
)
if TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING.get(self.config.model_type, None) is not None:
post_process_fn = TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING[self.config.model_type]
past_key_values = post_process_fn(past_key_values)
return past_key_values
else:
if peft_config.inference_mode:
prompts = prompt_encoder.embedding.weight.repeat(batch_size, 1, 1)
else:
prompts = prompt_encoder(prompt_tokens)
return prompts
def print_trainable_parameters(self):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in self.named_parameters():
num_params = param.numel()
# if using DS Zero 3 and the weights are initialized empty
if num_params == 0 and hasattr(param, "ds_numel"):
num_params = param.ds_numel
all_param += num_params
if param.requires_grad:
trainable_params += num_params
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
def __getattr__(self, name: str):
"""Forward missing attributes to the wrapped module."""
try:
return super().__getattr__(name) # defer to nn.Module's logic
except AttributeError:
return getattr(self.base_model, name)
def forward(self, *args, **kwargs):
"""
Forward pass of the model.
"""
return self.get_base_model()(*args, **kwargs)
@contextmanager
def disable_adapter(self):
"""
Disables the adapter module.
"""
try:
if isinstance(self.peft_config, PromptLearningConfig):
old_forward = self.forward
self.forward = self.base_model.forward
else:
self.base_model.disable_adapter_layers()
yield
finally:
if isinstance(self.peft_config, PromptLearningConfig):
self.forward = old_forward
else:
self.base_model.enable_adapter_layers()
def get_base_model(self):
"""
Returns the base model.
"""
return self.base_model if isinstance(self.active_peft_config, PromptLearningConfig) else self.base_model.model
def add_adapter(self, adapter_name, peft_config):
if peft_config.peft_type != self.peft_type:
raise ValueError(
f"Cannot combine adapters with different peft types. "
f"Found {self.peft_type} and {peft_config.peft_type}."
)
self.peft_config[adapter_name] = peft_config
if isinstance(peft_config, PromptLearningConfig):
self._setup_prompt_encoder(adapter_name)
else:
self.base_model.add_adapter(adapter_name, peft_config)
self.set_additional_trainable_modules(peft_config, adapter_name)
def set_additional_trainable_modules(self, peft_config, adapter_name):
if getattr(peft_config, "modules_to_save", None) is not None:
if self.modules_to_save is None:
self.modules_to_save = set(peft_config.modules_to_save)
else:
self.modules_to_save.update(peft_config.modules_to_save)
_set_trainable(self, adapter_name)
def load_adapter(self, model_id, adapter_name, is_trainable=False, **kwargs):
if adapter_name not in self.peft_config:
# load the config
peft_config = PEFT_TYPE_TO_CONFIG_MAPPING[
PeftConfig.from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)).peft_type
].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None))
if isinstance(peft_config, PromptLearningConfig) and is_trainable:
raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
else:
peft_config.inference_mode = not is_trainable
self.add_adapter(adapter_name, peft_config)
# load weights if any
path = os.path.join(model_id, kwargs["subfolder"]) if kwargs.get("subfolder", None) is not None else model_id
if os.path.exists(os.path.join(path, WEIGHTS_NAME)):
filename = os.path.join(path, WEIGHTS_NAME)
else:
try:
filename = hf_hub_download(model_id, WEIGHTS_NAME, subfolder=kwargs.get("subfolder", None))
except: # noqa
raise ValueError(
f"Can't find weights for {model_id} in {model_id} or in the Hugging Face Hub. "
f"Please check that the file {WEIGHTS_NAME} is present at {model_id}."
)
adapters_weights = torch.load(
filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu")
)
# load the weights into the model
set_peft_model_state_dict(self, adapters_weights, adapter_name=adapter_name)
if (
(getattr(self, "hf_device_map", None) is not None)
and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
and len(self.peft_config) == 1
):
device_map = kwargs.get("device_map", "auto")
max_memory = kwargs.get("max_memory", None)
offload_dir = kwargs.get("offload_folder", None)
offload_index = kwargs.get("offload_index", None)
dispatch_model_kwargs = {}
# Safety checker for previous `accelerate` versions
# `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
if "offload_index" in inspect.signature(dispatch_model).parameters:
dispatch_model_kwargs["offload_index"] = offload_index
no_split_module_classes = self._no_split_modules
if device_map != "sequential":
max_memory = get_balanced_memory(
self,
max_memory=max_memory,
no_split_module_classes=no_split_module_classes,
low_zero=(device_map == "balanced_low_0"),
)
if isinstance(device_map, str):
device_map = infer_auto_device_map(
self, max_memory=max_memory, no_split_module_classes=no_split_module_classes
)
dispatch_model(
self,
device_map=device_map,
offload_dir=offload_dir,
**dispatch_model_kwargs,
)
hook = AlignDevicesHook(io_same_device=True)
if isinstance(self.peft_config[adapter_name], PromptLearningConfig):
remove_hook_from_submodules(self.prompt_encoder)
add_hook_to_module(self.get_base_model(), hook)
# Set model in evaluation mode to deactivate Dropout modules by default
self.eval()
def set_adapter(self, adapter_name):
"""
Sets the active adapter.
"""
if adapter_name not in self.peft_config:
raise ValueError(f"Adapter {adapter_name} not found.")
self.active_adapter = adapter_name
if not isinstance(self.peft_config[adapter_name], PromptLearningConfig):
self.base_model.set_adapter(adapter_name)
_set_adapter(self, adapter_name)
@property
def active_peft_config(self):
return self.peft_config[self.active_adapter]
class PeftModelForSequenceClassification(PeftModel):
"""
Peft model for sequence classification tasks.
Args:
model ([`~transformers.PreTrainedModel`]): Base transformer model.
peft_config ([`PeftConfig`]): Peft config.
**Attributes**:
- **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model.
- **cls_layer_name** (`str`) -- The name of the classification layer.
Example:
```py
>>> from transformers import AutoModelForSequenceClassification
>>> from peft import PeftModelForSequenceClassification, get_peft_config
>>> config = {
... "peft_type": "PREFIX_TUNING",
... "task_type": "SEQ_CLS",
... "inference_mode": False,
... "num_virtual_tokens": 20,
... "token_dim": 768,
... "num_transformer_submodules": 1,
... "num_attention_heads": 12,
... "num_layers": 12,
... "encoder_hidden_size": 768,
... "prefix_projection": False,
... "postprocess_past_key_value_function": None,
... }
>>> peft_config = get_peft_config(config)
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")
>>> peft_model = PeftModelForSequenceClassification(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117
```
"""
def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
super().__init__(model, peft_config, adapter_name)
if self.modules_to_save is None:
self.modules_to_save = {"classifier", "score"}
else:
self.modules_to_save.update({"classifier", "score"})
for name, _ in self.base_model.named_children():
if any(module_name in name for module_name in self.modules_to_save):
self.cls_layer_name = name
break
# to make sure classifier layer is trainable
_set_trainable(self, adapter_name)
def forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
peft_config = self.active_peft_config
if not isinstance(peft_config, PromptLearningConfig):
return self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
labels=labels,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
batch_size = input_ids.shape[0]
if attention_mask is not None:
# concat prompt attention mask
prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(self.device)
attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
if kwargs.get("position_ids", None) is not None:
warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
kwargs["position_ids"] = None
kwargs.update(
{
"attention_mask": attention_mask,
"labels": labels,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
}
)
if peft_config.peft_type == PeftType.PREFIX_TUNING:
return self._prefix_tuning_forward(input_ids=input_ids, **kwargs)
else:
if kwargs.get("token_type_ids", None) is not None:
kwargs["token_type_ids"] = torch.cat(
(
torch.zeros(batch_size, peft_config.num_virtual_tokens).to(self.device),
kwargs["token_type_ids"],
),
dim=1,
).long()
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
prompts = self.get_prompt(batch_size=batch_size)
prompts = prompts.to(inputs_embeds.dtype)
inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
def _prefix_tuning_forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
batch_size = input_ids.shape[0]
past_key_values = self.get_prompt(batch_size)
fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys())
kwargs.update(
{
"input_ids": input_ids,
"attention_mask": attention_mask,
"inputs_embeds": inputs_embeds,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
"past_key_values": past_key_values,
}
)
if "past_key_values" in fwd_params:
return self.base_model(labels=labels, **kwargs)
else:
transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name)
fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys())
if "past_key_values" not in fwd_params:
raise ValueError("Model does not support past key values which are required for prefix tuning.")
outputs = transformer_backbone_name(**kwargs)
pooled_output = outputs[1] if len(outputs) > 1 else outputs[0]
if "dropout" in [name for name, _ in list(self.base_model.named_children())]:
pooled_output = self.base_model.dropout(pooled_output)
logits = self.base_model.get_submodule(self.cls_layer_name)(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.base_model.num_labels == 1:
self.config.problem_type = "regression"
elif self.base_model.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.base_model.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.base_model.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class PeftModelForCausalLM(PeftModel):
"""
Peft model for causal language modeling.
Args:
model ([`~transformers.PreTrainedModel`]): Base transformer model.
peft_config ([`PeftConfig`]): Peft config.
Example:
```py
>>> from transformers import AutoModelForCausalLM
>>> from peft import PeftModelForCausalLM, get_peft_config
>>> config = {
... "peft_type": "PREFIX_TUNING",
... "task_type": "CAUSAL_LM",
... "inference_mode": False,
... "num_virtual_tokens": 20,
... "token_dim": 1280,
... "num_transformer_submodules": 1,
... "num_attention_heads": 20,
... "num_layers": 36,
... "encoder_hidden_size": 1280,
... "prefix_projection": False,
... "postprocess_past_key_value_function": None,
... }
>>> peft_config = get_peft_config(config)
>>> model = AutoModelForCausalLM.from_pretrained("gpt2-large")
>>> peft_model = PeftModelForCausalLM(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 1843200 || all params: 775873280 || trainable%: 0.23756456724479544
```
"""
def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
super().__init__(model, peft_config, adapter_name)
self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
def forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
peft_config = self.active_peft_config
if not isinstance(peft_config, PromptLearningConfig):
return self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
labels=labels,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
batch_size = input_ids.shape[0]
if attention_mask is not None:
# concat prompt attention mask
prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(self.device)
attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
if kwargs.get("position_ids", None) is not None:
warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
kwargs["position_ids"] = None
if kwargs.get("token_type_ids", None) is not None:
warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
kwargs["token_type_ids"] = None
kwargs.update(
{
"attention_mask": attention_mask,
"labels": labels,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
}
)
if peft_config.peft_type == PeftType.PREFIX_TUNING:
past_key_values = self.get_prompt(batch_size)
return self.base_model(input_ids=input_ids, past_key_values=past_key_values, **kwargs)
else:
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
# concat prompt labels
if labels is not None:
prefix_labels = torch.full((batch_size, peft_config.num_virtual_tokens), -100).to(self.device)
kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
prompts = self.get_prompt(batch_size=batch_size)
prompts = prompts.to(inputs_embeds.dtype)
inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
def generate(self, **kwargs):
peft_config = self.active_peft_config
self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
try:
if not isinstance(peft_config, PromptLearningConfig):
outputs = self.base_model.generate(**kwargs)
else:
if "input_ids" not in kwargs:
raise ValueError("input_ids must be provided for Peft model generation")
# For gpt2 models, we construct postion_ids on the fly by using attention mask, and position ids need to match input_shape.
# for prefix tuning, input shape is determined using `input_ids`. Thus we should not expand 'attention_mask' here
# for prompt tuning input_ids is not passed but a concatenated input_embeds is passed. Thus attention_mask needs to be of same size of num_virtual_tokens + input_ids
if kwargs.get("attention_mask", None) is not None and peft_config.peft_type in [
PeftType.PROMPT_TUNING,
PeftType.P_TUNING,
]:
# concat prompt attention mask
prefix_attention_mask = torch.ones(
kwargs["input_ids"].shape[0], peft_config.num_virtual_tokens
).to(kwargs["input_ids"].device)
kwargs["attention_mask"] = torch.cat((prefix_attention_mask, kwargs["attention_mask"]), dim=1)
if kwargs.get("position_ids", None) is not None:
warnings.warn(
"Position ids are not supported for parameter efficient tuning. Ignoring position ids."
)
kwargs["position_ids"] = None
if kwargs.get("token_type_ids", None) is not None:
warnings.warn(
"Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
)
kwargs["token_type_ids"] = None
outputs = self.base_model.generate(**kwargs)
except:
self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
raise
else:
self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
return outputs
def prepare_inputs_for_generation(self, *args, **kwargs):
peft_config = self.active_peft_config
model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs)
if isinstance(peft_config, PromptLearningConfig):
if peft_config.peft_type == PeftType.PREFIX_TUNING:
prefix_attention_mask = torch.ones(
model_kwargs["input_ids"].shape[0], peft_config.num_virtual_tokens
).to(model_kwargs["input_ids"].device)
model_kwargs["attention_mask"] = torch.cat(
(prefix_attention_mask, model_kwargs["attention_mask"]), dim=1
)
if model_kwargs["past_key_values"] is None and peft_config.peft_type == PeftType.PREFIX_TUNING:
past_key_values = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0])
if self.base_model_torch_dtype is not None:
# handle the case for Bloom where it outputs tuple of tuples
if isinstance(past_key_values[0], tuple):
past_key_values = tuple(
tuple(
past_key_value.to(self.base_model_torch_dtype)
for past_key_value in past_key_value_tuple
)
for past_key_value_tuple in past_key_values
)
else:
past_key_values = tuple(
past_key_value.to(self.base_model_torch_dtype) for past_key_value in past_key_values
)
model_kwargs["past_key_values"] = past_key_values
else:
if model_kwargs["past_key_values"] is None:
inputs_embeds = self.word_embeddings(model_kwargs["input_ids"])
prompts = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0])
prompts = prompts.to(inputs_embeds.dtype)
model_kwargs["inputs_embeds"] = torch.cat((prompts, inputs_embeds), dim=1)
model_kwargs["input_ids"] = None
return model_kwargs
class PeftModelForSeq2SeqLM(PeftModel):
"""
Peft model for sequence-to-sequence language modeling.
Args:
model ([`~transformers.PreTrainedModel`]): Base transformer model.
peft_config ([`PeftConfig`]): Peft config.
Example:
```py
>>> from transformers import AutoModelForSeq2SeqLM
>>> from peft import PeftModelForSeq2SeqLM, get_peft_config
>>> config = {
... "peft_type": "LORA",
... "task_type": "SEQ_2_SEQ_LM",
... "inference_mode": False,
... "r": 8,
... "target_modules": ["q", "v"],
... "lora_alpha": 32,
... "lora_dropout": 0.1,
... "merge_weights": False,
... "fan_in_fan_out": False,
... "enable_lora": None,
... "bias": "none",
... }
>>> peft_config = get_peft_config(config)
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
>>> peft_model = PeftModelForSeq2SeqLM(model, peft_config)
>>> peft_model.print_trainable_parameters()
trainable params: 884736 || all params: 223843584 || trainable%: 0.3952474242013566
```
"""
def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
super().__init__(model, peft_config, adapter_name)
self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
self.base_model_prepare_encoder_decoder_kwargs_for_generation = (
self.base_model._prepare_encoder_decoder_kwargs_for_generation
)
def forward(
self,
input_ids=None,
attention_mask=None,
inputs_embeds=None,
decoder_input_ids=None,
decoder_attention_mask=None,
decoder_inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs,
):
peft_config = self.active_peft_config
if not isinstance(peft_config, PromptLearningConfig):
return self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_inputs_embeds=decoder_inputs_embeds,
labels=labels,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
batch_size = input_ids.shape[0]
if decoder_attention_mask is not None:
# concat prompt attention mask
prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(self.device)
decoder_attention_mask = torch.cat((prefix_attention_mask, decoder_attention_mask), dim=1)
if kwargs.get("position_ids", None) is not None:
warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
kwargs["position_ids"] = None
if kwargs.get("token_type_ids", None) is not None:
warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
kwargs["token_type_ids"] = None
kwargs.update(
{
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"labels": labels,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
}
)
if peft_config.peft_type == PeftType.PREFIX_TUNING:
past_key_values = self.get_prompt(batch_size)
return self.base_model(
input_ids=input_ids, decoder_input_ids=decoder_input_ids, past_key_values=past_key_values, **kwargs
)
else:
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if decoder_inputs_embeds is None and decoder_input_ids is None: | decoder_input_ids = shift_tokens_right( | 17 | 2023-10-19 10:55:50+00:00 | 24k |
YuroFR/freqtrade-modded-crypto-trading-bot | freqtrade/exchange/exchange.py | [
{
"identifier": "DEFAULT_AMOUNT_RESERVE_PERCENT",
"path": "freqtrade/constants.py",
"snippet": "DOCS_LINK = \"https://www.freqtrade.io/en/stable\"\nDEFAULT_CONFIG = 'config.json'\nPROCESS_THROTTLE_SECS = 5 # sec\nHYPEROPT_EPOCH = 100 # epochs\nRETRY_TIMEOUT = 30 # sec\nTIMEOUT_UNITS = ['minutes', 'se... | import asyncio
import inspect
import logging
import signal
import ccxt
import ccxt.async_support as ccxt_async
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from math import floor
from threading import Lock
from typing import Any, Coroutine, Dict, List, Literal, Optional, Tuple, Union
from cachetools import TTLCache
from ccxt import TICK_SIZE
from dateutil import parser
from pandas import DataFrame, concat
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BidAsk,
BuySell, Config, EntryExit, ExchangeConfig,
ListPairsWithTimeframes, MakerTaker, OBLiteral, PairWithTimeframe)
from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, PriceType, TradingMode
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
InvalidOrderException, OperationalException, PricingError,
RetryableOrderError, TemporaryError)
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, remove_exchange_credentials,
retrier, retrier_async)
from freqtrade.exchange.exchange_utils import (ROUND, ROUND_DOWN, ROUND_UP, CcxtModuleType,
amount_to_contract_precision, amount_to_contracts,
amount_to_precision, contracts_to_amount,
date_minus_candles, is_exchange_known_ccxt,
market_is_active, price_to_precision,
timeframe_to_minutes, timeframe_to_msecs,
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds)
from freqtrade.exchange.types import OHLCVResponse, OrderBook, Ticker, Tickers
from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
safe_value_fallback2)
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.util import dt_from_ts, dt_now
from freqtrade.util.datetime_helpers import dt_humanize, dt_ts
from freqtrade.persistence import Order | 15,462 | timeframe, candle_type, since_ms)
move_to = one_call * self.required_candle_call_count
now = timeframe_to_next_date(timeframe)
since_ms = int((now - timedelta(seconds=move_to // 1000)).timestamp() * 1000)
if since_ms:
return self._async_get_historic_ohlcv(
pair, timeframe, since_ms=since_ms, raise_=True, candle_type=candle_type)
else:
# One call ... "regular" refresh
return self._async_get_candle_history(
pair, timeframe, since_ms=since_ms, candle_type=candle_type)
def _build_ohlcv_dl_jobs(
self, pair_list: ListPairsWithTimeframes, since_ms: Optional[int],
cache: bool) -> Tuple[List[Coroutine], List[Tuple[str, str, CandleType]]]:
"""
Build Coroutines to execute as part of refresh_latest_ohlcv
"""
input_coroutines: List[Coroutine[Any, Any, OHLCVResponse]] = []
cached_pairs = []
for pair, timeframe, candle_type in set(pair_list):
if (timeframe not in self.timeframes
and candle_type in (CandleType.SPOT, CandleType.FUTURES)):
logger.warning(
f"Cannot download ({pair}, {timeframe}) combination as this timeframe is "
f"not available on {self.name}. Available timeframes are "
f"{', '.join(self.timeframes)}.")
continue
if ((pair, timeframe, candle_type) not in self._klines or not cache
or self._now_is_time_to_refresh(pair, timeframe, candle_type)):
input_coroutines.append(
self._build_coroutine(pair, timeframe, candle_type, since_ms, cache))
else:
logger.debug(
f"Using cached candle (OHLCV) data for {pair}, {timeframe}, {candle_type} ..."
)
cached_pairs.append((pair, timeframe, candle_type))
return input_coroutines, cached_pairs
def _process_ohlcv_df(self, pair: str, timeframe: str, c_type: CandleType, ticks: List[List],
cache: bool, drop_incomplete: bool) -> DataFrame:
# keeping last candle time as last refreshed time of the pair
if ticks and cache:
idx = -2 if drop_incomplete and len(ticks) > 1 else -1
self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[idx][0] // 1000
# keeping parsed dataframe in cache
ohlcv_df = ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=drop_incomplete)
if cache:
if (pair, timeframe, c_type) in self._klines:
old = self._klines[(pair, timeframe, c_type)]
# Reassign so we return the updated, combined df
ohlcv_df = clean_ohlcv_dataframe(concat([old, ohlcv_df], axis=0), timeframe, pair,
fill_missing=True, drop_incomplete=False)
candle_limit = self.ohlcv_candle_limit(timeframe, self._config['candle_type_def'])
# Age out old candles
ohlcv_df = ohlcv_df.tail(candle_limit + self._startup_candle_count)
ohlcv_df = ohlcv_df.reset_index(drop=True)
self._klines[(pair, timeframe, c_type)] = ohlcv_df
else:
self._klines[(pair, timeframe, c_type)] = ohlcv_df
return ohlcv_df
def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
since_ms: Optional[int] = None, cache: bool = True,
drop_incomplete: Optional[bool] = None
) -> Dict[PairWithTimeframe, DataFrame]:
"""
Refresh in-memory OHLCV asynchronously and set `_klines` with the result
Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
Only used in the dataprovider.refresh() method.
:param pair_list: List of 2 element tuples containing pair, interval to refresh
:param since_ms: time since when to download, in milliseconds
:param cache: Assign result to _klines. Usefull for one-off downloads like for pairlists
:param drop_incomplete: Control candle dropping.
Specifying None defaults to _ohlcv_partial_candle
:return: Dict of [{(pair, timeframe): Dataframe}]
"""
logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
# Gather coroutines to run
input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
results_df = {}
# Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
for input_coro in chunks(input_coroutines, 100):
async def gather_stuff():
return await asyncio.gather(*input_coro, return_exceptions=True)
with self._loop_lock:
results = self.loop.run_until_complete(gather_stuff())
for res in results:
if isinstance(res, Exception):
logger.warning(f"Async code raised an exception: {repr(res)}")
continue
# Deconstruct tuple (has 5 elements)
pair, timeframe, c_type, ticks, drop_hint = res
drop_incomplete_ = drop_hint if drop_incomplete is None else drop_incomplete
ohlcv_df = self._process_ohlcv_df(
pair, timeframe, c_type, ticks, cache, drop_incomplete_)
results_df[(pair, timeframe, c_type)] = ohlcv_df
# Return cached klines
for pair, timeframe, c_type in cached_pairs:
results_df[(pair, timeframe, c_type)] = self.klines(
(pair, timeframe, c_type),
copy=False
)
return results_df
def _now_is_time_to_refresh(self, pair: str, timeframe: str, candle_type: CandleType) -> bool:
# Timeframe in seconds
| # pragma pylint: disable=W0603
"""
Cryptocurrency Exchanges support
"""
logger = logging.getLogger(__name__)
class Exchange:
# Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
_params: Dict = {}
# Additional parameters - added to the ccxt object
_ccxt_params: Dict = {}
# Dict to specify which options each exchange implements
# This defines defaults, which can be selectively overridden by subclasses using _ft_has
# or by specifying them in the configuration.
_ft_has_default: Dict = {
"stoploss_on_exchange": False,
"stop_price_param": "stopLossPrice", # Used for stoploss_on_exchange request
"stop_price_prop": "stopLossPrice", # Used for stoploss_on_exchange response parsing
"order_time_in_force": ["GTC"],
"ohlcv_params": {},
"ohlcv_candle_limit": 500,
"ohlcv_has_history": True, # Some exchanges (Kraken) don't provide history via ohlcv
"ohlcv_partial_candle": True,
"ohlcv_require_since": False,
# Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
"ohlcv_volume_currency": "base", # "base" or "quote"
"tickers_have_quoteVolume": True,
"tickers_have_bid_ask": True, # bid / ask empty for fetch_tickers
"tickers_have_price": True,
"trades_pagination": "time", # Possible are "time" or "id"
"trades_pagination_arg": "since",
"l2_limit_range": None,
"l2_limit_range_required": True, # Allow Empty L2 limit (kucoin)
"mark_ohlcv_price": "mark",
"mark_ohlcv_timeframe": "8h",
"ccxt_futures_name": "swap",
"needs_trading_fees": False, # use fetch_trading_fees to cache fees
"order_props_in_contracts": ['amount', 'filled', 'remaining'],
# Override createMarketBuyOrderRequiresPrice where ccxt has it wrong
"marketOrderRequiresPrice": False,
}
_ft_has: Dict = {}
_ft_has_futures: Dict = {}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
# TradingMode.SPOT always supported and not required in this list
]
def __init__(self, config: Config, *, exchange_config: Optional[ExchangeConfig] = None,
validate: bool = True, load_leverage_tiers: bool = False) -> None:
"""
Initializes this module with the given config,
it does basic validation whether the specified exchange and pairs are valid.
:return: None
"""
self._api: ccxt.Exchange
self._api_async: ccxt_async.Exchange = None
self._markets: Dict = {}
self._trading_fees: Dict[str, Any] = {}
self._leverage_tiers: Dict[str, List[Dict]] = {}
# Lock event loop. This is necessary to avoid race-conditions when using force* commands
# Due to funding fee fetching.
self._loop_lock = Lock()
self.loop = self._init_async_loop()
self._config: Config = {}
self._config.update(config)
# Holds last candle refreshed time of each pair
self._pairs_last_refresh_time: Dict[PairWithTimeframe, int] = {}
# Timestamp of last markets refresh
self._last_markets_refresh: int = 0
# Cache for 10 minutes ...
self._cache_lock = Lock()
self._fetch_tickers_cache: TTLCache = TTLCache(maxsize=2, ttl=60 * 10)
# Cache values for 1800 to avoid frequent polling of the exchange for prices
# Caching only applies to RPC methods, so prices for open trades are still
# refreshed once every iteration.
self._exit_rate_cache: TTLCache = TTLCache(maxsize=100, ttl=1800)
self._entry_rate_cache: TTLCache = TTLCache(maxsize=100, ttl=1800)
# Holds candles
self._klines: Dict[PairWithTimeframe, DataFrame] = {}
# Holds all open sell orders for dry_run
self._dry_run_open_orders: Dict[str, Any] = {}
if config['dry_run']:
logger.info('Instance is running with dry_run enabled')
logger.info(f"Using CCXT {ccxt.__version__}")
exchange_conf: Dict[str, Any] = exchange_config if exchange_config else config['exchange']
remove_exchange_credentials(exchange_conf, config.get('dry_run', False))
self.log_responses = exchange_conf.get('log_responses', False)
# Leverage properties
self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
self.margin_mode: MarginMode = (
MarginMode(config.get('margin_mode'))
if config.get('margin_mode')
else MarginMode.NONE
)
self.liquidation_buffer = config.get('liquidation_buffer', 0.05)
# Deep merge ft_has with default ft_has options
self._ft_has = deep_merge_dicts(self._ft_has, deepcopy(self._ft_has_default))
if self.trading_mode == TradingMode.FUTURES:
self._ft_has = deep_merge_dicts(self._ft_has_futures, self._ft_has)
if exchange_conf.get('_ft_has_params'):
self._ft_has = deep_merge_dicts(exchange_conf.get('_ft_has_params'),
self._ft_has)
logger.info("Overriding exchange._ft_has with config params, result: %s", self._ft_has)
# Assign this directly for easy access
self._ohlcv_partial_candle = self._ft_has['ohlcv_partial_candle']
self._trades_pagination = self._ft_has['trades_pagination']
self._trades_pagination_arg = self._ft_has['trades_pagination_arg']
# Initialize ccxt objects
ccxt_config = self._ccxt_config
ccxt_config = deep_merge_dicts(exchange_conf.get('ccxt_config', {}), ccxt_config)
ccxt_config = deep_merge_dicts(exchange_conf.get('ccxt_sync_config', {}), ccxt_config)
self._api = self._init_ccxt(exchange_conf, ccxt_kwargs=ccxt_config)
ccxt_async_config = self._ccxt_config
ccxt_async_config = deep_merge_dicts(exchange_conf.get('ccxt_config', {}),
ccxt_async_config)
ccxt_async_config = deep_merge_dicts(exchange_conf.get('ccxt_async_config', {}),
ccxt_async_config)
self._api_async = self._init_ccxt(
exchange_conf, ccxt_async, ccxt_kwargs=ccxt_async_config)
logger.info(f'Using Exchange "{self.name}"')
self.required_candle_call_count = 1
if validate:
# Initial markets load
self._load_markets()
self.validate_config(config)
self._startup_candle_count: int = config.get('startup_candle_count', 0)
self.required_candle_call_count = self.validate_required_startup_candles(
self._startup_candle_count, config.get('timeframe', ''))
# Converts the interval provided in minutes in config to seconds
self.markets_refresh_interval: int = exchange_conf.get(
"markets_refresh_interval", 60) * 60 * 1000
if self.trading_mode != TradingMode.SPOT and load_leverage_tiers:
self.fill_leverage_tiers()
self.additional_exchange_init()
def __del__(self):
"""
Destructor - clean up async stuff
"""
self.close()
def close(self):
logger.debug("Exchange object destroyed, closing async loop")
if (self._api_async and inspect.iscoroutinefunction(self._api_async.close)
and self._api_async.session):
logger.debug("Closing async ccxt session.")
self.loop.run_until_complete(self._api_async.close())
if self.loop and not self.loop.is_closed():
self.loop.close()
def _init_async_loop(self) -> asyncio.AbstractEventLoop:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
return loop
def validate_config(self, config):
# Check if timeframe is available
self.validate_timeframes(config.get('timeframe'))
# Check if all pairs are available
self.validate_stakecurrency(config['stake_currency'])
if not config['exchange'].get('skip_pair_validation'):
self.validate_pairs(config['exchange']['pair_whitelist'])
self.validate_ordertypes(config.get('order_types', {}))
self.validate_order_time_in_force(config.get('order_time_in_force', {}))
self.validate_trading_mode_and_margin_mode(self.trading_mode, self.margin_mode)
self.validate_pricing(config['exit_pricing'])
self.validate_pricing(config['entry_pricing'])
def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
ccxt_kwargs: Dict = {}) -> ccxt.Exchange:
"""
Initialize ccxt with given config and return valid
ccxt instance.
"""
# Find matching class for the given exchange name
name = exchange_config['name']
if not is_exchange_known_ccxt(name, ccxt_module):
raise OperationalException(f'Exchange {name} is not supported by ccxt')
ex_config = {
'apiKey': exchange_config.get('key'),
'secret': exchange_config.get('secret'),
'password': exchange_config.get('password'),
'uid': exchange_config.get('uid', ''),
}
if ccxt_kwargs:
logger.info('Applying additional ccxt config: %s', ccxt_kwargs)
if self._ccxt_params:
# Inject static options after the above output to not confuse users.
ccxt_kwargs = deep_merge_dicts(self._ccxt_params, ccxt_kwargs)
if ccxt_kwargs:
ex_config.update(ccxt_kwargs)
try:
api = getattr(ccxt_module, name.lower())(ex_config)
except (KeyError, AttributeError) as e:
raise OperationalException(f'Exchange {name} is not supported') from e
except ccxt.BaseError as e:
raise OperationalException(f"Initialization of ccxt failed. Reason: {e}") from e
return api
@property
def _ccxt_config(self) -> Dict:
# Parameters to add directly to ccxt sync/async initialization.
if self.trading_mode == TradingMode.MARGIN:
return {
"options": {
"defaultType": "margin"
}
}
elif self.trading_mode == TradingMode.FUTURES:
return {
"options": {
"defaultType": self._ft_has["ccxt_futures_name"]
}
}
else:
return {}
@property
def name(self) -> str:
"""exchange Name (from ccxt)"""
return self._api.name
@property
def id(self) -> str:
"""exchange ccxt id"""
return self._api.id
@property
def timeframes(self) -> List[str]:
return list((self._api.timeframes or {}).keys())
@property
def markets(self) -> Dict[str, Any]:
"""exchange ccxt markets"""
if not self._markets:
logger.info("Markets were not loaded. Loading them now..")
self._load_markets()
return self._markets
@property
def precisionMode(self) -> int:
"""exchange ccxt precisionMode"""
return self._api.precisionMode
def additional_exchange_init(self) -> None:
"""
Additional exchange initialization logic.
.api will be available at this point.
Must be overridden in child methods if required.
"""
pass
def _log_exchange_response(self, endpoint, response) -> None:
""" Log exchange responses """
if self.log_responses:
logger.info(f"API {endpoint}: {response}")
def ohlcv_candle_limit(
self, timeframe: str, candle_type: CandleType, since_ms: Optional[int] = None) -> int:
"""
Exchange ohlcv candle limit
Uses ohlcv_candle_limit_per_timeframe if the exchange has different limits
per timeframe (e.g. bittrex), otherwise falls back to ohlcv_candle_limit
:param timeframe: Timeframe to check
:param candle_type: Candle-type
:param since_ms: Starting timestamp
:return: Candle limit as integer
"""
return int(self._ft_has.get('ohlcv_candle_limit_per_timeframe', {}).get(
timeframe, self._ft_has.get('ohlcv_candle_limit')))
def get_markets(self, base_currencies: List[str] = [], quote_currencies: List[str] = [],
spot_only: bool = False, margin_only: bool = False, futures_only: bool = False,
tradable_only: bool = True,
active_only: bool = False) -> Dict[str, Any]:
"""
Return exchange ccxt markets, filtered out by base currency and quote currency
if this was requested in parameters.
"""
markets = self.markets
if not markets:
raise OperationalException("Markets were not loaded.")
if base_currencies:
markets = {k: v for k, v in markets.items() if v['base'] in base_currencies}
if quote_currencies:
markets = {k: v for k, v in markets.items() if v['quote'] in quote_currencies}
if tradable_only:
markets = {k: v for k, v in markets.items() if self.market_is_tradable(v)}
if spot_only:
markets = {k: v for k, v in markets.items() if self.market_is_spot(v)}
if margin_only:
markets = {k: v for k, v in markets.items() if self.market_is_margin(v)}
if futures_only:
markets = {k: v for k, v in markets.items() if self.market_is_future(v)}
if active_only:
markets = {k: v for k, v in markets.items() if market_is_active(v)}
return markets
def get_quote_currencies(self) -> List[str]:
"""
Return a list of supported quote currencies
"""
markets = self.markets
return sorted(set([x['quote'] for _, x in markets.items()]))
def get_pair_quote_currency(self, pair: str) -> str:
""" Return a pair's quote currency (base/quote:settlement) """
return self.markets.get(pair, {}).get('quote', '')
def get_pair_base_currency(self, pair: str) -> str:
""" Return a pair's base currency (base/quote:settlement) """
return self.markets.get(pair, {}).get('base', '')
def market_is_future(self, market: Dict[str, Any]) -> bool:
return (
market.get(self._ft_has["ccxt_futures_name"], False) is True and
market.get('linear', False) is True
)
def market_is_spot(self, market: Dict[str, Any]) -> bool:
return market.get('spot', False) is True
def market_is_margin(self, market: Dict[str, Any]) -> bool:
return market.get('margin', False) is True
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
"""
Check if the market symbol is tradable by Freqtrade.
Ensures that Configured mode aligns to
"""
return (
market.get('quote', None) is not None
and market.get('base', None) is not None
and (self.precisionMode != TICK_SIZE
# Too low precision will falsify calculations
or market.get('precision', {}).get('price') > 1e-11)
and ((self.trading_mode == TradingMode.SPOT and self.market_is_spot(market))
or (self.trading_mode == TradingMode.MARGIN and self.market_is_margin(market))
or (self.trading_mode == TradingMode.FUTURES and self.market_is_future(market)))
)
def klines(self, pair_interval: PairWithTimeframe, copy: bool = True) -> DataFrame:
if pair_interval in self._klines:
return self._klines[pair_interval].copy() if copy else self._klines[pair_interval]
else:
return DataFrame()
def get_contract_size(self, pair: str) -> Optional[float]:
if self.trading_mode == TradingMode.FUTURES:
market = self.markets.get(pair, {})
contract_size: float = 1.0
if not market:
return None
if market.get('contractSize') is not None:
# ccxt has contractSize in markets as string
contract_size = float(market['contractSize'])
return contract_size
else:
return 1
def _trades_contracts_to_amount(self, trades: List) -> List:
if len(trades) > 0 and 'symbol' in trades[0]:
contract_size = self.get_contract_size(trades[0]['symbol'])
if contract_size != 1:
for trade in trades:
trade['amount'] = trade['amount'] * contract_size
return trades
def _order_contracts_to_amount(self, order: Dict) -> Dict:
if 'symbol' in order and order['symbol'] is not None:
contract_size = self.get_contract_size(order['symbol'])
if contract_size != 1:
for prop in self._ft_has.get('order_props_in_contracts', []):
if prop in order and order[prop] is not None:
order[prop] = order[prop] * contract_size
return order
def _amount_to_contracts(self, pair: str, amount: float) -> float:
contract_size = self.get_contract_size(pair)
return amount_to_contracts(amount, contract_size)
def _contracts_to_amount(self, pair: str, num_contracts: float) -> float:
contract_size = self.get_contract_size(pair)
return contracts_to_amount(num_contracts, contract_size)
def amount_to_contract_precision(self, pair: str, amount: float) -> float:
"""
Helper wrapper around amount_to_contract_precision
"""
contract_size = self.get_contract_size(pair)
return amount_to_contract_precision(amount, self.get_precision_amount(pair),
self.precisionMode, contract_size)
def _load_async_markets(self, reload: bool = False) -> None:
try:
if self._api_async:
self.loop.run_until_complete(
self._api_async.load_markets(reload=reload, params={}))
except (asyncio.TimeoutError, ccxt.BaseError) as e:
logger.warning('Could not load async markets. Reason: %s', e)
return
def _load_markets(self) -> None:
""" Initialize markets both sync and async """
try:
self._markets = self._api.load_markets(params={})
self._load_async_markets()
self._last_markets_refresh = dt_ts()
if self._ft_has['needs_trading_fees']:
self._trading_fees = self.fetch_trading_fees()
except ccxt.BaseError:
logger.exception('Unable to initialize markets.')
def reload_markets(self) -> None:
"""Reload markets both sync and async if refresh interval has passed """
# Check whether markets have to be reloaded
if (self._last_markets_refresh > 0) and (
self._last_markets_refresh + self.markets_refresh_interval > dt_ts()):
return None
logger.debug("Performing scheduled market reload..")
try:
self._markets = self._api.load_markets(reload=True, params={})
# Also reload async markets to avoid issues with newly listed pairs
self._load_async_markets(reload=True)
self._last_markets_refresh = dt_ts()
self.fill_leverage_tiers()
except ccxt.BaseError:
logger.exception("Could not reload markets.")
def validate_stakecurrency(self, stake_currency: str) -> None:
"""
Checks stake-currency against available currencies on the exchange.
Only runs on startup. If markets have not been loaded, there's been a problem with
the connection to the exchange.
:param stake_currency: Stake-currency to validate
:raise: OperationalException if stake-currency is not available.
"""
if not self._markets:
raise OperationalException(
'Could not load markets, therefore cannot start. '
'Please investigate the above error for more details.'
)
quote_currencies = self.get_quote_currencies()
if stake_currency not in quote_currencies:
raise OperationalException(
f"{stake_currency} is not available as stake on {self.name}. "
f"Available currencies are: {', '.join(quote_currencies)}")
def validate_pairs(self, pairs: List[str]) -> None:
"""
Checks if all given pairs are tradable on the current exchange.
:param pairs: list of pairs
:raise: OperationalException if one pair is not available
:return: None
"""
if not self.markets:
logger.warning('Unable to validate pairs (assuming they are correct).')
return
extended_pairs = expand_pairlist(pairs, list(self.markets), keep_invalid=True)
invalid_pairs = []
for pair in extended_pairs:
# Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
if self.markets and pair not in self.markets:
raise OperationalException(
f'Pair {pair} is not available on {self.name} {self.trading_mode.value}. '
f'Please remove {pair} from your whitelist.')
# From ccxt Documentation:
# markets.info: An associative array of non-common market properties,
# including fees, rates, limits and other general market information.
# The internal info array is different for each particular market,
# its contents depend on the exchange.
# It can also be a string or similar ... so we need to verify that first.
elif (isinstance(self.markets[pair].get('info'), dict)
and self.markets[pair].get('info', {}).get('prohibitedIn', False)):
# Warn users about restricted pairs in whitelist.
# We cannot determine reliably if Users are affected.
logger.warning(f"Pair {pair} is restricted for some users on this exchange."
f"Please check if you are impacted by this restriction "
f"on the exchange and eventually remove {pair} from your whitelist.")
if (self._config['stake_currency'] and
self.get_pair_quote_currency(pair) != self._config['stake_currency']):
invalid_pairs.append(pair)
if invalid_pairs:
raise OperationalException(
f"Stake-currency '{self._config['stake_currency']}' not compatible with "
f"pair-whitelist. Please remove the following pairs: {invalid_pairs}")
def get_valid_pair_combination(self, curr_1: str, curr_2: str) -> str:
"""
Get valid pair combination of curr_1 and curr_2 by trying both combinations.
"""
for pair in [f"{curr_1}/{curr_2}", f"{curr_2}/{curr_1}"]:
if pair in self.markets and self.markets[pair].get('active'):
return pair
raise ValueError(f"Could not combine {curr_1} and {curr_2} to get a valid pair.")
def validate_timeframes(self, timeframe: Optional[str]) -> None:
"""
Check if timeframe from config is a supported timeframe on the exchange
"""
if not hasattr(self._api, "timeframes") or self._api.timeframes is None:
# If timeframes attribute is missing (or is None), the exchange probably
# has no fetchOHLCV method.
# Therefore we also show that.
raise OperationalException(
f"The ccxt library does not provide the list of timeframes "
f"for the exchange {self.name} and this exchange "
f"is therefore not supported. ccxt fetchOHLCV: {self.exchange_has('fetchOHLCV')}")
if timeframe and (timeframe not in self.timeframes):
raise OperationalException(
f"Invalid timeframe '{timeframe}'. This exchange supports: {self.timeframes}")
if timeframe and timeframe_to_minutes(timeframe) < 1:
raise OperationalException("Timeframes < 1m are currently not supported by Freqtrade.")
def validate_ordertypes(self, order_types: Dict) -> None:
"""
Checks if order-types configured in strategy/config are supported
"""
if any(v == 'market' for k, v in order_types.items()):
if not self.exchange_has('createMarketOrder'):
raise OperationalException(
f'Exchange {self.name} does not support market orders.')
self.validate_stop_ordertypes(order_types)
def validate_stop_ordertypes(self, order_types: Dict) -> None:
"""
Validate stoploss order types
"""
if (order_types.get("stoploss_on_exchange")
and not self._ft_has.get("stoploss_on_exchange", False)):
raise OperationalException(
f'On exchange stoploss is not supported for {self.name}.'
)
if self.trading_mode == TradingMode.FUTURES:
price_mapping = self._ft_has.get('stop_price_type_value_mapping', {}).keys()
if (
order_types.get("stoploss_on_exchange", False) is True
and 'stoploss_price_type' in order_types
and order_types['stoploss_price_type'] not in price_mapping
):
raise OperationalException(
f'On exchange stoploss price type is not supported for {self.name}.'
)
def validate_pricing(self, pricing: Dict) -> None:
if pricing.get('use_order_book', False) and not self.exchange_has('fetchL2OrderBook'):
raise OperationalException(f'Orderbook not available for {self.name}.')
if (not pricing.get('use_order_book', False) and (
not self.exchange_has('fetchTicker')
or not self._ft_has['tickers_have_price'])):
raise OperationalException(f'Ticker pricing not available for {self.name}.')
def validate_order_time_in_force(self, order_time_in_force: Dict) -> None:
"""
Checks if order time in force configured in strategy/config are supported
"""
if any(v.upper() not in self._ft_has["order_time_in_force"]
for k, v in order_time_in_force.items()):
raise OperationalException(
f'Time in force policies are not supported for {self.name} yet.')
def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> int:
"""
Checks if required startup_candles is more than ohlcv_candle_limit().
Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
"""
candle_limit = self.ohlcv_candle_limit(
timeframe, self._config['candle_type_def'],
int(date_minus_candles(timeframe, startup_candles).timestamp() * 1000)
if timeframe else None)
# Require one more candle - to account for the still open candle.
candle_count = startup_candles + 1
# Allow 5 calls to the exchange per pair
required_candle_call_count = int(
(candle_count / candle_limit) + (0 if candle_count % candle_limit == 0 else 1))
if self._ft_has['ohlcv_has_history']:
if required_candle_call_count > 5:
# Only allow 5 calls per pair to somewhat limit the impact
raise OperationalException(
f"This strategy requires {startup_candles} candles to start, "
"which is more than 5x "
f"the amount of candles {self.name} provides for {timeframe}.")
elif required_candle_call_count > 1:
raise OperationalException(
f"This strategy requires {startup_candles} candles to start, which is more than "
f"the amount of candles {self.name} provides for {timeframe}.")
if required_candle_call_count > 1:
logger.warning(f"Using {required_candle_call_count} calls to get OHLCV. "
f"This can result in slower operations for the bot. Please check "
f"if you really need {startup_candles} candles for your strategy")
return required_candle_call_count
def validate_trading_mode_and_margin_mode(
self,
trading_mode: TradingMode,
margin_mode: Optional[MarginMode] # Only None when trading_mode = TradingMode.SPOT
):
"""
Checks if freqtrade can perform trades using the configured
trading mode(Margin, Futures) and MarginMode(Cross, Isolated)
Throws OperationalException:
If the trading_mode/margin_mode type are not supported by freqtrade on this exchange
"""
if trading_mode != TradingMode.SPOT and (
(trading_mode, margin_mode) not in self._supported_trading_mode_margin_pairs
):
mm_value = margin_mode and margin_mode.value
raise OperationalException(
f"Freqtrade does not support {mm_value} {trading_mode.value} on {self.name}"
)
def get_option(self, param: str, default: Optional[Any] = None) -> Any:
"""
Get parameter value from _ft_has
"""
return self._ft_has.get(param, default)
def exchange_has(self, endpoint: str) -> bool:
"""
Checks if exchange implements a specific API endpoint.
Wrapper around ccxt 'has' attribute
:param endpoint: Name of endpoint (e.g. 'fetchOHLCV', 'fetchTickers')
:return: bool
"""
return endpoint in self._api.has and self._api.has[endpoint]
def get_precision_amount(self, pair: str) -> Optional[float]:
"""
Returns the amount precision of the exchange.
:param pair: Pair to get precision for
:return: precision for amount or None. Must be used in combination with precisionMode
"""
return self.markets.get(pair, {}).get('precision', {}).get('amount', None)
def get_precision_price(self, pair: str) -> Optional[float]:
"""
Returns the price precision of the exchange.
:param pair: Pair to get precision for
:return: precision for price or None. Must be used in combination with precisionMode
"""
return self.markets.get(pair, {}).get('precision', {}).get('price', None)
def amount_to_precision(self, pair: str, amount: float) -> float:
"""
Returns the amount to buy or sell to a precision the Exchange accepts
"""
return amount_to_precision(amount, self.get_precision_amount(pair), self.precisionMode)
def price_to_precision(self, pair: str, price: float, *, rounding_mode: int = ROUND) -> float:
"""
Returns the price rounded to the precision the Exchange accepts.
The default price_rounding_mode in conf is ROUND.
For stoploss calculations, must use ROUND_UP for longs, and ROUND_DOWN for shorts.
"""
return price_to_precision(price, self.get_precision_price(pair),
self.precisionMode, rounding_mode=rounding_mode)
def price_get_one_pip(self, pair: str, price: float) -> float:
"""
Get's the "1 pip" value for this pair.
Used in PriceFilter to calculate the 1pip movements.
"""
precision = self.markets[pair]['precision']['price']
if self.precisionMode == TICK_SIZE:
return precision
else:
return 1 / pow(10, precision)
def get_min_pair_stake_amount(
self,
pair: str,
price: float,
stoploss: float,
leverage: Optional[float] = 1.0
) -> Optional[float]:
return self._get_stake_amount_limit(pair, price, stoploss, 'min', leverage)
def get_max_pair_stake_amount(self, pair: str, price: float, leverage: float = 1.0) -> float:
max_stake_amount = self._get_stake_amount_limit(pair, price, 0.0, 'max', leverage)
if max_stake_amount is None:
# * Should never be executed
raise OperationalException(f'{self.name}.get_max_pair_stake_amount should'
'never set max_stake_amount to None')
return max_stake_amount
def _get_stake_amount_limit(
self,
pair: str,
price: float,
stoploss: float,
limit: Literal['min', 'max'],
leverage: Optional[float] = 1.0
) -> Optional[float]:
isMin = limit == 'min'
try:
market = self.markets[pair]
except KeyError:
raise ValueError(f"Can't get market information for symbol {pair}")
if isMin:
# reserve some percent defined in config (5% default) + stoploss
margin_reserve: float = 1.0 + self._config.get('amount_reserve_percent',
DEFAULT_AMOUNT_RESERVE_PERCENT)
stoploss_reserve = (
margin_reserve / (1 - abs(stoploss)) if abs(stoploss) != 1 else 1.5
)
# it should not be more than 50%
stoploss_reserve = max(min(stoploss_reserve, 1.5), 1)
else:
margin_reserve = 1.0
stoploss_reserve = 1.0
stake_limits = []
limits = market['limits']
if (limits['cost'][limit] is not None):
stake_limits.append(
self._contracts_to_amount(pair, limits['cost'][limit]) * stoploss_reserve
)
if (limits['amount'][limit] is not None):
stake_limits.append(
self._contracts_to_amount(pair, limits['amount'][limit]) * price * margin_reserve
)
if not stake_limits:
return None if isMin else float('inf')
# The value returned should satisfy both limits: for amount (base currency) and
# for cost (quote, stake currency), so max() is used here.
# See also #2575 at github.
return self._get_stake_amount_considering_leverage(
max(stake_limits) if isMin else min(stake_limits),
leverage or 1.0
)
def _get_stake_amount_considering_leverage(self, stake_amount: float, leverage: float) -> float:
"""
Takes the minimum stake amount for a pair with no leverage and returns the minimum
stake amount when leverage is considered
:param stake_amount: The stake amount for a pair before leverage is considered
:param leverage: The amount of leverage being used on the current trade
"""
return stake_amount / leverage
# Dry-run methods
def create_dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
rate: float, leverage: float, params: Dict = {},
stop_loss: bool = False) -> Dict[str, Any]:
now = dt_now()
order_id = f'dry_run_{side}_{pair}_{now.timestamp()}'
# Rounding here must respect to contract sizes
_amount = self._contracts_to_amount(
pair, self.amount_to_precision(pair, self._amount_to_contracts(pair, amount)))
dry_order: Dict[str, Any] = {
'id': order_id,
'symbol': pair,
'price': rate,
'average': rate,
'amount': _amount,
'cost': _amount * rate,
'type': ordertype,
'side': side,
'filled': 0,
'remaining': _amount,
'datetime': now.strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
'timestamp': dt_ts(now),
'status': "open",
'fee': None,
'info': {},
'leverage': leverage
}
if stop_loss:
dry_order["info"] = {"stopPrice": dry_order["price"]}
dry_order[self._ft_has['stop_price_prop']] = dry_order["price"]
# Workaround to avoid filling stoploss orders immediately
dry_order["ft_order_type"] = "stoploss"
orderbook: Optional[OrderBook] = None
if self.exchange_has('fetchL2OrderBook'):
orderbook = self.fetch_l2_order_book(pair, 20)
if ordertype == "limit" and orderbook:
# Allow a 1% price difference
allowed_diff = 0.01
if self._dry_is_price_crossed(pair, side, rate, orderbook, allowed_diff):
logger.info(
f"Converted order {pair} to market order due to price {rate} crossing spread "
f"by more than {allowed_diff:.2%}.")
dry_order["type"] = "market"
if dry_order["type"] == "market" and not dry_order.get("ft_order_type"):
# Update market order pricing
average = self.get_dry_market_fill_price(pair, side, amount, rate, orderbook)
dry_order.update({
'average': average,
'filled': _amount,
'remaining': 0.0,
'status': "closed",
'cost': (dry_order['amount'] * average)
})
# market orders will always incurr taker fees
dry_order = self.add_dry_order_fee(pair, dry_order, 'taker')
dry_order = self.check_dry_limit_order_filled(
dry_order, immediate=True, orderbook=orderbook)
self._dry_run_open_orders[dry_order["id"]] = dry_order
# Copy order and close it - so the returned order is open unless it's a market order
return dry_order
def add_dry_order_fee(
self,
pair: str,
dry_order: Dict[str, Any],
taker_or_maker: MakerTaker,
) -> Dict[str, Any]:
fee = self.get_fee(pair, taker_or_maker=taker_or_maker)
dry_order.update({
'fee': {
'currency': self.get_pair_quote_currency(pair),
'cost': dry_order['cost'] * fee,
'rate': fee
}
})
return dry_order
def get_dry_market_fill_price(self, pair: str, side: str, amount: float, rate: float,
orderbook: Optional[OrderBook]) -> float:
"""
Get the market order fill price based on orderbook interpolation
"""
if self.exchange_has('fetchL2OrderBook'):
if not orderbook:
orderbook = self.fetch_l2_order_book(pair, 20)
ob_type: OBLiteral = 'asks' if side == 'buy' else 'bids'
slippage = 0.05
max_slippage_val = rate * ((1 + slippage) if side == 'buy' else (1 - slippage))
remaining_amount = amount
filled_value = 0.0
book_entry_price = 0.0
for book_entry in orderbook[ob_type]:
book_entry_price = book_entry[0]
book_entry_coin_volume = book_entry[1]
if remaining_amount > 0:
if remaining_amount < book_entry_coin_volume:
# Orderbook at this slot bigger than remaining amount
filled_value += remaining_amount * book_entry_price
break
else:
filled_value += book_entry_coin_volume * book_entry_price
remaining_amount -= book_entry_coin_volume
else:
break
else:
# If remaining_amount wasn't consumed completely (break was not called)
filled_value += remaining_amount * book_entry_price
forecast_avg_filled_price = max(filled_value, 0) / amount
# Limit max. slippage to specified value
if side == 'buy':
forecast_avg_filled_price = min(forecast_avg_filled_price, max_slippage_val)
else:
forecast_avg_filled_price = max(forecast_avg_filled_price, max_slippage_val)
return self.price_to_precision(pair, forecast_avg_filled_price)
return rate
def _dry_is_price_crossed(self, pair: str, side: str, limit: float,
orderbook: Optional[OrderBook] = None, offset: float = 0.0) -> bool:
if not self.exchange_has('fetchL2OrderBook'):
return True
if not orderbook:
orderbook = self.fetch_l2_order_book(pair, 1)
try:
if side == 'buy':
price = orderbook['asks'][0][0]
if limit * (1 - offset) >= price:
return True
else:
price = orderbook['bids'][0][0]
if limit * (1 + offset) <= price:
return True
except IndexError:
# Ignore empty orderbooks when filling - can be filled with the next iteration.
pass
return False
def check_dry_limit_order_filled(
self, order: Dict[str, Any], immediate: bool = False,
orderbook: Optional[OrderBook] = None) -> Dict[str, Any]:
"""
Check dry-run limit order fill and update fee (if it filled).
"""
if (order['status'] != "closed"
and order['type'] in ["limit"]
and not order.get('ft_order_type')):
pair = order['symbol']
if self._dry_is_price_crossed(pair, order['side'], order['price'], orderbook):
order.update({
'status': 'closed',
'filled': order['amount'],
'remaining': 0,
})
self.add_dry_order_fee(
pair,
order,
'taker' if immediate else 'maker',
)
return order
def fetch_dry_run_order(self, order_id) -> Dict[str, Any]:
"""
Return dry-run order
Only call if running in dry-run mode.
"""
try:
order = self._dry_run_open_orders[order_id]
order = self.check_dry_limit_order_filled(order)
return order
except KeyError as e:
order = Order.order_by_id(order_id)
if order:
ccxt_order = order.to_ccxt_object(self._ft_has['stop_price_prop'])
self._dry_run_open_orders[order_id] = ccxt_order
return ccxt_order
# Gracefully handle errors with dry-run orders.
raise InvalidOrderException(
f'Tried to get an invalid dry-run-order (id: {order_id}). Message: {e}') from e
# Order handling
def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False):
if self.trading_mode != TradingMode.SPOT:
self.set_margin_mode(pair, self.margin_mode, accept_fail)
self._set_leverage(leverage, pair, accept_fail)
def _get_params(
self,
side: BuySell,
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'GTC',
) -> Dict:
params = self._params.copy()
if time_in_force != 'GTC' and ordertype != 'market':
params.update({'timeInForce': time_in_force.upper()})
if reduceOnly:
params.update({'reduceOnly': True})
return params
def _order_needs_price(self, ordertype: str) -> bool:
return (
ordertype != 'market'
or self._api.options.get("createMarketBuyOrderRequiresPrice", False)
or self._ft_has.get('marketOrderRequiresPrice', False)
)
def create_order(
self,
*,
pair: str,
ordertype: str,
side: BuySell,
amount: float,
rate: float,
leverage: float,
reduceOnly: bool = False,
time_in_force: str = 'GTC',
) -> Dict:
if self._config['dry_run']:
dry_order = self.create_dry_run_order(
pair, ordertype, side, amount, self.price_to_precision(pair, rate), leverage)
return dry_order
params = self._get_params(side, ordertype, leverage, reduceOnly, time_in_force)
try:
# Set the precision for amount and price(rate) as accepted by the exchange
amount = self.amount_to_precision(pair, self._amount_to_contracts(pair, amount))
needs_price = self._order_needs_price(ordertype)
rate_for_order = self.price_to_precision(pair, rate) if needs_price else None
if not reduceOnly:
self._lev_prep(pair, leverage, side)
order = self._api.create_order(
pair,
ordertype,
side,
amount,
rate_for_order,
params,
)
if order.get('status') is None:
# Map empty status to open.
order['status'] = 'open'
if order.get('type') is None:
order['type'] = ordertype
self._log_exchange_response('create_order', order)
order = self._order_contracts_to_amount(order)
return order
except ccxt.InsufficientFunds as e:
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} {side} order on market {pair}. '
f'Tried to {side} amount {amount} at rate {rate}.'
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not create {ordertype} {side} order on market {pair}. '
f'Tried to {side} amount {amount} at rate {rate}. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not place {side} order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
if not self._ft_has.get('stoploss_on_exchange'):
raise OperationalException(f"stoploss is not implemented for {self.name}.")
price_param = self._ft_has['stop_price_prop']
return (
order.get(price_param, None) is None
or ((side == "sell" and stop_loss > float(order[price_param])) or
(side == "buy" and stop_loss < float(order[price_param])))
)
def _get_stop_order_type(self, user_order_type) -> Tuple[str, str]:
available_order_Types: Dict[str, str] = self._ft_has["stoploss_order_types"]
if user_order_type in available_order_Types.keys():
ordertype = available_order_Types[user_order_type]
else:
# Otherwise pick only one available
ordertype = list(available_order_Types.values())[0]
user_order_type = list(available_order_Types.keys())[0]
return ordertype, user_order_type
def _get_stop_limit_rate(self, stop_price: float, order_types: Dict, side: str) -> float:
# Limit price threshold: As limit price should always be below stop-price
limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
if side == "sell":
limit_rate = stop_price * limit_price_pct
else:
limit_rate = stop_price * (2 - limit_price_pct)
bad_stop_price = ((stop_price < limit_rate) if side ==
"sell" else (stop_price > limit_rate))
# Ensure rate is less than stop price
if bad_stop_price:
# This can for example happen if the stop / liquidation price is set to 0
# Which is possible if a market-order closes right away.
# The InvalidOrderException will bubble up to exit_positions, where it will be
# handled gracefully.
raise InvalidOrderException(
"In stoploss limit order, stop price should be more than limit price. "
f"Stop price: {stop_price}, Limit price: {limit_rate}, "
f"Limit Price pct: {limit_price_pct}"
)
return limit_rate
def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
params = self._params.copy()
# Verify if stopPrice works for your exchange, else configure stop_price_param
params.update({self._ft_has['stop_price_param']: stop_price})
return params
@retrier(retries=0)
def create_stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict,
side: BuySell, leverage: float) -> Dict:
"""
creates a stoploss order.
requires `_ft_has['stoploss_order_types']` to be set as a dict mapping limit and market
to the corresponding exchange type.
The precise ordertype is determined by the order_types dict or exchange default.
The exception below should never raise, since we disallow
starting the bot in validate_ordertypes()
This may work with a limited number of other exchanges, but correct working
needs to be tested individually.
WARNING: setting `stoploss_on_exchange` to True will NOT auto-enable stoploss on exchange.
`stoploss_adjust` must still be implemented for this to work.
"""
if not self._ft_has['stoploss_on_exchange']:
raise OperationalException(f"stoploss is not implemented for {self.name}.")
user_order_type = order_types.get('stoploss', 'market')
ordertype, user_order_type = self._get_stop_order_type(user_order_type)
round_mode = ROUND_DOWN if side == 'buy' else ROUND_UP
stop_price_norm = self.price_to_precision(pair, stop_price, rounding_mode=round_mode)
limit_rate = None
if user_order_type == 'limit':
limit_rate = self._get_stop_limit_rate(stop_price, order_types, side)
limit_rate = self.price_to_precision(pair, limit_rate, rounding_mode=round_mode)
if self._config['dry_run']:
dry_order = self.create_dry_run_order(
pair,
ordertype,
side,
amount,
stop_price_norm,
stop_loss=True,
leverage=leverage,
)
return dry_order
try:
params = self._get_stop_params(side=side, ordertype=ordertype,
stop_price=stop_price_norm)
if self.trading_mode == TradingMode.FUTURES:
params['reduceOnly'] = True
if 'stoploss_price_type' in order_types and 'stop_price_type_field' in self._ft_has:
price_type = self._ft_has['stop_price_type_value_mapping'][
order_types.get('stoploss_price_type', PriceType.LAST)]
params[self._ft_has['stop_price_type_field']] = price_type
amount = self.amount_to_precision(pair, self._amount_to_contracts(pair, amount))
self._lev_prep(pair, leverage, side, accept_fail=True)
order = self._api.create_order(symbol=pair, type=ordertype, side=side,
amount=amount, price=limit_rate, params=params)
self._log_exchange_response('create_stoploss_order', order)
order = self._order_contracts_to_amount(order)
logger.info(f"stoploss {user_order_type} order added for {pair}. "
f"stop price: {stop_price}. limit: {limit_rate}")
return order
except ccxt.InsufficientFunds as e:
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to sell amount {amount} at rate {limit_rate}. '
f'Message: {e}') from e
except ccxt.InvalidOrder as e:
# Errors:
# `Order would trigger immediately.`
raise InvalidOrderException(
f'Could not create {ordertype} sell order on market {pair}. '
f'Tried to sell amount {amount} at rate {limit_rate}. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f"Could not place stoploss order due to {e.__class__.__name__}. "
f"Message: {e}") from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
return self.fetch_dry_run_order(order_id)
try:
order = self._api.fetch_order(order_id, pair, params=params)
self._log_exchange_response('fetch_order', order)
order = self._order_contracts_to_amount(order)
return order
except ccxt.OrderNotFound as e:
raise RetryableOrderError(
f'Order not found (pair: {pair} id: {order_id}). Message: {e}') from e
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Tried to get an invalid order (pair: {pair} id: {order_id}). Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
return self.fetch_order(order_id, pair, params)
def fetch_order_or_stoploss_order(self, order_id: str, pair: str,
stoploss_order: bool = False) -> Dict:
"""
Simple wrapper calling either fetch_order or fetch_stoploss_order depending on
the stoploss_order parameter
:param order_id: OrderId to fetch order
:param pair: Pair corresponding to order_id
:param stoploss_order: If true, uses fetch_stoploss_order, otherwise fetch_order.
"""
if stoploss_order:
return self.fetch_stoploss_order(order_id, pair)
return self.fetch_order(order_id, pair)
def check_order_canceled_empty(self, order: Dict) -> bool:
"""
Verify if an order has been cancelled without being partially filled
:param order: Order dict as returned from fetch_order()
:return: True if order has been cancelled without being filled, False otherwise.
"""
return (order.get('status') in NON_OPEN_EXCHANGE_STATES
and order.get('filled') == 0.0)
@retrier
def cancel_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
try:
order = self.fetch_dry_run_order(order_id)
order.update({'status': 'canceled', 'filled': 0.0, 'remaining': order['amount']})
return order
except InvalidOrderException:
return {}
try:
order = self._api.cancel_order(order_id, pair, params=params)
self._log_exchange_response('cancel_order', order)
order = self._order_contracts_to_amount(order)
return order
except ccxt.InvalidOrder as e:
raise InvalidOrderException(
f'Could not cancel order. Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
return self.cancel_order(order_id, pair, params)
def is_cancel_order_result_suitable(self, corder) -> bool:
if not isinstance(corder, dict):
return False
required = ('fee', 'status', 'amount')
return all(corder.get(k, None) is not None for k in required)
def cancel_order_with_result(self, order_id: str, pair: str, amount: float) -> Dict:
"""
Cancel order returning a result.
Creates a fake result if cancel order returns a non-usable result
and fetch_order does not work (certain exchanges don't return cancelled orders)
:param order_id: Orderid to cancel
:param pair: Pair corresponding to order_id
:param amount: Amount to use for fake response
:return: Result from either cancel_order if usable, or fetch_order
"""
try:
corder = self.cancel_order(order_id, pair)
if self.is_cancel_order_result_suitable(corder):
return corder
except InvalidOrderException:
logger.warning(f"Could not cancel order {order_id} for {pair}.")
try:
order = self.fetch_order(order_id, pair)
except InvalidOrderException:
logger.warning(f"Could not fetch cancelled order {order_id}.")
order = {
'id': order_id,
'status': 'canceled',
'amount': amount,
'filled': 0.0,
'fee': {},
'info': {}
}
return order
def cancel_stoploss_order_with_result(self, order_id: str, pair: str, amount: float) -> Dict:
"""
Cancel stoploss order returning a result.
Creates a fake result if cancel order returns a non-usable result
and fetch_order does not work (certain exchanges don't return cancelled orders)
:param order_id: stoploss-order-id to cancel
:param pair: Pair corresponding to order_id
:param amount: Amount to use for fake response
:return: Result from either cancel_order if usable, or fetch_order
"""
corder = self.cancel_stoploss_order(order_id, pair)
if self.is_cancel_order_result_suitable(corder):
return corder
try:
order = self.fetch_stoploss_order(order_id, pair)
except InvalidOrderException:
logger.warning(f"Could not fetch cancelled stoploss order {order_id}.")
order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
return order
@retrier
def get_balances(self) -> dict:
try:
balances = self._api.fetch_balance()
# Remove additional info from ccxt results
balances.pop("info", None)
balances.pop("free", None)
balances.pop("total", None)
balances.pop("used", None)
return balances
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def fetch_positions(self, pair: Optional[str] = None) -> List[Dict]:
"""
Fetch positions from the exchange.
If no pair is given, all positions are returned.
:param pair: Pair for the query
"""
if self._config['dry_run'] or self.trading_mode != TradingMode.FUTURES:
return []
try:
symbols = []
if pair:
symbols.append(pair)
positions: List[Dict] = self._api.fetch_positions(symbols)
self._log_exchange_response('fetch_positions', positions)
return positions
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get positions due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def _fetch_orders_emulate(self, pair: str, since_ms: int) -> List[Dict]:
orders = []
if self.exchange_has('fetchClosedOrders'):
orders = self._api.fetch_closed_orders(pair, since=since_ms)
if self.exchange_has('fetchOpenOrders'):
orders_open = self._api.fetch_open_orders(pair, since=since_ms)
orders.extend(orders_open)
return orders
@retrier(retries=0)
def fetch_orders(self, pair: str, since: datetime, params: Optional[Dict] = None) -> List[Dict]:
"""
Fetch all orders for a pair "since"
:param pair: Pair for the query
:param since: Starting time for the query
"""
if self._config['dry_run']:
return []
try:
since_ms = int((since.timestamp() - 10) * 1000)
if self.exchange_has('fetchOrders'):
if not params:
params = {}
try:
orders: List[Dict] = self._api.fetch_orders(pair, since=since_ms, params=params)
except ccxt.NotSupported:
# Some exchanges don't support fetchOrders
# attempt to fetch open and closed orders separately
orders = self._fetch_orders_emulate(pair, since_ms)
else:
orders = self._fetch_orders_emulate(pair, since_ms)
self._log_exchange_response('fetch_orders', orders)
orders = [self._order_contracts_to_amount(o) for o in orders]
return orders
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not fetch positions due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def fetch_trading_fees(self) -> Dict[str, Any]:
"""
Fetch user account trading fees
Can be cached, should not update often.
"""
if (self._config['dry_run'] or self.trading_mode != TradingMode.FUTURES
or not self.exchange_has('fetchTradingFees')):
return {}
try:
trading_fees: Dict[str, Any] = self._api.fetch_trading_fees()
self._log_exchange_response('fetch_trading_fees', trading_fees)
return trading_fees
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not fetch trading fees due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def fetch_bids_asks(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
"""
:param cached: Allow cached result
:return: fetch_tickers result
"""
if not self.exchange_has('fetchBidsAsks'):
return {}
if cached:
with self._cache_lock:
tickers = self._fetch_tickers_cache.get('fetch_bids_asks')
if tickers:
return tickers
try:
tickers = self._api.fetch_bids_asks(symbols)
with self._cache_lock:
self._fetch_tickers_cache['fetch_bids_asks'] = tickers
return tickers
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching bids/asks in batch. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load bids/asks due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
"""
:param cached: Allow cached result
:return: fetch_tickers result
"""
tickers: Tickers
if not self.exchange_has('fetchTickers'):
return {}
if cached:
with self._cache_lock:
tickers = self._fetch_tickers_cache.get('fetch_tickers') # type: ignore
if tickers:
return tickers
try:
tickers = self._api.fetch_tickers(symbols)
with self._cache_lock:
self._fetch_tickers_cache['fetch_tickers'] = tickers
return tickers
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching tickers in batch. '
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load tickers due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
# Pricing info
@retrier
def fetch_ticker(self, pair: str) -> Ticker:
try:
if (pair not in self.markets or
self.markets[pair].get('active', False) is False):
raise ExchangeError(f"Pair {pair} not available")
data: Ticker = self._api.fetch_ticker(pair)
return data
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@staticmethod
def get_next_limit_in_list(limit: int, limit_range: Optional[List[int]],
range_required: bool = True):
"""
Get next greater value in the list.
Used by fetch_l2_order_book if the api only supports a limited range
"""
if not limit_range:
return limit
result = min([x for x in limit_range if limit <= x] + [max(limit_range)])
if not range_required and limit > result:
# Range is not required - we can use None as parameter.
return None
return result
@retrier
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> OrderBook:
"""
Get L2 order book from exchange.
Can be limited to a certain amount (if supported).
Returns a dict in the format
{'asks': [price, volume], 'bids': [price, volume]}
"""
limit1 = self.get_next_limit_in_list(limit, self._ft_has['l2_limit_range'],
self._ft_has['l2_limit_range_required'])
try:
return self._api.fetch_l2_order_book(pair, limit1)
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching order book.'
f'Message: {e}') from e
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get order book due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def _get_price_side(self, side: str, is_short: bool, conf_strategy: Dict) -> BidAsk:
price_side = conf_strategy['price_side']
if price_side in ('same', 'other'):
price_map = {
('entry', 'long', 'same'): 'bid',
('entry', 'long', 'other'): 'ask',
('entry', 'short', 'same'): 'ask',
('entry', 'short', 'other'): 'bid',
('exit', 'long', 'same'): 'ask',
('exit', 'long', 'other'): 'bid',
('exit', 'short', 'same'): 'bid',
('exit', 'short', 'other'): 'ask',
}
price_side = price_map[(side, 'short' if is_short else 'long', price_side)]
return price_side
def get_rate(self, pair: str, refresh: bool,
side: EntryExit, is_short: bool,
order_book: Optional[OrderBook] = None, ticker: Optional[Ticker] = None) -> float:
"""
Calculates bid/ask target
bid rate - between current ask price and last price
ask rate - either using ticker bid or first bid based on orderbook
or remain static in any other case since it's not updating.
:param pair: Pair to get rate for
:param refresh: allow cached data
:param side: "buy" or "sell"
:return: float: Price
:raises PricingError if orderbook price could not be determined.
"""
name = side.capitalize()
strat_name = 'entry_pricing' if side == "entry" else 'exit_pricing'
cache_rate: TTLCache = self._entry_rate_cache if side == "entry" else self._exit_rate_cache
if not refresh:
with self._cache_lock:
rate = cache_rate.get(pair)
# Check if cache has been invalidated
if rate:
logger.debug(f"Using cached {side} rate for {pair}.")
return rate
conf_strategy = self._config.get(strat_name, {})
price_side = self._get_price_side(side, is_short, conf_strategy)
if conf_strategy.get('use_order_book', False):
order_book_top = conf_strategy.get('order_book_top', 1)
if order_book is None:
order_book = self.fetch_l2_order_book(pair, order_book_top)
rate = self._get_rate_from_ob(pair, side, order_book, name, price_side,
order_book_top)
else:
logger.debug(f"Using Last {price_side.capitalize()} / Last Price")
if ticker is None:
ticker = self.fetch_ticker(pair)
rate = self._get_rate_from_ticker(side, ticker, conf_strategy, price_side)
if rate is None:
raise PricingError(f"{name}-Rate for {pair} was empty.")
with self._cache_lock:
cache_rate[pair] = rate
return rate
def _get_rate_from_ticker(self, side: EntryExit, ticker: Ticker, conf_strategy: Dict[str, Any],
price_side: BidAsk) -> Optional[float]:
"""
Get rate from ticker.
"""
ticker_rate = ticker[price_side]
if ticker['last'] and ticker_rate:
if side == 'entry' and ticker_rate > ticker['last']:
balance = conf_strategy.get('price_last_balance', 0.0)
ticker_rate = ticker_rate + balance * (ticker['last'] - ticker_rate)
elif side == 'exit' and ticker_rate < ticker['last']:
balance = conf_strategy.get('price_last_balance', 0.0)
ticker_rate = ticker_rate - balance * (ticker_rate - ticker['last'])
rate = ticker_rate
return rate
def _get_rate_from_ob(self, pair: str, side: EntryExit, order_book: OrderBook, name: str,
price_side: BidAsk, order_book_top: int) -> float:
"""
Get rate from orderbook
:raises: PricingError if rate could not be determined.
"""
logger.debug('order_book %s', order_book)
# top 1 = index 0
try:
obside: OBLiteral = 'bids' if price_side == 'bid' else 'asks'
rate = order_book[obside][order_book_top - 1][0]
except (IndexError, KeyError) as e:
logger.warning(
f"{pair} - {name} Price at location {order_book_top} from orderbook "
f"could not be determined. Orderbook: {order_book}"
)
raise PricingError from e
logger.debug(f"{pair} - {name} price from orderbook {price_side.capitalize()}"
f"side - top {order_book_top} order book {side} rate {rate:.8f}")
return rate
def get_rates(self, pair: str, refresh: bool, is_short: bool) -> Tuple[float, float]:
entry_rate = None
exit_rate = None
if not refresh:
with self._cache_lock:
entry_rate = self._entry_rate_cache.get(pair)
exit_rate = self._exit_rate_cache.get(pair)
if entry_rate:
logger.debug(f"Using cached buy rate for {pair}.")
if exit_rate:
logger.debug(f"Using cached sell rate for {pair}.")
entry_pricing = self._config.get('entry_pricing', {})
exit_pricing = self._config.get('exit_pricing', {})
order_book = ticker = None
if not entry_rate and entry_pricing.get('use_order_book', False):
order_book_top = max(entry_pricing.get('order_book_top', 1),
exit_pricing.get('order_book_top', 1))
order_book = self.fetch_l2_order_book(pair, order_book_top)
entry_rate = self.get_rate(pair, refresh, 'entry', is_short, order_book=order_book)
elif not entry_rate:
ticker = self.fetch_ticker(pair)
entry_rate = self.get_rate(pair, refresh, 'entry', is_short, ticker=ticker)
if not exit_rate:
exit_rate = self.get_rate(pair, refresh, 'exit',
is_short, order_book=order_book, ticker=ticker)
return entry_rate, exit_rate
# Fee handling
@retrier
def get_trades_for_order(self, order_id: str, pair: str, since: datetime,
params: Optional[Dict] = None) -> List:
"""
Fetch Orders using the "fetch_my_trades" endpoint and filter them by order-id.
The "since" argument passed in is coming from the database and is in UTC,
as timezone-native datetime object.
From the python documentation:
> Naive datetime instances are assumed to represent local time
Therefore, calling "since.timestamp()" will get the UTC timestamp, after applying the
transformation from local timezone to UTC.
This works for timezones UTC+ since then the result will contain trades from a few hours
instead of from the last 5 seconds, however fails for UTC- timezones,
since we're then asking for trades with a "since" argument in the future.
:param order_id order_id: Order-id as given when creating the order
:param pair: Pair the order is for
:param since: datetime object of the order creation time. Assumes object is in UTC.
"""
if self._config['dry_run']:
return []
if not self.exchange_has('fetchMyTrades'):
return []
try:
# Allow 5s offset to catch slight time offsets (discovered in #1185)
# since needs to be int in milliseconds
_params = params if params else {}
my_trades = self._api.fetch_my_trades(
pair, int((since.replace(tzinfo=timezone.utc).timestamp() - 5) * 1000),
params=_params)
matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
self._log_exchange_response('get_trades_for_order', matched_trades)
matched_trades = self._trades_contracts_to_amount(matched_trades)
return matched_trades
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get trades due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
return order['id']
@retrier
def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
price: float = 1, taker_or_maker: MakerTaker = 'maker') -> float:
"""
Retrieve fee from exchange
:param symbol: Pair
:param type: Type of order (market, limit, ...)
:param side: Side of order (buy, sell)
:param amount: Amount of order
:param price: Price of order
:param taker_or_maker: 'maker' or 'taker' (ignored if "type" is provided)
"""
if type and type == 'market':
taker_or_maker = 'taker'
try:
if self._config['dry_run'] and self._config.get('fee', None) is not None:
return self._config['fee']
# validate that markets are loaded before trying to get fee
if self._api.markets is None or len(self._api.markets) == 0:
self._api.load_markets(params={})
return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
price=price, takerOrMaker=taker_or_maker)['rate']
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not get fee info due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@staticmethod
def order_has_fee(order: Dict) -> bool:
"""
Verifies if the passed in order dict has the needed keys to extract fees,
and that these keys (currency, cost) are not empty.
:param order: Order or trade (one trade) dict
:return: True if the fee substructure contains currency and cost, false otherwise
"""
if not isinstance(order, dict):
return False
return ('fee' in order and order['fee'] is not None
and (order['fee'].keys() >= {'currency', 'cost'})
and order['fee']['currency'] is not None
and order['fee']['cost'] is not None
)
def calculate_fee_rate(
self, fee: Dict, symbol: str, cost: float, amount: float) -> Optional[float]:
"""
Calculate fee rate if it's not given by the exchange.
:param fee: ccxt Fee dict - must contain cost / currency / rate
:param symbol: Symbol of the order
:param cost: Total cost of the order
:param amount: Amount of the order
"""
if fee.get('rate') is not None:
return fee.get('rate')
fee_curr = fee.get('currency')
if fee_curr is None:
return None
fee_cost = float(fee['cost'])
# Calculate fee based on order details
if fee_curr == self.get_pair_base_currency(symbol):
# Base currency - divide by amount
return round(fee_cost / amount, 8)
elif fee_curr == self.get_pair_quote_currency(symbol):
# Quote currency - divide by cost
return round(fee_cost / cost, 8) if cost else None
else:
# If Fee currency is a different currency
if not cost:
# If cost is None or 0.0 -> falsy, return None
return None
try:
comb = self.get_valid_pair_combination(fee_curr, self._config['stake_currency'])
tick = self.fetch_ticker(comb)
fee_to_quote_rate = safe_value_fallback2(tick, tick, 'last', 'ask')
except (ValueError, ExchangeError):
fee_to_quote_rate = self._config['exchange'].get('unknown_fee_rate', None)
if not fee_to_quote_rate:
return None
return round((fee_cost * fee_to_quote_rate) / cost, 8)
def extract_cost_curr_rate(self, fee: Dict, symbol: str, cost: float,
amount: float) -> Tuple[float, str, Optional[float]]:
"""
Extract tuple of cost, currency, rate.
Requires order_has_fee to run first!
:param fee: ccxt Fee dict - must contain cost / currency / rate
:param symbol: Symbol of the order
:param cost: Total cost of the order
:param amount: Amount of the order
:return: Tuple with cost, currency, rate of the given fee dict
"""
return (float(fee['cost']),
fee['currency'],
self.calculate_fee_rate(
fee,
symbol,
cost,
amount
)
)
# Historic data
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False,
until_ms: Optional[int] = None) -> List:
"""
Get candle history using asyncio and returns the list of candles.
Handles all async work for this.
Async over one pair, assuming we get `self.ohlcv_candle_limit()` candles per call.
:param pair: Pair to download
:param timeframe: Timeframe to get data for
:param since_ms: Timestamp in milliseconds to get history from
:param until_ms: Timestamp in milliseconds to get history up to
:param candle_type: '', mark, index, premiumIndex, or funding_rate
:return: List with candle (OHLCV) data
"""
pair, _, _, data, _ = self.loop.run_until_complete(
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms, until_ms=until_ms,
is_new_pair=is_new_pair, candle_type=candle_type))
logger.info(f"Downloaded data for {pair} with length {len(data)}.")
return data
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: Optional[int] = None
) -> OHLCVResponse:
"""
Download historic ohlcv
:param is_new_pair: used by binance subclass to allow "fast" new pair downloading
:param candle_type: Any of the enum CandleType (must match trading mode!)
"""
one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(
timeframe, candle_type, since_ms)
logger.debug(
"one_call: %s msecs (%s)",
one_call,
dt_humanize(dt_now() - timedelta(milliseconds=one_call), only_distance=True)
)
input_coroutines = [self._async_get_candle_history(
pair, timeframe, candle_type, since) for since in
range(since_ms, until_ms or dt_ts(), one_call)]
data: List = []
# Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
for input_coro in chunks(input_coroutines, 100):
results = await asyncio.gather(*input_coro, return_exceptions=True)
for res in results:
if isinstance(res, Exception):
logger.warning(f"Async code raised an exception: {repr(res)}")
if raise_:
raise
continue
else:
# Deconstruct tuple if it's not an exception
p, _, c, new_data, _ = res
if p == pair and c == candle_type:
data.extend(new_data)
# Sort data again after extending the result - above calls return in "async order"
data = sorted(data, key=lambda x: x[0])
return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
def _build_coroutine(
self, pair: str, timeframe: str, candle_type: CandleType,
since_ms: Optional[int], cache: bool) -> Coroutine[Any, Any, OHLCVResponse]:
not_all_data = cache and self.required_candle_call_count > 1
if cache and (pair, timeframe, candle_type) in self._klines:
candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
min_date = date_minus_candles(timeframe, candle_limit - 5).timestamp()
# Check if 1 call can get us updated candles without hole in the data.
if min_date < self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0):
# Cache can be used - do one-off call.
not_all_data = False
else:
# Time jump detected, evict cache
logger.info(
f"Time jump detected. Evicting cache for {pair}, {timeframe}, {candle_type}")
del self._klines[(pair, timeframe, candle_type)]
if (not since_ms and (self._ft_has["ohlcv_require_since"] or not_all_data)):
# Multiple calls for one pair - to get more history
one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(
timeframe, candle_type, since_ms)
move_to = one_call * self.required_candle_call_count
now = timeframe_to_next_date(timeframe)
since_ms = int((now - timedelta(seconds=move_to // 1000)).timestamp() * 1000)
if since_ms:
return self._async_get_historic_ohlcv(
pair, timeframe, since_ms=since_ms, raise_=True, candle_type=candle_type)
else:
# One call ... "regular" refresh
return self._async_get_candle_history(
pair, timeframe, since_ms=since_ms, candle_type=candle_type)
def _build_ohlcv_dl_jobs(
self, pair_list: ListPairsWithTimeframes, since_ms: Optional[int],
cache: bool) -> Tuple[List[Coroutine], List[Tuple[str, str, CandleType]]]:
"""
Build Coroutines to execute as part of refresh_latest_ohlcv
"""
input_coroutines: List[Coroutine[Any, Any, OHLCVResponse]] = []
cached_pairs = []
for pair, timeframe, candle_type in set(pair_list):
if (timeframe not in self.timeframes
and candle_type in (CandleType.SPOT, CandleType.FUTURES)):
logger.warning(
f"Cannot download ({pair}, {timeframe}) combination as this timeframe is "
f"not available on {self.name}. Available timeframes are "
f"{', '.join(self.timeframes)}.")
continue
if ((pair, timeframe, candle_type) not in self._klines or not cache
or self._now_is_time_to_refresh(pair, timeframe, candle_type)):
input_coroutines.append(
self._build_coroutine(pair, timeframe, candle_type, since_ms, cache))
else:
logger.debug(
f"Using cached candle (OHLCV) data for {pair}, {timeframe}, {candle_type} ..."
)
cached_pairs.append((pair, timeframe, candle_type))
return input_coroutines, cached_pairs
def _process_ohlcv_df(self, pair: str, timeframe: str, c_type: CandleType, ticks: List[List],
cache: bool, drop_incomplete: bool) -> DataFrame:
# keeping last candle time as last refreshed time of the pair
if ticks and cache:
idx = -2 if drop_incomplete and len(ticks) > 1 else -1
self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[idx][0] // 1000
# keeping parsed dataframe in cache
ohlcv_df = ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=drop_incomplete)
if cache:
if (pair, timeframe, c_type) in self._klines:
old = self._klines[(pair, timeframe, c_type)]
# Reassign so we return the updated, combined df
ohlcv_df = clean_ohlcv_dataframe(concat([old, ohlcv_df], axis=0), timeframe, pair,
fill_missing=True, drop_incomplete=False)
candle_limit = self.ohlcv_candle_limit(timeframe, self._config['candle_type_def'])
# Age out old candles
ohlcv_df = ohlcv_df.tail(candle_limit + self._startup_candle_count)
ohlcv_df = ohlcv_df.reset_index(drop=True)
self._klines[(pair, timeframe, c_type)] = ohlcv_df
else:
self._klines[(pair, timeframe, c_type)] = ohlcv_df
return ohlcv_df
def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
since_ms: Optional[int] = None, cache: bool = True,
drop_incomplete: Optional[bool] = None
) -> Dict[PairWithTimeframe, DataFrame]:
"""
Refresh in-memory OHLCV asynchronously and set `_klines` with the result
Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
Only used in the dataprovider.refresh() method.
:param pair_list: List of 2 element tuples containing pair, interval to refresh
:param since_ms: time since when to download, in milliseconds
:param cache: Assign result to _klines. Usefull for one-off downloads like for pairlists
:param drop_incomplete: Control candle dropping.
Specifying None defaults to _ohlcv_partial_candle
:return: Dict of [{(pair, timeframe): Dataframe}]
"""
logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
# Gather coroutines to run
input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
results_df = {}
# Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
for input_coro in chunks(input_coroutines, 100):
async def gather_stuff():
return await asyncio.gather(*input_coro, return_exceptions=True)
with self._loop_lock:
results = self.loop.run_until_complete(gather_stuff())
for res in results:
if isinstance(res, Exception):
logger.warning(f"Async code raised an exception: {repr(res)}")
continue
# Deconstruct tuple (has 5 elements)
pair, timeframe, c_type, ticks, drop_hint = res
drop_incomplete_ = drop_hint if drop_incomplete is None else drop_incomplete
ohlcv_df = self._process_ohlcv_df(
pair, timeframe, c_type, ticks, cache, drop_incomplete_)
results_df[(pair, timeframe, c_type)] = ohlcv_df
# Return cached klines
for pair, timeframe, c_type in cached_pairs:
results_df[(pair, timeframe, c_type)] = self.klines(
(pair, timeframe, c_type),
copy=False
)
return results_df
def _now_is_time_to_refresh(self, pair: str, timeframe: str, candle_type: CandleType) -> bool:
# Timeframe in seconds | interval_in_sec = timeframe_to_seconds(timeframe) | 21 | 2023-10-21 10:02:05+00:00 | 24k |
yanzhh/HGERE | transformers/src/transformers/modeling_initdistilbert.py | [
{
"identifier": "gelu",
"path": "transformers/src/transformers/activations.py",
"snippet": "def swish(x):\ndef _gelu_python(x):\ndef gelu_new(x):\ndef get_activation(activation_string):\nACT2FN = {\n \"relu\": F.relu,\n \"swish\": swish,\n \"gelu\": gelu,\n \"tanh\": F.tanh,\n \"gelu_new\... | import copy
import logging
import math
import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from .activations import gelu
from .configuration_distilbert import DistilBertConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_utils import PreTrainedModel, prune_linear_layer
from .modeling_distilbert import DistilBertModel | 18,023 |
if (i in [1, mid]):
h1 = gelu(self.linear_1[w](hidden_states))
h2 = self.linear_2[w](h1).squeeze(-1)
if question_ends_mask is None:
prob = torch.sigmoid(h2)
# temp_loss = MSE(prob, rank_prob[:,w,:]) * seq_mask
temp_loss = torch.norm((prob - rank_prob[:,w,:]) * seq_mask, p=1)
loss += temp_loss / torch.sum(seq_mask)
else:
h2 = h2.view(-1, 4, num_items)
h2 = torch.mean(h2, dim=1)
prob = torch.sigmoid(h2)
temp_loss = torch.norm((prob - rank_prob[:,w,:]) * question_ends_mask, p=1)
loss += temp_loss / torch.sum(question_ends_mask)
layer_outputs = layer_module(
hidden_states, attn_mask, head_mask[i],
)
hidden_states = layer_outputs[0]
outputs = (hidden_states, loss)
return outputs
# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
class DistilBertPreTrainedModel(PreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = DistilBertConfig
pretrained_model_archive_map = DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
load_tf_weights = None
base_model_prefix = "distilbert"
def _init_weights(self, module):
""" Initialize the weights.
"""
if isinstance(module, nn.Embedding):
if module.weight.requires_grad:
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
DISTILBERT_START_DOCSTRING = r"""
This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
usage and behavior.
Parameters:
config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
DISTILBERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`transformers.DistilBertTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.encode_plus` for details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
`What are attention masks? <../glossary.html#attention-mask>`__
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
"""
@add_start_docstrings(
"The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",
DISTILBERT_START_DOCSTRING,
)
class InitDistilBertModel(DistilBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = Embeddings(config) # Embeddings
self.transformer = Transformer(config) # Encoder
self.init_weights()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, new_embeddings):
self.embeddings.word_embeddings = new_embeddings
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.transformer.layer[layer].attention.prune_heads(heads)
| # coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch DistilBERT model
adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
and in part from HuggingFace PyTorch version of Google AI Bert model (https://github.com/google-research/bert)
"""
logger = logging.getLogger(__name__)
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
"distilbert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-pytorch_model.bin",
"distilbert-base-uncased-distilled-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin",
"distilbert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-cased-pytorch_model.bin",
"distilbert-base-cased-distilled-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-cased-distilled-squad-pytorch_model.bin",
"distilbert-base-german-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-pytorch_model.bin",
"distilbert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-pytorch_model.bin",
"distilbert-base-uncased-finetuned-sst-2-english": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-finetuned-sst-2-english-pytorch_model.bin",
}
# UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE #
def create_sinusoidal_embeddings(n_pos, dim, out):
position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
out[:, 0::2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
out[:, 1::2] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
out.detach_()
out.requires_grad = False
class Embeddings(nn.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.dim, padding_idx=0)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.dim)
if config.sinusoidal_pos_embds:
create_sinusoidal_embeddings(
n_pos=config.max_position_embeddings, dim=config.dim, out=self.position_embeddings.weight
)
self.LayerNorm = nn.LayerNorm(config.dim, eps=1e-12)
self.dropout = nn.Dropout(config.dropout)
def forward(self, input_ids):
"""
Parameters
----------
input_ids: torch.tensor(bs, max_seq_length)
The token ids to embed.
Outputs
-------
embeddings: torch.tensor(bs, max_seq_length, dim)
The embedded tokens (plus position embeddings, no token_type embeddings)
"""
seq_length = input_ids.size(1)
position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device) # (max_seq_length)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids) # (bs, max_seq_length)
word_embeddings = self.word_embeddings(input_ids) # (bs, max_seq_length, dim)
position_embeddings = self.position_embeddings(position_ids) # (bs, max_seq_length, dim)
embeddings = word_embeddings + position_embeddings # (bs, max_seq_length, dim)
embeddings = self.LayerNorm(embeddings) # (bs, max_seq_length, dim)
embeddings = self.dropout(embeddings) # (bs, max_seq_length, dim)
return embeddings
class MultiHeadSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.n_heads = config.n_heads
self.dim = config.dim
self.dropout = nn.Dropout(p=config.attention_dropout)
self.output_attentions = config.output_attentions
assert self.dim % self.n_heads == 0
self.q_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
self.k_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
self.v_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
self.out_lin = nn.Linear(in_features=config.dim, out_features=config.dim)
self.pruned_heads = set()
def prune_heads(self, heads):
attention_head_size = self.dim // self.n_heads
if len(heads) == 0:
return
mask = torch.ones(self.n_heads, attention_head_size)
heads = set(heads) - self.pruned_heads
for head in heads:
head -= sum(1 if h < head else 0 for h in self.pruned_heads)
mask[head] = 0
mask = mask.view(-1).contiguous().eq(1)
index = torch.arange(len(mask))[mask].long()
# Prune linear layers
self.q_lin = prune_linear_layer(self.q_lin, index)
self.k_lin = prune_linear_layer(self.k_lin, index)
self.v_lin = prune_linear_layer(self.v_lin, index)
self.out_lin = prune_linear_layer(self.out_lin, index, dim=1)
# Update hyper params
self.n_heads = self.n_heads - len(heads)
self.dim = attention_head_size * self.n_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(self, query, key, value, mask, head_mask=None):
"""
Parameters
----------
query: torch.tensor(bs, seq_length, dim)
key: torch.tensor(bs, seq_length, dim)
value: torch.tensor(bs, seq_length, dim)
mask: torch.tensor(bs, seq_length)
Outputs
-------
weights: torch.tensor(bs, n_heads, seq_length, seq_length)
Attention weights
context: torch.tensor(bs, seq_length, dim)
Contextualized layer. Optional: only if `output_attentions=True`
"""
bs, q_length, dim = query.size()
k_length = key.size(1)
# assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
# assert key.size() == value.size()
dim_per_head = self.dim // self.n_heads
mask_reshp = (bs, 1, 1, k_length)
def shape(x):
""" separate heads """
return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)
def unshape(x):
""" group heads """
return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)
q = shape(self.q_lin(query)) # (bs, n_heads, q_length, dim_per_head)
k = shape(self.k_lin(key)) # (bs, n_heads, k_length, dim_per_head)
v = shape(self.v_lin(value)) # (bs, n_heads, k_length, dim_per_head)
q = q / math.sqrt(dim_per_head) # (bs, n_heads, q_length, dim_per_head)
scores = torch.matmul(q, k.transpose(2, 3)) # (bs, n_heads, q_length, k_length)
mask = (mask == 0).view(mask_reshp).expand_as(scores) # (bs, n_heads, q_length, k_length)
scores.masked_fill_(mask, -float("inf")) # (bs, n_heads, q_length, k_length)
weights = nn.Softmax(dim=-1)(scores) # (bs, n_heads, q_length, k_length)
weights = self.dropout(weights) # (bs, n_heads, q_length, k_length)
# Mask heads if we want to
if head_mask is not None:
weights = weights * head_mask
context = torch.matmul(weights, v) # (bs, n_heads, q_length, dim_per_head)
context = unshape(context) # (bs, q_length, dim)
context = self.out_lin(context) # (bs, q_length, dim)
if self.output_attentions:
return (context, weights)
else:
return (context,)
class FFN(nn.Module):
def __init__(self, config):
super().__init__()
self.dropout = nn.Dropout(p=config.dropout)
self.lin1 = nn.Linear(in_features=config.dim, out_features=config.hidden_dim)
self.lin2 = nn.Linear(in_features=config.hidden_dim, out_features=config.dim)
assert config.activation in ["relu", "gelu"], "activation ({}) must be in ['relu', 'gelu']".format(
config.activation
)
self.activation = gelu if config.activation == "gelu" else nn.ReLU()
def forward(self, input):
x = self.lin1(input)
x = self.activation(x)
x = self.lin2(x)
x = self.dropout(x)
return x
class TransformerBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.output_attentions = config.output_attentions
assert config.dim % config.n_heads == 0
self.attention = MultiHeadSelfAttention(config)
self.sa_layer_norm = nn.LayerNorm(normalized_shape=config.dim, eps=1e-12)
self.ffn = FFN(config)
self.output_layer_norm = nn.LayerNorm(normalized_shape=config.dim, eps=1e-12)
def forward(self, x, attn_mask=None, head_mask=None):
"""
Parameters
----------
x: torch.tensor(bs, seq_length, dim)
attn_mask: torch.tensor(bs, seq_length)
Outputs
-------
sa_weights: torch.tensor(bs, n_heads, seq_length, seq_length)
The attention weights
ffn_output: torch.tensor(bs, seq_length, dim)
The output of the transformer block contextualization.
"""
# Self-Attention
sa_output = self.attention(query=x, key=x, value=x, mask=attn_mask, head_mask=head_mask)
if self.output_attentions:
sa_output, sa_weights = sa_output # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length)
else: # To handle these `output_attention` or `output_hidden_states` cases returning tuples
assert type(sa_output) == tuple
sa_output = sa_output[0]
sa_output = self.sa_layer_norm(sa_output + x) # (bs, seq_length, dim)
# Feed Forward Network
ffn_output = self.ffn(sa_output) # (bs, seq_length, dim)
ffn_output = self.output_layer_norm(ffn_output + sa_output) # (bs, seq_length, dim)
output = (ffn_output,)
if self.output_attentions:
output = (sa_weights,) + output
return output
class Transformer(nn.Module):
def __init__(self, config):
super().__init__()
self.n_layers = config.n_layers
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
layer = TransformerBlock(config)
self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.n_layers)])
self.linear_size = 32
self.linear_1 = nn.ModuleList([nn.Linear(config.hidden_size, self.linear_size),
nn.Linear(config.hidden_size, self.linear_size),
# nn.Linear(config.hidden_size, self.linear_size),
# nn.Linear(config.hidden_size, self.linear_size),
# nn.Linear(config.hidden_size, self.linear_size),
# nn.Linear(config.hidden_size, self.linear_size),
# nn.Linear(config.hidden_size, self.linear_size),
# nn.Linear(config.hidden_size, self.linear_size)
])
self.linear_2 = nn.ModuleList([nn.Linear(self.linear_size, 1),
nn.Linear(self.linear_size, 1),
# nn.Linear(self.linear_size, 1),
# nn.Linear(self.linear_size, 1),
# nn.Linear(self.linear_size, 1),
# nn.Linear(self.linear_size, 1),
# nn.Linear(self.linear_size, 1),
# nn.Linear(self.linear_size, 1)
])
def forward(self, x, attn_mask=None, head_mask=None, rank_prob=None, question_ends_mask=None):
all_hidden_states = ()
all_attentions = ()
hidden_states = x
bsz, ori_num_items, dim = hidden_states.size()
seq_mask = attn_mask.to(dtype=next(self.parameters()).dtype)
num_items = hidden_states.size(1)
tot_zoom = None
tot_select_loss = 0
Ls = []
w = 0
mid = len(self.layer)//2
loss = 0
for i, layer_module in enumerate(self.layer):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if (i in [1, mid]):
h1 = gelu(self.linear_1[w](hidden_states))
h2 = self.linear_2[w](h1).squeeze(-1)
if question_ends_mask is None:
prob = torch.sigmoid(h2)
# temp_loss = MSE(prob, rank_prob[:,w,:]) * seq_mask
temp_loss = torch.norm((prob - rank_prob[:,w,:]) * seq_mask, p=1)
loss += temp_loss / torch.sum(seq_mask)
else:
h2 = h2.view(-1, 4, num_items)
h2 = torch.mean(h2, dim=1)
prob = torch.sigmoid(h2)
temp_loss = torch.norm((prob - rank_prob[:,w,:]) * question_ends_mask, p=1)
loss += temp_loss / torch.sum(question_ends_mask)
layer_outputs = layer_module(
hidden_states, attn_mask, head_mask[i],
)
hidden_states = layer_outputs[0]
outputs = (hidden_states, loss)
return outputs
# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
class DistilBertPreTrainedModel(PreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = DistilBertConfig
pretrained_model_archive_map = DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
load_tf_weights = None
base_model_prefix = "distilbert"
def _init_weights(self, module):
""" Initialize the weights.
"""
if isinstance(module, nn.Embedding):
if module.weight.requires_grad:
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
DISTILBERT_START_DOCSTRING = r"""
This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
usage and behavior.
Parameters:
config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
DISTILBERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`transformers.DistilBertTokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.encode_plus` for details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
`What are attention masks? <../glossary.html#attention-mask>`__
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
:obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
"""
@add_start_docstrings(
"The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",
DISTILBERT_START_DOCSTRING,
)
class InitDistilBertModel(DistilBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = Embeddings(config) # Embeddings
self.transformer = Transformer(config) # Encoder
self.init_weights()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, new_embeddings):
self.embeddings.word_embeddings = new_embeddings
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.transformer.layer[layer].attention.prune_heads(heads)
| @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING) | 3 | 2023-10-15 02:31:09+00:00 | 24k |
akashgreninja/GreSec | backend/venv/lib/python3.10/site-packages/pydantic/type_adapter.py | [
{
"identifier": "_config",
"path": "backend/venv/lib/python3.10/site-packages/pydantic/_internal/_config.py",
"snippet": "DEPRECATION_MESSAGE = 'Support for class-based `config` is deprecated, use ConfigDict instead.'\nV2_REMOVED_KEYS = {\n 'allow_mutation',\n 'error_msg_templates',\n 'fields',... | import sys
from dataclasses import is_dataclass
from typing import TYPE_CHECKING, Any, Dict, Generic, Iterable, Set, TypeVar, Union, overload
from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator, Some
from typing_extensions import Literal, is_typeddict
from pydantic.errors import PydanticUserError
from pydantic.main import BaseModel
from ._internal import _config, _core_utils, _discriminated_union, _generate_schema, _typing_extra
from .config import ConfigDict
from .json_schema import (
DEFAULT_REF_TEMPLATE,
GenerateJsonSchema,
JsonSchemaKeyT,
JsonSchemaMode,
JsonSchemaValue,
)
from .plugin._schema_validator import create_schema_validator | 15,770 |
def validate_strings(self, __obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) -> T:
"""Validate object contains string data against the model.
Args:
__obj: The object contains string data to validate.
strict: Whether to strictly check types.
context: Additional context to use during validation.
Returns:
The validated object.
"""
return self.validator.validate_strings(__obj, strict=strict, context=context)
def get_default_value(self, *, strict: bool | None = None, context: dict[str, Any] | None = None) -> Some[T] | None:
"""Get the default value for the wrapped type.
Args:
strict: Whether to strictly check types.
context: Additional context to pass to the validator.
Returns:
The default value wrapped in a `Some` if there is one or None if not.
"""
return self.validator.get_default_value(strict=strict, context=context)
def dump_python(
self,
__instance: T,
*,
mode: Literal['json', 'python'] = 'python',
include: IncEx | None = None,
exclude: IncEx | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool = True,
) -> Any:
"""Dump an instance of the adapted type to a Python object.
Args:
__instance: The Python object to serialize.
mode: The output format.
include: Fields to include in the output.
exclude: Fields to exclude from the output.
by_alias: Whether to use alias names for field names.
exclude_unset: Whether to exclude unset fields.
exclude_defaults: Whether to exclude fields with default values.
exclude_none: Whether to exclude fields with None values.
round_trip: Whether to output the serialized data in a way that is compatible with deserialization.
warnings: Whether to display serialization warnings.
Returns:
The serialized object.
"""
return self.serializer.to_python(
__instance,
mode=mode,
by_alias=by_alias,
include=include,
exclude=exclude,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
round_trip=round_trip,
warnings=warnings,
)
def dump_json(
self,
__instance: T,
*,
indent: int | None = None,
include: IncEx | None = None,
exclude: IncEx | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool = True,
) -> bytes:
"""Serialize an instance of the adapted type to JSON.
Args:
__instance: The instance to be serialized.
indent: Number of spaces for JSON indentation.
include: Fields to include.
exclude: Fields to exclude.
by_alias: Whether to use alias names for field names.
exclude_unset: Whether to exclude unset fields.
exclude_defaults: Whether to exclude fields with default values.
exclude_none: Whether to exclude fields with a value of `None`.
round_trip: Whether to serialize and deserialize the instance to ensure round-tripping.
warnings: Whether to emit serialization warnings.
Returns:
The JSON representation of the given instance as bytes.
"""
return self.serializer.to_json(
__instance,
indent=indent,
include=include,
exclude=exclude,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
round_trip=round_trip,
warnings=warnings,
)
def json_schema(
self,
*,
by_alias: bool = True,
ref_template: str = DEFAULT_REF_TEMPLATE,
schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
| """
You may have types that are not `BaseModel`s that you want to validate data against.
Or you may want to validate a `List[SomeModel]`, or dump it to JSON.
For use cases like this, Pydantic provides [`TypeAdapter`][pydantic.type_adapter.TypeAdapter],
which can be used for type validation, serialization, and JSON schema generation without creating a
[`BaseModel`][pydantic.main.BaseModel].
A [`TypeAdapter`][pydantic.type_adapter.TypeAdapter] instance exposes some of the functionality from
[`BaseModel`][pydantic.main.BaseModel] instance methods for types that do not have such methods
(such as dataclasses, primitive types, and more):
```py
from typing import List
from typing_extensions import TypedDict
from pydantic import TypeAdapter, ValidationError
class User(TypedDict):
name: str
id: int
UserListValidator = TypeAdapter(List[User])
print(repr(UserListValidator.validate_python([{'name': 'Fred', 'id': '3'}])))
#> [{'name': 'Fred', 'id': 3}]
try:
UserListValidator.validate_python(
[{'name': 'Fred', 'id': 'wrong', 'other': 'no'}]
)
except ValidationError as e:
print(e)
'''
1 validation error for list[typed-dict]
0.id
Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='wrong', input_type=str]
'''
```
Note:
Despite some overlap in use cases with [`RootModel`][pydantic.root_model.RootModel],
[`TypeAdapter`][pydantic.type_adapter.TypeAdapter] should not be used as a type annotation for
specifying fields of a `BaseModel`, etc.
## Parsing data into a specified type
[`TypeAdapter`][pydantic.type_adapter.TypeAdapter] can be used to apply the parsing logic to populate Pydantic models
in a more ad-hoc way. This function behaves similarly to
[`BaseModel.model_validate`][pydantic.main.BaseModel.model_validate],
but works with arbitrary Pydantic-compatible types.
This is especially useful when you want to parse results into a type that is not a direct subclass of
[`BaseModel`][pydantic.main.BaseModel]. For example:
```py
from typing import List
from pydantic import BaseModel, TypeAdapter
class Item(BaseModel):
id: int
name: str
# `item_data` could come from an API call, eg., via something like:
# item_data = requests.get('https://my-api.com/items').json()
item_data = [{'id': 1, 'name': 'My Item'}]
items = TypeAdapter(List[Item]).validate_python(item_data)
print(items)
#> [Item(id=1, name='My Item')]
```
[`TypeAdapter`][pydantic.type_adapter.TypeAdapter] is capable of parsing data into any of the types Pydantic can
handle as fields of a [`BaseModel`][pydantic.main.BaseModel].
""" # noqa: D212
from __future__ import annotations as _annotations
T = TypeVar('T')
if TYPE_CHECKING:
# should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
IncEx = Union[Set[int], Set[str], Dict[int, Any], Dict[str, Any]]
def _get_schema(type_: Any, config_wrapper: _config.ConfigWrapper, parent_depth: int) -> CoreSchema:
"""`BaseModel` uses its own `__module__` to find out where it was defined
and then look for symbols to resolve forward references in those globals.
On the other hand this function can be called with arbitrary objects,
including type aliases where `__module__` (always `typing.py`) is not useful.
So instead we look at the globals in our parent stack frame.
This works for the case where this function is called in a module that
has the target of forward references in its scope, but
does not work for more complex cases.
For example, take the following:
a.py
```python
from typing import Dict, List
IntList = List[int]
OuterDict = Dict[str, 'IntList']
```
b.py
```python test="skip"
from a import OuterDict
from pydantic import TypeAdapter
IntList = int # replaces the symbol the forward reference is looking for
v = TypeAdapter(OuterDict)
v({'x': 1}) # should fail but doesn't
```
If OuterDict were a `BaseModel`, this would work because it would resolve
the forward reference within the `a.py` namespace.
But `TypeAdapter(OuterDict)`
can't know what module OuterDict came from.
In other words, the assumption that _all_ forward references exist in the
module we are being called from is not technically always true.
Although most of the time it is and it works fine for recursive models and such,
`BaseModel`'s behavior isn't perfect either and _can_ break in similar ways,
so there is no right or wrong between the two.
But at the very least this behavior is _subtly_ different from `BaseModel`'s.
"""
local_ns = _typing_extra.parent_frame_namespace(parent_depth=parent_depth)
global_ns = sys._getframe(max(parent_depth - 1, 1)).f_globals.copy()
global_ns.update(local_ns or {})
gen = _generate_schema.GenerateSchema(config_wrapper, types_namespace=global_ns, typevars_map={})
schema = gen.generate_schema(type_)
schema = gen.collect_definitions(schema)
return schema
def _getattr_no_parents(obj: Any, attribute: str) -> Any:
"""Returns the attribute value without attempting to look up attributes from parent types."""
if hasattr(obj, '__dict__'):
try:
return obj.__dict__[attribute]
except KeyError:
pass
slots = getattr(obj, '__slots__', None)
if slots is not None and attribute in slots:
return getattr(obj, attribute)
else:
raise AttributeError(attribute)
class TypeAdapter(Generic[T]):
"""Type adapters provide a flexible way to perform validation and serialization based on a Python type.
A `TypeAdapter` instance exposes some of the functionality from `BaseModel` instance methods
for types that do not have such methods (such as dataclasses, primitive types, and more).
Note that `TypeAdapter` is not an actual type, so you cannot use it in type annotations.
Attributes:
core_schema: The core schema for the type.
validator (SchemaValidator): The schema validator for the type.
serializer: The schema serializer for the type.
"""
if TYPE_CHECKING:
@overload
def __new__(cls, __type: type[T], *, config: ConfigDict | None = ...) -> TypeAdapter[T]:
...
# this overload is for non-type things like Union[int, str]
# Pyright currently handles this "correctly", but MyPy understands this as TypeAdapter[object]
# so an explicit type cast is needed
@overload
def __new__(cls, __type: T, *, config: ConfigDict | None = ...) -> TypeAdapter[T]:
...
def __new__(cls, __type: Any, *, config: ConfigDict | None = ...) -> TypeAdapter[T]:
"""A class representing the type adapter."""
raise NotImplementedError
@overload
def __init__(self, type: type[T], *, config: ConfigDict | None = None, _parent_depth: int = 2) -> None:
...
# this overload is for non-type things like Union[int, str]
# Pyright currently handles this "correctly", but MyPy understands this as TypeAdapter[object]
# so an explicit type cast is needed
@overload
def __init__(self, type: T, *, config: ConfigDict | None = None, _parent_depth: int = 2) -> None:
...
def __init__(self, type: Any, *, config: ConfigDict | None = None, _parent_depth: int = 2) -> None:
"""Initializes the TypeAdapter object."""
config_wrapper = _config.ConfigWrapper(config)
try:
type_has_config = issubclass(type, BaseModel) or is_dataclass(type) or is_typeddict(type)
except TypeError:
# type is not a class
type_has_config = False
if type_has_config and config is not None:
raise PydanticUserError(
'Cannot use `config` when the type is a BaseModel, dataclass or TypedDict.'
' These types can have their own config and setting the config via the `config`'
' parameter to TypeAdapter will not override it, thus the `config` you passed to'
' TypeAdapter becomes meaningless, which is probably not what you want.',
code='type-adapter-config-unused',
)
core_schema: CoreSchema
try:
core_schema = _getattr_no_parents(type, '__pydantic_core_schema__')
except AttributeError:
core_schema = _get_schema(type, config_wrapper, parent_depth=_parent_depth + 1)
core_schema = _discriminated_union.apply_discriminators(_core_utils.simplify_schema_references(core_schema))
core_schema = _core_utils.validate_core_schema(core_schema)
core_config = config_wrapper.core_config(None)
validator: SchemaValidator
try:
validator = _getattr_no_parents(type, '__pydantic_validator__')
except AttributeError:
validator = create_schema_validator(core_schema, core_config, config_wrapper.plugin_settings)
serializer: SchemaSerializer
try:
serializer = _getattr_no_parents(type, '__pydantic_serializer__')
except AttributeError:
serializer = SchemaSerializer(core_schema, core_config)
self.core_schema = core_schema
self.validator = validator
self.serializer = serializer
def validate_python(
self,
__object: Any,
*,
strict: bool | None = None,
from_attributes: bool | None = None,
context: dict[str, Any] | None = None,
) -> T:
"""Validate a Python object against the model.
Args:
__object: The Python object to validate against the model.
strict: Whether to strictly check types.
from_attributes: Whether to extract data from object attributes.
context: Additional context to pass to the validator.
Returns:
The validated object.
"""
return self.validator.validate_python(__object, strict=strict, from_attributes=from_attributes, context=context)
def validate_json(
self, __data: str | bytes, *, strict: bool | None = None, context: dict[str, Any] | None = None
) -> T:
"""Validate a JSON string or bytes against the model.
Args:
__data: The JSON data to validate against the model.
strict: Whether to strictly check types.
context: Additional context to use during validation.
Returns:
The validated object.
"""
return self.validator.validate_json(__data, strict=strict, context=context)
def validate_strings(self, __obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) -> T:
"""Validate object contains string data against the model.
Args:
__obj: The object contains string data to validate.
strict: Whether to strictly check types.
context: Additional context to use during validation.
Returns:
The validated object.
"""
return self.validator.validate_strings(__obj, strict=strict, context=context)
def get_default_value(self, *, strict: bool | None = None, context: dict[str, Any] | None = None) -> Some[T] | None:
"""Get the default value for the wrapped type.
Args:
strict: Whether to strictly check types.
context: Additional context to pass to the validator.
Returns:
The default value wrapped in a `Some` if there is one or None if not.
"""
return self.validator.get_default_value(strict=strict, context=context)
def dump_python(
self,
__instance: T,
*,
mode: Literal['json', 'python'] = 'python',
include: IncEx | None = None,
exclude: IncEx | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool = True,
) -> Any:
"""Dump an instance of the adapted type to a Python object.
Args:
__instance: The Python object to serialize.
mode: The output format.
include: Fields to include in the output.
exclude: Fields to exclude from the output.
by_alias: Whether to use alias names for field names.
exclude_unset: Whether to exclude unset fields.
exclude_defaults: Whether to exclude fields with default values.
exclude_none: Whether to exclude fields with None values.
round_trip: Whether to output the serialized data in a way that is compatible with deserialization.
warnings: Whether to display serialization warnings.
Returns:
The serialized object.
"""
return self.serializer.to_python(
__instance,
mode=mode,
by_alias=by_alias,
include=include,
exclude=exclude,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
round_trip=round_trip,
warnings=warnings,
)
def dump_json(
self,
__instance: T,
*,
indent: int | None = None,
include: IncEx | None = None,
exclude: IncEx | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool = True,
) -> bytes:
"""Serialize an instance of the adapted type to JSON.
Args:
__instance: The instance to be serialized.
indent: Number of spaces for JSON indentation.
include: Fields to include.
exclude: Fields to exclude.
by_alias: Whether to use alias names for field names.
exclude_unset: Whether to exclude unset fields.
exclude_defaults: Whether to exclude fields with default values.
exclude_none: Whether to exclude fields with a value of `None`.
round_trip: Whether to serialize and deserialize the instance to ensure round-tripping.
warnings: Whether to emit serialization warnings.
Returns:
The JSON representation of the given instance as bytes.
"""
return self.serializer.to_json(
__instance,
indent=indent,
include=include,
exclude=exclude,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
round_trip=round_trip,
warnings=warnings,
)
def json_schema(
self,
*,
by_alias: bool = True,
ref_template: str = DEFAULT_REF_TEMPLATE,
schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, | mode: JsonSchemaMode = 'validation', | 6 | 2023-10-23 18:09:28+00:00 | 24k |
zju3dv/nr_in_a_room | data_gen/batch_real_scene_neural_render.py | [
{
"identifier": "read_json",
"path": "utils/util.py",
"snippet": "def read_json(fname):\n fname = Path(fname)\n with fname.open(\"rt\") as handle:\n return json.load(handle, object_hook=OrderedDict)"
},
{
"identifier": "read_yaml",
"path": "utils/util.py",
"snippet": "def re... | import sys
import os
import torch
import numpy as np
import imageio
import time
import cv2
from tqdm import tqdm
from argparse import ArgumentParser
from utils.util import read_json, read_yaml
from optim.room_optimizer import RoomOptimizer
from optim.misc_utils import read_real_scene_localization, read_testing_config
from scipy.spatial.transform import Rotation | 14,984 |
os.environ["OMP_NUM_THREADS"] = "1" # noqa
os.environ["MKL_NUM_THREADS"] = "1" # noqa
sys.path.append(".") # noqa
def render_frame(config, target_dir):
# or load from config
active_instance_id = config.active_instance_id
dataset_config = config.dataset_config["dataset"]
scene_info_json_path = config.scene_info_json
active_instance_id = [0]
for obj_info in read_json(scene_info_json_path)["objs"]:
active_instance_id += [obj_info["id"]]
bg_scale_factor = 1
bg_scene_center = [0, 0, 0]
if config.bg_dataset_config != "":
bg_dataset_config = config.bg_dataset_config["dataset"]
bg_scale_factor = bg_dataset_config["scale_factor"]
bg_scene_center = bg_dataset_config["scene_center"]
# intialize room optimizer
room_optimizer = RoomOptimizer(
scene_info_json_path=scene_info_json_path,
scale_factor=dataset_config["scale_factor"],
scale_factor_dict=dataset_config.get("scale_factor_dict", {}),
bg_scale_factor=bg_scale_factor,
bg_scene_center=bg_scene_center,
img_wh=config.img_wh,
near=0.3,
far=10.0,
chunk=config.chunk,
model_ckpt_path_dict=config.ckpt_path_dict,
active_instance_id=active_instance_id,
use_amp=True,
use_light_from_image_attr=True, # we use fixed light code (e.g. probe_03)
optimize_appearance_code=config.get("optimize_appearance_code", False),
use_appearance_from_image_attr=True,
)
# initialize object poses with no noise
room_optimizer.set_initial_object_poses_from_scene_meta(add_noise=False)
# we show an example to use pose
scene_meta = read_json(scene_info_json_path)
# localization_info = read_real_scene_localization(
# "/mnt/nas_54/group/BBYang/neural_scene_capture_360/capture_1104/processed/arrangement_panorama_select/arrangement1/traj.txt",
# "data/real_room_0/objects/000/background_hloc_neus_normal_converge/transform_info.json",
# )
pose = np.array(scene_meta["camera"]["cam3d2world"]).reshape(4, 4)
# Original poses has rotation in form "right down forward", change to NDC "right up back"
fix_rot = np.array([1, 0, 0, 0, -1, 0, 0, 0, -1]).reshape(3, 3)
pose[:3, :3] = pose[:3, :3] @ fix_rot
# from scipy.spatial.transform import Rotation as R
# print(pose)
# rot_fix_loc = np.array([0, 1, 0, 1, 0, 0, 0, 0, -1]).reshape(3, 3)
# pose[:3, :3] = pose[:3, :3] @ rot_fix_loc
# pose[:3, :3] = rot_fix_loc @ pose[:3, :3]
t1 = time.time()
# image_np = room_optimizer.render_full_scene(
# pose=pose,
# idx=-1,
# return_raw_image=True,
# refine_edge=True,
# # use_sphere_tracing=False,
# use_sphere_tracing=True,
# )
image_np, mask_np = room_optimizer.render_full_scene(
pose=pose,
idx=-1,
return_raw_image=True,
refine_edge=False,
# refine_edge=True,
# use_sphere_tracing=False,
use_sphere_tracing=True,
render_mask=True,
)
t2 = time.time()
print(f"Rendering finish in {t2-t1} s.")
os.makedirs("debug", exist_ok=True)
imageio.imwrite(f"{target_dir}/rgb.png", image_np)
cv2.imwrite(f"{target_dir}/seg.png", mask_np)
if __name__ == "__main__":
"""
Example:
python test/test_neural_scene_renderer.py \
config=test/config/ig_bedroom.yml \
"img_wh=[1024,512]" \
base_dir=data/real_room_rand_arrangement
"""
|
os.environ["OMP_NUM_THREADS"] = "1" # noqa
os.environ["MKL_NUM_THREADS"] = "1" # noqa
sys.path.append(".") # noqa
def render_frame(config, target_dir):
# or load from config
active_instance_id = config.active_instance_id
dataset_config = config.dataset_config["dataset"]
scene_info_json_path = config.scene_info_json
active_instance_id = [0]
for obj_info in read_json(scene_info_json_path)["objs"]:
active_instance_id += [obj_info["id"]]
bg_scale_factor = 1
bg_scene_center = [0, 0, 0]
if config.bg_dataset_config != "":
bg_dataset_config = config.bg_dataset_config["dataset"]
bg_scale_factor = bg_dataset_config["scale_factor"]
bg_scene_center = bg_dataset_config["scene_center"]
# intialize room optimizer
room_optimizer = RoomOptimizer(
scene_info_json_path=scene_info_json_path,
scale_factor=dataset_config["scale_factor"],
scale_factor_dict=dataset_config.get("scale_factor_dict", {}),
bg_scale_factor=bg_scale_factor,
bg_scene_center=bg_scene_center,
img_wh=config.img_wh,
near=0.3,
far=10.0,
chunk=config.chunk,
model_ckpt_path_dict=config.ckpt_path_dict,
active_instance_id=active_instance_id,
use_amp=True,
use_light_from_image_attr=True, # we use fixed light code (e.g. probe_03)
optimize_appearance_code=config.get("optimize_appearance_code", False),
use_appearance_from_image_attr=True,
)
# initialize object poses with no noise
room_optimizer.set_initial_object_poses_from_scene_meta(add_noise=False)
# we show an example to use pose
scene_meta = read_json(scene_info_json_path)
# localization_info = read_real_scene_localization(
# "/mnt/nas_54/group/BBYang/neural_scene_capture_360/capture_1104/processed/arrangement_panorama_select/arrangement1/traj.txt",
# "data/real_room_0/objects/000/background_hloc_neus_normal_converge/transform_info.json",
# )
pose = np.array(scene_meta["camera"]["cam3d2world"]).reshape(4, 4)
# Original poses has rotation in form "right down forward", change to NDC "right up back"
fix_rot = np.array([1, 0, 0, 0, -1, 0, 0, 0, -1]).reshape(3, 3)
pose[:3, :3] = pose[:3, :3] @ fix_rot
# from scipy.spatial.transform import Rotation as R
# print(pose)
# rot_fix_loc = np.array([0, 1, 0, 1, 0, 0, 0, 0, -1]).reshape(3, 3)
# pose[:3, :3] = pose[:3, :3] @ rot_fix_loc
# pose[:3, :3] = rot_fix_loc @ pose[:3, :3]
t1 = time.time()
# image_np = room_optimizer.render_full_scene(
# pose=pose,
# idx=-1,
# return_raw_image=True,
# refine_edge=True,
# # use_sphere_tracing=False,
# use_sphere_tracing=True,
# )
image_np, mask_np = room_optimizer.render_full_scene(
pose=pose,
idx=-1,
return_raw_image=True,
refine_edge=False,
# refine_edge=True,
# use_sphere_tracing=False,
use_sphere_tracing=True,
render_mask=True,
)
t2 = time.time()
print(f"Rendering finish in {t2-t1} s.")
os.makedirs("debug", exist_ok=True)
imageio.imwrite(f"{target_dir}/rgb.png", image_np)
cv2.imwrite(f"{target_dir}/seg.png", mask_np)
if __name__ == "__main__":
"""
Example:
python test/test_neural_scene_renderer.py \
config=test/config/ig_bedroom.yml \
"img_wh=[1024,512]" \
base_dir=data/real_room_rand_arrangement
""" | config = read_testing_config() | 4 | 2023-10-15 08:41:29+00:00 | 24k |
WenzhengZhang/Seq2seqCoref | main_trainer.py | [
{
"identifier": "DataArguments",
"path": "arguments.py",
"snippet": "class DataArguments:\n data_dir: Optional[str] = field(\n default=None, metadata={\"help\": \"Path to data directory\"}\n )\n\n max_train_len: Optional[int] = field(\n default=1536,\n metadata={\n ... | import logging
import os
import sys
from transformers import HfArgumentParser, set_seed
from transformers import AutoModelForSeq2SeqLM, \
DataCollatorForSeq2Seq, AutoConfig, AutoTokenizer
from transformers.integrations import TensorBoardCallback
from arguments import DataArguments, ModelArguments, CorefTrainingArguments \
as TrainingArguments
from data import CorefDataset, JointDataset
from constants import SPEAKER_START, SPEAKER_END, MENTION_START, MENTION_END, \
COPY, CLUSTER_NEW, CLUSTERS, SENTENCE_START, SENTENCE_END, SPECIAL_IDS, \
NON_INT_SPECIAL_IDS, MARK_SPECIAL_IDS, MENTION_END_NON_INT_SPECIAL_IDS, \
MENTION_ENDS
from trainer import CorefTrainer
from data import ConstrainedDataCollator
from model import ConstrainedT5 | 20,328 |
logger = logging.getLogger(__name__)
logger.addHandler(logging.StreamHandler(sys.stdout))
def main():
parser = HfArgumentParser(
(ModelArguments, DataArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
model_args: ModelArguments
data_args: DataArguments
training_args: TrainingArguments
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if training_args.local_rank in [-1,
0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, fp16 training: %s, bf16 training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
training_args.bf16,
)
logger.info("Training/evaluation parameters %s", training_args)
logger.info("MODEL parameters %s", model_args)
logger.info("Data arguments %s", data_args)
set_seed(training_args.seed)
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
if training_args.action_type == "integer":
num_new_tokens = tokenizer.add_tokens([SPEAKER_START, SPEAKER_END,
MENTION_START, MENTION_END,
COPY])
elif training_args.action_type == "non_integer":
if training_args.add_mention_end:
num_new_tokens = tokenizer.add_tokens([SPEAKER_START, SPEAKER_END,
MENTION_START, MENTION_END,
COPY,
CLUSTER_NEW] +
|
logger = logging.getLogger(__name__)
logger.addHandler(logging.StreamHandler(sys.stdout))
def main():
parser = HfArgumentParser(
(ModelArguments, DataArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
model_args: ModelArguments
data_args: DataArguments
training_args: TrainingArguments
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if training_args.local_rank in [-1,
0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, fp16 training: %s, bf16 training: %s",
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
training_args.fp16,
training_args.bf16,
)
logger.info("Training/evaluation parameters %s", training_args)
logger.info("MODEL parameters %s", model_args)
logger.info("Data arguments %s", data_args)
set_seed(training_args.seed)
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
if training_args.action_type == "integer":
num_new_tokens = tokenizer.add_tokens([SPEAKER_START, SPEAKER_END,
MENTION_START, MENTION_END,
COPY])
elif training_args.action_type == "non_integer":
if training_args.add_mention_end:
num_new_tokens = tokenizer.add_tokens([SPEAKER_START, SPEAKER_END,
MENTION_START, MENTION_END,
COPY,
CLUSTER_NEW] + | CLUSTERS) | 11 | 2023-10-17 17:39:16+00:00 | 24k |
giulio98/functional-diffusion-processes | src/functional_diffusion_processes/trainers/trainer.py | [
{
"identifier": "AudioDataset",
"path": "src/functional_diffusion_processes/datasets/audio_dataset.py",
"snippet": "class AudioDataset(BaseDataset, abc.ABC):\n \"\"\"Base class for defining audio datasets.\n\n This class serves as the foundation for defining datasets containing audio data.\n It... | import abc
import gc
import io
import logging
import os
import flax
import flax.jax_utils as flax_utils
import hydra.utils
import jax
import numpy as np
import tensorflow as tf
import wandb
from typing import Any, Callable, Tuple, Union
from cleanfid import fid
from flax import linen, traverse_util
from flax.training import checkpoints
from flax.training.checkpoints import restore_checkpoint
from jax import numpy as jnp
from omegaconf import DictConfig, OmegaConf
from tqdm.auto import tqdm
from wandb.sdk.lib import RunDisabled
from wandb.sdk.wandb_run import Run
from ..datasets import AudioDataset, ImageDataset
from ..datasets.base_dataset import BaseDataset
from ..losses.base_loss import Loss
from ..metrics import FIDMetric
from ..samplers import Sampler
from ..sdetools.base_sde import SDE
from ..utils.common import filter_mask, make_grid_image, process_images, save_samples, to_grayscale
from ..utils.scaler import get_data_inverse_scaler, get_data_scaler
from ..utils.training_state import TrainState
from .helpers import colorizing_fn, construct_sampling_fn, construct_train_step, inpainting_fn, sampling_fn | 14,855 | ):
self.save_checkpoint(step, run, state)
# Evaluate the model
if self.training_config.sampling and (step % self.training_config.eval_freq == 0):
# if step != 0:
if jax.host_id() == 0:
pylogger.info("Generating samples at step %d." % (step,))
_, *sample_rng = jax.random.split(rng, jax.local_device_count() + 1)
_, b, g, c = batch.shape
sample_rng = jnp.asarray(sample_rng)
if self.training_config.sampling_type == "full":
batch_sampled, batch_sampled_last, batch_sampled_all = sampling_fn(
sample_fn, (sample_rng, state), p_batch_input
)
elif self.training_config.sampling_type == "colorization":
batch_grayscale = to_grayscale(batch)
batch_grayscale = batch_grayscale.reshape(-1, b, g, 1)
batch_sampled, batch_sampled_last, batch_sampled_all = colorizing_fn(
sample_fn, (sample_rng, state), p_batch_input, batch_grayscale
)
elif self.training_config.sampling_type == "inpainting":
config_object = OmegaConf.create(
{
"_target_": "functional_diffusion_processes.datasets.mnist_dataset.MNISTDataset",
"data_config": {
"seed": 42,
"batch_size": ds_train.data_config.batch_size,
"image_height_size": ds_train.data_config.image_height_size,
"image_width_size": ds_train.data_config.image_width_size,
"output_size": 1,
"random_flip": False,
"uniform_dequantization": False,
"data_centered": False,
"data_dir": "${oc.env:DATA_ROOT}/tensorflow_datasets",
"download": True,
"is_mask": True,
},
"split": "train",
"evaluation": False,
}
)
ds_mask = hydra.utils.instantiate(config_object, _recursive_=False)
ds_mask_iter = iter(ds_mask)
batch_masked = jax.tree_map(f=lambda x: x._numpy(), tree=next(ds_mask_iter)["data"])
batch_sampled, batch_sampled_last, batch_sampled_all = inpainting_fn(
sample_fn, (sample_rng, state), p_batch_input, (batch * batch_masked), batch_masked
)
elif self.training_config.sampling_type == "deblurring":
n_rows, n_cols = ds_train.data_config.image_height_size, ds_train.data_config.image_width_size
batch_masked = filter_mask(batch.reshape(-1, b, n_rows, n_cols, c).shape, radius=10)
batch_freq = jnp.fft.fftshift(
jnp.fft.fft2(batch.reshape(-1, b, n_rows, n_cols, c), axes=(2, 3)),
axes=(2, 3),
)
batch_freq = batch_freq * batch_masked
batch_blurred = jnp.real(jnp.fft.ifft2(jnp.fft.ifftshift(batch_freq, axes=(2, 3)), axes=(2, 3)))
batch_blurred = batch_blurred.reshape(-1, b, g, c)
batch_masked = batch_masked.reshape(-1, b, g, c)
batch_sampled, batch_sampled_last, batch_sampled_all = inpainting_fn(
sample_fn, (sample_rng, state), p_batch_input, batch_blurred, batch_masked
)
if jax.host_id() == 0 and self.logging.use_wandb:
if isinstance(ds_train, ImageDataset):
this_sample_dir = os.path.join(
self.sample_dir,
"iter_{}_host_{}".format(step, jax.host_id()),
)
tf.io.gfile.makedirs(this_sample_dir)
# code below to show the gif of the sampled images
# processed_images = []
# for n in range(batch_sampled_all.shape[1]):
# batch_sampled_i = batch_sampled_all[:, n, :, :, :]
# batch_sampled_i = ds_train.postprocess_fn(
# batch_data=batch_sampled_i, inverse_scaler=inverse_scaler
# )
# processed_images.append(np.asarray(batch_sampled_i))
#
# # Log the sampled images as a GIF
# imageio.mimwrite(
# os.path.join(this_sample_dir, "image_sequence.gif"),
# processed_images,
# fps=10,
# )
# gif_wandb = wandb.Image(
# os.path.join(this_sample_dir, "image_sequence.gif"),
# caption="Sampled_all_gif",
# )
# wandb.log({"Sampled_all_gif": gif_wandb}, step=step)
batch_sampled = ds_train.postprocess_fn(batch_data=batch_sampled, inverse_scaler=inverse_scaler)
batch_sampled_last = ds_train.postprocess_fn(
batch_data=batch_sampled_last, inverse_scaler=inverse_scaler
)
batch_real = ds_train.postprocess_fn(
batch_data=batch.reshape(-1, b, g, c), inverse_scaler=inverse_scaler
)
if not self.training_config.sampling_only:
batch_target = ds_train.postprocess_fn(
batch_data=target.reshape(-1, b, g, c), inverse_scaler=inverse_scaler
)
if isinstance(ds_train, ImageDataset):
data_sampled = wandb.Image(np.asarray(batch_sampled), caption="Sampled")
data_sampled_rec = wandb.Image(np.asarray(batch_sampled_last), caption="Sampled Rec")
data_real = wandb.Image(np.asarray(batch_real), caption="Real")
if not self.training_config.sampling_only:
data_target = wandb.Image(np.asarray(batch_target), caption="Target")
|
# import imageio
# import imageio
pylogger = logging.getLogger(__name__)
class Trainer(abc.ABC):
"""Class for training a model."""
def __init__(
self,
mode: str,
model_name: str,
training_config: DictConfig,
optimizer,
evaluation_config: DictConfig,
trainer_logging: DictConfig,
sampler: Sampler,
loss_obj: Loss,
) -> None:
"""Initialize a Trainer instance with configurations and core components.
Args:
mode (str): Specifies the mode of the trainer which can be either "train" or "eval".
model_name (str): The name identifier for the model.
training_config (DictConfig): A configuration dictionary for training settings.
optimizer: The optimizer instance used for training.
evaluation_config (DictConfig): A configuration dictionary for evaluation settings.
trainer_logging (DictConfig): A configuration dictionary for logging settings.
sampler (Sampler): A sampler instance for sampling from the model.
loss_obj (Loss): A loss object used for computing the loss during training.
"""
self.mode = mode
self.model_name = model_name
self.training_config = training_config
self.optimizer = hydra.utils.instantiate(optimizer)
self.evaluation_config = evaluation_config
self.logging = trainer_logging
self.sampler = sampler
self.loss_obj = loss_obj
self.checkpoint_dir = os.path.join(self.training_config.save_dir, self.training_config.checkpoint_dir)
self.sample_dir = os.path.join(self.training_config.save_dir, self.training_config.sample_dir)
self.eval_dir = os.path.join(self.training_config.save_dir, self.evaluation_config.eval_dir)
# Create the directories for saving samples and checkpoints
tf.io.gfile.makedirs(self.checkpoint_dir)
tf.io.gfile.makedirs(self.sample_dir)
tf.io.gfile.makedirs(self.eval_dir)
tf.io.gfile.makedirs(os.path.join(self.eval_dir, "clean"))
def initialize_wandb(
self, dataset_config: DictConfig, sde_config: DictConfig, model_config: DictConfig
) -> Union[Run, RunDisabled, None]:
"""Initialize wandb if logging is enabled."""
if self.logging.use_wandb:
run = wandb.init(
name=os.path.basename(self.logging.wandb_init.name),
project=self.logging.wandb_init.project,
entity=self.logging.wandb_init.entity,
save_code=self.logging.wandb_init.save_code,
config={
**self.training_config,
**dataset_config,
**sde_config,
**model_config,
},
)
else:
run = None
return run
def initialize_run(self, model, ds_train, sde):
"""Perform all initialization steps required for training."""
run = self.initialize_wandb(ds_train.data_config, sde.sde_config, model.model_config)
scaler = get_data_scaler(is_centered=ds_train.data_config.data_centered)
inverse_scaler = get_data_inverse_scaler(is_centered=ds_train.data_config.data_centered)
rng = jax.random.PRNGKey(seed=self.training_config.seed)
rng, step_rng = jax.random.split(rng)
batch_input = model.initialize_input(
(ds_train.data_config.batch_size, *sde.sde_config.shape, ds_train.data_config.output_size)
)
params = jax.jit(model.initialize_model, backend="cpu")(step_rng, batch_input)
flat_params = traverse_util.flatten_dict(params).values()
tot_params = sum([jnp.size(p) for p in flat_params])
pylogger.info("Total number of parameters: {:.2f}M".format(tot_params / 1e6))
state = TrainState.create(
apply_fn=model.apply,
params=params,
tx=self.optimizer,
opt_state_params=self.optimizer.init(params),
rng=rng,
ema_params=params,
)
train_step_fn = construct_train_step(self.optimizer, self.loss_obj.construct_loss_fn(model))
sample_fn = construct_sampling_fn(model, self.sampler)
# Resume training when intermediate checkpoints are detected
if self.training_config.resume_training:
pylogger.warning("Resuming training from the latest checkpoint.")
if self.logging.use_wandb and self.model_name != "local":
model_file = wandb.use_artifact(self.model_name).download()
state = restore_checkpoint(ckpt_dir=model_file, prefix="checkpoint_", target=state)
else:
state = checkpoints.restore_checkpoint(ckpt_dir=self.checkpoint_dir, target=state)
return run, scaler, inverse_scaler, rng, state, train_step_fn, sample_fn, batch_input
def train_step(
self,
train_step_fn: Callable,
carry_state: Tuple,
batch: jnp.ndarray,
batch_input: jnp.ndarray,
) -> Tuple:
"""Perform a single training step, updating the model parameters.
Args:
train_step_fn (Callable): The train step function.
carry_state (Tuple): The current state of the model and optimizer.
batch (jnp.ndarray): The batch of data used for training.
batch_input (jnp.ndarray): The input data to the model.
Returns:
Tuple: The updated state after performing the training step.
"""
(rng, state) = carry_state
(
new_rng,
loss,
loss_inner,
new_params,
new_optim_state,
batch_reconstructed,
batch_corrupted,
target,
) = train_step_fn(
rng,
state.params,
state.opt_state_params,
state.step,
batch_input,
batch,
)
ema_rate = self.training_config.ema_rate
new_params_ema = jax.tree_map(
lambda p_ema, p: p_ema * ema_rate + p * (1.0 - ema_rate),
state.ema_params,
new_params,
)
# update the state
new_state = state.replace(
rng=flax.jax_utils.unreplicate(new_rng),
step=state.step + 1,
opt_state_params=new_optim_state,
params=new_params,
ema_params=new_params_ema,
)
new_carry_state = (new_rng, new_state)
loss = flax.jax_utils.unreplicate(loss)
step = int(flax_utils.unreplicate(state.step))
# Log the training progress
if jax.host_id() == 0 and step % self.training_config.log_freq == 0:
pylogger.info("step: %d, training_loss: %.5e" % (step, loss))
if self.logging.use_wandb:
wandb.log({"step": step, "loss": loss}, step=step)
if loss_inner is not None:
loss_inner = flax.jax_utils.unreplicate(loss_inner)
for inner_step, loss in enumerate(loss_inner):
pylogger.info("step: %d, training_loss_inner: %.5e" % (step, loss))
if self.logging.use_wandb:
wandb.log({"step": step, f"loss inner step {inner_step}": loss}, step=step)
return new_carry_state, batch_reconstructed, batch_corrupted, target
def save_checkpoint(self, step, run, state):
pylogger.info("Saving the model at step %d." % (step,))
# Log the evaluation progress
# Save the model parameters
(
params,
opt_state_params,
step_,
ema_params,
) = flax_utils.unreplicate(
(
state.params,
state.opt_state_params,
state.step,
state.ema_params,
)
)
saved_state = state.replace(
step=step_,
opt_state_params=opt_state_params,
params=params,
ema_params=ema_params,
)
checkpoint_file = checkpoints.save_checkpoint(
self.checkpoint_dir,
saved_state,
step=step_ // self.training_config.eval_freq,
keep=np.inf,
)
if self.logging.use_wandb:
wandb_model_artifact_name = str(step_) + "_" + run.id
wandb_model = wandb.Artifact(wandb_model_artifact_name, type="model")
wandb_model.add_file(checkpoint_file)
run.log_artifact(wandb_model)
# noinspection PyProtectedMember
def train(self, model: linen.Module, ds_train: BaseDataset, sde: SDE) -> None:
"""Train the model with optional evaluation and logging.
This method encapsulates the entire training process including initialization,
training loop, checkpointing, evaluation, and logging. It supports different
sampling types like colorization, inpainting, super resolution, and deblurring.
Args:
model (linen.Module): The model to be trained.
ds_train (BaseDataset): The training dataset.
sde (SDE): Stochastic differential equation object, governing the dynamics
for sampling.
Raises:
ValueError: If an unsupported dataset type is provided.
Note:
The method leverages the Weights & Biases (wandb) platform for logging
and checkpointing, make sure it's configured properly if logging is enabled.
"""
run, scaler, inverse_scaler, rng, state, train_step_fn, sample_fn, batch_input = self.initialize_run(
model, ds_train, sde
)
# `state.step` is JAX integer on the GPU/TPU devices
start_step = int(state.step)
rng = state.rng
# Replicate the train state on all devices
(
p_params,
p_opt_state_params,
p_step,
p_ema_params,
p_batch_input,
) = flax_utils.replicate(
(
state.params,
state.opt_state_params,
state.step,
state.ema_params,
batch_input,
)
)
# update the TrainState with replicated parameters and optimizer state
state = state.replace(
params=p_params,
opt_state_params=p_opt_state_params,
step=p_step,
ema_params=p_ema_params,
)
if jax.host_id() == 0:
pylogger.info("Starting training loop at step %d." % (start_step,))
rng = jax.random.fold_in(rng, jax.host_id())
assert (
self.training_config.log_freq % self.training_config.n_jitted_steps == 0
and self.training_config.eval_freq % self.training_config.n_jitted_steps == 0
), "Missing logs or checkpoints!"
ds_train_iter = iter(ds_train)
with tqdm(
total=self.training_config.total_steps + 1,
initial=start_step,
position=0,
leave=True,
) as pbar:
for step in range(
start_step,
self.training_config.total_steps + 1,
self.training_config.n_jitted_steps,
):
# Get the next batch of data and scale it
batch = jax.tree_map(f=lambda x: scaler(x._numpy()), tree=next(ds_train_iter)["data"])
if not self.training_config.sampling_only:
# Split the random number generator for the current step
rng, *next_rng = jax.random.split(key=rng, num=jax.local_device_count() + 1)
next_rng = jnp.asarray(next_rng)
((_, state), batch_reconstructed, batch_corrupted, target) = self.train_step(
train_step_fn=train_step_fn,
carry_state=(next_rng, state),
batch=batch,
batch_input=p_batch_input,
)
if not self.training_config.sampling_only and (
(jax.host_id() == 0 and step % self.training_config.checkpoint_freq == 0 and step != 0)
):
self.save_checkpoint(step, run, state)
# Evaluate the model
if self.training_config.sampling and (step % self.training_config.eval_freq == 0):
# if step != 0:
if jax.host_id() == 0:
pylogger.info("Generating samples at step %d." % (step,))
_, *sample_rng = jax.random.split(rng, jax.local_device_count() + 1)
_, b, g, c = batch.shape
sample_rng = jnp.asarray(sample_rng)
if self.training_config.sampling_type == "full":
batch_sampled, batch_sampled_last, batch_sampled_all = sampling_fn(
sample_fn, (sample_rng, state), p_batch_input
)
elif self.training_config.sampling_type == "colorization":
batch_grayscale = to_grayscale(batch)
batch_grayscale = batch_grayscale.reshape(-1, b, g, 1)
batch_sampled, batch_sampled_last, batch_sampled_all = colorizing_fn(
sample_fn, (sample_rng, state), p_batch_input, batch_grayscale
)
elif self.training_config.sampling_type == "inpainting":
config_object = OmegaConf.create(
{
"_target_": "functional_diffusion_processes.datasets.mnist_dataset.MNISTDataset",
"data_config": {
"seed": 42,
"batch_size": ds_train.data_config.batch_size,
"image_height_size": ds_train.data_config.image_height_size,
"image_width_size": ds_train.data_config.image_width_size,
"output_size": 1,
"random_flip": False,
"uniform_dequantization": False,
"data_centered": False,
"data_dir": "${oc.env:DATA_ROOT}/tensorflow_datasets",
"download": True,
"is_mask": True,
},
"split": "train",
"evaluation": False,
}
)
ds_mask = hydra.utils.instantiate(config_object, _recursive_=False)
ds_mask_iter = iter(ds_mask)
batch_masked = jax.tree_map(f=lambda x: x._numpy(), tree=next(ds_mask_iter)["data"])
batch_sampled, batch_sampled_last, batch_sampled_all = inpainting_fn(
sample_fn, (sample_rng, state), p_batch_input, (batch * batch_masked), batch_masked
)
elif self.training_config.sampling_type == "deblurring":
n_rows, n_cols = ds_train.data_config.image_height_size, ds_train.data_config.image_width_size
batch_masked = filter_mask(batch.reshape(-1, b, n_rows, n_cols, c).shape, radius=10)
batch_freq = jnp.fft.fftshift(
jnp.fft.fft2(batch.reshape(-1, b, n_rows, n_cols, c), axes=(2, 3)),
axes=(2, 3),
)
batch_freq = batch_freq * batch_masked
batch_blurred = jnp.real(jnp.fft.ifft2(jnp.fft.ifftshift(batch_freq, axes=(2, 3)), axes=(2, 3)))
batch_blurred = batch_blurred.reshape(-1, b, g, c)
batch_masked = batch_masked.reshape(-1, b, g, c)
batch_sampled, batch_sampled_last, batch_sampled_all = inpainting_fn(
sample_fn, (sample_rng, state), p_batch_input, batch_blurred, batch_masked
)
if jax.host_id() == 0 and self.logging.use_wandb:
if isinstance(ds_train, ImageDataset):
this_sample_dir = os.path.join(
self.sample_dir,
"iter_{}_host_{}".format(step, jax.host_id()),
)
tf.io.gfile.makedirs(this_sample_dir)
# code below to show the gif of the sampled images
# processed_images = []
# for n in range(batch_sampled_all.shape[1]):
# batch_sampled_i = batch_sampled_all[:, n, :, :, :]
# batch_sampled_i = ds_train.postprocess_fn(
# batch_data=batch_sampled_i, inverse_scaler=inverse_scaler
# )
# processed_images.append(np.asarray(batch_sampled_i))
#
# # Log the sampled images as a GIF
# imageio.mimwrite(
# os.path.join(this_sample_dir, "image_sequence.gif"),
# processed_images,
# fps=10,
# )
# gif_wandb = wandb.Image(
# os.path.join(this_sample_dir, "image_sequence.gif"),
# caption="Sampled_all_gif",
# )
# wandb.log({"Sampled_all_gif": gif_wandb}, step=step)
batch_sampled = ds_train.postprocess_fn(batch_data=batch_sampled, inverse_scaler=inverse_scaler)
batch_sampled_last = ds_train.postprocess_fn(
batch_data=batch_sampled_last, inverse_scaler=inverse_scaler
)
batch_real = ds_train.postprocess_fn(
batch_data=batch.reshape(-1, b, g, c), inverse_scaler=inverse_scaler
)
if not self.training_config.sampling_only:
batch_target = ds_train.postprocess_fn(
batch_data=target.reshape(-1, b, g, c), inverse_scaler=inverse_scaler
)
if isinstance(ds_train, ImageDataset):
data_sampled = wandb.Image(np.asarray(batch_sampled), caption="Sampled")
data_sampled_rec = wandb.Image(np.asarray(batch_sampled_last), caption="Sampled Rec")
data_real = wandb.Image(np.asarray(batch_real), caption="Real")
if not self.training_config.sampling_only:
data_target = wandb.Image(np.asarray(batch_target), caption="Target")
| elif isinstance(ds_train, AudioDataset): | 0 | 2023-10-24 22:01:35+00:00 | 24k |
violet-sto/HN-GFN | main_mobo.py | [
{
"identifier": "Dataset",
"path": "dataset.py",
"snippet": "class Dataset:\n\n def __init__(self, args, bpath, oracle, device):\n self.test_split_rng = np.random.RandomState(142857)\n self.train_rng = np.random.RandomState(int(time.time()))\n self.train_mols = []\n self.t... | from collections import defaultdict
from dataset import Dataset
from mol_mdp_ext import MolMDPExtended, BlockMoleculeDataExtended
from oracle.oracle import Oracle
from proxy import get_proxy
from generator import TBGFlowNet, FMGFlowNet, MOReinforce
from utils.utils import set_random_seed
from utils.metrics import compute_success, compute_diversity, compute_novelty, compute_correlation, circle_points
from utils.logging import get_logger
from datetime import datetime
from botorch.utils.multi_objective.hypervolume import Hypervolume
from botorch.utils.sampling import sample_simplex
from botorch.utils.transforms import normalize, unnormalize
from torch.distributions.dirichlet import Dirichlet
from main import RolloutWorker, get_test_mols
from pymoo.util.ref_dirs import get_reference_directions
from copy import deepcopy
import random
import os
import re
import argparse
import json
import time
import threading
import pdb
import pickle
import gzip
import torch.multiprocessing as mp
import torch.nn.functional as F
import torch
import pandas as pd
import numpy as np
import warnings | 15,557 | sampled_mols = []
sampled_raw_rewards = []
sampled_means = []
sampled_smis = []
while len(sampled_mols) < args.num_samples:
rollout_worker.rollout(generator, use_rand_policy=False, weights=torch.tensor(weights).unsqueeze(0).to(args.device))
(raw_r, _, m, trajectory_stats, inflow) = rollout_worker.sampled_mols[-1]
sampled_mols.append(m)
sampled_raw_rewards.append(raw_r[0].item())
sampled_means.append(raw_r[1])
sampled_smis.append(m.smiles)
idx_pick = np.argsort(sampled_raw_rewards)[::-1][:int(args.num_samples/len(rollout_worker.test_weights))]
picked_mols.extend(np.array(sampled_mols)[idx_pick].tolist())
means.extend(np.array(sampled_means)[idx_pick].tolist())
smis.extend(np.array(sampled_smis)[idx_pick].tolist())
raw_rewards.extend(np.array(sampled_raw_rewards)[idx_pick].tolist())
raw_rewards_weight[str(weights.cpu())] = np.array(sampled_raw_rewards)[idx_pick].mean()
raw_rewards_mean = np.mean(list(raw_rewards_weight.values()))
assert len(picked_mols) == args.num_samples
top_means = torch.tensor(means)
scores_dict = oracle.batch_get_scores(picked_mols)
scores = torch.tensor(pd.DataFrame.from_dict(scores_dict).values)
test_loss = F.mse_loss(top_means, scores)
hypervolume = Hypervolume(ref_point=torch.zeros(len(args.objectives)))
volume = hypervolume.compute(top_means)
volume_oracle = hypervolume.compute(scores)
diversity = compute_diversity(picked_mols)
batch_metrics = {'Hypervolume_reward': volume,
'Hypervolume_oracle': volume_oracle,
'Reward_mean': raw_rewards_mean,
'scores_max': pd.DataFrame.from_dict(scores_dict).max().to_dict(),
'scores_mean': pd.DataFrame.from_dict(scores_dict).mean().to_dict(),
'Test_loss': test_loss,
'Diversity': diversity}
print(batch_metrics)
print('Time: {}'.format(time.time()-time_start))
if not compute_multi_objective_metric:
return volume, volume_oracle, raw_rewards_weight, raw_rewards_mean, test_loss, diversity
else:
for i in range(len(picked_mols)):
picked_mols[i].score = scores_dict[i]
# success/diversity/novelty is computed among the top mols.
success, positive_mols = compute_success(
picked_mols, scores_dict, args.objectives, score_succ)
succ_diversity = compute_diversity(positive_mols)
if ref_mols:
novelty = compute_novelty(positive_mols, ref_mols)
else:
novelty = 1.
mo_metrics = {'success': success, 'novelty': novelty,
'succ_diversity': succ_diversity, }
picked_smis = [(raw_rewards[i], picked_mols[i].score, smis[i])
for i in range(len(raw_rewards))]
print(mo_metrics)
return (picked_mols, scores_dict, picked_smis), batch_metrics, mo_metrics
def log_overall_metrics(args, dataset, batch_infos=None, MultiObjective_metrics=None):
volume = dataset.compute_hypervolume()
print("Hypervolume for {}: {}".format(args.logger.context, volume))
args.logger.add_scalar('Metric/hypervolume', volume, use_context=False)
args.logger.add_object('scores', dataset.scores)
args.logger.add_object('smis', dataset.smis)
if batch_infos:
args.logger.add_scalar(
'Metric/test_loss', batch_infos['Test_loss'], use_context=False)
args.logger.add_object('collected_info', batch_infos)
if MultiObjective_metrics:
args.logger.add_scalars('Metric/MultiObjective',
MultiObjective_metrics, use_context=False)
def get_test_rays():
if args.n_objectives == 3:
n_partitions = 6
elif args.n_objectives == 4:
n_partitions = 7
test_rays = get_reference_directions("das-dennis", args.n_objectives, n_partitions=n_partitions).astype(np.float32)
test_rays = test_rays[[(r > 0).all() for r in test_rays]]
print(f"initialize {len(test_rays)} test rays")
return test_rays
def main(args):
set_random_seed(args.seed)
args.logger.set_context('iter_0')
bpath = "./data/blocks_105.json"
dpath = "./data/docked_mols.h5"
# Initialize oracle and dataset (for training surrogate function)
oracle = Oracle(args)
dataset = Dataset(args, bpath, oracle, args.device)
dataset.load_h5(dpath, num_init_examples=args.num_init_examples)
log_overall_metrics(args, dataset)
args.n_objectives = len(args.objectives)
# Initialize surrogate function
proxy = get_proxy(args, bpath, oracle)
proxy.update(dataset, 0, reset=False)
for i in range(1, args.num_outer_loop_iters+1):
print(f"====== Starting round {i} ======")
args.logger.set_context('iter_{}'.format(i))
test_weights = np.random.dirichlet(args.alpha_vector, 5*(2**(args.n_objectives-2))).astype(np.float32)
if args.criterion == 'TB':
generator = TBGFlowNet(args, bpath)
elif args.criterion == 'FM':
generator = FMGFlowNet(args, bpath)
elif args.criterion == 'Reinforce':
| warnings.filterwarnings('ignore')
def arg_parse():
parser = argparse.ArgumentParser()
parser.add_argument("--device", type=str, default='cuda')
parser.add_argument('--seed', type=int, default=42, help='seed')
parser.add_argument("--run", default=0, help="run", type=int)
parser.add_argument('--save', action='store_true',
default=False, help='Save model.')
parser.add_argument('--debug',action='store_true',
default=False, help='debug mode, no multi thread')
parser.add_argument("--enable_tensorboard",
action='store_true', default=False)
parser.add_argument("--log_dir", default='runs/mobo')
parser.add_argument("--include_nblocks", default=False)
parser.add_argument("--num_init_examples", default=200, type=int)
parser.add_argument("--num_outer_loop_iters", default=8, type=int)
parser.add_argument("--num_samples", default=100, type=int)
parser.add_argument("--floatX", default='float32')
parser.add_argument('--sample_iterations', type=int, default=1000, help='sample mols and compute metrics')
parser.add_argument("--log_weight_score", action='store_true', default=False)
# objectives
parser.add_argument("--objectives", type=str,
default='gsk3b,jnk3,qed,sa')
parser.add_argument("--acq_fn", default='UCB', type=str)
parser.add_argument("--beta", default=0.1, type=float)
parser.add_argument("--scalar", default='WeightedSum', type=str)
parser.add_argument("--alpha", default=1., type=float,
help='dirichlet distribution')
parser.add_argument("--alpha_vector", default='1,1,1,1', type=str)
# Proxy
parser.add_argument("--proxy_normalize", action='store_true',
default=False, help='normalize Y')
parser.add_argument("--proxy_num_iterations", default=10000, type=int)
parser.add_argument("--proxy_learning_rate", default=2.5e-4,
help="Learning rate", type=float)
parser.add_argument("--proxy_mbsize", default=64,
help="Minibatch size", type=int)
parser.add_argument("--proxy_early_stop_tol", default=10, type=int)
parser.add_argument("--proxy_repr_type", default='atom_graph')
parser.add_argument("--proxy_model_version", default='v2')
parser.add_argument("--proxy_num_conv_steps",
default=12, type=int)
parser.add_argument("--proxy_nemb", default=64, help="#hidden", type=int)
parser.add_argument("--proxy_weight_decay", default=1e-6,
help="Weight Decay in Proxy", type=float)
parser.add_argument("--proxy_uncertainty", default="evidential", type=str) # deep ensemble and GP
parser.add_argument("--proxy_dropout", default=0.1,
help="MC Dropout in Proxy", type=float)
parser.add_argument("--proxy_num_dropout_samples", default=5, type=int)
parser.add_argument("--evidential_lam", default=0.1, type=float)
parser.add_argument(
"--fp_radius", type=int, default=2, help="Morgan fingerprint radius."
)
parser.add_argument(
"--fp_nbits", type=int, default=1024, help="Morgan fingerprint nBits."
)
# GFlowNet
parser.add_argument("--min_blocks", default=2, type=int)
parser.add_argument("--max_blocks", default=8, type=int)
parser.add_argument("--num_iterations", default=5000, type=int)
parser.add_argument("--criterion", default="FM", type=str)
parser.add_argument("--learning_rate", default=5e-4,
help="Learning rate", type=float)
parser.add_argument("--Z_learning_rate", default=5e-3,
help="Learning rate", type=float)
parser.add_argument("--clip_grad", default=0, type=float)
parser.add_argument("--trajectories_mbsize", default=8, type=int)
parser.add_argument("--offline_mbsize", default=8, type=int)
parser.add_argument("--hindsight_prob", default=0.2, type=float)
parser.add_argument("--hindsight_buffer_mbsize", default=8, type=int)
parser.add_argument("--hindsight_trajectories_mbsize", default=8, type=int)
parser.add_argument("--reward_min", default=1e-2, type=float)
parser.add_argument("--reward_norm", default=1, type=float)
parser.add_argument("--reward_exp", default=8, type=float)
parser.add_argument("--reward_exp_ramping", default=0, type=float)
parser.add_argument("--logit_clipping", default=0., type=float)
# Hyperparameters for TB
parser.add_argument("--partition_init", default=1, type=float)
# Hyperparameters for FM
parser.add_argument("--log_reg_c", default=(0.1/8)**4, type=float)
parser.add_argument("--balanced_loss", default=True)
parser.add_argument("--leaf_coef", default=10, type=float)
# Architecture
parser.add_argument("--repr_type", default='block_graph')
parser.add_argument("--model_version", default='v4')
parser.add_argument("--num_conv_steps", default=10, type=int)
parser.add_argument("--nemb", default=256, help="#hidden", type=int)
parser.add_argument("--weight_decay", default=0, type=float)
parser.add_argument("--random_action_prob", default=0.05, type=float)
parser.add_argument("--bootstrap_tau", default=0, type=float)
parser.add_argument("--condition_type", type=str, default='HN')
parser.add_argument("--ray_hidden_dim", default=100, type=int)
return parser.parse_args()
class BoRolloutWorker(RolloutWorker):
def __init__(self, args, bpath, proxy, device):
super(BoRolloutWorker, self).__init__(args, bpath, proxy, device)
self.hindsight_prob = args.hindsight_prob
self.hindsight_mols = defaultdict(list)
self.hindsight_smiles = defaultdict(list)
self.replay_threshold = 0.9
def _get(self, i, dset, weights=None):
# Sample trajectories by walking backwards from the molecules in our dataset
# Handle possible multithreading issues when independent threads
# add/substract from dset:
m = dset[i]
if not isinstance(m, BlockMoleculeDataExtended):
m = m[-1]
r, raw_r = self._get_reward(m, weights)
done = 1
samples = []
# a sample is a tuple (parents(s), parent actions, reward(s), s, done)
# an action is (blockidx, stemidx) or (-1, x) for 'stop'
# so we start with the stop action, unless the molecule is already
# a "terminal" node (if it has no stems, no actions).
if len(m.stems) and len(m.blocks) < self.max_blocks:
samples.append(((m,), ((-1, 0),), weights, weights, r, m, done))
r = done = 0
while len(m.blocks): # and go backwards
if self.ignore_parents:
parents = self.mdp.parents(m)
parent, action = parents[self.train_rng.randint(len(parents))]
samples.append(((parent,), (action,), weights, weights, r, m, done))
r = done = 0
m = parent
else:
parents, actions = zip(*self.mdp.parents(m))
samples.append((parents, actions, weights.repeat(len(parents), 1), weights, r, m, done))
r = done = 0
m = parents[self.train_rng.randint(len(parents))]
return samples[::-1]
def _add_mol_to_replay(self, m):
for i, weights in enumerate(self.test_weights):
r, raw_r = self._get_reward(m, weights)
if len(self.hindsight_mols[i]) < self.max_hindsight_mols or raw_r[0] > self.hindsight_mols[i][0][0]:
if m.smiles not in self.hindsight_smiles[i]:
self.hindsight_mols[i].append((raw_r[0].item(), m.smiles, m))
self.hindsight_smiles[i].append(m.smiles)
if len(self.hindsight_mols[i]) > self.max_hindsight_mols:
self.hindsight_mols[i] = sorted(self.hindsight_mols[i], key=lambda x:(x[0]))[
max(int(0.05 * self.max_hindsight_mols), 1):]
self.hindsight_smiles[i] = [x[1] for x in self.hindsight_mols[i]]
def _add_mol_to_online(self, r, m, inflow):
if self.replay_mode == 'online':
r = r + self.train_rng.normal() * 0.01
if len(self.online_mols) < self.max_online_mols or r > self.online_mols[0][0]:
self.online_mols.append((r, m))
if len(self.online_mols) > self.max_online_mols:
self.online_mols = sorted(self.online_mols)[
max(int(0.05 * self.max_online_mols), 1):]
elif self.replay_mode == 'prioritized':
self.online_mols.append((abs(inflow - np.log(r)), m))
if len(self.online_mols) > self.max_online_mols * 1.1:
self.online_mols = self.online_mols[-self.max_online_mols:]
def _get_reward(self, m, weights=None):
rdmol = m.mol
if rdmol is None:
return self.reward_min
# get reward from proxy
raw_reward, score = self.proxy(m, weights)
raw_reward = raw_reward.clip(self.reward_min)
reward = self.l2r(raw_reward)
return reward, (raw_reward, score)
def execute_train_episode_batch(self, generator, dataset=None, Y_bounds=None, use_rand_policy=True):
if self.train_rng.uniform() < self.hindsight_prob:
idx = self.train_rng.randint(self.test_weights.shape[0])
weights = self.test_weights[idx].unsqueeze(0)
samples = sum((self.rollout(generator, use_rand_policy, weights)
for i in range(self.args.hindsight_trajectories_mbsize)), [])
if self.args.hindsight_buffer_mbsize > 0:
buffer = deepcopy(self.hindsight_mols[idx])
reward = np.array([x[0] for x in buffer])
prob = reward / sum(reward)
eidx = np.random.choice(list(range(len(buffer))), self.args.hindsight_buffer_mbsize, replace=False, p=prob)
offline_samples = sum((self._get(i, buffer, weights)
for i in eidx), [])
samples += offline_samples
else:
weights = Dirichlet(torch.tensor(self.args.alpha_vector)*self.args.alpha).sample_n(1).to(self.args.device) #* sample weights per batch, seem better
samples = sum((self.rollout(generator, use_rand_policy, weights, replay=True)
for i in range(self.args.trajectories_mbsize)), [])
# offline sampling from dataset
if self.args.offline_mbsize > 0 and dataset is not None:
# use the oracle reward
scores = torch.tensor(pd.DataFrame.from_dict(dataset.scores).values, dtype=torch.float32).to(args.device)
if Y_bounds is not None:
scores = normalize(scores, Y_bounds)
reward = torch.matmul(scores, weights.reshape(-1, 1))
prob = (reward / sum(reward)).squeeze(1).cpu().numpy()
eidx = np.random.choice(list(range(len(dataset.all_mols))), self.args.offline_mbsize, replace=False, p=prob)
offline_samples = sum((self._get(i, dataset.all_mols, weights)
for i in eidx), [])
samples += offline_samples
return zip(*samples)
def initialize_hindsight_mols(self, dataset):
for m in dataset.all_mols:
for i, weights in enumerate(self.test_weights):
r, raw_r = self._get_reward(m, weights)
self.hindsight_mols[i].append((raw_r[0].item(), m.smiles, m))
for i, weights in enumerate(self.test_weights):
self.hindsight_mols[i] = sorted(self.hindsight_mols[i], key=lambda x:(x[0]))
self.hindsight_smiles[i] = [x[1] for x in self.hindsight_mols[i]]
def train_generative_model(args, generator, bpath, proxy, oracle, dataset, test_weights,
round_idx, do_save=False):
print("Training generator...")
os.makedirs(os.path.join(args.log_dir, f'round_{round_idx}'), exist_ok=True)
device = args.device
rollout_worker = BoRolloutWorker(args, bpath, proxy, device)
rollout_worker.test_weights = torch.tensor(test_weights).to(device)
rollout_worker.initialize_hindsight_mols(dataset)
Y_bounds = torch.stack([proxy.partitioning.Y.min(dim=-2).values, proxy.partitioning.Y.max(dim=-2).values])
def save_stuff(round_idx, iter):
torch.save(generator.state_dict(), os.path.join(
args.log_dir, 'round_{}/{}_generator_checkpoint.pth'.format(round_idx, iter)))
pickle.dump(rollout_worker.sampled_mols,
gzip.open(f'{args.log_dir}/sampled_mols.pkl.gz', 'wb'))
multi_thread = not args.debug
if multi_thread:
sampler = rollout_worker.start_samplers(generator, 8, dataset)
def stop_everything():
print('joining')
rollout_worker.stop_samplers_and_join()
last_losses = []
train_losses = []
test_losses = []
test_infos = []
train_infos = []
time_last_check = time.time()
for i in range(args.num_iterations + 1):
if multi_thread:
r = sampler()
for thread in rollout_worker.sampler_threads:
if thread.failed:
stop_everything()
pdb.post_mortem(thread.exception.__traceback__)
return
p, pb, a, pw, w, r, s, d, mols = r
else:
p, pb, a, pw, w, r, s, d, mols = rollout_worker.sample2batch(
rollout_worker.execute_train_episode_batch(generator, dataset, Y_bounds, use_rand_policy=True))
loss = generator.train_step(p, pb, a, pw, w, r, s, d, mols, i)
last_losses.append(loss)
if not i % 100:
train_loss = [np.round(np.mean(i), 3) for i in zip(*last_losses)]
train_losses.append(train_loss)
args.logger.add_scalar(
'Loss/round{}/train'.format(round_idx), train_loss[0], use_context=False)
print('Iter {}: Loss {}, Time {}'.format(
i, train_loss, round(time.time() - time_last_check, 3)))
time_last_check = time.time()
last_losses = []
if not i % args.sample_iterations and i != 0:
volume, volume_oracle, reward_weight, reward_mean, test_loss, diversity = sample_batch(
args, generator, rollout_worker, oracle, proxy, Y_bounds, compute_multi_objective_metric=False)
args.logger.add_scalar(
'round{}/Top-100-sampled/volumes'.format(round_idx), volume, use_context=False)
args.logger.add_scalar(
'round{}/Top-100-sampled/volumes_oracle'.format(round_idx), volume_oracle, use_context=False)
args.logger.add_scalars(
'round{}/Top-100-sampled/reward_weight'.format(round_idx), reward_weight, use_context=False)
args.logger.add_scalar(
'round{}/Top-100-sampled/reward_mean'.format(round_idx), reward_mean, use_context=False) # reward_mean is a dict, the keys are test_weights
args.logger.add_scalar(
'round{}/Top-100-sampled/test_loss'.format(round_idx), test_loss, use_context=False)
args.logger.add_scalar(
'round{}/Top-100-sampled/dists'.format(round_idx), diversity, use_context=False)
if do_save:
save_stuff(round_idx, i)
stop_everything()
if do_save:
save_stuff(round_idx, i)
checkpoint_path = os.path.join(args.log_dir, f'round_{round_idx}/{i}_generator_checkpoint.pth')
generator.load_state_dict(torch.load(checkpoint_path))
return rollout_worker, {'train_losses': train_losses,
'test_losses': test_losses,
'test_infos': test_infos,
'train_infos': train_infos}
def sample_batch(args, generator, rollout_worker, oracle=None, proxy=None, ref_mols=None, Y_bounds=None, compute_multi_objective_metric=False):
score_succ = {'gsk3b': 0.5, 'jnk3': 0.5, 'drd2': 0.5,
'chemprop_sars': 0.5, 'chemprop_hiv': 0.5, "seh": 0.5,
'qed': 0.6, 'sa': 0.67}
if Y_bounds is None:
Y_bounds = torch.stack([proxy.partitioning.Y.min(
dim=-2).values, proxy.partitioning.Y.max(dim=-2).values])
time_start = time.time()
print(f"Sampling molecules...")
raw_rewards = []
raw_rewards_weight = {}
means = []
picked_mols = []
smis = []
for i, weights in enumerate(rollout_worker.test_weights):
sampled_mols = []
sampled_raw_rewards = []
sampled_means = []
sampled_smis = []
while len(sampled_mols) < args.num_samples:
rollout_worker.rollout(generator, use_rand_policy=False, weights=torch.tensor(weights).unsqueeze(0).to(args.device))
(raw_r, _, m, trajectory_stats, inflow) = rollout_worker.sampled_mols[-1]
sampled_mols.append(m)
sampled_raw_rewards.append(raw_r[0].item())
sampled_means.append(raw_r[1])
sampled_smis.append(m.smiles)
idx_pick = np.argsort(sampled_raw_rewards)[::-1][:int(args.num_samples/len(rollout_worker.test_weights))]
picked_mols.extend(np.array(sampled_mols)[idx_pick].tolist())
means.extend(np.array(sampled_means)[idx_pick].tolist())
smis.extend(np.array(sampled_smis)[idx_pick].tolist())
raw_rewards.extend(np.array(sampled_raw_rewards)[idx_pick].tolist())
raw_rewards_weight[str(weights.cpu())] = np.array(sampled_raw_rewards)[idx_pick].mean()
raw_rewards_mean = np.mean(list(raw_rewards_weight.values()))
assert len(picked_mols) == args.num_samples
top_means = torch.tensor(means)
scores_dict = oracle.batch_get_scores(picked_mols)
scores = torch.tensor(pd.DataFrame.from_dict(scores_dict).values)
test_loss = F.mse_loss(top_means, scores)
hypervolume = Hypervolume(ref_point=torch.zeros(len(args.objectives)))
volume = hypervolume.compute(top_means)
volume_oracle = hypervolume.compute(scores)
diversity = compute_diversity(picked_mols)
batch_metrics = {'Hypervolume_reward': volume,
'Hypervolume_oracle': volume_oracle,
'Reward_mean': raw_rewards_mean,
'scores_max': pd.DataFrame.from_dict(scores_dict).max().to_dict(),
'scores_mean': pd.DataFrame.from_dict(scores_dict).mean().to_dict(),
'Test_loss': test_loss,
'Diversity': diversity}
print(batch_metrics)
print('Time: {}'.format(time.time()-time_start))
if not compute_multi_objective_metric:
return volume, volume_oracle, raw_rewards_weight, raw_rewards_mean, test_loss, diversity
else:
for i in range(len(picked_mols)):
picked_mols[i].score = scores_dict[i]
# success/diversity/novelty is computed among the top mols.
success, positive_mols = compute_success(
picked_mols, scores_dict, args.objectives, score_succ)
succ_diversity = compute_diversity(positive_mols)
if ref_mols:
novelty = compute_novelty(positive_mols, ref_mols)
else:
novelty = 1.
mo_metrics = {'success': success, 'novelty': novelty,
'succ_diversity': succ_diversity, }
picked_smis = [(raw_rewards[i], picked_mols[i].score, smis[i])
for i in range(len(raw_rewards))]
print(mo_metrics)
return (picked_mols, scores_dict, picked_smis), batch_metrics, mo_metrics
def log_overall_metrics(args, dataset, batch_infos=None, MultiObjective_metrics=None):
volume = dataset.compute_hypervolume()
print("Hypervolume for {}: {}".format(args.logger.context, volume))
args.logger.add_scalar('Metric/hypervolume', volume, use_context=False)
args.logger.add_object('scores', dataset.scores)
args.logger.add_object('smis', dataset.smis)
if batch_infos:
args.logger.add_scalar(
'Metric/test_loss', batch_infos['Test_loss'], use_context=False)
args.logger.add_object('collected_info', batch_infos)
if MultiObjective_metrics:
args.logger.add_scalars('Metric/MultiObjective',
MultiObjective_metrics, use_context=False)
def get_test_rays():
if args.n_objectives == 3:
n_partitions = 6
elif args.n_objectives == 4:
n_partitions = 7
test_rays = get_reference_directions("das-dennis", args.n_objectives, n_partitions=n_partitions).astype(np.float32)
test_rays = test_rays[[(r > 0).all() for r in test_rays]]
print(f"initialize {len(test_rays)} test rays")
return test_rays
def main(args):
set_random_seed(args.seed)
args.logger.set_context('iter_0')
bpath = "./data/blocks_105.json"
dpath = "./data/docked_mols.h5"
# Initialize oracle and dataset (for training surrogate function)
oracle = Oracle(args)
dataset = Dataset(args, bpath, oracle, args.device)
dataset.load_h5(dpath, num_init_examples=args.num_init_examples)
log_overall_metrics(args, dataset)
args.n_objectives = len(args.objectives)
# Initialize surrogate function
proxy = get_proxy(args, bpath, oracle)
proxy.update(dataset, 0, reset=False)
for i in range(1, args.num_outer_loop_iters+1):
print(f"====== Starting round {i} ======")
args.logger.set_context('iter_{}'.format(i))
test_weights = np.random.dirichlet(args.alpha_vector, 5*(2**(args.n_objectives-2))).astype(np.float32)
if args.criterion == 'TB':
generator = TBGFlowNet(args, bpath)
elif args.criterion == 'FM':
generator = FMGFlowNet(args, bpath)
elif args.criterion == 'Reinforce': | generator = MOReinforce(args, bpath) | 7 | 2023-10-24 14:10:35+00:00 | 24k |
caglarkucuk/earthformer-satellite-to-radar | ef-sat2rad/earthformer/cuboid_transformer/cuboid_transformer_unet_dec.py | [
{
"identifier": "Upsample3DLayer",
"path": "ef-sat2rad/earthformer/cuboid_transformer/cuboid_transformer.py",
"snippet": "class Upsample3DLayer(nn.Module):\n \"\"\"Upsampling based on nn.UpSampling and Conv3x3.\n\n If the temporal dimension remains the same:\n x --> interpolation-2d (neares... | from typing import Sequence, Union
from functools import lru_cache
from collections import OrderedDict
from torch import nn
from einops import rearrange
from .cuboid_transformer import (
Upsample3DLayer, PatchMerging3D, PosEmbed,
InitialEncoder, FinalDecoder,
InitialStackPatchMergingEncoder, FinalStackUpsamplingDecoder,
StackCuboidSelfAttentionBlock, StackCuboidCrossAttentionBlock,
CuboidTransformerEncoder)
from .cuboid_transformer_patterns import CuboidSelfAttentionPatterns, CuboidCrossAttentionPatterns
from .utils import (
get_activation, get_norm_layer,
_generalize_padding, _generalize_unpadding,
apply_initialization, round_to)
import warnings
import torch
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint | 15,903 | cross_last_n_frames=None,
num_heads=4,
attn_drop=0.0,
proj_drop=0.0,
ffn_drop=0.0,
ffn_activation='leaky',
gated_ffn=False,
norm_layer='layer_norm',
use_inter_ffn=False,
hierarchical_pos_embed=False,
pos_embed_type='t+hw',
max_temporal_relative=50,
padding_type='ignore',
checkpoint_level=True,
use_relative_pos=True,
self_attn_use_final_proj=True,
# global vectors
use_self_global=False,
self_update_global=True,
use_cross_global=False,
use_global_vector_ffn=True,
use_global_self_attn=False,
separate_global_qkv=False,
global_dim_ratio=1,
# initialization
attn_linear_init_mode="0",
ffn_linear_init_mode="0",
conv_init_mode="0",
up_linear_init_mode="0",
norm_init_mode="0",
# different from `CuboidTransformerDecoder`, no arg `use_first_self_attn=False`
downsample=2,
downsample_type='patch_merge',
cross_mode="up",
down_linear_init_mode="0",
):
"""
Parameters
----------
target_temporal_length
mem_shapes
cross_start
The block to start cross attention
depth
Depth of each block
downsample
The downsample ratio
downsample_type
Type of the downsampling layer
upsample_type
The type of the upsampling layers
upsample_kernel_size
block_self_attn_patterns
Pattern of the block self attentions
block_self_cuboid_size
block_self_cuboid_strategy
block_self_shift_size
block_cross_attn_patterns
block_cross_cuboid_hw
block_cross_cuboid_strategy
block_cross_shift_hw
block_cross_n_temporal
cross_last_n_frames
cross_mode
Must be one of ("up", "down", "both")
Control whether the upsampling/downsampling/both phases cross attend to the encoded latent features
num_heads
attn_drop
proj_drop
ffn_drop
ffn_activation
gated_ffn
Whether to enable gated ffn or not
norm_layer
The normalization layer
use_inter_ffn
Whether to use intermediate FFN
hierarchical_pos_embed
Whether to add pos embedding for each hierarchy.
max_temporal_relative
padding_type
checkpoint_level
"""
super(CuboidTransformerUNetDecoder, self).__init__()
# initialization mode
self.attn_linear_init_mode = attn_linear_init_mode
self.ffn_linear_init_mode = ffn_linear_init_mode
self.conv_init_mode = conv_init_mode
self.up_linear_init_mode = up_linear_init_mode
self.norm_init_mode = norm_init_mode
assert len(depth) == len(mem_shapes)
self.target_temporal_length = target_temporal_length
self.num_blocks = len(mem_shapes)
self.cross_start = cross_start
self.mem_shapes = mem_shapes
self.block_units = tuple(mem_shape[-1] for mem_shape in self.mem_shapes)
self.depth = depth
if not isinstance(downsample, (tuple, list)):
downsample = (1, downsample, downsample)
self.downsample = downsample
self.downsample_type = downsample_type
self.upsample_type = upsample_type
self.hierarchical_pos_embed = hierarchical_pos_embed
self.checkpoint_level = checkpoint_level
self.use_self_global = use_self_global
self.self_update_global = self_update_global
self.use_cross_global = use_cross_global
self.use_global_vector_ffn = use_global_vector_ffn
assert cross_mode in ["up", "down", "both"], f"Invalid cross_mode {cross_mode}!"
self.cross_mode = cross_mode
self.up_use_cross = self.cross_mode in ["up", "both"]
self.down_use_cross = self.cross_mode in ["down", "both"]
if self.num_blocks > 1:
# Construct downsampling layers
if downsample_type == 'patch_merge':
self.downsample_layers = nn.ModuleList(
| """CuboidTransformer adapted for auxiliary inputs in decoder"""
class CuboidTransformerUNetDecoder(nn.Module):
"""U-Net style Decoder of the CuboidTransformer.
For each block, we first apply the StackCuboidSelfAttention and then apply the StackCuboidCrossAttention
We add cross attention following 3 modes:
cross_mode == "down":
x --> attn --> cross_attn --> downscale --> ... --> z --> attn --> upscale --> ... --> out
^ ^
| |
| |
mem mem
cross_mode == "up":
x --> attn --> downscale --> ... --> z --> attn --> cross_attn --> upscale --> ... --> out
^ ^
| |
| |
mem mem
cross_mode == "both":
x --> attn --> cross_attn --> downscale --> ... --> z --> attn --> cross_attn --> upscale --> ... --> out
^ ^ ^ ^
| | | |
| | | |
mem mem mem mem
"""
def __init__(self,
target_temporal_length,
mem_shapes,
cross_start=0,
depth=[2, 2],
upsample_type="upsample",
upsample_kernel_size=3,
block_self_attn_patterns=None,
block_self_cuboid_size=[(4, 4, 4), (4, 4, 4)],
block_self_cuboid_strategy=[('l', 'l', 'l'), ('d', 'd', 'd')],
block_self_shift_size=[(1, 1, 1), (0, 0, 0)],
block_cross_attn_patterns=None,
block_cross_cuboid_hw=[(4, 4), (4, 4)],
block_cross_cuboid_strategy=[('l', 'l', 'l'), ('d', 'l', 'l')],
block_cross_shift_hw=[(0, 0), (0, 0)],
block_cross_n_temporal=[1, 2],
cross_last_n_frames=None,
num_heads=4,
attn_drop=0.0,
proj_drop=0.0,
ffn_drop=0.0,
ffn_activation='leaky',
gated_ffn=False,
norm_layer='layer_norm',
use_inter_ffn=False,
hierarchical_pos_embed=False,
pos_embed_type='t+hw',
max_temporal_relative=50,
padding_type='ignore',
checkpoint_level=True,
use_relative_pos=True,
self_attn_use_final_proj=True,
# global vectors
use_self_global=False,
self_update_global=True,
use_cross_global=False,
use_global_vector_ffn=True,
use_global_self_attn=False,
separate_global_qkv=False,
global_dim_ratio=1,
# initialization
attn_linear_init_mode="0",
ffn_linear_init_mode="0",
conv_init_mode="0",
up_linear_init_mode="0",
norm_init_mode="0",
# different from `CuboidTransformerDecoder`, no arg `use_first_self_attn=False`
downsample=2,
downsample_type='patch_merge',
cross_mode="up",
down_linear_init_mode="0",
):
"""
Parameters
----------
target_temporal_length
mem_shapes
cross_start
The block to start cross attention
depth
Depth of each block
downsample
The downsample ratio
downsample_type
Type of the downsampling layer
upsample_type
The type of the upsampling layers
upsample_kernel_size
block_self_attn_patterns
Pattern of the block self attentions
block_self_cuboid_size
block_self_cuboid_strategy
block_self_shift_size
block_cross_attn_patterns
block_cross_cuboid_hw
block_cross_cuboid_strategy
block_cross_shift_hw
block_cross_n_temporal
cross_last_n_frames
cross_mode
Must be one of ("up", "down", "both")
Control whether the upsampling/downsampling/both phases cross attend to the encoded latent features
num_heads
attn_drop
proj_drop
ffn_drop
ffn_activation
gated_ffn
Whether to enable gated ffn or not
norm_layer
The normalization layer
use_inter_ffn
Whether to use intermediate FFN
hierarchical_pos_embed
Whether to add pos embedding for each hierarchy.
max_temporal_relative
padding_type
checkpoint_level
"""
super(CuboidTransformerUNetDecoder, self).__init__()
# initialization mode
self.attn_linear_init_mode = attn_linear_init_mode
self.ffn_linear_init_mode = ffn_linear_init_mode
self.conv_init_mode = conv_init_mode
self.up_linear_init_mode = up_linear_init_mode
self.norm_init_mode = norm_init_mode
assert len(depth) == len(mem_shapes)
self.target_temporal_length = target_temporal_length
self.num_blocks = len(mem_shapes)
self.cross_start = cross_start
self.mem_shapes = mem_shapes
self.block_units = tuple(mem_shape[-1] for mem_shape in self.mem_shapes)
self.depth = depth
if not isinstance(downsample, (tuple, list)):
downsample = (1, downsample, downsample)
self.downsample = downsample
self.downsample_type = downsample_type
self.upsample_type = upsample_type
self.hierarchical_pos_embed = hierarchical_pos_embed
self.checkpoint_level = checkpoint_level
self.use_self_global = use_self_global
self.self_update_global = self_update_global
self.use_cross_global = use_cross_global
self.use_global_vector_ffn = use_global_vector_ffn
assert cross_mode in ["up", "down", "both"], f"Invalid cross_mode {cross_mode}!"
self.cross_mode = cross_mode
self.up_use_cross = self.cross_mode in ["up", "both"]
self.down_use_cross = self.cross_mode in ["down", "both"]
if self.num_blocks > 1:
# Construct downsampling layers
if downsample_type == 'patch_merge':
self.downsample_layers = nn.ModuleList( | [PatchMerging3D(dim=self.block_units[i], | 1 | 2023-10-23 11:45:50+00:00 | 24k |
IBM/VillanDiffusion | make_latent_dataset.py | [
{
"identifier": "DiffuserModelSched",
"path": "model.py",
"snippet": "class DiffuserModelSched():\n LR_SCHED_CKPT: str = \"lr_sched.pth\"\n OPTIM_CKPT: str = \"optim.pth\"\n \n SDE_VP: str = \"SDE-VP\"\n SDE_VE: str = \"SDE-VE\"\n SDE_LDM: str = \"SDE-LDM\"\n CLIP_SAMPLE_DEFAULT = F... | import glob
import os
import pathlib
import torch
from dataclasses import dataclass
from typing import List, Union
from joblib import Parallel, delayed
from PIL import Image
from tqdm import tqdm
from datasets import Dataset
from torchvision import transforms
from torchvision.transforms import Compose
from torch.utils.data import DataLoader, ConcatDataset, Subset, IterableDataset
from model import DiffuserModelSched
from dataset import DatasetLoader, Backdoor | 21,517 |
@dataclass
class TrainingConfig:
latent_dataset_dir: str = 'celeba_hq_256_latents'
dataset_name: str = DatasetLoader.CELEBA_HQ
|
@dataclass
class TrainingConfig:
latent_dataset_dir: str = 'celeba_hq_256_latents'
dataset_name: str = DatasetLoader.CELEBA_HQ | trigger: str = Backdoor.TRIGGER_SM_STOP_SIGN | 2 | 2023-10-17 19:57:37+00:00 | 24k |
nchen909/Pass-Tuning | evaluator/CodeBLEU/syntax_match.py | [
{
"identifier": "DFG_python",
"path": "evaluator/CodeBLEU/parser/DFG.py",
"snippet": "def DFG_python(root_node,index_to_code,states):\n assignment=['assignment','augmented_assignment','for_in_clause']\n if_statement=['if_statement']\n for_statement=['for_statement']\n while_statement=['while... | from evaluator.CodeBLEU.parser import DFG_python, DFG_java, DFG_ruby, DFG_go, DFG_php, DFG_javascript, DFG_csharp,DFG_c
from evaluator.CodeBLEU.parser import (remove_comments_and_docstrings,
tree_to_token_index,
index_to_code_token,
tree_to_variable_index)
from tree_sitter import Language, Parser
| 17,407 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
parser_path = '/data/pretrain-attention/CodePrompt/evaluator/CodeBLEU/parser'
dfg_function = {
'python': DFG_python,
'java': DFG_java,
'ruby': DFG_ruby,
'go': DFG_go,
| # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
parser_path = '/data/pretrain-attention/CodePrompt/evaluator/CodeBLEU/parser'
dfg_function = {
'python': DFG_python,
'java': DFG_java,
'ruby': DFG_ruby,
'go': DFG_go,
| 'php': DFG_php,
| 4 | 2023-10-20 09:24:44+00:00 | 24k |
JoaoPedro9674/django-ledger | django_ledger/io/io_mixin.py | [
{
"identifier": "settings",
"path": "django_ledger/settings.py",
"snippet": " DJANGO_LEDGER_GRAPHQL_SUPPORT_ENABLED = True\n DJANGO_LEDGER_GRAPHQL_SUPPORT_ENABLED = False\n DJANGO_LEDGER_PDF_SUPPORT_ENABLED = True\n DJANGO_LEDGER_PDF_SUPPORT_ENABLED = False\nDJANGO_LEDGER_USE_CLOSING_ENTRIES... | from collections import defaultdict, namedtuple
from datetime import datetime, date
from itertools import groupby
from pathlib import Path
from random import choice
from typing import List, Set, Union, Tuple, Optional, Dict
from django.contrib.auth import get_user_model
from django.core.exceptions import ValidationError, ObjectDoesNotExist
from django.db.models import Sum, QuerySet
from django.db.models.functions import TruncMonth
from django.http import Http404
from django.utils.dateparse import parse_date, parse_datetime
from django.utils.timezone import make_aware, is_naive, localtime
from django.utils.translation import gettext_lazy as _
from django_ledger import settings
from django_ledger.exceptions import InvalidDateInputError, TransactionNotInBalanceError
from django_ledger.io import roles as roles_module
from django_ledger.io.io_context import (RoleContextManager, GroupContextManager, ActivityContextManager,
BalanceSheetStatementContextManager, IncomeStatementContextManager,
CashFlowStatementContextManager)
from django_ledger.io.io_digest import IODigestContextManager
from django_ledger.io.ratios import FinancialRatioManager
from django_ledger.models.utils import lazy_loader | 15,760 | a['account__uuid'],
a.get('journal_entry__entity_unit__uuid') if by_unit else None,
a.get('dt_idx').year if by_period else None,
a.get('dt_idx').month if by_period else None,
a.get('journal_entry__activity') if by_activity else None,
a.get('tx_type') if by_tx_type else None,
))
gb_digest = [self.aggregate_balances(k, g) for k, g in accounts_gb_code]
for acc in gb_digest:
acc['balance_abs'] = abs(acc['balance'])
if signs:
TransactionModel = lazy_loader.get_txs_model()
for acc in gb_digest:
if any([
all([acc['role_bs'] == roles_module.BS_ASSET_ROLE,
acc['balance_type'] == TransactionModel.CREDIT]),
all([acc['role_bs'] in (
roles_module.BS_LIABILITIES_ROLE,
roles_module.BS_EQUITY_ROLE
),
acc['balance_type'] == TransactionModel.DEBIT])
]):
acc['balance'] = -acc['balance']
return txs_queryset, gb_digest
@staticmethod
def aggregate_balances(k, g):
gl = list(g)
return {
'account_uuid': k[0],
'unit_uuid': k[1],
'unit_name': gl[0].get('journal_entry__entity_unit__name'),
'activity': gl[0].get('journal_entry__activity'),
'period_year': k[2],
'period_month': k[3],
'role_bs': roles_module.BS_ROLES.get(gl[0]['account__role']),
'role': gl[0]['account__role'],
'code': gl[0]['account__code'],
'name': gl[0]['account__name'],
'balance_type': gl[0]['account__balance_type'],
'tx_type': k[5],
'balance': sum(a['balance'] for a in gl),
}
def digest(self,
entity_slug: str = None,
unit_slug: str = None,
user_model: UserModel = None,
txs_queryset: QuerySet = None,
as_io_digest: bool = False,
accounts: Optional[Union[Set[str], List[str]]] = None,
role: Optional[Union[Set[str], List[str]]] = None,
activity: str = None,
signs: bool = True,
to_date: Union[str, datetime, date] = None,
from_date: Union[str, datetime, date] = None,
process_roles: bool = False,
process_groups: bool = False,
process_ratios: bool = False,
process_activity: bool = False,
equity_only: bool = False,
by_period: bool = False,
by_unit: bool = False,
by_activity: bool = False,
by_tx_type: bool = False,
digest_name: str = None,
balance_sheet_statement: bool = False,
income_statement: bool = False,
cash_flow_statement: bool = False,
**kwargs) -> Union[Tuple, IODigestContextManager]:
if balance_sheet_statement:
from_date = None
if cash_flow_statement:
by_activity = True
if activity:
activity = validate_activity(activity)
if role:
role = roles_module.validate_roles(role)
from_date, to_date = validate_dates(from_date, to_date)
io_data = defaultdict(lambda: dict())
io_data['io_model'] = self
io_data['from_date'] = from_date
io_data['to_date'] = to_date
io_data['by_unit'] = by_unit
io_data['by_period'] = by_period
io_data['by_activity'] = by_activity
io_data['by_tx_type'] = by_tx_type
txs_qs, accounts_digest = self.python_digest(
txs_queryset=txs_queryset,
user_model=user_model,
accounts=accounts,
role=role,
activity=activity,
entity_slug=entity_slug,
unit_slug=unit_slug,
to_date=to_date,
from_date=from_date,
signs=signs,
equity_only=equity_only,
by_period=by_period,
by_unit=by_unit,
by_activity=by_activity,
by_tx_type=by_tx_type,
**kwargs
)
io_data['txs_qs'] = txs_qs
io_data['accounts'] = accounts_digest
if process_roles:
| """
Django Ledger created by Miguel Sanda <msanda@arrobalytics.com>.
Copyright© EDMA Group Inc licensed under the GPLv3 Agreement.
Contributions to this module:
* Miguel Sanda <msanda@arrobalytics.com>
"""
UserModel = get_user_model()
def diff_tx_data(tx_data: list, raise_exception: bool = True):
IS_TX_MODEL = False
TransactionModel = lazy_loader.get_txs_model()
if isinstance(tx_data[0], TransactionModel):
CREDITS = sum(tx.amount for tx in tx_data if tx.tx_type == 'credit')
DEBITS = sum(tx.amount for tx in tx_data if tx.tx_type == 'debit')
IS_TX_MODEL = True
elif isinstance(tx_data[0], dict):
CREDITS = sum(tx['amount'] for tx in tx_data if tx['tx_type'] == 'credit')
DEBITS = sum(tx['amount'] for tx in tx_data if tx['tx_type'] == 'debit')
else:
raise ValidationError('Only Dictionary or TransactionModel allowed.')
is_valid = (CREDITS == DEBITS)
diff = CREDITS - DEBITS
if not is_valid and abs(diff) > settings.DJANGO_LEDGER_TRANSACTION_MAX_TOLERANCE:
if raise_exception:
raise TransactionNotInBalanceError(
f'Invalid tx data. Credits and debits must match. Currently cr: {CREDITS}, db {DEBITS}.'
f'Max Tolerance {settings.DJANGO_LEDGER_TRANSACTION_MAX_TOLERANCE}'
)
return IS_TX_MODEL, is_valid, diff
def check_tx_balance(tx_data: list, perform_correction: bool = False) -> bool:
if tx_data:
IS_TX_MODEL, is_valid, diff = diff_tx_data(tx_data, raise_exception=perform_correction)
if not perform_correction and abs(diff):
return False
if not perform_correction and abs(diff) > settings.DJANGO_LEDGER_TRANSACTION_MAX_TOLERANCE:
return False
while not is_valid:
tx_type_choice = choice(['debit', 'credit'])
txs_candidates = list(tx for tx in tx_data if tx['tx_type'] == tx_type_choice)
if len(txs_candidates) > 0:
tx = choice(list(tx for tx in tx_data if tx['tx_type'] == tx_type_choice))
if any([diff > 0 and tx_type_choice == 'debit',
diff < 0 and tx_type_choice == 'credit']):
if IS_TX_MODEL:
tx.amount += settings.DJANGO_LEDGER_TRANSACTION_CORRECTION
else:
tx['amount'] += settings.DJANGO_LEDGER_TRANSACTION_CORRECTION
elif any([diff < 0 and tx_type_choice == 'debit',
diff > 0 and tx_type_choice == 'credit']):
if IS_TX_MODEL:
tx.amount -= settings.DJANGO_LEDGER_TRANSACTION_CORRECTION
else:
tx['amount'] += settings.DJANGO_LEDGER_TRANSACTION_CORRECTION
IS_TX_MODEL, is_valid, diff = diff_tx_data(tx_data)
return True
def validate_io_date(dt: Union[str, date, datetime], no_parse_localdate: bool = True) -> Optional[datetime]:
if not dt:
return
if isinstance(dt, date):
dt = make_aware(
value=datetime.combine(
dt,
datetime.min.time()
))
return dt
elif isinstance(dt, datetime):
if is_naive(dt):
return make_aware(dt)
return dt
elif isinstance(dt, str):
# try to parse a date object from string...
fdt = parse_date(dt)
if not fdt:
# try to parse a datetime object from string...
fdt = parse_datetime(dt)
if not fdt:
raise InvalidDateInputError(
message=f'Could not parse date from {dt}'
)
elif is_naive(fdt):
fdt = make_aware(fdt)
return fdt
if no_parse_localdate:
return localtime()
def validate_dates(
from_date: Union[str, datetime, date] = None,
to_date: Union[str, datetime, date] = None) -> Tuple[date, date]:
from_date = validate_io_date(from_date, no_parse_localdate=False)
to_date = validate_io_date(to_date)
return from_date, to_date
def validate_activity(activity: str, raise_404: bool = False):
# idea: move to model???...
JournalEntryModel = lazy_loader.get_journal_entry_model()
valid = activity in JournalEntryModel.VALID_ACTIVITIES
if activity and not valid:
exception = ValidationError(f'{activity} is invalid. Choices are {JournalEntryModel.VALID_ACTIVITIES}.')
if raise_404:
raise Http404(exception)
raise exception
return activity
class IOValidationError(ValidationError):
pass
class IODatabaseMixIn:
"""
Controls how transactions are recorded into the ledger.
"""
def is_entity_model(self):
return isinstance(self, lazy_loader.get_entity_model())
def is_ledger_model(self):
return isinstance(self, lazy_loader.get_ledger_model())
def is_entity_unit_model(self):
return isinstance(self, lazy_loader.get_unit_model())
def get_entity_model_from_io(self):
if self.is_entity_model():
return self
elif self.is_ledger_model():
return self.entity
elif self.is_entity_unit_model():
return self.entity
# def is_time_bounded(self, from_date, to_date):
def database_digest(self,
txs_queryset: QuerySet,
entity_slug: str = None,
unit_slug: str = None,
user_model: UserModel = None,
from_date: date = None,
to_date: date = None,
activity: str = None,
role: str = None,
accounts: str or List[str] or Set[str] = None,
posted: bool = True,
exclude_zero_bal: bool = True,
by_activity: bool = False,
by_tx_type: bool = False,
by_period: bool = False,
by_unit: bool = False,
**kwargs):
if settings.DJANGO_LEDGER_USE_CLOSING_ENTRIES:
if not from_date:
entity_model = self.get_entity_model_from_io()
closing_entry_date = entity_model.select_closing_entry_for_io_date(to_date=to_date)
# print(closing_entry_date)
#
# if closing_entry_date:
# closing_entry_list = entity_model.get_closing_entry_cache_for_date(
# closing_date=closing_entry_date,
# force_cache_update=True
# )
# from_date_d = closing_entry_date + timedelta(days=1)
# print('Orig From:', from_date)
# print('New from:', from_date_d)
# print('To Date:', to_date)
# print(closing_entry_list)
if not txs_queryset:
TransactionModel = lazy_loader.get_txs_model()
if self.is_entity_model():
if entity_slug:
if entity_slug != self.slug:
raise IOValidationError('Inconsistent entity_slug. '
f'Provided {entity_slug} does not match actual {self.slug}')
if unit_slug:
txs_queryset = TransactionModel.objects.for_unit(
user_model=user_model,
entity_slug=entity_slug or self.slug,
unit_slug=unit_slug
)
else:
txs_queryset = TransactionModel.objects.for_entity(
user_model=user_model,
entity_slug=self
)
elif self.is_ledger_model():
if not entity_slug:
raise IOValidationError(
'Calling digest from Ledger Model requires entity_slug explicitly for safety')
txs_queryset = TransactionModel.objects.for_ledger(
user_model=user_model,
entity_slug=entity_slug,
ledger_model=self
)
elif self.is_entity_unit_model():
if not entity_slug:
raise IOValidationError(
'Calling digest from Entity Unit requires entity_slug explicitly for safety')
txs_queryset = TransactionModel.objects.for_unit(
user_model=user_model,
entity_slug=entity_slug,
unit_slug=unit_slug or self
)
else:
txs_queryset = TransactionModel.objects.none()
txs_queryset = txs_queryset.not_closing_entry()
if exclude_zero_bal:
txs_queryset = txs_queryset.filter(amount__gt=0)
if posted:
txs_queryset = txs_queryset.posted()
if from_date:
txs_queryset = txs_queryset.from_date(from_date=from_date)
if to_date:
txs_queryset = txs_queryset.to_date(to_date=to_date)
if accounts:
if not isinstance(accounts, str):
accounts = [accounts]
txs_queryset = txs_queryset.for_accounts(account_list=accounts)
if activity:
if isinstance(activity, str):
activity = [activity]
txs_queryset = txs_queryset.for_activity(activity_list=activity)
if role:
txs_queryset = txs_queryset.for_roles(role_list=role)
VALUES = [
'account__uuid',
'account__balance_type',
'tx_type',
'account__code',
'account__name',
'account__role',
]
ANNOTATE = {'balance': Sum('amount')}
ORDER_BY = ['account__uuid']
if by_unit:
ORDER_BY.append('journal_entry__entity_unit__uuid')
VALUES += ['journal_entry__entity_unit__uuid', 'journal_entry__entity_unit__name']
if by_period:
ORDER_BY.append('journal_entry__timestamp')
ANNOTATE['dt_idx'] = TruncMonth('journal_entry__timestamp')
if by_activity:
ORDER_BY.append('journal_entry__activity')
VALUES.append('journal_entry__activity')
if by_tx_type:
ORDER_BY.append('tx_type')
VALUES.append('tx_type')
return txs_queryset.values(*VALUES).annotate(**ANNOTATE).order_by(*ORDER_BY)
def python_digest(self,
txs_queryset: Optional[QuerySet] = None,
user_model: Optional[UserModel] = None,
to_date: date = None,
from_date: date = None,
equity_only: bool = False,
activity: str = None,
entity_slug: str = None,
unit_slug: str = None,
role: Optional[Union[Set[str], List[str]]] = None,
accounts: Optional[Union[Set[str], List[str]]] = None,
signs: bool = False,
by_unit: bool = False,
by_activity: bool = False,
by_tx_type: bool = False,
by_period: bool = False,
**kwargs) -> list or tuple:
if equity_only:
role = roles_module.GROUP_EARNINGS
txs_queryset = self.database_digest(
user_model=user_model,
txs_queryset=txs_queryset,
to_date=to_date,
from_date=from_date,
entity_slug=entity_slug,
unit_slug=unit_slug,
activity=activity,
role=role,
accounts=accounts,
by_unit=by_unit,
by_activity=by_activity,
by_tx_type=by_tx_type,
by_period=by_period,
**kwargs)
for tx_model in txs_queryset:
if tx_model['account__balance_type'] != tx_model['tx_type']:
tx_model['balance'] = -tx_model['balance']
# txs_list = list(txs_queryset)
# txs_list.sort(key=lambda a: (
# a['account__uuid'],
# str(a.get('journal_entry__entity_unit__uuid', '')) if by_unit else '',
# a['dt_idx'].year if by_period else 0,
# a['dt_idx'].month if by_period else 0,
# str(a['journal_entry__activity']) if by_activity else None,
# a['tx_type'] if by_tx_type else '',
# ))
accounts_gb_code = groupby(txs_queryset,
key=lambda a: (
a['account__uuid'],
a.get('journal_entry__entity_unit__uuid') if by_unit else None,
a.get('dt_idx').year if by_period else None,
a.get('dt_idx').month if by_period else None,
a.get('journal_entry__activity') if by_activity else None,
a.get('tx_type') if by_tx_type else None,
))
gb_digest = [self.aggregate_balances(k, g) for k, g in accounts_gb_code]
for acc in gb_digest:
acc['balance_abs'] = abs(acc['balance'])
if signs:
TransactionModel = lazy_loader.get_txs_model()
for acc in gb_digest:
if any([
all([acc['role_bs'] == roles_module.BS_ASSET_ROLE,
acc['balance_type'] == TransactionModel.CREDIT]),
all([acc['role_bs'] in (
roles_module.BS_LIABILITIES_ROLE,
roles_module.BS_EQUITY_ROLE
),
acc['balance_type'] == TransactionModel.DEBIT])
]):
acc['balance'] = -acc['balance']
return txs_queryset, gb_digest
@staticmethod
def aggregate_balances(k, g):
gl = list(g)
return {
'account_uuid': k[0],
'unit_uuid': k[1],
'unit_name': gl[0].get('journal_entry__entity_unit__name'),
'activity': gl[0].get('journal_entry__activity'),
'period_year': k[2],
'period_month': k[3],
'role_bs': roles_module.BS_ROLES.get(gl[0]['account__role']),
'role': gl[0]['account__role'],
'code': gl[0]['account__code'],
'name': gl[0]['account__name'],
'balance_type': gl[0]['account__balance_type'],
'tx_type': k[5],
'balance': sum(a['balance'] for a in gl),
}
def digest(self,
entity_slug: str = None,
unit_slug: str = None,
user_model: UserModel = None,
txs_queryset: QuerySet = None,
as_io_digest: bool = False,
accounts: Optional[Union[Set[str], List[str]]] = None,
role: Optional[Union[Set[str], List[str]]] = None,
activity: str = None,
signs: bool = True,
to_date: Union[str, datetime, date] = None,
from_date: Union[str, datetime, date] = None,
process_roles: bool = False,
process_groups: bool = False,
process_ratios: bool = False,
process_activity: bool = False,
equity_only: bool = False,
by_period: bool = False,
by_unit: bool = False,
by_activity: bool = False,
by_tx_type: bool = False,
digest_name: str = None,
balance_sheet_statement: bool = False,
income_statement: bool = False,
cash_flow_statement: bool = False,
**kwargs) -> Union[Tuple, IODigestContextManager]:
if balance_sheet_statement:
from_date = None
if cash_flow_statement:
by_activity = True
if activity:
activity = validate_activity(activity)
if role:
role = roles_module.validate_roles(role)
from_date, to_date = validate_dates(from_date, to_date)
io_data = defaultdict(lambda: dict())
io_data['io_model'] = self
io_data['from_date'] = from_date
io_data['to_date'] = to_date
io_data['by_unit'] = by_unit
io_data['by_period'] = by_period
io_data['by_activity'] = by_activity
io_data['by_tx_type'] = by_tx_type
txs_qs, accounts_digest = self.python_digest(
txs_queryset=txs_queryset,
user_model=user_model,
accounts=accounts,
role=role,
activity=activity,
entity_slug=entity_slug,
unit_slug=unit_slug,
to_date=to_date,
from_date=from_date,
signs=signs,
equity_only=equity_only,
by_period=by_period,
by_unit=by_unit,
by_activity=by_activity,
by_tx_type=by_tx_type,
**kwargs
)
io_data['txs_qs'] = txs_qs
io_data['accounts'] = accounts_digest
if process_roles: | roles_mgr = RoleContextManager( | 4 | 2023-10-20 01:07:20+00:00 | 24k |
acolas1/KGSimple | simplify.py | [
{
"identifier": "FluencyScorer",
"path": "scoring/fluency_scorer.py",
"snippet": "class FluencyScorer:\n def __init__(self, batch_size=1, reduce=\"mean\", log=True, laplace_smooth=False, prob_dict_path=None):\n self.device = \"cuda:1\" if torch.cuda.is_available() else \"cpu\"\n self.ba... | import os
import json
import numpy as np
import pandas as pd
import torch
import random
from collections import defaultdict
from transformers import BartTokenizer, T5Tokenizer
from transformers import AdamW, get_linear_schedule_with_warmup
from utils import *
from scoring.fluency_scorer import FluencyScorer
from scoring.saliency_scorer import SaliencyBERTScore
from scoring.simplicity_scorer import SimplicityTextScore
from scoring.guardrails import *
from scoring.aggregate_scorer import ScorerWrapper
from GAP.data_relations_as_nodes import GAPDataloader, EventDataset, WebNLGDataset
from GAP.data_relations_as_nodes import evaluate_bleu, get_t_emb_dim
from tqdm import tqdm, trange
from rake_nltk import Rake
from evaluate import load
from sentence_similarity import sentence_similarity
from GAP.modeling_gap_type import GAPBartForConditionalGeneration as GAP_Type_model
from GAP.modeling_gap import GAPBartForConditionalGeneration as GAP_model
| 21,357 |
# import yake
bertscore = load("bertscore")
## sentence model for merge
phrase_model = sentence_similarity(model_name='distilbert-base-uncased',embedding_type='cls_token_embedding')
## for sentence checking
ner_check = NERInaccuracyPenalty()
def run(args, logger):
#load in model for graph-to-text and tokenizer
checkpoint = args.model_path
tokenizer_path = args.tokenizer_path
tokenizer = BartTokenizer.from_pretrained(tokenizer_path)
n_gpu = torch.cuda.device_count()
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if args.type_encoding:
t_emb_dim = get_t_emb_dim(args)
model = GAP_Type_model.from_pretrained(checkpoint,t_emb_dim=t_emb_dim)
else:
|
# import yake
bertscore = load("bertscore")
## sentence model for merge
phrase_model = sentence_similarity(model_name='distilbert-base-uncased',embedding_type='cls_token_embedding')
## for sentence checking
ner_check = NERInaccuracyPenalty()
def run(args, logger):
#load in model for graph-to-text and tokenizer
checkpoint = args.model_path
tokenizer_path = args.tokenizer_path
tokenizer = BartTokenizer.from_pretrained(tokenizer_path)
n_gpu = torch.cuda.device_count()
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if args.type_encoding:
t_emb_dim = get_t_emb_dim(args)
model = GAP_Type_model.from_pretrained(checkpoint,t_emb_dim=t_emb_dim)
else:
| model = GAP_model.from_pretrained(checkpoint)
| 8 | 2023-10-24 13:24:23+00:00 | 24k |
S-LoRA/S-LoRA | slora/server/router/manager.py | [
{
"identifier": "SamplingParams",
"path": "slora/server/sampling_params.py",
"snippet": "class SamplingParams:\n\n def __init__(\n self,\n do_sample: bool = False,\n presence_penalty: float = 0.0,\n frequency_penalty: float = 0.0,\n temperature: float = 1.0,\n ... | import uvloop
import asyncio
import os
import pickle
import time
import torch
import zmq
import zmq.asyncio
import traceback
from typing import Dict, List, Optional
from ..sampling_params import SamplingParams
from ..io_struct import Req, Batch, BatchAbortReq
from .model_infer.model_rpc import start_model_process, ModelRpcClient
from .req_queue import ReqQueue
from rpyc.utils.classic import obtain
from slora.utils.infer_utils import calculate_time
from ..io_struct import BatchTokenIdOut, AbortReq
from .stats import Stats
from slora.server.input_params import InputParams
from slora.models.peft.lora_adapter import get_lora_config
from slora.server.router.profiler import AlphaModel, BetaModel
from slora.server.router.abort_req_queue import AbortReqQueue
from slora.server.router.cluster_req_queue import ClusterReqQueue
from slora.server.router.vtc_req_queue import VTCReqQueue
from slora.server.router.pets_req_queue import PETSReqQueue
from slora.server.router.peft_req_queue import PEFTReqQueue | 15,413 | self.has_wait_tokens += 1
return
else:
new_mini_batch = self.req_queue.generate_new_batch(self.running_batch, self.lora_ranks)
if self.input_params.enable_abort and len(self.req_queue.abort_req_list) > 0:
self.send_to_detokenization.send_pyobj(BatchAbortReq(self.req_queue.abort_req_list))
self.req_queue.reset_abort_list()
if new_mini_batch is not None:
self.stats_tool.count_prompt_tokens(new_mini_batch)
if not self.input_params.no_lora:
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].load_adapters(new_mini_batch.adapter_dirs))
await asyncio.gather(*ret)
await self._prefill_batch(new_mini_batch, minibatch=True)
if not new_mini_batch.is_clear():
await self._merge_batch(self.running_batch, new_mini_batch)
self.running_batch.merge(new_mini_batch)
self.has_wait_tokens = 0
else:
self.stats_tool.count_output_tokens(self.running_batch)
await self._decode_batch(self.running_batch)
await self._filter_runing_batch()
async def _init_batch(self, batch: Batch):
reqs = [r.to_rpc_obj() for r in batch.reqs]
rets = [self.model_rpcs[tp_rank].init_batch(batch.batch_id, reqs) for tp_rank in range(self.world_size)]
await asyncio.gather(*rets)
return
async def _prefill_batch(self, batch, minibatch=True):
await self._init_batch(batch)
rets = [self.model_rpcs[tp_rank].prefill_batch(batch.batch_id) for tp_rank in range(self.world_size)]
ans = await asyncio.gather(*rets)
if self.world_size != 1:
req_to_out_token_id = obtain(ans[0])
else:
req_to_out_token_id = ans[0]
self._add_token_id_to_req(batch, req_to_out_token_id)
has_new_finished_req = batch.mark_finished_req(self.eos_id)
self._send_to_detokenization_proc(batch, req_to_out_token_id)
await self._handle_finish_req(batch, has_new_finished_req, minibatch=True)
return
async def _decode_batch(self, batch:Batch):
self.req_queue.update_counter(batch)
rets = [self.model_rpcs[tp_rank].decode_batch(batch.batch_id) for tp_rank in range(self.world_size)]
ans = await asyncio.gather(*rets)
if self.world_size != 1:
req_to_out_token_id = obtain(ans[0])
else:
req_to_out_token_id = ans[0]
self._add_token_id_to_req(batch, req_to_out_token_id)
has_new_finished_req = batch.mark_finished_req(self.eos_id)
self._send_to_detokenization_proc(batch, req_to_out_token_id)
await self._handle_finish_req(batch, has_new_finished_req)
return
async def _filter_batch(self, batch: Batch):
req_id_list = [r.request_id for r in batch.reqs]
rets = [self.model_rpcs[tp_rank].filter_batch(batch.batch_id, req_id_list) for tp_rank in range(self.world_size)]
await asyncio.gather(*rets)
return
async def _merge_batch(self, batch1, batch2):
rets = [self.model_rpcs[tp_rank].merge_batch(batch1.batch_id, batch2.batch_id) for tp_rank in range(self.world_size)]
await asyncio.gather(*rets)
return
async def _remove_batch(self, batch):
rets = [self.model_rpcs[tp_rank].remove_batch(batch.batch_id) for tp_rank in range(self.world_size)]
await asyncio.gather(*rets)
return
async def _handle_finish_req(self, batch: Batch, has_new_finished_req, minibatch=False):
if has_new_finished_req:
batch.filter_finished()
# unmerge adapter from base model
if self.input_params.scheduler == "peft" and batch.is_clear():
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].unmerge_adapter())
await asyncio.gather(*ret)
if not minibatch and not self.input_params.no_lora:
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].offload_adapters(batch.adapter_dirs))
await asyncio.gather(*ret)
if batch.is_clear():
await self._remove_batch(batch)
else:
await self._filter_batch(batch)
return
async def _filter_runing_batch(self):
if self.running_batch is not None and self.running_batch.is_clear():
if not self.input_params.no_lora:
# offload model and adapters
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].offload_adapters())
await asyncio.gather(*ret)
self.running_batch = None
return
def _add_token_id_to_req(self, batch: Batch, req_ans):
for req_id, (new_token_id, new_gen_metadata) in req_ans.items():
req = batch.id_to_reqs[req_id]
req.output_ids.append(new_token_id)
req.output_metadata_list.append(new_gen_metadata)
return
def _send_to_detokenization_proc(self, batch: Batch, req_ans):
| asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
def get_scheduler(input_params, adapter_dirs):
if input_params.scheduler == "vtc_fair":
return VTCReqQueue(input_params.max_total_token_num, input_params.batch_max_tokens,
input_params.running_max_req_size, adapter_dirs, input_params.fair_weights)
elif input_params.scheduler == "pets":
return PETSReqQueue(input_params.max_total_token_num, input_params.batch_max_tokens,
input_params.running_max_req_size)
elif input_params.scheduler == "peft":
return PEFTReqQueue(input_params.max_total_token_num, input_params.batch_max_tokens,
input_params.running_max_req_size)
elif input_params.batch_num_adapters is not None:
return ClusterReqQueue(input_params.max_total_token_num, input_params.batch_max_tokens,
input_params.running_max_req_size, input_params.batch_num_adapters)
elif input_params.enable_abort:
return AbortReqQueue(input_params.max_total_token_num, input_params.batch_max_tokens,
input_params.running_max_req_size)
elif input_params.scheduler == "slora":
return ReqQueue(input_params.max_total_token_num, input_params.batch_max_tokens,
input_params.running_max_req_size)
else:
raise Exception("unrecognized scheduler")
class RouterManager:
def __init__(self, weightdir, adapter_dirs, load_way, world_size, eos_id,
router_port, detokenization_port, model_rpc_ports,
input_params,
mode=[], log_stats=True, log_stats_interval=10):
self.model_weightdir = weightdir
self.adapter_dirs = adapter_dirs
self.world_size = world_size
self.load_way = load_way
self.mode = mode
self.input_params = input_params
if self.input_params.prefetch:
self.prefetch_stream = torch.cuda.Stream()
else:
self.prefetch_stream = None
# get adapter rank
self.lora_ranks = {}
for lora_dir in adapter_dirs:
config, _ = get_lora_config(lora_dir, input_params.dummy)
self.lora_ranks[lora_dir] = config["r"]
self.lora_ranks[None] = 0
self.req_queue = get_scheduler(input_params, adapter_dirs)
self.running_batch: Batch = None
self.eos_id = eos_id
self.has_wait_tokens = 0
self.max_wait_tokens = 10
context = zmq.asyncio.Context(2)
self.recv_from_httpserver = context.socket(zmq.PULL)
self.recv_from_httpserver.bind(f"tcp://127.0.0.1:{router_port}")
self.send_to_detokenization = context.socket(zmq.PUSH)
self.send_to_detokenization.connect(f"tcp://127.0.0.1:{detokenization_port}")
self.model_rpc_ports = model_rpc_ports
self.stats_tool = Stats(log_stats, log_stats_interval)
async def wait_to_model_ready(self):
self.model_rpcs: List[ModelRpcClient] = []
for rank_id in range(self.world_size):
rpc_model = await start_model_process(port=self.model_rpc_ports[rank_id], world_size=self.world_size)
self.model_rpcs.append(rpc_model)
init_model_ret = []
for rank_id in range(self.world_size): # async init model process
init_model_ret.append(
self.model_rpcs[rank_id].init_model(
rank_id,
self.world_size,
self.model_weightdir,
self.adapter_dirs,
self.input_params.max_total_token_num,
self.load_way,
self.mode,
input_params=self.input_params,
prefetch_stream=self.prefetch_stream,
))
await asyncio.gather(*init_model_ret)
return
async def profile_prefill(self):
res = []
for rank_id in range(self.world_size): # async init model process
res.append(
self.model_rpcs[rank_id].profile_prefill())
results = await asyncio.gather(*res)
self.alpha_model = AlphaModel(results[0])
self.beta_model = BetaModel(results[0])
# check if the path exists else create it
cache_dir = os.path.expanduser("~/.cache/slora")
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
with open(cache_dir+"/profile_results.pkl", "wb") as f:
pickle.dump(results[0], f)
return
def add_req(
self,
adapter_dir: str,
prompt_ids: List[int],
sampling_params: SamplingParams,
request_id: str
):
req = Req(adapter_dir, request_id, prompt_ids, sampling_params)
self.req_queue.append(req)
self.send_to_detokenization.send_pyobj(req.to_req_detokenization_state())
return
async def abort(self, request_id):
if self.running_batch is not None:
for req in self.running_batch.reqs:
if req.request_id == request_id:
req.has_generate_finished = True
req.aborted = True
for req in self.req_queue.waiting_req_list:
if req.request_id == request_id:
req.has_generate_finished = True
req.aborted = True
return
async def loop_for_fwd(self,):
counter_count = 0
while True:
await self._step()
counter_count += 1
if self.running_batch is not None:
if counter_count % 50 == 0:
print("current batch size:", len(self.running_batch.reqs), "token used ratio:", self.running_batch.calcu_used_tokens() / self.input_params.max_total_token_num)
pass
self.stats_tool.print_stats()
if self.running_batch is None:
await asyncio.sleep(0.01) # 10ms
async def _step(self):
"""
事件处理循环
"""
# 删除所有已经 finished 的 req
if self.running_batch is None:
new_batch = self.req_queue.generate_new_batch(self.running_batch, self.lora_ranks)
if self.input_params.enable_abort and len(self.req_queue.abort_req_list) > 0:
self.send_to_detokenization.send_pyobj(BatchAbortReq(self.req_queue.abort_req_list))
self.req_queue.reset_abort_list()
if new_batch is not None:
self.stats_tool.count_prompt_tokens(new_batch)
self.running_batch = new_batch
if not self.input_params.no_lora:
# load adapters
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].load_adapters(new_batch.adapter_dirs))
await asyncio.gather(*ret)
# merge adapter to base model
if self.input_params.scheduler == "peft":
torch.cuda.synchronize()
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].merge_adapter())
await asyncio.gather(*ret)
torch.cuda.synchronize()
await self._prefill_batch(self.running_batch)
await self._filter_runing_batch()
self.has_wait_tokens = 0
return
if self.has_wait_tokens < self.max_wait_tokens:
self.stats_tool.count_output_tokens(self.running_batch)
# prefetch
if (not self.input_params.no_lora and
self.input_params.prefetch and (self.has_wait_tokens == self.max_wait_tokens // 2 or
self.has_wait_tokens == self.max_wait_tokens - 3) and self.input_params.scheduler != "peft"):
next_batch = self.req_queue.next_batch()
if next_batch is not None:
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].load_adapters(
next_batch.adapter_dirs, prefetch=True))
await asyncio.gather(*ret)
await self._decode_batch(self.running_batch)
await self._filter_runing_batch()
self.has_wait_tokens += 1
return
else:
new_mini_batch = self.req_queue.generate_new_batch(self.running_batch, self.lora_ranks)
if self.input_params.enable_abort and len(self.req_queue.abort_req_list) > 0:
self.send_to_detokenization.send_pyobj(BatchAbortReq(self.req_queue.abort_req_list))
self.req_queue.reset_abort_list()
if new_mini_batch is not None:
self.stats_tool.count_prompt_tokens(new_mini_batch)
if not self.input_params.no_lora:
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].load_adapters(new_mini_batch.adapter_dirs))
await asyncio.gather(*ret)
await self._prefill_batch(new_mini_batch, minibatch=True)
if not new_mini_batch.is_clear():
await self._merge_batch(self.running_batch, new_mini_batch)
self.running_batch.merge(new_mini_batch)
self.has_wait_tokens = 0
else:
self.stats_tool.count_output_tokens(self.running_batch)
await self._decode_batch(self.running_batch)
await self._filter_runing_batch()
async def _init_batch(self, batch: Batch):
reqs = [r.to_rpc_obj() for r in batch.reqs]
rets = [self.model_rpcs[tp_rank].init_batch(batch.batch_id, reqs) for tp_rank in range(self.world_size)]
await asyncio.gather(*rets)
return
async def _prefill_batch(self, batch, minibatch=True):
await self._init_batch(batch)
rets = [self.model_rpcs[tp_rank].prefill_batch(batch.batch_id) for tp_rank in range(self.world_size)]
ans = await asyncio.gather(*rets)
if self.world_size != 1:
req_to_out_token_id = obtain(ans[0])
else:
req_to_out_token_id = ans[0]
self._add_token_id_to_req(batch, req_to_out_token_id)
has_new_finished_req = batch.mark_finished_req(self.eos_id)
self._send_to_detokenization_proc(batch, req_to_out_token_id)
await self._handle_finish_req(batch, has_new_finished_req, minibatch=True)
return
async def _decode_batch(self, batch:Batch):
self.req_queue.update_counter(batch)
rets = [self.model_rpcs[tp_rank].decode_batch(batch.batch_id) for tp_rank in range(self.world_size)]
ans = await asyncio.gather(*rets)
if self.world_size != 1:
req_to_out_token_id = obtain(ans[0])
else:
req_to_out_token_id = ans[0]
self._add_token_id_to_req(batch, req_to_out_token_id)
has_new_finished_req = batch.mark_finished_req(self.eos_id)
self._send_to_detokenization_proc(batch, req_to_out_token_id)
await self._handle_finish_req(batch, has_new_finished_req)
return
async def _filter_batch(self, batch: Batch):
req_id_list = [r.request_id for r in batch.reqs]
rets = [self.model_rpcs[tp_rank].filter_batch(batch.batch_id, req_id_list) for tp_rank in range(self.world_size)]
await asyncio.gather(*rets)
return
async def _merge_batch(self, batch1, batch2):
rets = [self.model_rpcs[tp_rank].merge_batch(batch1.batch_id, batch2.batch_id) for tp_rank in range(self.world_size)]
await asyncio.gather(*rets)
return
async def _remove_batch(self, batch):
rets = [self.model_rpcs[tp_rank].remove_batch(batch.batch_id) for tp_rank in range(self.world_size)]
await asyncio.gather(*rets)
return
async def _handle_finish_req(self, batch: Batch, has_new_finished_req, minibatch=False):
if has_new_finished_req:
batch.filter_finished()
# unmerge adapter from base model
if self.input_params.scheduler == "peft" and batch.is_clear():
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].unmerge_adapter())
await asyncio.gather(*ret)
if not minibatch and not self.input_params.no_lora:
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].offload_adapters(batch.adapter_dirs))
await asyncio.gather(*ret)
if batch.is_clear():
await self._remove_batch(batch)
else:
await self._filter_batch(batch)
return
async def _filter_runing_batch(self):
if self.running_batch is not None and self.running_batch.is_clear():
if not self.input_params.no_lora:
# offload model and adapters
ret = []
for tp_rank in range(self.world_size):
ret.append(self.model_rpcs[tp_rank].offload_adapters())
await asyncio.gather(*ret)
self.running_batch = None
return
def _add_token_id_to_req(self, batch: Batch, req_ans):
for req_id, (new_token_id, new_gen_metadata) in req_ans.items():
req = batch.id_to_reqs[req_id]
req.output_ids.append(new_token_id)
req.output_metadata_list.append(new_gen_metadata)
return
def _send_to_detokenization_proc(self, batch: Batch, req_ans): | batch_out = BatchTokenIdOut() | 8 | 2023-11-05 04:08:36+00:00 | 24k |
ForceFledgling/proxyhub | proxyhub/api.py | [
{
"identifier": "Checker",
"path": "proxyhub/checker.py",
"snippet": "class Checker:\n \"\"\"Proxy checker.\"\"\"\n\n def __init__(\n self,\n judges,\n max_tries=3,\n timeout=8,\n verify_ssl=False,\n strict=False,\n dnsbl=None,\n real_ext_ip=... | import asyncio
import io
import signal
import warnings
from collections import Counter, defaultdict
from functools import partial
from pprint import pprint
from .checker import Checker
from .errors import ResolveError
from .providers import PROVIDERS, Provider
from .proxy import Proxy
from .resolver import Resolver
from .server import Server
from .utils import IPPortPatternLine, log | 14,841 | request. If not specified, it will use the value specified during
the creation of the :class:`Broker` object. Attempts can be made
with different proxies. The default value is 3
:param int strategy:
(optional) The strategy used for picking proxy from pool.
The default value is 'best'
:param int min_queue:
(optional) The minimum number of proxies to choose from
before deciding which is the most suitable to use.
The default value is 5
:param int min_req_proxy:
(optional) The minimum number of processed requests to estimate the
quality of proxy (in accordance with :attr:`max_error_rate` and
:attr:`max_resp_time`). The default value is 5
:param int max_error_rate:
(optional) The maximum percentage of requests that ended with
an error. For example: 0.5 = 50%. If proxy.error_rate exceeds this
value, proxy will be removed from the pool.
The default value is 0.5
:param int max_resp_time:
(optional) The maximum response time in seconds.
If proxy.avg_resp_time exceeds this value, proxy will be removed
from the pool. The default value is 8
:param bool prefer_connect:
(optional) Flag that indicates whether to use the CONNECT method
if possible. For example: If is set to True and a proxy supports
HTTP proto (GET or POST requests) and CONNECT method, the server
will try to use CONNECT method and only after that send the
original request. The default value is False
:param list http_allowed_codes:
(optional) Acceptable HTTP codes returned by proxy on requests.
If a proxy return code, not included in this list, it will be
considered as a proxy error, not a wrong/unavailable address.
For example, if a proxy will return a ``404 Not Found`` response -
this will be considered as an error of a proxy.
Checks only for HTTP protocol, HTTPS not supported at the moment.
By default the list is empty and the response code is not verified
:param int backlog:
(optional) The maximum number of queued connections passed to
listen. The default value is 100
:raises ValueError:
If :attr:`limit` is less than or equal to zero.
Because a parsing of providers will be endless
.. versionadded:: 0.2.0
"""
if limit <= 0:
raise ValueError(
'In serve mode value of the limit cannot be less than or '
'equal to zero. Otherwise, a parsing of providers will be '
'endless'
)
self._server = Server(
host=host,
port=port,
proxies=self._proxies,
timeout=self._timeout,
max_tries=kwargs.pop('max_tries', self._max_tries),
loop=self._loop,
**kwargs,
)
self._server.start()
task = asyncio.ensure_future(self.find(limit=limit, **kwargs))
self._all_tasks.append(task)
async def _load(self, data, check=True):
"""Looking for proxies in the passed data.
Transform the passed data from [raw string | file-like object | list]
to set {(host, port), ...}: {('192.168.0.1', '80'), }
"""
log.debug('Load proxies from the raw data')
if isinstance(data, io.TextIOWrapper):
data = data.read()
if isinstance(data, str):
data = IPPortPatternLine.findall(data)
proxies = set(data)
for proxy in proxies:
await self._handle(proxy, check=check)
await self._on_check.join()
self._done()
async def _grab(self, types=None, check=False):
def _get_tasks(by=MAX_CONCURRENT_PROVIDERS):
providers = [
pr
for pr in self._providers
if not types or not pr.proto or bool(pr.proto & types.keys())
]
while providers:
tasks = [
asyncio.ensure_future(pr.get_proxies()) for pr in providers[:by]
]
del providers[:by]
self._all_tasks.extend(tasks)
yield tasks
log.debug('Start grabbing proxies')
while True:
for tasks in _get_tasks():
for task in asyncio.as_completed(tasks):
proxies = await task
for proxy in proxies:
await self._handle(proxy, check=check)
log.debug('Grab cycle is complete')
if self._server:
log.debug('fall asleep for %d seconds' % GRAB_PAUSE)
await asyncio.sleep(GRAB_PAUSE)
log.debug('awaked')
else:
break
await self._on_check.join()
self._done()
async def _handle(self, proxy, check=False):
try:
|
# Pause between grabbing cycles; in seconds.
GRAB_PAUSE = 180
# The maximum number of providers that are parsed concurrently
MAX_CONCURRENT_PROVIDERS = 3
class Broker:
"""The Broker.
| One broker to rule them all, one broker to find them,
| One broker to bring them all and in the darkness bind them.
:param asyncio.Queue queue: (optional) Queue of found/checked proxies
:param int timeout: (optional) Timeout of a request in seconds
:param int max_conn:
(optional) The maximum number of concurrent checks of proxies
:param int max_tries:
(optional) The maximum number of attempts to check a proxy
:param list judges:
(optional) Urls of pages that show HTTP headers and IP address.
Or :class:`~proxyhub.judge.Judge` objects
:param list providers:
(optional) Urls of pages where to find proxies.
Or :class:`~proxyhub.providers.Provider` objects
:param bool verify_ssl:
(optional) Flag indicating whether to check the SSL certificates.
Set to True to check ssl certifications
:param loop: (optional) asyncio compatible event loop
:param stop_broker_on_sigint: (optional) whether set SIGINT signal on broker object.
Useful for a thread other than main thread.
.. deprecated:: 0.2.0
Use :attr:`max_conn` and :attr:`max_tries` instead of
:attr:`max_concurrent_conn` and :attr:`attempts_conn`.
"""
def __init__(
self,
queue=None,
timeout=8,
max_conn=200,
max_tries=3,
judges=None,
providers=None,
verify_ssl=False,
loop=None,
stop_broker_on_sigint=True,
**kwargs,
):
self._loop = loop or asyncio.get_event_loop_policy().get_event_loop()
self._proxies = queue or asyncio.Queue()
self._resolver = Resolver(loop=self._loop)
self._timeout = timeout
self._verify_ssl = verify_ssl
self.unique_proxies = {}
self._all_tasks = []
self._checker = None
self._server = None
self._limit = 0 # not limited
self._countries = None
max_concurrent_conn = kwargs.get('max_concurrent_conn')
if max_concurrent_conn:
warnings.warn(
'`max_concurrent_conn` is deprecated, use `max_conn` instead',
DeprecationWarning,
)
if isinstance(max_concurrent_conn, asyncio.Semaphore):
max_conn = max_concurrent_conn._value
else:
max_conn = max_concurrent_conn
attempts_conn = kwargs.get('attempts_conn')
if attempts_conn:
warnings.warn(
'`attempts_conn` is deprecated, use `max_tries` instead',
DeprecationWarning,
)
max_tries = attempts_conn
# The maximum number of concurrent checking proxies
self._on_check = asyncio.Queue(maxsize=max_conn)
self._max_tries = max_tries
self._judges = judges
self._providers = [
p if isinstance(p, Provider) else Provider(p)
for p in (providers or PROVIDERS)
]
if stop_broker_on_sigint:
try:
self._loop.add_signal_handler(signal.SIGINT, self.stop)
# add_signal_handler() is not implemented on Win
# https://docs.python.org/3.5/library/asyncio-eventloops.html#windows
except NotImplementedError:
pass
async def grab(self, *, countries=None, limit=0):
"""Gather proxies from the providers without checking.
:param list countries: (optional) List of ISO country codes
where should be located proxies
:param int limit: (optional) The maximum number of proxies
:ref:`Example of usage <proxyhub-examples-grab>`.
"""
self._countries = countries
self._limit = limit
task = asyncio.ensure_future(self._grab(check=False))
self._all_tasks.append(task)
async def find(
self,
*,
types=None,
data=None,
countries=None,
post=False,
strict=False,
dnsbl=None,
limit=0,
**kwargs,
):
"""Gather and check proxies from providers or from a passed data.
:ref:`Example of usage <proxyhub-examples-find>`.
:param list types:
Types (protocols) that need to be check on support by proxy.
Supported: HTTP, HTTPS, SOCKS4, SOCKS5, CONNECT:80, CONNECT:25
And levels of anonymity (HTTP only): Transparent, Anonymous, High
:param data:
(optional) String or list with proxies. Also can be a file-like
object supports `read()` method. Used instead of providers
:param list countries:
(optional) List of ISO country codes where should be located
proxies
:param bool post:
(optional) Flag indicating use POST instead of GET for requests
when checking proxies
:param bool strict:
(optional) Flag indicating that anonymity levels of types
(protocols) supported by a proxy must be equal to the requested
types and levels of anonymity. By default, strict mode is off and
for a successful check is enough to satisfy any one of the
requested types
:param list dnsbl:
(optional) Spam databases for proxy checking.
`Wiki <https://en.wikipedia.org/wiki/DNSBL>`_
:param int limit: (optional) The maximum number of proxies
:raises ValueError:
If :attr:`types` not given.
.. versionchanged:: 0.2.0
Added: :attr:`post`, :attr:`strict`, :attr:`dnsbl`.
Changed: :attr:`types` is required.
"""
ip = await self._resolver.get_real_ext_ip()
types = _update_types(types)
if not types:
raise ValueError('`types` is required')
self._checker = Checker(
judges=self._judges,
timeout=self._timeout,
verify_ssl=self._verify_ssl,
max_tries=self._max_tries,
real_ext_ip=ip,
types=types,
post=post,
strict=strict,
dnsbl=dnsbl,
loop=self._loop,
)
self._countries = countries
self._limit = limit
tasks = [asyncio.ensure_future(self._checker.check_judges())]
if data:
task = asyncio.ensure_future(self._load(data, check=True))
else:
task = asyncio.ensure_future(self._grab(types, check=True))
tasks.append(task)
self._all_tasks.extend(tasks)
def serve(self, host='127.0.0.1', port=8888, limit=100, **kwargs):
"""Start a local proxy server.
The server distributes incoming requests to a pool of found proxies.
When the server receives an incoming request, it chooses the optimal
proxy (based on the percentage of errors and average response time)
and passes to it the incoming request.
In addition to the parameters listed below are also accept all the
parameters of the :meth:`.find` method and passed it to gather proxies
to a pool.
:ref:`Example of usage <proxyhub-examples-server>`.
:param str host: (optional) Host of local proxy server
:param int port: (optional) Port of local proxy server
:param int limit:
(optional) When will be found a requested number of working
proxies, checking of new proxies will be lazily paused.
Checking will be resumed if all the found proxies will be discarded
in the process of working with them (see :attr:`max_error_rate`,
:attr:`max_resp_time`). And will continue until it finds one
working proxy and paused again. The default value is 100
:param int max_tries:
(optional) The maximum number of attempts to handle an incoming
request. If not specified, it will use the value specified during
the creation of the :class:`Broker` object. Attempts can be made
with different proxies. The default value is 3
:param int strategy:
(optional) The strategy used for picking proxy from pool.
The default value is 'best'
:param int min_queue:
(optional) The minimum number of proxies to choose from
before deciding which is the most suitable to use.
The default value is 5
:param int min_req_proxy:
(optional) The minimum number of processed requests to estimate the
quality of proxy (in accordance with :attr:`max_error_rate` and
:attr:`max_resp_time`). The default value is 5
:param int max_error_rate:
(optional) The maximum percentage of requests that ended with
an error. For example: 0.5 = 50%. If proxy.error_rate exceeds this
value, proxy will be removed from the pool.
The default value is 0.5
:param int max_resp_time:
(optional) The maximum response time in seconds.
If proxy.avg_resp_time exceeds this value, proxy will be removed
from the pool. The default value is 8
:param bool prefer_connect:
(optional) Flag that indicates whether to use the CONNECT method
if possible. For example: If is set to True and a proxy supports
HTTP proto (GET or POST requests) and CONNECT method, the server
will try to use CONNECT method and only after that send the
original request. The default value is False
:param list http_allowed_codes:
(optional) Acceptable HTTP codes returned by proxy on requests.
If a proxy return code, not included in this list, it will be
considered as a proxy error, not a wrong/unavailable address.
For example, if a proxy will return a ``404 Not Found`` response -
this will be considered as an error of a proxy.
Checks only for HTTP protocol, HTTPS not supported at the moment.
By default the list is empty and the response code is not verified
:param int backlog:
(optional) The maximum number of queued connections passed to
listen. The default value is 100
:raises ValueError:
If :attr:`limit` is less than or equal to zero.
Because a parsing of providers will be endless
.. versionadded:: 0.2.0
"""
if limit <= 0:
raise ValueError(
'In serve mode value of the limit cannot be less than or '
'equal to zero. Otherwise, a parsing of providers will be '
'endless'
)
self._server = Server(
host=host,
port=port,
proxies=self._proxies,
timeout=self._timeout,
max_tries=kwargs.pop('max_tries', self._max_tries),
loop=self._loop,
**kwargs,
)
self._server.start()
task = asyncio.ensure_future(self.find(limit=limit, **kwargs))
self._all_tasks.append(task)
async def _load(self, data, check=True):
"""Looking for proxies in the passed data.
Transform the passed data from [raw string | file-like object | list]
to set {(host, port), ...}: {('192.168.0.1', '80'), }
"""
log.debug('Load proxies from the raw data')
if isinstance(data, io.TextIOWrapper):
data = data.read()
if isinstance(data, str):
data = IPPortPatternLine.findall(data)
proxies = set(data)
for proxy in proxies:
await self._handle(proxy, check=check)
await self._on_check.join()
self._done()
async def _grab(self, types=None, check=False):
def _get_tasks(by=MAX_CONCURRENT_PROVIDERS):
providers = [
pr
for pr in self._providers
if not types or not pr.proto or bool(pr.proto & types.keys())
]
while providers:
tasks = [
asyncio.ensure_future(pr.get_proxies()) for pr in providers[:by]
]
del providers[:by]
self._all_tasks.extend(tasks)
yield tasks
log.debug('Start grabbing proxies')
while True:
for tasks in _get_tasks():
for task in asyncio.as_completed(tasks):
proxies = await task
for proxy in proxies:
await self._handle(proxy, check=check)
log.debug('Grab cycle is complete')
if self._server:
log.debug('fall asleep for %d seconds' % GRAB_PAUSE)
await asyncio.sleep(GRAB_PAUSE)
log.debug('awaked')
else:
break
await self._on_check.join()
self._done()
async def _handle(self, proxy, check=False):
try: | proxy = await Proxy.create( | 4 | 2023-11-05 13:28:57+00:00 | 24k |
TheFunny/ArisuAutoSweeper | module/webui/app.py | [
{
"identifier": "AzurLaneConfig",
"path": "module/config/config.py",
"snippet": "class AzurLaneConfig(ConfigUpdater, ManualConfig, GeneratedConfig, ConfigWatcher):\n stop_event: threading.Event = None\n bound = {}\n\n # Class property\n is_hoarding_task = True\n\n def __setattr__(self, ke... | import argparse
import queue
import threading
import time
import module.webui.lang as lang
from datetime import datetime
from functools import partial
from typing import Dict, List, Optional
from pywebio import config as webconfig
from pywebio.output import (
Output,
clear,
close_popup,
popup,
put_button,
put_buttons,
put_collapse,
put_column,
put_error,
put_html,
put_link,
put_loading,
put_markdown,
put_row,
put_scope,
put_table,
put_text,
put_warning,
toast,
use_scope,
)
from pywebio.pin import pin, pin_on_change
from pywebio.session import go_app, info, local, register_thread, run_js, set_env
from module.config.config import AzurLaneConfig, Function
from module.config.utils import (
alas_instance,
alas_template,
deep_get,
deep_iter,
deep_set,
dict_to_kv,
filepath_args,
filepath_config,
read_file,
)
from module.logger import logger
from module.webui.base import Frame
from module.webui.fake import (
get_config_mod,
load_config,
)
from module.webui.fastapi import asgi_app
from module.webui.lang import _t, t
from module.webui.pin import put_input, put_select
from module.webui.process_manager import ProcessManager
from module.webui.remote_access import RemoteAccess
from module.webui.setting import State
from module.webui.updater import updater
from module.webui.utils import (
Icon,
Switch,
TaskHandler,
add_css,
filepath_css,
get_alas_config_listen_path,
get_localstorage,
get_window_visibility_state,
login,
parse_pin_value,
raise_exception,
re_fullmatch,
)
from module.webui.widgets import (
BinarySwitchButton,
RichLog,
T_Output_Kwargs,
put_icon_buttons,
put_loading_text,
put_none,
put_output,
) | 15,235 | else:
name = arg
color = arg_dict.get("color", "#777777")
nodata = t("Gui.Dashboard.NoData")
def set_value(dic):
if "total" in dic.get("attrs", []) and config.get("total") is not None:
return [
put_text(config.get("value", nodata)).style("--dashboard-value--"),
put_text(f' / {config.get("total", "")}').style("--dashboard-time--"),
]
else:
return [
put_text(config.get("value", nodata)).style("--dashboard-value--"),
]
with use_scope(f"dashboard-row-{arg}", clear=True):
put_html(f'<div><div class="dashboard-icon" style="background-color:{color}"></div>'),
put_scope(f"dashboard-content-{arg}", [
put_scope(f"dashboard-value-{arg}", set_value(arg_dict)),
put_scope(f"dashboard-time-{arg}", [
put_text(f"{name} - {lang.readable_time(config.get('time', ''))}").style("--dashboard-time--"),
])
])
@use_scope("content", clear=True)
def alas_overview(self) -> None:
self.init_menu(name="Overview")
self.set_title(t(f"Gui.MenuAlas.Overview"))
put_scope("overview", [put_scope("schedulers"), put_scope("logs")])
with use_scope("schedulers"):
put_scope(
"scheduler-bar",
[
put_text(t("Gui.Overview.Scheduler")).style(
"font-size: 1.25rem; margin: auto .5rem auto;"
),
put_scope("scheduler_btn"),
],
)
put_scope(
"running",
[
put_text(t("Gui.Overview.Running")),
put_html('<hr class="hr-group">'),
put_scope("running_tasks"),
],
)
put_scope(
"pending",
[
put_text(t("Gui.Overview.Pending")),
put_html('<hr class="hr-group">'),
put_scope("pending_tasks"),
],
)
put_scope(
"waiting",
[
put_text(t("Gui.Overview.Waiting")),
put_html('<hr class="hr-group">'),
put_scope("waiting_tasks"),
],
)
switch_scheduler = BinarySwitchButton(
label_on=t("Gui.Button.Stop"),
label_off=t("Gui.Button.Start"),
onclick_on=lambda: self.alas.stop(),
onclick_off=lambda: self.alas.start(None, updater.event),
get_state=lambda: self.alas.alive,
color_on="off",
color_off="on",
scope="scheduler_btn",
)
log = RichLog("log")
with use_scope("logs"):
put_scope("log-bar", [
put_scope("log-title", [
put_text(t("Gui.Overview.Log")).style("font-size: 1.25rem; margin: auto .5rem auto;"),
put_scope("log-title-btns", [
put_scope("log_scroll_btn"),
]),
]),
put_html('<hr class="hr-group">'),
put_scope("dashboard", [
# Empty dashboard, values will be updated in alas_update_overview_task()
put_scope(f"dashboard-row-{arg}", [])
for arg in self.ALAS_STORED.keys() if deep_get(self.ALAS_STORED, keys=[arg, "order"], default=0)
# Empty content to left-align last row
] + [put_html("<i></i>")] * min(len(self.ALAS_STORED), 4))
])
put_scope("log", [put_html("")])
log.console.width = log.get_width()
switch_log_scroll = BinarySwitchButton(
label_on=t("Gui.Button.ScrollON"),
label_off=t("Gui.Button.ScrollOFF"),
onclick_on=lambda: log.set_scroll(False),
onclick_off=lambda: log.set_scroll(True),
get_state=lambda: log.keep_bottom,
color_on="on",
color_off="off",
scope="log_scroll_btn",
)
self.task_handler.add(switch_scheduler.g(), 1, True)
self.task_handler.add(switch_log_scroll.g(), 1, True)
self.task_handler.add(self.alas_update_overview_task, 10, True)
self.task_handler.add(log.put_log(self.alas), 0.25, True)
def _init_alas_config_watcher(self) -> None:
def put_queue(path, value):
self.modified_config_queue.put({"name": path, "value": value})
|
task_handler = TaskHandler()
class AlasGUI(Frame):
ALAS_MENU: Dict[str, Dict[str, List[str]]]
ALAS_ARGS: Dict[str, Dict[str, Dict[str, Dict[str, str]]]]
ALAS_STORED: Dict[str, Dict[str, Dict[str, str]]]
theme = "default"
def initial(self) -> None:
self.ALAS_MENU = read_file(filepath_args("menu", self.alas_mod))
self.ALAS_ARGS = read_file(filepath_args("args", self.alas_mod))
self.ALAS_STORED = read_file(filepath_args("stored", self.alas_mod))
self._init_alas_config_watcher()
def __init__(self) -> None:
super().__init__()
# modified keys, return values of pin_wait_change()
self.modified_config_queue = queue.Queue()
# alas config name
self.alas_name = ""
self.alas_mod = "alas"
self.alas_config = AzurLaneConfig("template")
self.initial()
@use_scope("aside", clear=True)
def set_aside(self) -> None:
# TODO: update put_icon_buttons()
put_icon_buttons(
Icon.DEVELOP,
buttons=[
{"label": t("Gui.Aside.Home"), "value": "Home", "color": "aside"}
],
onclick=[self.ui_develop],
),
for name in alas_instance():
put_icon_buttons(
Icon.RUN,
buttons=[{"label": name, "value": name, "color": "aside"}],
onclick=self.ui_alas,
)
put_icon_buttons(
Icon.ADD,
buttons=[
{"label": t("Gui.Aside.AddAlas"), "value": "AddAlas", "color": "aside"}
],
onclick=[self.ui_add_alas],
),
@use_scope("header_status")
def set_status(self, state: int) -> None:
"""
Args:
state (int):
1 (running)
2 (not running)
3 (warning, stop unexpectedly)
4 (stop for update)
0 (hide)
-1 (*state not changed)
"""
if state == -1:
return
clear()
if state == 1:
put_loading_text(t("Gui.Status.Running"), color="success")
elif state == 2:
put_loading_text(t("Gui.Status.Inactive"), color="secondary", fill=True)
elif state == 3:
put_loading_text(t("Gui.Status.Warning"), shape="grow", color="warning")
elif state == 4:
put_loading_text(t("Gui.Status.Updating"), shape="grow", color="success")
@classmethod
def set_theme(cls, theme="default") -> None:
cls.theme = theme
State.deploy_config.Theme = theme
State.theme = theme
webconfig(theme=theme)
@use_scope("menu", clear=True)
def alas_set_menu(self) -> None:
"""
Set menu
"""
put_buttons(
[{
"label": t("Gui.MenuAlas.Overview"),
"value": "Overview",
"color": "menu",
}],
onclick=[self.alas_overview],
).style(f"--menu-Overview--")
for menu, task_data in self.ALAS_MENU.items():
if task_data.get("page") == "tool":
_onclick = self.alas_daemon_overview
else:
_onclick = self.alas_set_group
if task_data.get("menu") == "collapse":
task_btn_list = [
put_buttons(
[{
"label": t(f"Task.{task}.name"),
"value": task,
"color": "menu",
}],
onclick=_onclick,
).style(f"--menu-{task}--")
for task in task_data.get("tasks", [])
]
put_collapse(title=t(f"Menu.{menu}.name"), content=task_btn_list)
else:
title = t(f"Menu.{menu}.name")
put_html('<div class="hr-task-group-box">'
'<span class="hr-task-group-line"></span>'
f'<span class="hr-task-group-text">{title}</span>'
'<span class="hr-task-group-line"></span>'
'</div>'
)
for task in task_data.get("tasks", []):
put_buttons(
[{
"label": t(f"Task.{task}.name"),
"value": task,
"color": "menu",
}],
onclick=_onclick,
).style(f"--menu-{task}--").style(f"padding-left: 0.75rem")
self.alas_overview()
@use_scope("content", clear=True)
def alas_set_group(self, task: str) -> None:
"""
Set arg groups from dict
"""
self.init_menu(name=task)
self.set_title(t(f"Task.{task}.name"))
put_scope("_groups", [put_none(), put_scope("groups"), put_scope("navigator")])
task_help: str = t(f"Task.{task}.help")
if task_help:
put_scope(
"group__info",
scope="groups",
content=[put_text(task_help).style("font-size: 1rem")],
)
config = self.alas_config.read_file(self.alas_name)
for group, arg_dict in deep_iter(self.ALAS_ARGS[task], depth=1):
if self.set_group(group, arg_dict, config, task):
self.set_navigator(group)
@use_scope("groups")
def set_group(self, group, arg_dict, config, task):
group_name = group[0]
output_list: List[Output] = []
for arg, arg_dict in deep_iter(arg_dict, depth=1):
output_kwargs: T_Output_Kwargs = arg_dict.copy()
# Skip hide
display: Optional[str] = output_kwargs.pop("display", None)
if display == "hide":
continue
# Disable
elif display == "disabled":
output_kwargs["disabled"] = True
# Output type
output_kwargs["widget_type"] = output_kwargs.pop("type")
arg_name = arg[0] # [arg_name,]
# Internal pin widget name
output_kwargs["name"] = f"{task}_{group_name}_{arg_name}"
# Display title
output_kwargs["title"] = t(f"{group_name}.{arg_name}.name")
# Get value from config
value = deep_get(
config, [task, group_name, arg_name], output_kwargs["value"]
)
# idk
value = str(value) if isinstance(value, datetime) else value
# Default value
output_kwargs["value"] = value
# Options
output_kwargs["options"] = options = output_kwargs.pop("option", [])
# Options label
options_label = []
for opt in options:
options_label.append(t(f"{group_name}.{arg_name}.{opt}"))
output_kwargs["options_label"] = options_label
# Help
arg_help = t(f"{group_name}.{arg_name}.help")
if arg_help == "" or not arg_help:
arg_help = None
output_kwargs["help"] = arg_help
# Invalid feedback
output_kwargs["invalid_feedback"] = t("Gui.Text.InvalidFeedBack", value)
o = put_output(output_kwargs)
if o is not None:
# output will inherit current scope when created, override here
o.spec["scope"] = f"#pywebio-scope-group_{group_name}"
output_list.append(o)
if not output_list:
return 0
with use_scope(f"group_{group_name}"):
put_text(t(f"{group_name}._info.name"))
group_help = t(f"{group_name}._info.help")
if group_help != "":
put_text(group_help)
put_html('<hr class="hr-group">')
for output in output_list:
output.show()
return len(output_list)
@use_scope("navigator")
def set_navigator(self, group):
js = f"""
$("#pywebio-scope-groups").scrollTop(
$("#pywebio-scope-group_{group[0]}").position().top
+ $("#pywebio-scope-groups").scrollTop() - 59
)
"""
put_button(
label=t(f"{group[0]}._info.name"),
onclick=lambda: run_js(js),
color="navigator",
)
def set_dashboard(self, arg, arg_dict, config):
i18n = arg_dict.get('i18n')
if i18n:
name = t(i18n)
else:
name = arg
color = arg_dict.get("color", "#777777")
nodata = t("Gui.Dashboard.NoData")
def set_value(dic):
if "total" in dic.get("attrs", []) and config.get("total") is not None:
return [
put_text(config.get("value", nodata)).style("--dashboard-value--"),
put_text(f' / {config.get("total", "")}').style("--dashboard-time--"),
]
else:
return [
put_text(config.get("value", nodata)).style("--dashboard-value--"),
]
with use_scope(f"dashboard-row-{arg}", clear=True):
put_html(f'<div><div class="dashboard-icon" style="background-color:{color}"></div>'),
put_scope(f"dashboard-content-{arg}", [
put_scope(f"dashboard-value-{arg}", set_value(arg_dict)),
put_scope(f"dashboard-time-{arg}", [
put_text(f"{name} - {lang.readable_time(config.get('time', ''))}").style("--dashboard-time--"),
])
])
@use_scope("content", clear=True)
def alas_overview(self) -> None:
self.init_menu(name="Overview")
self.set_title(t(f"Gui.MenuAlas.Overview"))
put_scope("overview", [put_scope("schedulers"), put_scope("logs")])
with use_scope("schedulers"):
put_scope(
"scheduler-bar",
[
put_text(t("Gui.Overview.Scheduler")).style(
"font-size: 1.25rem; margin: auto .5rem auto;"
),
put_scope("scheduler_btn"),
],
)
put_scope(
"running",
[
put_text(t("Gui.Overview.Running")),
put_html('<hr class="hr-group">'),
put_scope("running_tasks"),
],
)
put_scope(
"pending",
[
put_text(t("Gui.Overview.Pending")),
put_html('<hr class="hr-group">'),
put_scope("pending_tasks"),
],
)
put_scope(
"waiting",
[
put_text(t("Gui.Overview.Waiting")),
put_html('<hr class="hr-group">'),
put_scope("waiting_tasks"),
],
)
switch_scheduler = BinarySwitchButton(
label_on=t("Gui.Button.Stop"),
label_off=t("Gui.Button.Start"),
onclick_on=lambda: self.alas.stop(),
onclick_off=lambda: self.alas.start(None, updater.event),
get_state=lambda: self.alas.alive,
color_on="off",
color_off="on",
scope="scheduler_btn",
)
log = RichLog("log")
with use_scope("logs"):
put_scope("log-bar", [
put_scope("log-title", [
put_text(t("Gui.Overview.Log")).style("font-size: 1.25rem; margin: auto .5rem auto;"),
put_scope("log-title-btns", [
put_scope("log_scroll_btn"),
]),
]),
put_html('<hr class="hr-group">'),
put_scope("dashboard", [
# Empty dashboard, values will be updated in alas_update_overview_task()
put_scope(f"dashboard-row-{arg}", [])
for arg in self.ALAS_STORED.keys() if deep_get(self.ALAS_STORED, keys=[arg, "order"], default=0)
# Empty content to left-align last row
] + [put_html("<i></i>")] * min(len(self.ALAS_STORED), 4))
])
put_scope("log", [put_html("")])
log.console.width = log.get_width()
switch_log_scroll = BinarySwitchButton(
label_on=t("Gui.Button.ScrollON"),
label_off=t("Gui.Button.ScrollOFF"),
onclick_on=lambda: log.set_scroll(False),
onclick_off=lambda: log.set_scroll(True),
get_state=lambda: log.keep_bottom,
color_on="on",
color_off="off",
scope="log_scroll_btn",
)
self.task_handler.add(switch_scheduler.g(), 1, True)
self.task_handler.add(switch_log_scroll.g(), 1, True)
self.task_handler.add(self.alas_update_overview_task, 10, True)
self.task_handler.add(log.put_log(self.alas), 0.25, True)
def _init_alas_config_watcher(self) -> None:
def put_queue(path, value):
self.modified_config_queue.put({"name": path, "value": value})
| for path in get_alas_config_listen_path(self.ALAS_ARGS): | 29 | 2023-11-01 07:09:45+00:00 | 24k |
radekd91/inferno | inferno/models/DECA.py | [
{
"identifier": "EmoNetLoss",
"path": "inferno/layers/losses/EmoNetLoss.py",
"snippet": "class EmoNetLoss(EmoLossBase):\n# class EmoNetLoss(object):\n\n def __init__(self, device, emonet=None, trainable=False, normalize_features=False, emo_feat_loss=None, au_loss=None):\n if emonet is None:\n ... | import os, sys
import torch
import torchvision
import torch.nn.functional as F
import torchvision.transforms.functional as F_v
import numpy as np
import cv2
import inferno.layers.losses.DecaLosses as lossfunc
import inferno.layers.losses.MediaPipeLandmarkLosses as lossfunc_mp
import inferno.utils.DecaUtils as util
import pytorch_lightning.plugins.environments.lightning_environment as le
import psutil
import adabound
import copy
from pytorch_lightning import LightningModule
from pytorch_lightning.loggers import WandbLogger
from inferno.layers.losses.EmoNetLoss import EmoNetLoss, create_emo_loss, create_au_loss
from skimage.io import imread
from skimage.transform import resize
from pathlib import Path
from inferno.models.Renderer import SRenderY
from inferno.models.DecaEncoder import ResnetEncoder, SecondHeadResnet, SwinEncoder
from inferno.models.DecaDecoder import Generator, GeneratorAdaIn
from inferno.models.DecaFLAME import FLAME, FLAMETex, FLAME_mediapipe
from inferno.models.EmotionMLP import EmotionMLP
from inferno.datasets.AffWild2Dataset import Expression7
from inferno.datasets.AffectNetDataModule import AffectNetExpressions
from inferno.utils.lightning_logging import _log_array_image, _log_wandb_image, _torch_image2np
from enum import Enum
from inferno.utils.other import class_from_str, get_path_to_assets
from inferno.layers.losses.VGGLoss import VGG19Loss
from omegaconf import OmegaConf, open_dict
from inferno.models.temporal.external.LipReadingLoss import LipReadingLoss
from .StarGAN import StarGANWrapper
from inferno.models.EmoNetRegressor import EmoNetRegressor, EmonetRegressorStatic
from .mica.config import get_cfg_defaults
from .mica.mica import MICA
from .mica.MicaInputProcessing import MicaInputProcessor
from inferno.utils.other import get_path_to_assets
from inferno.models.IO import locate_checkpoint | 19,546 | uv_detail_vertices = uv_coarse_vertices + \
uv_z * uv_coarse_normals + \
self.fixed_uv_dis[None, None, :,:] * uv_coarse_normals #.detach()
dense_vertices = uv_detail_vertices.permute(0, 2, 3, 1).reshape([batch_size, -1, 3])
uv_detail_normals = util.vertex_normals(dense_vertices, self.render.dense_faces.expand(batch_size, -1, -1))
uv_detail_normals = uv_detail_normals.reshape(
[batch_size, uv_coarse_vertices.shape[2], uv_coarse_vertices.shape[3], 3]).permute(0, 3, 1, 2)
# uv_detail_normals = uv_detail_normals*self.uv_face_eye_mask + uv_coarse_normals*(1-self.uv_face_eye_mask)
# uv_detail_normals = util.gaussian_blur(uv_detail_normals)
return uv_detail_normals, uv_coarse_vertices
def visualize(self, visdict, savepath, catdim=1):
grids = {}
for key in visdict:
# print(key)
if visdict[key] is None:
continue
grids[key] = torchvision.utils.make_grid(
F.interpolate(visdict[key], [self.config.image_size, self.config.image_size])).detach().cpu()
grid = torch.cat(list(grids.values()), catdim)
grid_image = (grid.numpy().transpose(1, 2, 0).copy() * 255)[:, :, [2, 1, 0]]
grid_image = np.minimum(np.maximum(grid_image, 0), 255).astype(np.uint8)
if savepath is not None:
cv2.imwrite(savepath, grid_image)
return grid_image
def create_mesh(self, opdict, dense_template):
'''
vertices: [nv, 3], tensor
texture: [3, h, w], tensor
'''
i = 0
vertices = opdict['verts'][i].cpu().numpy()
faces = self.render.faces[0].cpu().numpy()
if 'uv_texture_gt' in opdict.keys():
texture = util.tensor2image(opdict['uv_texture_gt'][i])
else:
texture = None
uvcoords = self.render.raw_uvcoords[0].cpu().numpy()
uvfaces = self.render.uvfaces[0].cpu().numpy()
# save coarse mesh, with texture and normal map
if 'uv_detail_normals' in opdict.keys():
normal_map = util.tensor2image(opdict['uv_detail_normals'][i]*0.5 + 0.5)
# upsample mesh, save detailed mesh
texture = texture[:, :, [2, 1, 0]]
normals = opdict['normals'][i].cpu().numpy()
displacement_map = opdict['displacement_map'][i].detach().cpu().numpy().squeeze()
dense_vertices, dense_colors, dense_faces = util.upsample_mesh(vertices, normals, faces,
displacement_map, texture, dense_template)
else:
normal_map = None
dense_vertices = None
dense_colors = None
dense_faces = None
return vertices, faces, texture, uvcoords, uvfaces, normal_map, dense_vertices, dense_faces, dense_colors
def save_obj(self, filename, opdict, dense_template, mode ='detail'):
if mode not in ['coarse', 'detail', 'both']:
raise ValueError(f"Invalid mode '{mode}. Expected modes are: 'coarse', 'detail', 'both'")
vertices, faces, texture, uvcoords, uvfaces, normal_map, dense_vertices, dense_faces, dense_colors \
= self.create_mesh(opdict, dense_template)
if mode == 'both':
if isinstance(filename, list):
filename_coarse = filename[0]
filename_detail = filename[1]
else:
filename_coarse = filename
filename_detail = filename.replace('.obj', '_detail.obj')
elif mode == 'coarse':
filename_coarse = filename
else:
filename_detail = filename
if mode in ['coarse', 'both']:
util.write_obj(str(filename_coarse), vertices, faces,
texture=texture,
uvcoords=uvcoords,
uvfaces=uvfaces,
normal_map=normal_map)
if mode in ['detail', 'both']:
util.write_obj(str(filename_detail),
dense_vertices,
dense_faces,
colors = dense_colors,
inverse_face_order=True)
class ExpDECAInterface(object):
"""
This serves as an interface for EMOCA-like classes that need to use a different sub class but
retain the EMOCA functionality. See EMICA_v2 for an example.
"""
def _create_model(self):
# E_flame should be fixed for expression EMOCA
self.E_flame.requires_grad_(False)
# 2) add expression decoder
if self.config.expression_backbone == 'deca_parallel':
## a) Attach a parallel flow of FCs onto the original DECA coarse backbone. (Only the second FC head is trainable)
self.E_expression = SecondHeadResnet(self.E_flame, self.n_exp_param, 'same')
elif self.config.expression_backbone == 'deca_clone':
## b) Clones the original DECA coarse decoder (and the entire decoder will be trainable) - This is in final EMOCA.
#TODO this will only work for Resnet. Make this work for the other backbones (Swin) as well.
self.E_expression = ResnetEncoder(self.n_exp_param)
# clone parameters of the ResNet
self.E_expression.encoder.load_state_dict(self.E_flame.encoder.state_dict())
elif self.config.expression_backbone == 'emonet_trainable':
# Trainable EmoNet instead of Resnet (deprecated)
self.E_expression = EmoNetRegressor(self.n_exp_param)
elif self.config.expression_backbone == 'emonet_static':
# Frozen EmoNet with a trainable head instead of Resnet (deprecated)
| """
Author: Radek Danecek
Copyright (c) 2022, Radek Danecek
All rights reserved.
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# Using this computer program means that you agree to the terms
# in the LICENSE file included with this software distribution.
# Any use not explicitly granted by the LICENSE is prohibited.
#
# Copyright©2022 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# For comments or questions, please email us at emoca@tue.mpg.de
# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de
Parts of the code were adapted from the original DECA release:
https://github.com/YadiraF/DECA/
"""
# from time import time
torch.backends.cudnn.benchmark = True
class DecaMode(Enum):
COARSE = 1 # when switched on, only coarse part of DECA-based networks is used
DETAIL = 2 # when switched on, only coarse and detail part of DECA-based networks is used
class DecaModule(LightningModule):
"""
DecaModule is a PL module that implements DECA-inspired face reconstruction networks.
"""
def __init__(self, model_params, learning_params, inout_params, stage_name = ""):
"""
:param model_params: a DictConfig of parameters about the model itself
:param learning_params: a DictConfig of parameters corresponding to the learning process (such as optimizer, lr and others)
:param inout_params: a DictConfig of parameters about input and output (where checkpoints and visualizations are saved)
"""
super().__init__()
self.learning_params = learning_params
self.inout_params = inout_params
# detail conditioning - what is given as the conditioning input to the detail generator in detail stage training
if 'detail_conditioning' not in model_params.keys():
# jaw, expression and detail code by default
self.detail_conditioning = ['jawpose', 'expression', 'detail']
OmegaConf.set_struct(model_params, True)
with open_dict(model_params):
model_params.detail_conditioning = self.detail_conditioning
else:
self.detail_conditioning = model_params.detail_conditioning
# deprecated and is not used
if 'detailemo_conditioning' not in model_params.keys():
self.detailemo_conditioning = []
OmegaConf.set_struct(model_params, True)
with open_dict(model_params):
model_params.detailemo_conditioning = self.detailemo_conditioning
else:
self.detailemo_conditioning = model_params.detailemo_conditioning
supported_conditioning_keys = ['identity', 'jawpose', 'expression', 'detail', 'detailemo']
for c in self.detail_conditioning:
if c not in supported_conditioning_keys:
raise ValueError(f"Conditioning on '{c}' is not supported. Supported conditionings: {supported_conditioning_keys}")
for c in self.detailemo_conditioning:
if c not in supported_conditioning_keys:
raise ValueError(f"Conditioning on '{c}' is not supported. Supported conditionings: {supported_conditioning_keys}")
# which type of DECA network is used
if 'deca_class' not in model_params.keys() or model_params.deca_class is None:
print(f"Deca class is not specified. Defaulting to {str(DECA.__class__.__name__)}")
# vanilla DECA by default (not EMOCA)
deca_class = DECA
else:
# other type of DECA-inspired networks possible (such as ExpDECA, which is what EMOCA)
deca_class = class_from_str(model_params.deca_class, sys.modules[__name__])
# instantiate the network
self.deca = deca_class(config=model_params)
self.mode = DecaMode[str(model_params.mode).upper()]
self.stage_name = stage_name
if self.stage_name is None:
self.stage_name = ""
if len(self.stage_name) > 0:
self.stage_name += "_"
# initialize the emotion perceptual loss (used for EMOCA supervision)
self.emonet_loss = None
self._init_emotion_loss()
# initialize the au perceptual loss (not currently used in EMOCA)
self.au_loss = None
self._init_au_loss()
# initialize the lip reading perceptual loss (not currently used in original EMOCA)
self.lipread_loss = None
self._init_lipread_loss()
# MPL regressor from the encoded space to emotion labels (not used in EMOCA but could be used for direct emotion supervision)
if 'mlp_emotion_predictor' in self.deca.config.keys():
# self._build_emotion_mlp(self.deca.config.mlp_emotion_predictor)
self.emotion_mlp = EmotionMLP(self.deca.config.mlp_emotion_predictor, model_params)
else:
self.emotion_mlp = None
def get_input_image_size(self):
return (self.deca.config.image_size, self.deca.config.image_size)
def _instantiate_deca(self, model_params):
"""
Instantiate the DECA network.
"""
# which type of DECA network is used
if 'deca_class' not in model_params.keys() or model_params.deca_class is None:
print(f"Deca class is not specified. Defaulting to {str(DECA.__class__.__name__)}")
# vanilla DECA by default (not EMOCA)
deca_class = DECA
else:
# other type of DECA-inspired networks possible (such as ExpDECA, which is what EMOCA)
deca_class = class_from_str(model_params.deca_class, sys.modules[__name__])
# instantiate the network
self.deca = deca_class(config=model_params)
def _init_emotion_loss(self):
"""
Initialize the emotion perceptual loss (used for EMOCA supervision)
"""
if 'emonet_weight' in self.deca.config.keys() and bool(self.deca.config.get('emonet_model_path', False)):
if self.emonet_loss is not None:
emoloss_force_override = True if 'emoloss_force_override' in self.deca.config.keys() and self.deca.config.emoloss_force_override else False
if self.emonet_loss.is_trainable():
if not emoloss_force_override:
print("The old emonet loss is trainable and will not be overrided or replaced.")
return
# raise NotImplementedError("The old emonet loss was trainable. Changing a trainable loss is probably now "
# "what you want implicitly. If you need this, use the '`'emoloss_force_override' config.")
else:
print("The old emonet loss is trainable but override is set so it will be replaced.")
else:
print("The old emonet loss is not trainable. It will be replaced.")
if 'emonet_model_path' in self.deca.config.keys():
emonet_model_path = self.deca.config.emonet_model_path
else:
emonet_model_path=None
# self.emonet_loss = EmoNetLoss(self.device, emonet=emonet_model_path)
emoloss_trainable = True if 'emoloss_trainable' in self.deca.config.keys() and self.deca.config.emoloss_trainable else False
emoloss_dual = True if 'emoloss_dual' in self.deca.config.keys() and self.deca.config.emoloss_dual else False
normalize_features = self.deca.config.normalize_features if 'normalize_features' in self.deca.config.keys() else None
emo_feat_loss = self.deca.config.emo_feat_loss if 'emo_feat_loss' in self.deca.config.keys() else None
old_emonet_loss = self.emonet_loss
self.emonet_loss = create_emo_loss(self.device, emoloss=emonet_model_path, trainable=emoloss_trainable,
dual=emoloss_dual,
normalize_features=normalize_features,
emo_feat_loss=emo_feat_loss)
if old_emonet_loss is not None and type(old_emonet_loss) != self.emonet_loss:
print(f"The old emonet loss {old_emonet_loss.__class__.__name__} is replaced during reconfiguration by "
f"new emotion loss {self.emonet_loss.__class__.__name__}")
else:
self.emonet_loss = None
def _init_au_loss(self):
"""
Initialize the au perceptual loss (not currently used in EMOCA)
"""
if 'au_loss' in self.deca.config.keys():
if self.au_loss is not None:
force_override = True if 'force_override' in self.deca.config.au_loss.keys() \
and self.deca.config.au_loss.force_override else False
if self.au_loss.is_trainable():
if not force_override:
print("The old AU loss is trainable and will not be overrided or replaced.")
return
# raise NotImplementedError("The old emonet loss was trainable. Changing a trainable loss is probably now "
# "what you want implicitly. If you need this, use the '`'emoloss_force_override' config.")
else:
print("The old AU loss is trainable but override is set so it will be replaced.")
else:
print("The old AU loss is not trainable. It will be replaced.")
old_au_loss = self.emonet_loss
self.au_loss = create_au_loss(self.device, self.deca.config.au_loss)
else:
self.au_loss = None
def _init_lipread_loss(self):
"""
Initialize the au perceptual loss (not currently used in EMOCA)
"""
if 'lipread_loss' in self.deca.config.keys() and self.deca.config.lipread_loss.get('load', True):
if self.lipread_loss is not None:
force_override = True if 'force_override' in self.deca.config.lipread_loss.keys() \
and self.deca.config.lipread_loss.force_override else False
assert self.lipread_loss.is_trainable(), "Trainable lip reading loss is not supported yet."
if self.lipread_loss.is_trainable():
if not force_override:
print("The old lip reading loss is trainable and will not be overrided or replaced.")
return
# raise NotImplementedError("The old emonet loss was trainable. Changing a trainable loss is probably now "
# "what you want implicitly. If you need this, use the '`'emoloss_force_override' config.")
else:
print("The old lip reading loss is trainable but override is set so it will be replaced.")
else:
print("The old lip reading loss is not trainable. It will be replaced.")
# old_lipread_loss = self.emonet_loss
self.lipread_loss = LipReadingLoss(self.device, self.deca.config.lipread_loss.lipread_loss)
self.lipread_loss.eval()
self.lipread_loss.requires_grad_(False)
else:
self.lipread_loss = None
def reconfigure(self, model_params, inout_params, learning_params, stage_name="", downgrade_ok=False, train=True):
"""
Reconfigure the model. Usually used to switch between detail and coarse stages (which have separate configs)
"""
if (self.mode == DecaMode.DETAIL and model_params.mode != DecaMode.DETAIL) and not downgrade_ok:
raise RuntimeError("You're switching the EMOCA mode from DETAIL to COARSE. Is this really what you want?!")
self.inout_params = inout_params
self.learning_params = learning_params
if self.deca.__class__.__name__ != model_params.deca_class:
old_deca_class = self.deca.__class__.__name__
state_dict = self.deca.state_dict()
if 'deca_class' in model_params.keys():
deca_class = class_from_str(model_params.deca_class, sys.modules[__name__])
else:
deca_class = DECA
self.deca = deca_class(config=model_params)
diff = set(state_dict.keys()).difference(set(self.deca.state_dict().keys()))
if len(diff) > 0:
raise RuntimeError(f"Some values from old state dict will not be used. This is probably not what you "
f"want because it most likely means that the pretrained model's weights won't be used. "
f"Maybe you messed up backbone compatibility (i.e. SWIN vs ResNet?) {diff}")
ret = self.deca.load_state_dict(state_dict, strict=False)
if len(ret.unexpected_keys) > 0:
raise print(f"Unexpected keys: {ret.unexpected_keys}")
missing_modules = set([s.split(".")[0] for s in ret.missing_keys])
print(f"Missing modules when upgrading from {old_deca_class} to {model_params.deca_class}:")
print(missing_modules)
else:
self.deca._reconfigure(model_params)
self._init_emotion_loss()
self._init_au_loss()
self.stage_name = stage_name
if self.stage_name is None:
self.stage_name = ""
if len(self.stage_name) > 0:
self.stage_name += "_"
self.mode = DecaMode[str(model_params.mode).upper()]
self.train(mode=train)
print(f"EMOCA MODE RECONFIGURED TO: {self.mode}")
if 'shape_contrain_type' in self.deca.config.keys() and str(self.deca.config.shape_constrain_type).lower() != 'none':
shape_constraint = self.deca.config.shape_constrain_type
else:
shape_constraint = None
if 'expression_constrain_type' in self.deca.config.keys() and str(self.deca.config.expression_constrain_type).lower() != 'none':
expression_constraint = self.deca.config.expression_constrain_type
else:
expression_constraint = None
if shape_constraint is not None and expression_constraint is not None:
raise ValueError("Both shape constraint and expression constraint are active. This is probably not what we want.")
def uses_texture(self):
"""
Check if the model uses texture
"""
return self.deca.uses_texture()
def visualize(self, visdict, savepath, catdim=1):
return self.deca.visualize(visdict, savepath, catdim)
def train(self, mode: bool = True):
# super().train(mode) # not necessary
self.deca.train(mode)
if self.emotion_mlp is not None:
self.emotion_mlp.train(mode)
if self.emonet_loss is not None:
self.emonet_loss.eval()
if self.deca.perceptual_loss is not None:
self.deca.perceptual_loss.eval()
if self.deca.id_loss is not None:
self.deca.id_loss.eval()
return self
def to(self, *args, **kwargs):
super().to(*args, **kwargs)
return self
def cuda(self, device=None):
super().cuda(device)
return self
def cpu(self):
super().cpu()
return self
def forward(self, batch):
values = self.encode(batch, training=False)
values = self.decode(values, training=False)
return values
def _unwrap_list(self, codelist):
shapecode, texcode, expcode, posecode, cam, lightcode = codelist
return shapecode, texcode, expcode, posecode, cam, lightcode
def _unwrap_list_to_dict(self, codelist):
shapecode, texcode, expcode, posecode, cam, lightcode = codelist
return {'shape': shapecode, 'tex': texcode, 'exp': expcode, 'pose': posecode, 'cam': cam, 'light': lightcode}
# return shapecode, texcode, expcode, posecode, cam, lightcode
def _encode_flame(self, images, **kwargs):
if self.mode == DecaMode.COARSE or \
(self.mode == DecaMode.DETAIL and self.deca.config.train_coarse):
# forward pass with gradients (for coarse stage (used), or detail stage with coarse training (not used))
parameters = self.deca._encode_flame(images, **kwargs)
elif self.mode == DecaMode.DETAIL:
# in detail stage, the coarse forward pass does not need gradients
with torch.no_grad():
parameters = self.deca._encode_flame(images, **kwargs)
else:
raise ValueError(f"Invalid EMOCA Mode {self.mode}")
code_list, original_code = self.deca.decompose_code(parameters)
# shapecode, texcode, expcode, posecode, cam, lightcode = code_list
# return shapecode, texcode, expcode, posecode, cam, lightcode, original_code
return code_list, original_code
def _expression_ring_exchange(self, original_batch_size, K,
expcode, posecode, shapecode, lightcode, texcode,
images, cam, lmk, masks, va, expr7, affectnetexp,
detailcode=None, detailemocode=None, exprw=None, lmk_mp=None,
mica_images=None):
"""
Deprecated. Expression ring exchange is not used in EMOCA (nor DECA).
"""
new_order = np.array([np.random.permutation(K) + i * K for i in range(original_batch_size)])
new_order = new_order.flatten()
expcode_new = expcode[new_order]
## append new shape code data
expcode = torch.cat([expcode, expcode_new], dim=0)
texcode = torch.cat([texcode, texcode], dim=0)
shapecode = torch.cat([shapecode, shapecode], dim=0)
globpose = posecode[..., :3]
jawpose = posecode[..., 3:]
if self.deca.config.expression_constrain_use_jaw_pose:
jawpose_new = jawpose[new_order]
jawpose = torch.cat([jawpose, jawpose_new], dim=0)
else:
jawpose = torch.cat([jawpose, jawpose], dim=0)
if self.deca.config.expression_constrain_use_global_pose:
globpose_new = globpose[new_order]
globpose = torch.cat([globpose, globpose_new], dim=0)
else:
globpose = torch.cat([globpose, globpose], dim=0)
if self.deca.config.expression_constrain_use_jaw_pose or self.deca.config.expression_constrain_use_global_pose:
posecode = torch.cat([globpose, jawpose], dim=-1)
# posecode_new = torch.cat([globpose, jawpose], dim=-1)
else:
# posecode_new = posecode
# posecode_new = posecode
posecode = torch.cat([posecode, posecode], dim=0)
cam = torch.cat([cam, cam], dim=0)
lightcode = torch.cat([lightcode, lightcode], dim=0)
## append gt
images = torch.cat([images, images],
dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])
if mica_images is not None:
mica_images = torch.cat([mica_images, mica_images], dim=0)
lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])
if lmk_mp is not None:
lmk_mp = torch.cat([lmk_mp, lmk_mp], dim=0)
masks = torch.cat([masks, masks], dim=0)
# NOTE:
# Here we could think about what makes sense to exchange
# 1) Do we exchange all emotion GT (VA and expression) within the ring?
# 2) Do we exchange only the GT on which the ring is constructed (AffectNet ring based on binned VA or expression or Emonet feature?)
# note: if we use EmoMLP that goes from (expression, jawpose, detailcode) -> (v,a,expr) and we exchange
# ALL of these, the EmoMLP prediction will of course be the same. The output image still changes,
# so EmoNet loss (if used) would be different. Same for the photometric/landmark losses.
# TODO:
# For now I decided to exchange everything but this should probably be experimented with
# I would argue though, that exchanging the GT is the right thing to do
if va is not None:
va = torch.cat([va, va[new_order]], dim=0)
if expr7 is not None:
expr7 = torch.cat([expr7, expr7[new_order]], dim=0)
if affectnetexp is not None:
affectnetexp = torch.cat([affectnetexp, affectnetexp[new_order]], dim=0)
if exprw is not None:
exprw = torch.cat([exprw, exprw[new_order]], dim=0)
if detailcode is not None:
#TODO: to exchange or not to exchange, that is the question, the answer is probably NO
detailcode = torch.cat([detailcode, detailcode], dim=0)
# detailcode = torch.cat([detailcode, detailcode[new_order]], dim=0)
if detailemocode is not None:
# TODO: to exchange or not to exchange, that is the question, the answer is probably YES
detailemocode = torch.cat([detailemocode, detailemocode[new_order]], dim=0)
return expcode, posecode, shapecode, lightcode, texcode, images, cam, lmk, masks, va, expr7, affectnetexp, \
detailcode, detailemocode, exprw, lmk_mp, mica_images
# return expcode, posecode, shapecode, lightcode, texcode, images, cam, lmk, masks, va, expr7
def encode(self, batch, training=True) -> dict:
"""
Forward encoding pass of the model. Takes a batch of images and returns the corresponding latent codes for each image.
:param batch: Batch of images to encode. batch['image'] [batch_size, ring_size, 3, image_size, image_size].
For a training forward pass, additional corresponding data are necessery such as 'landmarks' and 'masks'.
For a testing pass, the images suffice.
:param training: Whether the forward pass is for training or testing.
"""
codedict = {}
original_batch_size = batch['image'].shape[0]
images = batch['image']
if 'mica_images' in batch.keys():
mica_images = batch['mica_images']
else:
mica_images = None
if len(images.shape) == 5:
K = images.shape[1]
elif len(images.shape) == 4:
K = 1
else:
raise RuntimeError("Invalid image batch dimensions.")
# [B, K, 3, size, size] ==> [BxK, 3, size, size]
images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])
if mica_images is not None:
mica_images = mica_images.view(-1, mica_images.shape[-3], mica_images.shape[-2], mica_images.shape[-1])
if 'landmark' in batch.keys():
lmk = batch['landmark']
lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])
if 'landmark_mediapipe' in batch.keys():
lmk_mp = batch['landmark_mediapipe']
lmk_mp = lmk_mp.view(-1, lmk_mp.shape[-2], lmk_mp.shape[-1])
else:
lmk_mp = None
if 'mask' in batch.keys():
masks = batch['mask']
masks = masks.view(-1, images.shape[-2], images.shape[-1])
# valence / arousal - not necessary unless we want to use VA for supervision (not done in EMOCA)
if 'va' in batch:
va = batch['va']
va = va.view(-1, va.shape[-1])
else:
va = None
# 7 basic expression - not necessary unless we want to use expression for supervision (not done in EMOCA or DECA)
if 'expr7' in batch:
expr7 = batch['expr7']
expr7 = expr7.view(-1, expr7.shape[-1])
else:
expr7 = None
# affectnet basic expression - not necessary unless we want to use expression for supervision (not done in EMOCA or DECA)
if 'affectnetexp' in batch:
affectnetexp = batch['affectnetexp']
affectnetexp = affectnetexp.view(-1, affectnetexp.shape[-1])
else:
affectnetexp = None
# expression weights if supervising by expression is used (to balance the classification loss) - not done in EMOCA or DECA
if 'expression_weight' in batch:
exprw = batch['expression_weight']
exprw = exprw.view(-1, exprw.shape[-1])
else:
exprw = None
# 1) COARSE STAGE
# forward pass of the coarse encoder
# shapecode, texcode, expcode, posecode, cam, lightcode = self._encode_flame(images)
code, original_code = self._encode_flame(images, mica_image=mica_images)
shapecode, texcode, expcode, posecode, cam, lightcode = self._unwrap_list(code)
if original_code is not None:
original_code = self._unwrap_list_to_dict(original_code)
if training:
# If training, we employ the disentanglement strategy
if self.mode == DecaMode.COARSE:
if self.deca.config.shape_constrain_type == 'same':
## Enforce that all identity shape codes within ring are the same. The batch is duplicated
## and the duplicated part's shape codes are shuffled.
# reshape shapecode => [B, K, n_shape]
# shapecode_idK = shapecode.view(self.batch_size, self.deca.K, -1)
shapecode_idK = shapecode.view(original_batch_size, K, -1)
# get mean id
shapecode_mean = torch.mean(shapecode_idK, dim=[1])
# shapecode_new = shapecode_mean[:, None, :].repeat(1, self.deca.K, 1)
shapecode_new = shapecode_mean[:, None, :].repeat(1, K, 1)
shapecode = shapecode_new.view(-1, self.deca._get_num_shape_params())
# do the same for the original code dict
shapecode_orig = original_code['shape']
shapecode_orig_idK = shapecode_orig.view(original_batch_size, K, -1)
shapecode_orig_mean = torch.mean(shapecode_orig_idK, dim=[1])
shapecode_orig_new = shapecode_orig_mean[:, None, :].repeat(1, K, 1)
original_code['shape'] = shapecode_orig_new.view(-1, self.deca._get_num_shape_params())
elif self.deca.config.shape_constrain_type == 'exchange':
## Shuffle identitys shape codes within ring (they should correspond to the same identity)
'''
make sure s0, s1 is something to make shape close
the difference from ||so - s1|| is
the later encourage s0, s1 is cloase in l2 space, but not really ensure shape will be close
'''
# new_order = np.array([np.random.permutation(self.deca.config.train_K) + i * self.deca.config.train_K for i in range(self.deca.config.batch_size_train)])
# new_order = np.array([np.random.permutation(self.deca.config.train_K) + i * self.deca.config.train_K for i in range(original_batch_size)])
new_order = np.array([np.random.permutation(K) + i * K for i in range(original_batch_size)])
new_order = new_order.flatten()
shapecode_new = shapecode[new_order]
## append new shape code data
shapecode = torch.cat([shapecode, shapecode_new], dim=0)
texcode = torch.cat([texcode, texcode], dim=0)
expcode = torch.cat([expcode, expcode], dim=0)
posecode = torch.cat([posecode, posecode], dim=0)
cam = torch.cat([cam, cam], dim=0)
lightcode = torch.cat([lightcode, lightcode], dim=0)
## append gt
images = torch.cat([images, images],
dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])
if mica_images is not None:
mica_images = torch.cat([mica_images, mica_images], dim=0)
lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])
if lmk_mp is not None:
lmk_mp = torch.cat([lmk_mp, lmk_mp], dim=0)
masks = torch.cat([masks, masks], dim=0)
if va is not None:
va = torch.cat([va, va], dim=0)
if expr7 is not None:
expr7 = torch.cat([expr7, expr7], dim=0)
# do the same for the original code dict
shapecode_orig = original_code['shape']
shapecode_orig_new = shapecode_orig[new_order]
original_code['shape'] = torch.cat([shapecode_orig, shapecode_orig_new], dim=0)
original_code['tex'] = torch.cat([original_code['tex'], original_code['tex']], dim=0)
original_code['exp'] = torch.cat([original_code['exp'], original_code['exp']], dim=0)
original_code['pose'] = torch.cat([original_code['pose'], original_code['pose']], dim=0)
original_code['cam'] = torch.cat([original_code['cam'], original_code['cam']], dim=0)
original_code['light'] = torch.cat([original_code['light'], original_code['light']], dim=0)
elif self.deca.config.shape_constrain_type == 'shuffle_expression':
assert original_code is not None
## DEPRECATED, NOT USED IN EMOCA OR DECA
new_order = np.random.permutation(K*original_batch_size)
old_order = np.arange(K*original_batch_size)
while (new_order == old_order).any(): # ugly hacky way of assuring that every element is permuted
new_order = np.random.permutation(K * original_batch_size)
codedict['new_order'] = new_order
# exchange expression
expcode_new = expcode[new_order]
expcode = torch.cat([expcode, expcode_new], dim=0)
# exchange jaw pose (but not global pose)
global_pose = posecode[:, :3]
jaw_pose = posecode[:, 3:]
jaw_pose_new = jaw_pose[new_order]
jaw_pose = torch.cat([jaw_pose, jaw_pose_new], dim=0)
global_pose = torch.cat([global_pose, global_pose], dim=0)
posecode = torch.cat([global_pose, jaw_pose], dim=1)
## duplicate the rest
shapecode = torch.cat([shapecode, shapecode], dim=0)
texcode = torch.cat([texcode, texcode], dim=0)
cam = torch.cat([cam, cam], dim=0)
lightcode = torch.cat([lightcode, lightcode], dim=0)
## duplicate gt if any
images = torch.cat([images, images],
dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])
if mica_images is not None:
mica_images = torch.cat([mica_images, mica_images], dim=0)
print(f"TRAINING: {training}")
if lmk is not None:
lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])
if lmk_mp is not None:
lmk_mp = torch.cat([lmk_mp, lmk_mp], dim=0)
masks = torch.cat([masks, masks], dim=0)
ref_images_identity_idxs = np.concatenate([old_order, old_order])
ref_images_expression_idxs = np.concatenate([old_order, new_order])
codedict["ref_images_identity_idxs"] = ref_images_identity_idxs
codedict["ref_images_expression_idxs"] = ref_images_expression_idxs
if va is not None:
va = torch.cat([va, va[new_order]], dim=0)
if expr7 is not None:
expr7 = torch.cat([expr7, expr7[new_order]], dim=0)
# do the same for the original code dict
original_code['shape'] = torch.cat([original_code['shape'], original_code['shape']], dim=0)
original_code['tex'] = torch.cat([original_code['tex'], original_code['tex']], dim=0)
original_code['exp'] = torch.cat([original_code['exp'], original_code['exp'][new_order]], dim=0)
original_global_pose = original_code['pose'][:, :3]
original_jaw_pose = original_code['pose'][:, 3:]
original_jaw_pose = torch.cat([original_jaw_pose, original_jaw_pose[new_order]], dim=0)
original_global_pose = torch.cat([original_global_pose, original_global_pose], dim=0)
original_code['pose'] = torch.cat([original_global_pose, original_jaw_pose], dim=1)
original_code['cam'] = torch.cat([original_code['cam'], original_code['cam']], dim=0)
original_code['light'] = torch.cat([original_code['light'], original_code['light']], dim=0)
elif self.deca.config.shape_constrain_type == 'shuffle_shape':
## The shape codes are shuffled without duplication
new_order = np.random.permutation(K*original_batch_size)
old_order = np.arange(K*original_batch_size)
while (new_order == old_order).any(): # ugly hacky way of assuring that every element is permuted
new_order = np.random.permutation(K * original_batch_size)
codedict['new_order'] = new_order
shapecode_new = shapecode[new_order]
## append new shape code data
shapecode = torch.cat([shapecode, shapecode_new], dim=0)
texcode = torch.cat([texcode, texcode], dim=0)
expcode = torch.cat([expcode, expcode], dim=0)
posecode = torch.cat([posecode, posecode], dim=0)
cam = torch.cat([cam, cam], dim=0)
lightcode = torch.cat([lightcode, lightcode], dim=0)
## append gt
images = torch.cat([images, images],
dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])
if mica_images is not None:
mica_images = torch.cat([mica_images, mica_images], dim=0)
if lmk is not None:
lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])
masks = torch.cat([masks, masks], dim=0)
ref_images_identity_idxs = np.concatenate([old_order, new_order])
ref_images_expression_idxs = np.concatenate([old_order, old_order])
codedict["ref_images_identity_idxs"] = ref_images_identity_idxs
codedict["ref_images_expression_idxs"] = ref_images_expression_idxs
if va is not None:
va = torch.cat([va, va], dim=0)
if expr7 is not None:
expr7 = torch.cat([expr7, expr7], dim=0)
# do the same for the original code dict
shapecode_orig = original_code['shape']
shapecode_orig_new = shapecode_orig[new_order]
original_code['shape'] = torch.cat([shapecode_orig, shapecode_orig_new], dim=0)
original_code['tex'] = torch.cat([original_code['tex'], original_code['tex']], dim=0)
original_code['exp'] = torch.cat([original_code['exp'], original_code['exp']], dim=0)
original_code['pose'] = torch.cat([original_code['pose'], original_code['pose']], dim=0)
original_code['cam'] = torch.cat([original_code['cam'], original_code['cam']], dim=0)
original_code['light'] = torch.cat([original_code['light'], original_code['light']], dim=0)
original_code['ref_images_identity_idxs'] = ref_images_identity_idxs
original_code['ref_images_expression_idxs'] = ref_images_expression_idxs
elif 'expression_constrain_type' in self.deca.config.keys() and \
self.deca.config.expression_constrain_type == 'same':
## NOT USED IN EMOCA OR DECA, deprecated
# reshape shapecode => [B, K, n_shape]
# shapecode_idK = shapecode.view(self.batch_size, self.deca.K, -1)
expcode_idK = expcode.view(original_batch_size, K, -1)
# get mean id
expcode_mean = torch.mean(expcode_idK, dim=[1])
# shapecode_new = shapecode_mean[:, None, :].repeat(1, self.deca.K, 1)
expcode_new = expcode_mean[:, None, :].repeat(1, K, 1)
expcode = expcode_new.view(-1, self.deca._get_num_shape_params())
# do the same thing for the original code dict
expcode_idK = original_code['exp'].view(original_batch_size, K, -1)
expcode_mean = torch.mean(expcode_idK, dim=[1])
expcode_new = expcode_mean[:, None, :].repeat(1, K, 1)
original_code['exp'] = expcode_new.view(-1, self.deca._get_num_shape_params())
elif 'expression_constrain_type' in self.deca.config.keys() and \
self.deca.config.expression_constrain_type == 'exchange':
## NOT USED IN EMOCA OR DECA, deprecated
expcode, posecode, shapecode, lightcode, texcode, images, cam, lmk, \
masks, va, expr7, affectnetexp, _, _, exprw, lmk_mp, mica_images = \
self._expression_ring_exchange(original_batch_size, K,
expcode, posecode, shapecode, lightcode, texcode,
images, cam, lmk, masks, va, expr7, affectnetexp, None, None, exprw, lmk_mp, mica_images)
# (self, original_batch_size, K,
# expcode, posecode, shapecode, lightcode, texcode,
# images, cam, lmk, masks, va, expr7, affectnetexp,
# detailcode=None, detailemocode=None, exprw=None):
# 2) DETAIL STAGE
if self.mode == DecaMode.DETAIL:
all_detailcode = self.deca.E_detail(images)
# identity-based detail code
detailcode = all_detailcode[:, :self.deca.n_detail]
# detail emotion code is deprecated and will be empty
detailemocode = all_detailcode[:, self.deca.n_detail:(self.deca.n_detail + self.deca.n_detail_emo)]
if training:
# If training, we employ the disentanglement strategy
if self.deca.config.detail_constrain_type == 'exchange':
# Identity within the same ring should be the same, so they should have the same code.
# This can be enforced by shuffling. The batch is duplicated and the duplicated part's code shuffled
'''
make sure s0, s1 is something to make shape close
the difference from ||so - s1|| is
the later encourage s0, s1 is cloase in l2 space, but not really ensure shape will be close
'''
# this creates a per-ring random permutation. The detail exchange happens ONLY between the same
# identities (within the ring) but not outside (no cross-identity detail exchange)
new_order = np.array(
# [np.random.permutation(self.deca.config.train_K) + i * self.deca.config.train_K for i in range(original_batch_size)])
[np.random.permutation(K) + i * K for i in range(original_batch_size)])
new_order = new_order.flatten()
detailcode_new = detailcode[new_order]
detailcode = torch.cat([detailcode, detailcode_new], dim=0)
detailemocode = torch.cat([detailemocode, detailemocode], dim=0)
## append new shape code data
shapecode = torch.cat([shapecode, shapecode], dim=0)
texcode = torch.cat([texcode, texcode], dim=0)
expcode = torch.cat([expcode, expcode], dim=0)
posecode = torch.cat([posecode, posecode], dim=0)
cam = torch.cat([cam, cam], dim=0)
lightcode = torch.cat([lightcode, lightcode], dim=0)
## append gt
images = torch.cat([images, images],
dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])
if mica_images is not None:
mica_images = torch.cat([mica_images, mica_images], dim=0)
lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])
masks = torch.cat([masks, masks], dim=0)
if va is not None:
va = torch.cat([va, va], dim=0)
if expr7 is not None:
expr7 = torch.cat([expr7, expr7], dim=0)
elif self.deca.config.detail_constrain_type == 'shuffle_expression':
## Deprecated and not used in EMOCA or DECA
new_order = np.random.permutation(K*original_batch_size)
old_order = np.arange(K*original_batch_size)
while (new_order == old_order).any(): # ugly hacky way of assuring that every element is permuted
new_order = np.random.permutation(K * original_batch_size)
codedict['new_order'] = new_order
# exchange expression
expcode_new = expcode[new_order]
expcode = torch.cat([expcode, expcode_new], dim=0)
# exchange emotion code, but not (identity-based) detailcode
detailemocode_new = detailemocode[new_order]
detailemocode = torch.cat([detailemocode, detailemocode_new], dim=0)
detailcode = torch.cat([detailcode, detailcode], dim=0)
# exchange jaw pose (but not global pose)
global_pose = posecode[:, :3]
jaw_pose = posecode[:, 3:]
jaw_pose_new = jaw_pose[new_order]
jaw_pose = torch.cat([jaw_pose, jaw_pose_new], dim=0)
global_pose = torch.cat([global_pose, global_pose], dim=0)
posecode = torch.cat([global_pose, jaw_pose], dim=1)
## duplicate the rest
shapecode = torch.cat([shapecode, shapecode], dim=0)
texcode = torch.cat([texcode, texcode], dim=0)
cam = torch.cat([cam, cam], dim=0)
lightcode = torch.cat([lightcode, lightcode], dim=0)
## duplicate gt if any
images = torch.cat([images, images],
dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])
if mica_images is not None:
mica_images = torch.cat([mica_images, mica_images], dim=0)
print(f"TRAINING: {training}")
if lmk is not None:
lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])
masks = torch.cat([masks, masks], dim=0)
ref_images_identity_idxs = np.concatenate([old_order, old_order])
ref_images_expression_idxs = np.concatenate([old_order, new_order])
codedict["ref_images_identity_idxs"] = ref_images_identity_idxs
codedict["ref_images_expression_idxs"] = ref_images_expression_idxs
if va is not None:
va = torch.cat([va, va[new_order]], dim=0)
if expr7 is not None:
expr7 = torch.cat([expr7, expr7[new_order]], dim=0)
elif self.deca.config.detail_constrain_type == 'shuffle_shape':
## Shuffles teh shape code without duplicating the batch
new_order = np.random.permutation(K*original_batch_size)
old_order = np.arange(K*original_batch_size)
while (new_order == old_order).any(): # ugly hacky way of assuring that every element is permuted
new_order = np.random.permutation(K * original_batch_size)
codedict['new_order'] = new_order
shapecode_new = shapecode[new_order]
## append new shape code data
shapecode = torch.cat([shapecode, shapecode_new], dim=0)
# exchange (identity-based) detailcode, but not emotion code
detailcode_new = detailcode[new_order]
detailcode = torch.cat([detailcode, detailcode_new], dim=0)
detailemocode = torch.cat([detailemocode, detailemocode], dim=0)
texcode = torch.cat([texcode, texcode], dim=0)
expcode = torch.cat([expcode, expcode], dim=0)
posecode = torch.cat([posecode, posecode], dim=0)
cam = torch.cat([cam, cam], dim=0)
lightcode = torch.cat([lightcode, lightcode], dim=0)
## append gt
images = torch.cat([images, images],
dim=0) # images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])
if mica_images is not None:
mica_images = torch.cat([mica_images, mica_images], dim=0)
if lmk is not None:
lmk = torch.cat([lmk, lmk], dim=0) # lmk = lmk.view(-1, lmk.shape[-2], lmk.shape[-1])
masks = torch.cat([masks, masks], dim=0)
ref_images_identity_idxs = np.concatenate([old_order, new_order])
ref_images_expression_idxs = np.concatenate([old_order, old_order])
codedict["ref_images_identity_idxs"] = ref_images_identity_idxs
codedict["ref_images_expression_idxs"] = ref_images_expression_idxs
if va is not None:
va = torch.cat([va, va], dim=0)
if expr7 is not None:
expr7 = torch.cat([expr7, expr7], dim=0)
elif 'expression_constrain_type' in self.deca.config.keys() and \
self.deca.config.expression_constrain_type == 'exchange':
expcode, posecode, shapecode, lightcode, texcode, images, cam, lmk, masks, va, expr7, affectnetexp, detailcode, detailemocode, exprw, lmk_mp, mica_images = \
self._expression_ring_exchange(original_batch_size, K,
expcode, posecode, shapecode, lightcode, texcode,
images, cam, lmk, masks, va, expr7, affectnetexp, detailcode, detailemocode, exprw, lmk_mp, mica_images)
codedict['shapecode'] = shapecode
codedict['texcode'] = texcode
codedict['expcode'] = expcode
codedict['posecode'] = posecode
codedict['cam'] = cam
codedict['lightcode'] = lightcode
if self.mode == DecaMode.DETAIL:
codedict['detailcode'] = detailcode
codedict['detailemocode'] = detailemocode
codedict['images'] = images
if mica_images is not None:
codedict['mica_images'] = mica_images
if 'mask' in batch.keys():
codedict['masks'] = masks
if 'landmark' in batch.keys():
codedict['lmk'] = lmk
if lmk_mp is not None:
codedict['lmk_mp'] = lmk_mp
if 'va' in batch.keys():
codedict['va'] = va
if 'expr7' in batch.keys():
codedict['expr7'] = expr7
if 'affectnetexp' in batch.keys():
codedict['affectnetexp'] = affectnetexp
if 'expression_weight' in batch.keys():
codedict['expression_weight'] = exprw
if original_code is not None:
codedict['original_code'] = original_code
return codedict
def _create_conditioning_lists(self, codedict, condition_list):
detail_conditioning_list = []
if 'globalpose' in condition_list:
detail_conditioning_list += [codedict["posecode"][:, :3]]
if 'jawpose' in condition_list:
detail_conditioning_list += [codedict["posecode"][:, 3:]]
if 'identity' in condition_list:
detail_conditioning_list += [codedict["shapecode"]]
if 'expression' in condition_list:
detail_conditioning_list += [codedict["expcode"]]
if isinstance(self.deca.D_detail, Generator):
# the detail codes might be excluded from conditioning based on the Generator architecture (for instance
# for AdaIn Generator)
if 'detail' in condition_list:
detail_conditioning_list += [codedict["detailcode"]]
if 'detailemo' in condition_list:
detail_conditioning_list += [codedict["detailemocode"]]
return detail_conditioning_list
def decode(self, codedict, training=True, render=True, **kwargs) -> dict:
"""
Forward decoding pass of the model. Takes the latent code predicted by the encoding stage and reconstructs and renders the shape.
:param codedict: Batch dict of the predicted latent codes
:param training: Whether the forward pass is for training or testing.
"""
shapecode = codedict['shapecode']
expcode = codedict['expcode']
posecode = codedict['posecode']
texcode = codedict['texcode']
cam = codedict['cam']
lightcode = codedict['lightcode']
images = codedict['images']
if 'masks' in codedict.keys():
masks = codedict['masks']
else:
masks = None
effective_batch_size = images.shape[0] # this is the current batch size after all training augmentations modifications
# 1) Reconstruct the face mesh
# FLAME - world space
if not isinstance(self.deca.flame, FLAME_mediapipe):
verts, landmarks2d, landmarks3d = self.deca.flame(shape_params=shapecode, expression_params=expcode,
pose_params=posecode)
landmarks2d_mediapipe = None
else:
verts, landmarks2d, landmarks3d, landmarks2d_mediapipe = self.deca.flame(shapecode, expcode, posecode)
# world to camera
trans_verts = util.batch_orth_proj(verts, cam)
predicted_landmarks = util.batch_orth_proj(landmarks2d, cam)[:, :, :2]
# camera to image space
trans_verts[:, :, 1:] = -trans_verts[:, :, 1:]
predicted_landmarks[:, :, 1:] = - predicted_landmarks[:, :, 1:]
if landmarks2d_mediapipe is not None:
predicted_landmarks_mediapipe = util.batch_orth_proj(landmarks2d_mediapipe, cam)[:, :, :2]
predicted_landmarks_mediapipe[:, :, 1:] = - predicted_landmarks_mediapipe[:, :, 1:]
if self.uses_texture():
albedo = self.deca.flametex(texcode)
else:
# if not using texture, default to gray
albedo = torch.ones([effective_batch_size, 3, self.deca.config.uv_size, self.deca.config.uv_size], device=images.device) * 0.5
# 2) Render the coarse image
if render:
ops = self.deca.render(verts, trans_verts, albedo, lightcode)
# mask
mask_face_eye = F.grid_sample(self.deca.uv_face_eye_mask.expand(effective_batch_size, -1, -1, -1),
ops['grid'].detach(),
align_corners=False)
# images
predicted_images = ops['images']
# predicted_images = ops['images'] * mask_face_eye * ops['alpha_images']
# predicted_images_no_mask = ops['images'] #* mask_face_eye * ops['alpha_images']
segmentation_type = None
if isinstance(self.deca.config.useSeg, bool):
if self.deca.config.useSeg:
segmentation_type = 'gt'
else:
segmentation_type = 'rend'
elif isinstance(self.deca.config.useSeg, str):
segmentation_type = self.deca.config.useSeg
else:
raise RuntimeError(f"Invalid 'useSeg' type: '{type(self.deca.config.useSeg)}'")
if segmentation_type not in ["gt", "rend", "intersection", "union"]:
raise ValueError(f"Invalid segmentation type for masking '{segmentation_type}'")
if masks is None: # if mask not provided, the only mask available is the rendered one
segmentation_type = 'rend'
elif masks.shape[-1] != predicted_images.shape[-1] or masks.shape[-2] != predicted_images.shape[-2]:
# resize masks if need be (this is only done if configuration was changed at some point after training)
dims = masks.ndim == 3
if dims:
masks = masks[:, None, :, :]
masks = F.interpolate(masks, size=predicted_images.shape[-2:], mode='bilinear')
if dims:
masks = masks[:, 0, ...]
# resize images if need be (this is only done if configuration was changed at some point after training)
if images.shape[-1] != predicted_images.shape[-1] or images.shape[-2] != predicted_images.shape[-2]:
## special case only for inference time if the rendering image sizes have been changed
images_resized = F.interpolate(images, size=predicted_images.shape[-2:], mode='bilinear')
else:
images_resized = images
# what type of segmentation we use
if segmentation_type == "gt": # GT stands for external segmetnation predicted by face parsing or similar
masks = masks[:, None, :, :]
elif segmentation_type == "rend": # mask rendered as a silhouette of the face mesh
masks = mask_face_eye * ops['alpha_images']
elif segmentation_type == "intersection": # intersection of the two above
masks = masks[:, None, :, :] * mask_face_eye * ops['alpha_images']
elif segmentation_type == "union": # union of the first two options
masks = torch.max(masks[:, None, :, :], mask_face_eye * ops['alpha_images'])
else:
raise RuntimeError(f"Invalid segmentation type for masking '{segmentation_type}'")
if self.deca.config.background_from_input in [True, "input"]:
if images.shape[-1] != predicted_images.shape[-1] or images.shape[-2] != predicted_images.shape[-2]:
## special case only for inference time if the rendering image sizes have been changed
predicted_images = (1. - masks) * images_resized + masks * predicted_images
else:
predicted_images = (1. - masks) * images + masks * predicted_images
elif self.deca.config.background_from_input in [False, "black"]:
predicted_images = masks * predicted_images
elif self.deca.config.background_from_input in ["none"]:
predicted_images = predicted_images
else:
raise ValueError(f"Invalid type of background modification {self.deca.config.background_from_input}")
# 3) Render the detail image
if self.mode == DecaMode.DETAIL:
detailcode = codedict['detailcode']
detailemocode = codedict['detailemocode']
# a) Create the detail conditioning lists
detail_conditioning_list = self._create_conditioning_lists(codedict, self.detail_conditioning)
detailemo_conditioning_list = self._create_conditioning_lists(codedict, self.detailemo_conditioning)
final_detail_conditioning_list = detail_conditioning_list + detailemo_conditioning_list
# b) Pass the detail code and the conditions through the detail generator to get displacement UV map
if isinstance(self.deca.D_detail, Generator):
uv_z = self.deca.D_detail(torch.cat(final_detail_conditioning_list, dim=1))
elif isinstance(self.deca.D_detail, GeneratorAdaIn):
uv_z = self.deca.D_detail(z=torch.cat([detailcode, detailemocode], dim=1),
cond=torch.cat(final_detail_conditioning_list, dim=1))
else:
raise ValueError(f"This class of generarator is not supported: '{self.deca.D_detail.__class__.__name__}'")
# if there is a displacement mask, apply it (DEPRECATED and not USED in DECA or EMOCA)
if hasattr(self.deca, 'displacement_mask') and self.deca.displacement_mask is not None:
if 'apply_displacement_masks' in self.deca.config.keys() and self.deca.config.apply_displacement_masks:
uv_z = uv_z * self.deca.displacement_mask
# uv_z = self.deca.D_detail(torch.cat([posecode[:, 3:], expcode, detailcode], dim=1))
# render detail
if render:
detach_from_coarse_geometry = not self.deca.config.train_coarse
uv_detail_normals, uv_coarse_vertices = self.deca.displacement2normal(uv_z, verts, ops['normals'],
detach=detach_from_coarse_geometry)
uv_shading = self.deca.render.add_SHlight(uv_detail_normals, lightcode.detach())
uv_texture = albedo.detach() * uv_shading
# batch size X image_rows X image_cols X 2
# you can query the grid for UV values of the face mesh at pixel locations
grid = ops['grid']
if detach_from_coarse_geometry:
# if the grid is detached, the gradient of the positions of UV-values in image space won't flow back to the geometry
grid = grid.detach()
predicted_detailed_image = F.grid_sample(uv_texture, grid, align_corners=False)
if self.deca.config.background_from_input in [True, "input"]:
if images.shape[-1] != predicted_images.shape[-1] or images.shape[-2] != predicted_images.shape[-2]:
## special case only for inference time if the rendering image sizes have been changed
# images_resized = F.interpolate(images, size=predicted_images.shape[-2:], mode='bilinear')
## before bugfix
# predicted_images = (1. - masks) * images_resized + masks * predicted_images
## after bugfix
predicted_detailed_image = (1. - masks) * images_resized + masks * predicted_detailed_image
else:
predicted_detailed_image = (1. - masks) * images + masks * predicted_detailed_image
elif self.deca.config.background_from_input in [False, "black"]:
predicted_detailed_image = masks * predicted_detailed_image
elif self.deca.config.background_from_input in ["none"]:
predicted_detailed_image = predicted_detailed_image
else:
raise ValueError(f"Invalid type of background modification {self.deca.config.background_from_input}")
# --- extract texture
uv_pverts = self.deca.render.world2uv(trans_verts).detach()
uv_gt = F.grid_sample(torch.cat([images_resized, masks], dim=1), uv_pverts.permute(0, 2, 3, 1)[:, :, :, :2],
mode='bilinear')
uv_texture_gt = uv_gt[:, :3, :, :].detach()
uv_mask_gt = uv_gt[:, 3:, :, :].detach()
# self-occlusion
normals = util.vertex_normals(trans_verts, self.deca.render.faces.expand(effective_batch_size, -1, -1))
uv_pnorm = self.deca.render.world2uv(normals)
uv_mask = (uv_pnorm[:, -1, :, :] < -0.05).float().detach()
uv_mask = uv_mask[:, None, :, :]
## combine masks
uv_vis_mask = uv_mask_gt * uv_mask * self.deca.uv_face_eye_mask
else:
uv_detail_normals = None
predicted_detailed_image = None
## 4) (Optional) NEURAL RENDERING - not used in neither DECA nor EMOCA
# If neural rendering is enabled, the differentiable rendered synthetic images are translated using an image translation net (such as StarGan)
predicted_translated_image = None
predicted_detailed_translated_image = None
translated_uv_texture = None
if render:
if self.deca._has_neural_rendering():
predicted_translated_image = self.deca.image_translator(
{
"input_image" : predicted_images,
"ref_image" : images,
"target_domain" : torch.tensor([0]*predicted_images.shape[0],
dtype=torch.int64, device=predicted_images.device)
}
)
if self.mode == DecaMode.DETAIL:
predicted_detailed_translated_image = self.deca.image_translator(
{
"input_image" : predicted_detailed_image,
"ref_image" : images,
"target_domain" : torch.tensor([0]*predicted_detailed_image.shape[0],
dtype=torch.int64, device=predicted_detailed_image.device)
}
)
translated_uv = F.grid_sample(torch.cat([predicted_detailed_translated_image, masks], dim=1), uv_pverts.permute(0, 2, 3, 1)[:, :, :, :2],
mode='bilinear')
translated_uv_texture = translated_uv[:, :3, :, :].detach()
else:
predicted_detailed_translated_image = None
translated_uv_texture = None
# no need in coarse mode
# translated_uv = F.grid_sample(torch.cat([predicted_translated_image, masks], dim=1), uv_pverts.permute(0, 2, 3, 1)[:, :, :, :2],
# mode='bilinear')
# translated_uv_texture = translated_uv_gt[:, :3, :, :].detach()
if self.emotion_mlp is not None:
codedict = self.emotion_mlp(codedict, "emo_mlp_")
# populate the value dict for metric computation/visualization
if render:
codedict['predicted_images'] = predicted_images
codedict['predicted_detailed_image'] = predicted_detailed_image
codedict['predicted_translated_image'] = predicted_translated_image
codedict['ops'] = ops
codedict['normals'] = ops['normals']
codedict['mask_face_eye'] = mask_face_eye
codedict['verts'] = verts
codedict['albedo'] = albedo
codedict['landmarks2d'] = landmarks2d
codedict['landmarks3d'] = landmarks3d
codedict['predicted_landmarks'] = predicted_landmarks
if landmarks2d_mediapipe is not None:
codedict['predicted_landmarks_mediapipe'] = predicted_landmarks_mediapipe
codedict['trans_verts'] = trans_verts
codedict['masks'] = masks
if self.mode == DecaMode.DETAIL:
if render:
codedict['predicted_detailed_translated_image'] = predicted_detailed_translated_image
codedict['translated_uv_texture'] = translated_uv_texture
codedict['uv_texture_gt'] = uv_texture_gt
codedict['uv_texture'] = uv_texture
codedict['uv_detail_normals'] = uv_detail_normals
codedict['uv_shading'] = uv_shading
codedict['uv_vis_mask'] = uv_vis_mask
codedict['uv_mask'] = uv_mask
codedict['uv_z'] = uv_z
codedict['displacement_map'] = uv_z + self.deca.fixed_uv_dis[None, None, :, :]
return codedict
def _compute_emotion_loss(self, images, predicted_images, loss_dict, metric_dict, prefix, va=None, expr7=None, with_grad=True,
batch_size=None, ring_size=None):
def loss_or_metric(name, loss, is_loss):
if not is_loss:
metric_dict[name] = loss
else:
loss_dict[name] = loss
# if self.deca.config.use_emonet_loss:
if with_grad:
d = loss_dict
emo_feat_loss_1, emo_feat_loss_2, valence_loss, arousal_loss, expression_loss, au_loss = \
self.emonet_loss.compute_loss(images, predicted_images, batch_size=batch_size, ring_size=ring_size)
else:
d = metric_dict
with torch.no_grad():
emo_feat_loss_1, emo_feat_loss_2, valence_loss, arousal_loss, expression_loss, au_loss = \
self.emonet_loss.compute_loss(images, predicted_images, batch_size=batch_size, ring_size=ring_size)
# EmoNet self-consistency loss terms
if emo_feat_loss_1 is not None:
loss_or_metric(prefix + '_emonet_feat_1_L1', emo_feat_loss_1 * self.deca.config.emonet_weight,
self.deca.config.use_emonet_feat_1 and self.deca.config.use_emonet_loss)
loss_or_metric(prefix + '_emonet_feat_2_L1', emo_feat_loss_2 * self.deca.config.emonet_weight,
self.deca.config.use_emonet_feat_2 and self.deca.config.use_emonet_loss)
loss_or_metric(prefix + '_emonet_valence_L1', valence_loss * self.deca.config.emonet_weight,
self.deca.config.use_emonet_valence and self.deca.config.use_emonet_loss)
loss_or_metric(prefix + '_emonet_arousal_L1', arousal_loss * self.deca.config.emonet_weight,
self.deca.config.use_emonet_arousal and self.deca.config.use_emonet_loss)
# loss_or_metric(prefix + 'emonet_expression_KL', expression_loss * self.deca.config.emonet_weight) # KL seems to be causing NaN's
loss_or_metric(prefix + '_emonet_expression_L1',expression_loss * self.deca.config.emonet_weight,
self.deca.config.use_emonet_expression and self.deca.config.use_emonet_loss)
loss_or_metric(prefix + '_emonet_combined', ((emo_feat_loss_1 if emo_feat_loss_1 is not None else 0)
+ emo_feat_loss_2 + valence_loss + arousal_loss + expression_loss) * self.deca.config.emonet_weight,
self.deca.config.use_emonet_combined and self.deca.config.use_emonet_loss)
# Log also the VA
metric_dict[prefix + "_valence_input"] = self.emonet_loss.input_emotion['valence'].mean().detach()
metric_dict[prefix + "_valence_output"] = self.emonet_loss.output_emotion['valence'].mean().detach()
metric_dict[prefix + "_arousal_input"] = self.emonet_loss.input_emotion['arousal'].mean().detach()
metric_dict[prefix + "_arousal_output"] = self.emonet_loss.output_emotion['arousal'].mean().detach()
input_ex = self.emonet_loss.input_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification'].detach().cpu().numpy()
input_ex = np.argmax(input_ex, axis=1).mean()
output_ex = self.emonet_loss.output_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification'].detach().cpu().numpy()
output_ex = np.argmax(output_ex, axis=1).mean()
metric_dict[prefix + "_expression_input"] = torch.tensor(input_ex, device=self.device)
metric_dict[prefix + "_expression_output"] = torch.tensor(output_ex, device=self.device)
# # GT emotion loss terms
# if self.deca.config.use_gt_emotion_loss:
# d = loss_dict
# else:
# d = metric_dict
# TODO: uncomment this after you handle the case when certain entries are NaN (GT missing, not a bug)
# if va is not None:
# d[prefix + 'emo_sup_val_L1'] = F.l1_loss(self.emonet_loss.output_emotion['valence'], va[:, 0]) \
# * self.deca.config.gt_emotion_reg
# d[prefix + 'emo_sup_ar_L1'] = F.l1_loss(self.emonet_loss.output_emotion['arousal'], va[:, 1]) \
# * self.deca.config.gt_emotion_reg
#
# metric_dict[prefix + "_valence_gt"] = va[:, 0].mean().detach()
# metric_dict[prefix + "_arousal_gt"] = va[:, 1].mean().detach()
#
# if expr7 is not None:
# affectnet_gt = [expr7_to_affect_net(int(expr7[i])).value for i in range(len(expr7))]
# affectnet_gt = torch.tensor(np.array(affectnet_gt), device=self.device, dtype=torch.long)
# d[prefix + '_emo_sup_expr_CE'] = F.cross_entropy(self.emonet_loss.output_emotion['expression'], affectnet_gt) * self.deca.config.gt_emotion_reg
# metric_dict[prefix + "_expr_gt"] = affectnet_gt.mean().detach()
def _compute_au_loss(self, images, predicted_images, loss_dict, metric_dict, prefix, au=None, with_grad=True):
def loss_or_metric(name, loss, is_loss):
if not is_loss:
metric_dict[name] = loss
else:
loss_dict[name] = loss
# if self.deca.config.use_emonet_loss:
if with_grad:
d = loss_dict
au_feat_loss_1, au_feat_loss_2, _, _, _, au_loss = \
self.au_loss.compute_loss(images, predicted_images)
else:
d = metric_dict
with torch.no_grad():
au_feat_loss_1, au_feat_loss_2, _, _, _, au_loss = \
self.au_loss.compute_loss(images, predicted_images)
# EmoNet self-consistency loss terms
if au_feat_loss_1 is not None:
loss_or_metric(prefix + '_au_feat_1_L1', au_feat_loss_1 * self.deca.config.au_loss.au_weight,
self.deca.config.au_loss.use_feat_1 and self.deca.config.au_loss.use_as_loss)
loss_or_metric(prefix + '_au_feat_2_L1', au_feat_loss_2 * self.deca.config.au_loss.au_weight,
self.deca.config.au_loss.use_feat_2 and self.deca.config.au_loss.use_as_loss)
loss_or_metric(prefix + '_au_loss', au_loss * self.deca.config.au_loss.au_weight,
self.deca.config.au_loss.use_aus and self.deca.config.au_loss.use_as_loss)
# loss_or_metric(prefix + '_au_losses_L1', arousal_loss * self.deca.config.au_loss.au_weight,
# self.deca.config.au_loss.use_emonet_arousal and self.deca.config.au_loss.use_as_loss)
# loss_or_metric(prefix + 'emonet_expression_KL', expression_loss * self.deca.config.au_loss.au_weight) # KL seems to be causing NaN's
# # Log also the VA
# metric_dict[prefix + "_valence_input"] = self.emonet_loss.input_emotion['valence'].mean().detach()
# metric_dict[prefix + "_valence_output"] = self.emonet_loss.output_emotion['valence'].mean().detach()
# metric_dict[prefix + "_arousal_input"] = self.emonet_loss.input_emotion['arousal'].mean().detach()
# metric_dict[prefix + "_arousal_output"] = self.emonet_loss.output_emotion['arousal'].mean().detach()
# input_ex = self.emonet_loss.input_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification'].detach().cpu().numpy()
# input_ex = np.argmax(input_ex, axis=1).mean()
# output_ex = self.emonet_loss.output_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification'].detach().cpu().numpy()
# output_ex = np.argmax(output_ex, axis=1).mean()
# metric_dict[prefix + "_expression_input"] = torch.tensor(input_ex, device=self.device)
# metric_dict[prefix + "_expression_output"] = torch.tensor(output_ex, device=self.device)
# # GT emotion loss terms
# if self.deca.config.use_gt_emotion_loss:
# d = loss_dict
# else:
# d = metric_dict
def _cut_mouth_vectorized(self, images, landmarks, convert_grayscale=True):
# mouth_window_margin = 12
mouth_window_margin = 1 # not temporal
mouth_crop_height = 96
mouth_crop_width = 96
mouth_landmark_start_idx = 48
mouth_landmark_stop_idx = 68
B, T = images.shape[:2]
landmarks = landmarks.to(torch.float32)
with torch.no_grad():
image_size = images.shape[-1] / 2
landmarks = landmarks * image_size + image_size
# #1) smooth the landmarks with temporal convolution
# landmarks are of shape (T, 68, 2)
# reshape to (T, 136)
landmarks_t = landmarks.reshape(*landmarks.shape[:2], -1)
# make temporal dimension last
landmarks_t = landmarks_t.permute(0, 2, 1)
# change chape to (N, 136, T)
# landmarks_t = landmarks_t.unsqueeze(0)
# smooth with temporal convolution
temporal_filter = torch.ones(mouth_window_margin, device=images.device) / mouth_window_margin
# pad the the landmarks
landmarks_t_padded = F.pad(landmarks_t, (mouth_window_margin // 2, mouth_window_margin // 2), mode='replicate')
# convolve each channel separately with the temporal filter
num_channels = landmarks_t.shape[1]
if temporal_filter.numel() > 1:
smooth_landmarks_t = F.conv1d(landmarks_t_padded,
temporal_filter.unsqueeze(0).unsqueeze(0).expand(num_channels,1,temporal_filter.numel()),
groups=num_channels, padding='valid'
)
smooth_landmarks_t = smooth_landmarks_t[..., 0:landmarks_t.shape[-1]]
else:
smooth_landmarks_t = landmarks_t
# reshape back to the original shape
smooth_landmarks_t = smooth_landmarks_t.permute(0, 2, 1).view(landmarks.shape)
smooth_landmarks_t = smooth_landmarks_t + landmarks.mean(dim=2, keepdims=True) - smooth_landmarks_t.mean(dim=2, keepdims=True)
# #2) get the mouth landmarks
mouth_landmarks_t = smooth_landmarks_t[..., mouth_landmark_start_idx:mouth_landmark_stop_idx, :]
# #3) get the mean of the mouth landmarks
mouth_landmarks_mean_t = mouth_landmarks_t.mean(dim=-2, keepdims=True)
# #4) get the center of the mouth
center_x_t = mouth_landmarks_mean_t[..., 0]
center_y_t = mouth_landmarks_mean_t[..., 1]
# #5) use grid_sample to crop the mouth in every image
# create the grid
height = mouth_crop_height//2
width = mouth_crop_width//2
torch.arange(0, mouth_crop_width, device=images.device)
grid = torch.stack(torch.meshgrid(torch.linspace(-height, height, mouth_crop_height).to(images.device) / (images.shape[-2] /2),
torch.linspace(-width, width, mouth_crop_width).to(images.device) / (images.shape[-1] /2) ),
dim=-1)
grid = grid[..., [1, 0]]
grid = grid.unsqueeze(0).unsqueeze(0).repeat(*images.shape[:2], 1, 1, 1)
center_x_t -= images.shape[-1] / 2
center_y_t -= images.shape[-2] / 2
center_x_t /= images.shape[-1] / 2
center_y_t /= images.shape[-2] / 2
grid = grid + torch.cat([center_x_t, center_y_t ], dim=-1).unsqueeze(-2).unsqueeze(-2)
images = images.view(B*T, *images.shape[2:])
grid = grid.view(B*T, *grid.shape[2:])
if convert_grayscale:
images = F_v.rgb_to_grayscale(images)
image_crops = F.grid_sample(
images,
grid,
align_corners=True,
padding_mode='zeros',
mode='bicubic'
)
image_crops = image_crops.view(B, T, *image_crops.shape[1:])
if convert_grayscale:
image_crops = image_crops#.squeeze(1)
# import matplotlib.pyplot as plt
# plt.figure()
# plt.imshow(image_crops[0, 0].permute(1,2,0).cpu().numpy())
# plt.show()
# plt.figure()
# plt.imshow(image_crops[0, 10].permute(1,2,0).cpu().numpy())
# plt.show()
# plt.figure()
# plt.imshow(image_crops[0, 20].permute(1,2,0).cpu().numpy())
# plt.show()
# plt.figure()
# plt.imshow(image_crops[1, 0].permute(1,2,0).cpu().numpy())
# plt.show()
# plt.figure()
# plt.imshow(image_crops[1, 10].permute(1,2,0).cpu().numpy())
# plt.show()
# plt.figure()
# plt.imshow(image_crops[1, 20].permute(1,2,0).cpu().numpy())
# plt.show()
return image_crops
def _compute_lipread_loss(self, images, predicted_images, landmarks, predicted_landmarks, loss_dict, metric_dict, prefix, with_grad=True):
def loss_or_metric(name, loss, is_loss):
if not is_loss:
metric_dict[name] = loss
else:
loss_dict[name] = loss
# shape of images is: (B, R, C, H, W)
# convert to (B * R, 1, H, W, C)
images = images.unsqueeze(1)
predicted_images = predicted_images.unsqueeze(1)
landmarks = landmarks.unsqueeze(1)
predicted_landmarks = predicted_landmarks.unsqueeze(1)
# cut out the mouth region
images_mouth = self._cut_mouth_vectorized(images, landmarks)
predicted_images_mouth = self._cut_mouth_vectorized(predicted_images, predicted_landmarks)
# make sure that the lip reading net interprests things with depth=1,
# if self.deca.config.use_emonet_loss:
if with_grad:
d = loss_dict
loss = self.lipread_loss.compute_loss(images_mouth, predicted_images_mouth)
else:
d = metric_dict
with torch.no_grad():
loss = self.lipread_loss.compute_loss(images_mouth, predicted_images_mouth)
d[prefix + '_lipread'] = loss * self.deca.config.lipread_loss.weight
def _metric_or_loss(self, loss_dict, metric_dict, is_loss):
if is_loss:
d = loss_dict
else:
d = metric_dict
return d
def _compute_id_loss(self, codedict, batch, training, testing, losses, batch_size,
ring_size):
# if self.deca.config.idw > 1e-3:
if self.deca.id_loss is not None:
images = codedict["images"]
ops = codedict["ops"]
mask_face_eye = codedict["mask_face_eye"]
shading_images = self.deca.render.add_SHlight(ops['normal_images'], codedict["lightcode"].detach())
albedo_images = F.grid_sample(codedict["albedo"].detach(), ops['grid'], align_corners=False)
# TODO: get to the bottom of this weird overlay thing - why is it there?
# answer: This renders the face and takes background from the image
overlay = albedo_images * shading_images * mask_face_eye + images * (1 - mask_face_eye)
if self.global_step >= self.deca.id_loss_start_step:
if 'id_metric' in self.deca.config.keys() and 'barlow_twins' in self.deca.config.id_metric:
assert ring_size == 1 or ring_size == 2
effective_bs = images.shape[0]
# losses['identity'] = self.deca.id_loss(overlay, images, batch_size=batch_size,
# ring_size=ring_size) * self.deca.config.idw
if "ref_images_identity_idxs" in codedict.keys():
# in case there was shuffling, this ensures that the proper images are used for identity loss
images_ = images[codedict["ref_images_identity_idxs"]]
else:
images_ = images
losses['identity'] = self.deca.id_loss(overlay, images_, batch_size=effective_bs,
ring_size=1) * self.deca.config.idw
if 'id_contrastive' in self.deca.config.keys() and bool(self.deca.config.id_contrastive):
if ring_size == 2:
assert effective_bs % 2 == 0
assert self.deca.id_loss.trainable
has_been_shuffled = 'new_order' in codedict.keys()
idxs_a = torch.arange(0, images.shape[0], 2) # indices of first images within the ring
idxs_b = torch.arange(1, images.shape[0], 2) # indices of second images within the ring
# WARNING - this assumes the ring is identity-based
if self.deca.config.id_contrastive in [True, "real", "both"]:
# we are taking this from the original batch dict because we do not care about the
# shuffled, duplicated samples (they don't have to be doubled)
images_0 = batch["image"][:, 0, ...]
images_1 = batch["image"][:, 1, ...]
losses['identity_contrastive_real'] = self.deca.id_loss(
images_0, # first images within the ring
images_1, # second images within the ring
batch_size=images_0.shape[0],
ring_size=1) * self.deca.config.idw * 2
if self.deca.config.id_contrastive in [True, "synth", "both"]:
if self.deca.config.shape_constrain_type in ['exchange', 'same']:
# we can take all when identity has been exchange within rings
overlay_0 = overlay[idxs_a]
overlay_1 = overlay[idxs_b]
else:
#if the batch was double otherwise (global shuffling) we only take the first half
# if batch_size * ring_size < effective_bs:
overlay_0 = overlay[0:batch_size * ring_size:2]
overlay_1 = overlay[1:batch_size * ring_size:2]
losses['identity_contrastive_synthetic'] = self.deca.id_loss(
overlay_0, # first images within the ring
overlay_1, # second images within the ring
batch_size=overlay_0.shape[0],
ring_size=1) * self.deca.config.idw
if has_been_shuffled:
new_order = codedict['new_order']
# TODO: compare the idxs to these:
# codedict["ref_images_identity_idxs"]
if self.deca.config.shape_constrain_type == 'shuffle_expression':
idxs_a_synth = np.arange(new_order.shape[0]) # first half of the batch
idxs_b_synth = np.arange(new_order.shape[0],
2 * new_order.shape[0]) # second half of the batch
elif self.deca.config.shape_constrain_type == 'shuffle_shape':
idxs_a_synth = new_order # shuffled first half of the batch
idxs_b_synth = np.arange(new_order.shape[0],
2 * new_order.shape[0]) # second half of the batch
else:
raise NotImplementedError("Unexpected shape consistency value ")
# if this doesn't go through, something went wrong with the shuffling indexations
assert codedict["shapecode"][idxs_a_synth].allclose(codedict["shapecode"][idxs_b_synth])
losses['identity_contrastive_synthetic_shuffled'] = self.deca.id_loss(
overlay[idxs_a_synth], # synthetic images of identities with reconstructed expressions
overlay[idxs_b_synth], # synthetic images of identities with shuffled expressions
batch_size=idxs_a_synth.size,
ring_size=1) * self.deca.config.idw
losses['identity_contrastive_synthetic2real_shuffled'] = self.deca.id_loss(
images[idxs_a_synth], # synthetic images of identities with reconstructed expressions
overlay[idxs_b_synth], # synthetic images of identities with shuffled expressions
batch_size=idxs_a_synth.size,
ring_size=1) * self.deca.config.idw
elif ring_size > 2:
raise NotImplementedError("Contrastive loss does not support ring sizes > 2.")
return losses
def _compute_emonet_loss_wrapper(self, codedict, batch, training, testing, losses, metrics, prefix, image_key,
with_grad, batch_size, ring_size):
if self.emonet_loss is not None:
if 'va' in codedict:
va = codedict['va']
va = va.view(-1, va.shape[-1])
else:
va = None
if 'expr7' in codedict:
expr7 = codedict['expr7']
expr7 = expr7.view(-1, expr7.shape[-1])
else:
expr7 = None
# with torch.no_grad():
# TODO: if expression shuffled, this needs to be changed, the input images no longer correspond
images = codedict["images"]
predicted_images = codedict[image_key]
effective_bs = images.shape[0]
if "ref_images_expression_idxs" in codedict.keys():
# in case there was shuffling, this ensures that the proper images are used for emotion loss
images_ = images[codedict["ref_images_expression_idxs"]]
else:
images_ = images
effective_bs = images.shape[0]
self._compute_emotion_loss(images_, predicted_images, losses, metrics, f"{prefix}",
va, expr7,
with_grad=with_grad,
batch_size=effective_bs, ring_size=1)
codedict[f"{prefix}_valence_input"] = self.emonet_loss.input_emotion['valence']
codedict[f"{prefix}_arousal_input"] = self.emonet_loss.input_emotion['arousal']
codedict[f"{prefix}_expression_input"] = self.emonet_loss.input_emotion[
'expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']
codedict[f"{prefix}_valence_output"] = self.emonet_loss.output_emotion['valence']
codedict[f"{prefix}_arousal_output"] = self.emonet_loss.output_emotion['arousal']
codedict[f"{prefix}_expression_output"] = self.emonet_loss.output_emotion[
'expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']
if 'emo_contrastive' in self.deca.config.keys() and self.deca.config.emo_contrastive:
assert ring_size == 2 or ring_size == 1
assert self.emonet_loss.trainable or (
hasattr(self.emonet_loss, 'clone_is_trainable') and self.emonet_lossclone_is_trainable)
has_been_shuffled = 'new_order' in codedict.keys()
# if self.deca.config.shape_constrain_type == 'shuffle_expression' and has_been_shuffled:
# new_order = codedict['new_order']
#
if self.deca.config.emo_contrastive in [True, "real", "both"]:
if ring_size == 2:
assert effective_bs % 2 == 0
if not isinstance(self.deca, ExpDECA):
raise NotImplementedError("Cross-ring emotion contrast means the ring has to be "
"expression based, not identity based. This is not guaranteed "
"for vanilla EMOCA (or its datasets).")
# we are taking this from the original batch dict because we do not care about the
# shuffled, duplicated samples (they don't have to be doubled)
images_0 = batch["image"][:, 0, ...]
images_1 = batch["image"][:, 1, ...]
self._compute_emotion_loss(images_0, # real images of first expressions in the ring
images_1, # real images of second expressions in the ring
losses, metrics, f"{prefix}_contrastive_real",
va, expr7, with_grad=self.deca.config.use_emonet_loss,
batch_size=images_0.shape[0], ring_size=1)
else:
print("[WARNING] Cannot compute real contrastive emotion loss because there is no ring!")
if self.deca.config.emo_contrastive in [True, "synth", "both"]:
if ring_size == 2:
assert effective_bs % 2 == 0
idxs_a = torch.arange(0, images.shape[0], 2) # indices of first expressions within a ring
idxs_b = torch.arange(1, images.shape[0], 2) # indices of second expressions within a ring
if 'expression_constrain_type' in self.deca.config.keys() and \
self.deca.config.expression_constrain_type in ['exchange', 'same']:
# we can take all when identity has been exchange within rings
predicted_images_0 = predicted_images[idxs_a]
predicted_images_1 = predicted_images[idxs_b]
raise RuntimeError("This should work but it was never tested or intended. Make sure this works.")
else:
# if the batch was double otherwise (global shuffling) we only take the first half
# if batch_size * ring_size < effective_bs:
predicted_images_0 = predicted_images[0:batch_size * ring_size:2]
predicted_images_1 = predicted_images[1:batch_size * ring_size:2]
if not isinstance(self.deca, ExpDECA):
raise NotImplementedError("Cross-ring emotion contrast means the ring has to be "
"expression based, not identity based. This is not guaranteed "
"for vanilla EMOCA.")
self._compute_emotion_loss(predicted_images_0,
# rec images of first expressions in the ring
predicted_images_1,
# rec images of second expressions in the ring
losses, metrics, f"{prefix}_contrastive_synth",
va, expr7, with_grad=self.deca.config.use_emonet_loss,
batch_size=predicted_images_1.shape[0], ring_size=1)
else:
print("[WARNING] Cannot compute synthetic contrastive emotion loss because there is no ring!")
if has_been_shuffled:
new_order = codedict['new_order']
if self.deca.config.shape_constrain_type == 'shuffle_expression':
# this gets tricky, in this case the images are not duplicates -> we need all, but the second
# half's order is shuffled, so we need to be careful here
idxs_a_synth = new_order # shuffled first half of the batch
idxs_b_synth = np.arange(new_order.shape[0],
2 * new_order.shape[0]) # second half of the batch
elif self.deca.config.shape_constrain_type == 'shuffle_shape':
idxs_a_synth = np.arange(new_order.shape[0]) # first half of the batch
idxs_b_synth = np.arange(new_order.shape[0],
2 * new_order.shape[0]) # second half of the batch
# if this doesn't go through, something went wrong with the shuffling indexations
assert codedict["expcode"][idxs_a_synth].allclose(codedict["expcode"][idxs_b_synth])
# the expressions at corresponding index positions of idxs_a_synth and idxs_b_synth should match now
self._compute_emotion_loss(predicted_images[idxs_a_synth],
# synthetic images of reconstructed expressions and corresponding identities
predicted_images[idxs_b_synth],
# synthetic images of reconstructed expressions and shuffled identities
losses, metrics, f"{prefix}_contrastive_synth_shuffled",
va, expr7,
with_grad=self.deca.config.use_emonet_loss and not self.deca._has_neural_rendering(),
batch_size=idxs_a_synth.size, ring_size=1)
self._compute_emotion_loss(images[idxs_a_synth],
# synthetic images of reconstructed expressions and corresponding identities
predicted_images[idxs_b_synth],
# synthetic images of reconstructed expressions and shuffled identities
losses, metrics, f"{prefix}_contrastive_synth2real_shuffled",
va, expr7,
with_grad=self.deca.config.use_emonet_loss and not self.deca._has_neural_rendering(),
batch_size=idxs_a_synth.size,
ring_size=1)
if va is not None:
codedict[f"{prefix}_valence_gt"] = va[:, 0]
codedict[f"{prefix}_arousal_gt"] = va[:, 1]
if expr7 is not None:
codedict[f"{prefix}_expression_gt"] = expr7
if self.deca._has_neural_rendering():
assert 'emo_contrastive' not in self.deca.config.keys() or self.deca.config.emo_contrastive is False
# TODO possible to make this more GPU efficient by not recomputing emotion for input image
self._compute_emotion_loss(images, predicted_translated_image, losses, metrics, f"{prefix}_translated",
va, expr7,
with_grad=self.deca.config.use_emonet_loss and self.deca._has_neural_rendering(),
batch_size=bs,
ring_size=1)
# codedict[f"{prefix}_valence_input"] = self.emonet_loss.input_emotion['valence']
# codedict[f"{prefix}_arousal_input"] = self.emonet_loss.input_emotion['arousal']
# codedict[f"{prefix}_expression_input"] = self.emonet_loss.input_emotion['expression']
codedict[f"{prefix}_translated_valence_output"] = self.emonet_loss.output_emotion['valence']
codedict[f"{prefix}_translated_arousal_output"] = self.emonet_loss.output_emotion['arousal']
codedict[f"{prefix}_translated_expression_output"] = self.emonet_loss.output_emotion[
'expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']
return losses, metrics, codedict
def _compute_loss(self, codedict, batch, training=True, testing=False):
#### ----------------------- Losses
losses = {}
metrics = {}
predicted_landmarks = codedict["predicted_landmarks"]
predicted_landmarks_mediapipe = codedict.get("predicted_landmarks_mediapipe", None)
if "lmk" in codedict.keys():
lmk = codedict["lmk"]
else:
lmk = None
if "lmk_mp" in codedict.keys():
lmk_mp = codedict["lmk_mp"]
else:
lmk_mp = None
if "masks" in codedict.keys():
masks = codedict["masks"]
else:
masks = None
batch_size = codedict["predicted_images"].shape[0]
use_geom_losses = 'use_geometric_losses_expression_exchange' in self.deca.config.keys() and \
self.deca.config.use_geometric_losses_expression_exchange
if training and ('expression_constrain_type' in self.deca.config.keys() \
and ('expression_constrain_type' in self.deca.config.keys() and self.deca.config.expression_constrain_type == 'exchange') or
( 'shape_constrain_type' in self.deca.config.keys() and
self.deca.config.shape_constrain_type in ['shuffle_expression', 'shuffle_shape'])) \
and (self.deca.mode == DecaMode.COARSE or self.deca.config.train_coarse) \
and (not use_geom_losses):
if batch_size % 2 != 0:
raise RuntimeError("The batch size should be even because it should have "
f"got doubled in expression ring exchange. Instead it was odd: {batch_size}")
# THIS IS DONE BECAUSE LANDMARK AND PHOTOMETRIC LOSSES MAKE NO SENSE FOR EXPRESSION EXCHANGE
geom_losses_idxs = batch_size // 2
else:
geom_losses_idxs = batch_size
predicted_images = codedict["predicted_images"]
images = codedict["images"]
lightcode = codedict["lightcode"]
albedo = codedict["albedo"]
mask_face_eye = codedict["mask_face_eye"]
shapecode = codedict["shapecode"]
expcode = codedict["expcode"]
texcode = codedict["texcode"]
ops = codedict["ops"]
if self.mode == DecaMode.DETAIL:
uv_texture = codedict["uv_texture"]
uv_texture_gt = codedict["uv_texture_gt"]
# this determines the configured batch size that is currently used (training, validation or testing)
# the reason why this is important is because of potential multi-gpu training and loss functions (such as Barlow Twins)
# that might need the full size of the batch (not just the chunk of the current GPU).
if training:
bs = self.learning_params.batch_size_train
rs = self.learning_params.train_K
else:
if not testing:
bs = self.learning_params.batch_size_val
rs = self.learning_params.val_K
else:
bs = self.learning_params.batch_size_test
rs = self.learning_params.test_K
## COARSE loss only
if self.mode == DecaMode.COARSE or (self.mode == DecaMode.DETAIL and self.deca.config.train_coarse):
# landmark losses (only useful if coarse model is being trained
# if training or lmk is not None:
if lmk is not None:
# if self.deca.config.use_landmarks:
# d = losses
# else:
# d = metrics
d = self._metric_or_loss(losses, metrics, self.deca.config.use_landmarks)
if self.deca.config.useWlmk:
d['landmark'] = \
lossfunc.weighted_landmark_loss(predicted_landmarks[:geom_losses_idxs, ...], lmk[:geom_losses_idxs, ...]) * self.deca.config.lmk_weight
else:
d['landmark'] = \
lossfunc.landmark_loss(predicted_landmarks[:geom_losses_idxs, ...], lmk[:geom_losses_idxs, ...]) * self.deca.config.lmk_weight
d = self._metric_or_loss(losses, metrics, 'use_eye_distance' not in self.deca.config.keys() or
self.deca.config.use_eye_distance)
# losses['eye_distance'] = lossfunc.eyed_loss(predicted_landmarks, lmk) * self.deca.config.lmk_weight * 2
d['eye_distance'] = lossfunc.eyed_loss(predicted_landmarks[:geom_losses_idxs, ...],
lmk[:geom_losses_idxs, ...]) * self.deca.config.eyed
d = self._metric_or_loss(losses, metrics, 'use_lip_distance' not in self.deca.config.keys() or
self.deca.config.use_lip_distance)
d['lip_distance'] = lossfunc.lipd_loss(predicted_landmarks[:geom_losses_idxs, ...],
lmk[:geom_losses_idxs, ...]) * self.deca.config.lipd
d = self._metric_or_loss(losses, metrics, 'use_mouth_corner_distance' in self.deca.config.keys() and
self.deca.config.use_mouth_corner_distance)
d['mouth_corner_distance'] = lossfunc.mouth_corner_loss(predicted_landmarks[:geom_losses_idxs, ...],
lmk[:geom_losses_idxs, ...]) * self.deca.config.lipd
if predicted_landmarks_mediapipe is not None and lmk_mp is not None:
use_mediapipe_landmarks = self.deca.config.get('use_mediapipe_landmarks', False)
d = self._metric_or_loss(losses, metrics, use_mediapipe_landmarks)
d['landmark_mediapipe'] =lossfunc_mp.landmark_loss(predicted_landmarks_mediapipe[:geom_losses_idxs, ...], lmk_mp[:geom_losses_idxs, ...]) * self.deca.config.lmk_weight_mp
d = self._metric_or_loss(losses, metrics, self.deca.config.get('use_eye_distance_mediapipe', False) )
d['eye_distance_mediapipe'] = lossfunc_mp.eyed_loss(predicted_landmarks_mediapipe[:geom_losses_idxs, ...],
lmk_mp[:geom_losses_idxs, ...]) * self.deca.config.eyed_mp
d = self._metric_or_loss(losses, metrics, self.deca.config.get('use_lip_distance_mediapipe', False) )
d['lip_distance_mediapipe'] = lossfunc_mp.lipd_loss(predicted_landmarks_mediapipe[:geom_losses_idxs, ...],
lmk_mp[:geom_losses_idxs, ...]) * self.deca.config.lipd_mp
d = self._metric_or_loss(losses, metrics, self.deca.config.get('use_mouth_corner_distance_mediapipe', False))
d['mouth_corner_distance_mediapipe'] = lossfunc_mp.mouth_corner_loss(predicted_landmarks_mediapipe[:geom_losses_idxs, ...],
lmk_mp[:geom_losses_idxs, ...]) * self.deca.config.lipd_mp
#TODO: fix this on the next iteration lipd_loss
# d['lip_distance'] = lossfunc.lipd_loss(predicted_landmarks, lmk) * self.deca.config.lipd
# photometric loss
# if training or masks is not None:
if masks is not None:
# if self.deca.config.use_photometric:
# d = losses
# else:
# d = metrics
# d['photometric_texture'] = (masks * (predicted_images - images).abs()).mean() * self.deca.config.photow
photometric = masks[:geom_losses_idxs, ...] * ((predicted_images[:geom_losses_idxs, ...] - images[:geom_losses_idxs, ...]).abs())
if 'photometric_normalization' not in self.deca.config.keys() or self.deca.config.photometric_normalization == 'mean':
photometric = photometric.mean()
elif self.deca.config.photometric_normalization == 'rel_mask_value':
photometric = photometric * masks[:geom_losses_idxs, ...].mean(dim=tuple(range(1,masks.ndim)), keepdim=True)
photometric = photometric.mean()
elif self.deca.config.photometric_normalization == 'neg_rel_mask_value':
mu = 1. - masks[:geom_losses_idxs, ...].mean(dim=tuple(range(1,masks.ndim)), keepdim=True)
photometric = photometric * mu
photometric = photometric.mean()
elif self.deca.config.photometric_normalization == 'inv_rel_mask_value':
mu = 1./ masks[:geom_losses_idxs, ...].mean(dim=tuple(range(1,masks.ndim)), keepdim=True)
photometric = photometric * mu
photometric = photometric.mean()
elif self.deca.config.photometric_normalization == 'abs_mask_value':
photometric = photometric * masks[:geom_losses_idxs, ...].sum(dim=tuple(range(1,masks.ndim)), keepdim=True)
photometric = photometric.mean()
else:
raise ValueError(f"Invalid photometric loss normalization: '{self.deca.config.photometric_normalization}'")
self._metric_or_loss(losses, metrics, self.deca.config.use_photometric)['photometric_texture'] = \
photometric * self.deca.config.photow
if self.deca.vgg_loss is not None:
vggl, _ = self.deca.vgg_loss(
masks[:geom_losses_idxs, ...] * images[:geom_losses_idxs, ...], # masked input image
masks[:geom_losses_idxs, ...] * predicted_images[:geom_losses_idxs, ...], # masked output image
)
self._metric_or_loss(losses, metrics, self.deca.config.use_vgg)['vgg'] = vggl * self.deca.config.vggw
if self.deca._has_neural_rendering():
predicted_translated_image = codedict["predicted_translated_image"]
photometric_translated = (masks[:geom_losses_idxs, ...] * (
predicted_translated_image[:geom_losses_idxs, ...] -
images[:geom_losses_idxs, ...]).abs()).mean() * self.deca.config.photow
if self.deca.config.use_photometric:
losses['photometric_translated_texture'] = photometric_translated
else:
metrics['photometric_translated_texture'] = photometric_translated
if self.deca.vgg_loss is not None:
vggl, _ = self.deca.vgg_loss(
masks[:geom_losses_idxs, ...] * images[:geom_losses_idxs, ...], # masked input image
masks[:geom_losses_idxs, ...] * predicted_translated_image[:geom_losses_idxs, ...],
# masked output image
)
self._metric_or_loss(losses, metrics, self.deca.config.use_vgg)['vgg_translated'] = vggl * self.deca.config.vggw
else:
raise ValueError("Is this line ever reached?")
losses = self._compute_id_loss(codedict, batch, training, testing, losses, batch_size=bs, ring_size=rs)
losses['shape_reg'] = (torch.sum(shapecode ** 2) / 2) * self.deca.config.shape_reg
losses['expression_reg'] = (torch.sum(expcode ** 2) / 2) * self.deca.config.exp_reg
losses['tex_reg'] = (torch.sum(texcode ** 2) / 2) * self.deca.config.tex_reg
losses['light_reg'] = ((torch.mean(lightcode, dim=2)[:, :,
None] - lightcode) ** 2).mean() * self.deca.config.light_reg
if 'original_code' in codedict.keys():
# original jaw pose regularization
if self.deca.config.get('exp_deca_jaw_pose', False) and \
'deca_jaw_reg' in self.deca.config.keys() and self.deca.config.deca_jaw_reg > 0:
jaw_pose_orig = codedict['original_code']['pose'][:, 3:]
jaw_pose = codedict['posecode'][..., 3:]
deca_jaw_pose_reg = (torch.sum((jaw_pose - jaw_pose_orig) ** 2) / 2) * self.deca.config.deca_jaw_reg
losses['deca_jaw_pose_reg'] = deca_jaw_pose_reg
if self.deca.config.get('exp_deca_global_pose', False) and \
'deca_global_reg' in self.deca.config.keys() and self.deca.config.deca_global_reg > 0:
global_pose_orig = codedict['original_code']['pose'][:, :3]
global_pose = codedict['posecode'][..., :3]
global_pose_reg = (torch.sum((global_pose - global_pose_orig) ** 2) / 2) * self.deca.config.deca_global_reg
losses['deca_global_pose_reg'] = global_pose_reg
# original expression regularization
if 'deca_expression_reg' in self.deca.config.keys() and self.deca.config.deca_expression_reg > 0:
expression_orig = codedict['original_code']['exp']
expression = codedict['expcode']
deca_expression_reg = (torch.sum((expression - expression_orig) ** 2) / 2) * self.deca.config.deca_expression_reg
losses['deca_expression_reg'] = deca_expression_reg
losses, metrics, codedict = self._compute_emonet_loss_wrapper(codedict, batch, training, testing, losses, metrics,
prefix="coarse", image_key="predicted_images",
with_grad=self.deca.config.use_emonet_loss and not self.deca._has_neural_rendering(),
batch_size=bs, ring_size=rs)
if self.deca._has_neural_rendering():
losses, metrics, codedict = self._compute_emonet_loss_wrapper(codedict, batch, training, testing, losses, metrics,
prefix="coarse_translated", image_key="predicted_translated_image",
with_grad=self.deca.config.use_emonet_loss and self.deca._has_neural_rendering(),
batch_size=bs, ring_size=rs
)
if self.au_loss is not None:
# with torch.no_grad():
self._compute_au_loss(images, predicted_images, losses, metrics, "coarse",
au=None,
with_grad=self.deca.config.au_loss.use_as_loss and not self.deca._has_neural_rendering())
if self.deca._has_neural_rendering():
self._compute_au_loss(images, predicted_translated_image, losses, metrics, "coarse",
au=None,
with_grad=self.deca.config.au_loss.use_as_loss and self.deca._has_neural_rendering())
if self.lipread_loss is not None:
# with torch.no_grad():
self._compute_lipread_loss(images, predicted_images, lmk, predicted_landmarks, losses, metrics, "coarse",
with_grad=self.deca.config.lipread_loss.use_as_loss and not self.deca._has_neural_rendering())
if self.deca._has_neural_rendering():
self._compute_lipread_loss(images, predicted_translated_image,
lmk, predicted_landmarks,
losses, metrics, "coarse",
with_grad=self.deca.config.lipread_loss.use_as_loss and self.deca._has_neural_rendering())
## DETAIL loss only
if self.mode == DecaMode.DETAIL:
predicted_detailed_image = codedict["predicted_detailed_image"]
uv_z = codedict["uv_z"] # UV displacement map
uv_shading = codedict["uv_shading"]
uv_vis_mask = codedict["uv_vis_mask"] # uv_mask of what is visible
photometric_detailed = (masks[:geom_losses_idxs, ...] * (
predicted_detailed_image[:geom_losses_idxs, ...] -
images[:geom_losses_idxs, ...]).abs()).mean() * self.deca.config.photow
if self.deca.config.use_detailed_photo:
losses['photometric_detailed_texture'] = photometric_detailed
else:
metrics['photometric_detailed_texture'] = photometric_detailed
if self.deca.vgg_loss is not None:
vggl, _ = self.deca.vgg_loss(
masks[:geom_losses_idxs, ...] * images[:geom_losses_idxs, ...], # masked input image
masks[:geom_losses_idxs, ...] * predicted_detailed_image[:geom_losses_idxs, ...],
# masked output image
)
self._metric_or_loss(losses, metrics, self.deca.config.use_vgg)['vgg_detailed'] = vggl * self.deca.config.vggw
if self.deca._has_neural_rendering():
predicted_detailed_translated_image = codedict["predicted_detailed_translated_image"]
photometric_detailed_translated = (masks[:geom_losses_idxs, ...] * (
predicted_detailed_translated_image[:geom_losses_idxs, ...] - images[:geom_losses_idxs,
...]).abs()).mean() * self.deca.config.photow
if self.deca.config.use_detailed_photo:
losses['photometric_translated_detailed_texture'] = photometric_detailed_translated
else:
metrics['photometric_translated_detailed_texture'] = photometric_detailed_translated
if self.deca.vgg_loss is not None:
vggl, _ = self.deca.vgg_loss(
masks[:geom_losses_idxs, ...] * images[:geom_losses_idxs, ...], # masked input image
masks[:geom_losses_idxs, ...] * predicted_detailed_translated_image[:geom_losses_idxs, ...],
# masked output image
)
self._metric_or_loss(losses, metrics, self.deca.config.use_vgg)[
'vgg_detailed_translated'] = vggl * self.deca.config.vggw
losses, metrics, codedict = self._compute_emonet_loss_wrapper(codedict, batch, training, testing, losses, metrics,
prefix="detail", image_key = "predicted_detailed_image",
with_grad=self.deca.config.use_emonet_loss and not self.deca._has_neural_rendering(),
batch_size=bs, ring_size=rs)
if self.deca._has_neural_rendering():
losses, metrics, codedict = self._compute_emonet_loss_wrapper(codedict, batch, training, testing, losses, metrics,
prefix="detail_translated",
image_key="predicted_detailed_translated_image",
with_grad=self.deca.config.use_emonet_loss and self.deca._has_neural_rendering(),
batch_size=bs, ring_size=rs)
# if self.emonet_loss is not None:
# self._compute_emotion_loss(images, predicted_detailed_image, losses, metrics, "detail",
# with_grad=self.deca.config.use_emonet_loss and not self.deca._has_neural_rendering(),
# batch_size=bs, ring_size=rs)
# codedict["detail_valence_input"] = self.emonet_loss.input_emotion['valence']
# codedict["detail_arousal_input"] = self.emonet_loss.input_emotion['arousal']
# codedict["detail_expression_input"] = self.emonet_loss.input_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']
# codedict["detail_valence_output"] = self.emonet_loss.output_emotion['valence']
# codedict["detail_arousal_output"] = self.emonet_loss.output_emotion['arousal']
# codedict["detail_expression_output"] = self.emonet_loss.output_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']
#
# if va is not None:
# codedict["detail_valence_gt"] = va[:,0]
# codedict["detail_arousal_gt"] = va[:,1]
# if expr7 is not None:
# codedict["detail_expression_gt"] = expr7
# if self.deca._has_neural_rendering():
# #TODO possible to make this more GPU efficient by not recomputing emotion for input image
# self._compute_emotion_loss(images, predicted_detailed_translated_image,
# losses, metrics, "detail_translated",
# va, expr7,
# with_grad= self.deca.config.use_emonet_loss and self.deca._has_neural_rendering(),
# batch_size=bs, ring_size=rs)
#
# # codedict["coarse_valence_input"] = self.emonet_loss.input_emotion['valence']
# # codedict["coarse_arousal_input"] = self.emonet_loss.input_emotion['arousal']
# # codedict["coarse_expression_input"] = self.emonet_loss.input_emotion['expression']
# codedict["detail_translated_valence_output"] = self.emonet_loss.output_emotion['valence']
# codedict["detail_translated_arousal_output"] = self.emonet_loss.output_emotion['arousal']
# codedict["detail_translated_expression_output"] = self.emonet_loss.output_emotion['expression' if 'expression' in self.emonet_loss.input_emotion.keys() else 'expr_classification']
if self.au_loss is not None:
self._compute_au_loss(images, predicted_images, losses, metrics, "detail",
au=None,
with_grad=self.deca.config.au_loss.use_as_loss and not self.deca._has_neural_rendering())
if self.deca._has_neural_rendering():
self._compute_au_loss(images, predicted_detailed_translated_image, losses, metrics, "detail",
au=None,
with_grad=self.deca.config.au_loss.use_as_loss and self.deca._has_neural_rendering())
for pi in range(3): # self.deca.face_attr_mask.shape[0]):
if self.deca.config.sfsw[pi] != 0:
# if pi==0:
new_size = 256
# else:
# new_size = 128
# if self.deca.config.uv_size != 256:
# new_size = 128
uv_texture_patch = F.interpolate(
uv_texture[:geom_losses_idxs, :, self.deca.face_attr_mask[pi][2]:self.deca.face_attr_mask[pi][3],
self.deca.face_attr_mask[pi][0]:self.deca.face_attr_mask[pi][1]],
[new_size, new_size], mode='bilinear')
uv_texture_gt_patch = F.interpolate(
uv_texture_gt[:geom_losses_idxs, :, self.deca.face_attr_mask[pi][2]:self.deca.face_attr_mask[pi][3],
self.deca.face_attr_mask[pi][0]:self.deca.face_attr_mask[pi][1]], [new_size, new_size],
mode='bilinear')
uv_vis_mask_patch = F.interpolate(
uv_vis_mask[:geom_losses_idxs, :, self.deca.face_attr_mask[pi][2]:self.deca.face_attr_mask[pi][3],
self.deca.face_attr_mask[pi][0]:self.deca.face_attr_mask[pi][1]],
[new_size, new_size], mode='bilinear')
detail_l1 = (uv_texture_patch * uv_vis_mask_patch - uv_texture_gt_patch * uv_vis_mask_patch).abs().mean() * \
self.deca.config.sfsw[pi]
if self.deca.config.use_detail_l1 and not self.deca._has_neural_rendering():
losses['detail_l1_{}'.format(pi)] = detail_l1
else:
metrics['detail_l1_{}'.format(pi)] = detail_l1
if self.deca.config.use_detail_mrf and not self.deca._has_neural_rendering():
mrf = self.deca.perceptual_loss(uv_texture_patch * uv_vis_mask_patch,
uv_texture_gt_patch * uv_vis_mask_patch) * \
self.deca.config.sfsw[pi] * self.deca.config.mrfwr
losses['detail_mrf_{}'.format(pi)] = mrf
else:
with torch.no_grad():
mrf = self.deca.perceptual_loss(uv_texture_patch * uv_vis_mask_patch,
uv_texture_gt_patch * uv_vis_mask_patch) * \
self.deca.config.sfsw[pi] * self.deca.config.mrfwr
metrics['detail_mrf_{}'.format(pi)] = mrf
if self.deca._has_neural_rendering():
# raise NotImplementedError("Gotta implement the texture extraction first.")
translated_uv_texture = codedict["translated_uv_texture"]
translated_uv_texture_patch = F.interpolate(
translated_uv_texture[:geom_losses_idxs, :,
self.deca.face_attr_mask[pi][2]:self.deca.face_attr_mask[pi][3],
self.deca.face_attr_mask[pi][0]:self.deca.face_attr_mask[pi][1]],
[new_size, new_size], mode='bilinear')
translated_detail_l1 = (translated_uv_texture_patch * uv_vis_mask_patch
- uv_texture_gt_patch * uv_vis_mask_patch).abs().mean() * \
self.deca.config.sfsw[pi]
if self.deca.config.use_detail_l1:
losses['detail_translated_l1_{}'.format(pi)] = translated_detail_l1
else:
metrics['detail_translated_l1_{}'.format(pi)] = translated_detail_l1
if self.deca.config.use_detail_mrf:
translated_mrf = self.deca.perceptual_loss(translated_uv_texture_patch * uv_vis_mask_patch,
uv_texture_gt_patch * uv_vis_mask_patch) * \
self.deca.config.sfsw[pi] * self.deca.config.mrfwr
losses['detail_translated_mrf_{}'.format(pi)] = translated_mrf
else:
with torch.no_grad():
mrf = self.deca.perceptual_loss(translated_uv_texture_patch * uv_vis_mask_patch,
uv_texture_gt_patch * uv_vis_mask_patch) * \
self.deca.config.sfsw[pi] * self.deca.config.mrfwr
metrics['detail_translated_mrf_{}'.format(pi)] = mrf
# Old piece of debug code. Good to delete.
# if pi == 2:
# uv_texture_gt_patch_ = uv_texture_gt_patch
# uv_texture_patch_ = uv_texture_patch
# uv_vis_mask_patch_ = uv_vis_mask_patch
losses['z_reg'] = torch.mean(uv_z.abs()) * self.deca.config.zregw
losses['z_diff'] = lossfunc.shading_smooth_loss(uv_shading) * self.deca.config.zdiffw
nonvis_mask = (1 - util.binary_erosion(uv_vis_mask))
losses['z_sym'] = (nonvis_mask * (uv_z - torch.flip(uv_z, [-1]).detach()).abs()).sum() * self.deca.config.zsymw
if self.emotion_mlp is not None:# and not testing:
mlp_losses, mlp_metrics = self.emotion_mlp.compute_loss(
codedict, batch, training=training, pred_prefix="emo_mlp_")
for key in mlp_losses.keys():
if key in losses.keys():
raise RuntimeError(f"Duplicate loss label {key}")
losses[key] = self.deca.config.mlp_emotion_predictor_weight * mlp_losses[key]
for key in mlp_metrics.keys():
if key in metrics.keys():
raise RuntimeError(f"Duplicate metric label {key}")
# let's report the metrics (which are a superset of losses when it comes to EmoMLP) without the weight,
# it's hard to plot the metrics otherwise
metrics[key] = mlp_metrics[key]
# metrics[key] = self.deca.config.mlp_emotion_predictor_weight * mlp_metrics[key]
# else:
# uv_texture_gt_patch_ = None
# uv_texture_patch_ = None
# uv_vis_mask_patch_ = None
return losses, metrics
def compute_loss(self, values, batch, training=True, testing=False) -> dict:
"""
The function used to compute the loss on a training batch.
:
training should be set to true when calling from training_step only
"""
losses, metrics = self._compute_loss(values, batch, training=training, testing=testing)
all_loss = 0.
losses_key = losses.keys()
for key in losses_key:
all_loss = all_loss + losses[key]
# losses['all_loss'] = all_loss
losses = {'loss_' + key: value for key, value in losses.items()} # add prefix loss for better logging
losses['loss'] = all_loss
# add metrics that do not effect the loss function (if any)
for key in metrics.keys():
losses['metric_' + key] = metrics[key]
return losses
def _val_to_be_logged(self, d):
if not hasattr(self, 'val_dict_list'):
self.val_dict_list = []
self.val_dict_list += [d]
def _train_to_be_logged(self, d):
if not hasattr(self, 'train_dict_list'):
self.train_dict_list = []
self.train_dict_list += [d]
def validation_step(self, batch, batch_idx, dataloader_idx=None):
"""
Training step override of pytorch lightning module. It makes the encoding, decoding passes, computes the loss and logs the losses/visualizations.
:param batch: Batch of images to encode. batch['image'] [batch_size, ring_size, 3, image_size, image_size].
For a training forward pass, additional corresponding data are necessery such as 'landmarks' and 'masks'.
:batch_idx batch index
"""
with torch.no_grad():
training = False
values = self.encode(batch, training=training)
values = self.decode(values, training=training)
losses_and_metrics = self.compute_loss(values, batch, training=training)
#### self.log_dict(losses_and_metrics, on_step=False, on_epoch=True)
# prefix = str(self.mode.name).lower()
prefix = self._get_logging_prefix()
# if dataloader_idx is not None:
# dataloader_str = str(dataloader_idx) + "_"
# else:
dataloader_str = ''
stage_str = dataloader_str + 'val_'
# losses_and_metrics_to_log = {prefix + dataloader_str +'_val_' + key: value.detach().cpu() for key, value in losses_and_metrics.items()}
# losses_and_metrics_to_log = {prefix + '_' + stage_str + key: value.detach() for key, value in losses_and_metrics.items()}
losses_and_metrics_to_log = {prefix + '_' + stage_str + key: value.detach().cpu().item() for key, value in losses_and_metrics.items()}
losses_and_metrics_to_log[prefix + '_' + stage_str + 'epoch'] = self.current_epoch
# losses_and_metrics_to_log[prefix + '_' + stage_str + 'epoch'] = torch.tensor(self.current_epoch, device=self.device)
# log val_loss also without any prefix for a model checkpoint to track it
losses_and_metrics_to_log[stage_str + 'loss'] = losses_and_metrics_to_log[prefix + '_' + stage_str + 'loss']
losses_and_metrics_to_log[prefix + '_' + stage_str + 'step'] = self.global_step
losses_and_metrics_to_log[prefix + '_' + stage_str + 'batch_idx'] = batch_idx
losses_and_metrics_to_log[stage_str + 'step'] = self.global_step
losses_and_metrics_to_log[stage_str + 'batch_idx'] = batch_idx
losses_and_metrics_to_log[prefix + '_' + stage_str + 'mem_usage'] = self.process.memory_info().rss
losses_and_metrics_to_log[stage_str + 'mem_usage'] = self.process.memory_info().rss
# self._val_to_be_logged(losses_and_metrics_to_log)
if self.logger is not None:
self.log_dict(losses_and_metrics_to_log, on_step=False, on_epoch=True, sync_dist=True) # log per epoch # recommended
if self.trainer.is_global_zero:
if self.deca.config.val_vis_frequency > 0:
if batch_idx % self.deca.config.val_vis_frequency == 0:
uv_detail_normals = None
if 'uv_detail_normals' in values.keys():
uv_detail_normals = values['uv_detail_normals']
visualizations, grid_image = self._visualization_checkpoint(values['verts'], values['trans_verts'], values['ops'],
uv_detail_normals, values, batch_idx, stage_str[:-1], prefix)
vis_dict = self._create_visualizations_to_log(stage_str[:-1], visualizations, values, batch_idx, indices=0, dataloader_idx=dataloader_idx)
# image = Image(grid_image, caption="full visualization")
# vis_dict[prefix + '_val_' + "visualization"] = image
if isinstance(self.logger, WandbLogger):
self.logger.log_metrics(vis_dict)
return None
def _get_logging_prefix(self):
prefix = self.stage_name + str(self.mode.name).lower()
return prefix
def test_step(self, batch, batch_idx, dataloader_idx=None):
"""
Testing step override of pytorch lightning module. It makes the encoding, decoding passes, computes the loss and logs the losses/visualizations
without gradient
:param batch: Batch of images to encode. batch['image'] [batch_size, ring_size, 3, image_size, image_size].
For a training forward pass, additional corresponding data are necessery such as 'landmarks' and 'masks'.
:batch_idx batch index
"""
prefix = self._get_logging_prefix()
losses_and_metrics_to_log = {}
# if dataloader_idx is not None:
# dataloader_str = str(dataloader_idx) + "_"
# else:
dataloader_str = ''
stage_str = dataloader_str + 'test_'
with torch.no_grad():
training = False
testing = True
values = self.encode(batch, training=training)
values = self.decode(values, training=training)
if 'mask' in batch.keys():
losses_and_metrics = self.compute_loss(values, batch, training=False, testing=testing)
# losses_and_metrics_to_log = {prefix + '_' + stage_str + key: value.detach().cpu() for key, value in losses_and_metrics.items()}
losses_and_metrics_to_log = {prefix + '_' + stage_str + key: value.detach().cpu().item() for key, value in losses_and_metrics.items()}
else:
losses_and_metric = None
# losses_and_metrics_to_log[prefix + '_' + stage_str + 'epoch'] = self.current_epoch
# losses_and_metrics_to_log[prefix + '_' + stage_str + 'epoch'] = torch.tensor(self.current_epoch, device=self.device)
# losses_and_metrics_to_log[prefix + '_' + stage_str + 'step'] = torch.tensor(self.global_step, device=self.device)
# losses_and_metrics_to_log[prefix + '_' + stage_str + 'batch_idx'] = torch.tensor(batch_idx, device=self.device)
# losses_and_metrics_to_log[stage_str + 'epoch'] = torch.tensor(self.current_epoch, device=self.device)
# losses_and_metrics_to_log[stage_str + 'step'] = torch.tensor(self.global_step, device=self.device)
# losses_and_metrics_to_log[stage_str + 'batch_idx'] = torch.tensor(batch_idx, device=self.device)
losses_and_metrics_to_log[prefix + '_' + stage_str + 'epoch'] = self.current_epoch
losses_and_metrics_to_log[prefix + '_' + stage_str + 'step'] = self.global_step
losses_and_metrics_to_log[prefix + '_' + stage_str + 'batch_idx'] = batch_idx
losses_and_metrics_to_log[prefix + '_' + stage_str + 'mem_usage'] = self.process.memory_info().rss
losses_and_metrics_to_log[stage_str + 'epoch'] = self.current_epoch
losses_and_metrics_to_log[stage_str + 'step'] = self.global_step
losses_and_metrics_to_log[stage_str + 'batch_idx'] = batch_idx
losses_and_metrics_to_log[stage_str + 'mem_usage'] = self.process.memory_info().rss
if self.logger is not None:
# self.logger.log_metrics(losses_and_metrics_to_log)
self.log_dict(losses_and_metrics_to_log, sync_dist=True, on_step=False, on_epoch=True)
# if self.global_step % 200 == 0:
uv_detail_normals = None
if 'uv_detail_normals' in values.keys():
uv_detail_normals = values['uv_detail_normals']
if self.deca.config.test_vis_frequency > 0:
# Log visualizations every once in a while
if batch_idx % self.deca.config.test_vis_frequency == 0:
# if self.trainer.is_global_zero:
visualizations, grid_image = self._visualization_checkpoint(values['verts'], values['trans_verts'], values['ops'],
uv_detail_normals, values, self.global_step, stage_str[:-1], prefix)
visdict = self._create_visualizations_to_log(stage_str[:-1], visualizations, values, batch_idx, indices=0, dataloader_idx=dataloader_idx)
self.logger.log_metrics(visdict)
return None
@property
def process(self):
if not hasattr(self,"process_"):
self.process_ = psutil.Process(os.getpid())
return self.process_
def training_step(self, batch, batch_idx, *args, **kwargs): #, debug=True):
"""
Training step override of pytorch lightning module. It makes the encoding, decoding passes, computes the loss and logs the losses/visualizations.
:param batch: Batch of images to encode. batch['image'] [batch_size, ring_size, 3, image_size, image_size].
For a training forward pass, additional corresponding data are necessery such as 'landmarks' and 'masks'.
:batch_idx batch index
"""
values = self.encode(batch, training=True)
values = self.decode(values, training=True)
losses_and_metrics = self.compute_loss(values, batch, training=True)
uv_detail_normals = None
if 'uv_detail_normals' in values.keys():
uv_detail_normals = values['uv_detail_normals']
# prefix = str(self.mode.name).lower()
prefix = self._get_logging_prefix()
# losses_and_metrics_to_log = {prefix + '_train_' + key: value.detach().cpu() for key, value in losses_and_metrics.items()}
# losses_and_metrics_to_log = {prefix + '_train_' + key: value.detach() for key, value in losses_and_metrics.items()}
losses_and_metrics_to_log = {prefix + '_train_' + key: value.detach().cpu().item() for key, value in losses_and_metrics.items()}
# losses_and_metrics_to_log[prefix + '_train_' + 'epoch'] = torch.tensor(self.current_epoch, device=self.device)
losses_and_metrics_to_log[prefix + '_train_' + 'epoch'] = self.current_epoch
losses_and_metrics_to_log[prefix + '_train_' + 'step'] = self.global_step
losses_and_metrics_to_log[prefix + '_train_' + 'batch_idx'] = batch_idx
losses_and_metrics_to_log[prefix + '_' + "train_" + 'mem_usage'] = self.process.memory_info().rss
# losses_and_metrics_to_log['train_' + 'epoch'] = torch.tensor(self.current_epoch, device=self.device)
losses_and_metrics_to_log['train_' + 'epoch'] = self.current_epoch
losses_and_metrics_to_log['train_' + 'step'] = self.global_step
losses_and_metrics_to_log['train_' + 'batch_idx'] = batch_idx
losses_and_metrics_to_log["train_" + 'mem_usage'] = self.process.memory_info().rss
# log loss also without any prefix for a model checkpoint to track it
losses_and_metrics_to_log['loss'] = losses_and_metrics_to_log[prefix + '_train_loss']
if self.logger is not None:
self.log_dict(losses_and_metrics_to_log, on_step=False, on_epoch=True, sync_dist=True) # log per epoch, # recommended
if self.deca.config.train_vis_frequency > 0:
if self.global_step % self.deca.config.train_vis_frequency == 0:
if self.trainer.is_global_zero:
visualizations, grid_image = self._visualization_checkpoint(values['verts'], values['trans_verts'], values['ops'],
uv_detail_normals, values, batch_idx, "train", prefix)
visdict = self._create_visualizations_to_log('train', visualizations, values, batch_idx, indices=0)
if isinstance(self.logger, WandbLogger):
self.logger.log_metrics(visdict)#, step=self.global_step)
# self.log_dict(visdict, sync_dist=True)
# self.log_dict(losses_and_metrics_to_log, on_step=True, on_epoch=False) # log per step
# self.log_dict(losses_and_metrics_to_log, on_step=True, on_epoch=True) # log per both
# return losses_and_metrics
return losses_and_metrics['loss']
### STEP ENDS ARE PROBABLY NOT NECESSARY BUT KEEP AN EYE ON THEM IF MULI-GPU TRAINING DOESN'T WORK
# def training_step_end(self, batch_parts):
# return self._step_end(batch_parts)
#
# def validation_step_end(self, batch_parts):
# return self._step_end(batch_parts)
#
# def _step_end(self, batch_parts):
# # gpu_0_prediction = batch_parts.pred[0]['pred']
# # gpu_1_prediction = batch_parts.pred[1]['pred']
# N = len(batch_parts)
# loss_dict = {}
# for key in batch_parts[0]:
# for i in range(N):
# if key not in loss_dict.keys():
# loss_dict[key] = batch_parts[i]
# else:
# loss_dict[key] = batch_parts[i]
# loss_dict[key] = loss_dict[key] / N
# return loss_dict
def vae_2_str(self, valence=None, arousal=None, affnet_expr=None, expr7=None, prefix=""):
caption = ""
if len(prefix) > 0:
prefix += "_"
if valence is not None and not np.isnan(valence).any():
caption += prefix + "valence= %.03f\n" % valence
if arousal is not None and not np.isnan(arousal).any():
caption += prefix + "arousal= %.03f\n" % arousal
if affnet_expr is not None and not np.isnan(affnet_expr).any():
caption += prefix + "expression= %s \n" % AffectNetExpressions(affnet_expr).name
if expr7 is not None and not np.isnan(expr7).any():
caption += prefix +"expression= %s \n" % Expression7(expr7).name
return caption
def _create_visualizations_to_log(self, stage, visdict, values, step, indices=None,
dataloader_idx=None, output_dir=None):
mode_ = str(self.mode.name).lower()
prefix = self._get_logging_prefix()
output_dir = output_dir or self.inout_params.full_run_dir
log_dict = {}
for key in visdict.keys():
images = _torch_image2np(visdict[key])
if images.dtype == np.float32 or images.dtype == np.float64 or images.dtype == np.float16:
images = np.clip(images, 0, 1)
if indices is None:
indices = np.arange(images.shape[0])
if isinstance(indices, int):
indices = [indices,]
if isinstance(indices, str) and indices == 'all':
image = np.concatenate([images[i] for i in range(images.shape[0])], axis=1)
savepath = Path(f'{output_dir}/{prefix}_{stage}/{key}/{self.current_epoch:04d}_{step:04d}_all.png')
# im2log = Image(image, caption=key)
if isinstance(self.logger, WandbLogger):
im2log = _log_wandb_image(savepath, image)
else:
im2log = _log_array_image(savepath, image)
name = prefix + "_" + stage + "_" + key
if dataloader_idx is not None:
name += "/dataloader_idx_" + str(dataloader_idx)
log_dict[name] = im2log
else:
for i in indices:
caption = key + f" batch_index={step}\n"
caption += key + f" index_in_batch={i}\n"
if self.emonet_loss is not None:
if key == 'inputs':
if mode_ + "_valence_input" in values.keys():
caption += self.vae_2_str(
values[mode_ + "_valence_input"][i].detach().cpu().item(),
values[mode_ + "_arousal_input"][i].detach().cpu().item(),
np.argmax(values[mode_ + "_expression_input"][i].detach().cpu().numpy()),
prefix="emonet") + "\n"
if 'va' in values.keys() and mode_ + "valence_gt" in values.keys():
# caption += self.vae_2_str(
# values[mode_ + "_valence_gt"][i].detach().cpu().item(),
# values[mode_ + "_arousal_gt"][i].detach().cpu().item(),
caption += self.vae_2_str(
values[mode_ + "valence_gt"][i].detach().cpu().item(),
values[mode_ + "arousal_gt"][i].detach().cpu().item(),
prefix="gt") + "\n"
if 'expr7' in values.keys() and mode_ + "_expression_gt" in values.keys():
caption += "\n" + self.vae_2_str(
expr7=values[mode_ + "_expression_gt"][i].detach().cpu().numpy(),
prefix="gt") + "\n"
if 'affectnetexp' in values.keys() and mode_ + "_expression_gt" in values.keys():
caption += "\n" + self.vae_2_str(
affnet_expr=values[mode_ + "_expression_gt"][i].detach().cpu().numpy(),
prefix="gt") + "\n"
elif 'geometry_detail' in key:
if "emo_mlp_valence" in values.keys():
caption += self.vae_2_str(
values["emo_mlp_valence"][i].detach().cpu().item(),
values["emo_mlp_arousal"][i].detach().cpu().item(),
prefix="mlp")
if 'emo_mlp_expr_classification' in values.keys():
caption += "\n" + self.vae_2_str(
affnet_expr=values["emo_mlp_expr_classification"][i].detach().cpu().argmax().numpy(),
prefix="mlp") + "\n"
elif key == 'output_images_' + mode_:
if mode_ + "_valence_output" in values.keys():
caption += self.vae_2_str(values[mode_ + "_valence_output"][i].detach().cpu().item(),
values[mode_ + "_arousal_output"][i].detach().cpu().item(),
np.argmax(values[mode_ + "_expression_output"][i].detach().cpu().numpy())) + "\n"
elif key == 'output_translated_images_' + mode_:
if mode_ + "_translated_valence_output" in values.keys():
caption += self.vae_2_str(values[mode_ + "_translated_valence_output"][i].detach().cpu().item(),
values[mode_ + "_translated_arousal_output"][i].detach().cpu().item(),
np.argmax(values[mode_ + "_translated_expression_output"][i].detach().cpu().numpy())) + "\n"
# elif key == 'output_images_detail':
# caption += "\n" + self.vae_2_str(values["detail_output_valence"][i].detach().cpu().item(),
# values["detail_output_valence"][i].detach().cpu().item(),
# np.argmax(values["detail_output_expression"][
# i].detach().cpu().numpy()))
savepath = Path(f'{output_dir}/{prefix}_{stage}/{key}/{self.current_epoch:04d}_{step:04d}_{i:02d}.png')
image = images[i]
# im2log = Image(image, caption=caption)
if isinstance(self.logger, WandbLogger):
im2log = _log_wandb_image(savepath, image, caption)
elif self.logger is not None:
im2log = _log_array_image(savepath, image, caption)
else:
im2log = _log_array_image(None, image, caption)
name = prefix + "_" + stage + "_" + key
if dataloader_idx is not None:
name += "/dataloader_idx_" + str(dataloader_idx)
log_dict[name] = im2log
return log_dict
def _visualization_checkpoint(self, verts, trans_verts, ops, uv_detail_normals, additional, batch_idx, stage, prefix,
save=False):
batch_size = verts.shape[0]
visind = np.arange(batch_size)
shape_images = self.deca.render.render_shape(verts, trans_verts)
if uv_detail_normals is not None:
detail_normal_images = F.grid_sample(uv_detail_normals.detach(), ops['grid'].detach(),
align_corners=False)
shape_detail_images = self.deca.render.render_shape(verts, trans_verts,
detail_normal_images=detail_normal_images)
else:
shape_detail_images = None
visdict = {}
if 'images' in additional.keys():
visdict['inputs'] = additional['images'][visind]
if 'images' in additional.keys() and 'lmk' in additional.keys():
visdict['landmarks_gt'] = util.tensor_vis_landmarks(additional['images'][visind], additional['lmk'][visind])
if 'images' in additional.keys() and 'predicted_landmarks' in additional.keys():
visdict['landmarks_predicted'] = util.tensor_vis_landmarks(additional['images'][visind],
additional['predicted_landmarks'][visind])
if 'predicted_images' in additional.keys():
visdict['output_images_coarse'] = additional['predicted_images'][visind]
if 'predicted_translated_image' in additional.keys() and additional['predicted_translated_image'] is not None:
visdict['output_translated_images_coarse'] = additional['predicted_translated_image'][visind]
visdict['geometry_coarse'] = shape_images[visind]
if shape_detail_images is not None:
visdict['geometry_detail'] = shape_detail_images[visind]
if 'albedo_images' in additional.keys():
visdict['albedo_images'] = additional['albedo_images'][visind]
if 'masks' in additional.keys():
visdict['mask'] = additional['masks'].repeat(1, 3, 1, 1)[visind]
if 'albedo' in additional.keys():
visdict['albedo'] = additional['albedo'][visind]
if 'predicted_detailed_image' in additional.keys() and additional['predicted_detailed_image'] is not None:
visdict['output_images_detail'] = additional['predicted_detailed_image'][visind]
if 'predicted_detailed_translated_image' in additional.keys() and additional['predicted_detailed_translated_image'] is not None:
visdict['output_translated_images_detail'] = additional['predicted_detailed_translated_image'][visind]
if 'shape_detail_images' in additional.keys():
visdict['shape_detail_images'] = additional['shape_detail_images'][visind]
if 'uv_detail_normals' in additional.keys():
visdict['uv_detail_normals'] = additional['uv_detail_normals'][visind] * 0.5 + 0.5
if 'uv_texture_patch' in additional.keys():
visdict['uv_texture_patch'] = additional['uv_texture_patch'][visind]
if 'uv_texture_gt' in additional.keys():
visdict['uv_texture_gt'] = additional['uv_texture_gt'][visind]
if 'translated_uv_texture' in additional.keys() and additional['translated_uv_texture'] is not None:
visdict['translated_uv_texture'] = additional['translated_uv_texture'][visind]
if 'uv_vis_mask_patch' in additional.keys():
visdict['uv_vis_mask_patch'] = additional['uv_vis_mask_patch'][visind]
if save:
savepath = f'{self.inout_params.full_run_dir}/{prefix}_{stage}/combined/{self.current_epoch:04d}_{batch_idx:04d}.png'
Path(savepath).parent.mkdir(exist_ok=True, parents=True)
visualization_image = self.deca.visualize(visdict, savepath)
return visdict, visualization_image[..., [2, 1, 0]]
else:
visualization_image = None
return visdict, None
def _get_trainable_parameters(self):
trainable_params = []
if self.mode == DecaMode.COARSE:
trainable_params += self.deca._get_coarse_trainable_parameters()
elif self.mode == DecaMode.DETAIL:
trainable_params += self.deca._get_detail_trainable_parameters()
else:
raise ValueError(f"Invalid deca mode: {self.mode}")
if self.emotion_mlp is not None:
trainable_params += list(self.emotion_mlp.parameters())
if self.emonet_loss is not None:
trainable_params += self.emonet_loss._get_trainable_params()
if self.deca.id_loss is not None:
trainable_params += self.deca.id_loss._get_trainable_params()
return trainable_params
def configure_optimizers(self):
# optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
print("Configuring optimizer")
trainable_params = self._get_trainable_parameters()
if self.learning_params.optimizer == 'Adam':
self.deca.opt = torch.optim.Adam(
trainable_params,
lr=self.learning_params.learning_rate,
amsgrad=False)
elif self.config.learning.optimizer == 'AdaBound':
self.deca.opt = adabound.AdaBound(
trainable_params,
lr=self.config.learning.learning_rate,
final_lr=self.config.learning.final_learning_rate
)
elif self.learning_params.optimizer == 'SGD':
self.deca.opt = torch.optim.SGD(
trainable_params,
lr=self.learning_params.learning_rate)
else:
raise ValueError(f"Unsupported optimizer: '{self.learning_params.optimizer}'")
optimizers = [self.deca.opt]
schedulers = []
if 'learning_rate_decay' in self.learning_params.keys():
scheduler = torch.optim.lr_scheduler.ExponentialLR(self.deca.opt, gamma=self.learning_params.learning_rate_decay)
schedulers += [scheduler]
if len(schedulers) == 0:
return self.deca.opt
return optimizers, schedulers
class DECA(torch.nn.Module):
"""
The original DECA class which contains the encoders, FLAME decoder and the detail decoder.
"""
def __init__(self, config):
"""
:config corresponds to a model_params from DecaModule
"""
super().__init__()
# ID-MRF perceptual loss (kept here from the original DECA implementation)
self.perceptual_loss = None
# Face Recognition loss
self.id_loss = None
# VGG feature loss
self.vgg_loss = None
self._reconfigure(config)
self._reinitialize()
def _dirty_init(self):
pass # not used here, implemented for EMICA
def get_input_image_size(self):
return (self.config.image_size, self.config.image_size)
def _reconfigure(self, config):
self.config = config
self.n_param = config.n_shape + config.n_tex + config.n_exp + config.n_pose + config.n_cam + config.n_light
# identity-based detail code
self.n_detail = config.n_detail
# emotion-based detail code (deprecated, not use by DECA or EMOCA)
self.n_detail_emo = config.n_detail_emo if 'n_detail_emo' in config.keys() else 0
# count the size of the conidition vector
if 'detail_conditioning' in self.config.keys():
self.n_cond = 0
if 'globalpose' in self.config.detail_conditioning:
self.n_cond += 3
if 'jawpose' in self.config.detail_conditioning:
self.n_cond += 3
if 'identity' in self.config.detail_conditioning:
self.n_cond += config.n_shape
if 'expression' in self.config.detail_conditioning:
self.n_cond += config.n_exp
else:
self.n_cond = 3 + config.n_exp
self.mode = DecaMode[str(config.mode).upper()]
self._create_detail_generator()
self._init_deep_losses()
self._setup_neural_rendering()
def _reinitialize(self):
self._create_model()
self._setup_renderer()
self._init_deep_losses()
self.face_attr_mask = util.load_local_mask(image_size=self.config.uv_size, mode='bbx')
def _get_num_shape_params(self):
return self.config.n_shape
def _init_deep_losses(self):
"""
Initialize networks for deep losses
"""
# TODO: ideally these networks should be moved out the DECA class and into DecaModule,
# but that would break backwards compatility with the original DECA and would not be able to load DECA's weights
if 'mrfwr' not in self.config.keys() or self.config.mrfwr == 0:
self.perceptual_loss = None
else:
if self.perceptual_loss is None:
self.perceptual_loss = lossfunc.IDMRFLoss().eval()
self.perceptual_loss.requires_grad_(False) # TODO, move this to the constructor
if 'idw' not in self.config.keys() or self.config.idw == 0:
self.id_loss = None
else:
if self.id_loss is None:
id_metric = self.config.id_metric if 'id_metric' in self.config.keys() else None
id_trainable = self.config.id_trainable if 'id_trainable' in self.config.keys() else False
self.id_loss_start_step = self.config.id_loss_start_step if 'id_loss_start_step' in self.config.keys() else 0
self.id_loss = lossfunc.VGGFace2Loss(self.config.pretrained_vgg_face_path, id_metric, id_trainable)
self.id_loss.freeze_nontrainable_layers()
if 'vggw' not in self.config.keys() or self.config.vggw == 0:
self.vgg_loss = None
else:
if self.vgg_loss is None:
vgg_loss_batch_norm = 'vgg_loss_batch_norm' in self.config.keys() and self.config.vgg_loss_batch_norm
self.vgg_loss = VGG19Loss(dict(zip(self.config.vgg_loss_layers, self.config.lambda_vgg_layers)), batch_norm=vgg_loss_batch_norm).eval()
self.vgg_loss.requires_grad_(False) # TODO, move this to the constructor
def _setup_renderer(self):
self.render = SRenderY(self.config.image_size, obj_filename=self.config.topology_path,
uv_size=self.config.uv_size) # .to(self.device)
# face mask for rendering details
mask = imread(self.config.face_mask_path).astype(np.float32) / 255.
mask = torch.from_numpy(mask[:, :, 0])[None, None, :, :].contiguous()
self.uv_face_mask = F.interpolate(mask, [self.config.uv_size, self.config.uv_size])
mask = imread(self.config.face_eye_mask_path).astype(np.float32) / 255.
mask = torch.from_numpy(mask[:, :, 0])[None, None, :, :].contiguous()
uv_face_eye_mask = F.interpolate(mask, [self.config.uv_size, self.config.uv_size])
self.register_buffer('uv_face_eye_mask', uv_face_eye_mask)
# displacement mask is deprecated and not used by DECA or EMOCA
if 'displacement_mask' in self.config.keys():
displacement_mask_ = 1-np.load(self.config.displacement_mask).astype(np.float32)
# displacement_mask_ = np.load(self.config.displacement_mask).astype(np.float32)
displacement_mask_ = torch.from_numpy(displacement_mask_)[None, None, ...].contiguous()
displacement_mask_ = F.interpolate(displacement_mask_, [self.config.uv_size, self.config.uv_size])
self.register_buffer('displacement_mask', displacement_mask_)
## displacement correct
if os.path.isfile(self.config.fixed_displacement_path):
fixed_dis = np.load(self.config.fixed_displacement_path)
fixed_uv_dis = torch.tensor(fixed_dis).float()
else:
fixed_uv_dis = torch.zeros([512, 512]).float()
print("Warning: fixed_displacement_path not found, using zero displacement")
self.register_buffer('fixed_uv_dis', fixed_uv_dis)
def uses_texture(self):
if 'use_texture' in self.config.keys():
return self.config.use_texture
return True # true by default
def _disable_texture(self, remove_from_model=False):
self.config.use_texture = False
if remove_from_model:
self.flametex = None
def _enable_texture(self):
self.config.use_texture = True
def _has_neural_rendering(self):
return hasattr(self.config, "neural_renderer") and bool(self.config.neural_renderer)
def _setup_neural_rendering(self):
if self._has_neural_rendering():
if self.config.neural_renderer.class_ == "StarGAN":
print("Creating StarGAN neural renderer")
self.image_translator = StarGANWrapper(self.config.neural_renderer.cfg, self.config.neural_renderer.stargan_repo)
else:
raise ValueError(f"Unsupported neural renderer class '{self.config.neural_renderer.class_}'")
if self.image_translator.background_mode == "input":
if self.config.background_from_input not in [True, "input"]:
raise NotImplementedError("The background mode of the neural renderer and deca is not synchronized. "
"Background should be inpainted from the input")
elif self.image_translator.background_mode == "black":
if self.config.background_from_input not in [False, "black"]:
raise NotImplementedError("The background mode of the neural renderer and deca is not synchronized. "
"Background should be black.")
elif self.image_translator.background_mode == "none":
if self.config.background_from_input not in ["none"]:
raise NotImplementedError("The background mode of the neural renderer and deca is not synchronized. "
"The background should not be handled")
else:
raise NotImplementedError(f"Unsupported mode of the neural renderer backroungd: "
f"'{self.image_translator.background_mode}'")
def _create_detail_generator(self):
#backwards compatibility hack:
if hasattr(self, 'D_detail'):
if (not "detail_conditioning_type" in self.config.keys() or self.config.detail_conditioning_type == "concat") \
and isinstance(self.D_detail, Generator):
return
if self.config.detail_conditioning_type == "adain" and isinstance(self.D_detail, GeneratorAdaIn):
return
print("[WARNING]: We are reinitializing the detail generator!")
del self.D_detail # just to make sure we free the CUDA memory, probably not necessary
if not "detail_conditioning_type" in self.config.keys() or str(self.config.detail_conditioning_type).lower() == "concat":
# concatenates detail latent and conditioning (this one is used by DECA/EMOCA)
print("Creating classic detail generator.")
self.D_detail = Generator(latent_dim=self.n_detail + self.n_detail_emo + self.n_cond, out_channels=1, out_scale=0.01,
sample_mode='bilinear')
elif str(self.config.detail_conditioning_type).lower() == "adain":
# conditioning passed in through adain layers (this one is experimental and not currently used)
print("Creating AdaIn detail generator.")
self.D_detail = GeneratorAdaIn(self.n_detail + self.n_detail_emo, self.n_cond, out_channels=1, out_scale=0.01,
sample_mode='bilinear')
else:
raise NotImplementedError(f"Detail conditioning invalid: '{self.config.detail_conditioning_type}'")
def _create_model(self):
# 1) build coarse encoder
e_flame_type = 'ResnetEncoder'
if 'e_flame_type' in self.config.keys():
e_flame_type = self.config.e_flame_type
if e_flame_type == 'ResnetEncoder':
self.E_flame = ResnetEncoder(outsize=self.n_param)
elif e_flame_type[:4] == 'swin':
self.E_flame = SwinEncoder(outsize=self.n_param, img_size=self.config.image_size, swin_type=e_flame_type)
else:
raise ValueError(f"Invalid 'e_flame_type' = {e_flame_type}")
flame_cfg = copy.deepcopy(self.config)
flame_cfg.n_shape = self._get_num_shape_params()
if 'flame_mediapipe_lmk_embedding_path' not in flame_cfg.keys():
self.flame = FLAME(flame_cfg)
else:
self.flame = FLAME_mediapipe(flame_cfg)
if self.uses_texture():
self.flametex = FLAMETex(self.config)
else:
self.flametex = None
# 2) build detail encoder
e_detail_type = 'ResnetEncoder'
if 'e_detail_type' in self.config.keys():
e_detail_type = self.config.e_detail_type
if e_detail_type == 'ResnetEncoder':
self.E_detail = ResnetEncoder(outsize=self.n_detail + self.n_detail_emo)
elif e_flame_type[:4] == 'swin':
self.E_detail = SwinEncoder(outsize=self.n_detail + self.n_detail_emo, img_size=self.config.image_size, swin_type=e_detail_type)
else:
raise ValueError(f"Invalid 'e_detail_type'={e_detail_type}")
self._create_detail_generator()
# self._load_old_checkpoint()
def _get_coarse_trainable_parameters(self):
print("Add E_flame.parameters() to the optimizer")
return list(self.E_flame.parameters())
def _get_detail_trainable_parameters(self):
trainable_params = []
if self.config.train_coarse:
trainable_params += self._get_coarse_trainable_parameters()
print("Add E_flame.parameters() to the optimizer")
trainable_params += list(self.E_detail.parameters())
print("Add E_detail.parameters() to the optimizer")
trainable_params += list(self.D_detail.parameters())
print("Add D_detail.parameters() to the optimizer")
return trainable_params
def train(self, mode: bool = True):
super().train(mode)
if mode:
if self.mode == DecaMode.COARSE:
self.E_flame.train()
# print("Setting E_flame to train")
self.E_detail.eval()
# print("Setting E_detail to eval")
self.D_detail.eval()
# print("Setting D_detail to eval")
elif self.mode == DecaMode.DETAIL:
if self.config.train_coarse:
# print("Setting E_flame to train")
self.E_flame.train()
else:
# print("Setting E_flame to eval")
self.E_flame.eval()
self.E_detail.train()
# print("Setting E_detail to train")
self.D_detail.train()
# print("Setting D_detail to train")
else:
raise ValueError(f"Invalid mode '{self.mode}'")
else:
self.E_flame.eval()
# print("Setting E_flame to eval")
self.E_detail.eval()
# print("Setting E_detail to eval")
self.D_detail.eval()
# print("Setting D_detail to eval")
# these are set to eval no matter what, they're never being trained (the FLAME shape and texture spaces are pretrained)
self.flame.eval()
if self.flametex is not None:
self.flametex.eval()
return self
def _load_old_checkpoint(self):
"""
Loads the DECA model weights from the original DECA implementation:
https://github.com/YadiraF/DECA
"""
if self.config.resume_training:
model_path = self.config.pretrained_modelpath
print(f"Loading model state from '{model_path}'")
checkpoint = torch.load(model_path)
# model
util.copy_state_dict(self.E_flame.state_dict(), checkpoint['E_flame'])
# util.copy_state_dict(self.opt.state_dict(), checkpoint['opt']) # deprecate
# detail model
if 'E_detail' in checkpoint.keys():
util.copy_state_dict(self.E_detail.state_dict(), checkpoint['E_detail'])
util.copy_state_dict(self.D_detail.state_dict(), checkpoint['D_detail'])
# training state
self.start_epoch = 0 # checkpoint['epoch']
self.start_iter = 0 # checkpoint['iter']
else:
print('Start training from scratch')
self.start_epoch = 0
self.start_iter = 0
def _encode_flame(self, images, **kwargs):
return self.E_flame(images)
def decompose_code(self, code):
'''
config.n_shape + config.n_tex + config.n_exp + config.n_pose + config.n_cam + config.n_light
'''
code_list = []
# num_list = [self.config.n_shape, self.config.n_tex, self.config.n_exp, self.config.n_pose, self.config.n_cam,
# self.config.n_light]
num_list = [self._get_num_shape_params(), self.config.n_tex, self.config.n_exp, self.config.n_pose, self.config.n_cam,
self.config.n_light]
start = 0
for i in range(len(num_list)):
code_list.append(code[:, start:start + num_list[i]])
start = start + num_list[i]
# shapecode, texcode, expcode, posecode, cam, lightcode = code_list
code_list[-1] = code_list[-1].reshape(code.shape[0], 9, 3)
return code_list, None
def displacement2normal(self, uv_z, coarse_verts, coarse_normals, detach=True):
"""
Converts the displacement uv map (uv_z) and coarse_verts to a normal map coarse_normals.
"""
batch_size = uv_z.shape[0]
uv_coarse_vertices = self.render.world2uv(coarse_verts)#.detach()
if detach:
uv_coarse_vertices = uv_coarse_vertices.detach()
uv_coarse_normals = self.render.world2uv(coarse_normals)#.detach()
if detach:
uv_coarse_normals = uv_coarse_normals.detach()
uv_z = uv_z * self.uv_face_eye_mask
# detail vertices = coarse vertice + predicted displacement*normals + fixed displacement*normals
uv_detail_vertices = uv_coarse_vertices + \
uv_z * uv_coarse_normals + \
self.fixed_uv_dis[None, None, :,:] * uv_coarse_normals #.detach()
dense_vertices = uv_detail_vertices.permute(0, 2, 3, 1).reshape([batch_size, -1, 3])
uv_detail_normals = util.vertex_normals(dense_vertices, self.render.dense_faces.expand(batch_size, -1, -1))
uv_detail_normals = uv_detail_normals.reshape(
[batch_size, uv_coarse_vertices.shape[2], uv_coarse_vertices.shape[3], 3]).permute(0, 3, 1, 2)
# uv_detail_normals = uv_detail_normals*self.uv_face_eye_mask + uv_coarse_normals*(1-self.uv_face_eye_mask)
# uv_detail_normals = util.gaussian_blur(uv_detail_normals)
return uv_detail_normals, uv_coarse_vertices
def visualize(self, visdict, savepath, catdim=1):
grids = {}
for key in visdict:
# print(key)
if visdict[key] is None:
continue
grids[key] = torchvision.utils.make_grid(
F.interpolate(visdict[key], [self.config.image_size, self.config.image_size])).detach().cpu()
grid = torch.cat(list(grids.values()), catdim)
grid_image = (grid.numpy().transpose(1, 2, 0).copy() * 255)[:, :, [2, 1, 0]]
grid_image = np.minimum(np.maximum(grid_image, 0), 255).astype(np.uint8)
if savepath is not None:
cv2.imwrite(savepath, grid_image)
return grid_image
def create_mesh(self, opdict, dense_template):
'''
vertices: [nv, 3], tensor
texture: [3, h, w], tensor
'''
i = 0
vertices = opdict['verts'][i].cpu().numpy()
faces = self.render.faces[0].cpu().numpy()
if 'uv_texture_gt' in opdict.keys():
texture = util.tensor2image(opdict['uv_texture_gt'][i])
else:
texture = None
uvcoords = self.render.raw_uvcoords[0].cpu().numpy()
uvfaces = self.render.uvfaces[0].cpu().numpy()
# save coarse mesh, with texture and normal map
if 'uv_detail_normals' in opdict.keys():
normal_map = util.tensor2image(opdict['uv_detail_normals'][i]*0.5 + 0.5)
# upsample mesh, save detailed mesh
texture = texture[:, :, [2, 1, 0]]
normals = opdict['normals'][i].cpu().numpy()
displacement_map = opdict['displacement_map'][i].detach().cpu().numpy().squeeze()
dense_vertices, dense_colors, dense_faces = util.upsample_mesh(vertices, normals, faces,
displacement_map, texture, dense_template)
else:
normal_map = None
dense_vertices = None
dense_colors = None
dense_faces = None
return vertices, faces, texture, uvcoords, uvfaces, normal_map, dense_vertices, dense_faces, dense_colors
def save_obj(self, filename, opdict, dense_template, mode ='detail'):
if mode not in ['coarse', 'detail', 'both']:
raise ValueError(f"Invalid mode '{mode}. Expected modes are: 'coarse', 'detail', 'both'")
vertices, faces, texture, uvcoords, uvfaces, normal_map, dense_vertices, dense_faces, dense_colors \
= self.create_mesh(opdict, dense_template)
if mode == 'both':
if isinstance(filename, list):
filename_coarse = filename[0]
filename_detail = filename[1]
else:
filename_coarse = filename
filename_detail = filename.replace('.obj', '_detail.obj')
elif mode == 'coarse':
filename_coarse = filename
else:
filename_detail = filename
if mode in ['coarse', 'both']:
util.write_obj(str(filename_coarse), vertices, faces,
texture=texture,
uvcoords=uvcoords,
uvfaces=uvfaces,
normal_map=normal_map)
if mode in ['detail', 'both']:
util.write_obj(str(filename_detail),
dense_vertices,
dense_faces,
colors = dense_colors,
inverse_face_order=True)
class ExpDECAInterface(object):
"""
This serves as an interface for EMOCA-like classes that need to use a different sub class but
retain the EMOCA functionality. See EMICA_v2 for an example.
"""
def _create_model(self):
# E_flame should be fixed for expression EMOCA
self.E_flame.requires_grad_(False)
# 2) add expression decoder
if self.config.expression_backbone == 'deca_parallel':
## a) Attach a parallel flow of FCs onto the original DECA coarse backbone. (Only the second FC head is trainable)
self.E_expression = SecondHeadResnet(self.E_flame, self.n_exp_param, 'same')
elif self.config.expression_backbone == 'deca_clone':
## b) Clones the original DECA coarse decoder (and the entire decoder will be trainable) - This is in final EMOCA.
#TODO this will only work for Resnet. Make this work for the other backbones (Swin) as well.
self.E_expression = ResnetEncoder(self.n_exp_param)
# clone parameters of the ResNet
self.E_expression.encoder.load_state_dict(self.E_flame.encoder.state_dict())
elif self.config.expression_backbone == 'emonet_trainable':
# Trainable EmoNet instead of Resnet (deprecated)
self.E_expression = EmoNetRegressor(self.n_exp_param)
elif self.config.expression_backbone == 'emonet_static':
# Frozen EmoNet with a trainable head instead of Resnet (deprecated) | self.E_expression = EmonetRegressorStatic(self.n_exp_param) | 22 | 2023-11-07 20:13:32+00:00 | 24k |
hxz393/ConfigCenterComparer | ui/action_start.py | [
{
"identifier": "COL_INFO",
"path": "config/settings.py",
"snippet": "COL_INFO = {\n \"name\": {\"col\": 0},\n \"group\": {\"col\": 1},\n \"key\": {\"col\": 2},\n \"pro_value\": {\"col\": 3},\n \"pro_time\": {\"col\": 4},\n \"pre_value\": {\"col\": 5},\n \"pre_time\": {\"col\": 6},\... | import logging
from typing import Dict, List
from PyQt5.QtCore import Qt, QThread, pyqtSignal, QObject
from PyQt5.QtGui import QIcon
from PyQt5.QtWidgets import QAction, QHeaderView
from config.settings import COL_INFO
from lib.get_resource_path import get_resource_path
from module.execute_queries import execute_queries
from ui.config_manager import ConfigManager
from ui.filter_bar import FilterBar
from ui.lang_manager import LangManager
from ui.message_show import message_show
from ui.table_main import TableMain | 15,024 |
def initialize(self) -> None:
"""
初始化界面和状态，在开始操作前执行。
此方法用于设置 UI 元素的初始状态，如禁用按钮、清空表格等。
:rtype: None
:return: 无返回值。
"""
logger.info('Start running')
# 状态栏发送提示消息
self.status_updated.emit(self.lang['ui.action_start_3'])
# 开始按钮不可点击
self.action_start.setEnabled(False)
# 禁用表格排序
self.table.setSortingEnabled(False)
# 禁用表格更新
self.table.setUpdatesEnabled(False)
# 禁用过滤栏组件
self.filter_bar.filter_app_box.setEnabled(False)
self.filter_bar.filter_table_box.setEnabled(False)
self.filter_bar.filter_table_check_box.setEnabled(False)
self.filter_bar.filter_value_box.setEnabled(False)
self.filter_bar.filter_value_button.setEnabled(False)
self.filter_bar.filter_reset_button.setEnabled(False)
# 清空表格数据
self.table.clear()
# 初始化表宽
self.table.set_header_resize()
logger.debug('Initialization finished')
def table_insert(self, table_rows: List[List[List[str]]]) -> None:
"""
将查询结果插入到主表格中。
此方法接收查询结果作为输入，并将其格式化后插入到应用程序的主表格中。每个元素是一个三重列表，表示表格的一行数据。
:param table_rows: 待插入的表格数据，每个元素代表一行数据。
:type table_rows: List[List[List[str]]]
:rtype: None
:return: 无返回值。
"""
for row in table_rows:
self.table.add_row(row)
logger.debug('Table filling finished.')
def table_column_hide(self, query_statuses: Dict[str, bool]) -> None:
"""
根据查询状态决定是否隐藏表格的某些列。
:param query_statuses: 各查询状态的字典，键为环境名，值为布尔值指示是否开启。
:type query_statuses: Dict[str, bool]
:rtype: None
:return: 无返回值。
"""
# env_name类似'PRO_CONFIG'，COL_INFO中的键类似'pro_value'，env_switch是布尔值。
for env_name, env_switch in query_statuses.items():
# 建立env_name和COL_INFO中的键的映射
column_name_mapping = {'PRO_CONFIG': 'pro_value', 'PRE_CONFIG': 'pre_value', 'TEST_CONFIG': 'test_value', 'DEV_CONFIG': 'dev_value'}
# 获取列序号
col = COL_INFO[column_name_mapping[env_name]]['col']
# 根据环境开关，决定列是否隐藏。
self.table.showColumn(col) if env_switch else self.table.hideColumn(col)
def finalize(self) -> None:
"""
完成查询后的收尾工作，包括重新启用表格排序和更新等。
:rtype: None
:return: 无返回值。
"""
# 先应用颜色和过滤器
self.table.apply_color_to_table()
self.filter_bar.filter_table()
# 启动排序
self.table.setSortingEnabled(True)
# 默认按第一列升序排序
self.table.sortByColumn(0, Qt.AscendingOrder)
# 允许用户调整列宽
self.table.horizontalHeader().setSectionResizeMode(QHeaderView.Interactive)
# 更新过滤器，过滤服务中插入值
self.filter_bar.filter_options_add()
# 调用过滤器
self.filter_bar.highlight_rows.clear()
self.filter_bar.filter_table()
# 启用表格更新
self.table.setUpdatesEnabled(True)
# 启用过滤栏组件
self.filter_bar.filter_app_box.setEnabled(True)
self.filter_bar.filter_table_box.setEnabled(True)
self.filter_bar.filter_table_check_box.setEnabled(True)
self.filter_bar.filter_value_box.setEnabled(True)
self.filter_bar.filter_value_button.setEnabled(True)
self.filter_bar.filter_reset_button.setEnabled(True)
def show_result_message(self, result: str) -> None:
"""
显示结果消息。
根据运行结果，显示不同的状态消息或错误信息。
:param result: 运行结果的描述。
:type result: str
:rtype: None
:return: 无返回值。
"""
self.action_start.setEnabled(True)
if result == 'done':
logger.info('Run Completed')
else:
message = {
'no query result': ('Warning', self.lang['ui.action_start_4']),
'prepare table rows failed': ('Warning', self.lang['ui.action_start_6']),
'run error': ('Critical', self.lang['ui.action_start_7'])
}.get(result)
if message:
| """
提供应用程序的主要功能，包括用户界面初始化、数据库查询执行、数据展示和处理。
本模块中包含的类负责应用程序的主要操作流程，如用户界面的初始化、按钮动作的处理、后台数据查询、数据展示等。主要类包括`ActionStart`和`StartWork`，分别负责处理用户界面动作和执行后台工作。
:author: assassing
:contact: https://github.com/hxz393
:copyright: Copyright 2023, hxz393. 保留所有权利。
"""
logger = logging.getLogger(__name__)
class ActionStart(QObject):
"""
负责处理用户界面动作，例如初始化界面、响应按钮点击等。
此类包含了界面的主要动作逻辑，如开始按钮的点击处理、用户界面语言的更新、表格的数据填充等。它与后台线程`StartWork`协作，实现数据的查询和展示。
:param lang_manager: 语言管理器，用于界面语言的加载和更新。
:param config_manager: 配置管理器，提供应用程序的配置信息。
:param table: 主表格界面，用于数据的展示。
:param filter_bar: 过滤条，用于数据的筛选。
:type lang_manager: LangManager
:type config_manager: ConfigManager
:type table: TableMain
:type filter_bar: FilterBar
"""
status_updated = pyqtSignal(str)
def __init__(self,
lang_manager: LangManager,
config_manager: ConfigManager,
table: TableMain,
filter_bar: FilterBar):
super().__init__()
# 实例化组件
self.lang_manager = lang_manager
self.lang_manager.lang_updated.connect(self.update_lang)
self.config_manager = config_manager
self.table = table
self.filter_bar = filter_bar
self.initUI()
def initUI(self) -> None:
"""
初始化用户界面。
创建并配置界面中的开始动作按钮，包括图标、快捷键和触发事件。
:rtype: None
:return: 无返回值。
"""
self.action_start = QAction(QIcon(get_resource_path('media/icons8-start-26.png')), 'Start')
self.action_start.setShortcut('F10')
self.action_start.triggered.connect(self.start)
self.update_lang()
def update_lang(self) -> None:
"""
更新界面语言设置。
:rtype: None
:return: 无返回值。
"""
self.lang = self.lang_manager.get_lang()
self.action_start.setText(self.lang['ui.action_start_1'])
self.action_start.setStatusTip(self.lang['ui.action_start_2'])
def start(self) -> None:
"""
启动更新动作的处理流程。
此方法负责初始化和启动一个后台线程 `StartWork`，该线程执行数据查询和表格更新。同时，该方法还负责连接信号和槽以进行 UI 更新。
:rtype: None
:return: 无返回值。
"""
try:
# 初始化子线程，传入语言字典和配置
self.start_work = StartWork(self.lang, self.config_manager)
# 连接信号槽，都是 UI 操作，必须主线程中进行
self.start_work.initialize_signal.connect(self.initialize)
self.start_work.table_insert_signal.connect(self.table_insert)
self.start_work.table_column_hide_signal.connect(self.table_column_hide)
self.start_work.finalize_signal.connect(self.finalize)
self.start_work.message.connect(self.show_result_message)
# 开始运行
self.start_work.start()
except Exception:
logger.exception('Failed to initiate start action.')
self.status_updated.emit(self.lang['label_status_error'])
def initialize(self) -> None:
"""
初始化界面和状态，在开始操作前执行。
此方法用于设置 UI 元素的初始状态，如禁用按钮、清空表格等。
:rtype: None
:return: 无返回值。
"""
logger.info('Start running')
# 状态栏发送提示消息
self.status_updated.emit(self.lang['ui.action_start_3'])
# 开始按钮不可点击
self.action_start.setEnabled(False)
# 禁用表格排序
self.table.setSortingEnabled(False)
# 禁用表格更新
self.table.setUpdatesEnabled(False)
# 禁用过滤栏组件
self.filter_bar.filter_app_box.setEnabled(False)
self.filter_bar.filter_table_box.setEnabled(False)
self.filter_bar.filter_table_check_box.setEnabled(False)
self.filter_bar.filter_value_box.setEnabled(False)
self.filter_bar.filter_value_button.setEnabled(False)
self.filter_bar.filter_reset_button.setEnabled(False)
# 清空表格数据
self.table.clear()
# 初始化表宽
self.table.set_header_resize()
logger.debug('Initialization finished')
def table_insert(self, table_rows: List[List[List[str]]]) -> None:
"""
将查询结果插入到主表格中。
此方法接收查询结果作为输入，并将其格式化后插入到应用程序的主表格中。每个元素是一个三重列表，表示表格的一行数据。
:param table_rows: 待插入的表格数据，每个元素代表一行数据。
:type table_rows: List[List[List[str]]]
:rtype: None
:return: 无返回值。
"""
for row in table_rows:
self.table.add_row(row)
logger.debug('Table filling finished.')
def table_column_hide(self, query_statuses: Dict[str, bool]) -> None:
"""
根据查询状态决定是否隐藏表格的某些列。
:param query_statuses: 各查询状态的字典，键为环境名，值为布尔值指示是否开启。
:type query_statuses: Dict[str, bool]
:rtype: None
:return: 无返回值。
"""
# env_name类似'PRO_CONFIG'，COL_INFO中的键类似'pro_value'，env_switch是布尔值。
for env_name, env_switch in query_statuses.items():
# 建立env_name和COL_INFO中的键的映射
column_name_mapping = {'PRO_CONFIG': 'pro_value', 'PRE_CONFIG': 'pre_value', 'TEST_CONFIG': 'test_value', 'DEV_CONFIG': 'dev_value'}
# 获取列序号
col = COL_INFO[column_name_mapping[env_name]]['col']
# 根据环境开关，决定列是否隐藏。
self.table.showColumn(col) if env_switch else self.table.hideColumn(col)
def finalize(self) -> None:
"""
完成查询后的收尾工作，包括重新启用表格排序和更新等。
:rtype: None
:return: 无返回值。
"""
# 先应用颜色和过滤器
self.table.apply_color_to_table()
self.filter_bar.filter_table()
# 启动排序
self.table.setSortingEnabled(True)
# 默认按第一列升序排序
self.table.sortByColumn(0, Qt.AscendingOrder)
# 允许用户调整列宽
self.table.horizontalHeader().setSectionResizeMode(QHeaderView.Interactive)
# 更新过滤器，过滤服务中插入值
self.filter_bar.filter_options_add()
# 调用过滤器
self.filter_bar.highlight_rows.clear()
self.filter_bar.filter_table()
# 启用表格更新
self.table.setUpdatesEnabled(True)
# 启用过滤栏组件
self.filter_bar.filter_app_box.setEnabled(True)
self.filter_bar.filter_table_box.setEnabled(True)
self.filter_bar.filter_table_check_box.setEnabled(True)
self.filter_bar.filter_value_box.setEnabled(True)
self.filter_bar.filter_value_button.setEnabled(True)
self.filter_bar.filter_reset_button.setEnabled(True)
def show_result_message(self, result: str) -> None:
"""
显示结果消息。
根据运行结果，显示不同的状态消息或错误信息。
:param result: 运行结果的描述。
:type result: str
:rtype: None
:return: 无返回值。
"""
self.action_start.setEnabled(True)
if result == 'done':
logger.info('Run Completed')
else:
message = {
'no query result': ('Warning', self.lang['ui.action_start_4']),
'prepare table rows failed': ('Warning', self.lang['ui.action_start_6']),
'run error': ('Critical', self.lang['ui.action_start_7'])
}.get(result)
if message: | message_show(*message) | 6 | 2023-11-07 01:02:38+00:00 | 24k |
google-research/semivl | model/builder.py | [
{
"identifier": "TIMMVisionTransformer",
"path": "model/backbone/timm_vit.py",
"snippet": "class TIMMVisionTransformer(nn.Module):\n\n def __init__(\n self,\n variant,\n timm_load_pretrained,\n drop_path_rate,\n img_size,\n out_indices,\n ):\n super... | import types
import torch
from functools import reduce
from mmcv.utils import Config
from mmseg.models import ASPPHead, DepthwiseSeparableASPPHead, build_segmentor
from mmseg.ops import resize
from torch.nn import functional as F
from model.backbone.timm_vit import TIMMVisionTransformer
from model.decode_heads.dlv3p_head import DLV3PHead
from model.decode_heads.vlg_head import VLGHead
from model.vlm import VLM
from third_party.maskclip.models.backbones.maskclip_vit import MaskClipVisionTransformer
from third_party.maskclip.models.decode_heads.maskclip2_head import MaskClip2Head
from third_party.maskclip.models.decode_heads.maskclip_head import MaskClipHead
from third_party.unimatch.model.semseg.deeplabv3plus import DeepLabV3Plus
from third_party.zegclip.losses.atm_loss import SegLossPlus
from third_party.zegclip.models.backbones.clip_vit import CLIPVisionTransformer
from third_party.zegclip.models.backbones.clip_vpt_vit import VPTCLIPVisionTransformer
from third_party.zegclip.models.backbones.text_encoder import CLIPTextEncoder
from third_party.zegclip.models.backbones.utils import DropPath
from third_party.zegclip.models.decode_heads.atm_head import ATMSingleHeadSeg | 19,928 | # Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
def nested_set(dic, key, value):
keys = key.split('.')
for key in keys[:-1]:
dic = dic.setdefault(key, {})
dic[keys[-1]] = value
def nested_get(dictionary, keys, default=None):
return reduce(lambda d, key: d.get(key, default) if isinstance(d, dict) else default, keys.split("."), dictionary)
def is_vlm(obj):
return isinstance(obj, VLM)
def forward_wrapper(self, img, gt=None, need_fp=False, only_fp=False, forward_mode='default'):
if forward_mode == 'maskclip_trust':
return self.train_maskclip_trust(img, gt)
elif forward_mode == 'default':
x = self.extract_feat(img)
if self.disable_dropout:
dropout_modules = [module for module in self.modules() if isinstance(module, torch.nn.Dropout) or isinstance(module, DropPath)]
for module in dropout_modules:
module.eval()
if only_fp:
if is_vlm(self):
feats = x[0][0]
x[0][0] = [F.dropout2d(f, self.fp_rate) for f in feats]
# perturb features from conv_encoder
if len(x) == 3 and x[2] is not None:
x[2] = [F.dropout2d(f, self.fp_rate) for f in x[2]]
# also provide unperturbed features
if hasattr(self.decode_head, 'dc_unperturbed') and self.decode_head.dc_unperturbed:
assert len(x[0]) == 2
x[0].append(feats)
else:
x = [F.dropout2d(f, self.fp_rate) for f in x]
elif need_fp:
if is_vlm(self):
feats = x[0][0]
x[0][0] = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in feats]
x[0][1] = torch.cat((x[0][1], x[0][1]))
# perturb features from conv_encoder
if len(x) == 3 and x[2] is not None:
x[2] = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in x[2]]
# also provide unperturbed features
if hasattr(self.decode_head, 'dc_unperturbed') and self.decode_head.dc_unperturbed:
assert len(x[0]) == 2
x[0].append([torch.cat((f, f)) for f in feats])
else:
x = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in x]
out = self._decode_head_forward_test(x, img_metas=None)
out = resize(
input=out,
size=img.shape[2:],
mode='bilinear',
align_corners=self.align_corners)
if need_fp:
out = out.chunk(2)
return out
else:
raise ValueError(forward_mode)
def build_model(cfg):
model_type = cfg['model']
if model_type == 'deeplabv3plus':
| # Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
def nested_set(dic, key, value):
keys = key.split('.')
for key in keys[:-1]:
dic = dic.setdefault(key, {})
dic[keys[-1]] = value
def nested_get(dictionary, keys, default=None):
return reduce(lambda d, key: d.get(key, default) if isinstance(d, dict) else default, keys.split("."), dictionary)
def is_vlm(obj):
return isinstance(obj, VLM)
def forward_wrapper(self, img, gt=None, need_fp=False, only_fp=False, forward_mode='default'):
if forward_mode == 'maskclip_trust':
return self.train_maskclip_trust(img, gt)
elif forward_mode == 'default':
x = self.extract_feat(img)
if self.disable_dropout:
dropout_modules = [module for module in self.modules() if isinstance(module, torch.nn.Dropout) or isinstance(module, DropPath)]
for module in dropout_modules:
module.eval()
if only_fp:
if is_vlm(self):
feats = x[0][0]
x[0][0] = [F.dropout2d(f, self.fp_rate) for f in feats]
# perturb features from conv_encoder
if len(x) == 3 and x[2] is not None:
x[2] = [F.dropout2d(f, self.fp_rate) for f in x[2]]
# also provide unperturbed features
if hasattr(self.decode_head, 'dc_unperturbed') and self.decode_head.dc_unperturbed:
assert len(x[0]) == 2
x[0].append(feats)
else:
x = [F.dropout2d(f, self.fp_rate) for f in x]
elif need_fp:
if is_vlm(self):
feats = x[0][0]
x[0][0] = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in feats]
x[0][1] = torch.cat((x[0][1], x[0][1]))
# perturb features from conv_encoder
if len(x) == 3 and x[2] is not None:
x[2] = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in x[2]]
# also provide unperturbed features
if hasattr(self.decode_head, 'dc_unperturbed') and self.decode_head.dc_unperturbed:
assert len(x[0]) == 2
x[0].append([torch.cat((f, f)) for f in feats])
else:
x = [torch.cat((f, F.dropout2d(f, self.fp_rate))) for f in x]
out = self._decode_head_forward_test(x, img_metas=None)
out = resize(
input=out,
size=img.shape[2:],
mode='bilinear',
align_corners=self.align_corners)
if need_fp:
out = out.chunk(2)
return out
else:
raise ValueError(forward_mode)
def build_model(cfg):
model_type = cfg['model']
if model_type == 'deeplabv3plus': | model = DeepLabV3Plus(cfg) | 7 | 2023-11-02 14:49:38+00:00 | 24k |
codefuse-ai/Collinear-Constrained-Attention | model/build_model.py | [
{
"identifier": "get_model_params_num",
"path": "utils/common_utils.py",
"snippet": "def get_model_params_num(model):\n \"\"\"\n Get params number of the model\n Args:\n model: model(required)\n Returns:\n the number of parameters of model\n \"\"\"\n num = 0\n for _, p... | import os
import torch
import sys
import peft
import model.peft.modeling_peft # noqa
import bitsandbytes as bnb # noqa
import accelerate # noqa
from utils.common_utils import get_model_params_num
from transformers import ( # noqa: E402
CONFIG_MAPPING,
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
PreTrainedTokenizerFast
)
from .gpt_neox.configuration_gpt_neox import GPTNeoXConfig
from .gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM
from .gpt_neox.tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
from .llama.configuration_llama import LlamaConfig
from .llama.modeling_llama import LlamaForCausalLM
from .llama.tokenization_llama import LlamaTokenizer
from .llama.tokenization_llama_fast import LlamaTokenizerFast
from torch.distributed.fsdp import (
FullyShardedDataParallel as FSDP,
StateDictType,
)
from utils.common_utils import print_rank_0, is_old_version
from tokenizer import build_tokenizer
from tokenizer.tokenizer import HFTokenizer
from peft.tuners.lora import LoraLayer
from model.peft.utils import prepare_model_for_kbit_training
from peft import ( # noqa
LoraConfig,
PrefixTuningConfig,
PromptEncoderConfig,
PromptEncoderReparameterizationType,
PromptTuningConfig,
PromptTuningInit,
TaskType,
get_peft_model
)
from model.peft.tuner import AdaLoraConfig
from transformers import BitsAndBytesConfig
from packaging import version
from .glm.tokenization_glm_deprecated import GLMChineseTokenizer | 17,414 | # coding=utf-8
# Copyright (c) 2023 Ant Group. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
sys.path.append("..")
# from .glm.modeling_glm import GLMForConditionalGeneration
# from .glm.configuration_glm import GLMConfig
# from .glm.tokenization_glm import GLMTokenizer
try:
except ImportError:
BitsAndBytesConfig = None
try:
except ImportError:
bnb = None
def find_all_linear_names(args, model):
cls = bnb.nn.Linear4bit if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)
lora_module_names = set()
for name, module in model.named_modules():
if isinstance(module, cls):
names = name.split('.')
lora_module_names.add(names[0] if len(names) == 1 else names[-1])
if 'lm_head' in lora_module_names: # needed for 16-bit
lora_module_names.remove('lm_head')
return list(lora_module_names)
def setup_model(args, logger, use_cache=False):
# Load pretrained model and tokenizer
if args.pretrained_model_path: # TODO: 实现from pretrained读tokenizer
if args.model_type == 'gpt_neox':
# if args.tokenizer_type:
# tokenizer = build_tokenizer(args)
# tokenizer.eod_token = "<|endoftext|>"
# tokenizer.pad_token = "<|pad|>"
# # tokenizer.sop_token = "<|endoftext|>" # 适配multi task dataset
# # tokenizer.eop_token = "<|endoftext|>"
# tokenizer.eod_id = tokenizer.tokenize(tokenizer.eod_token)[0]
# tokenizer.pad_id = tokenizer.tokenize(tokenizer.pad_token)[0]
# else:
tokenizer = GPTNeoXTokenizerFast.from_pretrained(args.pretrained_model_path)
# tokenizer = PreTrainedTokenizerFast(tokenizer_file=args.vocab_file)
tokenizer.eod_token = "<|endoftext|>"
tokenizer.pad_token = "<|pad|>"
tokenizer.sop_token = "<|endoftext|>" # 适配multi task dataset
tokenizer.eop_token = "<|endoftext|>"
tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)
tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')
print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')
elif args.model_type == 'llama':
tokenizer = LlamaTokenizerFast.from_pretrained(args.pretrained_model_path)
# tokenizer = AutoTokenizer.from_pretrained(
# args.pretrained_model_path,
# trust_remote_code=True,
# )
tokenizer.eod_token = "</s>"
tokenizer.eos_token = "</s>"
tokenizer.bos_token = "<s>"
tokenizer.pad_token = "[PAD]"
tokenizer.unk_token = "<unk>"
tokenizer.sop_token = "</s>" # 适配multi task dataset
tokenizer.eop_token = "</s>"
tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)
tokenizer.eos_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.bos_id = tokenizer.convert_tokens_to_ids(tokenizer.bos_token)
tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.unk_id = tokenizer.convert_tokens_to_ids(tokenizer.unk_token)
print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')
print_rank_0(f'tokenizer {tokenizer.eos_token} id: {tokenizer.eos_id}')
print_rank_0(f'tokenizer {tokenizer.bos_token} id: {tokenizer.bos_id}')
print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')
print_rank_0(f'tokenizer {tokenizer.unk_token} id: {tokenizer.unk_id}')
elif args.model_type == 'glm':
if is_old_version(args.pretrained_model_path):
tokenizer = GLMChineseTokenizer.from_pretrained(args.pretrained_model_path)
else:
tokenizer = GLMTokenizer.from_pretrained(args.pretrained_model_path)
elif args.train_mode == 'sst':
# tokenizer = build_tokenizer(args)
tokenizer = PreTrainedTokenizerFast(tokenizer_file=args.vocab_file)
tokenizer.eod_token = "<|endoftext|>"
tokenizer.pad_token = "<|pad|>"
tokenizer.sop_token = "<|endoftext|>" # 适配multi task dataset
tokenizer.eop_token = "<|endoftext|>"
tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)
tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')
print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_path."
)
if args.model_type == 'gpt_neox':
| # coding=utf-8
# Copyright (c) 2023 Ant Group. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
sys.path.append("..")
# from .glm.modeling_glm import GLMForConditionalGeneration
# from .glm.configuration_glm import GLMConfig
# from .glm.tokenization_glm import GLMTokenizer
try:
except ImportError:
BitsAndBytesConfig = None
try:
except ImportError:
bnb = None
def find_all_linear_names(args, model):
cls = bnb.nn.Linear4bit if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)
lora_module_names = set()
for name, module in model.named_modules():
if isinstance(module, cls):
names = name.split('.')
lora_module_names.add(names[0] if len(names) == 1 else names[-1])
if 'lm_head' in lora_module_names: # needed for 16-bit
lora_module_names.remove('lm_head')
return list(lora_module_names)
def setup_model(args, logger, use_cache=False):
# Load pretrained model and tokenizer
if args.pretrained_model_path: # TODO: 实现from pretrained读tokenizer
if args.model_type == 'gpt_neox':
# if args.tokenizer_type:
# tokenizer = build_tokenizer(args)
# tokenizer.eod_token = "<|endoftext|>"
# tokenizer.pad_token = "<|pad|>"
# # tokenizer.sop_token = "<|endoftext|>" # 适配multi task dataset
# # tokenizer.eop_token = "<|endoftext|>"
# tokenizer.eod_id = tokenizer.tokenize(tokenizer.eod_token)[0]
# tokenizer.pad_id = tokenizer.tokenize(tokenizer.pad_token)[0]
# else:
tokenizer = GPTNeoXTokenizerFast.from_pretrained(args.pretrained_model_path)
# tokenizer = PreTrainedTokenizerFast(tokenizer_file=args.vocab_file)
tokenizer.eod_token = "<|endoftext|>"
tokenizer.pad_token = "<|pad|>"
tokenizer.sop_token = "<|endoftext|>" # 适配multi task dataset
tokenizer.eop_token = "<|endoftext|>"
tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)
tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')
print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')
elif args.model_type == 'llama':
tokenizer = LlamaTokenizerFast.from_pretrained(args.pretrained_model_path)
# tokenizer = AutoTokenizer.from_pretrained(
# args.pretrained_model_path,
# trust_remote_code=True,
# )
tokenizer.eod_token = "</s>"
tokenizer.eos_token = "</s>"
tokenizer.bos_token = "<s>"
tokenizer.pad_token = "[PAD]"
tokenizer.unk_token = "<unk>"
tokenizer.sop_token = "</s>" # 适配multi task dataset
tokenizer.eop_token = "</s>"
tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)
tokenizer.eos_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
tokenizer.bos_id = tokenizer.convert_tokens_to_ids(tokenizer.bos_token)
tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.unk_id = tokenizer.convert_tokens_to_ids(tokenizer.unk_token)
print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')
print_rank_0(f'tokenizer {tokenizer.eos_token} id: {tokenizer.eos_id}')
print_rank_0(f'tokenizer {tokenizer.bos_token} id: {tokenizer.bos_id}')
print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')
print_rank_0(f'tokenizer {tokenizer.unk_token} id: {tokenizer.unk_id}')
elif args.model_type == 'glm':
if is_old_version(args.pretrained_model_path):
tokenizer = GLMChineseTokenizer.from_pretrained(args.pretrained_model_path)
else:
tokenizer = GLMTokenizer.from_pretrained(args.pretrained_model_path)
elif args.train_mode == 'sst':
# tokenizer = build_tokenizer(args)
tokenizer = PreTrainedTokenizerFast(tokenizer_file=args.vocab_file)
tokenizer.eod_token = "<|endoftext|>"
tokenizer.pad_token = "<|pad|>"
tokenizer.sop_token = "<|endoftext|>" # 适配multi task dataset
tokenizer.eop_token = "<|endoftext|>"
tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token)
tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}')
print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}')
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_path."
)
if args.model_type == 'gpt_neox': | auto_config = GPTNeoXConfig | 1 | 2023-11-02 01:37:01+00:00 | 24k |
bytedance/cryostar | projects/star/train_atom.py | [
{
"identifier": "SpatialGridTranslate",
"path": "cryostar/utils/transforms.py",
"snippet": "class SpatialGridTranslate(torch.nn.Module):\n\n def __init__(self, D, device=None) -> None:\n super().__init__()\n self.D = D\n # yapf: disable\n coords = torch.stack(torch.meshgri... | import os.path as osp
import warnings
import collections
import einops
import numpy as np
import biotite.structure as struc
import torch
import lightning.pytorch as pl
from pathlib import Path
from copy import deepcopy
from torch import nn
from torch import optim
from torch.utils.data import DataLoader
from torchinfo import summary
from lightning.fabric.utilities.warnings import PossibleUserWarning
from lightning.pytorch.utilities import rank_zero_only
from lightning.pytorch.strategies import DDPStrategy
from mmengine import mkdir_or_exist
from cryostar.utils.transforms import SpatialGridTranslate
from cryostar.utils.dataio import StarfileDataSet, StarfileDatasetConfig, Mask
from cryostar.utils.ctf_utils import CTFRelion, CTFCryoDRGN
from cryostar.utils.losses import calc_cor_loss, calc_kl_loss
from cryostar.utils.misc import log_to_current, \
pl_init_exp, pretty_dict, set_seed, warmup
from cryostar.utils.pdb_tools import bt_save_pdb
from cryostar.gmm.gmm import EMAN2Grid, batch_projection, Gaussian
from cryostar.gmm.deformer import E3Deformer, NMADeformer
from cryostar.utils.fft_utils import primal_to_fourier_2d, fourier_to_primal_2d
from cryostar.utils.polymer import Polymer, NT_ATOMS, AA_ATOMS
from cryostar.utils.dist_loss import (find_quaint_cutoff_pairs, find_range_cutoff_pairs, find_continuous_pairs,
calc_dist_by_pair_indices, remove_duplicate_pairs, filter_same_chain_pairs,
DistLoss)
from cryostar.utils.latent_space_utils import get_nearest_point, cluster_kmeans, run_pca, get_pc_traj, run_umap
from cryostar.utils.vis_utils import plot_z_dist, save_tensor_image
from cryostar.utils.pl_utils import merge_step_outputs, squeeze_dict_outputs_1st_dim, \
filter_outputs_by_indices, get_1st_unique_indices
from miscs import calc_pair_dist_loss, calc_clash_loss, low_pass_mask2d, VAE, infer_ctf_params_from_config | 14,716 |
# other
# avoid num_workers set as cpu_count warning
warnings.simplefilter("ignore", PossibleUserWarning)
# only log to rank_zero, comment this for debugging
log_to_current = rank_zero_only(log_to_current)
TASK_NAME = "atom"
def prepare_images(images: torch.FloatTensor, space: str):
assert space in ("real", "fourier")
if space == "real":
model_input = einops.rearrange(images, "b 1 ny nx -> b (1 ny nx)")
else:
fimages = primal_to_fourier_2d(images)
model_input = einops.rearrange(torch.view_as_real(fimages), "b 1 ny nx c2 -> b (1 ny nx c2)", c2=2)
return model_input
class InitTask(pl.LightningModule):
def __init__(self, em_module):
super().__init__()
self.cfg = em_module.cfg
self.em_module = em_module
self.loss_deque = collections.deque([
10,
], maxlen=20)
def on_train_batch_end(self, outputs, batch, batch_idx):
self.loss_deque.append(outputs['loss'].item())
if np.mean(self.loss_deque) < 1e-3:
self.trainer.should_stop = True
# update all process status
self.trainer.should_stop = self.trainer.strategy.broadcast(self.trainer.should_stop)
def training_step(self, batch, batch_idx):
images = batch["proj"]
idxes = batch["idx"]
rot_mats, trans_mats = self.em_module.get_batch_pose(batch)
pred_deformation, mu, log_var = self.em_module.model(prepare_images(images, self.cfg.model.input_space), idxes,
rot_mats)
shift_loss = torch.mean(torch.pow(pred_deformation.flatten(start_dim=-2), 2))
loss = shift_loss
if self.global_step % self.cfg.runner.log_every_n_step == 0:
log_to_current(f"loss {loss.item()}")
return loss
def configure_optimizers(self):
return optim.AdamW(self.em_module.model.parameters(), lr=1e-4)
def on_fit_end(self):
log_to_current(f"Init finished with loss {np.mean(self.loss_deque)}")
class CryoEMTask(pl.LightningModule):
def __init__(self, cfg, dataset):
super().__init__()
cfg = deepcopy(cfg)
self.cfg = cfg
# Define GMM
meta = Polymer.from_pdb(cfg.dataset_attr.ref_pdb_path)
log_to_current(f"Load reference structure from {cfg.dataset_attr.ref_pdb_path}")
# for save
self.template_pdb = meta.to_atom_arr()
log_to_current(f"Protein contains {len(meta)} atoms, "
f"{meta.num_amino_acids} amino acids, "
f"{meta.num_nucleotides} nucleotides, "
f"{meta.num_chains} chains.")
# ref
ref_centers = torch.from_numpy(meta.coord).float()
ref_amps = torch.from_numpy(meta.num_electron).float()
ref_sigmas = torch.ones_like(ref_amps)
ref_sigmas.fill_(2.)
log_to_current(f"1st GMM blob amplitude {ref_amps[0].item()}, sigma {ref_sigmas[0].item()}")
num_pts = len(meta)
log_to_current(f"Reference structure has {num_pts} atom coordinates")
# tunable params
# gmm
self.register_buffer("gmm_centers", ref_centers)
if cfg.gmm.tunable:
log_to_current("Set GMM sigmas, amplitudes tunable")
self.register_parameter("gmm_sigmas", nn.Parameter(ref_sigmas))
self.register_parameter("gmm_amps", nn.Parameter(ref_amps))
else:
self.register_buffer("gmm_sigmas", ref_sigmas)
self.register_buffer("gmm_amps", ref_amps)
nma_modes = None
if (hasattr(self.cfg.extra_input_data_attr, "nma_path") and
self.cfg.extra_input_data_attr.nma_path not in ["", None]):
nma_modes = torch.tensor(np.load(self.cfg.extra_input_data_attr.nma_path), dtype=torch.float32)
log_to_current(f"Load NMA coefficients from {self.cfg.extra_input_data_attr.nma_path}, "
f"whose shape is {nma_modes.shape}")
# model
if cfg.model.input_space == "fourier":
in_dim = 2 * cfg.data_process.down_side_shape ** 2
elif cfg.model.input_space == "real":
in_dim = cfg.data_process.down_side_shape ** 2
else:
raise NotImplementedError
self.model = VAE(in_dim=in_dim,
out_dim=num_pts * 3 if nma_modes is None else 6 + nma_modes.shape[1],
**cfg.model.model_cfg)
log_to_current('Model summary:\n' + str(summary(self.model, input_size=[(1, in_dim), (1,)], verbose=0)))
if nma_modes is None:
|
# other
# avoid num_workers set as cpu_count warning
warnings.simplefilter("ignore", PossibleUserWarning)
# only log to rank_zero, comment this for debugging
log_to_current = rank_zero_only(log_to_current)
TASK_NAME = "atom"
def prepare_images(images: torch.FloatTensor, space: str):
assert space in ("real", "fourier")
if space == "real":
model_input = einops.rearrange(images, "b 1 ny nx -> b (1 ny nx)")
else:
fimages = primal_to_fourier_2d(images)
model_input = einops.rearrange(torch.view_as_real(fimages), "b 1 ny nx c2 -> b (1 ny nx c2)", c2=2)
return model_input
class InitTask(pl.LightningModule):
def __init__(self, em_module):
super().__init__()
self.cfg = em_module.cfg
self.em_module = em_module
self.loss_deque = collections.deque([
10,
], maxlen=20)
def on_train_batch_end(self, outputs, batch, batch_idx):
self.loss_deque.append(outputs['loss'].item())
if np.mean(self.loss_deque) < 1e-3:
self.trainer.should_stop = True
# update all process status
self.trainer.should_stop = self.trainer.strategy.broadcast(self.trainer.should_stop)
def training_step(self, batch, batch_idx):
images = batch["proj"]
idxes = batch["idx"]
rot_mats, trans_mats = self.em_module.get_batch_pose(batch)
pred_deformation, mu, log_var = self.em_module.model(prepare_images(images, self.cfg.model.input_space), idxes,
rot_mats)
shift_loss = torch.mean(torch.pow(pred_deformation.flatten(start_dim=-2), 2))
loss = shift_loss
if self.global_step % self.cfg.runner.log_every_n_step == 0:
log_to_current(f"loss {loss.item()}")
return loss
def configure_optimizers(self):
return optim.AdamW(self.em_module.model.parameters(), lr=1e-4)
def on_fit_end(self):
log_to_current(f"Init finished with loss {np.mean(self.loss_deque)}")
class CryoEMTask(pl.LightningModule):
def __init__(self, cfg, dataset):
super().__init__()
cfg = deepcopy(cfg)
self.cfg = cfg
# Define GMM
meta = Polymer.from_pdb(cfg.dataset_attr.ref_pdb_path)
log_to_current(f"Load reference structure from {cfg.dataset_attr.ref_pdb_path}")
# for save
self.template_pdb = meta.to_atom_arr()
log_to_current(f"Protein contains {len(meta)} atoms, "
f"{meta.num_amino_acids} amino acids, "
f"{meta.num_nucleotides} nucleotides, "
f"{meta.num_chains} chains.")
# ref
ref_centers = torch.from_numpy(meta.coord).float()
ref_amps = torch.from_numpy(meta.num_electron).float()
ref_sigmas = torch.ones_like(ref_amps)
ref_sigmas.fill_(2.)
log_to_current(f"1st GMM blob amplitude {ref_amps[0].item()}, sigma {ref_sigmas[0].item()}")
num_pts = len(meta)
log_to_current(f"Reference structure has {num_pts} atom coordinates")
# tunable params
# gmm
self.register_buffer("gmm_centers", ref_centers)
if cfg.gmm.tunable:
log_to_current("Set GMM sigmas, amplitudes tunable")
self.register_parameter("gmm_sigmas", nn.Parameter(ref_sigmas))
self.register_parameter("gmm_amps", nn.Parameter(ref_amps))
else:
self.register_buffer("gmm_sigmas", ref_sigmas)
self.register_buffer("gmm_amps", ref_amps)
nma_modes = None
if (hasattr(self.cfg.extra_input_data_attr, "nma_path") and
self.cfg.extra_input_data_attr.nma_path not in ["", None]):
nma_modes = torch.tensor(np.load(self.cfg.extra_input_data_attr.nma_path), dtype=torch.float32)
log_to_current(f"Load NMA coefficients from {self.cfg.extra_input_data_attr.nma_path}, "
f"whose shape is {nma_modes.shape}")
# model
if cfg.model.input_space == "fourier":
in_dim = 2 * cfg.data_process.down_side_shape ** 2
elif cfg.model.input_space == "real":
in_dim = cfg.data_process.down_side_shape ** 2
else:
raise NotImplementedError
self.model = VAE(in_dim=in_dim,
out_dim=num_pts * 3 if nma_modes is None else 6 + nma_modes.shape[1],
**cfg.model.model_cfg)
log_to_current('Model summary:\n' + str(summary(self.model, input_size=[(1, in_dim), (1,)], verbose=0)))
if nma_modes is None: | self.deformer = E3Deformer() | 13 | 2023-11-06 07:15:26+00:00 | 24k |
KAIST-AILab/palr | train.py | [
{
"identifier": "BC",
"path": "imitation/bc.py",
"snippet": "class BC(nn.Module):\n def __init__(self, policy, env, best_policy=None,\n replay_buffer=None, replay_buffer_valid=None, seed=0, \n device='cpu', lr=3e-4, envname=None, wandb=None, save_policy_path=None, \n ... | import os
import wandb
import envs
import d4rl
import gym
import torch
from imitation.bc import BC
from imitation.rap import RAP
from imitation.fca import FCA
from imitation.mine import MINE_BC
from imitation.palr import PALR
from argparse import ArgumentParser
from itertools import product
from core.policy import TanhGaussianPolicyWithEmbedding, TanhGaussianRAPPolicy
from core.replay_buffer import EnvReplayBuffer
from core.preprocess import preprocess_dataset_with_prev_actions, data_select_num_transitions
from rlkit.envs.wrappers import NormalizedBoxEnv | 18,875 | wandb_dir = '.'
os.environ['WANDB_DIR'] = wandb_dir
os.environ['D4RL_DATASET_DIR'] = './dataset/'
def train(configs):
| wandb_dir = '.'
os.environ['WANDB_DIR'] = wandb_dir
os.environ['D4RL_DATASET_DIR'] = './dataset/'
def train(configs): | env = NormalizedBoxEnv(gym.make(configs['envname'])) | 10 | 2023-11-06 08:35:34+00:00 | 24k |
tylerlight071/Project-Cipher | main.py | [
{
"identifier": "clear_terminal",
"path": "components/common_functions.py",
"snippet": "def clear_terminal():\n os.system('cls' if os.name == 'nt' else 'clear')"
},
{
"identifier": "print_slow",
"path": "components/common_functions.py",
"snippet": "def print_slow(text, delay=0.00): #... | import msvcrt
import os
import pickle
import sys
import time
import colorama
import pygame
from colorama import Fore, Style
from components.common_functions import clear_terminal, print_slow, shop_help, help_user, connect_help, mail_help, \
system_help
from conversations.calls import intro_call, first_call, second_call, third_call, fourth_call, fifth_call, sixth_call, \
markus_seen_call
from conversations.minigame_calls import code_shatter_call
from minigames.code_shatter_minigame import code_shatter_minigame
from minigames.eye_spy_minigame import port_scanning
from systems.level_1.amy.amy_system import AmySystem
from systems.level_1.billy.billy_system import BillySystem
from systems.level_1.cameras.camera_1 import camera_first
from systems.level_1.markus.markus_system import MarkusSystem | 15,883 | print_slow(Fore.LIGHTBLUE_EX + f"\n{email['subject']} - From: {email['sender']}" + Style.RESET_ALL)
def read_email(emails, subject):
global has_read_email, evidence
global balance
email_found = False
for email in emails:
if email['subject'].lower() == subject.lower():
email_found = True
print_slow(
Fore.LIGHTBLUE_EX + f"\nFrom: {email['sender']}\nSubject: {email['subject']}\n\n{email['body']}" + Style.RESET_ALL)
# Check if the email is one of the specific emails that increases evidence count
if email['subject'].lower() in ["project update"]:
evidence_item = 3
if not has_evidence(evidence_item):
print_slow("Adding evidence to the list...")
print_slow("")
print_slow(Fore.GREEN + "Evidence Secured" + Style.RESET_ALL)
add_evidence(evidence_item)
print_slow("")
print_slow("")
time.sleep(3)
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
third_call()
if email['subject'].lower() in ["professional development"]:
evidence_item = 2
if not has_evidence(evidence_item):
print_slow("Adding evidence to the list...")
print_slow("")
print_slow(Fore.GREEN + "Evidence Secured" + Style.RESET_ALL)
add_evidence(evidence_item)
print_slow("")
print_slow("")
time.sleep(3)
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
second_call()
if email['subject'].lower() == "can't stop thinking about you" and email['sender'].lower() == 'amy':
evidence_item = 1
if not has_evidence(evidence_item):
print_slow("Adding evidence to the list...")
print_slow("")
print_slow(Fore.GREEN + "Evidence Secured" + Style.RESET_ALL)
add_evidence(evidence_item)
print_slow("")
print_slow("")
time.sleep(3)
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
first_call()
if email['subject'].lower() == "upcoming software update" and email['sender'].lower() == 'markus':
evidence_item = 6
if not has_evidence(evidence_item):
print_slow("Adding evidence to the list...")
print_slow("")
print_slow(Fore.GREEN + "Evidence Secured" + Style.RESET_ALL)
add_evidence(evidence_item)
print_slow("")
print_slow("")
time.sleep(3)
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
sixth_call()
# Add money to balance based on the email subject
if email['subject'].lower() == "professional development":
balance += 30
elif email['subject'].lower() == "project update":
balance += 50
elif email['subject'].lower() == "can't stop thinking about you":
balance += 20
elif email['subject'].lower() == "upcoming software update":
balance += 50
if not email_found:
print_slow(Fore.RED + "\nNo email found with that subject, please try again." + Style.RESET_ALL)
def connect():
if has_item("EnigmaLink"):
print_slow("")
print_slow(Fore.GREEN + "Connecting to Enigma Corps network using EnigmaLink..." + Style.RESET_ALL)
time.sleep(0.5)
print_slow("")
print_slow(Fore.GREEN + "Establishing connection...")
time.sleep(1)
print_slow("")
print_slow(Fore.GREEN + "Linking EnigmaLink to remote server...")
time.sleep(2)
print_slow("")
print_slow(Fore.GREEN + "Decrypting server security protocols...")
time.sleep(3)
print_slow("")
print_slow(Fore.GREEN + "Bypassing firewall...")
time.sleep(2)
print_slow("")
print_slow(Fore.GREEN + "Connection established!")
time.sleep(2)
print_slow("")
print_slow(Fore.GREEN + "You are now connected to Enigma Corps network.")
print_slow("")
# Network command loop
while True:
command = input(Fore.GREEN + "> " + Style.RESET_ALL)
# Scan the network for systems and vulnerabilities
if command.lower() == "scan":
scan()
# Hack into a system or vulnerability
elif command.lower().startswith("hack "):
target = command[5:]
hack(target)
# Display connect help message
elif command.lower() == "help":
|
# Set the PYGAME_HIDE_SUPPORT_PROMPT environment variable
os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "1"
# Initialize pygame mixer
pygame.mixer.init()
# Load the bg music file and loop it
pygame.mixer.music.load('bg_music.mp3')
pygame.mixer.music.play(-1)
# sets the volume to 20% (change value to adjust)
pygame.mixer.music.set_volume(0.2)
# Define the global variables at the module level
inventory = []
balance = 300
emails = []
has_read_email = False
has_read_file = False
has_intro_call = False
seen_markus = False
evidence = []
amy_system = AmySystem()
billy_system = BillySystem()
markus_system = MarkusSystem()
bg_music_enabled = True
player_level = 1
has_started_game = False
# Save the game state to a file
def save_game():
global inventory, balance, emails, has_read_email, evidence, player_level, has_intro_call, has_started_game, seen_markus
with open('savegame.pkl', 'wb') as f:
pickle.dump(
(inventory, balance, emails, has_read_email, evidence, player_level, has_intro_call, has_started_game, seen_markus),
f)
# Load the game state from a file
def load_game():
global inventory, balance, emails, has_read_email, evidence, player_level, has_intro_call, has_started_game, seen_markus
if os.path.exists('savegame.pkl'):
with open('savegame.pkl', 'rb') as f:
inventory, balance, emails, has_read_email, evidence, player_level, has_intro_call, has_started_game, seen_markus = pickle.load(
f)
else:
# If the savegame file doesn't exist, set the default values
inventory = []
player_level = 1
evidence = []
has_intro_call = False
has_started_game = False
seen_markus = False
balance = 30000
emails = [
{
"sender": "Hacker's Digest",
"subject": "Weekly Hacker's Digest",
"body": (
"Issue #143\n\n"
"Cipher,\n\n"
"Welcome to the latest edition of Hacker's Digest! In this issue: \n\n"
"- Unveiling the Latest Exploits\n"
"- Spotlight on Cryptocurrency Security\n"
"- Interview with a Grey Hat Hacker\n"
"- Tool of the Week: EnigmaLink\n\n"
"Don't miss out on the latest in the world of hacking and cybersecurity. Stay informed and stay secure!\n\n"
"Best regards,\n"
"Hacker's Digest Team"
)
},
{
"sender": "The Cyber Mythbuster",
"subject": "Busting Cybersecurity Myths",
"body": (
"Cipher,\n\n"
"Heard any wild cybersecurity myths lately? This week, we're busting the craziest ones, including:\n\n"
"- Using 'Password123' for Maximum Security\n"
"- Cyber Ninjas and Their Stealthy VPNs\n"
"- USB Drives: The Fountain of Eternal Data\n\n"
"Stay myth-free and keep on hacking (responsibly)!\n\n"
"Mythbustingly,\n"
"The Cyber Mythbuster"
)
},
{
"sender": "CyberSilliness",
"subject": "Where Cyber Meets Comedy",
"body": (
"Welcome to the CyberSilliness Gazette\n"
"Where we believe that a good laugh is the ultimate antivirus! In this week's hilarity-packed issue:\n\n"
"- Cyber Jokes to Crack You Up (Without Cracking Your Passwords)\n"
"- Tech Support Horror Stories: A Comedy of Errors\n"
"- Chuckle Challenge: Share Your Funniest Cybersecurity Anecdote\n"
"- Meet the Cyber Clowns: Our Team's Silly Security Habits Revealed\n\n"
"Laughter is contagious, and so is good cybersecurity. Dive into the giggles and stay safe!\n\n"
"Silly Regards,\n"
"The CyberSilliness Team"
)
},
{
"sender": "Security Insight Weekly",
"subject": "Navigating the Cybersecurity Landscape",
"body": (
"Hello Cipher,\n\n"
"Welcome to Security Insight Weekly, your reliable source for navigating the ever-evolving cybersecurity landscape. In this week's issue:\n\n"
"- Threat Analysis: Understanding Recent Cybersecurity Incidents\n"
"- Best Practices for Endpoint Security\n"
"- Industry Spotlight: Healthcare Cybersecurity Challenges\n"
"- Security Compliance Update: Staying Aligned with Regulations\n\n"
"Stay informed and empowered as we delve into the serious aspects of cybersecurity. Your security is our priority.\n\n"
"Best regards,\n"
"The Security Insight Team"
)
},
]
# New function for game settings
def game_settings():
global bg_music_enabled
print_slow(Fore.GREEN + "░██████╗███████╗████████╗████████╗██╗███╗░░██╗░██████╗░░██████╗")
print_slow(Fore.GREEN + "██╔════╝██╔════╝╚══██╔══╝╚══██╔══╝██║████╗░██║██╔════╝░██╔════╝")
print_slow(Fore.GREEN + "╚█████╗░█████╗░░░░░██║░░░░░░██║░░░██║██╔██╗██║██║░░██╗░╚█████╗░")
print_slow(Fore.GREEN + "░╚═══██╗██╔══╝░░░░░██║░░░░░░██║░░░██║██║╚████║██║░░╚██╗░╚═══██╗")
print_slow(Fore.GREEN + "██████╔╝███████╗░░░██║░░░░░░██║░░░██║██║░╚███║╚██████╔╝██████╔╝")
print_slow(Fore.GREEN + "╚═════╝░╚══════╝░░░╚═╝░░░░░░╚═╝░░░╚═╝╚═╝░░╚══╝░╚═════╝░╚═════╝░" + Style.RESET_ALL)
print_slow("")
print_slow("")
print_slow("")
print_slow(Fore.GREEN + " --------------------------------------------" + Style.RESET_ALL)
print_slow(
Fore.GREEN + f"| [Background Music] {'Enabled |' if bg_music_enabled else 'Disabled |'}" + Style.RESET_ALL)
print_slow(Fore.GREEN + "| |" + Style.RESET_ALL)
print_slow(Fore.GREEN + "| [Delete Savegame] |" + Style.RESET_ALL)
print_slow(Fore.GREEN + "| |" + Style.RESET_ALL)
print_slow(Fore.GREEN + "| [Back to Main Menu] |" + Style.RESET_ALL)
print_slow(Fore.GREEN + " --------------------------------------------" + Style.RESET_ALL)
choice = input(Fore.GREEN + "\n> " + Style.RESET_ALL)
if choice.lower() == "background music":
# Toggle background music
bg_music_enabled = not bg_music_enabled
if bg_music_enabled:
pygame.mixer.music.play(-1)
print_slow(Fore.GREEN + "\nBackground Music Enabled" + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
game_settings()
else:
pygame.mixer.music.stop()
print_slow(Fore.RED + "\nBackground Music Disabled" + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
game_settings()
elif choice.lower() == "delete savegame":
# Delete savegame
confirm = input(Fore.RED + "\nAre you sure you want to delete the savegame? (yes/no): " + Style.RESET_ALL)
if confirm.lower() == "yes":
try:
os.remove("savegame.pkl")
print_slow(Fore.GREEN + "\nSavegame Deleted" + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
game_settings()
except FileNotFoundError:
print_slow(Fore.RED + "\nSavegame not found" + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
game_settings()
elif choice.lower() == "back" or choice.lower() == "back to main menu":
# Return to Main Menu
print_slow(Fore.GREEN + "\nReturning to Main Menu..." + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
else:
print_slow(Fore.RED + "\nInvalid choice, please try again." + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
game_settings()
# Function to add an item to the inventory
def add_to_inventory(item):
inventory.append(item)
def remove_from_inventory(item):
if item in inventory:
inventory.remove(item)
def add_evidence(evidence_item):
evidence.append(evidence_item)
def has_evidence(evidence_item):
return evidence_item in evidence
# Prints the games title
def main():
clear_terminal()
colorama.init()
print_slow(Fore.GREEN + "██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗██╗░░██╗░█████╗░████████╗" + Style.RESET_ALL)
print_slow(Fore.GREEN + "██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝██║░░██║██╔══██╗╚══██╔══╝" + Style.RESET_ALL)
print_slow(Fore.GREEN + "██████╦╝██║░░░░░███████║██║░░╚═╝█████═╝░███████║███████║░░░██║░░░" + Style.RESET_ALL)
print_slow(Fore.GREEN + "██╔══██╗██║░░░░░██╔══██║██║░░██╗██╔═██╗░██╔══██║██╔══██║░░░██║░░░" + Style.RESET_ALL)
print_slow(Fore.GREEN + "██████╦╝███████╗██║░░██║╚█████╔╝██║░╚██╗██║░░██║██║░░██║░░░██║░░░" + Style.RESET_ALL)
print_slow(Fore.GREEN + "╚═════╝░╚══════╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝╚═╝░░╚═╝╚═╝░░╚═╝░░░╚═╝░░░" + Style.RESET_ALL)
# Pause for 2 seconds before clearing the console
time.sleep(5)
# Clear the console
clear_terminal()
# Main menu loop
while True:
print_slow(Fore.GREEN + "███╗░░░███╗░█████╗░██╗███╗░░██╗ ███╗░░░███╗███████╗███╗░░██╗██╗░░░██╗")
print_slow(Fore.GREEN + "████╗░████║██╔══██╗██║████╗░██║ ████╗░████║██╔════╝████╗░██║██║░░░██║")
print_slow(Fore.GREEN + "██╔████╔██║███████║██║██╔██╗██║ ██╔████╔██║█████╗░░██╔██╗██║██║░░░██║")
print_slow(Fore.GREEN + "██║╚██╔╝██║██╔══██║██║██║╚████║ ██║╚██╔╝██║██╔══╝░░██║╚████║██║░░░██║")
print_slow(Fore.GREEN + "██║░╚═╝░██║██║░░██║██║██║░╚███║ ██║░╚═╝░██║███████╗██║░╚███║╚██████╔╝")
print_slow(
Fore.GREEN + "╚═╝░░░░░╚═╝╚═╝░░╚═╝╚═╝╚═╝░░╚══╝ ╚═╝░░░░░╚═╝╚══════╝╚═╝░░╚══╝░╚═════╝░" + Style.RESET_ALL)
print_slow("")
print_slow("")
print_slow("")
print_slow(Fore.GREEN + " --------------------------------------------" + Style.RESET_ALL)
print_slow(Fore.GREEN + "| [Start] Start the game |" + Style.RESET_ALL)
print_slow(Fore.GREEN + "| |" + Style.RESET_ALL)
print_slow(Fore.GREEN + "| [Options] Change the settings |" + Style.RESET_ALL)
print_slow(Fore.GREEN + "| |" + Style.RESET_ALL)
print_slow(Fore.GREEN + "| [Exit] Exit the game |" + Style.RESET_ALL)
print_slow(Fore.GREEN + " --------------------------------------------" + Style.RESET_ALL)
choice = input(Fore.GREEN + "\n> " + Style.RESET_ALL)
# Start the game
if choice.lower() == "start":
load_game()
start_game()
# Open game settings
elif choice.lower() == "options":
clear_terminal()
game_settings()
# Exit the game
elif choice.lower() == "exit":
print_slow(Fore.GREEN + "\nExiting..." + Style.RESET_ALL)
pygame.mixer.music.stop()
sys.exit()
else:
print_slow(Fore.RED + "\nInvalid choice, please try again." + Style.RESET_ALL)
time.sleep(2)
clear_terminal()
# Function to get the user's balance
def get_balance():
return balance
# Function to add money to the user's balance
def add_money(amount):
global balance
balance += amount
# Function to subtract money from the user's balance
def subtract_money(amount):
global balance
balance -= amount
def add_level(level):
global player_level
player_level += level
# Function to print the user's balance
def print_balance():
print_slow(f"Your current balance is: £{get_balance()}")
# Function to read files and marks files as evidence
def read_file(file_content, file_name):
global has_read_file, evidence
global balance
# Print the file content
print_slow(Fore.LIGHTBLUE_EX + f"\n{file_name}:\n\n{file_content}" + Style.RESET_ALL)
print_slow("")
# Check if the file is one of the specific files that increases evidence count
if file_name.lower() in ["employee_performance_review.txt"]:
evidence_item = 4
if not has_evidence(evidence_item):
print_slow("Adding evidence to the list...")
print_slow("")
print_slow(Fore.GREEN + "Evidence Secured" + Style.RESET_ALL)
add_evidence(evidence_item)
print_slow("")
print_slow("")
time.sleep(3)
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
fourth_call()
if file_name.lower() in ["meeting_minutes.txt"]:
evidence_item = 5
if not has_evidence(evidence_item):
print_slow("Adding evidence to the list...")
print_slow("")
print_slow(Fore.GREEN + "Evidence Secured" + Style.RESET_ALL)
add_evidence(evidence_item)
print_slow("")
print_slow("")
time.sleep(3)
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
fifth_call()
# Add more file names here as needed
# Add money to balance based on the file name
if file_name.lower() == "employee_performance_review.txt":
balance += 30
elif file_name.lower() == "meeting_minutes.txt":
balance += 50
# List of available upgrades
upgrades = [
{"name": "EnigmaLink", "description": "Application required to connect to Enigma Corps network.", "price": 100},
{"name": "CodeShatter", "description": "A powerful password breaker that can crack even the strongest passwords.",
"price": 250},
{"name": "EyeSpy", "description": "A privacy breaker to gain access to the smallest of cameras.", "price": 500},
{"name": "Rift", "description": "Break the barrier between the Server and Network.", "price": 800}
]
# Function to display the shop
def shop():
clear_terminal()
print_slow(Fore.YELLOW + r''' ██╗░░██╗░█████╗░░█████╗░██╗░░██╗███████╗██████╗░ ███╗░░░███╗░█████╗░██████╗░██╗░░██╗███████╗████████╗
██║░░██║██╔══██╗██╔══██╗██║░██╔╝██╔════╝██╔══██╗ ████╗░████║██╔══██╗██╔══██╗██║░██╔╝██╔════╝╚══██╔══╝
███████║███████║██║░░╚═╝█████═╝░█████╗░░██████╔╝ ██╔████╔██║███████║██████╔╝█████═╝░█████╗░░░░░██║░░░
██╔══██║██╔══██║██║░░██╗██╔═██╗░██╔══╝░░██╔══██╗ ██║╚██╔╝██║██╔══██║██╔══██╗██╔═██╗░██╔══╝░░░░░██║░░░
██║░░██║██║░░██║╚█████╔╝██║░╚██╗███████╗██║░░██║ ██║░╚═╝░██║██║░░██║██║░░██║██║░╚██╗███████╗░░░██║░░░
╚═╝░░╚═╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝╚══════╝╚═╝░░╚═╝ ╚═╝░░░░░╚═╝╚═╝░░╚═╝╚═╝░░╚═╝╚═╝░░╚═╝╚══════╝░░░╚═╝░░░''' + Style.RESET_ALL)
print_slow(Fore.YELLOW + "\nWelcome to the Hacker's Market!" + Style.RESET_ALL)
print_slow("")
print_slow(Fore.YELLOW + "Here you can buy upgrades to improve your hacking abilities.\n" + Style.RESET_ALL)
while True:
# Display the list of available upgrades
for i, upgrade in enumerate(upgrades):
print_slow(
Fore.YELLOW + f"\n{upgrade['name']} - {upgrade['description']} - £{upgrade['price']}" + Style.RESET_ALL)
# Get the user's choice
command = input(Fore.YELLOW + "\n> " + Style.RESET_ALL)
# Buy the chosen upgrade
if command.lower() == 'exit':
print_slow(Fore.YELLOW + "\nExiting Hacker's Market" + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
start_game()
elif command.lower() == 'help':
shop_help()
elif command.lower().startswith('buy '):
upgrade_name = command[4:]
# [4:] removes first 4 characters
if has_item('EnigmaLink'):
if upgrade_name.lower() == 'enigmalink':
print_slow("")
print_slow(Fore.RED + "Sold Out" + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
shop()
else:
for upgrade in upgrades:
if upgrade_name.lower() == upgrade['name'].lower():
if get_balance() >= upgrade['price']:
print_slow("")
print_slow(
Fore.GREEN + f"You have successfully purchased {upgrade['name']} for ${upgrade['price']}!" + Style.RESET_ALL)
subtract_money(upgrade['price'])
print_slow("")
print_balance()
add_to_inventory(upgrade['name'])
time.sleep(2)
clear_terminal()
# Check if the purchased upgrade is CodeShatter
if upgrade_name.lower() == 'codeshatter':
print_slow("")
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
code_shatter_call()
shop()
else:
clear_terminal()
shop()
else:
print_slow(
Fore.RED + "You don't have enough money to buy this upgrade." + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
shop()
else:
print_slow(Fore.RED + "Invalid choice, please try again." + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
shop()
else:
for upgrade in upgrades:
if upgrade_name.lower() == upgrade['name'].lower():
if get_balance() >= upgrade['price']:
print_slow("")
print_slow(
Fore.GREEN + f"You have successfully purchased {upgrade['name']} for ${upgrade['price']}!" + Style.RESET_ALL)
subtract_money(upgrade['price'])
print_slow("")
print_balance()
add_to_inventory(upgrade['name'])
time.sleep(2)
clear_terminal()
shop()
else:
print_slow(
Fore.RED + "You don't have enough money to buy this upgrade." + Style.RESET_ALL)
shop()
else:
print_slow(Fore.RED + "Invalid choice, please try again." + Style.RESET_ALL)
time.sleep(1)
clear_terminal()
shop()
# Function to start the game
def start_game():
global has_intro_call, has_started_game, seen_markus
if has_intro_call:
clear_terminal()
pass
else:
print_slow("\nStarting game...")
time.sleep(1)
print_slow("\nLoading assets...")
time.sleep(1)
clear_terminal()
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
intro_call()
has_intro_call = True
has_started_game = True
print_slow(Fore.MAGENTA + "\nHint: Type 'help' to get a list of available commands." + Style.RESET_ALL)
pass
if seen_markus:
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
markus_seen_call()
else:
pass
# Game command loop
command = input(Fore.GREEN + "> " + Style.RESET_ALL)
# Connect to the network
if command.lower() == "connect":
connect()
# Access the mail system
elif command.lower() == "mail":
mail()
# Display help message
elif command.lower() == "help":
help_user()
# Check balance
elif command.lower() == "balance":
print_balance()
# Enter shop
elif command.lower() == "shop":
shop()
# Clear terminal
elif command.lower() == "clear":
clear_terminal()
# Return to the main menu
elif command.lower() == "exit":
print_slow("Returning to Main Menu...")
time.sleep(1)
main()
else:
print_slow("Invalid command, please try again.")
time.sleep(1)
clear_terminal()
start_game()
# Save the game state
save_game()
# Function to check if an item is in the inventory
def has_item(item):
return item in inventory
def scan():
print_slow("")
print_slow(Fore.YELLOW + "Scanning network..." + Style.RESET_ALL)
time.sleep(2)
print_slow("")
print_slow(Fore.YELLOW + "\nAvailable Systems:" + Style.RESET_ALL)
print_slow("")
for system in all_systems:
if system['level'] == player_level:
print_slow("")
print_slow(f"{system['name']} ({system['type']})")
print_slow("")
def getpass_star(prompt="Password: "):
print(prompt, end='', flush=True)
password = []
while True:
char = msvcrt.getch().decode('utf-8')
if char == '\r' or char == '\n':
break
elif char == '\b': # Backspace
if password:
password.pop()
print('\b \b', end='', flush=True)
else:
password.append(char)
print('*', end='', flush=True)
print() # Move to the next line
return ''.join(password)
def hack(system_name):
global seen_markus
# Find the system in the all_systems list
system = next((s for s in all_systems if s['name'].lower() == system_name.lower()), None)
if system:
if system['level'] == player_level:
# Check for CodeShatter before prompting for password
if system['name'] == 'Markus' and has_item("CodeShatter"):
clear_terminal()
code_shatter_minigame()
print_slow("Password Cracked: 735@&!//")
input("Press [Enter] to continue")
clear_terminal()
markus_system_command_loop(markus_system)
add_level(player_level)
remove_from_inventory(item="CodeShatter")
seen_markus = True
elif system['name'] == 'Lobby Camera' and has_item("EyeSpy"):
port_scanning()
add_level(player_level)
camera_first()
else:
# Prompt the user for the password
print_slow("")
password = getpass_star("Enter password: ")
print_slow("")
if password == system['password']:
print_slow("")
print_slow(Fore.GREEN + "Access granted!" + Style.RESET_ALL)
if system['name'] == 'Amy':
amy_system_command_loop(amy_system)
elif system['name'] == 'Billy':
billy_system_command_loop(billy_system)
elif system['name'] == 'Markus':
markus_system_command_loop(markus_system)
add_level(player_level)
seen_markus = True
elif system['name'] == 'Lobby Camera':
camera_first()
elif system['name'] == 'Kyle':
# Implement Kyle System
else:
# Add more conditions for other systems
pass
else:
print_slow("")
print_slow(Fore.RED + "Access denied! Incorrect password." + Style.RESET_ALL)
else:
print_slow("")
print_slow(Fore.RED + "System not found! Please try again." + Style.RESET_ALL)
else:
print_slow("")
print_slow(Fore.RED + "System not found! Please try again." + Style.RESET_ALL)
def list_emails(emails):
print_slow(Fore.LIGHTBLUE_EX + "\nEmails:" + Style.RESET_ALL)
for i, email in enumerate(emails):
print_slow(Fore.LIGHTBLUE_EX + f"\n{email['subject']} - From: {email['sender']}" + Style.RESET_ALL)
def read_email(emails, subject):
global has_read_email, evidence
global balance
email_found = False
for email in emails:
if email['subject'].lower() == subject.lower():
email_found = True
print_slow(
Fore.LIGHTBLUE_EX + f"\nFrom: {email['sender']}\nSubject: {email['subject']}\n\n{email['body']}" + Style.RESET_ALL)
# Check if the email is one of the specific emails that increases evidence count
if email['subject'].lower() in ["project update"]:
evidence_item = 3
if not has_evidence(evidence_item):
print_slow("Adding evidence to the list...")
print_slow("")
print_slow(Fore.GREEN + "Evidence Secured" + Style.RESET_ALL)
add_evidence(evidence_item)
print_slow("")
print_slow("")
time.sleep(3)
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
third_call()
if email['subject'].lower() in ["professional development"]:
evidence_item = 2
if not has_evidence(evidence_item):
print_slow("Adding evidence to the list...")
print_slow("")
print_slow(Fore.GREEN + "Evidence Secured" + Style.RESET_ALL)
add_evidence(evidence_item)
print_slow("")
print_slow("")
time.sleep(3)
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
second_call()
if email['subject'].lower() == "can't stop thinking about you" and email['sender'].lower() == 'amy':
evidence_item = 1
if not has_evidence(evidence_item):
print_slow("Adding evidence to the list...")
print_slow("")
print_slow(Fore.GREEN + "Evidence Secured" + Style.RESET_ALL)
add_evidence(evidence_item)
print_slow("")
print_slow("")
time.sleep(3)
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
first_call()
if email['subject'].lower() == "upcoming software update" and email['sender'].lower() == 'markus':
evidence_item = 6
if not has_evidence(evidence_item):
print_slow("Adding evidence to the list...")
print_slow("")
print_slow(Fore.GREEN + "Evidence Secured" + Style.RESET_ALL)
add_evidence(evidence_item)
print_slow("")
print_slow("")
time.sleep(3)
print_slow(Fore.GREEN + "Incoming Call..." + Style.RESET_ALL)
input(Fore.GREEN + "> " + Style.RESET_ALL)
sixth_call()
# Add money to balance based on the email subject
if email['subject'].lower() == "professional development":
balance += 30
elif email['subject'].lower() == "project update":
balance += 50
elif email['subject'].lower() == "can't stop thinking about you":
balance += 20
elif email['subject'].lower() == "upcoming software update":
balance += 50
if not email_found:
print_slow(Fore.RED + "\nNo email found with that subject, please try again." + Style.RESET_ALL)
def connect():
if has_item("EnigmaLink"):
print_slow("")
print_slow(Fore.GREEN + "Connecting to Enigma Corps network using EnigmaLink..." + Style.RESET_ALL)
time.sleep(0.5)
print_slow("")
print_slow(Fore.GREEN + "Establishing connection...")
time.sleep(1)
print_slow("")
print_slow(Fore.GREEN + "Linking EnigmaLink to remote server...")
time.sleep(2)
print_slow("")
print_slow(Fore.GREEN + "Decrypting server security protocols...")
time.sleep(3)
print_slow("")
print_slow(Fore.GREEN + "Bypassing firewall...")
time.sleep(2)
print_slow("")
print_slow(Fore.GREEN + "Connection established!")
time.sleep(2)
print_slow("")
print_slow(Fore.GREEN + "You are now connected to Enigma Corps network.")
print_slow("")
# Network command loop
while True:
command = input(Fore.GREEN + "> " + Style.RESET_ALL)
# Scan the network for systems and vulnerabilities
if command.lower() == "scan":
scan()
# Hack into a system or vulnerability
elif command.lower().startswith("hack "):
target = command[5:]
hack(target)
# Display connect help message
elif command.lower() == "help": | connect_help() | 4 | 2023-11-06 09:52:13+00:00 | 24k |
ziqi-zhang/TAOISM | python/test/test_conv.py | [
{
"identifier": "register_layer",
"path": "python/common_net.py",
"snippet": "def register_layer(layer, name):\n layer.register_forward_hook(hooking_layer(name))\n layer.register_backward_hook(hooking_layer_backward(name))\n layer_names.append(name)"
},
{
"identifier": "register_weight_... | import os
import sys
import numpy as np
import torch
import torch.distributed as dist
import sys
import pdb
from pdb import set_trace as st
from torch import optim, nn
from python.common_net import register_layer, register_weight_layer, get_layer_weight, get_layer_input, \
get_layer_weight_grad, get_layer_output, get_layer_output_grad, get_layer_input_grad
from python.enclave_interfaces import GlobalTensor
from python.layers.batch_norm_2d import SecretBatchNorm2dLayer
from python.layers.flatten import SecretFlattenLayer
from python.layers.input import SecretInputLayer
from python.layers.maxpool2d import SecretMaxpool2dLayer
from python.layers.output import SecretOutputLayer
from python.layers.relu import SecretReLULayer
from python.sgx_net import init_communicate, warming_up_cuda, SecretNeuralNetwork, SgdOptimizer
from python.layers.sgx_linear_base import SGXLinearBase
from python.layers.sgx_conv_base import SGXConvBase
from python.utils.basic_utils import ExecutionModeOptions
from python.utils.logger_utils import Logger
from python.quantize_net import NetQ
from python.test_sgx_net import argparser_distributed, marshal_process, load_cifar10, seed_torch
from python.utils.timer_utils import NamedTimerInstance, VerboseLevel, NamedTimer
from python.utils.torch_utils import compare_expected_actual
from pdb import set_trace as st | 21,043 |
device_cuda = torch.device("cuda:0")
torch.set_printoptions(precision=10)
def compare_layer_member(layer: SGXLinearBase, layer_name: str,
extract_func , member_name: str, save_path=None) -> None:
print(member_name)
layer.make_sure_cpu_is_latest(member_name)
compare_expected_actual(extract_func(layer_name), layer.get_cpu(member_name), get_relative=True, verbose=True)
if save_path is not None:
if not os.path.exists(save_path):
os.makedirs(save_path)
print("Directory ", save_path, " Created ")
else:
print("Directory ", save_path, " already exists")
torch.save(extract_func(layer_name), os.path.join(save_path, member_name + "_expected"))
torch.save(layer.get_cpu(member_name), os.path.join(save_path, member_name + "_actual"))
def compare_layer(layer: SGXLinearBase, layer_name: str, save_path=None) -> None:
print("comparing with layer in expected NN :", layer_name)
|
device_cuda = torch.device("cuda:0")
torch.set_printoptions(precision=10)
def compare_layer_member(layer: SGXLinearBase, layer_name: str,
extract_func , member_name: str, save_path=None) -> None:
print(member_name)
layer.make_sure_cpu_is_latest(member_name)
compare_expected_actual(extract_func(layer_name), layer.get_cpu(member_name), get_relative=True, verbose=True)
if save_path is not None:
if not os.path.exists(save_path):
os.makedirs(save_path)
print("Directory ", save_path, " Created ")
else:
print("Directory ", save_path, " already exists")
torch.save(extract_func(layer_name), os.path.join(save_path, member_name + "_expected"))
torch.save(layer.get_cpu(member_name), os.path.join(save_path, member_name + "_actual"))
def compare_layer(layer: SGXLinearBase, layer_name: str, save_path=None) -> None:
print("comparing with layer in expected NN :", layer_name) | compare_name_function = [("input", get_layer_input), ("output", get_layer_output), | 5 | 2023-11-01 10:37:37+00:00 | 24k |
Codra-Ingenierie-Informatique/DataLab | cdl/tests/scenarios/common.py | [
{
"identifier": "_",
"path": "cdl/config.py",
"snippet": "CONF_VERSION = \"1.0.0\"\nAPP_NAME = \"DataLab\"\nMOD_NAME = \"cdl\"\nAPP_DESC = _(\"\"\"DataLab is a generic signal and image processing platform\"\"\")\nAPP_PATH = osp.dirname(__file__)\nDEBUG = os.environ.get(\"DEBUG\", \"\").lower() in (\"1\"... | import numpy as np
import cdl.obj as dlo
import cdl.param as dlp
from cdl.config import _
from cdl.core.gui.main import CDLMainWindow
from cdl.core.gui.panel.image import ImagePanel
from cdl.core.gui.panel.signal import SignalPanel
from cdl.tests.data import (
create_paracetamol_signal,
create_peak2d_image,
create_sincos_image,
)
from cdl.tests.features.common.newobject_unit import (
iterate_image_creation,
iterate_signal_creation,
)
from cdl.widgets import fitdialog | 19,718 | panel.add_label_with_title()
__compute_11_operations(panel, 2)
def run_signal_computations(
win: CDLMainWindow, data_size: int = 500, all_types: bool = True
) -> None:
"""Testing signal features"""
panel = win.signalpanel
win.set_current_panel("signal")
if all_types:
for signal in iterate_signal_creation(data_size, non_zero=True):
panel.add_object(create_paracetamol_signal(data_size))
panel.add_object(signal)
compute_common_operations(panel)
panel.remove_all_objects()
sig1 = create_paracetamol_signal(data_size)
win.add_object(sig1)
# Add new signal based on s0
panel.objview.set_current_object(sig1)
newparam = dlo.new_signal_param(
_("Random function"), stype=dlo.SignalTypes.UNIFORMRANDOM
)
addparam = dlo.UniformRandomParam.create(vmin=0, vmax=sig1.y.max() * 0.2)
panel.new_object(newparam, addparam=addparam, edit=False)
compute_common_operations(panel)
win.add_object(create_paracetamol_signal(data_size))
param = dlp.NormalizeYParam()
for _name, method in param.methods:
param.method = method
panel.processor.compute_normalize(param)
param = dlp.XYCalibrateParam.create(a=1.2, b=0.1)
panel.processor.compute_calibration(param)
panel.processor.compute_derivative()
panel.processor.compute_integral()
param = dlp.PeakDetectionParam()
panel.processor.compute_peak_detection(param)
panel.processor.compute_multigaussianfit()
panel.objview.select_objects([-3])
sig = panel.objview.get_sel_objects()[0]
i1 = data_size // 10
i2 = len(sig.y) - i1
panel.processor.compute_roi_extraction(dlp.ROIDataParam.create([[i1, i2]]))
param = dlp.PolynomialFitParam()
panel.processor.compute_polyfit(param)
panel.processor.compute_fit(_("Gaussian fit"), fitdialog.gaussianfit)
panel.processor.compute_fit(_("Lorentzian fit"), fitdialog.lorentzianfit)
panel.processor.compute_fit(_("Voigt fit"), fitdialog.voigtfit)
newparam = dlo.new_signal_param(_("Gaussian"), stype=dlo.SignalTypes.GAUSS)
sig = dlo.create_signal_from_param(
newparam, dlo.GaussLorentzVoigtParam(), edit=False
)
panel.add_object(sig)
param = dlp.FWHMParam()
for fittype, _name in param.fittypes:
param.fittype = fittype
panel.processor.compute_fwhm(param)
panel.processor.compute_fw1e2()
# Create a new signal which X values are a subset of sig1
x = np.linspace(sig1.x.min(), sig1.x.max(), data_size // 2)[: data_size // 4]
y = x * 0.0
sig2 = dlo.create_signal("X values for interpolation", x, y)
panel.add_object(sig2)
# Test interpolation
for method_choice_tuple in dlp.InterpolationParam._methods:
method = method_choice_tuple[0]
for fill_value in (None, 0.0):
panel.objview.set_current_object(sig1)
param = dlp.InterpolationParam.create(method=method, fill_value=fill_value)
panel.processor.compute_interpolation(sig2, param)
# Test resampling
xmin, xmax = x[0], x[-1]
for mode, dx, nbpts in (("dx", 0.1, 10), ("nbpts", 0.0, 100)):
panel.objview.set_current_object(sig1)
param = dlp.ResamplingParam.create(
xmin=xmin, xmax=xmax, mode=mode, dx=dx, nbpts=nbpts
)
panel.processor.compute_resampling(param)
# Test convolution
panel.objview.set_current_object(sig1)
panel.processor.compute_derivative()
panel.processor.compute_convolution(sig1)
# Test detrending
panel.objview.set_current_object(sig1)
for method_choice_tuple in dlp.DetrendingParam._methods:
param = dlp.DetrendingParam.create(method=method_choice_tuple[0])
panel.processor.compute_detrending(param)
def run_image_computations(
win: CDLMainWindow, data_size: int = 150, all_types: bool = True
) -> None:
"""Testing signal features"""
win.set_current_panel("image")
panel = win.imagepanel
newparam = dlo.new_image_param(height=data_size, width=data_size)
if all_types:
| # -*- coding: utf-8 -*-
#
# Licensed under the terms of the BSD 3-Clause
# (see cdl/LICENSE for details)
"""
Scenarios common functions
"""
# pylint: disable=invalid-name # Allows short reference names like x, y, ...
# guitest: skip
from __future__ import annotations
def __compute_11_operations(panel: SignalPanel | ImagePanel, number: int) -> None:
"""Test compute_11 type operations on a signal or image
Requires that one signal or image has been added at index."""
assert len(panel) >= number - 1
panel.objview.select_objects((number,))
panel.processor.compute_gaussian_filter(dlp.GaussianParam())
panel.processor.compute_moving_average(dlp.MovingAverageParam())
panel.processor.compute_moving_median(dlp.MovingMedianParam())
panel.processor.compute_wiener()
panel.processor.compute_fft()
panel.processor.compute_ifft()
panel.processor.compute_abs()
panel.remove_object()
panel.processor.compute_re()
panel.remove_object()
panel.processor.compute_im()
panel.remove_object()
panel.processor.compute_astype(dlp.DataTypeIParam.create(dtype="float64"))
panel.processor.compute_log10()
panel.processor.compute_swap_axes()
panel.processor.compute_swap_axes()
def compute_common_operations(panel: SignalPanel | ImagePanel) -> None:
"""Test operations common to signal/image
Requires that two (and only two) signals/images are created/added to panel
First signal/image is supposed to be always the same (reference)
Second signal/image is the tested object
"""
assert len(panel) == 2
panel.objview.select_objects((2,))
panel.processor.compute_difference(panel[1]) # difference with obj #1
panel.remove_object()
panel.objview.select_objects((2,))
panel.processor.compute_quadratic_difference() # quadratic difference with itself
panel.delete_metadata()
panel.objview.select_objects((3,))
panel.remove_object()
panel.objview.select_objects((1, 2))
panel.processor.compute_sum()
panel.objview.select_objects((1, 2))
panel.processor.compute_sum()
panel.objview.select_objects((1, 2))
panel.processor.compute_product()
obj = panel.objmodel.get_groups()[0][-1]
param = dlp.ThresholdParam()
param.value = (obj.data.max() - obj.data.min()) * 0.2 + obj.data.min()
panel.processor.compute_threshold(param)
param = dlp.ClipParam() # Clipping before division...
param.value = (obj.data.max() - obj.data.min()) * 0.8 + obj.data.min()
panel.processor.compute_clip(param)
panel.objview.select_objects((3, 7))
panel.processor.compute_division()
panel.objview.select_objects((1, 2, 3))
panel.processor.compute_average()
panel.add_label_with_title()
__compute_11_operations(panel, 2)
def run_signal_computations(
win: CDLMainWindow, data_size: int = 500, all_types: bool = True
) -> None:
"""Testing signal features"""
panel = win.signalpanel
win.set_current_panel("signal")
if all_types:
for signal in iterate_signal_creation(data_size, non_zero=True):
panel.add_object(create_paracetamol_signal(data_size))
panel.add_object(signal)
compute_common_operations(panel)
panel.remove_all_objects()
sig1 = create_paracetamol_signal(data_size)
win.add_object(sig1)
# Add new signal based on s0
panel.objview.set_current_object(sig1)
newparam = dlo.new_signal_param(
_("Random function"), stype=dlo.SignalTypes.UNIFORMRANDOM
)
addparam = dlo.UniformRandomParam.create(vmin=0, vmax=sig1.y.max() * 0.2)
panel.new_object(newparam, addparam=addparam, edit=False)
compute_common_operations(panel)
win.add_object(create_paracetamol_signal(data_size))
param = dlp.NormalizeYParam()
for _name, method in param.methods:
param.method = method
panel.processor.compute_normalize(param)
param = dlp.XYCalibrateParam.create(a=1.2, b=0.1)
panel.processor.compute_calibration(param)
panel.processor.compute_derivative()
panel.processor.compute_integral()
param = dlp.PeakDetectionParam()
panel.processor.compute_peak_detection(param)
panel.processor.compute_multigaussianfit()
panel.objview.select_objects([-3])
sig = panel.objview.get_sel_objects()[0]
i1 = data_size // 10
i2 = len(sig.y) - i1
panel.processor.compute_roi_extraction(dlp.ROIDataParam.create([[i1, i2]]))
param = dlp.PolynomialFitParam()
panel.processor.compute_polyfit(param)
panel.processor.compute_fit(_("Gaussian fit"), fitdialog.gaussianfit)
panel.processor.compute_fit(_("Lorentzian fit"), fitdialog.lorentzianfit)
panel.processor.compute_fit(_("Voigt fit"), fitdialog.voigtfit)
newparam = dlo.new_signal_param(_("Gaussian"), stype=dlo.SignalTypes.GAUSS)
sig = dlo.create_signal_from_param(
newparam, dlo.GaussLorentzVoigtParam(), edit=False
)
panel.add_object(sig)
param = dlp.FWHMParam()
for fittype, _name in param.fittypes:
param.fittype = fittype
panel.processor.compute_fwhm(param)
panel.processor.compute_fw1e2()
# Create a new signal which X values are a subset of sig1
x = np.linspace(sig1.x.min(), sig1.x.max(), data_size // 2)[: data_size // 4]
y = x * 0.0
sig2 = dlo.create_signal("X values for interpolation", x, y)
panel.add_object(sig2)
# Test interpolation
for method_choice_tuple in dlp.InterpolationParam._methods:
method = method_choice_tuple[0]
for fill_value in (None, 0.0):
panel.objview.set_current_object(sig1)
param = dlp.InterpolationParam.create(method=method, fill_value=fill_value)
panel.processor.compute_interpolation(sig2, param)
# Test resampling
xmin, xmax = x[0], x[-1]
for mode, dx, nbpts in (("dx", 0.1, 10), ("nbpts", 0.0, 100)):
panel.objview.set_current_object(sig1)
param = dlp.ResamplingParam.create(
xmin=xmin, xmax=xmax, mode=mode, dx=dx, nbpts=nbpts
)
panel.processor.compute_resampling(param)
# Test convolution
panel.objview.set_current_object(sig1)
panel.processor.compute_derivative()
panel.processor.compute_convolution(sig1)
# Test detrending
panel.objview.set_current_object(sig1)
for method_choice_tuple in dlp.DetrendingParam._methods:
param = dlp.DetrendingParam.create(method=method_choice_tuple[0])
panel.processor.compute_detrending(param)
def run_image_computations(
win: CDLMainWindow, data_size: int = 150, all_types: bool = True
) -> None:
"""Testing signal features"""
win.set_current_panel("image")
panel = win.imagepanel
newparam = dlo.new_image_param(height=data_size, width=data_size)
if all_types: | for image in iterate_image_creation(data_size, non_zero=True): | 7 | 2023-11-09 16:56:03+00:00 | 24k |
ingra14m/Tensor4D-DNeRF | exp_runner.py | [
{
"identifier": "Dataset",
"path": "models/dataset.py",
"snippet": "class Dataset:\n def __init__(self, conf):\n super(Dataset, self).__init__()\n print('Load data: Begin')\n self.device = torch.device('cuda')\n self.conf = conf\n\n self.data_dir = conf.get_string('... | import os
import time
import logging
import argparse
import numpy as np
import cv2 as cv
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from shutil import copyfile
from tqdm import tqdm
from pyhocon import ConfigFactory
from models.dataset import Dataset, BlenderDataset
from models.fields import RenderingNetwork, FieldNetwork, SingleVarianceNetwork
from models.tensor4d import Tensor4D
from models.renderer import NeuSRenderer
from models.mask import Mask3D
from metrics import * | 15,678 | os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
class Runner:
def __init__(self, conf_path, mode='train', case='CASE_NAME', is_continue=False):
self.device = torch.device('cuda')
# Configuration
self.conf_path = conf_path
f = open(self.conf_path)
conf_text = f.read()
conf_text = conf_text.replace('CASE_NAME', case)
f.close()
self.conf = ConfigFactory.parse_string(conf_text)
self.conf['dataset.data_dir'] = self.conf['dataset.data_dir'].replace('CASE_NAME', case)
self.base_exp_dir = self.conf['general.base_exp_dir']
os.makedirs(self.base_exp_dir, exist_ok=True)
self.is_blender = self.conf['dataset'].get_bool('is_blender', default=False)
| os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
class Runner:
def __init__(self, conf_path, mode='train', case='CASE_NAME', is_continue=False):
self.device = torch.device('cuda')
# Configuration
self.conf_path = conf_path
f = open(self.conf_path)
conf_text = f.read()
conf_text = conf_text.replace('CASE_NAME', case)
f.close()
self.conf = ConfigFactory.parse_string(conf_text)
self.conf['dataset.data_dir'] = self.conf['dataset.data_dir'].replace('CASE_NAME', case)
self.base_exp_dir = self.conf['general.base_exp_dir']
os.makedirs(self.base_exp_dir, exist_ok=True)
self.is_blender = self.conf['dataset'].get_bool('is_blender', default=False) | self.dataset = BlenderDataset(self.conf['dataset']) if self.is_blender else Dataset(self.conf['dataset']) | 0 | 2023-11-07 10:16:33+00:00 | 24k |
Giftify-Bot/Giftify-Bot | bot.py | [
{
"identifier": "GuildConfig",
"path": "models/giveaway_settings.py",
"snippet": "class GuildConfig:\n \"\"\"Represents the configuration settings for a guild.\n\n Parameters\n ----------\n guild: discord.Guild\n The guild associated with the configuration.\n logging: Optional[disc... | import asyncio
import datetime
import logging
import os
import pathlib
import sys
import traceback
import aiohttp
import asyncpg
import discord
import dotenv
import jishaku
import sentry_sdk
import uvloop
from logging.handlers import RotatingFileHandler
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
from amari import AmariClient
from discord.ext import commands
from discord.utils import MISSING
from discord.utils import _ColourFormatter as ColourFormatter
from expiringdict import ExpiringDict
from sentry_sdk.integrations.logging import LoggingIntegration
from models.giveaway_settings import GuildConfig
from models.giveaways import Giveaway
from models.raffles import Raffle
from utils.constants import ERROR_EMOJI, SUCCESS_EMOJI, WARN_EMOJI
from utils.db import db_init
from utils.tree import CommandTree
from utils.view import ConfirmationView
from cogs.timer_manager import TimerManager
from models.donation_settings import GuildDonationConfig
| 14,886 |
async def fetch_level(self, member: discord.Member, /) -> int:
"""Fetches user level from Amari Bot API.
Parameters
-----------
member: discord.Member
The member whose level is to be fetched.
Returns
---------
int
The retrieved level.
"""
try:
user = await self.amari_client.fetch_user(member.guild.id, member.id)
except Exception:
return 0
else:
return user.level or 0
async def fetch_weekly_experience(self, member: discord.Member, /) -> int:
"""Fetches user's weekly experience from Amari Bot API.
Parameters
-----------
member: discord.Member
The member whose weekly experience is to be fetched.
Returns
---------
int
The retrieved weekly experience.
"""
try:
user = await self.amari_client.fetch_user(member.guild.id, member.id)
except Exception:
return 0
else:
return user.weeklyexp or 0
async def prompt(
self,
message: str,
*,
interaction: discord.Interaction[Giftify],
success_message: str,
cancel_message: str,
timeout: float = 60.0,
) -> Optional[bool]:
"""An interactive reaction confirmation dialog.
Parameters
-----------
message: str
The message to show along with the prompt.
timeout: float
How long to wait before returning.
interaction: Interaction
The interaction object to handle the confirmation dialog.
success_message: str
The message to show when the user clicks Confirm.
cancel_message: str
The message to show when the user clicks Cancel.
Returns
--------
Optional[bool]
``True`` if explicit confirm,
``False`` if explicit deny,
``None`` if deny due to timeout
"""
view = ConfirmationView(
timeout=timeout,
interaction=interaction,
success_message=success_message,
cancel_message=cancel_message,
)
view.message = await self.send(interaction, message, view=view, reason="warn")
await view.wait()
return view.value
class Giftify(GiftifyHelper, commands.AutoShardedBot):
user: discord.ClientUser
colour: int = 0xCB3045
__version_info__ = "1.1.4"
def __init__(
self,
*,
log_handler: LogHandler,
pool: asyncpg.Pool,
session: aiohttp.ClientSession,
amari_client: AmariClient,
) -> None:
self._log_handler = log_handler
self._pool = pool
self._session = session
self._amari_client = amari_client
intents = discord.Intents(messages=True, emojis=True, guilds=True)
allowed_mentions = discord.AllowedMentions(everyone=False, roles=False, users=True, replied_user=False)
member_cache_flags = discord.MemberCacheFlags.from_intents(intents=intents)
sentry_sdk.init(
dsn=os.environ["SENTRY_DSN"],
integrations=[
LoggingIntegration(
level=logging.INFO,
event_level=logging.ERROR,
)
],
traces_sample_rate=1.0,
)
super().__init__(
command_prefix=commands.when_mentioned,
| from __future__ import annotations
if TYPE_CHECKING:
dotenv.load_dotenv()
try:
except ImportError: # Windows
pass
else:
uvloop.install()
jishaku.Flags.HIDE = True
jishaku.Flags.RETAIN = True
jishaku.Flags.NO_UNDERSCORE = True
jishaku.Flags.NO_DM_TRACEBACK = True
OWNER_IDS = (747403406154399765,)
EXTENSIONS: Tuple[str, ...] = (
"meta",
"settings",
"timers",
"giveaways",
"donations",
"raffles",
"logger",
"webserver",
)
class RemoveNoise(logging.Filter):
def __init__(self) -> None:
super().__init__(name="discord.state")
def filter(self, record) -> bool:
if record.levelname == "WARNING" and "referencing an unknown" in record.msg:
return False
return True
class LogHandler:
def __init__(self, stream: bool = True) -> None:
self.log: logging.Logger = logging.getLogger()
self.max_bytes: int = 32 * 1024 * 1024
self.logging_path = pathlib.Path("./logs/")
self.logging_path.mkdir(exist_ok=True)
self.stream = stream
async def __aenter__(self) -> "LogHandler":
return self.__enter__()
def __enter__(self: "LogHandler") -> "LogHandler":
logging.getLogger("discord").setLevel(logging.INFO)
logging.getLogger("discord.http").setLevel(logging.INFO)
logging.getLogger("discord.state").addFilter(RemoveNoise())
self.log.setLevel(logging.INFO)
handler = RotatingFileHandler(
filename=self.logging_path / "Giftify.log",
encoding="utf-8",
mode="w",
maxBytes=self.max_bytes,
backupCount=5,
)
dt_fmt = "%Y-%m-%d %H:%M:%S"
fmt = logging.Formatter("[{asctime}] [{levelname:<7}] {name}: {message}", dt_fmt, style="{")
handler.setFormatter(fmt)
self.log.addHandler(handler)
if self.stream:
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(ColourFormatter())
self.log.addHandler(stream_handler)
return self
async def __aexit__(self, *args: Any) -> None:
return self.__exit__(*args)
def __exit__(self, *args: Any) -> None:
handlers = self.log.handlers[:]
for handler in handlers:
handler.close()
self.log.removeHandler(handler)
class GiftifyHelper:
configs: List[GuildConfig] = []
donation_configs: List[GuildDonationConfig] = []
cached_giveaways: List["Giveaway"] = []
webhook_cache: Dict[discord.TextChannel, discord.Webhook] = {}
raffles_cache: Dict[discord.Guild, List[Raffle]] = ExpiringDict(max_len=100, max_age_seconds=300)
pool: asyncpg.Pool
user: discord.ClientUser
amari_client: AmariClient
"""A helper class for Giftify's operations.
This class provides methods to send interaction messages with embeds,
fetch webhooks for a channel, and retrieve or fetch guild configuration.
"""
async def send(
self,
interaction: discord.Interaction,
message: str,
reason: str = "success",
ephemeral: bool = True,
view: discord.ui.View = MISSING,
) -> None:
"""Sends an interaction message with embed.
Parameters
-----------
interaction: discord.Interaction
The interaction to respond to.
message: str
The response message to send.
reason: str
The reason to send the message, can be "warn", "error" or "success".
ephemeral: bool
If the response should be sent ephemerally.
"""
emoji = WARN_EMOJI if reason == "warn" else ERROR_EMOJI if reason == "error" else SUCCESS_EMOJI
colour = (
discord.Colour.orange()
if reason == "warn"
else discord.Colour.red()
if reason == "error"
else discord.Colour.green()
)
embed = discord.Embed(description=f"> {emoji} {message}", colour=colour)
if interaction.response.is_done():
await interaction.followup.send(embed=embed, view=view, ephemeral=ephemeral)
else:
await interaction.response.send_message(embed=embed, view=view, ephemeral=ephemeral)
async def _get_webhook(self, channel: discord.TextChannel, force_create: bool = False) -> discord.Webhook:
if not force_create and (webhook := self.webhook_cache.get(channel)):
return webhook
webhook_list = await channel.webhooks()
if webhook_list:
for hook in webhook_list:
if hook.token:
if hook.user and hook.user.id == self.user.id:
self.webhook_cache[channel] = hook
return hook
# If no suitable webhook is found, create a new one
hook = await channel.create_webhook(name="Giftify Logging", avatar=await channel.guild.me.display_avatar.read())
self.webhook_cache[channel] = hook
return hook
async def send_to_webhook(self, channel: discord.TextChannel, embed: discord.Embed):
"""Sends an embed to a webhook associated with the provided channel.
Parameters
-----------
channel: discord.TextChannel
The channel to send message to.
"""
try:
webhook = await self._get_webhook(channel)
await webhook.send(embed=embed, username="Giftify Logging", avatar_url=self.user.display_avatar)
except discord.NotFound:
new_webhook = await self._get_webhook(channel, force_create=True)
await new_webhook.send(embed=embed, username="Giftify Logging", avatar_url=self.user.display_avatar)
except discord.HTTPException:
return
async def fetch_config(self, guild: discord.Guild) -> GuildConfig:
"""Looks up a guild config in cache or fetches if not found.
Parameters
-----------
guild: discord.Guild
The guild to look for.
Returns
---------
GuildConfig
The retrieved guild config object.
"""
config = discord.utils.get(self.configs, guild=guild)
if not config:
config = await GuildConfig.fetch(guild, self.pool)
self.configs.append(config)
return config
def get_donation_config(self, guild: discord.Guild, category: str) -> Optional[GuildDonationConfig]:
"""Finds the donation config of a guild for some category.
Parameters
-----------
guild: Guild
The guild to which the category belongs.
category: str
The name of the category.
Returns
--------
Optional[GuildDonationConfig]
The fetched donation config.
"""
for config in self.donation_configs:
if config.guild == guild and config.category == category:
return config
def get_guild_donation_categories(self, guild: discord.Guild) -> List[str]:
"""Finds the donation categories of a guild.
Parameters
-----------
guild: Guild
The guild for which categories will be fetched.
Returns
--------
List[str]
The of names of donation categories.
"""
return [config.category for config in self.donation_configs if config.guild == guild]
async def fetch_raffle(self, guild: discord.Guild, name: str) -> Optional[Raffle]:
"""Finds a raffle in some guild.
Parameters
-----------
guild: Guild
The guild to which the raffle belongs.
name: str
The name of the raffle.
Returns
--------
Optional[Raffle]
The fetched raffle.
"""
record = await self.pool.fetchrow("SELECT * FROM raffles WHERE guild = $1 AND name = $2", guild.id, name)
if record is not None:
return await Raffle.from_record(self, record=record) # type: ignore
async def fetch_raffles(self, guild: discord.Guild, use_cache: bool = True) -> List[Raffle]:
"""Fetch all the raffles in some guild
Parameters
-----------
guild: Guild
The guild for which raffles will be fetched.
use_cache: bool
Indicates wheter the bot should fetch the raffles from database or use internal cache.
Returns
--------
List[Raffle]
The of list of fetched raffles.
"""
if guild in self.raffles_cache and use_cache:
return self.raffles_cache[guild]
records = await self.pool.fetch("SELECT * FROM raffles WHERE guild = $1", guild.id)
raffles = [await Raffle.from_record(self, record=record) for record in records] # type: ignore
self.raffles_cache[guild] = raffles
return raffles
async def fetch_giveaway(self, *, guild_id: int, channel_id: int, message_id: int) -> Optional[Giveaway]:
"""Looks up a for a giveaway object in database.
Parameters
-----------
message_id: int
The ID of the giveaway message.
Returns
--------
Optional[Giveaway]
The retrieved giveaway object.
"""
giveaway = discord.utils.get(
self.cached_giveaways,
guild_id=guild_id,
channel_id=channel_id,
message_id=message_id,
)
if giveaway is not None:
return giveaway
record = await self.pool.fetchrow(
"SELECT * FROM giveaways WHERE guild = $1 AND channel = $2 AND message = $3",
guild_id,
channel_id,
message_id,
)
if record is not None:
giveaway = Giveaway(bot=self, record=record) # type: ignore
if giveaway.messages:
self.cached_giveaways.append(giveaway)
return giveaway
async def running_giveaways(self, *, guild_id: Optional[int] = None, sort_by_ends: bool = True) -> List[Giveaway]:
"""Looks up a list of active giveaways in the database.
Parameters
-----------
guild_id: Optional[int]
The ID of the guild. If provided, fetches giveaways only for that guild.
sort_by_ends: bool
If True, the results will be sorted by the 'ends' column in ascending order.
Returns
--------
List[Giveaway]
The list of fetched active giveaways.
"""
query = "SELECT * FROM giveaways WHERE ended = FALSE"
if guild_id is not None:
query += " AND guild = $1"
if sort_by_ends:
query += " ORDER BY ends ASC"
if guild_id is not None:
records = await self.pool.fetch(query, guild_id)
else:
records = await self.pool.fetch(query)
return [Giveaway(bot=self, record=record) for record in records] # type: ignore
async def fetch_level(self, member: discord.Member, /) -> int:
"""Fetches user level from Amari Bot API.
Parameters
-----------
member: discord.Member
The member whose level is to be fetched.
Returns
---------
int
The retrieved level.
"""
try:
user = await self.amari_client.fetch_user(member.guild.id, member.id)
except Exception:
return 0
else:
return user.level or 0
async def fetch_weekly_experience(self, member: discord.Member, /) -> int:
"""Fetches user's weekly experience from Amari Bot API.
Parameters
-----------
member: discord.Member
The member whose weekly experience is to be fetched.
Returns
---------
int
The retrieved weekly experience.
"""
try:
user = await self.amari_client.fetch_user(member.guild.id, member.id)
except Exception:
return 0
else:
return user.weeklyexp or 0
async def prompt(
self,
message: str,
*,
interaction: discord.Interaction[Giftify],
success_message: str,
cancel_message: str,
timeout: float = 60.0,
) -> Optional[bool]:
"""An interactive reaction confirmation dialog.
Parameters
-----------
message: str
The message to show along with the prompt.
timeout: float
How long to wait before returning.
interaction: Interaction
The interaction object to handle the confirmation dialog.
success_message: str
The message to show when the user clicks Confirm.
cancel_message: str
The message to show when the user clicks Cancel.
Returns
--------
Optional[bool]
``True`` if explicit confirm,
``False`` if explicit deny,
``None`` if deny due to timeout
"""
view = ConfirmationView(
timeout=timeout,
interaction=interaction,
success_message=success_message,
cancel_message=cancel_message,
)
view.message = await self.send(interaction, message, view=view, reason="warn")
await view.wait()
return view.value
class Giftify(GiftifyHelper, commands.AutoShardedBot):
user: discord.ClientUser
colour: int = 0xCB3045
__version_info__ = "1.1.4"
def __init__(
self,
*,
log_handler: LogHandler,
pool: asyncpg.Pool,
session: aiohttp.ClientSession,
amari_client: AmariClient,
) -> None:
self._log_handler = log_handler
self._pool = pool
self._session = session
self._amari_client = amari_client
intents = discord.Intents(messages=True, emojis=True, guilds=True)
allowed_mentions = discord.AllowedMentions(everyone=False, roles=False, users=True, replied_user=False)
member_cache_flags = discord.MemberCacheFlags.from_intents(intents=intents)
sentry_sdk.init(
dsn=os.environ["SENTRY_DSN"],
integrations=[
LoggingIntegration(
level=logging.INFO,
event_level=logging.ERROR,
)
],
traces_sample_rate=1.0,
)
super().__init__(
command_prefix=commands.when_mentioned,
| tree_cls=CommandTree,
| 7 | 2023-11-09 15:00:15+00:00 | 24k |
Kushalhk/AutoFilter | plugins/p_ttishow.py | [
{
"identifier": "ADMINS",
"path": "info.py",
"snippet": "ADMINS = [int(admin) if id_pattern.search(admin) else admin for admin in environ.get('ADMINS', '').split()]"
},
{
"identifier": "LOG_CHANNEL",
"path": "info.py",
"snippet": "LOG_CHANNEL = int(environ.get('LOG_CHANNEL', ''))"
},
... | from pyrogram import Client, filters, enums
from pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup, CallbackQuery
from pyrogram.errors.exceptions.bad_request_400 import MessageTooLong, PeerIdInvalid
from info import ADMINS, LOG_CHANNEL, SUPPORT_CHAT, MELCOW_NEW_USERS, MELCOW_VID, CHNL_LNK, GRP_LNK
from database.users_chats_db import db
from database.ia_filterdb import Media
from utils import get_size, temp, get_settings
from Script import script
from pyrogram.errors import ChatAdminRequired
import asyncio | 20,001 |
"""-----------------------------------------https://t.me/TG_LINKS_CHANNEL--------------------------------------"""
@Client.on_message(filters.new_chat_members & filters.group)
async def save_group(bot, message):
r_j_check = [u.id for u in message.new_chat_members]
if temp.ME in r_j_check:
if not await db.get_chat(message.chat.id):
total=await bot.get_chat_members_count(message.chat.id)
r_j = message.from_user.mention if message.from_user else "Anonymous"
await bot.send_message(LOG_CHANNEL, script.LOG_TEXT_G.format(message.chat.title, message.chat.id, total, r_j))
await db.add_chat(message.chat.id, message.chat.title)
if message.chat.id in temp.BANNED_CHATS:
# Inspired from a boat of a banana tree
buttons = [[
InlineKeyboardButton('Support', url=f'https://t.me/{SUPPORT_CHAT}')
]]
reply_markup=InlineKeyboardMarkup(buttons)
k = await message.reply(
text='<b>CHAT NOT ALLOWED 🐞\n\nMy admins has restricted me from working here ! If you want to know more about it contact support..</b>',
reply_markup=reply_markup,
)
try:
await k.pin()
except:
pass
await bot.leave_chat(message.chat.id)
return
buttons = [[
InlineKeyboardButton('🔸 ᴍᴇꜱꜱᴀɢᴇ ʜᴇʀᴇ 🔹', url="https://t.me/TG_Bots_Supporter")
],[
InlineKeyboardButton('ᴄʜᴀɴɴᴇʟ', url=CHNL_LNK),
InlineKeyboardButton('ɢʀᴏᴜᴘ', url=GRP_LNK)
]]
reply_markup=InlineKeyboardMarkup(buttons)
await message.reply_text(
text=f"<b>ᴛʜᴀɴᴋ ʏᴏᴜ ꜰᴏʀ ᴀᴅᴅɪɴɢ ᴍᴇ ɪɴ {message.chat.title} ❣️\n\nᴅᴏɴ'ᴛ ꜰᴏʀɢᴇᴛ ᴛᴏ ᴍᴀᴋᴇ ᴍᴇ ᴀᴅᴍɪɴ. ɪꜰ ʏᴏᴜ ʜᴀᴠᴇ ᴀɴʏ ǫᴜᴇꜱᴛɪᴏɴꜱ & ᴅᴏᴜʙᴛꜱ ᴀʙᴏᴜᴛ ᴜꜱɪɴɢ ᴍᴇ ᴄᴏɴᴛᴀᴄᴛ ꜰʀᴏᴍ ᴀᴅᴍɪɴ & ᴍᴇꜱꜱᴀɢᴇ ʜᴇʀᴇ 👇</b>",
reply_markup=reply_markup)
else:
|
"""-----------------------------------------https://t.me/TG_LINKS_CHANNEL--------------------------------------"""
@Client.on_message(filters.new_chat_members & filters.group)
async def save_group(bot, message):
r_j_check = [u.id for u in message.new_chat_members]
if temp.ME in r_j_check:
if not await db.get_chat(message.chat.id):
total=await bot.get_chat_members_count(message.chat.id)
r_j = message.from_user.mention if message.from_user else "Anonymous"
await bot.send_message(LOG_CHANNEL, script.LOG_TEXT_G.format(message.chat.title, message.chat.id, total, r_j))
await db.add_chat(message.chat.id, message.chat.title)
if message.chat.id in temp.BANNED_CHATS:
# Inspired from a boat of a banana tree
buttons = [[
InlineKeyboardButton('Support', url=f'https://t.me/{SUPPORT_CHAT}')
]]
reply_markup=InlineKeyboardMarkup(buttons)
k = await message.reply(
text='<b>CHAT NOT ALLOWED 🐞\n\nMy admins has restricted me from working here ! If you want to know more about it contact support..</b>',
reply_markup=reply_markup,
)
try:
await k.pin()
except:
pass
await bot.leave_chat(message.chat.id)
return
buttons = [[
InlineKeyboardButton('🔸 ᴍᴇꜱꜱᴀɢᴇ ʜᴇʀᴇ 🔹', url="https://t.me/TG_Bots_Supporter")
],[
InlineKeyboardButton('ᴄʜᴀɴɴᴇʟ', url=CHNL_LNK),
InlineKeyboardButton('ɢʀᴏᴜᴘ', url=GRP_LNK)
]]
reply_markup=InlineKeyboardMarkup(buttons)
await message.reply_text(
text=f"<b>ᴛʜᴀɴᴋ ʏᴏᴜ ꜰᴏʀ ᴀᴅᴅɪɴɢ ᴍᴇ ɪɴ {message.chat.title} ❣️\n\nᴅᴏɴ'ᴛ ꜰᴏʀɢᴇᴛ ᴛᴏ ᴍᴀᴋᴇ ᴍᴇ ᴀᴅᴍɪɴ. ɪꜰ ʏᴏᴜ ʜᴀᴠᴇ ᴀɴʏ ǫᴜᴇꜱᴛɪᴏɴꜱ & ᴅᴏᴜʙᴛꜱ ᴀʙᴏᴜᴛ ᴜꜱɪɴɢ ᴍᴇ ᴄᴏɴᴛᴀᴄᴛ ꜰʀᴏᴍ ᴀᴅᴍɪɴ & ᴍᴇꜱꜱᴀɢᴇ ʜᴇʀᴇ 👇</b>",
reply_markup=reply_markup)
else: | settings = await get_settings(message.chat.id) | 11 | 2023-11-03 12:21:26+00:00 | 24k |
apple/ml-reed | reed/algorithms/pebble.py | [
{
"identifier": "utils",
"path": "BPref/utils.py",
"snippet": "def make_env(cfg):\ndef ppo_make_env(env_id, seed):\ndef tie_weights(src, trg):\ndef make_metaworld_env(cfg):\ndef ppo_make_metaworld_env(env_id, seed):\n def __init__(self, *models):\n def __enter__(self):\n def __exit__(self, *arg... | import typing as t
import time
import numpy as np
import torch
import hydra
from pathlib import Path
from omegaconf import dictconfig, OmegaConf
from BPref import utils
from BPref.logger import Logger
from BPref.replay_buffer import TrajectoryReplayBuffer
from collections import deque
from reed.models.reward_model import StateActionRewardModel
from reed.data.preference_dataset import PreferenceDataset
from reed.data.preference_data_loader import PreferenceTripletEnsembleDataLoader
from reed.data.preprocess_images import PreProcessInference | 20,852 | Determine the dimensionality of the inputs to the reward model
Args:
observation_dim: the dimensionality of agent observations. If the observation is an image, the
dimensionality should have the following order: (num_channels, height, width)
action_dim: the dimensionality of agent actions
Returns:
the dimensionality of the reward model's inputs
"""
# compute the dimensions of the input to the reward function
if not self.experiment_config.reward_from_image_observations:
return observation_dim + action_dim
else:
# we need to concatenate the actions to last dimension of the image
# the input to the reward net also needs to have the channels first
# the image dimensions are given to us a (height, width, channels)
sample_shape = list(observation_dim)
if self.experiment_config.grayscale_images:
num_channels = action_dim + 1
else:
num_channels = sample_shape[0] + action_dim
# update the number of channels
sample_shape[0] = num_channels
# the dimensions of the input to the reward model
return sample_shape
def _determine_reward_observation_dimensions(self) -> t.Union[int, np.ndarray]:
"""
Check if the reward will use the image observations.
If so the reward input shape needs to be set accordingly
Returns:
the dimensionality of reward's observation space
"""
if self.experiment_config.reward_from_image_observations:
# get a sample image rendering of the environment and get its shape
self.env.reset()
if "metaworld" in self.experiment_config.env:
start_time = time.time()
img_obs = self.env.render(camera_name=self.experiment_config.camera_name,
resolution=(
self.experiment_config.image_height, self.experiment_config.image_width))
end_time = time.time()
print(f"Sample render time for metaworld is {end_time - start_time} seconds")
else:
start_time = time.time()
img_obs = self.env.render(mode="rgb_array",
height=self.experiment_config.image_height,
width=self.experiment_config.image_width)
end_time = time.time()
print(f"Sample render time for DMC is {end_time - start_time} seconds")
formatted_image_observation = self._reward_input_preprocessor.format_state(img_obs).squeeze(axis=0)
observation_space = formatted_image_observation.shape
print("--------------------------")
print("--------------------------")
print("--------------------------")
print("image observation shape", observation_space)
print("--------------------------")
print("--------------------------")
print("--------------------------")
else:
observation_space = self.env.observation_space.shape[0]
return observation_space
def _determine_observation_dimensions(self) -> t.Union[int, np.ndarray]:
"""
Check if the reward will use the image observations.
If so the replay buffer needs to be set up to
accumulate the image observations
Returns:
the dimensionality of reward's observation space
"""
if self.experiment_config.reward_from_image_observations:
# get a sample image rendering of the environment and get its shape
self.env.reset()
if "metaworld" in self.experiment_config.env:
start_time = time.time()
img_obs = self.env.render(camera_name=self.experiment_config.camera_name,
resolution=(
self.experiment_config.image_height, self.experiment_config.image_width))
end_time = time.time()
print(f"Sample render time for metaworld is {end_time - start_time} seconds")
else:
start_time = time.time()
img_obs = self.env.render(mode="rgb_array",
height=self.experiment_config.image_height,
width=self.experiment_config.image_width)
end_time = time.time()
print(f"Sample render time for DMC is {end_time - start_time} seconds")
observation_space = img_obs.shape
print("--------------------------")
print("--------------------------")
print("--------------------------")
print("image observation shape", observation_space)
print("--------------------------")
print("--------------------------")
print("--------------------------")
else:
observation_space = self.env.observation_space.shape[0]
return observation_space
def _render_image_observation(self) -> np.ndarray:
"""
Render the current image observation
"""
if "metaworld" in self.experiment_config.env:
img_obs = self.env.render(camera_name=self.experiment_config.camera_name,
resolution=(
self.experiment_config.image_height, self.experiment_config.image_width))
else:
img_obs = self.env.render(mode="rgb_array",
height=self.experiment_config.image_height,
width=self.experiment_config.image_width)
return img_obs
| #
# For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
#
class PEBBLE:
"""
Train a reward model in conjunction with policy training following the PEBBLE algorithm from (Lee et al. 2021)
"""
def __init__(self, experiment_config: dictconfig.DictConfig):
"""
Args:
experiment_config: contains the configuration for the experiment to be run. Access like a dictionry
"""
# track the experimental configuration
self.experiment_config = experiment_config
# create the logger to track policy learning progress
self.logger = Logger(
self.experiment_config.out_dir,
save_tb=self.experiment_config.log_save_tb,
log_frequency=self.experiment_config.log_frequency,
agent=self.experiment_config.agent.name)
# used to track where we are in training
# total amount of feedback the reward model has solicited
self.total_feedback = 0
# total amount of feedback given to the reward model
self.labeled_feedback = 0
# policy train step
self.step = 0
# we need to set the random seed for replication purposes
utils.set_seed_everywhere(self.experiment_config.seed)
# the device on which models will be trained
self.device = torch.device(self.experiment_config.device)
# flag to make sure we are handling multi-gpu training where we need to
self.multi_gpu = torch.cuda.device_count() > 1
print("----------------------------------------")
print("----------------------------------------")
print("----------------------------------------")
print("----------------------------------------")
print(f"There is {torch.cuda.device_count()} GPU, so models will be trained with torch.nn.DataParallel.")
print("----------------------------------------")
print("----------------------------------------")
print("----------------------------------------")
print("----------------------------------------")
# make the environment
if 'metaworld' in self.experiment_config.env:
self.env = utils.make_metaworld_env(self.experiment_config)
# we are not evaluating a domain where we need to log whether an agent has reached a goal state
self.log_success = True
else:
self.env = utils.make_env(self.experiment_config)
# we are not evaluating a domain where we need to log whether an agent has reached a goal state
self.log_success = False
print('----------------------')
print('----------------------')
print('----------------------')
print('----------------------')
print("observation space ", self.env.observation_space.shape[0])
print("action space ", self.env.action_space.shape[0])
print('----------------------')
print('----------------------')
print('----------------------')
print('----------------------')
# we need to set the policy's observation and action space
self.experiment_config.agent.params.obs_dim = self.env.observation_space.shape[0]
self.experiment_config.agent.params.action_dim = self.env.action_space.shape[0]
self.experiment_config.agent.params.action_range = [
float(self.env.action_space.low.min()),
float(self.env.action_space.high.max())
]
# create the agent specified in the configuration
self.agent = hydra.utils.instantiate(self.experiment_config.agent)
# the class that will format the observations and observation action pairs for consumption by the reward model
self._reward_input_preprocessor = PreProcessInference(
image_observations=self.experiment_config.reward_from_image_observations,
grayscale_images=self.experiment_config.grayscale_images,
normalize_images=self.experiment_config.normalized_images)
# determine the reward's observation space
# if the reward is trained on images then the reward's observation space differs from the policy's, which is
# trained on the state space
self._observation_dimensionality = self._determine_observation_dimensions()
self._reward_observation_dimensionality = self._determine_reward_observation_dimensions()
# create the agent's replay buffer setting if image observations will need to be tracked
self.replay_buffer = TrajectoryReplayBuffer(
int(self.experiment_config.replay_buffer_capacity),
self.device,
image_observations=(self._observation_dimensionality
if (self.experiment_config.reward_from_image_observations
or self.experiment_config.save_image_observations)
else None)
)
# determine the dimensionality of the input to the reward function
self.reward_in_dim = self._determine_reward_input_dimensions(
observation_dim=self._reward_observation_dimensionality,
action_dim=self.env.action_space.shape[0])
# instantiating the reward model
self.reward_model = self.construct_reward_ensemble()
# create the preference dataset that will solicit and hold labelled preference triplets
self.preference_dataset = PreferenceDataset(
observation_dim=self._reward_observation_dimensionality,
action_dim=self.env.action_space.shape[0],
capacity=self.experiment_config.preference_dataset_capacity,
size_segment=self.experiment_config.segment_size,
out_path=Path("/tmp/preference_dataset/"),
image_observations=self.experiment_config.reward_from_image_observations,
state_action_formatter=self._reward_input_preprocessor,
grayscale_images=self.experiment_config.grayscale_images,
collect_image_pref_dataset=self.experiment_config.save_image_observations,
teacher_beta=self.experiment_config.teacher_beta,
teacher_gamma=self.experiment_config.teacher_gamma,
teacher_eps_mistake=self.experiment_config.teacher_eps_mistake,
teacher_eps_skip=self.experiment_config.teacher_eps_skip,
teacher_eps_equal=self.experiment_config.teacher_eps_equal
)
# save the experimental configuration
with open(Path(self.experiment_config.out_dir) / "experiment_config.yaml", "w+") as f:
OmegaConf.save(config=self.experiment_config, f=f)
def _determine_reward_input_dimensions(self,
observation_dim: t.Union[int, np.ndarray],
action_dim: int) -> t.Union[int, t.Sequence]:
"""
Determine the dimensionality of the inputs to the reward model
Args:
observation_dim: the dimensionality of agent observations. If the observation is an image, the
dimensionality should have the following order: (num_channels, height, width)
action_dim: the dimensionality of agent actions
Returns:
the dimensionality of the reward model's inputs
"""
# compute the dimensions of the input to the reward function
if not self.experiment_config.reward_from_image_observations:
return observation_dim + action_dim
else:
# we need to concatenate the actions to last dimension of the image
# the input to the reward net also needs to have the channels first
# the image dimensions are given to us a (height, width, channels)
sample_shape = list(observation_dim)
if self.experiment_config.grayscale_images:
num_channels = action_dim + 1
else:
num_channels = sample_shape[0] + action_dim
# update the number of channels
sample_shape[0] = num_channels
# the dimensions of the input to the reward model
return sample_shape
def _determine_reward_observation_dimensions(self) -> t.Union[int, np.ndarray]:
"""
Check if the reward will use the image observations.
If so the reward input shape needs to be set accordingly
Returns:
the dimensionality of reward's observation space
"""
if self.experiment_config.reward_from_image_observations:
# get a sample image rendering of the environment and get its shape
self.env.reset()
if "metaworld" in self.experiment_config.env:
start_time = time.time()
img_obs = self.env.render(camera_name=self.experiment_config.camera_name,
resolution=(
self.experiment_config.image_height, self.experiment_config.image_width))
end_time = time.time()
print(f"Sample render time for metaworld is {end_time - start_time} seconds")
else:
start_time = time.time()
img_obs = self.env.render(mode="rgb_array",
height=self.experiment_config.image_height,
width=self.experiment_config.image_width)
end_time = time.time()
print(f"Sample render time for DMC is {end_time - start_time} seconds")
formatted_image_observation = self._reward_input_preprocessor.format_state(img_obs).squeeze(axis=0)
observation_space = formatted_image_observation.shape
print("--------------------------")
print("--------------------------")
print("--------------------------")
print("image observation shape", observation_space)
print("--------------------------")
print("--------------------------")
print("--------------------------")
else:
observation_space = self.env.observation_space.shape[0]
return observation_space
def _determine_observation_dimensions(self) -> t.Union[int, np.ndarray]:
"""
Check if the reward will use the image observations.
If so the replay buffer needs to be set up to
accumulate the image observations
Returns:
the dimensionality of reward's observation space
"""
if self.experiment_config.reward_from_image_observations:
# get a sample image rendering of the environment and get its shape
self.env.reset()
if "metaworld" in self.experiment_config.env:
start_time = time.time()
img_obs = self.env.render(camera_name=self.experiment_config.camera_name,
resolution=(
self.experiment_config.image_height, self.experiment_config.image_width))
end_time = time.time()
print(f"Sample render time for metaworld is {end_time - start_time} seconds")
else:
start_time = time.time()
img_obs = self.env.render(mode="rgb_array",
height=self.experiment_config.image_height,
width=self.experiment_config.image_width)
end_time = time.time()
print(f"Sample render time for DMC is {end_time - start_time} seconds")
observation_space = img_obs.shape
print("--------------------------")
print("--------------------------")
print("--------------------------")
print("image observation shape", observation_space)
print("--------------------------")
print("--------------------------")
print("--------------------------")
else:
observation_space = self.env.observation_space.shape[0]
return observation_space
def _render_image_observation(self) -> np.ndarray:
"""
Render the current image observation
"""
if "metaworld" in self.experiment_config.env:
img_obs = self.env.render(camera_name=self.experiment_config.camera_name,
resolution=(
self.experiment_config.image_height, self.experiment_config.image_width))
else:
img_obs = self.env.render(mode="rgb_array",
height=self.experiment_config.image_height,
width=self.experiment_config.image_width)
return img_obs
| def construct_reward_ensemble(self) -> StateActionRewardModel: | 3 | 2023-11-06 23:14:20+00:00 | 24k |
allenai/unified-io-2 | t5x/train.py | [
{
"identifier": "PackingStrategy",
"path": "t5x/examples/unified_io/packing.py",
"snippet": "class PackingStrategy:\n \"\"\"Defines how to pack data during training and handles batch-level constraints\n from the input/target encoders\"\"\"\n\n pack_max_len: Optional[Tuple[int, int]] = None\n \"\"... | import functools
import math
import os
import time
import warnings
import clu.data
import jax
import jax.numpy as jnp
import numpy as np
import seqio
import tensorflow as tf
import jax.profiler
import gin
from typing import Callable, Sequence, Mapping, Tuple, Type, Optional
from t5x.examples.unified_io.packing import PackingStrategy
from absl import logging
from clu import metric_writers
from jax import random
from jax.experimental import multihost_utils
from jax.experimental.global_device_array import GlobalDeviceArray
from t5x import checkpoints
from t5x import eval as eval_lib
from t5x import models
from t5x.examples.unified_io import evaluator
from t5x import partitioning
from t5x import train_state as train_state_lib
from t5x import trainer as trainer_lib
from t5x import utils
from os.path import expanduser
from t5x.examples.unified_io.utils import init_wandb
from t5x.examples.unified_io.metrics.metrics import null_metric
from t5x.examples.unified_io.data.postprocessing import return_example
from absl import app
from absl import flags
from t5x import gin_utils | 15,059 | metrics_by_task: A map of metrics keyed by task name.
Returns:
A bool indicating whether training should be halted.
Raises:
RuntimeError: When the metrics processed on host 0 is None.
"""
stop_training = False
if jax.process_index() == 0:
if not metrics_by_task:
raise RuntimeError('Metric is unexpectedly empty on process 0')
for action in actions.get(mode, []):
stop_training |= action.run(train_state, metrics_by_task=metrics_by_task)
# Broadcast result from host 0 to others.
return bool(multihost_utils.broadcast_one_to_all(jnp.array(stop_training)))
def train(
*,
model: models.BaseTransformerModel,
train_dataset_cfg: utils.DatasetConfig,
train_eval_dataset_cfg: Optional[utils.DatasetConfig],
infer_eval_dataset_cfg: Optional[utils.DatasetConfig],
checkpoint_cfg: utils.CheckpointConfig,
partitioner: partitioning.BasePartitioner,
trainer_cls: trainer_lib.BaseTrainerConstructor,
model_dir: str,
total_steps: int,
eval_steps: int,
eval_period: int,
stats_period: Optional[int] = None,
random_seed: Optional[int],
use_hardware_rng: bool = False,
summarize_config_fn: Callable[[str, metric_writers.MetricWriter, int], None],
inference_evaluator_cls: utils.EvaluatorConstructor = seqio.Evaluator,
get_dataset_fn: utils.GetDatasetCallable = utils.get_dataset,
concurrent_metrics: bool = True,
actions: Optional[Mapping[str, Sequence[trainer_lib.BaseAction]]] = None,
train_eval_get_dataset_fn: utils.GetEvalDatasetCallable = utils
.get_training_eval_datasets,
run_eval_before_training: bool = False,
use_wandb = True, weight_metrics="norm",
packing_strategy: PackingStrategy = None,
train_state_initializer_cls: Type[
utils.TrainStateInitializer] = utils.TrainStateInitializer,
use_gda: bool = True,
verify_matching_vocabs_fn: Optional[
Callable[[utils.DatasetConfig, models.BaseTransformerModel],
None]] = utils.verify_matching_vocabs,
shuffle_buffer_size=None,
cycle_length=None,
block_length=None
) -> Tuple[int, train_state_lib.TrainState]:
"""Train function.
Args:
model: The model object to use for training.
train_dataset_cfg: Specification for the dataset to train with.
train_eval_dataset_cfg: Specification for the dataset to evaluate with using
the train metrics and no inference (e.g., uses teacher forcing). If None,
train eval is disabled.
infer_eval_dataset_cfg: Specification for the dataset to evaluate with using
the inference metrics (e.g., uses sampled decoding). If None, inference
eval is disabled.
checkpoint_cfg: Specification for saving and restoring model parameters and
dataset state to/from checkpoints.
partitioner: Partitioner for model parameters and data across devices.
trainer_cls: An implementation of BaseTrainer.
model_dir: Path of directory to store checkpoints and metric summaries.
total_steps: The step number to stop training after. The number of actual
steps trained in this run will be this number minus the starting step from
the checkpoint. If this is set to the starting step from the checkpoint,
the model will not be compiled for training and training will not be run.
This can be used in conjunction with `run_eval_before_training` to only
evaluate a model.
eval_steps: The number of batches to process for each train-eval loop.
eval_period: The number of train steps between each evaluation (both
train-eval and infer-eval).
stats_period: The number of train steps between writing scalar stats. If
None, defaults to eval_period.
random_seed: A random seed to use for dropout and initialization. If None, a
fast, non-deterministic hardware-based RNG is used.
use_hardware_rng: Whether to force using the RngBitGenerator based hardware
rng, which takes seeds and acts similarly to software PRNG in that it
should be seed-deterministic. The new RngBitGenerator custom PRNG system
should be reproducible for a given sharding, but the numbers will change
for different shardings of the same model.
summarize_config_fn: A function that takes in the model directory, a
SummaryWriter, and the step number, and writes a summary of the
inference_evaluator_cls: seqio.Evaluator class to use for inference
evaluation, potentially with bound configuration args.
get_dataset_fn: The callable use to get the train and train-eval datasets
based on the DatasetConfig and shard information.
concurrent_metrics: If True, allow metrics computation and logging to
overlap with training. Will likely result in additional TPU memory usage.
actions: A mapping of actions that runs after train, eval or infer_eval, to
inspect the model and perform useful operations, e.g., early stopping. The
key must have a 1:1 mapping to ActionMode enum. For EVAL actions to
actually work, this requires `concurrent_metrics` to be turned off, since
chaining futures and mutating states concurrently might be error-prone.
train_eval_get_dataset_fn: Optional callable use to get the train-eval
datasets based on the DatasetConfig and shard information. If missing, it
defaults to `utils.get_training_eval_datasets`.
run_eval_before_training: If True, calculate training eval and inference
eval metrics before training begins.
train_state_initializer_cls: t5x.utils.TrainStateInitializer class for
initializing partitioned TrainState from checkpoints or scratch.
use_gda: if True, uses GlobalDeviceArray. Experimental feature.
verify_matching_vocabs_fn: Function to validate whether the task vocabulary
matches the model vocabulary. Should raise an exception on error.
Returns:
The tuple of (last_step, last_train_state).
"""
if jax.process_index() == 0 and use_wandb:
if not os.environ.get("WANDB_API_KEY"):
use_wandb = False
logging.warning("WANDB_API_KEY not found, wandb will not be used")
else:
| # Copyright 2022 The T5X Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""Script to pretrain or finetune in JAX using a SeqIO pipeline.
"""
# Set Linen to add profiling information when constructing Modules.
# Must be set before flax imports.
# pylint:disable=g-import-not-at-top
os.environ['FLAX_PROFILE'] = 'true'
# TODO(adarob): Re-enable once users are notified and tests are updated.
os.environ['FLAX_LAZY_RNG'] = 'no'
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = os.path.join(
expanduser("~"), ".config/gcloud/application_default_credentials.json")
# Automatically search for gin files relative to the T5X package.
_DEFAULT_GIN_SEARCH_PATHS = [
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
]
PyTreeDef = type(jax.tree_util.tree_structure(None))
P = partitioning.PartitionSpec
# Special key that used to distinguish train metrics.
TRAIN_METRIC_KEY = 'train'
# String keys that is acceptable from config.
_ACTION_KEYS = frozenset(trainer_lib.ActionMode.__members__.keys())
def run_actions(
mode: trainer_lib.ActionMode, actions: trainer_lib.ActionMapType,
train_state: train_state_lib.TrainState,
metrics_by_task: Mapping[str, trainer_lib.MetricValueMapType]) -> bool:
"""Invokes all actions on the given mode on host 0, then broadcasts to all.
Args:
mode: The mode to run the actions. e.g., if mode is `train`, only actions
configured to run with `train` mode will be invoked.
actions: A mapping of actions that runs after train, eval or infer_eval, to
inspect the model and perform useful operations, e.g., early stopping.
train_state: The current train_state of the trainer.
metrics_by_task: A map of metrics keyed by task name.
Returns:
A bool indicating whether training should be halted.
Raises:
RuntimeError: When the metrics processed on host 0 is None.
"""
stop_training = False
if jax.process_index() == 0:
if not metrics_by_task:
raise RuntimeError('Metric is unexpectedly empty on process 0')
for action in actions.get(mode, []):
stop_training |= action.run(train_state, metrics_by_task=metrics_by_task)
# Broadcast result from host 0 to others.
return bool(multihost_utils.broadcast_one_to_all(jnp.array(stop_training)))
def train(
*,
model: models.BaseTransformerModel,
train_dataset_cfg: utils.DatasetConfig,
train_eval_dataset_cfg: Optional[utils.DatasetConfig],
infer_eval_dataset_cfg: Optional[utils.DatasetConfig],
checkpoint_cfg: utils.CheckpointConfig,
partitioner: partitioning.BasePartitioner,
trainer_cls: trainer_lib.BaseTrainerConstructor,
model_dir: str,
total_steps: int,
eval_steps: int,
eval_period: int,
stats_period: Optional[int] = None,
random_seed: Optional[int],
use_hardware_rng: bool = False,
summarize_config_fn: Callable[[str, metric_writers.MetricWriter, int], None],
inference_evaluator_cls: utils.EvaluatorConstructor = seqio.Evaluator,
get_dataset_fn: utils.GetDatasetCallable = utils.get_dataset,
concurrent_metrics: bool = True,
actions: Optional[Mapping[str, Sequence[trainer_lib.BaseAction]]] = None,
train_eval_get_dataset_fn: utils.GetEvalDatasetCallable = utils
.get_training_eval_datasets,
run_eval_before_training: bool = False,
use_wandb = True, weight_metrics="norm",
packing_strategy: PackingStrategy = None,
train_state_initializer_cls: Type[
utils.TrainStateInitializer] = utils.TrainStateInitializer,
use_gda: bool = True,
verify_matching_vocabs_fn: Optional[
Callable[[utils.DatasetConfig, models.BaseTransformerModel],
None]] = utils.verify_matching_vocabs,
shuffle_buffer_size=None,
cycle_length=None,
block_length=None
) -> Tuple[int, train_state_lib.TrainState]:
"""Train function.
Args:
model: The model object to use for training.
train_dataset_cfg: Specification for the dataset to train with.
train_eval_dataset_cfg: Specification for the dataset to evaluate with using
the train metrics and no inference (e.g., uses teacher forcing). If None,
train eval is disabled.
infer_eval_dataset_cfg: Specification for the dataset to evaluate with using
the inference metrics (e.g., uses sampled decoding). If None, inference
eval is disabled.
checkpoint_cfg: Specification for saving and restoring model parameters and
dataset state to/from checkpoints.
partitioner: Partitioner for model parameters and data across devices.
trainer_cls: An implementation of BaseTrainer.
model_dir: Path of directory to store checkpoints and metric summaries.
total_steps: The step number to stop training after. The number of actual
steps trained in this run will be this number minus the starting step from
the checkpoint. If this is set to the starting step from the checkpoint,
the model will not be compiled for training and training will not be run.
This can be used in conjunction with `run_eval_before_training` to only
evaluate a model.
eval_steps: The number of batches to process for each train-eval loop.
eval_period: The number of train steps between each evaluation (both
train-eval and infer-eval).
stats_period: The number of train steps between writing scalar stats. If
None, defaults to eval_period.
random_seed: A random seed to use for dropout and initialization. If None, a
fast, non-deterministic hardware-based RNG is used.
use_hardware_rng: Whether to force using the RngBitGenerator based hardware
rng, which takes seeds and acts similarly to software PRNG in that it
should be seed-deterministic. The new RngBitGenerator custom PRNG system
should be reproducible for a given sharding, but the numbers will change
for different shardings of the same model.
summarize_config_fn: A function that takes in the model directory, a
SummaryWriter, and the step number, and writes a summary of the
inference_evaluator_cls: seqio.Evaluator class to use for inference
evaluation, potentially with bound configuration args.
get_dataset_fn: The callable use to get the train and train-eval datasets
based on the DatasetConfig and shard information.
concurrent_metrics: If True, allow metrics computation and logging to
overlap with training. Will likely result in additional TPU memory usage.
actions: A mapping of actions that runs after train, eval or infer_eval, to
inspect the model and perform useful operations, e.g., early stopping. The
key must have a 1:1 mapping to ActionMode enum. For EVAL actions to
actually work, this requires `concurrent_metrics` to be turned off, since
chaining futures and mutating states concurrently might be error-prone.
train_eval_get_dataset_fn: Optional callable use to get the train-eval
datasets based on the DatasetConfig and shard information. If missing, it
defaults to `utils.get_training_eval_datasets`.
run_eval_before_training: If True, calculate training eval and inference
eval metrics before training begins.
train_state_initializer_cls: t5x.utils.TrainStateInitializer class for
initializing partitioned TrainState from checkpoints or scratch.
use_gda: if True, uses GlobalDeviceArray. Experimental feature.
verify_matching_vocabs_fn: Function to validate whether the task vocabulary
matches the model vocabulary. Should raise an exception on error.
Returns:
The tuple of (last_step, last_train_state).
"""
if jax.process_index() == 0 and use_wandb:
if not os.environ.get("WANDB_API_KEY"):
use_wandb = False
logging.warning("WANDB_API_KEY not found, wandb will not be used")
else: | init_wandb() | 9 | 2023-12-12 20:23:33+00:00 | 24k |
alibaba/animate-anything | train.py | [
{
"identifier": "VideoJsonDataset",
"path": "utils/dataset.py",
"snippet": "class VideoJsonDataset(Dataset):\n def __init__(\n self,\n tokenizer=None,\n width: int = 256,\n height: int = 256,\n n_sample_frames: int = 16,\n fps: int = 8,\n video_dir: st... | import argparse
import datetime
import logging
import inspect
import math
import os
import json
import gc
import copy
import random
import cv2
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision.transforms as T
import diffusers
import transformers
import numpy as np
import imageio
import itertools
import bitsandbytes as bnb
from typing import Dict, Optional, Tuple
from omegaconf import OmegaConf
from tqdm.auto import tqdm
from PIL import Image
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers.models import AutoencoderKL
from diffusers import DPMSolverMultistepScheduler, DDPMScheduler
from diffusers.image_processor import VaeImageProcessor
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, export_to_video
from diffusers.utils.import_utils import is_xformers_available
from diffusers.models.attention_processor import AttnProcessor2_0, Attention
from diffusers.models.attention import BasicTransformerBlock
from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth import tensor2vid
from transformers import CLIPTextModel, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPEncoder
from utils.dataset import VideoJsonDataset, SingleVideoDataset, \
ImageDataset, VideoFolderDataset, CachedDataset, VideoBLIPDataset
from einops import rearrange, repeat
from models.unet_3d_condition_mask import UNet3DConditionModel
from models.pipeline import LatentToVideoPipeline
from utils.lora_handler import LoraHandler, LORA_VERSIONS
from utils.common import read_mask, generate_random_mask, slerp, calculate_motion_score, \
read_video, calculate_motion_precision, calculate_latent_motion_score, \
DDPM_forward, DDPM_forward_timesteps, DDPM_forward_mask, motion_mask_loss, \
generate_center_mask, tensor_to_vae_latent
from xformers.ops import MemoryEfficientAttentionFlashAttentionOp | 17,639 |
already_printed_trainables = False
logger = get_logger(__name__, log_level="INFO")
def create_logging(logging, logger, accelerator):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
def accelerate_set_verbose(accelerator):
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
def get_train_dataset(dataset_types, train_data, tokenizer):
train_datasets = []
|
already_printed_trainables = False
logger = get_logger(__name__, log_level="INFO")
def create_logging(logging, logger, accelerator):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
def accelerate_set_verbose(accelerator):
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
def get_train_dataset(dataset_types, train_data, tokenizer):
train_datasets = [] | dataset_cls = [VideoJsonDataset, SingleVideoDataset, ImageDataset, VideoFolderDataset, VideoBLIPDataset] | 5 | 2023-12-07 08:26:29+00:00 | 24k |
allenai/Holodeck | modules/object_selector.py | [
{
"identifier": "DFS_Solver_Floor",
"path": "modules/floor_objects.py",
"snippet": "class DFS_Solver_Floor():\n def __init__(self, grid_size, random_seed=0, max_duration=5, constraint_bouns=0.2):\n self.grid_size = grid_size\n self.random_seed = random_seed\n self.max_duration = ... | import re
import copy
import json
import torch
import random
import multiprocessing
import torch.nn.functional as F
import modules.prompts as prompts
from typing import Dict
from colorama import Fore
from shapely import Polygon
from langchain import PromptTemplate
from modules.floor_objects import DFS_Solver_Floor
from modules.wall_objects import DFS_Solver_Wall | 16,351 | valid_candidates = []
for candidate in candidates:
dimension = self.database[candidate[0]]["assetMetadata"]["boundingBox"]
size = [dimension["x"], dimension["y"], dimension["z"]]
if size[2] > size[0]: size = [size[2], size[1], size[0]] # make sure that x > z
if size[0] > room_size[0] * self.object_size_tolerance: continue
if size[1] > room_size[1] * self.object_size_tolerance: continue
if size[2] > room_size[2] * self.object_size_tolerance: continue
if size[0] * size[2] > room_size[0] * room_size[2] * 0.5: continue # TODO: consider using the floor area instead of the room area
valid_candidates.append(candidate)
return valid_candidates
def check_thin_object(self, candidates):
valid_candidates = []
for candidate in candidates:
dimension = self.database[candidate[0]]["assetMetadata"]["boundingBox"]
size = [dimension["x"], dimension["y"], dimension["z"]]
if size[2] > min(size[0], size[1]) * self.thin_threshold: continue
valid_candidates.append(candidate)
return valid_candidates
def random_select(self, candidates):
if self.random_selection:
selected_candidate = random.choice(candidates)
else:
scores = [candidate[1] for candidate in candidates]
scores_tensor = torch.Tensor(scores)
probas = F.softmax(scores_tensor, dim=0) # TODO: consider using normalized scores
selected_index = torch.multinomial(probas, 1).item()
selected_candidate = candidates[selected_index]
return selected_candidate
def update_floor_capacity(self, room2floor_capacity, scene):
for room in scene["rooms"]:
room_vertices = room["vertices"]
room_poly = Polygon(room_vertices)
for door in scene["doors"]:
for door_vertices in door["doorBoxes"]:
door_poly = Polygon(door_vertices)
door_center = door_poly.centroid
door_area = door_poly.area
if room_poly.contains(door_center):
room2floor_capacity[room["id"]][1] += door_area * 0.6
if scene["open_walls"] != []:
for open_wall_vertices in scene["open_walls"]["openWallBoxes"]:
open_wall_poly = Polygon(open_wall_vertices)
open_wall_center = open_wall_poly.centroid
if room_poly.contains(open_wall_center):
room2floor_capacity[room["id"]][1] += open_wall_poly.area * 0.6
return room2floor_capacity
def update_wall_capacity(self, room2wall_capacity, scene):
for room in scene["rooms"]:
room_vertices = room["vertices"]
room_poly = Polygon(room_vertices)
for window in scene["windows"]:
for window_vertices in window["windowBoxes"]:
window_poly = Polygon(window_vertices)
window_center = window_poly.centroid
window_x = window_poly.bounds[2] - window_poly.bounds[0]
window_y = window_poly.bounds[3] - window_poly.bounds[1]
window_width = max(window_x, window_y)
if room_poly.contains(window_center):
room2wall_capacity[room["id"]][1] += window_width * 0.6
if scene["open_walls"] != []:
for open_wall_vertices in scene["open_walls"]["openWallBoxes"]:
open_wall_poly = Polygon(open_wall_vertices)
open_wall_center = open_wall_poly.centroid
open_wall_x = open_wall_poly.bounds[2] - open_wall_poly.bounds[0]
open_wall_y = open_wall_poly.bounds[3] - open_wall_poly.bounds[1]
open_wall_width = max(open_wall_x, open_wall_y)
if room_poly.contains(open_wall_center):
room2wall_capacity[room["id"]][1] += open_wall_width * 0.6
return room2wall_capacity
def check_floor_placement(self, candidates, room_vertices, scene):
room_x = max([vertex[0] for vertex in room_vertices]) - min([vertex[0] for vertex in room_vertices])
room_z = max([vertex[1] for vertex in room_vertices]) - min([vertex[1] for vertex in room_vertices])
grid_size = int(max(room_x // 20, room_z // 20))
solver = DFS_Solver_Floor(grid_size=grid_size)
room_poly = Polygon(room_vertices)
initial_state = self.get_initial_state_floor(room_vertices, scene, add_window=False)
grid_points = solver.create_grids(room_poly)
grid_points = solver.remove_points(grid_points, initial_state)
valid_candidates = []
for candidate in candidates:
object_size = self.database[candidate[0]]["assetMetadata"]["boundingBox"]
object_dim = (object_size["x"]*100 + self.size_buffer, object_size["z"]*100 + self.size_buffer)
solutions = solver.get_all_solutions(room_poly, grid_points, object_dim)
solutions = solver.filter_collision(initial_state, solutions)
solutions = solver.place_edge(room_poly, solutions, object_dim)
if solutions != []: valid_candidates.append(candidate)
else: print(f"Floor Object {candidate[0]} (size: {object_dim}) cannot be placed in room"); continue
return valid_candidates
def check_wall_placement(self, candidates, room_vertices, scene):
room_x = max([vertex[0] for vertex in room_vertices]) - min([vertex[0] for vertex in room_vertices])
room_z = max([vertex[1] for vertex in room_vertices]) - min([vertex[1] for vertex in room_vertices])
grid_size = int(max(room_x // 20, room_z // 20))
|
class ObjectSelector:
def __init__(self, object_retriever, llm):
# object retriever
self.object_retriever = object_retriever
self.database = object_retriever.database
# language model and prompt templates
self.llm = llm
self.object_selection_template_1 = prompts.object_selection_prompt_new_1
self.object_selection_template_2 = PromptTemplate(input_variables=["object_selection_prompt_new_1", "object_selection_1", "room"], template=prompts.object_selection_prompt_new_2)
# hyperparameters
self.floor_capacity_ratio = 0.4
self.wall_capacity_ratio = 0.5
self.object_size_tolerance = 0.8
self.similarity_threshold_floor = 31 # need to be tuned
self.similarity_threshold_wall = 31 # need to be tuned
self.thin_threshold = 3
self.used_assets = []
self.consider_size = True
self.size_buffer = 10
self.random_selection = False
self.reuse_selection = False
self.multiprocessing = True
def select_objects(self, scene, additional_requirements="N/A"):
rooms_types = [room["roomType"] for room in scene["rooms"]]
room2area = {room["roomType"]: self.get_room_area(room) for room in scene["rooms"]}
room2size = {room["roomType"]: self.get_room_size(room, scene["wall_height"]) for room in scene["rooms"]}
room2perimeter = {room["roomType"]: self.get_room_perimeter(room) for room in scene["rooms"]}
room2vertices = {room["roomType"]: [(x * 100, y * 100) for (x, y) in room["vertices"]] for room in scene["rooms"]}
room2floor_capacity = {room_type: [room_area * self.floor_capacity_ratio, 0] for room_type, room_area in room2area.items()}
room2floor_capacity = self.update_floor_capacity(room2floor_capacity, scene)
room2wall_capacity = {room_type: [room_perimeter * self.wall_capacity_ratio, 0] for room_type, room_perimeter in room2perimeter.items()}
selected_objects = {room["roomType"]: {"floor": [], "wall": []} for room in scene["rooms"]}
if "object_selection_plan" in scene:
object_selection_plan = scene["object_selection_plan"]
if self.reuse_selection:
selected_objects = scene["selected_objects"]
else:
for room_type in rooms_types:
floor_objects, _, wall_objects, _ = self.get_objects_by_room(object_selection_plan[room_type], scene, room2size[room_type], room2floor_capacity[room_type], room2wall_capacity[room_type], room2vertices[room_type])
selected_objects[room_type]["floor"] = floor_objects
selected_objects[room_type]["wall"] = wall_objects
else:
object_selection_plan = {room["roomType"]: [] for room in scene["rooms"]}
packed_args = [(room_type, scene, additional_requirements, room2size, room2floor_capacity, room2wall_capacity, room2vertices) for room_type in rooms_types]
if self.multiprocessing:
pool = multiprocessing.Pool(processes=4)
results = pool.map(self.plan_room, packed_args)
pool.close()
pool.join()
else:
results = [self.plan_room(args) for args in packed_args]
for room_type, result in results:
selected_objects[room_type]["floor"] = result["floor"]
selected_objects[room_type]["wall"] = result["wall"]
object_selection_plan[room_type] = result["plan"]
print(f"\n{Fore.GREEN}AI: Here is the object selection plan:\n{object_selection_plan}{Fore.RESET}")
return object_selection_plan, selected_objects
def plan_room(self, args):
room_type, scene, additional_requirements, room2size, room2floor_capacity, room2wall_capacity, room2vertices = args
print(f"\n{Fore.GREEN}AI: Selecting objects for {room_type}...{Fore.RESET}\n")
result = {}
room_size_str = f"{int(room2size[room_type][0])*100}cm in length, {int(room2size[room_type][1])*100}cm in width, {int(room2size[room_type][2])*100}cm in height"
prompt_1 = self.object_selection_template_1.replace("INPUT", scene["query"]).replace("ROOM_TYPE", room_type).replace("ROOM_SIZE", room_size_str).replace("REQUIREMENTS", additional_requirements)
# print(f"\nUser: {prompt_1}\n")
output_1 = self.llm(prompt_1).lower()
plan_1 = self.extract_json(output_1)
if plan_1 is None:
print(f"Error while extracting the JSON for {room_type}.")
return result
floor_objects, floor_capacity, wall_objects, wall_capacity = self.get_objects_by_room(plan_1, scene, room2size[room_type], room2floor_capacity[room_type], room2wall_capacity[room_type], room2vertices[room_type])
if floor_capacity[1] / floor_capacity[0] >= 0.8:
result["floor"] = floor_objects
result["wall"] = wall_objects
result["plan"] = plan_1
else:
print(f"{Fore.RED}AI: The floor capacity of {room_type} is {floor_capacity[1]}m^2, which is less than 70% of the total floor capacity {floor_capacity[0]}m^2.{Fore.RESET}")
prompt_2 = self.object_selection_template_2.format(object_selection_prompt_new_1=prompt_1, object_selection_1=output_1, room=room_type)
output_2 = self.llm(prompt_2).lower()
plan_2 = self.extract_json(output_2)
new_plan = copy.deepcopy(plan_1)
for object in plan_2: new_plan[object] = plan_2[object]
floor_objects, _, wall_objects, _ = self.get_objects_by_room(new_plan, scene, room2size[room_type], room2floor_capacity[room_type], room2wall_capacity[room_type], room2vertices[room_type])
result["floor"] = floor_objects
result["wall"] = wall_objects
result["plan"] = new_plan
return room_type, result
def extract_json(self, input_string):
# Using regex to identify the JSON structure in the string
json_match = re.search(r'{.*}', input_string, re.DOTALL)
if json_match:
extracted_json = json_match.group(0)
try:
# Convert the extracted JSON string into a Python dictionary
json_dict = json.loads(extracted_json)
json_dict = self.check_dict(json_dict)
return json_dict
except json.JSONDecodeError:
print(input_string)
print("Error while decoding the JSON.")
return None
else:
print("No valid JSON found.")
return None
def check_dict(self, dict):
valid = True
attributes = ["description", "location", "size", "quantity", "variance_type", "objects_on_top"]
for key, value in dict.items():
if not isinstance(key, str): valid = False; break
if not isinstance(value, Dict): valid = False; break
for attribute in attributes:
if attribute not in value: valid = False; break
if not isinstance(value["description"], str): valid = False; break
if value["location"] not in ["floor", "wall"]: dict[key]["location"] = "floor"
if not isinstance(value["size"], list) or len(value["size"]) != 3 or not all(isinstance(i, int) for i in value["size"]): dict[key]["size"] = None
if not isinstance(value["quantity"], int): dict[key]["quantity"] = 1
if not isinstance(value["variance_type"], str) or value["variance_type"] not in ["same", "varied"]: dict[key]["variance_type"] = "same"
if not isinstance(value["objects_on_top"], list): dict[key]["objects_on_top"] = []
for i, child in enumerate(value["objects_on_top"]):
if not isinstance(child, Dict): valid = False; break
for attribute in ["object_name", "quantity", "variance_type"]:
if attribute not in child: valid = False; break
if not isinstance(child["object_name"], str): valid = False; break
if not isinstance(child["quantity"], int): dict[key]["objects_on_top"][i]["quantity"] = 1
if not isinstance(child["variance_type"], str) or child["variance_type"] not in ["same", "varied"]: dict[key]["objects_on_top"][i]["variance_type"] = "same"
if not valid: return None
else: return dict
def get_objects_by_room(self, parsed_plan, scene, room_size, floor_capacity, wall_capacity, vertices):
# get the floor and wall objects
floor_object_list = []
wall_object_list = []
for object_name, object_info in parsed_plan.items():
object_info["object_name"] = object_name
if object_info["location"] == "floor": floor_object_list.append(object_info)
else: wall_object_list.append(object_info)
floor_objects, floor_capacity = self.get_floor_objects(floor_object_list, floor_capacity, room_size, vertices, scene)
wall_objects, wall_capacity = self.get_wall_objects(wall_object_list, wall_capacity, room_size, vertices, scene)
return floor_objects, floor_capacity, wall_objects, wall_capacity
def get_room_size(self, room, wall_height):
floor_polygon = room["floorPolygon"]
x_values = [point['x'] for point in floor_polygon]
z_values = [point['z'] for point in floor_polygon]
x_dim = max(x_values) - min(x_values)
z_dim = max(z_values) - min(z_values)
if x_dim > z_dim: return (x_dim, wall_height, z_dim)
else: return (z_dim, wall_height, x_dim)
def get_room_area(self, room):
room_vertices = room["vertices"]
room_polygon = Polygon(room_vertices)
return room_polygon.area
def get_room_perimeter(self, room):
room_vertices = room["vertices"]
room_polygon = Polygon(room_vertices)
return room_polygon.length
def get_floor_objects(self, floor_object_list, floor_capacity, room_size, room_vertices, scene):
selected_floor_objects_all = []
for floor_object in floor_object_list:
object_type = floor_object["object_name"]
object_description = floor_object["description"]
object_size = floor_object["size"]
quantity = min(floor_object["quantity"], 10)
variance_type = floor_object["variance_type"]
candidates = self.object_retriever.retrieve([f"a 3D model of {object_type}, {object_description}"], self.similarity_threshold_floor)
# check on floor objects
candidates = [candidate for candidate in candidates if self.database[candidate[0]]["annotations"]["onFloor"] == True] # only select objects on the floor
candidates = [candidate for candidate in candidates if self.database[candidate[0]]["annotations"]["onCeiling"] == False] # only select objects not on the ceiling
# ignore doors and windows and frames
candidates = [candidate for candidate in candidates if "door" not in self.database[candidate[0]]["annotations"]["category"].lower()]
candidates = [candidate for candidate in candidates if "window" not in self.database[candidate[0]]["annotations"]["category"].lower()]
candidates = [candidate for candidate in candidates if "frame" not in self.database[candidate[0]]["annotations"]["category"].lower()]
# check if the object is too big
candidates = self.check_object_size(candidates, room_size)
# check if object can be placed on the floor
candidates = self.check_floor_placement(candidates[:20], room_vertices, scene)
# No candidates found
if len(candidates) == 0: print("No candidates found for {} {}".format(object_type, object_description)); continue
# remove used assets
top_one_candidate = candidates[0]
if len(candidates) > 1: candidates = [candidate for candidate in candidates if candidate[0] not in self.used_assets]
if len(candidates) == 0: candidates = [top_one_candidate]
# consider object size difference
if object_size is not None and self.consider_size:
candidates = self.object_retriever.compute_size_difference(object_size, candidates)
candidates = candidates[:10] # only select top 10 candidates
selected_asset_ids = []
if variance_type == "same":
selected_candidate = self.random_select(candidates)
selected_asset_id = selected_candidate[0]
selected_asset_ids = [selected_asset_id] * quantity
elif variance_type == "varied":
for i in range(quantity):
selected_candidate = self.random_select(candidates)
selected_asset_id = selected_candidate[0]
selected_asset_ids.append(selected_asset_id)
if len(candidates) > 1: candidates.remove(selected_candidate)
for i in range(quantity):
selected_asset_id = selected_asset_ids[i]
object_name = f"{object_type}-{i}"
selected_floor_objects_all.append((object_name, selected_asset_id))
# reselect objects if they exceed floor capacity, consider the diversity of objects
selected_floor_objects = []
while True:
if len(selected_floor_objects_all) == 0: break
current_selected_asset_ids = []
current_number_of_objects = len(selected_floor_objects)
for object_name, selected_asset_id in selected_floor_objects_all:
if selected_asset_id not in current_selected_asset_ids:
selected_asset_size = self.database[selected_asset_id]["assetMetadata"]["boundingBox"]
selected_asset_capacity = selected_asset_size["x"] * selected_asset_size["z"]
if floor_capacity[1] + selected_asset_capacity > floor_capacity[0] and len(selected_floor_objects) > 0:
print(f"{object_type} {object_description} exceeds floor capacity")
else:
current_selected_asset_ids.append(selected_asset_id)
selected_floor_objects.append((object_name, selected_asset_id))
selected_floor_objects_all.remove((object_name, selected_asset_id))
floor_capacity = (floor_capacity[0], floor_capacity[1] + selected_asset_capacity)
if len(selected_floor_objects) == current_number_of_objects: print("No more objects can be added"); break
# sort objects by object type
object_type2objects = {}
for object_name, selected_asset_id in selected_floor_objects:
object_type = object_name.split("-")[0]
if object_type not in object_type2objects: object_type2objects[object_type] = []
object_type2objects[object_type].append((object_name, selected_asset_id))
selected_floor_objects_ordered = []
for object_type in object_type2objects:
selected_floor_objects_ordered += sorted(object_type2objects[object_type])
return selected_floor_objects_ordered, floor_capacity
def get_wall_objects(self, wall_object_list, wall_capacity, room_size, room_vertices, scene):
selected_wall_objects_all = []
for wall_object in wall_object_list:
object_type = wall_object["object_name"]
object_description = wall_object["description"]
object_size = wall_object["size"]
quantity = min(wall_object["quantity"], 10)
variance_type = wall_object["variance_type"]
candidates = self.object_retriever.retrieve([f"a 3D model of {object_type}, {object_description}"], self.similarity_threshold_wall)
# check on wall objects
candidates = [candidate for candidate in candidates if self.database[candidate[0]]["annotations"]["onWall"] == True] # only select objects on the wall
# ignore doors and windows
candidates = [candidate for candidate in candidates if "door" not in self.database[candidate[0]]["annotations"]["category"].lower()]
candidates = [candidate for candidate in candidates if "window" not in self.database[candidate[0]]["annotations"]["category"].lower()]
# check if the object is too big
candidates = self.check_object_size(candidates, room_size)
# check thin objects
candidates = self.check_thin_object(candidates)
# check if object can be placed on the wall
candidates = self.check_wall_placement(candidates[:20], room_vertices, scene)
if len(candidates) == 0: print("No candidates found for {} {}".format(object_type, object_description)); continue
# remove used assets
top_one_candidate = candidates[0]
if len(candidates) > 1: candidates = [candidate for candidate in candidates if candidate[0] not in self.used_assets]
if len(candidates) == 0: candidates = [top_one_candidate]
# consider object size difference
if object_size is not None and self.consider_size:
candidates = self.object_retriever.compute_size_difference(object_size, candidates)
candidates = candidates[:10] # only select top 10 candidates
selected_asset_ids = []
if variance_type == "same":
selected_candidate = self.random_select(candidates)
selected_asset_id = selected_candidate[0]
selected_asset_ids = [selected_asset_id] * quantity
elif variance_type == "varied":
for i in range(quantity):
selected_candidate = self.random_select(candidates)
selected_asset_id = selected_candidate[0]
selected_asset_ids.append(selected_asset_id)
if len(candidates) > 1: candidates.remove(selected_candidate)
for i in range(quantity):
selected_asset_id = selected_asset_ids[i]
object_name = f"{object_type}-{i}"
selected_wall_objects_all.append((object_name, selected_asset_id))
# reselect objects if they exceed wall capacity, consider the diversity of objects
selected_wall_objects = []
while True:
if len(selected_wall_objects_all) == 0: break
current_selected_asset_ids = []
current_number_of_objects = len(selected_wall_objects)
for object_name, selected_asset_id in selected_wall_objects_all:
if selected_asset_id not in current_selected_asset_ids:
selected_asset_size = self.database[selected_asset_id]["assetMetadata"]["boundingBox"]
selected_asset_capacity = selected_asset_size["x"]
if wall_capacity[1] + selected_asset_capacity > wall_capacity[0] and len(selected_wall_objects) > 0:
print(f"{object_type} {object_description} exceeds wall capacity")
else:
current_selected_asset_ids.append(selected_asset_id)
selected_wall_objects.append((object_name, selected_asset_id))
selected_wall_objects_all.remove((object_name, selected_asset_id))
wall_capacity = (wall_capacity[0], wall_capacity[1] + selected_asset_capacity)
if len(selected_wall_objects) == current_number_of_objects: print("No more objects can be added"); break
# sort objects by object type
object_type2objects = {}
for object_name, selected_asset_id in selected_wall_objects:
object_type = object_name.split("-")[0]
if object_type not in object_type2objects: object_type2objects[object_type] = []
object_type2objects[object_type].append((object_name, selected_asset_id))
selected_wall_objects_ordered = []
for object_type in object_type2objects:
selected_wall_objects_ordered += sorted(object_type2objects[object_type])
return selected_wall_objects_ordered, wall_capacity
def check_object_size(self, candidates, room_size):
valid_candidates = []
for candidate in candidates:
dimension = self.database[candidate[0]]["assetMetadata"]["boundingBox"]
size = [dimension["x"], dimension["y"], dimension["z"]]
if size[2] > size[0]: size = [size[2], size[1], size[0]] # make sure that x > z
if size[0] > room_size[0] * self.object_size_tolerance: continue
if size[1] > room_size[1] * self.object_size_tolerance: continue
if size[2] > room_size[2] * self.object_size_tolerance: continue
if size[0] * size[2] > room_size[0] * room_size[2] * 0.5: continue # TODO: consider using the floor area instead of the room area
valid_candidates.append(candidate)
return valid_candidates
def check_thin_object(self, candidates):
valid_candidates = []
for candidate in candidates:
dimension = self.database[candidate[0]]["assetMetadata"]["boundingBox"]
size = [dimension["x"], dimension["y"], dimension["z"]]
if size[2] > min(size[0], size[1]) * self.thin_threshold: continue
valid_candidates.append(candidate)
return valid_candidates
def random_select(self, candidates):
if self.random_selection:
selected_candidate = random.choice(candidates)
else:
scores = [candidate[1] for candidate in candidates]
scores_tensor = torch.Tensor(scores)
probas = F.softmax(scores_tensor, dim=0) # TODO: consider using normalized scores
selected_index = torch.multinomial(probas, 1).item()
selected_candidate = candidates[selected_index]
return selected_candidate
def update_floor_capacity(self, room2floor_capacity, scene):
for room in scene["rooms"]:
room_vertices = room["vertices"]
room_poly = Polygon(room_vertices)
for door in scene["doors"]:
for door_vertices in door["doorBoxes"]:
door_poly = Polygon(door_vertices)
door_center = door_poly.centroid
door_area = door_poly.area
if room_poly.contains(door_center):
room2floor_capacity[room["id"]][1] += door_area * 0.6
if scene["open_walls"] != []:
for open_wall_vertices in scene["open_walls"]["openWallBoxes"]:
open_wall_poly = Polygon(open_wall_vertices)
open_wall_center = open_wall_poly.centroid
if room_poly.contains(open_wall_center):
room2floor_capacity[room["id"]][1] += open_wall_poly.area * 0.6
return room2floor_capacity
def update_wall_capacity(self, room2wall_capacity, scene):
for room in scene["rooms"]:
room_vertices = room["vertices"]
room_poly = Polygon(room_vertices)
for window in scene["windows"]:
for window_vertices in window["windowBoxes"]:
window_poly = Polygon(window_vertices)
window_center = window_poly.centroid
window_x = window_poly.bounds[2] - window_poly.bounds[0]
window_y = window_poly.bounds[3] - window_poly.bounds[1]
window_width = max(window_x, window_y)
if room_poly.contains(window_center):
room2wall_capacity[room["id"]][1] += window_width * 0.6
if scene["open_walls"] != []:
for open_wall_vertices in scene["open_walls"]["openWallBoxes"]:
open_wall_poly = Polygon(open_wall_vertices)
open_wall_center = open_wall_poly.centroid
open_wall_x = open_wall_poly.bounds[2] - open_wall_poly.bounds[0]
open_wall_y = open_wall_poly.bounds[3] - open_wall_poly.bounds[1]
open_wall_width = max(open_wall_x, open_wall_y)
if room_poly.contains(open_wall_center):
room2wall_capacity[room["id"]][1] += open_wall_width * 0.6
return room2wall_capacity
def check_floor_placement(self, candidates, room_vertices, scene):
room_x = max([vertex[0] for vertex in room_vertices]) - min([vertex[0] for vertex in room_vertices])
room_z = max([vertex[1] for vertex in room_vertices]) - min([vertex[1] for vertex in room_vertices])
grid_size = int(max(room_x // 20, room_z // 20))
solver = DFS_Solver_Floor(grid_size=grid_size)
room_poly = Polygon(room_vertices)
initial_state = self.get_initial_state_floor(room_vertices, scene, add_window=False)
grid_points = solver.create_grids(room_poly)
grid_points = solver.remove_points(grid_points, initial_state)
valid_candidates = []
for candidate in candidates:
object_size = self.database[candidate[0]]["assetMetadata"]["boundingBox"]
object_dim = (object_size["x"]*100 + self.size_buffer, object_size["z"]*100 + self.size_buffer)
solutions = solver.get_all_solutions(room_poly, grid_points, object_dim)
solutions = solver.filter_collision(initial_state, solutions)
solutions = solver.place_edge(room_poly, solutions, object_dim)
if solutions != []: valid_candidates.append(candidate)
else: print(f"Floor Object {candidate[0]} (size: {object_dim}) cannot be placed in room"); continue
return valid_candidates
def check_wall_placement(self, candidates, room_vertices, scene):
room_x = max([vertex[0] for vertex in room_vertices]) - min([vertex[0] for vertex in room_vertices])
room_z = max([vertex[1] for vertex in room_vertices]) - min([vertex[1] for vertex in room_vertices])
grid_size = int(max(room_x // 20, room_z // 20))
| solver = DFS_Solver_Wall(grid_size=grid_size) | 1 | 2023-12-08 19:19:57+00:00 | 24k |
modelscope/richdreamer | threestudio/models/geometry/tetrahedra_sdf_grid.py | [
{
"identifier": "BaseExplicitGeometry",
"path": "threestudio/models/geometry/base.py",
"snippet": "class BaseExplicitGeometry(BaseGeometry):\n @dataclass\n class Config(BaseGeometry.Config):\n radius: float = 1.0\n\n cfg: Config\n\n def configure(self) -> None:\n self.bbox: Flo... | import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
import trimesh
from dataclasses import dataclass, field
from threestudio.models.geometry.base import (BaseExplicitGeometry,
BaseGeometry,
contract_to_unisphere,)
from threestudio.models.geometry.implicit_sdf import ImplicitSDF
from threestudio.models.geometry.implicit_volume import ImplicitVolume
from threestudio.models.isosurface import MarchingTetrahedraHelper
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.misc import broadcast, get_rank
from threestudio.utils.ops import scale_tensor
from threestudio.utils.typing import *
from pysdf import SDF | 15,743 |
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh = other.isosurface()
# instance.isosurface_bbox = mesh.extras["bbox"]
instance.isosurface_bbox = mesh.extras["bbox"] * instance.cfg.nerf_scale
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
|
@threestudio.register("tetrahedra-sdf-grid")
class TetrahedraSDFGrid(BaseExplicitGeometry):
@dataclass
class Config(BaseExplicitGeometry.Config):
isosurface_resolution: int = 128
isosurface_deformable_grid: bool = True
isosurface_remove_outliers: bool = False
isosurface_outlier_n_faces_threshold: Union[int, float] = 0.01
nerf_scale: float = 1.0
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
shape_init_mesh_up: str = "+z"
shape_init_mesh_front: str = "+x"
force_shape_init: bool = False
geometry_only: bool = False
fix_geometry: bool = False
# sdf_bias: Union[float, str] = 0.0
# sdf_bias_params: Optional[Any] = None
cfg: Config
def configure(self) -> None:
super().configure()
# this should be saved to state_dict, register as buffer
self.isosurface_bbox: Float[Tensor, "2 3"]
self.register_buffer("isosurface_bbox", self.bbox.clone())
self.isosurface_helper = MarchingTetrahedraHelper(
self.cfg.isosurface_resolution,
f"load/tets/{self.cfg.isosurface_resolution}_tets.npz",
)
self.sdf: Float[Tensor, "Nv 1"]
self.deformation: Optional[Float[Tensor, "Nv 3"]]
if not self.cfg.fix_geometry:
self.register_parameter(
"sdf",
nn.Parameter(
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
)
),
)
if self.cfg.isosurface_deformable_grid:
self.register_parameter(
"deformation",
nn.Parameter(
torch.zeros_like(self.isosurface_helper.grid_vertices)
),
)
else:
self.deformation = None
else:
self.register_buffer(
"sdf",
torch.zeros(
(self.isosurface_helper.grid_vertices.shape[0], 1),
dtype=torch.float32,
),
)
if self.cfg.isosurface_deformable_grid:
self.register_buffer(
"deformation",
torch.zeros_like(self.isosurface_helper.grid_vertices),
)
else:
self.deformation = None
if not self.cfg.geometry_only:
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
self.mesh: Optional[Mesh] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
mesh = trimesh.load(mesh_path)
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
sdf_gt = get_gt_sdf(
scale_tensor(
self.isosurface_helper.grid_vertices,
self.isosurface_helper.points_range,
self.isosurface_bbox,
)
)
self.sdf.data = sdf_gt
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def isosurface(self) -> Mesh:
# return cached mesh if fix_geometry is True to save computation
if self.cfg.fix_geometry and self.mesh is not None:
return self.mesh
mesh = self.isosurface_helper(self.sdf, self.deformation)
mesh.v_pos = scale_tensor(
mesh.v_pos, self.isosurface_helper.points_range, self.isosurface_bbox
)
if self.cfg.isosurface_remove_outliers:
mesh = mesh.remove_outlier(self.cfg.isosurface_outlier_n_faces_threshold)
self.mesh = mesh
return mesh
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
if self.cfg.geometry_only:
return {}
assert (
output_normal == False
), f"Normal output is not supported for {self.__class__.__name__}"
points_unscaled = points # points in the original scale
points = contract_to_unisphere(points, self.bbox) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
return {"features": features}
@staticmethod
@torch.no_grad()
def create_from(
other: BaseGeometry,
cfg: Optional[Union[dict, DictConfig]] = None,
copy_net: bool = True,
**kwargs,
) -> "TetrahedraSDFGrid":
if isinstance(other, TetrahedraSDFGrid):
instance = TetrahedraSDFGrid(cfg, **kwargs)
assert instance.cfg.isosurface_resolution == other.cfg.isosurface_resolution
instance.isosurface_bbox = other.isosurface_bbox.clone()
instance.sdf.data = other.sdf.data.clone()
if (
instance.cfg.isosurface_deformable_grid
and other.cfg.isosurface_deformable_grid
):
assert (
instance.deformation is not None and other.deformation is not None
)
instance.deformation.data = other.deformation.data.clone()
if (
not instance.cfg.geometry_only
and not other.cfg.geometry_only
and copy_net
):
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance
elif isinstance(other, ImplicitVolume):
instance = TetrahedraSDFGrid(cfg, **kwargs)
if other.cfg.isosurface_method != "mt":
other.cfg.isosurface_method = "mt"
threestudio.warn(
f"Override isosurface_method of the source geometry to 'mt'"
)
if other.cfg.isosurface_resolution != instance.cfg.isosurface_resolution:
other.cfg.isosurface_resolution = instance.cfg.isosurface_resolution
threestudio.warn(
f"Override isosurface_resolution of the source geometry to {instance.cfg.isosurface_resolution}"
)
mesh = other.isosurface()
# instance.isosurface_bbox = mesh.extras["bbox"]
instance.isosurface_bbox = mesh.extras["bbox"] * instance.cfg.nerf_scale
instance.sdf.data = (
mesh.extras["grid_level"].to(instance.sdf.data).clamp(-1, 1)
)
if not instance.cfg.geometry_only and copy_net:
instance.encoding.load_state_dict(other.encoding.state_dict())
instance.feature_network.load_state_dict(
other.feature_network.state_dict()
)
return instance | elif isinstance(other, ImplicitSDF): | 3 | 2023-12-06 07:53:11+00:00 | 24k |
rehg-lab/RAVE | annotator/oneformer/detectron2/modeling/meta_arch/retinanet.py | [
{
"identifier": "configurable",
"path": "annotator/oneformer/detectron2/config/config.py",
"snippet": "def configurable(init_func=None, *, from_config=None):\r\n \"\"\"\r\n Decorate a function or a class's __init__ method so that it can be called\r\n with a :class:`CfgNode` object using a :func... | import logging
import math
import torch
from typing import List, Tuple
from fvcore.nn import sigmoid_focal_loss_jit
from torch import Tensor, nn
from torch.nn import functional as F
from annotator.oneformer.detectron2.config import configurable
from annotator.oneformer.detectron2.layers import CycleBatchNormList, ShapeSpec, batched_nms, cat, get_norm
from annotator.oneformer.detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
from annotator.oneformer.detectron2.utils.events import get_event_storage
from ..anchor_generator import build_anchor_generator
from ..backbone import Backbone, build_backbone
from ..box_regression import Box2BoxTransform, _dense_box_regression_loss
from ..matcher import Matcher
from .build import META_ARCH_REGISTRY
from .dense_detector import DenseDetector, permute_to_N_HWA_K # noqa
| 16,849 | # Vis parameters
self.vis_period = vis_period
self.input_format = input_format
@classmethod
def from_config(cls, cfg):
backbone = build_backbone(cfg)
backbone_shape = backbone.output_shape()
feature_shapes = [backbone_shape[f] for f in cfg.MODEL.RETINANET.IN_FEATURES]
head = RetinaNetHead(cfg, feature_shapes)
anchor_generator = build_anchor_generator(cfg, feature_shapes)
return {
"backbone": backbone,
"head": head,
"anchor_generator": anchor_generator,
"box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RETINANET.BBOX_REG_WEIGHTS),
"anchor_matcher": Matcher(
cfg.MODEL.RETINANET.IOU_THRESHOLDS,
cfg.MODEL.RETINANET.IOU_LABELS,
allow_low_quality_matches=True,
),
"pixel_mean": cfg.MODEL.PIXEL_MEAN,
"pixel_std": cfg.MODEL.PIXEL_STD,
"num_classes": cfg.MODEL.RETINANET.NUM_CLASSES,
"head_in_features": cfg.MODEL.RETINANET.IN_FEATURES,
# Loss parameters:
"focal_loss_alpha": cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA,
"focal_loss_gamma": cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA,
"smooth_l1_beta": cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA,
"box_reg_loss_type": cfg.MODEL.RETINANET.BBOX_REG_LOSS_TYPE,
# Inference parameters:
"test_score_thresh": cfg.MODEL.RETINANET.SCORE_THRESH_TEST,
"test_topk_candidates": cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST,
"test_nms_thresh": cfg.MODEL.RETINANET.NMS_THRESH_TEST,
"max_detections_per_image": cfg.TEST.DETECTIONS_PER_IMAGE,
# Vis parameters
"vis_period": cfg.VIS_PERIOD,
"input_format": cfg.INPUT.FORMAT,
}
def forward_training(self, images, features, predictions, gt_instances):
# Transpose the Hi*Wi*A dimension to the middle:
pred_logits, pred_anchor_deltas = self._transpose_dense_predictions(
predictions, [self.num_classes, 4]
)
anchors = self.anchor_generator(features)
gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances)
return self.losses(anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes)
def losses(self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes):
"""
Args:
anchors (list[Boxes]): a list of #feature level Boxes
gt_labels, gt_boxes: see output of :meth:`RetinaNet.label_anchors`.
Their shapes are (N, R) and (N, R, 4), respectively, where R is
the total number of anchors across levels, i.e. sum(Hi x Wi x Ai)
pred_logits, pred_anchor_deltas: both are list[Tensor]. Each element in the
list corresponds to one level and has shape (N, Hi * Wi * Ai, K or 4).
Where K is the number of classes used in `pred_logits`.
Returns:
dict[str, Tensor]:
mapping from a named loss to a scalar tensor storing the loss.
Used during training only. The dict keys are: "loss_cls" and "loss_box_reg"
"""
num_images = len(gt_labels)
gt_labels = torch.stack(gt_labels) # (N, R)
valid_mask = gt_labels >= 0
pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes)
num_pos_anchors = pos_mask.sum().item()
get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images)
normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 100)
# classification and regression loss
gt_labels_target = F.one_hot(gt_labels[valid_mask], num_classes=self.num_classes + 1)[
:, :-1
] # no loss for the last (background) class
loss_cls = sigmoid_focal_loss_jit(
cat(pred_logits, dim=1)[valid_mask],
gt_labels_target.to(pred_logits[0].dtype),
alpha=self.focal_loss_alpha,
gamma=self.focal_loss_gamma,
reduction="sum",
)
loss_box_reg = _dense_box_regression_loss(
anchors,
self.box2box_transform,
pred_anchor_deltas,
gt_boxes,
pos_mask,
box_reg_loss_type=self.box_reg_loss_type,
smooth_l1_beta=self.smooth_l1_beta,
)
return {
"loss_cls": loss_cls / normalizer,
"loss_box_reg": loss_box_reg / normalizer,
}
@torch.no_grad()
def label_anchors(self, anchors, gt_instances):
"""
Args:
anchors (list[Boxes]): A list of #feature level Boxes.
The Boxes contains anchors of this image on the specific feature level.
gt_instances (list[Instances]): a list of N `Instances`s. The i-th
`Instances` contains the ground-truth per-instance annotations
for the i-th input image.
Returns:
list[Tensor]: List of #img tensors. i-th element is a vector of labels whose length is
the total number of anchors across all feature maps (sum(Hi * Wi * A)).
Label values are in {-1, 0, ..., K}, with -1 means ignore, and K means background.
list[Tensor]: i-th element is a Rx4 tensor, where R is the total number of anchors
across feature maps. The values are the matched gt boxes for each anchor.
Values are undefined for those anchors not labeled as foreground.
"""
| # Copyright (c) Facebook, Inc. and its affiliates.
__all__ = ["RetinaNet"]
logger = logging.getLogger(__name__)
@META_ARCH_REGISTRY.register()
class RetinaNet(DenseDetector):
"""
Implement RetinaNet in :paper:`RetinaNet`.
"""
@configurable
def __init__(
self,
*,
backbone: Backbone,
head: nn.Module,
head_in_features,
anchor_generator,
box2box_transform,
anchor_matcher,
num_classes,
focal_loss_alpha=0.25,
focal_loss_gamma=2.0,
smooth_l1_beta=0.0,
box_reg_loss_type="smooth_l1",
test_score_thresh=0.05,
test_topk_candidates=1000,
test_nms_thresh=0.5,
max_detections_per_image=100,
pixel_mean,
pixel_std,
vis_period=0,
input_format="BGR",
):
"""
NOTE: this interface is experimental.
Args:
backbone: a backbone module, must follow detectron2's backbone interface
head (nn.Module): a module that predicts logits and regression deltas
for each level from a list of per-level features
head_in_features (Tuple[str]): Names of the input feature maps to be used in head
anchor_generator (nn.Module): a module that creates anchors from a
list of features. Usually an instance of :class:`AnchorGenerator`
box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to
instance boxes
anchor_matcher (Matcher): label the anchors by matching them with ground truth.
num_classes (int): number of classes. Used to label background proposals.
# Loss parameters:
focal_loss_alpha (float): focal_loss_alpha
focal_loss_gamma (float): focal_loss_gamma
smooth_l1_beta (float): smooth_l1_beta
box_reg_loss_type (str): Options are "smooth_l1", "giou", "diou", "ciou"
# Inference parameters:
test_score_thresh (float): Inference cls score threshold, only anchors with
score > INFERENCE_TH are considered for inference (to improve speed)
test_topk_candidates (int): Select topk candidates before NMS
test_nms_thresh (float): Overlap threshold used for non-maximum suppression
(suppress boxes with IoU >= this threshold)
max_detections_per_image (int):
Maximum number of detections to return per image during inference
(100 is based on the limit established for the COCO dataset).
pixel_mean, pixel_std: see :class:`DenseDetector`.
"""
super().__init__(
backbone, head, head_in_features, pixel_mean=pixel_mean, pixel_std=pixel_std
)
self.num_classes = num_classes
# Anchors
self.anchor_generator = anchor_generator
self.box2box_transform = box2box_transform
self.anchor_matcher = anchor_matcher
# Loss parameters:
self.focal_loss_alpha = focal_loss_alpha
self.focal_loss_gamma = focal_loss_gamma
self.smooth_l1_beta = smooth_l1_beta
self.box_reg_loss_type = box_reg_loss_type
# Inference parameters:
self.test_score_thresh = test_score_thresh
self.test_topk_candidates = test_topk_candidates
self.test_nms_thresh = test_nms_thresh
self.max_detections_per_image = max_detections_per_image
# Vis parameters
self.vis_period = vis_period
self.input_format = input_format
@classmethod
def from_config(cls, cfg):
backbone = build_backbone(cfg)
backbone_shape = backbone.output_shape()
feature_shapes = [backbone_shape[f] for f in cfg.MODEL.RETINANET.IN_FEATURES]
head = RetinaNetHead(cfg, feature_shapes)
anchor_generator = build_anchor_generator(cfg, feature_shapes)
return {
"backbone": backbone,
"head": head,
"anchor_generator": anchor_generator,
"box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RETINANET.BBOX_REG_WEIGHTS),
"anchor_matcher": Matcher(
cfg.MODEL.RETINANET.IOU_THRESHOLDS,
cfg.MODEL.RETINANET.IOU_LABELS,
allow_low_quality_matches=True,
),
"pixel_mean": cfg.MODEL.PIXEL_MEAN,
"pixel_std": cfg.MODEL.PIXEL_STD,
"num_classes": cfg.MODEL.RETINANET.NUM_CLASSES,
"head_in_features": cfg.MODEL.RETINANET.IN_FEATURES,
# Loss parameters:
"focal_loss_alpha": cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA,
"focal_loss_gamma": cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA,
"smooth_l1_beta": cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA,
"box_reg_loss_type": cfg.MODEL.RETINANET.BBOX_REG_LOSS_TYPE,
# Inference parameters:
"test_score_thresh": cfg.MODEL.RETINANET.SCORE_THRESH_TEST,
"test_topk_candidates": cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST,
"test_nms_thresh": cfg.MODEL.RETINANET.NMS_THRESH_TEST,
"max_detections_per_image": cfg.TEST.DETECTIONS_PER_IMAGE,
# Vis parameters
"vis_period": cfg.VIS_PERIOD,
"input_format": cfg.INPUT.FORMAT,
}
def forward_training(self, images, features, predictions, gt_instances):
# Transpose the Hi*Wi*A dimension to the middle:
pred_logits, pred_anchor_deltas = self._transpose_dense_predictions(
predictions, [self.num_classes, 4]
)
anchors = self.anchor_generator(features)
gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances)
return self.losses(anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes)
def losses(self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes):
"""
Args:
anchors (list[Boxes]): a list of #feature level Boxes
gt_labels, gt_boxes: see output of :meth:`RetinaNet.label_anchors`.
Their shapes are (N, R) and (N, R, 4), respectively, where R is
the total number of anchors across levels, i.e. sum(Hi x Wi x Ai)
pred_logits, pred_anchor_deltas: both are list[Tensor]. Each element in the
list corresponds to one level and has shape (N, Hi * Wi * Ai, K or 4).
Where K is the number of classes used in `pred_logits`.
Returns:
dict[str, Tensor]:
mapping from a named loss to a scalar tensor storing the loss.
Used during training only. The dict keys are: "loss_cls" and "loss_box_reg"
"""
num_images = len(gt_labels)
gt_labels = torch.stack(gt_labels) # (N, R)
valid_mask = gt_labels >= 0
pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes)
num_pos_anchors = pos_mask.sum().item()
get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images)
normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 100)
# classification and regression loss
gt_labels_target = F.one_hot(gt_labels[valid_mask], num_classes=self.num_classes + 1)[
:, :-1
] # no loss for the last (background) class
loss_cls = sigmoid_focal_loss_jit(
cat(pred_logits, dim=1)[valid_mask],
gt_labels_target.to(pred_logits[0].dtype),
alpha=self.focal_loss_alpha,
gamma=self.focal_loss_gamma,
reduction="sum",
)
loss_box_reg = _dense_box_regression_loss(
anchors,
self.box2box_transform,
pred_anchor_deltas,
gt_boxes,
pos_mask,
box_reg_loss_type=self.box_reg_loss_type,
smooth_l1_beta=self.smooth_l1_beta,
)
return {
"loss_cls": loss_cls / normalizer,
"loss_box_reg": loss_box_reg / normalizer,
}
@torch.no_grad()
def label_anchors(self, anchors, gt_instances):
"""
Args:
anchors (list[Boxes]): A list of #feature level Boxes.
The Boxes contains anchors of this image on the specific feature level.
gt_instances (list[Instances]): a list of N `Instances`s. The i-th
`Instances` contains the ground-truth per-instance annotations
for the i-th input image.
Returns:
list[Tensor]: List of #img tensors. i-th element is a vector of labels whose length is
the total number of anchors across all feature maps (sum(Hi * Wi * A)).
Label values are in {-1, 0, ..., K}, with -1 means ignore, and K means background.
list[Tensor]: i-th element is a Rx4 tensor, where R is the total number of anchors
across feature maps. The values are the matched gt boxes for each anchor.
Values are undefined for those anchors not labeled as foreground.
"""
| anchors = Boxes.cat(anchors) # Rx4
| 6 | 2023-12-05 02:51:53+00:00 | 24k |
DiffusionLight/DiffusionLight | relighting/inpainter.py | [
{
"identifier": "CustomStableDiffusionControlNetInpaintPipeline",
"path": "relighting/pipeline.py",
"snippet": "class CustomStableDiffusionControlNetInpaintPipeline(StableDiffusionControlNetInpaintPipeline):\n @torch.no_grad()\n def __call__(\n self,\n prompt: Union[str, List[str]] =... | import torch
import numpy as np
import os
import pickle
from diffusers import ControlNetModel, AutoencoderKL
from PIL import Image
from tqdm.auto import tqdm
from transformers import pipeline as transformers_pipeline
from relighting.pipeline import CustomStableDiffusionControlNetInpaintPipeline
from relighting.pipeline_inpaintonly import CustomStableDiffusionInpaintPipeline, CustomStableDiffusionXLInpaintPipeline
from relighting.argument import SAMPLERS, VAE_MODELS, DEPTH_ESTIMATOR, get_control_signal_type
from relighting.image_processor import (
estimate_scene_depth,
estimate_scene_normal,
merge_normal_map,
fill_depth_circular
)
from relighting.ball_processor import get_ideal_normal_ball, crop_ball
from relighting.pipeline_xl import CustomStableDiffusionXLControlNetInpaintPipeline | 18,556 |
control_image = fill_depth_circular(control_image, x, y, r)
return control_image
def process_sdxl_depth(self, input_image, normal_ball=None, mask_ball=None, x=None, y=None, r=None):
if getattr(self, 'depth_estimator', None) is None:
self.depth_estimator = transformers_pipeline("depth-estimation", model=DEPTH_ESTIMATOR, device=self.device.index)
control_image = estimate_scene_depth(input_image, depth_estimator=self.depth_estimator)
xs = [x] if not isinstance(x, list) else x
ys = [y] if not isinstance(y, list) else y
rs = [r] if not isinstance(r, list) else r
for x, y, r in zip(xs, ys, rs):
#print(f"depth at {x}, {y}, {r}")
control_image = fill_depth_circular(control_image, x, y, r)
return control_image
def process_sd_normal(self, input_image, normal_ball, mask_ball, x, y, r=None, normal_ball_path=None):
if getattr(self, 'depth_estimator', None) is None:
self.depth_estimator = transformers_pipeline("depth-estimation", model=DEPTH_ESTIMATOR, device=self.device.index)
normal_scene = estimate_scene_normal(input_image, depth_estimator=self.depth_estimator)
normal_image = merge_normal_map(normal_scene, normal_ball, mask_ball, x, y)
normal_image = (normal_image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
control_image = Image.fromarray(normal_image)
return control_image
def __call__(self, *args, **kwargs):
process_fn = getattr(self, f"process_{self.sd_arch}_{self.control_signal_type}", None)
if process_fn is None:
raise ValueError
else:
return process_fn(*args, **kwargs)
class BallInpainter():
def __init__(self, pipeline, sd_arch, control_generator, disable_water_mask=True):
self.pipeline = pipeline
self.sd_arch = sd_arch
self.control_generator = control_generator
self.median = {}
if disable_water_mask:
self._disable_water_mask()
def _disable_water_mask(self):
if hasattr(self.pipeline, "watermark"):
self.pipeline.watermark = NoWaterMark()
print("Disabled watermasking")
@classmethod
def from_sd(cls,
model,
controlnet=None,
device=0,
sampler="unipc",
torch_dtype=torch.float16,
disable_water_mask=True,
offload=False
):
if controlnet is not None:
control_signal_type = get_control_signal_type(controlnet)
controlnet = ControlNetModel.from_pretrained(controlnet, torch_dtype=torch.float16)
pipe = CustomStableDiffusionControlNetInpaintPipeline.from_pretrained(
model,
controlnet=controlnet,
torch_dtype=torch_dtype,
).to(device)
control_generator = ControlSignalGenerator("sd", control_signal_type, device=device)
else:
pipe = CustomStableDiffusionInpaintPipeline.from_pretrained(
model,
torch_dtype=torch_dtype,
).to(device)
control_generator = None
try:
if torch_dtype==torch.float16 and device != torch.device("cpu"):
pipe.enable_xformers_memory_efficient_attention()
except:
pass
pipe.set_progress_bar_config(disable=True)
pipe.scheduler = SAMPLERS[sampler].from_config(pipe.scheduler.config)
return BallInpainter(pipe, "sd", control_generator, disable_water_mask)
@classmethod
def from_sdxl(cls,
model,
controlnet=None,
device=0,
sampler="unipc",
torch_dtype=torch.float16,
disable_water_mask=True,
use_fixed_vae=True,
offload=False
):
vae = VAE_MODELS["sdxl"]
vae = AutoencoderKL.from_pretrained(vae, torch_dtype=torch_dtype).to(device) if use_fixed_vae else None
extra_kwargs = {"vae": vae} if vae is not None else {}
if controlnet is not None:
control_signal_type = get_control_signal_type(controlnet)
controlnet = ControlNetModel.from_pretrained(
controlnet,
variant="fp16" if torch_dtype == torch.float16 else None,
use_safetensors=True,
torch_dtype=torch_dtype,
).to(device)
pipe = CustomStableDiffusionXLControlNetInpaintPipeline.from_pretrained(
model,
controlnet=controlnet,
variant="fp16" if torch_dtype == torch.float16 else None,
use_safetensors=True,
torch_dtype=torch_dtype,
**extra_kwargs,
).to(device)
control_generator = ControlSignalGenerator("sdxl", control_signal_type, device=device)
else:
|
class NoWaterMark:
def apply_watermark(self, *args, **kwargs):
return args[0]
class ControlSignalGenerator():
def __init__(self, sd_arch, control_signal_type, device):
self.sd_arch = sd_arch
self.control_signal_type = control_signal_type
self.device = device
def process_sd_depth(self, input_image, normal_ball=None, mask_ball=None, x=None, y=None, r=None):
if getattr(self, 'depth_estimator', None) is None:
self.depth_estimator = transformers_pipeline("depth-estimation", device=self.device.index)
control_image = self.depth_estimator(input_image)['depth']
control_image = np.array(control_image)
control_image = control_image[:, :, None]
control_image = np.concatenate([control_image, control_image, control_image], axis=2)
control_image = Image.fromarray(control_image)
control_image = fill_depth_circular(control_image, x, y, r)
return control_image
def process_sdxl_depth(self, input_image, normal_ball=None, mask_ball=None, x=None, y=None, r=None):
if getattr(self, 'depth_estimator', None) is None:
self.depth_estimator = transformers_pipeline("depth-estimation", model=DEPTH_ESTIMATOR, device=self.device.index)
control_image = estimate_scene_depth(input_image, depth_estimator=self.depth_estimator)
xs = [x] if not isinstance(x, list) else x
ys = [y] if not isinstance(y, list) else y
rs = [r] if not isinstance(r, list) else r
for x, y, r in zip(xs, ys, rs):
#print(f"depth at {x}, {y}, {r}")
control_image = fill_depth_circular(control_image, x, y, r)
return control_image
def process_sd_normal(self, input_image, normal_ball, mask_ball, x, y, r=None, normal_ball_path=None):
if getattr(self, 'depth_estimator', None) is None:
self.depth_estimator = transformers_pipeline("depth-estimation", model=DEPTH_ESTIMATOR, device=self.device.index)
normal_scene = estimate_scene_normal(input_image, depth_estimator=self.depth_estimator)
normal_image = merge_normal_map(normal_scene, normal_ball, mask_ball, x, y)
normal_image = (normal_image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
control_image = Image.fromarray(normal_image)
return control_image
def __call__(self, *args, **kwargs):
process_fn = getattr(self, f"process_{self.sd_arch}_{self.control_signal_type}", None)
if process_fn is None:
raise ValueError
else:
return process_fn(*args, **kwargs)
class BallInpainter():
def __init__(self, pipeline, sd_arch, control_generator, disable_water_mask=True):
self.pipeline = pipeline
self.sd_arch = sd_arch
self.control_generator = control_generator
self.median = {}
if disable_water_mask:
self._disable_water_mask()
def _disable_water_mask(self):
if hasattr(self.pipeline, "watermark"):
self.pipeline.watermark = NoWaterMark()
print("Disabled watermasking")
@classmethod
def from_sd(cls,
model,
controlnet=None,
device=0,
sampler="unipc",
torch_dtype=torch.float16,
disable_water_mask=True,
offload=False
):
if controlnet is not None:
control_signal_type = get_control_signal_type(controlnet)
controlnet = ControlNetModel.from_pretrained(controlnet, torch_dtype=torch.float16)
pipe = CustomStableDiffusionControlNetInpaintPipeline.from_pretrained(
model,
controlnet=controlnet,
torch_dtype=torch_dtype,
).to(device)
control_generator = ControlSignalGenerator("sd", control_signal_type, device=device)
else:
pipe = CustomStableDiffusionInpaintPipeline.from_pretrained(
model,
torch_dtype=torch_dtype,
).to(device)
control_generator = None
try:
if torch_dtype==torch.float16 and device != torch.device("cpu"):
pipe.enable_xformers_memory_efficient_attention()
except:
pass
pipe.set_progress_bar_config(disable=True)
pipe.scheduler = SAMPLERS[sampler].from_config(pipe.scheduler.config)
return BallInpainter(pipe, "sd", control_generator, disable_water_mask)
@classmethod
def from_sdxl(cls,
model,
controlnet=None,
device=0,
sampler="unipc",
torch_dtype=torch.float16,
disable_water_mask=True,
use_fixed_vae=True,
offload=False
):
vae = VAE_MODELS["sdxl"]
vae = AutoencoderKL.from_pretrained(vae, torch_dtype=torch_dtype).to(device) if use_fixed_vae else None
extra_kwargs = {"vae": vae} if vae is not None else {}
if controlnet is not None:
control_signal_type = get_control_signal_type(controlnet)
controlnet = ControlNetModel.from_pretrained(
controlnet,
variant="fp16" if torch_dtype == torch.float16 else None,
use_safetensors=True,
torch_dtype=torch_dtype,
).to(device)
pipe = CustomStableDiffusionXLControlNetInpaintPipeline.from_pretrained(
model,
controlnet=controlnet,
variant="fp16" if torch_dtype == torch.float16 else None,
use_safetensors=True,
torch_dtype=torch_dtype,
**extra_kwargs,
).to(device)
control_generator = ControlSignalGenerator("sdxl", control_signal_type, device=device)
else: | pipe = CustomStableDiffusionXLInpaintPipeline.from_pretrained( | 2 | 2023-12-07 14:03:31+00:00 | 24k |
modelscope/normal-depth-diffusion | ldm/models/diffusion/mv_ddpm.py | [
{
"identifier": "AutoencoderKL",
"path": "ldm/models/autoencoder.py",
"snippet": "class AutoencoderKL(pl.LightningModule):\n\n def __init__(self,\n ddconfig,\n lossconfig,\n embed_dim,\n ckpt_path=None,\n ignore_keys=[],\... | import pdb
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
from contextlib import contextmanager
from functools import partial
from einops import rearrange, repeat
from ldm.models.autoencoder import (AutoencoderKL, IdentityFirstStage,
VQModelInterface)
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.dpm_solver import DPMSolverSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.modules.attention import CrossAttention
from ldm.modules.diffusionmodules.util import (extract_into_tensor,
make_beta_schedule, noise_like)
from ldm.modules.distributions.distributions import (
DiagonalGaussianDistribution, normal_kl)
from ldm.modules.ema import LitEma
from ldm.util import (count_params, default, exists, filter_nan_loss,
instantiate_from_config, isimage, ismap, log_txt_as_img,
mean_flat)
from torch.optim.lr_scheduler import LambdaLR
from torchvision.utils import make_grid
from tqdm import tqdm
from pytorch_lightning.utilities.distributed import rank_zero_only
from pytorch_lightning.utilities.rank_zero import rank_zero_only | 15,149 | else:
return int(self.start_steps + self.start_steps
* upper_bound(self.anneal_global_step, global_step))
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(
self,
unet_config,
timesteps=1000,
beta_schedule='linear',
loss_type='l2',
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor='val/loss',
use_ema=True,
first_stage_key='image',
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization='eps', # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
anneal_t=False, # we find at the begining, smaller t, larger denoise mse loss.
anneal_global_step=[],
anneal_ratio=0.9,
prior_model=None,
prior_normal=None,
input_keys=['rgb'],
):
super().__init__()
assert parameterization in [
'eps', 'x0'
], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(
f'{self.__class__.__name__}: Running in {self.parameterization}-prediction mode'
)
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
self.input_keys = input_keys
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(
ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(
given_betas=given_betas,
beta_schedule=beta_schedule,
timesteps=timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(
fill_value=logvar_init, size=(self.num_timesteps, ))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
### anneal t function
if not anneal_t:
self.anneal_func = anneal_identity()
else:
self.anneal_func = anneal_warmup(anneal_ratio, anneal_global_step,
self.num_timesteps)
if prior_model is not None:
self.prior_model = instantiate_from_config(prior_model)
else:
self.prior_model = None
if prior_normal is not None:
self.prior_normal = instantiate_from_config(prior_normal)
else:
self.prior_normal = None
def register_schedule(self,
given_betas=None,
beta_schedule='linear',
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3):
| """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""
try:
except:
__conditioning_keys__ = {
'concat': 'c_concat',
'crossattn': 'c_crossattn',
'adm': 'y'
}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2
class anneal_identity():
def __call__(self, x, global_step):
return x
def upper_bound(arr, key):
left = 0
right = len(arr)
while left < right:
mid = (left + right) >> 1
if arr[mid] < key:
left = mid + 1
else:
right = mid
return left
class anneal_warmup():
def __init__(self, anneal_ratio, anneal_global_step, num_steps):
self.anneal_ratio = anneal_ratio
self.anneal_global_step = anneal_global_step
self.steps = num_steps // (len(anneal_global_step) + 1)
self.start_steps = self.steps
def __call__(self, x, global_step):
if (torch.rand(1) > self.anneal_ratio).item():
return x
else:
return int(self.start_steps + self.start_steps
* upper_bound(self.anneal_global_step, global_step))
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
def __init__(
self,
unet_config,
timesteps=1000,
beta_schedule='linear',
loss_type='l2',
ckpt_path=None,
ignore_keys=[],
load_only_unet=False,
monitor='val/loss',
use_ema=True,
first_stage_key='image',
image_size=256,
channels=3,
log_every_t=100,
clip_denoised=True,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
given_betas=None,
original_elbo_weight=0.,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.,
conditioning_key=None,
parameterization='eps', # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.,
anneal_t=False, # we find at the begining, smaller t, larger denoise mse loss.
anneal_global_step=[],
anneal_ratio=0.9,
prior_model=None,
prior_normal=None,
input_keys=['rgb'],
):
super().__init__()
assert parameterization in [
'eps', 'x0'
], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
print(
f'{self.__class__.__name__}: Running in {self.parameterization}-prediction mode'
)
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
self.first_stage_key = first_stage_key
self.image_size = image_size # try conv?
self.channels = channels
self.use_positional_encodings = use_positional_encodings
self.model = DiffusionWrapper(unet_config, conditioning_key)
count_params(self.model, verbose=True)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
self.scheduler_config = scheduler_config
self.v_posterior = v_posterior
self.original_elbo_weight = original_elbo_weight
self.l_simple_weight = l_simple_weight
self.input_keys = input_keys
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(
ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
self.register_schedule(
given_betas=given_betas,
beta_schedule=beta_schedule,
timesteps=timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s)
self.loss_type = loss_type
self.learn_logvar = learn_logvar
self.logvar = torch.full(
fill_value=logvar_init, size=(self.num_timesteps, ))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
### anneal t function
if not anneal_t:
self.anneal_func = anneal_identity()
else:
self.anneal_func = anneal_warmup(anneal_ratio, anneal_global_step,
self.num_timesteps)
if prior_model is not None:
self.prior_model = instantiate_from_config(prior_model)
else:
self.prior_model = None
if prior_normal is not None:
self.prior_normal = instantiate_from_config(prior_normal)
else:
self.prior_normal = None
def register_schedule(self,
given_betas=None,
beta_schedule='linear',
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3): | if exists(given_betas): | 15 | 2023-12-06 07:29:34+00:00 | 24k |
RobertCsordas/moe_attention | tasks/simple/language_model/transformer_lm_mixin.py | [
{
"identifier": "TransformerLanguageModel",
"path": "models/transformer_language_model.py",
"snippet": "class TransformerLanguageModel(LoggingLayer, torch.nn.Module):\n def __init__(self, voc_size: int, embedding_size: Optional[int], state_size: int, dropout: float,\n tied_embedding: ... | import framework
import torch
import torch.nn
import torch.nn.functional as F
import torch.utils.data
import math
from typing import List, Tuple, Dict, Any
from models import TransformerLanguageModel
from ... import task, args
from layers.transformer import RelativeTransformerEncoderLayer, PrelnRelativeTransformerEncoderLayer
from layers.transformer.relative_moe_transformer import RelativeMoeTransformerEncoderLayer
from layers.transformer.fast_rope_transformer import FastRopeTransformerEncoderLayer
from layers.transformer.moe_attention_relative_transformer import MoeAttentionRelativeTransformerEncoderLayer
from layers.moe_layer import MoE
from interfaces import Result
from layers import LayerVisualizer
from layers.transformer.full_moe_relative_attention import FullMoeRelativeAttentionCore | 20,164 | parser.add_argument("-moe.att.n_experts", default=4)
parser.add_argument("-moe.att.variant", default="moa", choice=["moa", "simple", "qside", "full", "full_rope", "seq", "target"])
parser.add_argument("-moe.att.enable", default=False)
parser.add_argument("-moe.att.q_expert", default=True)
parser.add_argument("-moe.att.k_expert", default=True)
parser.add_argument("-moe.att.v_expert", default=True)
parser.add_argument("-moe.att.o_expert", default=True)
parser.add_argument("-moe.att.k", default=2)
parser.add_argument("-moe.att.norm_qk", default=False)
parser.add_argument("-moe.att.v_size", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-moe.att.same_sel", default=False)
parser.add_argument("-moe.att.expert_dropout", default="none", parser=parser.float_or_none_parser)
parser.add_argument("-moe.att.selection_mode", default="sigmoid", choice=["sigmoid", "softmax"])
parser.add_argument("-moe.att.perplexity_reg", default="none", parser=parser.float_or_none_parser)
parser.add_argument("-moe.att.qside_n_experts", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-moe.att.k", default=2)
parser.add_argument("-moe.att.norm_ret", default=False)
parser.add_argument("-moe.att.shared_experts", default=False)
parser.add_argument("-moe.att.drop_expert", default="none", parser=parser.float_or_none_parser)
parser.add_argument("-moe.att.kq_n_experts", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-moe.att.separate_kq_sel", default=False)
parser.add_argument("-moe.att.norm_init", default=False)
parser.add_argument("-rope.rotate_fraction", default=0.5)
parser.add_argument("-rope.base", default=10000.0)
parser.add_argument("-moa.mode", default="my", choice=["my", "moa"])
parser.add_argument("-moa.cvloss", default=0.0)
parser.add_argument("-moa.switchloss", default=0.0)
parser.add_argument("-moa.zloss", default=0.0)
parser.add_argument("-debug_plot_interval", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-transformer.plot_head_details", default=False)
parser.add_argument("-plot.n_steps", default=-128)
@task()
class TransformerLMMixin:
helper: framework.helpers.TrainingHelper
def is_preln(self) -> bool:
return "preln" in self.helper.args.transformer.variant
def topk_activation(self, x: torch.Tensor) -> torch.Tensor:
nx = -x
return torch.masked_fill(x, nx <= nx.kthvalue(self.helper.args.transformer.topk_value, keepdim=True)[0], 0)
def get_layers(self) -> List[torch.nn.Module]:
# pyright: reportOptionalMemberAccess=false
if self.helper.args.transformer.activation == "relu":
activation = F.relu
elif self.helper.args.transformer.activation == "topk":
activation = self.topk_activation
elif self.helper.args.transformer.activation == "identity":
activation = lambda x: x
elif self.helper.args.transformer.activation == "sigmoid":
activation = torch.sigmoid
elif self.helper.args.transformer.activation == "gelu":
activation = F.gelu
elif self.helper.args.transformer.activation == "softmax":
activation = lambda x: F.softmax(x, dim=-1)
else:
raise ValueError(f"Invalid activation: {self.helper.args.transformer.activation}")
base_args = dict(
d_model=self.helper.args.state_size,
nhead=self.helper.args.transformer.n_heads,
dropout=self.helper.args.dropout,
activation=activation
)
if self.helper.args.transformer.variant not in {"preln_moe", "moe"}:
base_args["dim_feedforward"]=int(self.helper.args.state_size * self.helper.args.transformer.ff_multiplier)
extra_args = {} if not self.helper.args.transformer.variant.endswith("_gelu") else {
"activation": F.gelu,
"drop_expand": False
}
if self.helper.args.transformer.variant in {"preln_relative"}:
mklayer = lambda: PrelnRelativeTransformerEncoderLayer(
**base_args, **extra_args, test_pos_clamp=self.helper.args.lm.trafo.test_pos_clamp,
n_layers=self.helper.args.transformer.encoder_n_layers,
head_projection_size=self.helper.args.transformer.head_projection_size,)
elif self.helper.args.transformer.variant in {"preln_moeatt"}:
mklayer = lambda: MoeAttentionRelativeTransformerEncoderLayer(
**base_args, **extra_args, moe_att_n_experts=self.helper.args.moe.att.n_experts,
n_layers=self.helper.args.transformer.encoder_n_layers,
head_projection_size=self.helper.args.transformer.head_projection_size,
att_perplexity_reg=self.helper.args.moe.perplexity_reg if self.helper.args.moe.att.perplexity_reg is None else self.helper.args.moe.att.perplexity_reg,
expert_dropout=self.helper.args.moe.drop_expert if self.helper.args.moe.att.drop_expert is None else self.helper.args.moe.att.drop_expert,
att_selection_mode=self.helper.args.moe.att.selection_mode,
preln=self.is_preln(),
attention_variant=self.helper.args.moe.att.variant,
q_expert=self.helper.args.moe.att.q_expert,
k_expert=self.helper.args.moe.att.k_expert,
v_expert=self.helper.args.moe.att.v_expert,
o_expert=self.helper.args.moe.att.o_expert,
norm_qk_score=self.helper.args.moe.att.norm_qk,
v_projection_size=self.helper.args.moe.att.v_size,
same_sel=self.helper.args.moe.att.same_sel,
moe_k=self.helper.args.moe.att.k,
qside_n_experts=self.helper.args.moe.att.qside_n_experts,
shared_experts=self.helper.args.moe.att.shared_experts,
kq_n_experts=self.helper.args.moe.att.kq_n_experts,
separate_kq_sel=self.helper.args.moe.att.separate_kq_sel,
moa_mode=self.helper.args.moa.mode,
cvloss=self.helper.args.moa.cvloss,
switchloss=self.helper.args.moa.switchloss,
zloss=self.helper.args.moa.zloss,
rotate_fraction=self.helper.args.rope.rotate_fraction,
rope_base=self.helper.args.rope.base,
moeatt_norm_init=self.helper.args.moe.att.norm_init)
elif self.helper.args.transformer.variant in {"preln_rope", "rope"}:
mklayer = lambda: FastRopeTransformerEncoderLayer(
**base_args, **extra_args,
n_layers=self.helper.args.transformer.encoder_n_layers,
head_projection_size=self.helper.args.transformer.head_projection_size,
preln=self.is_preln(), rotate_fraction = self.helper.args.rope.rotate_fraction,
rope_base=self.helper.args.rope.base)
elif self.helper.args.transformer.variant in {"preln_moe", "moe"}:
# def __init__(self, d_model, nhead, n_bins: int, bin_size: int, n_layers: int, dim_feedforward=2048,
|
@args
def a(parser: framework.helpers.ArgumentParser):
parser.add_argument("-lm.trafo.context_blocks", default=1)
parser.add_argument("-lm.trafo.test_context_blocks", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-lm.trafo.test_pos_clamp", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-lm.trafo.same_length_eval", default=False)
parser.add_argument("-lm.trafo.same_length", default=False)
parser.add_argument("-lm.trafo.last_layer_context", default=False)
parser.add_argument("-lm.trafo.xl_init", default=False)
parser.add_argument("-lm.trafo.embedding_mode_init", default="default", choice=["default", "scale_to_sqrt_dmodel", "init_to_sqrt_dmodel", "one_and_scale_to_sqrt_dmodel", "like_preln"])
parser.add_argument("-pkm.n_heads", default=1)
parser.add_argument("-moe.n_experts", default=128)
parser.add_argument("-moe.expert_size", default=128)
parser.add_argument("-moe.selection_mode", default="sigmoid", choice=["gate", "sigmoid", "mul"])
parser.add_argument("-moe.perplexity_reg", default=0.0)
parser.add_argument("-moe.perplexity_reg_mode", default="step", choice=["step", "global", "time", "global_time"])
parser.add_argument("-moe.reg_type", default="entropy", choice=["perplexity", "variance", "entropy", "l2", "switch", "normal"])
parser.add_argument("-moe.norm_keys", default=False)
parser.add_argument("-moe.n_random", default=0)
parser.add_argument("-moe.topk_mode", default="full", choice=["full", "l1_approx", "approx"])
parser.add_argument("-moe.activation_after_topk", default=False)
parser.add_argument("-moe.drop_parallel", default=True)
parser.add_argument("-moe.norm_key_init", default=False)
parser.add_argument("-moe.norm_value_init", default=False)
parser.add_argument("-moe.identical_init", default=False)
parser.add_argument("-moe.sel_lr_multipler", default=1.0)
parser.add_argument("-moe.expert_lr_multipler", default=1.0)
parser.add_argument("-moe.sel_norm", default="none", choice=["none", "cos", "input", "weights"])
parser.add_argument("-moe.dropout_factor", default=1.0)
parser.add_argument("-moe.drop_expert", default=0.0)
parser.add_argument("-moe.sync_distributed", default=True)
parser.add_argument("-moe.modulation_amplitude", default=0.5)
parser.add_argument("-moe.init_scale", default=1.0)
parser.add_argument("-moe.norm_expert_sel_init", default=False)
parser.add_argument("-kvmem.dropout", default="none", choice=["none", "early", "late", "weight", "score"])
parser.add_argument("-kvmem.norm_values", default=False)
parser.add_argument("-transformer.topk_value", default=32)
parser.add_argument("-transformer.activation", default="relu", choice=["relu", "topk", "gelu", "identity", "sigmoid", "softmax"])
parser.add_argument("-transformer.p_drop_layer", default=0.0)
parser.add_argument("-transformer.head_projection_size", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-transformer.ln_affine", default=True)
parser.add_argument("-transformer.ln_after_attention", default=True)
parser.add_argument("-moe.att.n_experts", default=4)
parser.add_argument("-moe.att.variant", default="moa", choice=["moa", "simple", "qside", "full", "full_rope", "seq", "target"])
parser.add_argument("-moe.att.enable", default=False)
parser.add_argument("-moe.att.q_expert", default=True)
parser.add_argument("-moe.att.k_expert", default=True)
parser.add_argument("-moe.att.v_expert", default=True)
parser.add_argument("-moe.att.o_expert", default=True)
parser.add_argument("-moe.att.k", default=2)
parser.add_argument("-moe.att.norm_qk", default=False)
parser.add_argument("-moe.att.v_size", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-moe.att.same_sel", default=False)
parser.add_argument("-moe.att.expert_dropout", default="none", parser=parser.float_or_none_parser)
parser.add_argument("-moe.att.selection_mode", default="sigmoid", choice=["sigmoid", "softmax"])
parser.add_argument("-moe.att.perplexity_reg", default="none", parser=parser.float_or_none_parser)
parser.add_argument("-moe.att.qside_n_experts", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-moe.att.k", default=2)
parser.add_argument("-moe.att.norm_ret", default=False)
parser.add_argument("-moe.att.shared_experts", default=False)
parser.add_argument("-moe.att.drop_expert", default="none", parser=parser.float_or_none_parser)
parser.add_argument("-moe.att.kq_n_experts", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-moe.att.separate_kq_sel", default=False)
parser.add_argument("-moe.att.norm_init", default=False)
parser.add_argument("-rope.rotate_fraction", default=0.5)
parser.add_argument("-rope.base", default=10000.0)
parser.add_argument("-moa.mode", default="my", choice=["my", "moa"])
parser.add_argument("-moa.cvloss", default=0.0)
parser.add_argument("-moa.switchloss", default=0.0)
parser.add_argument("-moa.zloss", default=0.0)
parser.add_argument("-debug_plot_interval", default="none", parser=parser.int_or_none_parser)
parser.add_argument("-transformer.plot_head_details", default=False)
parser.add_argument("-plot.n_steps", default=-128)
@task()
class TransformerLMMixin:
helper: framework.helpers.TrainingHelper
def is_preln(self) -> bool:
return "preln" in self.helper.args.transformer.variant
def topk_activation(self, x: torch.Tensor) -> torch.Tensor:
nx = -x
return torch.masked_fill(x, nx <= nx.kthvalue(self.helper.args.transformer.topk_value, keepdim=True)[0], 0)
def get_layers(self) -> List[torch.nn.Module]:
# pyright: reportOptionalMemberAccess=false
if self.helper.args.transformer.activation == "relu":
activation = F.relu
elif self.helper.args.transformer.activation == "topk":
activation = self.topk_activation
elif self.helper.args.transformer.activation == "identity":
activation = lambda x: x
elif self.helper.args.transformer.activation == "sigmoid":
activation = torch.sigmoid
elif self.helper.args.transformer.activation == "gelu":
activation = F.gelu
elif self.helper.args.transformer.activation == "softmax":
activation = lambda x: F.softmax(x, dim=-1)
else:
raise ValueError(f"Invalid activation: {self.helper.args.transformer.activation}")
base_args = dict(
d_model=self.helper.args.state_size,
nhead=self.helper.args.transformer.n_heads,
dropout=self.helper.args.dropout,
activation=activation
)
if self.helper.args.transformer.variant not in {"preln_moe", "moe"}:
base_args["dim_feedforward"]=int(self.helper.args.state_size * self.helper.args.transformer.ff_multiplier)
extra_args = {} if not self.helper.args.transformer.variant.endswith("_gelu") else {
"activation": F.gelu,
"drop_expand": False
}
if self.helper.args.transformer.variant in {"preln_relative"}:
mklayer = lambda: PrelnRelativeTransformerEncoderLayer(
**base_args, **extra_args, test_pos_clamp=self.helper.args.lm.trafo.test_pos_clamp,
n_layers=self.helper.args.transformer.encoder_n_layers,
head_projection_size=self.helper.args.transformer.head_projection_size,)
elif self.helper.args.transformer.variant in {"preln_moeatt"}:
mklayer = lambda: MoeAttentionRelativeTransformerEncoderLayer(
**base_args, **extra_args, moe_att_n_experts=self.helper.args.moe.att.n_experts,
n_layers=self.helper.args.transformer.encoder_n_layers,
head_projection_size=self.helper.args.transformer.head_projection_size,
att_perplexity_reg=self.helper.args.moe.perplexity_reg if self.helper.args.moe.att.perplexity_reg is None else self.helper.args.moe.att.perplexity_reg,
expert_dropout=self.helper.args.moe.drop_expert if self.helper.args.moe.att.drop_expert is None else self.helper.args.moe.att.drop_expert,
att_selection_mode=self.helper.args.moe.att.selection_mode,
preln=self.is_preln(),
attention_variant=self.helper.args.moe.att.variant,
q_expert=self.helper.args.moe.att.q_expert,
k_expert=self.helper.args.moe.att.k_expert,
v_expert=self.helper.args.moe.att.v_expert,
o_expert=self.helper.args.moe.att.o_expert,
norm_qk_score=self.helper.args.moe.att.norm_qk,
v_projection_size=self.helper.args.moe.att.v_size,
same_sel=self.helper.args.moe.att.same_sel,
moe_k=self.helper.args.moe.att.k,
qside_n_experts=self.helper.args.moe.att.qside_n_experts,
shared_experts=self.helper.args.moe.att.shared_experts,
kq_n_experts=self.helper.args.moe.att.kq_n_experts,
separate_kq_sel=self.helper.args.moe.att.separate_kq_sel,
moa_mode=self.helper.args.moa.mode,
cvloss=self.helper.args.moa.cvloss,
switchloss=self.helper.args.moa.switchloss,
zloss=self.helper.args.moa.zloss,
rotate_fraction=self.helper.args.rope.rotate_fraction,
rope_base=self.helper.args.rope.base,
moeatt_norm_init=self.helper.args.moe.att.norm_init)
elif self.helper.args.transformer.variant in {"preln_rope", "rope"}:
mklayer = lambda: FastRopeTransformerEncoderLayer(
**base_args, **extra_args,
n_layers=self.helper.args.transformer.encoder_n_layers,
head_projection_size=self.helper.args.transformer.head_projection_size,
preln=self.is_preln(), rotate_fraction = self.helper.args.rope.rotate_fraction,
rope_base=self.helper.args.rope.base)
elif self.helper.args.transformer.variant in {"preln_moe", "moe"}:
# def __init__(self, d_model, nhead, n_bins: int, bin_size: int, n_layers: int, dim_feedforward=2048, | mklayer = lambda: RelativeMoeTransformerEncoderLayer( | 5 | 2023-12-13 08:45:02+00:00 | 24k |
AIFSH/NativeDancer | nativedancer/third_part/detectron2/utils/visualizer.py | [
{
"identifier": "MetadataCatalog",
"path": "nativedancer/third_part/detectron2/data/catalog.py",
"snippet": "class _DatasetCatalog(UserDict):\nclass Metadata(types.SimpleNamespace):\nclass _MetadataCatalog(UserDict):\n def register(self, name, func):\n def get(self, name):\n def list(self) -> L... | import colorsys
import logging
import math
import numpy as np
import cv2
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import pycocotools.mask as mask_util
import torch
from enum import Enum, unique
from matplotlib.backends.backend_agg import FigureCanvasAgg
from PIL import Image
from ..data import MetadataCatalog
from ..structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes
from ..utils.file_io import PathManager
from .colormap import random_color
from panopticapi.utils import rgb2id | 17,119 | polygon = mpl.patches.Polygon(
segment,
fill=True,
facecolor=mplc.to_rgb(color) + (alpha,),
edgecolor=edge_color,
linewidth=max(self._default_font_size // 15 * self.output.scale, 1),
)
self.output.ax.add_patch(polygon)
return self.output
"""
Internal methods:
"""
def _jitter(self, color):
"""
Randomly modifies given color to produce a slightly different color than the color given.
Args:
color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color
picked. The values in the list are in the [0.0, 1.0] range.
Returns:
jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the
color after being jittered. The values in the list are in the [0.0, 1.0] range.
"""
color = mplc.to_rgb(color)
vec = np.random.rand(3)
# better to do it in another color space
vec = vec / np.linalg.norm(vec) * 0.5
res = np.clip(vec + color, 0, 1)
return tuple(res)
def _create_grayscale_image(self, mask=None):
"""
Create a grayscale version of the original image.
The colors in masked area, if given, will be kept.
"""
img_bw = self.img.astype("f4").mean(axis=2)
img_bw = np.stack([img_bw] * 3, axis=2)
if mask is not None:
img_bw[mask] = self.img[mask]
return img_bw
def _change_color_brightness(self, color, brightness_factor):
"""
Depending on the brightness_factor, gives a lighter or darker color i.e. a color with
less or more saturation than the original color.
Args:
color: color of the polygon. Refer to `matplotlib.colors` for a full list of
formats that are accepted.
brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of
0 will correspond to no change, a factor in [-1.0, 0) range will result in
a darker color and a factor in (0, 1.0] range will result in a lighter color.
Returns:
modified_color (tuple[double]): a tuple containing the RGB values of the
modified color. Each value in the tuple is in the [0.0, 1.0] range.
"""
assert brightness_factor >= -1.0 and brightness_factor <= 1.0
color = mplc.to_rgb(color)
polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))
modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])
modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness
modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness
modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])
return tuple(np.clip(modified_color, 0.0, 1.0))
def _convert_boxes(self, boxes):
"""
Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.
"""
if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):
return boxes.tensor.detach().numpy()
else:
return np.asarray(boxes)
def _convert_masks(self, masks_or_polygons):
"""
Convert different format of masks or polygons to a tuple of masks and polygons.
Returns:
list[GenericMask]:
"""
m = masks_or_polygons
if isinstance(m, PolygonMasks):
m = m.polygons
if isinstance(m, BitMasks):
m = m.tensor.numpy()
if isinstance(m, torch.Tensor):
m = m.numpy()
ret = []
for x in m:
if isinstance(x, GenericMask):
ret.append(x)
else:
ret.append(GenericMask(x, self.output.height, self.output.width))
return ret
def _draw_text_in_mask(self, binary_mask, text, color):
"""
Find proper places to draw text given a binary mask.
"""
# TODO sometimes drawn on wrong objects. the heuristics here can improve.
_num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)
if stats[1:, -1].size == 0:
return
largest_component_id = np.argmax(stats[1:, -1]) + 1
# draw text on the largest component, as well as other very large components.
for cid in range(1, _num_cc):
if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:
# median is more stable than centroid
# center = centroids[largest_component_id]
center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]
self.draw_text(text, center, color=color)
def _convert_keypoints(self, keypoints):
| # Copyright (c) Facebook, Inc. and its affiliates.
logger = logging.getLogger(__name__)
__all__ = ["ColorMode", "VisImage", "Visualizer"]
_SMALL_OBJECT_AREA_THRESH = 1000
_LARGE_MASK_AREA_THRESH = 120000
_OFF_WHITE = (1.0, 1.0, 240.0 / 255)
_BLACK = (0, 0, 0)
_RED = (1.0, 0, 0)
_KEYPOINT_THRESHOLD = 0.05
@unique
class ColorMode(Enum):
"""
Enum of different color modes to use for instance visualizations.
"""
IMAGE = 0
"""
Picks a random color for every instance and overlay segmentations with low opacity.
"""
SEGMENTATION = 1
"""
Let instances of the same category have similar colors
(from metadata.thing_colors), and overlay them with
high opacity. This provides more attention on the quality of segmentation.
"""
IMAGE_BW = 2
"""
Same as IMAGE, but convert all areas without masks to gray-scale.
Only available for drawing per-instance mask predictions.
"""
class GenericMask:
"""
Attribute:
polygons (list[ndarray]): list[ndarray]: polygons for this mask.
Each ndarray has format [x, y, x, y, ...]
mask (ndarray): a binary mask
"""
def __init__(self, mask_or_polygons, height, width):
self._mask = self._polygons = self._has_holes = None
self.height = height
self.width = width
m = mask_or_polygons
if isinstance(m, dict):
# RLEs
assert "counts" in m and "size" in m
if isinstance(m["counts"], list): # uncompressed RLEs
h, w = m["size"]
assert h == height and w == width
m = mask_util.frPyObjects(m, h, w)
self._mask = mask_util.decode(m)[:, :]
return
if isinstance(m, list): # list[ndarray]
self._polygons = [np.asarray(x).reshape(-1) for x in m]
return
if isinstance(m, np.ndarray): # assumed to be a binary mask
assert m.shape[1] != 2, m.shape
assert m.shape == (
height,
width,
), f"mask shape: {m.shape}, target dims: {height}, {width}"
self._mask = m.astype("uint8")
return
raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m)))
@property
def mask(self):
if self._mask is None:
self._mask = self.polygons_to_mask(self._polygons)
return self._mask
@property
def polygons(self):
if self._polygons is None:
self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
return self._polygons
@property
def has_holes(self):
if self._has_holes is None:
if self._mask is not None:
self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
else:
self._has_holes = False # if original format is polygon, does not have holes
return self._has_holes
def mask_to_polygons(self, mask):
# cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level
# hierarchy. External contours (boundary) of the object are placed in hierarchy-1.
# Internal contours (holes) are placed in hierarchy-2.
# cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.
mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr
res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
hierarchy = res[-1]
if hierarchy is None: # empty mask
return [], False
has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0
res = res[-2]
res = [x.flatten() for x in res]
# These coordinates from OpenCV are integers in range [0, W-1 or H-1].
# We add 0.5 to turn them into real-value coordinate space. A better solution
# would be to first +0.5 and then dilate the returned polygon by 0.5.
res = [x + 0.5 for x in res if len(x) >= 6]
return res, has_holes
def polygons_to_mask(self, polygons):
rle = mask_util.frPyObjects(polygons, self.height, self.width)
rle = mask_util.merge(rle)
return mask_util.decode(rle)[:, :]
def area(self):
return self.mask.sum()
def bbox(self):
p = mask_util.frPyObjects(self.polygons, self.height, self.width)
p = mask_util.merge(p)
bbox = mask_util.toBbox(p)
bbox[2] += bbox[0]
bbox[3] += bbox[1]
return bbox
class _PanopticPrediction:
"""
Unify different panoptic annotation/prediction formats
"""
def __init__(self, panoptic_seg, segments_info, metadata=None):
if segments_info is None:
assert metadata is not None
# If "segments_info" is None, we assume "panoptic_img" is a
# H*W int32 image storing the panoptic_id in the format of
# category_id * label_divisor + instance_id. We reserve -1 for
# VOID label.
label_divisor = metadata.label_divisor
segments_info = []
for panoptic_label in np.unique(panoptic_seg.numpy()):
if panoptic_label == -1:
# VOID region.
continue
pred_class = panoptic_label // label_divisor
isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()
segments_info.append(
{
"id": int(panoptic_label),
"category_id": int(pred_class),
"isthing": bool(isthing),
}
)
del metadata
self._seg = panoptic_seg
self._sinfo = {s["id"]: s for s in segments_info} # seg id -> seg info
segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)
areas = areas.numpy()
sorted_idxs = np.argsort(-areas)
self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]
self._seg_ids = self._seg_ids.tolist()
for sid, area in zip(self._seg_ids, self._seg_areas):
if sid in self._sinfo:
self._sinfo[sid]["area"] = float(area)
def non_empty_mask(self):
"""
Returns:
(H, W) array, a mask for all pixels that have a prediction
"""
empty_ids = []
for id in self._seg_ids:
if id not in self._sinfo:
empty_ids.append(id)
if len(empty_ids) == 0:
return np.zeros(self._seg.shape, dtype=np.uint8)
assert (
len(empty_ids) == 1
), ">1 ids corresponds to no labels. This is currently not supported"
return (self._seg != empty_ids[0]).numpy().astype(bool)
def semantic_masks(self):
for sid in self._seg_ids:
sinfo = self._sinfo.get(sid)
if sinfo is None or sinfo["isthing"]:
# Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.
continue
yield (self._seg == sid).numpy().astype(bool), sinfo
def instance_masks(self):
for sid in self._seg_ids:
sinfo = self._sinfo.get(sid)
if sinfo is None or not sinfo["isthing"]:
continue
mask = (self._seg == sid).numpy().astype(bool)
if mask.sum() > 0:
yield mask, sinfo
def _create_text_labels(classes, scores, class_names, is_crowd=None):
"""
Args:
classes (list[int] or None):
scores (list[float] or None):
class_names (list[str] or None):
is_crowd (list[bool] or None):
Returns:
list[str] or None
"""
labels = None
if classes is not None:
if class_names is not None and len(class_names) > 0:
labels = [class_names[i] for i in classes]
else:
labels = [str(i) for i in classes]
if scores is not None:
if labels is None:
labels = ["{:.0f}%".format(s * 100) for s in scores]
else:
labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)]
if labels is not None and is_crowd is not None:
labels = [l + ("|crowd" if crowd else "") for l, crowd in zip(labels, is_crowd)]
return labels
class VisImage:
def __init__(self, img, scale=1.0):
"""
Args:
img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].
scale (float): scale the input image
"""
self.img = img
self.scale = scale
self.width, self.height = img.shape[1], img.shape[0]
self._setup_figure(img)
def _setup_figure(self, img):
"""
Args:
Same as in :meth:`__init__()`.
Returns:
fig (matplotlib.pyplot.figure): top level container for all the image plot elements.
ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.
"""
fig = mplfigure.Figure(frameon=False)
self.dpi = fig.get_dpi()
# add a small 1e-2 to avoid precision lost due to matplotlib's truncation
# (https://github.com/matplotlib/matplotlib/issues/15363)
fig.set_size_inches(
(self.width * self.scale + 1e-2) / self.dpi,
(self.height * self.scale + 1e-2) / self.dpi,
)
self.canvas = FigureCanvasAgg(fig)
# self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
ax.axis("off")
self.fig = fig
self.ax = ax
self.reset_image(img)
def reset_image(self, img):
"""
Args:
img: same as in __init__
"""
img = img.astype("uint8")
self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")
def save(self, filepath):
"""
Args:
filepath (str): a string that contains the absolute path, including the file name, where
the visualized image will be saved.
"""
self.fig.savefig(filepath)
def get_image(self):
"""
Returns:
ndarray:
the visualized image of shape (H, W, 3) (RGB) in uint8 type.
The shape is scaled w.r.t the input image using the given `scale` argument.
"""
canvas = self.canvas
s, (width, height) = canvas.print_to_buffer()
# buf = io.BytesIO() # works for cairo backend
# canvas.print_rgba(buf)
# width, height = self.width, self.height
# s = buf.getvalue()
buffer = np.frombuffer(s, dtype="uint8")
img_rgba = buffer.reshape(height, width, 4)
rgb, alpha = np.split(img_rgba, [3], axis=2)
return rgb.astype("uint8")
class Visualizer:
"""
Visualizer that draws data about detection/segmentation on images.
It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`
that draw primitive objects to images, as well as high-level wrappers like
`draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`
that draw composite data in some pre-defined style.
Note that the exact visualization style for the high-level wrappers are subject to change.
Style such as color, opacity, label contents, visibility of labels, or even the visibility
of objects themselves (e.g. when the object is too small) may change according
to different heuristics, as long as the results still look visually reasonable.
To obtain a consistent style, you can implement custom drawing functions with the
abovementioned primitive methods instead. If you need more customized visualization
styles, you can process the data yourself following their format documented in
tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not
intend to satisfy everyone's preference on drawing styles.
This visualizer focuses on high rendering quality rather than performance. It is not
designed to be used for real-time applications.
"""
# TODO implement a fast, rasterized version using OpenCV
def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):
"""
Args:
img_rgb: a numpy array of shape (H, W, C), where H and W correspond to
the height and width of the image respectively. C is the number of
color channels. The image is required to be in RGB format since that
is a requirement of the Matplotlib library. The image is also expected
to be in the range [0, 255].
metadata (Metadata): dataset metadata (e.g. class names and colors)
instance_mode (ColorMode): defines one of the pre-defined style for drawing
instances on an image.
"""
self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
if metadata is None:
metadata = MetadataCatalog.get("__nonexist__")
self.metadata = metadata
self.output = VisImage(self.img, scale=scale)
self.cpu_device = torch.device("cpu")
# too small texts are useless, therefore clamp to 9
self._default_font_size = max(
np.sqrt(self.output.height * self.output.width) // 90, 10 // scale
)
self._instance_mode = instance_mode
self.keypoint_threshold = _KEYPOINT_THRESHOLD
def draw_instance_predictions(self, predictions):
"""
Draw instance-level prediction results on an image.
Args:
predictions (Instances): the output of an instance detection/segmentation
model. Following fields will be used to draw:
"pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
Returns:
output (VisImage): image object with visualizations.
"""
boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None
scores = predictions.scores if predictions.has("scores") else None
classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None
labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None))
keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None
if predictions.has("pred_masks"):
masks = np.asarray(predictions.pred_masks)
masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]
else:
masks = None
if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
colors = [
self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes
]
alpha = 0.8
else:
colors = None
alpha = 0.5
if self._instance_mode == ColorMode.IMAGE_BW:
self.output.reset_image(
self._create_grayscale_image(
(predictions.pred_masks.any(dim=0) > 0).numpy()
if predictions.has("pred_masks")
else None
)
)
alpha = 0.3
self.overlay_instances(
masks=masks,
boxes=boxes,
labels=labels,
keypoints=keypoints,
assigned_colors=colors,
alpha=alpha,
)
return self.output
def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):
"""
Draw semantic segmentation predictions/labels.
Args:
sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
Each value is the integer label of the pixel.
area_threshold (int): segments with less than `area_threshold` are not drawn.
alpha (float): the larger it is, the more opaque the segmentations are.
Returns:
output (VisImage): image object with visualizations.
"""
if isinstance(sem_seg, torch.Tensor):
sem_seg = sem_seg.numpy()
labels, areas = np.unique(sem_seg, return_counts=True)
sorted_idxs = np.argsort(-areas).tolist()
labels = labels[sorted_idxs]
for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):
try:
mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]
except (AttributeError, IndexError):
mask_color = None
binary_mask = (sem_seg == label).astype(np.uint8)
text = self.metadata.stuff_classes[label]
self.draw_binary_mask(
binary_mask,
color=mask_color,
edge_color=_OFF_WHITE,
text=text,
alpha=alpha,
area_threshold=area_threshold,
)
return self.output
def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7):
"""
Draw panoptic prediction annotations or results.
Args:
panoptic_seg (Tensor): of shape (height, width) where the values are ids for each
segment.
segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.
If it is a ``list[dict]``, each dict contains keys "id", "category_id".
If None, category id of each pixel is computed by
``pixel // metadata.label_divisor``.
area_threshold (int): stuff segments with less than `area_threshold` are not drawn.
Returns:
output (VisImage): image object with visualizations.
"""
pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)
if self._instance_mode == ColorMode.IMAGE_BW:
self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))
# draw mask for all semantic segments first i.e. "stuff"
for mask, sinfo in pred.semantic_masks():
category_idx = sinfo["category_id"]
try:
mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]
except AttributeError:
mask_color = None
text = self.metadata.stuff_classes[category_idx]
self.draw_binary_mask(
mask,
color=mask_color,
edge_color=_OFF_WHITE,
text=text,
alpha=alpha,
area_threshold=area_threshold,
)
# draw mask for all instances second
all_instances = list(pred.instance_masks())
if len(all_instances) == 0:
return self.output
masks, sinfo = list(zip(*all_instances))
category_ids = [x["category_id"] for x in sinfo]
try:
scores = [x["score"] for x in sinfo]
except KeyError:
scores = None
labels = _create_text_labels(
category_ids, scores, self.metadata.thing_classes, [x.get("iscrowd", 0) for x in sinfo]
)
try:
colors = [
self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in category_ids
]
except AttributeError:
colors = None
self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha)
return self.output
draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility
def draw_dataset_dict(self, dic):
"""
Draw annotations/segmentations in Detectron2 Dataset format.
Args:
dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.
Returns:
output (VisImage): image object with visualizations.
"""
annos = dic.get("annotations", None)
if annos:
if "segmentation" in annos[0]:
masks = [x["segmentation"] for x in annos]
else:
masks = None
if "keypoints" in annos[0]:
keypts = [x["keypoints"] for x in annos]
keypts = np.array(keypts).reshape(len(annos), -1, 3)
else:
keypts = None
boxes = [
BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS)
if len(x["bbox"]) == 4
else x["bbox"]
for x in annos
]
colors = None
category_ids = [x["category_id"] for x in annos]
if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
colors = [
self._jitter([x / 255 for x in self.metadata.thing_colors[c]])
for c in category_ids
]
names = self.metadata.get("thing_classes", None)
labels = _create_text_labels(
category_ids,
scores=None,
class_names=names,
is_crowd=[x.get("iscrowd", 0) for x in annos],
)
self.overlay_instances(
labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors
)
sem_seg = dic.get("sem_seg", None)
if sem_seg is None and "sem_seg_file_name" in dic:
with PathManager.open(dic["sem_seg_file_name"], "rb") as f:
sem_seg = Image.open(f)
sem_seg = np.asarray(sem_seg, dtype="uint8")
if sem_seg is not None:
self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)
pan_seg = dic.get("pan_seg", None)
if pan_seg is None and "pan_seg_file_name" in dic:
with PathManager.open(dic["pan_seg_file_name"], "rb") as f:
pan_seg = Image.open(f)
pan_seg = np.asarray(pan_seg)
pan_seg = rgb2id(pan_seg)
if pan_seg is not None:
segments_info = dic["segments_info"]
pan_seg = torch.tensor(pan_seg)
self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)
return self.output
def overlay_instances(
self,
*,
boxes=None,
labels=None,
masks=None,
keypoints=None,
assigned_colors=None,
alpha=0.5,
):
"""
Args:
boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
or a :class:`RotatedBoxes`,
or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
for the N objects in a single image,
labels (list[str]): the text to be displayed for each instance.
masks (masks-like object): Supported types are:
* :class:`detectron2.structures.PolygonMasks`,
:class:`detectron2.structures.BitMasks`.
* list[list[ndarray]]: contains the segmentation masks for all objects in one image.
The first level of the list corresponds to individual instances. The second
level to all the polygon that compose the instance, and the third level
to the polygon coordinates. The third level should have the format of
[x0, y0, x1, y1, ..., xn, yn] (n >= 3).
* list[ndarray]: each ndarray is a binary mask of shape (H, W).
* list[dict]: each dict is a COCO-style RLE.
keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),
where the N is the number of instances and K is the number of keypoints.
The last dimension corresponds to (x, y, visibility or score).
assigned_colors (list[matplotlib.colors]): a list of colors, where each color
corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
for full list of formats that the colors are accepted in.
Returns:
output (VisImage): image object with visualizations.
"""
num_instances = 0
if boxes is not None:
boxes = self._convert_boxes(boxes)
num_instances = len(boxes)
if masks is not None:
masks = self._convert_masks(masks)
if num_instances:
assert len(masks) == num_instances
else:
num_instances = len(masks)
if keypoints is not None:
if num_instances:
assert len(keypoints) == num_instances
else:
num_instances = len(keypoints)
keypoints = self._convert_keypoints(keypoints)
if labels is not None:
assert len(labels) == num_instances
if assigned_colors is None:
assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
if num_instances == 0:
return self.output
if boxes is not None and boxes.shape[1] == 5:
return self.overlay_rotated_instances(
boxes=boxes, labels=labels, assigned_colors=assigned_colors
)
# Display in largest to smallest order to reduce occlusion.
areas = None
if boxes is not None:
areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)
elif masks is not None:
areas = np.asarray([x.area() for x in masks])
if areas is not None:
sorted_idxs = np.argsort(-areas).tolist()
# Re-order overlapped instances in descending order.
boxes = boxes[sorted_idxs] if boxes is not None else None
labels = [labels[k] for k in sorted_idxs] if labels is not None else None
masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None
assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]
keypoints = keypoints[sorted_idxs] if keypoints is not None else None
for i in range(num_instances):
color = assigned_colors[i]
if boxes is not None:
self.draw_box(boxes[i], edge_color=color)
if masks is not None:
for segment in masks[i].polygons:
self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha)
if labels is not None:
# first get a box
if boxes is not None:
x0, y0, x1, y1 = boxes[i]
text_pos = (x0, y0) # if drawing boxes, put text on the box corner.
horiz_align = "left"
elif masks is not None:
# skip small mask without polygon
if len(masks[i].polygons) == 0:
continue
x0, y0, x1, y1 = masks[i].bbox()
# draw text in the center (defined by median) when box is not drawn
# median is less sensitive to outliers.
text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1]
horiz_align = "center"
else:
continue # drawing the box confidence for keypoints isn't very useful.
# for small objects, draw text at the side to avoid occlusion
instance_area = (y1 - y0) * (x1 - x0)
if (
instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
or y1 - y0 < 40 * self.output.scale
):
if y1 >= self.output.height - 5:
text_pos = (x1, y0)
else:
text_pos = (x0, y1)
height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
font_size = (
np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
* 0.5
* self._default_font_size
)
self.draw_text(
labels[i],
text_pos,
color=lighter_color,
horizontal_alignment=horiz_align,
font_size=font_size,
)
# draw keypoints
if keypoints is not None:
for keypoints_per_instance in keypoints:
self.draw_and_connect_keypoints(keypoints_per_instance)
return self.output
def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):
"""
Args:
boxes (ndarray): an Nx5 numpy array of
(x_center, y_center, width, height, angle_degrees) format
for the N objects in a single image.
labels (list[str]): the text to be displayed for each instance.
assigned_colors (list[matplotlib.colors]): a list of colors, where each color
corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
for full list of formats that the colors are accepted in.
Returns:
output (VisImage): image object with visualizations.
"""
num_instances = len(boxes)
if assigned_colors is None:
assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
if num_instances == 0:
return self.output
# Display in largest to smallest order to reduce occlusion.
if boxes is not None:
areas = boxes[:, 2] * boxes[:, 3]
sorted_idxs = np.argsort(-areas).tolist()
# Re-order overlapped instances in descending order.
boxes = boxes[sorted_idxs]
labels = [labels[k] for k in sorted_idxs] if labels is not None else None
colors = [assigned_colors[idx] for idx in sorted_idxs]
for i in range(num_instances):
self.draw_rotated_box_with_label(
boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None
)
return self.output
def draw_and_connect_keypoints(self, keypoints):
"""
Draws keypoints of an instance and follows the rules for keypoint connections
to draw lines between appropriate keypoints. This follows color heuristics for
line color.
Args:
keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints
and the last dimension corresponds to (x, y, probability).
Returns:
output (VisImage): image object with visualizations.
"""
visible = {}
keypoint_names = self.metadata.get("keypoint_names")
for idx, keypoint in enumerate(keypoints):
# draw keypoint
x, y, prob = keypoint
if prob > self.keypoint_threshold:
self.draw_circle((x, y), color=_RED)
if keypoint_names:
keypoint_name = keypoint_names[idx]
visible[keypoint_name] = (x, y)
if self.metadata.get("keypoint_connection_rules"):
for kp0, kp1, color in self.metadata.keypoint_connection_rules:
if kp0 in visible and kp1 in visible:
x0, y0 = visible[kp0]
x1, y1 = visible[kp1]
color = tuple(x / 255.0 for x in color)
self.draw_line([x0, x1], [y0, y1], color=color)
# draw lines from nose to mid-shoulder and mid-shoulder to mid-hip
# Note that this strategy is specific to person keypoints.
# For other keypoints, it should just do nothing
try:
ls_x, ls_y = visible["left_shoulder"]
rs_x, rs_y = visible["right_shoulder"]
mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2
except KeyError:
pass
else:
# draw line from nose to mid-shoulder
nose_x, nose_y = visible.get("nose", (None, None))
if nose_x is not None:
self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED)
try:
# draw line from mid-shoulder to mid-hip
lh_x, lh_y = visible["left_hip"]
rh_x, rh_y = visible["right_hip"]
except KeyError:
pass
else:
mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2
self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED)
return self.output
"""
Primitive drawing functions:
"""
def draw_text(
self,
text,
position,
*,
font_size=None,
color="g",
horizontal_alignment="center",
rotation=0,
):
"""
Args:
text (str): class label
position (tuple): a tuple of the x and y coordinates to place text on image.
font_size (int, optional): font of the text. If not provided, a font size
proportional to the image width is calculated and used.
color: color of the text. Refer to `matplotlib.colors` for full list
of formats that are accepted.
horizontal_alignment (str): see `matplotlib.text.Text`
rotation: rotation angle in degrees CCW
Returns:
output (VisImage): image object with text drawn.
"""
if not font_size:
font_size = self._default_font_size
# since the text background is dark, we don't want the text to be dark
color = np.maximum(list(mplc.to_rgb(color)), 0.2)
color[np.argmax(color)] = max(0.8, np.max(color))
x, y = position
self.output.ax.text(
x,
y,
text,
size=font_size * self.output.scale,
family="sans-serif",
bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
verticalalignment="top",
horizontalalignment=horizontal_alignment,
color=color,
zorder=10,
rotation=rotation,
)
return self.output
def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):
"""
Args:
box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0
are the coordinates of the image's top left corner. x1 and y1 are the
coordinates of the image's bottom right corner.
alpha (float): blending efficient. Smaller values lead to more transparent masks.
edge_color: color of the outline of the box. Refer to `matplotlib.colors`
for full list of formats that are accepted.
line_style (string): the string to use to create the outline of the boxes.
Returns:
output (VisImage): image object with box drawn.
"""
x0, y0, x1, y1 = box_coord
width = x1 - x0
height = y1 - y0
linewidth = max(self._default_font_size / 4, 1)
self.output.ax.add_patch(
mpl.patches.Rectangle(
(x0, y0),
width,
height,
fill=False,
edgecolor=edge_color,
linewidth=linewidth * self.output.scale,
alpha=alpha,
linestyle=line_style,
)
)
return self.output
def draw_rotated_box_with_label(
self, rotated_box, alpha=0.5, edge_color="g", line_style="-", label=None
):
"""
Draw a rotated box with label on its top-left corner.
Args:
rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle),
where cnt_x and cnt_y are the center coordinates of the box.
w and h are the width and height of the box. angle represents how
many degrees the box is rotated CCW with regard to the 0-degree box.
alpha (float): blending efficient. Smaller values lead to more transparent masks.
edge_color: color of the outline of the box. Refer to `matplotlib.colors`
for full list of formats that are accepted.
line_style (string): the string to use to create the outline of the boxes.
label (string): label for rotated box. It will not be rendered when set to None.
Returns:
output (VisImage): image object with box drawn.
"""
cnt_x, cnt_y, w, h, angle = rotated_box
area = w * h
# use thinner lines when the box is small
linewidth = self._default_font_size / (
6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3
)
theta = angle * math.pi / 180.0
c = math.cos(theta)
s = math.sin(theta)
rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)]
# x: left->right ; y: top->down
rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect]
for k in range(4):
j = (k + 1) % 4
self.draw_line(
[rotated_rect[k][0], rotated_rect[j][0]],
[rotated_rect[k][1], rotated_rect[j][1]],
color=edge_color,
linestyle="--" if k == 1 else line_style,
linewidth=linewidth,
)
if label is not None:
text_pos = rotated_rect[1] # topleft corner
height_ratio = h / np.sqrt(self.output.height * self.output.width)
label_color = self._change_color_brightness(edge_color, brightness_factor=0.7)
font_size = (
np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size
)
self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle)
return self.output
def draw_circle(self, circle_coord, color, radius=3):
"""
Args:
circle_coord (list(int) or tuple(int)): contains the x and y coordinates
of the center of the circle.
color: color of the polygon. Refer to `matplotlib.colors` for a full list of
formats that are accepted.
radius (int): radius of the circle.
Returns:
output (VisImage): image object with box drawn.
"""
x, y = circle_coord
self.output.ax.add_patch(
mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)
)
return self.output
def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None):
"""
Args:
x_data (list[int]): a list containing x values of all the points being drawn.
Length of list should match the length of y_data.
y_data (list[int]): a list containing y values of all the points being drawn.
Length of list should match the length of x_data.
color: color of the line. Refer to `matplotlib.colors` for a full list of
formats that are accepted.
linestyle: style of the line. Refer to `matplotlib.lines.Line2D`
for a full list of formats that are accepted.
linewidth (float or None): width of the line. When it's None,
a default value will be computed and used.
Returns:
output (VisImage): image object with line drawn.
"""
if linewidth is None:
linewidth = self._default_font_size / 3
linewidth = max(linewidth, 1)
self.output.ax.add_line(
mpl.lines.Line2D(
x_data,
y_data,
linewidth=linewidth * self.output.scale,
color=color,
linestyle=linestyle,
)
)
return self.output
def draw_binary_mask(
self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10
):
"""
Args:
binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and
W is the image width. Each value in the array is either a 0 or 1 value of uint8
type.
color: color of the mask. Refer to `matplotlib.colors` for a full list of
formats that are accepted. If None, will pick a random color.
edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
full list of formats that are accepted.
text (str): if None, will be drawn on the object
alpha (float): blending efficient. Smaller values lead to more transparent masks.
area_threshold (float): a connected component smaller than this area will not be shown.
Returns:
output (VisImage): image object with mask drawn.
"""
if color is None:
color = random_color(rgb=True, maximum=1)
color = mplc.to_rgb(color)
has_valid_segment = False
binary_mask = binary_mask.astype("uint8") # opencv needs uint8
mask = GenericMask(binary_mask, self.output.height, self.output.width)
shape2d = (binary_mask.shape[0], binary_mask.shape[1])
if not mask.has_holes:
# draw polygons for regular masks
for segment in mask.polygons:
area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1]))
if area < (area_threshold or 0):
continue
has_valid_segment = True
segment = segment.reshape(-1, 2)
self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha)
else:
# TODO: Use Path/PathPatch to draw vector graphics:
# https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon
rgba = np.zeros(shape2d + (4,), dtype="float32")
rgba[:, :, :3] = color
rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha
has_valid_segment = True
self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))
if text is not None and has_valid_segment:
lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
self._draw_text_in_mask(binary_mask, text, lighter_color)
return self.output
def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):
"""
Args:
soft_mask (ndarray): float array of shape (H, W), each value in [0, 1].
color: color of the mask. Refer to `matplotlib.colors` for a full list of
formats that are accepted. If None, will pick a random color.
text (str): if None, will be drawn on the object
alpha (float): blending efficient. Smaller values lead to more transparent masks.
Returns:
output (VisImage): image object with mask drawn.
"""
if color is None:
color = random_color(rgb=True, maximum=1)
color = mplc.to_rgb(color)
shape2d = (soft_mask.shape[0], soft_mask.shape[1])
rgba = np.zeros(shape2d + (4,), dtype="float32")
rgba[:, :, :3] = color
rgba[:, :, 3] = soft_mask * alpha
self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))
if text is not None:
lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
binary_mask = (soft_mask > 0.5).astype("uint8")
self._draw_text_in_mask(binary_mask, text, lighter_color)
return self.output
def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):
"""
Args:
segment: numpy array of shape Nx2, containing all the points in the polygon.
color: color of the polygon. Refer to `matplotlib.colors` for a full list of
formats that are accepted.
edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
full list of formats that are accepted. If not provided, a darker shade
of the polygon color will be used instead.
alpha (float): blending efficient. Smaller values lead to more transparent masks.
Returns:
output (VisImage): image object with polygon drawn.
"""
if edge_color is None:
# make edge color darker than the polygon color
if alpha > 0.8:
edge_color = self._change_color_brightness(color, brightness_factor=-0.7)
else:
edge_color = color
edge_color = mplc.to_rgb(edge_color) + (1,)
polygon = mpl.patches.Polygon(
segment,
fill=True,
facecolor=mplc.to_rgb(color) + (alpha,),
edgecolor=edge_color,
linewidth=max(self._default_font_size // 15 * self.output.scale, 1),
)
self.output.ax.add_patch(polygon)
return self.output
"""
Internal methods:
"""
def _jitter(self, color):
"""
Randomly modifies given color to produce a slightly different color than the color given.
Args:
color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color
picked. The values in the list are in the [0.0, 1.0] range.
Returns:
jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the
color after being jittered. The values in the list are in the [0.0, 1.0] range.
"""
color = mplc.to_rgb(color)
vec = np.random.rand(3)
# better to do it in another color space
vec = vec / np.linalg.norm(vec) * 0.5
res = np.clip(vec + color, 0, 1)
return tuple(res)
def _create_grayscale_image(self, mask=None):
"""
Create a grayscale version of the original image.
The colors in masked area, if given, will be kept.
"""
img_bw = self.img.astype("f4").mean(axis=2)
img_bw = np.stack([img_bw] * 3, axis=2)
if mask is not None:
img_bw[mask] = self.img[mask]
return img_bw
def _change_color_brightness(self, color, brightness_factor):
"""
Depending on the brightness_factor, gives a lighter or darker color i.e. a color with
less or more saturation than the original color.
Args:
color: color of the polygon. Refer to `matplotlib.colors` for a full list of
formats that are accepted.
brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of
0 will correspond to no change, a factor in [-1.0, 0) range will result in
a darker color and a factor in (0, 1.0] range will result in a lighter color.
Returns:
modified_color (tuple[double]): a tuple containing the RGB values of the
modified color. Each value in the tuple is in the [0.0, 1.0] range.
"""
assert brightness_factor >= -1.0 and brightness_factor <= 1.0
color = mplc.to_rgb(color)
polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))
modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])
modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness
modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness
modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])
return tuple(np.clip(modified_color, 0.0, 1.0))
def _convert_boxes(self, boxes):
"""
Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.
"""
if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):
return boxes.tensor.detach().numpy()
else:
return np.asarray(boxes)
def _convert_masks(self, masks_or_polygons):
"""
Convert different format of masks or polygons to a tuple of masks and polygons.
Returns:
list[GenericMask]:
"""
m = masks_or_polygons
if isinstance(m, PolygonMasks):
m = m.polygons
if isinstance(m, BitMasks):
m = m.tensor.numpy()
if isinstance(m, torch.Tensor):
m = m.numpy()
ret = []
for x in m:
if isinstance(x, GenericMask):
ret.append(x)
else:
ret.append(GenericMask(x, self.output.height, self.output.width))
return ret
def _draw_text_in_mask(self, binary_mask, text, color):
"""
Find proper places to draw text given a binary mask.
"""
# TODO sometimes drawn on wrong objects. the heuristics here can improve.
_num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)
if stats[1:, -1].size == 0:
return
largest_component_id = np.argmax(stats[1:, -1]) + 1
# draw text on the largest component, as well as other very large components.
for cid in range(1, _num_cc):
if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:
# median is more stable than centroid
# center = centroids[largest_component_id]
center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]
self.draw_text(text, center, color=color)
def _convert_keypoints(self, keypoints): | if isinstance(keypoints, Keypoints): | 3 | 2023-12-10 20:14:00+00:00 | 24k |
mkang315/ASF-YOLO | segment/val.py | [
{
"identifier": "DetectMultiBackend",
"path": "models/common.py",
"snippet": "class DetectMultiBackend(nn.Module):\n # YOLOv5 MultiBackend class for python inference on various backends\n def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=Tr... | import argparse
import json
import os
import sys
import numpy as np
import torch
import torch.nn.functional as F
import time
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tqdm import tqdm
from models.common import DetectMultiBackend
from models.yolo import SegmentationModel
from utils.callbacks import Callbacks
from utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size,
check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path,
non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
from utils.metrics import ConfusionMatrix, box_iou
from utils.plots import output_to_target, plot_val_study
from utils.segment.dataloaders import create_dataloader
from utils.segment.general import mask_iou, process_mask, process_mask_upsample, scale_image
from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
from utils.segment.plots import plot_images_and_masks
from utils.torch_utils import de_parallel, select_device, smart_inference_mode
from pycocotools.mask import encode
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval | 21,426 | labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
correct_bboxes = process_batch(predn, labelsn, iouv)
correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)
if plots:
confusion_matrix.process_batch(predn, labelsn)
stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)
pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
if plots and batch_i < 3:
plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot
# Save/log
if save_txt:
save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
if save_json:
pred_masks = scale_image(im[si].shape[1:],
pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1])
save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary
# callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
# Plot images
if plots and batch_i < 3:
if len(plot_masks):
plot_masks = torch.cat(plot_masks, dim=0)
plot_images_and_masks(im, targets, masks, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names)
plot_images_and_masks(im, output_to_target(preds, max_det=15), plot_masks, paths,
save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
# callbacks.run('on_val_batch_end')
# Compute metrics
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
if len(stats) and stats[0].any():
results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names)
metrics.update(results)
nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class
# Print results
pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format
LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results()))
if nt.sum() == 0:
LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels')
# Print results per class
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
for i, c in enumerate(metrics.ap_class_index):
LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i)))
# Print speeds
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
if not training:
shape = (batch_size, 3, imgsz, imgsz)
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
# Plots
if plots:
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
# callbacks.run('on_val_end')
mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results()
# Save JSON
if save_json and len(jdict):
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
with open(pred_json, 'w') as f:
json.dump(jdict, f)
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
anno = COCO(anno_json) # init annotations api
pred = anno.loadRes(pred_json) # init predictions api
results = []
for eval in COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm'):
if is_coco:
eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate
eval.evaluate()
eval.accumulate()
eval.summarize()
results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5)
map_bbox, map50_bbox, map_mask, map50_mask = results
except Exception as e:
LOGGER.info(f'pycocotools unable to run: {e}')
# Return results
model.float() # for training
if not training:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask
return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default=ROOT / 'data/BCC.yaml', help='dataset.yaml path')
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs_2/train-seg/base/weights/best.pt', help='model path(s)')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.01, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image')
parser.add_argument('--task', default='val', help='train, val, test, speed or study')
parser.add_argument('--device', default='6', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--verbose', action='store_true', help='report mAP by class')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
parser.add_argument('--project', default=ROOT / 'runs_2/val_test', help='save results to project/name')
parser.add_argument('--name', default='base', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
opt = parser.parse_args()
| # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Validate a trained YOLOv5 segment model on a segment dataset
Usage:
$ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images)
$ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments
Usage - formats:
$ python segment/val.py --weights yolov5s-seg.pt # PyTorch
yolov5s-seg.torchscript # TorchScript
yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s-seg_openvino_label # OpenVINO
yolov5s-seg.engine # TensorRT
yolov5s-seg.mlmodel # CoreML (macOS-only)
yolov5s-seg_saved_model # TensorFlow SavedModel
yolov5s-seg.pb # TensorFlow GraphDef
yolov5s-seg.tflite # TensorFlow Lite
yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
yolov5s-seg_paddle_model # PaddlePaddle
"""
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
def save_one_txt(predn, save_conf, shape, file):
# Save one txt result
gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(file, 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
def save_one_json(predn, jdict, path, class_map, pred_masks):
# Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
def single_encode(x):
rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
rle["counts"] = rle["counts"].decode("utf-8")
return rle
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
pred_masks = np.transpose(pred_masks, (2, 0, 1))
with ThreadPool(NUM_THREADS) as pool:
rles = pool.map(single_encode, pred_masks)
for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())):
jdict.append({
'image_id': image_id,
'category_id': class_map[int(p[5])],
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5),
'segmentation': rles[i]})
def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):
"""
Return correct prediction matrix
Arguments:
detections (array[N, 6]), x1, y1, x2, y2, conf, class
labels (array[M, 5]), class, x1, y1, x2, y2
Returns:
correct (array[N, 10]), for 10 IoU levels
"""
if masks:
if overlap:
nl = len(labels)
index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
if gt_masks.shape[1:] != pred_masks.shape[1:]:
gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
gt_masks = gt_masks.gt_(0.5)
iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
else: # boxes
iou = box_iou(labels[:, 1:], detections[:, :4])
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(iouv)):
x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
@smart_inference_mode()
def run(
data,
weights=None, # model.pt path(s)
batch_size=32, # batch size
imgsz=640, # inference size (pixels)
conf_thres=0.001, # confidence threshold
iou_thres=0.6, # NMS IoU threshold
max_det=300, # maximum detections per image
task='val', # train, val, test, speed or study
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
workers=8, # max dataloader workers (per RANK in DDP mode)
single_cls=False, # treat as single-class dataset
augment=False, # augmented inference
verbose=False, # verbose output
save_txt=False, # save results to *.txt
save_hybrid=False, # save label+prediction hybrid results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_json=False, # save a COCO-JSON results file
project=ROOT / 'runs/val-seg', # save to project/name
name='exp', # save to project/name
exist_ok=False, # existing project/name ok, do not increment
half=True, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
model=None,
dataloader=None,
save_dir=Path(''),
plots=True,
overlap=False,
mask_downsample_ratio=1,
compute_loss=None,
callbacks=Callbacks(),
):
if save_json:
check_requirements(['pycocotools'])
process = process_mask_upsample # more accurate
else:
process = process_mask # faster
# Initialize/load model and set device
training = model is not None
if training: # called by train.py
device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
half &= device.type != 'cpu' # half precision only supported on CUDA
model.half() if half else model.float()
nm = de_parallel(model).model[-1].nm # number of masks
else: # called directly
device = select_device(device, batch_size=batch_size)
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
half = model.fp16 # FP16 supported on limited backends with CUDA
nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks
if engine:
batch_size = model.batch_size
else:
device = model.device
if not (pt or jit):
batch_size = 1 # export.py models default to batch-size 1
LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
# Data
data = check_dataset(data) # check
# Configure
model.eval()
cuda = device.type != 'cpu'
is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset
nc = 1 if single_cls else int(data['nc']) # number of classes
iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
niou = iouv.numel()
# Dataloader
if not training:
if pt and not single_cls: # check --weights are trained on --data
ncm = model.model.nc
assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
f'classes). Pass correct combination of --weights and --data that are trained together.'
model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks
task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task],
imgsz,
batch_size,
stride,
single_cls,
pad=pad,
rect=rect,
workers=workers,
prefix=colorstr(f'{task}: '),
overlap_mask=overlap,
mask_downsample_ratio=mask_downsample_ratio)[0]
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
names = model.names if hasattr(model, 'names') else model.module.names # get class names
if isinstance(names, (list, tuple)): # old format
names = dict(enumerate(names))
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)", "Mask(P", "R",
"mAP50", "mAP50-95)")
dt = Profile(), Profile(), Profile()
metrics = Metrics()
loss = torch.zeros(4, device=device)
jdict, stats = [], []
# callbacks.run('on_val_start')
pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar):
# callbacks.run('on_val_batch_start')
with dt[0]:
if cuda:
im = im.to(device, non_blocking=True)
targets = targets.to(device)
masks = masks.to(device)
masks = masks.float()
im = im.half() if half else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
nb, _, height, width = im.shape # batch size, channels, height, width
# Inference
with dt[1]:
act = time.time()
preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None)
#print('time.time():',time.time()-act)
# Loss
if compute_loss:
loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls
# NMS
targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
with dt[2]:
preds = non_max_suppression(preds,
conf_thres,
iou_thres,
labels=lb,
multi_label=True,
agnostic=single_cls,
max_det=max_det,
nm=nm)
# Metrics
plot_masks = [] # masks for plotting
for si, (pred, proto) in enumerate(zip(preds, protos)):
labels = targets[targets[:, 0] == si, 1:]
nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
path, shape = Path(paths[si]), shapes[si][0]
correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
seen += 1
if npr == 0:
if nl:
stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0]))
if plots:
confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
continue
# Masks
midx = [si] if overlap else targets[:, 0] == si
gt_masks = masks[midx]
pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:])
# Predictions
if single_cls:
pred[:, 5] = 0
predn = pred.clone()
scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
# Evaluate
if nl:
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
correct_bboxes = process_batch(predn, labelsn, iouv)
correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)
if plots:
confusion_matrix.process_batch(predn, labelsn)
stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)
pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
if plots and batch_i < 3:
plot_masks.append(pred_masks[:15].cpu()) # filter top 15 to plot
# Save/log
if save_txt:
save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
if save_json:
pred_masks = scale_image(im[si].shape[1:],
pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1])
save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary
# callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
# Plot images
if plots and batch_i < 3:
if len(plot_masks):
plot_masks = torch.cat(plot_masks, dim=0)
plot_images_and_masks(im, targets, masks, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names)
plot_images_and_masks(im, output_to_target(preds, max_det=15), plot_masks, paths,
save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
# callbacks.run('on_val_batch_end')
# Compute metrics
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
if len(stats) and stats[0].any():
results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names)
metrics.update(results)
nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class
# Print results
pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format
LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results()))
if nt.sum() == 0:
LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels')
# Print results per class
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
for i, c in enumerate(metrics.ap_class_index):
LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i)))
# Print speeds
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
if not training:
shape = (batch_size, 3, imgsz, imgsz)
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
# Plots
if plots:
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
# callbacks.run('on_val_end')
mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results()
# Save JSON
if save_json and len(jdict):
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
with open(pred_json, 'w') as f:
json.dump(jdict, f)
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
anno = COCO(anno_json) # init annotations api
pred = anno.loadRes(pred_json) # init predictions api
results = []
for eval in COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm'):
if is_coco:
eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate
eval.evaluate()
eval.accumulate()
eval.summarize()
results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5)
map_bbox, map50_bbox, map_mask, map50_mask = results
except Exception as e:
LOGGER.info(f'pycocotools unable to run: {e}')
# Return results
model.float() # for training
if not training:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask
return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default=ROOT / 'data/BCC.yaml', help='dataset.yaml path')
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs_2/train-seg/base/weights/best.pt', help='model path(s)')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.01, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image')
parser.add_argument('--task', default='val', help='train, val, test, speed or study')
parser.add_argument('--device', default='6', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--verbose', action='store_true', help='report mAP by class')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
parser.add_argument('--project', default=ROOT / 'runs_2/val_test', help='save results to project/name')
parser.add_argument('--name', default='base', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
opt = parser.parse_args() | opt.data = check_yaml(opt.data) # check YAML | 10 | 2023-12-10 14:18:29+00:00 | 24k |
youngskkim/CRN | exps/base_exp.py | [
{
"identifier": "NuscDatasetRadarDet",
"path": "datasets/nusc_det_dataset.py",
"snippet": "class NuscDatasetRadarDet(Dataset):\n def __init__(self,\n ida_aug_conf,\n bda_aug_conf,\n rda_aug_conf,\n classes,\n data_root,\n... | from functools import partial
from pytorch_lightning.core import LightningModule
from torch.cuda.amp.autocast_mode import autocast
from torch.optim.lr_scheduler import MultiStepLR
from mmcv.runner import build_optimizer
from datasets.nusc_det_dataset import NuscDatasetRadarDet, collate_fn
from evaluators.det_evaluators import DetNuscEvaluator
from models.base_bev_depth import BaseBEVDepth
from utils.torch_dist import all_gather_object, synchronize
import mmcv
import torch
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
import torchvision.models as models | 16,127 | Output:
gt_depths: [B*N*h*w, d]
"""
B, N, H, W = gt_depths.shape
gt_depths = gt_depths.view(
B * N,
H // self.downsample_factor,
self.downsample_factor,
W // self.downsample_factor,
self.downsample_factor,
1,
)
gt_depths = gt_depths.permute(0, 1, 3, 5, 2, 4).contiguous()
gt_depths = gt_depths.view(
-1, self.downsample_factor * self.downsample_factor)
gt_depths_tmp = torch.where(gt_depths == 0.0,
1e5 * torch.ones_like(gt_depths),
gt_depths)
gt_depths = torch.min(gt_depths_tmp, dim=-1).values
gt_depths = gt_depths.view(B * N, H // self.downsample_factor,
W // self.downsample_factor)
gt_depths = (gt_depths -
(self.dbound[0] - self.dbound[2])) / self.dbound[2]
gt_depths = torch.where(
(gt_depths < self.depth_channels + 1) & (gt_depths > 0.),
gt_depths, torch.zeros_like(gt_depths))
gt_depths = F.one_hot(gt_depths.long(),
num_classes=self.depth_channels + 1).view(
-1, self.depth_channels + 1)[:, 1:]
return gt_depths.float()
def eval_step(self, batch, batch_idx, prefix: str):
(sweep_imgs, mats, img_metas, _, _, _, _, pts_pv) = batch
if torch.cuda.is_available():
if self.return_image:
sweep_imgs = sweep_imgs.cuda()
for key, value in mats.items():
mats[key] = value.cuda()
if self.return_radar_pv:
pts_pv = pts_pv.cuda()
preds = self(sweep_imgs, mats,
pts_pv=pts_pv,
is_train=False)
if isinstance(self.model, torch.nn.parallel.DistributedDataParallel):
results = self.model.module.get_bboxes(preds, img_metas)
else:
results = self.model.get_bboxes(preds, img_metas)
for i in range(len(results)):
results[i][0] = results[i][0].tensor.detach().cpu().numpy()
results[i][1] = results[i][1].detach().cpu().numpy()
results[i][2] = results[i][2].detach().cpu().numpy()
results[i].append(img_metas[i])
return results
def validation_epoch_end(self, validation_step_outputs):
detection_losses = list()
heatmap_losses = list()
bbox_losses = list()
depth_losses = list()
for validation_step_output in validation_step_outputs:
detection_losses.append(validation_step_output[0])
heatmap_losses.append(validation_step_output[1])
bbox_losses.append(validation_step_output[2])
depth_losses.append(validation_step_output[3])
synchronize()
self.log('val/detection', torch.mean(torch.stack(detection_losses)), on_epoch=True)
self.log('val/heatmap', torch.mean(torch.stack(heatmap_losses)), on_epoch=True)
self.log('val/bbox', torch.mean(torch.stack(bbox_losses)), on_epoch=True)
self.log('val/depth', torch.mean(torch.stack(depth_losses)), on_epoch=True)
def validation_step(self, batch, batch_idx):
(sweep_imgs, mats, _, gt_boxes_3d, gt_labels_3d, _, depth_labels, pts_pv) = batch
if torch.cuda.is_available():
if self.return_image:
sweep_imgs = sweep_imgs.cuda()
for key, value in mats.items():
mats[key] = value.cuda()
if self.return_radar_pv:
pts_pv = pts_pv.cuda()
gt_boxes_3d = [gt_box.cuda() for gt_box in gt_boxes_3d]
gt_labels_3d = [gt_label.cuda() for gt_label in gt_labels_3d]
with torch.no_grad():
preds, depth_preds = self(sweep_imgs, mats,
pts_pv=pts_pv,
is_train=True)
targets = self.model.get_targets(gt_boxes_3d, gt_labels_3d)
loss_detection, loss_heatmap, loss_bbox = self.model.loss(targets, preds)
if len(depth_labels.shape) == 5:
# only key-frame will calculate depth loss
depth_labels = depth_labels[:, 0, ...].contiguous()
loss_depth = self.get_depth_loss(depth_labels.cuda(), depth_preds, weight=3.)
return loss_detection, loss_heatmap, loss_bbox, loss_depth
def test_epoch_end(self, test_step_outputs):
all_pred_results = list()
all_img_metas = list()
for test_step_output in test_step_outputs:
for i in range(len(test_step_output)):
all_pred_results.append(test_step_output[i][:3])
all_img_metas.append(test_step_output[i][3])
synchronize()
# TODO: Change another way.
dataset_length = len(self.val_dataloader().dataset)
all_pred_results = sum(
map(list, zip(*all_gather_object(all_pred_results))),
[])[:dataset_length]
all_img_metas = sum(map(list, zip(*all_gather_object(all_img_metas))),
[])[:dataset_length]
if self.global_rank == 0:
self.evaluator.evaluate(all_pred_results, all_img_metas)
def configure_optimizers(self):
optimizer = build_optimizer(self.model, self.optimizer_config)
scheduler = MultiStepLR(optimizer, [19, 23])
return [[optimizer], [scheduler]]
def train_dataloader(self):
| # Copyright (c) Megvii Inc. All rights reserved.
pretrain_config = dict(
img_model_path=None,
img_load_key=[],
img_freeze_key=None,
pts_model_path=None,
pts_load_key=[])
optimizer_config = dict(
type='AdamW',
lr=2e-4,
weight_decay=1e-2)
H = 900
W = 1600
final_dim = (256, 704)
img_conf = dict(img_mean=[123.675, 116.28, 103.53],
img_std=[58.395, 57.12, 57.375],
to_rgb=True)
ida_aug_conf = {
'resize_lim': (0.386, 0.55),
'final_dim': final_dim,
'rot_lim': (-5.4, 5.4),
'H': 900,
'W': 1600,
'rand_flip': True,
'bot_pct_lim': (0.0, 0.0),
'cams': ['CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT',
'CAM_BACK_LEFT', 'CAM_BACK', 'CAM_BACK_RIGHT'],
'Ncams': 6,
}
bda_aug_conf = {
'rot_ratio': 1.0,
'rot_lim': (-22.5, 22.5),
'scale_lim': (0.95, 1.05),
'flip_dx_ratio': 0.5,
'flip_dy_ratio': 0.5
}
rda_aug_conf = {
'N_sweeps': 6,
'N_use': 5,
'drop_ratio': 0.1,
}
backbone_img_conf = {
'x_bound': [-51.2, 51.2, 0.8],
'y_bound': [-51.2, 51.2, 0.8],
'z_bound': [-5, 3, 8],
'd_bound': [2.0, 58.0, 0.8],
'final_dim': final_dim,
'output_channels': 80,
'downsample_factor': 16,
'img_backbone_conf': dict(
type='ResNet',
depth=50,
frozen_stages=0,
out_indices=[0, 1, 2, 3],
norm_eval=False,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
),
'img_neck_conf': dict(
type='SECONDFPN',
in_channels=[256, 512, 1024, 2048],
upsample_strides=[0.25, 0.5, 1, 2],
out_channels=[128, 128, 128, 128],
),
'depth_net_conf':
dict(in_channels=512, mid_channels=512),
'camera_aware': True
}
CLASSES = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone',
]
head_conf = {
'bev_backbone_conf': dict(
type='ResNet',
in_channels=80,
depth=18,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=[0, 1, 2],
norm_eval=False,
base_channels=160),
'bev_neck_conf': dict(
type='SECONDFPN',
in_channels=[80, 160, 320, 640],
upsample_strides=[1, 2, 4, 8],
out_channels=[64, 64, 64, 64]),
'tasks': [
dict(num_class=1, class_names=['car']),
dict(num_class=2, class_names=['truck', 'construction_vehicle']),
dict(num_class=2, class_names=['bus', 'trailer']),
dict(num_class=1, class_names=['barrier']),
dict(num_class=2, class_names=['motorcycle', 'bicycle']),
dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),],
'common_heads': dict(
reg=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),
'bbox_coder': dict(
type='CenterPointBBoxCoder',
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_num=500,
score_threshold=0.1,
out_size_factor=4,
voxel_size=[0.2, 0.2, 8],
pc_range=[-51.2, -51.2, -5, 51.2, 51.2, 3],
code_size=9),
'train_cfg': dict(
point_cloud_range=[-51.2, -51.2, -5, 51.2, 51.2, 3],
grid_size=[512, 512, 1],
voxel_size=[0.2, 0.2, 8],
out_size_factor=4,
dense_reg=1,
gaussian_overlap=0.1,
max_objs=500,
min_radius=2,
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5, 0.5]),
'test_cfg': dict(
post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_per_img=500,
max_pool_nms=False,
min_radius=[4, 12, 10, 1, 0.85, 0.175],
score_threshold=0.1,
out_size_factor=4,
voxel_size=[0.2, 0.2, 8],
nms_type='circle',
pre_max_size=1000,
post_max_size=83,
nms_thr=0.2),
'in_channels': 256, # Equal to bev_neck output_channels.
'loss_cls': dict(type='GaussianFocalLoss', reduction='mean'),
'loss_bbox': dict(type='L1Loss', reduction='mean', loss_weight=0.25),
'gaussian_overlap': 0.1,
'min_radius': 2,
}
class BEVDepthLightningModel(LightningModule):
MODEL_NAMES = sorted(name for name in models.__dict__
if name.islower() and not name.startswith('__')
and callable(models.__dict__[name]))
def __init__(self,
gpus: int = 1,
data_root='data/nuScenes',
eval_interval=1,
batch_size_per_device=8,
class_names=CLASSES,
backbone_img_conf=backbone_img_conf,
head_conf=head_conf,
ida_aug_conf=ida_aug_conf,
bda_aug_conf=bda_aug_conf,
rda_aug_conf=rda_aug_conf,
default_root_dir='./outputs/',
**kwargs):
super().__init__()
self.save_hyperparameters()
self.gpus = gpus
self.optimizer_config = optimizer_config
self.pretrain_config = pretrain_config
self.eval_interval = eval_interval
self.batch_size_per_device = batch_size_per_device
self.data_root = data_root
self.class_names = class_names
self.backbone_img_conf = backbone_img_conf
self.head_conf = head_conf
self.ida_aug_conf = ida_aug_conf
self.bda_aug_conf = bda_aug_conf
self.rda_aug_conf = rda_aug_conf
mmcv.mkdir_or_exist(default_root_dir)
self.default_root_dir = default_root_dir
self.evaluator = DetNuscEvaluator(class_names=self.class_names,
output_dir=self.default_root_dir)
self.model = BaseBEVDepth(self.backbone_img_conf,
self.head_conf)
self.mode = 'valid'
self.img_conf = img_conf
self.data_use_cbgs = False
self.load_interval = 1
self.num_sweeps = 1
self.sweep_idxes = list()
self.key_idxes = list()
self.data_return_depth = True
self.downsample_factor = self.backbone_img_conf['downsample_factor']
self.dbound = self.backbone_img_conf['d_bound']
self.depth_channels = int(
(self.dbound[1] - self.dbound[0]) / self.dbound[2])
self.use_fusion = False
self.train_info_paths = 'data/nuScenes/nuscenes_infos_train.pkl'
self.val_info_paths = 'data/nuScenes/nuscenes_infos_val.pkl'
self.predict_info_paths = 'data/nuScenes/nuscenes_infos_test.pkl'
self.return_image = True
self.return_depth = True
self.return_radar_pv = False
self.remove_z_axis = True
def forward(self, sweep_imgs, mats, is_train=False, **inputs):
return self.model(sweep_imgs, mats, is_train=is_train)
def training_step(self, batch):
if self.global_rank == 0:
for pg in self.trainer.optimizers[0].param_groups:
self.log('learning_rate', pg["lr"])
(sweep_imgs, mats, _, gt_boxes_3d, gt_labels_3d, _, depth_labels, pts_pv) = batch
if torch.cuda.is_available():
if self.return_image:
sweep_imgs = sweep_imgs.cuda()
for key, value in mats.items():
mats[key] = value.cuda()
if self.return_radar_pv:
pts_pv = pts_pv.cuda()
gt_boxes_3d = [gt_box.cuda() for gt_box in gt_boxes_3d]
gt_labels_3d = [gt_label.cuda() for gt_label in gt_labels_3d]
preds, depth_preds = self(sweep_imgs, mats,
pts_pv=pts_pv,
is_train=True)
targets = self.model.get_targets(gt_boxes_3d, gt_labels_3d)
loss_detection, loss_heatmap, loss_bbox = self.model.loss(targets, preds)
if len(depth_labels.shape) == 5:
# only key-frame will calculate depth loss
depth_labels = depth_labels[:, 0, ...].contiguous()
loss_depth = self.get_depth_loss(depth_labels.cuda(), depth_preds)
self.log('train/detection', loss_detection)
self.log('train/heatmap', loss_heatmap)
self.log('train/bbox', loss_bbox)
self.log('train/depth', loss_depth)
return loss_detection + loss_depth
def get_depth_loss(self, depth_labels, depth_preds, weight=3.):
depth_labels = self.get_downsampled_gt_depth(depth_labels)
depth_preds = depth_preds.permute(0, 2, 3, 1).contiguous().view(
-1, self.depth_channels)
fg_mask = torch.max(depth_labels, dim=1).values > 0.0
with autocast(enabled=False):
loss_depth = (F.binary_cross_entropy(
depth_preds[fg_mask],
depth_labels[fg_mask],
reduction='none',
).sum() / max(1.0, fg_mask.sum()))
return weight * loss_depth
def get_downsampled_gt_depth(self, gt_depths):
"""
Input:
gt_depths: [B, N, H, W]
Output:
gt_depths: [B*N*h*w, d]
"""
B, N, H, W = gt_depths.shape
gt_depths = gt_depths.view(
B * N,
H // self.downsample_factor,
self.downsample_factor,
W // self.downsample_factor,
self.downsample_factor,
1,
)
gt_depths = gt_depths.permute(0, 1, 3, 5, 2, 4).contiguous()
gt_depths = gt_depths.view(
-1, self.downsample_factor * self.downsample_factor)
gt_depths_tmp = torch.where(gt_depths == 0.0,
1e5 * torch.ones_like(gt_depths),
gt_depths)
gt_depths = torch.min(gt_depths_tmp, dim=-1).values
gt_depths = gt_depths.view(B * N, H // self.downsample_factor,
W // self.downsample_factor)
gt_depths = (gt_depths -
(self.dbound[0] - self.dbound[2])) / self.dbound[2]
gt_depths = torch.where(
(gt_depths < self.depth_channels + 1) & (gt_depths > 0.),
gt_depths, torch.zeros_like(gt_depths))
gt_depths = F.one_hot(gt_depths.long(),
num_classes=self.depth_channels + 1).view(
-1, self.depth_channels + 1)[:, 1:]
return gt_depths.float()
def eval_step(self, batch, batch_idx, prefix: str):
(sweep_imgs, mats, img_metas, _, _, _, _, pts_pv) = batch
if torch.cuda.is_available():
if self.return_image:
sweep_imgs = sweep_imgs.cuda()
for key, value in mats.items():
mats[key] = value.cuda()
if self.return_radar_pv:
pts_pv = pts_pv.cuda()
preds = self(sweep_imgs, mats,
pts_pv=pts_pv,
is_train=False)
if isinstance(self.model, torch.nn.parallel.DistributedDataParallel):
results = self.model.module.get_bboxes(preds, img_metas)
else:
results = self.model.get_bboxes(preds, img_metas)
for i in range(len(results)):
results[i][0] = results[i][0].tensor.detach().cpu().numpy()
results[i][1] = results[i][1].detach().cpu().numpy()
results[i][2] = results[i][2].detach().cpu().numpy()
results[i].append(img_metas[i])
return results
def validation_epoch_end(self, validation_step_outputs):
detection_losses = list()
heatmap_losses = list()
bbox_losses = list()
depth_losses = list()
for validation_step_output in validation_step_outputs:
detection_losses.append(validation_step_output[0])
heatmap_losses.append(validation_step_output[1])
bbox_losses.append(validation_step_output[2])
depth_losses.append(validation_step_output[3])
synchronize()
self.log('val/detection', torch.mean(torch.stack(detection_losses)), on_epoch=True)
self.log('val/heatmap', torch.mean(torch.stack(heatmap_losses)), on_epoch=True)
self.log('val/bbox', torch.mean(torch.stack(bbox_losses)), on_epoch=True)
self.log('val/depth', torch.mean(torch.stack(depth_losses)), on_epoch=True)
def validation_step(self, batch, batch_idx):
(sweep_imgs, mats, _, gt_boxes_3d, gt_labels_3d, _, depth_labels, pts_pv) = batch
if torch.cuda.is_available():
if self.return_image:
sweep_imgs = sweep_imgs.cuda()
for key, value in mats.items():
mats[key] = value.cuda()
if self.return_radar_pv:
pts_pv = pts_pv.cuda()
gt_boxes_3d = [gt_box.cuda() for gt_box in gt_boxes_3d]
gt_labels_3d = [gt_label.cuda() for gt_label in gt_labels_3d]
with torch.no_grad():
preds, depth_preds = self(sweep_imgs, mats,
pts_pv=pts_pv,
is_train=True)
targets = self.model.get_targets(gt_boxes_3d, gt_labels_3d)
loss_detection, loss_heatmap, loss_bbox = self.model.loss(targets, preds)
if len(depth_labels.shape) == 5:
# only key-frame will calculate depth loss
depth_labels = depth_labels[:, 0, ...].contiguous()
loss_depth = self.get_depth_loss(depth_labels.cuda(), depth_preds, weight=3.)
return loss_detection, loss_heatmap, loss_bbox, loss_depth
def test_epoch_end(self, test_step_outputs):
all_pred_results = list()
all_img_metas = list()
for test_step_output in test_step_outputs:
for i in range(len(test_step_output)):
all_pred_results.append(test_step_output[i][:3])
all_img_metas.append(test_step_output[i][3])
synchronize()
# TODO: Change another way.
dataset_length = len(self.val_dataloader().dataset)
all_pred_results = sum(
map(list, zip(*all_gather_object(all_pred_results))),
[])[:dataset_length]
all_img_metas = sum(map(list, zip(*all_gather_object(all_img_metas))),
[])[:dataset_length]
if self.global_rank == 0:
self.evaluator.evaluate(all_pred_results, all_img_metas)
def configure_optimizers(self):
optimizer = build_optimizer(self.model, self.optimizer_config)
scheduler = MultiStepLR(optimizer, [19, 23])
return [[optimizer], [scheduler]]
def train_dataloader(self): | train_dataset = NuscDatasetRadarDet( | 0 | 2023-12-06 14:57:49+00:00 | 24k |
jinxixiang/magic_animate_unofficial | animatediff/magic_animate/pipeline.py | [
{
"identifier": "UNet3DConditionModel",
"path": "animatediff/magic_animate/unet_controlnet.py",
"snippet": "class UNet3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Optiona... | import inspect, math
import numpy as np
import torch
import torch.distributed as dist
import einops
from typing import Callable, List, Optional, Union
from dataclasses import dataclass
from PIL import Image
from tqdm import tqdm
from diffusers.utils import is_accelerate_available
from packaging import version
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from diffusers.utils import deprecate, logging, BaseOutput
from einops import rearrange
from animatediff.magic_animate.unet_controlnet import UNet3DConditionModel
from animatediff.magic_animate.controlnet import ControlNetModel
from animatediff.magic_animate.mutual_self_attention import ReferenceAttentionControl
from animatediff.magic_animate.context import (
get_context_scheduler,
get_total_steps
)
from animatediff.utils.util import get_tensor_interpolation_method
from accelerate import cpu_offload | 17,204 | # *************************************************************************
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
# ytedance Inc..
# *************************************************************************
# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TODO:
1. support multi-controlnet
2. [DONE] support DDIM inversion
3. support Prompt-to-prompt
"""
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class AnimationPipeline(DiffusionPipeline):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel,
| # *************************************************************************
# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
# ytedance Inc..
# *************************************************************************
# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TODO:
1. support multi-controlnet
2. [DONE] support DDIM inversion
3. support Prompt-to-prompt
"""
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class AnimationPipeline(DiffusionPipeline):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel, | controlnet: ControlNetModel, | 1 | 2023-12-12 00:16:39+00:00 | 24k |
qitan/devops-backend-lite | common/ext_fun.py | [
{
"identifier": "generate_docu",
"path": "common/utils/ElasticSearchAPI.py",
"snippet": "def generate_docu(table, index_version=None):\n index_name = f\"{table.name}-{index_version}\" if index_version else table.name\n _tbindex = Index(index_name)\n _tbindex.analyzer(my_normalizer)\n _tbinde... | from gitlab.exceptions import GitlabGetError
from functools import reduce
from common.utils.ElasticSearchAPI import generate_docu, Search
from common.utils.GitLabAPI import GitLabAPI
from common.utils.HarborAPI import HarborAPI
from common.utils.JenkinsAPI import GlueJenkins
from common.custom_format import convert_xml_to_str_with_pipeline
from common.variables import DASHBOARD_TIME_FORMAT, DASHBOARD_TIME_FORMAT_T, DASHBOARD_TIME_FREQNAMES, \
DASHBOARD_TIME_FREQNAMES_T, SENSITIVE_KEYS, JENKINS_CALLBACK_KEY, \
JENKINS_STATUS_MAP, DEV_LANGUAGE_KEY
from dbapp.models import AppInfo, Product, KubernetesCluster, KubernetesDeploy, MicroApp, Project, ProjectConfig, DevLanguage, BuildJob, UserProfile, SystemConfig, Role, Permission, Menu, DataDict
from django.conf import settings
from django.core.cache import cache
from django.utils import timezone
from django.db.models import Q
from social_django.utils import load_strategy
from rest_framework.utils.serializer_helpers import ReturnDict
from config import SOCIAL_AUTH_GITLAB_API_URL, GITLAB_ADMIN_TOKEN
from common.utils.K8sAPI import K8sAPI
from urllib.parse import urlparse, quote_plus
from dateutil.relativedelta import relativedelta
from dateutil.rrule import rrule
from ruamel import yaml
from datetime import datetime, timedelta
from celery import current_app
import copy
import operator
import re
import time
import pytz
import os
import json
import requests
import math
import shortuuid
import logging | 17,589 | 'branch': 'master',
'content': content,
'author_email': user.email,
'author_name': user.username,
'commit_message': f'Create {instance.name} {filename} by {user.username}'})
content = project.files.raw(
f"{instance.name}/{filename}", ref='master')
return True, content
except GitlabGetError as e:
logger.info(f'获取异常，{e}')
if e.response_code == 404:
logger.info(f'{instance.name}/{filename}文件不存在')
return True, ''
except BaseException as e:
logger.error(f'GitLab开发语言模板异常, 原因: {e}')
return False, f'GitLab开发语言模板异常, 原因: {e}'
def snake_case(x):
"""
驼峰转下划线
"""
term_exclude = ['OS', 'GPU', 'DB', 'IA', 'IP',
'RR', 'TTL', 'SLB', 'CPU', 'MEMORY', 'QPS']
for i in term_exclude:
x = x.replace(i, i.lower())
return re.sub(r'(?P<key>[A-Z])', r'_\g<key>', x).lower().strip('_')
def node_filter(node_id, data):
"""
查找节点
:params: node_id int 节点ID
:params: data list 节点数组
"""
for i in data:
if i['id'] == node_id:
print('get node', i)
return i
else:
if i.get('children', None):
node = node_filter(node_id, i['children'])
if isinstance(node, (dict,)):
return node
def get_time_range(request):
"""
获取时间轴
"""
type_range = request.query_params.get('range_type', 'static')
if type_range == 'static':
time_range = request.query_params.get('range', '6-months')
else:
time_range = request.query_params.getlist('range[]', None)
if not time_range:
time_range = '6-months'
period = time_period(time_range, type_range)
time_line = timeline_generate(period, format_type='cmdb')
# 时间刻度, 以小时为刻度则删除年份
time_line_x = [i.split(' ')[-1]
for i in time_line] if period['name'] == 'hours' else time_line
return period, time_line, time_line_x
def compare_dict(data, old_data):
different_list = []
for k1 in data:
if k1 == 'update_time':
continue
v1 = data.get(k1)
v2 = old_data.get(k1)
if v1 != v2:
different_list.append({
'key': k1,
'new_value': v1,
'old_value': v2
})
return different_list
def get_project_mergerequest(project: Project, cli: GitLabAPI, **params):
"""
获取项目下所有应用的合并请求
"""
mrdata = []
git_project = [app.repo['id']
for app in project.microapp_set.all() if app.repo.get('id')]
for project_id in set(git_project):
try:
git_project = cli.get_project(project_id)
ok, data = cli.list_mrs(project=git_project, **params)
if ok is False:
continue
mrdata.extend([i.attributes for i in data])
except BaseException as e:
logger.error(f'获取应用合并请求异常，原因：{e}')
return mrdata
def gitlab_cli(user=None, admin=False, superadmin=False, merge=False):
"""
获取GitlabAPI
:param merge: 用于分支合并，管理员统一用配置文件里的token
"""
try:
payload = {'token': GITLAB_ADMIN_TOKEN, 'oauth': False}
cli = GitLabAPI(SOCIAL_AUTH_GITLAB_API_URL, **payload)
return True, cli
except BaseException as e:
logger.warning(f'获取GitlabAPI异常，原因：{e}')
return False, f'获取GitlabAPI异常，原因：{e}'
def get_deploy_image_list(app_id, appinfo_id=None, module=None, force=0):
# 可选发布镜像
# 获取关联应用ID
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author : Charles Lai
@Contact : qqing_lai@hotmail.com
@Time : 2020/12/21 上午10:00
@FileName: ext_fun.py
@Blog ：https://imaojia.com
"""
logger = logging.getLogger('drf')
class ThirdPartyUser(object):
def get_user(self):
user = UserProfile.objects.get_or_create(username='thirdparty')[0]
self.set_permission(user, self.get_role())
return user
def get_role(self):
return Role.objects.get_or_create(name='thirdparty')[0]
def get_perm(self):
return Permission.objects.get_or_create(name='Jenkins回调', method='jenkins_callback')[0]
def set_permission(self, user, role):
role.permissions.set([self.get_perm().id])
user.roles.set([role.id])
def set_redis_data(name, config):
cache.set(f"system:{name}", config, None)
def get_redis_data(name):
ret = cache.get(f"system:{name}")
if not ret:
try:
if name == 'cicd-harbor':
qs = SystemConfig.objects.filter(type=name)[0]
else:
qs = SystemConfig.objects.get(name=name)
except BaseException as e:
return None
ret = json.loads(qs.config)
set_redis_data(name, ret)
return ret
def get_datadict(name, config=0, default_value=None):
"""
从数据字典获取数据
"""
try:
qs = DataDict.objects.get(key=name)
except BaseException as e:
return default_value
if config:
ret = json.loads(qs.extra)
else:
ret = {'id': qs.id, 'key': qs.key,
'value': qs.value, 'desc': qs.desc}
return ret
def check_pods(cluster_id, k8s_config, namespace, **kwargs):
k8s = KubernetesCluster.objects.get(id=cluster_id)
cli = k8s_cli(k8s, k8s_config)
if not cli:
return False
count = 3
while count:
ret2 = cli.get_pods(namespace, **kwargs)
count -= 1
if len(ret2['items']) > 0:
return True
else:
check_pods(k8s_config, namespace, **kwargs)
return False
def template_svc_generate(appinfo_obj):
"""
生成Kubernetes Svc Yaml
### 格式：
{
"apiVersion": "v1",
"kind": "Service",
"metadata": {
"name": "appname",
"namespace": "env-product",
"labels": {
"app": "appname"
}
},
"spec": {
"ports": [{
"port": 8080,
"targetPort": 8080,
"protocol": "TCP",
"name": "http"
}],
"selector": {
"app": "appname"
}
}
}
"""
svc_temp = DataDict.objects.filter(key='yaml.svc')
if svc_temp.exists():
svc_temp = json.loads(svc_temp.first().extra)
if appinfo_obj.environment.name in svc_temp:
svc_temp = svc_temp[appinfo_obj.environment.name]
namespace = appinfo_obj.namespace
svc_temp['metadata']['name'] = appinfo_obj.app.name
svc_temp['metadata']['namespace'] = namespace
svc_temp['metadata']['labels'] = {'app': appinfo_obj.app.name}
labels = []
labels.extend([{'name': 'app', 'value': appinfo_obj.app.name}])
svc_temp['spec']['selector'] = {
i['name']: i['value'] for i in labels}
return True, svc_temp
return False, None
def harbor_cli(namespace, **filters):
try:
harbor = SystemConfig.objects.filter(**filters).first()
# 获取harbor配置
harbor_config = json.loads(harbor.config)
except BaseException as e:
logger.exception(f'创建任务失败, 原因: 获取harbor仓库异常, {e}')
return False, f"获取harbor仓库异常：{e}"
# 构建前创建harbor项目
cli = HarborAPI(url=harbor_config['url'], username=harbor_config['user'],
password=harbor_config['password'])
try:
cli.create_project(
namespace, public=harbor_config.get('public', False))
except BaseException as e:
pass
return True, harbor_config
def k8s_cli(k8s, k8s_config):
try:
if k8s_config['type'] == 'basic':
# basic auth or token auth
k8s_config.pop('config', None)
k8s_config.pop('type', None)
cli = K8sAPI(**k8s_config)
else:
eks = None
eks_token = None
k8s_config = yaml.safe_load(k8s_config['config'])
if k8s.idc.type == 1 and k8s.idc.supplier.split('.')[-1] == 'aws':
return False, 'not support.'
cli = K8sAPI(k8s_config=k8s_config, api_key=eks_token, eks=eks)
return True, cli
except BaseException as e:
return False, str(e)
def template_generate(appinfo_obj: AppInfo, image=None, partial_deploy_replicas: int = 0):
"""
生成Kubernetes Deployment Yaml
"""
def health_lifecycle_generate(item, enable=True):
_c = {}
for i in template[item]['data']:
_x = {}
if i.get('enable', enable):
for j in i['items']:
if '__' in j['name']:
_t = j['name'].split('__')
_value = j['value']
if j['name'] == 'exec__command':
_value = ["sh", "-c", j['value']]
if _x.get(_t[0], None):
_x[_t[0]][_t[1]] = _value
else:
_x[_t[0]] = {_t[1]: _value}
else:
_x[j['name']] = j['value']
_c[i['name']] = _x
return _c
def container_generate(container_data):
containers = []
for i in container_data:
if i.get('enable', None):
container = get_datadict(i['key'], config=1)
if not container:
container = i['extra']
containers.append(
container)
return containers
language_obj = DevLanguage.objects.get(name=appinfo_obj.app.language)
project_config = ProjectConfig.objects.filter(project_id=appinfo_obj.app.project.id,
environment_id=appinfo_obj.environment.id)
namespace = appinfo_obj.namespace
harbor_config = get_redis_data('cicd-harbor')
harbor_url = harbor_config['url'].split('://')[1]
image = f"{harbor_url}/{image}"
template = {}
# 模板优先级
# 应用模块 -> 应用 -> 项目 -> 环境
if project_config.first():
project_template = project_config.first().template
for k, v in project_template.items():
if v and isinstance(v, (dict,)):
if v.get('custom', False) is False:
if appinfo_obj.environment.template.get(k, None):
template[k] = appinfo_obj.environment.template[k]
else:
if project_template.get(k, None):
template[k] = project_template[k]
microapp_template = appinfo_obj.app.template
for k, v in microapp_template.items():
if '_on' in k and v:
_k = k.rstrip('_on')
if microapp_template.get(_k, None):
template[_k] = microapp_template[_k]
use_host_network = False
if appinfo_obj.template.get('userHostNetwork', 0):
use_host_network = True
for k, v in appinfo_obj.template.items():
if v and isinstance(v, (dict,)):
if v.get('custom', False) and appinfo_obj.template.get(k, None):
template[k] = appinfo_obj.template[k]
yaml_template = {'kind': 'Deployment', 'metadata': {}, 'spec':
{'strategy': {}, 'template': {'metadata': {}, 'spec':
{'containers': [{'ports': [{'containerPort': 8080}], 'resources': []}],
'imagePullSecrets': [{'name': 'loginharbor'}],
'terminationGracePeriodSeconds': 120}
}
}
}
try:
tz = appinfo_obj.app.project.product.region.extra['timezone']
except BaseException as e:
tz = 'Asia/Shanghai'
try:
if template.get('strategy', None):
for i in template['strategy']['data']:
if i['key'] in ['maxSurge', 'maxUnavailable']:
if yaml_template['spec']['strategy'].get('rollingUpdate', None) is None:
yaml_template['spec']['strategy']['rollingUpdate'] = {}
yaml_template['spec']['strategy']['rollingUpdate'][i['key']
] = f"{i['value']}%"
else:
yaml_template['spec'][i['key']] = i['value']
_d = {}
for i in template['resources']['data']:
_t = i['key'].split('_')
if _d.get(_t[0], None):
_d[_t[0]][_t[1]] = f"{i['value']}{i['slot']}"
else:
_d[_t[0]] = {_t[1]: f"{i['value']}{i['slot']}"}
yaml_template['spec']['template']['spec']['containers'][0]['resources'] = _d
yaml_template['metadata']['name'] = appinfo_obj.app.name
yaml_template['metadata']['namespace'] = namespace
yaml_template['spec']['template']['spec']['containers'][0]['name'] = appinfo_obj.app.name
yaml_template['spec']['template']['spec']['containers'][0]['image'] = image
command = appinfo_obj.app.template.get(
'command', None) or language_obj.labels.get('command', None)
if command:
if command.startswith('./'):
yaml_template['spec']['template']['spec']['containers'][0]['command'] = [
command]
else:
yaml_template['spec']['template']['spec']['containers'][0]['command'] = [
'sh', '-c', command]
# 优先级: 应用模块>应用>预设>开发语言
labels = template['label']['data']
labels.extend([{'name': 'app', 'value': appinfo_obj.app.name}])
yaml_template['spec']['template']['metadata']['labels'] = {
i['name']: i['value'] for i in labels}
yaml_template['spec']['template']['metadata']['labels'][
'status-app-name-for-ops-platform'] = appinfo_obj.app.name
yaml_template['spec']['selector'] = {
'matchLabels': {i['name']: i['value'] for i in labels}}
selectors = template['selector']['data']
yaml_template['spec']['template']['spec']['nodeSelector'] = {
i['name']: i['value'] for i in selectors}
if 'annotations' not in yaml_template['spec']['template']['metadata']:
yaml_template['spec']['template']['metadata']['annotations'] = {}
for i in template['prometheus']['data']:
yaml_template['spec']['template']['metadata'][
'annotations'][f'prometheus.io/{i["name"]}'] = i['value']
if 'prometheus.io/path' in yaml_template['spec']['template']['metadata']['annotations']:
yaml_template['spec']['template']['metadata']['annotations'][
'prometheus.io/app_product'] = appinfo_obj.app.project.product.name
yaml_template['spec']['template']['metadata']['annotations'][
'prometheus.io/app_env'] = appinfo_obj.environment.name
yaml_template['spec']['template']['metadata']['annotations'][
'prometheus.io/app_project'] = appinfo_obj.app.project.name
# 环境变量
envs = [{'name': 'TZ', 'value': tz}]
envs.extend(template['env']['data'])
envs.extend([
{'name': '_RESTART', 'value': datetime.now().strftime(
'%Y%m%d%H%M%S')}, # _RESTART变量用于强制更新deployment
{'name': 'PRODUCT_NAME', 'value': appinfo_obj.app.project.product.name},
{'name': 'PROJECT_NAME', 'value': appinfo_obj.app.project.name},
{'name': 'APPNAME', 'value': appinfo_obj.app.name},
{'name': 'APPID', 'value': appinfo_obj.app.appid},
{'name': 'ENV', 'value': appinfo_obj.environment.name},
{'name': 'POD_NAMESPACE', 'value': namespace}
])
envs = list({i['name']: i for i in envs}.values())
for i in envs:
try:
env_value = i.get('value', None)
cmname = i.pop('cmname', None)
cmkey = i.pop('cmkey', None)
if env_value:
env_value = env_value.lstrip('"').rstrip(
'"').lstrip("'").rstrip("'")
i.pop('value', None)
i['name'] = i['name'].lstrip('"').rstrip(
'"').lstrip("'").rstrip("'")
if i.get('valueFrom', None) == 'configMapKeyRef':
i['valueFrom'] = {'configMapKeyRef': {
'name': cmname, 'key': cmkey}}
else:
i['value'] = env_value
i['valueFrom'] = None
except BaseException as e:
pass
yaml_template['spec']['template']['spec']['containers'][0]['env'] = envs
if template.get('health', False):
_d = health_lifecycle_generate('health', True)
for k, v in _d.items():
yaml_template['spec']['template']['spec']['containers'][0][k] = v
if template.get('lifecycle', False):
yaml_template['spec']['template']['spec']['containers'][0]['lifecycle'] = {
}
_d = health_lifecycle_generate('lifecycle', False)
for k, v in _d.items():
yaml_template['spec']['template']['spec']['containers'][0]['lifecycle'][k] = v
_vo_mount = [{'mountPath': '/data/logs',
'name': 'logs', 'readOnly': False}]
_volumes = [{'name': 'logs', 'type': 'Directory', 'hostPath': {
'path': f'/data/{appinfo_obj.environment.name}-applogs/{appinfo_obj.app.project.name}/'}}]
if template.get('storage', None):
for k, v in template['storage']['data'].items():
for i in v:
_x = {}
for m, n in i.items():
if isinstance(n, (str,)):
n = n.replace('${APPNAME}', appinfo_obj.app.name)
if '_' in m:
_t = m.split('_')
if _x.get(_t[0], None):
_x[_t[0]][_t[1]] = n
else:
_x[_t[0]] = {_t[1]: n}
else:
_x[m] = n
_t = {'mountPath': _x['mount'], 'name': _x['name'],
'readOnly': True if _x.get('mode', None) == 'ReadOnly' else False}
if _x.get('file', None):
_t['subPath'] = _x['configMap']['items'][0]['key']
_vo_mount.append(_t)
_mode = _x.pop('mode', None)
_x.pop('file', None)
_x.pop('mount', None)
if _x.get('configMap', None):
_x['configMap']['defaultMode'] = 0o600 if _mode == 'ReadOnly' else 0o755
_volumes.append(_x)
yaml_template['spec']['template']['spec']['containers'][0]['volumeMounts'] = _vo_mount
yaml_template['spec']['template']['spec']['volumes'] = _volumes
if use_host_network:
yaml_template['spec']['template']['spec']['hostNetwork'] = True
partial_deploy_yaml_template = None
except BaseException as e:
logger.exception(f'generate yaml err {e.__class__} {e}')
return {'ecode': 500, 'message': str(e)}
# 多容器处理
if appinfo_obj.template.get('containers_custom', None):
containers = container_generate(
appinfo_obj.template.get('containers', []))
else:
containers = container_generate(
project_config.first().template.get('containers', []))
yaml_template['spec']['template']['spec']['containers'].extend(containers)
ret = {'ecode': 200, 'image': image, 'yaml': yaml_template}
if partial_deploy_yaml_template:
ret['partial_deploy_yaml'] = partial_deploy_yaml_template
return ret
def get_members(obj):
team_members = [j for i in obj.team_members.values() for j in i]
return list(set(team_members))
def get_permission_from_role(request):
try:
perms = request.user.roles.values(
'permissions__method',
).distinct()
return [p['permissions__method'] for p in perms]
except AttributeError:
return []
def get_headers(request=None):
"""
Function: get_headers(self, request)
Description: To get all the headers from request
"""
regex = re.compile('^HTTP_')
return dict((regex.sub('', header), value) for (header, value)
in request.META.items() if header.startswith('HTTP_'))
def mask_sensitive_data(data):
"""
Hides sensitive keys specified in sensitive_keys settings.
Loops recursively over nested dictionaries.
"""
if hasattr(settings, 'DRF_API_LOGGER_EXCLUDE_KEYS'):
if type(settings.DRF_API_LOGGER_EXCLUDE_KEYS) in (list, tuple):
SENSITIVE_KEYS.extend(settings.DRF_API_LOGGER_EXCLUDE_KEYS)
if type(data) != dict and type(data) != ReturnDict:
try:
data = json.loads(data)
except BaseException as e:
return data
for key, value in data.items():
if key in SENSITIVE_KEYS:
data[key] = "***FILTERED***"
if type(value) == dict:
data[key] = mask_sensitive_data(data[key])
return data
def time_convert(target_time):
"""
时间转字符串
"""
return target_time.astimezone(pytz.timezone('Asia/Shanghai')).strftime('%Y-%m-%d %H:%M:%S+08:00')
def time_comp(target_time, **kwargs):
"""
时间比较/统一使用utc时间对比
target_time: 目标时间
kwargs: 额外参数, 时间差 如{hours: 1}, {minutes: 1}, {seconds: 1}, 数值不取负数
"""
ctime = timezone.now()
if kwargs:
# 两个时间是否在期望时间差范围
if target_time > ctime:
return target_time - ctime <= timedelta(**kwargs)
else:
return ctime - target_time <= timedelta(**kwargs)
# 判断两个时间是否相等
return ctime == target_time
def timeline_generate(time_range, format_type='dashboard'):
"""
根据起始时间生成时间线
: params format_type: 默认为dashboard, 用于概览报表粗略显示, 其它用于监控类的展示则使用更细粒度的格式
"""
TIME_FREQNAMES = DASHBOARD_TIME_FREQNAMES
TIME_FORMAT = DASHBOARD_TIME_FORMAT
if format_type == 'cmdb':
TIME_FREQNAMES = DASHBOARD_TIME_FREQNAMES_T
TIME_FORMAT = DASHBOARD_TIME_FORMAT_T
start_time = time_range['start_time']
end_time = time_range['end_time']
time_line = rrule(
freq=TIME_FREQNAMES[time_range['name']], dtstart=start_time, until=end_time)
return [i.strftime(TIME_FORMAT[time_range['name']]) for i in time_line]
def time_period(time_range='6-months', type_range='static', time_zone='Asia/Shanghai', name=None):
"""
根据时间范围生成起止时间
"""
start_time = None
end_time = timezone.now().astimezone(pytz.timezone(time_zone))
if type_range == 'dynamic' and name is None:
start_time = datetime.strptime(time_range[0], '%Y-%m-%d %H:%M:%S')
end_time = datetime.strptime(time_range[1], '%Y-%m-%d %H:%M:%S')
if start_time > end_time:
start_time, end_time = end_time, start_time
if (end_time - start_time).days >= 60:
name = 'months'
elif (end_time - start_time).days >= 2:
name = 'days'
elif (end_time - start_time).days >= 1 or (end_time - start_time).seconds > 60 * 60:
name = 'hours'
else:
name = 'minutes'
return {'name': name, 'start_time': start_time, 'end_time': end_time}
if type_range == 'static':
_time = time_range.split('-')
if _time[-1] == 'week':
start_time = end_time - relativedelta(days=end_time.weekday(), hours=end_time.hour, minutes=end_time.minute,
seconds=end_time.second,
microseconds=end_time.microsecond)
return {'name': 'days', 'start_time': start_time, 'end_time': end_time}
if _time[-1] == 'lastweek':
start_time = end_time - relativedelta(days=end_time.weekday() + 7, hours=end_time.hour,
minutes=end_time.minute, seconds=end_time.second,
microseconds=end_time.microsecond)
end_time = end_time - relativedelta(days=end_time.weekday(), hours=end_time.hour, minutes=end_time.minute,
seconds=end_time.second, microseconds=end_time.microsecond)
return {'name': 'days', 'start_time': start_time, 'end_time': end_time}
if _time[-1] in ['today', 'yesterday']:
start_time = end_time - relativedelta(hours=end_time.hour, minutes=end_time.minute, seconds=end_time.second,
microseconds=end_time.microsecond)
if _time[-1] == 'yesterday':
end_time = start_time
start_time = end_time - relativedelta(days=1)
return {'name': 'hours', 'start_time': start_time, 'end_time': end_time}
name = _time[1]
if name is None:
if _time[1] in ['years', 'months']:
name = 'months'
if _time[1] == 'months' and int(_time[0]) < 2:
name = 'days'
if _time[1] == 'days' and int(_time[0]) < 2:
name = 'hours'
start_time = end_time + relativedelta(**{_time[1]: -int(_time[0])})
return {'name': name, 'start_time': start_time, 'end_time': end_time}
def extend_jenkins(data, env):
jenkins = get_redis_data('cicd-jenkins')
app = AppInfo.objects.filter(id=data['id'])[0]
category = DataDict.objects.get(key=app.app.category)
job_name = app.jenkins_jobname
jenkins_cli = GlueJenkins(jenkins.get('url', 'http://localhost'), username=jenkins.get('user', 'admin'),
password=jenkins.get('password', None))
try:
view_xml_config = f'''<?xml version="1.0" encoding="UTF-8"?>
<hudson.model.ListView>
<name>{app.app.project.alias}{env.alias}</name>
<filterExecutors>false</filterExecutors>
<filterQueue>false</filterQueue>
<properties class="hudson.model.View$PropertyList"/>
<jobNames>
<comparator class="hudson.util.CaseInsensitiveComparator"/>
</jobNames>
<jobFilters/>
<columns>
<hudson.views.StatusColumn/>
<hudson.views.WeatherColumn/>
<hudson.views.JobColumn/>
<jenkins.branch.DescriptionColumn/>
<hudson.views.LastSuccessColumn/>
<hudson.views.LastFailureColumn/>
<hudson.views.LastDurationColumn/>
<hudson.views.BuildButtonColumn/>
</columns>
<includeRegex>{env.name.lower()}-.*-{app.app.project.name.lower()}-.*</includeRegex>
</hudson.model.ListView>'''
jenkins_cli.create_view(
f'{app.app.project.alias}{env.alias}', view_xml_config)
except BaseException as e:
pass
try:
config_xml = convert_xml_to_str_with_pipeline(jenkins['xml'], jenkins['pipeline']['http_url_to_repo'],
jenkins['gitlab_credit'],
app.app.alias,
f'{app.app.language}/Jenkinsfile')
if not jenkins_cli.job_exists(job_name):
jenkins_cli.create_job(name=job_name, config_xml=config_xml)
else:
jenkins_cli.reconfig_job(name=job_name, config_xml=config_xml)
except Exception as e:
logger.error(f"创建Jenkins JOB: {job_name} 失败 ERROR: {e}")
def get_celery_tasks():
"""
获取celery任务
"""
current_app.loader.import_default_modules()
tasks = list(
sorted(name for name in current_app.tasks if not name.startswith('celery.')))
return tasks
def is_chinese(string):
"""
检查整个字符串是否包含中文
:param string: 需要检查的字符串
:return: bool
"""
for ch in string:
if u'\u4e00' <= ch <= u'\u9fff':
return True
return False
def get_word_list(string):
"""
切割字符串, 中文/-切割成单个字, 其它则切割成单个词
"""
res = re.compile(r"([\u4e00-\u9fa5\-])")
return [i for i in res.split(string.lower()) if len(i.strip()) > 0 and i != '-']
def devlanguage_template_manage(instance, filename, user=None, content=None, action='retrieve'):
jenkins = get_redis_data('cicd-jenkins')
ok, cli = gitlab_cli(admin=True)
if not ok:
return False, cli
project_id = jenkins['pipeline'].get('id', None)
if not project_id:
return False, '获取流水线失败，请检查Jenkins配置.'
project = cli.get_project(project_id)
items = project.repository_tree(path=instance.name)
try:
if action == 'update':
if filename in [i['name'] for i in items]:
# 文件已存在则更新
f = project.files.get(
f"{instance.name}/{filename}", ref='master')
f.content = content
f.save(
branch='master', commit_message=f'Update {instance.name} {filename} by {user.username}')
else:
# 文件不存在则创建
logger.info(f'{instance.name}/{filename}文件不存在则创建')
project.files.create({'file_path': f"{instance.name}/{filename}",
'branch': 'master',
'content': content,
'author_email': user.email,
'author_name': user.username,
'commit_message': f'Create {instance.name} {filename} by {user.username}'})
content = project.files.raw(
f"{instance.name}/{filename}", ref='master')
return True, content
except GitlabGetError as e:
logger.info(f'获取异常，{e}')
if e.response_code == 404:
logger.info(f'{instance.name}/{filename}文件不存在')
return True, ''
except BaseException as e:
logger.error(f'GitLab开发语言模板异常, 原因: {e}')
return False, f'GitLab开发语言模板异常, 原因: {e}'
def snake_case(x):
"""
驼峰转下划线
"""
term_exclude = ['OS', 'GPU', 'DB', 'IA', 'IP',
'RR', 'TTL', 'SLB', 'CPU', 'MEMORY', 'QPS']
for i in term_exclude:
x = x.replace(i, i.lower())
return re.sub(r'(?P<key>[A-Z])', r'_\g<key>', x).lower().strip('_')
def node_filter(node_id, data):
"""
查找节点
:params: node_id int 节点ID
:params: data list 节点数组
"""
for i in data:
if i['id'] == node_id:
print('get node', i)
return i
else:
if i.get('children', None):
node = node_filter(node_id, i['children'])
if isinstance(node, (dict,)):
return node
def get_time_range(request):
"""
获取时间轴
"""
type_range = request.query_params.get('range_type', 'static')
if type_range == 'static':
time_range = request.query_params.get('range', '6-months')
else:
time_range = request.query_params.getlist('range[]', None)
if not time_range:
time_range = '6-months'
period = time_period(time_range, type_range)
time_line = timeline_generate(period, format_type='cmdb')
# 时间刻度, 以小时为刻度则删除年份
time_line_x = [i.split(' ')[-1]
for i in time_line] if period['name'] == 'hours' else time_line
return period, time_line, time_line_x
def compare_dict(data, old_data):
different_list = []
for k1 in data:
if k1 == 'update_time':
continue
v1 = data.get(k1)
v2 = old_data.get(k1)
if v1 != v2:
different_list.append({
'key': k1,
'new_value': v1,
'old_value': v2
})
return different_list
def get_project_mergerequest(project: Project, cli: GitLabAPI, **params):
"""
获取项目下所有应用的合并请求
"""
mrdata = []
git_project = [app.repo['id']
for app in project.microapp_set.all() if app.repo.get('id')]
for project_id in set(git_project):
try:
git_project = cli.get_project(project_id)
ok, data = cli.list_mrs(project=git_project, **params)
if ok is False:
continue
mrdata.extend([i.attributes for i in data])
except BaseException as e:
logger.error(f'获取应用合并请求异常，原因：{e}')
return mrdata
def gitlab_cli(user=None, admin=False, superadmin=False, merge=False):
"""
获取GitlabAPI
:param merge: 用于分支合并，管理员统一用配置文件里的token
"""
try:
payload = {'token': GITLAB_ADMIN_TOKEN, 'oauth': False}
cli = GitLabAPI(SOCIAL_AUTH_GITLAB_API_URL, **payload)
return True, cli
except BaseException as e:
logger.warning(f'获取GitlabAPI异常，原因：{e}')
return False, f'获取GitlabAPI异常，原因：{e}'
def get_deploy_image_list(app_id, appinfo_id=None, module=None, force=0):
# 可选发布镜像
# 获取关联应用ID | app = MicroApp.objects.get(id=app_id) | 14 | 2023-12-13 03:09:32+00:00 | 24k |
MarilynKeller/aitviewer-skel | aitviewer/renderables/sdf.py | [
{
"identifier": "BoundingBoxes",
"path": "aitviewer/renderables/bounding_boxes.py",
"snippet": "class BoundingBoxes(Node):\n \"\"\"\n Draw bounding boxes.\n \"\"\"\n\n def __init__(self, vertices, thickness=0.005, color=(0.0, 0.0, 1.0, 1.0), **kwargs):\n \"\"\"\n Initializer.\n... | import numpy as np
from skimage import measure
from aitviewer.renderables.bounding_boxes import BoundingBoxes
from aitviewer.renderables.lines import Lines
from aitviewer.renderables.meshes import Meshes
from aitviewer.scene.node import Node
from aitviewer.utils.decorators import hooked | 19,602 | # Copyright (C) 2023 ETH Zurich, Manuel Kaufmann, Velko Vechev, Dario Mylonopoulos
class SDF(Node):
"""
Renderable that can be used to draw level sets of a dense SDF volume meshed using marching cubes.
This renderable internally uses the marching cubes algorithm from skimage.
For a faster marching cubes implementation see the Volume renderable.
"""
def __init__(
self,
volume,
size=(1, 1, 1),
level=0.0,
color=(0.7, 0.7, 0.7, 1.0),
level_sets=None,
level_set_colors=None,
mc_step_size=1,
**kwargs,
):
"""Initializer.
:param volume: np array of shape (X, Y, Z) of signed distance values
:param size: size of the volume in local units.
:param level: the level set used for the main mesh.
:param color: color of the main mesh.
:param level_sets: a list or array of additional level set values to display.
:param level_set_colors: a list or array of shape (L, 4) of the same length as
the level_set parameter with colors to use for the additional level sets.
:param mc_step_size: step size used for marching cubes.
:param **kwargs: arguments forwarded to the Node constructor.
"""
assert len(volume.shape) == 3 and len(size) == 3
kwargs["gui_material"] = False
super().__init__(**kwargs)
self.volume = volume
self.size = np.array((size), np.float32)
# Mesh.
verts, faces, normals, _ = measure.marching_cubes(
volume, level, spacing=self.size / (np.array(self.volume.shape) - 1.0), step_size=mc_step_size
)
self.mesh = Meshes(verts, faces, vertex_normals=-normals, color=color, name="Mesh")
# Level sets.
self.level_sets: list[Meshes] = []
if level_sets is not None:
if level_set_colors is not None:
assert len(level_sets) == len(level_set_colors)
for i, s in enumerate(level_sets):
verts, faces, normals, _ = measure.marching_cubes(
volume, s, spacing=self.size / (np.array(self.volume.shape) - 1.0), step_size=mc_step_size
)
shell = Meshes(verts, faces, vertex_normals=-normals, name=f"Level {s:.03f}", cast_shadow=False)
if level_set_colors is not None:
shell.color = tuple(level_set_colors[i])
shell.clip_control = np.array((1, 1, 1))
shell.clip_value = self.size.copy()
shell.backface_culling = False
self.level_sets.append(shell)
# Bounding box.
self.bounding_box = BoundingBoxes.from_min_max_diagonal(
np.array([[0.0, 0.0, 0.0]]),
np.array([self.size], dtype=np.float32),
color=(0, 0, 0, 1),
name="Bounding Box",
gui_affine=False,
)
# Clip plane lines.
self.clip_lines = []
for i, axis in enumerate(["X", "Y", "Z"]):
s0 = self.size[(i + 0) % 3]
s1 = self.size[(i + 1) % 3]
s2 = self.size[(i + 2) % 3]
lines = np.array(
(
[s0, 0, 0],
[s0, s1, 0],
[s0, s1, s2],
[s0, 0, s2],
[s0, 0, 0],
),
dtype=np.float32,
)
lines = np.roll(lines, axis=1, shift=(0, i))
color = np.array([0, 0, 0, 1])
color[i] = 1
| # Copyright (C) 2023 ETH Zurich, Manuel Kaufmann, Velko Vechev, Dario Mylonopoulos
class SDF(Node):
"""
Renderable that can be used to draw level sets of a dense SDF volume meshed using marching cubes.
This renderable internally uses the marching cubes algorithm from skimage.
For a faster marching cubes implementation see the Volume renderable.
"""
def __init__(
self,
volume,
size=(1, 1, 1),
level=0.0,
color=(0.7, 0.7, 0.7, 1.0),
level_sets=None,
level_set_colors=None,
mc_step_size=1,
**kwargs,
):
"""Initializer.
:param volume: np array of shape (X, Y, Z) of signed distance values
:param size: size of the volume in local units.
:param level: the level set used for the main mesh.
:param color: color of the main mesh.
:param level_sets: a list or array of additional level set values to display.
:param level_set_colors: a list or array of shape (L, 4) of the same length as
the level_set parameter with colors to use for the additional level sets.
:param mc_step_size: step size used for marching cubes.
:param **kwargs: arguments forwarded to the Node constructor.
"""
assert len(volume.shape) == 3 and len(size) == 3
kwargs["gui_material"] = False
super().__init__(**kwargs)
self.volume = volume
self.size = np.array((size), np.float32)
# Mesh.
verts, faces, normals, _ = measure.marching_cubes(
volume, level, spacing=self.size / (np.array(self.volume.shape) - 1.0), step_size=mc_step_size
)
self.mesh = Meshes(verts, faces, vertex_normals=-normals, color=color, name="Mesh")
# Level sets.
self.level_sets: list[Meshes] = []
if level_sets is not None:
if level_set_colors is not None:
assert len(level_sets) == len(level_set_colors)
for i, s in enumerate(level_sets):
verts, faces, normals, _ = measure.marching_cubes(
volume, s, spacing=self.size / (np.array(self.volume.shape) - 1.0), step_size=mc_step_size
)
shell = Meshes(verts, faces, vertex_normals=-normals, name=f"Level {s:.03f}", cast_shadow=False)
if level_set_colors is not None:
shell.color = tuple(level_set_colors[i])
shell.clip_control = np.array((1, 1, 1))
shell.clip_value = self.size.copy()
shell.backface_culling = False
self.level_sets.append(shell)
# Bounding box.
self.bounding_box = BoundingBoxes.from_min_max_diagonal(
np.array([[0.0, 0.0, 0.0]]),
np.array([self.size], dtype=np.float32),
color=(0, 0, 0, 1),
name="Bounding Box",
gui_affine=False,
)
# Clip plane lines.
self.clip_lines = []
for i, axis in enumerate(["X", "Y", "Z"]):
s0 = self.size[(i + 0) % 3]
s1 = self.size[(i + 1) % 3]
s2 = self.size[(i + 2) % 3]
lines = np.array(
(
[s0, 0, 0],
[s0, s1, 0],
[s0, s1, s2],
[s0, 0, s2],
[s0, 0, 0],
),
dtype=np.float32,
)
lines = np.roll(lines, axis=1, shift=(0, i))
color = np.array([0, 0, 0, 1])
color[i] = 1 | self.clip_lines.append(Lines(lines, cast_shadow=False, color=color, name=f"Clip {axis}", gui_affine=False)) | 1 | 2023-12-07 16:13:50+00:00 | 24k |
nexB/dejacode | dje/admin.py | [
{
"identifier": "IS_FILTER_LOOKUP_VAR",
"path": "dje/filters.py",
"snippet": "IS_FILTER_LOOKUP_VAR = \"_filter_lookup\""
},
{
"identifier": "CreatedByListFilter",
"path": "dje/filters.py",
"snippet": "class CreatedByListFilter(filters.SimpleListFilter):\n \"\"\"\n Filter by the use... | import csv
import operator
from collections import OrderedDict
from copy import copy
from functools import reduce
from django import forms
from django.conf import settings
from django.contrib import admin
from django.contrib import messages
from django.contrib.admin.helpers import ACTION_CHECKBOX_NAME
from django.contrib.admin.options import IS_POPUP_VAR
from django.contrib.admin.sites import AdminSite
from django.contrib.admin.templatetags.admin_urls import add_preserved_filters
from django.contrib.admin.utils import lookup_spawns_duplicates
from django.contrib.admin.utils import unquote
from django.contrib.admin.views.main import ChangeList
from django.contrib.admin.widgets import AdminDateWidget
from django.contrib.auth.admin import GroupAdmin
from django.contrib.auth.admin import UserAdmin
from django.contrib.auth.models import Group
from django.contrib.auth.models import Permission
from django.contrib.auth.views import LogoutView
from django.contrib.contenttypes.admin import GenericTabularInline
from django.contrib.contenttypes.models import ContentType
from django.core import checks
from django.core.exceptions import FieldDoesNotExist
from django.core.exceptions import FieldError
from django.core.exceptions import PermissionDenied
from django.db import models
from django.forms.formsets import DELETION_FIELD_NAME
from django.http import HttpResponse
from django.http.request import QueryDict
from django.shortcuts import get_object_or_404
from django.shortcuts import redirect
from django.template.loader import render_to_string
from django.template.response import TemplateResponse
from django.urls import path
from django.urls import reverse
from django.utils.encoding import force_str
from django.utils.html import format_html
from django.utils.html import format_html_join
from django.utils.http import urlencode
from django.utils.translation import gettext as _
from django.views.generic import RedirectView
from django_registration.backends.activation.views import RegistrationView
from dje.filters import IS_FILTER_LOOKUP_VAR
from dje.filters import CreatedByListFilter
from dje.filters import DataspaceFilter
from dje.filters import HistoryCreatedActionTimeListFilter
from dje.filters import HistoryModifiedActionTimeListFilter
from dje.filters import LimitToDataspaceListFilter
from dje.filters import MissingInFilter
from dje.forms import DataspaceAdminForm
from dje.forms import DataspacedAdminForm
from dje.forms import DejaCodeAuthenticationForm
from dje.importers import import_view
from dje.list_display import AsURL
from dje.mass_update import mass_update_action
from dje.models import Dataspace
from dje.models import DataspaceConfiguration
from dje.models import DejacodeUser
from dje.models import ExternalReference
from dje.models import ExternalSource
from dje.models import History
from dje.models import HistoryFieldsMixin
from dje.models import is_dataspace_related
from dje.notification import send_notification_email
from dje.notification import send_notification_email_on_queryset
from dje.permissions import get_protected_fields
from dje.search import advanced_search
from dje.utils import CHANGELIST_LINK_TEMPLATE
from dje.utils import class_wrap
from dje.utils import construct_changes_details_message
from dje.utils import get_previous_next
from dje.utils import group_by
from dje.utils import has_permission
from dje.utils import queryset_to_changelist_href
from dje.views import ActivityLog
from dje.views import clone_dataset_view
from dje.views import docs_models_view
from dje.views import manage_copy_defaults_view
from dje.views import manage_tab_permissions_view
from dje.views import object_compare_view
from dje.views import object_copy_view
from axes.admin import AccessAttemptAdmin
from axes.models import AccessAttempt | 19,188 | (
"Application Process Settings",
{
"fields": (
"set_usage_policy_on_new_component_from_licenses",
"enable_package_scanning",
"update_packages_from_scan",
"enable_purldb_access",
"enable_vulnerablecodedb_access",
)
},
),
)
search_fields = ("name",)
inlines = [DataspaceConfigurationInline]
form = DataspaceAdminForm
change_form_template = "admin/dje/dataspace/change_form.html"
change_list_template = "admin/change_list_extended.html"
def has_change_permission(self, request, obj=None):
"""
Bypass the ReferenceOnlyPermissions to allow regular Dataspace admins,
with the right permission, to edit their own Dataspace.
"""
return super(admin.ModelAdmin, self).has_change_permission(request, obj)
def get_readonly_fields(self, request, obj=None):
"""Make Dataspace.name field readonly on edit except for reference Dataspace superusers."""
readonly_fields = super().get_readonly_fields(request, obj)
user = request.user
if obj and not (user.dataspace.is_reference and user.is_superuser):
readonly_fields += ("name",)
return readonly_fields
def get_urls(self):
info = self.model._meta.app_label, self.model._meta.model_name
urls = [
path(
"<pk>/clonedataset/",
self.admin_site.admin_view(clone_dataset_view),
name="{}_{}_clonedataset".format(*info),
),
path(
"<pk>/tab_permissions/",
self.admin_site.admin_view(manage_tab_permissions_view),
name="{}_{}_tab_permissions".format(*info),
),
path(
"<pk>/copy_defaults/",
self.admin_site.admin_view(manage_copy_defaults_view),
name="{}_{}_copy_defaults".format(*info),
),
]
return urls + super().get_urls()
def get_queryset(self, request):
"""
Limit the QuerySet to the current user Dataspace. + the Reference one.
If the user Dataspace is the Reference then show all.
"""
qs = super().get_queryset(request)
if not request.user.dataspace.is_reference:
qs = qs.filter(id=request.user.dataspace_id)
return qs
def get_actions(self, request):
"""Remove the bulk delete action, it does not make sense for Dataspace."""
actions = super().get_actions(request)
if "delete_selected" in actions:
del actions["delete_selected"]
return actions
def changeform_view(self, request, object_id=None, form_url="", extra_context=None):
extra_context = extra_context or {}
extra_context["template_dataspace"] = settings.TEMPLATE_DATASPACE
return super().changeform_view(request, object_id, form_url, extra_context)
class ChildRelationshipInline(DataspacedFKMixin, admin.TabularInline):
fk_name = "parent"
extra = 0
classes = ("grp-collapse grp-open",)
raw_id_fields = ("child",)
autocomplete_lookup_fields = {"fk": ["child"]}
verbose_name = _("Child")
class ExternalReferenceInline(DataspacedFKMixin, GenericTabularInline):
model = ExternalReference
extra = 0
classes = ("grp-collapse grp-open",)
@admin.register(ExternalSource, site=dejacode_site)
class ExternalSourceAdmin(DataspacedAdmin):
def references(self, obj):
"""
Return links to the content_object changelist of ExternalReference instances,
for the given ExternalSource instance, grouped per ContentType.
"""
changelist_links = []
queryset = obj.externalreference_set
grouped = group_by(queryset, "content_type", count_on="object_id", distinct=True)
for value in grouped:
model_class = ContentType.objects.get(id=value["content_type"]).model_class()
opts = model_class._meta
url = reverse(f"admin:{opts.app_label}_{opts.model_name}_changelist")
params = {EXTERNAL_SOURCE_LOOKUP: obj.id}
href = f"{url}?{urlencode(params)}"
changelist_link = format_html(
CHANGELIST_LINK_TEMPLATE, href, value["count"], opts.verbose_name_plural
)
changelist_links.append([changelist_link])
html_list = "<ul>{}</ul>".format(format_html_join("", "<li>{}</li>", changelist_links))
| #
# Copyright (c) nexB Inc. and others. All rights reserved.
# DejaCode is a trademark of nexB Inc.
# SPDX-License-Identifier: AGPL-3.0-only
# See https://github.com/nexB/dejacode for support or download.
# See https://aboutcode.org for more information about AboutCode FOSS projects.
#
EXTERNAL_SOURCE_LOOKUP = "external_references__external_source_id"
ADDITION = History.ADDITION
CHANGE = History.CHANGE
DELETION = History.DELETION
class DejaCodeAdminSite(AdminSite):
login_template = "registration/login.html"
login_form = DejaCodeAuthenticationForm
site_title = _("DejaCode Administration")
site_header = _("DejaCode Administration")
index_title = _("DejaCode Administration")
empty_value_display = ""
def get_urls(self):
"""Override the admin:logout and admin:password_change views to the default ones."""
urls = [
path("logout/", LogoutView.as_view(next_page="login"), name="logout"),
path(
"password_change/",
RedirectView.as_view(url="/account/password_change/", permanent=True),
name="password_change",
),
path("docs/models/", docs_models_view, name="docs_models"),
]
return urls + super().get_urls()
dejacode_site = DejaCodeAdminSite()
@admin.display(description="")
def get_hierarchy_link(obj):
"""Return a link to the Hierarchy view if the obj has at least 1 parent or 1 child."""
if obj.has_parent_or_child():
return format_html(
'<a href="{}#hierarchy" target="_blank" class="hierarchy-icon"'
' title="Hierarchy"> </a>',
obj.get_absolute_url(),
)
def get_additional_information_fieldset(pre_fields=None):
fields = (
"dataspace",
"uuid",
"created_date",
"created_by",
"last_modified_date",
"last_modified_by",
)
if pre_fields:
fields = pre_fields + fields
return ("Additional Information", {"classes": ("grp-collapse grp-closed",), "fields": fields})
class ReferenceOnlyPermissions:
def has_add_permission(self, request):
"""Limits the addition to Reference dataspace users."""
perm = super().has_add_permission(request)
return perm and request.user.dataspace.is_reference
def has_change_permission(self, request, obj=None):
"""Limits the change to Reference dataspace users."""
perm = super().has_change_permission(request, obj)
return perm and request.user.dataspace.is_reference
def has_delete_permission(self, request, obj=None):
"""Limits the deletion to Reference dataspace users."""
perm = super().has_delete_permission(request, obj)
return perm and request.user.dataspace.is_reference
def has_view_permission(self, request, obj=None):
perm = super().has_view_permission(request, obj)
return perm and request.user.dataspace.is_reference
class DataspacedFKMixin:
"""
Limit the QuerySet of ForeignKeys to the current Dataspace,
or to the parent object in case of Inlines.
On ADDITION, the Dataspace is taken from the User
On MODIFICATION, it's taken on the current object instance or parent
instance in case of Inlines.
This class can be applied to ModelAdmins and Inlines.
The declared limit_choices_to on the model field will be respected.
"""
# The QuerySet for the fields in this list will be scoped by the Model content_type
content_type_scope_fields = []
def formfield_for_foreignkey(self, db_field, request=None, **kwargs):
# If a QuerySet was given in the kwargs of the calling method, we then
# assume that the filtering was done and we skip further processing.
qs = kwargs.get("queryset", None)
if qs is not None:
return super().formfield_for_foreignkey(db_field, request, **kwargs)
related_model = db_field.related_model
if is_dataspace_related(related_model):
# No instance, ADDITION, get dataspace from user
if not getattr(request, "_object", None):
dataspace = request.user.dataspace
# Parent instance, MODIFICATION, dataspace from instance
else:
dataspace = request._object.dataspace
kwargs["queryset"] = db_field.related_model.objects.scope(dataspace).complex_filter(
db_field.remote_field.limit_choices_to
)
if db_field.name in self.content_type_scope_fields:
kwargs["queryset"] = kwargs["queryset"].filter(
content_type=ContentType.objects.get_for_model(self.model)
)
return super().formfield_for_foreignkey(db_field, request, **kwargs)
class ProtectedFieldsMixin:
def get_readonly_fields(self, request, obj=None):
"""Add field level permissions."""
readonly_fields = super().get_readonly_fields(request, obj)
protected_fields = get_protected_fields(self.model, request.user)
if protected_fields:
readonly_fields += tuple(protected_fields)
return readonly_fields
class ChangelistPopupPermissionMixin:
"""
Allow the changelist view access in popup mode for users without change permission.
In the case of raw_id_fields feature, this view need be be available to select the related
object.
This mixin bypass the limitation in Django: https://code.djangoproject.com/ticket/11561
Only the changelist is available, the form is never accessible.
"""
def has_change_permission(self, request, obj=None):
if obj is None and IS_POPUP_VAR in request.GET:
return True
return super().has_change_permission(request, obj)
class ProhibitDataspaceLookupMixin:
"""
Prohibit all `dataspace` related lookups.
Remove the possibility to look into other Dataspaces.
"""
def lookup_allowed(self, lookup, value):
if lookup.startswith("dataspace"):
return False
return super().lookup_allowed(lookup, value)
def check(self, **kwargs):
errors = super().check(**kwargs)
has_dataspace_filter = DataspaceFilter in self.list_filter
if has_dataspace_filter:
errors.append(
checks.Error(f"Remove {DataspaceFilter} from {self}.list_filter", obj=self)
)
return errors
def get_queryset(self, request):
return super().get_queryset(request).scope_for_user(request.user)
class AdvancedSearchAdminMixin:
def get_search_results(self, request, queryset, search_term):
"""Replace default search with advanced system."""
use_distinct = False
search_fields = self.get_search_fields(request)
if search_fields and search_term:
filters = []
try:
filters = advanced_search(search_term, search_fields)
except FieldError as e:
messages.error(request, e)
except ValueError as e:
messages.error(request, f"Search terms error: {e}")
if filters:
queryset = queryset.filter(filters)
if not use_distinct:
for search_spec, __ in filters.children:
if lookup_spawns_duplicates(self.opts, search_spec):
use_distinct = True
break
return queryset, use_distinct
class HistoryAdminMixin:
def log_addition(self, request, object, change_message=None):
history_entry = History.log_addition(request.user, object)
if ADDITION in getattr(self, "email_notification_on", []):
send_notification_email(request.user, object, ADDITION)
return history_entry
def log_change(self, request, object, message):
"""
Add notification on object update.
The notification system can be disabled by setting _disable_notification
to True on the request.
"""
serialized_data = getattr(request, "_serialized_data", None)
history_entry = History.log_change(request.user, object, message, serialized_data)
message = history_entry.get_change_message()
disable_notification = getattr(request, "_disable_notification", False)
if CHANGE in getattr(self, "email_notification_on", []) and not disable_notification:
# Expending the base message with details
changes_details = getattr(request, "changes_details", {})
message += construct_changes_details_message(changes_details)
send_notification_email(request.user, object, CHANGE, message)
return history_entry
def log_deletion(self, request, object, object_repr):
"""
Log that an object will be deleted.
Note that this method must be called before the deletion.
"""
return History.log_deletion(request.user, object)
def history_view(self, request, object_id, extra_context=None):
response = super().history_view(request, object_id, extra_context)
context_data = getattr(response, "context_data", None)
if context_data: # In case response is a HttpResponseRedirect
obj = context_data["object"]
history_qs = History.objects
if is_dataspace_related(self.model):
history_qs = history_qs.filter(object_dataspace__id=obj.dataspace_id)
history_entries = (
history_qs.filter(
object_id=unquote(object_id),
content_type=ContentType.objects.get_for_model(self.model),
)
.select_related()
.order_by("-action_time")
)
obj_has_history_fields = isinstance(obj, HistoryFieldsMixin)
if obj_has_history_fields:
# Use the history fields from the model for the Addition entry.
addition_entry = History(
action_time=obj.created_date,
user=obj.created_by,
change_message="Added.",
)
history_entries = history_entries.exclude(action_flag=History.ADDITION)
history_entries = list(history_entries) + [addition_entry]
response.context_data["action_list"] = history_entries
return response
class ColoredIconAdminMixin:
class Media:
js = [
"fontawesomefree/js/all.min.js",
]
def colored_icon(self, obj):
if obj.icon and obj.color_code:
return obj.get_icon_as_html()
class DataspacedChangeList(ChangeList):
def get_results(self, request):
"""
Store the result_list ids in the session for the Previous and Next navigation button
and "Save and go to next" feature.
The session values are then used in the change_view() method of the ModelAdmin.
Injects the preserved_filters on each object of the result_list to be used in
list_display callable, as the request is not available there.
Hierarchy links on ComponentAdmin and OwnerAdmin, as well as the annotation link
on the LicenseAdmin requires this.
This workaround could be removed once if the the following gets solved in Django:
https://code.djangoproject.com/ticket/13659
"""
super().get_results(request)
for obj in self.result_list:
obj._preserved_filters = self.preserved_filters
self.set_reference_link(request)
@property
def has_filters_activated(self):
return bool(self.get_filters_params())
def get_filters_params(self, params=None):
lookup_params = super().get_filters_params(params)
if IS_FILTER_LOOKUP_VAR in lookup_params:
del lookup_params[IS_FILTER_LOOKUP_VAR]
return lookup_params
def set_reference_link(self, request):
"""Add a 'View Reference Data' or 'View My Data 'link in the changelist header."""
do_set_link = all(
[
DataspaceFilter in self.model_admin.list_filter,
self.model_admin.lookup_allowed(DataspaceFilter.parameter_name, None),
not self.is_popup,
]
)
if not do_set_link:
return
reference_dataspace = Dataspace.objects.get_reference()
if reference_dataspace and reference_dataspace != request.user.dataspace:
dataspace_id = request.GET.get(DataspaceFilter.parameter_name)
if dataspace_id and dataspace_id != request.user.dataspace_id:
self.my_dataspace_link = True
else:
params = f"?{DataspaceFilter.parameter_name}={reference_dataspace.id}"
self.reference_params = params
class DataspacedAdmin(
DataspacedFKMixin,
ProtectedFieldsMixin,
AdvancedSearchAdminMixin,
HistoryAdminMixin,
admin.ModelAdmin,
):
formfield_overrides = {
models.DateField: {"widget": AdminDateWidget(attrs={"placeholder": "YYYY-MM-DD"})},
}
list_filter = (DataspaceFilter,)
readonly_fields = (
"dataspace",
"uuid",
)
actions = ["copy_to", "compare_with"]
actions_to_remove = []
email_notification_on = [ADDITION, CHANGE, DELETION]
save_as = True
# Display only the current count
show_full_result_count = False
# Display a warning if any of the identifier_fields has changed
identifier_fields_warning = True
# Default form, customized form should always extend DataspacedAdminForm
form = DataspacedAdminForm
# Using extended version of base templates to avoid code duplication
change_form_template = "admin/change_form_extended.html"
change_list_template = "admin/change_list_extended.html"
# Set this to a BaseImporter extension of the Model to enable the import
importer_class = None
# Set this to a DejacodeMassUpdateForm to enable the mass update action
mass_update_form = None
# Set this to False to disable the Activity Log feature
activity_log = True
# Set this to True to enable the Previous and Next buttons in change view
navigation_buttons = False
preserve_filters = True
# Do not display the View on site links by default
# Set: view_on_site = DataspacedAdmin.changeform_view_on_site
# for the default obj.get_absolute_url()
view_on_site = False
def __init__(self, model, admin_site):
self.form.admin_site = admin_site
super().__init__(model, admin_site)
def check(self, **kwargs):
errors = super().check(**kwargs)
has_wrong_form_subclass = all(
[
not issubclass(self.form, DataspacedAdminForm),
self.model._meta.unique_together != (("dataspace", "uuid"),),
]
)
if has_wrong_form_subclass:
errors.extend(
[checks.Error(f"{self.form} is not a subclass of {DataspacedAdminForm}", obj=self)]
)
return errors
def changeform_view_on_site(self, obj):
return obj.get_absolute_url()
@admin.display(description=_("View"))
def changelist_view_on_site(self, obj):
return format_html('<a href="{}" target="_blank">View</a>', obj.get_absolute_url())
@admin.display(description=_("URN"))
def urn_link(self, instance):
"""Attach the URN link if URN is supported on the Model."""
if instance.pk:
return instance.urn_link
return f"URN will be available once the {instance._meta.verbose_name} is saved."
def get_queryset(self, request):
qs = super().get_queryset(request)
return qs.scope_for_user_in_admin(request.user)
def get_changelist(self, request, **kwargs):
return DataspacedChangeList
def get_list_filter(self, request):
"""Limit the availability of `MissingInFilter` to superusers."""
list_filter = list(super().get_list_filter(request))
if not request.user.is_superuser and MissingInFilter in list_filter:
del list_filter[list_filter.index(MissingInFilter)]
# Custom LogEntry-based filters when field not available on the model
history_filters = {
"created_by": CreatedByListFilter,
"last_modified_by": None,
"created_date": HistoryCreatedActionTimeListFilter,
"last_modified_date": HistoryModifiedActionTimeListFilter,
}
for field_name, default_filter in history_filters.items():
try:
field = self.model._meta.get_field(field_name)
except FieldDoesNotExist:
if default_filter:
list_filter.append(default_filter)
continue
filtr = field_name
if isinstance(field, models.ForeignKey):
filtr = (field_name, LimitToDataspaceListFilter)
list_filter.append(filtr)
return list_filter
def get_readonly_fields(self, request, obj=None):
readonly_fields = super().get_readonly_fields(request, obj)
if issubclass(self.model, HistoryFieldsMixin):
readonly_fields += (
"created_date",
"created_by",
"last_modified_date",
"last_modified_by",
)
return readonly_fields
def change_view(self, request, object_id, form_url="", extra_context=None):
"""
Render the changelist using the current preserved filters to gather the
previous and next id.
"""
context = extra_context or {}
# WARNING: request.GET is important as a condition since we do not want to run this
# expensive operation in case no given filter/search/sort is applied.
# For example when the admin form is reached directly from the user details view.
if self.navigation_buttons and request.method == "GET" and request.GET:
fake_request = copy(request)
query_dict = fake_request.GET.copy()
preserved_filters = query_dict.pop("_changelist_filters", "")
if preserved_filters:
preserved_filters = force_str(preserved_filters[0])
fake_request.GET = QueryDict(preserved_filters)
changelist_view = self.changelist_view(fake_request)
# Sanity check, if the _changelist_filters were manually changed for example.
if hasattr(changelist_view, "context_data"):
# Do not use ".values_list('id', flat=True)" to avoid an extra query
ids_list = [str(obj.id) for obj in changelist_view.context_data["cl"].result_list]
previous_id, next_id = get_previous_next(ids_list, str(object_id))
context.update(
{
"previous_id": previous_id,
"next_id": next_id,
}
)
if self.save_as and self.identifier_fields_warning:
identifier_fields = self.model.get_identifier_fields()
context["identifier_fields"] = identifier_fields
return super().change_view(request, object_id, form_url, context)
def render_change_form(self, request, context, add=False, change=False, form_url="", obj=None):
response = super().render_change_form(request, context, add, change, form_url, obj)
# De-activating the 'Save as' option if the user is editing an Object
# belonging to another Dataspace.
# We are able to update the context_data at that point as the
# TemplateResponse as not been rendered yet.
if obj and obj.dataspace != request.user.dataspace:
response.context_data["save_as"] = False
return response
@staticmethod
def get_selected_ids_from_request(request, queryset):
select_across = request.POST.get("select_across", 0)
if int(select_across):
# This is the "Selected all" case, we are using the all queryset
object_ids = list(queryset.values_list("id", flat=True))
else:
object_ids = request.POST.getlist(ACTION_CHECKBOX_NAME)
# Converting the ids list in a comma separated string, to be used
# in the GET parameters
return ",".join(str(id_) for id_ in object_ids)
def base_action_with_redirect(self, request, queryset, viewname):
ids = self.get_selected_ids_from_request(request, queryset)
opts = self.model._meta
preserved_filters = self.get_preserved_filters(request)
view_url = reverse(f"admin:{opts.app_label}_{opts.model_name}_{viewname}")
url_with_params = "{}?{}".format(view_url, urlencode({"ids": ids}))
redirect_url = add_preserved_filters(
{"preserved_filters": preserved_filters, "opts": opts}, url_with_params
)
return redirect(redirect_url)
@admin.display(description=_("Copy the selected objects"))
def copy_to(self, request, queryset):
"""Copy the selected objects to another Dataspace."""
return self.base_action_with_redirect(request, queryset, "copy")
@admin.display(description=_("Compare the selected object"))
def compare_with(self, request, queryset):
"""Compare one selected object with a matching object in another Dataspace."""
return self.base_action_with_redirect(request, queryset, "compare")
@admin.display(description=_("Check for updates in reference data"))
def check_updates_in_reference(self, request, queryset):
values = queryset.values_list("uuid", "last_modified_date")
orm_lookups = [
models.Q(**{"uuid": uuid, "last_modified_date__gt": last_modified_date})
for uuid, last_modified_date in values
]
return self.base_check_in_reference_action(request, self.model, orm_lookups)
@admin.display(description=_("Check for newer versions in reference data"))
def check_newer_version_in_reference(self, request, queryset):
values = queryset.values_list("name", "version")
orm_lookups = [
models.Q(**{"name": name, "version__gt": version}) for name, version in values
]
return self.base_check_in_reference_action(request, self.model, orm_lookups)
@staticmethod
def base_check_in_reference_action(request, model_class, orm_lookups):
reference_dataspace = Dataspace.objects.get_reference()
if not reference_dataspace or not orm_lookups:
return
updated_qs = model_class.objects.scope(reference_dataspace).filter(
reduce(operator.or_, orm_lookups)
)
params = {DataspaceFilter.parameter_name: reference_dataspace.pk}
changelist_href = queryset_to_changelist_href(updated_qs, params)
if changelist_href:
return redirect(changelist_href)
messages.warning(request, "No updates available in the reference dataspace.")
@staticmethod
def get_changes_details(form):
"""
Introspect a given form to collect the changes details.
Original values are collected on the DB instance (pre-save value)
and New values are collected on the form (post-save value)
"""
if not form.instance.pk:
return {}
model_class = form.instance.__class__
original_instance = model_class.objects.get(pk=form.instance.pk)
changes_details = []
# Using form.changed_data to only iterate on updated fields
for field in form.changed_data:
original_value = getattr(original_instance, field)
new_value = getattr(form.instance, field)
changes_details.append((field, original_value, new_value))
return {form.instance: changes_details}
def save_model(self, request, obj, form, change):
"""Set the created_by and last_modified_by fields at save time."""
# This have no impact on save() if the model does not declare those fields.
obj.last_modified_by = request.user
if not change:
obj.created_by = request.user
# Injecting the results in the request for future use in the
# log_change() method. This content will be used to add the changes
# details into the notification message.
# Using an OrderedDict to keep the main instance details first
# The related objects changes (inlines) are gathered in
# self.save_formset()
request.changes_details = OrderedDict()
if change and CHANGE in self.email_notification_on:
request.changes_details.update(self.get_changes_details(form))
super().save_model(request, obj, form, change)
def save_formset(self, request, form, formset, change):
"""
Set the Dataspace on the Inline instance before it's saved. Using the
Dataspace of the Model instance of this ModelAdmin.
Also craft the change details of the Inlines.
"""
for f in formset.forms:
# Skip if nothing has changed in the current inline form
if not f.changed_data:
continue
# Set the Dataspace on the Inline instance in case of addition of
# the current inline.
# The Dataspace is taken from the main form instance.
if not f.instance.dataspace_id:
f.instance.dataspace = form.instance.dataspace
# Only in case of a 'change' on the main instance
if change and CHANGE in self.email_notification_on:
# As the `change` param is only about the main instance, we use
# the pk of the inline instance to make sure we are in a
# MODIFICATION case.
#
# If the pk of the inline instance is None, this is an ADDITION,
# so skip the details creation. Also, if DELETION_FIELD_NAME is
# in changed_data, we are in an inline deletion case, skipping
# too.
if f.instance.pk and DELETION_FIELD_NAME not in f.changed_data:
# request.changes_details is created in self.save_model()
request.changes_details.update(self.get_changes_details(f))
super().save_formset(request, form, formset, change)
def delete_model(self, request, obj):
# We are using this rather than self.log_deletion because it's not called
# Here, History.log_deletion is called for each object in the bulk.
History.log_deletion(request.user, obj)
super().delete_model(request, obj)
if DELETION in self.email_notification_on:
send_notification_email(request.user, obj, DELETION)
def delete_queryset(self, request, queryset):
"""
Add the email notification on bulk deletion through the default django
'delete_selected' action.
"""
send_notification_email_on_queryset(request.user, queryset, DELETION)
super().delete_queryset(request, queryset)
def get_urls(self):
info = self.model._meta.app_label, self.model._meta.model_name
urls = []
if self.activity_log:
urls += [
path(
"activity_log/",
self.admin_site.admin_view(ActivityLog.as_view(model=self.model)),
name="{}_{}_activity_log".format(*info),
)
]
actions = getattr(self, "actions", [])
actions_to_remove = getattr(self, "actions_to_remove", [])
if "copy_to" in actions and "copy_to" not in actions_to_remove:
urls += [
path(
"copy/",
self.admin_site.admin_view(object_copy_view),
name="{}_{}_copy".format(*info),
),
]
if "compare_with" in actions and "compare_with" not in actions_to_remove:
urls += [
path(
"compare/",
self.admin_site.admin_view(object_compare_view),
name="{}_{}_compare".format(*info),
),
]
if self.importer_class:
urls += [
path(
"import/",
self.admin_site.admin_view(import_view),
{"importer_class": self.importer_class},
name="{}_{}_import".format(*info),
),
]
return urls + super().get_urls()
def get_form(self, request, obj=None, **kwargs):
"""
Set the `obj` instance on the `request` for future processing.
Set the serialized_data of the object on the `request` to be used in the
`log_change` method.
Set the `request` on the `form_class`.
"""
if obj:
request._object = obj
request._serialized_data = obj.as_json()
form_class = super().get_form(request, obj, **kwargs)
form_class.request = request
return form_class
def get_fieldsets(self, request, obj=None):
"""Exclude form fields from the ADMIN_FORMS_CONFIGURATION settings."""
fieldsets = super().get_fieldsets(request, obj)
forms_config = settings.ADMIN_FORMS_CONFIGURATION
if not forms_config:
return fieldsets
model_config = forms_config.get(self.model._meta.model_name, {})
exclude = model_config.get("exclude", [])
if not exclude:
return fieldsets
fieldsets_with_exclude = []
for label, entry in fieldsets:
fields = entry.get("fields")
if fields:
entry["fields"] = [field for field in fields if field not in exclude]
fieldsets_with_exclude.append((label, entry))
return fieldsets_with_exclude
def get_inline_instances(self, request, obj=None):
"""Injects the ``request`` in each inline form to be used in validation."""
base_instances = super().get_inline_instances(request, obj)
instances = []
request._object = obj
for inline in base_instances:
inline.form.request = request
instances.append(inline)
return instances
def get_actions(self, request):
"""Limit the available actions based on who you are and what you are looking at."""
if IS_POPUP_VAR in request.GET:
return OrderedDict()
actions = super().get_actions(request)
is_user_dataspace = DataspaceFilter.parameter_name not in request.GET
can_mass_update = all(
[
self.mass_update_form,
has_permission(self.model, request.user, "change"),
is_user_dataspace or request.user.dataspace.is_reference,
]
)
if can_mass_update:
actions["mass_update"] = (mass_update_action, "mass_update", "Mass update")
if not has_permission(self.model, request.user, "add") and "copy_to" in actions:
del actions["copy_to"]
if not request.user.dataspace.is_reference:
if is_user_dataspace: # The user is looking at his own Dataspace
if "copy_to" in actions:
del actions["copy_to"]
if "compare_with" in actions:
del actions["compare_with"]
else: # The user is looking at another Dataspace
if "delete_selected" in actions:
del actions["delete_selected"]
if request.user.dataspace.is_reference or not is_user_dataspace:
if "check_updates_in_reference" in actions:
del actions["check_updates_in_reference"]
if "check_newer_version_in_reference" in actions:
del actions["check_newer_version_in_reference"]
for action in self.actions_to_remove:
if action in actions:
del actions[action]
return actions
@admin.display(description=_("Copy to my Dataspace"))
def copy_link(self, obj):
return format_html(
'<strong><a href="{}&{}=1">{}</a></strong>',
obj.get_copy_url(),
IS_POPUP_VAR,
_("Copy to my Dataspace"),
)
@staticmethod
def hide_display_links(request):
return all(
[
DataspaceFilter.parameter_name in request.GET,
request.GET.get(DataspaceFilter.parameter_name) != str(request.user.dataspace_id),
]
)
def get_list_display(self, request):
"""
Remove the view_on_site and hierarchy links in popup mode.
Also insert the copy link when looking at another dataspace.
"""
list_display = super().get_list_display(request)
if IS_POPUP_VAR in request.GET:
list_display = list(list_display)
if "changelist_view_on_site" in list_display:
list_display.remove("changelist_view_on_site")
if get_hierarchy_link in list_display:
list_display.remove(get_hierarchy_link)
if self.hide_display_links(request):
list_display = list(list_display)
if "copy_to" not in self.actions_to_remove:
list_display.insert(0, "copy_link")
if get_hierarchy_link in list_display:
list_display.remove(get_hierarchy_link)
return list_display
def get_list_display_links(self, request, list_display):
"""Remove all the display_links when looking at another dataspace."""
if not self.hide_display_links(request):
return super().get_list_display_links(request, list_display)
def response_change(self, request, obj):
"""Add the logic for the "Save and go to next" feature."""
next_id = request.POST.get("next_id")
if "_next" in request.POST and next_id:
opts = self.model._meta
preserved_filters = self.get_preserved_filters(request)
msg_dict = {
"name": str(opts.verbose_name),
"obj": str(obj),
}
msg = 'The {name} "{obj}" was changed successfully.'.format(**msg_dict)
self.message_user(request, msg, messages.SUCCESS)
viewname = f"admin:{opts.app_label}_{opts.model_name}_change"
next_url = reverse(viewname, args=[next_id], current_app=self.admin_site.name)
redirect_url = add_preserved_filters(
{"preserved_filters": preserved_filters, "opts": opts}, next_url
)
return redirect(redirect_url)
return super().response_change(request, obj)
def lookup_allowed(self, lookup, value):
if lookup in [EXTERNAL_SOURCE_LOOKUP]:
return True
return super().lookup_allowed(lookup, value)
@staticmethod
def _limited_permission(request, obj, has_perm):
# Model permission
if not has_perm:
return False
# Object instance permission
if obj and obj.dataspace_id != request.user.dataspace_id:
return request.user.dataspace.is_reference
return True
def has_add_permission(self, request):
has_perm = super().has_add_permission(request)
# Do not display the "Add" link in filter lookup popup mode
if IS_FILTER_LOOKUP_VAR in request.GET:
return False
return has_perm
def has_change_permission(self, request, obj=None):
has_perm = super().has_change_permission(request, obj)
return self._limited_permission(request, obj, has_perm)
def has_delete_permission(self, request, obj=None):
has_perm = super().has_delete_permission(request, obj)
return self._limited_permission(request, obj, has_perm)
def has_view_permission(self, request, obj=None):
has_perm = super().has_view_permission(request, obj)
return self._limited_permission(request, obj, has_perm)
def has_importer(self):
"""Return True if the importer_class has been set."""
if self.importer_class:
return True
def has_activity_log(self):
"""Return True if the activity_log has been set."""
if self.activity_log:
return True
class HiddenValueWidget(forms.TextInput):
"""Render a hidden value in the UI."""
HIDDEN_VALUE = "*******"
def render(self, name, value, attrs=None, renderer=None):
value = self.HIDDEN_VALUE if value else None
return super().render(name, value, attrs, renderer)
class DataspaceConfigurationForm(forms.ModelForm):
"""
Configure Dataspace settings.
This form includes fields for various API keys, with sensitive values
hidden in the UI using the HiddenValueWidget.
"""
hidden_value_fields = [
"scancodeio_api_key",
"vulnerablecode_api_key",
"purldb_api_key",
]
def __init__(self, *args, **kwargs):
"""Initialize the form and set HiddenValueWidget for specified fields."""
super().__init__(*args, **kwargs)
for field_name in self.hidden_value_fields:
self.fields[field_name].widget = HiddenValueWidget()
def clean(self):
"""Clean the form data, excluding hidden values from cleaned_data."""
for field_name in self.hidden_value_fields:
value = self.cleaned_data.get(field_name)
if value == HiddenValueWidget.HIDDEN_VALUE:
del self.cleaned_data[field_name]
class DataspaceConfigurationInline(DataspacedFKMixin, admin.StackedInline):
model = DataspaceConfiguration
form = DataspaceConfigurationForm
verbose_name_plural = _("Configuration")
verbose_name = _("Dataspace configuration")
fields = [
"homepage_layout",
"scancodeio_url",
"scancodeio_api_key",
"vulnerablecode_url",
"vulnerablecode_api_key",
"purldb_url",
"purldb_api_key",
]
can_delete = False
@admin.register(Dataspace, site=dejacode_site)
class DataspaceAdmin(
ReferenceOnlyPermissions,
HistoryAdminMixin,
admin.ModelAdmin,
):
short_description = (
"A Dataspace is an independent, exclusive set of DejaCode data, "
"which can be either nexB reference data or installation-specific data."
)
long_description = (
"Each DJE application User is associated with exactly one Dataspace, "
"and the data owned by that Dataspace is presented to the user when "
"accessing the application. "
"An installation of DejaCode typically contains the following Dataspaces:"
"nexB: Reference reference data from nexB"
"{{mySite}}: Production data for a specific DejaCode installation"
"{{sandbox}}: Data for testing, training, or staging activities"
)
list_display = (
"name",
"full_name",
AsURL("homepage_url", short_description="Homepage URL"),
AsURL("contact_info", short_description="Contact information"),
)
fieldsets = (
(
"",
{
"fields": (
"name",
"homepage_url",
"notes",
"home_page_announcements",
"logo_url",
)
},
),
(
"Attribution Package Information",
{
"fields": (
"full_name",
"address",
"contact_info",
"open_source_information_url",
"open_source_download_url",
)
},
),
(
"User Interface Settings",
{
"fields": (
"show_license_profile_in_license_list_view",
"show_license_type_in_license_list_view",
"show_spdx_short_identifier_in_license_list_view",
"show_usage_policy_in_user_views",
"show_type_in_component_list_view",
"hide_empty_fields_in_component_details_view",
)
},
),
(
"Application Process Settings",
{
"fields": (
"set_usage_policy_on_new_component_from_licenses",
"enable_package_scanning",
"update_packages_from_scan",
"enable_purldb_access",
"enable_vulnerablecodedb_access",
)
},
),
)
search_fields = ("name",)
inlines = [DataspaceConfigurationInline]
form = DataspaceAdminForm
change_form_template = "admin/dje/dataspace/change_form.html"
change_list_template = "admin/change_list_extended.html"
def has_change_permission(self, request, obj=None):
"""
Bypass the ReferenceOnlyPermissions to allow regular Dataspace admins,
with the right permission, to edit their own Dataspace.
"""
return super(admin.ModelAdmin, self).has_change_permission(request, obj)
def get_readonly_fields(self, request, obj=None):
"""Make Dataspace.name field readonly on edit except for reference Dataspace superusers."""
readonly_fields = super().get_readonly_fields(request, obj)
user = request.user
if obj and not (user.dataspace.is_reference and user.is_superuser):
readonly_fields += ("name",)
return readonly_fields
def get_urls(self):
info = self.model._meta.app_label, self.model._meta.model_name
urls = [
path(
"<pk>/clonedataset/",
self.admin_site.admin_view(clone_dataset_view),
name="{}_{}_clonedataset".format(*info),
),
path(
"<pk>/tab_permissions/",
self.admin_site.admin_view(manage_tab_permissions_view),
name="{}_{}_tab_permissions".format(*info),
),
path(
"<pk>/copy_defaults/",
self.admin_site.admin_view(manage_copy_defaults_view),
name="{}_{}_copy_defaults".format(*info),
),
]
return urls + super().get_urls()
def get_queryset(self, request):
"""
Limit the QuerySet to the current user Dataspace. + the Reference one.
If the user Dataspace is the Reference then show all.
"""
qs = super().get_queryset(request)
if not request.user.dataspace.is_reference:
qs = qs.filter(id=request.user.dataspace_id)
return qs
def get_actions(self, request):
"""Remove the bulk delete action, it does not make sense for Dataspace."""
actions = super().get_actions(request)
if "delete_selected" in actions:
del actions["delete_selected"]
return actions
def changeform_view(self, request, object_id=None, form_url="", extra_context=None):
extra_context = extra_context or {}
extra_context["template_dataspace"] = settings.TEMPLATE_DATASPACE
return super().changeform_view(request, object_id, form_url, extra_context)
class ChildRelationshipInline(DataspacedFKMixin, admin.TabularInline):
fk_name = "parent"
extra = 0
classes = ("grp-collapse grp-open",)
raw_id_fields = ("child",)
autocomplete_lookup_fields = {"fk": ["child"]}
verbose_name = _("Child")
class ExternalReferenceInline(DataspacedFKMixin, GenericTabularInline):
model = ExternalReference
extra = 0
classes = ("grp-collapse grp-open",)
@admin.register(ExternalSource, site=dejacode_site)
class ExternalSourceAdmin(DataspacedAdmin):
def references(self, obj):
"""
Return links to the content_object changelist of ExternalReference instances,
for the given ExternalSource instance, grouped per ContentType.
"""
changelist_links = []
queryset = obj.externalreference_set
grouped = group_by(queryset, "content_type", count_on="object_id", distinct=True)
for value in grouped:
model_class = ContentType.objects.get(id=value["content_type"]).model_class()
opts = model_class._meta
url = reverse(f"admin:{opts.app_label}_{opts.model_name}_changelist")
params = {EXTERNAL_SOURCE_LOOKUP: obj.id}
href = f"{url}?{urlencode(params)}"
changelist_link = format_html(
CHANGELIST_LINK_TEMPLATE, href, value["count"], opts.verbose_name_plural
)
changelist_links.append([changelist_link])
html_list = "<ul>{}</ul>".format(format_html_join("", "<li>{}</li>", changelist_links)) | return class_wrap(html_list, "width200") | 26 | 2023-12-07 16:57:42+00:00 | 24k |
wusize/CLIM | src/open_clip/model.py | [
{
"identifier": "HFTextEncoder",
"path": "src/open_clip/hf_model.py",
"snippet": "class HFTextEncoder(nn.Module):\n \"\"\"HuggingFace model adapter\"\"\"\n output_tokens: torch.jit.Final[bool]\n\n def __init__(\n self,\n model_name_or_path: str,\n output_dim: in... | from dataclasses import dataclass
from typing import Optional, Tuple, Union
from torch import nn
from torch.utils.checkpoint import checkpoint
from .hf_model import HFTextEncoder
from .modified_resnet import ModifiedResNet
from .timm_model import TimmModel
from .transformer import LayerNormFp32, LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer
from .utils import to_2tuple
import logging
import math
import numpy as np
import torch
import torch.nn.functional as F | 15,880 | """ CLIP Model
Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
"""
@dataclass
class CLIPVisionCfg:
layers: Union[Tuple[int, int, int, int], int] = 12
width: int = 768
head_width: int = 64
mlp_ratio: float = 4.0
patch_size: int = 16
image_size: Union[Tuple[int, int], int] = 224
ls_init_value: Optional[float] = None # layer scale initial value
patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results
input_patchnorm: bool = False # whether to use dual patchnorm - would only apply the input layernorm on each patch, as post-layernorm already exist in original clip vit design
global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580)
attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer
n_queries: int = 256 # n_queries for attentional pooler
attn_pooler_heads: int = 8 # n heads for attentional_pooling
timm_model_name: str = None # a valid model name overrides layers, width, patch_size
timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model
timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '')
timm_proj_bias: bool = False # enable bias final projection
timm_drop: float = 0. # head dropout
timm_drop_path: Optional[float] = None # backbone stochastic depth
output_tokens: bool = False
freeze_output = True
freeze_all_bns = True
@dataclass
class CLIPTextCfg:
context_length: int = 77
vocab_size: int = 49408
width: int = 512
heads: int = 8
layers: int = 12
ls_init_value: Optional[float] = None # layer scale initial value
hf_model_name: str = None
hf_tokenizer_name: str = None
hf_model_pretrained: bool = True
proj: str = 'mlp'
pooler_type: str = 'mean_pooler'
embed_cls: bool = False
pad_id: int = 0
output_tokens: bool = False
def get_cast_dtype(precision: str):
cast_dtype = None
if precision == 'bf16':
cast_dtype = torch.bfloat16
elif precision == 'fp16':
cast_dtype = torch.float16
return cast_dtype
def _build_vision_tower(
embed_dim: int,
vision_cfg: CLIPVisionCfg,
quick_gelu: bool = False,
cast_dtype: Optional[torch.dtype] = None
):
if isinstance(vision_cfg, dict):
vision_cfg = CLIPVisionCfg(**vision_cfg)
# OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
# memory efficient in recent PyTorch releases (>= 1.10).
# NOTE: timm models always use native GELU regardless of quick_gelu flag.
act_layer = QuickGELU if quick_gelu else nn.GELU
if vision_cfg.timm_model_name:
visual = TimmModel(
vision_cfg.timm_model_name,
pretrained=vision_cfg.timm_model_pretrained,
pool=vision_cfg.timm_pool,
proj=vision_cfg.timm_proj,
proj_bias=vision_cfg.timm_proj_bias,
drop=vision_cfg.timm_drop,
drop_path=vision_cfg.timm_drop_path,
patch_drop=vision_cfg.patch_dropout if vision_cfg.patch_dropout > 0 else None,
embed_dim=embed_dim,
image_size=vision_cfg.image_size,
)
act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models
elif isinstance(vision_cfg.layers, (tuple, list)):
vision_heads = vision_cfg.width * 32 // vision_cfg.head_width
visual = ModifiedResNet(
layers=vision_cfg.layers,
output_dim=embed_dim,
heads=vision_heads,
image_size=vision_cfg.image_size,
width=vision_cfg.width,
freeze_output=vision_cfg.freeze_output,
freeze_all_bns=vision_cfg.freeze_all_bns
)
else:
vision_heads = vision_cfg.width // vision_cfg.head_width
| """ CLIP Model
Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
"""
@dataclass
class CLIPVisionCfg:
layers: Union[Tuple[int, int, int, int], int] = 12
width: int = 768
head_width: int = 64
mlp_ratio: float = 4.0
patch_size: int = 16
image_size: Union[Tuple[int, int], int] = 224
ls_init_value: Optional[float] = None # layer scale initial value
patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results
input_patchnorm: bool = False # whether to use dual patchnorm - would only apply the input layernorm on each patch, as post-layernorm already exist in original clip vit design
global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580)
attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer
n_queries: int = 256 # n_queries for attentional pooler
attn_pooler_heads: int = 8 # n heads for attentional_pooling
timm_model_name: str = None # a valid model name overrides layers, width, patch_size
timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model
timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '')
timm_proj_bias: bool = False # enable bias final projection
timm_drop: float = 0. # head dropout
timm_drop_path: Optional[float] = None # backbone stochastic depth
output_tokens: bool = False
freeze_output = True
freeze_all_bns = True
@dataclass
class CLIPTextCfg:
context_length: int = 77
vocab_size: int = 49408
width: int = 512
heads: int = 8
layers: int = 12
ls_init_value: Optional[float] = None # layer scale initial value
hf_model_name: str = None
hf_tokenizer_name: str = None
hf_model_pretrained: bool = True
proj: str = 'mlp'
pooler_type: str = 'mean_pooler'
embed_cls: bool = False
pad_id: int = 0
output_tokens: bool = False
def get_cast_dtype(precision: str):
cast_dtype = None
if precision == 'bf16':
cast_dtype = torch.bfloat16
elif precision == 'fp16':
cast_dtype = torch.float16
return cast_dtype
def _build_vision_tower(
embed_dim: int,
vision_cfg: CLIPVisionCfg,
quick_gelu: bool = False,
cast_dtype: Optional[torch.dtype] = None
):
if isinstance(vision_cfg, dict):
vision_cfg = CLIPVisionCfg(**vision_cfg)
# OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
# memory efficient in recent PyTorch releases (>= 1.10).
# NOTE: timm models always use native GELU regardless of quick_gelu flag.
act_layer = QuickGELU if quick_gelu else nn.GELU
if vision_cfg.timm_model_name:
visual = TimmModel(
vision_cfg.timm_model_name,
pretrained=vision_cfg.timm_model_pretrained,
pool=vision_cfg.timm_pool,
proj=vision_cfg.timm_proj,
proj_bias=vision_cfg.timm_proj_bias,
drop=vision_cfg.timm_drop,
drop_path=vision_cfg.timm_drop_path,
patch_drop=vision_cfg.patch_dropout if vision_cfg.patch_dropout > 0 else None,
embed_dim=embed_dim,
image_size=vision_cfg.image_size,
)
act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models
elif isinstance(vision_cfg.layers, (tuple, list)):
vision_heads = vision_cfg.width * 32 // vision_cfg.head_width
visual = ModifiedResNet(
layers=vision_cfg.layers,
output_dim=embed_dim,
heads=vision_heads,
image_size=vision_cfg.image_size,
width=vision_cfg.width,
freeze_output=vision_cfg.freeze_output,
freeze_all_bns=vision_cfg.freeze_all_bns
)
else:
vision_heads = vision_cfg.width // vision_cfg.head_width | norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm | 3 | 2023-12-09 05:43:08+00:00 | 24k |
LkPrtctrd/BSL-V53 | Heart/Logic/LogicLaserMessageFactory.py | [
{
"identifier": "ClientHelloMessage",
"path": "Heart/Packets/Client/Authentification/ClientHelloMessage.py",
"snippet": "class ClientHelloMessage(PiranhaMessage):\n def __init__(self, messageData):\n super().__init__(messageData)\n self.messageVersion = 0\n\n def encode(self, fields)... | from Heart.Packets.Client.Authentification.ClientHelloMessage import ClientHelloMessage
from Heart.Packets.Client.Authentification.LoginMessage import LoginMessage
from Heart.Packets.Client.Battle.AskForBattleEndMessage import AskForBattleEndMessage
from Heart.Packets.Client.Home.ChangeAvatarNameMessage import ChangeAvatarNameMessage
from Heart.Packets.Client.Home.EndClientTurnMessage import EndClientTurnMessage
from Heart.Packets.Client.Home.GoHomeFromOfflinePractiseMessage import GoHomeFromOfflinePractiseMessage
from Heart.Packets.Client.Home.GoHomeMessage import GoHomeMessage
from Heart.Packets.Client.Home.GetPlayerProfileMessage import GetPlayerProfileMessage
from Heart.Packets.Client.Home.AskForAllianceDataMessage import AskForAllianceDataMessage
from Heart.Packets.Client.Socket.KeepAliveMessage import KeepAliveMessage
from Heart.Packets.Server.Authentification.LoginFailedMessage import LoginFailedMessage
from Heart.Packets.Server.Authentification.LoginOkMessage import LoginOkMessage
from Heart.Packets.Server.Authentification.OutOfSyncMessage import OutOfSyncMessage
from Heart.Packets.Server.Authentification.ServerHelloMessage import ServerHelloMessage
from Heart.Packets.Server.Battle.BattleEndMessage import BattleEndMessage
from Heart.Packets.Server.Home.AvailableServerCommandMessage import AvailableServerCommandMessage
from Heart.Packets.Server.Home.LobbyInfoMessage import LobbyInfoMessage
from Heart.Packets.Server.Home.OwnHomeDataMessage import OwnHomeDataMessage
from Heart.Packets.Server.Socket.KeepAliveServerMessage import KeepAliveServerMessage
from Heart.Packets.Server.Home.PlayerProfileMessage import PlayerProfileMessage
from Heart.Packets.Server.Home.MyAllianceMessage import MyAllianceMessage
from Heart.Packets.Server.Home.AllianceDataMessage import AllianceDataMessage | 16,878 | 14362: 'TeamSetEventMessage',
14363: 'TeamSetLocationMessage',
14364: 'TeamReportChatMessage',
14365: 'TeamInviteMessage',
14366: 'PlayerStatusMessage',
14367: 'TeamClearInviteMessage',
14368: 'TeamInviteResponseMessage',
14369: 'TeamPremadeChatMessage',
14370: 'TeamAllianceMemberInviteMessage',
14371: 'TeamJoinOrCreateGameRoomMessage',
14372: 'TeamToggleSettingsMessage',
14373: 'TeamBotSlotDisableMessage',
14403: 'GetLeaderboardMessage',
14405: 'AskForAvatarStreamMessage',
14406: 'AskForBattleReplayStreamMessage',
14418: 'RemoveAvatarStreamEntryMessage',
14469: 'AlliancePremadeChatMessage',
14479: 'TeamInvitationResponseMessage',
14600: 'AvatarNameCheckRequestMessage',
14700: 'ListBrawlTvChannelsMessage',
14701: 'TuneBrawlTvChannelMessage',
14715: 'SendGlobalChatLineMessage',
14777: 'SetInvitesBlockedMessage',
14778: 'SetTeamChatMutedMessage',
14867: 'SetRegionMessage',
14880: 'TeamRequestJoinCancelMessage',
14881: 'TeamRequestJoinMessage',
14882: 'TeamRequestJoinApproveMessage',
15081: GetPlayerProfileMessage, #v50
15793: 'GetTokenFriendMessage',
16000: 'LogicDeviceLinkCodeRequestMessage',
16001: 'LogicDeviceLinkMenuClosedMessage',
16002: 'LogicDeviceLinkEnterCodeMessage',
16003: 'LogicDeviceLinkConfirmYesMessage',
16939: 'AskApiTokenMessage',
17000: 'LogicAccountTransferCodeRequestMessage',
17190: 'JoinAllianceUsingTokenMessage',
17337: 'UnbotifyReportMessage',
17338: 'AdjustPackageMessage',
17750: GoHomeFromOfflinePractiseMessage, #v50
18686: 'SetSupportedCreatorMessage',
19001: 'LatencyTestResultMessage',
19002: 'UdpLatencyTestRequestMessage',
19003: 'TriggerStartLatencyTestMessage',
19004: 'RequestLatencyTestStatusMessage',
20000: 'SetEncryptionMessage',
20100: ServerHelloMessage,
20101: 'CreateAccountOkMessage',
20103: LoginFailedMessage,
20104: LoginOkMessage,
20105: 'FriendListMessage',
20106: 'FriendListUpdateMessage',
20107: 'AddableFriendsMessage',
20108: KeepAliveServerMessage,
20109: 'FriendOnlineStatusMessage',
20110: 'FriendLoggedInMessage',
20111: 'FriendLoggedOutMessage',
20112: 'AddFriendFailedMessage',
20117: 'ReportUserStatusMessage',
20118: 'ChatAccountBanStatusMessage',
20121: 'BillingRequestFailedMessage',
20132: 'UnlockAccountOkMessage',
20133: 'UnlockAccountFailedMessage',
20151: 'AppleBillingProcessedByServerMessage',
20152: 'GoogleBillingProcessedByServerMessage',
20153: 'TencentBillingProcessedByServerMessage',
20154: 'CafeBazaarBillingProcessedByServerMessage',
20156: 'KunlunBillingProcessedByServerMessage',
20161: 'ShutdownStartedMessage',
20171: 'PersonalBreakStartedMessage',
20173: 'YoozooBillingProcessedByServerMessage',
20199: 'FriendSuggestionsMessage',
20205: 'AvatarNameChangeFailedMessage',
20206: 'AvatarOnlineStatusUpdated',
20207: 'AllianceOnlineStatusUpdatedMessage',
20300: 'AvatarNameCheckResponseMessage',
20402: 'CreateGameFailedMessage',
20405: 'MatchMakingStatusMessage',
20406: 'MatchMakingCancelledMessage',
20501: 'AcceptFriendFailedMessage',
20523: 'YoozooOrderAvailableMessage',
20545: 'YoozooOrderDeliveryFailedMessage',
20559: 'StartLoadingMessage',
20801: 'NotificationMessage',
20802: 'OpponentRejoinsMatchNotificationMessage',
20931: 'AntiAddictionDataUpdatedMessage',
22089: 'GetTokenFriendResultMessage',
22100: 'CreatePlayerMapResponseMessage',
22101: 'DeletePlayerMapResponseMessage',
22102: 'PlayerMapsMessage',
22103: 'UpdatePlayerMapResponseMessage',
22104: 'SubmitPlayerMapResponseMessage',
22105: 'PublishPlayerMapResponseMessage',
22106: 'ChangePlayerMapNameMResponseMessage',
22107: 'PlayerMapInfoUpdatedMessage',
22109: 'DebugPlayerMapReviewResultOverrideSetMessage',
22111: 'PlayerMapGreenlightedMessage',
22125: 'ReportPlayerMapResponseMessage',
22150: 'RankedMatchStartMessage',
22151: 'RankedMatchBanStartedMessage',
22152: 'RankedMatchBanHeroResponseMessage',
22153: 'RankedMatchBanEndedMessage',
22154: 'RankedMatchPickStartedMessage',
22155: 'RankedMatchPickHeroFailedMessage',
22156: 'RankedMatchHeroPickedMessage',
22157: 'RankedMatchHeroDataUpdatedMessage',
22158: 'RankedMatchFinalPreparationStartedMessage',
22159: 'RankedMatchTerminatedMessage',
22202: 'MapPreviewMessage',
22377: 'GoogleServiceAccountBoundMessage',
22687: 'GamecenterAccountAlreadyBoundMessage',
22957: 'PvpMatchmakeNotificationMessage',
23067: 'SCIDLogoutAllDevicesResultMessage',
23302: 'GetAllianceInviteTokenResultMessage',
23456: BattleEndMessage,
23457: LobbyInfoMessage,
23458: 'BattleLogMessage',
23459: 'BattleLogReplayAvailableMessage',
23494: 'GoogleServiceAccountAlreadyBoundMessage',
23774: 'PlayerJWTokenMessage',
|
class LogicLaserMessageFactory:
messagesList = {
10055: 'AskPlayerJWTokenMessage',
10099: 'ClientCryptoErrorMessage',
10100: ClientHelloMessage,
10101: LoginMessage,
10102: 'LoginUsingSessionMessage',
10103: 'CreateAccountMessage',
10107: 'ClientCapabilitiesMessage',
10108: KeepAliveMessage,
10109: 'UdpCheckConnectionMessage',
10110: 'AnalyticEventMessage',
10111: 'AccountIdentifiersMessage',
10112: 'AuthenticationCheckMessage',
10113: 'SetDeviceTokenMessage',
10116: 'ResetAccountMessage',
10117: 'ReportUserMessage',
10118: 'AccountSwitchedMessage',
10119: 'ReportAllianceStreamMessage',
10121: 'UnlockAccountMessage',
10150: 'AppleBillingRequestMessage',
10151: 'GoogleBillingRequestMessage',
10152: 'TencentBillingRequestMessage',
10153: 'CafeBazaarBillingRequestMessage',
10159: 'KunlunBillingRequestMessage',
10160: 'BillingCancelledByClientMessage',
10177: 'ClientInfoMessage',
10212: ChangeAvatarNameMessage,
10309: 'GetAllianceInviteTokenMessage',
10321: 'AttributionEventMessage',
10401: 'CreateGameMessage',
10501: 'AcceptFriendMessage',
10502: 'AddFriendMessage',
10503: 'AskForAddableFriendsMessage',
10504: 'AskForFriendListMessage',
10506: 'RemoveFriendMessage',
10507: 'AddFriendByEmailMessage',
10509: 'AddFriendByAvatarNameAndCodeMessage',
10512: 'AskForPlayingGamecenterFriendsMessage',
10513: 'AskForPlayingFacebookFriendsMessage',
10514: 'AskForPlayingKakaoFriendsMessage',
10515: 'AskForPlayingTencentFriendsMessage',
10516: 'AskForPlayingLineFriendsMessage',
10517: 'AskForPlayingSupercellFriendsMessage',
10523: 'YoozooBillingRequestMessage',
10555: 'ClientInputMessage',
10576: 'SetBlockFriendRequestsMessage',
10599: 'AskForFriendSuggestionsMessage',
10636: 'SCIDBindAccountMessage',
11736: 'SCIDLogoutAllDevicesMessage',
12100: 'CreatePlayerMapMessage',
12101: 'DeletePlayerMapMessage',
12102: 'GetPlayerMapsMessage',
12103: 'UpdatePlayerMapMessage',
12104: 'SubmitPlayerMapMessage',
12105: 'PublishPlayerMapMessage',
12106: 'ChangePlayerMapNameMessage',
12107: 'EnterMapEditorMessage',
12108: 'GoHomeFromMapEditorMessage',
12110: 'TeamSetPlayerMapMessage',
12111: 'SignoffPlayerMapMessage',
12125: 'ReportPlayerMapMessage',
12152: 'RankedMatchBanHeroMessage',
12155: 'RankedMatchPickHeroMessage',
12157: 'RankedMatchUpdateHeroDataMessage',
12905: 'GetCurrentBattleReplayDataMessage',
12998: 'SetCountryMessage',
13922: 'AcceptTokenFriendMessage',
14101: GoHomeMessage,
14102: EndClientTurnMessage,
14103: 'StartGameMessage',
14104: 'StartSpectateMessage',
14105: 'HomeLogicStoppedMessage',
14106: 'CancelMatchmakingMessage',
14107: 'StopSpectateMessage',
14108: 'GoHomeFromSpectateMessage',
#14109: GoHomeFromOfflinePractiseMessage, //before v50
14110: AskForBattleEndMessage,
#14113: GetPlayerProfileMessage, //before v50
14114: 'GetBattleLogMessage',
14115: 'BattleLogViewReplayMessage',
14116: 'ViewReplayByStringMessage',
14117: 'RequestMatchCancelMessage',
14118: 'SinglePlayerMatchRequestMessage',
14166: 'ChronosEventSeenMessage',
14167: 'ChronosEventSeenMessage',
14177: 'PlayAgainMessage',
14178: 'DebugCommandMessage',
14199: 'LookForGameRoomRequestMessage',
14211: 'UnbindFacebookAccountMessage',
14201: 'BindFacebookAccountMessage',
14202: 'BindKakaoAccountMessage',
14203: 'BingLineAccountMessage',
14212: 'BindGamecenterAccountMessage',
14213: 'UnbindKakaoAccountMessage',
14214: 'UnbindLineAccountMessage',
14262: 'BindGoogleServiceAccountMessage',
14266: 'BindTencentAccountMessage',
14268: 'TencentCheckCanPayMessage',
14276: 'TencentAntiAddictionInstructionExecutedMessage',
14277: 'GetSeasonRewardsMessage',
14299: 'SetAllianceCountryMessage',
14301: 'CreateAllianceMessage',
14302: AskForAllianceDataMessage,
14303: 'AskForJoinableAlliancesListMessage',
14304: 'AskForAllianceStreamMessage',
14305: 'JoinAllianceMessage',
14306: 'ChangeAllianceMemberRoleMessage',
14307: 'KickAllianceMemberMessage',
14308: 'LeaveAllianceMessage',
14315: 'ChatToAllianceStreamMessage',
14316: 'ChangeAllianceSettingsMessage',
14317: 'RequestJoinAllianceMessage',
14321: 'RespondToAllianceJoinRequestMessage',
14322: 'SendAllianceInvitationMessage',
14323: 'JoinAllianceUsingInvitationMessage',
14324: 'SearchAlliancesMessage',
14326: 'SendAllianceInvitationToFriendMessage',
14330: 'SendAllianceMailMessage',
14350: 'TeamCreateMessage',
14351: 'TeamJoinMessage',
14352: 'TeamKickMessage',
14353: 'TeamLeaveMessage',
14354: 'TeamChangeMemberSettingsMessage',
14355: 'TeamSetMemberReadyMessage',
14356: 'TeamTogglePractiseMessage',
14357: 'TeamToggleMemberSideMessage',
14358: 'TeamSpectateMessage',
14359: 'TeamChatMessage',
14360: 'TeamPostAdMessage',
14361: 'TeamMemberStatusMessage',
14362: 'TeamSetEventMessage',
14363: 'TeamSetLocationMessage',
14364: 'TeamReportChatMessage',
14365: 'TeamInviteMessage',
14366: 'PlayerStatusMessage',
14367: 'TeamClearInviteMessage',
14368: 'TeamInviteResponseMessage',
14369: 'TeamPremadeChatMessage',
14370: 'TeamAllianceMemberInviteMessage',
14371: 'TeamJoinOrCreateGameRoomMessage',
14372: 'TeamToggleSettingsMessage',
14373: 'TeamBotSlotDisableMessage',
14403: 'GetLeaderboardMessage',
14405: 'AskForAvatarStreamMessage',
14406: 'AskForBattleReplayStreamMessage',
14418: 'RemoveAvatarStreamEntryMessage',
14469: 'AlliancePremadeChatMessage',
14479: 'TeamInvitationResponseMessage',
14600: 'AvatarNameCheckRequestMessage',
14700: 'ListBrawlTvChannelsMessage',
14701: 'TuneBrawlTvChannelMessage',
14715: 'SendGlobalChatLineMessage',
14777: 'SetInvitesBlockedMessage',
14778: 'SetTeamChatMutedMessage',
14867: 'SetRegionMessage',
14880: 'TeamRequestJoinCancelMessage',
14881: 'TeamRequestJoinMessage',
14882: 'TeamRequestJoinApproveMessage',
15081: GetPlayerProfileMessage, #v50
15793: 'GetTokenFriendMessage',
16000: 'LogicDeviceLinkCodeRequestMessage',
16001: 'LogicDeviceLinkMenuClosedMessage',
16002: 'LogicDeviceLinkEnterCodeMessage',
16003: 'LogicDeviceLinkConfirmYesMessage',
16939: 'AskApiTokenMessage',
17000: 'LogicAccountTransferCodeRequestMessage',
17190: 'JoinAllianceUsingTokenMessage',
17337: 'UnbotifyReportMessage',
17338: 'AdjustPackageMessage',
17750: GoHomeFromOfflinePractiseMessage, #v50
18686: 'SetSupportedCreatorMessage',
19001: 'LatencyTestResultMessage',
19002: 'UdpLatencyTestRequestMessage',
19003: 'TriggerStartLatencyTestMessage',
19004: 'RequestLatencyTestStatusMessage',
20000: 'SetEncryptionMessage',
20100: ServerHelloMessage,
20101: 'CreateAccountOkMessage',
20103: LoginFailedMessage,
20104: LoginOkMessage,
20105: 'FriendListMessage',
20106: 'FriendListUpdateMessage',
20107: 'AddableFriendsMessage',
20108: KeepAliveServerMessage,
20109: 'FriendOnlineStatusMessage',
20110: 'FriendLoggedInMessage',
20111: 'FriendLoggedOutMessage',
20112: 'AddFriendFailedMessage',
20117: 'ReportUserStatusMessage',
20118: 'ChatAccountBanStatusMessage',
20121: 'BillingRequestFailedMessage',
20132: 'UnlockAccountOkMessage',
20133: 'UnlockAccountFailedMessage',
20151: 'AppleBillingProcessedByServerMessage',
20152: 'GoogleBillingProcessedByServerMessage',
20153: 'TencentBillingProcessedByServerMessage',
20154: 'CafeBazaarBillingProcessedByServerMessage',
20156: 'KunlunBillingProcessedByServerMessage',
20161: 'ShutdownStartedMessage',
20171: 'PersonalBreakStartedMessage',
20173: 'YoozooBillingProcessedByServerMessage',
20199: 'FriendSuggestionsMessage',
20205: 'AvatarNameChangeFailedMessage',
20206: 'AvatarOnlineStatusUpdated',
20207: 'AllianceOnlineStatusUpdatedMessage',
20300: 'AvatarNameCheckResponseMessage',
20402: 'CreateGameFailedMessage',
20405: 'MatchMakingStatusMessage',
20406: 'MatchMakingCancelledMessage',
20501: 'AcceptFriendFailedMessage',
20523: 'YoozooOrderAvailableMessage',
20545: 'YoozooOrderDeliveryFailedMessage',
20559: 'StartLoadingMessage',
20801: 'NotificationMessage',
20802: 'OpponentRejoinsMatchNotificationMessage',
20931: 'AntiAddictionDataUpdatedMessage',
22089: 'GetTokenFriendResultMessage',
22100: 'CreatePlayerMapResponseMessage',
22101: 'DeletePlayerMapResponseMessage',
22102: 'PlayerMapsMessage',
22103: 'UpdatePlayerMapResponseMessage',
22104: 'SubmitPlayerMapResponseMessage',
22105: 'PublishPlayerMapResponseMessage',
22106: 'ChangePlayerMapNameMResponseMessage',
22107: 'PlayerMapInfoUpdatedMessage',
22109: 'DebugPlayerMapReviewResultOverrideSetMessage',
22111: 'PlayerMapGreenlightedMessage',
22125: 'ReportPlayerMapResponseMessage',
22150: 'RankedMatchStartMessage',
22151: 'RankedMatchBanStartedMessage',
22152: 'RankedMatchBanHeroResponseMessage',
22153: 'RankedMatchBanEndedMessage',
22154: 'RankedMatchPickStartedMessage',
22155: 'RankedMatchPickHeroFailedMessage',
22156: 'RankedMatchHeroPickedMessage',
22157: 'RankedMatchHeroDataUpdatedMessage',
22158: 'RankedMatchFinalPreparationStartedMessage',
22159: 'RankedMatchTerminatedMessage',
22202: 'MapPreviewMessage',
22377: 'GoogleServiceAccountBoundMessage',
22687: 'GamecenterAccountAlreadyBoundMessage',
22957: 'PvpMatchmakeNotificationMessage',
23067: 'SCIDLogoutAllDevicesResultMessage',
23302: 'GetAllianceInviteTokenResultMessage',
23456: BattleEndMessage,
23457: LobbyInfoMessage,
23458: 'BattleLogMessage',
23459: 'BattleLogReplayAvailableMessage',
23494: 'GoogleServiceAccountAlreadyBoundMessage',
23774: 'PlayerJWTokenMessage', | 24101: OwnHomeDataMessage, | 17 | 2023-12-14 18:57:56+00:00 | 24k |
GXNU-ZhongLab/ODTrack | lib/train/base_functions.py | [
{
"identifier": "Lasot",
"path": "lib/train/dataset/lasot.py",
"snippet": "class Lasot(BaseVideoDataset):\n \"\"\" LaSOT dataset.\n\n Publication:\n LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking\n Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, H... | import os
import torch
import lib.train.data.transforms as tfm
from torch.utils.data.distributed import DistributedSampler
from lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet
from lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb
from lib.train.data import sampler, opencv_loader, processing, LTRLoader
from lib.utils.misc import is_main_process | 18,653 | # datasets related
def update_settings(settings, cfg):
settings.print_interval = cfg.TRAIN.PRINT_INTERVAL
settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR,
'search': cfg.DATA.SEARCH.FACTOR}
settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE,
'search': cfg.DATA.SEARCH.SIZE}
settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER,
'search': cfg.DATA.SEARCH.CENTER_JITTER}
settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER,
'search': cfg.DATA.SEARCH.SCALE_JITTER}
settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM
settings.print_stats = None
settings.batchsize = cfg.TRAIN.BATCH_SIZE
settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE
def names2datasets(name_list: list, settings, image_loader):
assert isinstance(name_list, list)
datasets = []
for name in name_list:
assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "GOT10K_official_val",
"COCO17", "VID", "TRACKINGNET",
]
# Tracking Task
if name == "LASOT":
if settings.use_lmdb:
print("Building lasot dataset from lmdb")
datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader))
else:
datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader))
if name == "GOT10K_vottrain":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader))
if name == "GOT10K_train_full":
if settings.use_lmdb:
print("Building got10k_train_full from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader))
if name == "GOT10K_votval":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader))
if name == "GOT10K_official_val":
if settings.use_lmdb:
raise ValueError("Not implement")
else:
datasets.append(Got10k(settings.env.got10k_val_dir, split=None, image_loader=image_loader))
if name == "COCO17":
if settings.use_lmdb:
print("Building COCO2017 from lmdb")
datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version="2017", image_loader=image_loader))
else:
datasets.append(MSCOCOSeq(settings.env.coco_dir, version="2017", image_loader=image_loader))
if name == "VID":
if settings.use_lmdb:
print("Building VID from lmdb")
datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader))
else:
datasets.append(ImagenetVID(settings.env.imagenet_dir, image_loader=image_loader))
if name == "TRACKINGNET":
if settings.use_lmdb:
print("Building TrackingNet from lmdb")
datasets.append(TrackingNet_lmdb(settings.env.trackingnet_lmdb_dir, image_loader=image_loader))
else:
# raise ValueError("NOW WE CAN ONLY USE TRACKINGNET FROM LMDB")
datasets.append(TrackingNet(settings.env.trackingnet_dir, image_loader=image_loader))
return datasets
def build_dataloaders(cfg, settings):
# Data transform
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05),
tfm.RandomHorizontalFlip(probability=0.5))
transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2),
tfm.RandomHorizontalFlip_Norm(probability=0.5),
# tfm.RandomHorizontalFlip(probability=0.5),
tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))
transform_val = tfm.Transform(tfm.ToTensor(),
tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))
# The tracking pairs processing module
output_sz = settings.output_sz
search_area_factor = settings.search_area_factor
data_processing_train = processing.STARKProcessing(search_area_factor=search_area_factor,
output_sz=output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
transform=transform_train,
joint_transform=transform_joint,
settings=settings)
data_processing_val = processing.STARKProcessing(search_area_factor=search_area_factor,
output_sz=output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
transform=transform_val,
joint_transform=transform_joint,
settings=settings)
# Train sampler and loader
settings.num_template = getattr(cfg.DATA.TEMPLATE, "NUMBER", 1)
settings.num_search = getattr(cfg.DATA.SEARCH, "NUMBER", 1)
sampler_mode = getattr(cfg.DATA, "SAMPLER_MODE", "causal")
train_cls = getattr(cfg.TRAIN, "TRAIN_CLS", False)
print("sampler_mode: ", sampler_mode)
| # datasets related
def update_settings(settings, cfg):
settings.print_interval = cfg.TRAIN.PRINT_INTERVAL
settings.search_area_factor = {'template': cfg.DATA.TEMPLATE.FACTOR,
'search': cfg.DATA.SEARCH.FACTOR}
settings.output_sz = {'template': cfg.DATA.TEMPLATE.SIZE,
'search': cfg.DATA.SEARCH.SIZE}
settings.center_jitter_factor = {'template': cfg.DATA.TEMPLATE.CENTER_JITTER,
'search': cfg.DATA.SEARCH.CENTER_JITTER}
settings.scale_jitter_factor = {'template': cfg.DATA.TEMPLATE.SCALE_JITTER,
'search': cfg.DATA.SEARCH.SCALE_JITTER}
settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM
settings.print_stats = None
settings.batchsize = cfg.TRAIN.BATCH_SIZE
settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE
def names2datasets(name_list: list, settings, image_loader):
assert isinstance(name_list, list)
datasets = []
for name in name_list:
assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full", "GOT10K_official_val",
"COCO17", "VID", "TRACKINGNET",
]
# Tracking Task
if name == "LASOT":
if settings.use_lmdb:
print("Building lasot dataset from lmdb")
datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader))
else:
datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader))
if name == "GOT10K_vottrain":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader))
if name == "GOT10K_train_full":
if settings.use_lmdb:
print("Building got10k_train_full from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader))
if name == "GOT10K_votval":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader))
if name == "GOT10K_official_val":
if settings.use_lmdb:
raise ValueError("Not implement")
else:
datasets.append(Got10k(settings.env.got10k_val_dir, split=None, image_loader=image_loader))
if name == "COCO17":
if settings.use_lmdb:
print("Building COCO2017 from lmdb")
datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version="2017", image_loader=image_loader))
else:
datasets.append(MSCOCOSeq(settings.env.coco_dir, version="2017", image_loader=image_loader))
if name == "VID":
if settings.use_lmdb:
print("Building VID from lmdb")
datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader))
else:
datasets.append(ImagenetVID(settings.env.imagenet_dir, image_loader=image_loader))
if name == "TRACKINGNET":
if settings.use_lmdb:
print("Building TrackingNet from lmdb")
datasets.append(TrackingNet_lmdb(settings.env.trackingnet_lmdb_dir, image_loader=image_loader))
else:
# raise ValueError("NOW WE CAN ONLY USE TRACKINGNET FROM LMDB")
datasets.append(TrackingNet(settings.env.trackingnet_dir, image_loader=image_loader))
return datasets
def build_dataloaders(cfg, settings):
# Data transform
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05),
tfm.RandomHorizontalFlip(probability=0.5))
transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2),
tfm.RandomHorizontalFlip_Norm(probability=0.5),
# tfm.RandomHorizontalFlip(probability=0.5),
tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))
transform_val = tfm.Transform(tfm.ToTensor(),
tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))
# The tracking pairs processing module
output_sz = settings.output_sz
search_area_factor = settings.search_area_factor
data_processing_train = processing.STARKProcessing(search_area_factor=search_area_factor,
output_sz=output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
transform=transform_train,
joint_transform=transform_joint,
settings=settings)
data_processing_val = processing.STARKProcessing(search_area_factor=search_area_factor,
output_sz=output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
transform=transform_val,
joint_transform=transform_joint,
settings=settings)
# Train sampler and loader
settings.num_template = getattr(cfg.DATA.TEMPLATE, "NUMBER", 1)
settings.num_search = getattr(cfg.DATA.SEARCH, "NUMBER", 1)
sampler_mode = getattr(cfg.DATA, "SAMPLER_MODE", "causal")
train_cls = getattr(cfg.TRAIN, "TRAIN_CLS", False)
print("sampler_mode: ", sampler_mode) | dataset_train = sampler.TrackingSampler(datasets=names2datasets(cfg.DATA.TRAIN.DATASETS_NAME, settings, opencv_loader), | 13 | 2023-12-10 03:57:19+00:00 | 24k |
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