0 or p_len - len(f0) - pad_size > 0:\\n f0 = np.pad(\\n f0, [[pad_size, p_len - len(f0) - pad_size]], mode=\\\"constant\\\"\\n )\\n elif f0_method == \\\"harvest\\\":\\n input_audio_path2wav[input_audio_path] = x.astype(np.double)\\n f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)\\n if filter_radius > 2:\\n f0 = signal.medfilt(f0, 3)\\n elif f0_method == \\\"crepe\\\":\\n model = \\\"full\\\"\\n # Pick a batch size that doesn't cause memory errors on your gpu\\n batch_size = 512\\n # Compute pitch using first gpu\\n audio = torch.tensor(np.copy(x))[None].float()\\n f0, pd = torchcrepe.predict(\\n audio,\\n self.sr,\\n self.window,\\n f0_min,\\n f0_max,\\n model,\\n batch_size=batch_size,\\n device=self.device,\\n return_periodicity=True,\\n )\\n pd = torchcrepe.filter.median(pd, 3)\\n f0 = torchcrepe.filter.mean(f0, 3)\\n f0[pd < 0.1] = 0\\n f0 = f0[0].cpu().numpy()\\n elif f0_method == \\\"rmvpe\\\":\\n if not hasattr(self, \\\"model_rmvpe\\\"):\\n from rvc.lib.rmvpe import RMVPE\\n\\n logger.info(\\n \\\"Loading rmvpe model,%s\\\" % \\\"%s/rmvpe.pt\\\" % os.environ[\\\"rmvpe_root\\\"]\\n )\\n self.model_rmvpe = RMVPE(\\n \\\"%s/rmvpe.pt\\\" % os.environ[\\\"rmvpe_root\\\"],\\n is_half=self.is_half,\\n device=self.device,\\n )\\n f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)\\n\\n if \\\"privateuseone\\\" in str(self.device): # clean ortruntime memory\\n del self.model_rmvpe.model\\n del self.model_rmvpe\\n logger.info(\\\"Cleaning ortruntime memory\\\")\\n\\n f0 *= pow(2, f0_up_key / 12)\\n # with open(\\\"test.txt\\\",\\\"w\\\")as f:f.write(\\\"\\\\n\\\".join([str(i)for i in f0.tolist()]))\\n tf0 = self.sr // self.window # 每秒f0点数\\n if inp_f0 is not None:\\n delta_t = np.round(\\n (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1\\n ).astype(\\\"int16\\\")\\n replace_f0 = np.interp(\\n list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]\\n )\\n shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]\\n f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[\\n :shape\\n ]\\n # with open(\\\"test_opt.txt\\\",\\\"w\\\")as f:f.write(\\\"\\\\n\\\".join([str(i)for i in f0.tolist()]))\\n f0bak = f0.copy()\\n f0_mel = 1127 * np.log(1 + f0 / 700)\\n f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (\\n f0_mel_max - f0_mel_min\\n ) + 1\\n f0_mel[f0_mel <= 1] = 1\\n f0_mel[f0_mel > 255] = 255\\n f0_coarse = np.rint(f0_mel).astype(np.int32)\\n return f0_coarse, f0bak # 1-0\\n\\n def vc(\\n self,\\n model,\\n net_g,\\n sid,\\n audio0,\\n pitch,\\n pitchf,\\n times,\\n index,\\n big_npy,\\n index_rate,\\n version,\\n protect,\\n ): # ,file_index,file_big_npy\\n feats = torch.from_numpy(audio0)\\n if self.is_half:\\n feats = feats.half()\\n else:\\n feats = feats.float()\\n if feats.dim() == 2: # double channels\\n feats = feats.mean(-1)\\n assert feats.dim() == 1, feats.dim()\\n feats = feats.view(1, -1)\\n padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)\\n\\n inputs = {\\n \\\"source\\\": feats.to(self.device),\\n \\\"padding_mask\\\": padding_mask,\\n \\\"output_layer\\\": 9 if version == \\\"v1\\\" else 12,\\n }\\n t0 = ttime()\\n with torch.no_grad():\\n logits = model.extract_features(**inputs)\\n feats = model.final_proj(logits[0]) if version == \\\"v1\\\" else logits[0]\\n if protect < 0.5 and pitch is not None and pitchf is not None:\\n feats0 = feats.clone()\\n if (\\n not isinstance(index, type(None))\\n and not isinstance(big_npy, type(None))\\n and index_rate != 0\\n ):\\n npy = feats[0].cpu().numpy()\\n if self.is_half:\\n npy = npy.astype(\\\"float32\\\")\\n\\n # _, I = index.search(npy, 1)\\n # npy = big_npy[I.squeeze()]\\n\\n score, ix = index.search(npy, k=8)\\n weight = np.square(1 / score)\\n weight /= weight.sum(axis=1, keepdims=True)\\n npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)\\n\\n if self.is_half:\\n npy = npy.astype(\\\"float16\\\")\\n feats = (\\n torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate\\n + (1 - index_rate) * feats\\n )\\n\\n feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)\\n if protect < 0.5 and pitch is not None and pitchf is not None:\\n feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(\\n 0, 2, 1\\n )\\n t1 = ttime()\\n p_len = audio0.shape[0] // self.window\\n if feats.shape[1] < p_len:\\n p_len = feats.shape[1]\\n if pitch is not None and pitchf is not None:\\n pitch = pitch[:, :p_len]\\n pitchf = pitchf[:, :p_len]\\n\\n if protect < 0.5 and pitch is not None and pitchf is not None:\\n pitchff = pitchf.clone()\\n pitchff[pitchf > 0] = 1\\n pitchff[pitchf < 1] = protect\\n pitchff = pitchff.unsqueeze(-1)\\n feats = feats * pitchff + feats0 * (1 - pitchff)\\n feats = feats.to(feats0.dtype)\\n p_len = torch.tensor([p_len], device=self.device).long()\\n with torch.no_grad():\\n hasp = pitch is not None and pitchf is not None\\n arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid)\\n audio1 = (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy()\\n del hasp, arg\\n del feats, p_len, padding_mask\\n if torch.cuda.is_available():\\n torch.cuda.empty_cache()\\n t2 = ttime()\\n times[\\\"npy\\\"] += t1 - t0\\n times[\\\"infer\\\"] += t2 - t1\\n return audio1\\n\\n def pipeline(\\n self,\\n model,\\n net_g,\\n sid,\\n audio,\\n input_audio_path,\\n times,\\n f0_up_key,\\n f0_method,\\n file_index,\\n index_rate,\\n if_f0,\\n filter_radius,\\n tgt_sr,\\n resample_sr,\\n rms_mix_rate,\\n version,\\n protect,\\n f0_file=None,\\n ):\\n if (\\n file_index\\n and file_index != \\\"\\\"\\n # and file_big_npy != \\\"\\\"\\n # and os.path.exists(file_big_npy) == True\\n and os.path.exists(file_index)\\n and index_rate != 0\\n ):\\n try:\\n index = faiss.read_index(file_index)\\n # big_npy = np.load(file_big_npy)\\n big_npy = index.reconstruct_n(0, index.ntotal)\\n except:\\n traceback.print_exc()\\n index = big_npy = None\\n else:\\n index = big_npy = None\\n audio = signal.filtfilt(bh, ah, audio)\\n audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode=\\\"reflect\\\")\\n opt_ts = []\\n if audio_pad.shape[0] > self.t_max:\\n audio_sum = np.zeros_like(audio)\\n for i in range(self.window):\\n audio_sum += np.abs(audio_pad[i : i - self.window])\\n for t in range(self.t_center, audio.shape[0], self.t_center):\\n opt_ts.append(\\n t\\n - self.t_query\\n + np.where(\\n audio_sum[t - self.t_query : t + self.t_query]\\n == audio_sum[t - self.t_query : t + self.t_query].min()\\n )[0][0]\\n )\\n s = 0\\n audio_opt = []\\n t = None\\n t1 = ttime()\\n audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode=\\\"reflect\\\")\\n p_len = audio_pad.shape[0] // self.window\\n inp_f0 = None\\n if hasattr(f0_file, \\\"name\\\"):\\n try:\\n with open(f0_file.name, \\\"r\\\") as f:\\n lines = f.read().strip(\\\"\\\\n\\\").split(\\\"\\\\n\\\")\\n inp_f0 = []\\n for line in lines:\\n inp_f0.append([float(i) for i in line.split(\\\",\\\")])\\n inp_f0 = np.array(inp_f0, dtype=\\\"float32\\\")\\n except:\\n traceback.print_exc()\\n sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()\\n pitch, pitchf = None, None\\n if if_f0 == 1:\\n pitch, pitchf = self.get_f0(\\n input_audio_path,\\n audio_pad,\\n p_len,\\n f0_up_key,\\n f0_method,\\n filter_radius,\\n inp_f0,\\n )\\n pitch = pitch[:p_len]\\n pitchf = pitchf[:p_len]\\n if \\\"mps\\\" not in str(self.device) or \\\"xpu\\\" not in str(self.device):\\n pitchf = pitchf.astype(np.float32)\\n pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()\\n pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()\\n t2 = ttime()\\n times[\\\"f0\\\"] += t2 - t1\\n for t in opt_ts:\\n t = t // self.window * self.window\\n if if_f0 == 1:\\n audio_opt.append(\\n self.vc(\\n model,\\n net_g,\\n sid,\\n audio_pad[s : t + self.t_pad2 + self.window],\\n pitch[:, s // self.window : (t + self.t_pad2) // self.window],\\n pitchf[:, s // self.window : (t + self.t_pad2) // self.window],\\n times,\\n index,\\n big_npy,\\n index_rate,\\n version,\\n protect,\\n )[self.t_pad_tgt : -self.t_pad_tgt]\\n )\\n else:\\n audio_opt.append(\\n self.vc(\\n model,\\n net_g,\\n sid,\\n audio_pad[s : t + self.t_pad2 + self.window],\\n None,\\n None,\\n times,\\n index,\\n big_npy,\\n index_rate,\\n version,\\n protect,\\n )[self.t_pad_tgt : -self.t_pad_tgt]\\n )\\n s = t\\n if if_f0 == 1:\\n audio_opt.append(\\n self.vc(\\n model,\\n net_g,\\n sid,\\n audio_pad[t:],\\n pitch[:, t // self.window :] if t is not None else pitch,\\n pitchf[:, t // self.window :] if t is not None else pitchf,\\n times,\\n index,\\n big_npy,\\n index_rate,\\n version,\\n protect,\\n )[self.t_pad_tgt : -self.t_pad_tgt]\\n )\\n else:\\n audio_opt.append(\\n self.vc(\\n model,\\n net_g,\\n sid,\\n audio_pad[t:],\\n None,\\n None,\\n times,\\n index,\\n big_npy,\\n index_rate,\\n version,\\n protect,\\n )[self.t_pad_tgt : -self.t_pad_tgt]\\n )\\n audio_opt = np.concatenate(audio_opt)\\n if rms_mix_rate != 1:\\n audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)\\n if tgt_sr != resample_sr >= 16000:\\n audio_opt = librosa.resample(\\n audio_opt, orig_sr=tgt_sr, target_sr=resample_sr\\n )\\n audio_max = np.abs(audio_opt).max() / 0.99\\n max_int16 = 32768\\n if audio_max > 1:\\n max_int16 /= audio_max\\n audio_opt = (audio_opt * max_int16).astype(np.int16)\\n del pitch, pitchf, sid\\n if torch.cuda.is_available():\\n torch.cuda.empty_cache()\\n return audio_opt\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import logging\nimport os\nimport traceback\nimport numpy as np\nimport soundfile as sf\nimport torch\nfrom collections import OrderedDict\nfrom io import BytesIO\nfrom pathlib import Path\nfrom rvc.configs.config import Config\nfrom rvc.lib.audio import load_audio, wav2\nfrom rvc.lib.infer_pack.models import (\n SynthesizerTrnMs256NSFsid,\n SynthesizerTrnMs256NSFsid_nono,\n SynthesizerTrnMs768NSFsid,\n SynthesizerTrnMs768NSFsid_nono,\n)\nfrom rvc.modules.vc.pipeline import Pipeline\nfrom rvc.modules.vc.utils import *"},"token_num":{"kind":"number","value":11768,"string":"11,768"},"cropped_code":{"kind":"string","value":"\n\n\nlogger: logging.Logger = logging.getLogger(__name__)\n\n\nclass VC:\n def __init__(self):\n self.n_spk: any = None\n self.tgt_sr: int | None = None\n self.net_g = None\n"},"all_code":{"kind":"string","value":"\n\n\nlogger: logging.Logger = logging.getLogger(__name__)\n\n\nclass VC:\n def __init__(self):\n self.n_spk: any = None\n self.tgt_sr: int | None = None\n self.net_g = None"},"next_line":{"kind":"string","value":" self.pipeline: Pipeline | None = None"},"gold_snippet_index":{"kind":"number","value":7,"string":"7"},"created_at":{"kind":"string","value":"2023-10-14 09:52:31+00:00"},"level":{"kind":"string","value":"16k"}}},{"rowIdx":4597,"cells":{"repo_name":{"kind":"string","value":"zhijie-group/LOVECon"},"file_path":{"kind":"string","value":"video_diffusion/pipelines/stable_diffusion_controlnet.py"},"context":{"kind":"list like","value":[{"identifier":"UNetPseudo3DConditionModel","path":"video_diffusion/models/unet_3d_condition.py","snippet":"class UNetPseudo3DConditionModel(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 \"CrossAttnDownBlockPseudo3D\",\n \"CrossAttnDownBlockPseudo3D\",\n \"CrossAttnDownBlockPseudo3D\",\n \"DownBlockPseudo3D\",\n ),\n mid_block_type: str = \"UNetMidBlockPseudo3DCrossAttn\",\n up_block_types: Tuple[str] = (\n \"UpBlockPseudo3D\",\n \"CrossAttnUpBlockPseudo3D\",\n \"CrossAttnUpBlockPseudo3D\",\n \"CrossAttnUpBlockPseudo3D\",\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 **kwargs\n ):\n super().__init__()\n\n self.sample_size = sample_size\n time_embed_dim = block_out_channels[0] * 4\n if 'temporal_downsample' in kwargs and kwargs['temporal_downsample'] is True:\n kwargs['temporal_downsample_time'] = 3\n self.temporal_downsample_time = kwargs.get('temporal_downsample_time', 0)\n \n # input\n self.conv_in = PseudoConv3d(in_channels, block_out_channels[0], \n kernel_size=3, padding=(1, 1), model_config=kwargs)\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 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 input_channel = output_channel\n output_channel = block_out_channels[i]\n is_final_block = i == len(block_out_channels) - 1\n kwargs_copy=copy.deepcopy(kwargs)\n temporal_downsample_i = ((i >= (len(down_block_types)-self.temporal_downsample_time))\n and (not is_final_block))\n kwargs_copy.update({'temporal_downsample': temporal_downsample_i} )\n # kwargs_copy.update({'SparseCausalAttention_index': temporal_downsample_i} )\n if temporal_downsample_i:\n print(f'Initialize model temporal downsample at layer {i}')\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 model_config=kwargs_copy\n )\n self.down_blocks.append(down_block)\n # mid\n if mid_block_type == \"UNetMidBlockPseudo3DCrossAttn\":\n self.mid_block = UNetMidBlockPseudo3DCrossAttn(\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 model_config=kwargs\n )\n else:\n raise ValueError(f\"unknown mid_block_type : {mid_block_type}\")\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 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 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 kwargs_copy=copy.deepcopy(kwargs)\n kwargs_copy.update({'temporal_downsample': \n i < (self.temporal_downsample_time-1)})\n if i < (self.temporal_downsample_time-1):\n print(f'Initialize model temporal updample at layer {i}')\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 model_config=kwargs_copy\n )\n self.up_blocks.append(up_block)\n prev_output_channel = output_channel\n\n # out\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 self.conv_out = PseudoConv3d(block_out_channels[0], out_channels, \n kernel_size=3, padding=1, model_config=kwargs)\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 = (\n num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\n )\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(\n module,\n (CrossAttnDownBlockPseudo3D, DownBlockPseudo3D, CrossAttnUpBlockPseudo3D, UpBlockPseudo3D),\n ):\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, # None\n attention_mask: Optional[torch.Tensor] = None, # None\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\n mid_block_additional_residual: Optional[torch.Tensor] = None,\n return_dict: bool = True,\n ) -> Union[UNetPseudo3DConditionOutput, Tuple]:\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 # 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: # None\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\n attention_mask = attention_mask.unsqueeze(1)\n\n # 0. center input if necessary\n if self.config.center_input_sample: # False\n sample = 2 * sample - 1.0\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 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 # 2. pre-process\n sample = self.conv_in(sample)\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 )\n else:\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\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 new_down_block_res_samples += (down_block_res_sample + down_block_additional_residual,)\n\n down_block_res_samples = new_down_block_res_samples\n\n # 4. mid\n sample = self.mid_block(\n sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\n )\n # for i in down_block_res_samples: print(i.shape) \n # torch.Size([1, 320, 16, 64, 64])\n # torch.Size([1, 320, 16, 64, 64])\n # torch.Size([1, 320, 16, 64, 64])\n # torch.Size([1, 320, 8, 32, 32])\n # torch.Size([1, 640, 8, 32, 32])\n # torch.Size([1, 640, 8, 32, 32])\n # torch.Size([1, 640, 4, 16, 16])\n # torch.Size([1, 1280, 4, 16, 16])\n # torch.Size([1, 1280, 4, 16, 16])\n # torch.Size([1, 1280, 2, 8, 8])\n # torch.Size([1, 1280, 2, 8, 8])\n # torch.Size([1, 1280, 2, 8, 8])\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 )\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 )\n # 6. 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 UNetPseudo3DConditionOutput(sample=sample)\n\n @classmethod\n def from_2d_model(cls, model_path, model_config):\n config_path = os.path.join(model_path, \"config.json\")\n if not os.path.isfile(config_path):\n raise RuntimeError(f\"{config_path} does not exist\")\n with open(config_path, \"r\") as f:\n config = json.load(f)\n\n config.pop(\"_class_name\")\n config.pop(\"_diffusers_version\")\n\n block_replacer = {\n \"CrossAttnDownBlock2D\": \"CrossAttnDownBlockPseudo3D\",\n \"DownBlock2D\": \"DownBlockPseudo3D\",\n \"UpBlock2D\": \"UpBlockPseudo3D\",\n \"CrossAttnUpBlock2D\": \"CrossAttnUpBlockPseudo3D\",\n }\n\n def convert_2d_to_3d_block(block):\n return block_replacer[block] if block in block_replacer else block\n\n config[\"down_block_types\"] = [\n convert_2d_to_3d_block(block) for block in config[\"down_block_types\"]\n ]\n config[\"up_block_types\"] = [convert_2d_to_3d_block(block) for block in config[\"up_block_types\"]]\n if model_config is not None:\n config.update(model_config)\n\n model = cls(**config)\n\n state_dict_path_condidates = glob.glob(os.path.join(model_path, \"*.bin\"))\n if state_dict_path_condidates:\n state_dict = torch.load(state_dict_path_condidates[0], map_location=\"cpu\")\n model.load_2d_state_dict(state_dict=state_dict)\n\n return model\n\n def load_2d_state_dict(self, state_dict, **kwargs):\n state_dict_3d = self.state_dict()\n\n for k, v in state_dict.items():\n if k not in state_dict_3d:\n raise KeyError(f\"2d state_dict key {k} does not exist in 3d model\")\n elif v.shape != state_dict_3d[k].shape:\n raise ValueError(f\"state_dict shape mismatch, 2d {v.shape}, 3d {state_dict_3d[k].shape}\")\n\n for k, v in state_dict_3d.items():\n if \"_temporal\" in k:\n continue\n if k not in state_dict:\n raise KeyError(f\"3d state_dict key {k} does not exist in 2d model\")\n\n state_dict_3d.update(state_dict)\n self.load_state_dict(state_dict_3d, **kwargs)"},{"identifier":"ControlNetPseudo3DModel","path":"video_diffusion/models/controlnet_3d_condition.py","snippet":"class ControlNetPseudo3DModel(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 \"CrossAttnDownBlockPseudo3D\",\n \"CrossAttnDownBlockPseudo3D\",\n \"CrossAttnDownBlockPseudo3D\",\n \"DownBlockPseudo3D\",\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 **kwargs\n ):\n super().__init__()\n\n if 'temporal_downsample' in kwargs and kwargs['temporal_downsample'] is True:\n kwargs['temporal_downsample_time'] = 3\n self.temporal_downsample_time = kwargs.get('temporal_downsample_time', 0)\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 = PseudoConv3d(\n # in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\n # )\n self.conv_in = InflatedConv3d(\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\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 = ControlNetPseudo3DConditioningEmbedding(\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 = PseudoConv3d(output_channel, output_channel, kernel_size=1)\n controlnet_block = InflatedConv3d(output_channel, output_channel, kernel_size=1)\n\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 #non temperal \n # kwargs_copy=copy.deepcopy(kwargs)\n # temporal_downsample_i = ((i >= (len(down_block_types)-self.temporal_downsample_time))\n # and (not is_final_block))\n # kwargs_copy.update({'temporal_downsample': temporal_downsample_i} )\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 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 # model_config=kwargs_copy\n )\n self.down_blocks.append(down_block)\n\n for _ in range(layers_per_block):\n # controlnet_block = PseudoConv3d(output_channel, output_channel, kernel_size=1)\n controlnet_block = InflatedConv3d(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 = PseudoConv3d(output_channel, output_channel, kernel_size=1)\n controlnet_block = InflatedConv3d(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 = PseudoConv3d(mid_block_channel, mid_block_channel, kernel_size=1)\n controlnet_block = InflatedConv3d(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 = UNetMidBlockPseudo3DCrossAttn(\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 attn_num_head_channels=attention_head_dim[-1],\n resnet_groups=norm_num_groups,\n use_linear_projection=use_linear_projection,\n upcast_attention=upcast_attention,\n # model_config=kwargs\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, module, value=False):\n if isinstance(module, (CrossAttnDownBlockPseudo3D, DownBlockPseudo3D)):\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 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[ControlNetPseudo3DOutput, Tuple]:\n # check channel order\n channel_order = self.config.controlnet_conditioning_channel_order\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\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)\n\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 # print(sample.shape,controlnet_cond.shape)\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 )\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 )\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 if not return_dict:\n return (down_block_res_samples, mid_block_res_sample)\n\n return ControlNetPseudo3DOutput(\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\n )\n\n @classmethod\n def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, control_temporal_idx=None, control_mid_temporal=None):\n if subfolder is not None:\n pretrained_model_path = os.path.join(pretrained_model_path, subfolder)\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 \"CrossAttnDownBlockPseudo3D\",\n \"CrossAttnDownBlockPseudo3D\",\n \"CrossAttnDownBlockPseudo3D\",\n \"DownBlockPseudo3D\"\n ]\n # config[\"control_temporal_idx\"] = control_temporal_idx\n # config[\"control_mid_temporal\"] = control_mid_temporal\n\n from diffusers.utils import WEIGHTS_NAME\n model = cls.from_config(config)\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\n state_dict = torch.load(model_file, map_location=\"cpu\")\n for k, v in model.state_dict().items():\n if '_temp.' in k:\n if 'conv' in k:\n state_dict.update({k: v})\n else:\n copyk = k\n copyk = copyk.replace('_temp.', '1.')\n state_dict.update({k: state_dict[copyk]})\n model.load_state_dict(state_dict)\n\n return model\n\n\n @classmethod\n def from_2d_model(cls, model_path, model_config):\n config_path = os.path.join(model_path, \"config.json\")\n if not os.path.isfile(config_path):\n raise RuntimeError(f\"{config_path} does not exist\")\n with open(config_path, \"r\") as f:\n config = json.load(f)\n\n config.pop(\"_class_name\")\n config.pop(\"_diffusers_version\")\n\n block_replacer = {\n \"CrossAttnDownBlock2D\": \"CrossAttnDownBlockPseudo3D\",\n \"DownBlock2D\": \"DownBlockPseudo3D\",\n \"UpBlock2D\": \"UpBlockPseudo3D\",\n \"CrossAttnUpBlock2D\": \"CrossAttnUpBlockPseudo3D\",\n }\n\n def convert_2d_to_3d_block(block):\n return block_replacer[block] if block in block_replacer else block\n\n config[\"down_block_types\"] = [\n convert_2d_to_3d_block(block) for block in config[\"down_block_types\"]\n ]\n \n if model_config is not None:\n config.update(model_config)\n\n model = cls(**config)\n\n state_dict_path_condidates = glob.glob(os.path.join(model_path, \"*.bin\"))\n if state_dict_path_condidates:\n state_dict = torch.load(state_dict_path_condidates[0], map_location=\"cpu\")\n model.load_2d_state_dict(state_dict=state_dict)\n\n return model\n\n def load_2d_state_dict(self, state_dict, **kwargs):\n state_dict_3d = self.state_dict()\n\n for k, v in state_dict.items():\n if k not in state_dict_3d:\n raise KeyError(f\"2d state_dict key {k} does not exist in 3d model\")\n elif v.shape != state_dict_3d[k].shape:\n raise ValueError(f\"state_dict shape mismatch, 2d {v.shape}, 3d {state_dict_3d[k].shape}\")\n\n for k, v in state_dict_3d.items():\n if \"_temporal\" in k:\n continue\n if k not in state_dict:\n raise KeyError(f\"3d state_dict key {k} does not exist in 2d model\")\n\n state_dict_3d.update(state_dict)\n self.load_state_dict(state_dict_3d, **kwargs)"},{"identifier":"attention_util","path":"video_diffusion/prompt_attention/attention_util.py","snippet":"class EmptyControl:\nclass AttentionControlEdit(AttentionStore, abc.ABC):\nclass AttentionReplace(AttentionControlEdit):\nclass AttentionRefine(AttentionControlEdit):\nclass AttentionReweight(AttentionControlEdit):\n def step_callback(self, x_t):\n def between_steps(self):\n def __call__(self, attn, is_cross: bool, place_in_unet: str):\n def step_callback(self, x_t):\n def replace_self_attention(self, attn_base, att_replace, reshaped_mask=None):\n def replace_cross_attention(self, attn_base, att_replace):\n def update_attention_position_dict(self, current_attention_key):\n def forward(self, attn, is_cross: bool, place_in_unet: str):\n def between_steps(self):\n def __init__(self, prompts, num_steps: int,\n cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],\n self_replace_steps: Union[float, Tuple[float, float]],\n latent_blend: Optional[SpatialBlender], tokenizer=None, \n additional_attention_store: AttentionStore =None,\n use_inversion_attention: bool=False,\n attention_blend: SpatialBlender= None,\n save_self_attention: bool=True,\n disk_store=False\n ):\n def replace_cross_attention(self, attn_base, att_replace):\n def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,\n latent_blend: Optional[SpatialBlender] = None, tokenizer=None,\n additional_attention_store=None,\n use_inversion_attention = False,\n attention_blend: SpatialBlender=None,\n save_self_attention: bool = True,\n disk_store=False):\n def replace_cross_attention(self, attn_base, att_replace):\n def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,\n latent_blend: Optional[SpatialBlender] = None, tokenizer=None,\n additional_attention_store=None,\n use_inversion_attention = False,\n attention_blend: SpatialBlender=None,\n save_self_attention : bool=True,\n disk_store = False\n ):\n def replace_cross_attention(self, attn_base, att_replace):\n def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, equalizer,\n latent_blend: Optional[SpatialBlender] = None, controller: Optional[AttentionControlEdit] = None, tokenizer=None,\n additional_attention_store=None,\n use_inversion_attention = False,\n attention_blend: SpatialBlender=None,\n save_self_attention:bool = True,\n disk_store = False\n ):\ndef get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float],\n Tuple[float, ...]], tokenizer=None):\ndef make_controller(tokenizer, prompts: List[str], is_replace_controller: bool,\n cross_replace_steps: Dict[str, float], self_replace_steps: float=0.0, \n blend_words=None, equilizer_params=None, \n additional_attention_store=None, use_inversion_attention = False, blend_th: float=(0.3, 0.3),\n NUM_DDIM_STEPS=None,\n blend_latents = False,\n blend_self_attention=False,\n save_path = None,\n save_self_attention = True,\n disk_store = False\n ) -> AttentionControlEdit:"}],"string":"[\n {\n \"identifier\": \"UNetPseudo3DConditionModel\",\n \"path\": \"video_diffusion/models/unet_3d_condition.py\",\n \"snippet\": \"class UNetPseudo3DConditionModel(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 \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"DownBlockPseudo3D\\\",\\n ),\\n mid_block_type: str = \\\"UNetMidBlockPseudo3DCrossAttn\\\",\\n up_block_types: Tuple[str] = (\\n \\\"UpBlockPseudo3D\\\",\\n \\\"CrossAttnUpBlockPseudo3D\\\",\\n \\\"CrossAttnUpBlockPseudo3D\\\",\\n \\\"CrossAttnUpBlockPseudo3D\\\",\\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 **kwargs\\n ):\\n super().__init__()\\n\\n self.sample_size = sample_size\\n time_embed_dim = block_out_channels[0] * 4\\n if 'temporal_downsample' in kwargs and kwargs['temporal_downsample'] is True:\\n kwargs['temporal_downsample_time'] = 3\\n self.temporal_downsample_time = kwargs.get('temporal_downsample_time', 0)\\n \\n # input\\n self.conv_in = PseudoConv3d(in_channels, block_out_channels[0], \\n kernel_size=3, padding=(1, 1), model_config=kwargs)\\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 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 input_channel = output_channel\\n output_channel = block_out_channels[i]\\n is_final_block = i == len(block_out_channels) - 1\\n kwargs_copy=copy.deepcopy(kwargs)\\n temporal_downsample_i = ((i >= (len(down_block_types)-self.temporal_downsample_time))\\n and (not is_final_block))\\n kwargs_copy.update({'temporal_downsample': temporal_downsample_i} )\\n # kwargs_copy.update({'SparseCausalAttention_index': temporal_downsample_i} )\\n if temporal_downsample_i:\\n print(f'Initialize model temporal downsample at layer {i}')\\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 model_config=kwargs_copy\\n )\\n self.down_blocks.append(down_block)\\n # mid\\n if mid_block_type == \\\"UNetMidBlockPseudo3DCrossAttn\\\":\\n self.mid_block = UNetMidBlockPseudo3DCrossAttn(\\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 model_config=kwargs\\n )\\n else:\\n raise ValueError(f\\\"unknown mid_block_type : {mid_block_type}\\\")\\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 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 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 kwargs_copy=copy.deepcopy(kwargs)\\n kwargs_copy.update({'temporal_downsample': \\n i < (self.temporal_downsample_time-1)})\\n if i < (self.temporal_downsample_time-1):\\n print(f'Initialize model temporal updample at layer {i}')\\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 model_config=kwargs_copy\\n )\\n self.up_blocks.append(up_block)\\n prev_output_channel = output_channel\\n\\n # out\\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 self.conv_out = PseudoConv3d(block_out_channels[0], out_channels, \\n kernel_size=3, padding=1, model_config=kwargs)\\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 = (\\n num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size\\n )\\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(\\n module,\\n (CrossAttnDownBlockPseudo3D, DownBlockPseudo3D, CrossAttnUpBlockPseudo3D, UpBlockPseudo3D),\\n ):\\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, # None\\n attention_mask: Optional[torch.Tensor] = None, # None\\n down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,\\n mid_block_additional_residual: Optional[torch.Tensor] = None,\\n return_dict: bool = True,\\n ) -> Union[UNetPseudo3DConditionOutput, Tuple]:\\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 # 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: # None\\n attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0\\n attention_mask = attention_mask.unsqueeze(1)\\n\\n # 0. center input if necessary\\n if self.config.center_input_sample: # False\\n sample = 2 * sample - 1.0\\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 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 # 2. pre-process\\n sample = self.conv_in(sample)\\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 )\\n else:\\n sample, res_samples = downsample_block(hidden_states=sample, temb=emb)\\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 new_down_block_res_samples += (down_block_res_sample + down_block_additional_residual,)\\n\\n down_block_res_samples = new_down_block_res_samples\\n\\n # 4. mid\\n sample = self.mid_block(\\n sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask\\n )\\n # for i in down_block_res_samples: print(i.shape) \\n # torch.Size([1, 320, 16, 64, 64])\\n # torch.Size([1, 320, 16, 64, 64])\\n # torch.Size([1, 320, 16, 64, 64])\\n # torch.Size([1, 320, 8, 32, 32])\\n # torch.Size([1, 640, 8, 32, 32])\\n # torch.Size([1, 640, 8, 32, 32])\\n # torch.Size([1, 640, 4, 16, 16])\\n # torch.Size([1, 1280, 4, 16, 16])\\n # torch.Size([1, 1280, 4, 16, 16])\\n # torch.Size([1, 1280, 2, 8, 8])\\n # torch.Size([1, 1280, 2, 8, 8])\\n # torch.Size([1, 1280, 2, 8, 8])\\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 )\\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 )\\n # 6. 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 UNetPseudo3DConditionOutput(sample=sample)\\n\\n @classmethod\\n def from_2d_model(cls, model_path, model_config):\\n config_path = os.path.join(model_path, \\\"config.json\\\")\\n if not os.path.isfile(config_path):\\n raise RuntimeError(f\\\"{config_path} does not exist\\\")\\n with open(config_path, \\\"r\\\") as f:\\n config = json.load(f)\\n\\n config.pop(\\\"_class_name\\\")\\n config.pop(\\\"_diffusers_version\\\")\\n\\n block_replacer = {\\n \\\"CrossAttnDownBlock2D\\\": \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"DownBlock2D\\\": \\\"DownBlockPseudo3D\\\",\\n \\\"UpBlock2D\\\": \\\"UpBlockPseudo3D\\\",\\n \\\"CrossAttnUpBlock2D\\\": \\\"CrossAttnUpBlockPseudo3D\\\",\\n }\\n\\n def convert_2d_to_3d_block(block):\\n return block_replacer[block] if block in block_replacer else block\\n\\n config[\\\"down_block_types\\\"] = [\\n convert_2d_to_3d_block(block) for block in config[\\\"down_block_types\\\"]\\n ]\\n config[\\\"up_block_types\\\"] = [convert_2d_to_3d_block(block) for block in config[\\\"up_block_types\\\"]]\\n if model_config is not None:\\n config.update(model_config)\\n\\n model = cls(**config)\\n\\n state_dict_path_condidates = glob.glob(os.path.join(model_path, \\\"*.bin\\\"))\\n if state_dict_path_condidates:\\n state_dict = torch.load(state_dict_path_condidates[0], map_location=\\\"cpu\\\")\\n model.load_2d_state_dict(state_dict=state_dict)\\n\\n return model\\n\\n def load_2d_state_dict(self, state_dict, **kwargs):\\n state_dict_3d = self.state_dict()\\n\\n for k, v in state_dict.items():\\n if k not in state_dict_3d:\\n raise KeyError(f\\\"2d state_dict key {k} does not exist in 3d model\\\")\\n elif v.shape != state_dict_3d[k].shape:\\n raise ValueError(f\\\"state_dict shape mismatch, 2d {v.shape}, 3d {state_dict_3d[k].shape}\\\")\\n\\n for k, v in state_dict_3d.items():\\n if \\\"_temporal\\\" in k:\\n continue\\n if k not in state_dict:\\n raise KeyError(f\\\"3d state_dict key {k} does not exist in 2d model\\\")\\n\\n state_dict_3d.update(state_dict)\\n self.load_state_dict(state_dict_3d, **kwargs)\"\n },\n {\n \"identifier\": \"ControlNetPseudo3DModel\",\n \"path\": \"video_diffusion/models/controlnet_3d_condition.py\",\n \"snippet\": \"class ControlNetPseudo3DModel(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 \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"DownBlockPseudo3D\\\",\\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 **kwargs\\n ):\\n super().__init__()\\n\\n if 'temporal_downsample' in kwargs and kwargs['temporal_downsample'] is True:\\n kwargs['temporal_downsample_time'] = 3\\n self.temporal_downsample_time = kwargs.get('temporal_downsample_time', 0)\\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 = PseudoConv3d(\\n # in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\\n # )\\n self.conv_in = InflatedConv3d(\\n in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding\\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 = ControlNetPseudo3DConditioningEmbedding(\\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 = PseudoConv3d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = InflatedConv3d(output_channel, output_channel, kernel_size=1)\\n\\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 #non temperal \\n # kwargs_copy=copy.deepcopy(kwargs)\\n # temporal_downsample_i = ((i >= (len(down_block_types)-self.temporal_downsample_time))\\n # and (not is_final_block))\\n # kwargs_copy.update({'temporal_downsample': temporal_downsample_i} )\\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 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 # model_config=kwargs_copy\\n )\\n self.down_blocks.append(down_block)\\n\\n for _ in range(layers_per_block):\\n # controlnet_block = PseudoConv3d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = InflatedConv3d(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 = PseudoConv3d(output_channel, output_channel, kernel_size=1)\\n controlnet_block = InflatedConv3d(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 = PseudoConv3d(mid_block_channel, mid_block_channel, kernel_size=1)\\n controlnet_block = InflatedConv3d(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 = UNetMidBlockPseudo3DCrossAttn(\\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 attn_num_head_channels=attention_head_dim[-1],\\n resnet_groups=norm_num_groups,\\n use_linear_projection=use_linear_projection,\\n upcast_attention=upcast_attention,\\n # model_config=kwargs\\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, module, value=False):\\n if isinstance(module, (CrossAttnDownBlockPseudo3D, DownBlockPseudo3D)):\\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 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[ControlNetPseudo3DOutput, Tuple]:\\n # check channel order\\n channel_order = self.config.controlnet_conditioning_channel_order\\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\\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)\\n\\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 # print(sample.shape,controlnet_cond.shape)\\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 )\\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 )\\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 if not return_dict:\\n return (down_block_res_samples, mid_block_res_sample)\\n\\n return ControlNetPseudo3DOutput(\\n down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample\\n )\\n\\n @classmethod\\n def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, control_temporal_idx=None, control_mid_temporal=None):\\n if subfolder is not None:\\n pretrained_model_path = os.path.join(pretrained_model_path, subfolder)\\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 \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"DownBlockPseudo3D\\\"\\n ]\\n # config[\\\"control_temporal_idx\\\"] = control_temporal_idx\\n # config[\\\"control_mid_temporal\\\"] = control_mid_temporal\\n\\n from diffusers.utils import WEIGHTS_NAME\\n model = cls.from_config(config)\\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\\n state_dict = torch.load(model_file, map_location=\\\"cpu\\\")\\n for k, v in model.state_dict().items():\\n if '_temp.' in k:\\n if 'conv' in k:\\n state_dict.update({k: v})\\n else:\\n copyk = k\\n copyk = copyk.replace('_temp.', '1.')\\n state_dict.update({k: state_dict[copyk]})\\n model.load_state_dict(state_dict)\\n\\n return model\\n\\n\\n @classmethod\\n def from_2d_model(cls, model_path, model_config):\\n config_path = os.path.join(model_path, \\\"config.json\\\")\\n if not os.path.isfile(config_path):\\n raise RuntimeError(f\\\"{config_path} does not exist\\\")\\n with open(config_path, \\\"r\\\") as f:\\n config = json.load(f)\\n\\n config.pop(\\\"_class_name\\\")\\n config.pop(\\\"_diffusers_version\\\")\\n\\n block_replacer = {\\n \\\"CrossAttnDownBlock2D\\\": \\\"CrossAttnDownBlockPseudo3D\\\",\\n \\\"DownBlock2D\\\": \\\"DownBlockPseudo3D\\\",\\n \\\"UpBlock2D\\\": \\\"UpBlockPseudo3D\\\",\\n \\\"CrossAttnUpBlock2D\\\": \\\"CrossAttnUpBlockPseudo3D\\\",\\n }\\n\\n def convert_2d_to_3d_block(block):\\n return block_replacer[block] if block in block_replacer else block\\n\\n config[\\\"down_block_types\\\"] = [\\n convert_2d_to_3d_block(block) for block in config[\\\"down_block_types\\\"]\\n ]\\n \\n if model_config is not None:\\n config.update(model_config)\\n\\n model = cls(**config)\\n\\n state_dict_path_condidates = glob.glob(os.path.join(model_path, \\\"*.bin\\\"))\\n if state_dict_path_condidates:\\n state_dict = torch.load(state_dict_path_condidates[0], map_location=\\\"cpu\\\")\\n model.load_2d_state_dict(state_dict=state_dict)\\n\\n return model\\n\\n def load_2d_state_dict(self, state_dict, **kwargs):\\n state_dict_3d = self.state_dict()\\n\\n for k, v in state_dict.items():\\n if k not in state_dict_3d:\\n raise KeyError(f\\\"2d state_dict key {k} does not exist in 3d model\\\")\\n elif v.shape != state_dict_3d[k].shape:\\n raise ValueError(f\\\"state_dict shape mismatch, 2d {v.shape}, 3d {state_dict_3d[k].shape}\\\")\\n\\n for k, v in state_dict_3d.items():\\n if \\\"_temporal\\\" in k:\\n continue\\n if k not in state_dict:\\n raise KeyError(f\\\"3d state_dict key {k} does not exist in 2d model\\\")\\n\\n state_dict_3d.update(state_dict)\\n self.load_state_dict(state_dict_3d, **kwargs)\"\n },\n {\n \"identifier\": \"attention_util\",\n \"path\": \"video_diffusion/prompt_attention/attention_util.py\",\n \"snippet\": \"class EmptyControl:\\nclass AttentionControlEdit(AttentionStore, abc.ABC):\\nclass AttentionReplace(AttentionControlEdit):\\nclass AttentionRefine(AttentionControlEdit):\\nclass AttentionReweight(AttentionControlEdit):\\n def step_callback(self, x_t):\\n def between_steps(self):\\n def __call__(self, attn, is_cross: bool, place_in_unet: str):\\n def step_callback(self, x_t):\\n def replace_self_attention(self, attn_base, att_replace, reshaped_mask=None):\\n def replace_cross_attention(self, attn_base, att_replace):\\n def update_attention_position_dict(self, current_attention_key):\\n def forward(self, attn, is_cross: bool, place_in_unet: str):\\n def between_steps(self):\\n def __init__(self, prompts, num_steps: int,\\n cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],\\n self_replace_steps: Union[float, Tuple[float, float]],\\n latent_blend: Optional[SpatialBlender], tokenizer=None, \\n additional_attention_store: AttentionStore =None,\\n use_inversion_attention: bool=False,\\n attention_blend: SpatialBlender= None,\\n save_self_attention: bool=True,\\n disk_store=False\\n ):\\n def replace_cross_attention(self, attn_base, att_replace):\\n def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,\\n latent_blend: Optional[SpatialBlender] = None, tokenizer=None,\\n additional_attention_store=None,\\n use_inversion_attention = False,\\n attention_blend: SpatialBlender=None,\\n save_self_attention: bool = True,\\n disk_store=False):\\n def replace_cross_attention(self, attn_base, att_replace):\\n def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,\\n latent_blend: Optional[SpatialBlender] = None, tokenizer=None,\\n additional_attention_store=None,\\n use_inversion_attention = False,\\n attention_blend: SpatialBlender=None,\\n save_self_attention : bool=True,\\n disk_store = False\\n ):\\n def replace_cross_attention(self, attn_base, att_replace):\\n def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float, equalizer,\\n latent_blend: Optional[SpatialBlender] = None, controller: Optional[AttentionControlEdit] = None, tokenizer=None,\\n additional_attention_store=None,\\n use_inversion_attention = False,\\n attention_blend: SpatialBlender=None,\\n save_self_attention:bool = True,\\n disk_store = False\\n ):\\ndef get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float],\\n Tuple[float, ...]], tokenizer=None):\\ndef make_controller(tokenizer, prompts: List[str], is_replace_controller: bool,\\n cross_replace_steps: Dict[str, float], self_replace_steps: float=0.0, \\n blend_words=None, equilizer_params=None, \\n additional_attention_store=None, use_inversion_attention = False, blend_th: float=(0.3, 0.3),\\n NUM_DDIM_STEPS=None,\\n blend_latents = False,\\n blend_self_attention=False,\\n save_path = None,\\n save_self_attention = True,\\n disk_store = False\\n ) -> AttentionControlEdit:\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import inspect\nimport os, sys\nimport PIL\nimport torch\nimport numpy as np\n import json\n import diffusers\n import bitsandbytes\nfrom dataclasses import dataclass\nfrom typing import Callable, List, Optional, Union,Dict,Any\nfrom einops import rearrange\nfrom tqdm import trange, 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\nfrom diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput\nfrom ..models.unet_3d_condition import UNetPseudo3DConditionModel\nfrom ..models.controlnet_3d_condition import ControlNetPseudo3DModel\nfrom video_diffusion.prompt_attention import attention_util\n from accelerate import cpu_offload"},"token_num":{"kind":"number","value":11286,"string":"11,286"},"cropped_code":{"kind":"string","value":"# code mostly taken from https://github.com/huggingface/diffusers\n\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass SpatioTemporalStableDiffusionControlnetPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-video generation using Spatio-Temporal Stable Diffusion.\n \"\"\"\n _optional_components = []\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNetPseudo3DConditionModel,\n"},"all_code":{"kind":"string","value":"# code mostly taken from https://github.com/huggingface/diffusers\n\n\n\nlogger = logging.get_logger(__name__) # pylint: disable=invalid-name\n\n\nclass SpatioTemporalStableDiffusionControlnetPipeline(DiffusionPipeline):\n r\"\"\"\n Pipeline for text-to-video generation using Spatio-Temporal Stable Diffusion.\n \"\"\"\n _optional_components = []\n\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNetPseudo3DConditionModel,"},"next_line":{"kind":"string","value":" controlnet : ControlNetPseudo3DModel,"},"gold_snippet_index":{"kind":"number","value":1,"string":"1"},"created_at":{"kind":"string","value":"2023-10-09 14:38:28+00:00"},"level":{"kind":"string","value":"16k"}}},{"rowIdx":4598,"cells":{"repo_name":{"kind":"string","value":"mlpc-ucsd/MaskCLIP"},"file_path":{"kind":"string","value":"train_net.py"},"context":{"kind":"list like","value":[{"identifier":"add_maskformer2_config","path":"maskclip/config.py","snippet":"def add_maskformer2_config(cfg):\n \"\"\"\n Add config for MASK_FORMER.\n \"\"\"\n # NOTE: configs from original maskformer\n # data config\n # select the dataset mapper\n cfg.INPUT.DATASET_MAPPER_NAME = \"mask_former_semantic\"\n # Color augmentation\n cfg.INPUT.COLOR_AUG_SSD = False\n # We retry random cropping until no single category in semantic segmentation GT occupies more\n # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0\n # Pad image and segmentation GT in dataset mapper.\n cfg.INPUT.SIZE_DIVISIBILITY = -1\n\n # solver config\n # weight decay on embedding\n cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0\n # optimizer\n cfg.SOLVER.OPTIMIZER = \"ADAMW\"\n cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1\n\n # mask_former model config\n cfg.MODEL.MASK_FORMER = CN()\n\n # loss\n cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True\n cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = 0.1\n cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = 1.0\n cfg.MODEL.MASK_FORMER.DICE_WEIGHT = 1.0\n cfg.MODEL.MASK_FORMER.MASK_WEIGHT = 20.0\n\n # transformer config\n cfg.MODEL.MASK_FORMER.NHEADS = 8\n cfg.MODEL.MASK_FORMER.DROPOUT = 0.1\n cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048\n cfg.MODEL.MASK_FORMER.ENC_LAYERS = 0\n cfg.MODEL.MASK_FORMER.DEC_LAYERS = 6\n cfg.MODEL.MASK_FORMER.PRE_NORM = False\n\n cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256\n cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 100\n\n cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE = \"res5\"\n cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ = False\n\n # mask_former inference config\n cfg.MODEL.MASK_FORMER.TEST = CN()\n cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True\n cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = False\n cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = False\n cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD = 0.0\n cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD = 0.0\n cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False\n\n # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)\n # you can use this config to override\n cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32\n\n # pixel decoder config\n cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256\n # adding transformer in pixel decoder\n cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0\n # pixel decoder\n cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = \"BasePixelDecoder\"\n\n # swin transformer backbone\n cfg.MODEL.SWIN = CN()\n cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224\n cfg.MODEL.SWIN.PATCH_SIZE = 4\n cfg.MODEL.SWIN.EMBED_DIM = 96\n cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]\n cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]\n cfg.MODEL.SWIN.WINDOW_SIZE = 7\n cfg.MODEL.SWIN.MLP_RATIO = 4.0\n cfg.MODEL.SWIN.QKV_BIAS = True\n cfg.MODEL.SWIN.QK_SCALE = None\n cfg.MODEL.SWIN.DROP_RATE = 0.0\n cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0\n cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3\n cfg.MODEL.SWIN.APE = False\n cfg.MODEL.SWIN.PATCH_NORM = True\n cfg.MODEL.SWIN.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]\n cfg.MODEL.SWIN.USE_CHECKPOINT = False\n\n # NOTE: maskformer2 extra configs\n # transformer module\n cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME = \"MultiScaleMaskedTransformerDecoder\"\n\n # LSJ aug\n cfg.INPUT.IMAGE_SIZE = 1024\n cfg.INPUT.MIN_SCALE = 0.1\n cfg.INPUT.MAX_SCALE = 2.0\n\n # MSDeformAttn encoder configs\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = [\"res3\", \"res4\", \"res5\"]\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8\n\n # point loss configs\n # Number of points sampled during training for a mask point head.\n cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = 112 * 112\n # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the\n # original paper.\n cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0\n # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in\n # the original paper.\n cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75\n\n # add MaskCLIP configs\n cfg.MODEL.CLIP_MODEL = CN()\n cfg.MODEL.CLIP_MODEL.NAME = 'ViT-L/14@336px'\n cfg.MODEL.CLIP_MODEL.INPUT_RESOLUTION = 336\n cfg.MODEL.CLIP_MODEL.PATCH_SIZE = 14\n cfg.MODEL.CLIP_MODEL.WIDTH = 1024\n cfg.MODEL.CLIP_MODEL.LAYERS = 24\n cfg.MODEL.CLIP_MODEL.HEADS = 16\n cfg.MODEL.CLIP_MODEL.OUTPUT_DIM = 768\n\n cfg.MODEL.CLIP_MODEL.TEMPERATURE = 0.01"},{"identifier":"COCOInstanceNewBaselineDatasetMapper","path":"maskclip/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py","snippet":"class COCOInstanceNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by MaskFormer.\n\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n tfm_gens,\n image_format,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n tfm_gens: data augmentation\n image_format: an image format supported by :func:`detection_utils.read_image`.\n \"\"\"\n self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[COCOInstanceNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(str(self.tfm_gens))\n )\n\n self.img_format = image_format\n self.is_train = is_train\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n\n ret = {\n \"is_train\": is_train,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n }\n return ret\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # TODO: get padding mask\n # by feeding a \"segmentation mask\" to the same transforms\n padding_mask = np.ones(image.shape[:2])\n\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n # the crop transformation has default padding value 0 for segmentation\n padding_mask = transforms.apply_segmentation(padding_mask)\n padding_mask = ~ padding_mask.astype(bool)\n\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n dataset_dict[\"padding_mask\"] = torch.as_tensor(np.ascontiguousarray(padding_mask))\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n return dataset_dict\n\n if \"annotations\" in dataset_dict:\n # USER: Modify this if you want to keep them for some reason.\n for anno in dataset_dict[\"annotations\"]:\n # Let's always keep mask\n # if not self.mask_on:\n # anno.pop(\"segmentation\", None)\n anno.pop(\"keypoints\", None)\n\n # USER: Implement additional transformations if you have other types of data\n annos = [\n utils.transform_instance_annotations(obj, transforms, image_shape)\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n # NOTE: does not support BitMask due to augmentation\n # Current BitMask cannot handle empty objects\n instances = utils.annotations_to_instances(annos, image_shape)\n # After transforms such as cropping are applied, the bounding box may no longer\n # tightly bound the object. As an example, imagine a triangle object\n # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight\n # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to\n # the intersection of original bounding box and the cropping box.\n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\n # Need to filter empty instances first (due to augmentation)\n instances = utils.filter_empty_instances(instances)\n # Generate masks from polygon\n h, w = instances.image_size\n # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)\n if hasattr(instances, 'gt_masks'):\n gt_masks = instances.gt_masks\n gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)\n instances.gt_masks = gt_masks\n dataset_dict[\"instances\"] = instances\n\n return dataset_dict"},{"identifier":"COCOPanopticNewBaselineDatasetMapper","path":"maskclip/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py","snippet":"class COCOPanopticNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by MaskFormer.\n\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n tfm_gens,\n image_format,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n crop_gen: crop augmentation\n tfm_gens: data augmentation\n image_format: an image format supported by :func:`detection_utils.read_image`.\n \"\"\"\n self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[COCOPanopticNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(\n str(self.tfm_gens)\n )\n )\n\n self.img_format = image_format\n self.is_train = is_train\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n\n ret = {\n \"is_train\": is_train,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n }\n return ret\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n return dataset_dict\n\n if \"pan_seg_file_name\" in dataset_dict:\n pan_seg_gt = utils.read_image(dataset_dict.pop(\"pan_seg_file_name\"), \"RGB\")\n segments_info = dataset_dict[\"segments_info\"]\n\n # apply the same transformation to panoptic segmentation\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\n\n from panopticapi.utils import rgb2id\n\n pan_seg_gt = rgb2id(pan_seg_gt)\n\n instances = Instances(image_shape)\n classes = []\n masks = []\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n classes.append(class_id)\n masks.append(pan_seg_gt == segment_info[\"id\"])\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_boxes = Boxes(torch.zeros((0, 4)))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_boxes = masks.get_bounding_boxes()\n\n dataset_dict[\"instances\"] = instances\n\n return dataset_dict"},{"identifier":"MaskFormerInstanceDatasetMapper","path":"maskclip/data/dataset_mappers/mask_former_instance_dataset_mapper.py","snippet":"class MaskFormerInstanceDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by MaskFormer for instance segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n augmentations,\n image_format,\n size_divisibility,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.size_divisibility = size_divisibility\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n\n # # Build augmentation\n # augs = [\n # T.ResizeShortestEdge(\n # cfg.INPUT.MIN_SIZE_TRAIN,\n # cfg.INPUT.MAX_SIZE_TRAIN,\n # cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n # )\n # ]\n # if cfg.INPUT.CROP.ENABLED:\n # augs.append(\n # T.RandomCrop(\n # cfg.INPUT.CROP.TYPE,\n # cfg.INPUT.CROP.SIZE,\n # )\n # )\n # if cfg.INPUT.COLOR_AUG_SSD:\n # augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n # augs.append(T.RandomFlip())\n\n augs = [\n T.Resize((1024, 1024))\n ]\n\n ret = {\n \"is_train\": is_train,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n }\n return ret\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"MaskFormerPanopticDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n aug_input = T.AugInput(image)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n\n # transform instnace masks\n assert \"annotations\" in dataset_dict\n for anno in dataset_dict[\"annotations\"]:\n anno.pop(\"keypoints\", None)\n\n annos = [\n utils.transform_instance_annotations(obj, transforms, image.shape[:2])\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n\n if len(annos):\n assert \"segmentation\" in annos[0]\n segms = [obj[\"segmentation\"] for obj in annos]\n masks = []\n for segm in segms:\n if isinstance(segm, list):\n # polygon\n masks.append(polygons_to_bitmask(segm, *image.shape[:2]))\n elif isinstance(segm, dict):\n # COCO RLE\n masks.append(mask_util.decode(segm))\n elif isinstance(segm, np.ndarray):\n assert segm.ndim == 2, \"Expect segmentation of 2 dimensions, got {}.\".format(\n segm.ndim\n )\n # mask array\n masks.append(segm)\n else:\n raise ValueError(\n \"Cannot convert segmentation of type '{}' to BitMasks!\"\n \"Supported types are: polygons as list[list[float] or ndarray],\"\n \" COCO-style RLE as a dict, or a binary segmentation mask \"\n \" in a 2D numpy array of shape HxW.\".format(type(segm))\n )\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks]\n\n classes = [int(obj[\"category_id\"]) for obj in annos]\n classes = torch.tensor(classes, dtype=torch.int64)\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n # pad image\n image = F.pad(image, padding_size, value=128).contiguous()\n # pad mask\n masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks]\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n # Prepare per-category binary masks\n instances = Instances(image_shape)\n instances.gt_classes = classes\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1]))\n else:\n masks = BitMasks(torch.stack(masks))\n instances.gt_masks = masks.tensor\n\n dataset_dict[\"instances\"] = instances\n\n return dataset_dict"},{"identifier":"MaskFormerPanopticDatasetMapper","path":"maskclip/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py","snippet":"class MaskFormerPanopticDatasetMapper(MaskFormerSemanticDatasetMapper):\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by MaskFormer for panoptic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n augmentations,\n image_format,\n ignore_label,\n size_divisibility,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n super().__init__(\n is_train,\n augmentations=augmentations,\n image_format=image_format,\n ignore_label=ignore_label,\n size_divisibility=size_divisibility,\n )\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"MaskFormerPanopticDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # semantic segmentation\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None\n\n # panoptic segmentation\n if \"pan_seg_file_name\" in dataset_dict:\n pan_seg_gt = utils.read_image(dataset_dict.pop(\"pan_seg_file_name\"), \"RGB\")\n segments_info = dataset_dict[\"segments_info\"]\n else:\n pan_seg_gt = None\n segments_info = None\n\n if pan_seg_gt is None:\n raise ValueError(\n \"Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.\".format(\n dataset_dict[\"file_name\"]\n )\n )\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n if sem_seg_gt is not None:\n sem_seg_gt = aug_input.sem_seg\n\n # apply the same transformation to panoptic segmentation\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\n\n from panopticapi.utils import rgb2id\n\n pan_seg_gt = rgb2id(pan_seg_gt)\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n pan_seg_gt = torch.as_tensor(pan_seg_gt.astype(\"long\"))\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n image = F.pad(image, padding_size, value=128).contiguous()\n if sem_seg_gt is not None:\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\n pan_seg_gt = F.pad(\n pan_seg_gt, padding_size, value=0\n ).contiguous() # 0 is the VOID panoptic label\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n if sem_seg_gt is not None:\n dataset_dict[\"sem_seg\"] = sem_seg_gt.long()\n\n if \"annotations\" in dataset_dict:\n raise ValueError(\"Pemantic segmentation dataset should not have 'annotations'.\")\n\n # Prepare per-category binary masks\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n classes = []\n masks = []\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n classes.append(class_id)\n masks.append(pan_seg_gt == segment_info[\"id\"])\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n\n dataset_dict[\"instances\"] = instances\n\n return dataset_dict"},{"identifier":"MaskFormerSemanticDatasetMapper","path":"maskclip/data/dataset_mappers/mask_former_semantic_dataset_mapper.py","snippet":"class MaskFormerSemanticDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by MaskFormer for semantic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n augmentations,\n image_format,\n ignore_label,\n size_divisibility,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.ignore_label = ignore_label\n self.size_divisibility = size_divisibility\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n\n # Build augmentation\n # augs = [\n # T.ResizeShortestEdge(\n # cfg.INPUT.MIN_SIZE_TRAIN,\n # cfg.INPUT.MAX_SIZE_TRAIN,\n # cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n # )\n # ]\n # if cfg.INPUT.CROP.ENABLED:\n # augs.append(\n # T.RandomCrop_CategoryAreaConstraint(\n # cfg.INPUT.CROP.TYPE,\n # cfg.INPUT.CROP.SIZE,\n # cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,\n # cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\n # )\n # )\n # if cfg.INPUT.COLOR_AUG_SSD:\n # augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n # augs.append(T.RandomFlip())\n\n augs = [\n T.Resize((1024, 1024))\n ]\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n ignore_label = meta.ignore_label\n\n ret = {\n \"is_train\": is_train,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"ignore_label\": ignore_label,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n }\n return ret\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"MaskFormerSemanticDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None\n\n if sem_seg_gt is None:\n raise ValueError(\n \"Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.\".format(\n dataset_dict[\"file_name\"]\n )\n )\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n sem_seg_gt = aug_input.sem_seg\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n image = F.pad(image, padding_size, value=128).contiguous()\n if sem_seg_gt is not None:\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n if sem_seg_gt is not None:\n dataset_dict[\"sem_seg\"] = sem_seg_gt.long()\n\n if \"annotations\" in dataset_dict:\n raise ValueError(\"Semantic segmentation dataset should not have 'annotations'.\")\n\n # Prepare per-category binary masks\n if sem_seg_gt is not None:\n sem_seg_gt = sem_seg_gt.numpy()\n instances = Instances(image_shape)\n classes = np.unique(sem_seg_gt)\n # remove ignored region\n classes = classes[classes != self.ignore_label]\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n\n masks = []\n for class_id in classes:\n masks.append(sem_seg_gt == class_id)\n\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n\n dataset_dict[\"instances\"] = instances\n\n return dataset_dict"},{"identifier":"SemanticSegmentorWithTTA","path":"maskclip/test_time_augmentation.py","snippet":"class SemanticSegmentorWithTTA(nn.Module):\n \"\"\"\n A SemanticSegmentor with test-time augmentation enabled.\n Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.\n \"\"\"\n\n def __init__(self, cfg, model, tta_mapper=None, batch_size=1):\n \"\"\"\n Args:\n cfg (CfgNode):\n model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.\n tta_mapper (callable): takes a dataset dict and returns a list of\n augmented versions of the dataset dict. Defaults to\n `DatasetMapperTTA(cfg)`.\n batch_size (int): batch the augmented images into this batch size for inference.\n \"\"\"\n super().__init__()\n if isinstance(model, DistributedDataParallel):\n model = model.module\n self.cfg = cfg.clone()\n\n self.model = model\n\n if tta_mapper is None:\n tta_mapper = DatasetMapperTTA(cfg)\n self.tta_mapper = tta_mapper\n self.batch_size = batch_size\n\n def __call__(self, batched_inputs):\n \"\"\"\n Same input/output format as :meth:`SemanticSegmentor.forward`\n \"\"\"\n\n def _maybe_read_image(dataset_dict):\n ret = copy.copy(dataset_dict)\n if \"image\" not in ret:\n image = read_image(ret.pop(\"file_name\"), self.model.input_format)\n image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW\n ret[\"image\"] = image\n if \"height\" not in ret and \"width\" not in ret:\n ret[\"height\"] = image.shape[1]\n ret[\"width\"] = image.shape[2]\n return ret\n\n processed_results = []\n for x in batched_inputs:\n result = self._inference_one_image(_maybe_read_image(x))\n processed_results.append(result)\n return processed_results\n\n def _inference_one_image(self, input):\n \"\"\"\n Args:\n input (dict): one dataset dict with \"image\" field being a CHW tensor\n Returns:\n dict: one output dict\n \"\"\"\n orig_shape = (input[\"height\"], input[\"width\"])\n augmented_inputs, tfms = self._get_augmented_inputs(input)\n\n final_predictions = None\n count_predictions = 0\n for input, tfm in zip(augmented_inputs, tfms):\n count_predictions += 1\n with torch.no_grad():\n if final_predictions is None:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions = self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions = self.model([input])[0].pop(\"sem_seg\")\n else:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions += self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions += self.model([input])[0].pop(\"sem_seg\")\n\n final_predictions = final_predictions / count_predictions\n return {\"sem_seg\": final_predictions}\n\n def _get_augmented_inputs(self, input):\n augmented_inputs = self.tta_mapper(input)\n tfms = [x.pop(\"transforms\") for x in augmented_inputs]\n return augmented_inputs, tfms"},{"identifier":"InstanceSegEvaluator","path":"maskclip/evaluation/instance_evaluation.py","snippet":"class InstanceSegEvaluator(COCOEvaluator):\n \"\"\"\n Evaluate AR for object proposals, AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n # all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n # num_classes = len(all_contiguous_ids)\n # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n # assert category_id < num_classes, (\n # f\"A prediction has class={category_id}, \"\n # f\"but the dataset only has {num_classes} classes and \"\n # f\"predicted class id should be in [0, {num_classes - 1}].\"\n # )\n assert category_id in reverse_id_mapping, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has class ids in {dataset_id_to_contiguous_id}.\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"bbox\", \"segm\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res"}],"string":"[\n {\n \"identifier\": \"add_maskformer2_config\",\n \"path\": \"maskclip/config.py\",\n \"snippet\": \"def add_maskformer2_config(cfg):\\n \\\"\\\"\\\"\\n Add config for MASK_FORMER.\\n \\\"\\\"\\\"\\n # NOTE: configs from original maskformer\\n # data config\\n # select the dataset mapper\\n cfg.INPUT.DATASET_MAPPER_NAME = \\\"mask_former_semantic\\\"\\n # Color augmentation\\n cfg.INPUT.COLOR_AUG_SSD = False\\n # We retry random cropping until no single category in semantic segmentation GT occupies more\\n # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.\\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0\\n # Pad image and segmentation GT in dataset mapper.\\n cfg.INPUT.SIZE_DIVISIBILITY = -1\\n\\n # solver config\\n # weight decay on embedding\\n cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0\\n # optimizer\\n cfg.SOLVER.OPTIMIZER = \\\"ADAMW\\\"\\n cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1\\n\\n # mask_former model config\\n cfg.MODEL.MASK_FORMER = CN()\\n\\n # loss\\n cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True\\n cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = 0.1\\n cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = 1.0\\n cfg.MODEL.MASK_FORMER.DICE_WEIGHT = 1.0\\n cfg.MODEL.MASK_FORMER.MASK_WEIGHT = 20.0\\n\\n # transformer config\\n cfg.MODEL.MASK_FORMER.NHEADS = 8\\n cfg.MODEL.MASK_FORMER.DROPOUT = 0.1\\n cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048\\n cfg.MODEL.MASK_FORMER.ENC_LAYERS = 0\\n cfg.MODEL.MASK_FORMER.DEC_LAYERS = 6\\n cfg.MODEL.MASK_FORMER.PRE_NORM = False\\n\\n cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256\\n cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 100\\n\\n cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE = \\\"res5\\\"\\n cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ = False\\n\\n # mask_former inference config\\n cfg.MODEL.MASK_FORMER.TEST = CN()\\n cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True\\n cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = False\\n cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = False\\n cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD = 0.0\\n cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD = 0.0\\n cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False\\n\\n # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)\\n # you can use this config to override\\n cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32\\n\\n # pixel decoder config\\n cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256\\n # adding transformer in pixel decoder\\n cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0\\n # pixel decoder\\n cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = \\\"BasePixelDecoder\\\"\\n\\n # swin transformer backbone\\n cfg.MODEL.SWIN = CN()\\n cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224\\n cfg.MODEL.SWIN.PATCH_SIZE = 4\\n cfg.MODEL.SWIN.EMBED_DIM = 96\\n cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]\\n cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]\\n cfg.MODEL.SWIN.WINDOW_SIZE = 7\\n cfg.MODEL.SWIN.MLP_RATIO = 4.0\\n cfg.MODEL.SWIN.QKV_BIAS = True\\n cfg.MODEL.SWIN.QK_SCALE = None\\n cfg.MODEL.SWIN.DROP_RATE = 0.0\\n cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0\\n cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3\\n cfg.MODEL.SWIN.APE = False\\n cfg.MODEL.SWIN.PATCH_NORM = True\\n cfg.MODEL.SWIN.OUT_FEATURES = [\\\"res2\\\", \\\"res3\\\", \\\"res4\\\", \\\"res5\\\"]\\n cfg.MODEL.SWIN.USE_CHECKPOINT = False\\n\\n # NOTE: maskformer2 extra configs\\n # transformer module\\n cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME = \\\"MultiScaleMaskedTransformerDecoder\\\"\\n\\n # LSJ aug\\n cfg.INPUT.IMAGE_SIZE = 1024\\n cfg.INPUT.MIN_SCALE = 0.1\\n cfg.INPUT.MAX_SCALE = 2.0\\n\\n # MSDeformAttn encoder configs\\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = [\\\"res3\\\", \\\"res4\\\", \\\"res5\\\"]\\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4\\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8\\n\\n # point loss configs\\n # Number of points sampled during training for a mask point head.\\n cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = 112 * 112\\n # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the\\n # original paper.\\n cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0\\n # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in\\n # the original paper.\\n cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75\\n\\n # add MaskCLIP configs\\n cfg.MODEL.CLIP_MODEL = CN()\\n cfg.MODEL.CLIP_MODEL.NAME = 'ViT-L/14@336px'\\n cfg.MODEL.CLIP_MODEL.INPUT_RESOLUTION = 336\\n cfg.MODEL.CLIP_MODEL.PATCH_SIZE = 14\\n cfg.MODEL.CLIP_MODEL.WIDTH = 1024\\n cfg.MODEL.CLIP_MODEL.LAYERS = 24\\n cfg.MODEL.CLIP_MODEL.HEADS = 16\\n cfg.MODEL.CLIP_MODEL.OUTPUT_DIM = 768\\n\\n cfg.MODEL.CLIP_MODEL.TEMPERATURE = 0.01\"\n },\n {\n \"identifier\": \"COCOInstanceNewBaselineDatasetMapper\",\n \"path\": \"maskclip/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py\",\n \"snippet\": \"class COCOInstanceNewBaselineDatasetMapper:\\n \\\"\\\"\\\"\\n A callable which takes a dataset dict in Detectron2 Dataset format,\\n and map it into a format used by MaskFormer.\\n\\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\\n\\n The callable currently does the following:\\n\\n 1. Read the image from \\\"file_name\\\"\\n 2. Applies geometric transforms to the image and annotation\\n 3. Find and applies suitable cropping to the image and annotation\\n 4. Prepare image and annotation to Tensors\\n \\\"\\\"\\\"\\n\\n @configurable\\n def __init__(\\n self,\\n is_train=True,\\n *,\\n tfm_gens,\\n image_format,\\n ):\\n \\\"\\\"\\\"\\n NOTE: this interface is experimental.\\n Args:\\n is_train: for training or inference\\n augmentations: a list of augmentations or deterministic transforms to apply\\n tfm_gens: data augmentation\\n image_format: an image format supported by :func:`detection_utils.read_image`.\\n \\\"\\\"\\\"\\n self.tfm_gens = tfm_gens\\n logging.getLogger(__name__).info(\\n \\\"[COCOInstanceNewBaselineDatasetMapper] Full TransformGens used in training: {}\\\".format(str(self.tfm_gens))\\n )\\n\\n self.img_format = image_format\\n self.is_train = is_train\\n \\n @classmethod\\n def from_config(cls, cfg, is_train=True):\\n # Build augmentation\\n tfm_gens = build_transform_gen(cfg, is_train)\\n\\n ret = {\\n \\\"is_train\\\": is_train,\\n \\\"tfm_gens\\\": tfm_gens,\\n \\\"image_format\\\": cfg.INPUT.FORMAT,\\n }\\n return ret\\n\\n def __call__(self, dataset_dict):\\n \\\"\\\"\\\"\\n Args:\\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\\n\\n Returns:\\n dict: a format that builtin models in detectron2 accept\\n \\\"\\\"\\\"\\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\\n image = utils.read_image(dataset_dict[\\\"file_name\\\"], format=self.img_format)\\n utils.check_image_size(dataset_dict, image)\\n\\n # TODO: get padding mask\\n # by feeding a \\\"segmentation mask\\\" to the same transforms\\n padding_mask = np.ones(image.shape[:2])\\n\\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\\n # the crop transformation has default padding value 0 for segmentation\\n padding_mask = transforms.apply_segmentation(padding_mask)\\n padding_mask = ~ padding_mask.astype(bool)\\n\\n image_shape = image.shape[:2] # h, w\\n\\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\\n # Therefore it's important to use torch.Tensor.\\n dataset_dict[\\\"image\\\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\\n dataset_dict[\\\"padding_mask\\\"] = torch.as_tensor(np.ascontiguousarray(padding_mask))\\n\\n if not self.is_train:\\n # USER: Modify this if you want to keep them for some reason.\\n dataset_dict.pop(\\\"annotations\\\", None)\\n return dataset_dict\\n\\n if \\\"annotations\\\" in dataset_dict:\\n # USER: Modify this if you want to keep them for some reason.\\n for anno in dataset_dict[\\\"annotations\\\"]:\\n # Let's always keep mask\\n # if not self.mask_on:\\n # anno.pop(\\\"segmentation\\\", None)\\n anno.pop(\\\"keypoints\\\", None)\\n\\n # USER: Implement additional transformations if you have other types of data\\n annos = [\\n utils.transform_instance_annotations(obj, transforms, image_shape)\\n for obj in dataset_dict.pop(\\\"annotations\\\")\\n if obj.get(\\\"iscrowd\\\", 0) == 0\\n ]\\n # NOTE: does not support BitMask due to augmentation\\n # Current BitMask cannot handle empty objects\\n instances = utils.annotations_to_instances(annos, image_shape)\\n # After transforms such as cropping are applied, the bounding box may no longer\\n # tightly bound the object. As an example, imagine a triangle object\\n # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight\\n # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to\\n # the intersection of original bounding box and the cropping box.\\n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\\n # Need to filter empty instances first (due to augmentation)\\n instances = utils.filter_empty_instances(instances)\\n # Generate masks from polygon\\n h, w = instances.image_size\\n # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)\\n if hasattr(instances, 'gt_masks'):\\n gt_masks = instances.gt_masks\\n gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)\\n instances.gt_masks = gt_masks\\n dataset_dict[\\\"instances\\\"] = instances\\n\\n return dataset_dict\"\n },\n {\n \"identifier\": \"COCOPanopticNewBaselineDatasetMapper\",\n \"path\": \"maskclip/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py\",\n \"snippet\": \"class COCOPanopticNewBaselineDatasetMapper:\\n \\\"\\\"\\\"\\n A callable which takes a dataset dict in Detectron2 Dataset format,\\n and map it into a format used by MaskFormer.\\n\\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\\n\\n The callable currently does the following:\\n\\n 1. Read the image from \\\"file_name\\\"\\n 2. Applies geometric transforms to the image and annotation\\n 3. Find and applies suitable cropping to the image and annotation\\n 4. Prepare image and annotation to Tensors\\n \\\"\\\"\\\"\\n\\n @configurable\\n def __init__(\\n self,\\n is_train=True,\\n *,\\n tfm_gens,\\n image_format,\\n ):\\n \\\"\\\"\\\"\\n NOTE: this interface is experimental.\\n Args:\\n is_train: for training or inference\\n augmentations: a list of augmentations or deterministic transforms to apply\\n crop_gen: crop augmentation\\n tfm_gens: data augmentation\\n image_format: an image format supported by :func:`detection_utils.read_image`.\\n \\\"\\\"\\\"\\n self.tfm_gens = tfm_gens\\n logging.getLogger(__name__).info(\\n \\\"[COCOPanopticNewBaselineDatasetMapper] Full TransformGens used in training: {}\\\".format(\\n str(self.tfm_gens)\\n )\\n )\\n\\n self.img_format = image_format\\n self.is_train = is_train\\n\\n @classmethod\\n def from_config(cls, cfg, is_train=True):\\n # Build augmentation\\n tfm_gens = build_transform_gen(cfg, is_train)\\n\\n ret = {\\n \\\"is_train\\\": is_train,\\n \\\"tfm_gens\\\": tfm_gens,\\n \\\"image_format\\\": cfg.INPUT.FORMAT,\\n }\\n return ret\\n\\n def __call__(self, dataset_dict):\\n \\\"\\\"\\\"\\n Args:\\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\\n\\n Returns:\\n dict: a format that builtin models in detectron2 accept\\n \\\"\\\"\\\"\\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\\n image = utils.read_image(dataset_dict[\\\"file_name\\\"], format=self.img_format)\\n utils.check_image_size(dataset_dict, image)\\n\\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\\n image_shape = image.shape[:2] # h, w\\n\\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\\n # Therefore it's important to use torch.Tensor.\\n dataset_dict[\\\"image\\\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\\n\\n if not self.is_train:\\n # USER: Modify this if you want to keep them for some reason.\\n dataset_dict.pop(\\\"annotations\\\", None)\\n return dataset_dict\\n\\n if \\\"pan_seg_file_name\\\" in dataset_dict:\\n pan_seg_gt = utils.read_image(dataset_dict.pop(\\\"pan_seg_file_name\\\"), \\\"RGB\\\")\\n segments_info = dataset_dict[\\\"segments_info\\\"]\\n\\n # apply the same transformation to panoptic segmentation\\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\\n\\n from panopticapi.utils import rgb2id\\n\\n pan_seg_gt = rgb2id(pan_seg_gt)\\n\\n instances = Instances(image_shape)\\n classes = []\\n masks = []\\n for segment_info in segments_info:\\n class_id = segment_info[\\\"category_id\\\"]\\n if not segment_info[\\\"iscrowd\\\"]:\\n classes.append(class_id)\\n masks.append(pan_seg_gt == segment_info[\\\"id\\\"])\\n\\n classes = np.array(classes)\\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\\n if len(masks) == 0:\\n # Some image does not have annotation (all ignored)\\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\\n instances.gt_boxes = Boxes(torch.zeros((0, 4)))\\n else:\\n masks = BitMasks(\\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\\n )\\n instances.gt_masks = masks.tensor\\n instances.gt_boxes = masks.get_bounding_boxes()\\n\\n dataset_dict[\\\"instances\\\"] = instances\\n\\n return dataset_dict\"\n },\n {\n \"identifier\": \"MaskFormerInstanceDatasetMapper\",\n \"path\": \"maskclip/data/dataset_mappers/mask_former_instance_dataset_mapper.py\",\n \"snippet\": \"class MaskFormerInstanceDatasetMapper:\\n \\\"\\\"\\\"\\n A callable which takes a dataset dict in Detectron2 Dataset format,\\n and map it into a format used by MaskFormer for instance segmentation.\\n\\n The callable currently does the following:\\n\\n 1. Read the image from \\\"file_name\\\"\\n 2. Applies geometric transforms to the image and annotation\\n 3. Find and applies suitable cropping to the image and annotation\\n 4. Prepare image and annotation to Tensors\\n \\\"\\\"\\\"\\n\\n @configurable\\n def __init__(\\n self,\\n is_train=True,\\n *,\\n augmentations,\\n image_format,\\n size_divisibility,\\n ):\\n \\\"\\\"\\\"\\n NOTE: this interface is experimental.\\n Args:\\n is_train: for training or inference\\n augmentations: a list of augmentations or deterministic transforms to apply\\n image_format: an image format supported by :func:`detection_utils.read_image`.\\n size_divisibility: pad image size to be divisible by this value\\n \\\"\\\"\\\"\\n self.is_train = is_train\\n self.tfm_gens = augmentations\\n self.img_format = image_format\\n self.size_divisibility = size_divisibility\\n\\n logger = logging.getLogger(__name__)\\n mode = \\\"training\\\" if is_train else \\\"inference\\\"\\n logger.info(f\\\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\\\")\\n\\n @classmethod\\n def from_config(cls, cfg, is_train=True):\\n\\n # # Build augmentation\\n # augs = [\\n # T.ResizeShortestEdge(\\n # cfg.INPUT.MIN_SIZE_TRAIN,\\n # cfg.INPUT.MAX_SIZE_TRAIN,\\n # cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\\n # )\\n # ]\\n # if cfg.INPUT.CROP.ENABLED:\\n # augs.append(\\n # T.RandomCrop(\\n # cfg.INPUT.CROP.TYPE,\\n # cfg.INPUT.CROP.SIZE,\\n # )\\n # )\\n # if cfg.INPUT.COLOR_AUG_SSD:\\n # augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\\n # augs.append(T.RandomFlip())\\n\\n augs = [\\n T.Resize((1024, 1024))\\n ]\\n\\n ret = {\\n \\\"is_train\\\": is_train,\\n \\\"augmentations\\\": augs,\\n \\\"image_format\\\": cfg.INPUT.FORMAT,\\n \\\"size_divisibility\\\": cfg.INPUT.SIZE_DIVISIBILITY,\\n }\\n return ret\\n\\n def __call__(self, dataset_dict):\\n \\\"\\\"\\\"\\n Args:\\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\\n\\n Returns:\\n dict: a format that builtin models in detectron2 accept\\n \\\"\\\"\\\"\\n assert self.is_train, \\\"MaskFormerPanopticDatasetMapper should only be used for training!\\\"\\n\\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\\n image = utils.read_image(dataset_dict[\\\"file_name\\\"], format=self.img_format)\\n utils.check_image_size(dataset_dict, image)\\n\\n aug_input = T.AugInput(image)\\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\\n image = aug_input.image\\n\\n # transform instnace masks\\n assert \\\"annotations\\\" in dataset_dict\\n for anno in dataset_dict[\\\"annotations\\\"]:\\n anno.pop(\\\"keypoints\\\", None)\\n\\n annos = [\\n utils.transform_instance_annotations(obj, transforms, image.shape[:2])\\n for obj in dataset_dict.pop(\\\"annotations\\\")\\n if obj.get(\\\"iscrowd\\\", 0) == 0\\n ]\\n\\n if len(annos):\\n assert \\\"segmentation\\\" in annos[0]\\n segms = [obj[\\\"segmentation\\\"] for obj in annos]\\n masks = []\\n for segm in segms:\\n if isinstance(segm, list):\\n # polygon\\n masks.append(polygons_to_bitmask(segm, *image.shape[:2]))\\n elif isinstance(segm, dict):\\n # COCO RLE\\n masks.append(mask_util.decode(segm))\\n elif isinstance(segm, np.ndarray):\\n assert segm.ndim == 2, \\\"Expect segmentation of 2 dimensions, got {}.\\\".format(\\n segm.ndim\\n )\\n # mask array\\n masks.append(segm)\\n else:\\n raise ValueError(\\n \\\"Cannot convert segmentation of type '{}' to BitMasks!\\\"\\n \\\"Supported types are: polygons as list[list[float] or ndarray],\\\"\\n \\\" COCO-style RLE as a dict, or a binary segmentation mask \\\"\\n \\\" in a 2D numpy array of shape HxW.\\\".format(type(segm))\\n )\\n\\n # Pad image and segmentation label here!\\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\\n masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks]\\n\\n classes = [int(obj[\\\"category_id\\\"]) for obj in annos]\\n classes = torch.tensor(classes, dtype=torch.int64)\\n\\n if self.size_divisibility > 0:\\n image_size = (image.shape[-2], image.shape[-1])\\n padding_size = [\\n 0,\\n self.size_divisibility - image_size[1],\\n 0,\\n self.size_divisibility - image_size[0],\\n ]\\n # pad image\\n image = F.pad(image, padding_size, value=128).contiguous()\\n # pad mask\\n masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks]\\n\\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\\n\\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\\n # Therefore it's important to use torch.Tensor.\\n dataset_dict[\\\"image\\\"] = image\\n\\n # Prepare per-category binary masks\\n instances = Instances(image_shape)\\n instances.gt_classes = classes\\n if len(masks) == 0:\\n # Some image does not have annotation (all ignored)\\n instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1]))\\n else:\\n masks = BitMasks(torch.stack(masks))\\n instances.gt_masks = masks.tensor\\n\\n dataset_dict[\\\"instances\\\"] = instances\\n\\n return dataset_dict\"\n },\n {\n \"identifier\": \"MaskFormerPanopticDatasetMapper\",\n \"path\": \"maskclip/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py\",\n \"snippet\": \"class MaskFormerPanopticDatasetMapper(MaskFormerSemanticDatasetMapper):\\n \\\"\\\"\\\"\\n A callable which takes a dataset dict in Detectron2 Dataset format,\\n and map it into a format used by MaskFormer for panoptic segmentation.\\n\\n The callable currently does the following:\\n\\n 1. Read the image from \\\"file_name\\\"\\n 2. Applies geometric transforms to the image and annotation\\n 3. Find and applies suitable cropping to the image and annotation\\n 4. Prepare image and annotation to Tensors\\n \\\"\\\"\\\"\\n\\n @configurable\\n def __init__(\\n self,\\n is_train=True,\\n *,\\n augmentations,\\n image_format,\\n ignore_label,\\n size_divisibility,\\n ):\\n \\\"\\\"\\\"\\n NOTE: this interface is experimental.\\n Args:\\n is_train: for training or inference\\n augmentations: a list of augmentations or deterministic transforms to apply\\n image_format: an image format supported by :func:`detection_utils.read_image`.\\n ignore_label: the label that is ignored to evaluation\\n size_divisibility: pad image size to be divisible by this value\\n \\\"\\\"\\\"\\n super().__init__(\\n is_train,\\n augmentations=augmentations,\\n image_format=image_format,\\n ignore_label=ignore_label,\\n size_divisibility=size_divisibility,\\n )\\n\\n def __call__(self, dataset_dict):\\n \\\"\\\"\\\"\\n Args:\\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\\n\\n Returns:\\n dict: a format that builtin models in detectron2 accept\\n \\\"\\\"\\\"\\n assert self.is_train, \\\"MaskFormerPanopticDatasetMapper should only be used for training!\\\"\\n\\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\\n image = utils.read_image(dataset_dict[\\\"file_name\\\"], format=self.img_format)\\n utils.check_image_size(dataset_dict, image)\\n\\n # semantic segmentation\\n if \\\"sem_seg_file_name\\\" in dataset_dict:\\n # PyTorch transformation not implemented for uint16, so converting it to double first\\n sem_seg_gt = utils.read_image(dataset_dict.pop(\\\"sem_seg_file_name\\\")).astype(\\\"double\\\")\\n else:\\n sem_seg_gt = None\\n\\n # panoptic segmentation\\n if \\\"pan_seg_file_name\\\" in dataset_dict:\\n pan_seg_gt = utils.read_image(dataset_dict.pop(\\\"pan_seg_file_name\\\"), \\\"RGB\\\")\\n segments_info = dataset_dict[\\\"segments_info\\\"]\\n else:\\n pan_seg_gt = None\\n segments_info = None\\n\\n if pan_seg_gt is None:\\n raise ValueError(\\n \\\"Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.\\\".format(\\n dataset_dict[\\\"file_name\\\"]\\n )\\n )\\n\\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\\n image = aug_input.image\\n if sem_seg_gt is not None:\\n sem_seg_gt = aug_input.sem_seg\\n\\n # apply the same transformation to panoptic segmentation\\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\\n\\n from panopticapi.utils import rgb2id\\n\\n pan_seg_gt = rgb2id(pan_seg_gt)\\n\\n # Pad image and segmentation label here!\\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\\n if sem_seg_gt is not None:\\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\\\"long\\\"))\\n pan_seg_gt = torch.as_tensor(pan_seg_gt.astype(\\\"long\\\"))\\n\\n if self.size_divisibility > 0:\\n image_size = (image.shape[-2], image.shape[-1])\\n padding_size = [\\n 0,\\n self.size_divisibility - image_size[1],\\n 0,\\n self.size_divisibility - image_size[0],\\n ]\\n image = F.pad(image, padding_size, value=128).contiguous()\\n if sem_seg_gt is not None:\\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\\n pan_seg_gt = F.pad(\\n pan_seg_gt, padding_size, value=0\\n ).contiguous() # 0 is the VOID panoptic label\\n\\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\\n\\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\\n # Therefore it's important to use torch.Tensor.\\n dataset_dict[\\\"image\\\"] = image\\n if sem_seg_gt is not None:\\n dataset_dict[\\\"sem_seg\\\"] = sem_seg_gt.long()\\n\\n if \\\"annotations\\\" in dataset_dict:\\n raise ValueError(\\\"Pemantic segmentation dataset should not have 'annotations'.\\\")\\n\\n # Prepare per-category binary masks\\n pan_seg_gt = pan_seg_gt.numpy()\\n instances = Instances(image_shape)\\n classes = []\\n masks = []\\n for segment_info in segments_info:\\n class_id = segment_info[\\\"category_id\\\"]\\n if not segment_info[\\\"iscrowd\\\"]:\\n classes.append(class_id)\\n masks.append(pan_seg_gt == segment_info[\\\"id\\\"])\\n\\n classes = np.array(classes)\\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\\n if len(masks) == 0:\\n # Some image does not have annotation (all ignored)\\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\\n else:\\n masks = BitMasks(\\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\\n )\\n instances.gt_masks = masks.tensor\\n\\n dataset_dict[\\\"instances\\\"] = instances\\n\\n return dataset_dict\"\n },\n {\n \"identifier\": \"MaskFormerSemanticDatasetMapper\",\n \"path\": \"maskclip/data/dataset_mappers/mask_former_semantic_dataset_mapper.py\",\n \"snippet\": \"class MaskFormerSemanticDatasetMapper:\\n \\\"\\\"\\\"\\n A callable which takes a dataset dict in Detectron2 Dataset format,\\n and map it into a format used by MaskFormer for semantic segmentation.\\n\\n The callable currently does the following:\\n\\n 1. Read the image from \\\"file_name\\\"\\n 2. Applies geometric transforms to the image and annotation\\n 3. Find and applies suitable cropping to the image and annotation\\n 4. Prepare image and annotation to Tensors\\n \\\"\\\"\\\"\\n\\n @configurable\\n def __init__(\\n self,\\n is_train=True,\\n *,\\n augmentations,\\n image_format,\\n ignore_label,\\n size_divisibility,\\n ):\\n \\\"\\\"\\\"\\n NOTE: this interface is experimental.\\n Args:\\n is_train: for training or inference\\n augmentations: a list of augmentations or deterministic transforms to apply\\n image_format: an image format supported by :func:`detection_utils.read_image`.\\n ignore_label: the label that is ignored to evaluation\\n size_divisibility: pad image size to be divisible by this value\\n \\\"\\\"\\\"\\n self.is_train = is_train\\n self.tfm_gens = augmentations\\n self.img_format = image_format\\n self.ignore_label = ignore_label\\n self.size_divisibility = size_divisibility\\n\\n logger = logging.getLogger(__name__)\\n mode = \\\"training\\\" if is_train else \\\"inference\\\"\\n logger.info(f\\\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\\\")\\n\\n @classmethod\\n def from_config(cls, cfg, is_train=True):\\n\\n # Build augmentation\\n # augs = [\\n # T.ResizeShortestEdge(\\n # cfg.INPUT.MIN_SIZE_TRAIN,\\n # cfg.INPUT.MAX_SIZE_TRAIN,\\n # cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\\n # )\\n # ]\\n # if cfg.INPUT.CROP.ENABLED:\\n # augs.append(\\n # T.RandomCrop_CategoryAreaConstraint(\\n # cfg.INPUT.CROP.TYPE,\\n # cfg.INPUT.CROP.SIZE,\\n # cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,\\n # cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,\\n # )\\n # )\\n # if cfg.INPUT.COLOR_AUG_SSD:\\n # augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\\n # augs.append(T.RandomFlip())\\n\\n augs = [\\n T.Resize((1024, 1024))\\n ]\\n\\n # Assume always applies to the training set.\\n dataset_names = cfg.DATASETS.TRAIN\\n meta = MetadataCatalog.get(dataset_names[0])\\n ignore_label = meta.ignore_label\\n\\n ret = {\\n \\\"is_train\\\": is_train,\\n \\\"augmentations\\\": augs,\\n \\\"image_format\\\": cfg.INPUT.FORMAT,\\n \\\"ignore_label\\\": ignore_label,\\n \\\"size_divisibility\\\": cfg.INPUT.SIZE_DIVISIBILITY,\\n }\\n return ret\\n\\n def __call__(self, dataset_dict):\\n \\\"\\\"\\\"\\n Args:\\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\\n\\n Returns:\\n dict: a format that builtin models in detectron2 accept\\n \\\"\\\"\\\"\\n assert self.is_train, \\\"MaskFormerSemanticDatasetMapper should only be used for training!\\\"\\n\\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\\n image = utils.read_image(dataset_dict[\\\"file_name\\\"], format=self.img_format)\\n utils.check_image_size(dataset_dict, image)\\n\\n if \\\"sem_seg_file_name\\\" in dataset_dict:\\n # PyTorch transformation not implemented for uint16, so converting it to double first\\n sem_seg_gt = utils.read_image(dataset_dict.pop(\\\"sem_seg_file_name\\\")).astype(\\\"double\\\")\\n else:\\n sem_seg_gt = None\\n\\n if sem_seg_gt is None:\\n raise ValueError(\\n \\\"Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.\\\".format(\\n dataset_dict[\\\"file_name\\\"]\\n )\\n )\\n\\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\\n image = aug_input.image\\n sem_seg_gt = aug_input.sem_seg\\n\\n # Pad image and segmentation label here!\\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\\n if sem_seg_gt is not None:\\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\\\"long\\\"))\\n\\n if self.size_divisibility > 0:\\n image_size = (image.shape[-2], image.shape[-1])\\n padding_size = [\\n 0,\\n self.size_divisibility - image_size[1],\\n 0,\\n self.size_divisibility - image_size[0],\\n ]\\n image = F.pad(image, padding_size, value=128).contiguous()\\n if sem_seg_gt is not None:\\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\\n\\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\\n\\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\\n # Therefore it's important to use torch.Tensor.\\n dataset_dict[\\\"image\\\"] = image\\n\\n if sem_seg_gt is not None:\\n dataset_dict[\\\"sem_seg\\\"] = sem_seg_gt.long()\\n\\n if \\\"annotations\\\" in dataset_dict:\\n raise ValueError(\\\"Semantic segmentation dataset should not have 'annotations'.\\\")\\n\\n # Prepare per-category binary masks\\n if sem_seg_gt is not None:\\n sem_seg_gt = sem_seg_gt.numpy()\\n instances = Instances(image_shape)\\n classes = np.unique(sem_seg_gt)\\n # remove ignored region\\n classes = classes[classes != self.ignore_label]\\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\\n\\n masks = []\\n for class_id in classes:\\n masks.append(sem_seg_gt == class_id)\\n\\n if len(masks) == 0:\\n # Some image does not have annotation (all ignored)\\n instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))\\n else:\\n masks = BitMasks(\\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\\n )\\n instances.gt_masks = masks.tensor\\n\\n dataset_dict[\\\"instances\\\"] = instances\\n\\n return dataset_dict\"\n },\n {\n \"identifier\": \"SemanticSegmentorWithTTA\",\n \"path\": \"maskclip/test_time_augmentation.py\",\n \"snippet\": \"class SemanticSegmentorWithTTA(nn.Module):\\n \\\"\\\"\\\"\\n A SemanticSegmentor with test-time augmentation enabled.\\n Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.\\n \\\"\\\"\\\"\\n\\n def __init__(self, cfg, model, tta_mapper=None, batch_size=1):\\n \\\"\\\"\\\"\\n Args:\\n cfg (CfgNode):\\n model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.\\n tta_mapper (callable): takes a dataset dict and returns a list of\\n augmented versions of the dataset dict. Defaults to\\n `DatasetMapperTTA(cfg)`.\\n batch_size (int): batch the augmented images into this batch size for inference.\\n \\\"\\\"\\\"\\n super().__init__()\\n if isinstance(model, DistributedDataParallel):\\n model = model.module\\n self.cfg = cfg.clone()\\n\\n self.model = model\\n\\n if tta_mapper is None:\\n tta_mapper = DatasetMapperTTA(cfg)\\n self.tta_mapper = tta_mapper\\n self.batch_size = batch_size\\n\\n def __call__(self, batched_inputs):\\n \\\"\\\"\\\"\\n Same input/output format as :meth:`SemanticSegmentor.forward`\\n \\\"\\\"\\\"\\n\\n def _maybe_read_image(dataset_dict):\\n ret = copy.copy(dataset_dict)\\n if \\\"image\\\" not in ret:\\n image = read_image(ret.pop(\\\"file_name\\\"), self.model.input_format)\\n image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW\\n ret[\\\"image\\\"] = image\\n if \\\"height\\\" not in ret and \\\"width\\\" not in ret:\\n ret[\\\"height\\\"] = image.shape[1]\\n ret[\\\"width\\\"] = image.shape[2]\\n return ret\\n\\n processed_results = []\\n for x in batched_inputs:\\n result = self._inference_one_image(_maybe_read_image(x))\\n processed_results.append(result)\\n return processed_results\\n\\n def _inference_one_image(self, input):\\n \\\"\\\"\\\"\\n Args:\\n input (dict): one dataset dict with \\\"image\\\" field being a CHW tensor\\n Returns:\\n dict: one output dict\\n \\\"\\\"\\\"\\n orig_shape = (input[\\\"height\\\"], input[\\\"width\\\"])\\n augmented_inputs, tfms = self._get_augmented_inputs(input)\\n\\n final_predictions = None\\n count_predictions = 0\\n for input, tfm in zip(augmented_inputs, tfms):\\n count_predictions += 1\\n with torch.no_grad():\\n if final_predictions is None:\\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\\n final_predictions = self.model([input])[0].pop(\\\"sem_seg\\\").flip(dims=[2])\\n else:\\n final_predictions = self.model([input])[0].pop(\\\"sem_seg\\\")\\n else:\\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\\n final_predictions += self.model([input])[0].pop(\\\"sem_seg\\\").flip(dims=[2])\\n else:\\n final_predictions += self.model([input])[0].pop(\\\"sem_seg\\\")\\n\\n final_predictions = final_predictions / count_predictions\\n return {\\\"sem_seg\\\": final_predictions}\\n\\n def _get_augmented_inputs(self, input):\\n augmented_inputs = self.tta_mapper(input)\\n tfms = [x.pop(\\\"transforms\\\") for x in augmented_inputs]\\n return augmented_inputs, tfms\"\n },\n {\n \"identifier\": \"InstanceSegEvaluator\",\n \"path\": \"maskclip/evaluation/instance_evaluation.py\",\n \"snippet\": \"class InstanceSegEvaluator(COCOEvaluator):\\n \\\"\\\"\\\"\\n Evaluate AR for object proposals, AP for instance detection/segmentation, AP\\n for keypoint detection outputs using COCO's metrics.\\n See http://cocodataset.org/#detection-eval and\\n http://cocodataset.org/#keypoints-eval to understand its metrics.\\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\\n the metric cannot be computed (e.g. due to no predictions made).\\n\\n In addition to COCO, this evaluator is able to support any bounding box detection,\\n instance segmentation, or keypoint detection dataset.\\n \\\"\\\"\\\"\\n\\n def _eval_predictions(self, predictions, img_ids=None):\\n \\\"\\\"\\\"\\n Evaluate predictions. Fill self._results with the metrics of the tasks.\\n \\\"\\\"\\\"\\n self._logger.info(\\\"Preparing results for COCO format ...\\\")\\n coco_results = list(itertools.chain(*[x[\\\"instances\\\"] for x in predictions]))\\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\\n\\n # unmap the category ids for COCO\\n if hasattr(self._metadata, \\\"thing_dataset_id_to_contiguous_id\\\"):\\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\\n # all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\\n # num_classes = len(all_contiguous_ids)\\n # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\\n\\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\\n for result in coco_results:\\n category_id = result[\\\"category_id\\\"]\\n # assert category_id < num_classes, (\\n # f\\\"A prediction has class={category_id}, \\\"\\n # f\\\"but the dataset only has {num_classes} classes and \\\"\\n # f\\\"predicted class id should be in [0, {num_classes - 1}].\\\"\\n # )\\n assert category_id in reverse_id_mapping, (\\n f\\\"A prediction has class={category_id}, \\\"\\n f\\\"but the dataset only has class ids in {dataset_id_to_contiguous_id}.\\\"\\n )\\n result[\\\"category_id\\\"] = reverse_id_mapping[category_id]\\n\\n if self._output_dir:\\n file_path = os.path.join(self._output_dir, \\\"coco_instances_results.json\\\")\\n self._logger.info(\\\"Saving results to {}\\\".format(file_path))\\n with PathManager.open(file_path, \\\"w\\\") as f:\\n f.write(json.dumps(coco_results))\\n f.flush()\\n\\n if not self._do_evaluation:\\n self._logger.info(\\\"Annotations are not available for evaluation.\\\")\\n return\\n\\n self._logger.info(\\n \\\"Evaluating predictions with {} COCO API...\\\".format(\\n \\\"unofficial\\\" if self._use_fast_impl else \\\"official\\\"\\n )\\n )\\n for task in sorted(tasks):\\n assert task in {\\\"bbox\\\", \\\"segm\\\", \\\"keypoints\\\"}, f\\\"Got unknown task: {task}!\\\"\\n coco_eval = (\\n _evaluate_predictions_on_coco(\\n self._coco_api,\\n coco_results,\\n task,\\n kpt_oks_sigmas=self._kpt_oks_sigmas,\\n use_fast_impl=self._use_fast_impl,\\n img_ids=img_ids,\\n max_dets_per_image=self._max_dets_per_image,\\n )\\n if len(coco_results) > 0\\n else None # cocoapi does not handle empty results very well\\n )\\n\\n res = self._derive_coco_results(\\n coco_eval, task, class_names=self._metadata.get(\\\"thing_classes\\\")\\n )\\n self._results[task] = res\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":" from shapely.errors import ShapelyDeprecationWarning\nfrom collections import OrderedDict\nfrom typing import Any, Dict, List, Set\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.config import get_cfg\nfrom detectron2.data import MetadataCatalog, build_detection_train_loader\nfrom detectron2.engine import (\n DefaultTrainer,\n default_argument_parser,\n default_setup,\n launch,\n)\nfrom detectron2.evaluation import (\n CityscapesInstanceEvaluator,\n CityscapesSemSegEvaluator,\n COCOEvaluator,\n COCOPanopticEvaluator,\n DatasetEvaluators,\n LVISEvaluator,\n SemSegEvaluator,\n verify_results,\n)\nfrom detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler\nfrom detectron2.solver.build import maybe_add_gradient_clipping\nfrom detectron2.utils.logger import setup_logger\nfrom maskclip import (\n COCOInstanceNewBaselineDatasetMapper,\n COCOPanopticNewBaselineDatasetMapper,\n InstanceSegEvaluator,\n MaskFormerInstanceDatasetMapper,\n MaskFormerPanopticDatasetMapper,\n MaskFormerSemanticDatasetMapper,\n SemanticSegmentorWithTTA,\n add_maskformer2_config,\n)\n import warnings\nimport copy\nimport itertools\nimport logging\nimport os\nimport torch\nimport detectron2.utils.comm as comm"},"token_num":{"kind":"number","value":11476,"string":"11,476"},"cropped_code":{"kind":"string","value":"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nMaskFormer Training Script.\n\nThis script is a simplified version of the training script in detectron2/tools.\n\"\"\"\ntry:\n # ignore ShapelyDeprecationWarning from fvcore\n warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)\nexcept:\n pass\n\n\n\n\n\n\n\nclass Trainer(DefaultTrainer):\n \"\"\"\n Extension of the Trainer class adapted to MaskFormer.\n \"\"\"\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name, output_folder=None):\n \"\"\"\n Create evaluator(s) for a given dataset.\n This uses the special metadata \"evaluator_type\" associated with each\n builtin dataset. For your own dataset, you can simply create an\n evaluator manually in your script and do not have to worry about the\n hacky if-else logic here.\n \"\"\"\n if output_folder is None:\n output_folder = os.path.join(cfg.OUTPUT_DIR, \"inference\")\n evaluator_list = []\n evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type\n # semantic segmentation\n if evaluator_type in [\"sem_seg\", \"ade20k_panoptic_seg\"]:\n evaluator_list.append(\n SemSegEvaluator(\n dataset_name,\n distributed=True,\n output_dir=output_folder,\n )\n )\n # instance segmentation\n if evaluator_type == \"coco\":\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n # panoptic segmentation\n if evaluator_type in [\n \"coco_panoptic_seg\",\n \"ade20k_panoptic_seg\",\n \"cityscapes_panoptic_seg\",\n \"mapillary_vistas_panoptic_seg\",\n ]:\n if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:\n evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))\n # COCO\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n # Mapillary Vistas\n if evaluator_type == \"mapillary_vistas_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"mapillary_vistas_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n # Cityscapes\n if evaluator_type == \"cityscapes_instance\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesInstanceEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_sem_seg\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesSemSegEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_panoptic_seg\":\n if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))\n if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))\n # ADE20K\n if evaluator_type == \"ade20k_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))\n # LVIS\n if evaluator_type == \"lvis\":\n return LVISEvaluator(dataset_name, output_dir=output_folder)\n if len(evaluator_list) == 0:\n raise NotImplementedError(\n \"no Evaluator for the dataset {} with the type {}\".format(\n dataset_name, evaluator_type\n )\n )\n elif len(evaluator_list) == 1:\n return evaluator_list[0]\n return DatasetEvaluators(evaluator_list)\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Semantic segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"mask_former_semantic\":\n mapper = MaskFormerSemanticDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # Panoptic segmentation dataset mapper\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"mask_former_panoptic\":\n"},"all_code":{"kind":"string","value":"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nMaskFormer Training Script.\n\nThis script is a simplified version of the training script in detectron2/tools.\n\"\"\"\ntry:\n # ignore ShapelyDeprecationWarning from fvcore\n warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)\nexcept:\n pass\n\n\n\n\n\n\n\nclass Trainer(DefaultTrainer):\n \"\"\"\n Extension of the Trainer class adapted to MaskFormer.\n \"\"\"\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name, output_folder=None):\n \"\"\"\n Create evaluator(s) for a given dataset.\n This uses the special metadata \"evaluator_type\" associated with each\n builtin dataset. For your own dataset, you can simply create an\n evaluator manually in your script and do not have to worry about the\n hacky if-else logic here.\n \"\"\"\n if output_folder is None:\n output_folder = os.path.join(cfg.OUTPUT_DIR, \"inference\")\n evaluator_list = []\n evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type\n # semantic segmentation\n if evaluator_type in [\"sem_seg\", \"ade20k_panoptic_seg\"]:\n evaluator_list.append(\n SemSegEvaluator(\n dataset_name,\n distributed=True,\n output_dir=output_folder,\n )\n )\n # instance segmentation\n if evaluator_type == \"coco\":\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n # panoptic segmentation\n if evaluator_type in [\n \"coco_panoptic_seg\",\n \"ade20k_panoptic_seg\",\n \"cityscapes_panoptic_seg\",\n \"mapillary_vistas_panoptic_seg\",\n ]:\n if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:\n evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))\n # COCO\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n # Mapillary Vistas\n if evaluator_type == \"mapillary_vistas_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"mapillary_vistas_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n # Cityscapes\n if evaluator_type == \"cityscapes_instance\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesInstanceEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_sem_seg\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesSemSegEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_panoptic_seg\":\n if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))\n if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))\n # ADE20K\n if evaluator_type == \"ade20k_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))\n # LVIS\n if evaluator_type == \"lvis\":\n return LVISEvaluator(dataset_name, output_dir=output_folder)\n if len(evaluator_list) == 0:\n raise NotImplementedError(\n \"no Evaluator for the dataset {} with the type {}\".format(\n dataset_name, evaluator_type\n )\n )\n elif len(evaluator_list) == 1:\n return evaluator_list[0]\n return DatasetEvaluators(evaluator_list)\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Semantic segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"mask_former_semantic\":\n mapper = MaskFormerSemanticDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # Panoptic segmentation dataset mapper\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"mask_former_panoptic\":"},"next_line":{"kind":"string","value":" mapper = MaskFormerPanopticDatasetMapper(cfg, True)"},"gold_snippet_index":{"kind":"number","value":4,"string":"4"},"created_at":{"kind":"string","value":"2023-10-13 02:32:25+00:00"},"level":{"kind":"string","value":"16k"}}},{"rowIdx":4599,"cells":{"repo_name":{"kind":"string","value":"mlpc-ucsd/MasQCLIP"},"file_path":{"kind":"string","value":"train_net.py"},"context":{"kind":"list like","value":[{"identifier":"add_maskformer2_config","path":"masqclip/config.py","snippet":"def add_maskformer2_config(cfg):\n \"\"\"\n Add config for MASK_FORMER.\n \"\"\"\n # NOTE: configs from original maskformer\n # data config\n # select the dataset mapper\n cfg.INPUT.DATASET_MAPPER_NAME = \"mask_former_semantic\"\n # Color augmentation\n cfg.INPUT.COLOR_AUG_SSD = False\n # We retry random cropping until no single category in semantic segmentation GT occupies more\n # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0\n # Pad image and segmentation GT in dataset mapper.\n cfg.INPUT.SIZE_DIVISIBILITY = -1\n\n # solver config\n # weight decay on embedding\n cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0\n # optimizer\n cfg.SOLVER.OPTIMIZER = \"ADAMW\"\n cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1\n\n # mask_former model config\n cfg.MODEL.MASK_FORMER = CN()\n\n # loss\n cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True\n cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = 0.1\n cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = 1.0\n cfg.MODEL.MASK_FORMER.DICE_WEIGHT = 1.0\n cfg.MODEL.MASK_FORMER.MASK_WEIGHT = 20.0\n\n # transformer config\n cfg.MODEL.MASK_FORMER.NHEADS = 8\n cfg.MODEL.MASK_FORMER.DROPOUT = 0.1\n cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048\n cfg.MODEL.MASK_FORMER.ENC_LAYERS = 0\n cfg.MODEL.MASK_FORMER.DEC_LAYERS = 6\n cfg.MODEL.MASK_FORMER.PRE_NORM = False\n\n cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256\n cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 100\n\n cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE = \"res5\"\n cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ = False\n\n # mask_former inference config\n cfg.MODEL.MASK_FORMER.TEST = CN()\n cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True\n cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = False\n cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = False\n cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD = 0.0\n cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD = 0.0\n cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False\n\n # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)\n # you can use this config to override\n cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32\n\n # pixel decoder config\n cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256\n # adding transformer in pixel decoder\n cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0\n # pixel decoder\n cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = \"BasePixelDecoder\"\n\n # swin transformer backbone\n cfg.MODEL.SWIN = CN()\n cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224\n cfg.MODEL.SWIN.PATCH_SIZE = 4\n cfg.MODEL.SWIN.EMBED_DIM = 96\n cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]\n cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]\n cfg.MODEL.SWIN.WINDOW_SIZE = 7\n cfg.MODEL.SWIN.MLP_RATIO = 4.0\n cfg.MODEL.SWIN.QKV_BIAS = True\n cfg.MODEL.SWIN.QK_SCALE = None\n cfg.MODEL.SWIN.DROP_RATE = 0.0\n cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0\n cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3\n cfg.MODEL.SWIN.APE = False\n cfg.MODEL.SWIN.PATCH_NORM = True\n cfg.MODEL.SWIN.OUT_FEATURES = [\"res2\", \"res3\", \"res4\", \"res5\"]\n cfg.MODEL.SWIN.USE_CHECKPOINT = False\n\n # NOTE: maskformer2 extra configs\n # transformer module\n cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME = \"MultiScaleMaskedTransformerDecoder\"\n\n # LSJ aug\n cfg.INPUT.IMAGE_SIZE = 1024\n cfg.INPUT.MIN_SCALE = 0.1\n cfg.INPUT.MAX_SCALE = 2.0\n\n # MSDeformAttn encoder configs\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = [\"res3\", \"res4\", \"res5\"]\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8\n\n # point loss configs\n # Number of points sampled during training for a mask point head.\n cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = 112 * 112\n # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the\n # original paper.\n cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0\n # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in\n # the original paper.\n cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75"},{"identifier":"add_masqclip_config","path":"masqclip/config.py","snippet":"def add_masqclip_config(cfg):\n \"\"\"\n Add config for MasQCLIP.\n \"\"\"\n cfg.MODEL.MASQ_CLIP = CN()\n cfg.MODEL.MASQ_CLIP.MODEL_NAME = [\"ViT-L/14@336px\"]\n \n cfg.MODEL.MASQ_CLIP.SCORE_THRESHOLD = 0.8\n cfg.MODEL.MASQ_CLIP.NMS_THRESHOLD = 0.1"},{"identifier":"COCOInstanceNewBaselineDatasetMapper","path":"masqclip/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py","snippet":"class COCOInstanceNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by MaskFormer.\n\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n tfm_gens,\n image_format,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n tfm_gens: data augmentation\n image_format: an image format supported by :func:`detection_utils.read_image`.\n \"\"\"\n self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[COCOInstanceNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(str(self.tfm_gens))\n )\n\n self.img_format = image_format\n self.is_train = is_train\n \n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n\n ret = {\n \"is_train\": is_train,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n }\n return ret\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # TODO: get padding mask\n # by feeding a \"segmentation mask\" to the same transforms\n padding_mask = np.ones(image.shape[:2])\n\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n # the crop transformation has default padding value 0 for segmentation\n padding_mask = transforms.apply_segmentation(padding_mask)\n padding_mask = ~ padding_mask.astype(bool)\n\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n dataset_dict[\"padding_mask\"] = torch.as_tensor(np.ascontiguousarray(padding_mask))\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n return dataset_dict\n\n if \"annotations\" in dataset_dict:\n # USER: Modify this if you want to keep them for some reason.\n for anno in dataset_dict[\"annotations\"]:\n # Let's always keep mask\n # if not self.mask_on:\n # anno.pop(\"segmentation\", None)\n anno.pop(\"keypoints\", None)\n\n # USER: Implement additional transformations if you have other types of data\n annos = [\n utils.transform_instance_annotations(obj, transforms, image_shape)\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n # NOTE: does not support BitMask due to augmentation\n # Current BitMask cannot handle empty objects\n instances = utils.annotations_to_instances(annos, image_shape)\n # After transforms such as cropping are applied, the bounding box may no longer\n # tightly bound the object. As an example, imagine a triangle object\n # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight\n # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to\n # the intersection of original bounding box and the cropping box.\n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\n # Need to filter empty instances first (due to augmentation)\n instances = utils.filter_empty_instances(instances)\n # Generate masks from polygon\n h, w = instances.image_size\n # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)\n if hasattr(instances, 'gt_masks'):\n gt_masks = instances.gt_masks\n gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)\n instances.gt_masks = gt_masks\n dataset_dict[\"instances\"] = instances\n\n return dataset_dict"},{"identifier":"COCOPanopticNewBaselineDatasetMapper","path":"masqclip/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py","snippet":"class COCOPanopticNewBaselineDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by MaskFormer.\n\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n tfm_gens,\n image_format,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n crop_gen: crop augmentation\n tfm_gens: data augmentation\n image_format: an image format supported by :func:`detection_utils.read_image`.\n \"\"\"\n self.tfm_gens = tfm_gens\n logging.getLogger(__name__).info(\n \"[COCOPanopticNewBaselineDatasetMapper] Full TransformGens used in training: {}\".format(\n str(self.tfm_gens)\n )\n )\n\n self.img_format = image_format\n self.is_train = is_train\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # Build augmentation\n tfm_gens = build_transform_gen(cfg, is_train)\n\n ret = {\n \"is_train\": is_train,\n \"tfm_gens\": tfm_gens,\n \"image_format\": cfg.INPUT.FORMAT,\n }\n return ret\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\n image_shape = image.shape[:2] # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n\n if not self.is_train:\n # USER: Modify this if you want to keep them for some reason.\n dataset_dict.pop(\"annotations\", None)\n return dataset_dict\n\n if \"pan_seg_file_name\" in dataset_dict:\n pan_seg_gt = utils.read_image(dataset_dict.pop(\"pan_seg_file_name\"), \"RGB\")\n segments_info = dataset_dict[\"segments_info\"]\n\n # apply the same transformation to panoptic segmentation\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\n\n from panopticapi.utils import rgb2id\n\n pan_seg_gt = rgb2id(pan_seg_gt)\n\n instances = Instances(image_shape)\n classes = []\n masks = []\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n classes.append(class_id)\n masks.append(pan_seg_gt == segment_info[\"id\"])\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n instances.gt_boxes = Boxes(torch.zeros((0, 4)))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n instances.gt_boxes = masks.get_bounding_boxes()\n\n dataset_dict[\"instances\"] = instances\n\n return dataset_dict"},{"identifier":"MaskFormerInstanceDatasetMapper","path":"masqclip/data/dataset_mappers/mask_former_instance_dataset_mapper.py","snippet":"class MaskFormerInstanceDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by MaskFormer for instance segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n augmentations,\n image_format,\n size_divisibility,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.size_divisibility = size_divisibility\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n # change_code_note\n\n # # Build augmentation\n # augs = [\n # T.ResizeShortestEdge(\n # cfg.INPUT.MIN_SIZE_TRAIN,\n # cfg.INPUT.MAX_SIZE_TRAIN,\n # cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\n # )\n # ]\n # if cfg.INPUT.CROP.ENABLED:\n # augs.append(\n # T.RandomCrop(\n # cfg.INPUT.CROP.TYPE,\n # cfg.INPUT.CROP.SIZE,\n # )\n # )\n # if cfg.INPUT.COLOR_AUG_SSD:\n # augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\n # augs.append(T.RandomFlip())\n\n augs = [\n T.Resize((1024, 1024))\n ]\n\n ret = {\n \"is_train\": is_train,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n }\n return ret\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"MaskFormerPanopticDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n aug_input = T.AugInput(image)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n\n # transform instnace masks\n assert \"annotations\" in dataset_dict\n for anno in dataset_dict[\"annotations\"]:\n anno.pop(\"keypoints\", None)\n\n annos = [\n utils.transform_instance_annotations(obj, transforms, image.shape[:2])\n for obj in dataset_dict.pop(\"annotations\")\n if obj.get(\"iscrowd\", 0) == 0\n ]\n\n if len(annos):\n assert \"segmentation\" in annos[0]\n segms = [obj[\"segmentation\"] for obj in annos]\n masks = []\n for segm in segms:\n if isinstance(segm, list):\n # polygon\n masks.append(polygons_to_bitmask(segm, *image.shape[:2]))\n elif isinstance(segm, dict):\n # COCO RLE\n masks.append(mask_util.decode(segm))\n elif isinstance(segm, np.ndarray):\n assert segm.ndim == 2, \"Expect segmentation of 2 dimensions, got {}.\".format(\n segm.ndim\n )\n # mask array\n masks.append(segm)\n else:\n raise ValueError(\n \"Cannot convert segmentation of type '{}' to BitMasks!\"\n \"Supported types are: polygons as list[list[float] or ndarray],\"\n \" COCO-style RLE as a dict, or a binary segmentation mask \"\n \" in a 2D numpy array of shape HxW.\".format(type(segm))\n )\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks]\n\n classes = [int(obj[\"category_id\"]) for obj in annos]\n classes = torch.tensor(classes, dtype=torch.int64)\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n # pad image\n image = F.pad(image, padding_size, value=128).contiguous()\n # pad mask\n masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks]\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n # Prepare per-category binary masks\n instances = Instances(image_shape)\n instances.gt_classes = classes\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1]))\n else:\n masks = BitMasks(torch.stack(masks))\n instances.gt_masks = masks.tensor\n\n dataset_dict[\"instances\"] = instances\n\n return dataset_dict"},{"identifier":"MaskFormerPanopticDatasetMapper","path":"masqclip/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py","snippet":"class MaskFormerPanopticDatasetMapper(MaskFormerSemanticDatasetMapper):\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by MaskFormer for panoptic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n augmentations,\n image_format,\n ignore_label,\n size_divisibility,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n super().__init__(\n is_train,\n augmentations=augmentations,\n image_format=image_format,\n ignore_label=ignore_label,\n size_divisibility=size_divisibility,\n )\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"MaskFormerPanopticDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n # semantic segmentation\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None\n\n # panoptic segmentation\n if \"pan_seg_file_name\" in dataset_dict:\n pan_seg_gt = utils.read_image(dataset_dict.pop(\"pan_seg_file_name\"), \"RGB\")\n segments_info = dataset_dict[\"segments_info\"]\n else:\n pan_seg_gt = None\n segments_info = None\n\n if pan_seg_gt is None:\n raise ValueError(\n \"Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.\".format(\n dataset_dict[\"file_name\"]\n )\n )\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n if sem_seg_gt is not None:\n sem_seg_gt = aug_input.sem_seg\n\n # apply the same transformation to panoptic segmentation\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\n\n from panopticapi.utils import rgb2id\n\n pan_seg_gt = rgb2id(pan_seg_gt)\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n pan_seg_gt = torch.as_tensor(pan_seg_gt.astype(\"long\"))\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n image = F.pad(image, padding_size, value=128).contiguous()\n if sem_seg_gt is not None:\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\n pan_seg_gt = F.pad(\n pan_seg_gt, padding_size, value=0\n ).contiguous() # 0 is the VOID panoptic label\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n if sem_seg_gt is not None:\n dataset_dict[\"sem_seg\"] = sem_seg_gt.long()\n\n if \"annotations\" in dataset_dict:\n raise ValueError(\"Pemantic segmentation dataset should not have 'annotations'.\")\n\n # Prepare per-category binary masks\n pan_seg_gt = pan_seg_gt.numpy()\n instances = Instances(image_shape)\n classes = []\n masks = []\n for segment_info in segments_info:\n class_id = segment_info[\"category_id\"]\n if not segment_info[\"iscrowd\"]:\n classes.append(class_id)\n masks.append(pan_seg_gt == segment_info[\"id\"])\n\n classes = np.array(classes)\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n\n dataset_dict[\"instances\"] = instances\n\n return dataset_dict"},{"identifier":"MaskFormerSemanticDatasetMapper","path":"masqclip/data/dataset_mappers/mask_former_semantic_dataset_mapper.py","snippet":"class MaskFormerSemanticDatasetMapper:\n \"\"\"\n A callable which takes a dataset dict in Detectron2 Dataset format,\n and map it into a format used by MaskFormer for semantic segmentation.\n\n The callable currently does the following:\n\n 1. Read the image from \"file_name\"\n 2. Applies geometric transforms to the image and annotation\n 3. Find and applies suitable cropping to the image and annotation\n 4. Prepare image and annotation to Tensors\n \"\"\"\n\n @configurable\n def __init__(\n self,\n is_train=True,\n *,\n augmentations,\n image_format,\n ignore_label,\n size_divisibility,\n ):\n \"\"\"\n NOTE: this interface is experimental.\n Args:\n is_train: for training or inference\n augmentations: a list of augmentations or deterministic transforms to apply\n image_format: an image format supported by :func:`detection_utils.read_image`.\n ignore_label: the label that is ignored to evaluation\n size_divisibility: pad image size to be divisible by this value\n \"\"\"\n self.is_train = is_train\n self.tfm_gens = augmentations\n self.img_format = image_format\n self.ignore_label = ignore_label\n self.size_divisibility = size_divisibility\n\n logger = logging.getLogger(__name__)\n mode = \"training\" if is_train else \"inference\"\n logger.info(f\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\")\n\n @classmethod\n def from_config(cls, cfg, is_train=True):\n\n augs = [\n T.Resize((1024, 1024))\n ]\n\n # Assume always applies to the training set.\n dataset_names = cfg.DATASETS.TRAIN\n meta = MetadataCatalog.get(dataset_names[0])\n ignore_label = meta.ignore_label\n\n ret = {\n \"is_train\": is_train,\n \"augmentations\": augs,\n \"image_format\": cfg.INPUT.FORMAT,\n \"ignore_label\": ignore_label,\n \"size_divisibility\": cfg.INPUT.SIZE_DIVISIBILITY,\n }\n return ret\n\n def __call__(self, dataset_dict):\n \"\"\"\n Args:\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\n\n Returns:\n dict: a format that builtin models in detectron2 accept\n \"\"\"\n assert self.is_train, \"MaskFormerSemanticDatasetMapper should only be used for training!\"\n\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\n image = utils.read_image(dataset_dict[\"file_name\"], format=self.img_format)\n utils.check_image_size(dataset_dict, image)\n\n if \"sem_seg_file_name\" in dataset_dict:\n # PyTorch transformation not implemented for uint16, so converting it to double first\n sem_seg_gt = utils.read_image(dataset_dict.pop(\"sem_seg_file_name\")).astype(\"double\")\n else:\n sem_seg_gt = None\n\n if sem_seg_gt is None:\n raise ValueError(\n \"Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.\".format(\n dataset_dict[\"file_name\"]\n )\n )\n\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\n image = aug_input.image\n sem_seg_gt = aug_input.sem_seg\n\n # Pad image and segmentation label here!\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\n if sem_seg_gt is not None:\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\"long\"))\n\n if self.size_divisibility > 0:\n image_size = (image.shape[-2], image.shape[-1])\n padding_size = [\n 0,\n self.size_divisibility - image_size[1],\n 0,\n self.size_divisibility - image_size[0],\n ]\n image = F.pad(image, padding_size, value=128).contiguous()\n if sem_seg_gt is not None:\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\n\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\n\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\n # Therefore it's important to use torch.Tensor.\n dataset_dict[\"image\"] = image\n\n if sem_seg_gt is not None:\n dataset_dict[\"sem_seg\"] = sem_seg_gt.long()\n\n if \"annotations\" in dataset_dict:\n raise ValueError(\"Semantic segmentation dataset should not have 'annotations'.\")\n\n # Prepare per-category binary masks\n if sem_seg_gt is not None:\n sem_seg_gt = sem_seg_gt.numpy()\n instances = Instances(image_shape)\n classes = np.unique(sem_seg_gt)\n # remove ignored region\n classes = classes[classes != self.ignore_label]\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\n\n masks = []\n for class_id in classes:\n masks.append(sem_seg_gt == class_id)\n\n if len(masks) == 0:\n # Some image does not have annotation (all ignored)\n instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))\n else:\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\n )\n instances.gt_masks = masks.tensor\n\n dataset_dict[\"instances\"] = instances\n\n return dataset_dict"},{"identifier":"SemanticSegmentorWithTTA","path":"masqclip/test_time_augmentation.py","snippet":"class SemanticSegmentorWithTTA(nn.Module):\n \"\"\"\n A SemanticSegmentor with test-time augmentation enabled.\n Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.\n \"\"\"\n\n def __init__(self, cfg, model, tta_mapper=None, batch_size=1):\n \"\"\"\n Args:\n cfg (CfgNode):\n model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.\n tta_mapper (callable): takes a dataset dict and returns a list of\n augmented versions of the dataset dict. Defaults to\n `DatasetMapperTTA(cfg)`.\n batch_size (int): batch the augmented images into this batch size for inference.\n \"\"\"\n super().__init__()\n if isinstance(model, DistributedDataParallel):\n model = model.module\n self.cfg = cfg.clone()\n\n self.model = model\n\n if tta_mapper is None:\n tta_mapper = DatasetMapperTTA(cfg)\n self.tta_mapper = tta_mapper\n self.batch_size = batch_size\n\n def __call__(self, batched_inputs):\n \"\"\"\n Same input/output format as :meth:`SemanticSegmentor.forward`\n \"\"\"\n\n def _maybe_read_image(dataset_dict):\n ret = copy.copy(dataset_dict)\n if \"image\" not in ret:\n image = read_image(ret.pop(\"file_name\"), self.model.input_format)\n image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW\n ret[\"image\"] = image\n if \"height\" not in ret and \"width\" not in ret:\n ret[\"height\"] = image.shape[1]\n ret[\"width\"] = image.shape[2]\n return ret\n\n processed_results = []\n for x in batched_inputs:\n result = self._inference_one_image(_maybe_read_image(x))\n processed_results.append(result)\n return processed_results\n\n def _inference_one_image(self, input):\n \"\"\"\n Args:\n input (dict): one dataset dict with \"image\" field being a CHW tensor\n Returns:\n dict: one output dict\n \"\"\"\n orig_shape = (input[\"height\"], input[\"width\"])\n augmented_inputs, tfms = self._get_augmented_inputs(input)\n\n final_predictions = None\n count_predictions = 0\n for input, tfm in zip(augmented_inputs, tfms):\n count_predictions += 1\n with torch.no_grad():\n if final_predictions is None:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions = self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions = self.model([input])[0].pop(\"sem_seg\")\n else:\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\n final_predictions += self.model([input])[0].pop(\"sem_seg\").flip(dims=[2])\n else:\n final_predictions += self.model([input])[0].pop(\"sem_seg\")\n\n final_predictions = final_predictions / count_predictions\n return {\"sem_seg\": final_predictions}\n\n def _get_augmented_inputs(self, input):\n augmented_inputs = self.tta_mapper(input)\n tfms = [x.pop(\"transforms\") for x in augmented_inputs]\n return augmented_inputs, tfms"},{"identifier":"InstanceSegEvaluator","path":"masqclip/evaluation/instance_evaluation.py","snippet":"class InstanceSegEvaluator(COCOEvaluator):\n \"\"\"\n Evaluate AR for object proposals, AP for instance detection/segmentation, AP\n for keypoint detection outputs using COCO's metrics.\n See http://cocodataset.org/#detection-eval and\n http://cocodataset.org/#keypoints-eval to understand its metrics.\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\n the metric cannot be computed (e.g. due to no predictions made).\n\n In addition to COCO, this evaluator is able to support any bounding box detection,\n instance segmentation, or keypoint detection dataset.\n \"\"\"\n\n def _eval_predictions(self, predictions, img_ids=None):\n \"\"\"\n Evaluate predictions. Fill self._results with the metrics of the tasks.\n \"\"\"\n self._logger.info(\"Preparing results for COCO format ...\")\n coco_results = list(itertools.chain(*[x[\"instances\"] for x in predictions]))\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\n\n # unmap the category ids for COCO\n if hasattr(self._metadata, \"thing_dataset_id_to_contiguous_id\"):\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\n # all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\n # num_classes = len(all_contiguous_ids)\n # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\n\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\n for result in coco_results:\n category_id = result[\"category_id\"]\n # assert category_id < num_classes, (\n # f\"A prediction has class={category_id}, \"\n # f\"but the dataset only has {num_classes} classes and \"\n # f\"predicted class id should be in [0, {num_classes - 1}].\"\n # )\n assert category_id in reverse_id_mapping, (\n f\"A prediction has class={category_id}, \"\n f\"but the dataset only has class ids in {dataset_id_to_contiguous_id}.\"\n )\n result[\"category_id\"] = reverse_id_mapping[category_id]\n\n if self._output_dir:\n file_path = os.path.join(self._output_dir, \"coco_instances_results.json\")\n self._logger.info(\"Saving results to {}\".format(file_path))\n with PathManager.open(file_path, \"w\") as f:\n f.write(json.dumps(coco_results))\n f.flush()\n\n if not self._do_evaluation:\n self._logger.info(\"Annotations are not available for evaluation.\")\n return\n\n self._logger.info(\n \"Evaluating predictions with {} COCO API...\".format(\n \"unofficial\" if self._use_fast_impl else \"official\"\n )\n )\n for task in sorted(tasks):\n assert task in {\"bbox\", \"segm\", \"keypoints\"}, f\"Got unknown task: {task}!\"\n coco_eval = (\n _evaluate_predictions_on_coco(\n self._coco_api,\n coco_results,\n task,\n kpt_oks_sigmas=self._kpt_oks_sigmas,\n use_fast_impl=self._use_fast_impl,\n img_ids=img_ids,\n max_dets_per_image=self._max_dets_per_image,\n )\n if len(coco_results) > 0\n else None # cocoapi does not handle empty results very well\n )\n\n res = self._derive_coco_results(\n coco_eval, task, class_names=self._metadata.get(\"thing_classes\")\n )\n self._results[task] = res"}],"string":"[\n {\n \"identifier\": \"add_maskformer2_config\",\n \"path\": \"masqclip/config.py\",\n \"snippet\": \"def add_maskformer2_config(cfg):\\n \\\"\\\"\\\"\\n Add config for MASK_FORMER.\\n \\\"\\\"\\\"\\n # NOTE: configs from original maskformer\\n # data config\\n # select the dataset mapper\\n cfg.INPUT.DATASET_MAPPER_NAME = \\\"mask_former_semantic\\\"\\n # Color augmentation\\n cfg.INPUT.COLOR_AUG_SSD = False\\n # We retry random cropping until no single category in semantic segmentation GT occupies more\\n # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.\\n cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0\\n # Pad image and segmentation GT in dataset mapper.\\n cfg.INPUT.SIZE_DIVISIBILITY = -1\\n\\n # solver config\\n # weight decay on embedding\\n cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0\\n # optimizer\\n cfg.SOLVER.OPTIMIZER = \\\"ADAMW\\\"\\n cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1\\n\\n # mask_former model config\\n cfg.MODEL.MASK_FORMER = CN()\\n\\n # loss\\n cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True\\n cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = 0.1\\n cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = 1.0\\n cfg.MODEL.MASK_FORMER.DICE_WEIGHT = 1.0\\n cfg.MODEL.MASK_FORMER.MASK_WEIGHT = 20.0\\n\\n # transformer config\\n cfg.MODEL.MASK_FORMER.NHEADS = 8\\n cfg.MODEL.MASK_FORMER.DROPOUT = 0.1\\n cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048\\n cfg.MODEL.MASK_FORMER.ENC_LAYERS = 0\\n cfg.MODEL.MASK_FORMER.DEC_LAYERS = 6\\n cfg.MODEL.MASK_FORMER.PRE_NORM = False\\n\\n cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256\\n cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 100\\n\\n cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE = \\\"res5\\\"\\n cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ = False\\n\\n # mask_former inference config\\n cfg.MODEL.MASK_FORMER.TEST = CN()\\n cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True\\n cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = False\\n cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = False\\n cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD = 0.0\\n cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD = 0.0\\n cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False\\n\\n # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)\\n # you can use this config to override\\n cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32\\n\\n # pixel decoder config\\n cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256\\n # adding transformer in pixel decoder\\n cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0\\n # pixel decoder\\n cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = \\\"BasePixelDecoder\\\"\\n\\n # swin transformer backbone\\n cfg.MODEL.SWIN = CN()\\n cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224\\n cfg.MODEL.SWIN.PATCH_SIZE = 4\\n cfg.MODEL.SWIN.EMBED_DIM = 96\\n cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]\\n cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]\\n cfg.MODEL.SWIN.WINDOW_SIZE = 7\\n cfg.MODEL.SWIN.MLP_RATIO = 4.0\\n cfg.MODEL.SWIN.QKV_BIAS = True\\n cfg.MODEL.SWIN.QK_SCALE = None\\n cfg.MODEL.SWIN.DROP_RATE = 0.0\\n cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0\\n cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3\\n cfg.MODEL.SWIN.APE = False\\n cfg.MODEL.SWIN.PATCH_NORM = True\\n cfg.MODEL.SWIN.OUT_FEATURES = [\\\"res2\\\", \\\"res3\\\", \\\"res4\\\", \\\"res5\\\"]\\n cfg.MODEL.SWIN.USE_CHECKPOINT = False\\n\\n # NOTE: maskformer2 extra configs\\n # transformer module\\n cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME = \\\"MultiScaleMaskedTransformerDecoder\\\"\\n\\n # LSJ aug\\n cfg.INPUT.IMAGE_SIZE = 1024\\n cfg.INPUT.MIN_SCALE = 0.1\\n cfg.INPUT.MAX_SCALE = 2.0\\n\\n # MSDeformAttn encoder configs\\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = [\\\"res3\\\", \\\"res4\\\", \\\"res5\\\"]\\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4\\n cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8\\n\\n # point loss configs\\n # Number of points sampled during training for a mask point head.\\n cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = 112 * 112\\n # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the\\n # original paper.\\n cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0\\n # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in\\n # the original paper.\\n cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75\"\n },\n {\n \"identifier\": \"add_masqclip_config\",\n \"path\": \"masqclip/config.py\",\n \"snippet\": \"def add_masqclip_config(cfg):\\n \\\"\\\"\\\"\\n Add config for MasQCLIP.\\n \\\"\\\"\\\"\\n cfg.MODEL.MASQ_CLIP = CN()\\n cfg.MODEL.MASQ_CLIP.MODEL_NAME = [\\\"ViT-L/14@336px\\\"]\\n \\n cfg.MODEL.MASQ_CLIP.SCORE_THRESHOLD = 0.8\\n cfg.MODEL.MASQ_CLIP.NMS_THRESHOLD = 0.1\"\n },\n {\n \"identifier\": \"COCOInstanceNewBaselineDatasetMapper\",\n \"path\": \"masqclip/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py\",\n \"snippet\": \"class COCOInstanceNewBaselineDatasetMapper:\\n \\\"\\\"\\\"\\n A callable which takes a dataset dict in Detectron2 Dataset format,\\n and map it into a format used by MaskFormer.\\n\\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\\n\\n The callable currently does the following:\\n\\n 1. Read the image from \\\"file_name\\\"\\n 2. Applies geometric transforms to the image and annotation\\n 3. Find and applies suitable cropping to the image and annotation\\n 4. Prepare image and annotation to Tensors\\n \\\"\\\"\\\"\\n\\n @configurable\\n def __init__(\\n self,\\n is_train=True,\\n *,\\n tfm_gens,\\n image_format,\\n ):\\n \\\"\\\"\\\"\\n NOTE: this interface is experimental.\\n Args:\\n is_train: for training or inference\\n augmentations: a list of augmentations or deterministic transforms to apply\\n tfm_gens: data augmentation\\n image_format: an image format supported by :func:`detection_utils.read_image`.\\n \\\"\\\"\\\"\\n self.tfm_gens = tfm_gens\\n logging.getLogger(__name__).info(\\n \\\"[COCOInstanceNewBaselineDatasetMapper] Full TransformGens used in training: {}\\\".format(str(self.tfm_gens))\\n )\\n\\n self.img_format = image_format\\n self.is_train = is_train\\n \\n @classmethod\\n def from_config(cls, cfg, is_train=True):\\n # Build augmentation\\n tfm_gens = build_transform_gen(cfg, is_train)\\n\\n ret = {\\n \\\"is_train\\\": is_train,\\n \\\"tfm_gens\\\": tfm_gens,\\n \\\"image_format\\\": cfg.INPUT.FORMAT,\\n }\\n return ret\\n\\n def __call__(self, dataset_dict):\\n \\\"\\\"\\\"\\n Args:\\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\\n\\n Returns:\\n dict: a format that builtin models in detectron2 accept\\n \\\"\\\"\\\"\\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\\n image = utils.read_image(dataset_dict[\\\"file_name\\\"], format=self.img_format)\\n utils.check_image_size(dataset_dict, image)\\n\\n # TODO: get padding mask\\n # by feeding a \\\"segmentation mask\\\" to the same transforms\\n padding_mask = np.ones(image.shape[:2])\\n\\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\\n # the crop transformation has default padding value 0 for segmentation\\n padding_mask = transforms.apply_segmentation(padding_mask)\\n padding_mask = ~ padding_mask.astype(bool)\\n\\n image_shape = image.shape[:2] # h, w\\n\\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\\n # Therefore it's important to use torch.Tensor.\\n dataset_dict[\\\"image\\\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\\n dataset_dict[\\\"padding_mask\\\"] = torch.as_tensor(np.ascontiguousarray(padding_mask))\\n\\n if not self.is_train:\\n # USER: Modify this if you want to keep them for some reason.\\n dataset_dict.pop(\\\"annotations\\\", None)\\n return dataset_dict\\n\\n if \\\"annotations\\\" in dataset_dict:\\n # USER: Modify this if you want to keep them for some reason.\\n for anno in dataset_dict[\\\"annotations\\\"]:\\n # Let's always keep mask\\n # if not self.mask_on:\\n # anno.pop(\\\"segmentation\\\", None)\\n anno.pop(\\\"keypoints\\\", None)\\n\\n # USER: Implement additional transformations if you have other types of data\\n annos = [\\n utils.transform_instance_annotations(obj, transforms, image_shape)\\n for obj in dataset_dict.pop(\\\"annotations\\\")\\n if obj.get(\\\"iscrowd\\\", 0) == 0\\n ]\\n # NOTE: does not support BitMask due to augmentation\\n # Current BitMask cannot handle empty objects\\n instances = utils.annotations_to_instances(annos, image_shape)\\n # After transforms such as cropping are applied, the bounding box may no longer\\n # tightly bound the object. As an example, imagine a triangle object\\n # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight\\n # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to\\n # the intersection of original bounding box and the cropping box.\\n instances.gt_boxes = instances.gt_masks.get_bounding_boxes()\\n # Need to filter empty instances first (due to augmentation)\\n instances = utils.filter_empty_instances(instances)\\n # Generate masks from polygon\\n h, w = instances.image_size\\n # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)\\n if hasattr(instances, 'gt_masks'):\\n gt_masks = instances.gt_masks\\n gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)\\n instances.gt_masks = gt_masks\\n dataset_dict[\\\"instances\\\"] = instances\\n\\n return dataset_dict\"\n },\n {\n \"identifier\": \"COCOPanopticNewBaselineDatasetMapper\",\n \"path\": \"masqclip/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py\",\n \"snippet\": \"class COCOPanopticNewBaselineDatasetMapper:\\n \\\"\\\"\\\"\\n A callable which takes a dataset dict in Detectron2 Dataset format,\\n and map it into a format used by MaskFormer.\\n\\n This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.\\n\\n The callable currently does the following:\\n\\n 1. Read the image from \\\"file_name\\\"\\n 2. Applies geometric transforms to the image and annotation\\n 3. Find and applies suitable cropping to the image and annotation\\n 4. Prepare image and annotation to Tensors\\n \\\"\\\"\\\"\\n\\n @configurable\\n def __init__(\\n self,\\n is_train=True,\\n *,\\n tfm_gens,\\n image_format,\\n ):\\n \\\"\\\"\\\"\\n NOTE: this interface is experimental.\\n Args:\\n is_train: for training or inference\\n augmentations: a list of augmentations or deterministic transforms to apply\\n crop_gen: crop augmentation\\n tfm_gens: data augmentation\\n image_format: an image format supported by :func:`detection_utils.read_image`.\\n \\\"\\\"\\\"\\n self.tfm_gens = tfm_gens\\n logging.getLogger(__name__).info(\\n \\\"[COCOPanopticNewBaselineDatasetMapper] Full TransformGens used in training: {}\\\".format(\\n str(self.tfm_gens)\\n )\\n )\\n\\n self.img_format = image_format\\n self.is_train = is_train\\n\\n @classmethod\\n def from_config(cls, cfg, is_train=True):\\n # Build augmentation\\n tfm_gens = build_transform_gen(cfg, is_train)\\n\\n ret = {\\n \\\"is_train\\\": is_train,\\n \\\"tfm_gens\\\": tfm_gens,\\n \\\"image_format\\\": cfg.INPUT.FORMAT,\\n }\\n return ret\\n\\n def __call__(self, dataset_dict):\\n \\\"\\\"\\\"\\n Args:\\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\\n\\n Returns:\\n dict: a format that builtin models in detectron2 accept\\n \\\"\\\"\\\"\\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\\n image = utils.read_image(dataset_dict[\\\"file_name\\\"], format=self.img_format)\\n utils.check_image_size(dataset_dict, image)\\n\\n image, transforms = T.apply_transform_gens(self.tfm_gens, image)\\n image_shape = image.shape[:2] # h, w\\n\\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\\n # Therefore it's important to use torch.Tensor.\\n dataset_dict[\\\"image\\\"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\\n\\n if not self.is_train:\\n # USER: Modify this if you want to keep them for some reason.\\n dataset_dict.pop(\\\"annotations\\\", None)\\n return dataset_dict\\n\\n if \\\"pan_seg_file_name\\\" in dataset_dict:\\n pan_seg_gt = utils.read_image(dataset_dict.pop(\\\"pan_seg_file_name\\\"), \\\"RGB\\\")\\n segments_info = dataset_dict[\\\"segments_info\\\"]\\n\\n # apply the same transformation to panoptic segmentation\\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\\n\\n from panopticapi.utils import rgb2id\\n\\n pan_seg_gt = rgb2id(pan_seg_gt)\\n\\n instances = Instances(image_shape)\\n classes = []\\n masks = []\\n for segment_info in segments_info:\\n class_id = segment_info[\\\"category_id\\\"]\\n if not segment_info[\\\"iscrowd\\\"]:\\n classes.append(class_id)\\n masks.append(pan_seg_gt == segment_info[\\\"id\\\"])\\n\\n classes = np.array(classes)\\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\\n if len(masks) == 0:\\n # Some image does not have annotation (all ignored)\\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\\n instances.gt_boxes = Boxes(torch.zeros((0, 4)))\\n else:\\n masks = BitMasks(\\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\\n )\\n instances.gt_masks = masks.tensor\\n instances.gt_boxes = masks.get_bounding_boxes()\\n\\n dataset_dict[\\\"instances\\\"] = instances\\n\\n return dataset_dict\"\n },\n {\n \"identifier\": \"MaskFormerInstanceDatasetMapper\",\n \"path\": \"masqclip/data/dataset_mappers/mask_former_instance_dataset_mapper.py\",\n \"snippet\": \"class MaskFormerInstanceDatasetMapper:\\n \\\"\\\"\\\"\\n A callable which takes a dataset dict in Detectron2 Dataset format,\\n and map it into a format used by MaskFormer for instance segmentation.\\n\\n The callable currently does the following:\\n\\n 1. Read the image from \\\"file_name\\\"\\n 2. Applies geometric transforms to the image and annotation\\n 3. Find and applies suitable cropping to the image and annotation\\n 4. Prepare image and annotation to Tensors\\n \\\"\\\"\\\"\\n\\n @configurable\\n def __init__(\\n self,\\n is_train=True,\\n *,\\n augmentations,\\n image_format,\\n size_divisibility,\\n ):\\n \\\"\\\"\\\"\\n NOTE: this interface is experimental.\\n Args:\\n is_train: for training or inference\\n augmentations: a list of augmentations or deterministic transforms to apply\\n image_format: an image format supported by :func:`detection_utils.read_image`.\\n size_divisibility: pad image size to be divisible by this value\\n \\\"\\\"\\\"\\n self.is_train = is_train\\n self.tfm_gens = augmentations\\n self.img_format = image_format\\n self.size_divisibility = size_divisibility\\n\\n logger = logging.getLogger(__name__)\\n mode = \\\"training\\\" if is_train else \\\"inference\\\"\\n logger.info(f\\\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\\\")\\n\\n @classmethod\\n def from_config(cls, cfg, is_train=True):\\n # change_code_note\\n\\n # # Build augmentation\\n # augs = [\\n # T.ResizeShortestEdge(\\n # cfg.INPUT.MIN_SIZE_TRAIN,\\n # cfg.INPUT.MAX_SIZE_TRAIN,\\n # cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,\\n # )\\n # ]\\n # if cfg.INPUT.CROP.ENABLED:\\n # augs.append(\\n # T.RandomCrop(\\n # cfg.INPUT.CROP.TYPE,\\n # cfg.INPUT.CROP.SIZE,\\n # )\\n # )\\n # if cfg.INPUT.COLOR_AUG_SSD:\\n # augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))\\n # augs.append(T.RandomFlip())\\n\\n augs = [\\n T.Resize((1024, 1024))\\n ]\\n\\n ret = {\\n \\\"is_train\\\": is_train,\\n \\\"augmentations\\\": augs,\\n \\\"image_format\\\": cfg.INPUT.FORMAT,\\n \\\"size_divisibility\\\": cfg.INPUT.SIZE_DIVISIBILITY,\\n }\\n return ret\\n\\n def __call__(self, dataset_dict):\\n \\\"\\\"\\\"\\n Args:\\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\\n\\n Returns:\\n dict: a format that builtin models in detectron2 accept\\n \\\"\\\"\\\"\\n assert self.is_train, \\\"MaskFormerPanopticDatasetMapper should only be used for training!\\\"\\n\\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\\n image = utils.read_image(dataset_dict[\\\"file_name\\\"], format=self.img_format)\\n utils.check_image_size(dataset_dict, image)\\n\\n aug_input = T.AugInput(image)\\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\\n image = aug_input.image\\n\\n # transform instnace masks\\n assert \\\"annotations\\\" in dataset_dict\\n for anno in dataset_dict[\\\"annotations\\\"]:\\n anno.pop(\\\"keypoints\\\", None)\\n\\n annos = [\\n utils.transform_instance_annotations(obj, transforms, image.shape[:2])\\n for obj in dataset_dict.pop(\\\"annotations\\\")\\n if obj.get(\\\"iscrowd\\\", 0) == 0\\n ]\\n\\n if len(annos):\\n assert \\\"segmentation\\\" in annos[0]\\n segms = [obj[\\\"segmentation\\\"] for obj in annos]\\n masks = []\\n for segm in segms:\\n if isinstance(segm, list):\\n # polygon\\n masks.append(polygons_to_bitmask(segm, *image.shape[:2]))\\n elif isinstance(segm, dict):\\n # COCO RLE\\n masks.append(mask_util.decode(segm))\\n elif isinstance(segm, np.ndarray):\\n assert segm.ndim == 2, \\\"Expect segmentation of 2 dimensions, got {}.\\\".format(\\n segm.ndim\\n )\\n # mask array\\n masks.append(segm)\\n else:\\n raise ValueError(\\n \\\"Cannot convert segmentation of type '{}' to BitMasks!\\\"\\n \\\"Supported types are: polygons as list[list[float] or ndarray],\\\"\\n \\\" COCO-style RLE as a dict, or a binary segmentation mask \\\"\\n \\\" in a 2D numpy array of shape HxW.\\\".format(type(segm))\\n )\\n\\n # Pad image and segmentation label here!\\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\\n masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks]\\n\\n classes = [int(obj[\\\"category_id\\\"]) for obj in annos]\\n classes = torch.tensor(classes, dtype=torch.int64)\\n\\n if self.size_divisibility > 0:\\n image_size = (image.shape[-2], image.shape[-1])\\n padding_size = [\\n 0,\\n self.size_divisibility - image_size[1],\\n 0,\\n self.size_divisibility - image_size[0],\\n ]\\n # pad image\\n image = F.pad(image, padding_size, value=128).contiguous()\\n # pad mask\\n masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks]\\n\\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\\n\\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\\n # Therefore it's important to use torch.Tensor.\\n dataset_dict[\\\"image\\\"] = image\\n\\n # Prepare per-category binary masks\\n instances = Instances(image_shape)\\n instances.gt_classes = classes\\n if len(masks) == 0:\\n # Some image does not have annotation (all ignored)\\n instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1]))\\n else:\\n masks = BitMasks(torch.stack(masks))\\n instances.gt_masks = masks.tensor\\n\\n dataset_dict[\\\"instances\\\"] = instances\\n\\n return dataset_dict\"\n },\n {\n \"identifier\": \"MaskFormerPanopticDatasetMapper\",\n \"path\": \"masqclip/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py\",\n \"snippet\": \"class MaskFormerPanopticDatasetMapper(MaskFormerSemanticDatasetMapper):\\n \\\"\\\"\\\"\\n A callable which takes a dataset dict in Detectron2 Dataset format,\\n and map it into a format used by MaskFormer for panoptic segmentation.\\n\\n The callable currently does the following:\\n\\n 1. Read the image from \\\"file_name\\\"\\n 2. Applies geometric transforms to the image and annotation\\n 3. Find and applies suitable cropping to the image and annotation\\n 4. Prepare image and annotation to Tensors\\n \\\"\\\"\\\"\\n\\n @configurable\\n def __init__(\\n self,\\n is_train=True,\\n *,\\n augmentations,\\n image_format,\\n ignore_label,\\n size_divisibility,\\n ):\\n \\\"\\\"\\\"\\n NOTE: this interface is experimental.\\n Args:\\n is_train: for training or inference\\n augmentations: a list of augmentations or deterministic transforms to apply\\n image_format: an image format supported by :func:`detection_utils.read_image`.\\n ignore_label: the label that is ignored to evaluation\\n size_divisibility: pad image size to be divisible by this value\\n \\\"\\\"\\\"\\n super().__init__(\\n is_train,\\n augmentations=augmentations,\\n image_format=image_format,\\n ignore_label=ignore_label,\\n size_divisibility=size_divisibility,\\n )\\n\\n def __call__(self, dataset_dict):\\n \\\"\\\"\\\"\\n Args:\\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\\n\\n Returns:\\n dict: a format that builtin models in detectron2 accept\\n \\\"\\\"\\\"\\n assert self.is_train, \\\"MaskFormerPanopticDatasetMapper should only be used for training!\\\"\\n\\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\\n image = utils.read_image(dataset_dict[\\\"file_name\\\"], format=self.img_format)\\n utils.check_image_size(dataset_dict, image)\\n\\n # semantic segmentation\\n if \\\"sem_seg_file_name\\\" in dataset_dict:\\n # PyTorch transformation not implemented for uint16, so converting it to double first\\n sem_seg_gt = utils.read_image(dataset_dict.pop(\\\"sem_seg_file_name\\\")).astype(\\\"double\\\")\\n else:\\n sem_seg_gt = None\\n\\n # panoptic segmentation\\n if \\\"pan_seg_file_name\\\" in dataset_dict:\\n pan_seg_gt = utils.read_image(dataset_dict.pop(\\\"pan_seg_file_name\\\"), \\\"RGB\\\")\\n segments_info = dataset_dict[\\\"segments_info\\\"]\\n else:\\n pan_seg_gt = None\\n segments_info = None\\n\\n if pan_seg_gt is None:\\n raise ValueError(\\n \\\"Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.\\\".format(\\n dataset_dict[\\\"file_name\\\"]\\n )\\n )\\n\\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\\n image = aug_input.image\\n if sem_seg_gt is not None:\\n sem_seg_gt = aug_input.sem_seg\\n\\n # apply the same transformation to panoptic segmentation\\n pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)\\n\\n from panopticapi.utils import rgb2id\\n\\n pan_seg_gt = rgb2id(pan_seg_gt)\\n\\n # Pad image and segmentation label here!\\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\\n if sem_seg_gt is not None:\\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\\\"long\\\"))\\n pan_seg_gt = torch.as_tensor(pan_seg_gt.astype(\\\"long\\\"))\\n\\n if self.size_divisibility > 0:\\n image_size = (image.shape[-2], image.shape[-1])\\n padding_size = [\\n 0,\\n self.size_divisibility - image_size[1],\\n 0,\\n self.size_divisibility - image_size[0],\\n ]\\n image = F.pad(image, padding_size, value=128).contiguous()\\n if sem_seg_gt is not None:\\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\\n pan_seg_gt = F.pad(\\n pan_seg_gt, padding_size, value=0\\n ).contiguous() # 0 is the VOID panoptic label\\n\\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\\n\\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\\n # Therefore it's important to use torch.Tensor.\\n dataset_dict[\\\"image\\\"] = image\\n if sem_seg_gt is not None:\\n dataset_dict[\\\"sem_seg\\\"] = sem_seg_gt.long()\\n\\n if \\\"annotations\\\" in dataset_dict:\\n raise ValueError(\\\"Pemantic segmentation dataset should not have 'annotations'.\\\")\\n\\n # Prepare per-category binary masks\\n pan_seg_gt = pan_seg_gt.numpy()\\n instances = Instances(image_shape)\\n classes = []\\n masks = []\\n for segment_info in segments_info:\\n class_id = segment_info[\\\"category_id\\\"]\\n if not segment_info[\\\"iscrowd\\\"]:\\n classes.append(class_id)\\n masks.append(pan_seg_gt == segment_info[\\\"id\\\"])\\n\\n classes = np.array(classes)\\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\\n if len(masks) == 0:\\n # Some image does not have annotation (all ignored)\\n instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))\\n else:\\n masks = BitMasks(\\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\\n )\\n instances.gt_masks = masks.tensor\\n\\n dataset_dict[\\\"instances\\\"] = instances\\n\\n return dataset_dict\"\n },\n {\n \"identifier\": \"MaskFormerSemanticDatasetMapper\",\n \"path\": \"masqclip/data/dataset_mappers/mask_former_semantic_dataset_mapper.py\",\n \"snippet\": \"class MaskFormerSemanticDatasetMapper:\\n \\\"\\\"\\\"\\n A callable which takes a dataset dict in Detectron2 Dataset format,\\n and map it into a format used by MaskFormer for semantic segmentation.\\n\\n The callable currently does the following:\\n\\n 1. Read the image from \\\"file_name\\\"\\n 2. Applies geometric transforms to the image and annotation\\n 3. Find and applies suitable cropping to the image and annotation\\n 4. Prepare image and annotation to Tensors\\n \\\"\\\"\\\"\\n\\n @configurable\\n def __init__(\\n self,\\n is_train=True,\\n *,\\n augmentations,\\n image_format,\\n ignore_label,\\n size_divisibility,\\n ):\\n \\\"\\\"\\\"\\n NOTE: this interface is experimental.\\n Args:\\n is_train: for training or inference\\n augmentations: a list of augmentations or deterministic transforms to apply\\n image_format: an image format supported by :func:`detection_utils.read_image`.\\n ignore_label: the label that is ignored to evaluation\\n size_divisibility: pad image size to be divisible by this value\\n \\\"\\\"\\\"\\n self.is_train = is_train\\n self.tfm_gens = augmentations\\n self.img_format = image_format\\n self.ignore_label = ignore_label\\n self.size_divisibility = size_divisibility\\n\\n logger = logging.getLogger(__name__)\\n mode = \\\"training\\\" if is_train else \\\"inference\\\"\\n logger.info(f\\\"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}\\\")\\n\\n @classmethod\\n def from_config(cls, cfg, is_train=True):\\n\\n augs = [\\n T.Resize((1024, 1024))\\n ]\\n\\n # Assume always applies to the training set.\\n dataset_names = cfg.DATASETS.TRAIN\\n meta = MetadataCatalog.get(dataset_names[0])\\n ignore_label = meta.ignore_label\\n\\n ret = {\\n \\\"is_train\\\": is_train,\\n \\\"augmentations\\\": augs,\\n \\\"image_format\\\": cfg.INPUT.FORMAT,\\n \\\"ignore_label\\\": ignore_label,\\n \\\"size_divisibility\\\": cfg.INPUT.SIZE_DIVISIBILITY,\\n }\\n return ret\\n\\n def __call__(self, dataset_dict):\\n \\\"\\\"\\\"\\n Args:\\n dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.\\n\\n Returns:\\n dict: a format that builtin models in detectron2 accept\\n \\\"\\\"\\\"\\n assert self.is_train, \\\"MaskFormerSemanticDatasetMapper should only be used for training!\\\"\\n\\n dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below\\n image = utils.read_image(dataset_dict[\\\"file_name\\\"], format=self.img_format)\\n utils.check_image_size(dataset_dict, image)\\n\\n if \\\"sem_seg_file_name\\\" in dataset_dict:\\n # PyTorch transformation not implemented for uint16, so converting it to double first\\n sem_seg_gt = utils.read_image(dataset_dict.pop(\\\"sem_seg_file_name\\\")).astype(\\\"double\\\")\\n else:\\n sem_seg_gt = None\\n\\n if sem_seg_gt is None:\\n raise ValueError(\\n \\\"Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.\\\".format(\\n dataset_dict[\\\"file_name\\\"]\\n )\\n )\\n\\n aug_input = T.AugInput(image, sem_seg=sem_seg_gt)\\n aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)\\n image = aug_input.image\\n sem_seg_gt = aug_input.sem_seg\\n\\n # Pad image and segmentation label here!\\n image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))\\n if sem_seg_gt is not None:\\n sem_seg_gt = torch.as_tensor(sem_seg_gt.astype(\\\"long\\\"))\\n\\n if self.size_divisibility > 0:\\n image_size = (image.shape[-2], image.shape[-1])\\n padding_size = [\\n 0,\\n self.size_divisibility - image_size[1],\\n 0,\\n self.size_divisibility - image_size[0],\\n ]\\n image = F.pad(image, padding_size, value=128).contiguous()\\n if sem_seg_gt is not None:\\n sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()\\n\\n image_shape = (image.shape[-2], image.shape[-1]) # h, w\\n\\n # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,\\n # but not efficient on large generic data structures due to the use of pickle & mp.Queue.\\n # Therefore it's important to use torch.Tensor.\\n dataset_dict[\\\"image\\\"] = image\\n\\n if sem_seg_gt is not None:\\n dataset_dict[\\\"sem_seg\\\"] = sem_seg_gt.long()\\n\\n if \\\"annotations\\\" in dataset_dict:\\n raise ValueError(\\\"Semantic segmentation dataset should not have 'annotations'.\\\")\\n\\n # Prepare per-category binary masks\\n if sem_seg_gt is not None:\\n sem_seg_gt = sem_seg_gt.numpy()\\n instances = Instances(image_shape)\\n classes = np.unique(sem_seg_gt)\\n # remove ignored region\\n classes = classes[classes != self.ignore_label]\\n instances.gt_classes = torch.tensor(classes, dtype=torch.int64)\\n\\n masks = []\\n for class_id in classes:\\n masks.append(sem_seg_gt == class_id)\\n\\n if len(masks) == 0:\\n # Some image does not have annotation (all ignored)\\n instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))\\n else:\\n masks = BitMasks(\\n torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])\\n )\\n instances.gt_masks = masks.tensor\\n\\n dataset_dict[\\\"instances\\\"] = instances\\n\\n return dataset_dict\"\n },\n {\n \"identifier\": \"SemanticSegmentorWithTTA\",\n \"path\": \"masqclip/test_time_augmentation.py\",\n \"snippet\": \"class SemanticSegmentorWithTTA(nn.Module):\\n \\\"\\\"\\\"\\n A SemanticSegmentor with test-time augmentation enabled.\\n Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.\\n \\\"\\\"\\\"\\n\\n def __init__(self, cfg, model, tta_mapper=None, batch_size=1):\\n \\\"\\\"\\\"\\n Args:\\n cfg (CfgNode):\\n model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.\\n tta_mapper (callable): takes a dataset dict and returns a list of\\n augmented versions of the dataset dict. Defaults to\\n `DatasetMapperTTA(cfg)`.\\n batch_size (int): batch the augmented images into this batch size for inference.\\n \\\"\\\"\\\"\\n super().__init__()\\n if isinstance(model, DistributedDataParallel):\\n model = model.module\\n self.cfg = cfg.clone()\\n\\n self.model = model\\n\\n if tta_mapper is None:\\n tta_mapper = DatasetMapperTTA(cfg)\\n self.tta_mapper = tta_mapper\\n self.batch_size = batch_size\\n\\n def __call__(self, batched_inputs):\\n \\\"\\\"\\\"\\n Same input/output format as :meth:`SemanticSegmentor.forward`\\n \\\"\\\"\\\"\\n\\n def _maybe_read_image(dataset_dict):\\n ret = copy.copy(dataset_dict)\\n if \\\"image\\\" not in ret:\\n image = read_image(ret.pop(\\\"file_name\\\"), self.model.input_format)\\n image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW\\n ret[\\\"image\\\"] = image\\n if \\\"height\\\" not in ret and \\\"width\\\" not in ret:\\n ret[\\\"height\\\"] = image.shape[1]\\n ret[\\\"width\\\"] = image.shape[2]\\n return ret\\n\\n processed_results = []\\n for x in batched_inputs:\\n result = self._inference_one_image(_maybe_read_image(x))\\n processed_results.append(result)\\n return processed_results\\n\\n def _inference_one_image(self, input):\\n \\\"\\\"\\\"\\n Args:\\n input (dict): one dataset dict with \\\"image\\\" field being a CHW tensor\\n Returns:\\n dict: one output dict\\n \\\"\\\"\\\"\\n orig_shape = (input[\\\"height\\\"], input[\\\"width\\\"])\\n augmented_inputs, tfms = self._get_augmented_inputs(input)\\n\\n final_predictions = None\\n count_predictions = 0\\n for input, tfm in zip(augmented_inputs, tfms):\\n count_predictions += 1\\n with torch.no_grad():\\n if final_predictions is None:\\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\\n final_predictions = self.model([input])[0].pop(\\\"sem_seg\\\").flip(dims=[2])\\n else:\\n final_predictions = self.model([input])[0].pop(\\\"sem_seg\\\")\\n else:\\n if any(isinstance(t, HFlipTransform) for t in tfm.transforms):\\n final_predictions += self.model([input])[0].pop(\\\"sem_seg\\\").flip(dims=[2])\\n else:\\n final_predictions += self.model([input])[0].pop(\\\"sem_seg\\\")\\n\\n final_predictions = final_predictions / count_predictions\\n return {\\\"sem_seg\\\": final_predictions}\\n\\n def _get_augmented_inputs(self, input):\\n augmented_inputs = self.tta_mapper(input)\\n tfms = [x.pop(\\\"transforms\\\") for x in augmented_inputs]\\n return augmented_inputs, tfms\"\n },\n {\n \"identifier\": \"InstanceSegEvaluator\",\n \"path\": \"masqclip/evaluation/instance_evaluation.py\",\n \"snippet\": \"class InstanceSegEvaluator(COCOEvaluator):\\n \\\"\\\"\\\"\\n Evaluate AR for object proposals, AP for instance detection/segmentation, AP\\n for keypoint detection outputs using COCO's metrics.\\n See http://cocodataset.org/#detection-eval and\\n http://cocodataset.org/#keypoints-eval to understand its metrics.\\n The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means\\n the metric cannot be computed (e.g. due to no predictions made).\\n\\n In addition to COCO, this evaluator is able to support any bounding box detection,\\n instance segmentation, or keypoint detection dataset.\\n \\\"\\\"\\\"\\n\\n def _eval_predictions(self, predictions, img_ids=None):\\n \\\"\\\"\\\"\\n Evaluate predictions. Fill self._results with the metrics of the tasks.\\n \\\"\\\"\\\"\\n self._logger.info(\\\"Preparing results for COCO format ...\\\")\\n coco_results = list(itertools.chain(*[x[\\\"instances\\\"] for x in predictions]))\\n tasks = self._tasks or self._tasks_from_predictions(coco_results)\\n\\n # unmap the category ids for COCO\\n if hasattr(self._metadata, \\\"thing_dataset_id_to_contiguous_id\\\"):\\n dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id\\n # all_contiguous_ids = list(dataset_id_to_contiguous_id.values())\\n # num_classes = len(all_contiguous_ids)\\n # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1\\n\\n reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}\\n for result in coco_results:\\n category_id = result[\\\"category_id\\\"]\\n # assert category_id < num_classes, (\\n # f\\\"A prediction has class={category_id}, \\\"\\n # f\\\"but the dataset only has {num_classes} classes and \\\"\\n # f\\\"predicted class id should be in [0, {num_classes - 1}].\\\"\\n # )\\n assert category_id in reverse_id_mapping, (\\n f\\\"A prediction has class={category_id}, \\\"\\n f\\\"but the dataset only has class ids in {dataset_id_to_contiguous_id}.\\\"\\n )\\n result[\\\"category_id\\\"] = reverse_id_mapping[category_id]\\n\\n if self._output_dir:\\n file_path = os.path.join(self._output_dir, \\\"coco_instances_results.json\\\")\\n self._logger.info(\\\"Saving results to {}\\\".format(file_path))\\n with PathManager.open(file_path, \\\"w\\\") as f:\\n f.write(json.dumps(coco_results))\\n f.flush()\\n\\n if not self._do_evaluation:\\n self._logger.info(\\\"Annotations are not available for evaluation.\\\")\\n return\\n\\n self._logger.info(\\n \\\"Evaluating predictions with {} COCO API...\\\".format(\\n \\\"unofficial\\\" if self._use_fast_impl else \\\"official\\\"\\n )\\n )\\n for task in sorted(tasks):\\n assert task in {\\\"bbox\\\", \\\"segm\\\", \\\"keypoints\\\"}, f\\\"Got unknown task: {task}!\\\"\\n coco_eval = (\\n _evaluate_predictions_on_coco(\\n self._coco_api,\\n coco_results,\\n task,\\n kpt_oks_sigmas=self._kpt_oks_sigmas,\\n use_fast_impl=self._use_fast_impl,\\n img_ids=img_ids,\\n max_dets_per_image=self._max_dets_per_image,\\n )\\n if len(coco_results) > 0\\n else None # cocoapi does not handle empty results very well\\n )\\n\\n res = self._derive_coco_results(\\n coco_eval, task, class_names=self._metadata.get(\\\"thing_classes\\\")\\n )\\n self._results[task] = res\"\n }\n]","isConversational":false},"import_statement":{"kind":"string","value":"import copy\nimport itertools\nimport logging\nimport os\nimport torch\nimport detectron2.utils.comm as comm\nimport warnings\nfrom collections import OrderedDict\nfrom typing import Any, Dict, List, Set\nfrom detectron2.checkpoint import DetectionCheckpointer\nfrom detectron2.config import get_cfg\nfrom detectron2.data import MetadataCatalog, build_detection_train_loader\nfrom detectron2.engine import (\n DefaultTrainer,\n default_argument_parser,\n default_setup,\n launch,\n)\nfrom detectron2.evaluation import (\n CityscapesInstanceEvaluator,\n CityscapesSemSegEvaluator,\n COCOEvaluator,\n COCOPanopticEvaluator,\n DatasetEvaluators,\n LVISEvaluator,\n SemSegEvaluator,\n verify_results,\n)\nfrom detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler\nfrom detectron2.solver.build import maybe_add_gradient_clipping\nfrom detectron2.utils.logger import setup_logger\nfrom masqclip import (\n COCOInstanceNewBaselineDatasetMapper,\n COCOPanopticNewBaselineDatasetMapper,\n InstanceSegEvaluator,\n MaskFormerInstanceDatasetMapper,\n MaskFormerPanopticDatasetMapper,\n MaskFormerSemanticDatasetMapper,\n SemanticSegmentorWithTTA,\n add_maskformer2_config,\n add_masqclip_config,\n)"},"token_num":{"kind":"number","value":12249,"string":"12,249"},"cropped_code":{"kind":"string","value":" @classmethod\n def build_train_loader(cls, cfg):\n # Semantic segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"mask_former_semantic\":\n mapper = MaskFormerSemanticDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # Panoptic segmentation dataset mapper\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"mask_former_panoptic\":\n mapper = MaskFormerPanopticDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # Instance segmentation dataset mapper\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"mask_former_instance\":\n mapper = MaskFormerInstanceDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco instance segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_instance_lsj\":\n mapper = COCOInstanceNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco panoptic segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_panoptic_lsj\":\n mapper = COCOPanopticNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,\n torch.nn.BatchNorm2d,\n torch.nn.BatchNorm3d,\n torch.nn.SyncBatchNorm,\n # NaiveSyncBatchNorm inherits from BatchNorm2d\n torch.nn.GroupNorm,\n torch.nn.InstanceNorm1d,\n torch.nn.InstanceNorm2d,\n torch.nn.InstanceNorm3d,\n torch.nn.LayerNorm,\n torch.nn.LocalResponseNorm,\n )\n\n params: List[Dict[str, Any]] = []\n memo: Set[torch.nn.parameter.Parameter] = set()\n for module_name, module in model.named_modules():\n for module_param_name, value in module.named_parameters(recurse=False):\n if not value.requires_grad:\n continue\n # Avoid duplicating parameters\n if value in memo:\n continue\n memo.add(value)\n\n hyperparams = copy.copy(defaults)\n if \"backbone\" in module_name:\n hyperparams[\"lr\"] = hyperparams[\"lr\"] * cfg.SOLVER.BACKBONE_MULTIPLIER\n if (\n \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n @classmethod\n def test_with_TTA(cls, cfg, model):\n logger = logging.getLogger(\"detectron2.trainer\")\n # In the end of training, run an evaluation with TTA.\n logger.info(\"Running inference with test-time augmentation ...\")\n"},"all_code":{"kind":"string","value":"# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nMasQCLIP Training Script.\n\"\"\"\n\n\n\n\n# MasQCLIP\n\nwarnings.filterwarnings(\"ignore\")\n\n\nclass Trainer(DefaultTrainer):\n \"\"\"\n Extension of the Trainer class adapted to MaskFormer.\n \"\"\"\n\n @classmethod\n def build_evaluator(cls, cfg, dataset_name, output_folder=None):\n \"\"\"\n Create evaluator(s) for a given dataset.\n This uses the special metadata \"evaluator_type\" associated with each\n builtin dataset. For your own dataset, you can simply create an\n evaluator manually in your script and do not have to worry about the\n hacky if-else logic here.\n \"\"\"\n if output_folder is None:\n output_folder = os.path.join(cfg.OUTPUT_DIR, \"inference\")\n evaluator_list = []\n evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type\n # semantic segmentation\n if evaluator_type in [\"sem_seg\", \"ade20k_panoptic_seg\"]:\n evaluator_list.append(\n SemSegEvaluator(\n dataset_name,\n distributed=True,\n output_dir=output_folder,\n )\n )\n # instance segmentation\n if evaluator_type == \"coco\":\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n # panoptic segmentation\n if evaluator_type in [\n \"coco_panoptic_seg\",\n \"ade20k_panoptic_seg\",\n \"cityscapes_panoptic_seg\",\n \"mapillary_vistas_panoptic_seg\",\n ]:\n if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:\n evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))\n # COCO\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"coco_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n # Mapillary Vistas\n if evaluator_type == \"mapillary_vistas_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))\n if evaluator_type == \"mapillary_vistas_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:\n evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))\n # Cityscapes\n if evaluator_type == \"cityscapes_instance\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesInstanceEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_sem_seg\":\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n return CityscapesSemSegEvaluator(dataset_name)\n if evaluator_type == \"cityscapes_panoptic_seg\":\n if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))\n if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n assert (\n torch.cuda.device_count() > comm.get_rank()\n ), \"CityscapesEvaluator currently do not work with multiple machines.\"\n evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))\n # ADE20K\n if evaluator_type == \"ade20k_panoptic_seg\" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:\n evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))\n # LVIS\n if evaluator_type == \"lvis\":\n return LVISEvaluator(dataset_name, output_dir=output_folder)\n if len(evaluator_list) == 0:\n raise NotImplementedError(\n \"no Evaluator for the dataset {} with the type {}\".format(\n dataset_name, evaluator_type\n )\n )\n elif len(evaluator_list) == 1:\n return evaluator_list[0]\n return DatasetEvaluators(evaluator_list)\n\n @classmethod\n def build_train_loader(cls, cfg):\n # Semantic segmentation dataset mapper\n if cfg.INPUT.DATASET_MAPPER_NAME == \"mask_former_semantic\":\n mapper = MaskFormerSemanticDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # Panoptic segmentation dataset mapper\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"mask_former_panoptic\":\n mapper = MaskFormerPanopticDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # Instance segmentation dataset mapper\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"mask_former_instance\":\n mapper = MaskFormerInstanceDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco instance segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_instance_lsj\":\n mapper = COCOInstanceNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n # coco panoptic segmentation lsj new baseline\n elif cfg.INPUT.DATASET_MAPPER_NAME == \"coco_panoptic_lsj\":\n mapper = COCOPanopticNewBaselineDatasetMapper(cfg, True)\n return build_detection_train_loader(cfg, mapper=mapper)\n else:\n mapper = None\n return build_detection_train_loader(cfg, mapper=mapper)\n\n @classmethod\n def build_lr_scheduler(cls, cfg, optimizer):\n \"\"\"\n It now calls :func:`detectron2.solver.build_lr_scheduler`.\n Overwrite it if you'd like a different scheduler.\n \"\"\"\n return build_lr_scheduler(cfg, optimizer)\n\n @classmethod\n def build_optimizer(cls, cfg, model):\n weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM\n weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED\n\n defaults = {}\n defaults[\"lr\"] = cfg.SOLVER.BASE_LR\n defaults[\"weight_decay\"] = cfg.SOLVER.WEIGHT_DECAY\n\n norm_module_types = (\n torch.nn.BatchNorm1d,\n torch.nn.BatchNorm2d,\n torch.nn.BatchNorm3d,\n torch.nn.SyncBatchNorm,\n # NaiveSyncBatchNorm inherits from BatchNorm2d\n torch.nn.GroupNorm,\n torch.nn.InstanceNorm1d,\n torch.nn.InstanceNorm2d,\n torch.nn.InstanceNorm3d,\n torch.nn.LayerNorm,\n torch.nn.LocalResponseNorm,\n )\n\n params: List[Dict[str, Any]] = []\n memo: Set[torch.nn.parameter.Parameter] = set()\n for module_name, module in model.named_modules():\n for module_param_name, value in module.named_parameters(recurse=False):\n if not value.requires_grad:\n continue\n # Avoid duplicating parameters\n if value in memo:\n continue\n memo.add(value)\n\n hyperparams = copy.copy(defaults)\n if \"backbone\" in module_name:\n hyperparams[\"lr\"] = hyperparams[\"lr\"] * cfg.SOLVER.BACKBONE_MULTIPLIER\n if (\n \"relative_position_bias_table\" in module_param_name\n or \"absolute_pos_embed\" in module_param_name\n ):\n print(module_param_name)\n hyperparams[\"weight_decay\"] = 0.0\n if isinstance(module, norm_module_types):\n hyperparams[\"weight_decay\"] = weight_decay_norm\n if isinstance(module, torch.nn.Embedding):\n hyperparams[\"weight_decay\"] = weight_decay_embed\n params.append({\"params\": [value], **hyperparams})\n\n def maybe_add_full_model_gradient_clipping(optim):\n # detectron2 doesn't have full model gradient clipping now\n clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE\n enable = (\n cfg.SOLVER.CLIP_GRADIENTS.ENABLED\n and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\"\n and clip_norm_val > 0.0\n )\n\n class FullModelGradientClippingOptimizer(optim):\n def step(self, closure=None):\n all_params = itertools.chain(*[x[\"params\"] for x in self.param_groups])\n torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)\n super().step(closure=closure)\n\n return FullModelGradientClippingOptimizer if enable else optim\n\n optimizer_type = cfg.SOLVER.OPTIMIZER\n if optimizer_type == \"SGD\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(\n params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM\n )\n elif optimizer_type == \"ADAMW\":\n optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(\n params, cfg.SOLVER.BASE_LR\n )\n else:\n raise NotImplementedError(f\"no optimizer type {optimizer_type}\")\n if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == \"full_model\":\n optimizer = maybe_add_gradient_clipping(cfg, optimizer)\n return optimizer\n\n @classmethod\n def test_with_TTA(cls, cfg, model):\n logger = logging.getLogger(\"detectron2.trainer\")\n # In the end of training, run an evaluation with TTA.\n logger.info(\"Running inference with test-time augmentation ...\")"},"next_line":{"kind":"string","value":" model = SemanticSegmentorWithTTA(cfg, model)"},"gold_snippet_index":{"kind":"number","value":7,"string":"7"},"created_at":{"kind":"string","value":"2023-10-13 02:43:53+00:00"},"level":{"kind":"string","value":"16k"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":45,"numItemsPerPage":100,"numTotalItems":8033,"offset":4500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3Mzk3ODY4NCwic3ViIjoiL2RhdGFzZXRzL3RpYW55YW5nL3JlcG9iZW5jaF9weXRob25fdjEuMSIsImV4cCI6MTc3Mzk4MjI4NCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.ukKnDsqRDxWE1L2rOLO0aFSiMSBdAV6eFGoNa55-kSiMmhW8fD4KNlvIjw7sGJimOpCgWVD3AZIpyl_UTcHNBQ","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 |
|---|---|---|---|---|---|---|---|---|---|---|
SqueezeBits/owlite | owlite/owlite.py | [
{
"identifier": "OWLITE_DEVICE_NAME",
"path": "owlite_core/cli/device.py",
"snippet": "OWLITE_DEVICE_NAME = CONNECTED_DEVICE[\"device\"] if CONNECTED_DEVICE else None"
},
{
"identifier": "OWLITE_FRONT_BASE_URL",
"path": "owlite_core/constants.py",
"snippet": "OWLITE_FRONT_BASE_URL = \"ht... | import json
import os
import torch
from dataclasses import asdict, dataclass
from typing import Any, Optional
from torch.fx import GraphModule # type: ignore
from torch.nn.parallel import DataParallel, DistributedDataParallel
from owlite_core.cli.device import OWLITE_DEVICE_NAME
from owlite_core.constants import (
OWLITE_FRONT_BASE_URL,
OWLITE_REPO_PATH,
OWLITE_REPORT_URL,
)
from owlite_core.owlite_settings import OWLITE_SETTINGS
from .api.device.devices import (
download_trt_engine,
poll_run_benchmark,
request_trt_benchmark,
)
from .api.dove.doves import get_configuration, upload_baseline
from .api.main.baselines import check_baseline_existence, create_baseline
from .api.main.projects import create_or_load_project
from .api.main.runs import (
copy_run,
create_run,
get_benchmark_key,
get_run_info,
update_run_info,
upload_run_onnx_proto,
)
from .backend.fx.trace import symbolic_trace
from .backend.onnx.dynamize import configure_dynamic_dimensions
from .backend.onnx.export import export, get_input_shape_signature
from .logger import log
from .options import GraphQuantizationOptions, ONNXExportOptions
from .quantize import quantize | 12,914 | self.project_name,
self.baseline_name,
self.experiment_name,
f"{self.project_name}_{self.baseline_name}_{self.experiment_name}.bin",
)
request_trt_benchmark(benchmark_key, bin_path)
log.info("TensorRT engine execution and benchmark successfully requested")
poll_run_benchmark(self.project_id, benchmark_key)
exp_info = get_run_info(self.project_id, self.baseline_name, self.experiment_name)
assert exp_info is not None
if self.is_baseline:
log.info(
"Latency\n"
f"\t\tBaseline - {exp_info['latency']} on {exp_info['device_name']}\n"
"\t\tConfigure the quantization settings located at "
f"{OWLITE_FRONT_BASE_URL}/project/detail/{self.project_id}"
)
else:
log.info(
"Latency\n"
f"\t\tConfigured - {exp_info['latency']} on {exp_info['device_name']}\n"
"\t\tRetrieve the specifics of the experiment at "
f"{OWLITE_FRONT_BASE_URL}/project/detail/{self.project_id}"
)
engine_path = os.path.join(
OWLITE_REPO_PATH,
self.project_name,
self.baseline_name,
self.experiment_name,
f"{self.project_name}_{self.baseline_name}_{self.experiment_name}.engine",
)
download_trt_engine(benchmark_key, engine_path)
def log(self, **kwargs) -> None:
"""Logs the model's metrics.
Notes:
Log metrics with OwLite like below
...
owl = owlite.init(...)
...
owl.log(accuracy=0.72, loss=1.2)
Raises:
TypeError: When data is not JSON serializable.
"""
try:
logs = json.dumps(kwargs)
except TypeError as e:
log.error("Data is not JSON serializable")
raise e
update_run_info(self.project_id, self.baseline_name, self.experiment_name, logs)
# pylint: disable-next=too-many-branches
def init(
project: str,
baseline: str,
experiment: Optional[str] = None,
duplicate_from: Optional[str] = None,
description: str = "",
onnx_export_options: Optional[ONNXExportOptions] = None,
) -> OwLite:
"""Sets project, baseline and experiment information in DB to proper state and creates `OwLite` instance.
Args:
project (str): OwLite project name.
baseline (str): OwLite baseline name.
experiment (str, optional): OwLite experiment name. Defaults to None.
duplicate_from (str, optional): OwLite source experiment name. Defaults to None.
description (str, optional): OwLite project description. Defaults to "".
onnx_export_options (ONNXExportOptions, optional): Options for ONNX export. Defaults to None.
Raises:
RuntimeError: When not authenticated.
ValueError: When invalid experiment name or baseline name is given.
Returns:
OwLite: Created `OwLite` instance.
"""
if OWLITE_SETTINGS.tokens is None:
log.error("Please log in using 'owlite login'. Account not found on this device")
raise RuntimeError("OwLite token not found")
if OWLITE_DEVICE_NAME is None:
log.warning("Connected device not found. Please connect device by 'owlite device connect --name (name)'")
else:
log.info(f"Connected device: {OWLITE_DEVICE_NAME}")
if experiment == baseline:
log.error(f"Experiment name '{baseline}' is reserved for baseline. Please try with a different experiment name")
raise ValueError("Invalid experiment name")
dir_path = os.path.join(
OWLITE_REPO_PATH,
project,
baseline,
experiment or baseline,
)
if os.path.exists(dir_path):
log.warning(f"Existing local directory found at {dir_path}. Continuing this code will overwrite the data")
else:
os.makedirs(dir_path, exist_ok=True)
log.info(f"Experiment data will be saved in {dir_path}")
# create or load project
project_id = create_or_load_project(project, description)
if experiment is None:
if duplicate_from:
log.warning(f"duplicate_from='{duplicate_from}' will be ignored as no value for experiment was provided")
| # type: ignore
"""OwLite Optimization Module
This module facilitates optimization and benchmarking of models using OwLite services."""
@dataclass
class OwLite:
"""Class handling OwLite project, baseline, and experiment configurations.
The OwLite class manages project, baseline, and experiment configurations within the OwLite system.
It allows users to create or load projects, set baselines, create or duplicate experiments, convert models,
and benchmark models against the specified configurations.
"""
project_id: str
project_name: str
baseline_name: str
experiment_name: str
onnx_export_options: ONNXExportOptions
module_args: Optional[tuple[Any, ...]] = None
module_kwargs: Optional[dict[str, Any]] = None
@property
def is_baseline(self) -> bool: # pylint: disable=missing-function-docstring
return self.baseline_name == self.experiment_name
def convert(self, model: torch.nn.Module, *args, **kwargs) -> GraphModule:
"""Converts input model to compressed model.
Args:
model (torch.nn.Module): Model to compress.
Returns:
GraphModule: Compressed graph module.
Raises:
HTTPError: When request for compression configuration was not successful.
"""
log.info("Model conversion initiated")
try:
model = symbolic_trace(model, *args, **kwargs)
except Exception as e: # pylint: disable=broad-exception-caught
log.error(
"Failed to extract the computation graph from the provided model. "
"Please check the error message for details.\n"
"If the issue persists, try replacing with a traceable node. "
"In case the problem remain unresolved, kindly report it at "
f"{OWLITE_REPORT_URL} for further assistance"
)
raise e
self.module_args = args
self.module_kwargs = kwargs
if self.is_baseline:
onnx_path = os.path.join(
OWLITE_REPO_PATH,
self.project_name,
self.baseline_name,
self.experiment_name,
f"{self.project_name}_{self.baseline_name}_{self.experiment_name}.onnx",
)
export(
model,
(*self.module_args, self.module_kwargs),
onnx_path,
**asdict(self.onnx_export_options),
)
log.info("Baseline ONNX saved")
upload_baseline(self.project_id, self.baseline_name, onnx_path, model)
log.info("Uploaded the model excluding parameters")
else:
exp_info = get_run_info(self.project_id, self.baseline_name, self.experiment_name)
assert exp_info is not None
if not exp_info["config_id"]:
log.warning("No compression configuration found, skipping the compression process")
else:
log.info(f"Compression configuration found for '{self.experiment_name}'")
configuration_string = get_configuration(self.project_id, self.baseline_name, self.experiment_name)
options = GraphQuantizationOptions.load(configuration_string)
log.info("Applying compression configuration")
model = quantize(model, options)
log.info("Converted the model")
return model
def benchmark(
self,
model: GraphModule,
dynamic_axes: Optional[dict[str, dict[int, dict[str, int]]]] = None,
) -> None:
"""Benchmarks given model.
Args:
model (GraphModule): Model to benchmark.
dynamic_axes (Optional[dict[str, dict[int, dict[str, int]]]]):
By default the exported model will have the shapes of all input tensors set to
exactly match those given when calling convert. To specify axes of tensors as
dynamic (i.e. known only at run-time), set `dynamic_axes` to a dict with schema:
* KEY (str): an input name.
* VALUE (dict[int, dict[str, int]]): a single item dictionary whose key is dynamic dimension of input
and value is a dynamic range setting dictionary containing min, opt, max, test dimension size
settings.
For example::
import owlite
owl = owlite.init( ... )
class SumModule(torch.nn.Module):
def forward(self, x):
return torch.sum(x, dim=1)
model = owl.convert( ... )
...
# set first(0-th) dimension of input x to be dynamic within the range of 1 ~ 8
# optimize for 4 and benchmark for 5
owl.benchmark(model, dynamic_axes={
"x": {
0: {
"min": 1,
"opt": 4,
"max": 8,
"test": 5,
}
}
})
Raises:
TypeError: When the `model` is an instance of `torch.nn.DataParallel` or `torch.nn.DistributedDataParallel`.
RuntimeError: When `dynamic_axes` is set for baseline benchmark.
ValueError: When invalid `dynamic_axes` is given.
"""
if isinstance(model, (DataParallel, DistributedDataParallel)):
_model_type = f"torch.nn.parallel.{type(model).__name__}"
log.error(
f"{_model_type} is not supported by benchmark, please use attribute module "
f"to unwrap model from {_model_type}. Try owlite.benchmark(model.module)"
)
raise TypeError(f"{_model_type} is not supported by benchmark")
if self.is_baseline:
log.info(
f"Benchmark initiated. '{self.baseline_name}' "
"ONNX will be uploaded to the connected device for TensorRT execution and benchmark"
)
if dynamic_axes is not None:
log.error(
"Baseline cannot be done with dynamic input. To benchmark baseline model with dynamic input, "
"please create a run without compression configuration and benchmark that run with dynamic input"
)
raise RuntimeError("Attempted dynamic baseline benchmark")
else:
log.info(
f"Benchmark initiated. '{self.experiment_name}' "
"ONNX will be created and uploaded to the connected device for TensorRT execution and benchmark"
)
dynamic_dimensions = None
if dynamic_axes is not None:
sep = "', '"
log.info(f"dynamic_axes setting for following inputs are provided. '{sep.join(dynamic_axes.keys())}'")
input_signature = get_input_shape_signature(
model, *(self.module_args or ()), **(self.module_kwargs or {})
)
dynamic_dimensions = configure_dynamic_dimensions(input_signature, dynamic_axes)
onnx_path = os.path.join(
OWLITE_REPO_PATH,
self.project_name,
self.baseline_name,
self.experiment_name,
f"{self.project_name}_{self.baseline_name}_{self.experiment_name}.onnx",
)
export(
model,
(*(self.module_args or ()), self.module_kwargs),
onnx_path,
**asdict(self.onnx_export_options),
dynamic_dimensions=dynamic_dimensions,
)
log.info("Experiment ONNX saved")
upload_run_onnx_proto(self.project_id, self.baseline_name, self.experiment_name, onnx_path, dynamic_axes)
log.info("Uploaded the model excluding parameters")
benchmark_key = get_benchmark_key(self.project_id, self.baseline_name, self.experiment_name)
bin_path = os.path.join(
OWLITE_REPO_PATH,
self.project_name,
self.baseline_name,
self.experiment_name,
f"{self.project_name}_{self.baseline_name}_{self.experiment_name}.bin",
)
request_trt_benchmark(benchmark_key, bin_path)
log.info("TensorRT engine execution and benchmark successfully requested")
poll_run_benchmark(self.project_id, benchmark_key)
exp_info = get_run_info(self.project_id, self.baseline_name, self.experiment_name)
assert exp_info is not None
if self.is_baseline:
log.info(
"Latency\n"
f"\t\tBaseline - {exp_info['latency']} on {exp_info['device_name']}\n"
"\t\tConfigure the quantization settings located at "
f"{OWLITE_FRONT_BASE_URL}/project/detail/{self.project_id}"
)
else:
log.info(
"Latency\n"
f"\t\tConfigured - {exp_info['latency']} on {exp_info['device_name']}\n"
"\t\tRetrieve the specifics of the experiment at "
f"{OWLITE_FRONT_BASE_URL}/project/detail/{self.project_id}"
)
engine_path = os.path.join(
OWLITE_REPO_PATH,
self.project_name,
self.baseline_name,
self.experiment_name,
f"{self.project_name}_{self.baseline_name}_{self.experiment_name}.engine",
)
download_trt_engine(benchmark_key, engine_path)
def log(self, **kwargs) -> None:
"""Logs the model's metrics.
Notes:
Log metrics with OwLite like below
...
owl = owlite.init(...)
...
owl.log(accuracy=0.72, loss=1.2)
Raises:
TypeError: When data is not JSON serializable.
"""
try:
logs = json.dumps(kwargs)
except TypeError as e:
log.error("Data is not JSON serializable")
raise e
update_run_info(self.project_id, self.baseline_name, self.experiment_name, logs)
# pylint: disable-next=too-many-branches
def init(
project: str,
baseline: str,
experiment: Optional[str] = None,
duplicate_from: Optional[str] = None,
description: str = "",
onnx_export_options: Optional[ONNXExportOptions] = None,
) -> OwLite:
"""Sets project, baseline and experiment information in DB to proper state and creates `OwLite` instance.
Args:
project (str): OwLite project name.
baseline (str): OwLite baseline name.
experiment (str, optional): OwLite experiment name. Defaults to None.
duplicate_from (str, optional): OwLite source experiment name. Defaults to None.
description (str, optional): OwLite project description. Defaults to "".
onnx_export_options (ONNXExportOptions, optional): Options for ONNX export. Defaults to None.
Raises:
RuntimeError: When not authenticated.
ValueError: When invalid experiment name or baseline name is given.
Returns:
OwLite: Created `OwLite` instance.
"""
if OWLITE_SETTINGS.tokens is None:
log.error("Please log in using 'owlite login'. Account not found on this device")
raise RuntimeError("OwLite token not found")
if OWLITE_DEVICE_NAME is None:
log.warning("Connected device not found. Please connect device by 'owlite device connect --name (name)'")
else:
log.info(f"Connected device: {OWLITE_DEVICE_NAME}")
if experiment == baseline:
log.error(f"Experiment name '{baseline}' is reserved for baseline. Please try with a different experiment name")
raise ValueError("Invalid experiment name")
dir_path = os.path.join(
OWLITE_REPO_PATH,
project,
baseline,
experiment or baseline,
)
if os.path.exists(dir_path):
log.warning(f"Existing local directory found at {dir_path}. Continuing this code will overwrite the data")
else:
os.makedirs(dir_path, exist_ok=True)
log.info(f"Experiment data will be saved in {dir_path}")
# create or load project
project_id = create_or_load_project(project, description)
if experiment is None:
if duplicate_from:
log.warning(f"duplicate_from='{duplicate_from}' will be ignored as no value for experiment was provided")
| created_baseline = create_baseline(project_id, baseline) | 11 | 2023-12-08 06:41:50+00:00 | 16k |
qitan/devops-backend-lite | apps/deploy/ext_func.py | [
{
"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 = xml... | import base64
import datetime
import os
import time
import logging
from django.core.cache import cache
from django.db.models import Q
from django.db import transaction
from django_q.tasks import async_task, schedule
from django_q.models import Schedule
from common.custom_format import convert_xml_to_str_with_pipeline
from common.utils.AesCipher import AesCipher
from common.variables import CI_LATEST_KEY
from deploy.documents import BuildJobDocument, DeployJobDocument
from deploy.serializers import BuildJobListSerializer, DeployJobSerializer
from deploy.rds_transfer import rds_transfer_es
from qtasks.tasks_build import JenkinsBuild
from dbapp.models import AppInfo
from dbapp.model.model_cmdb import DevLanguage, KubernetesDeploy
from common.ext_fun import get_datadict, get_redis_data, harbor_cli, template_generate
from config import PLAYBOOK_PATH
from dbapp.model.model_deploy import BuildJob, DeployJob, PublishOrder
from dbapp.model.model_ucenter import DataDict, UserProfile
from dbapp.model.model_workflow import Workflow | 11,866 |
logger = logging.getLogger(__name__)
@transaction.atomic
def app_build_handle(request_data, appinfo_obj: AppInfo, user: UserProfile):
"""
应用构建
"""
cipher = AesCipher('sdevops-platform')
commit_tag = request_data.get('commit_tag', None)
commits = request_data.get('commits', '')
modules = request_data.get('modules', 'dist')
custom_tag = request_data.get('custom_tag', None)
# {0: 构建, 1: 构建发布}
is_deploy = request_data.get('is_deploy', False)
language = DevLanguage.objects.get(name=appinfo_obj.app.language)
OPS_URL = get_redis_data('platform')['url']
jenkins = get_redis_data('cicd-jenkins')
category = appinfo_obj.app.category
namespace = appinfo_obj.namespace
job_name = appinfo_obj.jenkins_jobname
forward = 'no'
opshost = ''
# 定义harbor空配置
harbor_config = {}
build_time = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
JENKINS_CONFIG = get_redis_data('cicd-jenkins')
|
logger = logging.getLogger(__name__)
@transaction.atomic
def app_build_handle(request_data, appinfo_obj: AppInfo, user: UserProfile):
"""
应用构建
"""
cipher = AesCipher('sdevops-platform')
commit_tag = request_data.get('commit_tag', None)
commits = request_data.get('commits', '')
modules = request_data.get('modules', 'dist')
custom_tag = request_data.get('custom_tag', None)
# {0: 构建, 1: 构建发布}
is_deploy = request_data.get('is_deploy', False)
language = DevLanguage.objects.get(name=appinfo_obj.app.language)
OPS_URL = get_redis_data('platform')['url']
jenkins = get_redis_data('cicd-jenkins')
category = appinfo_obj.app.category
namespace = appinfo_obj.namespace
job_name = appinfo_obj.jenkins_jobname
forward = 'no'
opshost = ''
# 定义harbor空配置
harbor_config = {}
build_time = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
JENKINS_CONFIG = get_redis_data('cicd-jenkins') | jbuild = JenkinsBuild(JENKINS_CONFIG['url'], username=JENKINS_CONFIG['user'], | 3 | 2023-12-13 03:09:32+00:00 | 16k |
liujin112/PortraitDiffusion | app.py | [
{
"identifier": "AttentionBase",
"path": "utils/masactrl_utils.py",
"snippet": "class AttentionBase:\n def __init__(self):\n self.cur_step = 0\n self.num_att_layers = -1\n self.cur_att_layer = 0\n\n def after_step(self):\n pass\n\n def __call__(self, q, k, v, sim, at... | import os
import torch
import random
import numpy as np
import gradio as gr
import torch.nn.functional as F
from glob import glob
from datetime import datetime
from diffusers import StableDiffusionPipeline
from diffusers import DDIMScheduler, LCMScheduler
from PIL import Image,ImageDraw
from utils.masactrl_utils import (AttentionBase,
regiter_attention_editor_diffusers)
from utils.free_lunch_utils import register_upblock2d,register_crossattn_upblock2d,register_free_upblock2d, register_free_crossattn_upblock2d
from utils.style_attn_control import MaskPromptedStyleAttentionControl
from utils.pipeline import MasaCtrlPipeline
from torchvision.utils import save_image
from segment_anything import sam_model_registry, SamPredictor | 12,314 | self.personal_model_loaded = base_model_dropdown.split('.')[0]
print(f'load {base_model_dropdown} model success!')
return gr.Dropdown()
def update_lora_model(self, lora_model_dropdown,lora_alpha_slider):
if self.pipeline is None:
gr.Info(f"Please select a pretrained model path.")
return None
else:
if lora_model_dropdown == "none":
self.pipeline.unfuse_lora()
self.pipeline.unload_lora_weights()
self.lora_loaded = None
print("Restore lora.")
else:
lora_model_path = self.lora_model_list[lora_model_dropdown]
self.pipeline.load_lora_weights(lora_model_path)
self.pipeline.fuse_lora(lora_alpha_slider)
self.lora_loaded = lora_model_dropdown.split('.')[0]
print(f'load {lora_model_dropdown} LoRA Model Success!')
return gr.Dropdown()
def load_lcm_lora(self, lora_alpha_slider=1.0):
# set scheduler
self.pipeline = MasaCtrlPipeline.from_pretrained(self.stable_diffusion_list[0]).to(self.device)
self.pipeline.scheduler = LCMScheduler.from_config(self.pipeline.scheduler.config)
# load LCM-LoRA
self.pipeline.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
self.pipeline.fuse_lora(lora_alpha_slider)
self.lcm_lora_loaded = True
print(f'load LCM-LoRA model success!')
def generate(self, source, style, source_mask, style_mask,
start_step, start_layer, Style_attn_step,
Method, Style_Guidance, ddim_steps, scale, seed, de_bug,
target_prompt, negative_prompt_textbox,
inter_latents,
freeu, b1, b2, s1, s2,
width_slider,height_slider,
):
os.makedirs(self.savedir, exist_ok=True)
os.makedirs(self.savedir_sample, exist_ok=True)
os.makedirs(self.savedir_mask, exist_ok=True)
model = self.pipeline
if seed != -1 and seed != "": torch.manual_seed(int(seed))
else: torch.seed()
seed = torch.initial_seed()
sample_count = len(os.listdir(self.savedir_sample))
os.makedirs(os.path.join(self.savedir_mask, f"results_{sample_count}"), exist_ok=True)
# ref_prompt = [source_prompt, target_prompt]
# prompts = ref_prompt+['']
ref_prompt = [target_prompt, target_prompt]
prompts = ref_prompt+[target_prompt]
source_image,style_image,source_mask,style_mask = load_mask_images(source,style,source_mask,style_mask,self.device,width_slider,height_slider,out_dir=os.path.join(self.savedir_mask, f"results_{sample_count}"))
# global START_CODE, LATENTS_LIST
with torch.no_grad():
#import pdb;pdb.set_trace()
#prev_source
if self.start_code is None and self.latents_list is None:
content_style = torch.cat([style_image, source_image], dim=0)
editor = AttentionBase()
regiter_attention_editor_diffusers(model, editor)
st_code, latents_list = model.invert(content_style,
ref_prompt,
guidance_scale=scale,
num_inference_steps=ddim_steps,
return_intermediates=True)
start_code = torch.cat([st_code, st_code[1:]], dim=0)
self.start_code = start_code
self.latents_list = latents_list
else:
start_code = self.start_code
latents_list = self.latents_list
print('------------------------------------------ Use previous latents ------------------------------------------ ')
#["Without mask", "Only masked region", "Seperate Background Foreground"]
if Method == "Without mask":
style_mask = None
source_mask = None
only_masked_region = False
elif Method == "Only masked region":
assert style_mask is not None and source_mask is not None
only_masked_region = True
else:
assert style_mask is not None and source_mask is not None
only_masked_region = False
controller = MaskPromptedStyleAttentionControl(start_step, start_layer,
style_attn_step=Style_attn_step,
style_guidance=Style_Guidance,
style_mask=style_mask,
source_mask=source_mask,
only_masked_region=only_masked_region,
guidance=scale,
de_bug=de_bug,
)
if freeu:
# model.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
print(f'++++++++++++++++++ Run with FreeU {b1}_{b2}_{s1}_{s2} ++++++++++++++++')
if Method != "Without mask":
register_free_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=source_mask)
register_free_crossattn_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=source_mask)
else:
register_free_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=None)
register_free_crossattn_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=None)
else:
print(f'++++++++++++++++++ Run without FreeU ++++++++++++++++')
# model.disable_freeu()
|
css = """
.toolbutton {
margin-buttom: 0em 0em 0em 0em;
max-width: 2.5em;
min-width: 2.5em !important;
height: 2.5em;
}
"""
class GlobalText:
def __init__(self):
# config dirs
self.basedir = os.getcwd()
self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")
self.personalized_model_dir = './models/Stable-diffusion'
self.lora_model_dir = './models/Lora'
self.savedir = os.path.join(self.basedir, "samples", datetime.now().strftime("Gradio-%Y-%m-%dT%H-%M-%S"))
self.savedir_sample = os.path.join(self.savedir, "sample")
self.savedir_mask = os.path.join(self.savedir, "mask")
self.stable_diffusion_list = ["runwayml/stable-diffusion-v1-5",
"latent-consistency/lcm-lora-sdv1-5"]
self.personalized_model_list = []
self.lora_model_list = []
# config models
self.tokenizer = None
self.text_encoder = None
self.vae = None
self.unet = None
self.pipeline = None
self.lora_loaded = None
self.lcm_lora_loaded = False
self.personal_model_loaded = None
self.sam_predictor = None
self.lora_model_state_dict = {}
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# self.refresh_stable_diffusion()
self.refresh_personalized_model()
self.reset_start_code()
def load_base_pipeline(self, model_path):
print(f'loading {model_path} model')
scheduler = DDIMScheduler.from_pretrained(model_path,subfolder="scheduler")
self.pipeline = MasaCtrlPipeline.from_pretrained(model_path,
scheduler=scheduler).to(self.device)
def refresh_stable_diffusion(self):
self.load_base_pipeline(self.stable_diffusion_list[0])
self.lora_loaded = None
self.personal_model_loaded = None
self.lcm_lora_loaded = False
return self.stable_diffusion_list[0]
def refresh_personalized_model(self):
personalized_model_list = glob(os.path.join(self.personalized_model_dir, "**/*.safetensors"), recursive=True)
self.personalized_model_list = {os.path.basename(file): file for file in personalized_model_list}
lora_model_list = glob(os.path.join(self.lora_model_dir, "**/*.safetensors"), recursive=True)
self.lora_model_list = {os.path.basename(file): file for file in lora_model_list}
def update_stable_diffusion(self, stable_diffusion_dropdown):
if stable_diffusion_dropdown == 'latent-consistency/lcm-lora-sdv1-5':
self.load_lcm_lora()
else:
self.load_base_pipeline(stable_diffusion_dropdown)
self.lora_loaded = None
self.personal_model_loaded = None
return gr.Dropdown()
def update_base_model(self, base_model_dropdown):
if self.pipeline is None:
gr.Info(f"Please select a pretrained model path.")
return None
else:
base_model = self.personalized_model_list[base_model_dropdown]
mid_model = StableDiffusionPipeline.from_single_file(base_model)
self.pipeline.vae = mid_model.vae
self.pipeline.unet = mid_model.unet
self.pipeline.text_encoder = mid_model.text_encoder
self.pipeline.to(self.device)
self.personal_model_loaded = base_model_dropdown.split('.')[0]
print(f'load {base_model_dropdown} model success!')
return gr.Dropdown()
def update_lora_model(self, lora_model_dropdown,lora_alpha_slider):
if self.pipeline is None:
gr.Info(f"Please select a pretrained model path.")
return None
else:
if lora_model_dropdown == "none":
self.pipeline.unfuse_lora()
self.pipeline.unload_lora_weights()
self.lora_loaded = None
print("Restore lora.")
else:
lora_model_path = self.lora_model_list[lora_model_dropdown]
self.pipeline.load_lora_weights(lora_model_path)
self.pipeline.fuse_lora(lora_alpha_slider)
self.lora_loaded = lora_model_dropdown.split('.')[0]
print(f'load {lora_model_dropdown} LoRA Model Success!')
return gr.Dropdown()
def load_lcm_lora(self, lora_alpha_slider=1.0):
# set scheduler
self.pipeline = MasaCtrlPipeline.from_pretrained(self.stable_diffusion_list[0]).to(self.device)
self.pipeline.scheduler = LCMScheduler.from_config(self.pipeline.scheduler.config)
# load LCM-LoRA
self.pipeline.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
self.pipeline.fuse_lora(lora_alpha_slider)
self.lcm_lora_loaded = True
print(f'load LCM-LoRA model success!')
def generate(self, source, style, source_mask, style_mask,
start_step, start_layer, Style_attn_step,
Method, Style_Guidance, ddim_steps, scale, seed, de_bug,
target_prompt, negative_prompt_textbox,
inter_latents,
freeu, b1, b2, s1, s2,
width_slider,height_slider,
):
os.makedirs(self.savedir, exist_ok=True)
os.makedirs(self.savedir_sample, exist_ok=True)
os.makedirs(self.savedir_mask, exist_ok=True)
model = self.pipeline
if seed != -1 and seed != "": torch.manual_seed(int(seed))
else: torch.seed()
seed = torch.initial_seed()
sample_count = len(os.listdir(self.savedir_sample))
os.makedirs(os.path.join(self.savedir_mask, f"results_{sample_count}"), exist_ok=True)
# ref_prompt = [source_prompt, target_prompt]
# prompts = ref_prompt+['']
ref_prompt = [target_prompt, target_prompt]
prompts = ref_prompt+[target_prompt]
source_image,style_image,source_mask,style_mask = load_mask_images(source,style,source_mask,style_mask,self.device,width_slider,height_slider,out_dir=os.path.join(self.savedir_mask, f"results_{sample_count}"))
# global START_CODE, LATENTS_LIST
with torch.no_grad():
#import pdb;pdb.set_trace()
#prev_source
if self.start_code is None and self.latents_list is None:
content_style = torch.cat([style_image, source_image], dim=0)
editor = AttentionBase()
regiter_attention_editor_diffusers(model, editor)
st_code, latents_list = model.invert(content_style,
ref_prompt,
guidance_scale=scale,
num_inference_steps=ddim_steps,
return_intermediates=True)
start_code = torch.cat([st_code, st_code[1:]], dim=0)
self.start_code = start_code
self.latents_list = latents_list
else:
start_code = self.start_code
latents_list = self.latents_list
print('------------------------------------------ Use previous latents ------------------------------------------ ')
#["Without mask", "Only masked region", "Seperate Background Foreground"]
if Method == "Without mask":
style_mask = None
source_mask = None
only_masked_region = False
elif Method == "Only masked region":
assert style_mask is not None and source_mask is not None
only_masked_region = True
else:
assert style_mask is not None and source_mask is not None
only_masked_region = False
controller = MaskPromptedStyleAttentionControl(start_step, start_layer,
style_attn_step=Style_attn_step,
style_guidance=Style_Guidance,
style_mask=style_mask,
source_mask=source_mask,
only_masked_region=only_masked_region,
guidance=scale,
de_bug=de_bug,
)
if freeu:
# model.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
print(f'++++++++++++++++++ Run with FreeU {b1}_{b2}_{s1}_{s2} ++++++++++++++++')
if Method != "Without mask":
register_free_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=source_mask)
register_free_crossattn_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=source_mask)
else:
register_free_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=None)
register_free_crossattn_upblock2d(model, b1=b1, b2=b2, s1=s1, s2=s1,source_mask=None)
else:
print(f'++++++++++++++++++ Run without FreeU ++++++++++++++++')
# model.disable_freeu() | register_upblock2d(model) | 2 | 2023-12-06 01:18:39+00:00 | 16k |
AsuradaYuci/TF-CLIP | datasets/make_dataloader_clipreid.py | [
{
"identifier": "VideoDataset",
"path": "datasets/video_loader_xh.py",
"snippet": "class VideoDataset(Dataset):\n \"\"\"Video Person ReID Dataset.\n Note batch data has shape (batch, seq_len, channel, height, width).\n \"\"\"\n sample_methods = ['evenly', 'random', 'dense']\n\n def __init... | import torch
import utils.spatial_transforms as ST
import utils.temporal_transforms as TT
import utils.transforms as T
import utils.seqtransforms as SeqT
from torch.utils.data import DataLoader
from datasets.video_loader_xh import VideoDataset
from datasets.samplers import RandomIdentitySampler, RandomIdentitySamplerForSeq, RandomIdentitySamplerWYQ
from datasets.seqpreprocessor import SeqTrainPreprocessor, SeqTestPreprocessor
from datasets.set.mars import Mars
from datasets.set.ilidsvidsequence import iLIDSVIDSEQUENCE
from datasets.set.lsvid import LSVID | 12,722 |
# from torchvision.transforms import InterpolationMode
# import torchvision.transforms as T
__factory = {
'mars': Mars,
'ilidsvidsequence': iLIDSVIDSEQUENCE,
'lsvid': LSVID
}
def train_collate_fn(batch):
"""
# collate_fn这个函数的输入就是一个list，list的长度是一个batch size，list中的每个元素都是__getitem__得到的结果
"""
imgs, pids, camids, viewids, _ = zip(*batch)
pids = torch.tensor(pids, dtype=torch.int64)
viewids = torch.tensor(viewids, dtype=torch.int64)
camids = torch.tensor(camids, dtype=torch.int64)
return torch.stack(imgs, dim=0), pids, camids, viewids,
def val_collate_fn(batch):
imgs, pids, camids, viewids, img_paths = zip(*batch)
viewids = torch.tensor(viewids, dtype=torch.int64)
camids_batch = torch.tensor(camids, dtype=torch.int64)
return torch.stack(imgs, dim=0), pids, camids, camids_batch, viewids, img_paths
def train_collate_fn_seq(batch):
"""
# collate_fn这个函数的输入就是一个list，list的长度是一个batch size，list中的每个元素都是__getitem__得到的结果
"""
imgs, flows, pids, camids = zip(*batch)
viewids = 1
pids = torch.tensor(pids, dtype=torch.int64)
viewids = torch.tensor(viewids, dtype=torch.int64)
camids = torch.tensor(camids, dtype=torch.int64)
return torch.stack(imgs, dim=0), pids, camids, viewids,
def val_collate_fn_seq(batch):
imgs, flows, pids, camids = zip(*batch)
viewids = 1
img_paths = None
viewids = torch.tensor(viewids, dtype=torch.int64)
camids_batch = torch.tensor(camids, dtype=torch.int64)
return torch.stack(imgs, dim=0), pids, camids, camids_batch, viewids, img_paths
def make_dataloader(cfg):
split_id = cfg.DATASETS.SPLIT
seq_srd = cfg.INPUT.SEQ_SRD
seq_len = cfg.INPUT.SEQ_LEN
num_workers = cfg.DATALOADER.NUM_WORKERS
if cfg.DATASETS.NAMES != 'mars' and cfg.DATASETS.NAMES != 'duke' and cfg.DATASETS.NAMES != 'lsvid':
dataset = __factory[cfg.DATASETS.NAMES](root=cfg.DATASETS.ROOT_DIR, split_id=split_id, seq_len=seq_len,
seq_srd=seq_srd, num_val=1)
num_classes = dataset.num_trainval_ids
cam_num = dataset.num_train_cams
view_num = dataset.num_train_vids
train_set = SeqTrainPreprocessor(dataset.trainval, dataset, seq_len,
transform=SeqT.Compose([SeqT.RectScale(256, 128),
SeqT.RandomHorizontalFlip(),
SeqT.RandomSizedEarser(),
SeqT.ToTensor(),
SeqT.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])]))
train_set_normal = SeqTrainPreprocessor(dataset.trainval, dataset, seq_len,
transform=SeqT.Compose([SeqT.RectScale(256, 128),
SeqT.ToTensor(),
SeqT.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])]))
val_set = SeqTestPreprocessor(dataset.query + dataset.gallery, dataset, seq_len,
transform=SeqT.Compose([SeqT.RectScale(256, 128),
SeqT.ToTensor(),
SeqT.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])]))
train_loader_stage2 = DataLoader(
train_set,
|
# from torchvision.transforms import InterpolationMode
# import torchvision.transforms as T
__factory = {
'mars': Mars,
'ilidsvidsequence': iLIDSVIDSEQUENCE,
'lsvid': LSVID
}
def train_collate_fn(batch):
"""
# collate_fn这个函数的输入就是一个list，list的长度是一个batch size，list中的每个元素都是__getitem__得到的结果
"""
imgs, pids, camids, viewids, _ = zip(*batch)
pids = torch.tensor(pids, dtype=torch.int64)
viewids = torch.tensor(viewids, dtype=torch.int64)
camids = torch.tensor(camids, dtype=torch.int64)
return torch.stack(imgs, dim=0), pids, camids, viewids,
def val_collate_fn(batch):
imgs, pids, camids, viewids, img_paths = zip(*batch)
viewids = torch.tensor(viewids, dtype=torch.int64)
camids_batch = torch.tensor(camids, dtype=torch.int64)
return torch.stack(imgs, dim=0), pids, camids, camids_batch, viewids, img_paths
def train_collate_fn_seq(batch):
"""
# collate_fn这个函数的输入就是一个list，list的长度是一个batch size，list中的每个元素都是__getitem__得到的结果
"""
imgs, flows, pids, camids = zip(*batch)
viewids = 1
pids = torch.tensor(pids, dtype=torch.int64)
viewids = torch.tensor(viewids, dtype=torch.int64)
camids = torch.tensor(camids, dtype=torch.int64)
return torch.stack(imgs, dim=0), pids, camids, viewids,
def val_collate_fn_seq(batch):
imgs, flows, pids, camids = zip(*batch)
viewids = 1
img_paths = None
viewids = torch.tensor(viewids, dtype=torch.int64)
camids_batch = torch.tensor(camids, dtype=torch.int64)
return torch.stack(imgs, dim=0), pids, camids, camids_batch, viewids, img_paths
def make_dataloader(cfg):
split_id = cfg.DATASETS.SPLIT
seq_srd = cfg.INPUT.SEQ_SRD
seq_len = cfg.INPUT.SEQ_LEN
num_workers = cfg.DATALOADER.NUM_WORKERS
if cfg.DATASETS.NAMES != 'mars' and cfg.DATASETS.NAMES != 'duke' and cfg.DATASETS.NAMES != 'lsvid':
dataset = __factory[cfg.DATASETS.NAMES](root=cfg.DATASETS.ROOT_DIR, split_id=split_id, seq_len=seq_len,
seq_srd=seq_srd, num_val=1)
num_classes = dataset.num_trainval_ids
cam_num = dataset.num_train_cams
view_num = dataset.num_train_vids
train_set = SeqTrainPreprocessor(dataset.trainval, dataset, seq_len,
transform=SeqT.Compose([SeqT.RectScale(256, 128),
SeqT.RandomHorizontalFlip(),
SeqT.RandomSizedEarser(),
SeqT.ToTensor(),
SeqT.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])]))
train_set_normal = SeqTrainPreprocessor(dataset.trainval, dataset, seq_len,
transform=SeqT.Compose([SeqT.RectScale(256, 128),
SeqT.ToTensor(),
SeqT.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])]))
val_set = SeqTestPreprocessor(dataset.query + dataset.gallery, dataset, seq_len,
transform=SeqT.Compose([SeqT.RectScale(256, 128),
SeqT.ToTensor(),
SeqT.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])]))
train_loader_stage2 = DataLoader(
train_set, | sampler=RandomIdentitySamplerForSeq(dataset.trainval, batch_size=cfg.SOLVER.STAGE2.IMS_PER_BATCH, | 2 | 2023-12-11 04:03:46+00:00 | 16k |
MarilynKeller/aitviewer-skel | aitviewer/renderables/markers.py | [
{
"identifier": "Spheres",
"path": "aitviewer/renderables/spheres.py",
"snippet": "class Spheres(Node):\n \"\"\"Render some simple spheres.\"\"\"\n\n def __init__(\n self,\n positions,\n radius=0.01,\n color=(0.0, 0.0, 1.0, 1.0),\n rings=16,\n sectors=32,\... | import numpy as np
import os
import pickle as pkl
import tqdm
import nimblephysics as nimble
from aitviewer.renderables.spheres import Spheres
from aitviewer.scene.node import Node
from aitviewer.renderables.point_clouds import PointClouds
from aitviewer.utils import mocap
from aitviewer.utils.mocap import clean_CMU_mocap_labels | 11,724 |
# Code Developed by:
# Marilyn Keller, marilyn.keller@tuebingen.mpg.de
# Do not share or distribute without permission of the author
class Markers(Node):
"""
Draw a point clouds man!
"""
def __init__(self, points, markers_labels, name="Mocap data", colors=None, lengths=None, point_size=5.0, radius = 0.0075, color=(0.0, 0.0, 1.0, 1.0), as_spheres=True, **kwargs):
"""
A sequence of point clouds. Each point cloud can have a varying number of points.
Internally represented as a list of arrays.
:param points: Sequence of points (F, P, 3)
:param colors: Sequence of Colors (F, C, 4)
:param lengths: Length mask for each frame of points denoting the usable part of the array
:param point_size: Initial point size
"""
# self.points = points
super(Markers, self).__init__(name, n_frames=points.shape[0], color=color, **kwargs)
# Check that the marker labels are sorted
# markers_labels_copy = markers_labels.copy()
# markers_labels_copy.sort()
# assert markers_labels == markers_labels_copy
self.markers_labels = markers_labels
self.marker_trajectory = points # FxMx3
self.color = color
if self.marker_trajectory.shape[1]>200:
as_spheres = False
print(f"Too many markers ({self.marker_trajectory.shape[1]}). Switching to pointcloud.")
#todo fix color bug
for mi, marker_name in enumerate(self.markers_labels):
if colors is not None:
color = tuple(colors[mi])
if as_spheres:
markers_seq = Spheres(self.marker_trajectory[:,mi,:][:,np.newaxis,:], color=color, radius = radius, name=marker_name, **kwargs)
else:
|
# Code Developed by:
# Marilyn Keller, marilyn.keller@tuebingen.mpg.de
# Do not share or distribute without permission of the author
class Markers(Node):
"""
Draw a point clouds man!
"""
def __init__(self, points, markers_labels, name="Mocap data", colors=None, lengths=None, point_size=5.0, radius = 0.0075, color=(0.0, 0.0, 1.0, 1.0), as_spheres=True, **kwargs):
"""
A sequence of point clouds. Each point cloud can have a varying number of points.
Internally represented as a list of arrays.
:param points: Sequence of points (F, P, 3)
:param colors: Sequence of Colors (F, C, 4)
:param lengths: Length mask for each frame of points denoting the usable part of the array
:param point_size: Initial point size
"""
# self.points = points
super(Markers, self).__init__(name, n_frames=points.shape[0], color=color, **kwargs)
# Check that the marker labels are sorted
# markers_labels_copy = markers_labels.copy()
# markers_labels_copy.sort()
# assert markers_labels == markers_labels_copy
self.markers_labels = markers_labels
self.marker_trajectory = points # FxMx3
self.color = color
if self.marker_trajectory.shape[1]>200:
as_spheres = False
print(f"Too many markers ({self.marker_trajectory.shape[1]}). Switching to pointcloud.")
#todo fix color bug
for mi, marker_name in enumerate(self.markers_labels):
if colors is not None:
color = tuple(colors[mi])
if as_spheres:
markers_seq = Spheres(self.marker_trajectory[:,mi,:][:,np.newaxis,:], color=color, radius = radius, name=marker_name, **kwargs)
else: | markers_seq = PointClouds(self.marker_trajectory[:,mi,:][:,np.newaxis,:], name=marker_name, point_size=point_size, color=color, **kwargs) | 2 | 2023-12-07 16:13:50+00:00 | 16k |
nexB/dejacode | dejacode/urls.py | [
{
"identifier": "ComponentViewSet",
"path": "component_catalog/api.py",
"snippet": "class ComponentViewSet(CreateRetrieveUpdateListViewSet):\n queryset = Component.objects.all()\n serializer_class = ComponentSerializer\n filterset_class = ComponentFilterSet\n lookup_field = \"uuid\"\n sea... | from django.conf import settings
from django.conf.urls import include
from django.contrib import admin
from django.contrib.auth import views as auth_views
from django.contrib.auth.decorators import login_required
from django.template.loader import render_to_string
from django.urls import path
from django.views.defaults import page_not_found
from django.views.generic import RedirectView
from django.views.generic import TemplateView
from notifications.views import mark_all_as_read
from rest_framework.documentation import include_docs_urls
from rest_framework.routers import DefaultRouter
from component_catalog.api import ComponentViewSet
from component_catalog.api import PackageViewSet
from component_catalog.api import SubcomponentViewSet
from component_catalog.views import send_scan_notification
from dje import two_factor
from dje.admin import dejacode_site
from dje.api import ExternalReferenceViewSet
from dje.forms import DejaCodeAuthenticationForm
from dje.registration import DejaCodeActivationView
from dje.registration import DejaCodeRegistrationForm
from dje.views import AccountProfileView
from dje.views import AllNotificationsList
from dje.views import DataspaceAwareAutocompleteLookup
from dje.views import DataspaceAwareRelatedLookup
from dje.views import GlobalSearchListView
from dje.views import IntegrationsStatusView
from dje.views import UnreadNotificationsList
from dje.views import home_view
from dje.views import index_dispatch
from dje.views import urn_resolve_view
from license_library.api import LicenseAnnotationViewSet
from license_library.api import LicenseViewSet
from organization.api import OwnerViewSet
from policy.api import UsagePolicyViewSet
from product_portfolio.api import CodebaseResourceViewSet
from product_portfolio.api import ProductComponentViewSet
from product_portfolio.api import ProductPackageViewSet
from product_portfolio.api import ProductViewSet
from reporting.api import ReportViewSet
from workflow.api import RequestTemplateViewSet
from workflow.api import RequestViewSet
from django_registration.backends.activation.views import RegistrationView | 12,474 | #
# 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.
#
# Replace the default admin site with the DejaCode one.
admin.site = dejacode_site
# Restframework API
api_router = DefaultRouter()
api_router.register("owners", OwnerViewSet)
api_router.register("licenses", LicenseViewSet)
api_router.register("license_annotations", LicenseAnnotationViewSet)
api_router.register("components", ComponentViewSet)
api_router.register("subcomponents", SubcomponentViewSet)
api_router.register("packages", PackageViewSet)
api_router.register("products", ProductViewSet)
api_router.register("product_components", ProductComponentViewSet)
api_router.register("product_packages", ProductPackageViewSet)
api_router.register("codebase_resources", CodebaseResourceViewSet)
api_router.register("request_templates", RequestTemplateViewSet)
api_router.register("requests", RequestViewSet)
api_router.register("reports", ReportViewSet)
api_router.register("external_references", ExternalReferenceViewSet)
api_router.register("usage_policies", UsagePolicyViewSet)
urlpatterns = [
path("", index_dispatch, name="index_dispatch"),
path("home/", home_view, name="home"),
path("integrations_status/", IntegrationsStatusView.as_view(), name="integrations_status"),
path("account/", include("django.contrib.auth.urls")),
path("account/profile/", AccountProfileView.as_view(), name="account_profile"),
path("logout/", auth_views.LogoutView.as_view(next_page="login"), name="logout"),
path(
"login/",
two_factor.LoginView.as_view(
authentication_form=DejaCodeAuthenticationForm,
redirect_authenticated_user=True,
),
name="login",
),
# Activation and password views are required for the user creation flow.
# registration_activation_complete needs to be register before registration_activate
# so the 'complete/' segment is not caught as the activation_key
path(
"account/activate/complete/",
TemplateView.as_view(template_name="django_registration/activation_complete.html"),
name="django_registration_activation_complete",
),
path(
"account/activate/<str:activation_key>/",
DejaCodeActivationView.as_view(),
name="django_registration_activate",
),
# Two-factor authentication
path("account/2fa/enable/", two_factor.EnableView.as_view(), name="account_2fa_enable"),
path("account/2fa/disable/", two_factor.DisableView.as_view(), name="account_2fa_disable"),
path("account/2fa/verify/", two_factor.VerifyView.as_view(), name="account_2fa_verify"),
path("urn/", urn_resolve_view, name="urn_resolve"),
path("urn/<urn>/", urn_resolve_view, name="urn_resolve"),
path("admin/", dejacode_site.urls),
# Grappelli does not have a hook for replacing the ``RelatedLookup`` view
# class so we hijack the url used by that view and use our own version of
# ``RelatedLookup``. The same is done for ``AutocompleteLookup``.
path(
"grappelli/lookup/related/",
DataspaceAwareRelatedLookup.as_view(),
name="grp_related_lookup",
),
path(
"grappelli/lookup/autocomplete/",
DataspaceAwareAutocompleteLookup.as_view(),
name="grp_autocomplete_lookup",
),
# Disable Grappelli's M2M lookup.
path("grappelli/lookup/m2m/", page_not_found, name="grp_m2m_lookup"),
# This need to be registered after the overrides.
path("grappelli/", include("grappelli.urls")),
path("favicon.ico", RedirectView.as_view(url="/static/img/favicon.ico", permanent=True)),
]
urlpatterns += [
path("licenses/", include(("license_library.urls", "license_library"))),
path("", include(("component_catalog.urls", "component_catalog"))),
path("products/", include(("product_portfolio.urls", "product_portfolio"))),
path("owners/", include(("organization.urls", "organization"))),
path("requests/", include(("workflow.urls", "workflow"))),
path("reports/", include(("reporting.urls", "reporting"))),
path("global_search/", GlobalSearchListView.as_view(), name="global_search"),
]
notification_patterns = [
| #
# 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.
#
# Replace the default admin site with the DejaCode one.
admin.site = dejacode_site
# Restframework API
api_router = DefaultRouter()
api_router.register("owners", OwnerViewSet)
api_router.register("licenses", LicenseViewSet)
api_router.register("license_annotations", LicenseAnnotationViewSet)
api_router.register("components", ComponentViewSet)
api_router.register("subcomponents", SubcomponentViewSet)
api_router.register("packages", PackageViewSet)
api_router.register("products", ProductViewSet)
api_router.register("product_components", ProductComponentViewSet)
api_router.register("product_packages", ProductPackageViewSet)
api_router.register("codebase_resources", CodebaseResourceViewSet)
api_router.register("request_templates", RequestTemplateViewSet)
api_router.register("requests", RequestViewSet)
api_router.register("reports", ReportViewSet)
api_router.register("external_references", ExternalReferenceViewSet)
api_router.register("usage_policies", UsagePolicyViewSet)
urlpatterns = [
path("", index_dispatch, name="index_dispatch"),
path("home/", home_view, name="home"),
path("integrations_status/", IntegrationsStatusView.as_view(), name="integrations_status"),
path("account/", include("django.contrib.auth.urls")),
path("account/profile/", AccountProfileView.as_view(), name="account_profile"),
path("logout/", auth_views.LogoutView.as_view(next_page="login"), name="logout"),
path(
"login/",
two_factor.LoginView.as_view(
authentication_form=DejaCodeAuthenticationForm,
redirect_authenticated_user=True,
),
name="login",
),
# Activation and password views are required for the user creation flow.
# registration_activation_complete needs to be register before registration_activate
# so the 'complete/' segment is not caught as the activation_key
path(
"account/activate/complete/",
TemplateView.as_view(template_name="django_registration/activation_complete.html"),
name="django_registration_activation_complete",
),
path(
"account/activate/<str:activation_key>/",
DejaCodeActivationView.as_view(),
name="django_registration_activate",
),
# Two-factor authentication
path("account/2fa/enable/", two_factor.EnableView.as_view(), name="account_2fa_enable"),
path("account/2fa/disable/", two_factor.DisableView.as_view(), name="account_2fa_disable"),
path("account/2fa/verify/", two_factor.VerifyView.as_view(), name="account_2fa_verify"),
path("urn/", urn_resolve_view, name="urn_resolve"),
path("urn/<urn>/", urn_resolve_view, name="urn_resolve"),
path("admin/", dejacode_site.urls),
# Grappelli does not have a hook for replacing the ``RelatedLookup`` view
# class so we hijack the url used by that view and use our own version of
# ``RelatedLookup``. The same is done for ``AutocompleteLookup``.
path(
"grappelli/lookup/related/",
DataspaceAwareRelatedLookup.as_view(),
name="grp_related_lookup",
),
path(
"grappelli/lookup/autocomplete/",
DataspaceAwareAutocompleteLookup.as_view(),
name="grp_autocomplete_lookup",
),
# Disable Grappelli's M2M lookup.
path("grappelli/lookup/m2m/", page_not_found, name="grp_m2m_lookup"),
# This need to be registered after the overrides.
path("grappelli/", include("grappelli.urls")),
path("favicon.ico", RedirectView.as_view(url="/static/img/favicon.ico", permanent=True)),
]
urlpatterns += [
path("licenses/", include(("license_library.urls", "license_library"))),
path("", include(("component_catalog.urls", "component_catalog"))),
path("products/", include(("product_portfolio.urls", "product_portfolio"))),
path("owners/", include(("organization.urls", "organization"))),
path("requests/", include(("workflow.urls", "workflow"))),
path("reports/", include(("reporting.urls", "reporting"))),
path("global_search/", GlobalSearchListView.as_view(), name="global_search"),
]
notification_patterns = [ | path("", UnreadNotificationsList.as_view(), name="unread"), | 16 | 2023-12-07 16:57:42+00:00 | 16k |
wusize/CLIM | src/open_clip/factory.py | [
{
"identifier": "OPENAI_DATASET_MEAN",
"path": "src/open_clip/constants.py",
"snippet": "OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)"
},
{
"identifier": "OPENAI_DATASET_STD",
"path": "src/open_clip/constants.py",
"snippet": "OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.2... | import json
import logging
import os
import pathlib
import re
import torch
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, Optional, Tuple, Union
from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\
resize_pos_embed, get_cast_dtype
from .coca_model import CoCa
from .loss import ClipLoss, DistillClipLoss, CoCaLoss
from .openai import load_openai_model
from .pretrained import is_pretrained_cfg, get_pretrained_cfg, \
download_pretrained, list_pretrained_tags_by_model, download_pretrained_from_hf
from .transform import image_transform, AugmentationCfg, det_image_transform
from .tokenizer import HFTokenizer, tokenize
from open_clip import eva_clip
from open_clip import eva_clip | 13,630 | with open(config_path, 'r', encoding='utf-8') as f:
config = json.load(f)
pretrained_cfg = config['preprocess_cfg']
model_cfg = config['model_cfg']
else:
model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names
checkpoint_path = None
pretrained_cfg = {}
model_cfg = None
if isinstance(device, str):
device = torch.device(device)
if pretrained == 'eva':
return eva_clip.create_model(model_name=model_name,
pretrained=cache_dir, force_custom_clip=True,
precision=precision,
device=device,)
if pretrained and pretrained.lower() == 'openai':
logging.info(f'Loading pretrained {model_name} from OpenAI.')
model = load_openai_model(
model_name,
precision=precision,
device=device,
jit=jit,
cache_dir=cache_dir,
)
# to always output dict even if it is clip
if output_dict and hasattr(model, "output_dict"):
model.output_dict = True
else:
model_cfg = model_cfg or get_model_config(model_name)
if model_cfg is not None:
logging.info(f'Loaded {model_name} model config.')
else:
logging.error(f'Model config for {model_name} not found; available models {list_models()}.')
raise RuntimeError(f'Model config for {model_name} not found.')
if force_quick_gelu:
# override for use of QuickGELU on non-OpenAI transformer models
model_cfg["quick_gelu"] = True
if force_patch_dropout is not None:
# override the default patch dropout value
model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout
if force_image_size is not None:
# override model config's image size
model_cfg["vision_cfg"]["image_size"] = force_image_size
if pretrained_image:
if 'timm_model_name' in model_cfg.get('vision_cfg', {}):
# pretrained weight loading for timm models set via vision_cfg
model_cfg['vision_cfg']['timm_model_pretrained'] = True
else:
assert False, 'pretrained image towers currently only supported for timm models'
cast_dtype = get_cast_dtype(precision)
is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {})
custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model
if custom_text:
if is_hf_model:
model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf
if "coca" in model_name:
model = CoCa(**model_cfg, cast_dtype=cast_dtype)
else:
model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype)
else:
model = CLIP(**model_cfg, cast_dtype=cast_dtype)
pretrained_loaded = False
if pretrained:
checkpoint_path = ''
pretrained_cfg = get_pretrained_cfg(model_name, pretrained)
if pretrained_cfg:
checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)
elif os.path.exists(pretrained):
checkpoint_path = pretrained
if checkpoint_path:
print(f'Loading pretrained {model_name} weights ({pretrained}).', flush=True)
logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
load_checkpoint(model, checkpoint_path)
else:
error_str = (
f'Pretrained weights ({pretrained}) not found for model {model_name}.'
f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.')
logging.warning(error_str)
raise RuntimeError(error_str)
pretrained_loaded = True
elif has_hf_hub_prefix:
logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
load_checkpoint(model, checkpoint_path)
pretrained_loaded = True
if require_pretrained and not pretrained_loaded:
# callers of create_model_from_pretrained always expect pretrained weights
raise RuntimeError(
f'Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded.')
model.to(device=device)
if precision in ("fp16", "bf16"):
convert_weights_to_lp(model, dtype=torch.bfloat16 if precision == 'bf16' else torch.float16)
# set image / mean metadata from pretrained_cfg if available, or use default
model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN
model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD
# to always output dict even if it is clip
if output_dict and hasattr(model, "output_dict"):
model.output_dict = True
if jit:
model = torch.jit.script(model)
return model
def create_loss(args):
|
HF_HUB_PREFIX = 'hf-hub:'
_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs
def _natural_key(string_):
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def _rescan_model_configs():
global _MODEL_CONFIGS
config_ext = ('.json',)
config_files = []
for config_path in _MODEL_CONFIG_PATHS:
if config_path.is_file() and config_path.suffix in config_ext:
config_files.append(config_path)
elif config_path.is_dir():
for ext in config_ext:
config_files.extend(config_path.glob(f'*{ext}'))
for cf in config_files:
with open(cf, 'r') as f:
model_cfg = json.load(f)
if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')):
_MODEL_CONFIGS[cf.stem] = model_cfg
_MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))}
_rescan_model_configs() # initial populate of model config registry
def list_models():
""" enumerate available model architectures based on config files """
return list(_MODEL_CONFIGS.keys())
def add_model_config(path):
""" add model config path or file and update registry """
if not isinstance(path, Path):
path = Path(path)
_MODEL_CONFIG_PATHS.append(path)
_rescan_model_configs()
def get_model_config(model_name):
if model_name in _MODEL_CONFIGS:
return deepcopy(_MODEL_CONFIGS[model_name])
else:
return None
def get_tokenizer(model_name):
if 'EVA' in model_name:
return eva_clip.get_tokenizer(model_name)
if model_name.startswith(HF_HUB_PREFIX):
tokenizer = HFTokenizer(model_name[len(HF_HUB_PREFIX):])
else:
config = get_model_config(model_name)
tokenizer = HFTokenizer(
config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize
return tokenizer
def load_state_dict(checkpoint_path: str, map_location='cpu'):
checkpoint = torch.load(checkpoint_path, map_location=map_location)
if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
if next(iter(state_dict.items()))[0].startswith('module'):
state_dict = {k[7:]: v for k, v in state_dict.items()}
return state_dict
def load_checkpoint(model, checkpoint_path, strict=True):
state_dict = load_state_dict(checkpoint_path)
# detect old format and make compatible with new format
if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):
state_dict = convert_to_custom_text_state_dict(state_dict)
resize_pos_embed(state_dict, model)
incompatible_keys = model.load_state_dict(state_dict, strict=strict)
return incompatible_keys
def create_model(
model_name: str,
pretrained: Optional[str] = None,
precision: str = 'fp32',
device: Union[str, torch.device] = 'cpu',
jit: bool = False,
force_quick_gelu: bool = False,
force_custom_text: bool = False,
force_patch_dropout: Optional[float] = None,
force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
pretrained_image: bool = False,
pretrained_hf: bool = True,
cache_dir: Optional[str] = None,
output_dict: Optional[bool] = None,
require_pretrained: bool = False,
):
has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX)
if has_hf_hub_prefix:
model_id = model_name[len(HF_HUB_PREFIX):]
checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir)
with open(config_path, 'r', encoding='utf-8') as f:
config = json.load(f)
pretrained_cfg = config['preprocess_cfg']
model_cfg = config['model_cfg']
else:
model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names
checkpoint_path = None
pretrained_cfg = {}
model_cfg = None
if isinstance(device, str):
device = torch.device(device)
if pretrained == 'eva':
return eva_clip.create_model(model_name=model_name,
pretrained=cache_dir, force_custom_clip=True,
precision=precision,
device=device,)
if pretrained and pretrained.lower() == 'openai':
logging.info(f'Loading pretrained {model_name} from OpenAI.')
model = load_openai_model(
model_name,
precision=precision,
device=device,
jit=jit,
cache_dir=cache_dir,
)
# to always output dict even if it is clip
if output_dict and hasattr(model, "output_dict"):
model.output_dict = True
else:
model_cfg = model_cfg or get_model_config(model_name)
if model_cfg is not None:
logging.info(f'Loaded {model_name} model config.')
else:
logging.error(f'Model config for {model_name} not found; available models {list_models()}.')
raise RuntimeError(f'Model config for {model_name} not found.')
if force_quick_gelu:
# override for use of QuickGELU on non-OpenAI transformer models
model_cfg["quick_gelu"] = True
if force_patch_dropout is not None:
# override the default patch dropout value
model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout
if force_image_size is not None:
# override model config's image size
model_cfg["vision_cfg"]["image_size"] = force_image_size
if pretrained_image:
if 'timm_model_name' in model_cfg.get('vision_cfg', {}):
# pretrained weight loading for timm models set via vision_cfg
model_cfg['vision_cfg']['timm_model_pretrained'] = True
else:
assert False, 'pretrained image towers currently only supported for timm models'
cast_dtype = get_cast_dtype(precision)
is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {})
custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model
if custom_text:
if is_hf_model:
model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf
if "coca" in model_name:
model = CoCa(**model_cfg, cast_dtype=cast_dtype)
else:
model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype)
else:
model = CLIP(**model_cfg, cast_dtype=cast_dtype)
pretrained_loaded = False
if pretrained:
checkpoint_path = ''
pretrained_cfg = get_pretrained_cfg(model_name, pretrained)
if pretrained_cfg:
checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)
elif os.path.exists(pretrained):
checkpoint_path = pretrained
if checkpoint_path:
print(f'Loading pretrained {model_name} weights ({pretrained}).', flush=True)
logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
load_checkpoint(model, checkpoint_path)
else:
error_str = (
f'Pretrained weights ({pretrained}) not found for model {model_name}.'
f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.')
logging.warning(error_str)
raise RuntimeError(error_str)
pretrained_loaded = True
elif has_hf_hub_prefix:
logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
load_checkpoint(model, checkpoint_path)
pretrained_loaded = True
if require_pretrained and not pretrained_loaded:
# callers of create_model_from_pretrained always expect pretrained weights
raise RuntimeError(
f'Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded.')
model.to(device=device)
if precision in ("fp16", "bf16"):
convert_weights_to_lp(model, dtype=torch.bfloat16 if precision == 'bf16' else torch.float16)
# set image / mean metadata from pretrained_cfg if available, or use default
model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN
model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD
# to always output dict even if it is clip
if output_dict and hasattr(model, "output_dict"):
model.output_dict = True
if jit:
model = torch.jit.script(model)
return model
def create_loss(args): | return ClipLoss( | 9 | 2023-12-09 05:43:08+00:00 | 16k |
moonshot-admin/moonshot | third-party/tqdm-4.66.1/tqdm/tk.py | [
{
"identifier": "TqdmExperimentalWarning",
"path": "third-party/tqdm-4.66.1/tqdm/std.py",
"snippet": "class TqdmExperimentalWarning(TqdmWarning, FutureWarning):\n \"\"\"beta feature, unstable API and behaviour\"\"\"\n pass"
},
{
"identifier": "TqdmWarning",
"path": "third-party/tqdm-4.... | import re
import sys
import tkinter
import tkinter.ttk as ttk
from warnings import warn
from .std import TqdmExperimentalWarning, TqdmWarning
from .std import tqdm as std_tqdm | 13,102 | """
Tkinter GUI progressbar decorator for iterators.
Usage:
>>> from tqdm.tk import trange, tqdm
>>> for i in trange(10):
... ...
"""
__author__ = {"github.com/": ["richardsheridan", "casperdcl"]}
__all__ = ['tqdm_tk', 'ttkrange', 'tqdm', 'trange']
class tqdm_tk(std_tqdm): # pragma: no cover
"""
Experimental Tkinter GUI version of tqdm!
Note: Window interactivity suffers if `tqdm_tk` is not running within
a Tkinter mainloop and values are generated infrequently. In this case,
consider calling `tqdm_tk.refresh()` frequently in the Tk thread.
"""
# TODO: @classmethod: write()?
def __init__(self, *args, **kwargs):
"""
This class accepts the following parameters *in addition* to
the parameters accepted by `tqdm`.
Parameters
----------
grab : bool, optional
Grab the input across all windows of the process.
tk_parent : `tkinter.Wm`, optional
Parent Tk window.
cancel_callback : Callable, optional
Create a cancel button and set `cancel_callback` to be called
when the cancel or window close button is clicked.
"""
kwargs = kwargs.copy()
kwargs['gui'] = True
# convert disable = None to False
kwargs['disable'] = bool(kwargs.get('disable', False))
self._warn_leave = 'leave' in kwargs
grab = kwargs.pop('grab', False)
tk_parent = kwargs.pop('tk_parent', None)
self._cancel_callback = kwargs.pop('cancel_callback', None)
super(tqdm_tk, self).__init__(*args, **kwargs)
if self.disable:
return
if tk_parent is None: # Discover parent widget
try:
tk_parent = tkinter._default_root
except AttributeError:
raise AttributeError(
"`tk_parent` required when using `tkinter.NoDefaultRoot()`")
if tk_parent is None: # use new default root window as display
self._tk_window = tkinter.Tk()
else: # some other windows already exist
self._tk_window = tkinter.Toplevel()
else:
self._tk_window = tkinter.Toplevel(tk_parent)
| """
Tkinter GUI progressbar decorator for iterators.
Usage:
>>> from tqdm.tk import trange, tqdm
>>> for i in trange(10):
... ...
"""
__author__ = {"github.com/": ["richardsheridan", "casperdcl"]}
__all__ = ['tqdm_tk', 'ttkrange', 'tqdm', 'trange']
class tqdm_tk(std_tqdm): # pragma: no cover
"""
Experimental Tkinter GUI version of tqdm!
Note: Window interactivity suffers if `tqdm_tk` is not running within
a Tkinter mainloop and values are generated infrequently. In this case,
consider calling `tqdm_tk.refresh()` frequently in the Tk thread.
"""
# TODO: @classmethod: write()?
def __init__(self, *args, **kwargs):
"""
This class accepts the following parameters *in addition* to
the parameters accepted by `tqdm`.
Parameters
----------
grab : bool, optional
Grab the input across all windows of the process.
tk_parent : `tkinter.Wm`, optional
Parent Tk window.
cancel_callback : Callable, optional
Create a cancel button and set `cancel_callback` to be called
when the cancel or window close button is clicked.
"""
kwargs = kwargs.copy()
kwargs['gui'] = True
# convert disable = None to False
kwargs['disable'] = bool(kwargs.get('disable', False))
self._warn_leave = 'leave' in kwargs
grab = kwargs.pop('grab', False)
tk_parent = kwargs.pop('tk_parent', None)
self._cancel_callback = kwargs.pop('cancel_callback', None)
super(tqdm_tk, self).__init__(*args, **kwargs)
if self.disable:
return
if tk_parent is None: # Discover parent widget
try:
tk_parent = tkinter._default_root
except AttributeError:
raise AttributeError(
"`tk_parent` required when using `tkinter.NoDefaultRoot()`")
if tk_parent is None: # use new default root window as display
self._tk_window = tkinter.Tk()
else: # some other windows already exist
self._tk_window = tkinter.Toplevel()
else:
self._tk_window = tkinter.Toplevel(tk_parent)
| warn("GUI is experimental/alpha", TqdmExperimentalWarning, stacklevel=2) | 0 | 2023-12-14 07:43:03+00:00 | 16k |
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 | 14,187 |
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',
|
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, | 3 | 2023-12-14 18:57:56+00:00 | 16k |
pan-x-c/EE-LLM | megatron/core/models/gpt/gpt_layer_specs.py | [
{
"identifier": "get_bias_dropout_add",
"path": "megatron/core/fusions/fused_bias_dropout.py",
"snippet": "def get_bias_dropout_add(training, fused):\n if fused:\n # jit scripting for a nn.module (with dropout) is not\n # triggering the fusion kernel. For now, we use two\n # diff... | from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules
from megatron.core.transformer.custom_layers.transformer_engine import (
TEDotProductAttention,
TELayerNormColumnParallelLinear,
TERowParallelLinear,
)
from megatron.core.transformer.dot_product_attention import DotProductAttention
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.mlp import MLP, MLPSubmodules
from megatron.core.transformer.spec_utils import ModuleSpec
from megatron.core.transformer.switch_mlp import SwitchMLP
from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules | 12,878 |
# Use this spec to use lower level Transformer Engine modules (required for fp8 training)
gpt_layer_with_transformer_engine_spec = ModuleSpec(
module=TransformerLayer,
submodules=TransformerLayerSubmodules(
self_attention=ModuleSpec(
|
# Use this spec to use lower level Transformer Engine modules (required for fp8 training)
gpt_layer_with_transformer_engine_spec = ModuleSpec(
module=TransformerLayer,
submodules=TransformerLayerSubmodules(
self_attention=ModuleSpec( | module=SelfAttention, | 4 | 2023-12-07 08:29:38+00:00 | 16k |
tommy-xq/SA2VP | vit_train_swin.py | [
{
"identifier": "create_optimizer",
"path": "optim_factory.py",
"snippet": "def create_optimizer(args, model, get_num_layer=None, get_layer_scale=None, filter_bias_and_bn=True, skip_list=None):\n opt_lower = args.opt.lower()\n weight_decay = args.weight_decay\n if weight_decay and filter_bias_a... | import argparse
import datetime
import numpy as np
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import json
import os
import utils
import random
import deepspeed
from pathlib import Path
from time import sleep
from timm.data.mixup import Mixup
from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import ModelEma
from optim_factory import create_optimizer, get_parameter_groups, LayerDecayValueAssigner
from datasets import build_dataset
from datasets import build_beit_pretraining_dataset, build_beit_pretraining_dataset_val
from engine_for_train import train_one_epoch, evaluate # engine for vit
from utils import NativeScalerWithGradNormCount as NativeScaler
from scipy import interpolate
from timm.models.layers import trunc_normal_
from functools import partial
from vpt_main.src.models.build_swin_backbone import _build_swin_model # choose model
from deepspeed import DeepSpeedConfig
| 11,737 | utils.create_ds_config(args)
print(args)
device = torch.device(args.device)
seed = 42
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
if args.disable_eval_during_finetuning:
dataset_val = None
else:
dataset_val, _ = build_dataset(is_train=False, args=args)
print("Calculation of training examples = %d" % len(dataset_train))
print("Calculation of other examples = %d" % len(dataset_val))
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_train = torch.utils.data.DistributedSampler(
dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
)
print("Sampler_train = %s" % str(sampler_train))
if args.dist_eval:
if len(dataset_val) % num_tasks != 0:
print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
'This will slightly alter validation results as extra duplicate entries are added to achieve '
'equal num of samples per-process.')
sampler_val = torch.utils.data.DistributedSampler(
dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=False)
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
if global_rank == 0 and args.log_dir is not None:
os.makedirs(args.log_dir, exist_ok=True)
log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
else:
log_writer = None
data_loader_train = torch.utils.data.DataLoader(
dataset_train, sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
if dataset_val is not None:
data_loader_val = torch.utils.data.DataLoader(
dataset_val, sampler=sampler_val,
batch_size=int(4*args.batch_size),
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False
)
else:
data_loader_val = None
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
print("Mixup is activated!")
mixup_fn = Mixup(
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
label_smoothing=args.smoothing, num_classes=args.nb_classes)
model = Dual_model(args)
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
frozen_parameters = sum(p.numel() for p in model.parameters() if not p.requires_grad)
total_parameters = sum(p.numel() for p in model.parameters())
print('------------------------------')
for name, param in model.named_parameters():
print(name, param.requires_grad)
print('------------------------------')
model.to(device)
model_ema = None
if args.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
model_ema = ModelEma(
model,
decay=args.model_ema_decay,
device='cpu' if args.model_ema_force_cpu else '',
resume='')
print("Using EMA with decay = %.8f" % args.model_ema_decay)
model_without_ddp = model
# print("Model = %s" % str(model_without_ddp))
total_batch_size = args.batch_size * args.update_freq * utils.get_world_size()
num_training_steps_per_epoch = len(dataset_train) // total_batch_size
print("LR = %.8f" % args.lr)
print("Batch size = %d" % total_batch_size)
print("Update frequent = %d" % args.update_freq)
print("Number of training examples = %d" % len(dataset_train))
print("Number of training training per epoch = %d" % num_training_steps_per_epoch)
assigner = None
if assigner is not None:
print("Assigned values = %s" % str(assigner.values))
skip_weight_decay_list = None
if args.enable_deepspeed:
loss_scaler = None
| # --------------------------------------------------------
# SA2VP: Spatially Aligned-and-Adapted Visual Prompt code
# reference:
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Based on timm
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# --------------------------------------------------------'
#os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID'
#os.environ['CUDA_VISIBLE_DEVICES']='0'
class Dual_model(nn.Module):
def __init__(self, args):
super(Dual_model, self).__init__()
self.vit_base, feat_dim = _build_swin_model('swinb_imagenet22k_224', 224, './backbone_ckpt') # where to save pre-trained model ./backbone_ckpt
for k, p in self.vit_base.named_parameters():
name_list = k.split('.')
print(name_list)
if name_list[1] == 'deep_ppt' or name_list[1] == 'proj_ppt':
p.requires_grad = True
elif name_list[1] == '2':
if name_list[2] == 'cross_attn':
if name_list[4] == 'ffn' or name_list[4] == 'ffn_norm':
p.requires_grad = True
else:
p.requires_grad = False
else:
p.requires_grad = False
else:
p.requires_grad = False
self.class_head = nn.Linear(1024, args.nb_classes, bias=True)
trunc_normal_(self.class_head.weight, std=0.02)
def forward(self, x):
x, p = self.vit_base.forward_features(x) # B*768
return self.class_head(x), self.class_head(p)
def get_args():
parser = argparse.ArgumentParser('SA2VP script for image classification', add_help=False)
parser.add_argument('--batch_size', default=64, type=int)
parser.add_argument('--epochs', default=30, type=int)
parser.add_argument('--update_freq', default=1, type=int)
parser.add_argument('--save_ckpt_freq', default=50, type=int)
parser.add_argument("--discrete_vae_weight_path", type=str)
parser.add_argument("--discrete_vae_type", type=str, default="dall-e")
# Model parameters
parser.add_argument('--model', default='beit_base_patch16_224', type=str, metavar='MODEL',
help='Name of model to train')
parser.add_argument('--rel_pos_bias', action='store_true')
parser.add_argument('--disable_rel_pos_bias', action='store_false', dest='rel_pos_bias')
parser.set_defaults(rel_pos_bias=False)
parser.add_argument('--abs_pos_emb', action='store_true')
parser.set_defaults(abs_pos_emb=True)
parser.add_argument('--layer_scale_init_value', default=0.1, type=float,
help="0.1 for base, 1e-5 for large. set 0 to disable layer scale")
parser.add_argument('--input_size', default=224, type=int,
help='images input size')
parser.add_argument('--second_input_size', default=112, type=int,
help='images input size for discrete vae')
parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
help='Dropout rate (default: 0.)')
parser.add_argument('--attn_drop_rate', type=float, default=0.0, metavar='PCT',
help='Attention dropout rate (default: 0.)')
parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT',
help='Drop path rate (default: 0.1)')
parser.add_argument('--disable_eval_during_finetuning', action='store_true', default=False)
parser.add_argument('--model_ema', action='store_true', default=False)
parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
parser.add_argument('--model_ema_force_cpu', action='store_true', default=False, help='')
# Optimizer parameters
parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
help='Optimizer (default: "adamw"')
parser.add_argument('--opt_eps', default=1e-8, type=float, metavar='EPSILON',
help='Optimizer Epsilon (default: 1e-8)')
parser.add_argument('--opt_betas', default=None, type=float, nargs='+', metavar='BETA',
help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--weight_decay', type=float, default=0.05,
help='weight decay (default: 0.05)')
parser.add_argument('--weight_decay_end', type=float, default=None, help="""Final value of the
weight decay. We use a cosine schedule for WD and using a larger decay by
the end of training improves performance for ViTs.""")
parser.add_argument('--lr', type=float, default=5e-4, metavar='LR',
help='learning rate (default: 5e-4)')
parser.add_argument('--layer_decay', type=float, default=0.9)
parser.add_argument('--warmup_lr', type=float, default=1e-6, metavar='LR',
help='warmup learning rate (default: 1e-6)')
parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N',
help='epochs to warmup LR, if scheduler supports')
parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
help='num of steps to warmup LR, will overload warmup_epochs if set > 0')
# Augmentation parameters
parser.add_argument('--color_jitter', type=float, default=0.4, metavar='PCT',
help='Color jitter factor (default: 0.4)')
parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)'),
parser.add_argument('--smoothing', type=float, default=0,
help='Label smoothing (default: 0)')
parser.add_argument('--train_interpolation', type=str, default='bicubic',
help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
parser.add_argument('--second_interpolation', type=str, default='lanczos',
help='Interpolation for discrete vae (random, bilinear, bicubic default: "lanczos")')
# Evaluation parameters
parser.add_argument('--crop_pct', type=float, default=None)
# * Random Erase params
parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
help='Random erase prob (default: 0.25)')
parser.add_argument('--remode', type=str, default='pixel',
help='Random erase mode (default: "pixel")')
parser.add_argument('--recount', type=int, default=1,
help='Random erase count (default: 1)')
parser.add_argument('--resplit', action='store_true', default=False,
help='Do not random erase first (clean) augmentation split')
# * Mixup params
parser.add_argument('--mixup', type=float, default=0,
help='mixup alpha, mixup enabled if > 0.')
parser.add_argument('--cutmix', type=float, default=0,
help='cutmix alpha, cutmix enabled if > 0.')
parser.add_argument('--cutmix_minmax', type=float, nargs='+', default=None,
help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
parser.add_argument('--mixup_prob', type=float, default=1.0,
help='Probability of performing mixup or cutmix when either/both is enabled')
parser.add_argument('--mixup_switch_prob', type=float, default=0.5,
help='Probability of switching to cutmix when both mixup and cutmix enabled')
parser.add_argument('--mixup_mode', type=str, default='batch',
help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
# * Finetuning params
parser.add_argument('--finetune', default='',
help='finetune from checkpoint')
parser.add_argument('--model_key', default='model|module', type=str)
parser.add_argument('--model_prefix', default='', type=str)
parser.add_argument('--init_scale', default=0.001, type=float)
parser.add_argument('--use_mean_pooling', action='store_true')
parser.set_defaults(use_mean_pooling=True)
parser.add_argument('--use_cls', action='store_false', dest='use_mean_pooling')
parser.add_argument('--disable_weight_decay_on_rel_pos_bias', action='store_true', default=False)
# Dataset parameters
parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
help='dataset path')
parser.add_argument('--my_mode', default='train_val', type=str,
help='my mode to train or test')
parser.add_argument('--eval_data_path', default=None, type=str,
help='dataset path for evaluation')
parser.add_argument('--nb_classes', default=0, type=int,
help='number of the classification types')
parser.add_argument('--imagenet_default_mean_and_std', default=False, action='store_true')
parser.add_argument('--data_set', default='CUB', choices=['CIFAR', 'IMNET', 'image_folder', 'CUB', 'DOG', 'FLOWER', 'CAR', 'BIRD', 'CAL101', 'DMLAB','EUROSAT','PATCH_CAMELYON','CLEVR_COUNT','CIFAR100','FOOD101','SVHN','DTD','FLOWER_S','PET','SVHN_S','SUN','Resisc45','Retinopathy','CLEVR_DISTANCE','KITTI_DISTANCE','DS_LOC','DS_ORI','SN_AZI','SN_ELE', 'DTD_DAM', 'GTSRB_DAM', 'FOOD_DAM', 'CIFAR10_DAM', 'CIFAR100_DAM', 'SVHN_DAM'],
type=str, help='ImageNet dataset path')
parser.add_argument('--output_dir', default='',
help='path where to save, empty for no saving')
parser.add_argument('--log_dir', default=None,
help='path where to tensorboard log')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--resume', default='',
help='resume from checkpoint')
parser.add_argument('--auto_resume', action='store_true')
parser.add_argument('--no_auto_resume', action='store_false', dest='auto_resume')
parser.set_defaults(auto_resume=True)
parser.add_argument('--save_ckpt', action='store_true')
parser.add_argument('--no_save_ckpt', action='store_false', dest='save_ckpt')
parser.set_defaults(save_ckpt=True)
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--eval', action='store_true',
help='Perform evaluation only')
parser.add_argument('--dist_eval', action='store_true', default=False,
help='Enabling distributed evaluation')
parser.add_argument('--num_workers', default=10, type=int)
parser.add_argument('--pin_mem', action='store_true',
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
parser.set_defaults(pin_mem=True)
# distributed training parameters
parser.add_argument('--world_size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--local_rank', default=-1, type=int)
parser.add_argument('--dist_on_itp', action='store_true')
parser.add_argument('--dist_url', default='env://',
help='url used to set up distributed training')
parser.add_argument('--enable_deepspeed', action='store_true', default=False)
known_args, _ = parser.parse_known_args()
if known_args.enable_deepspeed:
try:
parser = deepspeed.add_config_arguments(parser)
ds_init = deepspeed.initialize
except:
print("Please 'pip install deepspeed==0.4.0'")
exit(0)
else:
ds_init = None
return parser.parse_args(), ds_init
def main(args, ds_init):
utils.init_distributed_mode(args)
if ds_init is not None:
utils.create_ds_config(args)
print(args)
device = torch.device(args.device)
seed = 42
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
if args.disable_eval_during_finetuning:
dataset_val = None
else:
dataset_val, _ = build_dataset(is_train=False, args=args)
print("Calculation of training examples = %d" % len(dataset_train))
print("Calculation of other examples = %d" % len(dataset_val))
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_train = torch.utils.data.DistributedSampler(
dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
)
print("Sampler_train = %s" % str(sampler_train))
if args.dist_eval:
if len(dataset_val) % num_tasks != 0:
print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
'This will slightly alter validation results as extra duplicate entries are added to achieve '
'equal num of samples per-process.')
sampler_val = torch.utils.data.DistributedSampler(
dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=False)
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
if global_rank == 0 and args.log_dir is not None:
os.makedirs(args.log_dir, exist_ok=True)
log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
else:
log_writer = None
data_loader_train = torch.utils.data.DataLoader(
dataset_train, sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
if dataset_val is not None:
data_loader_val = torch.utils.data.DataLoader(
dataset_val, sampler=sampler_val,
batch_size=int(4*args.batch_size),
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False
)
else:
data_loader_val = None
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
print("Mixup is activated!")
mixup_fn = Mixup(
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
label_smoothing=args.smoothing, num_classes=args.nb_classes)
model = Dual_model(args)
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
frozen_parameters = sum(p.numel() for p in model.parameters() if not p.requires_grad)
total_parameters = sum(p.numel() for p in model.parameters())
print('------------------------------')
for name, param in model.named_parameters():
print(name, param.requires_grad)
print('------------------------------')
model.to(device)
model_ema = None
if args.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
model_ema = ModelEma(
model,
decay=args.model_ema_decay,
device='cpu' if args.model_ema_force_cpu else '',
resume='')
print("Using EMA with decay = %.8f" % args.model_ema_decay)
model_without_ddp = model
# print("Model = %s" % str(model_without_ddp))
total_batch_size = args.batch_size * args.update_freq * utils.get_world_size()
num_training_steps_per_epoch = len(dataset_train) // total_batch_size
print("LR = %.8f" % args.lr)
print("Batch size = %d" % total_batch_size)
print("Update frequent = %d" % args.update_freq)
print("Number of training examples = %d" % len(dataset_train))
print("Number of training training per epoch = %d" % num_training_steps_per_epoch)
assigner = None
if assigner is not None:
print("Assigned values = %s" % str(assigner.values))
skip_weight_decay_list = None
if args.enable_deepspeed:
loss_scaler = None
| optimizer_params = get_parameter_groups(
| 1 | 2023-12-12 13:19:17+00:00 | 16k |
lumina-test/lumina | lumina/e2e_test/test_gbn.py | [
{
"identifier": "get_qp_info_list",
"path": "lumina/analyzer/main.py",
"snippet": "def get_qp_info_list(switch_msg_snapshot):\n \"\"\" Get the list of QP info from the switch message snapshot\n\n Args:\n switch_msg_snapshot (str): The path to the switch message snapshot\n\n Returns:\n ... | import argparse, os, math, glob, logging, time
import lumina.analyzer.checker.integrity_check as integrity_check
import lumina.analyzer.checker.host_check as host_check
import lumina.analyzer.checker.gbn_check as gbn_check
import lumina.analyzer.checker.read_gbn_check as read_gbn_check
import lumina.orchestrator.host as host
import lumina.orchestrator.switch as switch
from lumina.analyzer.main import get_qp_info_list
from lumina.orchestrator.main import Orchestrator
from lumina.analyzer.counter.switch_counter import SwitchCounter
from lumina.analyzer.counter.host_counter import MLNXHostCounter, IntelHostCounter
from lumina.analyzer.pcap_processor.pcap_process import get_packet_list
from lumina.analyzer.measurer.latency_measure import LatencyMeasure
from lumina.utils.config_loggers import config_stream_handler, config_file_handler
from lumina.analyzer.packet_parser.roce_packet import TRIGGER_OOS, TRIGGER_TIMEOUT | 13,636 |
## All logs will be logged into file LOG_FILENAME
LOG_FILENAME = "test_gbn.log"
## Results (checkers and measurements) will also be dumped into file RESULT_FILENAME
RESULT_FILENAME = "result.log"
## Max # of retries for each experiment iteration
MAX_NB_EXP_RETRIES = 3
def setup_root_logger(orchestrator):
""" Setup the root logger for the test
Args:
orchestrator (Orchestrator object): Orchestrator object that contains all the configurations
Returns:
N/A
"""
root_logger = logging.getLogger()
root_logger.handlers.clear()
config_stream_handler(root_logger)
config_file_handler(logger=root_logger,
log_file=os.path.join(orchestrator.result_path, LOG_FILENAME),
no_format=False)
def run_traffic(orchestrator):
""" Run the traffic and collect the results
Args:
orchestrator (Orchestrator object): Orchestrator object that contains all the configurations
Returns:
bool: True if the experiment is successful, False otherwise
"""
orchestrator.rm_old_files()
if orchestrator.sync_and_compile() == False:
logging.error("Failed to sync and compile the code")
sys.exit(-1)
logging.info("Sync and compile completed")
if orchestrator.generate_switch_config_file() == False:
logging.error("Failed to generate switch configuration file")
sys.exit(-1)
num_repeats = orchestrator.get_num_repeats()
for i in range(num_repeats):
logging.info("=" * 100)
nb_retry = 0
iter_result = False
while nb_retry < MAX_NB_EXP_RETRIES:
if orchestrator.run_experiment() == False:
logging.error("Iteration %d: Failed to complete experiment" % i)
logging.error("Iteration %d: Rerun experiment (retry: %d)" % i, nb_retry)
nb_retry += 1
orchestrator.clean_up()
time.sleep(5)
continue
logging.info("Iteration %d: Completed experiment" % i)
try:
orchestrator.clean_up()
orchestrator.fetch_results(i)
logging.info("Iteration %d: Fetch experiment results" % i)
orchestrator.merge_traces(i)
logging.info("Iteration %d: Merge the pcap files" % i)
except:
logging.error("Iteration %d: Result collection failed" % (i))
logging.error("Iteration %d: Rerun experiment (retry: %d)" % (i, nb_retry))
nb_retry += 1
time.sleep(5)
continue
if orchestrator.check_integrity(i) == False:
logging.error("Iteration %d: Integrity check failed" % (i))
logging.error("Iteration %d: Rerun experiment (retry: %d)" % (i, nb_retry))
nb_retry += 1
time.sleep(5)
continue
iter_result = True
break
if iter_result is False:
logging.error("Iteration %d: Still failed after %d retries" % (i, nb_retry))
return False
return True
def analyze_retrans_latency(pkt, latency_measurement, is_read, logger):
""" Analyze the retransmission latency breakdown for an undelivered packet
Args:
pkt (Packet object): The undelivered packet
latency_measurement (LatencyMeasure object): A LatencyMeasure object that can compute latency breakdown
is_read (bool): If we use RDMA READ in this experiment
logger (logging.Logger): A logger object
Returns:
N/A
"""
# All the undelivered packets should be retransmitted in our test cases
if latency_measurement.get_retransmit_pkt(pkt) == None:
logger.error("\t\t No retransmit packet found for this packet")
logger.error("\t\t It is possible that this undelivered packet is a redundant transmission")
return
retrans_latency = latency_measurement.get_retransmit_latency(pkt)
if is_read == True:
# For RDMA READ, we should always find a NACK READ request that triggers retransmission
nack = latency_measurement.get_nack(pkt)
if nack is not None:
trigger = nack.get_trigger()
|
## All logs will be logged into file LOG_FILENAME
LOG_FILENAME = "test_gbn.log"
## Results (checkers and measurements) will also be dumped into file RESULT_FILENAME
RESULT_FILENAME = "result.log"
## Max # of retries for each experiment iteration
MAX_NB_EXP_RETRIES = 3
def setup_root_logger(orchestrator):
""" Setup the root logger for the test
Args:
orchestrator (Orchestrator object): Orchestrator object that contains all the configurations
Returns:
N/A
"""
root_logger = logging.getLogger()
root_logger.handlers.clear()
config_stream_handler(root_logger)
config_file_handler(logger=root_logger,
log_file=os.path.join(orchestrator.result_path, LOG_FILENAME),
no_format=False)
def run_traffic(orchestrator):
""" Run the traffic and collect the results
Args:
orchestrator (Orchestrator object): Orchestrator object that contains all the configurations
Returns:
bool: True if the experiment is successful, False otherwise
"""
orchestrator.rm_old_files()
if orchestrator.sync_and_compile() == False:
logging.error("Failed to sync and compile the code")
sys.exit(-1)
logging.info("Sync and compile completed")
if orchestrator.generate_switch_config_file() == False:
logging.error("Failed to generate switch configuration file")
sys.exit(-1)
num_repeats = orchestrator.get_num_repeats()
for i in range(num_repeats):
logging.info("=" * 100)
nb_retry = 0
iter_result = False
while nb_retry < MAX_NB_EXP_RETRIES:
if orchestrator.run_experiment() == False:
logging.error("Iteration %d: Failed to complete experiment" % i)
logging.error("Iteration %d: Rerun experiment (retry: %d)" % i, nb_retry)
nb_retry += 1
orchestrator.clean_up()
time.sleep(5)
continue
logging.info("Iteration %d: Completed experiment" % i)
try:
orchestrator.clean_up()
orchestrator.fetch_results(i)
logging.info("Iteration %d: Fetch experiment results" % i)
orchestrator.merge_traces(i)
logging.info("Iteration %d: Merge the pcap files" % i)
except:
logging.error("Iteration %d: Result collection failed" % (i))
logging.error("Iteration %d: Rerun experiment (retry: %d)" % (i, nb_retry))
nb_retry += 1
time.sleep(5)
continue
if orchestrator.check_integrity(i) == False:
logging.error("Iteration %d: Integrity check failed" % (i))
logging.error("Iteration %d: Rerun experiment (retry: %d)" % (i, nb_retry))
nb_retry += 1
time.sleep(5)
continue
iter_result = True
break
if iter_result is False:
logging.error("Iteration %d: Still failed after %d retries" % (i, nb_retry))
return False
return True
def analyze_retrans_latency(pkt, latency_measurement, is_read, logger):
""" Analyze the retransmission latency breakdown for an undelivered packet
Args:
pkt (Packet object): The undelivered packet
latency_measurement (LatencyMeasure object): A LatencyMeasure object that can compute latency breakdown
is_read (bool): If we use RDMA READ in this experiment
logger (logging.Logger): A logger object
Returns:
N/A
"""
# All the undelivered packets should be retransmitted in our test cases
if latency_measurement.get_retransmit_pkt(pkt) == None:
logger.error("\t\t No retransmit packet found for this packet")
logger.error("\t\t It is possible that this undelivered packet is a redundant transmission")
return
retrans_latency = latency_measurement.get_retransmit_latency(pkt)
if is_read == True:
# For RDMA READ, we should always find a NACK READ request that triggers retransmission
nack = latency_measurement.get_nack(pkt)
if nack is not None:
trigger = nack.get_trigger() | if trigger == TRIGGER_OOS: | 9 | 2023-12-09 08:21:14+00:00 | 16k |
boweniac/autogan | autogan/agents/tool_agent_search.py | [
{
"identifier": "CodeExecution",
"path": "autogan/tools/code_execution_tool.py",
"snippet": "class CodeExecution:\n def __init__(self, work_dir: Optional[str] = None):\n \"\"\"A class for code execution\n 用于代码执行的类\n\n Supports python, bash, shell, powershell code\n 支持 pyth... | import re
from collections import defaultdict
from typing import Optional, Dict
from autogan.tools.code_execution_tool import CodeExecution
from autogan.tools.wolfram_alpha_tool import WolframAlphaAPIWrapper
from autogan.oai.count_tokens_utils import count_text_tokens
from autogan.agents.universal_agent import UniversalAgent
from autogan.utils.compressed_text_utils import compressed_text_universal
from autogan.tools.web_search_tool import WebSearch | 11,604 |
class ToolAgentSearch(UniversalAgent):
def __init__(
self,
search_config: Dict,
agent_config: Optional[Dict] = None,
retry_times: Optional[int] = 10,
name: Optional[str] = "WebSearchExp",
duty: Optional[str] = 'Not only can I search for information on the internet, '
'but I can also answer questions using the Wolfram engine.',
work_flow: Optional[str] = """I hope you are an internet search expert. When you receive a search request, you have the following two tools to choose from:
1. web: You can search for information on the internet. When using it, please enclose the search keywords in your output with the ```web\n ``` symbol, for example:
```web
Your search keywords
```
2. wolfram: You can use the Wolfram engine to help you calculate or query data related to Mathematics, finance, unit conversion, data analysis, science, geography, history, culture, movies, music, etc.
When using it, please enclose the English question that Wolfram can understand in your output with the ```wolfram\n ``` symbol, for example:
```wolfram
one wolfram query
```
Note: When you decide to use a tool, please do not @ anyone.""",
# duty: Optional[str] = '我不但可以从网络上搜索资料，还可以通过 wolfram 引擎来回答问题。',
# work_flow: Optional[str] = """我希望你是一个网络搜索专家，当你收到搜索请求时，你有一下两种工具可供选择：
#
# 1. web: 可以在网络上查找资料。使用时请在你的输出内容中，将搜索关键词用```web\n ``` 符号封装，例如：
# ```web
# Your search keywords
# ```
#
# 2.wolfram: 可以使用wolfram引擎，帮你计算或查询数学、金融、单位转换、数据分析、科学、地理、历史、文化、电影、音乐等相关数据。使用时请在你的输出内容中，将 wolfram 可以理解的英文问题用```wolfram\n ``` 符号封装，例如：
# ```wolfram
# one wolfram query
# ```
#
# 注意：当你决定使用工具时，请不要@任何人""",
):
"""WebSearchExpert
1.Receive the user's question and convert it into search keywords.
2.Call the Google Search API to obtain a result and extract the webpage content.
3.If no content related to the user's question is extracted,
call the Google Search API again to obtain the next result.
4.Repeat operations 2 and 3 until reaching retry_times.
Within the same task session domain, if the search keywords are the same,
the offset of the search results will accumulate and move backwards.
:param agent_config: The agent configuration includes:
agent 配置包括：
- main_model: The LLM configuration of the agent's main body.
agent 主体的 LLM 配置。
- summary_model: The LLM configuration used for compressing context and generating text summaries.
用于压缩上下文以及生成文本摘要的 LLM 配置。
- request_interval_time: The interval time of LLM requests.
LLM 请求间隔时间。
- request_timeout:The timeout of LLM requests.
LLM 请求超时时间。
- max_retries: The maximum number of retries for LLM requests.
LLM 请求最大重试次数。
:param search_config: JSON format of email_config {"cx": "", "key": ""}
:param retry_times: Represent the maximum number of attempts for each search, the default is 10.
:param name: The agent name should be unique in the organizational structure.
:param duty: Used to explain one's job responsibilities to other agents.
:param work_flow: Defines the workflow of the agent.
定义 agent 的工作流程。
"""
super().__init__(
name,
agent_config=agent_config,
duty=duty,
work_flow=work_flow,
use_tool="join"
)
self._web_search = WebSearch(search_config["google_search"]) if "google_search" in search_config else None
|
class ToolAgentSearch(UniversalAgent):
def __init__(
self,
search_config: Dict,
agent_config: Optional[Dict] = None,
retry_times: Optional[int] = 10,
name: Optional[str] = "WebSearchExp",
duty: Optional[str] = 'Not only can I search for information on the internet, '
'but I can also answer questions using the Wolfram engine.',
work_flow: Optional[str] = """I hope you are an internet search expert. When you receive a search request, you have the following two tools to choose from:
1. web: You can search for information on the internet. When using it, please enclose the search keywords in your output with the ```web\n ``` symbol, for example:
```web
Your search keywords
```
2. wolfram: You can use the Wolfram engine to help you calculate or query data related to Mathematics, finance, unit conversion, data analysis, science, geography, history, culture, movies, music, etc.
When using it, please enclose the English question that Wolfram can understand in your output with the ```wolfram\n ``` symbol, for example:
```wolfram
one wolfram query
```
Note: When you decide to use a tool, please do not @ anyone.""",
# duty: Optional[str] = '我不但可以从网络上搜索资料，还可以通过 wolfram 引擎来回答问题。',
# work_flow: Optional[str] = """我希望你是一个网络搜索专家，当你收到搜索请求时，你有一下两种工具可供选择：
#
# 1. web: 可以在网络上查找资料。使用时请在你的输出内容中，将搜索关键词用```web\n ``` 符号封装，例如：
# ```web
# Your search keywords
# ```
#
# 2.wolfram: 可以使用wolfram引擎，帮你计算或查询数学、金融、单位转换、数据分析、科学、地理、历史、文化、电影、音乐等相关数据。使用时请在你的输出内容中，将 wolfram 可以理解的英文问题用```wolfram\n ``` 符号封装，例如：
# ```wolfram
# one wolfram query
# ```
#
# 注意：当你决定使用工具时，请不要@任何人""",
):
"""WebSearchExpert
1.Receive the user's question and convert it into search keywords.
2.Call the Google Search API to obtain a result and extract the webpage content.
3.If no content related to the user's question is extracted,
call the Google Search API again to obtain the next result.
4.Repeat operations 2 and 3 until reaching retry_times.
Within the same task session domain, if the search keywords are the same,
the offset of the search results will accumulate and move backwards.
:param agent_config: The agent configuration includes:
agent 配置包括：
- main_model: The LLM configuration of the agent's main body.
agent 主体的 LLM 配置。
- summary_model: The LLM configuration used for compressing context and generating text summaries.
用于压缩上下文以及生成文本摘要的 LLM 配置。
- request_interval_time: The interval time of LLM requests.
LLM 请求间隔时间。
- request_timeout:The timeout of LLM requests.
LLM 请求超时时间。
- max_retries: The maximum number of retries for LLM requests.
LLM 请求最大重试次数。
:param search_config: JSON format of email_config {"cx": "", "key": ""}
:param retry_times: Represent the maximum number of attempts for each search, the default is 10.
:param name: The agent name should be unique in the organizational structure.
:param duty: Used to explain one's job responsibilities to other agents.
:param work_flow: Defines the workflow of the agent.
定义 agent 的工作流程。
"""
super().__init__(
name,
agent_config=agent_config,
duty=duty,
work_flow=work_flow,
use_tool="join"
)
self._web_search = WebSearch(search_config["google_search"]) if "google_search" in search_config else None | self._wolfram_alpha = WolframAlphaAPIWrapper( | 1 | 2023-12-06 03:24:34+00:00 | 16k |
ebb-earl-co/tidal-wave | tidal_wave/playlist.py | [
{
"identifier": "AudioFormat",
"path": "tidal_wave/media.py",
"snippet": "class AudioFormat(str, Enum):\n sony_360_reality_audio = \"360\"\n dolby_atmos = \"Atmos\"\n hi_res = \"HiRes\"\n mqa = \"MQA\"\n lossless = \"Lossless\"\n high = \"High\"\n low = \"Low\""
},
{
"identi... | from dataclasses import dataclass
from pathlib import Path
from types import SimpleNamespace
from typing import Dict, List, Optional, Set, Tuple, Union
from requests import HTTPError, Session
from .media import AudioFormat
from .models import (
PlaylistsEndpointResponseJSON,
TracksEndpointResponseJSON,
VideosEndpointResponseJSON,
)
from .requesting import request_playlists
from .track import Track
from .utils import download_cover_image, temporary_file, TIDAL_API_URL
from .video import Video
import json
import logging
import math
import shutil
import sys
import ffmpeg
import mutagen | 11,626 | for subdir in subdirs:
if subdir.exists():
shutil.rmtree(subdir)
else:
return self.playlist_dir
def craft_m3u8_text(self):
"""This method creates a file called playlist.m3u8 in self.playlist_dir
that is a standard M3U. Needs to be called after self.flatten_playlist_dir
in order to be able to access self.files
N.b. the already-written file is temporarily copied to a .mp4 version in a
temporary directory because .m4a files cannot be read with mutagen."""
m3u_text: str = f"#EXTM3U\n#EXTENC:UTF-8\n#EXTIMG:{str(self.cover_path.absolute())}\n#PLAYLIST:{self.name}\n"
logger.info(
f"Creating .m3u8 playlist file for Playlist with ID '{self.playlist_id}'"
)
for d in self.files:
file: str = next(iter(d.values()))
if file is None:
continue
elif file.endswith(".flac"):
m = mutagen.File(file)
artist: str = m.get("artist", [""])[0]
title: str = m.get("title", [""])[0]
extinf: str = (
f"#EXTINF:{math.ceil(m.info.length)},"
f"{artist} - {title}\n{file}\n"
)
m3u_text += extinf
elif file.endswith(".mka"):
m = mutagen.File(file)
artist: str = m.get("ARTI", [""])[0]
title: str = m.get("TITL", [""])[0]
extinf: str = (
f"#EXTINF:{math.ceil(m.info.length)},"
f"{artist} - {title}\n{file}\n"
)
m3u_text += extinf
elif file.endswith(".m4a"):
# Mutagen cannot read .m4a files, so make a copy with all
# of the metadata tags as a .mp4 in a temporary directory
with temporary_file(suffix=".mp4") as tf:
ffmpeg.input(file, hide_banner=None, y=None).output(
tf.name,
acodec="copy",
vcodec="copy",
loglevel="quiet",
).run()
m = mutagen.File(tf.name)
artist: str = m.get("\xa9ART", [""])[0]
title: str = m.get("\xa9nam", [""])[0]
extinf: str = (
f"#EXTINF:{math.ceil(m.info.length)},"
f"{artist} - {title}\n{file}\n"
)
m3u_text += extinf
else:
return m3u_text
def dumps(self):
return json.dumps(self.files)
def dump(self, fp=sys.stdout):
json.dump(self.files, fp)
def get(self, session: Session, audio_format: AudioFormat, out_dir: Path):
"""The main method of this class, executing a number of other methods
in a row:
- self.get_metadata()
- self.set_items()
- self.set_dir()
- self.save_cover_image()
- self.save_description()
- self.get_items()
- self.flatten_playlist_dir()
"""
self.get_metadata(session)
if self.metadata is None:
self.files = {}
return
self.set_items(session)
self.set_dir(out_dir)
self.save_cover_image(session, out_dir)
try:
self.save_description()
except Exception:
pass
_get_items = self.get_items(session, audio_format)
if _get_items is None:
logger.critical(f"Could not retrieve playlist with ID '{self.playlist_id}'")
return
self.flatten_playlist_dir()
try:
m3u8_text: str = self.craft_m3u8_text()
except Exception as e:
logger.warning(
"Unable to create playlist.m3u8 file for "
f"playlist with ID '{self.playlist_id}'"
)
logger.debug(e)
else:
with open(self.playlist_dir / "playlist.m3u8", "w") as f:
f.write(m3u8_text)
logger.info(f"Playlist files written to '{self.playlist_dir}'")
class TidalPlaylistException(Exception):
pass
def request_playlist_items(session: Session, playlist_id: str) -> Optional[dict]:
"""Request from TIDAL API /playlists/items endpoint."""
|
logger = logging.getLogger("__name__")
@dataclass
class Playlist:
playlist_id: str # UUID4
def __post_init__(self):
self.playlist_dir: Optional[Path] = None
self.playlist_cover_saved: bool = False
def get_metadata(self, session: Session):
"""Request from TIDAL API /playlists endpoint"""
self.metadata: Optional[PlaylistsEndpointResponseJSON] = request_playlists(
session=session, identifier=self.playlist_id
)
if self.metadata is None:
return
self.name = (
self.metadata.title.replace("/", "_")
.replace("|", "_")
.replace(":", " -")
.replace('"', "")
.replace("..", "")
)
def set_items(self, session: Session):
"""Uses data from TIDAL API /playlists/items endpoint to
populate self.items"""
playlist_items: Optional[PlaylistsItemsResponseJSON] = get_playlist(
session=session, playlist_id=self.playlist_id
)
if playlist_items is None:
self.items = tuple()
else:
self.items: Tuple[Optional[PlaylistItem]] = tuple(playlist_items.items)
def set_dir(self, out_dir: Path):
"""Populates self.playlist_dir based on self.name, self.playlist_id"""
playlist_substring: str = f"{self.name} [{self.playlist_id}]"
self.playlist_dir: Path = out_dir / "Playlists" / playlist_substring
self.playlist_dir.mkdir(parents=True, exist_ok=True)
def save_cover_image(self, session: Session, out_dir: Path):
"""Requests self.metadata.image and attempts to write it to disk"""
if self.playlist_dir is None:
self.set_dir(out_dir=out_dir)
self.cover_path: Path = self.playlist_dir / "cover.jpg"
if not self.cover_path.exists():
download_cover_image(
session=session,
cover_uuid=self.metadata.square_image,
output_dir=self.playlist_dir,
dimension=1080,
)
else:
self.playlist_cover_saved = True
def save_description(self):
"""Requests self.metadata.description and attempts to write it to disk"""
description_path: Path = self.playlist_dir / "PlaylistDescription.txt"
if self.metadata.description is not None and len(self.metadata.description) > 0:
if not description_path.exists():
description_path.write_text(f"{self.metadata.description}\n")
def get_items(self, session: Session, audio_format: AudioFormat):
"""Using either Track.get() or Video.get(), attempt to request
the data for each track or video in self.items"""
if len(self.items) == 0:
return
tracks_videos: list = [None] * len(self.items)
for i, item in enumerate(self.items):
if item is None:
tracks_videos[i] = None
continue
elif isinstance(item, TracksEndpointResponseJSON):
track: Track = Track(track_id=item.id)
track.get(
session=session,
audio_format=audio_format,
out_dir=self.playlist_dir,
metadata=item,
)
tracks_videos[i] = track
elif isinstance(item, VideosEndpointResponseJSON):
video: Video = Video(video_id=item.id)
video.get(
session=session,
out_dir=self.playlist_dir,
metadata=item,
)
tracks_videos[i] = video
else:
tracks_videos[i] = None
continue
else:
self.tracks_videos: Tuple[
Tuple[int, Optional[Union[Track, Video]]]
] = tuple(tracks_videos)
return tracks_videos
def flatten_playlist_dir(self):
"""When self.get_items() is called, the tracks and/or videos in
self.items are downloaded using their self-contained .get() logic;
this means that they will be downloaded to albums. This function
"flattens" self.playlist_dir, meaning that it moves all downloaded
audio and video files to self.playlist_dir, and removes the various
subdirectories created"""
files: List[Dict[int, Optional[str]]] = [None] * len(self.tracks_videos)
if len(self.tracks_videos) == 0:
return
subdirs: Set[Path] = set()
for i, tv in enumerate(self.tracks_videos, 1):
if getattr(tv, "outfile") is None:
try:
getattr(tv, "album_dir")
except AttributeError:
pass
else:
subdirs.add(tv.album_dir)
subdirs.add(tv.album_dir.parent)
files[i - 1] = {i: None}
continue
_path: Optional[Path] = Path(tv.outfile) if tv is not None else None
# if the item never got turned into a track or video
if _path is None:
files[i - 1] = {i: None}
continue
# if the track or video didn't download
if _path.exists():
if _path.stat().st_size == 0:
files[i - 1] = {i: None}
continue
else:
files[i - 1] = {i: None}
continue
# otherwise, move files and clean up
if isinstance(tv, Track):
new_path: Path = self.playlist_dir / f"{i:03d} - {tv.trackname}"
new_path.write_bytes(_path.read_bytes())
_path.unlink()
files[i - 1] = {i: str(new_path.absolute())}
elif isinstance(tv, Video):
new_path: Path = self.playlist_dir / f"{i:03d} - {_path.name}"
new_path.write_bytes(_path.read_bytes())
_path.unlink()
files[i - 1] = {i: str(new_path.absolute())}
else:
self.files: List[Dict[int, Optional[str]]] = files
# Find all subdirectories written to
subdirs: Set[Path] = set()
for tv in self.tracks_videos:
if isinstance(tv, Track):
try:
getattr(tv, "album_dir")
except AttributeError:
pass
else:
subdirs.add(tv.album_dir)
subdirs.add(tv.album_dir.parent)
elif isinstance(tv, Video):
subdirs.add(tv.artist_dir)
# Copy all artist images, artist bio JSON files out
# of subdirs
artist_images: Set[Path] = set()
for subdir in subdirs:
for p in subdir.glob("*.jpg"):
if p.name == "cover.jpg":
continue
artist_images.add(p)
else:
for artist_image_path in artist_images:
if artist_image_path.exists():
shutil.copyfile(
artist_image_path.absolute(),
self.playlist_dir / artist_image_path.name,
)
artist_bios: Set[Path] = set()
for subdir in subdirs:
for p in subdir.glob("*bio.json"):
artist_bios.add(p)
else:
for artist_bio_path in artist_bios:
if artist_bio_path.exists():
shutil.copyfile(
artist_bio_path.absolute(),
self.playlist_dir / artist_bio_path.name,
)
# Remove all subdirs
for subdir in subdirs:
if subdir.exists():
shutil.rmtree(subdir)
else:
return self.playlist_dir
def craft_m3u8_text(self):
"""This method creates a file called playlist.m3u8 in self.playlist_dir
that is a standard M3U. Needs to be called after self.flatten_playlist_dir
in order to be able to access self.files
N.b. the already-written file is temporarily copied to a .mp4 version in a
temporary directory because .m4a files cannot be read with mutagen."""
m3u_text: str = f"#EXTM3U\n#EXTENC:UTF-8\n#EXTIMG:{str(self.cover_path.absolute())}\n#PLAYLIST:{self.name}\n"
logger.info(
f"Creating .m3u8 playlist file for Playlist with ID '{self.playlist_id}'"
)
for d in self.files:
file: str = next(iter(d.values()))
if file is None:
continue
elif file.endswith(".flac"):
m = mutagen.File(file)
artist: str = m.get("artist", [""])[0]
title: str = m.get("title", [""])[0]
extinf: str = (
f"#EXTINF:{math.ceil(m.info.length)},"
f"{artist} - {title}\n{file}\n"
)
m3u_text += extinf
elif file.endswith(".mka"):
m = mutagen.File(file)
artist: str = m.get("ARTI", [""])[0]
title: str = m.get("TITL", [""])[0]
extinf: str = (
f"#EXTINF:{math.ceil(m.info.length)},"
f"{artist} - {title}\n{file}\n"
)
m3u_text += extinf
elif file.endswith(".m4a"):
# Mutagen cannot read .m4a files, so make a copy with all
# of the metadata tags as a .mp4 in a temporary directory
with temporary_file(suffix=".mp4") as tf:
ffmpeg.input(file, hide_banner=None, y=None).output(
tf.name,
acodec="copy",
vcodec="copy",
loglevel="quiet",
).run()
m = mutagen.File(tf.name)
artist: str = m.get("\xa9ART", [""])[0]
title: str = m.get("\xa9nam", [""])[0]
extinf: str = (
f"#EXTINF:{math.ceil(m.info.length)},"
f"{artist} - {title}\n{file}\n"
)
m3u_text += extinf
else:
return m3u_text
def dumps(self):
return json.dumps(self.files)
def dump(self, fp=sys.stdout):
json.dump(self.files, fp)
def get(self, session: Session, audio_format: AudioFormat, out_dir: Path):
"""The main method of this class, executing a number of other methods
in a row:
- self.get_metadata()
- self.set_items()
- self.set_dir()
- self.save_cover_image()
- self.save_description()
- self.get_items()
- self.flatten_playlist_dir()
"""
self.get_metadata(session)
if self.metadata is None:
self.files = {}
return
self.set_items(session)
self.set_dir(out_dir)
self.save_cover_image(session, out_dir)
try:
self.save_description()
except Exception:
pass
_get_items = self.get_items(session, audio_format)
if _get_items is None:
logger.critical(f"Could not retrieve playlist with ID '{self.playlist_id}'")
return
self.flatten_playlist_dir()
try:
m3u8_text: str = self.craft_m3u8_text()
except Exception as e:
logger.warning(
"Unable to create playlist.m3u8 file for "
f"playlist with ID '{self.playlist_id}'"
)
logger.debug(e)
else:
with open(self.playlist_dir / "playlist.m3u8", "w") as f:
f.write(m3u8_text)
logger.info(f"Playlist files written to '{self.playlist_dir}'")
class TidalPlaylistException(Exception):
pass
def request_playlist_items(session: Session, playlist_id: str) -> Optional[dict]:
"""Request from TIDAL API /playlists/items endpoint.""" | url: str = f"{TIDAL_API_URL}/playlists/{playlist_id}/items" | 8 | 2023-12-12 21:50:25+00:00 | 16k |
Deltares/imod-python | imod/mf6/npf.py | [
{
"identifier": "Package",
"path": "imod/mf6/package.py",
"snippet": "class Package(PackageBase, abc.ABC):\n \"\"\"\n Package is used to share methods for specific packages with no time\n component.\n\n It is not meant to be used directly, only to inherit from, to implement new\n packages... | import warnings
import numpy as np
from imod.mf6.package import Package
from imod.mf6.regridding_utils import RegridderType
from imod.mf6.validation import PKG_DIMS_SCHEMA
from imod.schemata import (
AllValueSchema,
DTypeSchema,
IdentityNoDataSchema,
IndexesSchema,
) | 11,181 | Default is False.
save_saturation: ({True, False}, optional)
keyword to indicate that cell saturation will be written to the budget
file, which is specified with "BUDGET SAVE FILE" in Output Control.
Saturation will be saved to the budget file as an auxiliary variable
saved with the DATA-SAT text label. Saturation is a cell variable that
ranges from zero to one and can be used by post processing programs to
determine how much of a cell volume is saturated. If ICELLTYPE is 0,
then saturation is always one.
xt3d_option: ({True, False}, optional)
If True, the XT3D formulation will be used. By default False.
rhs_option: ({True, False}, optional)
If True, then the XT3D additional terms will be added to the right-hand
side. If False, then the XT3D terms will be put into the coefficient
matrix. By default False.
validate: {True, False}
Flag to indicate whether the package should be validated upon
initialization. This raises a ValidationError if package input is
provided in the wrong manner. Defaults to True.
"""
_pkg_id = "npf"
_init_schemata = {
"icelltype": [
DTypeSchema(np.integer),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"k": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"rewet_layer": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"k22": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"k33": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"angle1": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"angle2": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"angle3": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"alternative_cell_averaging": [DTypeSchema(str)],
"save_flows": [DTypeSchema(np.bool_)],
"starting_head_as_confined_thickness": [DTypeSchema(np.bool_)],
"variable_vertical_conductance": [DTypeSchema(np.bool_)],
"dewatered": [DTypeSchema(np.bool_)],
"perched": [DTypeSchema(np.bool_)],
"save_specific_discharge": [DTypeSchema(np.bool_)],
}
_write_schemata = {
"k": (
AllValueSchema(">", 0.0),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
),
"rewet_layer": (
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
),
"k22": (
AllValueSchema(">", 0.0),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
# No need to check coords: dataset ensures they align with idomain.
),
"k33": (
AllValueSchema(">", 0.0),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
# No need to check coords: dataset ensures they align with idomain.
),
"angle1": (IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),),
"angle2": (IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),),
"angle3": (IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),),
}
_grid_data = {
"icelltype": np.int32,
"k": np.float64,
"rewet_layer": np.float64,
"k22": np.float64,
"k33": np.float64,
"angle1": np.float64,
"angle2": np.float64,
"angle3": np.float64,
}
_keyword_map = {
"rewet": "rewet_record",
"rewet_factor": "wetfct",
"rewet_method": "ihdwet",
"rewet_layer": "wetdry",
"variable_vertical_conductance": "variablecv",
"starting_head_as_confined_thickness": "thickstrt",
"rewet_iterations": "iwetit",
"xt3d_option": "xt3doptions",
"rhs_option": "rhs",
}
_template = Package._initialize_template(_pkg_id)
_regrid_method = {
|
class NodePropertyFlow(Package):
"""
Node Property Flow package.
In this package the hydraulic conductivity and rewetting in the model is
specified. A single NPF Package is required for each GWF model.
https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=51
A note about regridding: the fields k, k22, k33 define the principal
components of an anisotropic conductivity tensor. By default, k and k22 are
in the horizontal plane and k33 is vertical. Angle1, angle2 and angle3
define the rotation of this tensor. The regridding methods associated by
default are chosen based on the assumption that k and k22 are horizontal and
k33 is vertical. If this is not the case, it is up to the user to regrid the
npf package using other regridding methods. This may be recommended if for
example the rotation is such that k has become vertical and k33 horizontal.
Parameters
----------
icelltype: array of int (xr.DataArray)
flag for each cell that specifies how saturated thickness is treated. 0
means saturated thickness is held constant; >0 means saturated thickness
varies with computed head when head is below the cell top; <0 means
saturated thickness varies with computed head unless the
starting_head_as_confined_thickness option is in effect. When
starting_head_as_confined_thickness is in effect, a negative value of
icelltype indicates that saturated thickness will be computed as
strt-bot and held constant.
k: array of floats (xr.DataArray)
is the hydraulic conductivity. For the common case in which the user
would like to specify the horizontal hydraulic conductivity and the
vertical hydraulic conductivity, then K should be assigned as the
horizontal hydraulic conductivity, K33 should be assigned as the
vertical hydraulic conductivity, and K22 and the three rotation
angles should not be specified. When more sophisticated anisotropy is
required, then K corresponds to the K11 hydraulic conductivity axis. All
included cells (idomain > 0) must have a K value greater than zero
rewet: ({True, False}, optional)
activates model rewetting.
Default is False.
rewet_layer: float
is a combination of the wetting threshold and a flag to indicate which
neighboring cells can cause a cell to become wet. If rewet_layer < 0,
only a cell below a dry cell can cause the cell to become wet. If
rewet_layer > 0, the cell below a dry cell and horizontally adjacent
cells can cause a cell to become wet. If rewet_layer is 0, the cell
cannot be wetted. The absolute value of rewet_layer is the wetting
threshold. When the sum of BOT and the absolute value of rewet_layer at
a dry cell is equaled or exceeded by the head at an adjacent cell, the
cell is wetted. rewet_layer must be specified if "rewet" is specified in
the OPTIONS block. If "rewet" is not specified in the options block,
then rewet_layer can be entered, and memory will be allocated for it,
even though it is not used. (WETDRY)
Default is None.
rewet_factor:
is a keyword and factor that is included in the calculation of the head
that is initially established at a cell when that cell is converted from
dry to wet. (WETFCT)
Default is None.
rewet_iterations:
is a keyword and iteration interval for attempting to wet cells. Wetting
is attempted every rewet_iterations iteration. This applies to outer
iterations and not inner iterations. If rewet_iterations is specified as
zero or less, then the value is changed to 1. (IWETIT)
Default is None.
rewet_method:
is a keyword and integer flag that determines which equation is used to
define the initial head at cells that become wet. If rewet_method is 0,
h = BOT + rewet_factor (hm - BOT). If rewet_method is not 0, h = BOT +
rewet_factor (THRESH). (IHDWET)
Default is None.
k22: array of floats (xr.DataArray)
is the hydraulic conductivity of the second ellipsoid axis; for an
unrotated case this is the hydraulic conductivity in the y direction. If
K22 is not included, then K22 is set equal to K.
For a regular MODFLOW grid (DIS Package is used) in which no rotation
angles are specified, K22 is the hydraulic conductivity along columns in
the y direction. For an unstructured DISU grid, the user must assign
principal x and y axes and provide the angle for each cell face relative
to the assigned x direction. All included cells (idomain > 0) must have
a K22 value greater than zero.
Default is None.
k33: array of floats (xr.DataArray)
is the hydraulic conductivity of the third ellipsoid axis; for an
unrotated case, this is the vertical hydraulic conductivity. When
anisotropy is applied, K33 corresponds to the K33 tensor component. All
included cells (idomain > 0) must have a K33 value greater than zero.
Default is None.
angle1: float
is a rotation angle of the hydraulic conductivity tensor in degrees. The
angle represents the first of three sequential rotations of the
hydraulic conductivity ellipsoid. With the K11, K22, and K33 axes of the
ellipsoid initially aligned with the x, y, and z coordinate axes,
respectively, angle1 rotates the ellipsoid about its K33 axis (within
the x - y plane). A positive value represents counter-clockwise rotation
when viewed from any point on the positive K33 axis, looking toward the
center of the ellipsoid. A value of zero indicates that the K11 axis
lies within the x - z plane. If angle1 is not specified, default values
of zero are assigned to angle1, angle2, and angle3, in which case the
K11, K22, and K33 axes are aligned with the x, y, and z axes,
respectively.
Default is None.
angle2: float
is a rotation angle of the hydraulic conductivity tensor in degrees. The
angle represents the second of three sequential rotations of the
hydraulic conductivity ellipsoid. Following the rotation by angle1
described above, angle2 rotates the ellipsoid about its K22 axis (out of
the x - y plane). An array can be specified for angle2 only if angle1 is
also specified. A positive value of angle2 represents clockwise rotation
when viewed from any point on the positive K22 axis, looking toward the
center of the ellipsoid. A value of zero indicates that the K11 axis
lies within the x - y plane. If angle2 is not specified, default values
of zero are assigned to angle2 and angle3; connections that are not
user-designated as vertical are assumed to be strictly horizontal (that
is, to have no z component to their orientation); and connection lengths
are based on horizontal distances.
Default is None.
angle3: float
is a rotation angle of the hydraulic conductivity tensor in degrees. The
angle represents the third of three sequential rotations of the
hydraulic conductivity ellipsoid. Following the rotations by angle1 and
angle2 described above, angle3 rotates the ellipsoid about its K11 axis.
An array can be specified for angle3 only if angle1 and angle2 are also
specified. An array must be specified for angle3 if angle2 is specified.
A positive value of angle3 represents clockwise rotation when viewed
from any point on the positive K11 axis, looking toward the center of
the ellipsoid. A value of zero indicates that the K22 axis lies within
the x - y plane.
Default is None.
alternative_cell_averaging : str
Method calculating horizontal cell connection conductance.
Options: {"LOGARITHMIC", "AMT-LMK", or "AMT-HMK"}
Default: uses harmonic mean for averaging
save_flows: ({True, False}, optional)
keyword to indicate that cell-by-cell flow terms will be written to the
file specified with "budget save file" in Output Control.
Default is False.
starting_head_as_confined_thickness: ({True, False}, optional)
indicates that cells having a negative icelltype are confined, and their
cell thickness for conductance calculations will be computed as strt-bot
rather than top-bot.
(THICKSTRT)
Default is False.
variable_vertical_conductance: ({True, False}, optional)
keyword to indicate that the vertical conductance will be calculated
using the saturated thickness and properties of the overlying cell and
the thickness and properties of the underlying cell. if the dewatered
keyword is also specified, then the vertical conductance is calculated
using only the saturated thickness and properties of the overlying cell
if the head in the underlying cell is below its top. if these keywords
are not specified, then the default condition is to calculate the
vertical conductance at the start of the simulation using the initial
head and the cell properties. the vertical conductance remains constant
for the entire simulation.
(VARIABLECV)
Default is False.
dewatered: ({True, False}, optional)
If the dewatered keyword is specified, then the vertical conductance is
calculated using only the saturated thickness and properties of the
overlying cell if the head in the underlying cell is below its top.
Default is False.
perched: ({True, False}, optional)
keyword to indicate that when a cell is overlying a dewatered
convertible cell, the head difference used in Darcy’s Law is equal to
the head in the overlying cell minus the bottom elevation of the
overlying cell. If not specified, then the default is to use the head
difference between the two cells.
Default is False.
save_specific_discharge: ({True, False}, optional)
keyword to indicate that x, y, and z components of specific discharge
will be calculated at cell centers and written to the cell-by-cell flow
file, which is specified with"budget save file" in Output Control. If
this option is activated, then additional information may be required in
the discretization packages and the GWF Exchange package (if GWF models
are coupled). Specifically, angldegx must be specified in the
connectiondata block of the disu package; angldegx must also be
specified for the GWF Exchange as an auxiliary variable. disu package
has not been implemented yet.
Default is False.
save_saturation: ({True, False}, optional)
keyword to indicate that cell saturation will be written to the budget
file, which is specified with "BUDGET SAVE FILE" in Output Control.
Saturation will be saved to the budget file as an auxiliary variable
saved with the DATA-SAT text label. Saturation is a cell variable that
ranges from zero to one and can be used by post processing programs to
determine how much of a cell volume is saturated. If ICELLTYPE is 0,
then saturation is always one.
xt3d_option: ({True, False}, optional)
If True, the XT3D formulation will be used. By default False.
rhs_option: ({True, False}, optional)
If True, then the XT3D additional terms will be added to the right-hand
side. If False, then the XT3D terms will be put into the coefficient
matrix. By default False.
validate: {True, False}
Flag to indicate whether the package should be validated upon
initialization. This raises a ValidationError if package input is
provided in the wrong manner. Defaults to True.
"""
_pkg_id = "npf"
_init_schemata = {
"icelltype": [
DTypeSchema(np.integer),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"k": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"rewet_layer": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"k22": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"k33": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"angle1": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"angle2": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"angle3": [
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
],
"alternative_cell_averaging": [DTypeSchema(str)],
"save_flows": [DTypeSchema(np.bool_)],
"starting_head_as_confined_thickness": [DTypeSchema(np.bool_)],
"variable_vertical_conductance": [DTypeSchema(np.bool_)],
"dewatered": [DTypeSchema(np.bool_)],
"perched": [DTypeSchema(np.bool_)],
"save_specific_discharge": [DTypeSchema(np.bool_)],
}
_write_schemata = {
"k": (
AllValueSchema(">", 0.0),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
),
"rewet_layer": (
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
),
"k22": (
AllValueSchema(">", 0.0),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
# No need to check coords: dataset ensures they align with idomain.
),
"k33": (
AllValueSchema(">", 0.0),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
# No need to check coords: dataset ensures they align with idomain.
),
"angle1": (IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),),
"angle2": (IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),),
"angle3": (IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),),
}
_grid_data = {
"icelltype": np.int32,
"k": np.float64,
"rewet_layer": np.float64,
"k22": np.float64,
"k33": np.float64,
"angle1": np.float64,
"angle2": np.float64,
"angle3": np.float64,
}
_keyword_map = {
"rewet": "rewet_record",
"rewet_factor": "wetfct",
"rewet_method": "ihdwet",
"rewet_layer": "wetdry",
"variable_vertical_conductance": "variablecv",
"starting_head_as_confined_thickness": "thickstrt",
"rewet_iterations": "iwetit",
"xt3d_option": "xt3doptions",
"rhs_option": "rhs",
}
_template = Package._initialize_template(_pkg_id)
_regrid_method = { | "icelltype": (RegridderType.OVERLAP, "mean"), | 1 | 2023-12-08 13:57:59+00:00 | 16k |
camenduru/MotionDirector-hf | MotionDirector_train.py | [
{
"identifier": "UNet3DConditionModel",
"path": "models/unet_3d_condition.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 sh... | import argparse
import datetime
import logging
import inspect
import math
import os
import random
import gc
import copy
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import diffusers
import transformers
import imageio
import numpy as np
import itertools
import bitsandbytes as bnb
from typing import Dict, Optional, Tuple
from omegaconf import OmegaConf
from torchvision import transforms
from tqdm.auto import tqdm
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from models.unet_3d_condition import UNet3DConditionModel
from diffusers.models import AutoencoderKL
from diffusers import DDIMScheduler, TextToVideoSDPipeline
from diffusers.optimization import get_scheduler
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 transformers import CLIPTextModel, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPEncoder
from utils.dataset import VideoJsonDataset, SingleVideoDataset, \
ImageDataset, VideoFolderDataset, CachedDataset
from einops import rearrange, repeat
from utils.lora_handler import LoraHandler
from utils.lora import extract_lora_child_module
from utils.ddim_utils import ddim_inversion
from xformers.ops import MemoryEfficientAttentionFlashAttentionOp | 11,373 |
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 = []
# Loop through all available datasets, get the name, then add to list of data to process.
|
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 = []
# Loop through all available datasets, get the name, then add to list of data to process. | for DataSet in [VideoJsonDataset, SingleVideoDataset, ImageDataset, VideoFolderDataset]: | 1 | 2023-12-11 04:51:39+00:00 | 16k |
ZS-YANG/FemtoDet-v3 | projects/Detic_new/detic/detic_roi_head.py | [
{
"identifier": "CascadeRoIHead",
"path": "mmdet/models/roi_heads/cascade_roi_head.py",
"snippet": "class CascadeRoIHead(BaseRoIHead):\n \"\"\"Cascade roi head including one bbox head and one mask head.\n\n https://arxiv.org/abs/1712.00726\n \"\"\"\n\n def __init__(self,\n nu... | from typing import List, Sequence, Tuple
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.models.roi_heads import CascadeRoIHead
from mmdet.models.task_modules.samplers import SamplingResult
from mmdet.models.test_time_augs import merge_aug_masks
from mmdet.models.utils import empty_instances, unpack_gt_instances
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.structures.bbox import bbox2roi, get_box_tensor
from mmdet.utils import ConfigType, InstanceList, MultiConfig
import torch | 10,806 | # Copyright (c) OpenMMLab. All rights reserved.
@MODELS.register_module()
class DeticRoIHead(CascadeRoIHead):
def __init__(
self,
*,
mult_proposal_score: bool = False,
with_image_labels: bool = False,
add_image_box: bool = False,
image_box_size: float = 1.0,
ws_num_props: int = 128,
add_feature_to_prop: bool = False,
mask_weight: float = 1.0,
one_class_per_proposal: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.mult_proposal_score = mult_proposal_score
self.with_image_labels = with_image_labels
self.add_image_box = add_image_box
self.image_box_size = image_box_size
self.ws_num_props = ws_num_props
self.add_feature_to_prop = add_feature_to_prop
self.mask_weight = mask_weight
self.one_class_per_proposal = one_class_per_proposal
def init_mask_head(self, mask_roi_extractor: MultiConfig,
mask_head: MultiConfig) -> None:
"""Initialize mask head and mask roi extractor.
Args:
mask_head (dict): Config of mask in mask head.
mask_roi_extractor (:obj:`ConfigDict`, dict or list):
Config of mask roi extractor.
"""
self.mask_head = MODELS.build(mask_head)
if mask_roi_extractor is not None:
self.share_roi_extractor = False
self.mask_roi_extractor = MODELS.build(mask_roi_extractor)
else:
self.share_roi_extractor = True
self.mask_roi_extractor = self.bbox_roi_extractor
def _refine_roi(self, x: Tuple[Tensor], rois: Tensor,
batch_img_metas: List[dict],
num_proposals_per_img: Sequence[int], **kwargs) -> tuple:
"""Multi-stage refinement of RoI.
Args:
x (tuple[Tensor]): List of multi-level img features.
rois (Tensor): shape (n, 5), [batch_ind, x1, y1, x2, y2]
batch_img_metas (list[dict]): List of image information.
num_proposals_per_img (sequence[int]): number of proposals
in each image.
Returns:
tuple:
- rois (Tensor): Refined RoI.
- cls_scores (list[Tensor]): Average predicted
cls score per image.
- bbox_preds (list[Tensor]): Bbox branch predictions
for the last stage of per image.
"""
# "ms" in variable names means multi-stage
ms_scores = []
for stage in range(self.num_stages):
bbox_results = self._bbox_forward(
stage=stage, x=x, rois=rois, **kwargs)
# split batch bbox prediction back to each image
cls_scores = bbox_results['cls_score'].sigmoid()
bbox_preds = bbox_results['bbox_pred']
rois = rois.split(num_proposals_per_img, 0)
cls_scores = cls_scores.split(num_proposals_per_img, 0)
ms_scores.append(cls_scores)
bbox_preds = bbox_preds.split(num_proposals_per_img, 0)
if stage < self.num_stages - 1:
bbox_head = self.bbox_head[stage]
refine_rois_list = []
for i in range(len(batch_img_metas)):
if rois[i].shape[0] > 0:
bbox_label = cls_scores[i][:, :-1].argmax(dim=1)
# Refactor `bbox_head.regress_by_class` to only accept
# box tensor without img_idx concatenated.
refined_bboxes = bbox_head.regress_by_class(
rois[i][:, 1:], bbox_label, bbox_preds[i],
batch_img_metas[i])
refined_bboxes = get_box_tensor(refined_bboxes)
refined_rois = torch.cat(
[rois[i][:, [0]], refined_bboxes], dim=1)
refine_rois_list.append(refined_rois)
rois = torch.cat(refine_rois_list)
# ms_scores aligned
# average scores of each image by stages
cls_scores = [
sum([score[i] for score in ms_scores]) / float(len(ms_scores))
for i in range(len(batch_img_metas))
] # aligned
return rois, cls_scores, bbox_preds
def predict_bbox(self,
x: Tuple[Tensor],
batch_img_metas: List[dict],
| # Copyright (c) OpenMMLab. All rights reserved.
@MODELS.register_module()
class DeticRoIHead(CascadeRoIHead):
def __init__(
self,
*,
mult_proposal_score: bool = False,
with_image_labels: bool = False,
add_image_box: bool = False,
image_box_size: float = 1.0,
ws_num_props: int = 128,
add_feature_to_prop: bool = False,
mask_weight: float = 1.0,
one_class_per_proposal: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.mult_proposal_score = mult_proposal_score
self.with_image_labels = with_image_labels
self.add_image_box = add_image_box
self.image_box_size = image_box_size
self.ws_num_props = ws_num_props
self.add_feature_to_prop = add_feature_to_prop
self.mask_weight = mask_weight
self.one_class_per_proposal = one_class_per_proposal
def init_mask_head(self, mask_roi_extractor: MultiConfig,
mask_head: MultiConfig) -> None:
"""Initialize mask head and mask roi extractor.
Args:
mask_head (dict): Config of mask in mask head.
mask_roi_extractor (:obj:`ConfigDict`, dict or list):
Config of mask roi extractor.
"""
self.mask_head = MODELS.build(mask_head)
if mask_roi_extractor is not None:
self.share_roi_extractor = False
self.mask_roi_extractor = MODELS.build(mask_roi_extractor)
else:
self.share_roi_extractor = True
self.mask_roi_extractor = self.bbox_roi_extractor
def _refine_roi(self, x: Tuple[Tensor], rois: Tensor,
batch_img_metas: List[dict],
num_proposals_per_img: Sequence[int], **kwargs) -> tuple:
"""Multi-stage refinement of RoI.
Args:
x (tuple[Tensor]): List of multi-level img features.
rois (Tensor): shape (n, 5), [batch_ind, x1, y1, x2, y2]
batch_img_metas (list[dict]): List of image information.
num_proposals_per_img (sequence[int]): number of proposals
in each image.
Returns:
tuple:
- rois (Tensor): Refined RoI.
- cls_scores (list[Tensor]): Average predicted
cls score per image.
- bbox_preds (list[Tensor]): Bbox branch predictions
for the last stage of per image.
"""
# "ms" in variable names means multi-stage
ms_scores = []
for stage in range(self.num_stages):
bbox_results = self._bbox_forward(
stage=stage, x=x, rois=rois, **kwargs)
# split batch bbox prediction back to each image
cls_scores = bbox_results['cls_score'].sigmoid()
bbox_preds = bbox_results['bbox_pred']
rois = rois.split(num_proposals_per_img, 0)
cls_scores = cls_scores.split(num_proposals_per_img, 0)
ms_scores.append(cls_scores)
bbox_preds = bbox_preds.split(num_proposals_per_img, 0)
if stage < self.num_stages - 1:
bbox_head = self.bbox_head[stage]
refine_rois_list = []
for i in range(len(batch_img_metas)):
if rois[i].shape[0] > 0:
bbox_label = cls_scores[i][:, :-1].argmax(dim=1)
# Refactor `bbox_head.regress_by_class` to only accept
# box tensor without img_idx concatenated.
refined_bboxes = bbox_head.regress_by_class(
rois[i][:, 1:], bbox_label, bbox_preds[i],
batch_img_metas[i])
refined_bboxes = get_box_tensor(refined_bboxes)
refined_rois = torch.cat(
[rois[i][:, [0]], refined_bboxes], dim=1)
refine_rois_list.append(refined_rois)
rois = torch.cat(refine_rois_list)
# ms_scores aligned
# average scores of each image by stages
cls_scores = [
sum([score[i] for score in ms_scores]) / float(len(ms_scores))
for i in range(len(batch_img_metas))
] # aligned
return rois, cls_scores, bbox_preds
def predict_bbox(self,
x: Tuple[Tensor],
batch_img_metas: List[dict], | rpn_results_list: InstanceList, | 9 | 2023-12-11 15:23:03+00:00 | 16k |
merlresearch/PixPNet | pixpnet/protonets/lit_model.py | [
{
"identifier": "get_metadata",
"path": "pixpnet/data.py",
"snippet": "def get_metadata(config):\n dataset = config.dataset.name.upper().replace(\"-\", \"\")\n metadata = DatasetMeta(\n output_size=DATA_NUM_OUTPUTS[dataset],\n input_channels=DATA_CHANNELS[dataset],\n input_siz... | import argparse
import torch
from typing import Tuple
from torch import nn
from torchmetrics import Accuracy
from pytorch_lightning.loops import FitLoop
from pytorch_lightning.loops.fit_loop import _FitLoop as FitLoop
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.loggers import TensorBoardLogger
from pixpnet.data import get_metadata
from pixpnet.lightning.lightning_data import LitData
from pixpnet.lightning.lit_module import BaseLitModel
from pixpnet.optim import get_optimizer_cls, get_scheduler
from pixpnet.protonets.loss import ClusterLoss, L1ReadoutLoss, SeparationLoss
from pixpnet.protonets.models.protonet import ProtoNet, protonet
from pixpnet.protonets.push import push_prototypes
from pixpnet.utils import get_logger, intersect_func_and_kwargs | 11,497 | # Copyright (c) 2022-2023 Mitsubishi Electric Research Laboratories (MERL)
#
# SPDX-License-Identifier: AGPL-3.0-or-later
try:
except ImportError:
logger = get_logger(__name__)
def params_with_grad(parameters):
return filter(lambda p: p.requires_grad, parameters)
def make_optimizers_proto(
model: ProtoNet,
config: argparse.Namespace,
) -> Tuple[torch.optim.Optimizer, ...]:
""""""
optimizer_cls, hparams = get_optimizer_cls(config, ignore={"fine_tune_lr", "readout_lr"})
readout_params = None
if model.last_layer is not None:
readout_params = [
{
"params": params_with_grad(model.last_layer.parameters()),
"lr": config.optimizer.readout_lr,
"weight_decay": 0,
},
]
all_params = [
# feature extractor
{"params": params_with_grad(model.features.parameters()), "lr": config.optimizer.fine_tune_lr},
# add on layers
{"params": params_with_grad(model.add_on_layers.parameters())},
# prototype layers
{"params": params_with_grad([model.prototype_vectors]), "weight_decay": 0},
]
readout_optimizer = None
if readout_params is not None:
all_params += readout_params
readout_optimizer = optimizer_cls(params=readout_params, **hparams)
optimizer = optimizer_cls(params=all_params, **hparams)
return optimizer, readout_optimizer
def _set_grad(model, features=True, add_on_layers=True, prototype_vectors=True, last_layer=True):
for p in model.features.parameters():
p.requires_grad = features
for p in model.add_on_layers.parameters():
p.requires_grad = add_on_layers
model.prototype_vectors.requires_grad = prototype_vectors
if model.last_layer is not None:
for p in model.last_layer.parameters():
p.requires_grad = last_layer
def last_only(model):
_set_grad(model, features=False, add_on_layers=False, prototype_vectors=False)
def warm_only(model):
_set_grad(model, features=False)
def joint(model):
_set_grad(model)
class ProtoLitModel(BaseLitModel):
def __init__(self, config, feature_extractor=None):
super().__init__(config)
metadata = get_metadata(config)
self.num_classes = metadata.output_size
self.input_size = metadata.input_size
hparams, invalid_keys = intersect_func_and_kwargs(
protonet,
config.model,
exclude_func_args={"num_classes"},
exclude_kwargs={"name"},
)
if invalid_keys:
logger.warning(
f"Will not pass the following invalid model "
f"hyperparameters to {protonet.__name__}: "
f'{", ".join(invalid_keys)}'
)
logger.info(f"Model hyperparameters for {protonet.__name__}: " f"{hparams}")
if feature_extractor is not None:
logger.info(
f"feature_extractor is not None, ignoring config " f'option of {hparams.get("feature_extractor")}'
)
hparams["feature_extractor"] = feature_extractor
self.model = protonet(num_classes=self.num_classes, input_size=self.input_size, **hparams)
self.lr_scheduler = None
self.readout_optimizer = self.lr_scheduler_configs = None
# losses
self.xent = self._metric_per_split(nn.CrossEntropyLoss)
class_specific = self.config.model.class_specific
| # Copyright (c) 2022-2023 Mitsubishi Electric Research Laboratories (MERL)
#
# SPDX-License-Identifier: AGPL-3.0-or-later
try:
except ImportError:
logger = get_logger(__name__)
def params_with_grad(parameters):
return filter(lambda p: p.requires_grad, parameters)
def make_optimizers_proto(
model: ProtoNet,
config: argparse.Namespace,
) -> Tuple[torch.optim.Optimizer, ...]:
""""""
optimizer_cls, hparams = get_optimizer_cls(config, ignore={"fine_tune_lr", "readout_lr"})
readout_params = None
if model.last_layer is not None:
readout_params = [
{
"params": params_with_grad(model.last_layer.parameters()),
"lr": config.optimizer.readout_lr,
"weight_decay": 0,
},
]
all_params = [
# feature extractor
{"params": params_with_grad(model.features.parameters()), "lr": config.optimizer.fine_tune_lr},
# add on layers
{"params": params_with_grad(model.add_on_layers.parameters())},
# prototype layers
{"params": params_with_grad([model.prototype_vectors]), "weight_decay": 0},
]
readout_optimizer = None
if readout_params is not None:
all_params += readout_params
readout_optimizer = optimizer_cls(params=readout_params, **hparams)
optimizer = optimizer_cls(params=all_params, **hparams)
return optimizer, readout_optimizer
def _set_grad(model, features=True, add_on_layers=True, prototype_vectors=True, last_layer=True):
for p in model.features.parameters():
p.requires_grad = features
for p in model.add_on_layers.parameters():
p.requires_grad = add_on_layers
model.prototype_vectors.requires_grad = prototype_vectors
if model.last_layer is not None:
for p in model.last_layer.parameters():
p.requires_grad = last_layer
def last_only(model):
_set_grad(model, features=False, add_on_layers=False, prototype_vectors=False)
def warm_only(model):
_set_grad(model, features=False)
def joint(model):
_set_grad(model)
class ProtoLitModel(BaseLitModel):
def __init__(self, config, feature_extractor=None):
super().__init__(config)
metadata = get_metadata(config)
self.num_classes = metadata.output_size
self.input_size = metadata.input_size
hparams, invalid_keys = intersect_func_and_kwargs(
protonet,
config.model,
exclude_func_args={"num_classes"},
exclude_kwargs={"name"},
)
if invalid_keys:
logger.warning(
f"Will not pass the following invalid model "
f"hyperparameters to {protonet.__name__}: "
f'{", ".join(invalid_keys)}'
)
logger.info(f"Model hyperparameters for {protonet.__name__}: " f"{hparams}")
if feature_extractor is not None:
logger.info(
f"feature_extractor is not None, ignoring config " f'option of {hparams.get("feature_extractor")}'
)
hparams["feature_extractor"] = feature_extractor
self.model = protonet(num_classes=self.num_classes, input_size=self.input_size, **hparams)
self.lr_scheduler = None
self.readout_optimizer = self.lr_scheduler_configs = None
# losses
self.xent = self._metric_per_split(nn.CrossEntropyLoss)
class_specific = self.config.model.class_specific | self.l1 = self._metric_per_split(L1ReadoutLoss, class_specific=class_specific) | 6 | 2023-12-06 23:49:31+00:00 | 16k |
open-mmlab/PIA | animatediff/pipelines/i2v_pipeline.py | [
{
"identifier": "InflatedConv3d",
"path": "animatediff/models/resnet.py",
"snippet": "class InflatedConv3d(nn.Conv2d):\n def forward(self, x):\n video_length = x.shape[2]\n\n x = rearrange(x, \"b c f h w -> (b f) c h w\")\n x = super().forward(x)\n x = rearrange(x, \"(b f)... | import inspect
import os.path as osp
import numpy as np
import torch
from dataclasses import dataclass
from typing import Callable, List, Optional, Union
from diffusers.configuration_utils import FrozenDict
from diffusers.loaders import IPAdapterMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL
from diffusers.pipelines import DiffusionPipeline
from diffusers.schedulers import (DDIMScheduler, DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler, LMSDiscreteScheduler,
PNDMScheduler)
from diffusers.utils import (BaseOutput, deprecate, is_accelerate_available,
logging)
from diffusers.utils.import_utils import is_xformers_available
from einops import rearrange
from omegaconf import OmegaConf
from packaging import version
from safetensors import safe_open
from tqdm import tqdm
from transformers import (CLIPImageProcessor, CLIPTextModel, CLIPTokenizer,
CLIPVisionModelWithProjection)
from animatediff.models.resnet import InflatedConv3d
from animatediff.models.unet import UNet3DConditionModel
from animatediff.utils.convert_from_ckpt import (convert_ldm_clip_checkpoint,
convert_ldm_unet_checkpoint,
convert_ldm_vae_checkpoint)
from animatediff.utils.convert_lora_safetensor_to_diffusers import \
convert_lora_model_level
from animatediff.utils.util import prepare_mask_coef_by_statistics
from accelerate import cpu_offload | 13,276 |
class I2VPipeline(DiffusionPipeline, IPAdapterMixin, TextualInversionLoaderMixin):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
# memory_format: torch.memory_format,
feature_extractor: CLIPImageProcessor = None,
image_encoder: CLIPVisionModelWithProjection = None,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
# self.memory_format = memory_format
self.use_ip_adapter = False
@classmethod
def build_pipeline(cls,
base_cfg,
base_model: str,
unet_path: str,
dreambooth_path: Optional[str] = None,
lora_path: Optional[str] = None,
lora_alpha: float = 0,
vae_path: Optional[str] = None,
ip_adapter_path: Optional[str] = None,
ip_adapter_scale: float = 0.0,
only_load_vae_decoder: bool = False,
only_load_vae_encoder: bool = False) -> 'I2VPipeline':
"""Method to build pipeline in a faster way~
Args:
base_cfg: The config to build model
base_mode: The model id to initialize StableDiffusion
unet_path: Path for i2v unet
dreambooth_path: path for dreambooth model
lora_path: path for lora model
lora_alpha: value for lora scale
only_load_vae_decoder: Only load VAE decoder from dreambooth / VAE ckpt
and maitain encoder as original.
"""
# build unet
unet = UNet3DConditionModel.from_pretrained_2d(
base_model, subfolder="unet",
unet_additional_kwargs=OmegaConf.to_container(
base_cfg.unet_additional_kwargs))
old_weights = unet.conv_in.weight
old_bias = unet.conv_in.bias
| # Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
DEFAULT_N_PROMPT = ('wrong white balance, dark, sketches,worst quality,'
'low quality, deformed, distorted, disfigured, bad eyes, '
'wrong lips,weird mouth, bad teeth, mutated hands and fingers, '
'bad anatomy,wrong anatomy, amputation, extra limb, '
'missing limb, floating,limbs, disconnected limbs, mutation, '
'ugly, disgusting, bad_pictures, negative_hand-neg')
@dataclass
class AnimationPipelineOutput(BaseOutput):
videos: Union[torch.Tensor, np.ndarray]
class I2VPipeline(DiffusionPipeline, IPAdapterMixin, TextualInversionLoaderMixin):
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet3DConditionModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
# memory_format: torch.memory_format,
feature_extractor: CLIPImageProcessor = None,
image_encoder: CLIPVisionModelWithProjection = None,
):
super().__init__()
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["steps_offset"] = 1
scheduler._internal_dict = FrozenDict(new_config)
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
)
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(scheduler.config)
new_config["clip_sample"] = False
scheduler._internal_dict = FrozenDict(new_config)
is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
version.parse(unet.config._diffusers_version).base_version
) < version.parse("0.9.0.dev0")
is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
# self.memory_format = memory_format
self.use_ip_adapter = False
@classmethod
def build_pipeline(cls,
base_cfg,
base_model: str,
unet_path: str,
dreambooth_path: Optional[str] = None,
lora_path: Optional[str] = None,
lora_alpha: float = 0,
vae_path: Optional[str] = None,
ip_adapter_path: Optional[str] = None,
ip_adapter_scale: float = 0.0,
only_load_vae_decoder: bool = False,
only_load_vae_encoder: bool = False) -> 'I2VPipeline':
"""Method to build pipeline in a faster way~
Args:
base_cfg: The config to build model
base_mode: The model id to initialize StableDiffusion
unet_path: Path for i2v unet
dreambooth_path: path for dreambooth model
lora_path: path for lora model
lora_alpha: value for lora scale
only_load_vae_decoder: Only load VAE decoder from dreambooth / VAE ckpt
and maitain encoder as original.
"""
# build unet
unet = UNet3DConditionModel.from_pretrained_2d(
base_model, subfolder="unet",
unet_additional_kwargs=OmegaConf.to_container(
base_cfg.unet_additional_kwargs))
old_weights = unet.conv_in.weight
old_bias = unet.conv_in.bias | new_conv1 = InflatedConv3d( | 0 | 2023-12-21 03:29:34+00:00 | 16k |
xinghaochen/TinySAM | tinysam/hierarchical_mask_generator.py | [
{
"identifier": "Sam",
"path": "tinysam/modeling/sam.py",
"snippet": "class Sam(nn.Module):\n mask_threshold: float = 0.0\n image_format: str = \"RGB\"\n\n def __init__(\n self,\n image_encoder: Union[ImageEncoderViT, TinyViT],\n prompt_encoder: PromptEncoder,\n mask... | import numpy as np
import torch
import cv2 # type: ignore # noqa: F401
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing import Any, Dict, List, Optional, Tuple
from .modeling import Sam
from .predictor import SamPredictor
from .utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
from pycocotools import mask as mask_utils # type: ignore # noqa: F401 | 11,166 |
# Serialize predictions and store in MaskData
batch_data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
if self.pred_iou_thresh > 0.0:
keep_mask = batch_data["iou_preds"] > self.pred_iou_thresh
batch_data.filter(keep_mask)
# Calculate stability score
batch_data["stability_score"] = calculate_stability_score(
batch_data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
)
if self.stability_score_thresh > 0.0:
keep_mask = batch_data["stability_score"] >= self.stability_score_thresh
batch_data.filter(keep_mask)
if need_high:
batch_data["high_masks"] = batch_data["masks"] > self.high_score_thresh
batch_data["masks"] = batch_data["masks"] > self.predictor.model.mask_threshold
batch_data["boxes"] = batched_mask_to_box(batch_data["masks"])
keep_mask = ~is_box_near_crop_edge(batch_data["boxes"], [0, 0, orig_w, orig_h], [0, 0, orig_w, orig_h])
if not torch.all(keep_mask):
batch_data.filter(keep_mask)
# Compress to RLE
batch_data["rles"] = mask_to_rle_pytorch(batch_data["masks"])
data.cat(batch_data)
del batch_data
if need_high:
high_masks = data["high_masks"]
or_results = torch.zeros([high_masks.shape[1], high_masks.shape[2]]).to(high_masks.device)
for mask in high_masks:
or_results = torch.logical_or(or_results, mask)
del data["high_masks"]
or_results = or_results.permute(1, 0)
del data['masks']
return data, or_results
else:
del data['masks']
return data
@torch.no_grad()
def reset_image(self):
self.predictor.reset_image()
@torch.no_grad()
def post_process(self, image: np.ndarray, data: MaskData) -> List[Dict[str, Any]]:
orig_size = image.shape[:2]
orig_h, orig_w = orig_size
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros_like(data["boxes"][:, 0]), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
data = self.postprocess_small_regions(
data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle":
data["segmentations"] = [coco_encode_rle(rle) for rle in data["rles"]]
elif self.output_mode == "binary_mask":
data["segmentations"] = [rle_to_mask(rle) for rle in data["rles"]]
else:
data["segmentations"] = data["rles"]
# Write mask records
curr_anns = []
for idx in range(len(data["segmentations"])):
ann = {
"segmentation": data["segmentations"][idx],
"area": area_from_rle(data["rles"][idx]),
"bbox": box_xyxy_to_xywh(data["boxes"][idx]).tolist(),
"predicted_iou": data["iou_preds"][idx].item(),
"point_coords": [data["points"][idx].tolist()],
"stability_score": data["stability_score"][idx].item(),
}
curr_anns.append(ann)
# print("post use time: {}".format(time.time() - st))
return curr_anns
@staticmethod
def postprocess_small_regions(
mask_data: MaskData, min_area: int, nms_thresh: float
) -> MaskData:
"""
Removes small disconnected regions and holes in masks, then reruns
box NMS to remove any new duplicates.
Edits mask_data in place.
Requires open-cv as a dependency.
"""
if len(mask_data["rles"]) == 0:
return mask_data
# Filter small disconnected regions and holes
new_masks = []
scores = []
for rle in mask_data["rles"]:
mask = rle_to_mask(rle)
| # Copyright 2023 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
class SamHierarchicalMaskGenerator:
def __init__(
self,
model: Sam,
points_per_side: Optional[int] = 32,
points_per_batch: int = 64,
pred_iou_thresh: float = 0.88,
high_score_thresh: float = 8.5,
stability_score_thresh: float = 0.95,
stability_score_offset: float = 1.0,
box_nms_thresh: float = 0.7,
crop_n_layers: int = 0,
crop_nms_thresh: float = 0.7,
crop_overlap_ratio: float = 512 / 1500,
crop_n_points_downscale_factor: int = 1,
point_grids: Optional[List[np.ndarray]] = None,
min_mask_region_area: int = 0,
output_mode: str = "binary_mask",
) -> None:
"""
Using a SAM model, generates masks for the entire image.
Generates a grid of point prompts over the image, then filters
low quality and duplicate masks. The default settings are chosen
for SAM with a ViT-H backbone.
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
points_per_batch (int): Sets the number of points run simultaneously
by the model. Higher numbers may be faster but use more GPU memory.
pred_iou_thresh (float): A filtering threshold in [0,1], using the
model's predicted mask quality.
high_score_thresh (float): A filtering threshold in [-inf,inf], to find out
the unmasked area for the next generation.
stability_score_thresh (float): A filtering threshold in [0,1], using
the stability of the mask under changes to the cutoff used to binarize
the model's mask predictions.
stability_score_offset (float): The amount to shift the cutoff when
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
crop_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
crop_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
to remove disconnected regions and holes in masks with area smaller
than min_mask_region_area. Requires opencv.
output_mode (str): The form masks are returned in. Can be 'binary_mask',
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
For large resolutions, 'binary_mask' may consume large amounts of
memory.
"""
assert (points_per_side is None) != (
point_grids is None
), "Exactly one of points_per_side or point_grid must be provided."
if points_per_side is not None:
self.point_grids = build_all_layer_point_grids(
points_per_side,
crop_n_layers,
crop_n_points_downscale_factor,
)
elif point_grids is not None:
self.point_grids = point_grids
else:
raise ValueError("Can't have both points_per_side and point_grid be None.")
assert output_mode in [
"binary_mask",
"uncompressed_rle",
"coco_rle",
], f"Unknown output_mode {output_mode}."
if output_mode == "coco_rle":
if min_mask_region_area > 0:
self.predictor = SamPredictor(model)
self.points_per_side = points_per_side
self.points_per_batch = points_per_batch
self.pred_iou_thresh = pred_iou_thresh
self.high_score_thresh = high_score_thresh
self.stability_score_thresh = stability_score_thresh
self.stability_score_offset = stability_score_offset
self.box_nms_thresh = box_nms_thresh
self.crop_n_layers = crop_n_layers
self.crop_nms_thresh = crop_nms_thresh
self.crop_overlap_ratio = crop_overlap_ratio
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
self.min_mask_region_area = min_mask_region_area
self.output_mode = output_mode
def set_point_grids(self, point_grids):
self.point_grids = point_grids
def set_points_per_side(self, points_per_side):
self.point_grids = build_all_layer_point_grids(
points_per_side,
0,
1,
)
@torch.no_grad()
def set_image(self, image: np.ndarray) -> MaskData:
# Crop the image and calculate embeddings
self.predictor.set_image(image)
@torch.no_grad()
def hierarchical_generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
self.set_image(image)
self.set_points_per_side(self.points_per_side // 4)
ori_masks, or_results = self.generate(image, True)
ih, iw, _ = image.shape
hstride = ih // self.points_per_side
wstride = iw // self.points_per_side
new_points = []
pass_counter = 0
full_point_grids = np.array(self.point_grids)
for mask in range(full_point_grids.shape[1]):
point_coords = [full_point_grids[0, mask, 0] * iw, full_point_grids[0, mask, 1] * ih]
for sy in [-1, 0, 1]:
for sx in [-1, 0, 1]:
if (sy == 0 and sx == 0) or or_results[int(point_coords[0] + wstride * sy), int(point_coords[1] + hstride * sx)]:
continue
new_points.append([(point_coords[0] + wstride * sy) / iw, (point_coords[1] + hstride * sx) / ih])
if point_coords[0] + wstride * 2 < iw:
for sx in [-1, 0, 1]:
if or_results[int(point_coords[0] + wstride * 2), int(point_coords[1] + hstride * sx)]:
continue
new_points.append([(point_coords[0] + wstride * 2) / iw, (point_coords[1] + hstride * sx) / ih])
if point_coords[1] + hstride * 2 < ih:
for sy in [-1, 0, 1]:
if or_results[int(point_coords[0] + wstride * sy), int(point_coords[1] + hstride * 2)]:
continue
new_points.append([(point_coords[0] + wstride * sy) / iw, (point_coords[1] + hstride * 2) / ih])
if point_coords[0] + wstride * 2 < iw and point_coords[1] + hstride * 2 < ih:
if or_results[int(point_coords[0] + wstride * 2), int(point_coords[1] + hstride * 2)]:
continue
new_points.append([(point_coords[0] + wstride * 2) / iw, (point_coords[1] + hstride * 2) / ih])
self.set_point_grids([np.array(new_points)])
new_masks = self.generate(image, False)
new_masks.cat(ori_masks)
new_masks = self.post_process(image, new_masks)
return new_masks
@torch.no_grad()
def generate(self, image: np.ndarray, need_high: bool) -> MaskData:
orig_size = image.shape[:2]
# Get points for this crop
points_scale = np.array(orig_size)[None, ::-1]
points_for_image = self.point_grids[0] * points_scale
# Generate masks for this crop in batches
data = MaskData()
for (points,) in batch_iterator(self.points_per_batch, points_for_image):
orig_h, orig_w = orig_size
# Run model on this batch
transformed_points = self.predictor.transform.apply_coords(points, orig_size)
in_points = torch.as_tensor(transformed_points, device=self.predictor.device)
in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)
masks, iou_preds, _ = self.predictor.predict_torch(
in_points[:, None, :],
in_labels[:, None],
return_logits=True,
)
# Serialize predictions and store in MaskData
batch_data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
if self.pred_iou_thresh > 0.0:
keep_mask = batch_data["iou_preds"] > self.pred_iou_thresh
batch_data.filter(keep_mask)
# Calculate stability score
batch_data["stability_score"] = calculate_stability_score(
batch_data["masks"], self.predictor.model.mask_threshold, self.stability_score_offset
)
if self.stability_score_thresh > 0.0:
keep_mask = batch_data["stability_score"] >= self.stability_score_thresh
batch_data.filter(keep_mask)
if need_high:
batch_data["high_masks"] = batch_data["masks"] > self.high_score_thresh
batch_data["masks"] = batch_data["masks"] > self.predictor.model.mask_threshold
batch_data["boxes"] = batched_mask_to_box(batch_data["masks"])
keep_mask = ~is_box_near_crop_edge(batch_data["boxes"], [0, 0, orig_w, orig_h], [0, 0, orig_w, orig_h])
if not torch.all(keep_mask):
batch_data.filter(keep_mask)
# Compress to RLE
batch_data["rles"] = mask_to_rle_pytorch(batch_data["masks"])
data.cat(batch_data)
del batch_data
if need_high:
high_masks = data["high_masks"]
or_results = torch.zeros([high_masks.shape[1], high_masks.shape[2]]).to(high_masks.device)
for mask in high_masks:
or_results = torch.logical_or(or_results, mask)
del data["high_masks"]
or_results = or_results.permute(1, 0)
del data['masks']
return data, or_results
else:
del data['masks']
return data
@torch.no_grad()
def reset_image(self):
self.predictor.reset_image()
@torch.no_grad()
def post_process(self, image: np.ndarray, data: MaskData) -> List[Dict[str, Any]]:
orig_size = image.shape[:2]
orig_h, orig_w = orig_size
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros_like(data["boxes"][:, 0]), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
data = self.postprocess_small_regions(
data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle":
data["segmentations"] = [coco_encode_rle(rle) for rle in data["rles"]]
elif self.output_mode == "binary_mask":
data["segmentations"] = [rle_to_mask(rle) for rle in data["rles"]]
else:
data["segmentations"] = data["rles"]
# Write mask records
curr_anns = []
for idx in range(len(data["segmentations"])):
ann = {
"segmentation": data["segmentations"][idx],
"area": area_from_rle(data["rles"][idx]),
"bbox": box_xyxy_to_xywh(data["boxes"][idx]).tolist(),
"predicted_iou": data["iou_preds"][idx].item(),
"point_coords": [data["points"][idx].tolist()],
"stability_score": data["stability_score"][idx].item(),
}
curr_anns.append(ann)
# print("post use time: {}".format(time.time() - st))
return curr_anns
@staticmethod
def postprocess_small_regions(
mask_data: MaskData, min_area: int, nms_thresh: float
) -> MaskData:
"""
Removes small disconnected regions and holes in masks, then reruns
box NMS to remove any new duplicates.
Edits mask_data in place.
Requires open-cv as a dependency.
"""
if len(mask_data["rles"]) == 0:
return mask_data
# Filter small disconnected regions and holes
new_masks = []
scores = []
for rle in mask_data["rles"]:
mask = rle_to_mask(rle)
| mask, changed = remove_small_regions(mask, min_area, mode="holes") | 13 | 2023-12-19 11:25:54+00:00 | 16k |
DeepWok/mase | machop/chop/passes/graph/transforms/verilog/emit_tb.py | [
{
"identifier": "emit_data_in_tb_sv",
"path": "machop/chop/passes/graph/transforms/verilog/emit_tb_data_in.py",
"snippet": "def emit_data_in_tb_sv(data_width, load_path, out_file):\n buff = f\"\"\"\n`timescale 1 ns / 1 ps\n\nmodule AESL_autofifo_data_in_V (\n clk,\n reset,\n if_empty_n,\n ... | import math, time, os, logging, torch, glob, shutil
from chop.passes.graph.utils import vf, v2p, init_project
from chop.passes.graph.transforms.quantize.quantizers import integer_quantizer_for_hw
from .emit_tb_data_in import emit_data_in_tb_sv, emit_data_in_tb_dat
from .emit_tb_data_out import emit_data_out_tb_sv, emit_data_out_tb_dat
from .emit_tb_testbench import emit_top_tb
from pathlib import Path | 12,382 |
logger = logging.getLogger(__name__)
def emit_tb_verilog(graph, trans_num=1, project_dir="top"):
sim_dir = os.path.join(project_dir, "hardware", "sim")
tv_dir = os.path.join(sim_dir, "tv")
if not os.path.exists(tv_dir):
os.mkdir(tv_dir)
v_dir = os.path.join(sim_dir, "verilog")
if not os.path.exists(v_dir):
os.mkdir(v_dir)
# TODO : need to emit all the inputs
v_in_param = graph.nodes_in[0].meta["mase"].parameters["hardware"]["verilog_param"]
w_in_param = graph.nodes_in[0].meta["mase"].parameters["common"]["args"]
in_width = w_in_param["data_in_0"]["precision"][0]
in_size = v_in_param["DATA_IN_0_TENSOR_SIZE_DIM_0"]
data_width = in_width * in_size
# TODO : need to check
addr_width = 1
depth = 1
load_path = os.path.join(tv_dir, f"sw_data_in.dat")
out_file = os.path.join(v_dir, f"top_data_in_fifo.sv")
emit_data_in_tb_sv(data_width, load_path, out_file)
v_out_param = (
graph.nodes_out[0].meta["mase"].parameters["hardware"]["verilog_param"]
)
w_out_param = graph.nodes_in[0].meta["mase"].parameters["common"]["results"]
out_width = w_out_param["data_out_0"]["precision"][0]
out_size = v_out_param["DATA_OUT_0_TENSOR_SIZE_0_DIM_0"]
data_width = out_width * out_size
# TODO : need to check
addr_width = 1
depth = 1
load_path = os.path.join(tv_dir, f"sw_data_out.dat")
store_path = os.path.join(tv_dir, f"hw_data_out.dat")
out_file = os.path.join(v_dir, f"top_data_out_fifo.sv")
|
logger = logging.getLogger(__name__)
def emit_tb_verilog(graph, trans_num=1, project_dir="top"):
sim_dir = os.path.join(project_dir, "hardware", "sim")
tv_dir = os.path.join(sim_dir, "tv")
if not os.path.exists(tv_dir):
os.mkdir(tv_dir)
v_dir = os.path.join(sim_dir, "verilog")
if not os.path.exists(v_dir):
os.mkdir(v_dir)
# TODO : need to emit all the inputs
v_in_param = graph.nodes_in[0].meta["mase"].parameters["hardware"]["verilog_param"]
w_in_param = graph.nodes_in[0].meta["mase"].parameters["common"]["args"]
in_width = w_in_param["data_in_0"]["precision"][0]
in_size = v_in_param["DATA_IN_0_TENSOR_SIZE_DIM_0"]
data_width = in_width * in_size
# TODO : need to check
addr_width = 1
depth = 1
load_path = os.path.join(tv_dir, f"sw_data_in.dat")
out_file = os.path.join(v_dir, f"top_data_in_fifo.sv")
emit_data_in_tb_sv(data_width, load_path, out_file)
v_out_param = (
graph.nodes_out[0].meta["mase"].parameters["hardware"]["verilog_param"]
)
w_out_param = graph.nodes_in[0].meta["mase"].parameters["common"]["results"]
out_width = w_out_param["data_out_0"]["precision"][0]
out_size = v_out_param["DATA_OUT_0_TENSOR_SIZE_0_DIM_0"]
data_width = out_width * out_size
# TODO : need to check
addr_width = 1
depth = 1
load_path = os.path.join(tv_dir, f"sw_data_out.dat")
store_path = os.path.join(tv_dir, f"hw_data_out.dat")
out_file = os.path.join(v_dir, f"top_data_out_fifo.sv") | emit_data_out_tb_sv(data_width, load_path, store_path, out_file) | 2 | 2023-12-18 12:50:53+00:00 | 16k |
OPPOMKLab/u-LLaVA | models/GroundingDINO/groundingdino/models/GroundingDINO/groundingdino.py | [
{
"identifier": "box_ops",
"path": "models/GroundingDINO/groundingdino/util/box_ops.py",
"snippet": "def box_cxcywh_to_xyxy(x):\ndef box_xyxy_to_cxcywh(x):\ndef box_iou(boxes1, boxes2):\ndef generalized_box_iou(boxes1, boxes2):\ndef box_iou_pairwise(boxes1, boxes2):\ndef generalized_box_iou_pairwise(box... | import copy
import torch
import torch.nn.functional as F
from typing import List
from torch import nn
from torchvision.ops.boxes import nms
from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
from models.GroundingDINO.groundingdino.util import box_ops, get_tokenlizer
from models.GroundingDINO.groundingdino.util.misc import (
NestedTensor,
accuracy,
get_world_size,
interpolate,
inverse_sigmoid,
is_dist_avail_and_initialized,
nested_tensor_from_tensor_list,
)
from models.GroundingDINO.groundingdino.util.utils import get_phrases_from_posmap
from models.GroundingDINO.groundingdino.util.visualizer import COCOVisualizer
from models.GroundingDINO.groundingdino.util.vl_utils import create_positive_map_from_span
from ..registry import MODULE_BUILD_FUNCS
from .backbone import build_backbone
from .bertwarper import (
BertModelWarper,
generate_masks_with_special_tokens,
generate_masks_with_special_tokens_and_transfer_map,
)
from .transformer import build_transformer
from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss | 10,986 | # Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
class GroundingDINO(nn.Module):
"""This is the Cross-Attention Detector module that performs object detection"""
def __init__(
self,
backbone,
transformer,
num_queries,
aux_loss=False,
iter_update=False,
query_dim=2,
num_feature_levels=1,
nheads=8,
# two stage
two_stage_type="no", # ['no', 'standard']
dec_pred_bbox_embed_share=True,
two_stage_class_embed_share=True,
two_stage_bbox_embed_share=True,
num_patterns=0,
dn_number=100,
dn_box_noise_scale=0.4,
dn_label_noise_ratio=0.5,
dn_labelbook_size=100,
text_encoder_type="bert-base-uncased",
sub_sentence_present=True,
max_text_len=256,
):
"""Initializes the model.
Parameters:
backbone: torch module of the backbone to be used. See backbone.py
transformer: torch module of the transformer architecture. See transformer.py
num_queries: number of object queries, ie detection slot. This is the maximal number of objects
Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
"""
super().__init__()
self.num_queries = num_queries
self.transformer = transformer
self.hidden_dim = hidden_dim = transformer.d_model
self.num_feature_levels = num_feature_levels
self.nheads = nheads
self.max_text_len = 256
self.sub_sentence_present = sub_sentence_present
# setting query dim
self.query_dim = query_dim
assert query_dim == 4
# for dn training
self.num_patterns = num_patterns
self.dn_number = dn_number
self.dn_box_noise_scale = dn_box_noise_scale
self.dn_label_noise_ratio = dn_label_noise_ratio
self.dn_labelbook_size = dn_labelbook_size
# bert
self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
self.bert.pooler.dense.weight.requires_grad_(False)
self.bert.pooler.dense.bias.requires_grad_(False)
self.bert = BertModelWarper(bert_model=self.bert)
self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
nn.init.constant_(self.feat_map.bias.data, 0)
nn.init.xavier_uniform_(self.feat_map.weight.data)
# freeze
# special tokens
self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
# prepare input projection layers
if num_feature_levels > 1:
num_backbone_outs = len(backbone.num_channels)
input_proj_list = []
for _ in range(num_backbone_outs):
in_channels = backbone.num_channels[_]
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
)
for _ in range(num_feature_levels - num_backbone_outs):
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
nn.GroupNorm(32, hidden_dim),
)
)
in_channels = hidden_dim
self.input_proj = nn.ModuleList(input_proj_list)
else:
assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
self.input_proj = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
]
)
self.backbone = backbone
self.aux_loss = aux_loss
self.box_pred_damping = box_pred_damping = None
self.iter_update = iter_update
assert iter_update, "Why not iter_update?"
# prepare pred layers
self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
# prepare class & box embed
| # ------------------------------------------------------------------------
# Grounding DINO
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Conditional DETR model and criterion classes.
# Copyright (c) 2021 Microsoft. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
class GroundingDINO(nn.Module):
"""This is the Cross-Attention Detector module that performs object detection"""
def __init__(
self,
backbone,
transformer,
num_queries,
aux_loss=False,
iter_update=False,
query_dim=2,
num_feature_levels=1,
nheads=8,
# two stage
two_stage_type="no", # ['no', 'standard']
dec_pred_bbox_embed_share=True,
two_stage_class_embed_share=True,
two_stage_bbox_embed_share=True,
num_patterns=0,
dn_number=100,
dn_box_noise_scale=0.4,
dn_label_noise_ratio=0.5,
dn_labelbook_size=100,
text_encoder_type="bert-base-uncased",
sub_sentence_present=True,
max_text_len=256,
):
"""Initializes the model.
Parameters:
backbone: torch module of the backbone to be used. See backbone.py
transformer: torch module of the transformer architecture. See transformer.py
num_queries: number of object queries, ie detection slot. This is the maximal number of objects
Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
"""
super().__init__()
self.num_queries = num_queries
self.transformer = transformer
self.hidden_dim = hidden_dim = transformer.d_model
self.num_feature_levels = num_feature_levels
self.nheads = nheads
self.max_text_len = 256
self.sub_sentence_present = sub_sentence_present
# setting query dim
self.query_dim = query_dim
assert query_dim == 4
# for dn training
self.num_patterns = num_patterns
self.dn_number = dn_number
self.dn_box_noise_scale = dn_box_noise_scale
self.dn_label_noise_ratio = dn_label_noise_ratio
self.dn_labelbook_size = dn_labelbook_size
# bert
self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
self.bert.pooler.dense.weight.requires_grad_(False)
self.bert.pooler.dense.bias.requires_grad_(False)
self.bert = BertModelWarper(bert_model=self.bert)
self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
nn.init.constant_(self.feat_map.bias.data, 0)
nn.init.xavier_uniform_(self.feat_map.weight.data)
# freeze
# special tokens
self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
# prepare input projection layers
if num_feature_levels > 1:
num_backbone_outs = len(backbone.num_channels)
input_proj_list = []
for _ in range(num_backbone_outs):
in_channels = backbone.num_channels[_]
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
)
for _ in range(num_feature_levels - num_backbone_outs):
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
nn.GroupNorm(32, hidden_dim),
)
)
in_channels = hidden_dim
self.input_proj = nn.ModuleList(input_proj_list)
else:
assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
self.input_proj = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
]
)
self.backbone = backbone
self.aux_loss = aux_loss
self.box_pred_damping = box_pred_damping = None
self.iter_update = iter_update
assert iter_update, "Why not iter_update?"
# prepare pred layers
self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
# prepare class & box embed | _class_embed = ContrastiveEmbed() | 19 | 2023-12-21 08:10:23+00:00 | 16k |
chinhsuanwu/ifusion | 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 torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import itertools
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager, nullcontext
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities import rank_zero_only
from omegaconf import ListConfig
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 ldm.modules.attention import CrossAttention | 12,105 | log["diffusion_row"] = diffusion_grid
if sample:
# get denoise row
with ema_scope("Sampling"):
samples, z_denoise_row = self.sample_log(
cond=c,
batch_size=N,
ddim=use_ddim,
ddim_steps=ddim_steps,
eta=ddim_eta,
)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
if (
quantize_denoised
and not isinstance(self.first_stage_model, AutoencoderKL)
and not isinstance(self.first_stage_model, IdentityFirstStage)
):
# also display when quantizing x0 while sampling
with ema_scope("Plotting Quantized Denoised"):
samples, z_denoise_row = self.sample_log(
cond=c,
batch_size=N,
ddim=use_ddim,
ddim_steps=ddim_steps,
eta=ddim_eta,
quantize_denoised=True,
)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
# quantize_denoised=True)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_x0_quantized"] = x_samples
if unconditional_guidance_scale > 1.0:
uc = self.get_unconditional_conditioning(
N, unconditional_guidance_label, image_size=x.shape[-1]
)
# uc = torch.zeros_like(c)
with ema_scope("Sampling with classifier-free guidance"):
samples_cfg, _ = self.sample_log(
cond=c,
batch_size=N,
ddim=use_ddim,
ddim_steps=ddim_steps,
eta=ddim_eta,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=uc,
)
x_samples_cfg = self.decode_first_stage(samples_cfg)
log[
f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"
] = x_samples_cfg
if inpaint:
# make a simple center square
b, h, w = z.shape[0], z.shape[2], z.shape[3]
mask = torch.ones(N, h, w).to(self.device)
# zeros will be filled in
mask[:, h // 4 : 3 * h // 4, w // 4 : 3 * w // 4] = 0.0
mask = mask[:, None, ...]
with ema_scope("Plotting Inpaint"):
samples, _ = self.sample_log(
cond=c,
batch_size=N,
ddim=use_ddim,
eta=ddim_eta,
ddim_steps=ddim_steps,
x0=z[:N],
mask=mask,
)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_inpainting"] = x_samples
log["mask"] = mask
# outpaint
mask = 1.0 - mask
with ema_scope("Plotting Outpaint"):
samples, _ = self.sample_log(
cond=c,
batch_size=N,
ddim=use_ddim,
eta=ddim_eta,
ddim_steps=ddim_steps,
x0=z[:N],
mask=mask,
)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_outpainting"] = x_samples
if plot_progressive_rows:
with ema_scope("Plotting Progressives"):
img, progressives = self.progressive_denoising(
c,
shape=(self.channels, self.image_size, self.image_size),
batch_size=N,
)
prog_row = self._get_denoise_row_from_list(
progressives, desc="Progressive Generation"
)
log["progressive_row"] = prog_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 = []
if self.unet_trainable == "attn":
print("Training only unet attention layers")
for n, m in self.model.named_modules():
| """
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"}
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_target",
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=0.0,
v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
l_simple_weight=1.0,
conditioning_key=None,
parameterization="eps", # all assuming fixed variance schedules
scheduler_config=None,
use_positional_encodings=False,
learn_logvar=False,
logvar_init=0.0,
make_it_fit=False,
ucg_training=None,
):
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
self.make_it_fit = make_it_fit
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)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
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 = 1.0 - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1.0, 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(1.0 - alphas_cumprod))
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
)
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (
1.0 - alphas_cumprod_prev
) / (1.0 - 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) / (1.0 - alphas_cumprod)),
)
self.register_buffer(
"posterior_mean_coef2",
to_torch(
(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - 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))
/ (2.0 * 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")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
keys = list(sd.keys())
if self.make_it_fit:
n_params = len(
[
name
for name, _ in itertools.chain(
self.named_parameters(), self.named_buffers()
)
]
)
for name, param in tqdm(
itertools.chain(self.named_parameters(), self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params,
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[
i % old_shape[0], j % old_shape[1]
]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
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_(-1.0, 1.0)
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")
elif self.loss_type == "smooth_l1":
if mean:
loss = torch.nn.functional.smooth_l1_loss(target, pred)
else:
loss = torch.nn.functional.smooth_l1_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):
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):
for k in self.ucg_training:
p = self.ucg_training[k]["p"]
val = self.ucg_training[k]["val"]
if val is None:
val = ""
for i in range(len(batch[k])):
if self.ucg_prng.choice(2, p=[1 - p, p]):
batch[k][i] = val
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):
_, 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",
cond_stage_trainable=False,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
unet_trainable=True,
*args,
**kwargs,
):
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.unet_trainable = unet_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
# construct linear projection layer for concatenating image CLIP embedding and RT
self.cc_projection = nn.Linear(772, 768)
nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768])
nn.init.zeros_(list(self.cc_projection.parameters())[1])
self.cc_projection.requires_grad_(True)
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
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
@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 == 1.0
), "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", 1.0 / 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
# 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[0] * 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)
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[0] // 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,
uncond=0.05,
):
x = super().get_input(batch, k)
T = batch["T"].to(memory_format=torch.contiguous_format).float()
if bs is not None:
x = x[:bs]
T = T[:bs].to(self.device)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
cond_key = cond_key or self.cond_stage_key
xc = super().get_input(batch, cond_key).to(self.device)
if bs is not None:
xc = xc[:bs]
cond = {}
# To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
random = torch.rand(x.size(0), device=x.device)
prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
input_mask = 1 - rearrange(
(random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1"
)
null_prompt = self.get_learned_conditioning([""])
# z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768]
# print('=========== xc shape ===========', xc.shape)
with torch.enable_grad():
clip_emb = self.get_learned_conditioning(xc).detach()
null_prompt = self.get_learned_conditioning([""]).detach()
cond["c_crossattn"] = [
self.cc_projection(
torch.cat(
[
torch.where(prompt_mask, null_prompt, clip_emb),
T[:, None, :],
],
dim=-1,
)
)
]
cond["c_concat"] = [
input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach()
]
out = [z, cond]
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 = 1.0 / 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
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 )
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 shared_step(self, batch, step_ratio=None, **kwargs):
x, c = self.get_input(batch, self.first_stage_key)
loss = self(x, c, step_ratio=step_ratio)
return loss
def forward(self, x, c, step_ratio=None, *args, **kwargs):
if step_ratio is not None:
t = np.round((1 - step_ratio) * self.num_timesteps).clip(0, self.num_timesteps - 1)
t = torch.full((x.shape[0],), t, dtype=torch.long, device=self.device)
else:
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.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:]
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)
# 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)
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")
adapted_cond = self.get_learned_conditioning(adapted_cond)
adapted_cond = rearrange(
adapted_cond, "(l b) n d -> l b n d", l=z.shape[-1]
)
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()})
if self.logvar.device != self.device:
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_(-1.0, 1.0)
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=1.0,
noise_dropout=0.0,
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 > 0.0:
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=1.0,
noise_dropout=0.0,
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 + (1.0 - 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 + (1.0 - 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)
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 get_unconditional_conditioning(
self, batch_size, null_label=None, image_size=512
):
if null_label is not None:
xc = null_label
if isinstance(xc, ListConfig):
xc = list(xc)
if isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:
if hasattr(xc, "to"):
xc = xc.to(self.device)
c = self.get_learned_conditioning(xc)
else:
# todo: get null label from cond_stage_model
raise NotImplementedError()
c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
cond = {}
cond["c_crossattn"] = [c]
cond["c_concat"] = [
torch.zeros([batch_size, 4, image_size // 8, image_size // 8]).to(
self.device
)
]
return cond
@torch.no_grad()
def log_images(
self,
batch,
N=8,
n_row=4,
sample=True,
ddim_steps=200,
ddim_eta=1.0,
return_keys=None,
quantize_denoised=True,
inpaint=True,
plot_denoise_rows=False,
plot_progressive_rows=True,
plot_diffusion_rows=True,
unconditional_guidance_scale=1.0,
unconditional_guidance_label=None,
use_ema_scope=True,
**kwargs,
):
ema_scope = self.ema_scope if use_ema_scope else nullcontext
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,
)
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", "txt"]:
xc = log_txt_as_img(
(x.shape[2], x.shape[3]),
batch[self.cond_stage_key],
size=x.shape[2] // 25,
)
log["conditioning"] = xc
elif self.cond_stage_key == "class_label":
xc = log_txt_as_img(
(x.shape[2], x.shape[3]),
batch["human_label"],
size=x.shape[2] // 25,
)
log["conditioning"] = xc
elif isimage(xc):
log["conditioning"] = xc
if ismap(xc):
log["original_conditioning"] = self.to_rgb(xc)
if plot_diffusion_rows:
# get diffusion row
diffusion_row = list()
z_start = z[: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(z_start)
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
diffusion_row.append(self.decode_first_stage(z_noisy))
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
diffusion_grid = rearrange(diffusion_row, "n b c h w -> b n c h w")
diffusion_grid = rearrange(diffusion_grid, "b n c h w -> (b n) c h w")
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
log["diffusion_row"] = diffusion_grid
if sample:
# get denoise row
with ema_scope("Sampling"):
samples, z_denoise_row = self.sample_log(
cond=c,
batch_size=N,
ddim=use_ddim,
ddim_steps=ddim_steps,
eta=ddim_eta,
)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
if (
quantize_denoised
and not isinstance(self.first_stage_model, AutoencoderKL)
and not isinstance(self.first_stage_model, IdentityFirstStage)
):
# also display when quantizing x0 while sampling
with ema_scope("Plotting Quantized Denoised"):
samples, z_denoise_row = self.sample_log(
cond=c,
batch_size=N,
ddim=use_ddim,
ddim_steps=ddim_steps,
eta=ddim_eta,
quantize_denoised=True,
)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
# quantize_denoised=True)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_x0_quantized"] = x_samples
if unconditional_guidance_scale > 1.0:
uc = self.get_unconditional_conditioning(
N, unconditional_guidance_label, image_size=x.shape[-1]
)
# uc = torch.zeros_like(c)
with ema_scope("Sampling with classifier-free guidance"):
samples_cfg, _ = self.sample_log(
cond=c,
batch_size=N,
ddim=use_ddim,
ddim_steps=ddim_steps,
eta=ddim_eta,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=uc,
)
x_samples_cfg = self.decode_first_stage(samples_cfg)
log[
f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"
] = x_samples_cfg
if inpaint:
# make a simple center square
b, h, w = z.shape[0], z.shape[2], z.shape[3]
mask = torch.ones(N, h, w).to(self.device)
# zeros will be filled in
mask[:, h // 4 : 3 * h // 4, w // 4 : 3 * w // 4] = 0.0
mask = mask[:, None, ...]
with ema_scope("Plotting Inpaint"):
samples, _ = self.sample_log(
cond=c,
batch_size=N,
ddim=use_ddim,
eta=ddim_eta,
ddim_steps=ddim_steps,
x0=z[:N],
mask=mask,
)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_inpainting"] = x_samples
log["mask"] = mask
# outpaint
mask = 1.0 - mask
with ema_scope("Plotting Outpaint"):
samples, _ = self.sample_log(
cond=c,
batch_size=N,
ddim=use_ddim,
eta=ddim_eta,
ddim_steps=ddim_steps,
x0=z[:N],
mask=mask,
)
x_samples = self.decode_first_stage(samples.to(self.device))
log["samples_outpainting"] = x_samples
if plot_progressive_rows:
with ema_scope("Plotting Progressives"):
img, progressives = self.progressive_denoising(
c,
shape=(self.channels, self.image_size, self.image_size),
batch_size=N,
)
prog_row = self._get_denoise_row_from_list(
progressives, desc="Progressive Generation"
)
log["progressive_row"] = prog_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 = []
if self.unet_trainable == "attn":
print("Training only unet attention layers")
for n, m in self.model.named_modules(): | if isinstance(m, CrossAttention) and n.endswith("attn2"): | 18 | 2023-12-17 12:45:38+00:00 | 16k |
wangzhecheng/SkyScript | customized_train_and_test.py | [
{
"identifier": "create_model_and_transforms",
"path": "src/open_clip/factory.py",
"snippet": "def create_model_and_transforms(\n model_name: str,\n pretrained: Optional[str] = None,\n precision: str = 'fp32',\n device: Union[str, torch.device] = 'cpu',\n jit: bool = F... | import glob
import json
import logging
import os
import re
import subprocess
import sys
import random
import numpy as np
import torch
import wandb
import torch.utils.tensorboard as tensorboard
import horovod.torch as hvd
from datetime import datetime
from torch import optim
from torch.cuda.amp import GradScaler
from torchvision import transforms
from src.open_clip.factory import create_model_and_transforms, get_tokenizer, create_loss
from src.open_clip.model import trace_model
from src.training.data import get_data
from src.training.distributed import is_master, init_distributed_device, broadcast_object
from src.training.logger import setup_logging
from src.training.scheduler import cosine_lr, const_lr, const_lr_cooldown
from src.training.train import train_one_epoch, evaluate
from src.training.file_utils import pt_load, check_exists, start_sync_process, remote_sync
from src.training.main import natural_key, get_latest_checkpoint, copy_codebase
from test_zero_shot_classification import *
from params import parse_args | 13,187 | model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
ddp_args = {}
if args.ddp_static_graph:
# this doesn't exist in older PyTorch, arg only added if enabled
ddp_args['static_graph'] = True
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[device], **ddp_args)
if args.distill:
dist_model = torch.nn.parallel.DistributedDataParallel(dist_model, device_ids=[device], **ddp_args)
# create optimizer and scaler
optimizer = None
scaler = None
if args.train_data or args.dataset_type == "synthetic":
assert not args.trace, 'Cannot train with traced model'
exclude = lambda n, p: p.ndim < 2 or "bn" in n or "ln" in n or "bias" in n or 'logit_scale' in n
include = lambda n, p: not exclude(n, p)
named_parameters = list(model.named_parameters())
gain_or_bias_params = [p for n, p in named_parameters if exclude(n, p) and p.requires_grad]
rest_params = [p for n, p in named_parameters if include(n, p) and p.requires_grad]
optimizer = optim.AdamW(
[
{"params": gain_or_bias_params, "weight_decay": 0.},
{"params": rest_params, "weight_decay": args.wd},
],
lr=args.lr,
betas=(args.beta1, args.beta2),
eps=args.eps,
)
if args.horovod:
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
scaler = GradScaler() if args.precision == "amp" else None
# optionally resume from a checkpoint
start_epoch = 0
if args.resume is not None:
checkpoint = pt_load(args.resume, map_location='cpu')
if 'epoch' in checkpoint:
# resuming a train checkpoint w/ epoch and optimizer state
start_epoch = checkpoint["epoch"]
sd = checkpoint["state_dict"]
if not args.distributed and next(iter(sd.items()))[0].startswith('module'):
sd = {k[len('module.'):]: v for k, v in sd.items()}
model.load_state_dict(sd)
if optimizer is not None:
optimizer.load_state_dict(checkpoint["optimizer"])
if scaler is not None and 'scaler' in checkpoint:
scaler.load_state_dict(checkpoint['scaler'])
logging.info(f"=> resuming checkpoint '{args.resume}' (epoch {start_epoch})")
else:
# loading a bare (model only) checkpoint for fine-tune or evaluation
model.load_state_dict(checkpoint)
logging.info(f"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})")
# initialize datasets
data = get_data(args, (preprocess_train, preprocess_val), epoch=start_epoch, tokenizer=get_tokenizer(args.model))
assert len(data), 'At least one train or eval dataset must be specified.'
# initialize benchmark dataloaders for testing zero-shot classification
if args.datasets_for_testing is not None or args.test_data_name is not None:
test_dataloaders = get_test_dataloaders(args, preprocess_val)
else:
test_dataloaders = None
# create scheduler if train
scheduler = None
if 'train' in data and optimizer is not None:
total_steps = (data["train"].dataloader.num_batches // args.accum_freq) * args.epochs
if args.lr_scheduler == "cosine":
scheduler = cosine_lr(optimizer, args.lr, args.warmup, total_steps)
elif args.lr_scheduler == "const":
scheduler = const_lr(optimizer, args.lr, args.warmup, total_steps)
elif args.lr_scheduler == "const-cooldown":
assert args.epochs_cooldown is not None,\
"Please specify the number of cooldown epochs for this lr schedule."
cooldown_steps = (data["train"].dataloader.num_batches // args.accum_freq) * args.epochs_cooldown
scheduler = const_lr_cooldown(
optimizer, args.lr, args.warmup, total_steps,
cooldown_steps, args.lr_cooldown_power, args.lr_cooldown_end)
else:
logging.error(
f'Unknown scheduler, {args.lr_scheduler}. Available options are: cosine, const, const-cooldown.')
exit(1)
# determine if this worker should save logs and checkpoints. only do so if it is rank == 0
args.save_logs = args.logs and args.logs.lower() != 'none' and is_master(args)
writer = None
if args.save_logs and args.tensorboard:
assert tensorboard is not None, "Please install tensorboard."
writer = tensorboard.SummaryWriter(args.tensorboard_path)
if args.wandb and is_master(args):
assert wandb is not None, 'Please install wandb.'
logging.debug('Starting wandb.')
args.train_sz = data["train"].dataloader.num_samples
if args.val_data is not None:
args.val_sz = data["val"].dataloader.num_samples
# you will have to configure this for your project!
wandb.init(
project=args.wandb_project_name,
name=args.name,
id=args.name,
notes=args.wandb_notes,
tags=[],
resume='auto' if args.resume == "latest" else None,
config=vars(args),
)
if args.debug:
wandb.watch(model, log='all')
wandb.save(params_file)
logging.debug('Finished loading wandb.')
if 'train' not in data:
| """
Adapted from https://github.com/mlfoundations/open_clip.
Copyright (c) 2012-2021 Gabriel Ilharco, Mitchell Wortsman, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, John Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, Ludwig Schmidt
"""
try:
except ImportError:
wandb = None
try:
except ImportError:
tensorboard = None
try:
except ImportError:
hvd = None
# from src.open_clip import create_model_and_transforms, trace_model, get_tokenizer, create_loss
LATEST_CHECKPOINT_NAME = "epoch_latest.pt"
def RandomRotationNew(image):
angle = random.choice([0, 90, 180, 270])
image = transforms.functional.rotate(image, angle)
return image
def zero_shot_eval_during_training(model, test_dataloaders, epoch, args, tb_writer=None):
logging.info('Starting zero-shot evaluation.')
zero_shot_metrics = {}
for dataset_name in test_dataloaders:
logging.info(f'Evaluating zero-shot classification for dataset {dataset_name}')
results = test_zero_shot_classification(model, test_dataloaders[dataset_name]['dataloader'],
test_dataloaders[dataset_name]['labels'],
test_dataloaders[dataset_name]['is_binary'], args,
dataset_name=dataset_name, debugging=args.debugging)
for k, v in results.items():
if type(v) in [float, int, np.float16, np.float32, np.float64, np.int8, np.int16, np.int32, np.int64]:
zero_shot_metrics[k] = v
logging.info(
f"Zero-Shot Eval Epoch: {epoch} "
+ "\t".join([f"{k}: {round(v, 4):.4f}" for k, v in zero_shot_metrics.items()])
)
if args.save_logs:
for name, val in zero_shot_metrics.items():
if tb_writer is not None:
tb_writer.add_scalar(f"val/{name}", val, epoch)
with open(os.path.join(args.checkpoint_path, "results.jsonl"), "a+") as f:
f.write(json.dumps(zero_shot_metrics))
f.write("\n")
# if args.wandb:
# assert wandb is not None, 'Please install wandb.'
# for name, val in zero_shot_metrics.items():
# wandb.log({f"val/{name}": val, 'epoch': epoch})
logging.info('Finished zero-shot evaluation.')
return zero_shot_metrics
def train_and_test(args):
args = parse_args(args)
if torch.cuda.is_available():
# This enables tf32 on Ampere GPUs which is only 8% slower than
# float16 and almost as accurate as float32
# This was a default in pytorch until 1.12
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
# fully initialize distributed device environment
device = init_distributed_device(args)
# get the name of the experiments
if args.name is None:
# sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?
model_name_safe = args.model.replace('/', '-')
date_str = datetime.now().strftime("%Y_%m_%d-%H_%M_%S")
if args.distributed:
# sync date_str from master to all ranks
date_str = broadcast_object(args, date_str)
args.name = '-'.join([
date_str,
f"model_{model_name_safe}",
f"lr_{args.lr}",
f"b_{args.batch_size}",
f"j_{args.workers}",
f"p_{args.precision}",
])
resume_latest = args.resume == 'latest'
log_base_path = os.path.join(args.logs, args.name)
args.log_path = None
if is_master(args, local=args.log_local):
os.makedirs(log_base_path, exist_ok=True)
log_filename = f'out-{args.rank}' if args.log_local else 'out.log'
args.log_path = os.path.join(log_base_path, log_filename)
if os.path.exists(args.log_path) and not resume_latest:
print(
"Error. Experiment already exists. Use --name {} to specify a new experiment."
)
return -1
# Setup text logger
args.log_level = logging.DEBUG if args.debug else logging.INFO
setup_logging(args.log_path, args.log_level)
# Setup wandb, tensorboard, checkpoint logging
args.wandb = 'wandb' in args.report_to or 'all' in args.report_to
args.tensorboard = 'tensorboard' in args.report_to or 'all' in args.report_to
args.checkpoint_path = os.path.join(log_base_path, "checkpoints")
if is_master(args):
args.tensorboard_path = os.path.join(log_base_path, "tensorboard") if args.tensorboard else ''
for dirname in [args.tensorboard_path, args.checkpoint_path]:
if dirname:
os.makedirs(dirname, exist_ok=True)
else:
args.tensorboard_path = ''
if resume_latest:
resume_from = None
checkpoint_path = args.checkpoint_path
# If using remote_sync, need to check the remote instead of the local checkpoints folder.
if args.remote_sync is not None:
checkpoint_path = os.path.join(args.remote_sync, args.name, "checkpoints")
if args.save_most_recent:
print('Error. Cannot use save-most-recent with remote_sync and resume latest.')
return -1
if args.remote_sync_protocol != 's3':
print('Error. Sync protocol not supported when using resume latest.')
return -1
if is_master(args):
# Checking for existing checkpoint via master rank only. It is possible for
# different rank processes to see different files if a shared file-system is under
# stress, however it's very difficult to fully work around such situations.
if args.save_most_recent:
# if --save-most-recent flag is set, look for latest at a fixed filename
resume_from = os.path.join(checkpoint_path, LATEST_CHECKPOINT_NAME)
if not os.path.exists(resume_from):
# If no latest checkpoint has been saved yet, don't try to resume
resume_from = None
else:
# otherwise, list checkpoint dir contents and pick the newest checkpoint
resume_from = get_latest_checkpoint(checkpoint_path, remote=args.remote_sync is not None)
if resume_from:
logging.info(f'Found latest resume checkpoint at {resume_from}.')
else:
logging.info(f'No latest resume checkpoint found in {checkpoint_path}.')
if args.distributed:
# sync found checkpoint path to all ranks
resume_from = broadcast_object(args, resume_from)
args.resume = resume_from
if args.copy_codebase:
copy_codebase(args)
# start the sync proces if remote-sync is not None
remote_sync_process = None
if is_master(args) and args.remote_sync is not None:
# first make sure it works
result = remote_sync(
os.path.join(args.logs, args.name),
os.path.join(args.remote_sync, args.name),
args.remote_sync_protocol
)
if result:
logging.info('remote sync successful.')
else:
logging.info('Error: remote sync failed. Exiting.')
return -1
# if all looks good, start a process to do this every args.remote_sync_frequency seconds
remote_sync_process = start_sync_process(
args.remote_sync_frequency,
os.path.join(args.logs, args.name),
os.path.join(args.remote_sync, args.name),
args.remote_sync_protocol
)
remote_sync_process.start()
if args.precision == 'fp16':
logging.warning(
'It is recommended to use AMP mixed-precision instead of FP16. '
'FP16 support needs further verification and tuning, especially for train.')
if args.horovod:
logging.info(
f'Running in horovod mode with multiple processes / nodes. Device: {args.device}.'
f'Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}.')
elif args.distributed:
logging.info(
f'Running in distributed mode with multiple processes. Device: {args.device}.'
f'Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}.')
else:
logging.info(f'Running with a single process. Device {args.device}.')
dist_model = None
args.distill = args.distill_model is not None and args.distill_pretrained is not None
if args.distill:
#FIXME: support distillation with grad accum.
assert args.accum_freq == 1
#FIXME: support distillation with coca.
assert 'coca' not in args.model.lower()
if isinstance(args.force_image_size, (tuple, list)) and len(args.force_image_size) == 1:
# arg is nargs, single (square) image size list -> int
args.force_image_size = args.force_image_size[0]
random_seed(args.seed, 0)
model, preprocess_train, preprocess_val = create_model_and_transforms(
args.model,
args.pretrained,
precision=args.precision,
device=device,
jit=args.torchscript,
force_quick_gelu=args.force_quick_gelu,
force_custom_text=args.force_custom_text,
force_patch_dropout=args.force_patch_dropout,
force_image_size=args.force_image_size,
pretrained_image=args.pretrained_image,
image_mean=args.image_mean,
image_std=args.image_std,
aug_cfg=args.aug_cfg,
output_dict=True,
)
if args.random_rotation:
# add random rotation step into preprocess_train
for i, trans in enumerate(preprocess_train.transforms):
if type(trans) == transforms.transforms.ToTensor:
# insert random rotation right before ToTensor
preprocess_train.transforms.insert(i, transforms.Lambda(RandomRotationNew))
break
if args.distill:
# FIXME: currenlty assumes the model your distilling from has the same tokenizer & transforms.
dist_model, _, _ = create_model_and_transforms(
args.distill_model,
args.distill_pretrained,
device=device,
precision=args.precision,
output_dict=True,
)
random_seed(args.seed, args.rank)
if args.trace:
model = trace_model(model, batch_size=args.batch_size, device=device)
if args.lock_image:
# lock image tower as per LiT - https://arxiv.org/abs/2111.07991
model.lock_image_tower(
unlocked_groups=args.lock_image_unlocked_groups,
freeze_bn_stats=args.lock_image_freeze_bn_stats)
if args.lock_text:
model.lock_text_tower(
unlocked_layers=args.lock_text_unlocked_layers,
freeze_layer_norm=args.lock_text_freeze_layer_norm)
if args.grad_checkpointing:
model.set_grad_checkpointing()
if is_master(args):
logging.info("Model:")
logging.info(f"{str(model)}")
logging.info("Params:")
params_file = os.path.join(args.logs, args.name, "params.txt")
with open(params_file, "w") as f:
for name in sorted(vars(args)):
val = getattr(args, name)
logging.info(f" {name}: {val}")
f.write(f"{name}: {val}\n")
if args.distributed and not args.horovod:
if args.use_bn_sync:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
ddp_args = {}
if args.ddp_static_graph:
# this doesn't exist in older PyTorch, arg only added if enabled
ddp_args['static_graph'] = True
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[device], **ddp_args)
if args.distill:
dist_model = torch.nn.parallel.DistributedDataParallel(dist_model, device_ids=[device], **ddp_args)
# create optimizer and scaler
optimizer = None
scaler = None
if args.train_data or args.dataset_type == "synthetic":
assert not args.trace, 'Cannot train with traced model'
exclude = lambda n, p: p.ndim < 2 or "bn" in n or "ln" in n or "bias" in n or 'logit_scale' in n
include = lambda n, p: not exclude(n, p)
named_parameters = list(model.named_parameters())
gain_or_bias_params = [p for n, p in named_parameters if exclude(n, p) and p.requires_grad]
rest_params = [p for n, p in named_parameters if include(n, p) and p.requires_grad]
optimizer = optim.AdamW(
[
{"params": gain_or_bias_params, "weight_decay": 0.},
{"params": rest_params, "weight_decay": args.wd},
],
lr=args.lr,
betas=(args.beta1, args.beta2),
eps=args.eps,
)
if args.horovod:
optimizer = hvd.DistributedOptimizer(optimizer, named_parameters=model.named_parameters())
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
scaler = GradScaler() if args.precision == "amp" else None
# optionally resume from a checkpoint
start_epoch = 0
if args.resume is not None:
checkpoint = pt_load(args.resume, map_location='cpu')
if 'epoch' in checkpoint:
# resuming a train checkpoint w/ epoch and optimizer state
start_epoch = checkpoint["epoch"]
sd = checkpoint["state_dict"]
if not args.distributed and next(iter(sd.items()))[0].startswith('module'):
sd = {k[len('module.'):]: v for k, v in sd.items()}
model.load_state_dict(sd)
if optimizer is not None:
optimizer.load_state_dict(checkpoint["optimizer"])
if scaler is not None and 'scaler' in checkpoint:
scaler.load_state_dict(checkpoint['scaler'])
logging.info(f"=> resuming checkpoint '{args.resume}' (epoch {start_epoch})")
else:
# loading a bare (model only) checkpoint for fine-tune or evaluation
model.load_state_dict(checkpoint)
logging.info(f"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})")
# initialize datasets
data = get_data(args, (preprocess_train, preprocess_val), epoch=start_epoch, tokenizer=get_tokenizer(args.model))
assert len(data), 'At least one train or eval dataset must be specified.'
# initialize benchmark dataloaders for testing zero-shot classification
if args.datasets_for_testing is not None or args.test_data_name is not None:
test_dataloaders = get_test_dataloaders(args, preprocess_val)
else:
test_dataloaders = None
# create scheduler if train
scheduler = None
if 'train' in data and optimizer is not None:
total_steps = (data["train"].dataloader.num_batches // args.accum_freq) * args.epochs
if args.lr_scheduler == "cosine":
scheduler = cosine_lr(optimizer, args.lr, args.warmup, total_steps)
elif args.lr_scheduler == "const":
scheduler = const_lr(optimizer, args.lr, args.warmup, total_steps)
elif args.lr_scheduler == "const-cooldown":
assert args.epochs_cooldown is not None,\
"Please specify the number of cooldown epochs for this lr schedule."
cooldown_steps = (data["train"].dataloader.num_batches // args.accum_freq) * args.epochs_cooldown
scheduler = const_lr_cooldown(
optimizer, args.lr, args.warmup, total_steps,
cooldown_steps, args.lr_cooldown_power, args.lr_cooldown_end)
else:
logging.error(
f'Unknown scheduler, {args.lr_scheduler}. Available options are: cosine, const, const-cooldown.')
exit(1)
# determine if this worker should save logs and checkpoints. only do so if it is rank == 0
args.save_logs = args.logs and args.logs.lower() != 'none' and is_master(args)
writer = None
if args.save_logs and args.tensorboard:
assert tensorboard is not None, "Please install tensorboard."
writer = tensorboard.SummaryWriter(args.tensorboard_path)
if args.wandb and is_master(args):
assert wandb is not None, 'Please install wandb.'
logging.debug('Starting wandb.')
args.train_sz = data["train"].dataloader.num_samples
if args.val_data is not None:
args.val_sz = data["val"].dataloader.num_samples
# you will have to configure this for your project!
wandb.init(
project=args.wandb_project_name,
name=args.name,
id=args.name,
notes=args.wandb_notes,
tags=[],
resume='auto' if args.resume == "latest" else None,
config=vars(args),
)
if args.debug:
wandb.watch(model, log='all')
wandb.save(params_file)
logging.debug('Finished loading wandb.')
if 'train' not in data: | evaluate(model, data, start_epoch, args, writer) | 13 | 2023-12-19 11:50:56+00:00 | 16k |
penghao-wu/vstar | VisualSearch/train.py | [
{
"identifier": "VSMForCausalLM",
"path": "VisualSearch/model/VSM.py",
"snippet": "class VSMForCausalLM(LlavaLlamaForCausalLM):\n\tdef __init__(\n\t\tself,\n\t\tconfig,\n\t\t**kwargs,\n\t):\n\t\tif not hasattr(config, \"train_mask_decoder\"):\n\t\t\tconfig.mm_use_im_start_end = kwargs.pop(\"use_mm_start... | import argparse
import os
import shutil
import sys
import time
import deepspeed
import torch
import tqdm
import transformers
from functools import partial
from peft import LoraConfig, get_peft_model
from torch.utils.tensorboard import SummaryWriter
from VisualSearch.model.VSM import VSMForCausalLM
from VisualSearch.model.llava import conversation as conversation_lib
from VisualSearch.utils.dataset import HybridDataset, ValDataset, collate_fn
from VisualSearch.utils.utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
AverageMeter, ProgressMeter, Summary, dict_to_cuda,
intersectionAndUnionGPU) | 11,396 | "--version", default="LLaVA-7B-v1.1"
)
parser.add_argument(
"--precision",
default="bf16",
type=str,
choices=["fp32", "bf16", "fp16"],
help="precision for training",
)
parser.add_argument("--model_max_length", default=512, type=int)
parser.add_argument("--lora_r", default=8, type=int)
parser.add_argument(
"--vision-tower", default="openai/clip-vit-large-patch14", type=str
)
parser.add_argument("--load_in_8bit", action="store_true", default=False)
parser.add_argument("--load_in_4bit", action="store_true", default=False)
parser.add_argument(
"--dataset", default="general_segdet||refer_seg||mixed_grounding||vqa", type=str
)
parser.add_argument("--sample_rates", default="15,4,4,15", type=str)
parser.add_argument(
"--general_segdet_data",
default="objects365||cocostuff||paco_lvis",
type=str,
)
parser.add_argument("--general_segdet_sample_rates", default="2,1,1", type=str)
parser.add_argument(
"--refer_seg_data", default="refclef||refcoco||refcoco+||refcocog", type=str
)
parser.add_argument("--vqa_data", default="possible_locations_conv_86k||llava_instruct_80k", type=str)
parser.add_argument("--vqa_sample_rates", default="2,1", type=str)
parser.add_argument("--val_dataset", default="refcoco|unc|val", type=str)
parser.add_argument("--dataset_dir", default="data", type=str)
parser.add_argument("--log_base_dir", default="./runs", type=str)
parser.add_argument("--exp_name", default="vsm", type=str)
parser.add_argument("--epochs", default=40, type=int)
parser.add_argument("--steps_per_epoch", default=2500, type=int)
parser.add_argument(
"--batch_size", default=4, type=int, help="batch size per device per step"
)
parser.add_argument(
"--grad_accumulation_steps",
default=2,
type=int,
)
parser.add_argument("--val_batch_size", default=1, type=int)
parser.add_argument("--workers", default=2, type=int)
parser.add_argument("--lr", default=0.0001, type=float)
parser.add_argument("--ce_loss_weight", default=1.0, type=float)
parser.add_argument("--dice_loss_weight", default=0.5, type=float)
parser.add_argument("--bce_loss_weight", default=2.0, type=float)
parser.add_argument("--det_loss_weight", default=0.1, type=float)
parser.add_argument("--lora_alpha", default=16, type=int)
parser.add_argument("--lora_dropout", default=0.05, type=float)
parser.add_argument("--lora_target_modules", default="q_proj,v_proj", type=str)
parser.add_argument("--explanatory", default=0.1, type=float)
parser.add_argument("--beta1", default=0.9, type=float)
parser.add_argument("--beta2", default=0.95, type=float)
parser.add_argument("--num_classes_per_sample", default=3, type=int)
parser.add_argument("--exclude_val", action="store_true", default=False)
parser.add_argument("--no_eval", action="store_true", default=False)
parser.add_argument("--out_dim", default=512, type=int)
parser.add_argument("--weight", type=str)
parser.add_argument("--resume", default="", type=str)
parser.add_argument("--print_freq", default=1, type=int)
parser.add_argument("--start_epoch", default=0, type=int)
parser.add_argument("--gradient_checkpointing", action="store_true", default=True)
parser.add_argument("--train_mask_decoder", action="store_true", default=True)
parser.add_argument("--use_mm_start_end", action="store_true", default=True)
parser.add_argument("--auto_resume", action="store_true", default=False)
parser.add_argument(
"--conv_type",
default="llava_v1",
type=str,
choices=["llava_v1", "llava_llama_2"],
)
return parser.parse_args(args)
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=1)
def iou(bbox1, bbox2):
x1 = max(bbox1[0], bbox2[0])
y1 = max(bbox1[1], bbox2[1])
x2 = min(bbox1[2], bbox2[2])
y2 = min(bbox1[3], bbox2[3])
w1 = bbox1[2] - bbox1[0]
h1 = bbox1[3] - bbox1[1]
w2 = bbox2[2] - bbox2[0]
h2 = bbox2[3] - bbox2[1]
inter_area = max(0, x2 - x1) * max(0, y2 - y1)
return inter_area/(w1*h1+w2*h2-inter_area)
def main(args):
args = parse_args(args)
args.log_dir = os.path.join(args.log_base_dir, args.exp_name)
if args.local_rank == 0:
os.makedirs(args.log_dir, exist_ok=True)
writer = SummaryWriter(args.log_dir)
else:
writer = None
# Create model
tokenizer = transformers.AutoTokenizer.from_pretrained(
args.version,
cache_dir=None,
model_max_length=args.model_max_length,
padding_side="right",
use_fast=False,
)
tokenizer.pad_token = tokenizer.unk_token
num_added_tokens = tokenizer.add_tokens("[LOC]")
args.loc_token_idx = tokenizer("[LOC]", add_special_tokens=False).input_ids[0]
if args.use_mm_start_end:
tokenizer.add_tokens(
|
def parse_args(args):
parser = argparse.ArgumentParser(description="VisualSearch Model Training")
parser.add_argument("--local_rank", default=0, type=int, help="node rank")
parser.add_argument(
"--version", default="LLaVA-7B-v1.1"
)
parser.add_argument(
"--precision",
default="bf16",
type=str,
choices=["fp32", "bf16", "fp16"],
help="precision for training",
)
parser.add_argument("--model_max_length", default=512, type=int)
parser.add_argument("--lora_r", default=8, type=int)
parser.add_argument(
"--vision-tower", default="openai/clip-vit-large-patch14", type=str
)
parser.add_argument("--load_in_8bit", action="store_true", default=False)
parser.add_argument("--load_in_4bit", action="store_true", default=False)
parser.add_argument(
"--dataset", default="general_segdet||refer_seg||mixed_grounding||vqa", type=str
)
parser.add_argument("--sample_rates", default="15,4,4,15", type=str)
parser.add_argument(
"--general_segdet_data",
default="objects365||cocostuff||paco_lvis",
type=str,
)
parser.add_argument("--general_segdet_sample_rates", default="2,1,1", type=str)
parser.add_argument(
"--refer_seg_data", default="refclef||refcoco||refcoco+||refcocog", type=str
)
parser.add_argument("--vqa_data", default="possible_locations_conv_86k||llava_instruct_80k", type=str)
parser.add_argument("--vqa_sample_rates", default="2,1", type=str)
parser.add_argument("--val_dataset", default="refcoco|unc|val", type=str)
parser.add_argument("--dataset_dir", default="data", type=str)
parser.add_argument("--log_base_dir", default="./runs", type=str)
parser.add_argument("--exp_name", default="vsm", type=str)
parser.add_argument("--epochs", default=40, type=int)
parser.add_argument("--steps_per_epoch", default=2500, type=int)
parser.add_argument(
"--batch_size", default=4, type=int, help="batch size per device per step"
)
parser.add_argument(
"--grad_accumulation_steps",
default=2,
type=int,
)
parser.add_argument("--val_batch_size", default=1, type=int)
parser.add_argument("--workers", default=2, type=int)
parser.add_argument("--lr", default=0.0001, type=float)
parser.add_argument("--ce_loss_weight", default=1.0, type=float)
parser.add_argument("--dice_loss_weight", default=0.5, type=float)
parser.add_argument("--bce_loss_weight", default=2.0, type=float)
parser.add_argument("--det_loss_weight", default=0.1, type=float)
parser.add_argument("--lora_alpha", default=16, type=int)
parser.add_argument("--lora_dropout", default=0.05, type=float)
parser.add_argument("--lora_target_modules", default="q_proj,v_proj", type=str)
parser.add_argument("--explanatory", default=0.1, type=float)
parser.add_argument("--beta1", default=0.9, type=float)
parser.add_argument("--beta2", default=0.95, type=float)
parser.add_argument("--num_classes_per_sample", default=3, type=int)
parser.add_argument("--exclude_val", action="store_true", default=False)
parser.add_argument("--no_eval", action="store_true", default=False)
parser.add_argument("--out_dim", default=512, type=int)
parser.add_argument("--weight", type=str)
parser.add_argument("--resume", default="", type=str)
parser.add_argument("--print_freq", default=1, type=int)
parser.add_argument("--start_epoch", default=0, type=int)
parser.add_argument("--gradient_checkpointing", action="store_true", default=True)
parser.add_argument("--train_mask_decoder", action="store_true", default=True)
parser.add_argument("--use_mm_start_end", action="store_true", default=True)
parser.add_argument("--auto_resume", action="store_true", default=False)
parser.add_argument(
"--conv_type",
default="llava_v1",
type=str,
choices=["llava_v1", "llava_llama_2"],
)
return parser.parse_args(args)
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=1)
def iou(bbox1, bbox2):
x1 = max(bbox1[0], bbox2[0])
y1 = max(bbox1[1], bbox2[1])
x2 = min(bbox1[2], bbox2[2])
y2 = min(bbox1[3], bbox2[3])
w1 = bbox1[2] - bbox1[0]
h1 = bbox1[3] - bbox1[1]
w2 = bbox2[2] - bbox2[0]
h2 = bbox2[3] - bbox2[1]
inter_area = max(0, x2 - x1) * max(0, y2 - y1)
return inter_area/(w1*h1+w2*h2-inter_area)
def main(args):
args = parse_args(args)
args.log_dir = os.path.join(args.log_base_dir, args.exp_name)
if args.local_rank == 0:
os.makedirs(args.log_dir, exist_ok=True)
writer = SummaryWriter(args.log_dir)
else:
writer = None
# Create model
tokenizer = transformers.AutoTokenizer.from_pretrained(
args.version,
cache_dir=None,
model_max_length=args.model_max_length,
padding_side="right",
use_fast=False,
)
tokenizer.pad_token = tokenizer.unk_token
num_added_tokens = tokenizer.add_tokens("[LOC]")
args.loc_token_idx = tokenizer("[LOC]", add_special_tokens=False).input_ids[0]
if args.use_mm_start_end:
tokenizer.add_tokens( | [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True | 5 | 2023-12-15 14:58:24+00:00 | 16k |
foocker/Bert-VITS2-Faster | 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 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,
)
from models import (
SynthesizerTrn,
MultiPeriodDiscriminator,
DurationDiscriminator,
)
from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from text.symbols import symbols | 13,311 | y,
y_lengths,
speakers,
tone,
language,
bert,
ja_bert,
en_bert,
) in tqdm(enumerate(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.fp16_run):
(
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_),
) = 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(
y_hat.squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y = commons.slice_segments(
y, ids_slice * hps.data.hop_length, hps.train.segment_size
) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
y_d_hat_r, y_d_hat_g
)
loss_disc_all = loss_disc
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(
hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()
)
with autocast(enabled=False):
# TODO: I think need to mean using the mask, but for now, just mean all
(
loss_dur_disc,
losses_dur_disc_r,
losses_dur_disc_g,
) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
loss_dur_disc_all = loss_dur_disc
optim_dur_disc.zero_grad()
scaler.scale(loss_dur_disc_all).backward()
scaler.unscale_(optim_dur_disc)
commons.clip_grad_value_(net_dur_disc.parameters(), None)
scaler.step(optim_dur_disc)
optim_d.zero_grad()
scaler.scale(loss_disc_all).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)
with autocast(enabled=hps.train.fp16_run):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
with autocast(enabled=False):
loss_dur = torch.sum(l_length.float())
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
| # flake8: noqa: E402
logging.getLogger("numba").setLevel(logging.WARNING)
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.cudnn.benchmark = True
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
torch.backends.cuda.enable_math_sdp(True)
global_step = 0
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"],
)
)
# 多卡训练设置
backend = "nccl"
if platform.system() == "Windows":
backend = "gloo"
dist.init_process_group(
backend=backend,
init_method="env://", # If Windows,switch to gloo backend.
) # 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,
)
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
# 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=16,
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)
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)
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).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,
)
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])
net_d = DDP(net_d, device_ids=[local_rank])
dur_resume_lr = None
if net_dur_disc is not None:
net_dur_disc = DDP(
net_dur_disc, device_ids=[local_rank], find_unused_parameters=True
)
# 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,
)
try:
if net_dur_disc is not None:
_, _, 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,
)
_, 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
if not optim_dur_disc.param_groups[0].get("initial_lr"):
optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
epoch_str = max(epoch_str, 1)
global_step = (epoch_str - 1) * len(train_loader)
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
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
)
if net_dur_disc is not None:
if not optim_dur_disc.param_groups[0].get("initial_lr"):
optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
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.fp16_run)
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],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
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],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, None],
None,
None,
)
scheduler_g.step()
scheduler_d.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 = nets
optim_g, optim_d, optim_dur_disc = optims
scheduler_g, scheduler_d, scheduler_dur_disc = 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()
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 tqdm(enumerate(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.fp16_run):
(
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_),
) = 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(
y_hat.squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y = commons.slice_segments(
y, ids_slice * hps.data.hop_length, hps.train.segment_size
) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
y_d_hat_r, y_d_hat_g
)
loss_disc_all = loss_disc
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(
hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()
)
with autocast(enabled=False):
# TODO: I think need to mean using the mask, but for now, just mean all
(
loss_dur_disc,
losses_dur_disc_r,
losses_dur_disc_g,
) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
loss_dur_disc_all = loss_dur_disc
optim_dur_disc.zero_grad()
scaler.scale(loss_dur_disc_all).backward()
scaler.unscale_(optim_dur_disc)
commons.clip_grad_value_(net_dur_disc.parameters(), None)
scaler.step(optim_dur_disc)
optim_d.zero_grad()
scaler.scale(loss_disc_all).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)
with autocast(enabled=hps.train.fp16_run):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
with autocast(enabled=False):
loss_dur = torch.sum(l_length.float())
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
| loss_fm = feature_loss(fmap_r, fmap_g) | 9 | 2023-12-18 09:53:41+00:00 | 16k |
sinoyou/nelf-pro | nerfstudio/cameras/camera_paths.py | [
{
"identifier": "camera_utils",
"path": "nerfstudio/cameras/camera_utils.py",
"snippet": "_EPS = np.finfo(float).eps * 4.0\n M = np.array(matrix, dtype=np.float64, copy=False)[:4, :4]\n K = np.array(\n [\n [m00 - m11 - m22, 0.0, 0.0, 0.0],\n [m01 + m10,... | from typing import Any, Dict, Optional, Tuple
from nerfstudio.cameras import camera_utils
from nerfstudio.cameras.camera_utils import get_interpolated_poses_many
from nerfstudio.cameras.cameras import Cameras
from nerfstudio.viewer.server.utils import three_js_perspective_camera_focal_length
import numpy as np
import torch
import nerfstudio.utils.poses as pose_utils | 12,430 | # Copyright 2022 The Nerfstudio 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.
"""
Code for camera paths.
"""
def get_interpolated_camera_path(cameras: Cameras, steps: int) -> Cameras:
"""Generate a camera path between two cameras.
Args:
cameras: Cameras object containing intrinsics of all cameras.
steps: The number of steps to interpolate between the two cameras.
Returns:
A new set of cameras along a path.
"""
Ks = cameras.get_intrinsics_matrices().cpu().numpy()
poses = cameras.camera_to_worlds().cpu().numpy()
poses, Ks = get_interpolated_poses_many(poses, Ks, steps_per_transition=steps)
cameras = Cameras(fx=Ks[:, 0, 0], fy=Ks[:, 1, 1], cx=Ks[0, 0, 2], cy=Ks[0, 1, 2], camera_to_worlds=poses)
return cameras
# def get_path_from_json(camera_path, probe_config) -> Cameras:
# data = camera_path
# fl_x = data["fx"]
# fl_y = data["fy"]
# cx = data["cx"]
# cy = data["cy"]
# width = data["w"]
# height = data["h"]
# transforms = [x['transform_matrix'] for x in data['frames']]
# transforms = torch.tensor(transforms)
# return Cameras(fx=fl_x, fy=fl_y, cx=cx, cy=cy, width=width, height=height, camera_to_worlds=transforms, probe_config=probe_config)
def get_path_from_json(camera_path: Dict[str, Any], probe_config) -> Cameras:
"""Takes a camera path dictionary and returns a trajectory as a Camera instance.
Args:
camera_path: A dictionary of the camera path information coming from the viewer.
Returns:
A Cameras instance with the camera path.
"""
image_height = camera_path["render_height"]
image_width = camera_path["render_width"]
c2ws = []
fxs = []
fys = []
for camera in camera_path["camera_path"]:
# pose
c2w = torch.tensor(camera["camera_to_world"]).view(4, 4)[:3]
c2ws.append(c2w)
# field of view
fov = camera["fov"]
| # Copyright 2022 The Nerfstudio 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.
"""
Code for camera paths.
"""
def get_interpolated_camera_path(cameras: Cameras, steps: int) -> Cameras:
"""Generate a camera path between two cameras.
Args:
cameras: Cameras object containing intrinsics of all cameras.
steps: The number of steps to interpolate between the two cameras.
Returns:
A new set of cameras along a path.
"""
Ks = cameras.get_intrinsics_matrices().cpu().numpy()
poses = cameras.camera_to_worlds().cpu().numpy()
poses, Ks = get_interpolated_poses_many(poses, Ks, steps_per_transition=steps)
cameras = Cameras(fx=Ks[:, 0, 0], fy=Ks[:, 1, 1], cx=Ks[0, 0, 2], cy=Ks[0, 1, 2], camera_to_worlds=poses)
return cameras
# def get_path_from_json(camera_path, probe_config) -> Cameras:
# data = camera_path
# fl_x = data["fx"]
# fl_y = data["fy"]
# cx = data["cx"]
# cy = data["cy"]
# width = data["w"]
# height = data["h"]
# transforms = [x['transform_matrix'] for x in data['frames']]
# transforms = torch.tensor(transforms)
# return Cameras(fx=fl_x, fy=fl_y, cx=cx, cy=cy, width=width, height=height, camera_to_worlds=transforms, probe_config=probe_config)
def get_path_from_json(camera_path: Dict[str, Any], probe_config) -> Cameras:
"""Takes a camera path dictionary and returns a trajectory as a Camera instance.
Args:
camera_path: A dictionary of the camera path information coming from the viewer.
Returns:
A Cameras instance with the camera path.
"""
image_height = camera_path["render_height"]
image_width = camera_path["render_width"]
c2ws = []
fxs = []
fys = []
for camera in camera_path["camera_path"]:
# pose
c2w = torch.tensor(camera["camera_to_world"]).view(4, 4)[:3]
c2ws.append(c2w)
# field of view
fov = camera["fov"] | focal_length = three_js_perspective_camera_focal_length(fov, image_height) | 3 | 2023-12-15 20:07:22+00:00 | 16k |
amazon-science/c2f-seg | src/image_model.py | [
{
"identifier": "VQModel",
"path": "taming_src/taming_models.py",
"snippet": "class VQModel(nn.Module):\n def __init__(self, config):\n super(VQModel, self).__init__()\n self.config = config\n self.iteration = 0\n self.name = config.model_type\n self.m_path = os.pat... | import os
import math
import random
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from torchvision import transforms
from taming_src.taming_models import VQModel
from src.image_component import MaskedTransformer, Resnet_Encoder, Refine_Module
from src.loss import VGG19, PerceptualLoss
from utils.pytorch_optimization import AdamW, get_linear_schedule_with_warmup
from utils.utils import torch_show_all_params, torch_init_model
from utils.utils import Config
from utils.evaluation import evaluation_image
from utils.loss import CrossEntropyLoss
from tqdm import tqdm | 11,249 | mask_out = []
for t in range(start_iter, T):
logits = self.transformer(img_feat[-1], src_indices, cur_ids, mask=None) # [B, L, N]
logits = logits[..., :-1]
logits = self.top_k_logits(logits, k=3)
probs = F.softmax(logits, dim=-1) # convert logits into probs [B, 256, vocab_size+1]
sampled_ids = torch.distributions.categorical.Categorical(probs=probs).sample() # [B, L]
unknown_map = (cur_ids == self.mask_token_idx) # which tokens need to be sampled -> bool [B, 256]
sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) # replace all -1 with their samples and leave the others untouched [B, 256]
seq_out.append(sampled_ids)
mask_out.append(1. * unknown_map)
ratio = 1. * (t + 1) / T # just a percentage e.g. 1 / 12
mask_ratio = gamma(ratio)
selected_probs = probs.gather(dim=-1, index=sampled_ids.unsqueeze(-1)).squeeze(-1)
selected_probs = torch.where(unknown_map, selected_probs, torch.Tensor([np.inf]).to(logits.device)) # ignore tokens which are already sampled [B, 256]
mask_len = torch.unsqueeze(torch.floor(unknown_number_in_the_beginning * mask_ratio), 1) # floor(256 * 0.99) = 254 --> [254, 254, 254, 254, ....] (B x 1)
mask_len = torch.maximum(torch.ones_like(mask_len), torch.minimum(torch.sum(unknown_map, dim=-1, keepdim=True) - 1, mask_len))
# Adds noise for randomness
masking = self.mask_by_random_topk(mask_len, selected_probs, temperature=self.choice_temperature * (1. - ratio))
# Masks tokens with lower confidence.
cur_ids = torch.where(masking, self.mask_token_idx, sampled_ids) # [B, L]
seq_ids = torch.stack(seq_out, dim=1) # [B, T, L]
quant_z = self.g_model.quantize.get_codebook_entry(seq_ids[:,-1,:].reshape(-1), shape=bhwc)
pred_fm_crop = self.g_model.decode(quant_z)
pred_fm_crop = pred_fm_crop.mean(dim=1, keepdim=True)
pred_fm_crop_old = torch.clamp(pred_fm_crop, min=0, max=1)
pred_vm_crop, pred_fm_crop = self.refine_module(img_feat, pred_fm_crop_old)
pred_vm_crop = F.interpolate(pred_vm_crop, size=(256, 256), mode="nearest")
pred_vm_crop = torch.sigmoid(pred_vm_crop)
loss_vm = self.refine_criterion(pred_vm_crop, meta['vm_crop_gt'])
# pred_vm_crop = (pred_vm_crop>=0.5).to(torch.float32)
pred_fm_crop = F.interpolate(pred_fm_crop, size=(256, 256), mode="nearest")
pred_fm_crop = torch.sigmoid(pred_fm_crop)
loss_fm = self.refine_criterion(pred_fm_crop, meta['fm_crop'])
# pred_fm_crop = (pred_fm_crop>=0.5).to(torch.float32)
pred_vm = self.align_raw_size(pred_vm_crop, meta['obj_position'], meta["vm_pad"], meta)
pred_fm = self.align_raw_size(pred_fm_crop, meta['obj_position'], meta["vm_pad"], meta)
# visualization
self.visualize(pred_vm, pred_fm, meta, mode, iter)
loss_eval = self.loss_and_evaluation(pred_fm, meta, iter, mode, pred_vm=pred_vm)
loss_eval["loss_fm"] = loss_fm
loss_eval["loss_vm"] = loss_vm
return loss_eval
def visualize(self, pred_vm, pred_fm, meta, mode, iteration):
pred_fm = pred_fm.squeeze()
pred_vm = pred_vm.squeeze()
gt_vm = meta["vm_no_crop"].squeeze()
gt_fm = meta["fm_no_crop"].squeeze()
to_plot = torch.cat((pred_vm, pred_fm, gt_vm, gt_fm)).cpu().numpy()
save_dir = os.path.join(self.root_path, '{}_samples'.format(mode))
image_id, anno_id= meta["img_id"], meta["anno_id"]
plt.imsave("{}/{}_{}_{}.png".format(save_dir, iteration, int(image_id.item()), int(anno_id.item())), to_plot)
# def visualize_crop(self, pred_vm, pred_fm, meta, mode, count, pred_fm_crop_old):
# pred_fm = pred_fm.squeeze()
# pred_vm = pred_vm.squeeze()
# pred_fm_crop_old = pred_fm_crop_old.squeeze()
# gt_vm = meta["vm_crop"].squeeze()
# gt_fm = meta["fm_crop"].squeeze()
# to_plot = torch.cat((pred_vm, gt_vm, pred_fm_crop_old, pred_fm, gt_fm)).cpu().numpy()
# save_dir = os.path.join(self.root_path, '{}_samples'.format(mode))
# image_id, anno_id= meta["img_id"], meta["anno_id"]
# plt.imsave("{}/{}_{}_{}_{}.png".format(save_dir, count, int(image_id.item()), int(anno_id.item()), "crop"), to_plot)
def create_inputs_tokens_normal(self, num, device):
self.num_latent_size = self.config['resolution'] // self.config['patch_size']
blank_tokens = torch.ones((num, self.num_latent_size ** 2), device=device)
masked_tokens = self.mask_token_idx * blank_tokens
return masked_tokens.to(torch.int64)
def gamma_func(self, mode="cosine"):
if mode == "linear":
return lambda r: 1 - r
elif mode == "cosine":
return lambda r: np.cos(r * np.pi / 2)
elif mode == "square":
return lambda r: 1 - r ** 2
elif mode == "cubic":
return lambda r: 1 - r ** 3
elif mode == "log":
return lambda r, total_unknown: - np.log2(r) / np.log2(total_unknown)
else:
raise NotImplementedError
def mask_by_random_topk(self, mask_len, probs, temperature=1.0):
confidence = torch.log(probs) + temperature * torch.distributions.gumbel.Gumbel(0, 1).sample(probs.shape).to(probs.device)
sorted_confidence, _ = torch.sort(confidence, dim=-1) # from small to large
# Obtains cut off threshold given the mask lengths.
# cut_off = torch.take_along_dim(sorted_confidence, mask_len.to(torch.long), dim=-1)
cut_off = sorted_confidence.gather(dim=-1, index=mask_len.to(torch.long))
# Masks tokens with lower confidence.
masking = (confidence < cut_off)
return masking
def load(self, is_test=False, prefix=None):
if prefix is not None:
transformer_path = self.transformer_path + prefix + '.pth'
else:
transformer_path = self.transformer_path + '_last.pth'
if self.config.restore or is_test:
if os.path.exists(transformer_path):
print('Rank {} is loading {} Transformer...'.format(self.rank, transformer_path))
data = torch.load(transformer_path, map_location="cpu")
|
class C2F_Seg(nn.Module):
def __init__(self, config, g_path, mode, logger=None, save_eval_dict={}):
super(C2F_Seg, self).__init__()
self.config = config
self.iteration = 0
self.sample_iter = 0
self.name = config.model_type
# load g model for mask
self.g_config = Config(os.path.join(g_path, 'vqgan_{}.yml'.format(config.dataset)))
self.g_path = os.path.join(g_path, self.g_config.model_type)
self.root_path = config.path
self.transformer_path = os.path.join(config.path, self.name)
self.mode = mode
self.save_eval_dict = save_eval_dict
self.eps = 1e-6
self.train_sample_iters = config.train_sample_iters
self.g_model = VQModel(self.g_config).to(config.device)
self.img_encoder = Resnet_Encoder().to(config.device)
self.refine_module = Refine_Module().to(config.device)
self.transformer = MaskedTransformer(config).to(config.device)
self.g_model.eval()
self.refine_criterion = nn.BCELoss()
self.criterion = CrossEntropyLoss(num_classes=config.vocab_size+1, device=config.device)
if config.train_with_dec:
if not config.gumbel_softmax:
self.temperature = nn.Parameter(torch.tensor([config.tp], dtype=torch.float32),
requires_grad=True).to(config.device)
if config.use_vgg:
vgg = VGG19(pretrained=True, vgg_norm=config.vgg_norm).to(config.device)
vgg.eval()
reduction = 'mean' if config.balanced_loss is False else 'none'
self.perceptual_loss = PerceptualLoss(vgg, weights=config.vgg_weights,
reduction=reduction).to(config.device)
else:
self.perceptual_loss = None
if config.init_gpt_with_vqvae:
self.transformer.z_emb.weight = self.g_model.quantize.embedding.weight
if logger is not None:
logger.info('Gen Parameters:{}'.format(torch_show_all_params(self.g_model)))
logger.info('Transformer Parameters:{}'.format(torch_show_all_params(self.transformer)))
else:
print('Gen Parameters:{}'.format(torch_show_all_params(self.g_model)))
print('Transformer Parameters:{}'.format(torch_show_all_params(self.transformer)))
# loss
no_decay = ['bias', 'ln1.bias', 'ln1.weight', 'ln2.bias', 'ln2.weight']
param_optimizer = self.transformer.named_parameters()
param_optimizer_encoder = self.img_encoder.named_parameters()
param_optimizer_refine= self.refine_module.named_parameters()
optimizer_parameters = [
{'params': [p for n, p in param_optimizer if not any([nd in n for nd in no_decay])],
'weight_decay': config.weight_decay},
{'params': [p for n, p in param_optimizer if any([nd in n for nd in no_decay])],
'weight_decay': 0.0},
{'params': [p for n, p in param_optimizer_encoder], 'weight_decay': config.weight_decay},
{'params': [p for n, p in param_optimizer_refine], 'weight_decay': config.weight_decay},
]
self.opt = AdamW(params=optimizer_parameters,
lr=float(config.lr), betas=(config.beta1, config.beta2))
self.sche = get_linear_schedule_with_warmup(self.opt, num_warmup_steps=config.warmup_iters,
num_training_steps=config.max_iters)
self.rank = dist.get_rank()
self.gamma = self.gamma_func(mode=config.gamma_mode)
self.mask_token_idx = config.vocab_size
self.choice_temperature = 4.5
self.Image_W = config.Image_W
self.Image_H = config.Image_H
self.patch_W = config.patch_W
self.patch_H = config.patch_H
@torch.no_grad()
def encode_to_z(self, x, mask=None):
if len(x.size())==5:
x = x[0]
quant_z, _, info = self.g_model.encode(x.float(), mask) # [B,D,H,W]
indices = info[2].view(quant_z.shape[0], -1) # [B, L]
return quant_z, indices
def get_attn_map(self, feature, guidance):
guidance = F.interpolate(guidance, scale_factor=(1/16))
b,c,h,w = guidance.shape
q = torch.flatten(guidance, start_dim=2)
v = torch.flatten(feature, start_dim=2)
k = v * q
k = k.sum(dim=-1, keepdim=True) / (q.sum(dim=-1, keepdim=True) + 1e-6)
attn = (k.transpose(-2, -1) @ v) / 1
attn = F.softmax(attn, dim=-1)
attn = attn.reshape(b, c, h, w)
return attn
def get_losses(self, meta):
self.iteration += 1
z_loss = 0
img_feat = self.img_encoder(meta['img_crop'].permute((0,3,1,2)).to(torch.float32))
_, src_indices = self.encode_to_z(meta['vm_crop'])
_, tgt_indices = self.encode_to_z(meta['fm_crop'])
bhwc = (_.shape[0], _.shape[2], _.shape[3], _.shape[1])
r = np.maximum(self.gamma(np.random.uniform()), self.config.min_mask_rate)
r = math.floor(r * tgt_indices.shape[1])
sample = torch.rand(tgt_indices.shape, device=tgt_indices.device).topk(r, dim=1).indices
random_mask = torch.zeros(tgt_indices.shape, dtype=torch.bool, device=tgt_indices.device)
random_mask.scatter_(dim=1, index=sample, value=True) # [B, L]
# concat mask
mask = random_mask
masked_indices = self.mask_token_idx * torch.ones_like(tgt_indices, device=tgt_indices.device) # [B, L]
z_indices = (~mask) * tgt_indices + mask * masked_indices # [B, L]
logits_z = self.transformer(img_feat[-1], src_indices, z_indices, mask=None)
target = tgt_indices
z_loss = self.criterion(logits_z.view(-1, logits_z.size(-1)), target.view(-1))
with torch.no_grad():
logits_z = logits_z[..., :-1]
logits_z = self.top_k_logits(logits_z, k=5)
probs = F.softmax(logits_z, dim=-1)
seq_ids = torch.distributions.categorical.Categorical(probs=probs).sample() # [B, L]
quant_z = self.g_model.quantize.get_codebook_entry(seq_ids.reshape(-1), shape=bhwc)
pred_fm_crop = self.g_model.decode(quant_z)
pred_fm_crop = pred_fm_crop.mean(dim=1, keepdim=True)
pred_fm_crop = torch.clamp(pred_fm_crop, min=0, max=1)
pred_vm_crop, pred_fm_crop = self.refine_module(img_feat, pred_fm_crop.detach())
pred_vm_crop = F.interpolate(pred_vm_crop, size=(256, 256), mode="nearest")
pred_vm_crop = torch.sigmoid(pred_vm_crop)
loss_vm = self.refine_criterion(pred_vm_crop, meta['vm_crop_gt'])
# pred_vm_crop = (pred_vm_crop>=0.5).to(torch.float32)
pred_fm_crop = F.interpolate(pred_fm_crop, size=(256, 256), mode="nearest")
pred_fm_crop = torch.sigmoid(pred_fm_crop)
loss_fm = self.refine_criterion(pred_fm_crop, meta['fm_crop'])
# pred_fm_crop = (pred_fm_crop>=0.5).to(torch.float32)
logs = [
("z_loss", z_loss.item()),
("loss_vm", loss_vm.item()),
("loss_fm", loss_fm.item()),
]
return z_loss, loss_vm+loss_fm, logs
def align_raw_size(self, full_mask, obj_position, vm_pad, meta):
vm_np_crop = meta["vm_no_crop"].squeeze()
H, W = vm_np_crop.shape[-2], vm_np_crop.shape[-1]
bz, seq_len = full_mask.shape[:2]
new_full_mask = torch.zeros((bz, seq_len, H, W)).to(torch.float32).cuda()
if len(vm_pad.shape)==3:
vm_pad = vm_pad[0]
obj_position = obj_position[0]
for b in range(bz):
paddings = vm_pad[b]
position = obj_position[b]
new_fm = full_mask[
b, :,
:-int(paddings[0]) if int(paddings[0]) !=0 else None,
:-int(paddings[1]) if int(paddings[1]) !=0 else None
]
vx_min = int(position[0])
vx_max = min(H, int(position[1])+1)
vy_min = int(position[2])
vy_max = min(W, int(position[3])+1)
resize = transforms.Resize([vx_max-vx_min, vy_max-vy_min])
try:
new_fm = resize(new_fm)
new_full_mask[b, :, vx_min:vx_max, vy_min:vy_max] = new_fm[0]
except:
new_fm = new_fm
return new_full_mask
def loss_and_evaluation(self, pred_fm, meta, iter, mode, pred_vm=None):
loss_eval = {}
pred_fm = pred_fm.squeeze()
counts = meta["counts"].reshape(-1).to(pred_fm.device)
fm_no_crop = meta["fm_no_crop"].squeeze()
vm_no_crop = meta["vm_no_crop"].squeeze()
pred_vm = pred_vm.squeeze()
# post-process
pred_fm = (pred_fm > 0.5).to(torch.int64)
pred_vm = (pred_vm > 0.5).to(torch.int64)
iou, invisible_iou_, iou_count = evaluation_image((pred_fm > 0.5).to(torch.int64), fm_no_crop, counts, meta, self.save_eval_dict)
loss_eval["iou"] = iou
loss_eval["invisible_iou_"] = invisible_iou_
loss_eval["occ_count"] = iou_count
loss_eval["iou_count"] = torch.Tensor([1]).cuda()
pred_fm_post = pred_fm + vm_no_crop
pred_fm_post = (pred_fm_post>0.5).to(torch.int64)
iou_post, invisible_iou_post, iou_count_post = evaluation_image(pred_fm_post, fm_no_crop, counts, meta, self.save_eval_dict)
loss_eval["iou_post"] = iou_post
loss_eval["invisible_iou_post"] = invisible_iou_post
return loss_eval
def backward(self, loss=None):
self.opt.zero_grad()
loss.backward()
self.opt.step()
self.sche.step()
def top_k_logits(self, logits, k):
v, ix = torch.topk(logits, k)
out = logits.clone()
out[out < v[..., [-1]]] = -float('Inf')
return out
@torch.no_grad()
def batch_predict_maskgit(self, meta, iter, mode, T=3, start_iter=0):
'''
:param x:[B,3,H,W] image
:param c:[b,X,H,W] condition
:param mask: [1,1,H,W] mask
'''
self.sample_iter += 1
img_feat = self.img_encoder(meta['img_crop'].permute((0,3,1,2)).to(torch.float32))
_, src_indices = self.encode_to_z(meta['vm_crop'])
# _, tgt_indices = self.encode_to_z(meta['fm_crop'])
bhwc = (_.shape[0], _.shape[2], _.shape[3], _.shape[1])
masked_indices = self.mask_token_idx * torch.ones_like(src_indices, device=src_indices.device) # [B, L]
unknown_number_in_the_beginning = torch.sum(masked_indices == self.mask_token_idx, dim=-1) # [B]
gamma = self.gamma_func("cosine")
cur_ids = masked_indices # [B, L]
seq_out = []
mask_out = []
for t in range(start_iter, T):
logits = self.transformer(img_feat[-1], src_indices, cur_ids, mask=None) # [B, L, N]
logits = logits[..., :-1]
logits = self.top_k_logits(logits, k=3)
probs = F.softmax(logits, dim=-1) # convert logits into probs [B, 256, vocab_size+1]
sampled_ids = torch.distributions.categorical.Categorical(probs=probs).sample() # [B, L]
unknown_map = (cur_ids == self.mask_token_idx) # which tokens need to be sampled -> bool [B, 256]
sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) # replace all -1 with their samples and leave the others untouched [B, 256]
seq_out.append(sampled_ids)
mask_out.append(1. * unknown_map)
ratio = 1. * (t + 1) / T # just a percentage e.g. 1 / 12
mask_ratio = gamma(ratio)
selected_probs = probs.gather(dim=-1, index=sampled_ids.unsqueeze(-1)).squeeze(-1)
selected_probs = torch.where(unknown_map, selected_probs, torch.Tensor([np.inf]).to(logits.device)) # ignore tokens which are already sampled [B, 256]
mask_len = torch.unsqueeze(torch.floor(unknown_number_in_the_beginning * mask_ratio), 1) # floor(256 * 0.99) = 254 --> [254, 254, 254, 254, ....] (B x 1)
mask_len = torch.maximum(torch.ones_like(mask_len), torch.minimum(torch.sum(unknown_map, dim=-1, keepdim=True) - 1, mask_len))
# Adds noise for randomness
masking = self.mask_by_random_topk(mask_len, selected_probs, temperature=self.choice_temperature * (1. - ratio))
# Masks tokens with lower confidence.
cur_ids = torch.where(masking, self.mask_token_idx, sampled_ids) # [B, L]
seq_ids = torch.stack(seq_out, dim=1) # [B, T, L]
quant_z = self.g_model.quantize.get_codebook_entry(seq_ids[:,-1,:].reshape(-1), shape=bhwc)
pred_fm_crop = self.g_model.decode(quant_z)
pred_fm_crop = pred_fm_crop.mean(dim=1, keepdim=True)
pred_fm_crop_old = torch.clamp(pred_fm_crop, min=0, max=1)
pred_vm_crop, pred_fm_crop = self.refine_module(img_feat, pred_fm_crop_old)
pred_vm_crop = F.interpolate(pred_vm_crop, size=(256, 256), mode="nearest")
pred_vm_crop = torch.sigmoid(pred_vm_crop)
loss_vm = self.refine_criterion(pred_vm_crop, meta['vm_crop_gt'])
# pred_vm_crop = (pred_vm_crop>=0.5).to(torch.float32)
pred_fm_crop = F.interpolate(pred_fm_crop, size=(256, 256), mode="nearest")
pred_fm_crop = torch.sigmoid(pred_fm_crop)
loss_fm = self.refine_criterion(pred_fm_crop, meta['fm_crop'])
# pred_fm_crop = (pred_fm_crop>=0.5).to(torch.float32)
pred_vm = self.align_raw_size(pred_vm_crop, meta['obj_position'], meta["vm_pad"], meta)
pred_fm = self.align_raw_size(pred_fm_crop, meta['obj_position'], meta["vm_pad"], meta)
# visualization
self.visualize(pred_vm, pred_fm, meta, mode, iter)
loss_eval = self.loss_and_evaluation(pred_fm, meta, iter, mode, pred_vm=pred_vm)
loss_eval["loss_fm"] = loss_fm
loss_eval["loss_vm"] = loss_vm
return loss_eval
def visualize(self, pred_vm, pred_fm, meta, mode, iteration):
pred_fm = pred_fm.squeeze()
pred_vm = pred_vm.squeeze()
gt_vm = meta["vm_no_crop"].squeeze()
gt_fm = meta["fm_no_crop"].squeeze()
to_plot = torch.cat((pred_vm, pred_fm, gt_vm, gt_fm)).cpu().numpy()
save_dir = os.path.join(self.root_path, '{}_samples'.format(mode))
image_id, anno_id= meta["img_id"], meta["anno_id"]
plt.imsave("{}/{}_{}_{}.png".format(save_dir, iteration, int(image_id.item()), int(anno_id.item())), to_plot)
# def visualize_crop(self, pred_vm, pred_fm, meta, mode, count, pred_fm_crop_old):
# pred_fm = pred_fm.squeeze()
# pred_vm = pred_vm.squeeze()
# pred_fm_crop_old = pred_fm_crop_old.squeeze()
# gt_vm = meta["vm_crop"].squeeze()
# gt_fm = meta["fm_crop"].squeeze()
# to_plot = torch.cat((pred_vm, gt_vm, pred_fm_crop_old, pred_fm, gt_fm)).cpu().numpy()
# save_dir = os.path.join(self.root_path, '{}_samples'.format(mode))
# image_id, anno_id= meta["img_id"], meta["anno_id"]
# plt.imsave("{}/{}_{}_{}_{}.png".format(save_dir, count, int(image_id.item()), int(anno_id.item()), "crop"), to_plot)
def create_inputs_tokens_normal(self, num, device):
self.num_latent_size = self.config['resolution'] // self.config['patch_size']
blank_tokens = torch.ones((num, self.num_latent_size ** 2), device=device)
masked_tokens = self.mask_token_idx * blank_tokens
return masked_tokens.to(torch.int64)
def gamma_func(self, mode="cosine"):
if mode == "linear":
return lambda r: 1 - r
elif mode == "cosine":
return lambda r: np.cos(r * np.pi / 2)
elif mode == "square":
return lambda r: 1 - r ** 2
elif mode == "cubic":
return lambda r: 1 - r ** 3
elif mode == "log":
return lambda r, total_unknown: - np.log2(r) / np.log2(total_unknown)
else:
raise NotImplementedError
def mask_by_random_topk(self, mask_len, probs, temperature=1.0):
confidence = torch.log(probs) + temperature * torch.distributions.gumbel.Gumbel(0, 1).sample(probs.shape).to(probs.device)
sorted_confidence, _ = torch.sort(confidence, dim=-1) # from small to large
# Obtains cut off threshold given the mask lengths.
# cut_off = torch.take_along_dim(sorted_confidence, mask_len.to(torch.long), dim=-1)
cut_off = sorted_confidence.gather(dim=-1, index=mask_len.to(torch.long))
# Masks tokens with lower confidence.
masking = (confidence < cut_off)
return masking
def load(self, is_test=False, prefix=None):
if prefix is not None:
transformer_path = self.transformer_path + prefix + '.pth'
else:
transformer_path = self.transformer_path + '_last.pth'
if self.config.restore or is_test:
if os.path.exists(transformer_path):
print('Rank {} is loading {} Transformer...'.format(self.rank, transformer_path))
data = torch.load(transformer_path, map_location="cpu")
| torch_init_model(self.transformer, transformer_path, 'model') | 9 | 2023-12-21 04:25:47+00:00 | 16k |
MingtaoGuo/AnimateAnyone_unofficial | 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 torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import itertools
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager, nullcontext
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig
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 IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler | 12,316 | 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)
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 get_unconditional_conditioning(self, batch_size, null_label=None):
if null_label is not None:
xc = null_label
if isinstance(xc, ListConfig):
xc = list(xc)
if isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:
if hasattr(xc, "to"):
xc = xc.to(self.device)
c = self.get_learned_conditioning(xc)
else:
if self.cond_stage_key in ["class_label", "cls"]:
xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
return self.get_learned_conditioning(xc)
else:
raise NotImplementedError("todo")
if isinstance(c, list): # in case the encoder gives us a list
for i in range(len(c)):
c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
else:
c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
return c
@torch.no_grad()
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
use_ema_scope=True,
**kwargs):
ema_scope = self.ema_scope if use_ema_scope else nullcontext
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)
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", "txt"]:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
log["conditioning"] = xc
elif self.cond_stage_key in ['class_label', "cls"]:
try:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
log['conditioning'] = xc
except KeyError:
# probably no "human_label" in batch
pass
| """
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'}
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.,
make_it_fit=False,
ucg_training=None,
reset_ema=False,
reset_num_ema_updates=False,
):
super().__init__()
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
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
self.make_it_fit = make_it_fit
if reset_ema: assert exists(ckpt_path)
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
if reset_ema:
assert self.use_ema
print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
self.model_ema = LitEma(self.model)
if reset_num_ema_updates:
print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
assert self.use_ema
self.model_ema.reset_num_updates()
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
logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
else:
self.register_buffer('logvar', logvar)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
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))
elif self.parameterization == "v":
lvlb_weights = torch.ones_like(self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
else:
raise NotImplementedError("mu not supported")
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")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
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]
if self.make_it_fit:
n_params = len([name for name, _ in
itertools.chain(self.named_parameters(),
self.named_buffers())])
for name, param in tqdm(
itertools.chain(self.named_parameters(),
self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
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:\n {missing}")
if len(unexpected) > 0:
print(f"\nUnexpected Keys:\n {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 predict_start_from_z_and_v(self, x_t, t, v):
# 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)))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
)
def predict_eps_from_z_and_v(self, x_t, t, v):
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
)
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_v(self, x, noise, t):
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
)
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
elif self.parameterization == "v":
target = self.get_v(x_start, noise, t)
else:
raise NotImplementedError(f"Parameterization {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):
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):
for k in self.ucg_training:
p = self.ucg_training[k]["p"]
val = self.ucg_training[k]["val"]
if val is None:
val = ""
for i in range(len(batch[k])):
if self.ucg_prng.choice(2, p=[1 - p, p]):
batch[k][i] = val
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):
_, 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,
force_null_conditioning=False,
*args, **kwargs):
self.force_null_conditioning = force_null_conditioning
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__' and not self.force_null_conditioning:
conditioning_key = None
ckpt_path = kwargs.pop("ckpt_path", None)
reset_ema = kwargs.pop("reset_ema", False)
reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
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
if reset_ema:
assert self.use_ema
print(
f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
self.model_ema = LitEma(self.model)
if reset_num_ema_updates:
print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
assert self.use_ema
self.model_ema.reset_num_updates()
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
@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
# 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[0] * 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)
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[0] // 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, return_x=False):
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 and not self.force_null_conditioning:
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', 'txt', 'vision']:
xc = batch[cond_key]
xc = rearrange(xc, 'b h w c -> b c h w')
elif cond_key in ['class_label', 'cls']:
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 isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:
c = self.get_learned_conditioning(xc.to(self.device))
else:
c = xc
if bs is not None:
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]
if return_first_stage_outputs:
xrec = self.decode_first_stage(z)
out.extend([x, xrec])
if return_x:
out.extend([x])
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
return self.first_stage_model.decode(z)
@torch.no_grad()
def encode_first_stage(self, x):
return self.first_stage_model.encode(x)
def shared_step(self, batch, **kwargs):
x, c = self.get_input(batch, self.first_stage_key)
loss = self(x, c)
return loss
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.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 apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is expected 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}
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
elif self.parameterization == "v":
target = self.get_v(x_start, noise, t)
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()})
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)
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 get_unconditional_conditioning(self, batch_size, null_label=None):
if null_label is not None:
xc = null_label
if isinstance(xc, ListConfig):
xc = list(xc)
if isinstance(xc, dict) or isinstance(xc, list):
c = self.get_learned_conditioning(xc)
else:
if hasattr(xc, "to"):
xc = xc.to(self.device)
c = self.get_learned_conditioning(xc)
else:
if self.cond_stage_key in ["class_label", "cls"]:
xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
return self.get_learned_conditioning(xc)
else:
raise NotImplementedError("todo")
if isinstance(c, list): # in case the encoder gives us a list
for i in range(len(c)):
c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
else:
c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
return c
@torch.no_grad()
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
use_ema_scope=True,
**kwargs):
ema_scope = self.ema_scope if use_ema_scope else nullcontext
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)
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", "txt"]:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
log["conditioning"] = xc
elif self.cond_stage_key in ['class_label', "cls"]:
try:
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
log['conditioning'] = xc
except KeyError:
# probably no "human_label" in batch
pass | elif isimage(xc): | 4 | 2023-12-16 03:31:33+00:00 | 16k |
yasserben/CLOUDS | train_net.py | [
{
"identifier": "add_maskformer2_config",
"path": "clouds/config.py",
"snippet": "def add_maskformer2_config(cfg):\n \"\"\"\n Add config for MASK_FORMER.\n \"\"\"\n # NOTE: configs from original maskformer\n # data config\n # select the dataset mapper\n cfg.INPUT.DATASET_MAPPER_NAME... | from shapely.errors import ShapelyDeprecationWarning
from collections import OrderedDict
from typing import Any, Dict, List, Set
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import (
MetadataCatalog,
build_detection_train_loader,
build_detection_test_loader,
)
from detectron2.engine import (
DefaultTrainer,
default_argument_parser,
default_setup,
launch,
)
from detectron2.modeling import build_model
from detectron2.evaluation import (
CityscapesInstanceEvaluator,
CityscapesSemSegEvaluator,
COCOEvaluator,
COCOPanopticEvaluator,
DatasetEvaluators,
LVISEvaluator,
SemSegEvaluator,
verify_results,
inference_on_dataset,
print_csv_format,
DatasetEvaluator,
)
from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
from detectron2.solver.build import maybe_add_gradient_clipping
from detectron2.utils.logger import setup_logger
from detectron2.engine import hooks
from fvcore.nn.precise_bn import get_bn_modules
from clouds import (
CityscapesSemSegEvaluator,
ClassicalSemSegEvaluator,
MapperTrain,
MapperTest,
add_maskformer2_config,
add_clouds_config,
add_wandb_config,
add_prerocessing_training_set_config,
PersoEvalHook,
add_repeat_factors,
)
from clouds.utils import setup_wandb, WandbWriter
import warnings
import copy
import itertools
import logging
import os
import ast
import torch
import detectron2.utils.comm as comm | 12,300 | """
Copyright 2023 Telecom Paris, Yasser BENIGMIM. All rights reserved.
Licensed under the Apache License, Version 2.0
Reference: https://github.com/facebookresearch/Mask2Former/blob/main/train_net.py
CLOUDS Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
try:
# ignore ShapelyDeprecationWarning from fvcore
warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)
except:
pass
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to CLOUDS.
"""
def build_writers(self):
writers = super().build_writers()
# use wandb writer instead.
| """
Copyright 2023 Telecom Paris, Yasser BENIGMIM. All rights reserved.
Licensed under the Apache License, Version 2.0
Reference: https://github.com/facebookresearch/Mask2Former/blob/main/train_net.py
CLOUDS Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
try:
# ignore ShapelyDeprecationWarning from fvcore
warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)
except:
pass
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to CLOUDS.
"""
def build_writers(self):
writers = super().build_writers()
# use wandb writer instead. | writers[-1] = WandbWriter() | 10 | 2023-12-15 15:40:58+00:00 | 16k |
modelscope/scepter | scepter/modules/inference/diffusion_inference.py | [
{
"identifier": "GaussianDiffusion",
"path": "scepter/modules/model/network/diffusion/diffusion.py",
"snippet": "class GaussianDiffusion(object):\n def __init__(self, sigmas, prediction_type='eps'):\n assert prediction_type in {'x0', 'eps', 'v'}\n self.sigmas = sigmas # noise coefficie... | import copy
import hashlib
import json
import os.path
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as TT
from collections import OrderedDict
from peft.utils import CONFIG_NAME, SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME
from PIL.Image import Image
from swift import Swift, SwiftModel
from scepter.modules.model.network.diffusion.diffusion import GaussianDiffusion
from scepter.modules.model.network.diffusion.schedules import noise_schedule
from scepter.modules.model.registry import (BACKBONES, EMBEDDERS, MODELS,
TOKENIZERS, TUNERS)
from scepter.modules.utils.config import Config
from scepter.modules.utils.distribute import we
from scepter.modules.utils.file_system import FS
from safetensors.torch import \
load_file as safe_load_file
from safetensors.torch import \
save_file as safe_save_file
from safetensors.torch import load_file as load_safetensors
from safetensors.torch import load_file as load_safetensors | 12,587 | cond_stage_model = OrderedDict()
diffusion_model = OrderedDict()
for k, v in sd.items():
if k.startswith('first_stage_model.'):
first_stage_model[k.replace(
'first_stage_model.', '')] = v
elif k.startswith('conditioner.'):
cond_stage_model[k.replace('conditioner.', '')] = v
elif k.startswith('cond_stage_model.'):
if k.startswith('cond_stage_model.model.'):
cond_stage_model[k.replace(
'cond_stage_model.model.', '')] = v
else:
cond_stage_model[k.replace(
'cond_stage_model.', '')] = v
elif k.startswith('model.diffusion_model.'):
diffusion_model[k.replace('model.diffusion_model.',
'')] = v
else:
continue
if cfg.have('FIRST_STAGE_MODEL'):
with open(first_stage_model_path + 'cache', 'wb') as f:
torch.save(first_stage_model, f)
os.rename(first_stage_model_path + 'cache',
first_stage_model_path)
self.logger.info(
'First stage model has been processed.')
if cfg.have('COND_STAGE_MODEL'):
with open(cond_stage_model_path + 'cache', 'wb') as f:
torch.save(cond_stage_model, f)
os.rename(cond_stage_model_path + 'cache',
cond_stage_model_path)
self.logger.info(
'Cond stage model has been processed.')
if cfg.have('DIFFUSION_MODEL'):
with open(diffusion_model_path + 'cache', 'wb') as f:
torch.save(diffusion_model, f)
os.rename(diffusion_model_path + 'cache',
diffusion_model_path)
self.logger.info('Diffusion model has been processed.')
if not cfg.FIRST_STAGE_MODEL.get('PRETRAINED_MODEL', None):
cfg.FIRST_STAGE_MODEL.PRETRAINED_MODEL = first_stage_model_path
else:
cfg.FIRST_STAGE_MODEL.RELOAD_MODEL = first_stage_model_path
if not cfg.COND_STAGE_MODEL.get('PRETRAINED_MODEL', None):
cfg.COND_STAGE_MODEL.PRETRAINED_MODEL = cond_stage_model_path
else:
cfg.COND_STAGE_MODEL.RELOAD_MODEL = cond_stage_model_path
if not cfg.DIFFUSION_MODEL.get('PRETRAINED_MODEL', None):
cfg.DIFFUSION_MODEL.PRETRAINED_MODEL = diffusion_model_path
else:
cfg.DIFFUSION_MODEL.RELOAD_MODEL = diffusion_model_path
return cfg
def init_from_modules(self, modules):
for k, v in modules.items():
self.__setattr__(k, v)
def infer_model(self, cfg, module_paras=None):
module = {
'model': None,
'cfg': cfg,
'device': 'offline',
'name': cfg.NAME,
'function_info': {},
'paras': {}
}
if module_paras is None:
return module
function_info = {}
paras = {
k.lower(): v
for k, v in module_paras.get('PARAS', {}).items()
}
for function in module_paras.get('FUNCTION', []):
input_dict = {}
for inp in function.get('INPUT', []):
if inp.lower() in self.input:
input_dict[inp.lower()] = self.input[inp.lower()]
function_info[function.NAME] = {
'dtype': function.get('DTYPE', 'float32'),
'input': input_dict
}
module['paras'] = paras
module['function_info'] = function_info
return module
def init_from_ckpt(self, path, model, ignore_keys=list()):
if path.endswith('safetensors'):
sd = load_safetensors(path)
else:
sd = torch.load(path, map_location='cpu')
new_sd = OrderedDict()
for k, v in sd.items():
ignored = False
for ik in ignore_keys:
if ik in k:
if we.rank == 0:
self.logger.info(
'Ignore key {} from state_dict.'.format(k))
ignored = True
break
if not ignored:
new_sd[k] = v
missing, unexpected = model.load_state_dict(new_sd, strict=False)
if we.rank == 0:
self.logger.info(
f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
)
if len(missing) > 0:
self.logger.info(f'Missing Keys:\n {missing}')
if len(unexpected) > 0:
self.logger.info(f'\nUnexpected Keys:\n {unexpected}')
def load(self, module):
if module['device'] == 'offline':
if module['cfg'].NAME in MODELS.class_map:
model = MODELS.build(module['cfg'], logger=self.logger).eval()
| # -*- coding: utf-8 -*-
def get_model(model_tuple):
assert 'model' in model_tuple
return model_tuple['model']
class DiffusionInference():
'''
define vae, unet, text-encoder, tuner, refiner components
support to load the components dynamicly.
create and load model when run this model at the first time.
'''
def __init__(self, logger=None):
self.logger = logger
def init_from_cfg(self, cfg):
self.name = cfg.NAME
self.is_default = cfg.get('IS_DEFAULT', False)
module_paras = self.load_default(cfg.get('DEFAULT_PARAS', None))
assert cfg.have('MODEL')
cfg.MODEL = self.redefine_paras(cfg.MODEL)
self.diffusion = self.load_schedule(cfg.MODEL.SCHEDULE)
self.diffusion_model = self.infer_model(
cfg.MODEL.DIFFUSION_MODEL, module_paras.get(
'DIFFUSION_MODEL',
None)) if cfg.MODEL.have('DIFFUSION_MODEL') else None
self.first_stage_model = self.infer_model(
cfg.MODEL.FIRST_STAGE_MODEL,
module_paras.get(
'FIRST_STAGE_MODEL',
None)) if cfg.MODEL.have('FIRST_STAGE_MODEL') else None
self.cond_stage_model = self.infer_model(
cfg.MODEL.COND_STAGE_MODEL,
module_paras.get(
'COND_STAGE_MODEL',
None)) if cfg.MODEL.have('COND_STAGE_MODEL') else None
self.refiner_cond_model = self.infer_model(
cfg.MODEL.REFINER_COND_MODEL,
module_paras.get(
'REFINER_COND_MODEL',
None)) if cfg.MODEL.have('REFINER_COND_MODEL') else None
self.refiner_diffusion_model = self.infer_model(
cfg.MODEL.REFINER_MODEL, module_paras.get(
'REFINER_MODEL',
None)) if cfg.MODEL.have('REFINER_MODEL') else None
self.tokenizer = TOKENIZERS.build(
cfg.MODEL.TOKENIZER,
logger=self.logger) if cfg.MODEL.have('TOKENIZER') else None
if self.tokenizer is not None:
self.cond_stage_model['cfg'].KWARGS = {
'vocab_size': self.tokenizer.vocab_size
}
def register_tuner(self, tuner_model_list):
if len(tuner_model_list) < 1:
if isinstance(self.diffusion_model['model'], SwiftModel):
for adapter_name in self.diffusion_model['model'].adapters:
self.diffusion_model['model'].deactivate_adapter(
adapter_name, offload='cpu')
if isinstance(self.cond_stage_model['model'], SwiftModel):
for adapter_name in self.cond_stage_model['model'].adapters:
self.cond_stage_model['model'].deactivate_adapter(
adapter_name, offload='cpu')
return
all_diffusion_tuner = {}
all_cond_tuner = {}
save_root_dir = '.cache_tuner'
for tuner_model in tuner_model_list:
tunner_model_folder = tuner_model.MODEL_PATH
local_tuner_model = FS.get_dir_to_local_dir(tunner_model_folder)
all_tuner_datas = os.listdir(local_tuner_model)
cur_tuner_md5 = hashlib.md5(
tunner_model_folder.encode('utf-8')).hexdigest()
local_diffusion_cache = os.path.join(
save_root_dir, cur_tuner_md5 + '_' + 'diffusion')
local_cond_cache = os.path.join(save_root_dir,
cur_tuner_md5 + '_' + 'cond')
meta_file = os.path.join(save_root_dir,
cur_tuner_md5 + '_meta.json')
if not os.path.exists(meta_file):
diffusion_tuner = {}
cond_tuner = {}
for sub in all_tuner_datas:
sub_file = os.path.join(local_tuner_model, sub)
config_file = os.path.join(sub_file, CONFIG_NAME)
safe_file = os.path.join(sub_file,
SAFETENSORS_WEIGHTS_NAME)
bin_file = os.path.join(sub_file, WEIGHTS_NAME)
if os.path.isdir(sub_file) and os.path.isfile(config_file):
# diffusion or cond
cfg = json.load(open(config_file, 'r'))
if 'cond_stage_model.' in cfg['target_modules']:
cond_cfg = copy.deepcopy(cfg)
if 'cond_stage_model.*' in cond_cfg[
'target_modules']:
cond_cfg['target_modules'] = cond_cfg[
'target_modules'].replace(
'cond_stage_model.*', '.*')
else:
cond_cfg['target_modules'] = cond_cfg[
'target_modules'].replace(
'cond_stage_model.', '')
if cond_cfg['target_modules'].startswith('*'):
cond_cfg['target_modules'] = '.' + cond_cfg[
'target_modules']
os.makedirs(local_cond_cache + '_' + sub,
exist_ok=True)
cond_tuner[os.path.basename(local_cond_cache) +
'_' + sub] = hashlib.md5(
(local_cond_cache + '_' +
sub).encode('utf-8')).hexdigest()
os.makedirs(local_cond_cache + '_' + sub,
exist_ok=True)
json.dump(
cond_cfg,
open(
os.path.join(local_cond_cache + '_' + sub,
CONFIG_NAME), 'w'))
if 'model.' in cfg['target_modules'].replace(
'cond_stage_model.', ''):
diffusion_cfg = copy.deepcopy(cfg)
if 'model.*' in diffusion_cfg['target_modules']:
diffusion_cfg[
'target_modules'] = diffusion_cfg[
'target_modules'].replace(
'model.*', '.*')
else:
diffusion_cfg[
'target_modules'] = diffusion_cfg[
'target_modules'].replace(
'model.', '')
if diffusion_cfg['target_modules'].startswith('*'):
diffusion_cfg[
'target_modules'] = '.' + diffusion_cfg[
'target_modules']
os.makedirs(local_diffusion_cache + '_' + sub,
exist_ok=True)
diffusion_tuner[
os.path.basename(local_diffusion_cache) + '_' +
sub] = hashlib.md5(
(local_diffusion_cache + '_' +
sub).encode('utf-8')).hexdigest()
json.dump(
diffusion_cfg,
open(
os.path.join(
local_diffusion_cache + '_' + sub,
CONFIG_NAME), 'w'))
state_dict = {}
is_bin_file = True
if os.path.isfile(bin_file):
state_dict = torch.load(bin_file)
elif os.path.isfile(safe_file):
is_bin_file = False
state_dict = safe_load_file(
safe_file,
device='cuda'
if torch.cuda.is_available() else 'cpu')
save_diffusion_state_dict = {}
save_cond_state_dict = {}
for key, value in state_dict.items():
if key.startswith('model.'):
save_diffusion_state_dict[
key[len('model.'):].replace(
sub,
os.path.basename(local_diffusion_cache)
+ '_' + sub)] = value
elif key.startswith('cond_stage_model.'):
save_cond_state_dict[
key[len('cond_stage_model.'):].replace(
sub,
os.path.basename(local_cond_cache) +
'_' + sub)] = value
if is_bin_file:
if len(save_diffusion_state_dict) > 0:
torch.save(
save_diffusion_state_dict,
os.path.join(
local_diffusion_cache + '_' + sub,
WEIGHTS_NAME))
if len(save_cond_state_dict) > 0:
torch.save(
save_cond_state_dict,
os.path.join(local_cond_cache + '_' + sub,
WEIGHTS_NAME))
else:
if len(save_diffusion_state_dict) > 0:
safe_save_file(
save_diffusion_state_dict,
os.path.join(
local_diffusion_cache + '_' + sub,
SAFETENSORS_WEIGHTS_NAME),
metadata={'format': 'pt'})
if len(save_cond_state_dict) > 0:
safe_save_file(
save_cond_state_dict,
os.path.join(local_cond_cache + '_' + sub,
SAFETENSORS_WEIGHTS_NAME),
metadata={'format': 'pt'})
json.dump(
{
'diffusion_tuner': diffusion_tuner,
'cond_tuner': cond_tuner
}, open(meta_file, 'w'))
else:
meta_conf = json.load(open(meta_file, 'r'))
diffusion_tuner = meta_conf['diffusion_tuner']
cond_tuner = meta_conf['cond_tuner']
all_diffusion_tuner.update(diffusion_tuner)
all_cond_tuner.update(cond_tuner)
if len(all_diffusion_tuner) > 0:
self.load(self.diffusion_model)
self.diffusion_model['model'] = Swift.from_pretrained(
self.diffusion_model['model'],
save_root_dir,
adapter_name=all_diffusion_tuner)
self.diffusion_model['model'].set_active_adapters(
list(all_diffusion_tuner.values()))
self.unload(self.diffusion_model)
if len(all_cond_tuner) > 0:
self.load(self.cond_stage_model)
self.cond_stage_model['model'] = Swift.from_pretrained(
self.cond_stage_model['model'],
save_root_dir,
adapter_name=all_cond_tuner)
self.cond_stage_model['model'].set_active_adapters(
list(all_cond_tuner.values()))
self.unload(self.cond_stage_model)
def register_controllers(self, control_model_ins):
if control_model_ins is None or control_model_ins == '':
if isinstance(self.diffusion_model['model'], SwiftModel):
if (hasattr(self.diffusion_model['model'].base_model,
'control_blocks') and
self.diffusion_model['model'].base_model.control_blocks
): # noqa
del self.diffusion_model['model'].base_model.control_blocks
self.diffusion_model[
'model'].base_model.control_blocks = None
self.diffusion_model['model'].base_model.control_name = []
else:
del self.diffusion_model['model'].control_blocks
self.diffusion_model['model'].control_blocks = None
self.diffusion_model['model'].control_name = []
return
if not isinstance(control_model_ins, list):
control_model_ins = [control_model_ins]
control_model = nn.ModuleList([])
control_model_folder = []
for one_control in control_model_ins:
one_control_model_folder = one_control.MODEL_PATH
control_model_folder.append(one_control_model_folder)
have_list = getattr(self.diffusion_model['model'], 'control_name',
[])
if one_control_model_folder in have_list:
ind = have_list.index(one_control_model_folder)
csc_tuners = copy.deepcopy(
self.diffusion_model['model'].control_blocks[ind])
else:
one_local_control_model = FS.get_dir_to_local_dir(
one_control_model_folder)
control_cfg = Config(cfg_file=os.path.join(
one_local_control_model, 'configuration.json'))
assert hasattr(control_cfg, 'CONTROL_MODEL')
control_cfg.CONTROL_MODEL[
'INPUT_BLOCK_CHANS'] = self.diffusion_model[
'model']._input_block_chans
control_cfg.CONTROL_MODEL[
'INPUT_DOWN_FLAG'] = self.diffusion_model[
'model']._input_down_flag
control_cfg.CONTROL_MODEL.PRETRAINED_MODEL = os.path.join(
one_local_control_model, 'pytorch_model.bin')
csc_tuners = TUNERS.build(control_cfg.CONTROL_MODEL,
logger=self.logger)
control_model.append(csc_tuners)
if isinstance(self.diffusion_model['model'], SwiftModel):
del self.diffusion_model['model'].base_model.control_blocks
self.diffusion_model[
'model'].base_model.control_blocks = control_model
self.diffusion_model[
'model'].base_model.control_name = control_model_folder
else:
del self.diffusion_model['model'].control_blocks
self.diffusion_model['model'].control_blocks = control_model
self.diffusion_model['model'].control_name = control_model_folder
def redefine_paras(self, cfg):
if cfg.get('PRETRAINED_MODEL', None):
assert FS.isfile(cfg.PRETRAINED_MODEL)
with FS.get_from(cfg.PRETRAINED_MODEL,
wait_finish=True) as local_path:
if local_path.endswith('safetensors'):
sd = load_safetensors(local_path)
else:
sd = torch.load(local_path, map_location='cpu')
first_stage_model_path = os.path.join(
os.path.dirname(local_path), 'first_stage_model.pth')
cond_stage_model_path = os.path.join(
os.path.dirname(local_path), 'cond_stage_model.pth')
diffusion_model_path = os.path.join(
os.path.dirname(local_path), 'diffusion_model.pth')
if (not os.path.exists(first_stage_model_path)
or not os.path.exists(cond_stage_model_path)
or not os.path.exists(diffusion_model_path)):
self.logger.info(
'Now read the whole model and rearrange the modules, it may take several mins.'
)
first_stage_model = OrderedDict()
cond_stage_model = OrderedDict()
diffusion_model = OrderedDict()
for k, v in sd.items():
if k.startswith('first_stage_model.'):
first_stage_model[k.replace(
'first_stage_model.', '')] = v
elif k.startswith('conditioner.'):
cond_stage_model[k.replace('conditioner.', '')] = v
elif k.startswith('cond_stage_model.'):
if k.startswith('cond_stage_model.model.'):
cond_stage_model[k.replace(
'cond_stage_model.model.', '')] = v
else:
cond_stage_model[k.replace(
'cond_stage_model.', '')] = v
elif k.startswith('model.diffusion_model.'):
diffusion_model[k.replace('model.diffusion_model.',
'')] = v
else:
continue
if cfg.have('FIRST_STAGE_MODEL'):
with open(first_stage_model_path + 'cache', 'wb') as f:
torch.save(first_stage_model, f)
os.rename(first_stage_model_path + 'cache',
first_stage_model_path)
self.logger.info(
'First stage model has been processed.')
if cfg.have('COND_STAGE_MODEL'):
with open(cond_stage_model_path + 'cache', 'wb') as f:
torch.save(cond_stage_model, f)
os.rename(cond_stage_model_path + 'cache',
cond_stage_model_path)
self.logger.info(
'Cond stage model has been processed.')
if cfg.have('DIFFUSION_MODEL'):
with open(diffusion_model_path + 'cache', 'wb') as f:
torch.save(diffusion_model, f)
os.rename(diffusion_model_path + 'cache',
diffusion_model_path)
self.logger.info('Diffusion model has been processed.')
if not cfg.FIRST_STAGE_MODEL.get('PRETRAINED_MODEL', None):
cfg.FIRST_STAGE_MODEL.PRETRAINED_MODEL = first_stage_model_path
else:
cfg.FIRST_STAGE_MODEL.RELOAD_MODEL = first_stage_model_path
if not cfg.COND_STAGE_MODEL.get('PRETRAINED_MODEL', None):
cfg.COND_STAGE_MODEL.PRETRAINED_MODEL = cond_stage_model_path
else:
cfg.COND_STAGE_MODEL.RELOAD_MODEL = cond_stage_model_path
if not cfg.DIFFUSION_MODEL.get('PRETRAINED_MODEL', None):
cfg.DIFFUSION_MODEL.PRETRAINED_MODEL = diffusion_model_path
else:
cfg.DIFFUSION_MODEL.RELOAD_MODEL = diffusion_model_path
return cfg
def init_from_modules(self, modules):
for k, v in modules.items():
self.__setattr__(k, v)
def infer_model(self, cfg, module_paras=None):
module = {
'model': None,
'cfg': cfg,
'device': 'offline',
'name': cfg.NAME,
'function_info': {},
'paras': {}
}
if module_paras is None:
return module
function_info = {}
paras = {
k.lower(): v
for k, v in module_paras.get('PARAS', {}).items()
}
for function in module_paras.get('FUNCTION', []):
input_dict = {}
for inp in function.get('INPUT', []):
if inp.lower() in self.input:
input_dict[inp.lower()] = self.input[inp.lower()]
function_info[function.NAME] = {
'dtype': function.get('DTYPE', 'float32'),
'input': input_dict
}
module['paras'] = paras
module['function_info'] = function_info
return module
def init_from_ckpt(self, path, model, ignore_keys=list()):
if path.endswith('safetensors'):
sd = load_safetensors(path)
else:
sd = torch.load(path, map_location='cpu')
new_sd = OrderedDict()
for k, v in sd.items():
ignored = False
for ik in ignore_keys:
if ik in k:
if we.rank == 0:
self.logger.info(
'Ignore key {} from state_dict.'.format(k))
ignored = True
break
if not ignored:
new_sd[k] = v
missing, unexpected = model.load_state_dict(new_sd, strict=False)
if we.rank == 0:
self.logger.info(
f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
)
if len(missing) > 0:
self.logger.info(f'Missing Keys:\n {missing}')
if len(unexpected) > 0:
self.logger.info(f'\nUnexpected Keys:\n {unexpected}')
def load(self, module):
if module['device'] == 'offline':
if module['cfg'].NAME in MODELS.class_map:
model = MODELS.build(module['cfg'], logger=self.logger).eval() | elif module['cfg'].NAME in BACKBONES.class_map: | 2 | 2023-12-21 02:01:48+00:00 | 16k |
RomGai/BrainVis | dc_ldm/models/diffusion/ddpm.py | [
{
"identifier": "log_txt_as_img",
"path": "dc_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 os
import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import torch.nn.functional as F
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from dc_ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from dc_ldm.modules.ema import LitEma
from dc_ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from dc_ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from dc_ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from dc_ldm.models.diffusion.ddim import DDIMSampler
from dc_ldm.models.diffusion.plms import PLMSSampler
from PIL import Image
from eval_metrics import get_similarity_metric
from dc_ldm.modules.encoders.modules import FrozenImageEmbedder | 14,233 | 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)
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)
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"])
log["conditioning"] = xc
elif self.cond_stage_key == 'class_label':
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
log['conditioning'] = xc
elif isimage(xc):
log["conditioning"] = xc
if ismap(xc):
log["original_conditioning"] = self.to_rgb(xc)
if plot_diffusion_rows:
# get diffusion row
diffusion_row = list()
z_start = z[: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(z_start)
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
diffusion_row.append(self.decode_first_stage(z_noisy))
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
log["diffusion_row"] = diffusion_grid
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
| """
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'}
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.,
ddim_steps=300
):
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)
self.validation_count = 0
self.ddim_steps = ddim_steps
self.return_cond = False
self.output_path = None
self.main_config = None
self.best_val = 0.0
self.run_full_validation_threshold = 0.0
self.eval_avg = True
def re_init_ema(self):
if self.use_ema:
self.model_ema = LitEma(self.model)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
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")
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):
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):
self.train()
self.cond_stage_model.train() ###到底是在哪里训练的
loss, loss_dict = self.shared_step(batch)
self.log_dict(loss_dict, prog_bar=True,
logger=True, on_step=False, on_epoch=True)
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=False, on_epoch=True)
return loss
@torch.no_grad()
def generate(self, data, num_samples, ddim_steps=300, HW=None, limit=None, state=None):
# fmri_embedding: n, seq_len, embed_dim
all_samples = []
if HW is None:
shape = (self.p_channels,
self.p_image_size, self.p_image_size)
else:
num_resolutions = len(self.ch_mult)
shape = (self.p_channels,
HW[0] // 2**(num_resolutions-1), HW[1] // 2**(num_resolutions-1))
model = self
sampler = PLMSSampler(model)
# sampler = DDIMSampler(model)
model.eval()
if torch.cuda.is_available():
state = torch.cuda.get_rng_state() if state is None else state
torch.cuda.set_rng_state(state)
else:
state = torch.get_rng_state() if state is None else state
torch.set_rng_state(state)
# rng = torch.Generator(device=self.device).manual_seed(2022).set_state(state)
# state = torch.cuda.get_rng_state()
with model.ema_scope():
for count, item in enumerate(zip(data['eeg'], data['image'])):
if limit is not None:
if count >= limit:
break
latent = item[0] # fmri embedding
gt_image = rearrange(item[1], 'h w c -> 1 c h w') # h w c
print(f"rendering {num_samples} examples in {ddim_steps} steps.")
# c = model.get_learned_conditioning(repeat(latent, 'h w -> c h w', c=num_samples).to(self.device))
c, re_latent = model.get_learned_conditioning(repeat(latent, 'h w -> c h w', c=num_samples).to(self.device))
samples_ddim, _ = sampler.sample(S=ddim_steps,
conditioning=c,
batch_size=num_samples,
shape=shape,
verbose=False,
generator=None)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0,min=0.0, max=1.0)
gt_image = torch.clamp((gt_image+1.0)/2.0,min=0.0, max=1.0)
all_samples.append(torch.cat([gt_image.detach().cpu(), x_samples_ddim.detach().cpu()], dim=0)) # put groundtruth at first
# display as grid
grid = torch.stack(all_samples, 0)
grid = rearrange(grid, 'n b c h w -> (n b) c h w')
grid = make_grid(grid, nrow=num_samples+1)
# to image
grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
return grid, (255. * torch.stack(all_samples, 0).cpu().numpy()).astype(np.uint8), state
def save_images(self, all_samples, suffix=0):
# print('output_path')
# print(self.output_path)
if self.output_path is not None:
os.makedirs(os.path.join(self.output_path, 'val', f'{self.validation_count}_{suffix}'), exist_ok=True)
for sp_idx, imgs in enumerate(all_samples):
# for copy_idx, img in enumerate(imgs[1:]):
for copy_idx, img in enumerate(imgs):
img = rearrange(img, 'c h w -> h w c')
Image.fromarray(img).save(os.path.join(self.output_path, 'val',
f'{self.validation_count}_{suffix}', f'test{sp_idx}-{copy_idx}.png'))
def full_validation(self, batch, state=None):
print('###### run full validation! ######\n')
grid, all_samples, state = self.generate(batch, ddim_steps=self.ddim_steps, num_samples=5, limit=None, state=state)
metric, metric_list = self.get_eval_metric(all_samples)
self.save_images(all_samples, suffix='%.4f'%metric[-1])
metric_dict = {f'val/{k}_full':v for k, v in zip(metric_list, metric)}
# self.logger.log_metrics(metric_dict)
grid_imgs = Image.fromarray(grid.astype(np.uint8))
# self.logger.log_image(key=f'samples_test_full', images=[grid_imgs])
if metric[-1] > self.best_val:
self.best_val = metric[-1]
torch.save(
{
'model_state_dict': self.state_dict(),
'config': self.main_config,
'state': state
},
os.path.join(self.output_path, 'checkpoint_best.pth')
)
@torch.no_grad()
def validation_step(self, batch, batch_idx):
if batch_idx != 0:
return
if self.validation_count % 5 == 0 and self.trainer.current_epoch != 0:
self.full_validation(batch)
else:
# pass
grid, all_samples, state = self.generate(batch, ddim_steps=self.ddim_steps, num_samples=3, limit=5)
metric, metric_list = self.get_eval_metric(all_samples, avg=self.eval_avg)
grid_imgs = Image.fromarray(grid.astype(np.uint8))
# self.logger.log_image(key=f'samples_test', images=[grid_imgs])
metric_dict = {f'val/{k}':v for k, v in zip(metric_list, metric)}
# self.logger.log_metrics(metric_dict)
if metric[-1] > self.run_full_validation_threshold:
self.full_validation(batch, state=state)
self.validation_count += 1
def get_eval_metric(self, samples, avg=True):
metric_list = ['mse', 'pcc', 'ssim', 'psm']
res_list = []
gt_images = [img[0] for img in samples]
gt_images = rearrange(np.stack(gt_images), 'n c h w -> n h w c')
samples_to_run = np.arange(1, len(samples[0])) if avg else [1]
for m in metric_list:
res_part = []
for s in samples_to_run:
pred_images = [img[s] for img in samples]
pred_images = rearrange(np.stack(pred_images), 'n c h w -> n h w c')
res = get_similarity_metric(pred_images, gt_images, method='pair-wise', metric_name=m)
res_part.append(np.mean(res))
res_list.append(np.mean(res_part))
res_part = []
for s in samples_to_run:
pred_images = [img[s] for img in samples]
pred_images = rearrange(np.stack(pred_images), 'n c h w -> n h w c')
res = get_similarity_metric(pred_images, gt_images, 'class', None,
n_way=50, num_trials=50, top_k=1, device='cuda')
res_part.append(np.mean(res))
res_list.append(np.mean(res_part))
res_list.append(np.max(res_part))
metric_list.append('top-1-class')
metric_list.append('top-1-class (max)')
return res_list, metric_list
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=True,
concat_mode=True,
cond_stage_forward=None,
conditioning_key=None,
scale_factor=1.0,
scale_by_std=False,
*args, **kwargs):
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.train_cond_stage_only = False
self.clip_tune = True
if self.clip_tune:
self.image_embedder = FrozenImageEmbedder()
self.cls_tune = False
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
@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()
def freeze_diffusion_model(self):
for param in self.model.parameters():
param.requires_grad = False
def unfreeze_diffusion_model(self):
for param in self.model.parameters():
param.requires_grad = True
def freeze_cond_stage(self):
for param in self.cond_stage_model.parameters():
param.requires_grad = False
def unfreeze_cond_stage(self):
for param in self.cond_stage_model.parameters():
param.requires_grad = True
def freeze_first_stage(self):
self.first_stage_model.trainable = False
for param in self.first_stage_model.parameters():
param.requires_grad = False
def unfreeze_first_stage(self):
self.first_stage_model.trainable = True
for param in self.first_stage_model.parameters():
param.requires_grad = True
def freeze_whole_model(self):
self.first_stage_model.trainable = False
for param in self.parameters():
param.requires_grad = False
def unfreeze_whole_model(self):
self.first_stage_model.trainable = True
for param in self.parameters():
param.requires_grad = True
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):
# self.cond_stage_model.eval()
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
c, re_latent = self.cond_stage_model.encode(c)
# c = self.cond_stage_model.encode(c)
else:
c, re_latent = self.cond_stage_model(c)
# c = self.cond_stage_model(c)
# return c
return c, re_latent
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
# 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[0] * 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)
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[0] // 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)
# print('encoder_posterior.shape')
# print(encoder_posterior.shape)
z = self.get_first_stage_encoding(encoder_posterior).detach()
# print('z.shape')
# print(z.shape)
# print(cond_key)
# print(self.cond_stage_key)
# print(cond_key)
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','fmri', 'eeg']:
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
# print('get input')
# print(not self.cond_stage_trainable)
# print(force_c_encode)
if not self.cond_stage_trainable or force_c_encode :
# print('get learned condition')
if isinstance(xc, dict) or isinstance(xc, list):
# import pudb; pudb.set_trace()
c, re_latent = self.get_learned_conditioning(xc)
# c = self.get_learned_conditioning(xc)
else:
c, re_latent = self.get_learned_conditioning(xc.to(self.device))
# c = self.get_learned_conditioning(xc.to(self.device))
else:
c = xc
if bs is not None:
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 , batch['label'], batch['image_raw']]
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
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)
# 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):
return self.first_stage_model.encode(x)
def shared_step(self, batch, **kwargs):
self.freeze_first_stage()
# print('share step\'s get input')
x, c, label, image_raw = self.get_input(batch, self.first_stage_key)
# print('get input shape')
# print('x.shape')
# print(x.shape)
# print('c.shape')
# print(c.shape)
if self.return_cond:
loss, cc = self(x, c, label, image_raw)
return loss, cc
else:
loss = self(x, c, label, image_raw)
return loss
def forward(self, x, c, label, image_raw, *args, **kwargs):
# print(self.num_timesteps)
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
# print('t.shape')
# print(t.shape)
if self.model.conditioning_key is not None:
assert c is not None
imgs = c
if self.cond_stage_trainable:
# c = self.get_learned_conditioning(c)
c, re_latent = self.get_learned_conditioning(c)
# print('c.shape')
# print(c.shape)
prefix = 'train' if self.training else 'val'
loss, loss_dict = self.p_losses(x, c, t, *args, **kwargs)
# pre_cls = self.cond_stage_model.get_cls(re_latent)
# rencon = self.cond_stage_model.recon(re_latent)
if self.clip_tune:
image_embeds = self.image_embedder(image_raw)
loss_clip = self.cond_stage_model.get_clip_loss(re_latent, image_embeds)
# loss_recon = self.recon_loss(imgs, rencon)
# loss_cls = self.cls_loss(label, pre_cls)
loss += loss_clip
# loss += loss_cls # loss_recon + #(self.original_elbo_weight * loss_vlb)
# loss_dict.update({f'{prefix}/loss_recon': loss_recon})
# loss_dict.update({f'{prefix}/loss_cls': loss_cls})
loss_dict.update({f'{prefix}/loss_clip': loss_clip})
if self.cls_tune:
pre_cls = self.cond_stage_model.get_cls(re_latent)
loss_cls = self.cls_loss(label, pre_cls)
# image_embeds = self.image_embedder(image_raw)
# loss_clip = self.cond_stage_model.get_clip_loss(re_latent, image_embeds)
# loss_recon = self.recon_loss(imgs, rencon)
# loss_cls = self.cls_loss(label, pre_cls)
loss += loss_cls
# loss += loss_cls # loss_recon + #(self.original_elbo_weight * loss_vlb)
# loss_dict.update({f'{prefix}/loss_recon': loss_recon})
# loss_dict.update({f'{prefix}/loss_cls': loss_cls})
loss_dict.update({f'{prefix}/loss_cls': loss_cls})
# if self.return_cond:
# return self.p_losses(x, c, t, *args, **kwargs), c
# return self.p_losses(x, c, t, *args, **kwargs)
if self.return_cond:
return loss, loss_dict, c
return loss, loss_dict
# def recon_loss(self, )
def recon_loss(self, imgs, pred):
"""
imgs: [N, 1, num_voxels]
pred: [N, L, p]
mask: [N, L], 0 is keep, 1 is remove,
"""
# target = self.patchify(imgs)
loss = (pred - imgs) ** 2
loss = loss.mean()
# loss = loss.mean(dim=-1) # [N, L], mean loss per patch
# loss = (loss * mask).sum() / mask.sum() if mask.sum() != 0 else (loss * mask).sum() # mean loss on removed patches
return loss
def cls_loss(self, label, pred):
return torch.nn.CrossEntropyLoss()(pred, label)
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = torch.clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = torch.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}
x_recon = self.model(x_noisy, t, **cond)
# print('x_recon')
# if isinstance(x_recon, tuple):
# print('is tuple')
# # print(len(x_recon))
# # print(x_recon[0].shape)
# else:
# print(x_recon.shape)
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))
# print('p_losses')
# print('noise.shape')
# print(noise.shape)
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
# print('x_noisy[0].shape')
# print(x_noisy[0].shape)
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()})
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_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_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)
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)
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"])
log["conditioning"] = xc
elif self.cond_stage_key == 'class_label':
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
log['conditioning'] = xc
elif isimage(xc):
log["conditioning"] = xc
if ismap(xc):
log["original_conditioning"] = self.to_rgb(xc)
if plot_diffusion_rows:
# get diffusion row
diffusion_row = list()
z_start = z[: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(z_start)
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
diffusion_row.append(self.decode_first_stage(z_noisy))
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
log["diffusion_row"] = diffusion_grid
if sample:
# get denoise row
with self.ema_scope("Plotting"):
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
ddim_steps=ddim_steps,eta=ddim_eta)
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( | self.first_stage_model, IdentityFirstStage): | 12 | 2023-12-16 12:52:14+00:00 | 16k |
tonnetonne814/PL-Bert-VITS2 | train_ms.py | [
{
"identifier": "DistributedBucketSampler",
"path": "data_utils.py",
"snippet": "class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):\n \"\"\"\n Maintain similar input lengths in a batch.\n Length groups are specified by boundaries.\n Ex) boundaries = [b1, b2, b3]... | import argparse
import itertools
import json
import math
import os
import logging
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import tqdm
import commons
import models
import utils
from torch import nn, optim
from torch.cuda.amp import GradScaler, autocast
from torch.nn import functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from data_utils import (DistributedBucketSampler, TextAudioSpeakerCollate,
TextAudioSpeakerLoader)
from losses import discriminator_loss, feature_loss, generator_loss, kl_loss
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from models import (AVAILABLE_DURATION_DISCRIMINATOR_TYPES,
AVAILABLE_FLOW_TYPES,
DurationDiscriminatorV1, DurationDiscriminatorV2,
MultiPeriodDiscriminator, SynthesizerTrn)
from PL_BERT_ja.text.symbols import symbols | 12,128 | y,
y_lengths,
bert,
bert_lengths,
speakers,
) in enumerate(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(rank, non_blocking=True), x_lengths.cuda(
rank, non_blocking=True
)
spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(
rank, non_blocking=True
)
y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(
rank, non_blocking=True
)
bert, bert_lengths = bert.cuda(rank, non_blocking=True), bert_lengths.cuda(
rank, non_blocking=True
)
speakers = speakers.cuda(rank, non_blocking=True)
with autocast(enabled=hps.train.fp16_run):
(
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_),
) = net_g(x, x_lengths, spec, spec_lengths, bert, bert_lengths, speakers)
if (
hps.model.use_mel_posterior_encoder
or hps.data.use_mel_posterior_encoder
):
mel = spec
else:
# comment - choihkk
# for numerical stable when using fp16 and torch>=2.0.0,
# spec.float() could be help in the training stage
# https://github.com/jaywalnut310/vits/issues/15
mel = spec_to_mel_torch(
spec.float(),
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(
y_hat.squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y = commons.slice_segments(
y, ids_slice * hps.data.hop_length, hps.train.segment_size
) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
y_d_hat_r, y_d_hat_g
)
loss_disc_all = loss_disc
# Duration Discriminator
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(
hidden_x.detach(), x_mask.detach(), logw_.detach(), logw.detach()
)
with autocast(enabled=False):
# TODO: I think need to mean using the mask, but for now, just mean all
(
loss_dur_disc,
losses_dur_disc_r,
losses_dur_disc_g,
) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
loss_dur_disc_all = loss_dur_disc
optim_dur_disc.zero_grad()
scaler.scale(loss_dur_disc_all).backward()
scaler.unscale_(optim_dur_disc)
grad_norm_dur_disc = commons.clip_grad_value_(
net_dur_disc.parameters(), None
)
scaler.step(optim_dur_disc)
optim_d.zero_grad()
scaler.scale(loss_disc_all).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)
with autocast(enabled=hps.train.fp16_run):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw_, logw)
with autocast(enabled=False):
loss_dur = torch.sum(l_length.float())
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
loss_fm = feature_loss(fmap_r, fmap_g)
|
numba_logger = logging.getLogger('numba')
numba_logger.setLevel(logging.WARNING)
# from tensorboardX import SummaryWriter
torch.backends.cudnn.benchmark = True
global_step = 0
def main():
"""Assume Single Node Multi GPUs Training Only"""
assert torch.cuda.is_available(), "CPU training is not allowed."
n_gpus = torch.cuda.device_count()
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "6060"
hps = utils.get_hparams()
mp.spawn(
run,
nprocs=n_gpus,
args=(
n_gpus,
hps,
),
)
def run(rank, n_gpus, hps):
net_dur_disc = None
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"))
dist.init_process_group(
backend="nccl", init_method="env://", world_size=n_gpus, rank=rank
)
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(rank)
if (
"use_mel_posterior_encoder" in hps.model.keys()
and hps.model.use_mel_posterior_encoder == True
):
print("Using mel posterior encoder for VITS2")
posterior_channels = 128 # vits2
hps.data.use_mel_posterior_encoder = True
else:
print("Using lin posterior encoder for VITS1")
posterior_channels = hps.data.filter_length // 2 + 1
hps.data.use_mel_posterior_encoder = False
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size,
[32, 300, 500, 700, 900, 1100, 1300, 1500, 3000],
num_replicas=n_gpus,
rank=rank,
shuffle=True,
)
collate_fn = TextAudioSpeakerCollate()
train_loader = DataLoader(
train_dataset,
num_workers=8,
shuffle=False,
pin_memory=True,
collate_fn=collate_fn,
batch_sampler=train_sampler,
)
if rank == 0:
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
eval_loader = DataLoader(
eval_dataset,
num_workers=8,
shuffle=False,
batch_size=hps.train.batch_size,
pin_memory=True,
drop_last=False,
collate_fn=collate_fn,
)
# some of these flags are not being used in the code and directly set in hps json file.
# they are kept here for reference and prototyping.
if (
"use_transformer_flows" in hps.model.keys()
and hps.model.use_transformer_flows == True
):
use_transformer_flows = True
transformer_flow_type = hps.model.transformer_flow_type
print(f"Using transformer flows {transformer_flow_type} for VITS2")
assert (
transformer_flow_type in AVAILABLE_FLOW_TYPES
), f"transformer_flow_type must be one of {AVAILABLE_FLOW_TYPES}"
else:
print("Using normal flows for VITS1")
use_transformer_flows = False
if (
"use_spk_conditioned_encoder" in hps.model.keys()
and hps.model.use_spk_conditioned_encoder == True
):
if hps.data.n_speakers == 0:
raise ValueError(
"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
)
use_spk_conditioned_encoder = True
else:
print("Using normal encoder for VITS1")
use_spk_conditioned_encoder = False
if (
"use_noise_scaled_mas" in hps.model.keys()
and hps.model.use_noise_scaled_mas == True
):
print("Using noise scaled MAS for VITS2")
use_noise_scaled_mas = True
mas_noise_scale_initial = 0.01
noise_scale_delta = 2e-6
else:
print("Using normal MAS for VITS1")
use_noise_scaled_mas = False
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 == True
):
# print("Using duration discriminator for VITS2")
use_duration_discriminator = True
# comment - choihkk
# add duration discriminator type here
# I think it would be a good idea to come up with a method to input this part accurately, like a hydra
duration_discriminator_type = getattr(
hps.model, "duration_discriminator_type", "dur_disc_1"
)
print(f"Using duration_discriminator {duration_discriminator_type} for VITS2")
assert (
duration_discriminator_type in AVAILABLE_DURATION_DISCRIMINATOR_TYPES
), f"duration_discriminator_type must be one of {AVAILABLE_DURATION_DISCRIMINATOR_TYPES}"
# duration_discriminator_type = AVAILABLE_DURATION_DISCRIMINATOR_TYPES # ここ修正
if duration_discriminator_type == "dur_disc_1":
net_dur_disc = DurationDiscriminatorV1(
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(rank)
elif duration_discriminator_type == "dur_disc_2":
net_dur_disc = DurationDiscriminatorV2(
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(rank)
else:
print("NOT using any duration discriminator like VITS1")
net_dur_disc = None
use_duration_discriminator = False
net_g = SynthesizerTrn(
len(symbols)+1,
posterior_channels,
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(rank)
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
optim_g = torch.optim.AdamW(
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,
)
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
# comment - choihkk
# if we comment out unused parameter like DurationDiscriminator's self.pre_out_norm1,2 self.norm_1,2
# and ResidualCouplingTransformersLayer's self.post_transformer
# we don't have to set find_unused_parameters=True
# but I will not proceed with commenting out for compatibility with the latest work for others
net_g = DDP(net_g, device_ids=[rank])
net_d = DDP(net_d, device_ids=[rank])
if net_dur_disc is not None:
net_dur_disc = DDP(
net_dur_disc, device_ids=[rank])
try:
_, _, _, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g
)
_, _, _, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d
)
if net_dur_disc is not None:
_, _, _, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"),
net_dur_disc,
optim_dur_disc,
)
global_step = (epoch_str - 1) * len(train_loader)
input = input("Initialize Global Steps and Epochs ??? y/n")
if input == "y":
epoch_str = 1
global_step = 0
except:
epoch_str = 1
global_step = 0
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
)
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.fp16_run)
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank == 0:
train_and_evaluate(
rank,
epoch,
hps,
[net_g, net_d, net_dur_disc],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, eval_loader],
logger,
[writer, writer_eval],
)
else:
train_and_evaluate(
rank,
epoch,
hps,
[net_g, net_d, net_dur_disc],
[optim_g, optim_d, optim_dur_disc],
[scheduler_g, scheduler_d, scheduler_dur_disc],
scaler,
[train_loader, None],
None,
None,
)
scheduler_g.step()
scheduler_d.step()
if net_dur_disc is not None:
scheduler_dur_disc.step()
def train_and_evaluate(
rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers
):
net_g, net_d, net_dur_disc = nets
optim_g, optim_d, optim_dur_disc = optims
scheduler_g, scheduler_d, scheduler_dur_disc = 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()
if net_dur_disc is not None:
net_dur_disc.train()
if rank == 0:
loader = tqdm.tqdm(train_loader, desc="Loading train data")
else:
loader = train_loader
for batch_idx, (
x,
x_lengths,
spec,
spec_lengths,
y,
y_lengths,
bert,
bert_lengths,
speakers,
) in enumerate(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(rank, non_blocking=True), x_lengths.cuda(
rank, non_blocking=True
)
spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(
rank, non_blocking=True
)
y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(
rank, non_blocking=True
)
bert, bert_lengths = bert.cuda(rank, non_blocking=True), bert_lengths.cuda(
rank, non_blocking=True
)
speakers = speakers.cuda(rank, non_blocking=True)
with autocast(enabled=hps.train.fp16_run):
(
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_),
) = net_g(x, x_lengths, spec, spec_lengths, bert, bert_lengths, speakers)
if (
hps.model.use_mel_posterior_encoder
or hps.data.use_mel_posterior_encoder
):
mel = spec
else:
# comment - choihkk
# for numerical stable when using fp16 and torch>=2.0.0,
# spec.float() could be help in the training stage
# https://github.com/jaywalnut310/vits/issues/15
mel = spec_to_mel_torch(
spec.float(),
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(
y_hat.squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax,
)
y = commons.slice_segments(
y, ids_slice * hps.data.hop_length, hps.train.segment_size
) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
y_d_hat_r, y_d_hat_g
)
loss_disc_all = loss_disc
# Duration Discriminator
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(
hidden_x.detach(), x_mask.detach(), logw_.detach(), logw.detach()
)
with autocast(enabled=False):
# TODO: I think need to mean using the mask, but for now, just mean all
(
loss_dur_disc,
losses_dur_disc_r,
losses_dur_disc_g,
) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
loss_dur_disc_all = loss_dur_disc
optim_dur_disc.zero_grad()
scaler.scale(loss_dur_disc_all).backward()
scaler.unscale_(optim_dur_disc)
grad_norm_dur_disc = commons.clip_grad_value_(
net_dur_disc.parameters(), None
)
scaler.step(optim_dur_disc)
optim_d.zero_grad()
scaler.scale(loss_disc_all).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)
with autocast(enabled=hps.train.fp16_run):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw_, logw)
with autocast(enabled=False):
loss_dur = torch.sum(l_length.float())
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
loss_fm = feature_loss(fmap_r, fmap_g) | loss_gen, losses_gen = generator_loss(y_d_hat_g) | 5 | 2023-12-16 05:34:02+00:00 | 16k |
Ruiyuan-Zhang/CCS | multi_part_assembly/utils/wx_transformer_utilities/multihead_attention.py | [
{
"identifier": "FairseqDropout",
"path": "multi_part_assembly/utils/wx_transformer_utilities/fairseq_dropout.py",
"snippet": "class FairseqDropout(nn.Module):\n\n def __init__(self, p, module_name=None):\n super().__init__()\n self.p = p\n self.module_name = module_name\n ... | import math
import time
import numpy as np
import torch
import torch.nn.functional as F
import multi_part_assembly.utils.wx_transformer_utilities.fairseq_utils as utils
from typing import Dict, Optional, Tuple
from torch import Tensor, nn
from torch.nn import Parameter
from .fairseq_dropout import FairseqDropout
from .attention_rim import MultiHeadAttention as MHAMemory
from .quant_noise import quant_noise
from .group_linear_layer import GroupLinearLayer
from .relational_memory_volatile import RelationalMemory
from .relational_memory_regressive import RelationalMemory as RelationalMemoryRegressive | 13,834 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#import models.fairseq_util
#from fairseq.incremental_decoding_utils import with_incremental_state
#from .relational_memory_lstm import RelationalMemory # 为什么作者没有从这两个类别中引入relmem？
#from fairseq.modules.shared_group_linear_layer import SharedGroupLinearLayer as GroupLinearLayer
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(
self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
q_noise=0.0,
qn_block_size=8,
nblocks=1,
top_k_ratio=None,
use_value_competition=True,
shared_memory_attention = False,
use_topk = False,
topk = 3,
num_steps = 5,
mem_slots = 4,
null_attention = False,
regressive = False
):
super().__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#import models.fairseq_util
#from fairseq.incremental_decoding_utils import with_incremental_state
#from .relational_memory_lstm import RelationalMemory # 为什么作者没有从这两个类别中引入relmem？
#from fairseq.modules.shared_group_linear_layer import SharedGroupLinearLayer as GroupLinearLayer
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(
self,
embed_dim,
num_heads,
kdim=None,
vdim=None,
dropout=0.0,
bias=True,
add_bias_kv=False,
add_zero_attn=False,
self_attention=False,
encoder_decoder_attention=False,
q_noise=0.0,
qn_block_size=8,
nblocks=1,
top_k_ratio=None,
use_value_competition=True,
shared_memory_attention = False,
use_topk = False,
topk = 3,
num_steps = 5,
mem_slots = 4,
null_attention = False,
regressive = False
):
super().__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads | self.dropout_module = FairseqDropout( | 0 | 2023-12-15 13:13:01+00:00 | 16k |
camenduru/FreeInit-hf | app.py | [
{
"identifier": "UNet3DConditionModel",
"path": "animatediff/models/unet.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... | import os
import torch
import random
import gradio as gr
from glob import glob
from omegaconf import OmegaConf
from safetensors import safe_open
from diffusers import AutoencoderKL
from diffusers import EulerDiscreteScheduler, DDIMScheduler
from diffusers.utils.import_utils import is_xformers_available
from transformers import CLIPTextModel, CLIPTokenizer
from animatediff.models.unet import UNet3DConditionModel
from animatediff.pipelines.pipeline_animation import AnimationFreeInitPipeline
from animatediff.utils.util import save_videos_grid
from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint
from diffusers.training_utils import set_seed
from animatediff.utils.freeinit_utils import get_freq_filter
from collections import namedtuple | 14,182 | "A cute raccoon playing guitar in a boat on the ocean",
"worst quality, low quality, nsfw, logo",
512, 512, "1566149281915957",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 4-MajicMix
[
"majicmixRealistic_v5Preview.safetensors",
"mm_sd_v14.ckpt",
"1girl, reading book",
"(ng_deepnegative_v1_75t:1.2), (badhandv4:1), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, watermark, moles",
512, 512, "2005563494988190",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# # 5-RealisticVision
# [
# "realisticVisionV51_v20Novae.safetensors",
# "mm_sd_v14.ckpt",
# "A panda standing on a surfboard in the ocean in sunset.",
# "worst quality, low quality, nsfw, logo",
# 512, 512, "2005563494988190",
# "butterworth", 0.25, 0.25, 3,
# ["use_fp16"]
# ]
]
# clean unrelated ckpts
# ckpts = [
# "realisticVisionV40_v20Novae.safetensors",
# "majicmixRealistic_v5Preview.safetensors",
# "rcnzCartoon3d_v10.safetensors",
# "lyriel_v16.safetensors",
# "toonyou_beta3.safetensors"
# ]
# for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")):
# for ckpt in ckpts:
# if path.endswith(ckpt): break
# else:
# print(f"### Cleaning {path} ...")
# os.system(f"rm -rf {path}")
# os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}")
# os.system(f"bash download_bashscripts/1-ToonYou.sh")
# os.system(f"bash download_bashscripts/2-Lyriel.sh")
# os.system(f"bash download_bashscripts/3-RcnzCartoon.sh")
# os.system(f"bash download_bashscripts/4-MajicMix.sh")
# os.system(f"bash download_bashscripts/5-RealisticVision.sh")
# # clean Gradio cache
# print(f"### Cleaning cached examples ...")
# os.system(f"rm -rf gradio_cached_examples/")
class AnimateController:
def __init__(self):
# config dirs
self.basedir = os.getcwd()
self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")
self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module")
self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA")
self.savedir = os.path.join(self.basedir, "samples")
os.makedirs(self.savedir, exist_ok=True)
self.base_model_list = []
self.motion_module_list = []
self.filter_type_list = [
"butterworth",
"gaussian",
"box",
"ideal"
]
self.selected_base_model = None
self.selected_motion_module = None
self.selected_filter_type = None
self.set_width = None
self.set_height = None
self.set_d_s = None
self.set_d_t = None
self.refresh_motion_module()
self.refresh_personalized_model()
# config models
self.inference_config = OmegaConf.load(inference_config_path)
self.tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda()
self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda()
self.unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()
self.freq_filter = None
self.update_base_model(self.base_model_list[-2])
self.update_motion_module(self.motion_module_list[0])
self.update_filter(512, 512, self.filter_type_list[0], 0.25, 0.25)
def refresh_motion_module(self):
motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt"))
self.motion_module_list = sorted([os.path.basename(p) for p in motion_module_list])
def refresh_personalized_model(self):
base_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors"))
self.base_model_list = sorted([os.path.basename(p) for p in base_model_list])
def update_base_model(self, base_model_dropdown):
self.selected_base_model = base_model_dropdown
base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown)
base_model_state_dict = {}
with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:
for key in f.keys(): base_model_state_dict[key] = f.get_tensor(key)
|
pretrained_model_path = "models/StableDiffusion/stable-diffusion-v1-5"
inference_config_path = "configs/inference/inference-v1.yaml"
css = """
.toolbutton {
margin-buttom: 0em 0em 0em 0em;
max-width: 2.5em;
min-width: 2.5em !important;
height: 2.5em;
}
"""
examples = [
# 0-RealisticVision
[
"realisticVisionV51_v20Novae.safetensors",
"mm_sd_v14.ckpt",
"A panda standing on a surfboard in the ocean under moonlight.",
"worst quality, low quality, nsfw, logo",
512, 512, "2005563494988190",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 1-ToonYou
[
"toonyou_beta3.safetensors",
"mm_sd_v14.ckpt",
"(best quality, masterpiece), 1girl, looking at viewer, blurry background, upper body, contemporary, dress",
"(worst quality, low quality)",
512, 512, "478028150728261",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 2-Lyriel
[
"lyriel_v16.safetensors",
"mm_sd_v14.ckpt",
"hypercars cyberpunk moving, muted colors, swirling color smokes, legend, cityscape, space",
"3d, cartoon, anime, sketches, worst quality, low quality, nsfw, logo",
512, 512, "1566149281915957",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 3-RCNZ
[
"rcnzCartoon3d_v10.safetensors",
"mm_sd_v14.ckpt",
"A cute raccoon playing guitar in a boat on the ocean",
"worst quality, low quality, nsfw, logo",
512, 512, "1566149281915957",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# 4-MajicMix
[
"majicmixRealistic_v5Preview.safetensors",
"mm_sd_v14.ckpt",
"1girl, reading book",
"(ng_deepnegative_v1_75t:1.2), (badhandv4:1), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, watermark, moles",
512, 512, "2005563494988190",
"butterworth", 0.25, 0.25, 3,
["use_fp16"]
],
# # 5-RealisticVision
# [
# "realisticVisionV51_v20Novae.safetensors",
# "mm_sd_v14.ckpt",
# "A panda standing on a surfboard in the ocean in sunset.",
# "worst quality, low quality, nsfw, logo",
# 512, 512, "2005563494988190",
# "butterworth", 0.25, 0.25, 3,
# ["use_fp16"]
# ]
]
# clean unrelated ckpts
# ckpts = [
# "realisticVisionV40_v20Novae.safetensors",
# "majicmixRealistic_v5Preview.safetensors",
# "rcnzCartoon3d_v10.safetensors",
# "lyriel_v16.safetensors",
# "toonyou_beta3.safetensors"
# ]
# for path in glob(os.path.join("models", "DreamBooth_LoRA", "*.safetensors")):
# for ckpt in ckpts:
# if path.endswith(ckpt): break
# else:
# print(f"### Cleaning {path} ...")
# os.system(f"rm -rf {path}")
# os.system(f"rm -rf {os.path.join('models', 'DreamBooth_LoRA', '*.safetensors')}")
# os.system(f"bash download_bashscripts/1-ToonYou.sh")
# os.system(f"bash download_bashscripts/2-Lyriel.sh")
# os.system(f"bash download_bashscripts/3-RcnzCartoon.sh")
# os.system(f"bash download_bashscripts/4-MajicMix.sh")
# os.system(f"bash download_bashscripts/5-RealisticVision.sh")
# # clean Gradio cache
# print(f"### Cleaning cached examples ...")
# os.system(f"rm -rf gradio_cached_examples/")
class AnimateController:
def __init__(self):
# config dirs
self.basedir = os.getcwd()
self.stable_diffusion_dir = os.path.join(self.basedir, "models", "StableDiffusion")
self.motion_module_dir = os.path.join(self.basedir, "models", "Motion_Module")
self.personalized_model_dir = os.path.join(self.basedir, "models", "DreamBooth_LoRA")
self.savedir = os.path.join(self.basedir, "samples")
os.makedirs(self.savedir, exist_ok=True)
self.base_model_list = []
self.motion_module_list = []
self.filter_type_list = [
"butterworth",
"gaussian",
"box",
"ideal"
]
self.selected_base_model = None
self.selected_motion_module = None
self.selected_filter_type = None
self.set_width = None
self.set_height = None
self.set_d_s = None
self.set_d_t = None
self.refresh_motion_module()
self.refresh_personalized_model()
# config models
self.inference_config = OmegaConf.load(inference_config_path)
self.tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder").cuda()
self.vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").cuda()
self.unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(self.inference_config.unet_additional_kwargs)).cuda()
self.freq_filter = None
self.update_base_model(self.base_model_list[-2])
self.update_motion_module(self.motion_module_list[0])
self.update_filter(512, 512, self.filter_type_list[0], 0.25, 0.25)
def refresh_motion_module(self):
motion_module_list = glob(os.path.join(self.motion_module_dir, "*.ckpt"))
self.motion_module_list = sorted([os.path.basename(p) for p in motion_module_list])
def refresh_personalized_model(self):
base_model_list = glob(os.path.join(self.personalized_model_dir, "*.safetensors"))
self.base_model_list = sorted([os.path.basename(p) for p in base_model_list])
def update_base_model(self, base_model_dropdown):
self.selected_base_model = base_model_dropdown
base_model_dropdown = os.path.join(self.personalized_model_dir, base_model_dropdown)
base_model_state_dict = {}
with safe_open(base_model_dropdown, framework="pt", device="cpu") as f:
for key in f.keys(): base_model_state_dict[key] = f.get_tensor(key)
| converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_model_state_dict, self.vae.config) | 5 | 2023-12-19 21:06:32+00:00 | 16k |
exislow/tidal-dl-ng | tidal_dl_ng/gui.py | [
{
"identifier": "get_format_template",
"path": "tidal_dl_ng/helper/path.py",
"snippet": "def get_format_template(\n media: Track | Album | Playlist | UserPlaylist | Video | Mix | MediaType, settings\n) -> str | bool:\n result = False\n\n if isinstance(media, Track) or media == MediaType.TRACK:\... | import math
import sys
import qdarktheme
import coloredlogs.converter
from collections.abc import Callable
from tidal_dl_ng.helper.path import get_format_template
from PySide6 import QtCore, QtGui, QtWidgets
from rich.progress import Progress
from tidalapi import Album, Mix, Playlist, Quality, Track, UserPlaylist, Video
from tidalapi.session import SearchTypes
from tidal_dl_ng.config import Settings, Tidal
from tidal_dl_ng.constants import QualityVideo, TidalLists
from tidal_dl_ng.download import Download
from tidal_dl_ng.logger import XStream, logger_gui
from tidal_dl_ng.model.gui_data import ProgressBars, ResultSearch
from tidal_dl_ng.ui.main import Ui_MainWindow
from tidal_dl_ng.ui.spinner import QtWaitingSpinner
from tidal_dl_ng.worker import Worker | 11,836 |
try:
except ImportError as e:
print(e)
print("Qt dependencies missing. Cannot start GUI. Please execute: 'pip install pyside6 pyqtdarktheme'")
sys.exit(1)
# TODO: Make more use of Exceptions
# TODO: Add File -> Version
class MainWindow(QtWidgets.QMainWindow, Ui_MainWindow):
|
try:
except ImportError as e:
print(e)
print("Qt dependencies missing. Cannot start GUI. Please execute: 'pip install pyside6 pyqtdarktheme'")
sys.exit(1)
# TODO: Make more use of Exceptions
# TODO: Add File -> Version
class MainWindow(QtWidgets.QMainWindow, Ui_MainWindow): | settings: Settings = None | 1 | 2023-12-19 23:05:47+00:00 | 16k |
zyrant/SPGroup3D | tests/test_data/test_datasets/test_scannet_dataset.py | [
{
"identifier": "ScanNetDataset",
"path": "mmdet3d/datasets/scannet_dataset.py",
"snippet": "class ScanNetDataset(Custom3DDataset):\n r\"\"\"ScanNet Dataset for Detection Task.\n\n This class serves as the API for experiments on the ScanNet Dataset.\n\n Please refer to the `github repo <https:/... | import copy
import numpy as np
import pytest
import torch
import tempfile
import tempfile
import mmcv
import tempfile
import tempfile
import mmcv
import mmcv
from mmdet3d.datasets import (ScanNetDataset, ScanNetInstanceSegDataset,
ScanNetSegDataset, ScanNetInstanceSegV2Dataset)
from mmdet3d.core.bbox.structures import DepthInstance3DBoxes
from os import path as osp
from mmdet3d.core.bbox import DepthInstance3DBoxes
from os import path as osp
from os import path as osp | 11,219 | 2.0221e-02, 2.6153e+00, 1.5109e-02, 7.3335e-01,
1.0429e+00, 1.0251e+00, 0.0000e+00
]]))
scores_3d = torch.tensor(
[1.2058e-04, 2.3012e-03, 6.2324e-06, 6.6139e-06, 6.7965e-05])
labels_3d = torch.tensor([0, 0, 0, 0, 0])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
# show function with pipeline
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
scannet_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
def test_seg_getitem():
np.random.seed(0)
root_path = './tests/data/scannet/'
ann_file = './tests/data/scannet/scannet_infos.pkl'
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
palette = [
[174, 199, 232],
[152, 223, 138],
[31, 119, 180],
[255, 187, 120],
[188, 189, 34],
[140, 86, 75],
[255, 152, 150],
[214, 39, 40],
[197, 176, 213],
[148, 103, 189],
[196, 156, 148],
[23, 190, 207],
[247, 182, 210],
[219, 219, 141],
[255, 127, 14],
[158, 218, 229],
[44, 160, 44],
[112, 128, 144],
[227, 119, 194],
[82, 84, 163],
]
scene_idxs = [0 for _ in range(20)]
# test network inputs are (xyz, rgb, normalized_xyz)
pipelines = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39),
max_cat_id=40),
dict(
type='IndoorPatchPointSample',
num_points=5,
block_size=1.5,
ignore_index=len(class_names),
use_normalized_coord=True,
enlarge_size=0.2,
min_unique_num=None),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=['points', 'pts_semantic_mask'],
meta_keys=['file_name', 'sample_idx'])
]
| # Copyright (c) OpenMMLab. All rights reserved.
def test_getitem():
np.random.seed(0)
root_path = './tests/data/scannet/'
ann_file = './tests/data/scannet/scannet_infos.pkl'
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
pipelines = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_mask_3d=True,
with_seg_3d=True),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='PointSegClassMapping',
valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33,
34, 36, 39)),
dict(type='PointSample', num_points=5),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=1.0,
flip_ratio_bev_vertical=1.0),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[1.0, 1.0],
shift_height=True),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
'pts_instance_mask'
],
meta_keys=['file_name', 'sample_idx', 'pcd_rotation']),
]
scannet_dataset = ScanNetDataset(root_path, ann_file, pipelines)
data = scannet_dataset[0]
points = data['points']._data
gt_bboxes_3d = data['gt_bboxes_3d']._data
gt_labels = data['gt_labels_3d']._data
pts_semantic_mask = data['pts_semantic_mask']._data
pts_instance_mask = data['pts_instance_mask']._data
file_name = data['img_metas']._data['file_name']
pcd_rotation = data['img_metas']._data['pcd_rotation']
sample_idx = data['img_metas']._data['sample_idx']
expected_rotation = np.array([[0.99654, 0.08311407, 0.],
[-0.08311407, 0.99654, 0.], [0., 0., 1.]])
assert file_name == './tests/data/scannet/points/scene0000_00.bin'
assert np.allclose(pcd_rotation, expected_rotation, 1e-3)
assert sample_idx == 'scene0000_00'
expected_points = torch.tensor(
[[1.8339e+00, 2.1093e+00, 2.2900e+00, 2.3895e+00],
[3.6079e+00, 1.4592e-01, 2.0687e+00, 2.1682e+00],
[4.1886e+00, 5.0614e+00, -1.0841e-01, -8.8736e-03],
[6.8790e+00, 1.5086e+00, -9.3154e-02, 6.3816e-03],
[4.8253e+00, 2.6668e-01, 1.4917e+00, 1.5912e+00]])
expected_gt_bboxes_3d = torch.tensor(
[[-1.1835, -3.6317, 1.5704, 1.7577, 0.3761, 0.5724, 0.0000],
[-3.1832, 3.2269, 1.1911, 0.6727, 0.2251, 0.6715, 0.0000],
[-0.9598, -2.2864, 0.0093, 0.7506, 2.5709, 1.2145, 0.0000],
[-2.6988, -2.7354, 0.8288, 0.7680, 1.8877, 0.2870, 0.0000],
[3.2989, 0.2885, -0.0090, 0.7600, 3.8814, 2.1603, 0.0000]])
expected_gt_labels = np.array([
6, 6, 4, 9, 11, 11, 10, 0, 15, 17, 17, 17, 3, 12, 4, 4, 14, 1, 0, 0, 0,
0, 0, 0, 5, 5, 5
])
expected_pts_semantic_mask = np.array([0, 18, 18, 18, 18])
expected_pts_instance_mask = np.array([44, 22, 10, 10, 57])
original_classes = scannet_dataset.CLASSES
assert scannet_dataset.CLASSES == class_names
assert torch.allclose(points, expected_points, 1e-2)
assert gt_bboxes_3d.tensor[:5].shape == (5, 7)
assert torch.allclose(gt_bboxes_3d.tensor[:5], expected_gt_bboxes_3d, 1e-2)
assert np.all(gt_labels.numpy() == expected_gt_labels)
assert np.all(pts_semantic_mask.numpy() == expected_pts_semantic_mask)
assert np.all(pts_instance_mask.numpy() == expected_pts_instance_mask)
assert original_classes == class_names
scannet_dataset = ScanNetDataset(
root_path, ann_file, pipeline=None, classes=['cabinet', 'bed'])
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ['cabinet', 'bed']
scannet_dataset = ScanNetDataset(
root_path, ann_file, pipeline=None, classes=('cabinet', 'bed'))
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ('cabinet', 'bed')
# Test load classes from file
with tempfile.TemporaryDirectory() as tmpdir:
path = tmpdir + 'classes.txt'
with open(path, 'w') as f:
f.write('cabinet\nbed\n')
scannet_dataset = ScanNetDataset(
root_path, ann_file, pipeline=None, classes=path)
assert scannet_dataset.CLASSES != original_classes
assert scannet_dataset.CLASSES == ['cabinet', 'bed']
def test_evaluate():
if not torch.cuda.is_available():
pytest.skip()
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetDataset(root_path, ann_file)
results = []
pred_boxes = dict()
pred_boxes['boxes_3d'] = DepthInstance3DBoxes(
torch.tensor([[
1.4813e+00, 3.5207e+00, 1.5704e+00, 1.7445e+00, 2.3196e-01,
5.7235e-01, 0.0000e+00
],
[
2.9040e+00, -3.4803e+00, 1.1911e+00, 6.6078e-01,
1.7072e-01, 6.7154e-01, 0.0000e+00
],
[
1.1466e+00, 2.1987e+00, 9.2576e-03, 5.4184e-01,
2.5346e+00, 1.2145e+00, 0.0000e+00
],
[
2.9168e+00, 2.5016e+00, 8.2875e-01, 6.1697e-01,
1.8428e+00, 2.8697e-01, 0.0000e+00
],
[
-3.3114e+00, -1.3351e-02, -8.9524e-03, 4.4082e-01,
3.8582e+00, 2.1603e+00, 0.0000e+00
],
[
-2.0135e+00, -3.4857e+00, 9.3848e-01, 1.9911e+00,
2.1603e-01, 1.2767e+00, 0.0000e+00
],
[
-2.1945e+00, -3.1402e+00, -3.8165e-02, 1.4801e+00,
6.8676e-01, 1.0586e+00, 0.0000e+00
],
[
-2.7553e+00, 2.4055e+00, -2.9972e-02, 1.4764e+00,
1.4927e+00, 2.3380e+00, 0.0000e+00
]]))
pred_boxes['labels_3d'] = torch.tensor([6, 6, 4, 9, 11, 11])
pred_boxes['scores_3d'] = torch.tensor([0.5, 1.0, 1.0, 1.0, 1.0, 0.5])
results.append(pred_boxes)
metric = [0.25, 0.5]
ret_dict = scannet_dataset.evaluate(results, metric)
assert abs(ret_dict['table_AP_0.25'] - 0.3333) < 0.01
assert abs(ret_dict['window_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['counter_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['curtain_AP_0.25'] - 1.0) < 0.01
# test evaluate with pipeline
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
ret_dict = scannet_dataset.evaluate(
results, metric, pipeline=eval_pipeline)
assert abs(ret_dict['table_AP_0.25'] - 0.3333) < 0.01
assert abs(ret_dict['window_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['counter_AP_0.25'] - 1.0) < 0.01
assert abs(ret_dict['curtain_AP_0.25'] - 1.0) < 0.01
def test_show():
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
root_path = './tests/data/scannet'
ann_file = './tests/data/scannet/scannet_infos.pkl'
scannet_dataset = ScanNetDataset(root_path, ann_file)
boxes_3d = DepthInstance3DBoxes(
torch.tensor([[
-2.4053e+00, 9.2295e-01, 8.0661e-02, 2.4054e+00, 2.1468e+00,
8.5990e-01, 0.0000e+00
],
[
-1.9341e+00, -2.0741e+00, 3.0698e-03, 3.2206e-01,
2.5322e-01, 3.5144e-01, 0.0000e+00
],
[
-3.6908e+00, 8.0684e-03, 2.6201e-01, 4.1515e-01,
7.6489e-01, 5.3585e-01, 0.0000e+00
],
[
2.6332e+00, 8.5143e-01, -4.9964e-03, 3.0367e-01,
1.3448e+00, 1.8329e+00, 0.0000e+00
],
[
2.0221e-02, 2.6153e+00, 1.5109e-02, 7.3335e-01,
1.0429e+00, 1.0251e+00, 0.0000e+00
]]))
scores_3d = torch.tensor(
[1.2058e-04, 2.3012e-03, 6.2324e-06, 6.6139e-06, 6.7965e-05])
labels_3d = torch.tensor([0, 0, 0, 0, 0])
result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
results = [result]
scannet_dataset.show(results, temp_dir, show=False)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
# show function with pipeline
class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'garbagebin')
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2]),
dict(type='GlobalAlignment', rotation_axis=2),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
tmp_dir = tempfile.TemporaryDirectory()
temp_dir = tmp_dir.name
scannet_dataset.show(results, temp_dir, show=False, pipeline=eval_pipeline)
pts_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_points.obj')
gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
pred_file_path = osp.join(temp_dir, 'scene0000_00',
'scene0000_00_pred.obj')
mmcv.check_file_exist(pts_file_path)
mmcv.check_file_exist(gt_file_path)
mmcv.check_file_exist(pred_file_path)
tmp_dir.cleanup()
def test_seg_getitem():
np.random.seed(0)
root_path = './tests/data/scannet/'
ann_file = './tests/data/scannet/scannet_infos.pkl'
class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
'curtain', 'refrigerator', 'showercurtrain', 'toilet',
'sink', 'bathtub', 'otherfurniture')
palette = [
[174, 199, 232],
[152, 223, 138],
[31, 119, 180],
[255, 187, 120],
[188, 189, 34],
[140, 86, 75],
[255, 152, 150],
[214, 39, 40],
[197, 176, 213],
[148, 103, 189],
[196, 156, 148],
[23, 190, 207],
[247, 182, 210],
[219, 219, 141],
[255, 127, 14],
[158, 218, 229],
[44, 160, 44],
[112, 128, 144],
[227, 119, 194],
[82, 84, 163],
]
scene_idxs = [0 for _ in range(20)]
# test network inputs are (xyz, rgb, normalized_xyz)
pipelines = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
28, 33, 34, 36, 39),
max_cat_id=40),
dict(
type='IndoorPatchPointSample',
num_points=5,
block_size=1.5,
ignore_index=len(class_names),
use_normalized_coord=True,
enlarge_size=0.2,
min_unique_num=None),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D',
keys=['points', 'pts_semantic_mask'],
meta_keys=['file_name', 'sample_idx'])
]
| scannet_dataset = ScanNetSegDataset( | 2 | 2023-12-21 12:50:35+00:00 | 16k |
v3ucn/Bert-vits2-V2.2 | 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,
)
from models import (
SynthesizerTrn,
MultiPeriodDiscriminator,
DurationDiscriminator,
)
from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from text.symbols import symbols | 10,900 | "--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,
)
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
# 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)
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
| # flake8: noqa: E402
logging.getLogger("numba").setLevel(logging.WARNING)
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
torch.backends.cuda.enable_math_sdp(True)
global_step = 0
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,
)
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
# 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)
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) | 5 | 2023-12-18 04:54:46+00:00 | 16k |
m-abr/FCPCodebase | scripts/utils/Inv_Kinematics.py | [
{
"identifier": "Base_Agent",
"path": "agent/Base_Agent.py",
"snippet": "class Base_Agent():\n all_agents = []\n\n def __init__(self, host:str, agent_port:int, monitor_port:int, unum:int, robot_type:int, team_name:str, enable_log:bool=True,\n enable_draw:bool=True, apply_play_mode... | from agent.Base_Agent import Base_Agent as Agent
from itertools import count
from math_ops.Inverse_Kinematics import Inverse_Kinematics
from scripts.commons.Script import Script
from world.commons.Draw import Draw
import numpy as np | 11,392 |
class Inv_Kinematics():
def __init__(self, script:Script) -> None:
self.args = script.args
self.last_action = (0,0,0)
self.gravity = True
# Initial pose is a neutral pose where all angles are 0
leg_y_dev, upper_leg_height, upper_leg_depth, lower_leg_len, _, _ = Inverse_Kinematics.NAO_SPECS_PER_ROBOT[self.args.r]
leg_height = upper_leg_height + lower_leg_len
self.feet_pose = [ [[upper_leg_depth,leg_y_dev,-leg_height],[0,0,0]], [[upper_leg_depth,-leg_y_dev,-leg_height], [0,0,0]] ]
def _user_control(self):
while True:
inp = input("Command:")
if inp == "": return 2
elif inp == ".": return 1
elif inp == "h": self.print_help(); continue
elif inp == "g":
self.gravity = not self.gravity
print("Using gravity:",self.gravity)
if self.gravity:
return 6 # extra steps for beam to take effect
else:
return 1
#Check if user input is a value
try:
val = float(inp)
self.feet_pose[self.last_action[0]][self.last_action[1]][self.last_action[2]] = val
continue
except:
pass
if inp[0] not in ['l','r'] or inp[1] not in ['x','y','z','X','Y','Z']:
print("Illegal command!")
continue
side = 0 if inp[0]=='l' else 1
pos_rot = 0 if inp[1].islower() else 1
axis = {'x':0,'y':1,'z':2}[inp[1].lower()]
self.last_action = (side,pos_rot,axis)
try:
val = float(inp[2:])
self.feet_pose[side][pos_rot][axis] = val
except:
print("Illegal value conversion!")
|
class Inv_Kinematics():
def __init__(self, script:Script) -> None:
self.args = script.args
self.last_action = (0,0,0)
self.gravity = True
# Initial pose is a neutral pose where all angles are 0
leg_y_dev, upper_leg_height, upper_leg_depth, lower_leg_len, _, _ = Inverse_Kinematics.NAO_SPECS_PER_ROBOT[self.args.r]
leg_height = upper_leg_height + lower_leg_len
self.feet_pose = [ [[upper_leg_depth,leg_y_dev,-leg_height],[0,0,0]], [[upper_leg_depth,-leg_y_dev,-leg_height], [0,0,0]] ]
def _user_control(self):
while True:
inp = input("Command:")
if inp == "": return 2
elif inp == ".": return 1
elif inp == "h": self.print_help(); continue
elif inp == "g":
self.gravity = not self.gravity
print("Using gravity:",self.gravity)
if self.gravity:
return 6 # extra steps for beam to take effect
else:
return 1
#Check if user input is a value
try:
val = float(inp)
self.feet_pose[self.last_action[0]][self.last_action[1]][self.last_action[2]] = val
continue
except:
pass
if inp[0] not in ['l','r'] or inp[1] not in ['x','y','z','X','Y','Z']:
print("Illegal command!")
continue
side = 0 if inp[0]=='l' else 1
pos_rot = 0 if inp[1].islower() else 1
axis = {'x':0,'y':1,'z':2}[inp[1].lower()]
self.last_action = (side,pos_rot,axis)
try:
val = float(inp[2:])
self.feet_pose[side][pos_rot][axis] = val
except:
print("Illegal value conversion!")
| def _draw_labels(self, player:Agent): | 5 | 2023-12-16 23:40:23+00:00 | 16k |
daihaojun554/biliscrapy | biliscrapy/network/bilibili_danmu.py | [
{
"identifier": "bili_pb2",
"path": "biliscrapy/network/protobuf/bili_pb2.py",
"snippet": "DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\\n\\x08my.proto\\x12 bilibili.community.service.dm.v1\\\"d\\n\\x06\\x41vatar\\x12\\n\\n\\x02id\\x18\\x01 \\x01(\\t\\x12\\x0b\\n\\x03url\\x18\\x02 \\x01(... | import logging
import os
import json
import sys
import requests
from datetime import datetime
from .protobuf import bili_pb2 as Danmaku
from .bilibili_utils import bili_utils | 12,925 |
headers = {
'authority': 'message.bilibili.com',
'accept': 'application/json, text/plain, */*',
'accept-language': 'zh-CN,zh;q=0.9',
'cache-control': 'no-cache',
'origin': 'https://www.bilibili.com',
'pragma': 'no-cache',
'referer': 'https://www.bilibili.com/',
'sec-ch-ua': '"Chromium";v="118", "Google Chrome";v="118", "Not=A?Brand";v="99"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-site',
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36',
}
# import bili_pb2 as Danmaku
class Danmu:
def __init__(self):
self.utils = bili_utils()
self.script_dir = os.path.dirname(os.path.abspath(__file__))
# 构建文件路径
file_path = os.path.join(self.script_dir, 'bilibili_cookies.json')
if not file_path:
self.cookies = {}
with open(file_path, 'r') as file:
self.cookies_data = json.load(file)
self.cookies = {cookie['name']: cookie['value'] for cookie in self.cookies_data}
self.headers = headers
self.logger = logging.getLogger('log')
def bv2cid(self, bvorurl):
try:
bv = self.utils.bv_get(bvorurl)
cid = self.utils.bv2cid(bv)
return cid
except Exception as e:
self.logger.error(e)
return None
# 获取某个 oid 下存在弹幕的日期列表
def get_available_dates(self, oid, year=None, month=None):
if not year or not month:
now = datetime.now()
year = now.year
month = now.month
# 如果month 是1.2.3.4.5.6.7.8.9 前面补0
if month < 10:
month = '0' + str(month)
url = f'https://api.bilibili.com/x/v2/dm/history/index?type=1&oid={oid}&month={year}-{month}'
response = requests.get(url, cookies=self.cookies, headers=self.headers)
if response.status_code == 200:
data = response.json()
return data.get("data", [])
else:
self.logger.error("请检查你输入的 oid 号码!!")
self.logger.error(f"当前请求的 URL 为: {url}")
return []
'''
下载某个视频的弹幕文件
'''
def down_so_files(self, oid, dates):
if dates == None:
return
if oid == None:
self.logger.info("请输入正确的 oid 号码!!")
return
if not os.path.exists(os.path.join(self.script_dir, 'data/danmaku')):
os.mkdir(os.path.join(self.script_dir, 'data/danmaku'))
elif dates:
url = f'https://api.bilibili.com/x/v2/dm/web/history/seg.so?type=1&oid={oid}'
for date in dates:
url_ = f'{url}&date={date}'
self.logger.info(f"正在下载 {oid}-{date}.so 文件，请稍后...")
response = requests.get(url_, cookies=self.cookies, headers=self.headers)
if response.status_code == 200:
with open(os.path.join(self.script_dir, 'data/danmaku/', f'{oid}-{date}.so'), 'wb') as f:
f.write(response.content)
else:
self.logger.info("请检查你输入的 oid 号码!!")
self.logger.info(f"当前请求的 URL 为: {url}")
return
self.logger.info(f"下载完成！")
# 将.so文件解析并保存为JSON文件
def parse_so_to_json(self, oid, dates):
try:
if dates == None:
self.logger.error("日期为空")
return
all_danmaku = set() # 用集合存储所有弹幕数据
for date in dates:
file_path = os.path.join(self.script_dir, 'data/danmaku/', f'{oid}-{date}.so')
with open(file_path, 'rb') as f:
data = f.read()
|
headers = {
'authority': 'message.bilibili.com',
'accept': 'application/json, text/plain, */*',
'accept-language': 'zh-CN,zh;q=0.9',
'cache-control': 'no-cache',
'origin': 'https://www.bilibili.com',
'pragma': 'no-cache',
'referer': 'https://www.bilibili.com/',
'sec-ch-ua': '"Chromium";v="118", "Google Chrome";v="118", "Not=A?Brand";v="99"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Windows"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-site',
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36',
}
# import bili_pb2 as Danmaku
class Danmu:
def __init__(self):
self.utils = bili_utils()
self.script_dir = os.path.dirname(os.path.abspath(__file__))
# 构建文件路径
file_path = os.path.join(self.script_dir, 'bilibili_cookies.json')
if not file_path:
self.cookies = {}
with open(file_path, 'r') as file:
self.cookies_data = json.load(file)
self.cookies = {cookie['name']: cookie['value'] for cookie in self.cookies_data}
self.headers = headers
self.logger = logging.getLogger('log')
def bv2cid(self, bvorurl):
try:
bv = self.utils.bv_get(bvorurl)
cid = self.utils.bv2cid(bv)
return cid
except Exception as e:
self.logger.error(e)
return None
# 获取某个 oid 下存在弹幕的日期列表
def get_available_dates(self, oid, year=None, month=None):
if not year or not month:
now = datetime.now()
year = now.year
month = now.month
# 如果month 是1.2.3.4.5.6.7.8.9 前面补0
if month < 10:
month = '0' + str(month)
url = f'https://api.bilibili.com/x/v2/dm/history/index?type=1&oid={oid}&month={year}-{month}'
response = requests.get(url, cookies=self.cookies, headers=self.headers)
if response.status_code == 200:
data = response.json()
return data.get("data", [])
else:
self.logger.error("请检查你输入的 oid 号码!!")
self.logger.error(f"当前请求的 URL 为: {url}")
return []
'''
下载某个视频的弹幕文件
'''
def down_so_files(self, oid, dates):
if dates == None:
return
if oid == None:
self.logger.info("请输入正确的 oid 号码!!")
return
if not os.path.exists(os.path.join(self.script_dir, 'data/danmaku')):
os.mkdir(os.path.join(self.script_dir, 'data/danmaku'))
elif dates:
url = f'https://api.bilibili.com/x/v2/dm/web/history/seg.so?type=1&oid={oid}'
for date in dates:
url_ = f'{url}&date={date}'
self.logger.info(f"正在下载 {oid}-{date}.so 文件，请稍后...")
response = requests.get(url_, cookies=self.cookies, headers=self.headers)
if response.status_code == 200:
with open(os.path.join(self.script_dir, 'data/danmaku/', f'{oid}-{date}.so'), 'wb') as f:
f.write(response.content)
else:
self.logger.info("请检查你输入的 oid 号码!!")
self.logger.info(f"当前请求的 URL 为: {url}")
return
self.logger.info(f"下载完成！")
# 将.so文件解析并保存为JSON文件
def parse_so_to_json(self, oid, dates):
try:
if dates == None:
self.logger.error("日期为空")
return
all_danmaku = set() # 用集合存储所有弹幕数据
for date in dates:
file_path = os.path.join(self.script_dir, 'data/danmaku/', f'{oid}-{date}.so')
with open(file_path, 'rb') as f:
data = f.read()
| my_seg = Danmaku.DmSegMobileReply() | 0 | 2023-12-14 10:14:24+00:00 | 16k |
Angryrou/udao | udao/optimization/tests/moo/test_weighted_sum.py | [
{
"identifier": "logger",
"path": "udao/utils/logging.py",
"snippet": "def _get_logger(name: str = \"udao\", level: int = logging.DEBUG) -> logging.Logger:"
},
{
"identifier": "Constraint",
"path": "udao/optimization/concepts/constraint.py",
"snippet": "class Constraint:\n \"\"\"An op... | from typing import Dict, Optional
from ....utils.logging import logger
from ...concepts import Constraint, Objective
from ...concepts.problem import MOProblem
from ...moo.weighted_sum import WeightedSum
from ...soo.grid_search_solver import GridSearchSolver
from ...soo.mogd import MOGD
from ...soo.random_sampler_solver import RandomSamplerSolver
from ...soo.so_solver import SOSolver
from ...utils.exceptions import NoSolutionError
from ...utils.moo_utils import even_weights
import numpy as np
import pytest
import torch as th | 11,710 |
class TestWeightedSum:
@pytest.mark.parametrize(
"inner_solver",
[
GridSearchSolver(GridSearchSolver.Params(n_grids_per_var=[2, 7])),
RandomSamplerSolver(RandomSamplerSolver.Params(n_samples_per_param=30)),
],
)
def test_solve_without_input_parameters(
self, inner_solver: SOSolver, simple_problem: MOProblem
) -> None:
"""solve a dummy minimization problem with 2 objectives and 1 constraint"""
ws_pairs = np.array([[0.3, 0.7], [0.6, 0.4]])
simple_problem.input_parameters = None
ws_algo = WeightedSum(
WeightedSum.Params(
so_solver=inner_solver,
ws_pairs=ws_pairs,
)
)
po_objs, po_vars = ws_algo.solve(problem=simple_problem, seed=0)
np.testing.assert_array_almost_equal(po_objs, np.array([[0, 0.2]]))
np.testing.assert_equal(po_vars, np.array({"v1": 0, "v2": 2}))
@pytest.mark.parametrize(
"inner_solver",
[
GridSearchSolver(GridSearchSolver.Params(n_grids_per_var=[2, 7])),
RandomSamplerSolver(RandomSamplerSolver.Params(n_samples_per_param=30)),
],
)
def test_solve_with_input_parameters(
self, inner_solver: SOSolver, simple_problem: MOProblem
) -> None:
"""solve a dummy minimization problem with 2 objectives and 1 constraint"""
ws_pairs = np.array([[0.3, 0.7], [0.6, 0.4]])
ws_algo = WeightedSum(
WeightedSum.Params(
so_solver=inner_solver,
ws_pairs=ws_pairs,
)
)
po_objs, po_vars = ws_algo.solve(problem=simple_problem, seed=0)
np.testing.assert_almost_equal(po_objs, np.array([[1, 1.3]]))
np.testing.assert_equal(po_vars, np.array([{"v1": 0, "v2": 3}]))
@pytest.mark.parametrize(
"inner_solver",
[
GridSearchSolver(GridSearchSolver.Params(n_grids_per_var=[2, 7])),
RandomSamplerSolver(RandomSamplerSolver.Params(n_samples_per_param=1000)),
],
)
def test_solver_with_two_obj_problem(
self, inner_solver: SOSolver, two_obj_problem: MOProblem
) -> None:
ws_pairs = np.array(
[
[0.3, 0.7],
[0.6, 0.4],
[0.1, 0.9],
[0.2, 0.8],
[0.4, 0.6],
[0.5, 0.5],
]
)
ws_algo = WeightedSum(
WeightedSum.Params(
so_solver=inner_solver,
ws_pairs=ws_pairs,
)
)
po_objs, po_vars = ws_algo.solve(problem=two_obj_problem, seed=0)
np.testing.assert_almost_equal(po_objs, np.array([[0, 0]]), decimal=5)
np.testing.assert_almost_equal(po_vars[0]["v1"], 0.0, decimal=3)
assert po_vars[0]["v2"] == 1.0
@pytest.mark.parametrize(
"strict_rounding",
[
True,
False,
],
)
def test_solver_with_two_obj_problem_mogd(
self, strict_rounding: bool, two_obj_problem: MOProblem
) -> None:
inner_solver = MOGD(
MOGD.Params(
learning_rate=0.1,
max_iters=100,
patience=20,
multistart=2,
batch_size=10,
strict_rounding=strict_rounding,
)
)
|
class TestWeightedSum:
@pytest.mark.parametrize(
"inner_solver",
[
GridSearchSolver(GridSearchSolver.Params(n_grids_per_var=[2, 7])),
RandomSamplerSolver(RandomSamplerSolver.Params(n_samples_per_param=30)),
],
)
def test_solve_without_input_parameters(
self, inner_solver: SOSolver, simple_problem: MOProblem
) -> None:
"""solve a dummy minimization problem with 2 objectives and 1 constraint"""
ws_pairs = np.array([[0.3, 0.7], [0.6, 0.4]])
simple_problem.input_parameters = None
ws_algo = WeightedSum(
WeightedSum.Params(
so_solver=inner_solver,
ws_pairs=ws_pairs,
)
)
po_objs, po_vars = ws_algo.solve(problem=simple_problem, seed=0)
np.testing.assert_array_almost_equal(po_objs, np.array([[0, 0.2]]))
np.testing.assert_equal(po_vars, np.array({"v1": 0, "v2": 2}))
@pytest.mark.parametrize(
"inner_solver",
[
GridSearchSolver(GridSearchSolver.Params(n_grids_per_var=[2, 7])),
RandomSamplerSolver(RandomSamplerSolver.Params(n_samples_per_param=30)),
],
)
def test_solve_with_input_parameters(
self, inner_solver: SOSolver, simple_problem: MOProblem
) -> None:
"""solve a dummy minimization problem with 2 objectives and 1 constraint"""
ws_pairs = np.array([[0.3, 0.7], [0.6, 0.4]])
ws_algo = WeightedSum(
WeightedSum.Params(
so_solver=inner_solver,
ws_pairs=ws_pairs,
)
)
po_objs, po_vars = ws_algo.solve(problem=simple_problem, seed=0)
np.testing.assert_almost_equal(po_objs, np.array([[1, 1.3]]))
np.testing.assert_equal(po_vars, np.array([{"v1": 0, "v2": 3}]))
@pytest.mark.parametrize(
"inner_solver",
[
GridSearchSolver(GridSearchSolver.Params(n_grids_per_var=[2, 7])),
RandomSamplerSolver(RandomSamplerSolver.Params(n_samples_per_param=1000)),
],
)
def test_solver_with_two_obj_problem(
self, inner_solver: SOSolver, two_obj_problem: MOProblem
) -> None:
ws_pairs = np.array(
[
[0.3, 0.7],
[0.6, 0.4],
[0.1, 0.9],
[0.2, 0.8],
[0.4, 0.6],
[0.5, 0.5],
]
)
ws_algo = WeightedSum(
WeightedSum.Params(
so_solver=inner_solver,
ws_pairs=ws_pairs,
)
)
po_objs, po_vars = ws_algo.solve(problem=two_obj_problem, seed=0)
np.testing.assert_almost_equal(po_objs, np.array([[0, 0]]), decimal=5)
np.testing.assert_almost_equal(po_vars[0]["v1"], 0.0, decimal=3)
assert po_vars[0]["v2"] == 1.0
@pytest.mark.parametrize(
"strict_rounding",
[
True,
False,
],
)
def test_solver_with_two_obj_problem_mogd(
self, strict_rounding: bool, two_obj_problem: MOProblem
) -> None:
inner_solver = MOGD(
MOGD.Params(
learning_rate=0.1,
max_iters=100,
patience=20,
multistart=2,
batch_size=10,
strict_rounding=strict_rounding,
)
)
| ws_pairs = even_weights(0.1, 2) | 10 | 2023-12-20 09:10:42+00:00 | 16k |
XLearning-SCU/2023-TPAMI-SMILE | Net.py | [
{
"identifier": "get_dist_release",
"path": "DistComput.py",
"snippet": "def get_dist_release(loader, dist_path):\r\n if not os.path.exists(dist_path):\r\n # loader = test_loader\r\n num_data = [10]\r\n with torch.no_grad():\r\n dist_list = [[] for i in range(len(num_d... | import math
import os
import time
import warnings
import numpy as np
import torch
import torchvision
import torch.nn.functional as F
import evaluate
import faiss
import scipy.io as sio
from torch import nn
from torch.autograd import Variable
from DistComput import get_dist_release
from _Utils.Calculator import get_nearest_k
from _Utils.Logs import update_log
from _Utils.Scatter import visualize2
from _Utils.Visualize import visualize, visual_matrix_console, visualize_image, plot_heat_map
from _Utils import TimeOperator, DirectoryOperator
from DataSetMaster.dataset import get_clusters
from classification import svm_classify
from evaluate import UMAP, evaluate2
from sklearn import metrics
from munkres import Munkres
from figures.ScatterMaster import visual_image_scatter
| 10,809 | elif args.reAlign == 'Copy':
if torch.sum(to_realign):
h1[to_realign] = h0[to_realign]
# class_labels1[is_pair == 0] = class_labels0[is_pair == 0]
elif args.reAlign == 'KnnMapMean':
if torch.sum(to_realign):
targ_v1 = h1[is_pair]
nearest = get_nearest_k(h0[to_realign], h0[is_pair], args.reAlignK)
h1[to_realign] = torch.cat([torch.mean(targ_v1[ns], dim=0) for ns in nearest])
# class_labels1[is_pair == 0] = ...
elif args.reAlign == 'Ignore':
pass
else:
raise NotImplementedError('')
if args.Rev:
fea0_rec, fea1_rec = self.decode([h1, h0])
else:
fea0_rec, fea1_rec = self.decode([h0, h1])
# if len(fea0_rec[0]) == len(fea1_rec[0]):
# fea_rec = torch.concat([fea0_rec, fea1_rec])
# fea = torch.concat([fea0, fea1])
# mask_c = torch.concat([mask[:, 0], mask[:, 1]])
# if torch.sum(mask_c == 0):
# rnmse_vec[0].extend(
# evaluate.get_rnmse(xs_hat=fea_rec[mask_c == 0], xs=fea[mask_c == 0]).cpu().numpy())
# if torch.sum(mask_c == 1):
# rnmse_vec[1].extend(
# evaluate.get_rnmse(xs_hat=fea_rec[mask_c == 1], xs=fea[mask_c == 1]).cpu().numpy())
# else:
# if torch.sum(mask == 0):
# n0_v0 = evaluate.get_rnmse(
# xs_hat=fea0_rec[mask[:, 0] == 0], xs=fea0[mask[:, 0] == 0]).cpu().numpy()
# n0_v1 = evaluate.get_rnmse(
# xs_hat=fea1_rec[mask[:, 1] == 0], xs=fea1[mask[:, 1] == 0]).cpu().numpy()
# rnmse_vec[0].extend(n0_v0)
# rnmse_vec[0].extend(n0_v1)
# if torch.sum(mask == 1):
# n1_v0 = evaluate.get_rnmse(
# xs_hat=fea0_rec[mask[:, 0] == 1], xs=fea0[mask[:, 0] == 1]).cpu().numpy()
# n1_v1 = evaluate.get_rnmse(
# xs_hat=fea1_rec[mask[:, 1] == 1], xs=fea1[mask[:, 1] == 1]).cpu().numpy()
# rnmse_vec[1].extend(n1_v0)
# rnmse_vec[1].extend(n1_v1)
g = torch.concat((torch.zeros(len(fea0), device=fea0.device, dtype=torch.int),
torch.ones(len(fea1), device=fea0.device, dtype=torch.int)))
h = torch.cat([h0, h1]).detach().cpu().numpy()
feature_vec.extend(h)
data_vec.extend(torch.cat([fea0, fea1]).detach().cpu().numpy())
group_vec.extend(g.cpu().numpy())
type_vec.extend(torch.concat((class_labels0, class_labels1)).numpy())
inf_data_t = time.time()
feature_vec = np.array(feature_vec)
data_vec = np.array(data_vec)
feature_vec_cluster = np.array(feature_vec_cluster)
is_pair_all = np.array(is_pair_all)
feature_vec_classification = np.array(feature_vec_classification)
group_vec = np.array(group_vec)
group_vec_cluster = np.array(group_vec_cluster)
type_vec = np.array(type_vec)
type_vec_cluster = np.array(type_vec_cluster)
rnmse_vec[0] = np.array(rnmse_vec[0])
rnmse_vec[1] = np.array(rnmse_vec[1])
kmeans_time = TimeOperator.Timer()
if args.ShowReconstruct:
if args.dataset == 'MNISTUSPS':
dims = [np.product(d.data.shape[1:]) for d in test_dataloader.dataset.datasets]
data_list = [np.asarray(it.data, dtype=np.float32) for it in test_dataloader.dataset.datasets]
Y = test_dataloader.dataset.datasets[0].targets
else:
dims = [d.shape[1] for d in test_dataloader.dataset.data]
data_list = [np.asarray(it, dtype=np.float32) for it in test_dataloader.dataset.data]
Y = test_dataloader.dataset.class_labels0
mask = test_dataloader.dataset.mask
n_per_cat = 10
rec0, rec1 = self.decode([
torch.from_numpy(feature_vec[group_vec == 0]).cuda(),
torch.from_numpy(feature_vec[group_vec == 1]).cuda()])
rec0 = rec0.detach().cpu().numpy()
rec1 = rec1.detach().cpu().numpy()
show_img = np.asarray([])
inds_map = np.asarray([])
for v in range(2):
col = np.asarray([])
inds_map_col = np.asarray([])
for y in range(10):
inds = np.arange(len(Y))[
np.logical_and(np.logical_and(mask[:, v] == 1, mask[:, 1 - v] == 0), Y == y)
]
np.random.shuffle(inds)
assert len(inds) >= n_per_cat
inds = inds[:n_per_cat]
raw_imgs = data_list[v][inds]
missing_imgs = data_list[1 - v][inds]
rec_imgs = [rec0, rec1][v][inds]
rec_imgs_miss = [rec0, rec1][1 - v][inds]
pack = np.asarray(
[raw_imgs, rec_imgs, missing_imgs, rec_imgs_miss]).reshape([-1, n_per_cat, 28, 28])
if len(col):
col = np.concatenate([col, pack], axis=0)
else:
col = pack
if len(inds_map_col):
inds_map_col = np.concatenate([inds_map_col, inds.reshape([1, -1])], axis=0)
else:
inds_map_col = inds.reshape([1, -1])
if len(show_img):
show_img = np.concatenate([show_img, col], axis=1)
else:
show_img = col
if len(inds_map):
inds_map = np.concatenate([inds_map, inds_map_col], axis=1)
else:
inds_map = inds_map_col
plot_heat_map(inds_map, show=True, fig_path='/xlearning/pengxin/Temp/MissingRecIM.svg')
|
def show_distribution_ct(type_vec, group_vec, pred_vec, class_num, group_num):
v = np.zeros((class_num, class_num, group_num), dtype=int)
for t, c, g in zip(type_vec, pred_vec, group_vec):
v[t, c, g] += 1
visual_matrix_console(x=v)
def kmeans(feature_vec, class_num):
d = feature_vec.shape[1]
kmeans = faiss.Clustering(d, class_num)
kmeans.verbose = False
kmeans.niter = 300
kmeans.nredo = 10
# kmeans.spherical = True
# if LimitKmeans:
# kmeans.max_points_per_centroid = 1000
# kmeans.min_points_per_centroid = 10
res = faiss.StandardGpuResources()
cfg = faiss.GpuIndexFlatConfig()
cfg.useFloat16 = True
cfg.device = 0
index = faiss.GpuIndexFlatL2(res, d, cfg)
# print(feature_vec.shape)
kmeans.train(feature_vec, index)
centroids = faiss.vector_to_array(kmeans.centroids).reshape(class_num, d)
return centroids
def show_distribution(cluster_vec, group_vec, class_num, group_num):
for it in np.arange(group_num):
print('{:4d}, '.format(it), end='')
print('')
cluster_group = torch.zeros((class_num, group_num), dtype=torch.int)
for i, j in zip(cluster_vec, group_vec):
cluster_group[i, j] += 1
# cluster_group = cluster_group[torch.argsort(torch.sum(cluster_group, dim=1))]
for line in cluster_group:
print('{:4d}: '.format(torch.sum(line)), end='')
for it in line:
print('{:4d}, '.format(it), end='')
print('')
def save_checkpoint(state, epoch):
"""
it has been trained for *epoch* epochs
"""
filename = 'Epoch{:03d}.checkpoint'.format(epoch)
checkpoint_dir = os.path.join(
os.path.dirname(os.getcwd()),
'Checkpoints',
filename
)
DirectoryOperator.FoldOperator(directory=checkpoint_dir).make_fold()
if os.path.exists(checkpoint_dir):
warnings.warn('Checkpoint exist and been replaced.({})'.format(checkpoint_dir))
print('Save check point into {}'.format(checkpoint_dir))
torch.save(state, checkpoint_dir)
def get_ffn(dims, last_layers=None, with_bn=False, drop_out=0):
layers = []
for ind in range(len(dims) - 1):
in_dim = dims[ind]
out_dim = dims[ind + 1]
layers.append(nn.Linear(in_dim, out_dim))
if with_bn:
layers.append(nn.BatchNorm1d(out_dim))
layers.append(nn.ReLU())
if drop_out:
layers.append(nn.Dropout(drop_out))
if last_layers is not None:
layers.extend(last_layers)
return nn.Sequential(*layers)
def get_cov(dims, strides, last_layers=None, with_bn=False, drop_out=0):
layers = []
for ind in range(len(dims) - 1):
in_dim = dims[ind]
out_dim = dims[ind + 1]
stride = strides[ind]
# layers.append(nn.Linear(in_dim, out_dim))
if stride >= 0:
layers.append(nn.Conv2d(in_dim, out_dim, kernel_size=3, stride=stride, padding=1))
else:
layers.append(nn.ConvTranspose2d(
in_dim, out_dim, kernel_size=3, stride=-stride, padding=1, output_padding=0 if stride == -1 else 1))
if with_bn:
# layers.append(nn.BatchNorm1d(out_dim))
layers.append(nn.BatchNorm2d(out_dim))
layers.append(nn.ReLU())
if drop_out:
layers.append(nn.Dropout(drop_out))
if last_layers is not None:
layers.extend(last_layers)
return nn.Sequential(*layers)
class Net(nn.Module):
def __init__(self, args, in_dims, class_num, group_num):
super(Net, self).__init__()
self.encoder_adaption = nn.ModuleList([
get_ffn([in_dims[i], 1024], with_bn=args.BatchNormType[0] == '1', drop_out=args.Dropout)
for i in range(group_num if args.GroupWiseLayer[0] == '1' else 1)])
self.encoder = nn.ModuleList([
get_ffn([1024, 1024, 512], with_bn=args.BatchNormType[1] == '1', drop_out=args.Dropout)
for _ in range(group_num if args.GroupWiseLayer[1] == '1' else 1)])
if args.representation_dim == 0:
args.representation_dim = class_num
self.class_num = class_num
self.group_num = group_num
self.pred_cac = None
self.pred_center_cac = None
if args.ElActivationType == 'None':
el_activation_ = []
elif args.ElActivationType == 'Normalize':
el_activation_ = []
elif args.ElActivationType == 'BnNormalize':
el_activation_ = [nn.BatchNorm1d(args.representation_dim)]
elif args.ElActivationType == 'BnReNormalize':
el_activation_ = [nn.BatchNorm1d(args.representation_dim), nn.ReLU()]
elif args.ElActivationType == 'BnRe':
el_activation_ = [nn.BatchNorm1d(args.representation_dim), nn.ReLU()]
else:
raise NotImplementedError('')
self.el_activation_ = el_activation_
self.encoder_linear = nn.ModuleList([
get_ffn([512, 256], with_bn=args.BatchNormType[2] == '1', drop_out=args.Dropout,
last_layers=[nn.Linear(256, args.representation_dim)] + self.el_activation_)
for _ in range(group_num if args.GroupWiseLayer[2] == '1' else 1)])
dec_in = args.representation_dim
if args.McDecoder:
dec_in *= group_num
self.dec_in = dec_in
self.decoder_linear = nn.ModuleList([
get_ffn([self.dec_in, 256, 512], with_bn=args.BatchNormType[3] == '1', drop_out=args.Dropout)
for _ in range(group_num if args.GroupWiseLayer[3] == '1' else 1)])
if args.ActivationType == 'None':
final_activation_ = []
elif args.ActivationType == 'Sigmoid':
final_activation_ = [nn.Sigmoid()]
elif args.ActivationType == 'Tanh':
final_activation_ = [nn.Tanh()]
else:
raise NotImplementedError('')
self.final_activation_ = final_activation_
self.decoder = nn.ModuleList([
get_ffn([512, 1024, 1024], with_bn=args.BatchNormType[4] == '1', drop_out=args.Dropout)
for _ in range(group_num if args.GroupWiseLayer[4] == '1' else 1)])
self.decoder_adaption = nn.ModuleList([
get_ffn([], last_layers=[nn.Linear(1024, in_dims[i])] + self.final_activation_)
for i in range(group_num if args.GroupWiseLayer[5] == '1' else 1)])
self.args = args
self.in_dims = in_dims
# def update_cluster_center(self, center):
# self.cluster_centers = F.normalize(torch.from_numpy(center), dim=1).cuda()
def forward(self, x, **kwargs):
return self.decode(self.encode([x]))
def encode(self, xs: list):
hs = []
for g, x in enumerate(xs):
if self.args.noise_type == 'None':
pass
elif self.args.noise_type == 'Drop':
x = x * (Variable(x.data.new(x.size()).normal_(0, 0.1)) < self.args.noise_weight).type_as(x)
elif self.args.noise_type == 'Add':
x = x + Variable(x.data.new(x.size()).normal_(0, self.args.noise_weight)).type_as(x)
else:
raise NotImplementedError('')
if len(x) != 0:
if len(x) == 1:
x = torch.concat([x, x])
# print(x.shape)
# x = x.view((len(x), -1))
# print(x.shape)
x = self.encoder_adaption[g if self.args.GroupWiseLayer[0] == '1' else 0](x)
x = self.encoder[g if self.args.GroupWiseLayer[1] == '1' else 0](x)
x = self.encoder_linear[g if self.args.GroupWiseLayer[2] == '1' else 0](x)
if len(x) == 1:
x = x[[0]]
if self.args.ElActivationType in ['Normalize', 'BnNormalize', 'BnReNormalize']:
x = F.normalize(x, dim=1)
else:
x = torch.zeros([0, self.args.representation_dim], device=torch.device('cuda:0'))
hs.append(x)
return hs
def soft_ass(self, h, centroids):
if self.args.ElActivationType in ['Normalize', 'BnNormalize', 'BnReNormalize']:
return h @ centroids.T
else:
dst = torch.cdist(h, centroids)
# return (torch.mean(dst) - dst) / (torch.amax(dst) - torch.amin(dst)) * 2
return -dst / 2
# def encode_class(self, hs):
# cs = []
# for h in hs:
# c = h @ self.cluster_centers.T
# cs.append(c)
# return cs
def decode(self, hs):
xs = []
for g, h in enumerate(hs):
if self.args.McDecoder:
h = torch.cat(hs, dim=1)
if len(h) != 0:
if len(h) == 1:
h = torch.concat([h, h])
h = self.decoder_linear[g if self.args.GroupWiseLayer[3] == '1' else 0](h)
h = self.decoder[g if self.args.GroupWiseLayer[4] == '1' else 0](h)
h = self.decoder_adaption[g if self.args.GroupWiseLayer[5] == '1' else 0](h)
if len(h) == 1:
h = h[[0]]
else:
h = torch.zeros([0, self.in_dims[g]], device=torch.device('cuda:0'))
xs.append(h)
return xs
def run(self, epochs, train_dataloader, test_dataloader, args):
# if args.loss_self_cons:
# clusters = get_clusters(args=args)
optimizer_g = torch.optim.Adam(
self.parameters(),
lr=args.LearnRate,
betas=(args.betas_a, args.betas_v),
weight_decay=args.WeightDecay
)
mse_loss = nn.MSELoss().cuda()
timer_all = TimeOperator.Timer()
timer_train = TimeOperator.Timer()
timer_save = TimeOperator.Timer()
ce_loss = nn.CrossEntropyLoss().cuda()
type_detail_shown = False
start_epoch = 0
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
# if args.gpu is None:
# checkpoint = torch.load(args.resume)
# else:
# # Map model to be loaded to specified single gpu.
# loc = 'cuda:{}'.format(args.gpu)
# checkpoint = torch.load(args.resume, map_location=loc)
start_epoch = checkpoint['epoch']
self.load_state_dict(checkpoint['state_dict'])
optimizer_g.load_state_dict(checkpoint['optimizer']['optimizer_g'])
# self.__dict__ = checkpoint['self_dic']
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
# self.args = args
# warnings.warn('This is not equal to start from the beginning due to different rands states.')
#
else:
raise NotImplementedError("=> no checkpoint found at '{}'".format(args.resume))
if args.CodeTest:
args.train_epoch = start_epoch + 1
epochs = start_epoch + 1
best_acc = 0
for epoch in range(start_epoch, epochs):
if (epoch + 1) <= args.LearnRateWarm:
lr = args.LearnRate * (epoch + 1) / args.LearnRateWarm
else:
if args.LearnRateDecayType == 'None':
lr = args.LearnRate
elif args.LearnRateDecayType == 'Exp':
lr = args.LearnRate * ((1 + 10 * (epoch + 1 - args.LearnRateWarm) / (
args.train_epoch - args.LearnRateWarm)) ** -0.75)
elif args.LearnRateDecayType == 'Cosine':
lr = args.LearnRate * 0.5 * (1. + math.cos(
math.pi * (epoch + 1 - args.LearnRateWarm) / (args.train_epoch - args.LearnRateWarm)))
else:
raise NotImplementedError('args.LearnRateDecayType')
if lr != args.LearnRate:
def adjust_learning_rate(optimizer):
print('adjust_learning_rate: {}'.format(lr))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
adjust_learning_rate(optimizer_g)
timer_all_time = time.time()
# inf_t = time.time()
# print('start epoch {}'.format(epoch))
self.eval()
feature_vec, type_vec, group_vec = [], [], []
feature_vec_cluster = []
group_vec_cluster = []
feature_vec_classification = []
type_vec_cluster = []
data_vec = []
is_pair_all = []
timer_infer_data = TimeOperator.Timer()
rnmse_vec = [[], []] # mask = 0 1
with torch.no_grad():
inf_data_t = time.time()
for (fea0, fea1, class_labels0, class_labels1, mask, is_pair, index) in test_dataloader:
timer_infer_data.update(time.time() - inf_data_t)
# timer_infer_data.show(prefix='InferDataTime', total_count=len(test_dataloader),
# print_end_time=False)
fea0 = fea0.cuda()
fea1 = fea1.cuda()
if args.Rev:
h1, h0 = self.encode([fea0, fea1])
if args.SingleView != -1:
for v in range(len(mask[0])):
if v != 1 - args.SingleView:
mask[:, v] = 0
else:
h0, h1 = self.encode([fea0, fea1])
if args.SingleView != -1:
for v in range(len(mask[0])):
if v != args.SingleView:
mask[:, v] = 0
cluster_h0 = h0[mask[:, 0] == 1]
cluster_h1 = h1[mask[:, 1] == 1]
# if args.SingleView != -1:
# mask[:, args.SingleView] = 0
# # if args.SingleView == 0:
# # cluster_h1 = cluster_h1[[]]
# # class_labels1 = class_labels1[[]]
# # elif args.SingleView == 1:
# # class_labels0 = class_labels0[[]]
# # cluster_h0 = cluster_h0[[]]
# # else:
# # raise NotImplementedError('')
is_pair_all.extend(is_pair)
feature_vec_cluster.extend(torch.cat([cluster_h0, cluster_h1]).detach().cpu().numpy())
group_vec_cluster.extend(torch.concat((torch.zeros(len(cluster_h0), dtype=torch.int),
torch.ones(len(cluster_h1), dtype=torch.int))).numpy())
type_vec_cluster.extend(torch.concat((class_labels0[mask[:, 0] == 1],
class_labels1[mask[:, 1] == 1])).numpy())
feature_vec_classification.extend(torch.cat([h0, h1]).detach().cpu().numpy())
if (epoch + 1) == epochs or (epoch + 1) % args.VisualFreq == 0:
if torch.sum(torch.logical_not(torch.logical_or(mask[:, 1], mask[:, 0]))):
raise NotImplementedError('存在一个pair两个模态都缺失')
if args.reFill == 'Copy':
if torch.sum(mask[:, 0] == 0):
h0[mask[:, 0] == 0] = h1[mask[:, 0] == 0]
if torch.sum(mask[:, 1] == 0):
h1[mask[:, 1] == 0] = h0[mask[:, 1] == 0]
elif args.reFill == 'Center':
# raise NotImplementedError('')
if self.pred_center_cac is None:
pass
warnings.warn('self.pred_center_cac == None')
else:
centors = torch.zeros((len(mask), 2, len(self.pred_center_cac[0]))).cuda()
centors[mask[:, 0] == 1, 0] = self.pred_center_cac[
self.pred_cac[:torch.sum(mask[:, 0] == 1)]]
centors[mask[:, 1] == 1, 1] = self.pred_center_cac[
self.pred_cac[torch.sum(mask[:, 0] == 1):]]
if torch.sum(mask[:, 0] == 0):
h0[mask[:, 0] == 0] = centors[mask[:, 0] == 0, 1]
if torch.sum(mask[:, 1] == 0):
h1[mask[:, 1] == 0] = centors[mask[:, 1] == 0, 0]
elif args.reFill == 'KnnMapMean':
if torch.sum(mask[:, 0] == 0):
nearest = get_nearest_k(h1[mask[:, 0] == 0], h1[is_pair], args.reAlignK)
h0p = h0[is_pair]
h1[mask[:, 0] == 0] = torch.cat([torch.mean(h0p[ns], dim=0) for ns in nearest])
if torch.sum(mask[:, 1] == 0):
nearest = get_nearest_k(h0[mask[:, 1] == 0], h0[is_pair], args.reAlignK)
h1p = h1[is_pair]
h1[mask[:, 1] == 0] = torch.cat([torch.mean(h1p[ns], dim=0) for ns in nearest])
# raise NotImplementedError('')
elif args.reFill == 'KnnMean':
# 关联对齐, xi1 不变, xi2替换成离xi1最近的k个view2的点的mean
if torch.sum(mask[:, 1] == 0):
hs0 = h0[mask[:, 1] == 0]
he1 = h1[mask[:, 1] == 1]
nearest = get_nearest_k(hs0, he1, args.reAlignK)
# nearest = torch.argsort(torch.cdist(hs0.cpu(), he1.cpu()), dim=1)[:, :args.reAlignK]
h1[mask[:, 1] == 0] = torch.cat([torch.mean(he1[ns], dim=0) for ns in nearest])
# class_labels1[mask[:, 1] == 0] = class_labels1[mask[:, 1] == 1][nearest[:, 0]]
if torch.sum(mask[:, 0] == 0):
hs1 = h1[mask[:, 0] == 0]
he0 = h0[mask[:, 0] == 1]
nearest = get_nearest_k(hs1, he0, args.reAlignK)
# nearest = torch.argsort(torch.cdist(hs1.cpu(), he0.cpu()), dim=1)[:, :args.reAlignK]
h0[mask[:, 0] == 0] = torch.cat([torch.mean(he0[ns], dim=0) for ns in nearest])
# class_labels0[mask[:, 0] == 0] = class_labels0[mask[:, 0] == 1][nearest[:, 0]]
###############################################################
# 缺失补全, xi2 = mean(离xi1最近的k个view2的点)
# fill_num = k
# C = euclidean_dist(h0, h1)
# row_idx = C.argsort()
# col_idx = (C.t()).argsort()
# # Mij denotes the flag of i-th sample in view 0 and j-th sample in view 1
# M = torch.logical_and((mask[:, 0].repeat(test_num, 1)).t(), mask[:, 1].repeat(test_num, 1))
# for i in range(test_num):
# idx0 = col_idx[i, :][
# M[col_idx[i, :], i]] # idx for view 0 to sort and find the non-missing neighbors
# idx1 = row_idx[i, :][
# M[i, row_idx[i, :]]] # idx for view 1 to sort and find the non-missing neighbors
# if len(idx1) != 0 and len(idx0) == 0: # i-th sample in view 1 is missing
# avg_fill = h1[idx1[0:fill_num], :].sum(dim=0) / fill_num
# cnt += (class_labels1[idx1[0:fill_num]] == class_labels1[i]).sum()
# missing_cnt += 1
# recover_out0[i, :] = h0[i, :]
# recover_out1[i, :] = avg_fill # missing
# elif len(idx0) != 0 and len(idx1) == 0:
# avg_fill = h0[idx0[0:fill_num], :].sum(dim=0) / fill_num
# cnt += (class_labels0[idx0[0:fill_num]] == class_labels0[i]).sum()
# missing_cnt += 1
# recover_out0[i, :] = avg_fill # missing
# recover_out1[i, :] = h1[i, :]
# elif len(idx0) != 0 and len(idx1) != 0:
# recover_out0[i, :] = h0[i, :]
# recover_out1[i, :] = h1[i, :]
# else:
# raise Exception('error')
# if setting == 1:
# align_out0.extend((recover_out0.cpu()).numpy())
# align_out1.extend((recover_out1.cpu()).numpy())
# continue
#
else:
raise NotImplementedError('')
to_realign = torch.logical_and(is_pair == 0, torch.logical_and(mask[:, 1], mask[:, 0]))
if args.reAlign == 'KnnMean':
# 关联对齐, xi1 不变, xi2替换成离xi1最近的k个view2的点的mean
if torch.sum(to_realign):
ha1 = h1[to_realign]
nearest = get_nearest_k(h0[to_realign], ha1, args.reAlignK)
# dist = torch.cdist(h0[to_realign].cpu(), ha1.cpu())
# nearest = torch.argsort(dist, dim=1)[:, :args.reAlignK]
h1[to_realign] = torch.cat([torch.mean(ha1[ns], dim=0) for ns in nearest])
# class_labels1[is_pair == 0] = class_labels1[is_pair == 0][nearest[:, 0]]
elif args.reAlign == 'Copy':
if torch.sum(to_realign):
h1[to_realign] = h0[to_realign]
# class_labels1[is_pair == 0] = class_labels0[is_pair == 0]
elif args.reAlign == 'KnnMapMean':
if torch.sum(to_realign):
targ_v1 = h1[is_pair]
nearest = get_nearest_k(h0[to_realign], h0[is_pair], args.reAlignK)
h1[to_realign] = torch.cat([torch.mean(targ_v1[ns], dim=0) for ns in nearest])
# class_labels1[is_pair == 0] = ...
elif args.reAlign == 'Ignore':
pass
else:
raise NotImplementedError('')
if args.Rev:
fea0_rec, fea1_rec = self.decode([h1, h0])
else:
fea0_rec, fea1_rec = self.decode([h0, h1])
# if len(fea0_rec[0]) == len(fea1_rec[0]):
# fea_rec = torch.concat([fea0_rec, fea1_rec])
# fea = torch.concat([fea0, fea1])
# mask_c = torch.concat([mask[:, 0], mask[:, 1]])
# if torch.sum(mask_c == 0):
# rnmse_vec[0].extend(
# evaluate.get_rnmse(xs_hat=fea_rec[mask_c == 0], xs=fea[mask_c == 0]).cpu().numpy())
# if torch.sum(mask_c == 1):
# rnmse_vec[1].extend(
# evaluate.get_rnmse(xs_hat=fea_rec[mask_c == 1], xs=fea[mask_c == 1]).cpu().numpy())
# else:
# if torch.sum(mask == 0):
# n0_v0 = evaluate.get_rnmse(
# xs_hat=fea0_rec[mask[:, 0] == 0], xs=fea0[mask[:, 0] == 0]).cpu().numpy()
# n0_v1 = evaluate.get_rnmse(
# xs_hat=fea1_rec[mask[:, 1] == 0], xs=fea1[mask[:, 1] == 0]).cpu().numpy()
# rnmse_vec[0].extend(n0_v0)
# rnmse_vec[0].extend(n0_v1)
# if torch.sum(mask == 1):
# n1_v0 = evaluate.get_rnmse(
# xs_hat=fea0_rec[mask[:, 0] == 1], xs=fea0[mask[:, 0] == 1]).cpu().numpy()
# n1_v1 = evaluate.get_rnmse(
# xs_hat=fea1_rec[mask[:, 1] == 1], xs=fea1[mask[:, 1] == 1]).cpu().numpy()
# rnmse_vec[1].extend(n1_v0)
# rnmse_vec[1].extend(n1_v1)
g = torch.concat((torch.zeros(len(fea0), device=fea0.device, dtype=torch.int),
torch.ones(len(fea1), device=fea0.device, dtype=torch.int)))
h = torch.cat([h0, h1]).detach().cpu().numpy()
feature_vec.extend(h)
data_vec.extend(torch.cat([fea0, fea1]).detach().cpu().numpy())
group_vec.extend(g.cpu().numpy())
type_vec.extend(torch.concat((class_labels0, class_labels1)).numpy())
inf_data_t = time.time()
feature_vec = np.array(feature_vec)
data_vec = np.array(data_vec)
feature_vec_cluster = np.array(feature_vec_cluster)
is_pair_all = np.array(is_pair_all)
feature_vec_classification = np.array(feature_vec_classification)
group_vec = np.array(group_vec)
group_vec_cluster = np.array(group_vec_cluster)
type_vec = np.array(type_vec)
type_vec_cluster = np.array(type_vec_cluster)
rnmse_vec[0] = np.array(rnmse_vec[0])
rnmse_vec[1] = np.array(rnmse_vec[1])
kmeans_time = TimeOperator.Timer()
if args.ShowReconstruct:
if args.dataset == 'MNISTUSPS':
dims = [np.product(d.data.shape[1:]) for d in test_dataloader.dataset.datasets]
data_list = [np.asarray(it.data, dtype=np.float32) for it in test_dataloader.dataset.datasets]
Y = test_dataloader.dataset.datasets[0].targets
else:
dims = [d.shape[1] for d in test_dataloader.dataset.data]
data_list = [np.asarray(it, dtype=np.float32) for it in test_dataloader.dataset.data]
Y = test_dataloader.dataset.class_labels0
mask = test_dataloader.dataset.mask
n_per_cat = 10
rec0, rec1 = self.decode([
torch.from_numpy(feature_vec[group_vec == 0]).cuda(),
torch.from_numpy(feature_vec[group_vec == 1]).cuda()])
rec0 = rec0.detach().cpu().numpy()
rec1 = rec1.detach().cpu().numpy()
show_img = np.asarray([])
inds_map = np.asarray([])
for v in range(2):
col = np.asarray([])
inds_map_col = np.asarray([])
for y in range(10):
inds = np.arange(len(Y))[
np.logical_and(np.logical_and(mask[:, v] == 1, mask[:, 1 - v] == 0), Y == y)
]
np.random.shuffle(inds)
assert len(inds) >= n_per_cat
inds = inds[:n_per_cat]
raw_imgs = data_list[v][inds]
missing_imgs = data_list[1 - v][inds]
rec_imgs = [rec0, rec1][v][inds]
rec_imgs_miss = [rec0, rec1][1 - v][inds]
pack = np.asarray(
[raw_imgs, rec_imgs, missing_imgs, rec_imgs_miss]).reshape([-1, n_per_cat, 28, 28])
if len(col):
col = np.concatenate([col, pack], axis=0)
else:
col = pack
if len(inds_map_col):
inds_map_col = np.concatenate([inds_map_col, inds.reshape([1, -1])], axis=0)
else:
inds_map_col = inds.reshape([1, -1])
if len(show_img):
show_img = np.concatenate([show_img, col], axis=1)
else:
show_img = col
if len(inds_map):
inds_map = np.concatenate([inds_map, inds_map_col], axis=1)
else:
inds_map = inds_map_col
plot_heat_map(inds_map, show=True, fig_path='/xlearning/pengxin/Temp/MissingRecIM.svg')
| visualize_image(show_img, show=True, fig_path='/xlearning/pengxin/Temp/MissingRec.svg')
| 6 | 2023-12-21 08:50:36+00:00 | 16k |
Azure-Samples/functions-python-web-crawler | .venv/Lib/site-packages/charset_normalizer/cd.py | [
{
"identifier": "FREQUENCIES",
"path": ".venv/Lib/site-packages/charset_normalizer/constant.py",
"snippet": "FREQUENCIES: Dict[str, List[str]] = {\n \"English\": [\n \"e\",\n \"a\",\n \"t\",\n \"i\",\n \"o\",\n \"n\",\n \"s\",\n \"r\",\n ... | import importlib
from codecs import IncrementalDecoder
from collections import Counter
from functools import lru_cache
from typing import Counter as TypeCounter, Dict, List, Optional, Tuple
from .constant import (
FREQUENCIES,
KO_NAMES,
LANGUAGE_SUPPORTED_COUNT,
TOO_SMALL_SEQUENCE,
ZH_NAMES,
)
from .md import is_suspiciously_successive_range
from .models import CoherenceMatches
from .utils import (
is_accentuated,
is_latin,
is_multi_byte_encoding,
is_unicode_range_secondary,
unicode_range,
) | 11,400 |
def characters_popularity_compare(
language: str, ordered_characters: List[str]
) -> float:
"""
Determine if a ordered characters list (by occurrence from most appearance to rarest) match a particular language.
The result is a ratio between 0. (absolutely no correspondence) and 1. (near perfect fit).
Beware that is function is not strict on the match in order to ease the detection. (Meaning close match is 1.)
"""
if language not in FREQUENCIES:
raise ValueError("{} not available".format(language))
character_approved_count: int = 0
FREQUENCIES_language_set = set(FREQUENCIES[language])
ordered_characters_count: int = len(ordered_characters)
target_language_characters_count: int = len(FREQUENCIES[language])
large_alphabet: bool = target_language_characters_count > 26
for character, character_rank in zip(
ordered_characters, range(0, ordered_characters_count)
):
if character not in FREQUENCIES_language_set:
continue
character_rank_in_language: int = FREQUENCIES[language].index(character)
expected_projection_ratio: float = (
target_language_characters_count / ordered_characters_count
)
character_rank_projection: int = int(character_rank * expected_projection_ratio)
if (
large_alphabet is False
and abs(character_rank_projection - character_rank_in_language) > 4
):
continue
if (
large_alphabet is True
and abs(character_rank_projection - character_rank_in_language)
< target_language_characters_count / 3
):
character_approved_count += 1
continue
characters_before_source: List[str] = FREQUENCIES[language][
0:character_rank_in_language
]
characters_after_source: List[str] = FREQUENCIES[language][
character_rank_in_language:
]
characters_before: List[str] = ordered_characters[0:character_rank]
characters_after: List[str] = ordered_characters[character_rank:]
before_match_count: int = len(
set(characters_before) & set(characters_before_source)
)
after_match_count: int = len(
set(characters_after) & set(characters_after_source)
)
if len(characters_before_source) == 0 and before_match_count <= 4:
character_approved_count += 1
continue
if len(characters_after_source) == 0 and after_match_count <= 4:
character_approved_count += 1
continue
if (
before_match_count / len(characters_before_source) >= 0.4
or after_match_count / len(characters_after_source) >= 0.4
):
character_approved_count += 1
continue
return character_approved_count / len(ordered_characters)
def alpha_unicode_split(decoded_sequence: str) -> List[str]:
"""
Given a decoded text sequence, return a list of str. Unicode range / alphabet separation.
Ex. a text containing English/Latin with a bit a Hebrew will return two items in the resulting list;
One containing the latin letters and the other hebrew.
"""
layers: Dict[str, str] = {}
for character in decoded_sequence:
if character.isalpha() is False:
continue
character_range: Optional[str] = unicode_range(character)
if character_range is None:
continue
layer_target_range: Optional[str] = None
for discovered_range in layers:
if (
is_suspiciously_successive_range(discovered_range, character_range)
is False
):
layer_target_range = discovered_range
break
if layer_target_range is None:
layer_target_range = character_range
if layer_target_range not in layers:
layers[layer_target_range] = character.lower()
continue
layers[layer_target_range] += character.lower()
return list(layers.values())
|
def encoding_unicode_range(iana_name: str) -> List[str]:
"""
Return associated unicode ranges in a single byte code page.
"""
if is_multi_byte_encoding(iana_name):
raise IOError("Function not supported on multi-byte code page")
decoder = importlib.import_module(
"encodings.{}".format(iana_name)
).IncrementalDecoder
p: IncrementalDecoder = decoder(errors="ignore")
seen_ranges: Dict[str, int] = {}
character_count: int = 0
for i in range(0x40, 0xFF):
chunk: str = p.decode(bytes([i]))
if chunk:
character_range: Optional[str] = unicode_range(chunk)
if character_range is None:
continue
if is_unicode_range_secondary(character_range) is False:
if character_range not in seen_ranges:
seen_ranges[character_range] = 0
seen_ranges[character_range] += 1
character_count += 1
return sorted(
[
character_range
for character_range in seen_ranges
if seen_ranges[character_range] / character_count >= 0.15
]
)
def unicode_range_languages(primary_range: str) -> List[str]:
"""
Return inferred languages used with a unicode range.
"""
languages: List[str] = []
for language, characters in FREQUENCIES.items():
for character in characters:
if unicode_range(character) == primary_range:
languages.append(language)
break
return languages
@lru_cache()
def encoding_languages(iana_name: str) -> List[str]:
"""
Single-byte encoding language association. Some code page are heavily linked to particular language(s).
This function does the correspondence.
"""
unicode_ranges: List[str] = encoding_unicode_range(iana_name)
primary_range: Optional[str] = None
for specified_range in unicode_ranges:
if "Latin" not in specified_range:
primary_range = specified_range
break
if primary_range is None:
return ["Latin Based"]
return unicode_range_languages(primary_range)
@lru_cache()
def mb_encoding_languages(iana_name: str) -> List[str]:
"""
Multi-byte encoding language association. Some code page are heavily linked to particular language(s).
This function does the correspondence.
"""
if (
iana_name.startswith("shift_")
or iana_name.startswith("iso2022_jp")
or iana_name.startswith("euc_j")
or iana_name == "cp932"
):
return ["Japanese"]
if iana_name.startswith("gb") or iana_name in ZH_NAMES:
return ["Chinese"]
if iana_name.startswith("iso2022_kr") or iana_name in KO_NAMES:
return ["Korean"]
return []
@lru_cache(maxsize=LANGUAGE_SUPPORTED_COUNT)
def get_target_features(language: str) -> Tuple[bool, bool]:
"""
Determine main aspects from a supported language if it contains accents and if is pure Latin.
"""
target_have_accents: bool = False
target_pure_latin: bool = True
for character in FREQUENCIES[language]:
if not target_have_accents and is_accentuated(character):
target_have_accents = True
if target_pure_latin and is_latin(character) is False:
target_pure_latin = False
return target_have_accents, target_pure_latin
def alphabet_languages(
characters: List[str], ignore_non_latin: bool = False
) -> List[str]:
"""
Return associated languages associated to given characters.
"""
languages: List[Tuple[str, float]] = []
source_have_accents = any(is_accentuated(character) for character in characters)
for language, language_characters in FREQUENCIES.items():
target_have_accents, target_pure_latin = get_target_features(language)
if ignore_non_latin and target_pure_latin is False:
continue
if target_have_accents is False and source_have_accents:
continue
character_count: int = len(language_characters)
character_match_count: int = len(
[c for c in language_characters if c in characters]
)
ratio: float = character_match_count / character_count
if ratio >= 0.2:
languages.append((language, ratio))
languages = sorted(languages, key=lambda x: x[1], reverse=True)
return [compatible_language[0] for compatible_language in languages]
def characters_popularity_compare(
language: str, ordered_characters: List[str]
) -> float:
"""
Determine if a ordered characters list (by occurrence from most appearance to rarest) match a particular language.
The result is a ratio between 0. (absolutely no correspondence) and 1. (near perfect fit).
Beware that is function is not strict on the match in order to ease the detection. (Meaning close match is 1.)
"""
if language not in FREQUENCIES:
raise ValueError("{} not available".format(language))
character_approved_count: int = 0
FREQUENCIES_language_set = set(FREQUENCIES[language])
ordered_characters_count: int = len(ordered_characters)
target_language_characters_count: int = len(FREQUENCIES[language])
large_alphabet: bool = target_language_characters_count > 26
for character, character_rank in zip(
ordered_characters, range(0, ordered_characters_count)
):
if character not in FREQUENCIES_language_set:
continue
character_rank_in_language: int = FREQUENCIES[language].index(character)
expected_projection_ratio: float = (
target_language_characters_count / ordered_characters_count
)
character_rank_projection: int = int(character_rank * expected_projection_ratio)
if (
large_alphabet is False
and abs(character_rank_projection - character_rank_in_language) > 4
):
continue
if (
large_alphabet is True
and abs(character_rank_projection - character_rank_in_language)
< target_language_characters_count / 3
):
character_approved_count += 1
continue
characters_before_source: List[str] = FREQUENCIES[language][
0:character_rank_in_language
]
characters_after_source: List[str] = FREQUENCIES[language][
character_rank_in_language:
]
characters_before: List[str] = ordered_characters[0:character_rank]
characters_after: List[str] = ordered_characters[character_rank:]
before_match_count: int = len(
set(characters_before) & set(characters_before_source)
)
after_match_count: int = len(
set(characters_after) & set(characters_after_source)
)
if len(characters_before_source) == 0 and before_match_count <= 4:
character_approved_count += 1
continue
if len(characters_after_source) == 0 and after_match_count <= 4:
character_approved_count += 1
continue
if (
before_match_count / len(characters_before_source) >= 0.4
or after_match_count / len(characters_after_source) >= 0.4
):
character_approved_count += 1
continue
return character_approved_count / len(ordered_characters)
def alpha_unicode_split(decoded_sequence: str) -> List[str]:
"""
Given a decoded text sequence, return a list of str. Unicode range / alphabet separation.
Ex. a text containing English/Latin with a bit a Hebrew will return two items in the resulting list;
One containing the latin letters and the other hebrew.
"""
layers: Dict[str, str] = {}
for character in decoded_sequence:
if character.isalpha() is False:
continue
character_range: Optional[str] = unicode_range(character)
if character_range is None:
continue
layer_target_range: Optional[str] = None
for discovered_range in layers:
if (
is_suspiciously_successive_range(discovered_range, character_range)
is False
):
layer_target_range = discovered_range
break
if layer_target_range is None:
layer_target_range = character_range
if layer_target_range not in layers:
layers[layer_target_range] = character.lower()
continue
layers[layer_target_range] += character.lower()
return list(layers.values())
| def merge_coherence_ratios(results: List[CoherenceMatches]) -> CoherenceMatches: | 6 | 2023-12-16 04:12:01+00:00 | 16k |
YaoFANGUK/video-subtitle-remover | backend/scenedetect/scene_manager.py | [
{
"identifier": "SimpleTableCell",
"path": "backend/scenedetect/_thirdparty/simpletable.py",
"snippet": "class SimpleTableCell(object):\n \"\"\"A table class to create table cells.\n\n Example:\n cell = SimpleTableCell('Hello, world!')\n \"\"\"\n\n def __init__(self, text, header=False):\... | import csv
import threading
import queue
import logging
import math
import sys
import cv2
import numpy as np
from enum import Enum
from typing import Iterable, List, Tuple, Optional, Dict, Callable, Union, TextIO
from backend.scenedetect._thirdparty.simpletable import (SimpleTableCell, SimpleTableImage, SimpleTableRow,
SimpleTable, HTMLPage)
from backend.scenedetect.platform import (tqdm, get_and_create_path, get_cv2_imwrite_params, Template)
from backend.scenedetect.frame_timecode import FrameTimecode
from backend.scenedetect.video_stream import VideoStream
from backend.scenedetect.scene_detector import SceneDetector, SparseSceneDetector
from backend.scenedetect.stats_manager import StatsManager, FrameMetricRegistered | 14,375 | csv_writer = csv.writer(output_csv_file, lineterminator='\n')
# If required, output the cutting list as the first row (i.e. before the header row).
if include_cut_list:
csv_writer.writerow(
["Timecode List:"] +
cut_list if cut_list else [start.get_timecode() for start, _ in scene_list[1:]])
csv_writer.writerow([
"Scene Number", "Start Frame", "Start Timecode", "Start Time (seconds)", "End Frame",
"End Timecode", "End Time (seconds)", "Length (frames)", "Length (timecode)",
"Length (seconds)"
])
for i, (start, end) in enumerate(scene_list):
duration = end - start
csv_writer.writerow([
'%d' % (i + 1),
'%d' % (start.get_frames() + 1),
start.get_timecode(),
'%.3f' % start.get_seconds(),
'%d' % end.get_frames(),
end.get_timecode(),
'%.3f' % end.get_seconds(),
'%d' % duration.get_frames(),
duration.get_timecode(),
'%.3f' % duration.get_seconds()
])
def write_scene_list_html(output_html_filename,
scene_list,
cut_list=None,
css=None,
css_class='mytable',
image_filenames=None,
image_width=None,
image_height=None):
"""Writes the given list of scenes to an output file handle in html format.
Arguments:
output_html_filename: filename of output html file
scene_list: List of pairs of FrameTimecodes denoting each scene's start/end FrameTimecode.
cut_list: Optional list of FrameTimecode objects denoting the cut list (i.e. the frames
in the video that need to be split to generate individual scenes). If not passed,
the start times of each scene (besides the 0th scene) is used instead.
css: String containing all the css information for the resulting html page.
css_class: String containing the named css class
image_filenames: dict where key i contains a list with n elements (filenames of
the n saved images from that scene)
image_width: Optional desired width of images in table in pixels
image_height: Optional desired height of images in table in pixels
"""
if not css:
css = """
table.mytable {
font-family: times;
font-size:12px;
color:#000000;
border-width: 1px;
border-color: #eeeeee;
border-collapse: collapse;
background-color: #ffffff;
width=100%;
max-width:550px;
table-layout:fixed;
}
table.mytable th {
border-width: 1px;
padding: 8px;
border-style: solid;
border-color: #eeeeee;
background-color: #e6eed6;
color:#000000;
}
table.mytable td {
border-width: 1px;
padding: 8px;
border-style: solid;
border-color: #eeeeee;
}
#code {
display:inline;
font-family: courier;
color: #3d9400;
}
#string {
display:inline;
font-weight: bold;
}
"""
# Output Timecode list
timecode_table = SimpleTable(
[["Timecode List:"] +
(cut_list if cut_list else [start.get_timecode() for start, _ in scene_list[1:]])],
css_class=css_class)
# Output list of scenes
header_row = [
"Scene Number", "Start Frame", "Start Timecode", "Start Time (seconds)", "End Frame",
"End Timecode", "End Time (seconds)", "Length (frames)", "Length (timecode)",
"Length (seconds)"
]
for i, (start, end) in enumerate(scene_list):
duration = end - start
row = SimpleTableRow([
'%d' % (i + 1),
'%d' % (start.get_frames() + 1),
start.get_timecode(),
'%.3f' % start.get_seconds(),
'%d' % end.get_frames(),
end.get_timecode(),
'%.3f' % end.get_seconds(),
'%d' % duration.get_frames(),
duration.get_timecode(),
'%.3f' % duration.get_seconds()
])
if image_filenames:
for image in image_filenames[i]:
row.add_cell(
| # -*- coding: utf-8 -*-
#
# PySceneDetect: Python-Based Video Scene Detector
# -------------------------------------------------------------------
# [ Site: https://scenedetect.com ]
# [ Docs: https://scenedetect.com/docs/ ]
# [ Github: https://github.com/Breakthrough/PySceneDetect/ ]
#
# Copyright (C) 2014-2023 Brandon Castellano <http://www.bcastell.com>.
# PySceneDetect is licensed under the BSD 3-Clause License; see the
# included LICENSE file, or visit one of the above pages for details.
#
"""``scenedetect.scene_manager`` Module
This module implements :class:`SceneManager`, coordinates running a
:mod:`SceneDetector <scenedetect.detectors>` over the frames of a video
(:mod:`VideoStream <scenedetect.video_stream>`). Video decoding is done in a separate thread to
improve performance.
This module also contains other helper functions (e.g. :func:`save_images`) which can be used to
process the resulting scene list.
===============================================================
Usage
===============================================================
The following example shows basic usage of a :class:`SceneManager`:
.. code:: python
from scenedetect import open_video, SceneManager, ContentDetector
video = open_video(video_path)
scene_manager = SceneManager()
scene_manager.add_detector(ContentDetector())
# Detect all scenes in video from current position to end.
scene_manager.detect_scenes(video)
# `get_scene_list` returns a list of start/end timecode pairs
# for each scene that was found.
scenes = scene_manager.get_scene_list()
An optional callback can also be invoked on each detected scene, for example:
.. code:: python
from scenedetect import open_video, SceneManager, ContentDetector
# Callback to invoke on the first frame of every new scene detection.
def on_new_scene(frame_img: numpy.ndarray, frame_num: int):
print("New scene found at frame %d." % frame_num)
video = open_video(test_video_file)
scene_manager = SceneManager()
scene_manager.add_detector(ContentDetector())
scene_manager.detect_scenes(video=video, callback=on_new_scene)
To use a `SceneManager` with a webcam/device or existing `cv2.VideoCapture` device, use the
:class:`VideoCaptureAdapter <scenedetect.backends.opencv.VideoCaptureAdapter>` instead of
`open_video`.
=======================================================================
Storing Per-Frame Statistics
=======================================================================
`SceneManager` can use an optional
:class:`StatsManager <scenedetect.stats_manager.StatsManager>` to save frame statistics to disk:
.. code:: python
from scenedetect import open_video, ContentDetector, SceneManager, StatsManager
video = open_video(test_video_file)
scene_manager = SceneManager(stats_manager=StatsManager())
scene_manager.add_detector(ContentDetector())
scene_manager.detect_scenes(video=video)
scene_list = scene_manager.get_scene_list()
print_scenes(scene_list=scene_list)
# Save per-frame statistics to disk.
scene_manager.stats_manager.save_to_csv(csv_file=STATS_FILE_PATH)
The statsfile can be used to find a better threshold for certain inputs, or perform statistical
analysis of the video.
"""
logger = logging.getLogger('pyscenedetect')
# TODO: This value can and should be tuned for performance improvements as much as possible,
# until accuracy falls, on a large enough dataset. This has yet to be done, but the current
# value doesn't seem to have caused any issues at least.
DEFAULT_MIN_WIDTH: int = 256
"""The default minimum width a frame will be downscaled to when calculating a downscale factor."""
MAX_FRAME_QUEUE_LENGTH: int = 4
"""Maximum number of decoded frames which can be buffered while waiting to be processed."""
PROGRESS_BAR_DESCRIPTION = 'Detected: %d | Progress'
"""Template to use for progress bar."""
class Interpolation(Enum):
"""Interpolation method used for image resizing. Based on constants defined in OpenCV."""
NEAREST = cv2.INTER_NEAREST
"""Nearest neighbor interpolation."""
LINEAR = cv2.INTER_LINEAR
"""Bilinear interpolation."""
CUBIC = cv2.INTER_CUBIC
"""Bicubic interpolation."""
AREA = cv2.INTER_AREA
"""Pixel area relation resampling. Provides moire'-free downscaling."""
LANCZOS4 = cv2.INTER_LANCZOS4
"""Lanczos interpolation over 8x8 neighborhood."""
def compute_downscale_factor(frame_width: int, effective_width: int = DEFAULT_MIN_WIDTH) -> int:
"""Get the optimal default downscale factor based on a video's resolution (currently only
the width in pixels is considered).
The resulting effective width of the video will be between frame_width and 1.5 * frame_width
pixels (e.g. if frame_width is 200, the range of effective widths will be between 200 and 300).
Arguments:
frame_width: Actual width of the video frame in pixels.
effective_width: Desired minimum width in pixels.
Returns:
int: The default downscale factor to use to achieve at least the target effective_width.
"""
assert not (frame_width < 1 or effective_width < 1)
if frame_width < effective_width:
return 1
return frame_width // effective_width
def get_scenes_from_cuts(
cut_list: Iterable[FrameTimecode],
start_pos: Union[int, FrameTimecode],
end_pos: Union[int, FrameTimecode],
base_timecode: Optional[FrameTimecode] = None,
) -> List[Tuple[FrameTimecode, FrameTimecode]]:
"""Returns a list of tuples of start/end FrameTimecodes for each scene based on a
list of detected scene cuts/breaks.
This function is called when using the :meth:`SceneManager.get_scene_list` method.
The scene list is generated from a cutting list (:meth:`SceneManager.get_cut_list`),
noting that each scene is contiguous, starting from the first to last frame of the input.
If `cut_list` is empty, the resulting scene will span from `start_pos` to `end_pos`.
Arguments:
cut_list: List of FrameTimecode objects where scene cuts/breaks occur.
base_timecode: The base_timecode of which all FrameTimecodes in the cut_list are based on.
num_frames: The number of frames, or FrameTimecode representing duration, of the video that
was processed (used to generate last scene's end time).
start_frame: The start frame or FrameTimecode of the cut list. Used to generate the first
scene's start time.
base_timecode: [DEPRECATED] DO NOT USE. For backwards compatibility only.
Returns:
List of tuples in the form (start_time, end_time), where both start_time and
end_time are FrameTimecode objects representing the exact time/frame where each
scene occupies based on the input cut_list.
"""
# TODO(v0.7): Use the warnings module to turn this into a warning.
if base_timecode is not None:
logger.error('`base_timecode` argument is deprecated has no effect.')
# Scene list, where scenes are tuples of (Start FrameTimecode, End FrameTimecode).
scene_list = []
if not cut_list:
scene_list.append((start_pos, end_pos))
return scene_list
# Initialize last_cut to the first frame we processed,as it will be
# the start timecode for the first scene in the list.
last_cut = start_pos
for cut in cut_list:
scene_list.append((last_cut, cut))
last_cut = cut
# Last scene is from last cut to end of video.
scene_list.append((last_cut, end_pos))
return scene_list
def write_scene_list(output_csv_file: TextIO,
scene_list: Iterable[Tuple[FrameTimecode, FrameTimecode]],
include_cut_list: bool = True,
cut_list: Optional[Iterable[FrameTimecode]] = None) -> None:
"""Writes the given list of scenes to an output file handle in CSV format.
Arguments:
output_csv_file: Handle to open file in write mode.
scene_list: List of pairs of FrameTimecodes denoting each scene's start/end FrameTimecode.
include_cut_list: Bool indicating if the first row should include the timecodes where
each scene starts. Should be set to False if RFC 4180 compliant CSV output is required.
cut_list: Optional list of FrameTimecode objects denoting the cut list (i.e. the frames
in the video that need to be split to generate individual scenes). If not specified,
the cut list is generated using the start times of each scene following the first one.
"""
csv_writer = csv.writer(output_csv_file, lineterminator='\n')
# If required, output the cutting list as the first row (i.e. before the header row).
if include_cut_list:
csv_writer.writerow(
["Timecode List:"] +
cut_list if cut_list else [start.get_timecode() for start, _ in scene_list[1:]])
csv_writer.writerow([
"Scene Number", "Start Frame", "Start Timecode", "Start Time (seconds)", "End Frame",
"End Timecode", "End Time (seconds)", "Length (frames)", "Length (timecode)",
"Length (seconds)"
])
for i, (start, end) in enumerate(scene_list):
duration = end - start
csv_writer.writerow([
'%d' % (i + 1),
'%d' % (start.get_frames() + 1),
start.get_timecode(),
'%.3f' % start.get_seconds(),
'%d' % end.get_frames(),
end.get_timecode(),
'%.3f' % end.get_seconds(),
'%d' % duration.get_frames(),
duration.get_timecode(),
'%.3f' % duration.get_seconds()
])
def write_scene_list_html(output_html_filename,
scene_list,
cut_list=None,
css=None,
css_class='mytable',
image_filenames=None,
image_width=None,
image_height=None):
"""Writes the given list of scenes to an output file handle in html format.
Arguments:
output_html_filename: filename of output html file
scene_list: List of pairs of FrameTimecodes denoting each scene's start/end FrameTimecode.
cut_list: Optional list of FrameTimecode objects denoting the cut list (i.e. the frames
in the video that need to be split to generate individual scenes). If not passed,
the start times of each scene (besides the 0th scene) is used instead.
css: String containing all the css information for the resulting html page.
css_class: String containing the named css class
image_filenames: dict where key i contains a list with n elements (filenames of
the n saved images from that scene)
image_width: Optional desired width of images in table in pixels
image_height: Optional desired height of images in table in pixels
"""
if not css:
css = """
table.mytable {
font-family: times;
font-size:12px;
color:#000000;
border-width: 1px;
border-color: #eeeeee;
border-collapse: collapse;
background-color: #ffffff;
width=100%;
max-width:550px;
table-layout:fixed;
}
table.mytable th {
border-width: 1px;
padding: 8px;
border-style: solid;
border-color: #eeeeee;
background-color: #e6eed6;
color:#000000;
}
table.mytable td {
border-width: 1px;
padding: 8px;
border-style: solid;
border-color: #eeeeee;
}
#code {
display:inline;
font-family: courier;
color: #3d9400;
}
#string {
display:inline;
font-weight: bold;
}
"""
# Output Timecode list
timecode_table = SimpleTable(
[["Timecode List:"] +
(cut_list if cut_list else [start.get_timecode() for start, _ in scene_list[1:]])],
css_class=css_class)
# Output list of scenes
header_row = [
"Scene Number", "Start Frame", "Start Timecode", "Start Time (seconds)", "End Frame",
"End Timecode", "End Time (seconds)", "Length (frames)", "Length (timecode)",
"Length (seconds)"
]
for i, (start, end) in enumerate(scene_list):
duration = end - start
row = SimpleTableRow([
'%d' % (i + 1),
'%d' % (start.get_frames() + 1),
start.get_timecode(),
'%.3f' % start.get_seconds(),
'%d' % end.get_frames(),
end.get_timecode(),
'%.3f' % end.get_seconds(),
'%d' % duration.get_frames(),
duration.get_timecode(),
'%.3f' % duration.get_seconds()
])
if image_filenames:
for image in image_filenames[i]:
row.add_cell( | SimpleTableCell( | 0 | 2023-10-25 02:50:01+00:00 | 16k |
EulerSearch/embedding_studio | embedding_studio/workers/fine_tuning/finetune_embedding_one_param.py | [
{
"identifier": "QueryRetriever",
"path": "embedding_studio/embeddings/data/clickstream/query_retriever.py",
"snippet": "class QueryRetriever(object):\n \"\"\"As we can't exactly predict a schema of storing queries:\n 1. As text exceptly in clickstream service\n 2. As ID of a record with a text... | import logging
import torch
from typing import Optional
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import EarlyStopping
from torch.utils.data import DataLoader
from embedding_studio.embeddings.data.clickstream.query_retriever import (
QueryRetriever,
)
from embedding_studio.embeddings.data.ranking_data import RankingData
from embedding_studio.embeddings.models.interface import (
EmbeddingsModelInterface,
)
from embedding_studio.embeddings.training.embeddings_finetuner import (
EmbeddingsFineTuner,
)
from embedding_studio.workers.fine_tuning.experiments.experiments_tracker import (
ExperimentsManager,
)
from embedding_studio.workers.fine_tuning.experiments.finetuning_params import (
FineTuningParams,
)
from embedding_studio.workers.fine_tuning.experiments.finetuning_settings import (
FineTuningSettings,
) | 11,796 |
logger = logging.getLogger(__name__)
class CustomDataCollator:
def __call__(self, batch):
return batch
def fine_tune_embedding_model_one_param(
initial_model: EmbeddingsModelInterface,
settings: FineTuningSettings,
ranking_data: RankingData,
|
logger = logging.getLogger(__name__)
class CustomDataCollator:
def __call__(self, batch):
return batch
def fine_tune_embedding_model_one_param(
initial_model: EmbeddingsModelInterface,
settings: FineTuningSettings,
ranking_data: RankingData, | query_retriever: QueryRetriever, | 0 | 2023-10-31 00:33:13+00:00 | 16k |
masked-spacetime-hashing/msth | dataparser.py | [
{
"identifier": "camera_utils",
"path": "nerfstudio/cameras/camera_utils.py",
"snippet": "_EPS = np.finfo(float).eps * 4.0\n M = np.array(matrix, dtype=np.float64, copy=False)[:4, :4]\n K = np.array(\n [\n [m00 - m11 - m22, 0.0, 0.0, 0.0],\n [m01 + m10,... | import json
import math
import os
import cv2
import numpy as np
import torch
from copy import deepcopy
from dataclasses import dataclass, field
from pathlib import Path, PurePath
from typing import List, Optional, Type
from typing import *
from PIL import Image
from rich.console import Console
from typing_extensions import Literal
from nerfstudio.cameras import camera_utils
from nerfstudio.cameras.cameras import CAMERA_MODEL_TO_TYPE, Cameras, CameraType
from nerfstudio.configs.config_utils import to_immutable_dict
from nerfstudio.data.dataparsers.base_dataparser import (
DataParser,
DataParserConfig,
DataparserOutputs,
)
from nerfstudio.data.scene_box import SceneBox
from nerfstudio.utils.io import load_from_json
from torchtyping import TensorType | 12,904 | from __future__ import annotations
CONSOLE = Console(width=120)
MAX_AUTO_RESOLUTION = 1600
@dataclass
class VideoDataParserOutputs:
data_dir: Path
video_filenames: List[Path]
start_frame: int
num_frames: int
"""Dataparser outputs for the which will be used by the DataManager
for creating RayBundle and RayGT objects."""
"""Filenames for the images."""
| from __future__ import annotations
CONSOLE = Console(width=120)
MAX_AUTO_RESOLUTION = 1600
@dataclass
class VideoDataParserOutputs:
data_dir: Path
video_filenames: List[Path]
start_frame: int
num_frames: int
"""Dataparser outputs for the which will be used by the DataManager
for creating RayBundle and RayGT objects."""
"""Filenames for the images.""" | cameras: Cameras | 2 | 2023-10-26 04:39:15+00:00 | 16k |
Trustworthy-AI-Group/TransferAttack | transferattack/model_related/ghost.py | [
{
"identifier": "Attack",
"path": "transferattack/attack.py",
"snippet": "class Attack(object):\n \"\"\"\n Base class for all attacks.\n \"\"\"\n def __init__(self, attack, model_name, epsilon, targeted, random_start, norm, loss, device=None):\n \"\"\"\n Initialize the hyperpar... | from ..utils import *
from ..attack import Attack
from .ghost_networks.resnet import ghost_resnet101, ghost_resnet152
from ..gradient.mifgsm import MIFGSM
from ..gradient.nifgsm import NIFGSM
from ..gradient.vmifgsm import VMIFGSM
from ..input_transformation.dim import DIM
from ..input_transformation.tim import TIM
from ..input_transformation.sim import SIM
from ..input_transformation.admix import Admix
from torch import Tensor
from ..utils import *
from ..gradient.mifgsm import MIFGSM
from ..gradient.nifgsm import NIFGSM
from ..input_transformation.dim import DIM
from ..input_transformation.tim import TIM
from ..input_transformation.sim import SIM
from ..input_transformation.admix import Admix | 11,180 | # example bash: python main.py --attack=ghost_network
support_models = {
"resnet101": ghost_resnet101,
"resnet152": ghost_resnet152,
}
class GhostNetwork_MIFGSM(MIFGSM):
"""
Ghost Network Attack:
Arguments:
model (str): the surrogate model for attack.
ghost_keep_prob (float): the dropout rate when generating ghost networks.
ghost_random_range (float): the dropout rate when generating ghost networks of residual structure.
"""
def __init__(self, model_name='inc_v3', ghost_keep_prob=0.994, ghost_random_range=0.16, *args, **kwargs):
self.ghost_keep_prob = ghost_keep_prob # do not use
self.ghost_random_range = ghost_random_range # do not use
super().__init__(model_name, *args, **kwargs)
def load_model(self, model_name):
if model_name in support_models.keys():
# The ghost_keep_prob and ghost_random_range are correctly set as param default value,
# in the __init__ function of each GhostNetwork.
model = wrap_model(support_models[model_name](weights='DEFAULT').eval().cuda())
else:
raise ValueError('Model {} not supported for GhostNetwork'.format(model_name))
return model
class GhostNetwork_IFGSM(MIFGSM):
"""
Ghost Network Attack:
Arguments:
model (str): the surrogate model for attack.
ghost_keep_prob (float): the dropout rate when generating ghost networks.
ghost_random_range (float): the dropout rate when generating ghost networks of residual structure.
"""
def __init__(self, model_name='inc_v3', ghost_keep_prob=0.994, ghost_random_range=0.16, *args, **kwargs):
self.ghost_keep_prob = ghost_keep_prob # do not use
self.ghost_random_range = ghost_random_range # do not use
super().__init__(model_name, *args, **kwargs)
self.decay = 0.
def load_model(self, model_name):
if model_name in support_models.keys():
# The ghost_keep_prob and ghost_random_range are correctly set as param default value,
# in the __init__ function of each GhostNetwork.
model = wrap_model(support_models[model_name](weights='DEFAULT').eval().cuda())
else:
raise ValueError('Model {} not supported for GhostNetwork'.format(model_name))
return model
| # example bash: python main.py --attack=ghost_network
support_models = {
"resnet101": ghost_resnet101,
"resnet152": ghost_resnet152,
}
class GhostNetwork_MIFGSM(MIFGSM):
"""
Ghost Network Attack:
Arguments:
model (str): the surrogate model for attack.
ghost_keep_prob (float): the dropout rate when generating ghost networks.
ghost_random_range (float): the dropout rate when generating ghost networks of residual structure.
"""
def __init__(self, model_name='inc_v3', ghost_keep_prob=0.994, ghost_random_range=0.16, *args, **kwargs):
self.ghost_keep_prob = ghost_keep_prob # do not use
self.ghost_random_range = ghost_random_range # do not use
super().__init__(model_name, *args, **kwargs)
def load_model(self, model_name):
if model_name in support_models.keys():
# The ghost_keep_prob and ghost_random_range are correctly set as param default value,
# in the __init__ function of each GhostNetwork.
model = wrap_model(support_models[model_name](weights='DEFAULT').eval().cuda())
else:
raise ValueError('Model {} not supported for GhostNetwork'.format(model_name))
return model
class GhostNetwork_IFGSM(MIFGSM):
"""
Ghost Network Attack:
Arguments:
model (str): the surrogate model for attack.
ghost_keep_prob (float): the dropout rate when generating ghost networks.
ghost_random_range (float): the dropout rate when generating ghost networks of residual structure.
"""
def __init__(self, model_name='inc_v3', ghost_keep_prob=0.994, ghost_random_range=0.16, *args, **kwargs):
self.ghost_keep_prob = ghost_keep_prob # do not use
self.ghost_random_range = ghost_random_range # do not use
super().__init__(model_name, *args, **kwargs)
self.decay = 0.
def load_model(self, model_name):
if model_name in support_models.keys():
# The ghost_keep_prob and ghost_random_range are correctly set as param default value,
# in the __init__ function of each GhostNetwork.
model = wrap_model(support_models[model_name](weights='DEFAULT').eval().cuda())
else:
raise ValueError('Model {} not supported for GhostNetwork'.format(model_name))
return model
| class GhostNetwork_NIFGSM(NIFGSM): | 11 | 2023-10-31 03:43:26+00:00 | 16k |
chenruduan/OAReactDiff | demo.py | [
{
"identifier": "LEFTNet",
"path": "oa_reactdiff/model/leftnet.py",
"snippet": "class LEFTNet(torch.nn.Module):\n r\"\"\"\n LEFTNet\n\n Args:\n pos_require_grad (bool, optional): If set to :obj:`True`, will require to take derivative of model output with respect to the atomic positions. ... | import torch
import py3Dmol
import numpy as np
import plotly.express as px
import json
from typing import Optional
from torch import tensor
from e3nn import o3
from torch_scatter import scatter_mean
from oa_reactdiff.model import LEFTNet
from oa_reactdiff.tests.model.utils import (
generate_full_eij,
get_cut_graph_mask,
)
from torch.utils.data import DataLoader
from oa_reactdiff.trainer.pl_trainer import DDPMModule
from oa_reactdiff.dataset import ProcessedTS1x
from oa_reactdiff.diffusion._schedule import DiffSchedule, PredefinedNoiseSchedule
from oa_reactdiff.diffusion._normalizer import FEATURE_MAPPING
from oa_reactdiff.analyze.rmsd import batch_rmsd
from oa_reactdiff.utils.sampling_tools import (
assemble_sample_inputs,
write_tmp_xyz,
)
from glob import glob
from oa_reactdiff.analyze.rmsd import xyz2pmg, pymatgen_rmsd
from pymatgen.core import Molecule
from collections import OrderedDict
from sklearn.cluster import KMeans
from glob import glob
from pymatgen.io.xyz import XYZ
from openbabel import pybel
from oa_reactdiff.analyze.rmsd import pymatgen_rmsd | 11,579 | # --- 导入和定义一些函数 ----
default_float = torch.float64
torch.set_default_dtype(default_float) # 使用双精度，测试更准确
def remove_mean_batch(
x: tensor,
indices: Optional[tensor] = None
) -> tensor:
"""将x中的每个batch的均值去掉
Args:
x (tensor): input tensor.
indices (Optional[tensor], optional): batch indices. Defaults to None.
Returns:
tensor: output tensor with batch mean as 0.
"""
if indices == None:
return x - torch.mean(x, dim=0)
mean = scatter_mean(x, indices, dim=0)
x = x - mean[indices]
return x
def draw_in_3dmol(mol: str, fmt: str = "xyz") -> py3Dmol.view:
"""画分子
Args:
mol (str): str content of molecule.
fmt (str, optional): format. Defaults to "xyz".
Returns:
py3Dmol.view: output viewer
"""
viewer = py3Dmol.view(1024, 576)
viewer.addModel(mol, fmt)
viewer.setStyle({'stick': {}, "sphere": {"radius": 0.36}})
viewer.zoomTo()
return viewer
def assemble_xyz(z: list, pos: tensor) -> str:
"""将原子序数和位置组装成xyz格式
Args:
z (list): chemical elements
pos (tensor): 3D coordinates
Returns:
str: xyz string
"""
natoms =len(z)
xyz = f"{natoms}\n\n"
for _z, _pos in zip(z, pos.numpy()):
xyz += f"{_z}\t" + "\t".join([str(x) for x in _pos]) + "\n"
return xyz
num_layers = 2
hidden_channels = 8
in_hidden_channels = 4
num_radial = 4
| # --- 导入和定义一些函数 ----
default_float = torch.float64
torch.set_default_dtype(default_float) # 使用双精度，测试更准确
def remove_mean_batch(
x: tensor,
indices: Optional[tensor] = None
) -> tensor:
"""将x中的每个batch的均值去掉
Args:
x (tensor): input tensor.
indices (Optional[tensor], optional): batch indices. Defaults to None.
Returns:
tensor: output tensor with batch mean as 0.
"""
if indices == None:
return x - torch.mean(x, dim=0)
mean = scatter_mean(x, indices, dim=0)
x = x - mean[indices]
return x
def draw_in_3dmol(mol: str, fmt: str = "xyz") -> py3Dmol.view:
"""画分子
Args:
mol (str): str content of molecule.
fmt (str, optional): format. Defaults to "xyz".
Returns:
py3Dmol.view: output viewer
"""
viewer = py3Dmol.view(1024, 576)
viewer.addModel(mol, fmt)
viewer.setStyle({'stick': {}, "sphere": {"radius": 0.36}})
viewer.zoomTo()
return viewer
def assemble_xyz(z: list, pos: tensor) -> str:
"""将原子序数和位置组装成xyz格式
Args:
z (list): chemical elements
pos (tensor): 3D coordinates
Returns:
str: xyz string
"""
natoms =len(z)
xyz = f"{natoms}\n\n"
for _z, _pos in zip(z, pos.numpy()):
xyz += f"{_z}\t" + "\t".join([str(x) for x in _pos]) + "\n"
return xyz
num_layers = 2
hidden_channels = 8
in_hidden_channels = 4
num_radial = 4
| model = LEFTNet( | 0 | 2023-10-30 02:53:38+00:00 | 16k |
Weitheskmt/WeiDMD | build/lib/weidmd/cdmd.py | [
{
"identifier": "DMDBase",
"path": "build/lib/weidmd/dmdbase.py",
"snippet": "class DMDBase:\n \"\"\"\n Dynamic Mode Decomposition base class.\n\n :param svd_rank: the rank for the truncation; If 0, the method computes the\n optimal rank and uses it for truncation; if positive interger, ... | import numpy as np
import scipy.sparse
from scipy.linalg import sqrtm
from .dmdbase import DMDBase
from .dmdoperator import DMDOperator
from .snapshots import Snapshots
from .utils import compute_svd, compute_tlsq | 10,856 |
from __future__ import division
class CDMDOperator(DMDOperator):
"""
DMD operator for Compressed-DMD.
:param svd_rank: the rank for the truncation; If 0, the method computes the
optimal rank and uses it for truncation; if positive interger, the
method uses the argument for the truncation; if float between 0 and 1,
the rank is the number of the biggest singular values that are needed
to reach the 'energy' specified by `svd_rank`; if -1, the method does
not compute truncation.
:type svd_rank: int or float
:param rescale_mode: Scale Atilde as shown in
10.1016/j.jneumeth.2015.10.010 (section 2.4) before computing its
eigendecomposition. None means no rescaling, 'auto' means automatic
rescaling using singular values, otherwise the scaling factors.
:type rescale_mode: {'auto'} or None or numpy.ndarray
:param bool forward_backward: If True, the low-rank operator is computed
like in fbDMD (reference: https://arxiv.org/abs/1507.02264). Default is
False.
:param sorted_eigs: Sort eigenvalues (and modes/dynamics accordingly) by
magnitude if `sorted_eigs='abs'`, by real part (and then by imaginary
part to break ties) if `sorted_eigs='real'`. Default: False.
:type sorted_eigs: {'real', 'abs'} or False
:param tikhonov_regularization: Tikhonov parameter for the regularization.
If `None`, no regularization is applied, if `float`, it is used as the
:math:`\lambda` tikhonov parameter.
:type tikhonov_regularization: int or float
"""
def __init__(
self,
svd_rank,
rescale_mode,
forward_backward,
sorted_eigs,
tikhonov_regularization,
):
super().__init__(
svd_rank=svd_rank,
exact=True,
rescale_mode=rescale_mode,
forward_backward=forward_backward,
sorted_eigs=sorted_eigs,
tikhonov_regularization=tikhonov_regularization,
)
self._Atilde = None
def compute_operator(self, compressedX, compressedY, nonCompressedY):
"""
Compute the low-rank operator.
:param numpy.ndarray compressedX: the compressed version of the matrix
containing the snapshots x0,..x{n-1} by column.
:param numpy.ndarray compressedY: the compressed version of the matrix
containing the snapshots x1,..x{n} by column.
:param numpy.ndarray nonCompressedY: the matrix containing the
snapshots x1,..x{n} by column.
:return: the (truncated) left-singular vectors matrix, the (truncated)
singular values array, the (truncated) right-singular vectors
matrix of compressedX.
:rtype: numpy.ndarray, numpy.ndarray, numpy.ndarray
"""
U, s, V = compute_svd(compressedX, svd_rank=self._svd_rank)
atilde = self._least_square_operator(U, s, V, compressedY)
if self._forward_backward:
# b stands for "backward"
bU, bs, bV = compute_svd(compressedY, svd_rank=self._svd_rank)
atilde_back = self._least_square_operator(bU, bs, bV, compressedX)
atilde = sqrtm(atilde.dot(np.linalg.inv(atilde_back)))
self._Atilde = atilde
self._compute_eigenquantities()
self._compute_modes(nonCompressedY, U, s, V)
return U, s, V
|
from __future__ import division
class CDMDOperator(DMDOperator):
"""
DMD operator for Compressed-DMD.
:param svd_rank: the rank for the truncation; If 0, the method computes the
optimal rank and uses it for truncation; if positive interger, the
method uses the argument for the truncation; if float between 0 and 1,
the rank is the number of the biggest singular values that are needed
to reach the 'energy' specified by `svd_rank`; if -1, the method does
not compute truncation.
:type svd_rank: int or float
:param rescale_mode: Scale Atilde as shown in
10.1016/j.jneumeth.2015.10.010 (section 2.4) before computing its
eigendecomposition. None means no rescaling, 'auto' means automatic
rescaling using singular values, otherwise the scaling factors.
:type rescale_mode: {'auto'} or None or numpy.ndarray
:param bool forward_backward: If True, the low-rank operator is computed
like in fbDMD (reference: https://arxiv.org/abs/1507.02264). Default is
False.
:param sorted_eigs: Sort eigenvalues (and modes/dynamics accordingly) by
magnitude if `sorted_eigs='abs'`, by real part (and then by imaginary
part to break ties) if `sorted_eigs='real'`. Default: False.
:type sorted_eigs: {'real', 'abs'} or False
:param tikhonov_regularization: Tikhonov parameter for the regularization.
If `None`, no regularization is applied, if `float`, it is used as the
:math:`\lambda` tikhonov parameter.
:type tikhonov_regularization: int or float
"""
def __init__(
self,
svd_rank,
rescale_mode,
forward_backward,
sorted_eigs,
tikhonov_regularization,
):
super().__init__(
svd_rank=svd_rank,
exact=True,
rescale_mode=rescale_mode,
forward_backward=forward_backward,
sorted_eigs=sorted_eigs,
tikhonov_regularization=tikhonov_regularization,
)
self._Atilde = None
def compute_operator(self, compressedX, compressedY, nonCompressedY):
"""
Compute the low-rank operator.
:param numpy.ndarray compressedX: the compressed version of the matrix
containing the snapshots x0,..x{n-1} by column.
:param numpy.ndarray compressedY: the compressed version of the matrix
containing the snapshots x1,..x{n} by column.
:param numpy.ndarray nonCompressedY: the matrix containing the
snapshots x1,..x{n} by column.
:return: the (truncated) left-singular vectors matrix, the (truncated)
singular values array, the (truncated) right-singular vectors
matrix of compressedX.
:rtype: numpy.ndarray, numpy.ndarray, numpy.ndarray
"""
U, s, V = compute_svd(compressedX, svd_rank=self._svd_rank)
atilde = self._least_square_operator(U, s, V, compressedY)
if self._forward_backward:
# b stands for "backward"
bU, bs, bV = compute_svd(compressedY, svd_rank=self._svd_rank)
atilde_back = self._least_square_operator(bU, bs, bV, compressedX)
atilde = sqrtm(atilde.dot(np.linalg.inv(atilde_back)))
self._Atilde = atilde
self._compute_eigenquantities()
self._compute_modes(nonCompressedY, U, s, V)
return U, s, V
| class CDMD(DMDBase): | 0 | 2023-10-30 12:37:40+00:00 | 16k |
lewandofskee/DiAD | 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 torch
import os
import logging
import timm
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import itertools
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager, nullcontext
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig
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 IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from scipy.ndimage import gaussian_filter
from utils.util import cal_anomaly_map, log_local, create_logger
from utils.eval_helper import dump, log_metrics, merge_together, performances | 14,186 | @torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
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]
if self.make_it_fit:
n_params = len([name for name, _ in
itertools.chain(self.named_parameters(),
self.named_buffers())])
for name, param in tqdm(
itertools.chain(self.named_parameters(),
self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
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:\n {missing}")
if len(unexpected) > 0:
print(f"\nUnexpected Keys:\n {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 predict_start_from_z_and_v(self, x_t, t, v):
# 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)))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
)
def predict_eps_from_z_and_v(self, x_t, t, v):
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
)
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)
| """
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'}
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.,
make_it_fit=False,
ucg_training=None,
reset_ema=False,
reset_num_ema_updates=False,
):
super().__init__()
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
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
self.make_it_fit = make_it_fit
if reset_ema: assert exists(ckpt_path)
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
if reset_ema:
assert self.use_ema
print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
self.model_ema = LitEma(self.model)
if reset_num_ema_updates:
print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
assert self.use_ema
self.model_ema.reset_num_updates()
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
logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
else:
self.register_buffer('logvar', logvar)
self.ucg_training = ucg_training or dict()
if self.ucg_training:
self.ucg_prng = np.random.RandomState()
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))
elif self.parameterization == "v":
lvlb_weights = torch.ones_like(self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
else:
raise NotImplementedError("mu not supported")
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")
@torch.no_grad()
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
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]
if self.make_it_fit:
n_params = len([name for name, _ in
itertools.chain(self.named_parameters(),
self.named_buffers())])
for name, param in tqdm(
itertools.chain(self.named_parameters(),
self.named_buffers()),
desc="Fitting old weights to new weights",
total=n_params
):
if not name in sd:
continue
old_shape = sd[name].shape
new_shape = param.shape
assert len(old_shape) == len(new_shape)
if len(new_shape) > 2:
# we only modify first two axes
assert new_shape[2:] == old_shape[2:]
# assumes first axis corresponds to output dim
if not new_shape == old_shape:
new_param = param.clone()
old_param = sd[name]
if len(new_shape) == 1:
for i in range(new_param.shape[0]):
new_param[i] = old_param[i % old_shape[0]]
elif len(new_shape) >= 2:
for i in range(new_param.shape[0]):
for j in range(new_param.shape[1]):
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
n_used_old = torch.ones(old_shape[1])
for j in range(new_param.shape[1]):
n_used_old[j % old_shape[1]] += 1
n_used_new = torch.zeros(new_shape[1])
for j in range(new_param.shape[1]):
n_used_new[j] = n_used_old[j % old_shape[1]]
n_used_new = n_used_new[None, :]
while len(n_used_new.shape) < len(new_shape):
n_used_new = n_used_new.unsqueeze(-1)
new_param /= n_used_new
sd[name] = new_param
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:\n {missing}")
if len(unexpected) > 0:
print(f"\nUnexpected Keys:\n {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 predict_start_from_z_and_v(self, x_t, t, v):
# 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)))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
)
def predict_eps_from_z_and_v(self, x_t, t, v):
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
)
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) | 15 | 2023-10-30 14:21:09+00:00 | 16k |
nv-tlabs/trace | tbsim/evaluation/env_builders.py | [
{
"identifier": "UnifiedDataset",
"path": "trajdata/src/trajdata/dataset.py",
"snippet": "class UnifiedDataset(Dataset):\n # @profile\n def __init__(\n self,\n desired_data: List[str],\n scene_description_contains: Optional[List[str]] = None,\n centric: str = \"agent\",... | from collections import defaultdict
from trajdata import UnifiedDataset
from tbsim.configs.eval_config import EvaluationConfig
from tbsim.configs.base import ExperimentConfig
from tbsim.envs.env_trajdata import EnvUnifiedSimulation
from tbsim.utils.config_utils import translate_pass_trajdata_cfg
from tbsim.utils.trajdata_utils import TRAJDATA_AGENT_TYPE_MAP
import tbsim.envs.env_metrics as EnvMetrics | 13,174 |
class EnvironmentBuilder(object):
"""Builds an simulation environment for evaluation."""
def __init__(self, eval_config: EvaluationConfig, exp_config: ExperimentConfig, device):
self.eval_cfg = eval_config
self.exp_cfg = exp_config
self.device = device
def _get_analytical_metrics(self):
metrics = dict(
all_off_road_rate=EnvMetrics.OffRoadRate(),
all_disk_off_road_rate=EnvMetrics.DiskOffRoadRate(),
all_sem_layer_rate=EnvMetrics.SemLayerRate(),
all_collision_rate=EnvMetrics.CollisionRate(),
all_disk_collision_rate=EnvMetrics.DiskCollisionRate(),
agents_collision_rate=EnvMetrics.CollisionRate(),
all_failure=EnvMetrics.CriticalFailure(num_offroad_frames=2),
all_comfort=EnvMetrics.Comfort(sim_dt=self.exp_cfg.algo.step_time, stat_dt=0.5),
)
return metrics
def get_env(self):
raise NotImplementedError
class EnvUnifiedBuilder(EnvironmentBuilder):
def get_env(self):
exp_cfg = self.exp_cfg.clone()
exp_cfg.unlock()
exp_cfg.env.simulation.num_simulation_steps = self.eval_cfg.num_simulation_steps
exp_cfg.env.simulation.start_frame_index = exp_cfg.algo.history_num_frames + 1
exp_cfg.lock()
# the config used at training time
data_cfg = translate_pass_trajdata_cfg(exp_cfg)
future_sec = data_cfg.future_num_frames * data_cfg.step_time
history_sec = data_cfg.history_num_frames * data_cfg.step_time
neighbor_distance = data_cfg.max_agents_distance
|
class EnvironmentBuilder(object):
"""Builds an simulation environment for evaluation."""
def __init__(self, eval_config: EvaluationConfig, exp_config: ExperimentConfig, device):
self.eval_cfg = eval_config
self.exp_cfg = exp_config
self.device = device
def _get_analytical_metrics(self):
metrics = dict(
all_off_road_rate=EnvMetrics.OffRoadRate(),
all_disk_off_road_rate=EnvMetrics.DiskOffRoadRate(),
all_sem_layer_rate=EnvMetrics.SemLayerRate(),
all_collision_rate=EnvMetrics.CollisionRate(),
all_disk_collision_rate=EnvMetrics.DiskCollisionRate(),
agents_collision_rate=EnvMetrics.CollisionRate(),
all_failure=EnvMetrics.CriticalFailure(num_offroad_frames=2),
all_comfort=EnvMetrics.Comfort(sim_dt=self.exp_cfg.algo.step_time, stat_dt=0.5),
)
return metrics
def get_env(self):
raise NotImplementedError
class EnvUnifiedBuilder(EnvironmentBuilder):
def get_env(self):
exp_cfg = self.exp_cfg.clone()
exp_cfg.unlock()
exp_cfg.env.simulation.num_simulation_steps = self.eval_cfg.num_simulation_steps
exp_cfg.env.simulation.start_frame_index = exp_cfg.algo.history_num_frames + 1
exp_cfg.lock()
# the config used at training time
data_cfg = translate_pass_trajdata_cfg(exp_cfg)
future_sec = data_cfg.future_num_frames * data_cfg.step_time
history_sec = data_cfg.history_num_frames * data_cfg.step_time
neighbor_distance = data_cfg.max_agents_distance | agent_only_types = [TRAJDATA_AGENT_TYPE_MAP[cur_type] for cur_type in data_cfg.trajdata_only_types] | 5 | 2023-10-31 18:43:07+00:00 | 16k |
nv-tlabs/pacer | uhc/smpllib/np_smpl_humanoid_batch.py | [
{
"identifier": "dict_to_torch",
"path": "uhc/utils/torch_ext.py",
"snippet": "def dict_to_torch(input_dict, dtype = None, device = None, add_dim = False):\n if not isinstance(input_dict, dict):\n return None\n out_dict = {}\n for key, value in input_dict.items():\n if isinstance(... | import torch
import glob
import os
import sys
import pdb
import os.path as osp
import joblib
import pytorch3d.transforms as tR
import autograd.numpy as np
import time
import ipdb
from uhc.utils.torch_ext import dict_to_torch
from uhc.utils.torch_utils import *
from uhc.utils.transform_utils import *
from scipy.spatial.transform import Rotation as sRot
from uhc.smpllib.smpl_mujoco import SMPLConverter, smpl_to_qpose, smpl_to_qpose_torch, SMPL_BONE_ORDER_NAMES
from uhc.smpllib.smpl_parser import SMPL_EE_NAMES
from uhc.utils.tools import get_expert, get_expert_master
from uhc.smpllib.smpl_parser import (
SMPL_Parser,
SMPLH_Parser,
SMPLX_Parser,
)
from autograd import elementwise_grad as egrad
from uhc.smpllib.smpl_robot import Robot
from uhc.smpllib.torch_smpl_humanoid import Humanoid
from uhc.utils.config_utils.copycat_config import Config
from uhc.data_loaders.dataset_amass_single import DatasetAMASSSingle
from uhc.utils.torch_ext import dict_to_torch
from uhc.smpllib.smpl_mujoco import smpl_to_qpose_torch, smplh_to_smpl | 11,659 | pred_joints2d.squeeze()[7:8]).squeeze().mean()
def fk_batch(self, pose, trans, convert_to_mat=True, count_offset=True):
pose, trans = pose.cpu().numpy(), trans.cpu().numpy()
B, seq_len = pose.shape[:2]
if convert_to_mat:
pose_mat = rodrigues(pose.reshape(B * seq_len * 24, 1, 3)).reshape(
B, seq_len, -1, 3, 3)
else:
pose_mat = pose
if pose_mat.shape != 5:
pose_mat = pose_mat.reshape(B, seq_len, -1, 3, 3)
J = pose_mat.shape[2] - 1 # Exclude root
if count_offset:
trans = trans + self._offsets[:, 0:1]
pose_mat_ordered = pose_mat[:, :, self.smpl_index]
wbody_pos, wbody_mat = self.forward_kinematics_batch(
pose_mat_ordered[:, :, 1:], pose_mat_ordered[:, :, 0:1], trans)
return_dic = {}
return_dic["wbpos"] = wbody_pos
return_dic["wbmat"] = wbody_mat
return return_dic
def fk_batch_grad(self, input_vec, count_offset=True):
trans, pose = input_vec[:, :, :3], input_vec[:, :, 3:]
B, seq_len = pose.shape[:2]
pose_mat = rodrigues(pose.reshape(-1, 1,
3)).reshape(B, seq_len, -1, 3, 3)
# pose_mat = [
# rodrigues_vec_to_rotation_mat(a) for a in pose.reshape(-1, 3)
# ]
# pose_mat = np.stack(pose_mat).reshape(B, seq_len, -1, 3, 3)
J = pose_mat.shape[2] - 1 # Exclude root
if count_offset:
trans = trans + self._offsets[:, 0:1]
pose_mat_ordered = pose_mat[:, :, self.smpl_index]
wbody_pos, wbody_mat = self.forward_kinematics_batch(
pose_mat_ordered[:, :, 1:], pose_mat_ordered[:, :, 0:1], trans)
return wbody_pos
def get_ee_pos(self, body_xpos, root_q, transform):
ee_name = SMPL_EE_NAMES
ee_pos = []
root_pos = body_xpos[:, 0, :]
for name in ee_name:
bone_id = self.model._body_name2id[name] - 1
bone_vec = body_xpos[:, bone_id]
if transform is not None:
bone_vec = bone_vec - root_pos
bone_vec = transform_vec_batch(bone_vec, root_q, transform)
ee_pos.append(bone_vec)
return torch.swapaxes(torch.stack(ee_pos, dim=0), 0, 1)
def forward_kinematics_batch(self, rotations, root_rotations,
root_positions):
"""
Perform forward kinematics using the given trajectory and local rotations.
Arguments (where B = batch size, J = number of joints):
-- rotations: (B, J, 4) tensor of unit quaternions describing the local rotations of each joint.
-- root_positions: (B, 3) tensor describing the root joint positions.
Output: joint positions (B, J, 3)
"""
B, seq_len = rotations.shape[0:2]
J = self._offsets.shape[1]
positions_world = []
rotations_world = []
expanded_offsets = np.repeat(np.repeat(self._offsets, B,
axis=0)[:, None, :],
seq_len,
axis=1)
for i in range(J):
if self._parents[i] == -1:
positions_world.append(root_positions)
rotations_world.append(root_rotations)
else:
jpos = (
np.matmul(rotations_world[self._parents[i]][:, :, 0],
expanded_offsets[:, :, i, :, None]).squeeze(-1) +
positions_world[self._parents[i]])
rot_mat = np.matmul(rotations_world[self._parents[i]],
rotations[:, :, (i - 1):i, :])
positions_world.append(jpos)
rotations_world.append(rot_mat)
positions_world = np.stack(positions_world, axis=2)
rotations_world = np.concatenate(rotations_world, axis=2)
return positions_world, rotations_world
if __name__ == "__main__":
torch.manual_seed(0)
cfg = Config(
cfg_id="copycat_44",
create_dirs=False,
)
smpl_robot = Robot(
cfg.robot_cfg,
data_dir=osp.join(cfg.base_dir, "data/smpl"),
masterfoot=False,
)
dataset = DatasetAMASSSingle(cfg.data_specs, "test")
humanoid_batch = Humanoid_Batch()
data_test = dataset.sample_seq()
| # import numpy as np
sys.path.append(os.getcwd())
def smpl_op_to_op(pred_joints2d):
new_2d = np.concatenate([pred_joints2d[..., [1, 4], :].mean(axis = -2, keepdims = True), \
pred_joints2d[..., 1:7, :], \
pred_joints2d[..., [7, 8, 11], :].mean(axis = -2, keepdims = True), \
pred_joints2d[..., 9:11, :], \
pred_joints2d[..., 12:, :]], \
axis = -2)
return new_2d
def normalize_screen_coordinates(X, w=1920, h=1080):
assert X.shape[-1] == 2
# Normalize so that [0, w] is mapped to
# [-1, 1], while preserving the aspect ratio
return X / w * 2 - np.array([1, h / w])
def rodrigues(r):
"""
Rodrigues' rotation formula that turns axis-angle vector into rotation
matrix in a batch-ed manner.
Parameter:
----------
r: Axis-angle rotation vector of shape [batch_size, 1, 3].
Return:
-------
Rotation matrix of shape [batch_size, 3, 3].
"""
theta = np.linalg.norm(r, axis=(1, 2))[:, None, None]
# avoid zero divide
theta = np.maximum(theta, np.finfo(r.dtype).eps)
r_hat = r / theta
cos = np.cos(theta)
z_stick = np.zeros(theta.shape[0])
m = np.stack([
z_stick, -r_hat[:, 0, 2], r_hat[:, 0, 1], r_hat[:, 0, 2], z_stick,
-r_hat[:, 0, 0], -r_hat[:, 0, 1], r_hat[:, 0, 0], z_stick
],
axis=1).reshape([-1, 3, 3])
i_cube = np.broadcast_to(np.expand_dims(np.eye(3), axis=0),
[theta.shape[0], 3, 3])
A = np.transpose(r_hat, axes=[0, 2, 1])
B = r_hat
dot = np.matmul(A, B)
R = cos * i_cube + (1 - cos) * dot + np.sin(theta) * m
return R
def rodrigues_vec_to_rotation_mat(rot):
theta = np.linalg.norm(rot, axis=0)
if theta < sys.float_info.epsilon:
rotation_mat = np.eye(3, dtype=float)
else:
rot = rot / theta
I = np.eye(3, dtype=float)
r_rT = np.array([[rot[0] * rot[0], rot[0] * rot[1], rot[0] * rot[2]],
[rot[1] * rot[0], rot[1] * rot[1], rot[1] * rot[2]],
[rot[2] * rot[0], rot[2] * rot[1], rot[2] * rot[2]]])
r_cross = np.array([[0, -rot[2], rot[1]], [rot[2], 0, -rot[0]],
[-rot[1], rot[0], 0]])
rotation_mat = np.cos(theta) * I + (
1 - np.cos(theta)) * r_rT + np.sin(theta) * r_cross
return rotation_mat
class Humanoid_Batch:
def __init__(self, smpl_model="smpl", data_dir="data/smpl"):
self.smpl_model = smpl_model
if self.smpl_model == "smpl":
self.smpl_parser_n = SMPL_Parser(model_path=data_dir,
gender="neutral")
self.smpl_parser_m = SMPL_Parser(model_path=data_dir,
gender="male")
self.smpl_parser_f = SMPL_Parser(model_path=data_dir,
gender="female")
elif self.smpl_model == "smplh":
self.smpl_parser_n = SMPLH_Parser(
model_path=data_dir,
gender="neutral",
use_pca=False,
create_transl=False,
)
self.smpl_parser_m = SMPLH_Parser(model_path=data_dir,
gender="male",
use_pca=False,
create_transl=False)
self.smpl_parser_f = SMPLH_Parser(model_path=data_dir,
gender="female",
use_pca=False,
create_transl=False)
elif self.smpl_model == "smplx":
self.smpl_parser_n = SMPLX_Parser(
model_path=data_dir,
gender="neutral",
use_pca=False,
create_transl=False,
)
self.smpl_parser_m = SMPLX_Parser(model_path=data_dir,
gender="male",
use_pca=False,
create_transl=False)
self.smpl_parser_f = SMPLX_Parser(model_path=data_dir,
gender="female",
use_pca=False,
create_transl=False)
self.model_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'
]
self._parents = [
-1, 0, 1, 2, 3, 0, 5, 6, 7, 0, 9, 10, 11, 12, 11, 14, 15, 16, 17,
11, 19, 20, 21, 22
]
self.smpl_index = [
SMPL_BONE_ORDER_NAMES.index(i) for i in self.model_names
]
def update_model(self, betas, gender):
betas, gender = betas.cpu().float(), gender.cpu().long()
B, _ = betas.shape
betas_f = betas[gender == 2]
if len(betas_f) > 0:
_, _, _, _, joint_offsets_f, _, _, _, _, _, _, = self.smpl_parser_f.get_mesh_offsets_batch(
betas=betas_f[:, :10])
betas_n = betas[gender == 0]
if len(betas_n) > 0:
_, _, _, _, joint_offsets_n, _, _, _, _, _, _, = self.smpl_parser_n.get_mesh_offsets_batch(
betas=betas_n[:, :10])
betas_m = betas[gender == 1]
if len(betas_m) > 0:
_, _, _, _, joint_offsets_m, _, _, _, _, _, _, = self.smpl_parser_m.get_mesh_offsets_batch(
betas=betas_m[:, :10])
joint_offsets_all = dict()
for n in SMPL_BONE_ORDER_NAMES:
joint_offsets_all[n] = torch.zeros([B, 3]).float()
if len(betas_f) > 0:
joint_offsets_all[n][gender == 2] = joint_offsets_f[n]
if len(betas_n) > 0:
joint_offsets_all[n][gender == 0] = joint_offsets_n[n]
if len(betas_m) > 0:
joint_offsets_all[n][gender == 1] = joint_offsets_m[n]
off_sets = []
for n in self.model_names:
off_sets.append(joint_offsets_all[n])
# self._offsets = torch.from_numpy(np.stack(off_sets, axis=1))
self._offsets = np.round(np.stack(off_sets, axis=1), decimals=5)
self.trans2joint = -self._offsets[:, 0:1]
self.trans2joint[:, :, 2] = 0
# self._offsets = joblib.load("curr_offset.pkl")[None, ]
def update_projection(self, cam_params, smpl2op_map, MUJOCO_2_SMPL):
self.full_R = cam_params['full_R']
self.full_t = cam_params['full_t']
self.K = cam_params['K']
self.img_w = cam_params['img_w']
self.img_h = cam_params['img_h']
self.openpose_subindex = smpl2op_map < 22
self.smpl2op_map = smpl2op_map
self.smpl2op_partial = self.smpl2op_map[self.openpose_subindex]
self.MUJOCO_2_SMPL = MUJOCO_2_SMPL
def update_tgt_joints(self, tgt_joints, inliers):
self.gt_2d_joints = tgt_joints
self.inliers = inliers.astype(bool)
num_joints = self.gt_2d_joints.shape[-2]
self.gt_2d_joints_norm = normalize_screen_coordinates(self.gt_2d_joints, self.img_w, self.img_h)
self.num_frames = self.gt_2d_joints.shape[0]
self.camera_rays = np.concatenate([self.gt_2d_joints, np.ones([self.num_frames, num_joints, 1])],
axis=2).dot(np.linalg.inv(self.K).T)
self.camera_rays /= np.linalg.norm(self.camera_rays, axis=2)[..., None]
lam = 0.3
self.weighting = np.exp(lam * -np.arange(self.num_frames)) / np.sum(
np.exp(lam * -np.arange(self.num_frames)))
self.weighting = np.tile(self.weighting[:, None, None], [1, num_joints, 2])
# self.weighting = np.ones(self.num_frames) / self.num_frames
def proj2d(self, wbpos, return_cam_3d=False):
# wbpos in mujoco
pred_joints3d = wbpos.squeeze()[self.MUJOCO_2_SMPL][
self.smpl2op_partial][None, ]
pred_joints3d = pred_joints3d @ self.full_R.T + self.full_t
pred_joints2d = pred_joints3d @ (self.K.T)
z = pred_joints2d[:, :, 2:]
pred_joints2d = pred_joints2d[:, :, :2] / z
pred_joints2d = smpl_op_to_op(pred_joints2d)
if return_cam_3d:
return pred_joints2d, pred_joints3d
else:
return pred_joints2d
def proj_2d_line_loss(self, input_vec):
wbpos = self.fk_batch_grad(input_vec)
_, pred_joints3d = self.proj2d(wbpos, return_cam_3d=True)
dist = np.cross(pred_joints3d[0],
pred_joints3d[0] - self.camera_rays)**2
return dist.mean()
def proj_2d_loss(self, input_vec, ord=2, normalize = True):
wbpos = self.fk_batch_grad(input_vec)
pred_joints2d = self.proj2d(wbpos)
curr_weighting = np.array(self.weighting)
if normalize:
pred_joints2d = normalize_screen_coordinates(pred_joints2d, self.img_w, self.img_h)
gt_2d_joints = self.gt_2d_joints_norm
else:
gt_2d_joints = self.gt_2d_joints
if ord == 1:
loss = np.abs(
gt_2d_joints[self.inliers] -
pred_joints2d.squeeze()[self.inliers]).squeeze().mean()
else:
diff = (gt_2d_joints - pred_joints2d.squeeze())**2
curr_weighting[~self.inliers] = 0
loss = (diff * curr_weighting).sum(axis=0).mean()
return loss
def proj_2d_body_loss(self, input_vec, ord=2, normalize = False):
# Has to use the current translation (to roughly put at the same position, and then zero out the translation)
wbpos = self.fk_batch_grad(input_vec)
pred_joints2d = self.proj2d(wbpos)
gt2d_center = self.gt_2d_joints[..., 7:8, :].copy()
pred_joints2d += (gt2d_center - pred_joints2d[..., 7:8, :])
curr_weighting = np.array(self.weighting)
if normalize:
pred_joints2d = normalize_screen_coordinates(pred_joints2d, self.img_w, self.img_h)
gt_2d_joints = self.gt_2d_joints_norm
else:
gt_2d_joints = self.gt_2d_joints
if ord == 1:
loss = np.abs(gt_2d_joints[self.inliers] - pred_joints2d.squeeze()[self.inliers]).squeeze().mean()
else:
diff = (gt_2d_joints - pred_joints2d.squeeze())**2
curr_weighting[~self.inliers] = 0
loss = (diff * curr_weighting).sum(axis=0).mean()
return loss
def proj_2d_root_loss(self, root_pos_rot):
input_vec = np.concatenate(
[root_pos_rot.reshape([1, 1, 6]),
np.zeros([1, 1, 69])], axis=2)
wbpos = self.fk_batch_grad(input_vec)
pred_joints2d = self.proj2d(wbpos)
return np.abs(self.gt_2d_joints[7:8] -
pred_joints2d.squeeze()[7:8]).squeeze().mean()
def fk_batch(self, pose, trans, convert_to_mat=True, count_offset=True):
pose, trans = pose.cpu().numpy(), trans.cpu().numpy()
B, seq_len = pose.shape[:2]
if convert_to_mat:
pose_mat = rodrigues(pose.reshape(B * seq_len * 24, 1, 3)).reshape(
B, seq_len, -1, 3, 3)
else:
pose_mat = pose
if pose_mat.shape != 5:
pose_mat = pose_mat.reshape(B, seq_len, -1, 3, 3)
J = pose_mat.shape[2] - 1 # Exclude root
if count_offset:
trans = trans + self._offsets[:, 0:1]
pose_mat_ordered = pose_mat[:, :, self.smpl_index]
wbody_pos, wbody_mat = self.forward_kinematics_batch(
pose_mat_ordered[:, :, 1:], pose_mat_ordered[:, :, 0:1], trans)
return_dic = {}
return_dic["wbpos"] = wbody_pos
return_dic["wbmat"] = wbody_mat
return return_dic
def fk_batch_grad(self, input_vec, count_offset=True):
trans, pose = input_vec[:, :, :3], input_vec[:, :, 3:]
B, seq_len = pose.shape[:2]
pose_mat = rodrigues(pose.reshape(-1, 1,
3)).reshape(B, seq_len, -1, 3, 3)
# pose_mat = [
# rodrigues_vec_to_rotation_mat(a) for a in pose.reshape(-1, 3)
# ]
# pose_mat = np.stack(pose_mat).reshape(B, seq_len, -1, 3, 3)
J = pose_mat.shape[2] - 1 # Exclude root
if count_offset:
trans = trans + self._offsets[:, 0:1]
pose_mat_ordered = pose_mat[:, :, self.smpl_index]
wbody_pos, wbody_mat = self.forward_kinematics_batch(
pose_mat_ordered[:, :, 1:], pose_mat_ordered[:, :, 0:1], trans)
return wbody_pos
def get_ee_pos(self, body_xpos, root_q, transform):
ee_name = SMPL_EE_NAMES
ee_pos = []
root_pos = body_xpos[:, 0, :]
for name in ee_name:
bone_id = self.model._body_name2id[name] - 1
bone_vec = body_xpos[:, bone_id]
if transform is not None:
bone_vec = bone_vec - root_pos
bone_vec = transform_vec_batch(bone_vec, root_q, transform)
ee_pos.append(bone_vec)
return torch.swapaxes(torch.stack(ee_pos, dim=0), 0, 1)
def forward_kinematics_batch(self, rotations, root_rotations,
root_positions):
"""
Perform forward kinematics using the given trajectory and local rotations.
Arguments (where B = batch size, J = number of joints):
-- rotations: (B, J, 4) tensor of unit quaternions describing the local rotations of each joint.
-- root_positions: (B, 3) tensor describing the root joint positions.
Output: joint positions (B, J, 3)
"""
B, seq_len = rotations.shape[0:2]
J = self._offsets.shape[1]
positions_world = []
rotations_world = []
expanded_offsets = np.repeat(np.repeat(self._offsets, B,
axis=0)[:, None, :],
seq_len,
axis=1)
for i in range(J):
if self._parents[i] == -1:
positions_world.append(root_positions)
rotations_world.append(root_rotations)
else:
jpos = (
np.matmul(rotations_world[self._parents[i]][:, :, 0],
expanded_offsets[:, :, i, :, None]).squeeze(-1) +
positions_world[self._parents[i]])
rot_mat = np.matmul(rotations_world[self._parents[i]],
rotations[:, :, (i - 1):i, :])
positions_world.append(jpos)
rotations_world.append(rot_mat)
positions_world = np.stack(positions_world, axis=2)
rotations_world = np.concatenate(rotations_world, axis=2)
return positions_world, rotations_world
if __name__ == "__main__":
torch.manual_seed(0)
cfg = Config(
cfg_id="copycat_44",
create_dirs=False,
)
smpl_robot = Robot(
cfg.robot_cfg,
data_dir=osp.join(cfg.base_dir, "data/smpl"),
masterfoot=False,
)
dataset = DatasetAMASSSingle(cfg.data_specs, "test")
humanoid_batch = Humanoid_Batch()
data_test = dataset.sample_seq() | data_test = dict_to_torch(data_test) | 0 | 2023-10-31 20:47:12+00:00 | 16k |
Improbable-AI/dexenv | dexenv/envs/dclaw_base.py | [
{
"identifier": "VecTask",
"path": "dexenv/envs/base/vec_task.py",
"snippet": "class VecTask(Env):\n\n def __init__(self, config, sim_device, rl_device, graphics_device_id, headless):\n \"\"\"Initialise the `VecTask`.\n Args:\n config: config dictionary for the environment.\n... | import time
import torch
import dexenv
from isaacgym import gymapi
from isaacgym import gymtorch
from isaacgym.gymutil import get_property_getter_map
from isaacgym.gymutil import get_property_setter_map
from isaacgymenvs.utils.torch_jit_utils import *
from loguru import logger
from dexenv.envs.base.vec_task import VecTask
from dexenv.envs.rewards import compute_dclaw_reward
from dexenv.utils.common import get_module_path
from dexenv.utils.common import pathlib_file
from dexenv.utils.hand_color import dclaw_body_color_mapping
from dexenv.utils.isaac_utils import get_camera_params
from dexenv.utils.torch_utils import random_quaternions
from dexenv.utils.torch_utils import torch_long | 10,857 | cam_focus_pt = np.array([0.08, 0, 0.15])
cam_focus_pt = gymapi.Vec3(*cam_focus_pt)
cam_pos = gymapi.Vec3(*cam_pos)
camera_poses = [(cam_pos, cam_focus_pt)]
camera_params = get_camera_params(width=self.cfg.cam.visual_render_width,
height=self.cfg.cam.visual_render_height,
hov=45,
cuda=False)
return camera_poses, camera_params
def create_hand_actor(self, env_ptr, dclaw_asset, dclaw_start_pose, dclaw_dof_props, env_id):
dclaw_actor = self.gym.create_actor(env_ptr, dclaw_asset, dclaw_start_pose, "hand", env_id, 0, 0)
if self.cfg.env.dof_torque_on:
self.gym.enable_actor_dof_force_sensors(env_ptr, dclaw_actor)
self.hand_start_states.append(
[dclaw_start_pose.p.x, dclaw_start_pose.p.y, dclaw_start_pose.p.z,
dclaw_start_pose.r.x, dclaw_start_pose.r.y, dclaw_start_pose.r.z,
dclaw_start_pose.r.w,
0, 0, 0, 0, 0, 0])
self.gym.set_actor_dof_properties(env_ptr, dclaw_actor, dclaw_dof_props)
hand_idx = self.gym.get_actor_index(env_ptr, dclaw_actor, gymapi.DOMAIN_SIM)
self.hand_indices.append(hand_idx)
self.gym.set_actor_dof_states(env_ptr, dclaw_actor, self.dclaw_default_dof_states, gymapi.STATE_ALL)
if self.obs_type == "full_state":
self.gym.enable_actor_dof_force_sensors(env_ptr, dclaw_actor)
self.dclaws.append(dclaw_actor)
self.set_hand_color(env_ptr, dclaw_actor)
def set_hand_color(self, env_ptr, dclaw_actor):
rgd_dict = self.gym.get_actor_rigid_body_dict(env_ptr, dclaw_actor)
for bd, bd_id in rgd_dict.items():
if bd not in dclaw_body_color_mapping:
continue
color = gymapi.Vec3(*dclaw_body_color_mapping[bd])
self.gym.set_rigid_body_color(env_ptr, dclaw_actor,
bd_id, gymapi.MESH_VISUAL,
color)
def get_table_asset(self):
asset_options = gymapi.AssetOptions()
asset_options.armature = 0.001
asset_options.fix_base_link = True
asset_options.thickness = 0.001
asset_options.disable_gravity = True
table_dims = gymapi.Vec3(0.6, 0.6, 0.1)
table_asset = self.gym.create_box(self.sim,
table_dims.x,
table_dims.y,
table_dims.z,
asset_options)
table_props = self.gym.get_asset_rigid_shape_properties(table_asset)
for p in table_props:
p.friction = self.cfg.env.table.friction
p.torsion_friction = self.cfg.env.table.torsion_friction
p.restitution = self.cfg.env.table.restitution
p.rolling_friction = self.cfg.env.table.rolling_friction
self.gym.set_asset_rigid_shape_properties(table_asset, table_props)
return table_asset
def get_table_pose(self):
object_start_pose = gymapi.Transform()
object_start_pose.p = gymapi.Vec3()
object_start_pose.p.x = 0
object_start_pose.p.y = 0
object_start_pose.p.z = -0.05
return object_start_pose
def get_dclaw_start_pose(self):
dclaw_start_pose = gymapi.Transform()
dclaw_start_pose.p = gymapi.Vec3(*get_axis_params(0.25, self.up_axis_idx))
dclaw_start_pose.r = gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 1, 0), np.pi)
return dclaw_start_pose
def setup_torch_states(self):
self.render_rgb_obs_buf = None
if self.cfg.rgb_render:
self.gym.set_light_parameters(self.sim, 0, gymapi.Vec3(0.9, 0.9, 0.9),
gymapi.Vec3(0.9, 0.9, 0.9), gymapi.Vec3(0, 0, 0))
else:
self.gym.set_light_parameters(self.sim, 0, gymapi.Vec3(0.9, 0.9, 0.9),
gymapi.Vec3(0.7, 0.7, 0.7), gymapi.Vec3(0, 0, 0))
self.object_init_state = to_torch(self.object_init_state, device=self.device, dtype=torch.float).view(
self.num_envs, 13)
self.goal_states = self.object_init_state.clone()
self.goal_states[:, self.up_axis_idx] -= 0.04
self.goal_init_state = self.goal_states.clone()
self.hand_start_states = to_torch(self.hand_start_states, device=self.device).view(self.num_envs, 13)
self.fingertip_handles = to_torch(self.fingertip_handles, dtype=torch.long, device=self.device)
self.object_rb_handles = to_torch(self.object_rb_handles, dtype=torch.long, device=self.device)
self.object_rb_masses = None
self.update_obj_mass()
self.hand_indices = to_torch(self.hand_indices, dtype=torch.long, device=self.device)
self.object_indices = to_torch(self.object_indices, dtype=torch.long, device=self.device)
self.goal_object_indices = to_torch(self.goal_object_indices, dtype=torch.long, device=self.device)
def get_dclaw_asset(self, asset_root=None, asset_options=None):
# load dclaw asset
if asset_options is None:
asset_options = gymapi.AssetOptions()
asset_options.flip_visual_attachments = False
asset_options.fix_base_link = True
asset_options.collapse_fixed_joints = False
asset_options.disable_gravity = False
asset_options.thickness = 0.001
asset_options.angular_damping = 0.01
asset_options.override_inertia = True
asset_options.override_com = True
logger.info(f'VHACD:{self.cfg.env.vhacd}')
if self.cfg.env.vhacd:
asset_options.convex_decomposition_from_submeshes = True
if self.cfg.physics_engine == "physx":
# if self.physics_engine == gymapi.SIM_PHYSX:
asset_options.use_physx_armature = True
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS
if asset_root is None:
asset_root = dexenv.LIB_PATH.joinpath('assets', 'dclaw_4f').as_posix()
robot_name = self.cfg.env.robot
|
class DClawBase(VecTask):
def __init__(self, cfg, sim_device, rl_device, graphics_device_id):
self.cfg = cfg
headless = self.cfg.headless
self.randomize = self.cfg["task"]["randomize"]
if self.randomize:
logger.warning(f'Domain randomization is enabled!')
self.randomization_params = self.cfg["task"]["randomization_params"]
self.aggregate_mode = self.cfg["env"]["aggregateMode"]
self.dist_reward_scale = self.cfg["env"]["rew"]["distRewardScale"]
self.rot_reward_scale = self.cfg["env"]["rew"]["rotRewardScale"]
self.success_tolerance = self.cfg["env"]["rew"]["successTolerance"]
self.reach_goal_bonus = self.cfg["env"]["rew"]["reachGoalBonus"]
self.fall_dist = self.cfg["env"]["rew"]["fallDistance"]
self.fall_penalty = self.cfg["env"]["rew"]["fallPenalty"]
self.rot_eps = self.cfg["env"]["rew"]["rotEps"]
self.vel_obs_scale = 0.2 # scale factor of velocity based observations
self.force_torque_obs_scale = 10.0 # scale factor of velocity based observations
self.reset_position_noise = self.cfg["env"]["resetPositionNoise"]
self.reset_rotation_noise = self.cfg["env"]["resetRotationNoise"]
self.reset_dof_pos_noise = self.cfg["env"]["resetDofPosRandomInterval"]
self.reset_dof_vel_noise = self.cfg["env"]["resetDofVelRandomInterval"]
self.force_scale = self.cfg["env"].get("forceScale", 0.0)
self.force_prob_range = self.cfg["env"].get("forceProbRange", [0.001, 0.1])
self.force_decay = self.cfg["env"].get("forceDecay", 0.99)
self.force_decay_interval = self.cfg["env"].get("forceDecayInterval", 0.08)
self.dclaw_dof_speed_scale = self.cfg["env"]["dofSpeedScale"]
# self.act_moving_average = self.cfg["env"]["actionsMovingAverage"]
self.debug_viz = self.cfg["env"]["enableDebugVis"]
self.max_episode_length = self.cfg["env"]["episodeLength"]
self.reset_time = self.cfg["env"].get("resetTime", -1.0)
self.print_success_stat = self.cfg["env"]["printNumSuccesses"]
self.max_consecutive_successes = self.cfg["env"]["maxConsecutiveSuccesses"]
self.av_factor = self.cfg["env"].get("averFactor", 0.1)
self.object_type = self.cfg["env"]["objectType"]
self.asset_files_dict = {
"block": "urdf/objects/cube_multicolor.urdf",
"egg": "mjcf/open_ai_assets/hand/egg.xml",
"airplane": "single_objects/airplane/model.urdf",
'power_drill': 'single_objects/power_drill/model.urdf',
'mug': 'single_objects/mug/model.urdf',
'elephant': 'asymm/train/elephant/var_000/model.urdf',
'train': 'asymm/train/train/var_000/model.urdf',
'stanford_bunny': 'asymm/train/stanford_bunny/var_004/model.urdf'
}
self.objs_in_isaacgym = ['block', 'egg']
if "asset" in self.cfg["env"]:
self.asset_files_dict["block"] = self.cfg["env"]["asset"].get("assetFileNameBlock",
self.asset_files_dict["block"])
self.asset_files_dict["egg"] = self.cfg["env"]["asset"].get("assetFileNameEgg",
self.asset_files_dict["egg"])
self.obs_type = self.cfg["env"]["observationType"]
if not (self.obs_type in ["full_no_vel", "full", "full_state"]):
raise Exception(
"Unknown type of observations!\nobservationType should be one of: [openai, full_no_vel, full, full_state]")
print("Obs type:", self.obs_type)
## TODO: change value here
self.num_obs_dict = {
"full_no_vel": 42,
"full": 87,
"full_state": 114
}
self.up_axis = 'z'
num_states = 0
self.cfg["env"]["numObservations"] = self.num_obs_dict[self.obs_type]
self.cfg["env"]["numStates"] = num_states
self.cfg["env"]["numActions"] = 12
self.hist_buf_reset_env_ids = None
super().__init__(config=self.cfg,
sim_device=sim_device,
rl_device=rl_device,
graphics_device_id=graphics_device_id,
headless=headless)
self.dt = self.sim_params.dt
control_freq_inv = self.cfg["env"].get("controlFrequencyInv", 1)
if self.reset_time > 0.0:
self.max_episode_length = int(round(self.reset_time / (control_freq_inv * self.dt)))
print("Reset time: ", self.reset_time)
print("New episode length: ", self.max_episode_length)
if self.viewer != None:
cam_pos = gymapi.Vec3(0.16, -0.5, 0.5)
cam_target = gymapi.Vec3(0.0, 0.0, 0.15)
self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)
actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor(self.sim)
dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
rigid_body_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim)
dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
if self.obs_type == "full_state":
sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, self.num_fingertips * 6)
dof_force_tensor = self.gym.acquire_dof_force_tensor(self.sim)
self.dof_force_tensor = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs,
self.num_dclaw_dofs)
self.gym.refresh_actor_root_state_tensor(self.sim)
self.gym.refresh_dof_state_tensor(self.sim)
if self.cfg.env.dof_torque_on:
self.gym.refresh_dof_force_tensor(self.sim)
self.gym.refresh_rigid_body_state_tensor(self.sim)
self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
self.dclaw_dof_state = self.dof_state.view(self.num_envs, -1, 2)[:, :self.num_dclaw_dofs]
self.dclaw_dof_pos = self.dclaw_dof_state[..., 0]
self.dclaw_dof_vel = self.dclaw_dof_state[..., 1]
if self.cfg.env.dof_torque_on:
self.dclaw_dof_torque = gymtorch.wrap_tensor(dof_force_tensor).view(self.num_envs, -1)
else:
self.dclaw_dof_torque = None
self.rigid_body_states = gymtorch.wrap_tensor(rigid_body_tensor).view(self.num_envs, -1, 13)
self.num_bodies = self.rigid_body_states.shape[1]
self.root_state_tensor = gymtorch.wrap_tensor(actor_root_state_tensor).view(-1, 13)
if self.cfg.env.rew.pen_tb_contact:
_net_cf = self.gym.acquire_net_contact_force_tensor(self.sim)
self.net_contact_force = gymtorch.wrap_tensor(_net_cf).view(self.num_envs, -1, 3)
table_handle = self.gym.find_actor_handle(self.envs[0], 'table')
self.table_body_index = self.gym.find_actor_rigid_body_index(self.envs[0],
table_handle,
'table',
gymapi.DOMAIN_ENV)
logger.warning(f'Table body index:{self.table_body_index}')
self.table_contact_force = self.net_contact_force[:, self.table_body_index]
self.num_dofs = self.gym.get_sim_dof_count(self.sim) // self.num_envs
self.prev_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.cur_targets = torch.zeros((self.num_envs, self.num_dofs), dtype=torch.float, device=self.device)
self.global_indices = torch.arange(self.num_envs * 3, dtype=torch.int32, device=self.device).view(self.num_envs, -1)
self.reset_goal_buf = self.reset_buf.clone()
self.successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.consecutive_successes = torch.zeros(1, dtype=torch.float, device=self.device)
self.av_factor = to_torch(self.av_factor, dtype=torch.float, device=self.device)
self.total_successes = 0
self.total_resets = 0
self.force_decay = to_torch(self.force_decay, dtype=torch.float, device=self.device)
self.force_prob_range = to_torch(self.force_prob_range, dtype=torch.float, device=self.device)
self.random_force_prob = torch.exp((torch.log(self.force_prob_range[0]) - torch.log(self.force_prob_range[1]))
* torch.rand(self.num_envs, device=self.device) + torch.log(
self.force_prob_range[1]))
self.rb_forces = torch.zeros((self.num_envs, self.num_bodies, 3), dtype=torch.float, device=self.device)
self.num_actions = self.num_dclaw_dofs
self.actions = self.zero_actions()
DClawBase.compute_observations(self)
self.num_observations = self.obs_buf.shape[-1]
self.cfg.env.numObservations = self.num_observations
self.create_ob_act_space()
def create_sim(self):
self.dt = self.cfg["sim"]["dt"]
self.up_axis_idx = self.set_sim_params_up_axis(self.sim_params, self.up_axis)
self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)
self._create_ground_plane()
self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))
if self.randomize:
self.apply_randomizations(self.randomization_params)
def _create_ground_plane(self):
plane_params = gymapi.PlaneParams()
plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
plane_params.distance = 0.1
self.gym.add_ground(self.sim, plane_params)
def _create_envs(self, num_envs, spacing, num_per_row):
lower = gymapi.Vec3(-spacing, -spacing, 0.0)
upper = gymapi.Vec3(spacing, spacing, spacing)
asset_root = dexenv.LIB_PATH.joinpath('assets', 'dclaw').as_posix()
object_asset_file = self.asset_files_dict[self.object_type]
dclaw_asset, dclaw_dof_props = self.get_dclaw_asset(asset_root=asset_root)
table_asset = self.get_table_asset()
table_pose = self.get_table_pose()
if self.obs_type == "full_state":
sensor_pose = gymapi.Transform()
for ft_handle in self.fingertip_handles:
self.gym.create_asset_force_sensor(dclaw_asset, ft_handle, sensor_pose)
if self.object_type in self.objs_in_isaacgym:
asset_root = get_module_path('isaacgymenvs').parent.joinpath('assets').as_posix()
else:
asset_root = dexenv.LIB_PATH.joinpath('assets').as_posix()
object_asset_options = gymapi.AssetOptions()
if self.cfg.env.vhacd:
object_asset_options.convex_decomposition_from_submeshes = True
object_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options)
object_asset_options.disable_gravity = True
goal_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options)
dclaw_start_pose = self.get_dclaw_start_pose()
object_start_pose = self.get_object_start_pose(dclaw_start_pose)
goal_start_pose = self.get_goal_object_start_pose(object_start_pose=object_start_pose)
self.dclaws = []
self.envs = []
self.object_init_state = []
self.hand_start_states = []
self.hand_indices = []
self.fingertip_indices = []
self.object_indices = []
self.goal_object_indices = []
self.render_camera_handles = []
if self.cfg.rgb_render:
render_cam_pose, render_cam_params = self.get_visual_render_camera_setup()
self.fingertip_handles = [self.gym.find_asset_rigid_body_index(dclaw_asset, name) for name in
self.fingertips]
print(f'Fingertip handles:{self.fingertip_handles}')
dclaw_rb_count = self.gym.get_asset_rigid_body_count(dclaw_asset)
object_rb_count = self.gym.get_asset_rigid_body_count(object_asset)
object_rs_count = self.gym.get_asset_rigid_shape_count(object_asset)
self.object_rb_handles = list(range(dclaw_rb_count, dclaw_rb_count + object_rb_count))
self.object_handles = []
max_agg_bodies = self.num_dclaw_bodies + 2 * object_rb_count + 1
max_agg_shapes = self.num_dclaw_shapes + 2 * object_rs_count + 1
for i in range(self.num_envs):
env_ptr = self.gym.create_env(
self.sim, lower, upper, num_per_row
)
if self.aggregate_mode >= 1:
self.gym.begin_aggregate(env_ptr, max_agg_bodies, max_agg_shapes, True)
self.create_hand_actor(env_ptr=env_ptr,
dclaw_asset=dclaw_asset,
dclaw_start_pose=dclaw_start_pose,
dclaw_dof_props=dclaw_dof_props,
env_id=i)
object_handle = self.gym.create_actor(env_ptr, object_asset, object_start_pose, "object", i, 0, 1)
self.object_handles.append(object_handle)
self.object_init_state.append([object_start_pose.p.x, object_start_pose.p.y, object_start_pose.p.z,
object_start_pose.r.x, object_start_pose.r.y, object_start_pose.r.z,
object_start_pose.r.w,
0, 0, 0, 0, 0, 0])
object_idx = self.gym.get_actor_index(env_ptr, object_handle, gymapi.DOMAIN_SIM)
self.object_indices.append(object_idx)
goal_handle = self.gym.create_actor(env_ptr, goal_asset, goal_start_pose, "goal_object", i + self.num_envs,
0, 2)
goal_object_idx = self.gym.get_actor_index(env_ptr, goal_handle, gymapi.DOMAIN_SIM)
self.goal_object_indices.append(goal_object_idx)
if self.cfg.env.blockscale is not None and self.cfg.env.objectType == 'block':
blockscale = float(self.cfg.env.blockscale)
self.gym.set_actor_scale(env_ptr, object_handle, blockscale)
self.gym.set_actor_scale(env_ptr, goal_handle, blockscale)
if self.object_type != "block":
self.gym.set_rigid_body_color(
env_ptr, object_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
self.gym.set_rigid_body_color(
env_ptr, goal_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98))
table_handle = self.gym.create_actor(env_ptr, table_asset, table_pose, "table", i, 0)
if self.cfg.rgb_render:
render_camera_handle = self.create_camera(render_cam_pose, env_ptr, render_cam_params)
self.render_camera_handles.append(render_camera_handle[0])
if self.aggregate_mode > 0:
self.gym.end_aggregate(env_ptr)
self.envs.append(env_ptr)
self.setup_torch_states()
def create_camera(self, camera_poses, env_ptr, camera_params):
cam_handles = []
for ic in range(min(len(camera_poses), self.cfg.cam.cam_num)):
camera_handle = self.gym.create_camera_sensor(env_ptr, camera_params)
if isinstance(camera_poses[ic], tuple):
self.gym.set_camera_location(camera_handle, env_ptr, camera_poses[ic][0], camera_poses[ic][1])
else:
self.gym.set_camera_transform(camera_handle, env_ptr, camera_poses[ic])
cam_handles.append(camera_handle)
return cam_handles
def get_visual_render_camera_setup(self):
cam_pos = np.array([-0.7, 0, 0.5])
cam_focus_pt = np.array([0.08, 0, 0.15])
cam_focus_pt = gymapi.Vec3(*cam_focus_pt)
cam_pos = gymapi.Vec3(*cam_pos)
camera_poses = [(cam_pos, cam_focus_pt)]
camera_params = get_camera_params(width=self.cfg.cam.visual_render_width,
height=self.cfg.cam.visual_render_height,
hov=45,
cuda=False)
return camera_poses, camera_params
def create_hand_actor(self, env_ptr, dclaw_asset, dclaw_start_pose, dclaw_dof_props, env_id):
dclaw_actor = self.gym.create_actor(env_ptr, dclaw_asset, dclaw_start_pose, "hand", env_id, 0, 0)
if self.cfg.env.dof_torque_on:
self.gym.enable_actor_dof_force_sensors(env_ptr, dclaw_actor)
self.hand_start_states.append(
[dclaw_start_pose.p.x, dclaw_start_pose.p.y, dclaw_start_pose.p.z,
dclaw_start_pose.r.x, dclaw_start_pose.r.y, dclaw_start_pose.r.z,
dclaw_start_pose.r.w,
0, 0, 0, 0, 0, 0])
self.gym.set_actor_dof_properties(env_ptr, dclaw_actor, dclaw_dof_props)
hand_idx = self.gym.get_actor_index(env_ptr, dclaw_actor, gymapi.DOMAIN_SIM)
self.hand_indices.append(hand_idx)
self.gym.set_actor_dof_states(env_ptr, dclaw_actor, self.dclaw_default_dof_states, gymapi.STATE_ALL)
if self.obs_type == "full_state":
self.gym.enable_actor_dof_force_sensors(env_ptr, dclaw_actor)
self.dclaws.append(dclaw_actor)
self.set_hand_color(env_ptr, dclaw_actor)
def set_hand_color(self, env_ptr, dclaw_actor):
rgd_dict = self.gym.get_actor_rigid_body_dict(env_ptr, dclaw_actor)
for bd, bd_id in rgd_dict.items():
if bd not in dclaw_body_color_mapping:
continue
color = gymapi.Vec3(*dclaw_body_color_mapping[bd])
self.gym.set_rigid_body_color(env_ptr, dclaw_actor,
bd_id, gymapi.MESH_VISUAL,
color)
def get_table_asset(self):
asset_options = gymapi.AssetOptions()
asset_options.armature = 0.001
asset_options.fix_base_link = True
asset_options.thickness = 0.001
asset_options.disable_gravity = True
table_dims = gymapi.Vec3(0.6, 0.6, 0.1)
table_asset = self.gym.create_box(self.sim,
table_dims.x,
table_dims.y,
table_dims.z,
asset_options)
table_props = self.gym.get_asset_rigid_shape_properties(table_asset)
for p in table_props:
p.friction = self.cfg.env.table.friction
p.torsion_friction = self.cfg.env.table.torsion_friction
p.restitution = self.cfg.env.table.restitution
p.rolling_friction = self.cfg.env.table.rolling_friction
self.gym.set_asset_rigid_shape_properties(table_asset, table_props)
return table_asset
def get_table_pose(self):
object_start_pose = gymapi.Transform()
object_start_pose.p = gymapi.Vec3()
object_start_pose.p.x = 0
object_start_pose.p.y = 0
object_start_pose.p.z = -0.05
return object_start_pose
def get_dclaw_start_pose(self):
dclaw_start_pose = gymapi.Transform()
dclaw_start_pose.p = gymapi.Vec3(*get_axis_params(0.25, self.up_axis_idx))
dclaw_start_pose.r = gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 1, 0), np.pi)
return dclaw_start_pose
def setup_torch_states(self):
self.render_rgb_obs_buf = None
if self.cfg.rgb_render:
self.gym.set_light_parameters(self.sim, 0, gymapi.Vec3(0.9, 0.9, 0.9),
gymapi.Vec3(0.9, 0.9, 0.9), gymapi.Vec3(0, 0, 0))
else:
self.gym.set_light_parameters(self.sim, 0, gymapi.Vec3(0.9, 0.9, 0.9),
gymapi.Vec3(0.7, 0.7, 0.7), gymapi.Vec3(0, 0, 0))
self.object_init_state = to_torch(self.object_init_state, device=self.device, dtype=torch.float).view(
self.num_envs, 13)
self.goal_states = self.object_init_state.clone()
self.goal_states[:, self.up_axis_idx] -= 0.04
self.goal_init_state = self.goal_states.clone()
self.hand_start_states = to_torch(self.hand_start_states, device=self.device).view(self.num_envs, 13)
self.fingertip_handles = to_torch(self.fingertip_handles, dtype=torch.long, device=self.device)
self.object_rb_handles = to_torch(self.object_rb_handles, dtype=torch.long, device=self.device)
self.object_rb_masses = None
self.update_obj_mass()
self.hand_indices = to_torch(self.hand_indices, dtype=torch.long, device=self.device)
self.object_indices = to_torch(self.object_indices, dtype=torch.long, device=self.device)
self.goal_object_indices = to_torch(self.goal_object_indices, dtype=torch.long, device=self.device)
def get_dclaw_asset(self, asset_root=None, asset_options=None):
# load dclaw asset
if asset_options is None:
asset_options = gymapi.AssetOptions()
asset_options.flip_visual_attachments = False
asset_options.fix_base_link = True
asset_options.collapse_fixed_joints = False
asset_options.disable_gravity = False
asset_options.thickness = 0.001
asset_options.angular_damping = 0.01
asset_options.override_inertia = True
asset_options.override_com = True
logger.info(f'VHACD:{self.cfg.env.vhacd}')
if self.cfg.env.vhacd:
asset_options.convex_decomposition_from_submeshes = True
if self.cfg.physics_engine == "physx":
# if self.physics_engine == gymapi.SIM_PHYSX:
asset_options.use_physx_armature = True
asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS
if asset_root is None:
asset_root = dexenv.LIB_PATH.joinpath('assets', 'dclaw_4f').as_posix()
robot_name = self.cfg.env.robot | asset_root = pathlib_file(asset_root).parent.joinpath(f'{robot_name}').as_posix() | 3 | 2023-10-25 17:22:41+00:00 | 16k |
CVHub520/yolov5_obb | detect.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=None, dnn=False):\n # Usage:\n # PyTorch: ... | import argparse
import os
import sys
import cv2
import torch
import torch.backends.cudnn as cudnn
from pathlib import Path
from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
increment_path, non_max_suppression, non_max_suppression_obb, print_args, scale_coords, scale_polys, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync
from utils.rboxs_utils import poly2rbox, rbox2poly | 14,097 | """
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # 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
@torch.no_grad()
def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
project=ROOT / 'runs/detect', # save results to project/name
name='exp', # save results to project/name
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
):
source = str(source)
save_img = not nosave and not source.endswith('.txt') # save inference images
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
if is_url and is_file:
source = check_file(source) # download
# 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
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn)
stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
# Half
half &= (pt or jit or engine) and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
if pt or jit:
model.model.half() if half else model.model.float()
# Dataloader
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
bs = len(dataset) # batch_size
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
bs = 1 # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
model.warmup(imgsz=(1, 3, *imgsz), half=half) # warmup
dt, seen = [0.0, 0.0, 0.0], 0
for path, im, im0s, vid_cap, s in dataset:
t1 = time_sync()
im = torch.from_numpy(im).to(device)
im = im.half() if half else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
t2 = time_sync()
dt[0] += t2 - t1
# Inference
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(im, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
# NMS
# pred: list*(n, [xylsθ, conf, cls]) θ ∈ [-pi/2, pi/2)
pred = non_max_suppression_obb(pred, conf_thres, iou_thres, classes, agnostic_nms, multi_label=True, max_det=max_det)
dt[2] += time_sync() - t3
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Process predictions
for i, det in enumerate(pred): # per image
pred_poly = rbox2poly(det[:, :5]) # (n, [x1 y1 x2 y2 x3 y3 x4 y4])
seen += 1
if webcam: # batch_size >= 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
# Rescale polys from img_size to im0 size
# det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
| # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run inference on images, videos, directories, streams, etc.
Usage:
$ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
"""
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # 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
@torch.no_grad()
def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
project=ROOT / 'runs/detect', # save results to project/name
name='exp', # save results to project/name
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
):
source = str(source)
save_img = not nosave and not source.endswith('.txt') # save inference images
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
if is_url and is_file:
source = check_file(source) # download
# 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
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn)
stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
# Half
half &= (pt or jit or engine) and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
if pt or jit:
model.model.half() if half else model.model.float()
# Dataloader
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
bs = len(dataset) # batch_size
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
bs = 1 # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
model.warmup(imgsz=(1, 3, *imgsz), half=half) # warmup
dt, seen = [0.0, 0.0, 0.0], 0
for path, im, im0s, vid_cap, s in dataset:
t1 = time_sync()
im = torch.from_numpy(im).to(device)
im = im.half() if half else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
t2 = time_sync()
dt[0] += t2 - t1
# Inference
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(im, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
# NMS
# pred: list*(n, [xylsθ, conf, cls]) θ ∈ [-pi/2, pi/2)
pred = non_max_suppression_obb(pred, conf_thres, iou_thres, classes, agnostic_nms, multi_label=True, max_det=max_det)
dt[2] += time_sync() - t3
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Process predictions
for i, det in enumerate(pred): # per image
pred_poly = rbox2poly(det[:, :5]) # (n, [x1 y1 x2 y2 x3 y3 x4 y4])
seen += 1
if webcam: # batch_size >= 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
# Rescale polys from img_size to im0 size
# det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round() | pred_poly = scale_polys(im.shape[2:], pred_poly, im0.shape) | 16 | 2023-10-31 06:06:41+00:00 | 16k |
DataCanvasIO/LMS | lms/runtime/prune/llm_pruner/LLMPruner/peft/mapping.py | [
{
"identifier": "PeftModel",
"path": "lms/runtime/prune/llm_pruner/LLMPruner/peft/peft_model.py",
"snippet": "class PeftModel(PushToHubMixin, torch.nn.Module):\n \"\"\"\n Base model encompassing various Peft methods.\n\n Args:\n model ([`~transformers.PreTrainedModel`]): The base transfo... | from .peft_model import (
PeftModel,
PeftModelForCausalLM,
PeftModelForSeq2SeqLM,
PeftModelForSequenceClassification,
PeftModelForTokenClassification,
)
from .tuners import AdaLoraConfig, LoraConfig, PrefixTuningConfig, PromptEncoderConfig, PromptTuningConfig
from .utils import PromptLearningConfig | 14,279 | # 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.
MODEL_TYPE_TO_PEFT_MODEL_MAPPING = {
"SEQ_CLS": PeftModelForSequenceClassification,
"SEQ_2_SEQ_LM": PeftModelForSeq2SeqLM,
"CAUSAL_LM": PeftModelForCausalLM,
"TOKEN_CLS": PeftModelForTokenClassification,
}
PEFT_TYPE_TO_CONFIG_MAPPING = {
"PROMPT_TUNING": PromptTuningConfig,
| # 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.
MODEL_TYPE_TO_PEFT_MODEL_MAPPING = {
"SEQ_CLS": PeftModelForSequenceClassification,
"SEQ_2_SEQ_LM": PeftModelForSeq2SeqLM,
"CAUSAL_LM": PeftModelForCausalLM,
"TOKEN_CLS": PeftModelForTokenClassification,
}
PEFT_TYPE_TO_CONFIG_MAPPING = {
"PROMPT_TUNING": PromptTuningConfig, | "PREFIX_TUNING": PrefixTuningConfig, | 8 | 2023-10-30 10:50:32+00:00 | 16k |
chenran-li/RQL-release | stable_baselines3/a2c/a2c.py | [
{
"identifier": "OnPolicyAlgorithm",
"path": "stable_baselines3/common/on_policy_algorithm.py",
"snippet": "class OnPolicyAlgorithm(BaseAlgorithm):\n \"\"\"\n The base for On-Policy algorithms (ex: A2C/PPO).\n\n :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)\n :param env:... | from typing import Any, Dict, Optional, Type, TypeVar, Union
from gym import spaces
from torch.nn import functional as F
from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy, BasePolicy, MultiInputActorCriticPolicy
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.common.utils import explained_variance
import torch as th | 10,962 | :param _init_setup_model: Whether or not to build the network at the creation of the instance
"""
policy_aliases: Dict[str, Type[BasePolicy]] = {
"MlpPolicy": ActorCriticPolicy,
"CnnPolicy": ActorCriticCnnPolicy,
"MultiInputPolicy": MultiInputActorCriticPolicy,
}
def __init__(
self,
policy: Union[str, Type[ActorCriticPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Schedule] = 7e-4,
n_steps: int = 5,
gamma: float = 0.99,
gae_lambda: float = 1.0,
ent_coef: float = 0.0,
vf_coef: float = 0.5,
max_grad_norm: float = 0.5,
rms_prop_eps: float = 1e-5,
use_rms_prop: bool = True,
use_sde: bool = False,
sde_sample_freq: int = -1,
normalize_advantage: bool = False,
tensorboard_log: Optional[str] = None,
policy_kwargs: Optional[Dict[str, Any]] = None,
verbose: int = 0,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
_init_setup_model: bool = True,
):
super().__init__(
policy,
env,
learning_rate=learning_rate,
n_steps=n_steps,
gamma=gamma,
gae_lambda=gae_lambda,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
use_sde=use_sde,
sde_sample_freq=sde_sample_freq,
tensorboard_log=tensorboard_log,
policy_kwargs=policy_kwargs,
verbose=verbose,
device=device,
seed=seed,
_init_setup_model=False,
supported_action_spaces=(
spaces.Box,
spaces.Discrete,
spaces.MultiDiscrete,
spaces.MultiBinary,
),
)
self.normalize_advantage = normalize_advantage
# Update optimizer inside the policy if we want to use RMSProp
# (original implementation) rather than Adam
if use_rms_prop and "optimizer_class" not in self.policy_kwargs:
self.policy_kwargs["optimizer_class"] = th.optim.RMSprop
self.policy_kwargs["optimizer_kwargs"] = dict(alpha=0.99, eps=rms_prop_eps, weight_decay=0)
if _init_setup_model:
self._setup_model()
def train(self) -> None:
"""
Update policy using the currently gathered
rollout buffer (one gradient step over whole data).
"""
# Switch to train mode (this affects batch norm / dropout)
self.policy.set_training_mode(True)
# Update optimizer learning rate
self._update_learning_rate(self.policy.optimizer)
# This will only loop once (get all data in one go)
for rollout_data in self.rollout_buffer.get(batch_size=None):
actions = rollout_data.actions
if isinstance(self.action_space, spaces.Discrete):
# Convert discrete action from float to long
actions = actions.long().flatten()
values, log_prob, entropy = self.policy.evaluate_actions(rollout_data.observations, actions)
values = values.flatten()
# Normalize advantage (not present in the original implementation)
advantages = rollout_data.advantages
if self.normalize_advantage:
advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
# Policy gradient loss
policy_loss = -(advantages * log_prob).mean()
# Value loss using the TD(gae_lambda) target
value_loss = F.mse_loss(rollout_data.returns, values)
# Entropy loss favor exploration
if entropy is None:
# Approximate entropy when no analytical form
entropy_loss = -th.mean(-log_prob)
else:
entropy_loss = -th.mean(entropy)
loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss
# Optimization step
self.policy.optimizer.zero_grad()
loss.backward()
# Clip grad norm
th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
self.policy.optimizer.step()
|
SelfA2C = TypeVar("SelfA2C", bound="A2C")
class A2C(OnPolicyAlgorithm):
"""
Advantage Actor Critic (A2C)
Paper: https://arxiv.org/abs/1602.01783
Code: This implementation borrows code from https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail and
and Stable Baselines (https://github.com/hill-a/stable-baselines)
Introduction to A2C: https://hackernoon.com/intuitive-rl-intro-to-advantage-actor-critic-a2c-4ff545978752
:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
:param env: The environment to learn from (if registered in Gym, can be str)
:param learning_rate: The learning rate, it can be a function
of the current progress remaining (from 1 to 0)
:param n_steps: The number of steps to run for each environment per update
(i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
:param gamma: Discount factor
:param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator
Equivalent to classic advantage when set to 1.
:param ent_coef: Entropy coefficient for the loss calculation
:param vf_coef: Value function coefficient for the loss calculation
:param max_grad_norm: The maximum value for the gradient clipping
:param rms_prop_eps: RMSProp epsilon. It stabilizes square root computation in denominator
of RMSProp update
:param use_rms_prop: Whether to use RMSprop (default) or Adam as optimizer
:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
instead of action noise exploration (default: False)
:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
Default: -1 (only sample at the beginning of the rollout)
:param normalize_advantage: Whether to normalize or not the advantage
:param tensorboard_log: the log location for tensorboard (if None, no logging)
:param policy_kwargs: additional arguments to be passed to the policy on creation
:param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
debug messages
:param seed: Seed for the pseudo random generators
:param device: Device (cpu, cuda, ...) on which the code should be run.
Setting it to auto, the code will be run on the GPU if possible.
:param _init_setup_model: Whether or not to build the network at the creation of the instance
"""
policy_aliases: Dict[str, Type[BasePolicy]] = {
"MlpPolicy": ActorCriticPolicy,
"CnnPolicy": ActorCriticCnnPolicy,
"MultiInputPolicy": MultiInputActorCriticPolicy,
}
def __init__(
self,
policy: Union[str, Type[ActorCriticPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Schedule] = 7e-4,
n_steps: int = 5,
gamma: float = 0.99,
gae_lambda: float = 1.0,
ent_coef: float = 0.0,
vf_coef: float = 0.5,
max_grad_norm: float = 0.5,
rms_prop_eps: float = 1e-5,
use_rms_prop: bool = True,
use_sde: bool = False,
sde_sample_freq: int = -1,
normalize_advantage: bool = False,
tensorboard_log: Optional[str] = None,
policy_kwargs: Optional[Dict[str, Any]] = None,
verbose: int = 0,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
_init_setup_model: bool = True,
):
super().__init__(
policy,
env,
learning_rate=learning_rate,
n_steps=n_steps,
gamma=gamma,
gae_lambda=gae_lambda,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
use_sde=use_sde,
sde_sample_freq=sde_sample_freq,
tensorboard_log=tensorboard_log,
policy_kwargs=policy_kwargs,
verbose=verbose,
device=device,
seed=seed,
_init_setup_model=False,
supported_action_spaces=(
spaces.Box,
spaces.Discrete,
spaces.MultiDiscrete,
spaces.MultiBinary,
),
)
self.normalize_advantage = normalize_advantage
# Update optimizer inside the policy if we want to use RMSProp
# (original implementation) rather than Adam
if use_rms_prop and "optimizer_class" not in self.policy_kwargs:
self.policy_kwargs["optimizer_class"] = th.optim.RMSprop
self.policy_kwargs["optimizer_kwargs"] = dict(alpha=0.99, eps=rms_prop_eps, weight_decay=0)
if _init_setup_model:
self._setup_model()
def train(self) -> None:
"""
Update policy using the currently gathered
rollout buffer (one gradient step over whole data).
"""
# Switch to train mode (this affects batch norm / dropout)
self.policy.set_training_mode(True)
# Update optimizer learning rate
self._update_learning_rate(self.policy.optimizer)
# This will only loop once (get all data in one go)
for rollout_data in self.rollout_buffer.get(batch_size=None):
actions = rollout_data.actions
if isinstance(self.action_space, spaces.Discrete):
# Convert discrete action from float to long
actions = actions.long().flatten()
values, log_prob, entropy = self.policy.evaluate_actions(rollout_data.observations, actions)
values = values.flatten()
# Normalize advantage (not present in the original implementation)
advantages = rollout_data.advantages
if self.normalize_advantage:
advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
# Policy gradient loss
policy_loss = -(advantages * log_prob).mean()
# Value loss using the TD(gae_lambda) target
value_loss = F.mse_loss(rollout_data.returns, values)
# Entropy loss favor exploration
if entropy is None:
# Approximate entropy when no analytical form
entropy_loss = -th.mean(-log_prob)
else:
entropy_loss = -th.mean(entropy)
loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss
# Optimization step
self.policy.optimizer.zero_grad()
loss.backward()
# Clip grad norm
th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
self.policy.optimizer.step()
| explained_var = explained_variance(self.rollout_buffer.values.flatten(), self.rollout_buffer.returns.flatten()) | 6 | 2023-10-28 01:09:21+00:00 | 16k |
zyang1580/CoLLM | minigpt4/runners/runner_base_rec.py | [
{
"identifier": "download_cached_file",
"path": "minigpt4/common/dist_utils.py",
"snippet": "def download_cached_file(url, check_hash=True, progress=False):\n \"\"\"\n Download a file from a URL and cache it locally. If the file already exists, it is not downloaded again.\n If distributed, only... | import datetime
import json
import logging
import os
import time
import torch
import torch.distributed as dist
import webdataset as wds
from pathlib import Path
from minigpt4.common.dist_utils import (
download_cached_file,
get_rank,
get_world_size,
is_main_process,
main_process,
)
from minigpt4.common.registry import registry
from minigpt4.common.utils import is_url
from minigpt4.datasets.data_utils import concat_datasets, reorg_datasets_by_split, ChainDataset
from minigpt4.datasets.datasets.dataloader_utils import (
IterLoader,
MultiIterLoader,
PrefetchLoader,
)
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, DistributedSampler
from minigpt4.runners.runner_base import RunnerBase | 11,148 | # sampler=sampler,
# shuffle=sampler is None and is_train,
# collate_fn=collate_fn,
# drop_last=True if is_train else False,
# )
# loader = PrefetchLoader(loader)
# if is_train:
# loader = IterLoader(loader, use_distributed=self.use_distributed)
# return loader
# loaders = []
# for dataset, bsz, is_train, collate_fn in zip(
# datasets, batch_sizes, is_trains, collate_fns
# ):
# if isinstance(dataset, list) or isinstance(dataset, tuple):
# if hasattr(dataset[0], 'sample_ratio') and dataset_ratios is None:
# dataset_ratios = [d.sample_ratio for d in dataset]
# loader = MultiIterLoader(
# loaders=[
# _create_loader(d, num_workers, bsz, is_train, collate_fn[i])
# for i, d in enumerate(dataset)
# ],
# ratios=dataset_ratios,
# )
# else:
# loader = _create_loader(dataset, num_workers, bsz, is_train, collate_fn)
# loaders.append(loader)
# return loaders
# @main_process
# def _save_checkpoint(self, cur_epoch, is_best=False):
# """
# Save the checkpoint at the current epoch.
# """
# model_no_ddp = self.unwrap_dist_model(self.model)
# param_grad_dic = {
# k: v.requires_grad for (k, v) in model_no_ddp.named_parameters()
# }
# state_dict = model_no_ddp.state_dict()
# for k in list(state_dict.keys()):
# if k in param_grad_dic.keys() and not param_grad_dic[k]:
# # delete parameters that do not require gradient
# del state_dict[k]
# save_obj = {
# "model": state_dict,
# "optimizer": self.optimizer.state_dict(),
# "config": self.config.to_dict(),
# "scaler": self.scaler.state_dict() if self.scaler else None,
# "epoch": cur_epoch,
# }
# save_to = os.path.join(
# self.output_dir,
# "checkpoint_{}.pth".format("best" if is_best else cur_epoch),
# )
# logging.info("Saving checkpoint at epoch {} to {}.".format(cur_epoch, save_to))
# torch.save(save_obj, save_to)
# def _reload_best_model(self, model):
# """
# Load the best checkpoint for evaluation.
# """
# checkpoint_path = os.path.join(self.output_dir, "checkpoint_best.pth")
# logging.info("Loading checkpoint from {}.".format(checkpoint_path))
# checkpoint = torch.load(checkpoint_path, map_location="cpu")
# try:
# model.load_state_dict(checkpoint["model"])
# except RuntimeError as e:
# logging.warning(
# """
# Key mismatch when loading checkpoint. This is expected if only part of the model is saved.
# Trying to load the model with strict=False.
# """
# )
# model.load_state_dict(checkpoint["model"], strict=False)
# return model
# def _load_checkpoint(self, url_or_filename):
# """
# Resume from a checkpoint.
# """
# if is_url(url_or_filename):
# cached_file = download_cached_file(
# url_or_filename, check_hash=False, progress=True
# )
# checkpoint = torch.load(cached_file, map_location=self.device)
# elif os.path.isfile(url_or_filename):
# checkpoint = torch.load(url_or_filename, map_location=self.device)
# else:
# raise RuntimeError("checkpoint url or path is invalid")
# state_dict = checkpoint["model"]
# self.unwrap_dist_model(self.model).load_state_dict(state_dict,strict=False)
# self.optimizer.load_state_dict(checkpoint["optimizer"])
# if self.scaler and "scaler" in checkpoint:
# self.scaler.load_state_dict(checkpoint["scaler"])
# self.start_epoch = checkpoint["epoch"] + 1
# logging.info("Resume checkpoint from {}".format(url_or_filename))
# @main_process
# def log_stats(self, stats, split_name):
# if isinstance(stats, dict):
# log_stats = {**{f"{split_name}_{k}": v for k, v in stats.items()}}
# with open(os.path.join(self.output_dir, "log.txt"), "a") as f:
# f.write(json.dumps(log_stats) + "\n")
# elif isinstance(stats, list):
# pass
# @main_process
# def log_config(self):
# with open(os.path.join(self.output_dir, "log.txt"), "a") as f:
# f.write(json.dumps(self.config.to_dict(), indent=4) + "\n")
| """
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
# @registry.register_runner("rec_runner_base")
# class RecRunnerBase:
# """
# A runner class to train and evaluate a model given a task and datasets.
# The runner uses pytorch distributed data parallel by default. Future release
# will support other distributed frameworks.
# """
# def __init__(self, cfg, task, model, datasets, job_id):
# self.config = cfg
# self.job_id = job_id
# self.task = task
# self.datasets = datasets
# self._model = model
# self._wrapped_model = None
# self._device = None
# self._optimizer = None
# self._scaler = None
# self._dataloaders = None
# self._lr_sched = None
# self.start_epoch = 0
# # self.setup_seeds()
# self.setup_output_dir()
# @property
# def device(self):
# if self._device is None:
# self._device = torch.device(self.config.run_cfg.device)
# return self._device
# @property
# def use_distributed(self):
# return self.config.run_cfg.distributed
# @property
# def model(self):
# """
# A property to get the DDP-wrapped model on the device.
# """
# # move model to device
# if self._model.device != self.device:
# self._model = self._model.to(self.device)
# # distributed training wrapper
# if self.use_distributed:
# if self._wrapped_model is None:
# self._wrapped_model = DDP(
# self._model, device_ids=[self.config.run_cfg.gpu]
# )
# else:
# self._wrapped_model = self._model
# return self._wrapped_model
# @property
# def optimizer(self):
# # TODO make optimizer class and configurations
# if self._optimizer is None:
# num_parameters = 0
# p_wd, p_non_wd = [], []
# for n, p in self.model.named_parameters():
# if not p.requires_grad:
# continue # frozen weights
# print(n)
# if p.ndim < 2 or "bias" in n or "ln" in n or "bn" in n:
# p_non_wd.append(p)
# else:
# p_wd.append(p)
# num_parameters += p.data.nelement()
# logging.info("number of trainable parameters: %d" % num_parameters)
# optim_params = [
# {
# "params": p_wd,
# "weight_decay": float(self.config.run_cfg.weight_decay),
# },
# {"params": p_non_wd, "weight_decay": 0},
# ]
# beta2 = self.config.run_cfg.get("beta2", 0.999)
# self._optimizer = torch.optim.AdamW(
# optim_params,
# lr=float(self.config.run_cfg.init_lr),
# weight_decay=float(self.config.run_cfg.weight_decay),
# betas=(0.9, beta2),
# )
# return self._optimizer
# @property
# def scaler(self):
# amp = self.config.run_cfg.get("amp", False)
# if amp:
# if self._scaler is None:
# self._scaler = torch.cuda.amp.GradScaler()
# return self._scaler
# @property
# def lr_scheduler(self):
# """
# A property to get and create learning rate scheduler by split just in need.
# """
# if self._lr_sched is None:
# lr_sched_cls = registry.get_lr_scheduler_class(self.config.run_cfg.lr_sched)
# # max_epoch = self.config.run_cfg.max_epoch
# max_epoch = self.max_epoch
# # min_lr = self.config.run_cfg.min_lr
# min_lr = self.min_lr
# # init_lr = self.config.run_cfg.init_lr
# init_lr = self.init_lr
# # optional parameters
# decay_rate = self.config.run_cfg.get("lr_decay_rate", None)
# warmup_start_lr = self.config.run_cfg.get("warmup_lr", -1)
# warmup_steps = self.config.run_cfg.get("warmup_steps", 0)
# iters_per_epoch = self.config.run_cfg.get("iters_per_epoch", None)
# if iters_per_epoch is None:
# try:
# iters_per_epoch = len(self.dataloaders['train'])
# except (AttributeError, TypeError):
# iters_per_epoch = 10000
# self._lr_sched = lr_sched_cls(
# optimizer=self.optimizer,
# max_epoch=max_epoch,
# iters_per_epoch=iters_per_epoch,
# min_lr=min_lr,
# init_lr=init_lr,
# decay_rate=decay_rate,
# warmup_start_lr=warmup_start_lr,
# warmup_steps=warmup_steps,
# )
# return self._lr_sched
# @property
# def dataloaders(self) -> dict:
# """
# A property to get and create dataloaders by split just in need.
# If no train_dataset_ratio is provided, concatenate map-style datasets and
# chain wds.DataPipe datasets separately. Training set becomes a tuple
# (ConcatDataset, ChainDataset), both are optional but at least one of them is
# required. The resultant ConcatDataset and ChainDataset will be sampled evenly.
# If train_dataset_ratio is provided, create a MultiIterLoader to sample
# each dataset by ratios during training.
# Currently do not support multiple datasets for validation and test.
# Returns:
# dict: {split_name: (tuples of) dataloader}
# """
# if self._dataloaders is None:
# # concatenate map-style datasets and chain wds.DataPipe datasets separately
# # training set becomes a tuple (ConcatDataset, ChainDataset), both are
# # optional but at least one of them is required. The resultant ConcatDataset
# # and ChainDataset will be sampled evenly.
# logging.info(
# "dataset_ratios not specified, datasets will be concatenated (map-style datasets) or chained (webdataset.DataPipeline)."
# )
# datasets = reorg_datasets_by_split(self.datasets)
# self.datasets = datasets
# # self.datasets = concat_datasets(datasets)
# # print dataset statistics after concatenation/chaining
# for split_name in self.datasets:
# if isinstance(self.datasets[split_name], tuple) or isinstance(
# self.datasets[split_name], list
# ):
# # mixed wds.DataPipeline and torch.utils.data.Dataset
# num_records = sum(
# [
# len(d)
# if not type(d) in [wds.DataPipeline, ChainDataset]
# else 0
# for d in self.datasets[split_name]
# ]
# )
# else:
# if hasattr(self.datasets[split_name], "__len__"):
# # a single map-style dataset
# num_records = len(self.datasets[split_name])
# else:
# # a single wds.DataPipeline
# num_records = -1
# logging.info(
# "Only a single wds.DataPipeline dataset, no __len__ attribute."
# )
# if num_records >= 0:
# logging.info(
# "Loaded {} records for {} split from the dataset.".format(
# num_records, split_name
# )
# )
# # create dataloaders
# split_names = sorted(self.datasets.keys())
# datasets = [self.datasets[split] for split in split_names]
# is_trains = [split in self.train_splits for split in split_names]
# batch_sizes = [
# self.config.run_cfg.batch_size_train
# if split == "train"
# else self.config.run_cfg.batch_size_eval
# for split in split_names
# ]
# collate_fns = []
# for dataset in datasets:
# if isinstance(dataset, tuple) or isinstance(dataset, list):
# collate_fns.append([getattr(d, "collater", None) for d in dataset])
# else:
# collate_fns.append(getattr(dataset, "collater", None))
# dataloaders = self.create_loaders(
# datasets=datasets,
# num_workers=self.config.run_cfg.num_workers,
# batch_sizes=batch_sizes,
# is_trains=is_trains,
# collate_fns=collate_fns,
# )
# self._dataloaders = {k: v for k, v in zip(split_names, dataloaders)}
# return self._dataloaders
# @property
# def cuda_enabled(self):
# return self.device.type == "cuda"
# @property
# def max_epoch(self):
# return int(self.config.run_cfg.max_epoch)
# @property
# def log_freq(self):
# log_freq = self.config.run_cfg.get("log_freq", 50)
# return int(log_freq)
# @property
# def init_lr(self):
# return float(self.config.run_cfg.init_lr)
# @property
# def min_lr(self):
# return float(self.config.run_cfg.min_lr)
# @property
# def accum_grad_iters(self):
# return int(self.config.run_cfg.get("accum_grad_iters", 1))
# @property
# def valid_splits(self):
# valid_splits = self.config.run_cfg.get("valid_splits", [])
# if len(valid_splits) == 0:
# logging.info("No validation splits found.")
# return valid_splits
# @property
# def test_splits(self):
# test_splits = self.config.run_cfg.get("test_splits", [])
# return test_splits
# @property
# def train_splits(self):
# train_splits = self.config.run_cfg.get("train_splits", [])
# if len(train_splits) == 0:
# logging.info("Empty train splits.")
# return train_splits
# @property
# def evaluate_only(self):
# """
# Set to True to skip training.
# """
# return self.config.run_cfg.evaluate
# @property
# def use_dist_eval_sampler(self):
# return self.config.run_cfg.get("use_dist_eval_sampler", True)
# @property
# def resume_ckpt_path(self):
# return self.config.run_cfg.get("resume_ckpt_path", None)
# @property
# def train_loader(self):
# train_dataloader = self.dataloaders["train"]
# return train_dataloader
# def setup_output_dir(self):
# lib_root = Path(registry.get_path("library_root"))
# output_dir = lib_root / self.config.run_cfg.output_dir / self.job_id
# result_dir = output_dir / "result"
# output_dir.mkdir(parents=True, exist_ok=True)
# result_dir.mkdir(parents=True, exist_ok=True)
# registry.register_path("result_dir", str(result_dir))
# registry.register_path("output_dir", str(output_dir))
# self.result_dir = result_dir
# self.output_dir = output_dir
# def train(self):
# start_time = time.time()
# best_agg_metric = 0
# best_epoch = 0
# self.log_config()
# # resume from checkpoint if specified
# if not self.evaluate_only and self.resume_ckpt_path is not None:
# self._load_checkpoint(self.resume_ckpt_path)
# for cur_epoch in range(self.start_epoch, self.max_epoch):
# # training phase
# if not self.evaluate_only:
# logging.info("Start training")
# train_stats = self.train_epoch(cur_epoch)
# self.log_stats(split_name="train", stats=train_stats)
# # evaluation phase
# if len(self.valid_splits) > 0:
# for split_name in self.valid_splits:
# logging.info("Evaluating on {}.".format(split_name))
# val_log = self.eval_epoch(
# split_name=split_name, cur_epoch=cur_epoch
# )
# if val_log is not None:
# if is_main_process():
# assert (
# "agg_metrics" in val_log
# ), "No agg_metrics found in validation log."
# agg_metrics = val_log["agg_metrics"]
# if agg_metrics > best_agg_metric and split_name == "val":
# best_epoch, best_agg_metric = cur_epoch, agg_metrics
# self._save_checkpoint(cur_epoch, is_best=True)
# val_log.update({"best_epoch": best_epoch})
# self.log_stats(val_log, split_name)
# else:
# # if no validation split is provided, we just save the checkpoint at the end of each epoch.
# if not self.evaluate_only:
# self._save_checkpoint(cur_epoch, is_best=False)
# if self.evaluate_only:
# break
# if self.config.run_cfg.distributed:
# dist.barrier()
# # testing phase
# test_epoch = "best" if len(self.valid_splits) > 0 else cur_epoch
# self.evaluate(cur_epoch=test_epoch, skip_reload=self.evaluate_only)
# total_time = time.time() - start_time
# total_time_str = str(datetime.timedelta(seconds=int(total_time)))
# logging.info("Training time {}".format(total_time_str))
# def evaluate(self, cur_epoch="best", skip_reload=False):
# test_logs = dict()
# if len(self.test_splits) > 0:
# for split_name in self.test_splits:
# test_logs[split_name] = self.eval_epoch(
# split_name=split_name, cur_epoch=cur_epoch, skip_reload=skip_reload
# )
# return test_logs
# def train_epoch(self, epoch):
# # train
# self.model.train()
# return self.task.train_epoch(
# epoch=epoch,
# model=self.model,
# data_loader=self.train_loader,
# optimizer=self.optimizer,
# scaler=self.scaler,
# lr_scheduler=self.lr_scheduler,
# cuda_enabled=self.cuda_enabled,
# log_freq=self.log_freq,
# accum_grad_iters=self.accum_grad_iters,
# )
# @torch.no_grad()
# def eval_epoch(self, split_name, cur_epoch, skip_reload=False):
# """
# Evaluate the model on a given split.
# Args:
# split_name (str): name of the split to evaluate on.
# cur_epoch (int): current epoch.
# skip_reload_best (bool): whether to skip reloading the best checkpoint.
# During training, we will reload the best checkpoint for validation.
# During testing, we will use provided weights and skip reloading the best checkpoint .
# """
# self.model.eval()
# data_loader = self.dataloaders.get(split_name, None)
# assert data_loader, "data_loader for split {} is None.".format(split_name)
# # TODO In validation, you need to compute loss as well as metrics
# # TODO consider moving to model.before_evaluation()
# model = self.unwrap_dist_model(self.model)
# if not skip_reload and cur_epoch == "best":
# model = self._reload_best_model(model)
# model.eval()
# self.task.before_evaluation(
# model=model,
# dataset=self.datasets[split_name],
# )
# results = self.task.evaluation(model, data_loader)
# if results is not None:
# return self.task.after_evaluation(
# val_result=results,
# split_name=split_name,
# epoch=cur_epoch,
# )
# @torch.no_grad()
# def eval_epoch_new(self, split_name, cur_epoch):
# """
# Evaluate the model on a given split.
# Args:
# split_name (str): name of the split to evaluate on.
# cur_epoch (int): current epoch.
# skip_reload_best (bool): whether to skip reloading the best checkpoint.
# During training, we will reload the best checkpoint for validation.
# During testing, we will use provided weights and skip reloading the best checkpoint .
# """
# data_loader = self.dataloaders.get(split_name, None)
# assert data_loader, "data_loader for split {} is None.".format(split_name)
# # TODO In validation, you need to compute loss as well as metrics
# # TODO consider moving to model.before_evaluation()
# model = self.unwrap_dist_model(self.model)
# # if not skip_reload and cur_epoch == "best":
# # model = self._reload_best_model(model)
# model.eval()
# self.task.before_evaluation(
# model=model,
# dataset=self.datasets[split_name],
# )
# results = self.task.evaluation(model, data_loader)
# return results
# # if results is not None:
# # return self.task.after_evaluation(
# # val_result=results,
# # split_name=split_name,
# # epoch=cur_epoch,
# # )
# def unwrap_dist_model(self, model):
# if self.use_distributed:
# return model.module
# else:
# return model
# def create_loaders(
# self,
# datasets,
# num_workers,
# batch_sizes,
# is_trains,
# collate_fns,
# dataset_ratios=None,
# ):
# """
# Create dataloaders for training and validation.
# """
# def _create_loader(dataset, num_workers, bsz, is_train, collate_fn):
# # create a single dataloader for each split
# if isinstance(dataset, ChainDataset) or isinstance(
# dataset, wds.DataPipeline
# ):
# # wds.WebdDataset instance are chained together
# # webdataset.DataPipeline has its own sampler and collate_fn
# loader = iter(
# DataLoader(
# dataset,
# batch_size=bsz,
# num_workers=num_workers,
# pin_memory=True,
# )
# )
# else:
# # map-style dataset are concatenated together
# # setup distributed sampler
# if self.use_distributed:
# sampler = DistributedSampler(
# dataset,
# shuffle=is_train,
# num_replicas=get_world_size(),
# rank=get_rank(),
# )
# if not self.use_dist_eval_sampler:
# # e.g. retrieval evaluation
# sampler = sampler if is_train else None
# else:
# sampler = None
# loader = DataLoader(
# dataset,
# batch_size=bsz,
# num_workers=num_workers,
# pin_memory=True,
# sampler=sampler,
# shuffle=sampler is None and is_train,
# collate_fn=collate_fn,
# drop_last=True if is_train else False,
# )
# loader = PrefetchLoader(loader)
# if is_train:
# loader = IterLoader(loader, use_distributed=self.use_distributed)
# return loader
# loaders = []
# for dataset, bsz, is_train, collate_fn in zip(
# datasets, batch_sizes, is_trains, collate_fns
# ):
# if isinstance(dataset, list) or isinstance(dataset, tuple):
# if hasattr(dataset[0], 'sample_ratio') and dataset_ratios is None:
# dataset_ratios = [d.sample_ratio for d in dataset]
# loader = MultiIterLoader(
# loaders=[
# _create_loader(d, num_workers, bsz, is_train, collate_fn[i])
# for i, d in enumerate(dataset)
# ],
# ratios=dataset_ratios,
# )
# else:
# loader = _create_loader(dataset, num_workers, bsz, is_train, collate_fn)
# loaders.append(loader)
# return loaders
# @main_process
# def _save_checkpoint(self, cur_epoch, is_best=False):
# """
# Save the checkpoint at the current epoch.
# """
# model_no_ddp = self.unwrap_dist_model(self.model)
# param_grad_dic = {
# k: v.requires_grad for (k, v) in model_no_ddp.named_parameters()
# }
# state_dict = model_no_ddp.state_dict()
# for k in list(state_dict.keys()):
# if k in param_grad_dic.keys() and not param_grad_dic[k]:
# # delete parameters that do not require gradient
# del state_dict[k]
# save_obj = {
# "model": state_dict,
# "optimizer": self.optimizer.state_dict(),
# "config": self.config.to_dict(),
# "scaler": self.scaler.state_dict() if self.scaler else None,
# "epoch": cur_epoch,
# }
# save_to = os.path.join(
# self.output_dir,
# "checkpoint_{}.pth".format("best" if is_best else cur_epoch),
# )
# logging.info("Saving checkpoint at epoch {} to {}.".format(cur_epoch, save_to))
# torch.save(save_obj, save_to)
# def _reload_best_model(self, model):
# """
# Load the best checkpoint for evaluation.
# """
# checkpoint_path = os.path.join(self.output_dir, "checkpoint_best.pth")
# logging.info("Loading checkpoint from {}.".format(checkpoint_path))
# checkpoint = torch.load(checkpoint_path, map_location="cpu")
# try:
# model.load_state_dict(checkpoint["model"])
# except RuntimeError as e:
# logging.warning(
# """
# Key mismatch when loading checkpoint. This is expected if only part of the model is saved.
# Trying to load the model with strict=False.
# """
# )
# model.load_state_dict(checkpoint["model"], strict=False)
# return model
# def _load_checkpoint(self, url_or_filename):
# """
# Resume from a checkpoint.
# """
# if is_url(url_or_filename):
# cached_file = download_cached_file(
# url_or_filename, check_hash=False, progress=True
# )
# checkpoint = torch.load(cached_file, map_location=self.device)
# elif os.path.isfile(url_or_filename):
# checkpoint = torch.load(url_or_filename, map_location=self.device)
# else:
# raise RuntimeError("checkpoint url or path is invalid")
# state_dict = checkpoint["model"]
# self.unwrap_dist_model(self.model).load_state_dict(state_dict,strict=False)
# self.optimizer.load_state_dict(checkpoint["optimizer"])
# if self.scaler and "scaler" in checkpoint:
# self.scaler.load_state_dict(checkpoint["scaler"])
# self.start_epoch = checkpoint["epoch"] + 1
# logging.info("Resume checkpoint from {}".format(url_or_filename))
# @main_process
# def log_stats(self, stats, split_name):
# if isinstance(stats, dict):
# log_stats = {**{f"{split_name}_{k}": v for k, v in stats.items()}}
# with open(os.path.join(self.output_dir, "log.txt"), "a") as f:
# f.write(json.dumps(log_stats) + "\n")
# elif isinstance(stats, list):
# pass
# @main_process
# def log_config(self):
# with open(os.path.join(self.output_dir, "log.txt"), "a") as f:
# f.write(json.dumps(self.config.to_dict(), indent=4) + "\n")
| @registry.register_runner("rec_runner_base") | 5 | 2023-10-29 12:47:25+00:00 | 16k |
tobagin/whakarere | whakarere/windows/whakarere.py | [
{
"identifier": "ConfigManager",
"path": "whakarere/managers/config.py",
"snippet": "class ConfigManager:\n def __init__(self, window):\n self.window = window\n self.config = {}\n self.config_file_path = os.path.expanduser(\"~/.config/whakarere/config.json\")\n atexit.regi... | import gi
from whakarere.managers.config import ConfigManager
from whakarere.managers.session import SessionManager
from whakarere.managers.whatsapp import WhatsAppSessionManager
from whakarere.widgets.titlebar import WindowTitlebarWidget
from whakarere.widgets.main_menu import MainMenuButtonWidget
from whakarere.pages.session import SessionManagerPage
from whakarere.pages.session2 import SessionManagerPage2
from whakarere.windows.account_wizard import AccountWizardWindow
from gi.repository import Adw, Gtk, Gdk | 11,819 |
gi.require_version('Gtk', '4.0')
gi.require_version('Adw', '1')
gi.require_version("Gdk", "4.0")
class WhakarereMainWindow(Adw.ApplicationWindow):
def __init__(self, app, debug=False, dev=False):
super().__init__(application=app)
self.app = app
self.debug = debug
self.dev = dev
self.settings = Gtk.Settings.get_default()
self.settings.connect("notify::gtk-theme-name", self.on_theme_changed)
# Initial CSS application
self.update_css_for_theme()
# Set the window size and default close behavior
self.set_default_size(800, 600)
self.set_hide_on_close(True)
# Create the config manager and load the config file
self.config_manager = ConfigManager(self)
self.config_manager.load_config()
# Create the session manager and load the sessions
self.session_manager = SessionManager(self)
self.session_manager.load_sessions()
# Create the whatsapp manager and initialize the active sessions
self.whatsapp_manager = WhatsAppSessionManager(self)
self.whatsapp_manager.initialize()
# Create TitleBar Widget
self.window_titlebar_widget = Adw.WindowTitle()
self.window_titlebar_widget.set_title("Whakarere")
self.window_titlebar_widget.set_subtitle("Your Gtk4 Whatsapp Client.")
# Create MainMenu Button Widget
|
gi.require_version('Gtk', '4.0')
gi.require_version('Adw', '1')
gi.require_version("Gdk", "4.0")
class WhakarereMainWindow(Adw.ApplicationWindow):
def __init__(self, app, debug=False, dev=False):
super().__init__(application=app)
self.app = app
self.debug = debug
self.dev = dev
self.settings = Gtk.Settings.get_default()
self.settings.connect("notify::gtk-theme-name", self.on_theme_changed)
# Initial CSS application
self.update_css_for_theme()
# Set the window size and default close behavior
self.set_default_size(800, 600)
self.set_hide_on_close(True)
# Create the config manager and load the config file
self.config_manager = ConfigManager(self)
self.config_manager.load_config()
# Create the session manager and load the sessions
self.session_manager = SessionManager(self)
self.session_manager.load_sessions()
# Create the whatsapp manager and initialize the active sessions
self.whatsapp_manager = WhatsAppSessionManager(self)
self.whatsapp_manager.initialize()
# Create TitleBar Widget
self.window_titlebar_widget = Adw.WindowTitle()
self.window_titlebar_widget.set_title("Whakarere")
self.window_titlebar_widget.set_subtitle("Your Gtk4 Whatsapp Client.")
# Create MainMenu Button Widget | self.button_settings_menu = MainMenuButtonWidget() | 4 | 2023-10-29 15:46:50+00:00 | 16k |
KHU-VLL/CAST | dataset/datasets.py | [
{
"identifier": "TubeMaskingGenerator",
"path": "util_tools/masking_generator.py",
"snippet": "class TubeMaskingGenerator:\n def __init__(self, input_size, mask_ratio):\n self.frames, self.height, self.width = input_size\n self.num_patches_per_frame = self.height * self.width\n ... | import os
from torchvision import transforms
from util_tools.transforms import *
from util_tools.masking_generator import TubeMaskingGenerator
from .kinetics import VideoClsDataset, VideoMAE
from .ssv2 import SSVideoClsDataset
from .epic import EpicVideoClsDataset | 11,175 | self.input_std = [0.229, 0.224, 0.225] # IMAGENET_DEFAULT_STD
normalize = GroupNormalize(self.input_mean, self.input_std)
self.train_augmentation = GroupMultiScaleCrop(args.input_size, [1, .875, .75, .66])
self.transform = transforms.Compose([
self.train_augmentation,
Stack(roll=False),
ToTorchFormatTensor(div=True),
normalize,
])
if args.mask_type == 'tube':
self.masked_position_generator = TubeMaskingGenerator(
args.window_size, args.mask_ratio
)
def __call__(self, images):
process_data, _ = self.transform(images)
return process_data, self.masked_position_generator()
def __repr__(self):
repr = "(DataAugmentationForVideoMAE,\n"
repr += " transform = %s,\n" % str(self.transform)
repr += " Masked position generator = %s,\n" % str(self.masked_position_generator)
repr += ")"
return repr
def build_pretraining_dataset(args):
transform = DataAugmentationForVideoMAE(args)
dataset = VideoMAE(
root=None,
setting=args.data_path,
video_ext='mp4',
is_color=True,
modality='rgb',
new_length=args.num_frames,
new_step=args.sampling_rate,
transform=transform,
temporal_jitter=False,
video_loader=True,
use_decord=True,
lazy_init=False)
print("Data Aug = %s" % str(transform))
return dataset
def build_dataset(is_train, test_mode, args):
if args.data_set == 'Kinetics-400':
mode = None
anno_path = args.anno_path
if is_train is True:
mode = 'train'
anno_path = os.path.join(args.anno_path, 'train.csv')
elif test_mode is True:
mode = 'test'
anno_path = os.path.join(args.anno_path, 'val.csv')
else:
mode = 'validation'
anno_path = os.path.join(args.anno_path, 'val.csv')
dataset = VideoClsDataset(
anno_path=anno_path,
data_path=args.data_path,
mode=mode,
clip_len=args.num_frames,
frame_sample_rate=args.sampling_rate,
num_segment=1,
test_num_segment=args.test_num_segment,
test_num_crop=args.test_num_crop,
num_crop=1 if not test_mode else 3,
keep_aspect_ratio=True,
crop_size=args.input_size,
short_side_size=args.short_side_size,
new_height=256,
new_width=320,
args=args)
nb_classes = 400
elif args.data_set == 'SSV2':
mode = None
anno_path = None
if is_train is True:
mode = 'train'
anno_path = os.path.join(args.anno_path, 'train.csv')
elif test_mode is True:
mode = 'test'
anno_path = os.path.join(args.anno_path, 'val.csv')
else:
mode = 'validation'
anno_path = os.path.join(args.anno_path, 'val.csv')
dataset = SSVideoClsDataset(
anno_path=anno_path,
data_path=args.data_path,
mode=mode,
clip_len=1,
num_segment=args.num_frames,
test_num_segment=args.test_num_segment,
test_num_crop=args.test_num_crop,
num_crop=1 if not test_mode else 3,
keep_aspect_ratio=True,
crop_size=args.input_size,
short_side_size=args.short_side_size,
new_height=256,
new_width=320,
args=args)
nb_classes = 174
elif args.data_set == 'EPIC':
mode = None
anno_path = None
if is_train is True:
mode = 'train'
anno_path = os.path.join(args.anno_path, 'train.csv')
elif test_mode is True:
mode = 'test'
anno_path = os.path.join(args.anno_path, 'val.csv')
else:
mode = 'validation'
anno_path = os.path.join(args.anno_path, 'val.csv')
|
class DataAugmentationForVideoMAE(object):
def __init__(self, args):
self.input_mean = [0.485, 0.456, 0.406] # IMAGENET_DEFAULT_MEAN
self.input_std = [0.229, 0.224, 0.225] # IMAGENET_DEFAULT_STD
normalize = GroupNormalize(self.input_mean, self.input_std)
self.train_augmentation = GroupMultiScaleCrop(args.input_size, [1, .875, .75, .66])
self.transform = transforms.Compose([
self.train_augmentation,
Stack(roll=False),
ToTorchFormatTensor(div=True),
normalize,
])
if args.mask_type == 'tube':
self.masked_position_generator = TubeMaskingGenerator(
args.window_size, args.mask_ratio
)
def __call__(self, images):
process_data, _ = self.transform(images)
return process_data, self.masked_position_generator()
def __repr__(self):
repr = "(DataAugmentationForVideoMAE,\n"
repr += " transform = %s,\n" % str(self.transform)
repr += " Masked position generator = %s,\n" % str(self.masked_position_generator)
repr += ")"
return repr
def build_pretraining_dataset(args):
transform = DataAugmentationForVideoMAE(args)
dataset = VideoMAE(
root=None,
setting=args.data_path,
video_ext='mp4',
is_color=True,
modality='rgb',
new_length=args.num_frames,
new_step=args.sampling_rate,
transform=transform,
temporal_jitter=False,
video_loader=True,
use_decord=True,
lazy_init=False)
print("Data Aug = %s" % str(transform))
return dataset
def build_dataset(is_train, test_mode, args):
if args.data_set == 'Kinetics-400':
mode = None
anno_path = args.anno_path
if is_train is True:
mode = 'train'
anno_path = os.path.join(args.anno_path, 'train.csv')
elif test_mode is True:
mode = 'test'
anno_path = os.path.join(args.anno_path, 'val.csv')
else:
mode = 'validation'
anno_path = os.path.join(args.anno_path, 'val.csv')
dataset = VideoClsDataset(
anno_path=anno_path,
data_path=args.data_path,
mode=mode,
clip_len=args.num_frames,
frame_sample_rate=args.sampling_rate,
num_segment=1,
test_num_segment=args.test_num_segment,
test_num_crop=args.test_num_crop,
num_crop=1 if not test_mode else 3,
keep_aspect_ratio=True,
crop_size=args.input_size,
short_side_size=args.short_side_size,
new_height=256,
new_width=320,
args=args)
nb_classes = 400
elif args.data_set == 'SSV2':
mode = None
anno_path = None
if is_train is True:
mode = 'train'
anno_path = os.path.join(args.anno_path, 'train.csv')
elif test_mode is True:
mode = 'test'
anno_path = os.path.join(args.anno_path, 'val.csv')
else:
mode = 'validation'
anno_path = os.path.join(args.anno_path, 'val.csv')
dataset = SSVideoClsDataset(
anno_path=anno_path,
data_path=args.data_path,
mode=mode,
clip_len=1,
num_segment=args.num_frames,
test_num_segment=args.test_num_segment,
test_num_crop=args.test_num_crop,
num_crop=1 if not test_mode else 3,
keep_aspect_ratio=True,
crop_size=args.input_size,
short_side_size=args.short_side_size,
new_height=256,
new_width=320,
args=args)
nb_classes = 174
elif args.data_set == 'EPIC':
mode = None
anno_path = None
if is_train is True:
mode = 'train'
anno_path = os.path.join(args.anno_path, 'train.csv')
elif test_mode is True:
mode = 'test'
anno_path = os.path.join(args.anno_path, 'val.csv')
else:
mode = 'validation'
anno_path = os.path.join(args.anno_path, 'val.csv')
| dataset = EpicVideoClsDataset( | 4 | 2023-10-25 07:07:05+00:00 | 16k |
OpenProteinAI/PoET | scripts/score.py | [
{
"identifier": "Uniprot21",
"path": "poet/alphabets.py",
"snippet": "class Uniprot21(Alphabet):\n def __init__(\n self,\n mask=False,\n include_gap=False,\n include_startstop=False,\n distinct_startstop=False,\n ):\n chars = b\"ARNDCQEGHILKMFPSTWYV\"\n ... | import argparse
import itertools
import string
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pathlib import Path
from typing import Callable, Optional, Sequence, TypeVar
from torch.nn.utils.rnn import pad_sequence
from tqdm import tqdm, trange
from poet.alphabets import Uniprot21
from poet.fasta import parse_stream
from poet.models.modules.packed_sequence import PackedTensorSequences
from poet.models.poet import PoET
from poet.msa.sampling import MSASampler, NeighborsSampler | 11,929 | [torch.from_numpy(v).long() for v in this_variants],
batch_first=True,
padding_value=alphabet.mask_token,
)
if this_variants.size(1) < max_variant_length:
this_variants = F.pad(
this_variants,
(0, max_variant_length - this_variants.size(1)),
value=alphabet.mask_token,
)
assert (this_variants == alphabet.gap_token).sum() == 0
this_variants = this_variants.cuda()
logits = model.logits(this_variants[:, :-1], memory, preallocated_memory=True)
targets = this_variants[:, 1:]
score = -criteria.forward(logits.transpose(1, 2), targets).float().sum(dim=1)
logps.append(score.cpu().numpy())
return np.hstack(logps)
def get_logps_tiered_fast(
msa_sequences: Sequence[np.ndarray],
variants: Sequence[np.ndarray],
model: PoET,
batch_size: int,
alphabet: Uniprot21,
pbar_position: Optional[int] = None,
) -> np.ndarray:
if len(msa_sequences) > 0:
segment_sizes = torch.tensor([len(s) for s in msa_sequences]).cuda()
msa_sequences: torch.Tensor = torch.cat(
[torch.from_numpy(s).long() for s in msa_sequences]
).cuda()
memory = model.embed(
msa_sequences.unsqueeze(0),
segment_sizes.unsqueeze(0),
pbar_position=pbar_position,
)
else:
memory = None
return _get_logps_tiered_fast(
memory=memory,
variants=variants,
model=model,
batch_size=batch_size,
alphabet=alphabet,
pbar_position=pbar_position,
)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--ckpt_path", type=str, default="data/poet.ckpt")
parser.add_argument(
"--msa_a3m_path", type=str, default="data/BLAT_ECOLX_ColabFold_2202.a3m"
)
parser.add_argument(
"--variants_fasta_path",
type=str,
default="data/BLAT_ECOLX_Jacquier_2013_variants.fasta",
)
parser.add_argument(
"--output_npy_path",
type=str,
default="data/BLAT_ECOLX_Jacquier_2013_variants.npy",
)
parser.add_argument("--batch_size", type=int, default=8)
parser.add_argument("--seed", type=int, default=188257)
parser.add_argument(
"--debug",
action="store_true",
help="run only 1/15 params from the msa sampling and filtering ensemble",
)
args = parser.parse_args()
args.msa_a3m_path = Path(args.msa_a3m_path)
args.variants_fasta_path = Path(args.variants_fasta_path)
args.output_npy_path = Path(args.output_npy_path)
return args
@torch.inference_mode()
def main():
args = parse_args()
# load model
ckpt = torch.load(args.ckpt_path)
model = PoET(**ckpt["hyper_parameters"]["model_spec"]["init_args"])
model.load_state_dict(
{k.split(".", 1)[1]: v for k, v in ckpt["state_dict"].items()}
)
del ckpt
model = model.cuda().half().eval()
alphabet = Uniprot21(
include_gap=True, include_startstop=True, distinct_startstop=True
)
jit_warmup(model, alphabet)
# get variants to score
variants = [
append_startstop(alphabet.encode(v), alphabet=alphabet)
for v in get_seqs_from_fastalike(args.variants_fasta_path)
]
# process msa
msa_sequences = get_seqs_from_fastalike(args.msa_a3m_path)
msa = get_encoded_msa_from_a3m_seqs(msa_sequences=msa_sequences, alphabet=alphabet)
# score the variants
logps = []
if not args.debug:
params = list(
itertools.product(
[6144, 12288, 24576],
[1.0, 0.95, 0.90, 0.70, 0.50],
)
)
else:
params = [(12288, 0.95)]
for max_tokens, max_similarity in tqdm(params, desc="ensemble"):
|
ASCII_LOWERCASE_BYTES = string.ascii_lowercase.encode()
PBAR_POSITION = 1
T = TypeVar("T", np.ndarray, torch.Tensor)
def append_startstop(x: T, alphabet: Uniprot21) -> T:
x_ndim = x.ndim
assert x_ndim in {1, 2}
if x_ndim == 1:
x = x[None, :]
if isinstance(x, torch.Tensor):
empty_func = torch.empty
else:
empty_func = np.empty
x_ = empty_func((x.shape[0], x.shape[1] + 2), dtype=x.dtype)
x_[:, 0] = alphabet.start_token
x_[:, -1] = alphabet.stop_token
x_[:, 1:-1] = x
if x_ndim == 1:
x_ = x_.flatten()
return x_
def get_seqs_from_fastalike(filepath: Path) -> list[bytes]:
return [s for _, s in parse_stream(open(filepath, "rb"), upper=False)]
def get_encoded_msa_from_a3m_seqs(
msa_sequences: list[bytes], alphabet: Uniprot21
) -> np.ndarray:
return np.vstack(
[
alphabet.encode(s.translate(None, delete=ASCII_LOWERCASE_BYTES))
for s in msa_sequences
]
)
def sample_msa_sequences(
get_sequence_fn: Callable[[int], bytes],
sample_idxs: Sequence[int],
max_tokens: int,
alphabet: Uniprot21,
shuffle: bool = True,
shuffle_seed: Optional[int] = None,
truncate: bool = True,
) -> list[np.ndarray]:
assert alphabet.start_token != -1
assert alphabet.stop_token != -1
if not shuffle:
assert shuffle_seed is None
seqs, total_tokens = [], 0
for idx in sample_idxs:
next_sequence = get_sequence_fn(idx)
seqs.append(append_startstop(alphabet.encode(next_sequence), alphabet=alphabet))
total_tokens += len(seqs[-1])
if total_tokens > max_tokens:
break
# shuffle order and truncate to max tokens
if shuffle:
rng = (
np.random.default_rng(shuffle_seed)
if shuffle_seed is not None
else np.random
)
final_permutation = rng.permutation(len(seqs))
else:
final_permutation = np.arange(len(seqs))
final_seqs, total_tokens = [], 0
for seq in [seqs[i] for i in final_permutation]:
if truncate and (total_tokens + len(seq) > max_tokens):
seq = seq[: max_tokens - total_tokens]
total_tokens += len(seq)
final_seqs.append(seq)
if total_tokens >= max_tokens:
break
return final_seqs
def jit_warmup(embedding_model: PoET, alphabet: Uniprot21):
x = b"$WAAAGH*$WAAGW*"
segment_sizes = [8, 7]
x = alphabet.encode(x) # encode x into the uniprot21 alphabet
x = torch.from_numpy(x).long().cuda()
segment_sizes = torch.tensor(segment_sizes).long().cuda()
_ = embedding_model.embed(x.unsqueeze(0), segment_sizes.unsqueeze(0))
def _get_logps_tiered_fast(
memory: Optional[list[PackedTensorSequences]],
variants: Sequence[np.ndarray],
model: PoET,
batch_size: int,
alphabet: Uniprot21,
pbar_position: Optional[int] = None,
) -> np.ndarray:
max_variant_length = max(len(v) for v in variants)
memory = model.logits_allocate_memory(
memory=memory,
batch_size=batch_size,
length=max_variant_length - 1, # discount stop token
)
criteria = nn.CrossEntropyLoss(ignore_index=alphabet.mask_token, reduction="none")
logps = []
if pbar_position is not None:
pbar = trange(
0,
len(variants),
batch_size,
desc=f"[{pbar_position}] decoding",
leave=False,
position=pbar_position,
)
else:
pbar = range(0, len(variants), batch_size)
for start_idx in pbar:
this_variants = variants[start_idx : start_idx + batch_size]
this_variants = pad_sequence(
[torch.from_numpy(v).long() for v in this_variants],
batch_first=True,
padding_value=alphabet.mask_token,
)
if this_variants.size(1) < max_variant_length:
this_variants = F.pad(
this_variants,
(0, max_variant_length - this_variants.size(1)),
value=alphabet.mask_token,
)
assert (this_variants == alphabet.gap_token).sum() == 0
this_variants = this_variants.cuda()
logits = model.logits(this_variants[:, :-1], memory, preallocated_memory=True)
targets = this_variants[:, 1:]
score = -criteria.forward(logits.transpose(1, 2), targets).float().sum(dim=1)
logps.append(score.cpu().numpy())
return np.hstack(logps)
def get_logps_tiered_fast(
msa_sequences: Sequence[np.ndarray],
variants: Sequence[np.ndarray],
model: PoET,
batch_size: int,
alphabet: Uniprot21,
pbar_position: Optional[int] = None,
) -> np.ndarray:
if len(msa_sequences) > 0:
segment_sizes = torch.tensor([len(s) for s in msa_sequences]).cuda()
msa_sequences: torch.Tensor = torch.cat(
[torch.from_numpy(s).long() for s in msa_sequences]
).cuda()
memory = model.embed(
msa_sequences.unsqueeze(0),
segment_sizes.unsqueeze(0),
pbar_position=pbar_position,
)
else:
memory = None
return _get_logps_tiered_fast(
memory=memory,
variants=variants,
model=model,
batch_size=batch_size,
alphabet=alphabet,
pbar_position=pbar_position,
)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--ckpt_path", type=str, default="data/poet.ckpt")
parser.add_argument(
"--msa_a3m_path", type=str, default="data/BLAT_ECOLX_ColabFold_2202.a3m"
)
parser.add_argument(
"--variants_fasta_path",
type=str,
default="data/BLAT_ECOLX_Jacquier_2013_variants.fasta",
)
parser.add_argument(
"--output_npy_path",
type=str,
default="data/BLAT_ECOLX_Jacquier_2013_variants.npy",
)
parser.add_argument("--batch_size", type=int, default=8)
parser.add_argument("--seed", type=int, default=188257)
parser.add_argument(
"--debug",
action="store_true",
help="run only 1/15 params from the msa sampling and filtering ensemble",
)
args = parser.parse_args()
args.msa_a3m_path = Path(args.msa_a3m_path)
args.variants_fasta_path = Path(args.variants_fasta_path)
args.output_npy_path = Path(args.output_npy_path)
return args
@torch.inference_mode()
def main():
args = parse_args()
# load model
ckpt = torch.load(args.ckpt_path)
model = PoET(**ckpt["hyper_parameters"]["model_spec"]["init_args"])
model.load_state_dict(
{k.split(".", 1)[1]: v for k, v in ckpt["state_dict"].items()}
)
del ckpt
model = model.cuda().half().eval()
alphabet = Uniprot21(
include_gap=True, include_startstop=True, distinct_startstop=True
)
jit_warmup(model, alphabet)
# get variants to score
variants = [
append_startstop(alphabet.encode(v), alphabet=alphabet)
for v in get_seqs_from_fastalike(args.variants_fasta_path)
]
# process msa
msa_sequences = get_seqs_from_fastalike(args.msa_a3m_path)
msa = get_encoded_msa_from_a3m_seqs(msa_sequences=msa_sequences, alphabet=alphabet)
# score the variants
logps = []
if not args.debug:
params = list(
itertools.product(
[6144, 12288, 24576],
[1.0, 0.95, 0.90, 0.70, 0.50],
)
)
else:
params = [(12288, 0.95)]
for max_tokens, max_similarity in tqdm(params, desc="ensemble"): | sampler = MSASampler( | 4 | 2023-10-28 01:30:26+00:00 | 16k |
Transconnectome/SwiFT | interpretation/integrated_gradient.py | [
{
"identifier": "SwinTransformer4D",
"path": "project/module/models/swin4d_transformer_ver7.py",
"snippet": "class SwinTransformer4D(nn.Module):\n \"\"\"\n Swin Transformer based on: \"Liu et al.,\n Swin Transformer: Hierarchical Vision Transformer using Shifted Windows\n <https://arxiv.org/... | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import json
import numpy as np
import torchvision
import matplotlib.pyplot as plt
from PIL import Image
from tqdm import tqdm
from matplotlib.colors import LinearSegmentedColormap
from torchvision import models
from torchvision import transforms
from captum.attr import IntegratedGradients
from captum.attr import GradientShap
from captum.attr import Occlusion
from captum.attr import NoiseTunnel
from captum.attr import visualization as viz
from matplotlib.colors import LogNorm
from project.module.models.swin4d_transformer_ver7 import SwinTransformer4D
from project.module.pl_classifier import LitClassifier
from project.module.utils.data_module import fMRIDataModule
from pathlib import Path | 12,871 |
save_dir = # write path to save_dir
jobid = # write project number
neptune_project_id = # write project id. ex)user_id/project_name
for i in Path(f'SwiFT/output/{neptune_project_id}/RSTOT-{jobid}/').glob('checkpt*'):
ckpt_path = i
ckpt = torch.load(ckpt_path, map_location='cuda:0' if torch.cuda.is_available() else 'cpu')
ckpt['hyper_parameters']['image_path'] = # write path to MNI_to_TRs folder
ckpt['hyper_parameters']['default_root_dir'] = # write path to use default_root_dir
ckpt['hyper_parameters']['shuffle_time_sequence'] = False
ckpt['hyper_parameters']['time_as_channel'] = False
ckpt['hyper_parameters']['eval_batch_size'] = 1
args = ckpt['hyper_parameters']
|
save_dir = # write path to save_dir
jobid = # write project number
neptune_project_id = # write project id. ex)user_id/project_name
for i in Path(f'SwiFT/output/{neptune_project_id}/RSTOT-{jobid}/').glob('checkpt*'):
ckpt_path = i
ckpt = torch.load(ckpt_path, map_location='cuda:0' if torch.cuda.is_available() else 'cpu')
ckpt['hyper_parameters']['image_path'] = # write path to MNI_to_TRs folder
ckpt['hyper_parameters']['default_root_dir'] = # write path to use default_root_dir
ckpt['hyper_parameters']['shuffle_time_sequence'] = False
ckpt['hyper_parameters']['time_as_channel'] = False
ckpt['hyper_parameters']['eval_batch_size'] = 1
args = ckpt['hyper_parameters']
| model = LitClassifier(**args) | 1 | 2023-10-28 09:26:03+00:00 | 16k |
TheCompAce/ShellSpeak | main.py | [
{
"identifier": "VectorDatabase",
"path": "modules/vectorDatabase.py",
"snippet": "class VectorDatabase:\n def __init__(self, path, name):\n self.path = path\n self.name = name\n self.db_path = os.path.join(path, f'{name}.db')\n self.model_path = os.path.join(path, f'{name... | import json
import os
import sys
import asyncio
import json
from modules.vectorDatabase import VectorDatabase
from datetime import datetime
from modules.menus.setup_menu import save_settings, setup_menu
from modules.shellSpeak import ShellSpeak
from modules.utils import load_settings | 11,491 | # from modules.vectors import load_faiss_index, build_and_save_faiss_index, load_index_data, needs_index_update
def run_async_function(func, *args):
asyncio.run(func(*args))
async def start_shell_speak(settings, base_path, vector_db):
await main_start(settings, base_path, vector_db)
async def main_start(settings, base_path, vector_db):
# Initialize VectorDatabase here if needed globally
shellSpeak = ShellSpeak(settings, base_path, vector_db)
await shellSpeak.run()
def main():
base_path = os.path.abspath(".")
settings = load_settings(base_path)
# FAISS Index check and build prompt
if settings.get('use_indexing', False):
system_folder_path = os.path.join(base_path, 'system')
# history_json_path = os.path.join(system_folder_path, 'history.json')
vector_db_path = os.path.join(system_folder_path, 'vector')
vector_db = VectorDatabase(path=settings.get('vector_db_path', system_folder_path),
name=settings.get('vector_db_name', vector_db_path))
if not os.path.exists(system_folder_path):
os.makedirs(system_folder_path)
# Check if 'system' folder and 'history.json' exist
# if not os.path.exists(system_folder_path) or not os.path.exists(history_json_path):
# settings['use_indexing'] = False
if vector_db.needs_index_update():
user_decision = input("A new index needs to be built. Do you want to build it now? (yes/no): ")
if user_decision.lower() in ['yes', 'y']:
print("Building index... (Grab a Coffee.)")
vector_db.train_untrained_responses()
print("Index built and saved successfully.")
settings['last_build_date'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
save_settings(settings, os.path.join(base_path, 'settings.json'))
else:
print("Skipping index building.")
# Check for command-line arguments
if len(sys.argv) > 1 and sys.argv[1] == '/start':
run_async_function(start_shell_speak, settings, base_path, vector_db)
return
# Display menu
while True:
print("\nMenu:")
print("1. Setup")
print("2. Run")
print("3. Exit")
print("-----------------------------------------------------------------")
print("(You can also start the script with /start to Start automaticly.)")
print("-----------------------------------------------------------------")
choice = input("Choose an option: ")
if choice == '1':
| # from modules.vectors import load_faiss_index, build_and_save_faiss_index, load_index_data, needs_index_update
def run_async_function(func, *args):
asyncio.run(func(*args))
async def start_shell_speak(settings, base_path, vector_db):
await main_start(settings, base_path, vector_db)
async def main_start(settings, base_path, vector_db):
# Initialize VectorDatabase here if needed globally
shellSpeak = ShellSpeak(settings, base_path, vector_db)
await shellSpeak.run()
def main():
base_path = os.path.abspath(".")
settings = load_settings(base_path)
# FAISS Index check and build prompt
if settings.get('use_indexing', False):
system_folder_path = os.path.join(base_path, 'system')
# history_json_path = os.path.join(system_folder_path, 'history.json')
vector_db_path = os.path.join(system_folder_path, 'vector')
vector_db = VectorDatabase(path=settings.get('vector_db_path', system_folder_path),
name=settings.get('vector_db_name', vector_db_path))
if not os.path.exists(system_folder_path):
os.makedirs(system_folder_path)
# Check if 'system' folder and 'history.json' exist
# if not os.path.exists(system_folder_path) or not os.path.exists(history_json_path):
# settings['use_indexing'] = False
if vector_db.needs_index_update():
user_decision = input("A new index needs to be built. Do you want to build it now? (yes/no): ")
if user_decision.lower() in ['yes', 'y']:
print("Building index... (Grab a Coffee.)")
vector_db.train_untrained_responses()
print("Index built and saved successfully.")
settings['last_build_date'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
save_settings(settings, os.path.join(base_path, 'settings.json'))
else:
print("Skipping index building.")
# Check for command-line arguments
if len(sys.argv) > 1 and sys.argv[1] == '/start':
run_async_function(start_shell_speak, settings, base_path, vector_db)
return
# Display menu
while True:
print("\nMenu:")
print("1. Setup")
print("2. Run")
print("3. Exit")
print("-----------------------------------------------------------------")
print("(You can also start the script with /start to Start automaticly.)")
print("-----------------------------------------------------------------")
choice = input("Choose an option: ")
if choice == '1': | setup_menu() | 1 | 2023-10-31 23:35:19+00:00 | 16k |
qym7/SparseDiff | sparse_diffusion/diffusion_model_sparse.py | [
{
"identifier": "utils",
"path": "sparse_diffusion/utils.py",
"snippet": "def setup_wandb(cfg):\ndef create_folders(args):\ndef to_dense(x, edge_index, edge_attr, batch, charge):\ndef to_dense_node(x, batch):\ndef to_dense_edge(edge_index, edge_attr, batch, max_num_nodes):\ndef encode_no_edge(E):\ndef t... | import time
import os
import math
import pickle
import json
import torch
import wandb
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
from tqdm import tqdm
from models.conv_transformer_model import GraphTransformerConv
from diffusion.noise_schedule import (
PredefinedNoiseScheduleDiscrete,
MarginalUniformTransition,
)
from metrics.train_metrics import TrainLossDiscrete
from metrics.abstract_metrics import SumExceptBatchMetric, SumExceptBatchKL, NLL
from analysis.visualization import Visualizer
from sparse_diffusion import utils
from sparse_diffusion.diffusion import diffusion_utils
from sparse_diffusion.diffusion.sample_edges_utils import (
get_computational_graph,
mask_query_graph_from_comp_graph,
sample_non_existing_edge_attr,
condensed_to_matrix_index_batch,
)
from sparse_diffusion.diffusion.sample_edges import (
sample_query_edges,
sample_non_existing_edges_batched,
sampled_condensed_indices_uniformly,
)
from sparse_diffusion.models.sign_pos_encoder import SignNetNodeEncoder | 10,971 |
def sample_node_edge(
self, pred, p_s_and_t_given_0_X, p_s_and_t_given_0_E, node_mask
):
_, prob_X = self.sample_node(pred.X, p_s_and_t_given_0_X, node_mask)
_, prob_E = self.sample_edge(pred.E, p_s_and_t_given_0_E, node_mask)
sampled_s = diffusion_utils.sample_discrete_features(
prob_X, prob_E, node_mask=node_mask
)
return sampled_s
def sample_sparse_node(self, pred_node, p_s_and_t_given_0_X):
# Normalize predictions
pred_X = F.softmax(pred_node, dim=-1) # N, dx
# Dim of the second tensor: N, dx, dx
weighted_X = pred_X.unsqueeze(-1) * p_s_and_t_given_0_X # N, dx, dx
unnormalized_prob_X = weighted_X.sum(dim=1) # N, dx
unnormalized_prob_X[
torch.sum(unnormalized_prob_X, dim=-1) == 0
] = 1e-5 # TODO: delete/masking?
prob_X = unnormalized_prob_X / torch.sum(
unnormalized_prob_X, dim=-1, keepdim=True
) # N, dx
assert ((prob_X.sum(dim=-1) - 1).abs() < 1e-4).all()
X_t = prob_X.multinomial(1)[:, 0]
return X_t
def sample_sparse_edge(self, pred_edge, p_s_and_t_given_0_E):
# Normalize predictions
pred_E = F.softmax(pred_edge, dim=-1) # N, d0
# Dim of the second tensor: N, d0, dt-1
weighted_E = pred_E.unsqueeze(-1) * p_s_and_t_given_0_E # N, d0, dt-1
unnormalized_prob_E = weighted_E.sum(dim=1) # N, dt-1
unnormalized_prob_E[torch.sum(unnormalized_prob_E, dim=-1) == 0] = 1e-5
prob_E = unnormalized_prob_E / torch.sum(
unnormalized_prob_E, dim=-1, keepdim=True
)
assert ((prob_E.sum(dim=-1) - 1).abs() < 1e-4).all()
E_t = prob_E.multinomial(1)[:, 0]
return E_t
def sample_sparse_node_edge(
self,
pred_node,
pred_edge,
p_s_and_t_given_0_X,
p_s_and_t_given_0_E,
pred_charge,
p_s_and_t_given_0_charge,
):
sampled_node = self.sample_sparse_node(pred_node, p_s_and_t_given_0_X).long()
sampled_edge = self.sample_sparse_edge(pred_edge, p_s_and_t_given_0_E).long()
if pred_charge.size(-1) > 0:
sampled_charge = self.sample_sparse_node(
pred_charge, p_s_and_t_given_0_charge
).long()
else:
sampled_charge = pred_charge
return sampled_node, sampled_edge, sampled_charge
def sample_p_zs_given_zt(self, s_float, t_float, data):
"""
Samples from zs ~ p(zs | zt). Only used during sampling.
if last_step, return the graph prediction as well
"""
node = data.node
edge_index = data.edge_index
edge_attr = data.edge_attr
y = data.y
charge = data.charge
ptr = data.ptr
batch = data.batch
beta_t = self.noise_schedule(t_normalized=t_float) # (bs, 1)
alpha_s_bar = self.noise_schedule.get_alpha_bar(t_normalized=s_float)
alpha_t_bar = self.noise_schedule.get_alpha_bar(t_normalized=t_float)
# Retrieve transitions matrix
Qtb = self.transition_model.get_Qt_bar(alpha_t_bar, self.device)
Qsb = self.transition_model.get_Qt_bar(alpha_s_bar, self.device)
Qt = self.transition_model.get_Qt(beta_t, self.device)
# Prior distribution
# (N, dx, dx)
p_s_and_t_given_0_X = (
diffusion_utils.compute_sparse_batched_over0_posterior_distribution(
input_data=node, batch=batch, Qt=Qt.X, Qsb=Qsb.X, Qtb=Qtb.X
)
)
p_s_and_t_given_0_charge = None
if self.use_charge:
p_s_and_t_given_0_charge = (
diffusion_utils.compute_sparse_batched_over0_posterior_distribution(
input_data=charge,
batch=batch,
Qt=Qt.charge,
Qsb=Qsb.charge,
Qtb=Qtb.charge,
)
)
# prepare sparse information
num_nodes = ptr.diff().long()
num_edges = (num_nodes * (num_nodes - 1) / 2).long()
# If we had one graph, we will iterate on all edges for each step
# we also make sure that the non existing edge number remains the same with the training process
(
all_condensed_index,
all_edge_batch,
all_edge_mask,
|
class DiscreteDenoisingDiffusion(pl.LightningModule):
model_dtype = torch.float32
best_val_nll = 1e8
val_counter = 0
start_epoch_time = None
val_iterations = None
def __init__(
self,
cfg,
dataset_infos,
train_metrics,
extra_features,
domain_features,
val_sampling_metrics,
test_sampling_metrics,
):
super().__init__()
self.in_dims = dataset_infos.input_dims
self.out_dims = dataset_infos.output_dims
self.use_charge = cfg.model.use_charge and self.out_dims.charge > 1
self.node_dist = dataset_infos.nodes_dist
self.extra_features = extra_features
self.domain_features = domain_features
self.sign_net = cfg.model.sign_net
if not self.sign_net:
cfg.model.sn_hidden_dim = 0
# sparse settings
self.edge_fraction = cfg.model.edge_fraction
self.autoregressive = cfg.model.autoregressive
self.cfg = cfg
self.test_variance = cfg.general.test_variance
self.dataset_info = dataset_infos
self.visualization_tools = Visualizer(dataset_infos)
self.name = cfg.general.name
self.T = cfg.model.diffusion_steps
self.train_loss = TrainLossDiscrete(cfg.model.lambda_train, self.edge_fraction)
self.train_metrics = train_metrics
self.val_sampling_metrics = val_sampling_metrics
self.test_sampling_metrics = test_sampling_metrics
# TODO: transform to torchmetrics.MetricCollection
self.val_nll = NLL()
# self.val_metrics = torchmetrics.MetricCollection([])
self.val_X_kl = SumExceptBatchKL()
self.val_E_kl = SumExceptBatchKL()
self.val_X_logp = SumExceptBatchMetric()
self.val_E_logp = SumExceptBatchMetric()
self.best_nll = 1e8
self.best_epoch = 0
# TODO: transform to torchmetrics.MetricCollection
self.test_nll = NLL()
self.test_X_kl = SumExceptBatchKL()
self.test_E_kl = SumExceptBatchKL()
self.test_X_logp = SumExceptBatchMetric()
self.test_E_logp = SumExceptBatchMetric()
if self.use_charge:
self.val_charge_kl = SumExceptBatchKL()
self.val_charge_logp = SumExceptBatchMetric()
self.test_charge_kl = SumExceptBatchKL()
self.test_charge_logp = SumExceptBatchMetric()
self.model = GraphTransformerConv(
n_layers=cfg.model.n_layers,
input_dims=self.in_dims,
hidden_dims=cfg.model.hidden_dims,
output_dims=self.out_dims,
sn_hidden_dim=cfg.model.sn_hidden_dim,
output_y=cfg.model.output_y,
dropout=cfg.model.dropout
)
# whether to use sign net
if self.sign_net and cfg.model.extra_features == "all":
self.sign_net = SignNetNodeEncoder(
dataset_infos, cfg.model.sn_hidden_dim, cfg.model.num_eigenvectors
)
# whether to use scale layers
self.scaling_layer = cfg.model.scaling_layer
(
self.node_scaling_layer,
self.edge_scaling_layer,
self.graph_scaling_layer,
) = self.get_scaling_layers()
self.noise_schedule = PredefinedNoiseScheduleDiscrete(
cfg.model.diffusion_noise_schedule, timesteps=cfg.model.diffusion_steps
)
# Marginal transition
node_types = self.dataset_info.node_types.float()
x_marginals = node_types / torch.sum(node_types)
edge_types = self.dataset_info.edge_types.float()
e_marginals = edge_types / torch.sum(edge_types)
if not self.use_charge:
charge_marginals = node_types.new_zeros(0)
else:
charge_marginals = (
self.dataset_info.charge_types * node_types[:, None]
).sum(dim=0)
print(
f"Marginal distribution of the classes: {x_marginals} for nodes, {e_marginals} for edges"
)
self.transition_model = MarginalUniformTransition(
x_marginals=x_marginals,
e_marginals=e_marginals,
y_classes=self.out_dims.y,
charge_marginals=charge_marginals,
)
self.limit_dist = utils.PlaceHolder(
X=x_marginals,
E=e_marginals,
y=torch.ones(self.out_dims.y) / self.out_dims.y,
charge=charge_marginals,
)
self.save_hyperparameters(ignore=["train_metrics", "sampling_metrics"])
self.log_every_steps = cfg.general.log_every_steps
self.number_chain_steps = cfg.general.number_chain_steps
def training_step(self, data, i):
# The above code is using the Python debugger module `pdb` to set a breakpoint at a specific
# line of code. When the code is executed, it will pause at that line and allow you to
# interactively debug the program.
if data.edge_index.numel() == 0:
print("Found a batch with no edges. Skipping.")
return
# Map discrete classes to one hot encoding
data = self.dataset_info.to_one_hot(data)
start_time = time.time()
sparse_noisy_data = self.apply_sparse_noise(data)
if hasattr(self, "apply_noise_time"):
self.apply_noise_time.append(round(time.time() - start_time, 2))
# Sample the query edges and build the computational graph = union(noisy graph, query edges)
start_time = time.time()
# print(data.ptr.diff())
triu_query_edge_index, _ = sample_query_edges(
num_nodes_per_graph=data.ptr.diff(), edge_proportion=self.edge_fraction
)
query_mask, comp_edge_index, comp_edge_attr = get_computational_graph(
triu_query_edge_index=triu_query_edge_index,
clean_edge_index=sparse_noisy_data["edge_index_t"],
clean_edge_attr=sparse_noisy_data["edge_attr_t"],
)
# pass sparse comp_graph to dense comp_graph for ease calculation
sparse_noisy_data["comp_edge_index_t"] = comp_edge_index
sparse_noisy_data["comp_edge_attr_t"] = comp_edge_attr
self.sample_query_time.append(round(time.time() - start_time, 2))
sparse_pred = self.forward(sparse_noisy_data)
# Compute the loss on the query edges only
sparse_pred.edge_attr = sparse_pred.edge_attr[query_mask]
sparse_pred.edge_index = comp_edge_index[:, query_mask]
# mask true label for query edges
# We have the true edge index at time 0, and the query edge index at time t. This function
# merge the query edges and edge index at time 0, delete repeated one, and retune the mask
# for the true attr of query edges
start_time = time.time()
(
query_mask2,
true_comp_edge_attr,
true_comp_edge_index,
) = mask_query_graph_from_comp_graph(
triu_query_edge_index=triu_query_edge_index,
edge_index=data.edge_index,
edge_attr=data.edge_attr,
num_classes=self.out_dims.E,
)
query_true_edge_attr = true_comp_edge_attr[query_mask2]
assert (
true_comp_edge_index[:, query_mask2] - sparse_pred.edge_index == 0
).all()
self.query_count.append(len(query_true_edge_attr))
true_data = utils.SparsePlaceHolder(
node=data.x,
charge=data.charge,
edge_attr=query_true_edge_attr,
edge_index=sparse_pred.edge_index,
y=data.y,
batch=data.batch,
)
true_data.collapse() # Map one-hot to discrete class
self.coalesce_time.append(round(time.time() - start_time, 2))
# Loss calculation
start_time = time.time()
loss = self.train_loss.forward(
pred=sparse_pred,
true_data=true_data,
log=i % self.log_every_steps == 0
)
self.train_metrics(
pred=sparse_pred, true_data=true_data, log=i % self.log_every_steps == 0
)
self.loss_time.append(round(time.time() - start_time, 2))
return {"loss": loss}
def on_fit_start(self) -> None:
print(
f"Size of the input features:"
f" X {self.in_dims.X}, E {self.in_dims.E}, charge {self.in_dims.charge}, y {self.in_dims.y}"
)
if self.local_rank == 0:
utils.setup_wandb(
self.cfg
) # Initialize wandb only on one process to log metrics only once
def on_train_epoch_start(self) -> None:
self.print("Starting train epoch...")
self.start_epoch_time = time.time()
self.train_loss.reset()
self.train_metrics.reset()
self.query_count = []
self.apply_noise_time = []
self.extra_data_time = []
self.forward_time = []
self.sample_query_time = []
self.coalesce_time = []
self.loss_time = []
self.cycle_time = []
self.eigen_time = []
def on_train_epoch_end(self) -> None:
epoch_loss = self.train_loss.log_epoch_metrics()
self.print(
f"Epoch {self.current_epoch} finished: X: {epoch_loss['train_epoch/x_CE'] :.2f} -- "
f"E: {epoch_loss['train_epoch/E_CE'] :.2f} --"
f"charge: {epoch_loss['train_epoch/charge_CE'] :.2f} --"
f"y: {epoch_loss['train_epoch/y_CE'] :.2f}"
)
self.train_metrics.log_epoch_metrics()
if wandb.run:
wandb.log({"epoch": self.current_epoch}, commit=False)
def on_validation_epoch_start(self) -> None:
val_metrics = [self.val_nll, self.val_X_kl, self.val_E_kl, self.val_X_logp, self.val_E_logp,
self.val_sampling_metrics]
if self.use_charge:
val_metrics.extend([self.val_charge_kl, self.val_charge_logp])
for metric in val_metrics:
metric.reset()
def validation_step(self, data, i):
data = self.dataset_info.to_one_hot(data)
sparse_noisy_data = self.apply_sparse_noise(data)
# Sample the query edges and build the computational graph = union(noisy graph, query edges)
triu_query_edge_index, _ = sample_query_edges(
num_nodes_per_graph=data.ptr.diff(), edge_proportion=self.edge_fraction
)
_, comp_edge_index, comp_edge_attr = get_computational_graph(
triu_query_edge_index=triu_query_edge_index,
clean_edge_index=sparse_noisy_data["edge_index_t"],
clean_edge_attr=sparse_noisy_data["edge_attr_t"]
)
# pass sparse comp_graph to dense comp_graph for ease calculation
sparse_noisy_data["comp_edge_index_t"] = comp_edge_index
sparse_noisy_data["comp_edge_attr_t"] = comp_edge_attr
sparse_pred = self.forward(sparse_noisy_data)
# to dense
dense_pred, node_mask = utils.to_dense(
x=sparse_pred.node,
edge_index=sparse_pred.edge_index,
edge_attr=sparse_pred.edge_attr,
batch=sparse_pred.batch,
charge=sparse_pred.charge,
)
dense_original, _ = utils.to_dense(
x=data.x,
edge_index=data.edge_index,
edge_attr=data.edge_attr,
batch=data.batch,
charge=data.charge,
)
noisy_data = utils.densify_noisy_data(sparse_noisy_data)
nll = self.compute_val_loss(
dense_pred,
noisy_data,
dense_original.X,
dense_original.E,
dense_original.y,
node_mask,
charge=dense_original.charge,
test=False,
)
return {"loss": nll}
def on_validation_epoch_end(self) -> None:
metrics = [
self.val_nll.compute(),
self.val_X_kl.compute() * self.T,
self.val_E_kl.compute() * self.T,
self.val_X_logp.compute(),
self.val_E_logp.compute(),
]
if self.use_charge:
metrics += [
self.val_charge_kl.compute() * self.T,
self.val_charge_logp.compute(),
]
else:
metrics += [-1, -1]
if self.val_nll.compute() < self.best_nll:
self.best_epoch = self.current_epoch
self.best_nll = self.val_nll.compute()
metrics += [self.best_epoch, self.best_nll]
if wandb.run:
wandb.log(
{
"val/epoch_NLL": metrics[0],
"val/X_kl": metrics[1],
"val/E_kl": metrics[2],
"val/X_logp": metrics[3],
"val/E_logp": metrics[4],
"val/charge_kl": metrics[5],
"val/charge_logp": metrics[6],
"val/best_nll_epoch": metrics[7],
"val/best_nll": metrics[8],
},
commit=False,
)
self.print(
f"Epoch {self.current_epoch}: Val NLL {metrics[0] :.2f} -- Val Atom type KL {metrics[1] :.2f} -- ",
f"Val Edge type KL: {metrics[2] :.2f}",
)
# Log val nll with default Lightning logger, so it can be monitored by checkpoint callback
val_nll = metrics[0]
self.log("val/epoch_NLL", val_nll, sync_dist=True)
if val_nll < self.best_val_nll:
self.best_val_nll = val_nll
self.print(
"Val loss: %.4f \t Best val loss: %.4f\n" % (val_nll, self.best_val_nll)
)
self.val_counter += 1
print("Starting to sample")
if self.val_counter % self.cfg.general.sample_every_val == 0:
start = time.time()
samples_left_to_generate = self.cfg.general.samples_to_generate
samples_left_to_save = self.cfg.general.samples_to_save
chains_left_to_save = self.cfg.general.chains_to_save # multi gpu operation
samples_left_to_generate = math.ceil(samples_left_to_generate / max(self._trainer.num_devices, 1))
self.print(
f"Samples to generate: {samples_left_to_generate} for each of the {max(self._trainer.num_devices, 1)} devices"
)
print(f"Sampling start on GR{self.global_rank}")
print('multi-gpu metrics for uniqueness is not accurate in the validation step.')
generated_graphs = []
ident = 0
while samples_left_to_generate > 0:
bs = self.cfg.train.batch_size * 2
to_generate = min(samples_left_to_generate, bs)
to_save = min(samples_left_to_save, bs)
chains_save = min(chains_left_to_save, bs)
sampled_batch = self.sample_batch(
batch_id=ident,
batch_size=to_generate,
save_final=to_save,
keep_chain=chains_save,
number_chain_steps=self.number_chain_steps,
)
generated_graphs.append(sampled_batch)
ident += to_generate
samples_left_to_save -= to_save
samples_left_to_generate -= to_generate
chains_left_to_save -= chains_save
generated_graphs = utils.concat_sparse_graphs(generated_graphs)
print(
f"Sampled {generated_graphs.batch.max().item()+1} batches on local rank {self.local_rank}. ",
"Sampling took {time.time() - start:.2f} seconds\n"
)
print("Computing sampling metrics...")
self.val_sampling_metrics.compute_all_metrics(
generated_graphs, self.current_epoch, local_rank=self.local_rank
)
def on_test_epoch_start(self) -> None:
print("Starting test...")
if self.local_rank == 0:
utils.setup_wandb(
self.cfg
) # Initialize wandb only on one process to log metrics only once
test_metrics = [self.test_nll, self.test_X_kl, self.test_E_kl, self.test_X_logp, self.test_E_logp,
self.test_sampling_metrics]
if self.use_charge:
test_metrics.extend([self.test_charge_kl, self.test_charge_logp])
for metric in test_metrics:
metric.reset()
def test_step(self, data, i):
pass
def on_test_epoch_end(self) -> None:
"""Measure likelihood on a test set and compute stability metrics."""
if self.cfg.general.generated_path:
self.print("Loading generated samples...")
samples = np.load(self.cfg.general.generated_path)
with open(self.cfg.general.generated_path, "rb") as f:
samples = pickle.load(f)
else:
samples_left_to_generate = self.cfg.general.final_model_samples_to_generate
samples_left_to_save = self.cfg.general.final_model_samples_to_save
chains_left_to_save = self.cfg.general.final_model_chains_to_save
# multi gpu operation
samples_left_to_generate = math.ceil(samples_left_to_generate / max(self._trainer.num_devices, 1))
self.print(
f"Samples to generate: {samples_left_to_generate} for each of the {max(self._trainer.num_devices, 1)} devices"
)
print(f"Sampling start on GR{self.global_rank}")
samples = []
id = 0
while samples_left_to_generate > 0:
print(
f"Samples left to generate: {samples_left_to_generate}/"
f"{self.cfg.general.final_model_samples_to_generate}",
end="",
flush=True,
)
bs = self.cfg.train.batch_size * 2
to_generate = min(samples_left_to_generate, bs)
to_save = min(samples_left_to_save, bs)
chains_save = min(chains_left_to_save, bs)
sampled_batch = self.sample_batch(
batch_id=id,
batch_size=to_generate,
num_nodes=None,
save_final=to_save,
keep_chain=chains_save,
number_chain_steps=self.number_chain_steps,
)
samples.append(sampled_batch)
id += to_generate
samples_left_to_save -= to_save
samples_left_to_generate -= to_generate
chains_left_to_save -= chains_save
print("Saving the generated graphs")
samples = utils.concat_sparse_graphs(samples)
filename = f"generated_samples1.txt"
# Save the samples list as pickle to a file that depends on the local rank
# This is needed to avoid overwriting the same file on different GPUs
with open(f"generated_samples_rank{self.local_rank}.pkl", "wb") as f:
pickle.dump(samples, f)
# This line is used to sync between gpus
self._trainer.strategy.barrier()
for i in range(2, 10):
if os.path.exists(filename):
filename = f"generated_samples{i}.txt"
else:
break
with open(filename, "w") as f:
for i in range(samples.batch.max().item() + 1):
atoms = samples.node[samples.batch == i]
f.write(f"N={atoms.shape[0]}\n")
atoms = atoms.tolist()
f.write("X: \n")
for at in atoms:
f.write(f"{at} ")
f.write("\n")
f.write("E: \n")
bonds = samples.edge_attr[samples.batch[samples.edge_index[0]] == i]
for bond in bonds:
f.write(f"{bond} ")
f.write("\n")
print("Saved.")
print("Computing sampling metrics...")
# Load the pickles of the other GPUs
samples = []
for i in range(self._trainer.num_devices):
with open(f"generated_samples_rank{i}.pkl", "rb") as f:
samples.append(pickle.load(f))
samples = utils.concat_sparse_graphs(samples)
print('saving all samples')
with open(f"generated_samples.pkl", "wb") as f:
pickle.dump(samples, f)
if self.test_variance == 1:
to_log, _ = self.test_sampling_metrics.compute_all_metrics(
samples, self.current_epoch, self.local_rank
)
# save results for testing
print('saving results for testing')
current_path = os.getcwd()
res_path = os.path.join(
current_path,
f"test_epoch{self.current_epoch}.json",
)
with open(res_path, 'w') as file:
# Convert the dictionary to a JSON string and write it to the file
json.dump(to_log, file)
else:
to_log = {}
for i in range(self.test_variance):
start_idx = int(self.cfg.general.final_model_samples_to_generate / self.test_variance * i)
end_idx = int(self.cfg.general.final_model_samples_to_generate / self.test_variance * (i + 1))
cur_samples = utils.split_samples(samples, start_idx, end_idx)
cur_to_log, _ = self.test_sampling_metrics.compute_all_metrics(cur_samples, self.current_epoch, self.local_rank)
if i == 0:
to_log = {i: [cur_to_log[i]] for i in cur_to_log}
else:
to_log = {i: to_log[i].append(cur_to_log[i]) for i in cur_to_log}
# get the variance and mean value of the metrics
final_to_log = {i: [np.mean(i), np.var(i)] for i in to_log}
to_log.update(final_to_log)
# save results for testing
print('saving results for testing')
current_path = os.getcwd()
res_path = os.path.join(
current_path,
f"test_epoch{self.current_epoch}_fold{self.test_variance}.json",
)
with open(res_path, 'w') as file:
# Convert the dictionary to a JSON string and write it to the file
json.dump(to_log, file)
print("Test sampling metrics computed.")
def apply_sparse_noise(self, data):
"""Sample noise and apply it to the data."""
bs = int(data.batch.max() + 1)
t_int = torch.randint(
1, self.T + 1, size=(bs, 1), device=self.device
).float() # (bs, 1)
s_int = t_int - 1
t_float = t_int / self.T
s_float = s_int / self.T
# beta_t and alpha_s_bar are used for denoising/loss computation
beta_t = self.noise_schedule(t_normalized=t_float) # (bs, 1)
alpha_s_bar = self.noise_schedule.get_alpha_bar(t_normalized=s_float) # (bs, 1)
alpha_t_bar = self.noise_schedule.get_alpha_bar(t_normalized=t_float) # (bs, 1)
Qtb = self.transition_model.get_Qt_bar(
alpha_t_bar, device=self.device
) # (bs, dx_in, dx_out), (bs, de_in, de_out)
assert (abs(Qtb.X.sum(dim=2) - 1.0) < 1e-4).all(), Qtb.X.sum(dim=2) - 1
assert (abs(Qtb.E.sum(dim=2) - 1.0) < 1e-4).all()
# Compute transition probabilities
# get charge distribution
if self.use_charge:
prob_charge = data.charge.unsqueeze(1) @ Qtb.charge[data.batch]
charge_t = prob_charge.squeeze(1).multinomial(1).flatten() # (N, )
charge_t = F.one_hot(charge_t, num_classes=self.out_dims.charge)
else:
charge_t = data.charge
# Diffuse sparse nodes and sample sparse node labels
probN = data.x.unsqueeze(1) @ Qtb.X[data.batch] # (N, dx)
node_t = probN.squeeze(1).multinomial(1).flatten() # (N, )
# count node numbers and edge numbers for existing edges for each graph
num_nodes = data.ptr.diff().long()
batch_edge = data.batch[data.edge_index[0]]
num_edges = torch.zeros(num_nodes.shape).to(self.device)
unique, counts = torch.unique(batch_edge, sorted=True, return_counts=True)
num_edges[unique] = counts.float()
# count number of non-existing edges for each graph
num_neg_edge = ((num_nodes - 1) * num_nodes - num_edges) / 2 # (bs, )
# Step1: diffuse on existing edges
# get edges defined in the top triangle of the adjacency matrix
dir_edge_index, dir_edge_attr = utils.undirected_to_directed(
data.edge_index, data.edge_attr
)
batch_edge = data.batch[dir_edge_index[0]]
batch_Qtb = Qtb.E[batch_edge]
probE = dir_edge_attr.unsqueeze(1) @ batch_Qtb
dir_edge_attr = probE.squeeze(1).multinomial(1).flatten()
# Step2: diffuse on non-existing edges
# get number of new edges according to Qtb
emerge_prob = Qtb.E[:, 0, 1:].sum(-1) # (bs, )
num_emerge_edges = (
torch.distributions.binomial.Binomial(num_neg_edge, emerge_prob)
.sample()
.int()
)
# combine existing and non-existing edges (both are directed, i.e. triu)
if num_emerge_edges.max() > 0:
# sample non-existing edges
neg_edge_index = sample_non_existing_edges_batched(
num_edges_to_sample=num_emerge_edges,
existing_edge_index=dir_edge_index,
num_nodes=num_nodes,
batch=data.batch,
)
neg_edge_attr = sample_non_existing_edge_attr(
query_edges_dist_batch=Qtb.E[:, 0, 1:],
num_edges_to_sample=num_emerge_edges,
)
E_t_attr = torch.hstack([dir_edge_attr, neg_edge_attr])
E_t_index = torch.hstack([dir_edge_index, neg_edge_index])
else:
E_t_attr = dir_edge_attr
E_t_index = dir_edge_index
# mask non-existing edges
mask = E_t_attr != 0
E_t_attr = E_t_attr[mask]
E_t_index = E_t_index[:, mask]
E_t_index, E_t_attr = utils.to_undirected(E_t_index, E_t_attr)
E_t_attr = F.one_hot(E_t_attr, num_classes=self.out_dims.E)
node_t = F.one_hot(node_t, num_classes=self.out_dims.X)
sparse_noisy_data = {
"t_int": t_int,
"t_float": t_float,
"beta_t": beta_t,
"alpha_s_bar": alpha_s_bar,
"alpha_t_bar": alpha_t_bar,
"node_t": node_t,
"edge_index_t": E_t_index,
"edge_attr_t": E_t_attr,
"comp_edge_index_t": None,
"comp_edge_attr_t": None, # computational graph
"y_t": data.y,
"batch": data.batch,
"ptr": data.ptr,
"charge_t": charge_t,
}
return sparse_noisy_data
def compute_val_loss(self, pred, noisy_data, X, E, y, node_mask, charge, test):
"""Computes an estimator for the variational lower bound.
pred: (batch_size, n, total_features)
noisy_data: dict
X, E, y : (bs, n, dx), (bs, n, n, de), (bs, dy)
node_mask : (bs, n)
Output: nll (size 1)
"""
t = noisy_data["t_float"]
# 1.
N = node_mask.sum(1).long()
log_pN = self.node_dist.log_prob(N)
# 2. The KL between q(z_T | x) and p(z_T) = Uniform(1/num_classes). Should be close to zero.
kl_prior = self.kl_prior(X, E, node_mask, charge=charge)
# 3. Diffusion loss
loss_all_t = self.compute_Lt(
X, E, y, charge, pred, noisy_data, node_mask, test=test
)
# Combine terms
nlls = - log_pN + kl_prior + loss_all_t
assert (~nlls.isnan()).all(), f"NLLs contain NaNs: {nlls}"
assert len(nlls.shape) == 1, f"{nlls.shape} has more than only batch dim."
# Update NLL metric object and return batch nll
nll = (self.test_nll if test else self.val_nll)(nlls) # Average over the batch
if wandb.run:
wandb.log(
{
"kl prior": kl_prior.mean(),
"Estimator loss terms": loss_all_t.mean(),
"log_pn": log_pN.mean(),
"val_nll": nll,
"epoch": self.current_epoch
},
commit=False,
)
return nll
def kl_prior(self, X, E, node_mask, charge):
"""Computes the KL between q(z1 | x) and the prior p(z1) = Normal(0, 1).
This is essentially a lot of work for something that is in practice negligible in the loss. However, you
compute it so that you see it when you've made a mistake in your noise schedule.
"""
# Compute the last alpha value, alpha_T.
ones = torch.ones((X.size(0), 1), device=X.device)
Ts = self.T * ones
alpha_t_bar = self.noise_schedule.get_alpha_bar(t_int=Ts) # (bs, 1)
Qtb = self.transition_model.get_Qt_bar(alpha_t_bar, self.device)
# Compute transition probabilities
probX = X @ Qtb.X # (bs, n, dx_out)
probE = E @ Qtb.E.unsqueeze(1) # (bs, n, n, de_out)
assert probX.shape == X.shape
bs, n, _ = probX.shape
limit_X = self.limit_dist.X[None, None, :].expand(bs, n, -1).type_as(probX)
limit_E = (
self.limit_dist.E[None, None, None, :].expand(bs, n, n, -1).type_as(probE)
)
if self.use_charge:
prob_charge = charge @ Qtb.charge # (bs, n, de_out)
limit_charge = (
self.limit_dist.charge[None, None, :]
.expand(bs, n, -1)
.type_as(prob_charge)
)
limit_charge = limit_charge.clone()
else:
prob_charge = limit_charge = None
# Make sure that masked rows do not contribute to the loss
(
limit_dist_X,
limit_dist_E,
probX,
probE,
limit_dist_charge,
prob_charge,
) = diffusion_utils.mask_distributions(
true_X=limit_X.clone(),
true_E=limit_E.clone(),
pred_X=probX,
pred_E=probE,
node_mask=node_mask,
true_charge=limit_charge,
pred_charge=prob_charge,
)
kl_distance_X = F.kl_div(
input=probX.log(), target=limit_dist_X, reduction="none"
)
kl_distance_E = F.kl_div(
input=probE.log(), target=limit_dist_E, reduction="none"
)
# not all edges are used for loss calculation
E_mask = torch.logical_or(
kl_distance_E.sum(-1).isnan(), kl_distance_E.sum(-1).isinf()
)
kl_distance_E[E_mask] = 0
X_mask = torch.logical_or(
kl_distance_X.sum(-1).isnan(), kl_distance_X.sum(-1).isinf()
)
kl_distance_X[X_mask] = 0
loss = diffusion_utils.sum_except_batch(
kl_distance_X
) + diffusion_utils.sum_except_batch(kl_distance_E)
# The above code is using the Python debugger module `pdb` to set a breakpoint in the code.
# When the code is executed, it will pause at this line and allow you to interactively debug
# the program.
if self.use_charge:
kl_distance_charge = F.kl_div(
input=prob_charge.log(), target=limit_dist_charge, reduction="none"
)
kl_distance_charge[X_mask] = 0
loss = loss + diffusion_utils.sum_except_batch(kl_distance_charge)
assert (~loss.isnan()).any()
return loss
def compute_Lt(self, X, E, y, charge, pred, noisy_data, node_mask, test):
pred_probs_X = F.softmax(pred.X, dim=-1)
pred_probs_E = F.softmax(pred.E, dim=-1)
if self.use_charge:
pred_probs_charge = F.softmax(pred.charge, dim=-1)
else:
pred_probs_charge = None
charge = None
Qtb = self.transition_model.get_Qt_bar(noisy_data["alpha_t_bar"], self.device)
Qsb = self.transition_model.get_Qt_bar(noisy_data["alpha_s_bar"], self.device)
Qt = self.transition_model.get_Qt(noisy_data["beta_t"], self.device)
# Compute distributions to compare with KL
bs, n, d = X.shape
prob_true = diffusion_utils.posterior_distributions(
X=X,
E=E,
X_t=noisy_data["X_t"],
E_t=noisy_data["E_t"],
charge=charge,
charge_t=noisy_data["charge_t"],
y_t=noisy_data["y_t"],
Qt=Qt,
Qsb=Qsb,
Qtb=Qtb,
)
prob_true.E = prob_true.E.reshape((bs, n, n, -1))
prob_pred = diffusion_utils.posterior_distributions(
X=pred_probs_X,
E=pred_probs_E,
X_t=noisy_data["X_t"],
E_t=noisy_data["E_t"],
charge=pred_probs_charge,
charge_t=noisy_data["charge_t"],
y_t=noisy_data["y_t"],
Qt=Qt,
Qsb=Qsb,
Qtb=Qtb,
)
prob_pred.E = prob_pred.E.reshape((bs, n, n, -1))
# Reshape and filter masked rows
(
prob_true_X,
prob_true_E,
prob_pred.X,
prob_pred.E,
prob_true.charge,
prob_pred.charge,
) = diffusion_utils.mask_distributions(
true_X=prob_true.X,
true_E=prob_true.E,
pred_X=prob_pred.X,
pred_E=prob_pred.E,
node_mask=node_mask,
true_charge=prob_true.charge,
pred_charge=prob_pred.charge,
)
kl_x = (self.test_X_kl if test else self.val_X_kl)(prob_true_X, torch.log(prob_pred.X))
kl_e = (self.test_E_kl if test else self.val_E_kl)(prob_true_E, torch.log(prob_pred.E))
assert (~(kl_x + kl_e).isnan()).any()
loss = kl_x + kl_e
if self.use_charge:
kl_charge = (self.test_charge_kl if test else self.val_charge_kl)(
prob_true.charge, torch.log(prob_pred.charge)
)
assert (~(kl_charge).isnan()).any()
loss = loss + kl_charge
return self.T * loss
def reconstruction_logp(self, t, X, E, node_mask, charge):
# Compute noise values for t = 0.
t_zeros = torch.zeros_like(t)
beta_0 = self.noise_schedule(t_zeros)
Q0 = self.transition_model.get_Qt(beta_t=beta_0, device=self.device)
probX0 = X @ Q0.X # (bs, n, dx_out)
probE0 = E @ Q0.E.unsqueeze(1) # (bs, n, n, de_out)
prob_charge0 = None
if self.use_charge:
prob_charge0 = charge @ Q0.charge
sampled0 = diffusion_utils.sample_discrete_features(
probX=probX0, probE=probE0, node_mask=node_mask, prob_charge=prob_charge0
)
X0 = F.one_hot(sampled0.X, num_classes=self.out_dims.X).float()
E0 = F.one_hot(sampled0.E, num_classes=self.out_dims.E).float()
y0 = sampled0.y
assert (X.shape == X0.shape) and (E.shape == E0.shape)
charge0 = X0.new_zeros((*X0.shape[:-1], 0))
if self.use_charge:
charge0 = F.one_hot(
sampled0.charge, num_classes=self.out_dims.charge
).float()
sampled_0 = utils.PlaceHolder(X=X0, E=E0, y=y0, charge=charge0).mask(node_mask)
# Predictions
noisy_data = {
"X_t": sampled_0.X,
"E_t": sampled_0.E,
"y_t": sampled_0.y,
"node_mask": node_mask,
"t_int": torch.zeros((X0.shape[0], 1), dtype=torch.long).to(self.device),
"t_float": torch.zeros((X0.shape[0], 1), dtype=torch.float).to(self.device),
"charge_t": sampled_0.charge,
}
sparse_noisy_data = utils.to_sparse(
noisy_data["X_t"],
noisy_data["E_t"],
noisy_data["y_t"],
node_mask,
charge=noisy_data["charge_t"],
)
noisy_data.update(sparse_noisy_data)
noisy_data["comp_edge_index_t"] = sparse_noisy_data["edge_index_t"]
noisy_data["comp_edge_attr_t"] = sparse_noisy_data["edge_attr_t"]
pred0 = self.forward(noisy_data)
pred0, _ = utils.to_dense(
pred0.node, pred0.edge_index, pred0.edge_attr, pred0.batch, pred0.charge
)
# Normalize predictions
probX0 = F.softmax(pred0.X, dim=-1)
probE0 = F.softmax(pred0.E, dim=-1)
# Set masked rows to arbitrary values that don't contribute to loss
probX0[~node_mask] = torch.ones(self.out_dims.X).type_as(probX0)
probE0[~(node_mask.unsqueeze(1) * node_mask.unsqueeze(2))] = torch.ones(
self.out_dims.E
).type_as(probE0)
diag_mask = torch.eye(probE0.size(1)).type_as(probE0).bool()
diag_mask = diag_mask.unsqueeze(0).expand(probE0.size(0), -1, -1)
probE0[diag_mask] = torch.ones(self.out_dims.E).type_as(probE0)
assert (~probX0.isnan()).any()
assert (~probE0.isnan()).any()
prob_charge0 = charge
if self.use_charge:
prob_charge0 = F.softmax(pred0.charge, dim=-1)
prob_charge0[~node_mask] = torch.ones(self.out_dims.charge).type_as(
prob_charge0
)
assert (~prob_charge0.isnan()).any()
return utils.PlaceHolder(X=probX0, E=probE0, y=None, charge=prob_charge0)
def forward_sparse(self, sparse_noisy_data):
start_time = time.time()
node = sparse_noisy_data["node_t"]
edge_attr = sparse_noisy_data["edge_attr_t"].float()
edge_index = sparse_noisy_data["edge_index_t"].to(torch.int64)
y = sparse_noisy_data["y_t"]
batch = sparse_noisy_data["batch"].long()
if hasattr(self, "forward_time"):
self.forward_time.append(round(time.time() - start_time, 2))
return self.model(node, edge_attr, edge_index, y, batch)
def forward(self, noisy_data):
"""
noisy data contains: node_t, comp_edge_index_t, comp_edge_attr_t, batch
"""
# build the sparse_noisy_data for the forward function of the sparse model
start_time = time.time()
sparse_noisy_data = self.compute_extra_data(sparse_noisy_data=noisy_data)
if self.sign_net and self.cfg.model.extra_features == "all":
x = self.sign_net(
sparse_noisy_data["node_t"],
sparse_noisy_data["edge_index_t"],
sparse_noisy_data["batch"],
)
sparse_noisy_data["node_t"] = torch.hstack(
[sparse_noisy_data["node_t"], x]
)
if hasattr(self, "extra_data_time"):
self.extra_data_time.append(round(time.time() - start_time, 2))
return self.forward_sparse(sparse_noisy_data)
@torch.no_grad()
def sample_batch(
self,
batch_id: int,
batch_size: int,
keep_chain: int,
number_chain_steps: int,
save_final: int,
num_nodes=None,
):
"""
:param batch_id: int
:param batch_size: int
:param num_nodes: int, <int>tensor (batch_size) (optional) for specifying number of nodes
:param save_final: int: number of predictions to save to file
:param keep_chain: int: number of chains to save to file
:param keep_chain_steps: number of timesteps to save for each chain
:return: molecule_list. Each element of this list is a tuple (node_types, charge, positions)
"""
if num_nodes is None:
num_nodes = self.node_dist.sample_n(batch_size, self.device)
elif type(num_nodes) == int:
num_nodes = num_nodes * torch.ones(
batch_size, device=self.device, dtype=torch.int
)
else:
assert isinstance(num_nodes, torch.Tensor)
num_nodes = num_nodes
num_max = torch.max(num_nodes)
# Build the masks
arange = (
torch.arange(num_max, device=self.device)
.unsqueeze(0)
.expand(batch_size, -1)
)
node_mask = arange < num_nodes.unsqueeze(1)
# Sample noise -- z has size ( num_samples, num_nodes, num_features)
sparse_sampled_data = diffusion_utils.sample_sparse_discrete_feature_noise(
limit_dist=self.limit_dist, node_mask=node_mask
)
assert number_chain_steps < self.T
chain = utils.SparseChainPlaceHolder(keep_chain=keep_chain)
# Iteratively sample p(z_s | z_t) for t = 1, ..., T, with s = t - 1.
for s_int in tqdm(reversed(range(self.T)), total=self.T):
s_array = (s_int * torch.ones((batch_size, 1))).to(self.device)
t_array = s_array + 1
s_norm = s_array / self.T
t_norm = t_array / self.T
# Sample z_s
sparse_sampled_data = self.sample_p_zs_given_zt(
s_norm, t_norm, sparse_sampled_data
)
# keep_chain can be very small, e.g., 1
if ((s_int * number_chain_steps) % self.T == 0) and (keep_chain != 0):
chain.append(sparse_sampled_data)
# get generated graphs
generated_graphs = sparse_sampled_data.to_device("cpu")
generated_graphs.edge_attr = sparse_sampled_data.edge_attr.argmax(-1)
generated_graphs.node = sparse_sampled_data.node.argmax(-1)
if self.use_charge:
generated_graphs.charge = sparse_sampled_data.charge.argmax(-1) - 1
if self.visualization_tools is not None:
current_path = os.getcwd()
# Visualize chains
if keep_chain > 0:
print("Visualizing chains...")
chain_path = os.path.join(
current_path,
f"chains/{self.cfg.general.name}/" f"epoch{self.current_epoch}/",
)
try:
_ = self.visualization_tools.visualize_chain(
chain_path, batch_id, chain, local_rank=self.local_rank
)
except OSError:
print("Warn: image chains failed to be visualized ")
# Visualize the final molecules
print("\nVisualizing molecules...")
result_path = os.path.join(
current_path,
f"graphs/{self.name}/epoch{self.current_epoch}_b{batch_id}/",
)
try:
self.visualization_tools.visualize(
result_path,
generated_graphs,
save_final,
local_rank=self.local_rank,
)
except OSError:
print("Warn: image failed to be visualized ")
print("Done.")
return generated_graphs
def sample_node(self, pred_X, p_s_and_t_given_0_X, node_mask):
# Normalize predictions
pred_X = F.softmax(pred_X, dim=-1) # bs, n, d0
# Dim of these two tensors: bs, N, d0, d_t-1
weighted_X = pred_X.unsqueeze(-1) * p_s_and_t_given_0_X # bs, n, d0, d_t-1
unnormalized_prob_X = weighted_X.sum(dim=2) # bs, n, d_t-1
unnormalized_prob_X[torch.sum(unnormalized_prob_X, dim=-1) == 0] = 1e-5
prob_X = unnormalized_prob_X / torch.sum(
unnormalized_prob_X, dim=-1, keepdim=True
) # bs, n, d_t
assert ((prob_X.sum(dim=-1) - 1).abs() < 1e-4).all()
X_t = diffusion_utils.sample_discrete_node_features(prob_X, node_mask)
return X_t, prob_X
def sample_edge(self, pred_E, p_s_and_t_given_0_E, node_mask):
# Normalize predictions
bs, n, n, de = pred_E.shape
pred_E = F.softmax(pred_E, dim=-1) # bs, n, n, d0
pred_E = pred_E.reshape((bs, -1, pred_E.shape[-1]))
weighted_E = pred_E.unsqueeze(-1) * p_s_and_t_given_0_E # bs, N, d0, d_t-1
unnormalized_prob_E = weighted_E.sum(dim=-2)
unnormalized_prob_E[torch.sum(unnormalized_prob_E, dim=-1) == 0] = 1e-5
prob_E = unnormalized_prob_E / torch.sum(
unnormalized_prob_E, dim=-1, keepdim=True
)
prob_E = prob_E.reshape(bs, n, n, de)
assert ((prob_E.sum(dim=-1) - 1).abs() < 1e-4).all()
E_t = diffusion_utils.sample_discrete_edge_features(prob_E, node_mask)
return E_t, prob_E
def sample_node_edge(
self, pred, p_s_and_t_given_0_X, p_s_and_t_given_0_E, node_mask
):
_, prob_X = self.sample_node(pred.X, p_s_and_t_given_0_X, node_mask)
_, prob_E = self.sample_edge(pred.E, p_s_and_t_given_0_E, node_mask)
sampled_s = diffusion_utils.sample_discrete_features(
prob_X, prob_E, node_mask=node_mask
)
return sampled_s
def sample_sparse_node(self, pred_node, p_s_and_t_given_0_X):
# Normalize predictions
pred_X = F.softmax(pred_node, dim=-1) # N, dx
# Dim of the second tensor: N, dx, dx
weighted_X = pred_X.unsqueeze(-1) * p_s_and_t_given_0_X # N, dx, dx
unnormalized_prob_X = weighted_X.sum(dim=1) # N, dx
unnormalized_prob_X[
torch.sum(unnormalized_prob_X, dim=-1) == 0
] = 1e-5 # TODO: delete/masking?
prob_X = unnormalized_prob_X / torch.sum(
unnormalized_prob_X, dim=-1, keepdim=True
) # N, dx
assert ((prob_X.sum(dim=-1) - 1).abs() < 1e-4).all()
X_t = prob_X.multinomial(1)[:, 0]
return X_t
def sample_sparse_edge(self, pred_edge, p_s_and_t_given_0_E):
# Normalize predictions
pred_E = F.softmax(pred_edge, dim=-1) # N, d0
# Dim of the second tensor: N, d0, dt-1
weighted_E = pred_E.unsqueeze(-1) * p_s_and_t_given_0_E # N, d0, dt-1
unnormalized_prob_E = weighted_E.sum(dim=1) # N, dt-1
unnormalized_prob_E[torch.sum(unnormalized_prob_E, dim=-1) == 0] = 1e-5
prob_E = unnormalized_prob_E / torch.sum(
unnormalized_prob_E, dim=-1, keepdim=True
)
assert ((prob_E.sum(dim=-1) - 1).abs() < 1e-4).all()
E_t = prob_E.multinomial(1)[:, 0]
return E_t
def sample_sparse_node_edge(
self,
pred_node,
pred_edge,
p_s_and_t_given_0_X,
p_s_and_t_given_0_E,
pred_charge,
p_s_and_t_given_0_charge,
):
sampled_node = self.sample_sparse_node(pred_node, p_s_and_t_given_0_X).long()
sampled_edge = self.sample_sparse_edge(pred_edge, p_s_and_t_given_0_E).long()
if pred_charge.size(-1) > 0:
sampled_charge = self.sample_sparse_node(
pred_charge, p_s_and_t_given_0_charge
).long()
else:
sampled_charge = pred_charge
return sampled_node, sampled_edge, sampled_charge
def sample_p_zs_given_zt(self, s_float, t_float, data):
"""
Samples from zs ~ p(zs | zt). Only used during sampling.
if last_step, return the graph prediction as well
"""
node = data.node
edge_index = data.edge_index
edge_attr = data.edge_attr
y = data.y
charge = data.charge
ptr = data.ptr
batch = data.batch
beta_t = self.noise_schedule(t_normalized=t_float) # (bs, 1)
alpha_s_bar = self.noise_schedule.get_alpha_bar(t_normalized=s_float)
alpha_t_bar = self.noise_schedule.get_alpha_bar(t_normalized=t_float)
# Retrieve transitions matrix
Qtb = self.transition_model.get_Qt_bar(alpha_t_bar, self.device)
Qsb = self.transition_model.get_Qt_bar(alpha_s_bar, self.device)
Qt = self.transition_model.get_Qt(beta_t, self.device)
# Prior distribution
# (N, dx, dx)
p_s_and_t_given_0_X = (
diffusion_utils.compute_sparse_batched_over0_posterior_distribution(
input_data=node, batch=batch, Qt=Qt.X, Qsb=Qsb.X, Qtb=Qtb.X
)
)
p_s_and_t_given_0_charge = None
if self.use_charge:
p_s_and_t_given_0_charge = (
diffusion_utils.compute_sparse_batched_over0_posterior_distribution(
input_data=charge,
batch=batch,
Qt=Qt.charge,
Qsb=Qsb.charge,
Qtb=Qtb.charge,
)
)
# prepare sparse information
num_nodes = ptr.diff().long()
num_edges = (num_nodes * (num_nodes - 1) / 2).long()
# If we had one graph, we will iterate on all edges for each step
# we also make sure that the non existing edge number remains the same with the training process
(
all_condensed_index,
all_edge_batch,
all_edge_mask, | ) = sampled_condensed_indices_uniformly( | 8 | 2023-10-30 12:12:16+00:00 | 16k |
akekic/causal-component-analysis | experiments/cauca/main.py | [
{
"identifier": "DGP",
"path": "config.py",
"snippet": "DGP = {\n \"graph-4-0\": {\n \"num_causal_variables\": 4, # N\n \"adj_matrix\": np.array(\n [[0, 1, 1, 1], [0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]\n ),\n \"int_targets\": torch.tensor(\n [[0, ... | import argparse
import os
import pytorch_lightning as pl
from pathlib import Path
from pytorch_lightning.loggers import WandbLogger
from config import DGP
from data_generator import MultiEnvDataModule, make_multi_env_dgp
from model.cauca_model import LinearCauCAModel, NaiveNonlinearModel, NonlinearCauCAModel | 12,283 | )
parser.add_argument(
"--fix-mechanisms",
type=bool,
default=True,
action=argparse.BooleanOptionalAction,
help="Fix fixable mechanisms in latents.",
)
parser.add_argument(
"--fix-all-intervention-targets",
type=bool,
default=False,
action=argparse.BooleanOptionalAction,
help="Fix all intervention targets.",
)
parser.add_argument(
"--nonparametric-base-distr",
type=bool,
default=False,
action=argparse.BooleanOptionalAction,
help="Use nonparametric base distribution for flows.",
)
parser.add_argument(
"--wandb",
type=bool,
default=True,
action=argparse.BooleanOptionalAction,
help="Whether to log to weights and biases.",
)
parser.add_argument(
"--wandb-project",
type=str,
default="cauca",
help="Weights & Biases project name.",
)
args = parser.parse_args()
if args.function_misspec:
assert (
args.mixing == "nonlinear" and args.model == "linear"
), "Function not misspecified."
if args.wandb:
wandb_logger = WandbLogger(project=args.wandb_project)
wandb_logger.experiment.config.update(args, allow_val_change=True)
checkpoint_dir = (
Path(args.checkpoint_root_dir) / f"{wandb_logger.experiment.id}"
)
logger = [wandb_logger]
else:
checkpoint_dir = Path(args.checkpoint_root_dir) / "default"
logger = None
checkpoint_callback = pl.callbacks.ModelCheckpoint(
dirpath=checkpoint_dir,
save_last=True,
every_n_epochs=args.check_val_every_n_epoch,
)
multi_env_dgp = make_multi_env_dgp(
latent_dim=DGP[args.dgp]["num_causal_variables"],
observation_dim=DGP[args.dgp]["observation_dim"],
adjacency_matrix=DGP[args.dgp]["adj_matrix"],
intervention_targets_per_env=DGP[args.dgp]["int_targets"],
noise_shift_type=args.noise_shift_type,
mixing=args.mixing,
scm=args.scm,
n_nonlinearities=args.n_nonlinearities,
scm_coeffs_low=args.scm_coeffs_low,
scm_coeffs_high=args.scm_coeffs_high,
coeffs_min_abs_value=args.scm_coeffs_min_abs_value,
edge_prob=DGP[args.dgp].get("edge_prob", None),
snr=args.snr,
)
data_module = MultiEnvDataModule(
multi_env_dgp=multi_env_dgp,
num_samples_per_env=DGP[args.dgp]["num_samples_per_env"],
batch_size=args.batch_size,
num_workers=os.cpu_count(),
intervention_targets_per_env=DGP[args.dgp]["int_targets"],
log_dir=checkpoint_dir / "data_stats",
)
data_module.setup()
pl.seed_everything(args.training_seed, workers=True)
intervention_targets_per_env = DGP[args.dgp]["int_targets"]
# Model Initialization
if args.model == "nonlinear":
model = NonlinearCauCAModel(
latent_dim=DGP[args.dgp]["num_causal_variables"],
adjacency_matrix=data_module.medgp.adjacency_matrix,
k_flows=args.k_flows,
lr=args.lr,
intervention_targets_per_env=intervention_targets_per_env,
lr_scheduler=args.lr_scheduler,
lr_min=args.lr_min,
adjacency_misspecified=args.adjacency_misspec,
net_hidden_dim=args.net_hidden_dim,
net_hidden_layers=args.net_hidden_layers,
fix_mechanisms=args.fix_mechanisms,
fix_all_intervention_targets=args.fix_all_intervention_targets,
nonparametric_base_distr=args.nonparametric_base_distr,
K_cbn=args.k_flows_cbn,
net_hidden_dim_cbn=args.net_hidden_dim_cbn,
net_hidden_layers_cbn=args.net_hidden_layers_cbn,
)
elif args.model == "linear":
model = LinearCauCAModel(
latent_dim=DGP[args.dgp]["num_causal_variables"],
adjacency_matrix=data_module.medgp.adjacency_matrix,
lr=args.lr,
intervention_targets_per_env=intervention_targets_per_env,
lr_scheduler=args.lr_scheduler,
lr_min=args.lr_min,
adjacency_misspecified=args.adjacency_misspec,
fix_mechanisms=args.fix_mechanisms,
nonparametric_base_distr=args.nonparametric_base_distr,
)
elif args.model == "naive":
|
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Run experiment for Causal Component Analysis (CauCA)."
)
parser.add_argument(
"--max-epochs",
type=int,
default=10,
help="Number of epochs to train for.",
)
parser.add_argument(
"--accelerator",
type=str,
default="gpu",
help="Accelerator to use for training.",
)
parser.add_argument(
"--batch-size",
type=int,
default=1024,
help="Number of samples per batch.",
)
parser.add_argument(
"--lr",
type=float,
default=1e-4,
help="Learning rate for Adam optimizer.",
)
parser.add_argument(
"--checkpoint-root-dir",
type=str,
default="checkpoints",
help="Checkpoint root directory.",
)
parser.add_argument(
"--noise-shift-type",
type=str,
default="mean",
choices=["mean", "std"],
help="Property of noise distribution that is shifted between environments.",
)
parser.add_argument(
"--check-val-every-n-epoch",
type=int,
default=1,
help="Check validation loss every n epochs.",
)
parser.add_argument(
"--dgp",
type=str,
default="graph-4-0",
help="Data generation process to use.",
)
parser.add_argument(
"--k-flows",
type=int,
default=1,
help="Number of flows to use in nonlinear ICA model.",
)
parser.add_argument(
"--k-flows-cbn",
type=int,
default=3,
help="Number of flows to use in nonlinear latent CBN model.",
)
parser.add_argument(
"--model",
type=str,
default="nonlinear",
help="Type of encoder to use.",
choices=["linear", "nonlinear", "naive"],
)
parser.add_argument(
"--seed",
type=int,
default=42,
)
parser.add_argument(
"--training-seed",
type=int,
default=42,
)
parser.add_argument(
"--mixing",
type=str,
default="nonlinear",
help="Type of mixing function to use.",
choices=["linear", "nonlinear"],
)
parser.add_argument(
"--scm",
type=str,
default="linear",
help="Type of SCM to use.",
choices=["linear", "location-scale"],
)
parser.add_argument(
"--n-nonlinearities",
type=int,
default=1,
help="Number of nonlinearities to use in nonlinear mixing function.",
)
parser.add_argument(
"--learn-scm-params",
type=bool,
default=True,
action=argparse.BooleanOptionalAction,
help="Whether to learn SCM parameters.",
)
parser.add_argument(
"--lr-scheduler",
type=str,
default=None,
help="Learning rate scheduler.",
choices=[None, "cosine"],
)
parser.add_argument(
"--lr-min",
type=float,
default=0.0,
help="Minimum learning rate for cosine learning rate scheduler.",
)
parser.add_argument(
"--scm-coeffs-low",
type=float,
default=-1,
help="Lower bound for SCM coefficients.",
)
parser.add_argument(
"--scm-coeffs-high",
type=float,
default=1,
help="Upper bound for SCM coefficients.",
)
parser.add_argument(
"--scm-coeffs-min-abs-value",
type=float,
default=None,
help="Minimum absolute value for SCM coefficients.",
)
parser.add_argument(
"--snr",
type=float,
default=1.0,
help="Signal-to-noise ratio in latent SCM.",
)
parser.add_argument(
"--adjacency-misspec",
type=bool,
default=False,
action=argparse.BooleanOptionalAction,
help="Misspecify adjacency matrix - assume ICA.",
)
parser.add_argument(
"--function-misspec",
type=bool,
default=False,
action=argparse.BooleanOptionalAction,
help="Misspecify function class - assume linear.",
)
parser.add_argument(
"--net-hidden-layers",
type=int,
default=3,
help="Number of hidden layers in nonlinear encoder.",
)
parser.add_argument(
"--net-hidden-layers-cbn",
type=int,
default=3,
help="Number of hidden layers in latent CBN model.",
)
parser.add_argument(
"--net-hidden-dim",
type=int,
default=128,
help="Number of hidden dimensions in nonlinear encoder.",
)
parser.add_argument(
"--net-hidden-dim-cbn",
type=int,
default=128,
help="Number of hidden dimensions in latent CBN model.",
)
parser.add_argument(
"--fix-mechanisms",
type=bool,
default=True,
action=argparse.BooleanOptionalAction,
help="Fix fixable mechanisms in latents.",
)
parser.add_argument(
"--fix-all-intervention-targets",
type=bool,
default=False,
action=argparse.BooleanOptionalAction,
help="Fix all intervention targets.",
)
parser.add_argument(
"--nonparametric-base-distr",
type=bool,
default=False,
action=argparse.BooleanOptionalAction,
help="Use nonparametric base distribution for flows.",
)
parser.add_argument(
"--wandb",
type=bool,
default=True,
action=argparse.BooleanOptionalAction,
help="Whether to log to weights and biases.",
)
parser.add_argument(
"--wandb-project",
type=str,
default="cauca",
help="Weights & Biases project name.",
)
args = parser.parse_args()
if args.function_misspec:
assert (
args.mixing == "nonlinear" and args.model == "linear"
), "Function not misspecified."
if args.wandb:
wandb_logger = WandbLogger(project=args.wandb_project)
wandb_logger.experiment.config.update(args, allow_val_change=True)
checkpoint_dir = (
Path(args.checkpoint_root_dir) / f"{wandb_logger.experiment.id}"
)
logger = [wandb_logger]
else:
checkpoint_dir = Path(args.checkpoint_root_dir) / "default"
logger = None
checkpoint_callback = pl.callbacks.ModelCheckpoint(
dirpath=checkpoint_dir,
save_last=True,
every_n_epochs=args.check_val_every_n_epoch,
)
multi_env_dgp = make_multi_env_dgp(
latent_dim=DGP[args.dgp]["num_causal_variables"],
observation_dim=DGP[args.dgp]["observation_dim"],
adjacency_matrix=DGP[args.dgp]["adj_matrix"],
intervention_targets_per_env=DGP[args.dgp]["int_targets"],
noise_shift_type=args.noise_shift_type,
mixing=args.mixing,
scm=args.scm,
n_nonlinearities=args.n_nonlinearities,
scm_coeffs_low=args.scm_coeffs_low,
scm_coeffs_high=args.scm_coeffs_high,
coeffs_min_abs_value=args.scm_coeffs_min_abs_value,
edge_prob=DGP[args.dgp].get("edge_prob", None),
snr=args.snr,
)
data_module = MultiEnvDataModule(
multi_env_dgp=multi_env_dgp,
num_samples_per_env=DGP[args.dgp]["num_samples_per_env"],
batch_size=args.batch_size,
num_workers=os.cpu_count(),
intervention_targets_per_env=DGP[args.dgp]["int_targets"],
log_dir=checkpoint_dir / "data_stats",
)
data_module.setup()
pl.seed_everything(args.training_seed, workers=True)
intervention_targets_per_env = DGP[args.dgp]["int_targets"]
# Model Initialization
if args.model == "nonlinear":
model = NonlinearCauCAModel(
latent_dim=DGP[args.dgp]["num_causal_variables"],
adjacency_matrix=data_module.medgp.adjacency_matrix,
k_flows=args.k_flows,
lr=args.lr,
intervention_targets_per_env=intervention_targets_per_env,
lr_scheduler=args.lr_scheduler,
lr_min=args.lr_min,
adjacency_misspecified=args.adjacency_misspec,
net_hidden_dim=args.net_hidden_dim,
net_hidden_layers=args.net_hidden_layers,
fix_mechanisms=args.fix_mechanisms,
fix_all_intervention_targets=args.fix_all_intervention_targets,
nonparametric_base_distr=args.nonparametric_base_distr,
K_cbn=args.k_flows_cbn,
net_hidden_dim_cbn=args.net_hidden_dim_cbn,
net_hidden_layers_cbn=args.net_hidden_layers_cbn,
)
elif args.model == "linear":
model = LinearCauCAModel(
latent_dim=DGP[args.dgp]["num_causal_variables"],
adjacency_matrix=data_module.medgp.adjacency_matrix,
lr=args.lr,
intervention_targets_per_env=intervention_targets_per_env,
lr_scheduler=args.lr_scheduler,
lr_min=args.lr_min,
adjacency_misspecified=args.adjacency_misspec,
fix_mechanisms=args.fix_mechanisms,
nonparametric_base_distr=args.nonparametric_base_distr,
)
elif args.model == "naive": | model = NaiveNonlinearModel( | 4 | 2023-10-25 09:25:26+00:00 | 16k |
endo-yuki-t/MAG | 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 torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
import itertools
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager, nullcontext
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig
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 IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler | 11,199 | """
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'}
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.,
make_it_fit=False,
ucg_training=None,
reset_ema=False,
reset_num_ema_updates=False,
):
super().__init__()
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
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:
| """
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'}
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.,
make_it_fit=False,
ucg_training=None,
reset_ema=False,
reset_num_ema_updates=False,
):
super().__init__()
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
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) | 8 | 2023-10-27 06:56:37+00:00 | 16k |
Gene-Weaver/VoucherVision | vouchervision/VoucherVision_GUI.py | [
{
"identifier": "write_config_file",
"path": "vouchervision/LeafMachine2_Config_Builder.py",
"snippet": "def write_config_file(config_data, dir_home, filename=\"LeafMachine2.yaml\"):\n file_path = os.path.join(dir_home, filename)\n\n # Write the data to a YAML file\n with open(file_path, \"w\")... | import streamlit as st
import yaml, os, json, random, time, re
import matplotlib.pyplot as plt
import plotly.graph_objs as go
import numpy as np
import pandas as pd
from itertools import chain
from PIL import Image
from typing import Union
from streamlit_extras.let_it_rain import rain
from vouchervision.LeafMachine2_Config_Builder import write_config_file
from vouchervision.VoucherVision_Config_Builder import build_VV_config, run_demo_tests_GPT, run_demo_tests_Palm , TestOptionsGPT, TestOptionsPalm, check_if_usable, run_api_tests
from vouchervision.vouchervision_main import voucher_vision, voucher_vision_OCR_test
from vouchervision.general_utils import test_GPU, get_cfg_from_full_path, summarize_expense_report, create_google_ocr_yaml_config, validate_dir | 10,839 |
if st.session_state.config['leafmachine']['project']['prompt_version'] in PROMPTS_THAT_NEED_DOMAIN_KNOWLEDGE:
st.session_state.config['leafmachine']['project']['use_domain_knowledge'] = st.checkbox("Use domain knowledge", True, disabled=True)
else:
st.session_state.config['leafmachine']['project']['use_domain_knowledge'] = st.checkbox("Use domain knowledge", False, disabled=True)
st.write("")
if st.session_state.config['leafmachine']['project']['use_domain_knowledge']:
st.session_state.config['leafmachine']['project']['embeddings_database_name'] = st.text_input("Embeddings database name (only use underscores)", st.session_state.config['leafmachine']['project'].get('embeddings_database_name', ''))
st.session_state.config['leafmachine']['project']['build_new_embeddings_database'] = st.checkbox("Build *new* embeddings database", st.session_state.config['leafmachine']['project'].get('build_new_embeddings_database', False))
st.session_state.config['leafmachine']['project']['path_to_domain_knowledge_xlsx'] = st.text_input("Path to domain knowledge CSV file (will be used to create new embeddings database)", st.session_state.config['leafmachine']['project'].get('path_to_domain_knowledge_xlsx', ''))
else:
st.session_state.config['leafmachine']['project']['embeddings_database_name'] = st.text_input("Embeddings database name (only use underscores)", st.session_state.config['leafmachine']['project'].get('embeddings_database_name', ''), disabled=True)
st.session_state.config['leafmachine']['project']['build_new_embeddings_database'] = st.checkbox("Build *new* embeddings database", st.session_state.config['leafmachine']['project'].get('build_new_embeddings_database', False), disabled=True)
st.session_state.config['leafmachine']['project']['path_to_domain_knowledge_xlsx'] = st.text_input("Path to domain knowledge CSV file (will be used to create new embeddings database)", st.session_state.config['leafmachine']['project'].get('path_to_domain_knowledge_xlsx', ''), disabled=True)
def render_expense_report_summary():
expense_summary = st.session_state.expense_summary
expense_report = st.session_state.expense_report
st.header('Expense Report Summary')
if expense_summary:
st.metric(label="Total Cost", value=f"${round(expense_summary['total_cost_sum'], 4):,}")
col1, col2 = st.columns(2)
# Run count and total costs
with col1:
st.metric(label="Run Count", value=expense_summary['run_count'])
st.metric(label="Tokens In", value=f"{expense_summary['tokens_in_sum']:,}")
# Token information
with col2:
st.metric(label="Total Images", value=expense_summary['n_images_sum'])
st.metric(label="Tokens Out", value=f"{expense_summary['tokens_out_sum']:,}")
# Calculate cost proportion per image for each API version
st.subheader('Average Cost per Image by API Version')
cost_labels = []
cost_values = []
total_images = 0
cost_per_image_dict = {}
# Iterate through the expense report to accumulate costs and image counts
for index, row in expense_report.iterrows():
api_version = row['api_version']
total_cost = row['total_cost']
n_images = row['n_images']
total_images += n_images # Keep track of total images processed
if api_version not in cost_per_image_dict:
cost_per_image_dict[api_version] = {'total_cost': 0, 'n_images': 0}
cost_per_image_dict[api_version]['total_cost'] += total_cost
cost_per_image_dict[api_version]['n_images'] += n_images
api_versions = list(cost_per_image_dict.keys())
colors = [COLORS_EXPENSE_REPORT[version] if version in COLORS_EXPENSE_REPORT else '#DDDDDD' for version in api_versions]
# Calculate the cost per image for each API version
for version, cost_data in cost_per_image_dict.items():
total_cost = cost_data['total_cost']
n_images = cost_data['n_images']
# Calculate the cost per image for this version
cost_per_image = total_cost / n_images if n_images > 0 else 0
cost_labels.append(version)
cost_values.append(cost_per_image)
# Generate the pie chart
cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_values, hole=.3)])
# Update traces for custom text in hoverinfo, displaying cost with a dollar sign and two decimal places
cost_pie_chart.update_traces(
marker=dict(colors=colors),
text=[f"${value:.2f}" for value in cost_values], # Formats the cost as a string with a dollar sign and two decimals
textinfo='percent+label',
hoverinfo='label+percent+text' # Adds custom text (formatted cost) to the hover information
)
st.plotly_chart(cost_pie_chart, use_container_width=True)
st.subheader('Proportion of Total Cost by API Version')
cost_labels = []
cost_proportions = []
total_cost_by_version = {}
# Sum the total cost for each API version
for index, row in expense_report.iterrows():
api_version = row['api_version']
total_cost = row['total_cost']
if api_version not in total_cost_by_version:
total_cost_by_version[api_version] = 0
total_cost_by_version[api_version] += total_cost
# Calculate the combined total cost for all versions
combined_total_cost = sum(total_cost_by_version.values())
# Calculate the proportion of total cost for each API version
for version, total_cost in total_cost_by_version.items():
proportion = (total_cost / combined_total_cost) * 100 if combined_total_cost > 0 else 0
cost_labels.append(version)
cost_proportions.append(proportion)
# Generate the pie chart
cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_proportions, hole=.3)])
# Update traces for custom text in hoverinfo
cost_pie_chart.update_traces(
marker=dict(colors=colors),
text=[f"${cost:.2f}" for cost in total_cost_by_version.values()], # This will format the cost to 2 decimal places
textinfo='percent+label',
hoverinfo='label+percent+text' # This tells Plotly to show the label, percent, and custom text (cost) on hover
)
st.plotly_chart(cost_pie_chart, use_container_width=True)
# API version usage percentages pie chart
st.subheader('Runs by API Version')
api_versions = list(expense_summary['api_version_percentages'].keys())
percentages = [expense_summary['api_version_percentages'][version] for version in api_versions]
pie_chart = go.Figure(data=[go.Pie(labels=api_versions, values=percentages, hole=.3)])
pie_chart.update_layout(margin=dict(t=0, b=0, l=0, r=0))
pie_chart.update_traces(marker=dict(colors=colors),)
st.plotly_chart(pie_chart, use_container_width=True)
else:
st.error('No expense report data available.')
def sidebar_content():
if not os.path.exists(os.path.join(st.session_state.dir_home,'expense_report')):
|
PROMPTS_THAT_NEED_DOMAIN_KNOWLEDGE = ["Version 1","Version 1 PaLM 2"]
COLORS_EXPENSE_REPORT = {
'GPT_4': '#8fff66', # Bright Green
'GPT_3_5': '#006400', # Dark Green
'PALM2': '#66a8ff' # blue
}
class ProgressReport:
def __init__(self, overall_bar, batch_bar, text_overall, text_batch):
self.overall_bar = overall_bar
self.batch_bar = batch_bar
self.text_overall = text_overall
self.text_batch = text_batch
self.current_overall_step = 0
self.total_overall_steps = 20 # number of major steps in machine function
self.current_batch = 0
self.total_batches = 20
def update_overall(self, step_name=""):
self.current_overall_step += 1
self.overall_bar.progress(self.current_overall_step / self.total_overall_steps)
self.text_overall.text(step_name)
def update_batch(self, step_name=""):
self.current_batch += 1
self.batch_bar.progress(self.current_batch / self.total_batches)
self.text_batch.text(step_name)
def set_n_batches(self, n_batches):
self.total_batches = n_batches
def set_n_overall(self, total_overall_steps):
self.current_overall_step = 0
self.overall_bar.progress(0)
self.total_overall_steps = total_overall_steps
def reset_batch(self, step_name):
self.current_batch = 0
self.batch_bar.progress(0)
self.text_batch.text(step_name)
def reset_overall(self, step_name):
self.current_overall_step = 0
self.overall_bar.progress(0)
self.text_overall.text(step_name)
def get_n_images(self):
return self.n_images
def get_n_overall(self):
return self.total_overall_steps
def does_private_file_exist():
dir_home = os.path.dirname(os.path.dirname(__file__))
path_cfg_private = os.path.join(dir_home, 'PRIVATE_DATA.yaml')
return os.path.exists(path_cfg_private)
def setup_streamlit_config(dir_home):
# Define the directory path and filename
dir_path = os.path.join(dir_home, ".streamlit")
file_path = os.path.join(dir_path, "config.toml")
# Check if directory exists, if not create it
if not os.path.exists(dir_path):
os.makedirs(dir_path)
# Create or modify the file with the provided content
config_content = f"""
[theme]
base = "dark"
primaryColor = "#00ff00"
[server]
enableStaticServing = false
runOnSave = true
port = 8524
"""
with open(file_path, "w") as f:
f.write(config_content.strip())
def display_scrollable_results(JSON_results, test_results, OPT2, OPT3):
"""
Display the results from JSON_results in a scrollable container.
"""
# Initialize the container
con_results = st.empty()
with con_results.container():
# Start the custom container for all the results
results_html = """<div class='scrollable-results-container'>"""
for idx, (test_name, _) in enumerate(sorted(test_results.items())):
_, ind_opt1, ind_opt2, ind_opt3 = test_name.split('__')
opt2_readable = "Use LeafMachine2" if OPT2[int(ind_opt2.split('-')[1])] else "Don't use LeafMachine2"
opt3_readable = f"{OPT3[int(ind_opt3.split('-')[1])]}"
if JSON_results[idx] is None:
results_html += f"<p>None</p>"
else:
formatted_json = json.dumps(JSON_results[idx], indent=4, sort_keys=False)
results_html += f"<pre>[{opt2_readable}] + [{opt3_readable}]<br/>{formatted_json}</pre>"
# End the custom container
results_html += """</div>"""
# The CSS to make this container scrollable
css = """
<style>
.scrollable-results-container {
overflow-y: auto;
height: 600px;
width: 100%;
white-space: pre-wrap; # To wrap the content
font-family: monospace; # To give the JSON a code-like appearance
}
</style>
"""
# Apply the CSS and then the results
st.markdown(css, unsafe_allow_html=True)
st.markdown(results_html, unsafe_allow_html=True)
def refresh():
st.write('')
def display_test_results(test_results, JSON_results, llm_version):
if llm_version == 'gpt':
OPT1, OPT2, OPT3 = TestOptionsGPT.get_options()
elif llm_version == 'palm':
OPT1, OPT2, OPT3 = TestOptionsPalm.get_options()
else:
raise
widths = [1] * (len(OPT1) + 2) + [2]
columns = st.columns(widths)
with columns[0]:
st.write("LeafMachine2")
with columns[1]:
st.write("Prompt")
with columns[len(OPT1) + 2]:
st.write("Scroll to See Last Transcription in Each Test")
already_written = set()
for test_name, result in sorted(test_results.items()):
_, ind_opt1, _, _ = test_name.split('__')
option_value = OPT1[int(ind_opt1.split('-')[1])]
if option_value not in already_written:
with columns[int(ind_opt1.split('-')[1]) + 2]:
st.write(option_value)
already_written.add(option_value)
printed_options = set()
with columns[-1]:
display_scrollable_results(JSON_results, test_results, OPT2, OPT3)
# Close the custom container
st.write('</div>', unsafe_allow_html=True)
for idx, (test_name, result) in enumerate(sorted(test_results.items())):
_, ind_opt1, ind_opt2, ind_opt3 = test_name.split('__')
opt2_readable = "Use LeafMachine2" if OPT2[int(ind_opt2.split('-')[1])] else "Don't use LeafMachine2"
opt3_readable = f"{OPT3[int(ind_opt3.split('-')[1])]}"
if (opt2_readable, opt3_readable) not in printed_options:
with columns[0]:
st.info(f"{opt2_readable}")
st.write('---')
with columns[1]:
st.info(f"{opt3_readable}")
st.write('---')
printed_options.add((opt2_readable, opt3_readable))
with columns[int(ind_opt1.split('-')[1]) + 2]:
if result:
st.success(f"Test Passed")
else:
st.error(f"Test Failed")
st.write('---')
# success_count = sum(1 for result in test_results.values() if result)
# failure_count = len(test_results) - success_count
# proportional_rain("🥇", success_count, "💔", failure_count, font_size=72, falling_speed=5, animation_length="infinite")
rain_emojis(test_results)
def add_emoji_delay():
time.sleep(0.3)
def rain_emojis(test_results):
# test_results = {
# 'test1': True, # Test passed
# 'test2': True, # Test passed
# 'test3': True, # Test passed
# 'test4': False, # Test failed
# 'test5': False, # Test failed
# 'test6': False, # Test failed
# 'test7': False, # Test failed
# 'test8': False, # Test failed
# 'test9': False, # Test failed
# 'test10': False, # Test failed
# }
success_emojis = ["🥇", "🏆", "🍾", "🙌"]
failure_emojis = ["💔", "😭"]
success_count = sum(1 for result in test_results.values() if result)
failure_count = len(test_results) - success_count
chosen_emoji = random.choice(success_emojis)
for _ in range(success_count):
rain(
emoji=chosen_emoji,
font_size=72,
falling_speed=4,
animation_length=2,
)
add_emoji_delay()
chosen_emoji = random.choice(failure_emojis)
for _ in range(failure_count):
rain(
emoji=chosen_emoji,
font_size=72,
falling_speed=5,
animation_length=1,
)
add_emoji_delay()
def get_prompt_versions(LLM_version):
yaml_files = [f for f in os.listdir(os.path.join(st.session_state.dir_home, 'custom_prompts')) if f.endswith('.yaml')]
if LLM_version in ["gpt-4-1106-preview", "GPT 4", "GPT 3.5", "Azure GPT 4", "Azure GPT 3.5"]:
versions = ["Version 1", "Version 1 No Domain Knowledge", "Version 2"]
return (versions + yaml_files, "Version 2")
elif LLM_version in ["PaLM 2",]:
versions = ["Version 1 PaLM 2", "Version 1 PaLM 2 No Domain Knowledge", "Version 2 PaLM 2"]
return (versions + yaml_files, "Version 2 PaLM 2")
else:
# Handle other cases or raise an error
return (yaml_files, None)
def get_private_file():
dir_home = os.path.dirname(os.path.dirname(__file__))
path_cfg_private = os.path.join(dir_home, 'PRIVATE_DATA.yaml')
return get_cfg_from_full_path(path_cfg_private)
def create_space_saver():
st.subheader("Space Saving Options")
col_ss_1, col_ss_2 = st.columns([2,2])
with col_ss_1:
st.write("Several folders are created and populated with data during the VoucherVision transcription process.")
st.write("Below are several options that will allow you to automatically delete temporary files that you may not need for everyday operations.")
st.write("VoucherVision creates the following folders. Folders marked with a :star: are required if you want to use VoucherVisionEditor for quality control.")
st.write("`../[Run Name]/Archival_Components`")
st.write("`../[Run Name]/Config_File`")
st.write("`../[Run Name]/Cropped_Images` :star:")
st.write("`../[Run Name]/Logs`")
st.write("`../[Run Name]/Original_Images` :star:")
st.write("`../[Run Name]/Transcription` :star:")
with col_ss_2:
st.session_state.config['leafmachine']['project']['delete_temps_keep_VVE'] = st.checkbox("Delete Temporary Files (KEEP files required for VoucherVisionEditor)", st.session_state.config['leafmachine']['project'].get('delete_temps_keep_VVE', False))
st.session_state.config['leafmachine']['project']['delete_all_temps'] = st.checkbox("Keep only the final transcription file", st.session_state.config['leafmachine']['project'].get('delete_all_temps', False),help="*WARNING:* This limits your ability to do quality assurance. This will delete all folders created by VoucherVision, leaving only the `transcription.xlsx` file.")
# def create_private_file():
# st.session_state.proceed_to_main = False
# if st.session_state.private_file:
# cfg_private = get_private_file()
# create_private_file_0(cfg_private)
# else:
# st.title("VoucherVision")
# create_private_file_0()
def create_private_file():
st.session_state.proceed_to_main = False
st.title("VoucherVision")
col_private,_= st.columns([12,2])
if st.session_state.private_file:
cfg_private = get_private_file()
else:
cfg_private = {}
cfg_private['openai'] = {}
cfg_private['openai']['OPENAI_API_KEY'] =''
cfg_private['openai_azure'] = {}
cfg_private['openai_azure']['openai_api_key'] = ''
cfg_private['openai_azure']['api_version'] = ''
cfg_private['openai_azure']['openai_api_base'] =''
cfg_private['openai_azure']['openai_organization'] =''
cfg_private['openai_azure']['openai_api_type'] =''
cfg_private['google_cloud'] = {}
cfg_private['google_cloud']['path_json_file'] =''
cfg_private['google_palm'] = {}
cfg_private['google_palm']['google_palm_api'] =''
with col_private:
st.header("Set API keys")
st.info("***Note:*** There is a known bug with tabs in Streamlit. If you update an input field it may take you back to the 'Project Settings' tab. Changes that you made are saved, it's just an annoying glitch. We are aware of this issue and will fix it as soon as we can.")
st.warning("To commit changes to API keys you must press the 'Set API Keys' button at the bottom of the page.")
st.write("Before using VoucherVision you must set your API keys. All keys are stored locally on your computer and are never made public.")
st.write("API keys are stored in `../VoucherVision/PRIVATE_DATA.yaml`.")
st.write("Deleting this file will allow you to reset API keys. Alternatively, you can edit the keys in the user interface.")
st.write("Leave keys blank if you do not intend to use that service.")
st.write("---")
st.subheader("Google Vision (*Required*)")
st.markdown("VoucherVision currently uses [Google Vision API](https://cloud.google.com/vision/docs/ocr) for OCR. Generating an API key for this is more involved than the others. [Please carefully follow the instructions outlined here to create and setup your account.](https://cloud.google.com/vision/docs/setup) ")
st.markdown("""
Once your account is created, [visit this page](https://console.cloud.google.com) and create a project. Then follow these instructions:
- **Select your Project**: If you have multiple projects, ensure you select the one where you've enabled the Vision API.
- **Open the Navigation Menu**: Click on the hamburger menu (three horizontal lines) in the top left corner.
- **Go to IAM & Admin**: In the navigation pane, hover over "IAM & Admin" and then click on "Service accounts."
- **Locate Your Service Account**: Find the service account for which you wish to download the JSON key. If you haven't created a service account yet, you'll need to do so by clicking the "CREATE SERVICE ACCOUNT" button at the top.
- **Download the JSON Key**:
- Click on the three dots (actions menu) on the right side of your service account name.
- Select "Manage keys."
- In the pop-up window, click on the "ADD KEY" button and select "JSON."
- The JSON key file will automatically be downloaded to your computer.
- **Store Safely**: This file contains sensitive data that can be used to authenticate and bill your Google Cloud account. Never commit it to public repositories or expose it in any way. Always keep it safe and secure.
""")
with st.container():
c_in_ocr, c_button_ocr = st.columns([10,2])
with c_in_ocr:
google_vision = st.text_input(label = 'Full path to Google Cloud JSON API key file', value = cfg_private['google_cloud'].get('path_json_file', ''),
placeholder = 'e.g. C:/Documents/Secret_Files/google_API/application_default_credentials.json',
help ="This API Key is in the form of a JSON file. Please save the JSON file in a safe directory. DO NOT store the JSON key inside of the VoucherVision directory.",
type='password',key='924857298734590283750932809238')
with c_button_ocr:
st.empty()
st.write("---")
st.subheader("OpenAI")
st.markdown("API key for first-party OpenAI API. Create an account with OpenAI [here](https://platform.openai.com/signup), then create an API key [here](https://platform.openai.com/account/api-keys).")
with st.container():
c_in_openai, c_button_openai = st.columns([10,2])
with c_in_openai:
openai_api_key = st.text_input("openai_api_key", cfg_private['openai'].get('OPENAI_API_KEY', ''),
help='The actual API key. Likely to be a string of 2 character, a dash, and then a 48-character string: sk-XXXXXXXX...',
placeholder = 'e.g. sk-XXXXXXXX...',
type='password')
with c_button_openai:
st.empty()
st.write("---")
st.subheader("OpenAI - Azure")
st.markdown("This version OpenAI relies on Azure servers directly as is intended for private enterprise instances of OpenAI's services, such as [UM-GPT](https://its.umich.edu/computing/ai). Administrators will provide you with the following information.")
azure_openai_api_version = st.text_input("azure_openai_api_version", cfg_private['openai_azure'].get('api_version', ''),
help='API Version e.g. "2023-05-15"',
placeholder = 'e.g. 2023-05-15',
type='password')
azure_openai_api_key = st.text_input("azure_openai_api_key", cfg_private['openai_azure'].get('openai_api_key', ''),
help='The actual API key. Likely to be a 32-character string',
placeholder = 'e.g. 12333333333333333333333333333332',
type='password')
azure_openai_api_base = st.text_input("azure_openai_api_base", cfg_private['openai_azure'].get('openai_api_base', ''),
help='The base url for the API e.g. "https://api.umgpt.umich.edu/azure-openai-api"',
placeholder = 'e.g. https://api.umgpt.umich.edu/azure-openai-api',
type='password')
azure_openai_organization = st.text_input("azure_openai_organization", cfg_private['openai_azure'].get('openai_organization', ''),
help='Your organization code. Likely a short string',
placeholder = 'e.g. 123456',
type='password')
azure_openai_api_type = st.text_input("azure_openai_api_type", cfg_private['openai_azure'].get('openai_api_type', ''),
help='The API type. Typically "azure"',
placeholder = 'e.g. azure',
type='password')
with st.container():
c_in_azure, c_button_azure = st.columns([10,2])
with c_button_azure:
st.empty()
st.write("---")
st.subheader("Google PaLM 2")
st.markdown('Follow these [instructions](https://developers.generativeai.google/tutorials/setup) to generate an API key for PaLM 2. You may need to also activate an account with [MakerSuite](https://makersuite.google.com/app/apikey) and enable "early access."')
with st.container():
c_in_palm, c_button_palm = st.columns([10,2])
with c_in_palm:
google_palm = st.text_input("Google PaLM 2 API Key", cfg_private['google_palm'].get('google_palm_api', ''),
help='The MakerSuite API key e.g. a 32-character string',
placeholder='e.g. SATgthsykuE64FgrrrrEervr3S4455t_geyDeGq',
type='password')
with st.container():
with c_button_ocr:
st.write("##")
st.button("Test OCR", on_click=test_API, args=['google_vision',c_in_ocr, cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key,
azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm])
with st.container():
with c_button_openai:
st.write("##")
st.button("Test OpenAI", on_click=test_API, args=['openai',c_in_openai, cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key,
azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm])
with st.container():
with c_button_azure:
st.write("##")
st.button("Test Azure OpenAI", on_click=test_API, args=['azure_openai',c_in_azure, cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key,
azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm])
with st.container():
with c_button_palm:
st.write("##")
st.button("Test PaLM 2", on_click=test_API, args=['palm',c_in_palm, cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key,
azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm])
st.button("Set API Keys",type='primary', on_click=save_changes_to_API_keys, args=[cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key,
azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm])
if st.button('Proceed to VoucherVision'):
st.session_state.proceed_to_private = False
st.session_state.proceed_to_main = True
def test_API(api, message_loc, cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key, azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm):
# Save the API keys
save_changes_to_API_keys(cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key,azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm)
with st.spinner('Performing validation checks...'):
if api == 'google_vision':
print("*** Google Vision OCR API Key ***")
try:
demo_config_path = os.path.join(st.session_state.dir_home,'demo','validation_configs','google_vision_ocr_test.yaml')
demo_images_path = os.path.join(st.session_state.dir_home, 'demo', 'demo_images')
demo_out_path = os.path.join(st.session_state.dir_home, 'demo', 'demo_output','run_name')
create_google_ocr_yaml_config(demo_config_path, demo_images_path, demo_out_path)
voucher_vision_OCR_test(demo_config_path, st.session_state.dir_home, None, demo_images_path)
with message_loc:
st.success("Google Vision OCR API Key Valid :white_check_mark:")
return True
except Exception as e:
with message_loc:
st.error(f"Google Vision OCR API Key Failed! {e}")
return False
elif api == 'openai':
print("*** OpenAI API Key ***")
try:
if run_api_tests('openai'):
with message_loc:
st.success("OpenAI API Key Valid :white_check_mark:")
else:
with message_loc:
st.error("OpenAI API Key Failed:exclamation:")
return False
except Exception as e:
with message_loc:
st.error(f"OpenAI API Key Failed:exclamation: {e}")
elif api == 'azure_openai':
print("*** Azure OpenAI API Key ***")
try:
if run_api_tests('azure_openai'):
with message_loc:
st.success("Azure OpenAI API Key Valid :white_check_mark:")
else:
with message_loc:
st.error(f"Azure OpenAI API Key Failed:exclamation:")
return False
except Exception as e:
with message_loc:
st.error(f"Azure OpenAI API Key Failed:exclamation: {e}")
elif api == 'palm':
print("*** Google PaLM 2 API Key ***")
try:
if run_api_tests('palm'):
with message_loc:
st.success("Google PaLM 2 API Key Valid :white_check_mark:")
else:
with message_loc:
st.error("Google PaLM 2 API Key Failed:exclamation:")
return False
except Exception as e:
with message_loc:
st.error(f"Google PaLM 2 API Key Failed:exclamation: {e}")
def save_changes_to_API_keys(cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key,
azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm):
# Update the configuration dictionary with the new values
cfg_private['openai']['OPENAI_API_KEY'] = openai_api_key
cfg_private['openai_azure']['api_version'] = azure_openai_api_version
cfg_private['openai_azure']['openai_api_key'] = azure_openai_api_key
cfg_private['openai_azure']['openai_api_base'] = azure_openai_api_base
cfg_private['openai_azure']['openai_organization'] = azure_openai_organization
cfg_private['openai_azure']['openai_api_type'] = azure_openai_api_type
cfg_private['google_cloud']['path_json_file'] = google_vision
cfg_private['google_palm']['google_palm_api'] = google_palm
# Call the function to write the updated configuration to the YAML file
write_config_file(cfg_private, st.session_state.dir_home, filename="PRIVATE_DATA.yaml")
st.session_state.private_file = does_private_file_exist()
# Function to load a YAML file and update session_state
def load_prompt_yaml(filename):
with open(filename, 'r') as file:
st.session_state['prompt_info'] = yaml.safe_load(file)
st.session_state['prompt_author'] = st.session_state['prompt_info'].get('prompt_author', st.session_state['default_prompt_author'])
st.session_state['prompt_author_institution'] = st.session_state['prompt_info'].get('prompt_author_institution', st.session_state['default_prompt_author_institution'])
st.session_state['prompt_description'] = st.session_state['prompt_info'].get('prompt_description', st.session_state['default_prompt_description'])
st.session_state['instructions'] = st.session_state['prompt_info'].get('instructions', st.session_state['default_instructions'])
st.session_state['json_formatting_instructions'] = st.session_state['prompt_info'].get('json_formatting_instructions', st.session_state['default_json_formatting_instructions'] )
st.session_state['rules'] = st.session_state['prompt_info'].get('rules', {})
st.session_state['mapping'] = st.session_state['prompt_info'].get('mapping', {})
st.session_state['LLM'] = st.session_state['prompt_info'].get('LLM', 'gpt')
# Placeholder:
st.session_state['assigned_columns'] = list(chain.from_iterable(st.session_state['mapping'].values()))
def save_prompt_yaml(filename):
yaml_content = {
'prompt_author': st.session_state['prompt_author'],
'prompt_author_institution': st.session_state['prompt_author_institution'],
'prompt_description': st.session_state['prompt_description'],
'LLM': st.session_state['LLM'],
'instructions': st.session_state['instructions'],
'json_formatting_instructions': st.session_state['json_formatting_instructions'],
'rules': st.session_state['rules'],
'mapping': st.session_state['mapping'],
}
dir_prompt = os.path.join(st.session_state.dir_home, 'custom_prompts')
filepath = os.path.join(dir_prompt, f"{filename}.yaml")
with open(filepath, 'w') as file:
yaml.safe_dump(dict(yaml_content), file, sort_keys=False)
st.success(f"Prompt saved as '{filename}.yaml'.")
def check_unique_mapping_assignments():
if len(st.session_state['assigned_columns']) != len(set(st.session_state['assigned_columns'])):
st.error("Each column name must be assigned to only one category.")
return False
else:
st.success("Mapping confirmed.")
return True
def check_prompt_yaml_filename(fname):
# Check if the filename only contains letters, numbers, underscores, and dashes
pattern = r'^[\w-]+$'
# The \w matches any alphanumeric character and is equivalent to the character class [a-zA-Z0-9_].
# The hyphen - is literally matched.
if re.match(pattern, fname):
return True
else:
return False
def btn_load_prompt(selected_yaml_file, dir_prompt):
if selected_yaml_file:
yaml_file_path = os.path.join(dir_prompt, selected_yaml_file)
load_prompt_yaml(yaml_file_path)
elif not selected_yaml_file:
# Directly assigning default values since no file is selected
st.session_state['prompt_info'] = {}
st.session_state['prompt_author'] = st.session_state['default_prompt_author']
st.session_state['prompt_author_institution'] = st.session_state['default_prompt_author_institution']
st.session_state['prompt_description'] = st.session_state['default_prompt_description']
st.session_state['instructions'] = st.session_state['default_instructions']
st.session_state['json_formatting_instructions'] = st.session_state['default_json_formatting_instructions']
st.session_state['rules'] = {}
st.session_state['LLM'] = 'gpt'
st.session_state['assigned_columns'] = []
st.session_state['prompt_info'] = {
'prompt_author': st.session_state['prompt_author'],
'prompt_author_institution': st.session_state['prompt_author_institution'],
'prompt_description': st.session_state['prompt_description'],
'instructions': st.session_state['instructions'],
'json_formatting_instructions': st.session_state['json_formatting_instructions'],
'rules': st.session_state['rules'],
'mapping': st.session_state['mapping'],
'LLM': st.session_state['LLM']
}
def build_LLM_prompt_config():
st.session_state['assigned_columns'] = []
st.session_state['default_prompt_author'] = 'unknown'
st.session_state['default_prompt_author_institution'] = 'unknown'
st.session_state['default_prompt_description'] = 'unknown'
st.session_state['default_instructions'] = """1. Refactor the unstructured OCR text into a dictionary based on the JSON structure outlined below.
2. You should map the unstructured OCR text to the appropriate JSON key and then populate the field based on its rules.
3. Some JSON key fields are permitted to remain empty if the corresponding information is not found in the unstructured OCR text.
4. Ignore any information in the OCR text that doesn't fit into the defined JSON structure.
5. Duplicate dictionary fields are not allowed.
6. Ensure that all JSON keys are in lowercase.
7. Ensure that new JSON field values follow sentence case capitalization.
8. Ensure all key-value pairs in the JSON dictionary strictly adhere to the format and data types specified in the template.
9. Ensure the output JSON string is valid JSON format. It should not have trailing commas or unquoted keys.
10. Only return a JSON dictionary represented as a string. You should not explain your answer."""
st.session_state['default_json_formatting_instructions'] = """The next section of instructions outlines how to format the JSON dictionary. The keys are the same as those of the final formatted JSON object.
For each key there is a format requirement that specifies how to transcribe the information for that key.
The possible formatting options are:
1. "verbatim transcription" - field is populated with verbatim text from the unformatted OCR.
2. "spell check transcription" - field is populated with spelling corrected text from the unformatted OCR.
3. "boolean yes no" - field is populated with only yes or no.
4. "boolean 1 0" - field is populated with only 1 or 0.
5. "integer" - field is populated with only an integer.
6. "[list]" - field is populated from one of the values in the list.
7. "yyyy-mm-dd" - field is populated with a date in the format year-month-day.
The desired null value is also given. Populate the field with the null value of the information for that key is not present in the unformatted OCR text."""
# Start building the Streamlit app
col_prompt_main_left, ___, col_prompt_main_right = st.columns([6,1,3])
with col_prompt_main_left:
st.title("Custom LLM Prompt Builder")
st.subheader('About')
st.write("This form allows you to craft a prompt for your specific task.")
st.subheader('How it works')
st.write("1. Edit this page until you are happy with your instructions. We recommend looking at the basic structure, writing down your prompt inforamtion in a Word document so that it does not randomly disappear, and then copying and pasting that info into this form once your whole prompt structure is defined.")
st.write("2. After you enter all of your prompt instructions, click 'Save' and give your file a name.")
st.write("3. This file will be saved as a yaml configuration file in the `..VoucherVision/custom_prompts` folder.")
st.write("4. When you go back the main VoucherVision page you will now see your custom prompt available in the 'Prompt Version' dropdown menu.")
st.write("5. Select your custom prompt. Note, your prompt will only be available for the LLM that you set when filling out the form below.")
dir_prompt = os.path.join(st.session_state.dir_home, 'custom_prompts')
yaml_files = [f for f in os.listdir(dir_prompt) if f.endswith('.yaml')]
col_load_text, col_load_btn = st.columns([8,2])
with col_load_text:
# Dropdown for selecting a YAML file
selected_yaml_file = st.selectbox('Select a prompt YAML file to load:', [''] + yaml_files)
with col_load_btn:
st.write('##')
# Button to load the selected prompt
st.button('Load Prompt', on_click=btn_load_prompt, args=[selected_yaml_file, dir_prompt])
# Prompt Author Information
st.header("Prompt Author Information")
st.write("We value community contributions! Please provide your name(s) (or pseudonym if you prefer) for credit. If you leave this field blank, it will say 'unknown'.")
st.session_state['prompt_author'] = st.text_input("Enter names of prompt author(s)", value=st.session_state['default_prompt_author'])
st.write("Please provide your institution name. If you leave this field blank, it will say 'unknown'.")
st.session_state['prompt_author_institution'] = st.text_input("Enter name of institution", value=st.session_state['default_prompt_author_institution'])
st.write("Please provide a description of your prompt and its intended task. Is it designed for a specific collection? Taxa? Database structure?")
st.session_state['prompt_description'] = st.text_input("Enter description of prompt", value=st.session_state['default_prompt_description'])
st.write('---')
st.header("Set LLM Model Type")
# Define the options for the dropdown
llm_options = ['gpt', 'palm']
# Create the dropdown and set the value to session_state['LLM']
st.write("Which LLM is this prompt designed for? This will not restrict its use to a specific LLM, but some prompts will behave in different ways across models.")
st.write("For example, VoucherVision will automatically add multiple JSON formatting blocks to all PaLM 2 prompts to coax PaLM 2 to return a valid JSON object.")
st.session_state['LLM'] = st.selectbox('Set LLM', llm_options, index=llm_options.index(st.session_state.get('LLM', 'gpt')))
st.write('---')
# Instructions Section
st.header("Instructions")
st.write("These are the general instructions that guide the LLM through the transcription task. We recommend using the default instructions unless you have a specific reason to change them.")
st.session_state['instructions'] = st.text_area("Enter instructions", value=st.session_state['default_instructions'].strip(), height=350, disabled=True)
st.write('---')
# Column Instructions Section
st.header("JSON Formatting Instructions")
st.write("The following section tells the LLM how we want to structure the JSON dictionary. We do not recommend changing this section because it would likely result in unstable and inconsistent behavior.")
st.session_state['json_formatting_instructions'] = st.text_area("Enter column instructions", value=st.session_state['default_json_formatting_instructions'], height=350, disabled=True)
st.write('---')
col_left, col_right = st.columns([6,4])
with col_left:
st.subheader('Add/Edit Columns')
# Initialize rules in session state if not already present
if 'rules' not in st.session_state or not st.session_state['rules']:
st.session_state['rules']['Dictionary'] = {
"catalog_number": {
"format": "verbatim transcription",
"null_value": "",
"description": "The barcode identifier, typically a number with at least 6 digits, but fewer than 30 digits."
}
}
st.session_state['rules']['SpeciesName'] = {
"taxonomy": ["Genus_species"]
}
# Layout for adding a new column name
# col_text, col_textbtn = st.columns([8, 2])
# with col_text:
new_column_name = st.text_input("Enter a new column name:")
# with col_textbtn:
# st.write('##')
if st.button("Add New Column") and new_column_name:
if new_column_name not in st.session_state['rules']['Dictionary']:
st.session_state['rules']['Dictionary'][new_column_name] = {"format": "", "null_value": "", "description": ""}
st.success(f"New column '{new_column_name}' added. Now you can edit its properties.")
else:
st.error("Column name already exists. Please enter a unique column name.")
# Get columns excluding the protected "catalog_number"
st.write('#')
editable_columns = [col for col in st.session_state['rules']['Dictionary'] if col != "catalog_number"]
column_name = st.selectbox("Select a column to edit:", [""] + editable_columns)
# Handle rules editing
current_rule = st.session_state['rules']['Dictionary'].get(column_name, {
"format": "",
"null_value": "",
"description": ""
})
if 'selected_column' not in st.session_state:
st.session_state['selected_column'] = column_name
# Form for input fields
with st.form(key='rule_form'):
format_options = ["verbatim transcription", "spell check transcription", "boolean yes no", "boolean 1 0", "integer", "[list]", "yyyy-mm-dd"]
current_rule["format"] = st.selectbox("Format:", format_options, index=format_options.index(current_rule["format"]) if current_rule["format"] else 0)
current_rule["null_value"] = st.text_input("Null value:", value=current_rule["null_value"])
current_rule["description"] = st.text_area("Description:", value=current_rule["description"])
commit_button = st.form_submit_button("Commit Column")
default_rule = {
"format": format_options[0], # default format
"null_value": "", # default null value
"description": "", # default description
}
if st.session_state['selected_column'] != column_name:
# Column has changed. Update the session_state selected column.
st.session_state['selected_column'] = column_name
# Reset the current rule to the default for this new column, or a blank rule if not set.
current_rule = st.session_state['rules']['Dictionary'].get(column_name, default_rule.copy())
# Handle commit action
if commit_button and column_name:
# Commit the rules to the session state.
st.session_state['rules']['Dictionary'][column_name] = current_rule.copy()
st.success(f"Column '{column_name}' added/updated in rules.")
# Force the form to reset by clearing the fields from the session state
st.session_state.pop('selected_column', None) # Clear the selected column to force reset
# st.session_state['rules'][column_name] = current_rule
# st.success(f"Column '{column_name}' added/updated in rules.")
# # Reset current_rule to default values for the next input
# current_rule["format"] = default_rule["format"]
# current_rule["null_value"] = default_rule["null_value"]
# current_rule["description"] = default_rule["description"]
# # To ensure that the form fields are reset, we can clear them from the session state
# for key in current_rule.keys():
# st.session_state[key] = default_rule[key]
# Layout for removing an existing column
# del_col, del_colbtn = st.columns([8, 2])
# with del_col:
delete_column_name = st.selectbox("Select a column to delete:", [""] + editable_columns, key='delete_column')
# with del_colbtn:
# st.write('##')
if st.button("Delete Column") and delete_column_name:
del st.session_state['rules'][delete_column_name]
st.success(f"Column '{delete_column_name}' removed from rules.")
with col_right:
# Display the current state of the JSON rules
st.subheader('Formatted Columns')
st.json(st.session_state['rules']['Dictionary'])
# st.subheader('All Prompt Info')
# st.json(st.session_state['prompt_info'])
st.write('---')
col_left_mapping, col_right_mapping = st.columns([6,4])
with col_left_mapping:
st.header("Mapping")
st.write("Assign each column name to a single category.")
st.session_state['refresh_mapping'] = False
# Dynamically create a list of all column names that can be assigned
# This assumes that the column names are the keys in the dictionary under 'rules'
all_column_names = list(st.session_state['rules']['Dictionary'].keys())
categories = ['TAXONOMY', 'GEOGRAPHY', 'LOCALITY', 'COLLECTING', 'MISCELLANEOUS']
if ('mapping' not in st.session_state) or (st.session_state['mapping'] == {}):
st.session_state['mapping'] = {category: [] for category in categories}
for category in categories:
# Filter out the already assigned columns
available_columns = [col for col in all_column_names if col not in st.session_state['assigned_columns'] or col in st.session_state['mapping'].get(category, [])]
# Ensure the current mapping is a subset of the available options
current_mapping = [col for col in st.session_state['mapping'].get(category, []) if col in available_columns]
# Provide a safe default if the current mapping is empty or contains invalid options
safe_default = current_mapping if all(col in available_columns for col in current_mapping) else []
# Create a multi-select widget for the category with a safe default
selected_columns = st.multiselect(
f"Select columns for {category}:",
available_columns,
default=safe_default,
key=f"mapping_{category}"
)
# Update the assigned_columns based on the selections
for col in current_mapping:
if col not in selected_columns and col in st.session_state['assigned_columns']:
st.session_state['assigned_columns'].remove(col)
st.session_state['refresh_mapping'] = True
for col in selected_columns:
if col not in st.session_state['assigned_columns']:
st.session_state['assigned_columns'].append(col)
st.session_state['refresh_mapping'] = True
# Update the mapping in session state when there's a change
st.session_state['mapping'][category] = selected_columns
if st.session_state['refresh_mapping']:
st.session_state['refresh_mapping'] = False
# Button to confirm and save the mapping configuration
if st.button('Confirm Mapping'):
if check_unique_mapping_assignments():
# Proceed with further actions since the mapping is confirmed and unique
pass
with col_right_mapping:
# Display the current state of the JSON rules
st.subheader('Formatted Column Maps')
st.json(st.session_state['mapping'])
col_left_save, col_right_save = st.columns([6,4])
with col_left_save:
# Input for new file name
new_filename = st.text_input("Enter filename to save your prompt as a configuration YAML:",placeholder='my_prompt_name')
# Button to save the new YAML file
if st.button('Save YAML', type='primary'):
if new_filename:
if check_unique_mapping_assignments():
if check_prompt_yaml_filename(new_filename):
save_prompt_yaml(new_filename)
else:
st.error("File name can only contain letters, numbers, underscores, and dashes. Cannot contain spaces.")
else:
st.error("Mapping contains an error. Make sure that each column is assigned to only ***one*** category.")
else:
st.error("Please enter a filename.")
if st.button('Exit'):
st.session_state.proceed_to_build_llm_prompt = False
st.session_state.proceed_to_main = True
st.rerun()
with col_prompt_main_right:
st.subheader('All Prompt Components')
st.session_state['prompt_info'] = {
'prompt_author': st.session_state['prompt_author'],
'prompt_author_institution': st.session_state['prompt_author_institution'],
'prompt_description': st.session_state['prompt_description'],
'LLM': st.session_state['LLM'],
'instructions': st.session_state['instructions'],
'json_formatting_instructions': st.session_state['json_formatting_instructions'],
'rules': st.session_state['rules'],
'mapping': st.session_state['mapping'],
}
st.json(st.session_state['prompt_info'])
def show_header_welcome():
st.session_state.logo_path = os.path.join(st.session_state.dir_home, 'img','logo.png')
st.session_state.logo = Image.open(st.session_state.logo_path)
st.image(st.session_state.logo, width=250)
def determine_n_images():
try:
# Check if 'dir_uploaded_images' key exists and it is not empty
if 'dir_uploaded_images' in st and st['dir_uploaded_images']:
dir_path = st['dir_uploaded_images'] # This would be the path to the directory
return len([f for f in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, f))])
else:
return None
except:
return None
def content_header():
col_run_1, col_run_2, col_run_3 = st.columns([4,4,2])
col_test = st.container()
st.write("")
st.write("")
st.write("")
st.write("")
st.subheader("Overall Progress")
col_run_info_1 = st.columns([1])[0]
st.write("")
st.write("")
st.write("")
st.write("")
st.header("Configuration Settings")
with col_run_info_1:
# Progress
# Progress
# st.subheader('Project')
# bar = st.progress(0)
# new_text = st.empty() # Placeholder for current step name
# progress_report = ProgressReportVV(bar, new_text, n_images=10)
# Progress
overall_progress_bar = st.progress(0)
text_overall = st.empty() # Placeholder for current step name
st.subheader('Transcription Progress')
batch_progress_bar = st.progress(0)
text_batch = st.empty() # Placeholder for current step name
progress_report = ProgressReport(overall_progress_bar, batch_progress_bar, text_overall, text_batch)
st.info("***Note:*** There is a known bug with tabs in Streamlit. If you update an input field it may take you back to the 'Project Settings' tab. Changes that you made are saved, it's just an annoying glitch. We are aware of this issue and will fix it as soon as we can.")
st.write("If you use VoucherVision frequently, you can change the default values that are auto-populated in the form below. In a text editor or IDE, edit the first few rows in the file `../VoucherVision/vouchervision/VoucherVision_Config_Builder.py`")
with col_run_1:
show_header_welcome()
st.subheader('Run VoucherVision')
N_STEPS = 6
if determine_n_images():
st.session_state['processing_add_on'] = f" {determine_n_images()} Images"
else:
st.session_state['processing_add_on'] = ''
if check_if_usable():
if st.button(f"Start Processing{st.session_state['processing_add_on']}", type='primary'):
# Define number of overall steps
progress_report.set_n_overall(N_STEPS)
progress_report.update_overall(f"Starting VoucherVision...")
# First, write the config file.
write_config_file(st.session_state.config, st.session_state.dir_home, filename="VoucherVision.yaml")
path_custom_prompts = os.path.join(st.session_state.dir_home,'custom_prompts',st.session_state.config['leafmachine']['project']['prompt_version'])
# Call the machine function.
last_JSON_response, total_cost = voucher_vision(None, st.session_state.dir_home, path_custom_prompts, None, progress_report,path_api_cost=os.path.join(st.session_state.dir_home,'api_cost','api_cost.yaml'), is_real_run=True)
if total_cost:
st.success(f":money_with_wings: This run cost :heavy_dollar_sign:{total_cost:.4f}")
# Format the JSON string for display.
if last_JSON_response is None:
st.markdown(f"Last JSON object in the batch: NONE")
else:
try:
formatted_json = json.dumps(json.loads(last_JSON_response), indent=4, sort_keys=False)
except:
formatted_json = json.dumps(last_JSON_response, indent=4, sort_keys=False)
st.markdown(f"Last JSON object in the batch:\n```\n{formatted_json}\n```")
st.balloons()
else:
st.button("Start Processing", type='primary', disabled=True)
st.error(":heavy_exclamation_mark: Required API keys not set. Please visit the 'API Keys' tab and set the Google Vision OCR API key and at least one LLM key.")
st.button("Refresh", on_click=refresh)
with col_run_2:
if st.button("Test GPT"):
progress_report.set_n_overall(TestOptionsGPT.get_length())
test_results, JSON_results = run_demo_tests_GPT(progress_report)
with col_test:
display_test_results(test_results, JSON_results, 'gpt')
st.balloons()
if st.button("Test PaLM2"):
progress_report.set_n_overall(TestOptionsPalm.get_length())
test_results, JSON_results = run_demo_tests_Palm(progress_report)
with col_test:
display_test_results(test_results, JSON_results, 'palm')
st.balloons()
with col_run_3:
st.subheader('Check GPU')
if st.button("GPU"):
success, info = test_GPU()
if success:
st.balloons()
for message in info:
st.success(message)
else:
for message in info:
st.error(message)
def content_tab_settings():
st.header('Project')
col_project_1, col_project_2 = st.columns([4,2])
st.write("---")
st.header('Input Images')
col_local_1, col_local_2 = st.columns([4,2])
st.write("---")
st.header('LeafMachine2 Label Collage')
col_cropped_1, col_cropped_2 = st.columns([4,4])
st.write("---")
st.header('OCR Overlay Image')
col_ocr_1, col_ocr_2 = st.columns([4,4])
os.path.join(st.session_state.dir_home, )
### Project
with col_project_1:
st.session_state.config['leafmachine']['project']['run_name'] = st.text_input("Run name", st.session_state.config['leafmachine']['project'].get('run_name', ''))
st.session_state.config['leafmachine']['project']['dir_output'] = st.text_input("Output directory", st.session_state.config['leafmachine']['project'].get('dir_output', ''))
### Input Images Local
with col_local_1:
st.session_state.config['leafmachine']['project']['dir_images_local'] = st.text_input("Input images directory", st.session_state.config['leafmachine']['project'].get('dir_images_local', ''))
st.session_state.config['leafmachine']['project']['continue_run_from_partial_xlsx'] = st.text_input("Continue run from partially completed project XLSX", st.session_state.config['leafmachine']['project'].get('continue_run_from_partial_xlsx', ''), disabled=True)
st.write("---")
st.subheader('LLM Version')
st.markdown(
"""
***Note:*** GPT-4 is 20x more expensive than GPT-3.5
"""
)
st.session_state.config['leafmachine']['LLM_version'] = st.selectbox("LLM version", ["gpt-4-1106-preview", "GPT 4", "GPT 3.5", "Azure GPT 4", "Azure GPT 3.5", "PaLM 2"], index=["gpt-4-1106-preview", "GPT 4", "GPT 3.5", "Azure GPT 4", "Azure GPT 3.5", "PaLM 2"].index(st.session_state.config['leafmachine'].get('LLM_version', 'Azure GPT 4')))
st.write("---")
st.subheader('Prompt Version')
versions, default_version = get_prompt_versions(st.session_state.config['leafmachine']['LLM_version'])
if versions:
selected_version = st.session_state.config['leafmachine']['project'].get('prompt_version', default_version)
if selected_version not in versions:
selected_version = default_version
st.session_state.config['leafmachine']['project']['prompt_version'] = st.selectbox("Prompt Version", versions, index=versions.index(selected_version))
with col_cropped_1:
default_crops = st.session_state.config['leafmachine']['cropped_components'].get('save_cropped_annotations', ['leaf_whole'])
st.write("Prior to transcription, use LeafMachine2 to crop all labels from input images to create label collages for each specimen image. (Requires GPU)")
st.session_state.config['leafmachine']['use_RGB_label_images'] = st.checkbox("Use LeafMachine2 label collage for transcriptions", st.session_state.config['leafmachine'].get('use_RGB_label_images', False))
st.session_state.config['leafmachine']['cropped_components']['save_cropped_annotations'] = st.multiselect("Components to crop",
['ruler', 'barcode','label', 'colorcard','map','envelope','photo','attached_item','weights',
'leaf_whole', 'leaf_partial', 'leaflet', 'seed_fruit_one', 'seed_fruit_many', 'flower_one', 'flower_many', 'bud','specimen','roots','wood'],default=default_crops)
with col_cropped_2:
ba = os.path.join(st.session_state.dir_home,'demo', 'ba','ba2.png')
image = Image.open(ba)
st.image(image, caption='LeafMachine2 Collage', output_format = "PNG")
with col_ocr_1:
st.write('This will plot bounding boxes around all text that Google Vision was able to detect. If there are no boxes around text, then the OCR failed, so that missing text will not be seen by the LLM when it is creating the JSON object. The created image will be viewable in the VoucherVisionEditor.')
st.session_state.config['leafmachine']['do_create_OCR_helper_image'] = st.checkbox("Create image showing an overlay of the OCR detections", st.session_state.config['leafmachine'].get('do_create_OCR_helper_image', False))
with col_ocr_2:
ocr = os.path.join(st.session_state.dir_home,'demo', 'ba','ocr.png')
image_ocr = Image.open(ocr)
st.image(image_ocr, caption='OCR Overlay Images', output_format = "PNG")
def content_tab_component():
st.header('Archival Components')
ACD_version = st.selectbox("Archival Component Detector (ACD) Version", ["Version 2.1", "Version 2.2"])
ACD_confidence_default = int(st.session_state.config['leafmachine']['archival_component_detector']['minimum_confidence_threshold'] * 100)
ACD_confidence = st.number_input("ACD Confidence Threshold (%)", min_value=0, max_value=100,value=ACD_confidence_default)
st.session_state.config['leafmachine']['archival_component_detector']['minimum_confidence_threshold'] = float(ACD_confidence/100)
st.session_state.config['leafmachine']['archival_component_detector']['do_save_prediction_overlay_images'] = st.checkbox("Save Archival Prediction Overlay Images", st.session_state.config['leafmachine']['archival_component_detector'].get('do_save_prediction_overlay_images', True))
st.session_state.config['leafmachine']['archival_component_detector']['ignore_objects_for_overlay'] = st.multiselect("Hide Archival Components in Prediction Overlay Images",
['ruler', 'barcode','label', 'colorcard','map','envelope','photo','attached_item','weights',],
default=[])
# Depending on the selected version, set the configuration
if ACD_version == "Version 2.1":
st.session_state.config['leafmachine']['archival_component_detector']['detector_type'] = 'Archival_Detector'
st.session_state.config['leafmachine']['archival_component_detector']['detector_version'] = 'PREP_final'
st.session_state.config['leafmachine']['archival_component_detector']['detector_iteration'] = 'PREP_final'
st.session_state.config['leafmachine']['archival_component_detector']['detector_weights'] = 'best.pt'
elif ACD_version == "Version 2.2": #TODO update this to version 2.2
st.session_state.config['leafmachine']['archival_component_detector']['detector_type'] = 'Archival_Detector'
st.session_state.config['leafmachine']['archival_component_detector']['detector_version'] = 'PREP_final'
st.session_state.config['leafmachine']['archival_component_detector']['detector_iteration'] = 'PREP_final'
st.session_state.config['leafmachine']['archival_component_detector']['detector_weights'] = 'best.pt'
def content_tab_processing():
st.header('Processing Options')
col_processing_1, col_processing_2 = st.columns([2,2,])
with col_processing_1:
st.subheader('Compute Options')
st.session_state.config['leafmachine']['project']['num_workers'] = st.number_input("Number of CPU workers", value=st.session_state.config['leafmachine']['project'].get('num_workers', 1), disabled=True)
st.session_state.config['leafmachine']['project']['batch_size'] = st.number_input("Batch size", value=st.session_state.config['leafmachine']['project'].get('batch_size', 500), help='Sets the batch size for the LeafMachine2 cropping. If computer RAM is filled, lower this value to ~100.')
with col_processing_2:
st.subheader('Misc')
st.session_state.config['leafmachine']['project']['prefix_removal'] = st.text_input("Remove prefix from catalog number", st.session_state.config['leafmachine']['project'].get('prefix_removal', ''))
st.session_state.config['leafmachine']['project']['suffix_removal'] = st.text_input("Remove suffix from catalog number", st.session_state.config['leafmachine']['project'].get('suffix_removal', ''))
st.session_state.config['leafmachine']['project']['catalog_numerical_only'] = st.checkbox("Require 'Catalog Number' to be numerical only", st.session_state.config['leafmachine']['project'].get('catalog_numerical_only', True))
### Logging and Image Validation - col_v1
st.header('Logging and Image Validation')
col_v1, col_v2 = st.columns(2)
with col_v1:
st.session_state.config['leafmachine']['do']['check_for_illegal_filenames'] = st.checkbox("Check for illegal filenames", st.session_state.config['leafmachine']['do'].get('check_for_illegal_filenames', True))
st.session_state.config['leafmachine']['do']['check_for_corrupt_images_make_vertical'] = st.checkbox("Check for corrupt images", st.session_state.config['leafmachine']['do'].get('check_for_corrupt_images_make_vertical', True))
st.session_state.config['leafmachine']['print']['verbose'] = st.checkbox("Print verbose", st.session_state.config['leafmachine']['print'].get('verbose', True))
st.session_state.config['leafmachine']['print']['optional_warnings'] = st.checkbox("Show optional warnings", st.session_state.config['leafmachine']['print'].get('optional_warnings', True))
with col_v2:
log_level = st.session_state.config['leafmachine']['logging'].get('log_level', None)
log_level_display = log_level if log_level is not None else 'default'
selected_log_level = st.selectbox("Logging Level", ['default', 'DEBUG', 'INFO', 'WARNING', 'ERROR'], index=['default', 'DEBUG', 'INFO', 'WARNING', 'ERROR'].index(log_level_display))
if selected_log_level == 'default':
st.session_state.config['leafmachine']['logging']['log_level'] = None
else:
st.session_state.config['leafmachine']['logging']['log_level'] = selected_log_level
def content_tab_domain():
st.header('Embeddings Database')
col_emb_1, col_emb_2 = st.columns([4,2])
with col_emb_1:
st.markdown(
"""
VoucherVision includes the option of using domain knowledge inside of the dynamically generated prompts. The OCR text is queried against a database of existing label transcriptions. The most similar existing transcriptions act as an example of what the LLM should emulate and are shown to the LLM as JSON objects. VoucherVision uses cosine similarity search to return the most similar existing transcription.
- Note: Using domain knowledge may increase the chance that foreign text is included in the final transcription
- Disabling this feature will show the LLM multiple examples of an empty JSON skeleton structure instead
- Enabling this option requires a GPU with at least 8GB of VRAM
- The domain knowledge files can be located in the directory "../VoucherVision/domain_knowledge". On first run the embeddings database must be created, which takes time. If the database creation runs each time you use VoucherVision, then something is wrong.
"""
)
st.write(f"Domain Knowledge is only available for the following prompts:")
for available_prompts in PROMPTS_THAT_NEED_DOMAIN_KNOWLEDGE:
st.markdown(f"- {available_prompts}")
if st.session_state.config['leafmachine']['project']['prompt_version'] in PROMPTS_THAT_NEED_DOMAIN_KNOWLEDGE:
st.session_state.config['leafmachine']['project']['use_domain_knowledge'] = st.checkbox("Use domain knowledge", True, disabled=True)
else:
st.session_state.config['leafmachine']['project']['use_domain_knowledge'] = st.checkbox("Use domain knowledge", False, disabled=True)
st.write("")
if st.session_state.config['leafmachine']['project']['use_domain_knowledge']:
st.session_state.config['leafmachine']['project']['embeddings_database_name'] = st.text_input("Embeddings database name (only use underscores)", st.session_state.config['leafmachine']['project'].get('embeddings_database_name', ''))
st.session_state.config['leafmachine']['project']['build_new_embeddings_database'] = st.checkbox("Build *new* embeddings database", st.session_state.config['leafmachine']['project'].get('build_new_embeddings_database', False))
st.session_state.config['leafmachine']['project']['path_to_domain_knowledge_xlsx'] = st.text_input("Path to domain knowledge CSV file (will be used to create new embeddings database)", st.session_state.config['leafmachine']['project'].get('path_to_domain_knowledge_xlsx', ''))
else:
st.session_state.config['leafmachine']['project']['embeddings_database_name'] = st.text_input("Embeddings database name (only use underscores)", st.session_state.config['leafmachine']['project'].get('embeddings_database_name', ''), disabled=True)
st.session_state.config['leafmachine']['project']['build_new_embeddings_database'] = st.checkbox("Build *new* embeddings database", st.session_state.config['leafmachine']['project'].get('build_new_embeddings_database', False), disabled=True)
st.session_state.config['leafmachine']['project']['path_to_domain_knowledge_xlsx'] = st.text_input("Path to domain knowledge CSV file (will be used to create new embeddings database)", st.session_state.config['leafmachine']['project'].get('path_to_domain_knowledge_xlsx', ''), disabled=True)
def render_expense_report_summary():
expense_summary = st.session_state.expense_summary
expense_report = st.session_state.expense_report
st.header('Expense Report Summary')
if expense_summary:
st.metric(label="Total Cost", value=f"${round(expense_summary['total_cost_sum'], 4):,}")
col1, col2 = st.columns(2)
# Run count and total costs
with col1:
st.metric(label="Run Count", value=expense_summary['run_count'])
st.metric(label="Tokens In", value=f"{expense_summary['tokens_in_sum']:,}")
# Token information
with col2:
st.metric(label="Total Images", value=expense_summary['n_images_sum'])
st.metric(label="Tokens Out", value=f"{expense_summary['tokens_out_sum']:,}")
# Calculate cost proportion per image for each API version
st.subheader('Average Cost per Image by API Version')
cost_labels = []
cost_values = []
total_images = 0
cost_per_image_dict = {}
# Iterate through the expense report to accumulate costs and image counts
for index, row in expense_report.iterrows():
api_version = row['api_version']
total_cost = row['total_cost']
n_images = row['n_images']
total_images += n_images # Keep track of total images processed
if api_version not in cost_per_image_dict:
cost_per_image_dict[api_version] = {'total_cost': 0, 'n_images': 0}
cost_per_image_dict[api_version]['total_cost'] += total_cost
cost_per_image_dict[api_version]['n_images'] += n_images
api_versions = list(cost_per_image_dict.keys())
colors = [COLORS_EXPENSE_REPORT[version] if version in COLORS_EXPENSE_REPORT else '#DDDDDD' for version in api_versions]
# Calculate the cost per image for each API version
for version, cost_data in cost_per_image_dict.items():
total_cost = cost_data['total_cost']
n_images = cost_data['n_images']
# Calculate the cost per image for this version
cost_per_image = total_cost / n_images if n_images > 0 else 0
cost_labels.append(version)
cost_values.append(cost_per_image)
# Generate the pie chart
cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_values, hole=.3)])
# Update traces for custom text in hoverinfo, displaying cost with a dollar sign and two decimal places
cost_pie_chart.update_traces(
marker=dict(colors=colors),
text=[f"${value:.2f}" for value in cost_values], # Formats the cost as a string with a dollar sign and two decimals
textinfo='percent+label',
hoverinfo='label+percent+text' # Adds custom text (formatted cost) to the hover information
)
st.plotly_chart(cost_pie_chart, use_container_width=True)
st.subheader('Proportion of Total Cost by API Version')
cost_labels = []
cost_proportions = []
total_cost_by_version = {}
# Sum the total cost for each API version
for index, row in expense_report.iterrows():
api_version = row['api_version']
total_cost = row['total_cost']
if api_version not in total_cost_by_version:
total_cost_by_version[api_version] = 0
total_cost_by_version[api_version] += total_cost
# Calculate the combined total cost for all versions
combined_total_cost = sum(total_cost_by_version.values())
# Calculate the proportion of total cost for each API version
for version, total_cost in total_cost_by_version.items():
proportion = (total_cost / combined_total_cost) * 100 if combined_total_cost > 0 else 0
cost_labels.append(version)
cost_proportions.append(proportion)
# Generate the pie chart
cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_proportions, hole=.3)])
# Update traces for custom text in hoverinfo
cost_pie_chart.update_traces(
marker=dict(colors=colors),
text=[f"${cost:.2f}" for cost in total_cost_by_version.values()], # This will format the cost to 2 decimal places
textinfo='percent+label',
hoverinfo='label+percent+text' # This tells Plotly to show the label, percent, and custom text (cost) on hover
)
st.plotly_chart(cost_pie_chart, use_container_width=True)
# API version usage percentages pie chart
st.subheader('Runs by API Version')
api_versions = list(expense_summary['api_version_percentages'].keys())
percentages = [expense_summary['api_version_percentages'][version] for version in api_versions]
pie_chart = go.Figure(data=[go.Pie(labels=api_versions, values=percentages, hole=.3)])
pie_chart.update_layout(margin=dict(t=0, b=0, l=0, r=0))
pie_chart.update_traces(marker=dict(colors=colors),)
st.plotly_chart(pie_chart, use_container_width=True)
else:
st.error('No expense report data available.')
def sidebar_content():
if not os.path.exists(os.path.join(st.session_state.dir_home,'expense_report')): | validate_dir(os.path.join(st.session_state.dir_home,'expense_report')) | 14 | 2023-10-30 23:25:20+00:00 | 16k |
medsagou/massar-direction-sagoubot | main.py | [
{
"identifier": "C_File",
"path": "utilities/Class_Files.py",
"snippet": "class C_File():\n #____________________________________________________________________________________________________________________________________________________________\n # Le constructeur d'une instance d'un fichier\... | import tkinter as tk
import customtkinter
import time
import os
import threading
import logging
import sys
from tkinter import filedialog
from PIL import Image
from validate_email import validate_email
from utilities import C_File, C_Dossier
from dotenv import set_key, load_dotenv
from absence_app import Read_Db
from absence_app import Absence
from Interaction_browser import Massar_Direction_Sagou | 13,411 |
self.run_bot = customtkinter.CTkButton(self.tabview_fill_bot.tab("Review & Submit"), text="Run",
command=self.run_bot_interaction, width=50)
self.run_bot.grid(row=6, column=5, padx=10, pady=(5, 5))
self.return_btn5 = customtkinter.CTkButton(self.tabview_fill_bot.tab("Review & Submit"), text="Back", command=self.back2,
width=50, fg_color="gray30")
self.return_btn5.grid(row=6, column=4, padx=10, pady=(5, 5))
if self.about_us_text is not None:
self.about_us_text.grid_remove()
self.about_us_logo.grid_remove()
self.console_text.grid()
self.try_again_fill = True
self.select_frame_by_name("Fill Absence Bot")
else:
self.tabview_generate_lists.grid_remove()
self.tabview_fill_bot.grid()
self.console_text.grid()
if self.about_us_text is not None:
self.about_us_text.grid_remove()
self.about_us_logo.grid_remove()
self.select_frame_by_name("Fill Absence Bot")
def about_us_button_event(self):
if self.tabview_generate_lists.grid_info():
self.tabview_generate_lists.grid_remove()
if self.tabview_fill_bot is not None:
if self.tabview_fill_bot.grid_info():
self.tabview_fill_bot.grid_remove()
if self.about_us_text is not None:
self.about_us_text.grid()
self.about_us_logo.grid()
self.console_text.grid_remove()
self.select_frame_by_name("About us")
else:
self.about_us_logo = customtkinter.CTkLabel(self, text="",
image=self.about_us_image)
self.about_us_logo.grid(row=0, column=1, padx=10, pady=10)
self.about_us_text = customtkinter.CTkTextbox(self, height=200, wrap="word", font=("Arial", 18))
self.about_us_text.grid(row=1, column=1,rowspan=3, columnspan=6, padx=(20, 20), pady = (15, 20), sticky = "nsew")
self.console_text.grid_remove()
self.about_us_text.tag_config("Title", foreground="gray92")
self.about_us_text.tag_config("subTitle", foreground="gray65")
self.about_us_text.tag_config("Paragraph", foreground="gray50")
# Content to be displayed
# Insert the formatted text into the Text widget
self.about_us_text.insert("end", "\n About Us", "LargeText")
self.about_us_text.insert("end", "\n\nMassar Direction Sagoubot is a cutting-edge automation project designed to streamline and simplify the process of managing absence data for multiple classes within a web application. Our solution is meticulously crafted using modern technologies and software tools to optimize efficiency and save valuable time for teachers and administrators.\n", "Paragraph")
self.about_us_text.insert("end", "\n\n Terms and Privacy", "Title")
self.about_us_text.insert("end", "\n\nAccount Access", "subTitle")
self.about_us_text.insert("end",
"\nTo enhance your experience with Massar Direction Sagoubot, the application utilizes your account credentials to securely log in to the Massar website. Your privacy and security are of utmost importance to us. We ensure that your login information is encrypted and used solely for the purpose of automating absence data management.\n", "Paragraph")
self.about_us_text.insert("end", "\n\nData Handling", "subTitle")
self.about_us_text.insert("end",
"\nYour data, specifically related to absence records and class information, is processed within the confines of the application to facilitate automation. We do not store or retain any of your personal data beyond the scope of improving application functionality.\n",
"Paragraph")
self.about_us_text.insert("end", "\n\nSecurity Measures", "subTitle")
self.about_us_text.insert("end",
"\nWe employ industry-standard security measures to safeguard your account information. This includes encryption protocols and best practices to prevent unauthorized access or misuse of your credentials.\n", "Paragraph")
self.about_us_text.insert("end", "\n\nUser Consent", "subTitle")
self.about_us_text.insert("end",
"\nBy using Massar Direction Sagoubot, you consent to the utilization of your Massar account credentials for the sole purpose of automating absence data management. We prioritize transparency and security in handling your login information.\n", "Paragraph")
self.about_us_text.insert("end", "\n\nQuestions or Concerns", "subTitle")
self.about_us_text.insert("end",
"\nIf you have any questions, concerns, or require further clarification regarding our terms, privacy practices, or the usage of your account information, please feel free to reach out to us at sakou81833@gmail.com. Your satisfaction and trust are our top priorities.\n", "Paragraph")
self.about_us_text.configure(state="disabled")
self.select_frame_by_name("About us")
def exit(self):
result = tk.messagebox.askokcancel("Confirmation", "Are you sure you want to exit?")
if result: # If the user confirms
app.quit()
# backend functions
def generate_absence_file(self):
self.generate_progress_bar()
self.submit3.configure(state="disabled")
self.return_btn3.configure(state="disabled")
self.console_text.configure(state="normal")
self.label_all_review1.configure(text_color="gray35")
def run_fill_all_class_sheets():
reader = Read_Db(input_file=self.entry_path.get(),
template_file=self.entry_path2.get(),
output_file=str(self.output_path.get()) + "\\absence.xlsx",
required_classes=self.selected_classes,
progress_bar=self.progressbar_1,
console=self.console_text)
reader.fill_all_class_sheets()
time.sleep(3)
self.submit3.configure(state="normal")
self.return_btn3.configure(state="normal")
self.progressbar_1.grid_remove()
self.console_text.configure(state="disabled")
self.label_all_review1.configure(text_color="gray70")
thread = threading.Thread(target=run_fill_all_class_sheets)
thread.start()
return
def run_bot_interaction(self):
self.generate_progress_bar(determinate=False)
self.console_text.configure(state="normal")
self.run_bot.configure(state="disabled")
self.return_btn5.configure(state="disabled")
self.label_all_review2.configure(text_color="gray35")
def run_fill_absence():
# loading the class here because of the .env file not getting refreshed
interaction_object = Massar_Direction_Sagou(console=self.console_text)
driver_test = interaction_object.main_interaction()
if driver_test:
interaction_object.get_list_page()
|
# https://stackoverflow.com/questions/31836104/pyinstaller-and-onefile-how-to-include-an-image-in-the-exe-file
def resource_path(relative_path):
""" Get absolute path to resource, works for dev and for PyInstaller """
try:
# PyInstaller creates a temp folder and stores path in _MEIPASS
base_path = sys._MEIPASS2
except Exception:
base_path = os.path.abspath(".")
return os.path.join(base_path, relative_path)
logging.basicConfig(filename='app.log', level=logging.DEBUG,
format='%(asctime)s - %(levelname)s - %(message)s')
customtkinter.set_appearance_mode("Dark") # Modes: "System" (standard), "Dark", "Light"
customtkinter.set_default_color_theme("dark-blue") # Themes: "blue" (standard), "green", "dark-blue"
dirPath = os.path.dirname(os.path.realpath(__file__))
class App(customtkinter.CTk):
def __init__(self):
super().__init__()
self.tabview_generate_lists = None
self.tabview_fill_bot= None
self.generate_list_menu = None
self.about_us_text = None
self.fill_absence_menu = None
self.try_again_generate = False
self.try_again_fill = False
self.progressbar_1 = None
image_path = resource_path("images")
self.main_logo_image = customtkinter.CTkImage(
light_image=Image.open(os.path.join(image_path, "logo_black.png")),
dark_image=Image.open(os.path.join(image_path, "logo_white.png")), size=(200,200))
self.about_us_image = customtkinter.CTkImage(
light_image=Image.open(os.path.join(image_path, "logo_black.png")),
dark_image=Image.open(os.path.join(image_path, "logo_white.png")), size=(150, 150))
# self.main_logo_photo = ImageTk.PhotoImage(self.main_logo_image)
# configure window
self.title("SagouBot Massar Direction")
self.iconbitmap(resource_path("icon.ico"))
self.geometry(f"{1100}x{580}")
# configure grid layout (4x4)
self.grid_columnconfigure(1, weight=1)
self.grid_columnconfigure((2, 3), weight=0)
self.grid_rowconfigure((0, 1, 2), weight=1)
# create sidebar frame with widgets
self.sidebar_frame = customtkinter.CTkFrame(self, width=200, corner_radius=0)
self.sidebar_frame.grid(row=0, column=0, rowspan=4, sticky="nsew")
self.sidebar_frame.grid_rowconfigure(5, weight=1)
self.sidebar_frame.grid(row=0, column=0)
self.sideBar_logo = customtkinter.CTkLabel(self.sidebar_frame, text="",
image=self.main_logo_image)
self.sideBar_logo.grid(row=5, column=0, padx=20, pady=20)
self.entry_default_bordercolor = customtkinter.CTkEntry(self).cget("border_color")
# self.logo_label = customtkinter.CTkLabel(self.sidebar_frame, text="SagouBot", font=customtkinter.CTkFont(size=40, weight="bold"))
# self.logo_label.grid(row=1, column=0, padx=20, pady=(20, 10))
self.generate_list_menu_button_event()
# Console (Text area)
self.console_text = customtkinter.CTkTextbox(self, height=200, width=400, fg_color="gray1")
self.console_text.insert("0.0", "CONSOLE")
self.console_text.insert(F"{len('CONSOLE')}.0", "--------" * 28)
self.console_text.configure(state="disabled")
self.console_text.grid(row=1, column=1, padx=(20, 20), pady=(5, 15), sticky="nsew")
self.console_text.tag_config("error", foreground="red")
self.console_text.tag_config("note", foreground="orange")
self.console_text.tag_config("successes", foreground="blue")
# self.generate_progress_bar()
# Progress Bar
# progress_bar = customtkinter.CTkProgressBar(self, mode='determinate')
# progress_bar.grid(row=1, column=1, padx=(20, 20), pady=(5, 0), sticky="nsew")
# # Button to trigger updates
# update_button = customtkinter.CTkButton(self, text="Start Processing", command=())
# update_button.grid(row=1, column=1, padx=(20, 20), pady=(5, 0), sticky="nsew")
def high_school_switch(self):
state = self.high_school_options.get()
options = [self.TCS,
self.TCSF,
self.TCLSH,
self.BACSC,
self.BACSH,
self.BACSE,
self.BACSVT,
self.BACSH2]
if state:
for option in options:
option.configure(state="normal")
else:
for option in options:
option.configure(state="disabled")
return
def college_switch(self):
state = self.college_options.get()
if state:
self.college_generale.configure(state="normal")
self.college_aspeb.configure(state="normal")
self.college_inter.configure(state="normal")
else:
self.college_generale.configure(state="disabled")
self.college_aspeb.configure(state="disabled")
self.college_inter.configure(state="disabled")
def college_label_error(self):
current_text = self.label_college.cget("text")
self.label_college.configure(text=current_text.replace("*", "") + "*", text_color="red")
return
def high_school_label_eroor(self):
current_text = self.label_high_school.cget("text")
self.label_high_school.configure(text=current_text.replace("*", "") + "*", text_color="red")
return
def reset_label_high_college(self):
current_text1 = self.label_college.cget("text")
current_text = self.label_high_school.cget("text")
self.label_high_school.configure(text=current_text.replace("*", ""), text_color="gray90")
self.label_college.configure(text=current_text1.replace("*", ""), text_color="gray90")
def label_data_file_error(self):
current_text = self.label_data_file.cget("text")
self.label_data_file.configure(text=current_text.replace("*", "") + "*", text_color="red")
return
def label_template_file_error(self):
current_text = self.label_template_entry.cget("text")
self.label_template_entry.configure(text=current_text.replace("*", "") + "*", text_color="red")
return
def reset_error1(self):
current_text = self.label_data_file.cget("text")
self.label_data_file.configure(text=current_text.replace("*", ""), text_color="gray90")
return
def reset_error2(self):
current_text = self.label_template_entry.cget("text")
self.label_template_entry.configure(text=current_text.replace("*", ""), text_color="gray90")
return
def directory_error(self):
current_text = self.label_output_folder.cget("text")
self.label_output_folder.configure(text=current_text + "*", text_color="red")
return
def reset_error3(self):
current_text = self.label_output_folder.cget("text")
self.label_output_folder.configure(text=current_text.replace("*", ""), text_color="gray90")
return
def go_to_review2(self):
if self.email_entry.get() == "" or self.password_entry.get() == "" or not self.validate_path(self.entry_path_absence) or not self.check_terms_and_condition.get():
if self.email_entry.get() == "":
self.error_label(self.label_email_entry)
self.entry_error(self.email_entry)
if len(self.password_entry.get()) < 8:
self.error_label(self.label_password_entry)
self.entry_error(self.password_entry)
if not self.validate_path(self.entry_path_absence):
self.error_label(self.label_absence_data_file)
self.entry_error(self.entry_path_absence)
if not self.check_terms_and_condition.get():
self.check_terms_and_condition.configure(border_color="red", text_color="red")
self.error_label(self.label_terms)
else:
paths = C_File(resource_path("db/paths.txt"))
L = paths.fichier_to_Liste()
L[3] = "ABSENCE_FILE" + "=" + self.entry_path_absence.get() +"\n"
L[4] = "EMAIL" + "=" + self.email_entry.get() +"\n"
paths.Liste_to_Fichier(L)
set_key(dotenv_path=os.path.join(dirPath,".env"), key_to_set="EMAIL", value_to_set=self.email_entry.get())
set_key(dotenv_path=os.path.join(dirPath,".env"), key_to_set="PASSWORD", value_to_set=self.password_entry.get())
load_dotenv(dotenv_path=os.path.join(dirPath,".env"))
self.tabview_fill_bot.set("Review & Submit")
self.label_all_review2 = customtkinter.CTkTextbox(self.tabview_fill_bot.tab("Review & Submit"))
self.label_all_review2.grid(row=0, column=0, columnspan=6, sticky="nsew")
# self.label_all_review2.insert("1.0", text)
text = f"Email:"
text += " " * (30 - len("Email:"))
text += str(self.email_entry.get()) + "\n\n"
self.label_all_review2.insert("end", text)
text = "Absence Excel File:"
text += " " * (30 - len("Absence Excel File:"))
text += str(self.entry_path_absence.get())+ "\n\n"
self.label_all_review2.insert("end", text)
text = "Browser:"
text += " " * (30 - len("Browser:"))
if self.browser_type.get() == 2:
text += "FireFox"
else:
text += "Chrome"
self.label_all_review2.insert("end", text)
self.label_all_review2.configure(state="disabled", text_color="gray70")
return
def go_to_output_location(self):
if self.tabview_generate_lists.grid_info():
tabview = self.tabview_generate_lists
tab = tabview.get()
optionsHighSchool = [self.TCS,
self.TCSF,
self.TCLSH,
self.BACSC,
self.BACSH,
self.BACSE,
self.BACSVT,
self.BACSH2]
optionsCollege = [
self.college_inter,
self.college_aspeb,
self.college_generale
]
selected_classes = []
paths = C_File(resource_path("db/paths.txt"))
if tab == "Setup":
# path validation
if self.validate_path(self.entry_path) and self.validate_path(self.entry_path2) and (
self.college_options.get() or self.high_school_options.get()):
if self.high_school_options.get():
for option in optionsHighSchool:
if option.get():
selected_classes.append((option.cget("text")))
if self.college_options.get():
for option in optionsCollege:
if option.get():
selected_classes.append((option.cget("text")))
if len(selected_classes) == 0:
self.college_label_error()
self.high_school_label_eroor()
else:
self.selected_classes = selected_classes
self.tabview_generate_lists.set("Output Location")
L = paths.fichier_to_Liste()
L[0] = "DATA" + "=" + self.entry_path.get() + "\n"
L[1] = "TEMPLATE" + "=" + self.entry_path2.get() + "\n"
paths.Liste_to_Fichier(L)
else:
if not self.validate_path(self.entry_path):
self.label_data_file_error()
if not self.validate_path(self.entry_path2):
self.label_template_file_error()
if self.high_school_options.get():
for option in optionsHighSchool:
if option.get():
selected_classes.append((option.cget("text")))
if self.college_options.get():
for option in optionsCollege:
if option.get():
selected_classes.append((option.cget("text")))
if len(selected_classes) == 0:
self.college_label_error()
self.high_school_label_eroor()
if tab == "Output Location":
if self.validate_dir(self.output_path):
self.tabview_generate_lists.set("Review & Submit")
L = paths.fichier_to_Liste()
L[-1] = "DIR" + "=" + self.output_path.get()
paths.Liste_to_Fichier(L)
self.label_all_review1 = customtkinter.CTkTextbox(self.tabview_generate_lists.tab("Review & Submit"))
self.label_all_review1.grid(row=0, column=0, columnspan=6, sticky="nsew")
# self.label_all_review2.insert("1.0", text)
text = f"Data file path:"
text += " " * (30 - len("Data file path:"))
text += str(self.entry_path.get()) + "\n\n"
self.label_all_review1.insert("end", text)
text = "Template file path:"
text += " " * (30 - len("Template file path:"))
text += str(self.entry_path2.get()) + "\n\n"
self.label_all_review1.insert("end", text)
text = "Classes:"
text += " " * (30 - len("Classes:"))
for c in self.selected_classes:
text = text + c + ",\t"
self.label_all_review1.insert("end", text + "\n\n")
text = "Output directory:"
text += " " * (30 - len("Output directory:"))
text += str(self.output_path.get()) + "\n\n"
self.label_all_review1.insert("end", text)
self.label_all_review1.configure(state="disabled", text_color="gray70")
else:
self.directory_error()
return
def browse_path(self):
filetypes = (
("Text files", "*.xls"), # Display only .txt files
("All files", "*.*") # Display all files
)
path = filedialog.askopenfilename(filetypes=filetypes, initialdir=os.path.dirname(self.path["DATA"]) if self.path["DATA"] != "" else os.path.join(os.path.expanduser('~'), 'Documents'))
if path == "":
return
self.entry_path.delete(0, tk.END) # Clear the entry
self.entry_path.insert(0, os.path.abspath(path))
self.path["DATA"] = path
file = C_File(file_name=path)
if file.existe_fichier():
self.reset_error1()
def browse_path2(self):
filetypes = (
("Text files", "*.xlsx"), # Display only .txt files
("All files", "*.*") # Display all files
)
path = filedialog.askopenfilename(filetypes=filetypes, initialdir=os.path.dirname(self.path["TEMPLATE"]) if self.path["TEMPLATE"] != "" else os.path.join(os.path.expanduser('~'), 'Documents'))
if path == "":
return
self.entry_path2.delete(0, tk.END) # Clear the entry
self.entry_path2.insert(0, os.path.abspath(path))
self.path["TEMPLATE"] = path
file = C_File(file_name=path)
if file.existe_fichier():
self.reset_error2()
def browser_path3(self):
filetypes = (
("Text files", "*.xlsx"), # Display only .txt files
("All files", "*.*") # Display all files
)
path = filedialog.askopenfilename(filetypes=filetypes, initialdir=os.path.dirname(self.path["ABSENCE_FILE"]) if self.path["ABSENCE_FILE"] != "" else os.path.join(os.path.expanduser('~'), 'Documents'))
if path == "":
return
self.path["ABSENCE_FILE"] = path
self.entry_path_absence.delete(0, tk.END) # Clear the entry
self.entry_path_absence.insert(0, os.path.abspath(path))
file = C_File(file_name=path)
if file.existe_fichier():
self.reset_label(self.label_absence_data_file)
self.entry_reset(self.entry_path_absence)
def browse_folder(self):
path = filedialog.askdirectory(initialdir=self.path["DIR"] if self.path["DIR"] != "" else os.path.join(os.path.expanduser('~'), 'Documents'))
if path == "":
return
self.output_path.delete(0, tk.END)
self.output_path.insert(0, os.path.abspath(path))
self.path["DIR"] = path
dir = C_Dossier()
if dir.existe_dossier(Chemin=path):
self.reset_error3()
return
# Function to validate the path entry
def validate_path(self, path):
if path.get() == "":
return False
file = C_File(file_name=path.get())
return file.existe_fichier()
def validate_dir(self, path):
if path.get() == "":
return False
dir = C_Dossier()
return dir.existe_dossier(Chemin=path.get())
def back(self):
if self.tabview_generate_lists.grid_info():
tab = self.tabview_generate_lists
else:
tab = self.tabview_fill_bot
if tab.get() == "Review & Submit":
tab.set("Output Location")
elif tab.get() == "Output Location":
tab.set("Setup")
return
def back2(self):
self.tabview_fill_bot.set("Setup") if self.tabview_fill_bot.grid_info() else self.tabview_generate_lists.set("Setup")
return
def select_frame_by_name(self, name):
# set button color for selected button
self.generate_list_menu.configure(fg_color=("gray75", "gray25") if name == "Generate Lists" else "transparent")
self.fill_absence_menu.configure(fg_color=("gray75", "gray25") if name == "Fill Absence Bot" else "transparent")
self.about_us_menu.configure(fg_color=("gray75", "gray25") if name == "About us" else "transparent")
def generate_progress_bar(self, determinate=True):
if self.progressbar_1 is None:
self.progressbar_1 = customtkinter.CTkProgressBar(self.sidebar_frame,
mode="determinate" if determinate == True else "indeterminate")
state = True
else:
self.progressbar_1.configure(mode="determinate" if determinate == True else "indeterminate")
state = False
if determinate:
self.progressbar_1.set(0)
else:
self.progressbar_1.start()
if state:
self.progressbar_1.grid(row=6, column=0, padx=20, pady=20, sticky="ew")
else:
self.progressbar_1.grid()
def generate_list_menu_button_event(self):
if self.try_again_generate != False:
test = self.generate_list_menu.cget("fg_color")
if test == ("gray75", "gray25"):
self.tabview_generate_lists.set("Setup")
return
if self.try_again_generate == False:
self.generate_list_menu = customtkinter.CTkButton(self.sidebar_frame, corner_radius=0, height=40,
border_spacing=10,
text="Generate Lists",
fg_color="transparent", text_color=("gray10", "gray90"),
hover_color=("gray70", "gray30"), anchor="w",
command=self.generate_list_menu_button_event)
self.generate_list_menu.grid(row=1, column=0, sticky="ew", pady=(20, 0))
self.fill_absence_menu = customtkinter.CTkButton(self.sidebar_frame, corner_radius=0, height=40,
border_spacing=10, text="Fill Absence Bot",
fg_color="transparent", text_color=("gray10", "gray90"),
hover_color=("gray70", "gray30"), anchor="w",
command=self.fill_absence_button_event
)
self.fill_absence_menu.grid(row=2, column=0, sticky="ew")
self.about_us_menu = customtkinter.CTkButton(self.sidebar_frame, corner_radius=0, height=40,
border_spacing=10, text="About us",
fg_color="transparent", text_color=("gray10", "gray90"),
hover_color=("gray70", "gray30"), anchor="w",
command=self.about_us_button_event
)
self.about_us_menu.grid(row=3, column=0, sticky="ew")
# end of side bar
# generate lists page
self.tabview_generate_lists = customtkinter.CTkTabview(self, width=250, state='disabled', text_color_disabled='white',
height=250)
self.tabview_generate_lists.grid(row=0, column=1, padx=(20, 20), pady=(5, 0), sticky="nsew")
self.tabview_generate_lists.add("Setup")
self.tabview_generate_lists.add("Output Location")
self.tabview_generate_lists.add("Review & Submit")
self.tabview_generate_lists.tab("Setup").grid_columnconfigure(0, weight=1)
# setup tab
self.tabview_generate_lists.tab("Setup").grid_rowconfigure(0, weight=1)
self.tabview_generate_lists.tab("Setup").grid_columnconfigure(0, weight=1)
# data entry
# check if file exist
paths = C_File(file_name=resource_path("db/paths.txt"))
self.path={}
if paths.existe_fichier():
self.paths = paths.fichier_to_Liste()
for path in self.paths:
path_splited = path.split("=")
self.path[path_splited[0]]=path_splited[-1].strip()
self.data_entry_frame = customtkinter.CTkFrame(self.tabview_generate_lists.tab("Setup"))
self.data_entry_frame.grid(sticky='nw', row=0, column=0, padx=5, pady=(0, 0))
self.label_data_file = customtkinter.CTkLabel(self.data_entry_frame, text="Data File (.xls):",
text_color="gray90")
self.label_data_file.grid(row=0, column=0, padx=(0, 5), pady=(15, 0))
self.entry_path = customtkinter.CTkEntry(self.data_entry_frame, placeholder_text="C:\\", validate='focusout',
validatecommand=((), '%P'),
width=250)
self.entry_path.grid(row=0, column=1, padx=(100, 5), pady=(15, 0))
self.browse_button = customtkinter.CTkButton(self.data_entry_frame, text="Browse", command=self.browse_path,
width=50)
self.browse_button.grid(row=0, column=2, padx=(0, 5), pady=(15, 0))
self.label_template_entry = customtkinter.CTkLabel(self.data_entry_frame, text="Template file (.xlsx):")
self.label_template_entry.grid(row=1, column=0, padx=(0, 5), pady=(15, 0))
self.entry_path2 = customtkinter.CTkEntry(self.data_entry_frame, placeholder_text="C:\\", validate='focusout',
width=250)
self.entry_path2.grid(row=1, column=1, padx=(100, 5), pady=(15, 10))
self.browse_button2 = customtkinter.CTkButton(self.data_entry_frame, text="Browse", command=self.browse_path2,
width=50)
self.browse_button2.grid(row=1, column=2, padx=(0, 5), pady=(15, 10))
if self.path["DATA"] != "":
self.entry_path.insert(0, self.path["DATA"])
if self.path["TEMPLATE"] != "":
self.entry_path2.insert(0, self.path["TEMPLATE"])
self.class_type_options_frame = customtkinter.CTkFrame(self.tabview_generate_lists.tab("Setup"), fg_color="gray25", height=100)
self.class_type_options_frame.grid(sticky="nsew", row=2, column=0, columnspan=6, padx=10, pady=(20, 20))
# self.error_label = customtkinter.CTkLabel(self.class_type_options_frame, text="You have to choose atlease one class", text_color="black")
# self.error_label.grid(row=0, column=0, padx=(0,0))
self.label_college = customtkinter.CTkLabel(self.class_type_options_frame, text="College Classes")
self.label_college.grid(row=0, column=0, padx=(0, 0))
self.college_options = customtkinter.CTkSwitch(self.class_type_options_frame, text="College", state="switched",
command=self.college_switch)
self.college_options.select()
self.college_options.grid(row=1, column=0, padx=(0, 0))
self.college_inter = customtkinter.CTkCheckBox(self.class_type_options_frame, text="APIC", state="normal",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.college_inter.grid(row=2, column=0, padx=(20, 0), pady=(10, 0), sticky="n")
self.college_generale = customtkinter.CTkCheckBox(self.class_type_options_frame, text="ASCG", state="normal",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.college_generale.grid(row=2, column=1, padx=(0, 0), pady=(10, 0), sticky="n")
self.college_aspeb = customtkinter.CTkCheckBox(self.class_type_options_frame, text="ASCPEB", state="normal",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.college_aspeb.grid(row=3, column=0, padx=(20, 0), pady=(5, 5), sticky="n")
self.label_high_school = customtkinter.CTkLabel(self.class_type_options_frame, text="High School Classes",
anchor="e")
self.label_high_school.grid(row=0, column=2, padx=(100, 0))
self.high_school_options = customtkinter.CTkSwitch(self.class_type_options_frame, text="High School",
state="switched",
command=self.high_school_switch)
# self.high_school_options.select()
self.high_school_options.grid(row=1, column=2, padx=(80, 0))
self.TCS = customtkinter.CTkCheckBox(self.class_type_options_frame, text="TCS", state="disabled",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.TCS.grid(row=2, column=2, padx=(100, 0), pady=(5, 0), sticky="nsew")
self.TCSF = customtkinter.CTkCheckBox(self.class_type_options_frame, text="TCSF", state="disabled",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.TCSF.grid(row=2, column=3, padx=(0, 0), pady=(5, 0), sticky="nsew")
self.TCLSH = customtkinter.CTkCheckBox(self.class_type_options_frame, text="TCLSH", state="disabled",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.TCLSH.grid(row=3, column=2, padx=(100, 0), pady=(5, 5), sticky="nsew")
self.BACSE = customtkinter.CTkCheckBox(self.class_type_options_frame, text="1BACSE", state="disabled",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.BACSE.grid(row=3, column=3, padx=(0, 0), pady=(5, 5), sticky="nsew")
self.BACSH = customtkinter.CTkCheckBox(self.class_type_options_frame, text="1BACSH", state="disabled",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.BACSH.grid(row=3, column=4, padx=(0, 0), pady=(5, 5), sticky="nsew")
self.BACSC = customtkinter.CTkCheckBox(self.class_type_options_frame, text="2BACSC", state="disabled",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.BACSC.grid(row=3, column=5, padx=(0, 0), pady=(5, 5), sticky="nsew")
self.BACSH2 = customtkinter.CTkCheckBox(self.class_type_options_frame, text="2BACSH", state="disabled",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.BACSH2.grid(row=2, column=4, padx=(0, 0), pady=(5, 0), sticky="nsew")
self.BACSVT = customtkinter.CTkCheckBox(self.class_type_options_frame, text="2BACSVT", state="disabled",
checkbox_width=20, checkbox_height=20,
command=self.reset_label_high_college)
self.BACSVT.grid(row=2, column=5, padx=(0, 0), pady=(5, 0), sticky="nsew")
self.submit = customtkinter.CTkButton(self.tabview_generate_lists.tab("Setup"), text="Next",
command=self.go_to_output_location, width=50)
self.submit.grid(row=6, column=5, padx=10, pady=(5, 5))
self.return_btn = customtkinter.CTkButton(self.tabview_generate_lists.tab("Setup"), text="Exit",
width=50, fg_color="gray30", command=self.exit)
self.return_btn.grid(row=6, column=4, padx=10, pady=(5, 5))
# output location tab
self.tabview_generate_lists.tab("Output Location").grid_rowconfigure(0, weight=1)
self.tabview_generate_lists.tab("Output Location").grid_columnconfigure((0, 1, 2), weight=1)
self.output_location_frame = customtkinter.CTkFrame(self.tabview_generate_lists.tab("Output Location"), height=200)
self.output_location_frame.grid(sticky='nw', row=0, column=0, padx=5, pady=(20, 0))
self.label_output_folder = customtkinter.CTkLabel(self.output_location_frame, text="Output Folder")
self.label_output_folder.grid(row=0, column=0, padx=(0, 5), pady=(15, 0))
self.output_path = customtkinter.CTkEntry(self.output_location_frame,
placeholder_text=self.path["DIR"] if self.path["DIR"] != "" else os.path.join(os.path.expanduser('~'), 'Documents'),
validate='focusout',
width=250)
self.output_path.insert("0", str(self.path["DIR"] if self.path["DIR"] != "" else os.path.join(os.path.expanduser('~'), 'Documents')))
self.output_path.grid(row=0, column=1, padx=(100, 5), pady=(15, 0))
self.browse_button3 = customtkinter.CTkButton(self.output_location_frame, text="Browse",
command=self.browse_folder, width=50)
self.browse_button3.grid(row=0, column=2, padx=(0, 5), pady=(15, 0))
self.submit2 = customtkinter.CTkButton(self.tabview_generate_lists.tab("Output Location"), text="Next",
command=self.go_to_output_location, width=50)
self.submit2.grid(row=3, column=5, padx=10, pady=(5, 5))
self.return_btn2 = customtkinter.CTkButton(self.tabview_generate_lists.tab("Output Location"), text="Back", command=self.back,
width=50, fg_color="gray30")
self.return_btn2.grid(row=3, column=4, padx=10, pady=(5, 5))
# review tab
self.tabview_generate_lists.tab("Review & Submit").grid_rowconfigure(0, weight=1)
self.tabview_generate_lists.tab("Review & Submit").grid_columnconfigure((0, 1, 2), weight=1)
self.submit3 = customtkinter.CTkButton(self.tabview_generate_lists.tab("Review & Submit"), text="Submit",
command=self.generate_absence_file, width=60)
self.submit3.grid(row=4, column=5, padx=10, pady=(5, 5))
self.return_btn3 = customtkinter.CTkButton(self.tabview_generate_lists.tab("Review & Submit"), text="Back", command=self.back,
width=50, fg_color="gray30")
self.return_btn3.grid(row=4, column=4, padx=10, pady=(5, 5))
self.select_frame_by_name("Generate Lists")
self.try_again_generate = True
else:
self.tabview_fill_bot.grid_remove()
self.tabview_generate_lists.grid()
if not self.console_text.grid_info():
self.console_text.grid()
if self.about_us_text is not None:
self.about_us_text.grid_remove()
self.about_us_logo.grid_remove()
self.select_frame_by_name("Generate Lists")
def entry_error(self, entry):
entry.configure(border_color="red")
def entry_reset(self, entry):
entry.configure(border_color=self.entry_default_bordercolor)
def error_label(self, label):
current_text = label.cget("text")
label.configure(text=current_text.replace("*", "") + "*", text_color="red")
return
def reset_label(self, label):
current_text = label.cget("text")
label.configure(text=current_text.replace("*", ""), text_color="gray90")
return
def validate_email_entry(self):
email = self.email_entry.get()
is_valid = validate_email(email)
if is_valid:
self.reset_label(self.label_email_entry)
self.entry_reset(self.email_entry)
else:
self.error_label(self.label_email_entry)
self.entry_error(self.email_entry)
def check_terms_box(self):
if self.check_terms_and_condition.get():
self.check_terms_and_condition.configure(border_color="gray72", text_color="gray72")
self.reset_label(self.label_terms)
else:
self.check_terms_and_condition.configure(border_color="red", text_color="red")
self.error_label(self.label_terms)
def fill_absence_button_event(self):
test = self.fill_absence_menu.cget("fg_color")
if test == ("gray75", "gray25"):
self.tabview_fill_bot.set("Setup")
return
if self.try_again_fill == False:
self.tabview_fill_bot = customtkinter.CTkTabview(self, width=250, state='disabled',
text_color_disabled='white',
height=250)
self.tabview_fill_bot.grid(row=0, column=1, padx=(20, 20), pady=(5, 0), sticky="nsew")
self.tabview_fill_bot.add("Setup")
self.tabview_fill_bot.add("Review & Submit")
# setup tab
self.tabview_fill_bot.tab("Setup").grid_rowconfigure(0, weight=1)
self.tabview_fill_bot.tab("Setup").grid_columnconfigure(0, weight=1)
self.tabview_fill_bot.tab("Review & Submit").grid_rowconfigure(0, weight=1)
self.tabview_fill_bot.tab("Review & Submit").grid_columnconfigure((0, 1, 2), weight=1)
# self.generate_list_menu_button_event()
self.tabview_fill_bot.set("Setup")
# self.submit.destroy()
# self.return_btn.destroy()
self.data_entry_frame = customtkinter.CTkFrame(self.tabview_fill_bot.tab("Setup"))
self.data_entry_frame.grid(sticky='nw', row=0, column=0, padx=5, pady=(0, 0))
self.label_email_entry = customtkinter.CTkLabel(self.data_entry_frame, text="Email:", text_color="gray90")
self.label_email_entry.grid(row=0, column=0, padx=(0, 5), pady=(15, 0))
self.email_entry = customtkinter.CTkEntry(self.data_entry_frame, placeholder_text="email@taalim.ma", width=250)
self.email_entry.grid(row=0, column=1, padx=(100, 5), pady=(15, 0))
if self.path["EMAIL"] != "":
self.email_entry.insert(0, self.path["EMAIL"])
self.email_entry.bind("<KeyRelease>", lambda _ : self.validate_email_entry())
self.label_password_entry = customtkinter.CTkLabel(self.data_entry_frame, text="Password:")
self.label_password_entry.grid(row=1, column=0, padx=(0, 5), pady=(15, 0))
self.password_entry = customtkinter.CTkEntry(self.data_entry_frame, show="*" ,placeholder_text="Your Password", width=250)
self.password_entry.grid(row=1, column=1, padx=(100, 5), pady=(15, 0))
self.password_entry.bind("<KeyRelease>", lambda _ : (self.reset_label(self.label_password_entry), self.entry_reset(self.password_entry)) if len(self.password_entry.get()) > 8 else (self.error_label(self.label_password_entry), self.entry_error(self.password_entry)))
self.label_absence_data_file = customtkinter.CTkLabel(self.data_entry_frame, text="Absence File (.xlsx):",
text_color="gray90")
self.label_absence_data_file.grid(row=2, column=0, padx=(0, 5), pady=(15, 0))
self.entry_path_absence = customtkinter.CTkEntry(self.data_entry_frame, placeholder_text=self.path["ABSENCE_FILE"] if self.path["ABSENCE_FILE"] != "" else "C://", validate='focusout',
validatecommand=((), '%P'),
width=250)
self.entry_path_absence.grid(row=2, column=1, padx=(100, 5), pady=(15, 0))
if self.path["ABSENCE_FILE"] != "":
self.entry_path_absence.insert(0, self.path["ABSENCE_FILE"])
self.browse_button_absence = customtkinter.CTkButton(self.data_entry_frame, text="Browse", command=self.browser_path3,
width=50)
self.browse_button_absence.grid(row=2, column=2, padx=(0, 5), pady=(15,0))
self.label_browser_chrome_firefox = customtkinter.CTkLabel(self.data_entry_frame, text="Browser:", text_color="gray90")
self.label_browser_chrome_firefox.grid(row=3, column=0, padx=(0, 5), pady=(15, 0))
self.browser_type = customtkinter.IntVar()
self.chrome_radio = customtkinter.CTkRadioButton(self.data_entry_frame, text="Chrome", variable=self.browser_type, value=1, state="disabled")
self.chrome_radio.grid(row=3, column=1, padx=(10, 5), pady=(15, 0))
self.firefox_radio = customtkinter.CTkRadioButton(self.data_entry_frame, text="Firefox", variable=self.browser_type, value=2)
self.firefox_radio.grid(row=3, column=2, padx=(10, 5), pady=(15,0))
self.firefox_radio.select()
self.label_terms = customtkinter.CTkLabel(self.data_entry_frame, text="Terms and conditions:",
text_color="gray90")
self.label_terms.grid(row=4, column=0, padx=(0, 5), pady=(20, 0))
self.check_terms_and_condition = customtkinter.CTkCheckBox(self.data_entry_frame, text="I accept the Terms and the Conditions", state="normal",checkbox_width=20, checkbox_height=20, command=self.check_terms_box)
self.check_terms_and_condition.grid(row=4, column=1, padx=(0, 0), pady=(20,0), sticky="ne")
self.submit4 = customtkinter.CTkButton(self.tabview_fill_bot.tab("Setup"), text="Next",
command=self.go_to_review2, width=50)
self.submit4.grid(row=6, column=5, padx=10, pady=(5, 5))
self.return_btn4 = customtkinter.CTkButton(self.tabview_fill_bot.tab("Setup"), text="Exit",
width=50, fg_color="gray30", command=self.exit)
self.return_btn4.grid(row=6, column=4, padx=10, pady=(5, 5))
self.run_bot = customtkinter.CTkButton(self.tabview_fill_bot.tab("Review & Submit"), text="Run",
command=self.run_bot_interaction, width=50)
self.run_bot.grid(row=6, column=5, padx=10, pady=(5, 5))
self.return_btn5 = customtkinter.CTkButton(self.tabview_fill_bot.tab("Review & Submit"), text="Back", command=self.back2,
width=50, fg_color="gray30")
self.return_btn5.grid(row=6, column=4, padx=10, pady=(5, 5))
if self.about_us_text is not None:
self.about_us_text.grid_remove()
self.about_us_logo.grid_remove()
self.console_text.grid()
self.try_again_fill = True
self.select_frame_by_name("Fill Absence Bot")
else:
self.tabview_generate_lists.grid_remove()
self.tabview_fill_bot.grid()
self.console_text.grid()
if self.about_us_text is not None:
self.about_us_text.grid_remove()
self.about_us_logo.grid_remove()
self.select_frame_by_name("Fill Absence Bot")
def about_us_button_event(self):
if self.tabview_generate_lists.grid_info():
self.tabview_generate_lists.grid_remove()
if self.tabview_fill_bot is not None:
if self.tabview_fill_bot.grid_info():
self.tabview_fill_bot.grid_remove()
if self.about_us_text is not None:
self.about_us_text.grid()
self.about_us_logo.grid()
self.console_text.grid_remove()
self.select_frame_by_name("About us")
else:
self.about_us_logo = customtkinter.CTkLabel(self, text="",
image=self.about_us_image)
self.about_us_logo.grid(row=0, column=1, padx=10, pady=10)
self.about_us_text = customtkinter.CTkTextbox(self, height=200, wrap="word", font=("Arial", 18))
self.about_us_text.grid(row=1, column=1,rowspan=3, columnspan=6, padx=(20, 20), pady = (15, 20), sticky = "nsew")
self.console_text.grid_remove()
self.about_us_text.tag_config("Title", foreground="gray92")
self.about_us_text.tag_config("subTitle", foreground="gray65")
self.about_us_text.tag_config("Paragraph", foreground="gray50")
# Content to be displayed
# Insert the formatted text into the Text widget
self.about_us_text.insert("end", "\n About Us", "LargeText")
self.about_us_text.insert("end", "\n\nMassar Direction Sagoubot is a cutting-edge automation project designed to streamline and simplify the process of managing absence data for multiple classes within a web application. Our solution is meticulously crafted using modern technologies and software tools to optimize efficiency and save valuable time for teachers and administrators.\n", "Paragraph")
self.about_us_text.insert("end", "\n\n Terms and Privacy", "Title")
self.about_us_text.insert("end", "\n\nAccount Access", "subTitle")
self.about_us_text.insert("end",
"\nTo enhance your experience with Massar Direction Sagoubot, the application utilizes your account credentials to securely log in to the Massar website. Your privacy and security are of utmost importance to us. We ensure that your login information is encrypted and used solely for the purpose of automating absence data management.\n", "Paragraph")
self.about_us_text.insert("end", "\n\nData Handling", "subTitle")
self.about_us_text.insert("end",
"\nYour data, specifically related to absence records and class information, is processed within the confines of the application to facilitate automation. We do not store or retain any of your personal data beyond the scope of improving application functionality.\n",
"Paragraph")
self.about_us_text.insert("end", "\n\nSecurity Measures", "subTitle")
self.about_us_text.insert("end",
"\nWe employ industry-standard security measures to safeguard your account information. This includes encryption protocols and best practices to prevent unauthorized access or misuse of your credentials.\n", "Paragraph")
self.about_us_text.insert("end", "\n\nUser Consent", "subTitle")
self.about_us_text.insert("end",
"\nBy using Massar Direction Sagoubot, you consent to the utilization of your Massar account credentials for the sole purpose of automating absence data management. We prioritize transparency and security in handling your login information.\n", "Paragraph")
self.about_us_text.insert("end", "\n\nQuestions or Concerns", "subTitle")
self.about_us_text.insert("end",
"\nIf you have any questions, concerns, or require further clarification regarding our terms, privacy practices, or the usage of your account information, please feel free to reach out to us at sakou81833@gmail.com. Your satisfaction and trust are our top priorities.\n", "Paragraph")
self.about_us_text.configure(state="disabled")
self.select_frame_by_name("About us")
def exit(self):
result = tk.messagebox.askokcancel("Confirmation", "Are you sure you want to exit?")
if result: # If the user confirms
app.quit()
# backend functions
def generate_absence_file(self):
self.generate_progress_bar()
self.submit3.configure(state="disabled")
self.return_btn3.configure(state="disabled")
self.console_text.configure(state="normal")
self.label_all_review1.configure(text_color="gray35")
def run_fill_all_class_sheets():
reader = Read_Db(input_file=self.entry_path.get(),
template_file=self.entry_path2.get(),
output_file=str(self.output_path.get()) + "\\absence.xlsx",
required_classes=self.selected_classes,
progress_bar=self.progressbar_1,
console=self.console_text)
reader.fill_all_class_sheets()
time.sleep(3)
self.submit3.configure(state="normal")
self.return_btn3.configure(state="normal")
self.progressbar_1.grid_remove()
self.console_text.configure(state="disabled")
self.label_all_review1.configure(text_color="gray70")
thread = threading.Thread(target=run_fill_all_class_sheets)
thread.start()
return
def run_bot_interaction(self):
self.generate_progress_bar(determinate=False)
self.console_text.configure(state="normal")
self.run_bot.configure(state="disabled")
self.return_btn5.configure(state="disabled")
self.label_all_review2.configure(text_color="gray35")
def run_fill_absence():
# loading the class here because of the .env file not getting refreshed
interaction_object = Massar_Direction_Sagou(console=self.console_text)
driver_test = interaction_object.main_interaction()
if driver_test:
interaction_object.get_list_page() | absence = Absence(driver=interaction_object.driver, console=self.console_text) | 3 | 2023-10-29 18:10:27+00:00 | 16k |
hsma-programme/Teaching_DES_Concepts_Streamlit | pages/4_🏥_The_Full_Model.py | [
{
"identifier": "add_logo",
"path": "helper_functions.py",
"snippet": "def add_logo():\n '''\n Add a logo at the top of the page navigation sidebar\n\n Approach written by blackary on\n https://discuss.streamlit.io/t/put-logo-and-title-above-on-top-of-page-navigation-in-sidebar-of-multipage-... | import gc
import asyncio
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
import numpy as np
from helper_functions import add_logo, mermaid, center_running
from model_classes import Scenario, multiple_replications
from output_animation_functions import reshape_for_animations, animate_activity_log | 11,011 | st.subheader("Non-Trauma Treatment")
n_cubicles_1 = st.slider("👨⚕️👩⚕️ Number of Treatment Cubicles for Non-Trauma", 1, 10, step=1, value=2)
non_trauma_treat_p = st.slider("🤕 Probability that a non-trauma patient will need treatment",
0.0, 1.0, step=0.01, value=0.7,
help="0 = No non-trauma patients need treatment\n\n1 = All non-trauma patients need treatment")
col5, col6 = st.columns(2)
with col5:
st.write("Total rooms in use is {}".format(n_cubicles_1+n_cubicles_2+n_exam+n_trauma+n_triage+n_reg))
with col6:
with st.expander("Advanced Parameters"):
seed = st.slider("🎲 Set a random number for the computer to start from",
1, 1000,
step=1, value=42)
n_reps = st.slider("🔁 How many times should the simulation run? WARNING: Fast/modern computer required to take this above 5 replications.",
1, 10,
step=1, value=3)
run_time_days = st.slider("🗓️ How many days should we run the simulation for each time?",
1, 60,
step=1, value=5)
args = Scenario(
random_number_set=seed,
n_triage=n_triage,
n_reg=n_reg,
n_exam=n_exam,
n_trauma=n_trauma,
n_cubicles_1=n_cubicles_1,
n_cubicles_2=n_cubicles_2,
non_trauma_treat_p=non_trauma_treat_p,
prob_trauma=prob_trauma)
# A user must press a streamlit button to run the model
button_run_pressed = st.button("Run simulation")
if button_run_pressed:
# add a spinner and then display success box
with st.spinner('Simulating the minor injuries unit...'):
await asyncio.sleep(0.1)
my_bar = st.progress(0, text="Simulating the minor injuries unit...")
# run multiple replications of experment
detailed_outputs = multiple_replications(
args,
n_reps=n_reps,
rc_period=run_time_days*60*24,
return_detailed_logs=True
)
my_bar.progress(40, text="Collating Simulation Outputs...")
results = pd.concat([detailed_outputs[i]['results']['summary_df'].assign(rep= i+1)
for i in range(n_reps)]).set_index('rep')
full_event_log = pd.concat([detailed_outputs[i]['results']['full_event_log'].assign(rep= i+1)
for i in range(n_reps)])
del detailed_outputs
gc.collect()
my_bar.progress(60, text="Logging Results...")
# print(len(st.session_state['session_results']))
# results_for_state = pd.DataFrame(results.median()).T.drop(['Rep'], axis=1)
results_for_state = results
original_cols = results_for_state.columns.values
results_for_state['Triage\nCubicles'] = args.n_triage
results_for_state['Registration\nClerks'] = args.n_reg
results_for_state['Examination\nRooms'] = args.n_exam
results_for_state['Non-Trauma\nTreatment Cubicles'] = args.n_cubicles_1
results_for_state['Trauma\nStabilisation Bays'] = args.n_trauma
results_for_state['Trauma\nTreatment Cubicles'] = args.n_cubicles_2
results_for_state['Probability patient\nis a trauma patient'] = args.prob_trauma
results_for_state['Probability non-trauma patients\nrequire treatment'] = args.non_trauma_treat_p
results_for_state['Model Run'] = len(st.session_state['session_results']) + 1
results_for_state['Random Seed'] = seed
# Reorder columns
column_order = ['Model Run', 'Triage\nCubicles', 'Registration\nClerks', 'Examination\nRooms',
'Non-Trauma\nTreatment Cubicles', 'Trauma\nStabilisation Bays',
'Trauma\nTreatment Cubicles', 'Probability patient\nis a trauma patient',
'Probability non-trauma patients\nrequire treatment', 'Random Seed'
] + list(original_cols)
results_for_state = results_for_state[column_order]
current_state = st.session_state['session_results']
current_state.append(results_for_state)
del results_for_state
gc.collect()
st.session_state['session_results'] = current_state
del current_state
gc.collect()
# print(len(st.session_state['session_results']))
# UTILISATION AUDIT - BRING BACK WHEN NEEDED
# full_utilisation_audit = pd.concat([detailed_outputs[i]['results']['utilisation_audit'].assign(Rep= i+1)
# for i in range(n_reps)])
# animation_dfs_queue = reshape_for_animations(
# full_event_log[
# (full_event_log['rep']==1) &
# ((full_event_log['event_type']=='queue') | (full_event_log['event_type']=='arrival_departure'))
# ]
# )
my_bar.progress(80, text="Creating Animations...")
| '''
A Streamlit application based on Monks and
Allows users to interact with an increasingly more complex treatment simulation
'''
st.set_page_config(
page_title="The Full Model",
layout="wide",
initial_sidebar_state="expanded",
)
# Initialise session state
if 'session_results' not in st.session_state:
st.session_state['session_results'] = []
add_logo()
center_running()
with open("style.css") as css:
st.markdown( f'<style>{css.read()}</style>' , unsafe_allow_html= True)
## We add in a title for our web app's page
st.title("Discrete Event Simulation Playground")
st.subheader("How can we optimise the full system?")
st.markdown("Once you have run more than one scenario, try out the new tab 'compare scenario outputs'.")
gc.collect()
# tab1, tab2, tab3, tab4 = st.tabs(["Introduction", "Exercises", "Playground", "Compare Scenario Outputs"])
tab1, tab2, tab3, tab4 = st.tabs(["Playground", "Exercise", "Compare Scenario Outputs", "Information"])
with tab4:
st.markdown("""
So now we have explored every component of the model:
- Generating arrivals
- Generating and using resources
- Sending people down different paths
So now let's create a version of the model that uses all of these aspects.
For now, we won't consider nurses separately - we will assume that each nurse on shift has one room that is theirs to always use.
"""
)
mermaid(height=600, code=
"""
%%{ init: { 'flowchart': { 'curve': 'step' } } }%%
%%{ init: { 'theme': 'base', 'themeVariables': {'lineColor': '#b4b4b4'} } }%%
flowchart LR
A[Arrival] --> BX[Triage]
BX -.-> T([Triage Bay\n<b>RESOURCE</b>])
T -.-> BX
BX --> BY{Trauma or non-trauma}
BY ----> B1{Trauma Pathway}
BY ----> B2{Non-Trauma Pathway}
B1 --> C[Stabilisation]
C --> E[Treatment]
B2 --> D[Registration]
D --> G[Examination]
G --> H[Treat?]
H ----> F
H --> I[Non-Trauma Treatment]
I --> F
C -.-> Z([Trauma Room\n<b>RESOURCE</b>])
Z -.-> C
E -.-> Y([Cubicle - 1\n<b>RESOURCE</b>])
Y -.-> E
D -.-> X([Clerks\n<b>RESOURCE</b>])
X -.-> D
G -.-> W([Exam Room\n<b>RESOURCE</b>])
W -.-> G
I -.-> V([Cubicle - 2\n<b>RESOURCE</b>])
V -.-> I
E ----> F[Discharge]
classDef ZZ1 fill:#8B5E0F,font-family:lexend, color:#FFF
classDef ZZ2 fill:#5DFDA0,font-family:lexend
classDef ZZ2a fill:#02CD55,font-family:lexend, color:#FFF
classDef ZZ3 fill: #D45E5E,font-family:lexend
classDef ZZ3a fill: #932727,font-family:lexend, color:#FFF
classDef ZZ4 fill: #611D67,font-family:lexend, color:#FFF
classDef ZZ5 fill:#47D7FF,font-family:lexend
classDef ZZ5a fill:#00AADA,font-family:lexend
class A ZZ1
class C,E ZZ2
class D,G ZZ3
class X,W ZZ3a
class Z,Y ZZ2a
class I,V ZZ4
class BX ZZ5
class T ZZ5a
;
"""
)
with tab2:
st.header("Things to Try")
st.markdown(
"""
- First, just run the model with the default settings.
- Look at the graphs and animated patient log. What is the performance of the system like?
- Are the queues consistent throughout the day?
---
- Due to building work taking place, the hospital will temporarily need to close several bays.
It will be possible to have a maximum of 20 bays/cubicles/rooms in total across the whole system.
- What is the best configuration you can find to keep the average wait times as low as possible across both trauma and non-trauma pathways?
*Make sure you are using the default probabilities for trauma/non-trauma patients (0.3) and treatment of non-trauma patients (0.7)*
"""
)
with tab1:
# n_triage: int
# The number of triage cubicles
# n_reg: int
# The number of registration clerks
# n_exam: int
# The number of examination rooms
# n_trauma: int
# The number of trauma bays for stablisation
# n_cubicles_1: int
# The number of non-trauma treatment cubicles
# n_cubicles_2: int
# The number of trauma treatment cubicles
# non_trauma_treat_p: float
# Probability non trauma patient requires treatment
# prob_trauma: float
# probability that a new arrival is a trauma patient.
col1, col2, col3, col4 = st.columns(4)
with col1:
st.subheader("Triage")
n_triage = st.slider("👨⚕️👩⚕️ Number of Triage Cubicles", 1, 10, step=1, value=4)
prob_trauma = st.slider("🚑 Probability that a new arrival is a trauma patient",
0.0, 1.0, step=0.01, value=0.3,
help="0 = No arrivals are trauma patients\n\n1 = All arrivals are trauma patients")
with col2:
st.subheader("Trauma Pathway")
n_trauma = st.slider("👨⚕️👩⚕️ Number of Trauma Bays for Stabilisation", 1, 10, step=1, value=6)
n_cubicles_2 = st.slider("👨⚕️👩⚕️ Number of Treatment Cubicles for Trauma", 1, 10, step=1, value=6)
with col3:
st.subheader("Non-Trauma Pathway")
n_reg = st.slider("👨⚕️👩⚕️ Number of Registration Cubicles", 1, 10, step=1, value=3)
n_exam = st.slider("👨⚕️👩⚕️ Number of Examination Rooms for non-trauma patients", 1, 10, step=1, value=3)
with col4:
st.subheader("Non-Trauma Treatment")
n_cubicles_1 = st.slider("👨⚕️👩⚕️ Number of Treatment Cubicles for Non-Trauma", 1, 10, step=1, value=2)
non_trauma_treat_p = st.slider("🤕 Probability that a non-trauma patient will need treatment",
0.0, 1.0, step=0.01, value=0.7,
help="0 = No non-trauma patients need treatment\n\n1 = All non-trauma patients need treatment")
col5, col6 = st.columns(2)
with col5:
st.write("Total rooms in use is {}".format(n_cubicles_1+n_cubicles_2+n_exam+n_trauma+n_triage+n_reg))
with col6:
with st.expander("Advanced Parameters"):
seed = st.slider("🎲 Set a random number for the computer to start from",
1, 1000,
step=1, value=42)
n_reps = st.slider("🔁 How many times should the simulation run? WARNING: Fast/modern computer required to take this above 5 replications.",
1, 10,
step=1, value=3)
run_time_days = st.slider("🗓️ How many days should we run the simulation for each time?",
1, 60,
step=1, value=5)
args = Scenario(
random_number_set=seed,
n_triage=n_triage,
n_reg=n_reg,
n_exam=n_exam,
n_trauma=n_trauma,
n_cubicles_1=n_cubicles_1,
n_cubicles_2=n_cubicles_2,
non_trauma_treat_p=non_trauma_treat_p,
prob_trauma=prob_trauma)
# A user must press a streamlit button to run the model
button_run_pressed = st.button("Run simulation")
if button_run_pressed:
# add a spinner and then display success box
with st.spinner('Simulating the minor injuries unit...'):
await asyncio.sleep(0.1)
my_bar = st.progress(0, text="Simulating the minor injuries unit...")
# run multiple replications of experment
detailed_outputs = multiple_replications(
args,
n_reps=n_reps,
rc_period=run_time_days*60*24,
return_detailed_logs=True
)
my_bar.progress(40, text="Collating Simulation Outputs...")
results = pd.concat([detailed_outputs[i]['results']['summary_df'].assign(rep= i+1)
for i in range(n_reps)]).set_index('rep')
full_event_log = pd.concat([detailed_outputs[i]['results']['full_event_log'].assign(rep= i+1)
for i in range(n_reps)])
del detailed_outputs
gc.collect()
my_bar.progress(60, text="Logging Results...")
# print(len(st.session_state['session_results']))
# results_for_state = pd.DataFrame(results.median()).T.drop(['Rep'], axis=1)
results_for_state = results
original_cols = results_for_state.columns.values
results_for_state['Triage\nCubicles'] = args.n_triage
results_for_state['Registration\nClerks'] = args.n_reg
results_for_state['Examination\nRooms'] = args.n_exam
results_for_state['Non-Trauma\nTreatment Cubicles'] = args.n_cubicles_1
results_for_state['Trauma\nStabilisation Bays'] = args.n_trauma
results_for_state['Trauma\nTreatment Cubicles'] = args.n_cubicles_2
results_for_state['Probability patient\nis a trauma patient'] = args.prob_trauma
results_for_state['Probability non-trauma patients\nrequire treatment'] = args.non_trauma_treat_p
results_for_state['Model Run'] = len(st.session_state['session_results']) + 1
results_for_state['Random Seed'] = seed
# Reorder columns
column_order = ['Model Run', 'Triage\nCubicles', 'Registration\nClerks', 'Examination\nRooms',
'Non-Trauma\nTreatment Cubicles', 'Trauma\nStabilisation Bays',
'Trauma\nTreatment Cubicles', 'Probability patient\nis a trauma patient',
'Probability non-trauma patients\nrequire treatment', 'Random Seed'
] + list(original_cols)
results_for_state = results_for_state[column_order]
current_state = st.session_state['session_results']
current_state.append(results_for_state)
del results_for_state
gc.collect()
st.session_state['session_results'] = current_state
del current_state
gc.collect()
# print(len(st.session_state['session_results']))
# UTILISATION AUDIT - BRING BACK WHEN NEEDED
# full_utilisation_audit = pd.concat([detailed_outputs[i]['results']['utilisation_audit'].assign(Rep= i+1)
# for i in range(n_reps)])
# animation_dfs_queue = reshape_for_animations(
# full_event_log[
# (full_event_log['rep']==1) &
# ((full_event_log['event_type']=='queue') | (full_event_log['event_type']=='arrival_departure'))
# ]
# )
my_bar.progress(80, text="Creating Animations...")
| animation_dfs_log = reshape_for_animations( | 5 | 2023-10-26 09:57:52+00:00 | 16k |
hyperspy/exspy | exspy/models/edsmodel.py | [
{
"identifier": "_get_element_and_line",
"path": "exspy/misc/eds/utils.py",
"snippet": "def _get_element_and_line(xray_line):\n \"\"\"\n Returns the element name and line character for a particular X-ray line as\n a tuple.\n\n By example, if xray_line = 'Mn_Ka' this function returns ('Mn', '... | import warnings
import numpy as np
import math
import logging
import hyperspy.components1d as create_component
from hyperspy.misc.utils import stash_active_state
from exspy.misc.eds.utils import _get_element_and_line
from hyperspy.models.model1d import Model1D
from exspy.signals.eds import EDSSpectrum
from exspy.misc.elements import elements as elements_db
from exspy.misc.eds import utils as utils_eds
from hyperspy import utils | 12,074 | # -*- coding: utf-8 -*-
# Copyright 2007-2023 The exSpy developers
#
# This file is part of exSpy.
#
# exSpy 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.
#
# exSpy 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 exSpy. If not, see <https://www.gnu.org/licenses/#GPL>.
from __future__ import division
_logger = logging.getLogger(__name__)
eV2keV = 1000.0
sigma2fwhm = 2 * math.sqrt(2 * math.log(2))
def _get_weight(element, line, weight_line=None):
if weight_line is None:
| # -*- coding: utf-8 -*-
# Copyright 2007-2023 The exSpy developers
#
# This file is part of exSpy.
#
# exSpy 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.
#
# exSpy 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 exSpy. If not, see <https://www.gnu.org/licenses/#GPL>.
from __future__ import division
_logger = logging.getLogger(__name__)
eV2keV = 1000.0
sigma2fwhm = 2 * math.sqrt(2 * math.log(2))
def _get_weight(element, line, weight_line=None):
if weight_line is None: | weight_line = elements_db[element]["Atomic_properties"]["Xray_lines"][line][ | 1 | 2023-10-28 20:04:10+00:00 | 16k |
Sllambias/yucca | yucca/training/augmentation/YuccaAugmentationComposer.py | [
{
"identifier": "get_max_rotated_size",
"path": "yucca/image_processing/matrix_ops.py",
"snippet": "def get_max_rotated_size(patch_size):\n if len(patch_size) == 2:\n max_dim = int(np.sqrt(patch_size[0] ** 2 + patch_size[1] ** 2))\n return (max_dim, max_dim)\n\n max_dim_0 = max(\n ... | from torchvision import transforms
from yucca.image_processing.matrix_ops import get_max_rotated_size
from yucca.image_processing.transforms.formatting import (
AddBatchDimension,
RemoveBatchDimension,
)
from yucca.image_processing.transforms.BiasField import BiasField
from yucca.image_processing.transforms.Blur import Blur
from yucca.image_processing.transforms.CopyImageToSeg import CopyImageToSeg
from yucca.image_processing.transforms.Gamma import Gamma
from yucca.image_processing.transforms.Ghosting import MotionGhosting
from yucca.image_processing.transforms.Masking import Masking
from yucca.image_processing.transforms.Mirror import Mirror
from yucca.image_processing.transforms.Noise import (
AdditiveNoise,
MultiplicativeNoise,
)
from yucca.image_processing.transforms.Ringing import GibbsRinging
from yucca.image_processing.transforms.sampling import DownsampleSegForDS
from yucca.image_processing.transforms.SimulateLowres import SimulateLowres
from yucca.image_processing.transforms.Spatial import Spatial
from yucca.network_architectures.utils.model_memory_estimation import (
find_optimal_tensor_dims,
) | 13,389 | self.gamma_range = (0.5, 2.0)
self.gibbs_ringing_p_per_sample = 0.2
self.gibbs_ringing_cutfreq = (96, 129)
self.gibbs_ringing_axes = (0, 2) if is_2d else (0, 3)
self.mirror_p_per_sample = 0.0
self.mirror_p_per_axis = 0.33
self.mirror_axes = (0, 1) if is_2d else (0, 1, 2)
self.motion_ghosting_p_per_sample = 0.2
self.motion_ghosting_alpha = (0.85, 0.95)
self.motion_ghosting_numreps = (2, 11)
self.motion_ghosting_axes = (0, 2) if is_2d else (0, 3)
self.multiplicative_noise_p_per_sample = 0.2
self.multiplicative_noise_mean = (0, 0)
self.multiplicative_noise_sigma = (1e-3, 1e-4)
self.rotation_p_per_sample = 0.2
self.rotation_p_per_axis = 0.66
self.rotation_x = (-30.0, 30.0)
self.rotation_y = (-0.0, 0.0) if is_2d else (-30.0, 30.0)
self.rotation_z = (-0.0, 0.0) if is_2d else (-30.0, 30.0)
self.scale_p_per_sample = 0.2
self.scale_factor = (0.9, 1.1)
self.simulate_lowres_p_per_sample = 0.2
self.simulate_lowres_p_per_channel = 0.5
self.simulate_lowres_p_per_axis = 0.33
self.simulate_lowres_zoom_range = (0.5, 1.0)
@property
def pre_aug_patch_size(self):
# First check if any spatial transforms are included
if self.elastic_deform_p_per_sample > 0 or self.rotation_p_per_sample > 0 or self.scale_p_per_sample > 0:
self._pre_aug_patch_size = get_max_rotated_size(self.patch_size)
return self._pre_aug_patch_size
def apply_task_type_specific_preset(self, task_type_preset):
if task_type_preset == "classification":
self.skip_label = True
if task_type_preset == "unsupervised":
self.skip_label = True
self.copy_image_to_label = True
# This should be uncommented when masking is properly implemented
# augmentation_parameter_dict["mask_image_for_reconstruction"] = True
def overwrite_params(self, parameter_dict):
for key, value in parameter_dict.items():
setattr(self, key, value)
def compose_train_transforms(self):
tr_transforms = transforms.Compose(
[
AddBatchDimension(),
Spatial(
patch_size=self.patch_size,
crop=True,
random_crop=self.random_crop,
p_deform_per_sample=self.elastic_deform_p_per_sample,
deform_sigma=self.elastic_deform_sigma,
deform_alpha=self.elastic_deform_alpha,
p_rot_per_sample=self.rotation_p_per_sample,
p_rot_per_axis=self.rotation_p_per_axis,
x_rot_in_degrees=self.rotation_x,
y_rot_in_degrees=self.rotation_y,
z_rot_in_degrees=self.rotation_z,
p_scale_per_sample=self.scale_p_per_sample,
scale_factor=self.scale_factor,
skip_label=self.skip_label,
),
AdditiveNoise(
p_per_sample=self.additive_noise_p_per_sample,
mean=self.additive_noise_mean,
sigma=self.additive_noise_sigma,
),
Blur(
p_per_sample=self.blurring_p_per_sample,
p_per_channel=self.blurring_p_per_channel,
sigma=self.blurring_sigma,
),
MultiplicativeNoise(
p_per_sample=self.multiplicative_noise_p_per_sample,
mean=self.multiplicative_noise_mean,
sigma=self.multiplicative_noise_sigma,
),
MotionGhosting(
p_per_sample=self.motion_ghosting_p_per_sample,
alpha=self.motion_ghosting_alpha,
numReps=self.motion_ghosting_numreps,
axes=self.motion_ghosting_axes,
),
GibbsRinging(
p_per_sample=self.gibbs_ringing_p_per_sample,
cutFreq=self.gibbs_ringing_cutfreq,
axes=self.gibbs_ringing_axes,
),
SimulateLowres(
p_per_sample=self.simulate_lowres_p_per_sample,
p_per_channel=self.simulate_lowres_p_per_channel,
p_per_axis=self.simulate_lowres_p_per_axis,
zoom_range=self.simulate_lowres_zoom_range,
),
BiasField(p_per_sample=self.biasfield_p_per_sample),
Gamma(
p_per_sample=self.gamma_p_per_sample,
p_invert_image=self.gamma_p_invert_image,
gamma_range=self.gamma_range,
),
Mirror(
p_per_sample=self.mirror_p_per_sample,
axes=self.mirror_axes,
p_mirror_per_axis=self.mirror_p_per_axis,
skip_label=self.skip_label,
),
DownsampleSegForDS(deep_supervision=self.deep_supervision),
CopyImageToSeg(copy=self.copy_image_to_label),
|
class YuccaAugmentationComposer:
def __init__(
self,
patch_size: list | tuple,
deep_supervision: bool = False,
is_2D: bool = False,
parameter_dict: dict = {},
task_type_preset: str = None,
):
self._pre_aug_patch_size = None
self.deep_supervision = deep_supervision
self.setup_default_params(is_2D, patch_size)
self.apply_task_type_specific_preset(task_type_preset)
self.overwrite_params(parameter_dict)
self.train_transforms = self.compose_train_transforms()
self.val_transforms = self.compose_val_transforms()
def setup_default_params(self, is_2d, patch_size):
print("Composing Transforms")
# Define whether we crop before or after applying augmentations
# Define if cropping is random or always centered
self.random_crop = True
self.mask_image_for_reconstruction = False
self.patch_size = patch_size
# label/segmentation transforms
self.skip_label = False
self.label_dtype = int
self.copy_image_to_label = False
self.additive_noise_p_per_sample = 0.2
self.additive_noise_mean = (0.0, 0.0)
self.additive_noise_sigma = (1e-3, 1e-4)
self.biasfield_p_per_sample = 0.33
self.blurring_p_per_sample = 0.2
self.blurring_sigma = (0.0, 1.0)
self.blurring_p_per_channel = 0.5
self.elastic_deform_p_per_sample = 0.33
self.elastic_deform_alpha = (200, 600)
self.elastic_deform_sigma = (20, 30)
self.gamma_p_per_sample = 0.2
self.gamma_p_invert_image = 0.05
self.gamma_range = (0.5, 2.0)
self.gibbs_ringing_p_per_sample = 0.2
self.gibbs_ringing_cutfreq = (96, 129)
self.gibbs_ringing_axes = (0, 2) if is_2d else (0, 3)
self.mirror_p_per_sample = 0.0
self.mirror_p_per_axis = 0.33
self.mirror_axes = (0, 1) if is_2d else (0, 1, 2)
self.motion_ghosting_p_per_sample = 0.2
self.motion_ghosting_alpha = (0.85, 0.95)
self.motion_ghosting_numreps = (2, 11)
self.motion_ghosting_axes = (0, 2) if is_2d else (0, 3)
self.multiplicative_noise_p_per_sample = 0.2
self.multiplicative_noise_mean = (0, 0)
self.multiplicative_noise_sigma = (1e-3, 1e-4)
self.rotation_p_per_sample = 0.2
self.rotation_p_per_axis = 0.66
self.rotation_x = (-30.0, 30.0)
self.rotation_y = (-0.0, 0.0) if is_2d else (-30.0, 30.0)
self.rotation_z = (-0.0, 0.0) if is_2d else (-30.0, 30.0)
self.scale_p_per_sample = 0.2
self.scale_factor = (0.9, 1.1)
self.simulate_lowres_p_per_sample = 0.2
self.simulate_lowres_p_per_channel = 0.5
self.simulate_lowres_p_per_axis = 0.33
self.simulate_lowres_zoom_range = (0.5, 1.0)
@property
def pre_aug_patch_size(self):
# First check if any spatial transforms are included
if self.elastic_deform_p_per_sample > 0 or self.rotation_p_per_sample > 0 or self.scale_p_per_sample > 0:
self._pre_aug_patch_size = get_max_rotated_size(self.patch_size)
return self._pre_aug_patch_size
def apply_task_type_specific_preset(self, task_type_preset):
if task_type_preset == "classification":
self.skip_label = True
if task_type_preset == "unsupervised":
self.skip_label = True
self.copy_image_to_label = True
# This should be uncommented when masking is properly implemented
# augmentation_parameter_dict["mask_image_for_reconstruction"] = True
def overwrite_params(self, parameter_dict):
for key, value in parameter_dict.items():
setattr(self, key, value)
def compose_train_transforms(self):
tr_transforms = transforms.Compose(
[
AddBatchDimension(),
Spatial(
patch_size=self.patch_size,
crop=True,
random_crop=self.random_crop,
p_deform_per_sample=self.elastic_deform_p_per_sample,
deform_sigma=self.elastic_deform_sigma,
deform_alpha=self.elastic_deform_alpha,
p_rot_per_sample=self.rotation_p_per_sample,
p_rot_per_axis=self.rotation_p_per_axis,
x_rot_in_degrees=self.rotation_x,
y_rot_in_degrees=self.rotation_y,
z_rot_in_degrees=self.rotation_z,
p_scale_per_sample=self.scale_p_per_sample,
scale_factor=self.scale_factor,
skip_label=self.skip_label,
),
AdditiveNoise(
p_per_sample=self.additive_noise_p_per_sample,
mean=self.additive_noise_mean,
sigma=self.additive_noise_sigma,
),
Blur(
p_per_sample=self.blurring_p_per_sample,
p_per_channel=self.blurring_p_per_channel,
sigma=self.blurring_sigma,
),
MultiplicativeNoise(
p_per_sample=self.multiplicative_noise_p_per_sample,
mean=self.multiplicative_noise_mean,
sigma=self.multiplicative_noise_sigma,
),
MotionGhosting(
p_per_sample=self.motion_ghosting_p_per_sample,
alpha=self.motion_ghosting_alpha,
numReps=self.motion_ghosting_numreps,
axes=self.motion_ghosting_axes,
),
GibbsRinging(
p_per_sample=self.gibbs_ringing_p_per_sample,
cutFreq=self.gibbs_ringing_cutfreq,
axes=self.gibbs_ringing_axes,
),
SimulateLowres(
p_per_sample=self.simulate_lowres_p_per_sample,
p_per_channel=self.simulate_lowres_p_per_channel,
p_per_axis=self.simulate_lowres_p_per_axis,
zoom_range=self.simulate_lowres_zoom_range,
),
BiasField(p_per_sample=self.biasfield_p_per_sample),
Gamma(
p_per_sample=self.gamma_p_per_sample,
p_invert_image=self.gamma_p_invert_image,
gamma_range=self.gamma_range,
),
Mirror(
p_per_sample=self.mirror_p_per_sample,
axes=self.mirror_axes,
p_mirror_per_axis=self.mirror_p_per_axis,
skip_label=self.skip_label,
),
DownsampleSegForDS(deep_supervision=self.deep_supervision),
CopyImageToSeg(copy=self.copy_image_to_label), | Masking(mask=self.mask_image_for_reconstruction), | 8 | 2023-10-26 08:13:03+00:00 | 16k |
Elfenreigen/UniChest | optim/optim_factory.py | [
{
"identifier": "Adafactor",
"path": "optim/adafactor.py",
"snippet": "class Adafactor(torch.optim.Optimizer):\n \"\"\"Implements Adafactor algorithm.\n This implementation is based on: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost`\n (see https://arxiv.org/abs/1804.04235)\n\n ... | import torch
from torch import optim as optim
from .adafactor import Adafactor
from .adahessian import Adahessian
from .adamp import AdamP
from .lookahead import Lookahead
from .nadam import Nadam
from .novograd import NovoGrad
from .nvnovograd import NvNovoGrad
from .radam import RAdam
from .rmsprop_tf import RMSpropTF
from .sgdp import SGDP
from apex.optimizers import FusedNovoGrad, FusedAdam, FusedLAMB, FusedSGD | 12,364 | """ Optimizer Factory w/ Custom Weight Decay
Hacked together by / Copyright 2020 Ross Wightman
"""
try:
has_apex = True
except ImportError:
has_apex = False
def add_weight_decay(model, image_encoder,text_encoder, weight_decay=1e-5, skip_list=()):
decay = []
no_decay = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue # frozen weights
if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
no_decay.append(param)
else:
decay.append(param)
for name, param in image_encoder.named_parameters():
if not param.requires_grad:
continue # frozen weights
if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
no_decay.append(param)
else:
decay.append(param)
for name, param in text_encoder.named_parameters():
if not param.requires_grad:
continue # frozen weights
if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
no_decay.append(param)
else:
decay.append(param)
return [
{'params': no_decay, 'weight_decay': 0.},
{'params': decay, 'weight_decay': weight_decay}]
def create_optimizer(args, model, image_encoder,text_encoder, filter_bias_and_bn=True):
opt_lower = args.opt.lower()
weight_decay = args.weight_decay
if weight_decay and filter_bias_and_bn:
skip = {}
if hasattr(model, 'no_weight_decay'):
skip = model.no_weight_decay()
parameters = add_weight_decay(model,image_encoder,text_encoder, weight_decay, skip)
weight_decay = 0.
else:
parameters = [filter(lambda p: p.requires_grad, model.parameters()),filter(lambda p: p.requires_grad, image_encoder.parameters()),filter(lambda p: p.requires_grad, text_encoder.parameters())]
#model.parameters()
# print(parameters)
if 'fused' in opt_lower:
assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
opt_args = dict(lr=args.lr, weight_decay=weight_decay)
if hasattr(args, 'opt_eps') and args.opt_eps is not None:
opt_args['eps'] = args.opt_eps
if hasattr(args, 'opt_betas') and args.opt_betas is not None:
opt_args['betas'] = args.opt_betas
if hasattr(args, 'opt_args') and args.opt_args is not None:
opt_args.update(args.opt_args)
opt_split = opt_lower.split('_')
opt_lower = opt_split[-1]
if opt_lower == 'sgd' or opt_lower == 'nesterov':
opt_args.pop('eps', None)
optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
elif opt_lower == 'momentum':
opt_args.pop('eps', None)
optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
elif opt_lower == 'adam':
optimizer = optim.Adam(parameters, **opt_args)
elif opt_lower == 'adamw':
optimizer = optim.AdamW(parameters, **opt_args)
elif opt_lower == 'nadam':
optimizer = Nadam(parameters, **opt_args)
elif opt_lower == 'radam':
| """ Optimizer Factory w/ Custom Weight Decay
Hacked together by / Copyright 2020 Ross Wightman
"""
try:
has_apex = True
except ImportError:
has_apex = False
def add_weight_decay(model, image_encoder,text_encoder, weight_decay=1e-5, skip_list=()):
decay = []
no_decay = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue # frozen weights
if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
no_decay.append(param)
else:
decay.append(param)
for name, param in image_encoder.named_parameters():
if not param.requires_grad:
continue # frozen weights
if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
no_decay.append(param)
else:
decay.append(param)
for name, param in text_encoder.named_parameters():
if not param.requires_grad:
continue # frozen weights
if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
no_decay.append(param)
else:
decay.append(param)
return [
{'params': no_decay, 'weight_decay': 0.},
{'params': decay, 'weight_decay': weight_decay}]
def create_optimizer(args, model, image_encoder,text_encoder, filter_bias_and_bn=True):
opt_lower = args.opt.lower()
weight_decay = args.weight_decay
if weight_decay and filter_bias_and_bn:
skip = {}
if hasattr(model, 'no_weight_decay'):
skip = model.no_weight_decay()
parameters = add_weight_decay(model,image_encoder,text_encoder, weight_decay, skip)
weight_decay = 0.
else:
parameters = [filter(lambda p: p.requires_grad, model.parameters()),filter(lambda p: p.requires_grad, image_encoder.parameters()),filter(lambda p: p.requires_grad, text_encoder.parameters())]
#model.parameters()
# print(parameters)
if 'fused' in opt_lower:
assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
opt_args = dict(lr=args.lr, weight_decay=weight_decay)
if hasattr(args, 'opt_eps') and args.opt_eps is not None:
opt_args['eps'] = args.opt_eps
if hasattr(args, 'opt_betas') and args.opt_betas is not None:
opt_args['betas'] = args.opt_betas
if hasattr(args, 'opt_args') and args.opt_args is not None:
opt_args.update(args.opt_args)
opt_split = opt_lower.split('_')
opt_lower = opt_split[-1]
if opt_lower == 'sgd' or opt_lower == 'nesterov':
opt_args.pop('eps', None)
optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
elif opt_lower == 'momentum':
opt_args.pop('eps', None)
optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
elif opt_lower == 'adam':
optimizer = optim.Adam(parameters, **opt_args)
elif opt_lower == 'adamw':
optimizer = optim.AdamW(parameters, **opt_args)
elif opt_lower == 'nadam':
optimizer = Nadam(parameters, **opt_args)
elif opt_lower == 'radam': | optimizer = RAdam(parameters, **opt_args) | 7 | 2023-10-30 00:24:16+00:00 | 16k |
YichenZW/Coh-MGT-Detection | run_detector.py | [
{
"identifier": "glue_compute_metrics",
"path": "util.py",
"snippet": "def glue_compute_metrics(task_name, preds, labels):\n assert len(preds) == len(labels)\n if task_name == \"cola\":\n return {\"mcc\": matthews_corrcoef(labels, preds)}\n elif task_name == \"sst-2\":\n return {\... | import os
import torch
import argparse
import logging
import random
import wandb
import numpy as np
import ray
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from torch.optim import AdamW
from transformers import (
set_seed,
AutoTokenizer,
AutoConfig,
AutoModel,
AutoModelForSequenceClassification,
get_linear_schedule_with_warmup,
)
from functools import partial
from util import glue_compute_metrics as compute_metrics
from util import (
glue_convert_examples_to_features as convert_examples_to_features,
)
from util import glue_output_modes as output_modes
from util import glue_processors as processors
from modeling_roberta import (
RobertaForGraphBasedSequenceClassification,
RobertaForGraphBasedSequenceClassification_CL,
RobertaForGraphBasedSequenceClassification_MBCL,
EncoderForMBCL,
RobertaForGraphBasedSequenceClassification_RFCL,
)
from ray import tune
from ray.tune import CLIReporter
from ray.tune.schedulers import ASHAScheduler
from apex import amp | 11,050 | )
torch.save(
scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")
)
logger.info("Saving optimizer and scheduler states to %s", output_dir)
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
return_res = {
"max_acc": max_acc,
"max_acc_f1": max_acc_f1,
"max_f1": max_f1,
"max_f1_acc": max_f1_acc,
}
if args.do_ray:
tune.report(
accuracy=max_acc, max_acc_f1=max_acc_f1, f1=max_f1, max_f1_acc=max_f1_acc
)
return global_step, tr_loss / global_step, return_res, output_dir
def evaluate(args, model, tokenizer, checkpoint=None, prefix="", mode="dev"):
eval_task_names = (args.task_name,)
eval_outputs_dirs = (args.output_dir,)
results = {}
for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
eval_dataset = load_and_cache_examples(
args, eval_task, tokenizer, evaluate=True, mode=mode
)
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly.
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(
eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size
)
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Evaluation
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
preds, out_label_ids = None, None
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"labels": batch[3],
"nodes_index_mask": batch[4],
"adj_metric": batch[5],
"node_mask": batch[6],
"sen2node": batch[7],
"sentence_mask": batch[8],
"sentence_length": batch[9],
}
if args.model_type != "distilbert":
inputs["token_type_ids"] = (
batch[2]
if args.model_type in ["bert", "xlnet", "albert"]
else None
) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
outputs, _ = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(
out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0
)
probs = preds
eval_loss = eval_loss / nb_eval_steps
if args.output_mode == "classification":
preds = np.argmax(preds, axis=1)
elif args.output_mode == "regression":
preds = np.squeeze(preds)
result = compute_metrics(eval_task, preds, out_label_ids)
results.update(result)
output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
wandb.log(
{
"eval/acc": result["acc"],
"eval/f1": result["f1"],
"eval/acc_and_f1": result["acc_and_f1"],
}
)
return results
def load_and_cache_examples(
args, task, tokenizer, evaluate=False, mode="train", dataset_name="", rel=""
):
if args.local_rank not in [-1, 0] and not evaluate:
torch.distributed.barrier()
| # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Based on code from the above authors, modifications made by Xi'an Jiaotong University.
#
# 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.getLogger(__name__)
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def number_h(num):
for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]:
if abs(num) < 1000.0:
return "%3.1f%s" % (num, unit)
num /= 1000.0
return "%.1f%s" % (num, "Yi")
def generate_shaped_nodes_mask(nodes, max_seq_length, max_nodes_num):
nodes_mask = np.zeros(shape=(max_nodes_num, max_seq_length))
nodes_num = min(len(nodes), max_nodes_num)
for i in range(nodes_num):
span = nodes[i]
if span[0] != -1:
if span[0] < max_seq_length - 1:
end_pos = (
span[1] if span[1] < max_seq_length - 1 else max_seq_length - 1
)
nodes_mask[i, span[0] + 1 : end_pos + 1] = 1
else:
continue
return nodes_mask, nodes_num
def generate_shaped_edge_mask(adj_metric, nodes_num, max_nodes_num, relation_n):
if nodes_num != 0:
if relation_n != 0:
new_adj_metric = np.zeros(shape=(relation_n, max_nodes_num, max_nodes_num))
for i in range(relation_n):
new_adj_metric[i][:nodes_num, :nodes_num] = adj_metric[i][
:nodes_num, :nodes_num
]
else:
new_adj_metric = np.zeros(shape=(max_nodes_num, max_nodes_num))
new_adj_metric[:nodes_num, :nodes_num] = adj_metric[:nodes_num, :nodes_num]
return new_adj_metric
def train(args, train_dataset, model, tokenizer):
"""Train the model"""
total_params = sum(p.numel() for p in model.parameters())
total_trainable_params = sum(
p.numel() for p in model.parameters() if p.requires_grad
)
print("Total Params:", number_h(total_params))
print("Total Trainable Params:", number_h(total_trainable_params))
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = (
RandomSampler(train_dataset)
if args.local_rank == -1
else DistributedSampler(train_dataset)
)
train_dataloader = DataLoader(
train_dataset, sampler=train_sampler, batch_size=args.train_batch_size
)
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = (
args.max_steps
// (len(train_dataloader) // args.gradient_accumulation_steps)
+ 1
)
else:
t_total = (
len(train_dataloader)
// args.gradient_accumulation_steps
* args.num_train_epochs
)
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [
p
for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
"weight_decay": args.weight_decay,
},
{
"params": [
p
for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
"weight_decay": 0.01,
},
]
optimizer = AdamW(
optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon
)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
)
# Check if saved optimizer or scheduler states exist
if os.path.isfile(
os.path.join(args.model_name_or_path, "optimizer.pt")
) and os.path.isfile(os.path.join(args.model_name_or_path, "scheduler.pt")):
optimizer.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))
)
scheduler.load_state_dict(
torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))
)
if args.fp16:
try:
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(
model, optimizer, opt_level=args.fp16_opt_level
)
# Multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True,
)
# Training
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(
" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size
)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size
* args.gradient_accumulation_steps
* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
best_acc, best_f1 = 0.0, 0.0
global_step, epochs_trained, steps_trained_in_current_epoch = 0, 0, 0
# Check if continuing training from a checkpoint
if os.path.exists(args.model_name_or_path):
# set global_step to gobal_step of last saved checkpoint from model path
global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
epochs_trained = global_step // (
len(train_dataloader) // args.gradient_accumulation_steps
)
steps_trained_in_current_epoch = global_step % (
len(train_dataloader) // args.gradient_accumulation_steps
)
logger.info(
" Continuing training from checkpoint, will skip to saved global_step"
)
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", global_step)
logger.info(
" Will skip the first %d steps in the first epoch",
steps_trained_in_current_epoch,
)
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(
epochs_trained,
int(args.num_train_epochs),
desc="Epoch",
disable=args.local_rank not in [-1, 0],
)
set_seed(args)
max_acc, max_acc_f1, max_f1, max_f1_acc = 0.0, 0.0, 0.0, 0.0
for idx, _ in enumerate(train_iterator):
tr_loss = 0.0
epoch_iterator = tqdm(
train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]
)
for step, batch in enumerate(epoch_iterator):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"labels": batch[3],
"nodes_index_mask": batch[4],
"adj_metric": batch[5],
"node_mask": batch[6],
"sen2node": batch[7],
"sentence_mask": batch[8],
"sentence_length": batch[9],
"batch_id": batch[10],
}
if args.model_type != "distilbert":
inputs["token_type_ids"] = (
batch[2] if args.model_type in ["bert", "xlnet", "albert"] else None
)
outputs, _ = model(**inputs)
loss = outputs[0]
wandb.log({"train/loss": loss})
if args.n_gpu > 1:
loss = loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
epoch_iterator.set_description(
"loss {}".format(
round(tr_loss * args.gradient_accumulation_steps / (step + 1), 4)
)
)
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm
)
else:
torch.nn.utils.clip_grad_norm_(
model.parameters(), args.max_grad_norm
)
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
if (
args.local_rank in [-1, 0]
and args.logging_steps > 0
and global_step % args.logging_steps == 0
):
logs = {}
if (
args.local_rank == -1 and args.evaluate_during_training
):
results = evaluate(args, model, tokenizer)
for key, value in results.items():
eval_key = "eval_{}".format(key)
logs[eval_key] = value
loss_scalar = (tr_loss - logging_loss) / args.logging_steps
learning_rate_scalar = scheduler.get_lr()[0]
logs["learning_rate"] = learning_rate_scalar
logs["loss"] = loss_scalar
logging_loss = tr_loss
wandb.log({"eval/loss": loss_scalar})
if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
break
if args.local_rank in [-1, 0] and args.save_steps > 0 and args.do_eval:
results = evaluate(args, model, tokenizer, checkpoint=str(idx))
logger.info("the results is {}".format(results))
if results["acc"] > max_acc:
max_acc = results["acc"]
max_acc_f1 = results["f1"]
if results["f1"] > max_f1:
max_f1 = results["f1"]
max_f1_acc = results["acc"]
if results["f1"] > best_f1:
best_f1 = results["f1"]
output_dir = os.path.join(
args.output_dir,
"seed-{}".format(args.seed),
"checkpoint-{}-{}".format(idx, best_f1),
)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = (
model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(
args, os.path.join(output_dir, "training_{}.bin".format(idx))
)
logger.info("Saving model checkpoint to %s", output_dir)
torch.save(
optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")
)
torch.save(
scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")
)
logger.info("Saving optimizer and scheduler states to %s", output_dir)
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
return_res = {
"max_acc": max_acc,
"max_acc_f1": max_acc_f1,
"max_f1": max_f1,
"max_f1_acc": max_f1_acc,
}
if args.do_ray:
tune.report(
accuracy=max_acc, max_acc_f1=max_acc_f1, f1=max_f1, max_f1_acc=max_f1_acc
)
return global_step, tr_loss / global_step, return_res, output_dir
def mb_train(args, train_dataset, encoder_q, encoder_k, dataloader, tokenizer):
"""Train the model"""
global memory_queue
encoder_q.train()
total_params = sum(p.numel() for p in encoder_q.parameters())
total_trainable_params = sum(
p.numel() for p in encoder_q.parameters() if p.requires_grad
)
print("Encoder Params:", number_h(total_params))
print("Encoder Trainable Params:", number_h(total_trainable_params))
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = (
RandomSampler(train_dataset)
if args.local_rank == -1
else DistributedSampler(train_dataset)
)
train_dataloader = DataLoader(
train_dataset, sampler=train_sampler, batch_size=args.train_batch_size
)
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = (
args.max_steps
// (len(train_dataloader) // args.gradient_accumulation_steps)
+ 1
)
else:
t_total = (
len(train_dataloader)
// args.gradient_accumulation_steps
* args.num_train_epochs
)
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [
p
for n, p in encoder_q.named_parameters()
if not any(nd in n for nd in no_decay)
],
"weight_decay": args.weight_decay,
},
{
"params": [
p
for n, p in encoder_q.named_parameters()
if any(nd in n for nd in no_decay)
],
"weight_decay": 0.01,
},
]
optimizer = AdamW(
optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon
)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
)
# Training
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(
" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size
)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size
* args.gradient_accumulation_steps
* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
best_f1 = 0.0
global_step, epochs_trained, steps_trained_in_current_epoch = 0, 0, 0
tr_loss, logging_loss = 0.0, 0.0
encoder_q.zero_grad()
train_iterator = trange(
epochs_trained,
int(args.num_train_epochs),
desc="Epoch",
disable=args.local_rank not in [-1, 0],
)
set_seed(args)
max_acc, max_acc_f1, max_f1, max_f1_acc = 0.0, 0.0, 0.0, 0.0
for idx, _ in enumerate(train_iterator):
tr_loss = 0.0
epoch_iterator = tqdm(
train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]
)
for step, batch in enumerate(epoch_iterator):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
encoder_q.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"labels": batch[3],
"nodes_index_mask": batch[4],
"adj_metric": batch[5],
"node_mask": batch[6],
"sen2node": batch[7],
"sentence_mask": batch[8],
"sentence_length": batch[9],
"batch_id": batch[10],
}
if args.model_type != "distilbert":
inputs["token_type_ids"] = (
batch[2] if args.model_type in ["bert", "xlnet", "albert"] else None
) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
q_outputs, q_rep = encoder_q(**inputs)
# Model outputs are always tuple in transformers (see doc).
if args.n_gpu > 1:
loss = loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
epoch_iterator.set_description(
"loss {}".format(
round(tr_loss * args.gradient_accumulation_steps / (step + 1), 4)
)
)
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm
)
else:
torch.nn.utils.clip_grad_norm_(
encoder_q.parameters(), args.max_grad_norm
)
optimizer.step()
scheduler.step()
encoder_q.zero_grad()
global_step += 1
if (
args.local_rank in [-1, 0]
and args.logging_steps > 0
and global_step % args.logging_steps == 0
):
logs = {}
if (
args.local_rank == -1 and args.evaluate_during_training
): # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, encoder_q, tokenizer)
for key, value in results.items():
eval_key = "eval_{}".format(key)
logs[eval_key] = value
loss_scalar = (tr_loss - logging_loss) / args.logging_steps
learning_rate_scalar = scheduler.get_lr()[0]
logs["learning_rate"] = learning_rate_scalar
logs["loss"] = loss_scalar
logging_loss = tr_loss
wandb.log({"train/loss": loss_scalar})
if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
break
if args.local_rank in [-1, 0] and args.save_steps > 0 and args.do_eval:
results = evaluate(args, encoder_q, tokenizer, checkpoint=str(idx))
logger.info("the results is {}".format(results))
if results["f1"] > max_f1:
max_f1 = results["f1"]
max_f1_acc = results["acc"]
if results["acc"] > max_acc:
max_acc = results["acc"]
max_acc_f1 = results["f1"]
if results["f1"] > best_f1:
best_f1 = results["f1"]
output_dir = os.path.join(
args.output_dir,
"seed-{}".format(args.seed),
"checkpoint-{}-{}".format(idx, best_f1),
)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = (
encoder_q.module if hasattr(encoder_q, "module") else encoder_q
) # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(
args, os.path.join(output_dir, "training_{}.bin".format(idx))
)
logger.info("Saving model checkpoint to %s", output_dir)
torch.save(
optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")
)
torch.save(
scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")
)
logger.info("Saving optimizer and scheduler states to %s", output_dir)
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
return_res = {
"max_acc": max_acc,
"max_acc_f1": max_acc_f1,
"max_f1": max_f1,
"max_f1_acc": max_f1_acc,
}
if args.do_ray:
tune.report(
accuracy=max_acc, max_acc_f1=max_acc_f1, f1=max_f1, max_f1_acc=max_f1_acc
)
return global_step, tr_loss / global_step, return_res, output_dir
def evaluate(args, model, tokenizer, checkpoint=None, prefix="", mode="dev"):
eval_task_names = (args.task_name,)
eval_outputs_dirs = (args.output_dir,)
results = {}
for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
eval_dataset = load_and_cache_examples(
args, eval_task, tokenizer, evaluate=True, mode=mode
)
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly.
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(
eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size
)
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Evaluation
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
preds, out_label_ids = None, None
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"labels": batch[3],
"nodes_index_mask": batch[4],
"adj_metric": batch[5],
"node_mask": batch[6],
"sen2node": batch[7],
"sentence_mask": batch[8],
"sentence_length": batch[9],
}
if args.model_type != "distilbert":
inputs["token_type_ids"] = (
batch[2]
if args.model_type in ["bert", "xlnet", "albert"]
else None
) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
outputs, _ = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(
out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0
)
probs = preds
eval_loss = eval_loss / nb_eval_steps
if args.output_mode == "classification":
preds = np.argmax(preds, axis=1)
elif args.output_mode == "regression":
preds = np.squeeze(preds)
result = compute_metrics(eval_task, preds, out_label_ids)
results.update(result)
output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
wandb.log(
{
"eval/acc": result["acc"],
"eval/f1": result["f1"],
"eval/acc_and_f1": result["acc_and_f1"],
}
)
return results
def load_and_cache_examples(
args, task, tokenizer, evaluate=False, mode="train", dataset_name="", rel=""
):
if args.local_rank not in [-1, 0] and not evaluate:
torch.distributed.barrier()
| processor = processors[task]() | 0 | 2023-10-24 14:03:11+00:00 | 16k |
deforum-studio/deforum | src/deforum/models/depth_models/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py | [
{
"identifier": "DepthModel",
"path": "src/deforum/models/depth_models/zoedepth/models/depth_model.py",
"snippet": "class DepthModel(nn.Module):\n def __init__(self):\n super().__init__()\n self.device = 'cuda'\n \n def to(self, device) -> nn.Module:\n self.device = device\... | import itertools
import torch
import torch.nn as nn
from ..depth_model import DepthModel
from ..base_models.midas import MidasCore
from ..layers.attractor import AttractorLayer, AttractorLayerUnnormed
from ..layers.dist_layers import ConditionalLogBinomial
from ..layers.localbins_layers import (Projector, SeedBinRegressor,
SeedBinRegressorUnnormed)
from ..layers.patch_transformer import PatchTransformerEncoder
from ..model_io import load_state_from_resource | 11,174 | b_prev = (seed_b_centers - min_depth) / (max_depth - min_depth)
else:
b_prev = seed_b_centers
prev_b_embedding = self.seed_projector(x)
attractors = self.attractors[bin_conf_name]
for projector, attractor, x in zip(self.projectors, attractors, x_blocks):
b_embedding = projector(x)
b, b_centers = attractor(
b_embedding, b_prev, prev_b_embedding, interpolate=True)
b_prev = b
prev_b_embedding = b_embedding
last = outconv_activation
b_centers = nn.functional.interpolate(
b_centers, last.shape[-2:], mode='bilinear', align_corners=True)
b_embedding = nn.functional.interpolate(
b_embedding, last.shape[-2:], mode='bilinear', align_corners=True)
clb = self.conditional_log_binomial[bin_conf_name]
x = clb(last, b_embedding)
# Now depth value is Sum px * cx , where cx are bin_centers from the last bin tensor
# print(x.shape, b_centers.shape)
# b_centers = nn.functional.interpolate(b_centers, x.shape[-2:], mode='bilinear', align_corners=True)
out = torch.sum(x * b_centers, dim=1, keepdim=True)
output = dict(domain_logits=domain_logits, metric_depth=out)
if return_final_centers or return_probs:
output['bin_centers'] = b_centers
if return_probs:
output['probs'] = x
return output
def get_lr_params(self, lr):
"""
Learning rate configuration for different layers of the model
Args:
lr (float) : Base learning rate
Returns:
list : list of parameters to optimize and their learning rates, in the format required by torch optimizers.
"""
param_conf = []
if self.train_midas:
def get_rel_pos_params():
for name, p in self.core.core.pretrained.named_parameters():
if "relative_position" in name:
yield p
def get_enc_params_except_rel_pos():
for name, p in self.core.core.pretrained.named_parameters():
if "relative_position" not in name:
yield p
encoder_params = get_enc_params_except_rel_pos()
rel_pos_params = get_rel_pos_params()
midas_params = self.core.core.scratch.parameters()
midas_lr_factor = self.midas_lr_factor if self.is_midas_pretrained else 1.0
param_conf.extend([
{'params': encoder_params, 'lr': lr / self.encoder_lr_factor},
{'params': rel_pos_params, 'lr': lr / self.pos_enc_lr_factor},
{'params': midas_params, 'lr': lr / midas_lr_factor}
])
remaining_modules = []
for name, child in self.named_children():
if name != 'core':
remaining_modules.append(child)
remaining_params = itertools.chain(
*[child.parameters() for child in remaining_modules])
param_conf.append({'params': remaining_params, 'lr': lr})
return param_conf
def get_conf_parameters(self, conf_name):
"""
Returns parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
"""
params = []
for name, child in self.named_children():
if isinstance(child, nn.ModuleDict):
for bin_conf_name, module in child.items():
if bin_conf_name == conf_name:
params += list(module.parameters())
return params
def freeze_conf(self, conf_name):
"""
Freezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
"""
for p in self.get_conf_parameters(conf_name):
p.requires_grad = False
def unfreeze_conf(self, conf_name):
"""
Unfreezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
"""
for p in self.get_conf_parameters(conf_name):
p.requires_grad = True
def freeze_all_confs(self):
"""
Freezes all the parameters of all the ModuleDicts children
"""
for name, child in self.named_children():
if isinstance(child, nn.ModuleDict):
for bin_conf_name, module in child.items():
for p in module.parameters():
p.requires_grad = False
@staticmethod
def build(midas_model_type="DPT_BEiT_L_384", pretrained_resource=None, use_pretrained_midas=False,
train_midas=False, freeze_midas_bn=True, **kwargs):
core = MidasCore.build(midas_model_type=midas_model_type, use_pretrained_midas=use_pretrained_midas,
train_midas=train_midas, fetch_features=True, freeze_bn=freeze_midas_bn, **kwargs)
model = ZoeDepthNK(core, **kwargs)
if pretrained_resource:
assert isinstance(pretrained_resource, str), "pretrained_resource must be a string"
| # MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# File author: Shariq Farooq Bhat
class ZoeDepthNK(DepthModel):
def __init__(self, core, bin_conf, bin_centers_type="softplus", bin_embedding_dim=128,
n_attractors=[16, 8, 4, 1], attractor_alpha=300, attractor_gamma=2, attractor_kind='sum',
attractor_type='exp',
min_temp=5, max_temp=50,
memory_efficient=False, train_midas=True,
is_midas_pretrained=True, midas_lr_factor=1, encoder_lr_factor=10, pos_enc_lr_factor=10,
inverse_midas=False, **kwargs):
"""ZoeDepthNK model. This is the version of ZoeDepth that has two metric heads and uses a learned router to route to experts.
Args:
core (models.base_models.midas.MidasCore): The base midas model that is used for extraction of "relative" features
bin_conf (List[dict]): A list of dictionaries that contain the bin configuration for each metric head. Each dictionary should contain the following keys:
"name" (str, typically same as the dataset name), "n_bins" (int), "min_depth" (float), "max_depth" (float)
The length of this list determines the number of metric heads.
bin_centers_type (str, optional): "normed" or "softplus". Activation type used for bin centers. For "normed" bin centers, linear normalization trick is applied. This results in bounded bin centers.
For "softplus", softplus activation is used and thus are unbounded. Defaults to "normed".
bin_embedding_dim (int, optional): bin embedding dimension. Defaults to 128.
n_attractors (List[int], optional): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1].
attractor_alpha (int, optional): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 300.
attractor_gamma (int, optional): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2.
attractor_kind (str, optional): Attraction aggregation "sum" or "mean". Defaults to 'sum'.
attractor_type (str, optional): Type of attractor to use; "inv" (Inverse attractor) or "exp" (Exponential attractor). Defaults to 'exp'.
min_temp (int, optional): Lower bound for temperature of output probability distribution. Defaults to 5.
max_temp (int, optional): Upper bound for temperature of output probability distribution. Defaults to 50.
memory_efficient (bool, optional): Whether to use memory efficient version of attractor layers. Memory efficient version is slower but is recommended incase of multiple metric heads in order save GPU memory. Defaults to False.
train_midas (bool, optional): Whether to train "core", the base midas model. Defaults to True.
is_midas_pretrained (bool, optional): Is "core" pretrained? Defaults to True.
midas_lr_factor (int, optional): Learning rate reduction factor for base midas model except its encoder and positional encodings. Defaults to 10.
encoder_lr_factor (int, optional): Learning rate reduction factor for the encoder in midas model. Defaults to 10.
pos_enc_lr_factor (int, optional): Learning rate reduction factor for positional encodings in the base midas model. Defaults to 10.
"""
super().__init__()
self.core = core
self.bin_conf = bin_conf
self.min_temp = min_temp
self.max_temp = max_temp
self.memory_efficient = memory_efficient
self.train_midas = train_midas
self.is_midas_pretrained = is_midas_pretrained
self.midas_lr_factor = midas_lr_factor
self.encoder_lr_factor = encoder_lr_factor
self.pos_enc_lr_factor = pos_enc_lr_factor
self.inverse_midas = inverse_midas
N_MIDAS_OUT = 32
btlnck_features = self.core.output_channels[0]
num_out_features = self.core.output_channels[1:]
# self.scales = [16, 8, 4, 2] # spatial scale factors
self.conv2 = nn.Conv2d(
btlnck_features, btlnck_features, kernel_size=1, stride=1, padding=0)
# Transformer classifier on the bottleneck
self.patch_transformer = PatchTransformerEncoder(
btlnck_features, 1, 128, use_class_token=True)
self.mlp_classifier = nn.Sequential(
nn.Linear(128, 128),
nn.ReLU(),
nn.Linear(128, 2)
)
if bin_centers_type == "normed":
SeedBinRegressorLayer = SeedBinRegressor
Attractor = AttractorLayer
elif bin_centers_type == "softplus":
SeedBinRegressorLayer = SeedBinRegressorUnnormed
Attractor = AttractorLayerUnnormed
elif bin_centers_type == "hybrid1":
SeedBinRegressorLayer = SeedBinRegressor
Attractor = AttractorLayerUnnormed
elif bin_centers_type == "hybrid2":
SeedBinRegressorLayer = SeedBinRegressorUnnormed
Attractor = AttractorLayer
else:
raise ValueError(
"bin_centers_type should be one of 'normed', 'softplus', 'hybrid1', 'hybrid2'")
self.bin_centers_type = bin_centers_type
# We have bins for each bin conf.
# Create a map (ModuleDict) of 'name' -> seed_bin_regressor
self.seed_bin_regressors = nn.ModuleDict(
{conf['name']: SeedBinRegressorLayer(btlnck_features, conf["n_bins"], mlp_dim=bin_embedding_dim // 2,
min_depth=conf["min_depth"], max_depth=conf["max_depth"])
for conf in bin_conf}
)
self.seed_projector = Projector(
btlnck_features, bin_embedding_dim, mlp_dim=bin_embedding_dim // 2)
self.projectors = nn.ModuleList([
Projector(num_out, bin_embedding_dim, mlp_dim=bin_embedding_dim // 2)
for num_out in num_out_features
])
# Create a map (ModuleDict) of 'name' -> attractors (ModuleList)
self.attractors = nn.ModuleDict(
{conf['name']: nn.ModuleList([
Attractor(bin_embedding_dim, n_attractors[i],
mlp_dim=bin_embedding_dim, alpha=attractor_alpha,
gamma=attractor_gamma, kind=attractor_kind,
attractor_type=attractor_type, memory_efficient=memory_efficient,
min_depth=conf["min_depth"], max_depth=conf["max_depth"])
for i in range(len(n_attractors))
])
for conf in bin_conf}
)
last_in = N_MIDAS_OUT
# conditional log binomial for each bin conf
self.conditional_log_binomial = nn.ModuleDict(
{conf['name']: ConditionalLogBinomial(last_in, bin_embedding_dim, conf['n_bins'], bottleneck_factor=4,
min_temp=self.min_temp, max_temp=self.max_temp)
for conf in bin_conf}
)
def forward(self, x, return_final_centers=False, denorm=False, return_probs=False, **kwargs):
"""
Args:
x (torch.Tensor): Input image tensor of shape (B, C, H, W). Assumes all images are from the same domain.
return_final_centers (bool, optional): Whether to return the final centers of the attractors. Defaults to False.
denorm (bool, optional): Whether to denormalize the input image. Defaults to False.
return_probs (bool, optional): Whether to return the probabilities of the bins. Defaults to False.
Returns:
dict: Dictionary of outputs with keys:
- "rel_depth": Relative depth map of shape (B, 1, H, W)
- "metric_depth": Metric depth map of shape (B, 1, H, W)
- "domain_logits": Domain logits of shape (B, 2)
- "bin_centers": Bin centers of shape (B, N, H, W). Present only if return_final_centers is True
- "probs": Bin probabilities of shape (B, N, H, W). Present only if return_probs is True
"""
b, c, h, w = x.shape
self.orig_input_width = w
self.orig_input_height = h
rel_depth, out = self.core(x, denorm=denorm, return_rel_depth=True)
outconv_activation = out[0]
btlnck = out[1]
x_blocks = out[2:]
x_d0 = self.conv2(btlnck)
x = x_d0
# Predict which path to take
embedding = self.patch_transformer(x)[0] # N, E
domain_logits = self.mlp_classifier(embedding) # N, 2
domain_vote = torch.softmax(domain_logits.sum(
dim=0, keepdim=True), dim=-1) # 1, 2
# Get the path
bin_conf_name = ["nyu", "kitti"][torch.argmax(
domain_vote, dim=-1).squeeze().item()]
try:
conf = [c for c in self.bin_conf if c["name"] == bin_conf_name][0]
except IndexError:
raise ValueError(
f"bin_conf_name {bin_conf_name} not found in bin_confs")
min_depth = conf['min_depth']
max_depth = conf['max_depth']
seed_bin_regressor = self.seed_bin_regressors[bin_conf_name]
_, seed_b_centers = seed_bin_regressor(x)
if self.bin_centers_type == 'normed' or self.bin_centers_type == 'hybrid2':
b_prev = (seed_b_centers - min_depth) / (max_depth - min_depth)
else:
b_prev = seed_b_centers
prev_b_embedding = self.seed_projector(x)
attractors = self.attractors[bin_conf_name]
for projector, attractor, x in zip(self.projectors, attractors, x_blocks):
b_embedding = projector(x)
b, b_centers = attractor(
b_embedding, b_prev, prev_b_embedding, interpolate=True)
b_prev = b
prev_b_embedding = b_embedding
last = outconv_activation
b_centers = nn.functional.interpolate(
b_centers, last.shape[-2:], mode='bilinear', align_corners=True)
b_embedding = nn.functional.interpolate(
b_embedding, last.shape[-2:], mode='bilinear', align_corners=True)
clb = self.conditional_log_binomial[bin_conf_name]
x = clb(last, b_embedding)
# Now depth value is Sum px * cx , where cx are bin_centers from the last bin tensor
# print(x.shape, b_centers.shape)
# b_centers = nn.functional.interpolate(b_centers, x.shape[-2:], mode='bilinear', align_corners=True)
out = torch.sum(x * b_centers, dim=1, keepdim=True)
output = dict(domain_logits=domain_logits, metric_depth=out)
if return_final_centers or return_probs:
output['bin_centers'] = b_centers
if return_probs:
output['probs'] = x
return output
def get_lr_params(self, lr):
"""
Learning rate configuration for different layers of the model
Args:
lr (float) : Base learning rate
Returns:
list : list of parameters to optimize and their learning rates, in the format required by torch optimizers.
"""
param_conf = []
if self.train_midas:
def get_rel_pos_params():
for name, p in self.core.core.pretrained.named_parameters():
if "relative_position" in name:
yield p
def get_enc_params_except_rel_pos():
for name, p in self.core.core.pretrained.named_parameters():
if "relative_position" not in name:
yield p
encoder_params = get_enc_params_except_rel_pos()
rel_pos_params = get_rel_pos_params()
midas_params = self.core.core.scratch.parameters()
midas_lr_factor = self.midas_lr_factor if self.is_midas_pretrained else 1.0
param_conf.extend([
{'params': encoder_params, 'lr': lr / self.encoder_lr_factor},
{'params': rel_pos_params, 'lr': lr / self.pos_enc_lr_factor},
{'params': midas_params, 'lr': lr / midas_lr_factor}
])
remaining_modules = []
for name, child in self.named_children():
if name != 'core':
remaining_modules.append(child)
remaining_params = itertools.chain(
*[child.parameters() for child in remaining_modules])
param_conf.append({'params': remaining_params, 'lr': lr})
return param_conf
def get_conf_parameters(self, conf_name):
"""
Returns parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
"""
params = []
for name, child in self.named_children():
if isinstance(child, nn.ModuleDict):
for bin_conf_name, module in child.items():
if bin_conf_name == conf_name:
params += list(module.parameters())
return params
def freeze_conf(self, conf_name):
"""
Freezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
"""
for p in self.get_conf_parameters(conf_name):
p.requires_grad = False
def unfreeze_conf(self, conf_name):
"""
Unfreezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
"""
for p in self.get_conf_parameters(conf_name):
p.requires_grad = True
def freeze_all_confs(self):
"""
Freezes all the parameters of all the ModuleDicts children
"""
for name, child in self.named_children():
if isinstance(child, nn.ModuleDict):
for bin_conf_name, module in child.items():
for p in module.parameters():
p.requires_grad = False
@staticmethod
def build(midas_model_type="DPT_BEiT_L_384", pretrained_resource=None, use_pretrained_midas=False,
train_midas=False, freeze_midas_bn=True, **kwargs):
core = MidasCore.build(midas_model_type=midas_model_type, use_pretrained_midas=use_pretrained_midas,
train_midas=train_midas, fetch_features=True, freeze_bn=freeze_midas_bn, **kwargs)
model = ZoeDepthNK(core, **kwargs)
if pretrained_resource:
assert isinstance(pretrained_resource, str), "pretrained_resource must be a string" | model = load_state_from_resource(model, pretrained_resource) | 9 | 2023-10-28 14:23:27+00:00 | 16k |
samholt/ActiveObservingInContinuous-timeControl | mppi_dataset_collector.py | [
{
"identifier": "dotdict",
"path": "config.py",
"snippet": "class dotdict(dict):\n \"\"\"dot.notation access to dictionary attributes\"\"\"\n\n __getattr__ = dict.get\n __setattr__ = dict.__setitem__\n __delattr__ = dict.__delitem__"
},
{
"identifier": "create_env",
"path": "over... | import logging
import os
import time
import imageio
import numpy as np
import torch
import torch.multiprocessing as multiprocessing
from functools import partial
from tqdm import tqdm
from config import dotdict
from overlay import create_env, setup_logger, start_virtual_display, step_env
from planners.mppi import MPPI
from planners.mppi_active_observing import MPPIActiveObserving
from oracle import pendulum_dynamics_dt
from oracle import cartpole_dynamics_dt
from oracle import acrobot_dynamics_dt
from oracle import cancer_dynamics_dt
from pathlib import Path
from config import get_config, seed_all | 10,832 | env_name,
roll_outs=1000,
time_steps=30,
lambda_=1.0,
sigma=1.0,
dt=0.05,
model_seed=11,
save_video=False,
state_constraint=False,
change_goal=False,
encode_obs_time=False,
model=None,
uniq=None,
log_debug=False,
episodes_per_sampler_task=10,
config={},
iter_=200,
change_goal_flipped_iter_=False,
ts_grid="exp",
intermediate_run=False,
):
config = dotdict(config)
env = create_env(env_name, dt=dt, ts_grid=ts_grid, friction=config.friction)
ACTION_LOW = env.action_space.low[0]
ACTION_HIGH = env.action_space.high[0]
if env_name == "oderl-cancer":
limit_actions_to_only_positive = True
else:
limit_actions_to_only_positive = False
nx = env.get_obs().shape[0]
nu = env.action_space.shape[0]
dtype = torch.float32
gamma = sigma**2
off_diagonal = 0.5 * gamma
mppi_noise_sigma = torch.ones((nu, nu), device=device, dtype=dtype) * off_diagonal + torch.eye(
nu, device=device, dtype=dtype
) * (gamma - off_diagonal)
logger.info(mppi_noise_sigma)
mppi_lambda_ = 1.0
random_action_noise = config.collect_expert_random_action_noise
if model_name == "random":
def dynamics(state, perturbed_action):
pass
elif model_name == "oracle":
oracle_sigma = config.observation_noise
if env_name == "oderl-pendulum":
dynamics_oracle = pendulum_dynamics_dt
elif env_name == "oderl-cartpole":
dynamics_oracle = cartpole_dynamics_dt
elif env_name == "oderl-acrobot":
dynamics_oracle = acrobot_dynamics_dt
elif env_name == "oderl-cancer":
dynamics_oracle = cancer_dynamics_dt
def dynamics(*args, **kwargs):
state_mu = dynamics_oracle(*args, **kwargs)
return state_mu, torch.ones_like(state_mu) * oracle_sigma
dynamics = partial(dynamics, friction=config.friction)
def running_cost(state, action):
if state_constraint:
reward = env.diff_obs_reward_(
state, exp_reward=False, state_constraint=state_constraint
) + env.diff_ac_reward_(action)
elif change_goal:
global change_goal_flipped
reward = env.diff_obs_reward_(
state, exp_reward=False, change_goal=change_goal, change_goal_flipped=change_goal_flipped
) + env.diff_ac_reward_(action)
else:
reward = env.diff_obs_reward_(state, exp_reward=False) + env.diff_ac_reward_(action)
cost = -reward
return cost
if config.planner == "mppi":
mppi_gym = MPPI(
dynamics,
running_cost,
nx,
mppi_noise_sigma,
num_samples=roll_outs,
horizon=time_steps,
device=device,
lambda_=mppi_lambda_,
u_min=torch.tensor(ACTION_LOW),
u_max=torch.tensor(ACTION_HIGH),
u_scale=ACTION_HIGH,
)
elif config.planner == "mppi_active_observing":
mppi_gym = MPPIActiveObserving(
dynamics,
running_cost,
nx,
mppi_noise_sigma,
num_samples=roll_outs,
horizon=time_steps,
device=device,
lambda_=mppi_lambda_,
u_min=torch.tensor(ACTION_LOW),
u_max=torch.tensor(ACTION_HIGH),
u_scale=ACTION_HIGH,
observing_cost=config.observing_cost,
sampling_policy=config.sampling_policy,
observing_var_threshold=config.observing_var_threshold,
limit_actions_to_only_positive=limit_actions_to_only_positive,
dt=dt,
)
if save_video:
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger = logging.getLogger()
def inner_mppi_with_model_collect_data(
seed,
model_name,
env_name,
roll_outs=1000,
time_steps=30,
lambda_=1.0,
sigma=1.0,
dt=0.05,
model_seed=11,
save_video=False,
state_constraint=False,
change_goal=False,
encode_obs_time=False,
model=None,
uniq=None,
log_debug=False,
episodes_per_sampler_task=10,
config={},
iter_=200,
change_goal_flipped_iter_=False,
ts_grid="exp",
intermediate_run=False,
):
config = dotdict(config)
env = create_env(env_name, dt=dt, ts_grid=ts_grid, friction=config.friction)
ACTION_LOW = env.action_space.low[0]
ACTION_HIGH = env.action_space.high[0]
if env_name == "oderl-cancer":
limit_actions_to_only_positive = True
else:
limit_actions_to_only_positive = False
nx = env.get_obs().shape[0]
nu = env.action_space.shape[0]
dtype = torch.float32
gamma = sigma**2
off_diagonal = 0.5 * gamma
mppi_noise_sigma = torch.ones((nu, nu), device=device, dtype=dtype) * off_diagonal + torch.eye(
nu, device=device, dtype=dtype
) * (gamma - off_diagonal)
logger.info(mppi_noise_sigma)
mppi_lambda_ = 1.0
random_action_noise = config.collect_expert_random_action_noise
if model_name == "random":
def dynamics(state, perturbed_action):
pass
elif model_name == "oracle":
oracle_sigma = config.observation_noise
if env_name == "oderl-pendulum":
dynamics_oracle = pendulum_dynamics_dt
elif env_name == "oderl-cartpole":
dynamics_oracle = cartpole_dynamics_dt
elif env_name == "oderl-acrobot":
dynamics_oracle = acrobot_dynamics_dt
elif env_name == "oderl-cancer":
dynamics_oracle = cancer_dynamics_dt
def dynamics(*args, **kwargs):
state_mu = dynamics_oracle(*args, **kwargs)
return state_mu, torch.ones_like(state_mu) * oracle_sigma
dynamics = partial(dynamics, friction=config.friction)
def running_cost(state, action):
if state_constraint:
reward = env.diff_obs_reward_(
state, exp_reward=False, state_constraint=state_constraint
) + env.diff_ac_reward_(action)
elif change_goal:
global change_goal_flipped
reward = env.diff_obs_reward_(
state, exp_reward=False, change_goal=change_goal, change_goal_flipped=change_goal_flipped
) + env.diff_ac_reward_(action)
else:
reward = env.diff_obs_reward_(state, exp_reward=False) + env.diff_ac_reward_(action)
cost = -reward
return cost
if config.planner == "mppi":
mppi_gym = MPPI(
dynamics,
running_cost,
nx,
mppi_noise_sigma,
num_samples=roll_outs,
horizon=time_steps,
device=device,
lambda_=mppi_lambda_,
u_min=torch.tensor(ACTION_LOW),
u_max=torch.tensor(ACTION_HIGH),
u_scale=ACTION_HIGH,
)
elif config.planner == "mppi_active_observing":
mppi_gym = MPPIActiveObserving(
dynamics,
running_cost,
nx,
mppi_noise_sigma,
num_samples=roll_outs,
horizon=time_steps,
device=device,
lambda_=mppi_lambda_,
u_min=torch.tensor(ACTION_LOW),
u_max=torch.tensor(ACTION_HIGH),
u_scale=ACTION_HIGH,
observing_cost=config.observing_cost,
sampling_policy=config.sampling_policy,
observing_var_threshold=config.observing_var_threshold,
limit_actions_to_only_positive=limit_actions_to_only_positive,
dt=dt,
)
if save_video: | start_virtual_display() | 3 | 2023-10-24 16:19:14+00:00 | 16k |
s1tools/s1-etad | s1etad/_jupyter_support.py | [
{
"identifier": "Sentinel1Etad",
"path": "s1etad/product.py",
"snippet": "class Sentinel1Etad:\n \"\"\"Sentinel-1 ETAD product.\n\n Class to decode and access the elements of the Sentinel ETAD product\n which specification is governed by ETAD-DLR-PS-0014.\n\n The index operator [] (implement... | from .product import Sentinel1Etad, Sentinel1EtadSwath, Sentinel1EtadBurst | 13,421 | # -*- coding: utf-8 -*-
def _sentinel1_etad_repr_pretty_(obj, p, cycle):
if cycle:
p.text(repr(obj))
else:
p.text(repr(obj))
p.break_()
plist = obj.s1_product_list()
if isinstance(plist, str):
plist = [plist]
p.text(f"Number of Sentinel-1 slices: {len(plist)}")
p.break_()
with p.group(2, "Sentinel-1 products list:"):
for name in plist:
p.break_()
p.text(name)
p.break_()
p.text(f"Number of swaths: {obj.number_of_swath}")
p.break_()
p.text("Swath list: {}".format(", ".join(obj.swath_list)))
p.break_()
with p.group(2, "Azimuth time:"):
p.break_()
p.text(f"min: {obj.min_azimuth_time}")
p.break_()
p.text(f"max: {obj.max_azimuth_time}")
p.break_()
with p.group(2, "Range time:"):
p.break_()
p.text(f"min: {obj.min_range_time}")
p.break_()
p.text(f"max: {obj.max_range_time}")
p.break_()
with p.group(2, "Grid sampling:"):
for key, value in obj.grid_sampling.items():
p.break_()
p.text(f"{key}: {value}")
p.break_()
with p.group(2, "Grid spacing:"):
for key, value in obj.grid_spacing.items():
p.break_()
p.text(f"{key}: {value}")
p.break_()
with p.group(2, "Processing settings:"):
for key, value in obj.processing_setting().items():
p.break_()
p.text(f"{key}: {value}")
def _sentinel1_etad_swath_repr_pretty_(obj, p, cycle):
if cycle:
p.text(repr(obj))
else:
p.text(repr(obj))
p.break_()
p.text(f"Swaths ID: {obj.swath_id}")
p.break_()
p.text(f"Number of bursts: {obj.number_of_burst}")
p.break_()
p.text("Burst list: " + str(obj.burst_list))
p.break_()
with p.group(2, "Sampling start:"):
for key, value in obj.sampling_start.items():
p.break_()
p.text(f"{key}: {value}")
p.break_()
with p.group(2, "Sampling:"):
for key, value in obj.sampling.items():
p.break_()
p.text(f"{key}: {value}")
def _sentinel1_etad_burst_repr_pretty_(obj, p, cycle):
if cycle:
p.text(repr(obj))
else:
p.text(repr(obj))
p.break_()
p.text(f"Swaths ID: {obj.swath_id}")
p.break_()
p.text(f"Burst index: {obj.burst_index}")
p.break_()
p.text(f"Shape: ({obj.lines}, {obj.samples})")
p.break_()
with p.group(2, "Sampling start:"):
for key, value in obj.sampling_start.items():
p.break_()
p.text(f"{key}: {value}")
p.break_()
with p.group(2, "Sampling:"):
for key, value in obj.sampling.items():
p.break_()
p.text(f"{key}: {value}")
def _register_jupyter_formatters():
try:
ipy = get_ipython() # noqa
except NameError:
return False
else:
formatter = ipy.display_formatter.formatters["text/plain"]
| # -*- coding: utf-8 -*-
def _sentinel1_etad_repr_pretty_(obj, p, cycle):
if cycle:
p.text(repr(obj))
else:
p.text(repr(obj))
p.break_()
plist = obj.s1_product_list()
if isinstance(plist, str):
plist = [plist]
p.text(f"Number of Sentinel-1 slices: {len(plist)}")
p.break_()
with p.group(2, "Sentinel-1 products list:"):
for name in plist:
p.break_()
p.text(name)
p.break_()
p.text(f"Number of swaths: {obj.number_of_swath}")
p.break_()
p.text("Swath list: {}".format(", ".join(obj.swath_list)))
p.break_()
with p.group(2, "Azimuth time:"):
p.break_()
p.text(f"min: {obj.min_azimuth_time}")
p.break_()
p.text(f"max: {obj.max_azimuth_time}")
p.break_()
with p.group(2, "Range time:"):
p.break_()
p.text(f"min: {obj.min_range_time}")
p.break_()
p.text(f"max: {obj.max_range_time}")
p.break_()
with p.group(2, "Grid sampling:"):
for key, value in obj.grid_sampling.items():
p.break_()
p.text(f"{key}: {value}")
p.break_()
with p.group(2, "Grid spacing:"):
for key, value in obj.grid_spacing.items():
p.break_()
p.text(f"{key}: {value}")
p.break_()
with p.group(2, "Processing settings:"):
for key, value in obj.processing_setting().items():
p.break_()
p.text(f"{key}: {value}")
def _sentinel1_etad_swath_repr_pretty_(obj, p, cycle):
if cycle:
p.text(repr(obj))
else:
p.text(repr(obj))
p.break_()
p.text(f"Swaths ID: {obj.swath_id}")
p.break_()
p.text(f"Number of bursts: {obj.number_of_burst}")
p.break_()
p.text("Burst list: " + str(obj.burst_list))
p.break_()
with p.group(2, "Sampling start:"):
for key, value in obj.sampling_start.items():
p.break_()
p.text(f"{key}: {value}")
p.break_()
with p.group(2, "Sampling:"):
for key, value in obj.sampling.items():
p.break_()
p.text(f"{key}: {value}")
def _sentinel1_etad_burst_repr_pretty_(obj, p, cycle):
if cycle:
p.text(repr(obj))
else:
p.text(repr(obj))
p.break_()
p.text(f"Swaths ID: {obj.swath_id}")
p.break_()
p.text(f"Burst index: {obj.burst_index}")
p.break_()
p.text(f"Shape: ({obj.lines}, {obj.samples})")
p.break_()
with p.group(2, "Sampling start:"):
for key, value in obj.sampling_start.items():
p.break_()
p.text(f"{key}: {value}")
p.break_()
with p.group(2, "Sampling:"):
for key, value in obj.sampling.items():
p.break_()
p.text(f"{key}: {value}")
def _register_jupyter_formatters():
try:
ipy = get_ipython() # noqa
except NameError:
return False
else:
formatter = ipy.display_formatter.formatters["text/plain"] | formatter.for_type(Sentinel1Etad, _sentinel1_etad_repr_pretty_) | 0 | 2023-10-27 13:47:30+00:00 | 16k |
ifrit98/storage-subnet | storage/validator/store.py | [
{
"identifier": "EventSchema",
"path": "storage/validator/event.py",
"snippet": "class EventSchema:\n task_name: str # Task type, e.g. 'store', 'challenge', 'retrieve' 'broadcast'\n successful: List[bool] # List of whether or not the task was successful or not\n completion_times: List[float] ... | import os
import sys
import copy
import time
import torch
import base64
import typing
import asyncio
import aioredis
import bittensor as bt
import websocket
from pprint import pformat
from pyinstrument import Profiler
from Crypto.Random import get_random_bytes, random
from dataclasses import asdict
from storage.validator.event import EventSchema
from storage import protocol
from storage.shared.ecc import (
hash_data,
setup_CRS,
ecc_point_to_hex,
)
from storage.shared.utils import b64_encode
from storage.validator.utils import (
make_random_file,
compute_chunk_distribution_mut_exclusive_numpy_reuse_uids,
)
from storage.validator.encryption import encrypt_data
from storage.validator.verify import verify_store_with_seed
from storage.validator.reward import apply_reward_scores
from storage.validator.database import (
add_metadata_to_hotkey,
store_chunk_metadata,
store_file_chunk_mapping_ordered,
get_ordered_metadata,
hotkey_at_capacity,
)
from storage.validator.bonding import update_statistics
from .reward import create_reward_vector
from .network import ping_and_retry_uids, compute_and_ping_chunks, reroll_distribution
from .utils import compute_chunk_distribution_mut_exclusive_numpy_reuse_uids | 10,992 | # Update event log with moving averaged scores
event.moving_averaged_scores = self.moving_averaged_scores.tolist()
return event
async def store_random_data(self):
"""
Stores data on the network and ensures it is correctly committed by the miners.
Parameters:
- data (bytes, optional): The data to be stored.
- wallet (bt.wallet, optional): The wallet to be used for encrypting the data.
Returns:
- The status of the data storage operation.
"""
# Setup CRS for this round of validation
g, h = setup_CRS(curve=self.config.neuron.curve)
# Make a random bytes file to test the miner if none provided
data = make_random_file(maxsize=self.config.neuron.maxsize)
bt.logging.debug(f"Random store data size: {sys.getsizeof(data)}")
# Encrypt the data
# TODO: create and use a throwaway wallet (never decrypable)
encrypted_data, encryption_payload = encrypt_data(data, self.encryption_wallet)
return await store_encrypted_data(
self,
encrypted_data,
encryption_payload,
k=self.config.neuron.store_sample_size,
ttl=self.config.neuron.data_ttl,
)
async def store_broadband(
self,
encrypted_data,
encryption_payload,
R=3,
k=10,
data_hash=None,
exclude_uids=None,
):
"""
Asynchronously stores encrypted data across a distributed network by splitting it into chunks and
assigning these chunks to various miners for storage. This method ensures redundancy and efficient
data distribution while handling network requests concurrently.
The process includes chunking the data, selecting miners for storage, and verifying the integrity
of stored data through response validation.
Parameters:
encrypted_data (bytes): The encrypted data to be stored across the network.
encryption_payload (dict): Additional payload information required for encryption.
R (int, optional): The redundancy factor, denoting how many times each chunk is replicated. Default is 3.
k (int, optional): The number of miners to query for each chunk. Default is 10.
data_hash (str, optional): The hash of the data to be stored. If not provided, compute it. Default is None.
exclude_uids: (list of int, optional): A list of UIDs to exclude from the storage process. Default is None.
Returns:
str: The hash of the full data, representing its unique identifier in the network.
Raises:
Exception: If the process of creating initial distributions fails after multiple retries.
Note:
- Uses a semaphore to limit the number of concurrent network requests.
- Employs a retry mechanism for handling network and miner availability issues.
- Logs various stages of the process for debugging and monitoring purposes.
"""
if self.config.neuron.profile:
# Create a profiler instance
profiler = Profiler()
profiler.start()
semaphore = asyncio.Semaphore(self.config.neuron.semaphore_size)
async def store_chunk_group(chunk_hash, chunk, uids):
event = EventSchema(
task_name="Store",
successful=[],
completion_times=[],
task_status_messages=[],
task_status_codes=[],
block=self.subtensor.get_current_block(),
uids=[],
step_length=0.0,
best_uid="",
best_hotkey="",
rewards=[],
moving_averaged_scores=[],
)
g, h = setup_CRS(curve=self.config.neuron.curve)
bt.logging.debug(f"type(chunk): {type(chunk)}")
bt.logging.debug(f"chunk: {chunk[:100]}")
chunk = chunk.encode("utf-8") if isinstance(chunk, str) else chunk
b64_encoded_chunk = await asyncio.to_thread(base64.b64encode, chunk)
b64_encoded_chunk = b64_encoded_chunk.decode("utf-8")
bt.logging.debug(f"b64_encoded_chunk: {b64_encoded_chunk[:100]}")
random_seed = get_random_bytes(32).hex()
synapse = protocol.Store(
encrypted_data=b64_encoded_chunk,
curve=self.config.neuron.curve,
g=ecc_point_to_hex(g),
h=ecc_point_to_hex(h),
seed=random_seed,
)
uids = [
uid
for uid in uids
| # The MIT License (MIT)
# Copyright © 2023 Yuma Rao
# Copyright © 2023 philanthrope
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the “Software”), to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
# and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
# the Software.
# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
# THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
async def store_encrypted_data(
self,
encrypted_data: typing.Union[bytes, str],
encryption_payload: dict,
exclude_uids: typing.List[str] = [],
ttl: int = 0,
k: int = None,
max_retries: int = 3,
) -> bool:
event = EventSchema(
task_name="Store",
successful=[],
completion_times=[],
task_status_messages=[],
task_status_codes=[],
block=self.subtensor.get_current_block(),
uids=[],
step_length=0.0,
best_uid="",
best_hotkey="",
rewards=[],
moving_averaged_scores=[],
)
start_time = time.time()
encrypted_data = (
encrypted_data.encode("utf-8")
if isinstance(encrypted_data, str)
else encrypted_data
)
# Setup CRS for this round of validation
g, h = setup_CRS(curve=self.config.neuron.curve)
# Hash the data
data_hash = hash_data(encrypted_data)
# Convert to base64 for compactness
# TODO: Don't do this if it's already b64 encoded. (Check first)
b64_encrypted_data = base64.b64encode(encrypted_data).decode("utf-8")
if self.config.neuron.verbose:
bt.logging.debug(f"storing user data: {encrypted_data[:12]}...")
bt.logging.debug(f"storing user hash: {data_hash}")
bt.logging.debug(f"b64 encrypted data: {b64_encrypted_data[:12]}...")
synapse = protocol.Store(
encrypted_data=b64_encrypted_data,
curve=self.config.neuron.curve,
g=ecc_point_to_hex(g),
h=ecc_point_to_hex(h),
seed=get_random_bytes(32).hex(), # 256-bit seed
)
# Select subset of miners to query (e.g. redunancy factor of N)
uids, _ = await ping_and_retry_uids(
self,
k=k or self.config.neuron.store_redundancy,
max_retries=max_retries,
exclude_uids=exclude_uids,
)
bt.logging.debug(f"store_encrypted_data() uids: {uids}")
axons = [self.metagraph.axons[uid] for uid in uids]
failed_uids = [None]
retries = 0
while len(failed_uids) and retries < max_retries:
if failed_uids == [None]:
# initial loop
failed_uids = []
# Broadcast the query to selected miners on the network.
responses = await self.dendrite(
axons,
synapse,
deserialize=False,
timeout=self.config.neuron.store_timeout,
)
# Compute the rewards for the responses given proc time.
rewards: torch.FloatTensor = torch.zeros(
len(responses), dtype=torch.float32
).to(self.device)
async def success(hotkey, idx, uid, response):
# Prepare storage for the data for particular miner
response_storage = {
"prev_seed": synapse.seed,
"size": sys.getsizeof(encrypted_data), # in bytes, not len(data)
"encryption_payload": encryption_payload,
}
bt.logging.trace(f"Storing UID {uid} data {pformat(response_storage)}")
# Store in the database according to the data hash and the miner hotkey
await add_metadata_to_hotkey(
hotkey,
data_hash,
response_storage,
self.database,
)
if ttl > 0:
await self.database.expire(
f"{hotkey}:{data_hash}",
ttl,
)
bt.logging.debug(
f"Stored data in database with hotkey: {hotkey} | uid {uid} | {data_hash}"
)
def failure(uid):
failed_uids.append(uid)
await create_reward_vector(
self, synapse, rewards, uids, responses, event, success, failure
)
event.rewards.extend(rewards.tolist())
if self.config.neuron.verbose and self.config.neuron.log_responses:
bt.logging.debug(f"Store responses round: {retries}")
[
bt.logging.debug(f"Store response: {response.dendrite.dict()}")
for response in responses
]
bt.logging.trace(f"Applying store rewards for retry: {retries}")
apply_reward_scores(
self,
uids,
responses,
rewards,
timeout=self.config.neuron.store_timeout,
mode=self.config.neuron.reward_mode,
)
# Get a new set of UIDs to query for those left behind
if failed_uids != []:
bt.logging.trace(f"Failed to store on uids: {failed_uids}")
uids, _ = await ping_and_retry_uids(
self, k=len(failed_uids), exclude_uids=exclude_uids
)
bt.logging.trace(f"Retrying with new uids: {uids}")
axons = [self.metagraph.axons[uid] for uid in uids]
failed_uids = [] # reset failed uids for next round
retries += 1
# Calculate step length
end_time = time.time()
event.step_length = end_time - start_time
# Determine the best UID based on rewards
if event.rewards:
best_index = max(range(len(event.rewards)), key=event.rewards.__getitem__)
event.best_uid = event.uids[best_index]
event.best_hotkey = self.metagraph.hotkeys[event.best_uid]
# Update event log with moving averaged scores
event.moving_averaged_scores = self.moving_averaged_scores.tolist()
return event
async def store_random_data(self):
"""
Stores data on the network and ensures it is correctly committed by the miners.
Parameters:
- data (bytes, optional): The data to be stored.
- wallet (bt.wallet, optional): The wallet to be used for encrypting the data.
Returns:
- The status of the data storage operation.
"""
# Setup CRS for this round of validation
g, h = setup_CRS(curve=self.config.neuron.curve)
# Make a random bytes file to test the miner if none provided
data = make_random_file(maxsize=self.config.neuron.maxsize)
bt.logging.debug(f"Random store data size: {sys.getsizeof(data)}")
# Encrypt the data
# TODO: create and use a throwaway wallet (never decrypable)
encrypted_data, encryption_payload = encrypt_data(data, self.encryption_wallet)
return await store_encrypted_data(
self,
encrypted_data,
encryption_payload,
k=self.config.neuron.store_sample_size,
ttl=self.config.neuron.data_ttl,
)
async def store_broadband(
self,
encrypted_data,
encryption_payload,
R=3,
k=10,
data_hash=None,
exclude_uids=None,
):
"""
Asynchronously stores encrypted data across a distributed network by splitting it into chunks and
assigning these chunks to various miners for storage. This method ensures redundancy and efficient
data distribution while handling network requests concurrently.
The process includes chunking the data, selecting miners for storage, and verifying the integrity
of stored data through response validation.
Parameters:
encrypted_data (bytes): The encrypted data to be stored across the network.
encryption_payload (dict): Additional payload information required for encryption.
R (int, optional): The redundancy factor, denoting how many times each chunk is replicated. Default is 3.
k (int, optional): The number of miners to query for each chunk. Default is 10.
data_hash (str, optional): The hash of the data to be stored. If not provided, compute it. Default is None.
exclude_uids: (list of int, optional): A list of UIDs to exclude from the storage process. Default is None.
Returns:
str: The hash of the full data, representing its unique identifier in the network.
Raises:
Exception: If the process of creating initial distributions fails after multiple retries.
Note:
- Uses a semaphore to limit the number of concurrent network requests.
- Employs a retry mechanism for handling network and miner availability issues.
- Logs various stages of the process for debugging and monitoring purposes.
"""
if self.config.neuron.profile:
# Create a profiler instance
profiler = Profiler()
profiler.start()
semaphore = asyncio.Semaphore(self.config.neuron.semaphore_size)
async def store_chunk_group(chunk_hash, chunk, uids):
event = EventSchema(
task_name="Store",
successful=[],
completion_times=[],
task_status_messages=[],
task_status_codes=[],
block=self.subtensor.get_current_block(),
uids=[],
step_length=0.0,
best_uid="",
best_hotkey="",
rewards=[],
moving_averaged_scores=[],
)
g, h = setup_CRS(curve=self.config.neuron.curve)
bt.logging.debug(f"type(chunk): {type(chunk)}")
bt.logging.debug(f"chunk: {chunk[:100]}")
chunk = chunk.encode("utf-8") if isinstance(chunk, str) else chunk
b64_encoded_chunk = await asyncio.to_thread(base64.b64encode, chunk)
b64_encoded_chunk = b64_encoded_chunk.decode("utf-8")
bt.logging.debug(f"b64_encoded_chunk: {b64_encoded_chunk[:100]}")
random_seed = get_random_bytes(32).hex()
synapse = protocol.Store(
encrypted_data=b64_encoded_chunk,
curve=self.config.neuron.curve,
g=ecc_point_to_hex(g),
h=ecc_point_to_hex(h),
seed=random_seed,
)
uids = [
uid
for uid in uids | if not await hotkey_at_capacity(self.metagraph.hotkeys[uid], self.database) | 15 | 2023-10-26 18:54:47+00:00 | 16k |
Eclectic-Sheep/sheeprlhf | sheeprlhf/task/train/ppo.py | [
{
"identifier": "PPOAgent",
"path": "sheeprlhf/agent/ppo.py",
"snippet": "class PPOAgent:\n \"\"\"Agent model for PPO training.\"\"\"\n\n _reference: ActorModel\n _reward: RewardModel\n _finetune_mode: FINETUNE_MODE\n _actor: Optional[ActorModel] = None\n _critic: Optional[CriticModel]... | import copy
import time
import torch
from pathlib import Path
from typing import Dict
from lightning import Fabric
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import GenerationConfig, PreTrainedTokenizer
from sheeprlhf.agent.ppo import PPOAgent
from sheeprlhf.data.base import TextDataset
from sheeprlhf.data.collate import LeftPadCollate
from sheeprlhf.loss.ppo import policy_loss, value_loss
from sheeprlhf.model.actor import ActorModel
from sheeprlhf.structure.data import DataConfig
from sheeprlhf.structure.generation import GenConfig
from sheeprlhf.structure.model import ModelConfig
from sheeprlhf.structure.task import PPOConfig
from sheeprlhf.utils.data import prepare_generation_config, validate_dataset
from sheeprlhf.utils.helper import create_tensorboard_logger, get_log_dir, log_text
from sheeprlhf.utils.hydra import instantiate_from_config
from sheeprlhf.utils.metric import PPOMetricManager
from sheeprlhf.utils.model import compute_grad_norm, prepare_optimizer_parameters
from sheeprlhf.utils.ppo import AdaptiveKLController, FixedKLController, collect_rollout, masked_normalize
from sheeprlhf.utils.registry import register_task | 11,159 | tokenizer=tokenizer,
model_cfg=model_cfg,
gen_cfg=gen_cfg,
fabric=fabric,
)
eval_gen_cfg = copy.deepcopy(gen_cfg)
eval_gen_cfg.do_sample = False
eval_generation_config = prepare_generation_config(
tokenizer=tokenizer,
model_cfg=model_cfg,
gen_cfg=eval_gen_cfg,
fabric=fabric,
)
# Setup Optimizer Scheduler fabric models
actor_trainable_params, _, _ = prepare_optimizer_parameters(agent.actor, weight_decay=optim_cfg.weight_decay)
actor_optimizer = instantiate_from_config(
optim_cfg,
params=actor_trainable_params,
_convert_="partial",
)
actor_optimizer = fabric.setup_optimizers(actor_optimizer)
critic_trainable_params, _, _ = prepare_optimizer_parameters(agent.critic, weight_decay=optim_cfg.weight_decay)
critic_optimizer = instantiate_from_config(
optim_cfg,
params=critic_trainable_params,
_convert_="partial",
)
critic_optimizer = fabric.setup_optimizers(critic_optimizer)
if fabric.is_global_zero:
gen_text, score = generate(
agent=agent,
tokenizer=tokenizer,
generation_config=eval_generation_config,
example_prompt=example_prompt,
device=fabric.device,
)
log_text(fabric, gen_text, "info/example_sample", step=0)
fabric.log("info/example_last_reward", score, step=0)
num_training_steps = 2 if cfg.dry_run else task_cfg.epochs * len(train_dataloader)
# KL Controller
if task_cfg.adaptive_kl_coeff:
kl_controller = AdaptiveKLController(
init_kl_coef=task_cfg.init_kl_coeff, target=task_cfg.target_kl_coeff, kl_horizon=num_training_steps
)
else:
kl_controller = FixedKLController(kl_coeff=task_cfg.init_kl_coeff)
fabric.print("Model Checkpoint interval: ", task_cfg.save_interval, "steps")
fabric.print("Model Evaluation interval: ", task_cfg.eval_interval, "steps")
iterator = tqdm(range(num_training_steps), disable=not fabric.is_global_zero)
data_iterator = iter(train_dataloader)
agent.reward.eval()
for k in iterator:
# Setup counters and data
if k % len(train_dataloader) == 0 or data_iterator is None:
data_iterator = iter(train_dataloader)
is_accumulating = (k) % task_cfg.gradient_accumulation_steps != 0
last_step = k == num_training_steps - 1
# Setup batch data
batch = next(data_iterator)
max_prompt_length = batch["prompt_input_ids"].shape[1]
agent.actor.eval()
agent.critic.eval()
t0 = time.time()
rollout, sample_output = collect_rollout(
batch=batch,
agent=agent,
generation_config=generation_config,
kl_controller=kl_controller,
task_cfg=task_cfg,
tokenizer=tokenizer,
fabric=fabric,
metrics=metrics,
)
time_rollout = time.time() - t0
rollout_dataloader = DataLoader(
rollout, batch_size=task_cfg.micro_batch_size, shuffle=True, collate_fn=lambda x: x
)
rollout_dataloader = fabric.setup_dataloaders(rollout_dataloader, use_distributed_sampler=False)
agent.actor.train()
agent.critic.train()
for _ in range(task_cfg.ppo_epochs):
accumulator_counter = 0
for micro_batch in rollout_dataloader:
is_accumulating = (accumulator_counter) % task_cfg.gradient_accumulation_steps != 0
generated_data = {
"input_ids": micro_batch["input_ids"],
"attention_mask": micro_batch["attention_mask"],
}
old_log_probs = micro_batch["actor_log_probs"]
old_values = micro_batch["values"]
advantages = micro_batch["advantages"]
returns = micro_batch["returns"]
start_token_idx = max_prompt_length - 1
action_mask = micro_batch["attention_mask"][:, start_token_idx:-1].int()
if task_cfg.normalize_advantages:
advantages = masked_normalize(advantages, action_mask)
with fabric.no_backward_sync(agent.actor, enabled=is_accumulating):
log_probs = agent.actor(**generated_data)[:, start_token_idx:] # (B, num_new_tokens)
p_loss = policy_loss(
log_probs=log_probs,
old_log_probs=old_log_probs,
advantages=advantages,
clip_coeff=task_cfg.clip_coeff,
action_mask=action_mask,
)
fabric.backward(p_loss / task_cfg.gradient_accumulation_steps)
with fabric.no_backward_sync(agent.critic, enabled=is_accumulating):
values = agent.critic(**generated_data)[:, start_token_idx:-1] # (B, num_new_tokens)
|
@torch.no_grad()
def generate( # noqa: D103
agent: PPOAgent,
tokenizer: PreTrainedTokenizer,
generation_config: GenerationConfig,
example_prompt: Dict[str, torch.Tensor],
device: torch.device,
):
generated_input_ids = agent.actor.module.generate(
input_ids=example_prompt["input_ids"].to(device),
attention_mask=example_prompt["attention_mask"].to(device),
generation_config=generation_config,
use_cache=True,
)
prompt_length = example_prompt["input_ids"].shape[1]
generated_attention_mask = (generated_input_ids != generation_config.pad_token_id).int()
generated_data = {"input_ids": generated_input_ids, "attention_mask": generated_attention_mask}
reward = agent.reward(**generated_data)[:, prompt_length:]
action_mask = (generated_input_ids != generation_config.pad_token_id).int()[:, prompt_length:]
last_token_idx = torch.argmax(torch.cumsum(action_mask, dim=1) * action_mask, dim=1, keepdim=True)
reward_score = torch.gather(reward, dim=-1, index=last_token_idx).squeeze(-1)
return tokenizer.decode(generated_input_ids[0], skip_special_tokens=True), reward_score.item()
@register_task()
def main(fabric: Fabric, cfg: Dict): # noqa: D103
task_cfg = PPOConfig(**cfg.task)
model_cfg = ModelConfig(**cfg.model)
data_cfg = DataConfig(**cfg.data)
gen_cfg = GenConfig(**cfg.generation)
optim_cfg = cfg.optim
fabric.seed_everything(cfg.seed + fabric.global_rank)
# Create TensorBoardLogger. This will create the logger only on the
# rank-0 process
logger = create_tensorboard_logger(fabric, cfg, override_log_level=True)
if logger and fabric.is_global_zero:
fabric._loggers = [logger]
fabric.logger.log_hyperparams(cfg)
log_dir = get_log_dir(fabric, cfg.root_dir, cfg.run_name)
experiment_dir = Path(log_dir).parent
# Setup Metrics
metrics = PPOMetricManager(log_interval=task_cfg.log_interval).to(fabric.device)
# Setup Dataloaders
data_processor = validate_dataset(fabric, data_cfg)
dataset_path = Path(data_processor.full_path)
tokenizer = data_processor.tokenizer
collator = LeftPadCollate(pad_value=tokenizer.pad_token_id, ignore_index=data_cfg.ignore_index)
train_dataset = TextDataset(dataframe_path=dataset_path / "finetune_train.pkl")
train_dataloader = DataLoader(
train_dataset,
shuffle=True,
batch_size=task_cfg.micro_batch_size,
collate_fn=collator,
num_workers=task_cfg.num_workers,
)
train_dataloader = fabric.setup_dataloaders(train_dataloader)
example_prompt = torch.load(dataset_path / "example_prompt.pt")
# Setup Model
with fabric.init_module(empty_init=model_cfg.fabric_empty_init):
agent = PPOAgent(model_cfg=model_cfg, task_cfg=task_cfg)
agent.load_checkpoint(device=fabric.device)
agent.setup_finetuning()
agent.actor = fabric.setup_module(agent.actor)
agent.critic = fabric.setup_module(agent.critic)
if not agent.share_critic_reward:
agent.reward = fabric.setup_module(agent.reward)
if not agent.share_actor_critic and not agent.lora_enabled:
agent.reference = fabric.setup_module(agent.reference)
# Setup Generation Configs
generation_config = prepare_generation_config(
tokenizer=tokenizer,
model_cfg=model_cfg,
gen_cfg=gen_cfg,
fabric=fabric,
)
eval_gen_cfg = copy.deepcopy(gen_cfg)
eval_gen_cfg.do_sample = False
eval_generation_config = prepare_generation_config(
tokenizer=tokenizer,
model_cfg=model_cfg,
gen_cfg=eval_gen_cfg,
fabric=fabric,
)
# Setup Optimizer Scheduler fabric models
actor_trainable_params, _, _ = prepare_optimizer_parameters(agent.actor, weight_decay=optim_cfg.weight_decay)
actor_optimizer = instantiate_from_config(
optim_cfg,
params=actor_trainable_params,
_convert_="partial",
)
actor_optimizer = fabric.setup_optimizers(actor_optimizer)
critic_trainable_params, _, _ = prepare_optimizer_parameters(agent.critic, weight_decay=optim_cfg.weight_decay)
critic_optimizer = instantiate_from_config(
optim_cfg,
params=critic_trainable_params,
_convert_="partial",
)
critic_optimizer = fabric.setup_optimizers(critic_optimizer)
if fabric.is_global_zero:
gen_text, score = generate(
agent=agent,
tokenizer=tokenizer,
generation_config=eval_generation_config,
example_prompt=example_prompt,
device=fabric.device,
)
log_text(fabric, gen_text, "info/example_sample", step=0)
fabric.log("info/example_last_reward", score, step=0)
num_training_steps = 2 if cfg.dry_run else task_cfg.epochs * len(train_dataloader)
# KL Controller
if task_cfg.adaptive_kl_coeff:
kl_controller = AdaptiveKLController(
init_kl_coef=task_cfg.init_kl_coeff, target=task_cfg.target_kl_coeff, kl_horizon=num_training_steps
)
else:
kl_controller = FixedKLController(kl_coeff=task_cfg.init_kl_coeff)
fabric.print("Model Checkpoint interval: ", task_cfg.save_interval, "steps")
fabric.print("Model Evaluation interval: ", task_cfg.eval_interval, "steps")
iterator = tqdm(range(num_training_steps), disable=not fabric.is_global_zero)
data_iterator = iter(train_dataloader)
agent.reward.eval()
for k in iterator:
# Setup counters and data
if k % len(train_dataloader) == 0 or data_iterator is None:
data_iterator = iter(train_dataloader)
is_accumulating = (k) % task_cfg.gradient_accumulation_steps != 0
last_step = k == num_training_steps - 1
# Setup batch data
batch = next(data_iterator)
max_prompt_length = batch["prompt_input_ids"].shape[1]
agent.actor.eval()
agent.critic.eval()
t0 = time.time()
rollout, sample_output = collect_rollout(
batch=batch,
agent=agent,
generation_config=generation_config,
kl_controller=kl_controller,
task_cfg=task_cfg,
tokenizer=tokenizer,
fabric=fabric,
metrics=metrics,
)
time_rollout = time.time() - t0
rollout_dataloader = DataLoader(
rollout, batch_size=task_cfg.micro_batch_size, shuffle=True, collate_fn=lambda x: x
)
rollout_dataloader = fabric.setup_dataloaders(rollout_dataloader, use_distributed_sampler=False)
agent.actor.train()
agent.critic.train()
for _ in range(task_cfg.ppo_epochs):
accumulator_counter = 0
for micro_batch in rollout_dataloader:
is_accumulating = (accumulator_counter) % task_cfg.gradient_accumulation_steps != 0
generated_data = {
"input_ids": micro_batch["input_ids"],
"attention_mask": micro_batch["attention_mask"],
}
old_log_probs = micro_batch["actor_log_probs"]
old_values = micro_batch["values"]
advantages = micro_batch["advantages"]
returns = micro_batch["returns"]
start_token_idx = max_prompt_length - 1
action_mask = micro_batch["attention_mask"][:, start_token_idx:-1].int()
if task_cfg.normalize_advantages:
advantages = masked_normalize(advantages, action_mask)
with fabric.no_backward_sync(agent.actor, enabled=is_accumulating):
log_probs = agent.actor(**generated_data)[:, start_token_idx:] # (B, num_new_tokens)
p_loss = policy_loss(
log_probs=log_probs,
old_log_probs=old_log_probs,
advantages=advantages,
clip_coeff=task_cfg.clip_coeff,
action_mask=action_mask,
)
fabric.backward(p_loss / task_cfg.gradient_accumulation_steps)
with fabric.no_backward_sync(agent.critic, enabled=is_accumulating):
values = agent.critic(**generated_data)[:, start_token_idx:-1] # (B, num_new_tokens) | v_loss = value_loss( | 4 | 2023-10-31 12:02:02+00:00 | 16k |
cpacker/MemGPT | tests/test_storage.py | [
{
"identifier": "StorageConnector",
"path": "memgpt/agent_store/storage.py",
"snippet": "class StorageConnector:\n \"\"\"Defines a DB connection that is user-specific to access data: Documents, Passages, Archival/Recall Memory\"\"\"\n\n def __init__(self, table_type: TableType, config: MemGPTConfi... | import os
import uuid
import pytest
from sqlalchemy.ext.declarative import declarative_base
from memgpt.agent_store.storage import StorageConnector, TableType
from memgpt.embeddings import embedding_model
from memgpt.data_types import Message, Passage, EmbeddingConfig, AgentState, OpenAIEmbeddingConfig
from memgpt.config import MemGPTConfig
from memgpt.credentials import MemGPTCredentials
from memgpt.agent_store.storage import StorageConnector, TableType
from memgpt.metadata import MetadataStore
from memgpt.data_types import User
from datetime import datetime, timedelta | 12,773 |
# Note: the database will filter out rows that do not correspond to agent1 and test_user by default.
texts = ["This is a test passage", "This is another test passage", "Cinderella wept"]
start_date = datetime(2009, 10, 5, 18, 00)
dates = [start_date, start_date - timedelta(weeks=1), start_date + timedelta(weeks=1)]
roles = ["user", "assistant", "assistant"]
agent_1_id = uuid.uuid4()
agent_2_id = uuid.uuid4()
agent_ids = [agent_1_id, agent_2_id, agent_1_id]
ids = [uuid.uuid4(), uuid.uuid4(), uuid.uuid4()]
user_id = uuid.uuid4()
# Data generation functions: Passages
def generate_passages(embed_model):
"""Generate list of 3 Passage objects"""
# embeddings: use openai if env is set, otherwise local
passages = []
for text, _, _, agent_id, id in zip(texts, dates, roles, agent_ids, ids):
embedding = None
if embed_model:
embedding = embed_model.get_text_embedding(text)
passages.append(Passage(user_id=user_id, text=text, agent_id=agent_id, embedding=embedding, data_source="test_source", id=id))
return passages
# Data generation functions: Messages
def generate_messages(embed_model):
"""Generate list of 3 Message objects"""
messages = []
for text, date, role, agent_id, id in zip(texts, dates, roles, agent_ids, ids):
embedding = None
if embed_model:
embedding = embed_model.get_text_embedding(text)
messages.append(
Message(user_id=user_id, text=text, agent_id=agent_id, role=role, created_at=date, id=id, model="gpt-4", embedding=embedding)
)
print(messages[-1].text)
return messages
@pytest.fixture(autouse=True)
def clear_dynamically_created_models():
"""Wipe globals for SQLAlchemy"""
yield
for key in list(globals().keys()):
if key.endswith("Model"):
del globals()[key]
@pytest.fixture(autouse=True)
def recreate_declarative_base():
"""Recreate the declarative base before each test"""
global Base
Base = declarative_base()
yield
Base.metadata.clear()
@pytest.mark.parametrize("storage_connector", ["postgres", "chroma", "sqlite"])
# @pytest.mark.parametrize("storage_connector", ["sqlite", "chroma"])
# @pytest.mark.parametrize("storage_connector", ["postgres"])
@pytest.mark.parametrize("table_type", [TableType.RECALL_MEMORY, TableType.ARCHIVAL_MEMORY])
def test_storage(storage_connector, table_type, clear_dynamically_created_models, recreate_declarative_base):
# setup memgpt config
# TODO: set env for different config path
# hacky way to cleanup globals that scruw up tests
# for table_name in ['Message']:
# if 'Message' in globals():
# print("Removing messages", globals()['Message'])
# del globals()['Message']
config = MemGPTConfig()
if storage_connector == "postgres":
if not os.getenv("PGVECTOR_TEST_DB_URL"):
print("Skipping test, missing PG URI")
return
config.archival_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL")
config.recall_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL")
config.archival_storage_type = "postgres"
config.recall_storage_type = "postgres"
if storage_connector == "lancedb":
# TODO: complete lancedb implementation
if not os.getenv("LANCEDB_TEST_URL"):
print("Skipping test, missing LanceDB URI")
return
config.archival_storage_uri = os.getenv("LANCEDB_TEST_URL")
config.recall_storage_uri = os.getenv("LANCEDB_TEST_URL")
config.archival_storage_type = "lancedb"
config.recall_storage_type = "lancedb"
if storage_connector == "chroma":
if table_type == TableType.RECALL_MEMORY:
print("Skipping test, chroma only supported for archival memory")
return
config.archival_storage_type = "chroma"
config.archival_storage_path = "./test_chroma"
if storage_connector == "sqlite":
if table_type == TableType.ARCHIVAL_MEMORY:
print("Skipping test, sqlite only supported for recall memory")
return
config.recall_storage_type = "sqlite"
# get embedding model
embed_model = None
if os.getenv("OPENAI_API_KEY"):
|
# Note: the database will filter out rows that do not correspond to agent1 and test_user by default.
texts = ["This is a test passage", "This is another test passage", "Cinderella wept"]
start_date = datetime(2009, 10, 5, 18, 00)
dates = [start_date, start_date - timedelta(weeks=1), start_date + timedelta(weeks=1)]
roles = ["user", "assistant", "assistant"]
agent_1_id = uuid.uuid4()
agent_2_id = uuid.uuid4()
agent_ids = [agent_1_id, agent_2_id, agent_1_id]
ids = [uuid.uuid4(), uuid.uuid4(), uuid.uuid4()]
user_id = uuid.uuid4()
# Data generation functions: Passages
def generate_passages(embed_model):
"""Generate list of 3 Passage objects"""
# embeddings: use openai if env is set, otherwise local
passages = []
for text, _, _, agent_id, id in zip(texts, dates, roles, agent_ids, ids):
embedding = None
if embed_model:
embedding = embed_model.get_text_embedding(text)
passages.append(Passage(user_id=user_id, text=text, agent_id=agent_id, embedding=embedding, data_source="test_source", id=id))
return passages
# Data generation functions: Messages
def generate_messages(embed_model):
"""Generate list of 3 Message objects"""
messages = []
for text, date, role, agent_id, id in zip(texts, dates, roles, agent_ids, ids):
embedding = None
if embed_model:
embedding = embed_model.get_text_embedding(text)
messages.append(
Message(user_id=user_id, text=text, agent_id=agent_id, role=role, created_at=date, id=id, model="gpt-4", embedding=embedding)
)
print(messages[-1].text)
return messages
@pytest.fixture(autouse=True)
def clear_dynamically_created_models():
"""Wipe globals for SQLAlchemy"""
yield
for key in list(globals().keys()):
if key.endswith("Model"):
del globals()[key]
@pytest.fixture(autouse=True)
def recreate_declarative_base():
"""Recreate the declarative base before each test"""
global Base
Base = declarative_base()
yield
Base.metadata.clear()
@pytest.mark.parametrize("storage_connector", ["postgres", "chroma", "sqlite"])
# @pytest.mark.parametrize("storage_connector", ["sqlite", "chroma"])
# @pytest.mark.parametrize("storage_connector", ["postgres"])
@pytest.mark.parametrize("table_type", [TableType.RECALL_MEMORY, TableType.ARCHIVAL_MEMORY])
def test_storage(storage_connector, table_type, clear_dynamically_created_models, recreate_declarative_base):
# setup memgpt config
# TODO: set env for different config path
# hacky way to cleanup globals that scruw up tests
# for table_name in ['Message']:
# if 'Message' in globals():
# print("Removing messages", globals()['Message'])
# del globals()['Message']
config = MemGPTConfig()
if storage_connector == "postgres":
if not os.getenv("PGVECTOR_TEST_DB_URL"):
print("Skipping test, missing PG URI")
return
config.archival_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL")
config.recall_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL")
config.archival_storage_type = "postgres"
config.recall_storage_type = "postgres"
if storage_connector == "lancedb":
# TODO: complete lancedb implementation
if not os.getenv("LANCEDB_TEST_URL"):
print("Skipping test, missing LanceDB URI")
return
config.archival_storage_uri = os.getenv("LANCEDB_TEST_URL")
config.recall_storage_uri = os.getenv("LANCEDB_TEST_URL")
config.archival_storage_type = "lancedb"
config.recall_storage_type = "lancedb"
if storage_connector == "chroma":
if table_type == TableType.RECALL_MEMORY:
print("Skipping test, chroma only supported for archival memory")
return
config.archival_storage_type = "chroma"
config.archival_storage_path = "./test_chroma"
if storage_connector == "sqlite":
if table_type == TableType.ARCHIVAL_MEMORY:
print("Skipping test, sqlite only supported for recall memory")
return
config.recall_storage_type = "sqlite"
# get embedding model
embed_model = None
if os.getenv("OPENAI_API_KEY"): | embedding_config = EmbeddingConfig( | 5 | 2023-10-11 07:38:37+00:00 | 16k |
xxlong0/Wonder3D | mvdiffusion/models/unet_mv2d_condition.py | [
{
"identifier": "CrossAttnDownBlockMV2D",
"path": "mvdiffusion/models/unet_mv2d_blocks.py",
"snippet": "class CrossAttnDownBlockMV2D(nn.Module):\n def __init__(\n self,\n in_channels: int,\n out_channels: int,\n temb_channels: int,\n dropout: float = 0.0,\n n... | from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import UNet2DConditionLoadersMixin
from diffusers.utils import BaseOutput, logging
from diffusers.models.activations import get_activation
from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
from diffusers.models.embeddings import (
GaussianFourierProjection,
ImageHintTimeEmbedding,
ImageProjection,
ImageTimeEmbedding,
TextImageProjection,
TextImageTimeEmbedding,
TextTimeEmbedding,
TimestepEmbedding,
Timesteps,
)
from diffusers.models.modeling_utils import ModelMixin, load_state_dict, _load_state_dict_into_model
from diffusers.models.unet_2d_blocks import (
CrossAttnDownBlock2D,
CrossAttnUpBlock2D,
DownBlock2D,
UNetMidBlock2DCrossAttn,
UNetMidBlock2DSimpleCrossAttn,
UpBlock2D,
)
from diffusers.utils import (
CONFIG_NAME,
DIFFUSERS_CACHE,
FLAX_WEIGHTS_NAME,
HF_HUB_OFFLINE,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
_add_variant,
_get_model_file,
deprecate,
is_accelerate_available,
is_safetensors_available,
is_torch_version,
logging,
)
from diffusers import __version__
from mvdiffusion.models.unet_mv2d_blocks import (
CrossAttnDownBlockMV2D,
CrossAttnUpBlockMV2D,
UNetMidBlockMV2DCrossAttn,
get_down_block,
get_up_block,
)
import os
import torch
import torch.nn as nn
import torch.utils.checkpoint
import copy | 11,839 | image_embed_dim=cross_attention_dim,
cross_attention_dim=cross_attention_dim,
)
elif encoder_hid_dim_type == "image_proj":
# Kandinsky 2.2
self.encoder_hid_proj = ImageProjection(
image_embed_dim=encoder_hid_dim,
cross_attention_dim=cross_attention_dim,
)
elif encoder_hid_dim_type is not None:
raise ValueError(
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
)
else:
self.encoder_hid_proj = None
# class embedding
if class_embed_type is None and num_class_embeds is not None:
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
elif class_embed_type == "timestep":
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
elif class_embed_type == "identity":
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
elif class_embed_type == "projection":
if projection_class_embeddings_input_dim is None:
raise ValueError(
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
)
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
# 2. it projects from an arbitrary input dimension.
#
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
elif class_embed_type == "simple_projection":
if projection_class_embeddings_input_dim is None:
raise ValueError(
"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
)
self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
else:
self.class_embedding = None
if addition_embed_type == "text":
if encoder_hid_dim is not None:
text_time_embedding_from_dim = encoder_hid_dim
else:
text_time_embedding_from_dim = cross_attention_dim
self.add_embedding = TextTimeEmbedding(
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
)
elif addition_embed_type == "text_image":
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
self.add_embedding = TextImageTimeEmbedding(
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
)
elif addition_embed_type == "text_time":
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
elif addition_embed_type == "image":
# Kandinsky 2.2
self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
elif addition_embed_type == "image_hint":
# Kandinsky 2.2 ControlNet
self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
elif addition_embed_type is not None:
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
if time_embedding_act_fn is None:
self.time_embed_act = None
else:
self.time_embed_act = get_activation(time_embedding_act_fn)
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(only_cross_attention, bool):
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = only_cross_attention
only_cross_attention = [only_cross_attention] * len(down_block_types)
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = False
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
if isinstance(attention_head_dim, int):
attention_head_dim = (attention_head_dim,) * len(down_block_types)
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
if isinstance(layers_per_block, int):
layers_per_block = [layers_per_block] * len(down_block_types)
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
if class_embeddings_concat:
# The time embeddings are concatenated with the class embeddings. The dimension of the
# time embeddings passed to the down, middle, and up blocks is twice the dimension of the
# regular time embeddings
blocks_time_embed_dim = time_embed_dim * 2
else:
blocks_time_embed_dim = time_embed_dim
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
| # 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
@dataclass
class UNetMV2DConditionOutput(BaseOutput):
"""
The output of [`UNet2DConditionModel`].
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
"""
sample: torch.FloatTensor = None
class UNetMV2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
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.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use.
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or
`UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
The tuple of upsample blocks to use.
only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
Whether to include self-attention in the basic transformer blocks, see
[`~models.attention.BasicTransformerBlock`].
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.
downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
If `None`, normalization and activation layers is skipped in post-processing.
norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
The dimension of the cross attention features.
transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
encoder_hid_dim (`int`, *optional*, defaults to None):
If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
dimension to `cross_attention_dim`.
encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
num_attention_heads (`int`, *optional*):
The number of attention heads. If not defined, defaults to `attention_head_dim`
resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
class_embed_type (`str`, *optional*, defaults to `None`):
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
addition_embed_type (`str`, *optional*, defaults to `None`):
Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
"text". "text" will use the `TextTimeEmbedding` layer.
addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
Dimension for the timestep embeddings.
num_class_embeds (`int`, *optional*, defaults to `None`):
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
class conditioning with `class_embed_type` equal to `None`.
time_embedding_type (`str`, *optional*, defaults to `positional`):
The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
time_embedding_dim (`int`, *optional*, defaults to `None`):
An optional override for the dimension of the projected time embedding.
time_embedding_act_fn (`str`, *optional*, defaults to `None`):
Optional activation function to use only once on the time embeddings before they are passed to the rest of
the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
timestep_post_act (`str`, *optional*, defaults to `None`):
The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
time_cond_proj_dim (`int`, *optional*, defaults to `None`):
The dimension of `cond_proj` layer in the timestep embedding.
conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
`class_embed_type="projection"`. Required when `class_embed_type="projection"`.
class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
embeddings with the class embeddings.
mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
`only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
`only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
otherwise.
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
center_input_sample: bool = False,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str] = (
"CrossAttnDownBlockMV2D",
"CrossAttnDownBlockMV2D",
"CrossAttnDownBlockMV2D",
"DownBlock2D",
),
mid_block_type: Optional[str] = "UNetMidBlockMV2DCrossAttn",
up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlockMV2D", "CrossAttnUpBlockMV2D", "CrossAttnUpBlockMV2D"),
only_cross_attention: Union[bool, Tuple[bool]] = False,
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
layers_per_block: Union[int, Tuple[int]] = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
act_fn: str = "silu",
norm_num_groups: Optional[int] = 32,
norm_eps: float = 1e-5,
cross_attention_dim: Union[int, Tuple[int]] = 1280,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
encoder_hid_dim: Optional[int] = None,
encoder_hid_dim_type: Optional[str] = None,
attention_head_dim: Union[int, Tuple[int]] = 8,
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
dual_cross_attention: bool = False,
use_linear_projection: bool = False,
class_embed_type: Optional[str] = None,
addition_embed_type: Optional[str] = None,
addition_time_embed_dim: Optional[int] = None,
num_class_embeds: Optional[int] = None,
upcast_attention: bool = False,
resnet_time_scale_shift: str = "default",
resnet_skip_time_act: bool = False,
resnet_out_scale_factor: int = 1.0,
time_embedding_type: str = "positional",
time_embedding_dim: Optional[int] = None,
time_embedding_act_fn: Optional[str] = None,
timestep_post_act: Optional[str] = None,
time_cond_proj_dim: Optional[int] = None,
conv_in_kernel: int = 3,
conv_out_kernel: int = 3,
projection_class_embeddings_input_dim: Optional[int] = None,
class_embeddings_concat: bool = False,
mid_block_only_cross_attention: Optional[bool] = None,
cross_attention_norm: Optional[str] = None,
addition_embed_type_num_heads=64,
num_views: int = 1,
cd_attention_last: bool = False,
cd_attention_mid: bool = False,
multiview_attention: bool = True,
sparse_mv_attention: bool = False,
mvcd_attention: bool = False
):
super().__init__()
self.sample_size = sample_size
if num_attention_heads is not None:
raise ValueError(
"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
)
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
num_attention_heads = num_attention_heads or attention_head_dim
# Check inputs
if len(down_block_types) != len(up_block_types):
raise ValueError(
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}."
)
if len(block_out_channels) != len(down_block_types):
raise ValueError(
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}."
)
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
raise ValueError(
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}."
)
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
)
if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
raise ValueError(
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}."
)
if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
)
if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
)
# input
conv_in_padding = (conv_in_kernel - 1) // 2
self.conv_in = nn.Conv2d(
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
)
# time
if time_embedding_type == "fourier":
time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
if time_embed_dim % 2 != 0:
raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
self.time_proj = GaussianFourierProjection(
time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
)
timestep_input_dim = time_embed_dim
elif time_embedding_type == "positional":
time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]
else:
raise ValueError(
f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
)
self.time_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim,
act_fn=act_fn,
post_act_fn=timestep_post_act,
cond_proj_dim=time_cond_proj_dim,
)
if encoder_hid_dim_type is None and encoder_hid_dim is not None:
encoder_hid_dim_type = "text_proj"
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
if encoder_hid_dim is None and encoder_hid_dim_type is not None:
raise ValueError(
f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
)
if encoder_hid_dim_type == "text_proj":
self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
elif encoder_hid_dim_type == "text_image_proj":
# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
# case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
self.encoder_hid_proj = TextImageProjection(
text_embed_dim=encoder_hid_dim,
image_embed_dim=cross_attention_dim,
cross_attention_dim=cross_attention_dim,
)
elif encoder_hid_dim_type == "image_proj":
# Kandinsky 2.2
self.encoder_hid_proj = ImageProjection(
image_embed_dim=encoder_hid_dim,
cross_attention_dim=cross_attention_dim,
)
elif encoder_hid_dim_type is not None:
raise ValueError(
f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
)
else:
self.encoder_hid_proj = None
# class embedding
if class_embed_type is None and num_class_embeds is not None:
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
elif class_embed_type == "timestep":
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
elif class_embed_type == "identity":
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
elif class_embed_type == "projection":
if projection_class_embeddings_input_dim is None:
raise ValueError(
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
)
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
# 2. it projects from an arbitrary input dimension.
#
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
elif class_embed_type == "simple_projection":
if projection_class_embeddings_input_dim is None:
raise ValueError(
"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
)
self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
else:
self.class_embedding = None
if addition_embed_type == "text":
if encoder_hid_dim is not None:
text_time_embedding_from_dim = encoder_hid_dim
else:
text_time_embedding_from_dim = cross_attention_dim
self.add_embedding = TextTimeEmbedding(
text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
)
elif addition_embed_type == "text_image":
# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
self.add_embedding = TextImageTimeEmbedding(
text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
)
elif addition_embed_type == "text_time":
self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
elif addition_embed_type == "image":
# Kandinsky 2.2
self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
elif addition_embed_type == "image_hint":
# Kandinsky 2.2 ControlNet
self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
elif addition_embed_type is not None:
raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
if time_embedding_act_fn is None:
self.time_embed_act = None
else:
self.time_embed_act = get_activation(time_embedding_act_fn)
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(only_cross_attention, bool):
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = only_cross_attention
only_cross_attention = [only_cross_attention] * len(down_block_types)
if mid_block_only_cross_attention is None:
mid_block_only_cross_attention = False
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
if isinstance(attention_head_dim, int):
attention_head_dim = (attention_head_dim,) * len(down_block_types)
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
if isinstance(layers_per_block, int):
layers_per_block = [layers_per_block] * len(down_block_types)
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
if class_embeddings_concat:
# The time embeddings are concatenated with the class embeddings. The dimension of the
# time embeddings passed to the down, middle, and up blocks is twice the dimension of the
# regular time embeddings
blocks_time_embed_dim = time_embed_dim * 2
else:
blocks_time_embed_dim = time_embed_dim
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
| down_block = get_down_block( | 3 | 2023-10-14 12:18:38+00:00 | 16k |
PixArt-alpha/PixArt-alpha | train_scripts/train_pixart_lcm_lora.py | [
{
"identifier": "IDDPM",
"path": "diffusion/iddpm.py",
"snippet": "def IDDPM(\n timestep_respacing,\n noise_schedule=\"linear\",\n use_kl=False,\n sigma_small=False,\n predict_xstart=False,\n learn_sigma=True,\n pred_sigma=True,\n rescale_learned_s... | import os
import sys
import types
import argparse
import datetime
import time
import warnings
import torch
import torch.nn.functional as F
import numpy as np
import re
import accelerate
from pathlib import Path
from accelerate import Accelerator, InitProcessGroupKwargs
from accelerate.utils import DistributedType
from torch.utils.data import RandomSampler
from mmcv.runner import LogBuffer
from packaging import version
from diffusion import IDDPM
from diffusion.utils.dist_utils import get_world_size, clip_grad_norm_
from diffusion.data.builder import build_dataset, build_dataloader, set_data_root
from diffusion.utils.logger import get_root_logger
from diffusion.utils.misc import set_random_seed, read_config, init_random_seed, DebugUnderflowOverflow
from diffusion.utils.optimizer import build_optimizer, auto_scale_lr
from diffusion.utils.lr_scheduler import build_lr_scheduler
from diffusion.utils.data_sampler import AspectRatioBatchSampler, BalancedAspectRatioBatchSampler
from peft import LoraConfig, get_peft_model, get_peft_model_state_dict
from diffusers import AutoencoderKL, Transformer2DModel, StableDiffusionPipeline, PixArtAlphaPipeline
from accelerate import FullyShardedDataParallelPlugin
from torch.distributed.fsdp.fully_sharded_data_parallel import FullStateDictConfig | 11,110 |
logger = get_root_logger(os.path.join(config.work_dir, 'train_log.log'))
logger.info(accelerator.state)
config.seed = init_random_seed(config.get('seed', None))
set_random_seed(config.seed)
if accelerator.is_main_process:
config.dump(os.path.join(config.work_dir, 'config.py'))
logger.info(f"Config: \n{config.pretty_text}")
logger.info(f"World_size: {get_world_size()}, seed: {config.seed}")
logger.info(f"Initializing: {init_train} for training")
image_size = config.image_size # @param [256, 512]
latent_size = int(image_size) // 8
pred_sigma = getattr(config, 'pred_sigma', True)
learn_sigma = getattr(config, 'learn_sigma', True) and pred_sigma
# prepare null_embedding for training
if not os.path.exists('output/pretrained_models/null_embed.pth'):
logger.info(f"Creating output/pretrained_models/null_embed.pth")
os.makedirs('output/pretrained_models/', exist_ok=True)
pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16, use_safetensors=True,).to("cuda")
torch.save(pipe.encode_prompt(""), 'output/pretrained_models/null_embed.pth')
del pipe
torch.cuda.empty_cache()
# build models
train_diffusion = IDDPM(str(config.train_sampling_steps), learn_sigma=learn_sigma, pred_sigma=pred_sigma, return_startx=True)
model_teacher = Transformer2DModel.from_pretrained(config.load_from, subfolder="transformer")
model_teacher.requires_grad_(False)
model = Transformer2DModel.from_pretrained(config.load_from, subfolder="transformer").train()
logger.info(f"{model.__class__.__name__} Model Parameters: {sum(p.numel() for p in model.parameters()):}")
lora_config = LoraConfig(
r=config.lora_rank,
target_modules=[
"to_q",
"to_k",
"to_v",
"to_out.0",
"proj_in",
"proj_out",
"ff.net.0.proj",
"ff.net.2",
"proj",
"linear",
"linear_1",
"linear_2",
# "scale_shift_table", # not available due to the implementation in huggingface/peft, working on it.
],
)
print(lora_config)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# 9. Handle mixed precision and device placement
# For mixed precision training we cast all non-trainable weigths to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# 11. Enable optimizations
# model.enable_xformers_memory_efficient_attention()
# model_teacher.enable_xformers_memory_efficient_attention()
lora_layers = filter(lambda p: p.requires_grad, model.parameters())
# for name, params in model.named_parameters():
# if params.requires_grad == False: logger.info(f"freeze param: {name}")
#
# for name, params in model.named_parameters():
# if params.requires_grad == True: logger.info(f"trainable param: {name}")
# 10. Handle saving and loading of checkpoints
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
transformer_ = accelerator.unwrap_model(models[0])
lora_state_dict = get_peft_model_state_dict(transformer_, adapter_name="default")
StableDiffusionPipeline.save_lora_weights(os.path.join(output_dir, "transformer_lora"), lora_state_dict)
# save weights in peft format to be able to load them back
transformer_.save_pretrained(output_dir)
for _, model in enumerate(models):
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
# load the LoRA into the model
transformer_ = accelerator.unwrap_model(models[0])
transformer_.load_adapter(input_dir, "default", is_trainable=True)
for _ in range(len(models)):
# pop models so that they are not loaded again
models.pop()
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
if config.grad_checkpointing:
model.enable_gradient_checkpointing()
if not config.data.load_vae_feat:
vae = AutoencoderKL.from_pretrained(config.vae_pretrained).cuda()
# prepare for FSDP clip grad norm calculation
if accelerator.distributed_type == DistributedType.FSDP:
for m in accelerator._models:
m.clip_grad_norm_ = types.MethodType(clip_grad_norm_, m)
# build dataloader
set_data_root(config.data_root)
dataset = build_dataset(config.data, resolution=image_size, aspect_ratio_type=config.aspect_ratio_type)
if config.multi_scale:
| current_file_path = Path(__file__).resolve()
sys.path.insert(0, str(current_file_path.parent.parent))
warnings.filterwarnings("ignore") # ignore warning
def set_fsdp_env():
os.environ["ACCELERATE_USE_FSDP"] = 'true'
os.environ["FSDP_AUTO_WRAP_POLICY"] = 'TRANSFORMER_BASED_WRAP'
os.environ["FSDP_BACKWARD_PREFETCH"] = 'BACKWARD_PRE'
os.environ["FSDP_TRANSFORMER_CLS_TO_WRAP"] = 'PixArtBlock'
def filter_keys(key_set):
def _f(dictionary):
return {k: v for k, v in dictionary.items() if k in key_set}
return _f
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
return x[(...,) + (None,) * dims_to_append]
# From LCMScheduler.get_scalings_for_boundary_condition_discrete
def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
c_skip = sigma_data**2 / ((timestep / 0.1) ** 2 + sigma_data**2)
c_out = (timestep / 0.1) / ((timestep / 0.1) ** 2 + sigma_data**2) ** 0.5
return c_skip, c_out
# Compare LCMScheduler.step, Step 4
def predicted_origin(model_output, timesteps, sample, prediction_type, alphas, sigmas):
if prediction_type == "epsilon":
sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
alphas = extract_into_tensor(alphas, timesteps, sample.shape)
pred_x_0 = (sample - sigmas * model_output) / alphas
elif prediction_type == "v_prediction":
sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
alphas = extract_into_tensor(alphas, timesteps, sample.shape)
pred_x_0 = alphas * sample - sigmas * model_output
else:
raise ValueError(f"Prediction type {prediction_type} currently not supported.")
return pred_x_0
def extract_into_tensor(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
class DDIMSolver:
def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
# DDIM sampling parameters
step_ratio = timesteps // ddim_timesteps
self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]
self.ddim_alpha_cumprods_prev = np.asarray(
[alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()
)
# convert to torch tensors
self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()
self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)
self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)
def to(self, device):
self.ddim_timesteps = self.ddim_timesteps.to(device)
self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)
self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)
return self
def ddim_step(self, pred_x0, pred_noise, timestep_index):
alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)
dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise
x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt
return x_prev
def train(model):
if config.get('debug_nan', False):
DebugUnderflowOverflow(model)
logger.info('NaN debugger registered. Start to detect overflow during training.')
time_start, last_tic = time.time(), time.time()
log_buffer = LogBuffer()
global_step = start_step
load_vae_feat = getattr(train_dataloader.dataset, 'load_vae_feat', False)
# Create uncond embeds for classifier free guidance
uncond_prompt_embeds = torch.load('output/pretrained_models/null_embed.pth', map_location='cpu').to(accelerator.device).repeat(config.train_batch_size, 1, 1, 1)
# Now you train the model
for epoch in range(start_epoch + 1, config.num_epochs + 1):
data_time_start= time.time()
data_time_all = 0
for step, batch in enumerate(train_dataloader):
data_time_all += time.time() - data_time_start
if load_vae_feat:
z = batch[0]
else:
with torch.no_grad():
with torch.cuda.amp.autocast(enabled=config.mixed_precision == 'fp16'):
posterior = vae.encode(batch[0]).latent_dist
if config.sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
latents = (z * config.scale_factor).to(weight_dtype)
y = batch[1].squeeze(1).to(weight_dtype)
y_mask = batch[2].squeeze(1).squeeze(1).to(weight_dtype)
data_info = {'resolution': batch[3]['img_hw'].to(weight_dtype), 'aspect_ratio': batch[3]['aspect_ratio'].to(weight_dtype),}
# Sample a random timestep for each image
grad_norm = None
with accelerator.accumulate(model):
# Predict the noise residual
optimizer.zero_grad()
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image t_n ~ U[0, N - k - 1] without bias.
topk = config.train_sampling_steps // config.num_ddim_timesteps
index = torch.randint(0, config.num_ddim_timesteps, (bsz,), device=latents.device).long()
start_timesteps = solver.ddim_timesteps[index]
timesteps = start_timesteps - topk
timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)
# Get boundary scalings for start_timesteps and (end) timesteps.
c_skip_start, c_out_start = scalings_for_boundary_conditions(start_timesteps)
c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]
c_skip, c_out = scalings_for_boundary_conditions(timesteps)
c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]
# Sample a random guidance scale w from U[w_min, w_max] and embed it
# w = (config.w_max - config.w_min) * torch.rand((bsz,)) + config.w_min
w = config.cfg_scale * torch.ones((bsz,))
w = w.reshape(bsz, 1, 1, 1)
w = w.to(device=latents.device, dtype=latents.dtype)
# Get online LCM prediction on z_{t_{n + k}}, w, c, t_{n + k}
_, pred_x_0, noisy_model_input = train_diffusion.training_losses_diffusers(
model, latents, start_timesteps,
model_kwargs=dict(encoder_hidden_states=y, encoder_attention_mask=y_mask, added_cond_kwargs=data_info),
noise=noise
)
model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0
with torch.no_grad():
with torch.autocast("cuda"):
cond_teacher_output, cond_pred_x0, _ = train_diffusion.training_losses_diffusers(
model_teacher, latents, start_timesteps,
model_kwargs=dict(encoder_hidden_states=y, encoder_attention_mask=y_mask, added_cond_kwargs=data_info),
noise=noise
)
# Get teacher model prediction on noisy_latents and unconditional embedding
uncond_teacher_output, uncond_pred_x0, _ = train_diffusion.training_losses_diffusers(
model_teacher, latents, start_timesteps,
model_kwargs=dict(encoder_hidden_states=uncond_prompt_embeds, encoder_attention_mask=y_mask, added_cond_kwargs=data_info),
noise=noise
)
# Perform "CFG" to get x_prev estimate (using the LCM paper's CFG formulation)
pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)
pred_noise = cond_teacher_output + w * (cond_teacher_output - uncond_teacher_output)
x_prev = solver.ddim_step(pred_x0, pred_noise, index)
# Get target LCM prediction on x_prev, w, c, t_n
with torch.no_grad():
with torch.autocast("cuda", enabled=True):
_, pred_x_0, _ = train_diffusion.training_losses_diffusers(
model, x_prev.float(), timesteps,
model_kwargs=dict(encoder_hidden_states=y, encoder_attention_mask=y_mask, added_cond_kwargs=data_info),
skip_noise=True
)
target = c_skip * x_prev + c_out * pred_x_0
# Calculate loss
if config.loss_type == "l2":
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
elif config.loss_type == "huber":
loss = torch.mean(torch.sqrt((model_pred.float() - target.float()) ** 2 + config.huber_c**2) - config.huber_c)
accelerator.backward(loss)
if accelerator.sync_gradients:
grad_norm = accelerator.clip_grad_norm_(model.parameters(), config.gradient_clip)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
lr = lr_scheduler.get_last_lr()[0]
logs = {"loss": accelerator.gather(loss).mean().item()}
if grad_norm is not None:
logs.update(grad_norm=accelerator.gather(grad_norm).mean().item())
log_buffer.update(logs)
if (step + 1) % config.log_interval == 0 or (step + 1) == 1:
t = (time.time() - last_tic) / config.log_interval
t_d = data_time_all / config.log_interval
avg_time = (time.time() - time_start) / (global_step + 1)
eta = str(datetime.timedelta(seconds=int(avg_time * (total_steps - start_step - global_step - 1))))
eta_epoch = str(datetime.timedelta(seconds=int(avg_time * (len(train_dataloader) - step - 1))))
# avg_loss = sum(loss_buffer) / len(loss_buffer)
log_buffer.average()
info = f"Step/Epoch [{(epoch-1)*len(train_dataloader)+step+1}/{epoch}][{step + 1}/{len(train_dataloader)}]:total_eta: {eta}, " \
f"epoch_eta:{eta_epoch}, time_all:{t:.3f}, time_data:{t_d:.3f}, lr:{lr:.3e}, s:({data_info['resolution'][0][0].item()}, {data_info['resolution'][0][1].item()}), "
info += ', '.join([f"{k}:{v:.4f}" for k, v in log_buffer.output.items()])
logger.info(info)
last_tic = time.time()
log_buffer.clear()
data_time_all = 0
logs.update(lr=lr)
accelerator.log(logs, step=global_step + start_step)
global_step += 1
data_time_start= time.time()
accelerator.wait_for_everyone()
if accelerator.is_main_process:
if ((epoch - 1) * len(train_dataloader) + step + 1) % config.save_model_steps == 0:
save_path = os.path.join(os.path.join(config.work_dir, 'checkpoints'), f"checkpoint-{(epoch - 1) * len(train_dataloader) + step + 1}")
os.umask(0o000)
logger.info(f"Start to save state to {save_path}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
accelerator.wait_for_everyone()
if epoch % config.save_model_epochs == 0 or epoch == config.num_epochs:
os.umask(0o000)
save_path = os.path.join(os.path.join(config.work_dir, 'checkpoints'), f"checkpoint-{(epoch - 1) * len(train_dataloader) + step + 1}")
logger.info(f"Start to save state to {save_path}")
model = accelerator.unwrap_model(model)
model.save_pretrained(save_path)
lora_state_dict = get_peft_model_state_dict(model, adapter_name="default")
StableDiffusionPipeline.save_lora_weights(os.path.join(save_path, "transformer_lora"), lora_state_dict)
logger.info(f"Saved state to {save_path}")
def parse_args():
parser = argparse.ArgumentParser(description="Process some integers.")
parser.add_argument("config", type=str, help="config")
parser.add_argument("--cloud", action='store_true', default=False, help="cloud or local machine")
parser.add_argument("--work-dir", default='output', help='the dir to save logs and models')
parser.add_argument("--resume-from", help='the dir to save logs and models')
parser.add_argument("--local-rank", type=int, default=-1)
parser.add_argument("--local_rank", type=int, default=-1)
parser.add_argument("--debug", action='store_true')
parser.add_argument("--lora_rank", type=int, default=64, help="The rank of the LoRA projection matrix.", )
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
config = read_config(args.config)
config.resume_from = None
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
config.work_dir = args.work_dir
if args.cloud:
config.data_root = '/data/data'
if args.resume_from is not None:
config.resume_from = args.resume_from
if args.debug:
config.log_interval = 1
config.train_batch_size = 4
config.valid_num = 10
config.save_model_steps = 10
os.umask(0o000)
os.makedirs(config.work_dir, exist_ok=True)
init_handler = InitProcessGroupKwargs()
init_handler.timeout = datetime.timedelta(seconds=5400) # change timeout to avoid a strange NCCL bug
# Initialize accelerator and tensorboard logging
if config.use_fsdp:
init_train = 'FSDP'
set_fsdp_env()
fsdp_plugin = FullyShardedDataParallelPlugin(state_dict_config=FullStateDictConfig(offload_to_cpu=False, rank0_only=False),)
else:
init_train = 'DDP'
fsdp_plugin = None
even_batches = True
if config.multi_scale:
even_batches=False,
accelerator = Accelerator(
mixed_precision=config.mixed_precision,
gradient_accumulation_steps=config.gradient_accumulation_steps,
log_with="tensorboard",
project_dir=os.path.join(config.work_dir, "logs"),
fsdp_plugin=fsdp_plugin,
even_batches=even_batches,
kwargs_handlers=[init_handler]
)
logger = get_root_logger(os.path.join(config.work_dir, 'train_log.log'))
logger.info(accelerator.state)
config.seed = init_random_seed(config.get('seed', None))
set_random_seed(config.seed)
if accelerator.is_main_process:
config.dump(os.path.join(config.work_dir, 'config.py'))
logger.info(f"Config: \n{config.pretty_text}")
logger.info(f"World_size: {get_world_size()}, seed: {config.seed}")
logger.info(f"Initializing: {init_train} for training")
image_size = config.image_size # @param [256, 512]
latent_size = int(image_size) // 8
pred_sigma = getattr(config, 'pred_sigma', True)
learn_sigma = getattr(config, 'learn_sigma', True) and pred_sigma
# prepare null_embedding for training
if not os.path.exists('output/pretrained_models/null_embed.pth'):
logger.info(f"Creating output/pretrained_models/null_embed.pth")
os.makedirs('output/pretrained_models/', exist_ok=True)
pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16, use_safetensors=True,).to("cuda")
torch.save(pipe.encode_prompt(""), 'output/pretrained_models/null_embed.pth')
del pipe
torch.cuda.empty_cache()
# build models
train_diffusion = IDDPM(str(config.train_sampling_steps), learn_sigma=learn_sigma, pred_sigma=pred_sigma, return_startx=True)
model_teacher = Transformer2DModel.from_pretrained(config.load_from, subfolder="transformer")
model_teacher.requires_grad_(False)
model = Transformer2DModel.from_pretrained(config.load_from, subfolder="transformer").train()
logger.info(f"{model.__class__.__name__} Model Parameters: {sum(p.numel() for p in model.parameters()):}")
lora_config = LoraConfig(
r=config.lora_rank,
target_modules=[
"to_q",
"to_k",
"to_v",
"to_out.0",
"proj_in",
"proj_out",
"ff.net.0.proj",
"ff.net.2",
"proj",
"linear",
"linear_1",
"linear_2",
# "scale_shift_table", # not available due to the implementation in huggingface/peft, working on it.
],
)
print(lora_config)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# 9. Handle mixed precision and device placement
# For mixed precision training we cast all non-trainable weigths to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# 11. Enable optimizations
# model.enable_xformers_memory_efficient_attention()
# model_teacher.enable_xformers_memory_efficient_attention()
lora_layers = filter(lambda p: p.requires_grad, model.parameters())
# for name, params in model.named_parameters():
# if params.requires_grad == False: logger.info(f"freeze param: {name}")
#
# for name, params in model.named_parameters():
# if params.requires_grad == True: logger.info(f"trainable param: {name}")
# 10. Handle saving and loading of checkpoints
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
transformer_ = accelerator.unwrap_model(models[0])
lora_state_dict = get_peft_model_state_dict(transformer_, adapter_name="default")
StableDiffusionPipeline.save_lora_weights(os.path.join(output_dir, "transformer_lora"), lora_state_dict)
# save weights in peft format to be able to load them back
transformer_.save_pretrained(output_dir)
for _, model in enumerate(models):
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
# load the LoRA into the model
transformer_ = accelerator.unwrap_model(models[0])
transformer_.load_adapter(input_dir, "default", is_trainable=True)
for _ in range(len(models)):
# pop models so that they are not loaded again
models.pop()
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
if config.grad_checkpointing:
model.enable_gradient_checkpointing()
if not config.data.load_vae_feat:
vae = AutoencoderKL.from_pretrained(config.vae_pretrained).cuda()
# prepare for FSDP clip grad norm calculation
if accelerator.distributed_type == DistributedType.FSDP:
for m in accelerator._models:
m.clip_grad_norm_ = types.MethodType(clip_grad_norm_, m)
# build dataloader
set_data_root(config.data_root)
dataset = build_dataset(config.data, resolution=image_size, aspect_ratio_type=config.aspect_ratio_type)
if config.multi_scale: | batch_sampler = AspectRatioBatchSampler(sampler=RandomSampler(dataset), dataset=dataset, | 14 | 2023-10-12 14:16:33+00:00 | 16k |
showlab/MotionDirector | MotionDirector_train.py | [
{
"identifier": "UNet3DConditionModel",
"path": "models/unet_3d_condition.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 sh... | import argparse
import datetime
import logging
import inspect
import math
import os
import random
import gc
import copy
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import diffusers
import transformers
import imageio
import numpy as np
import itertools
import bitsandbytes as bnb
from typing import Dict, Optional, Tuple
from omegaconf import OmegaConf
from torchvision import transforms
from tqdm.auto import tqdm
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from models.unet_3d_condition import UNet3DConditionModel
from diffusers.models import AutoencoderKL
from diffusers import DDIMScheduler, TextToVideoSDPipeline
from diffusers.optimization import get_scheduler
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 transformers import CLIPTextModel, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPEncoder
from utils.dataset import VideoJsonDataset, SingleVideoDataset, \
ImageDataset, VideoFolderDataset, CachedDataset
from einops import rearrange, repeat
from utils.lora_handler import LoraHandler
from utils.lora import extract_lora_child_module
from utils.ddim_utils import ddim_inversion
from xformers.ops import MemoryEfficientAttentionFlashAttentionOp | 11,326 |
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 = []
# Loop through all available datasets, get the name, then add to list of data to process.
|
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 = []
# Loop through all available datasets, get the name, then add to list of data to process. | for DataSet in [VideoJsonDataset, SingleVideoDataset, ImageDataset, VideoFolderDataset]: | 1 | 2023-10-12 12:06:55+00:00 | 16k |
NVlabs/EmerNeRF | datasets/base/split_wrapper.py | [
{
"identifier": "SceneLidarSource",
"path": "datasets/base/lidar_source.py",
"snippet": "class SceneLidarSource(abc.ABC):\n \"\"\"\n The base class for the lidar source of a scene.\n \"\"\"\n\n data_cfg: OmegaConf = None\n # the normalized timestamps of all points (normalized to [0, 1]), ... | from typing import List, Union
from .lidar_source import SceneLidarSource
from .pixel_source import ScenePixelSource
import torch
import torch.nn.functional as F | 12,378 |
class SplitWrapper(torch.utils.data.Dataset):
# a sufficiently large number to make sure we don't run out of data
_num_iters = 1000000
def __init__(
self,
|
class SplitWrapper(torch.utils.data.Dataset):
# a sufficiently large number to make sure we don't run out of data
_num_iters = 1000000
def __init__(
self, | datasource: Union[ScenePixelSource, SceneLidarSource], | 1 | 2023-10-11 20:56:27+00:00 | 16k |
alibaba-damo-academy/FunCodec | funcodec/models/decoder/contextual_decoder.py | [
{
"identifier": "utils",
"path": "funcodec/modules/streaming_utils/utils.py",
"snippet": "def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):\ndef apply_cmvn(inputs, mvn):\ndef drop_and_add(inputs: torch.Tensor,\n outputs: torch.Tensor,\n training: ... | from typing import List
from typing import Tuple
from funcodec.modules.streaming_utils import utils as myutils
from funcodec.models.decoder.transformer_decoder import BaseTransformerDecoder
from typeguard import check_argument_types
from funcodec.modules.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
from funcodec.modules.embedding import PositionalEncoding
from funcodec.modules.layer_norm import LayerNorm
from funcodec.modules.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
from funcodec.modules.repeat import repeat
from funcodec.models.decoder.sanm_decoder import DecoderLayerSANM, ParaformerSANMDecoder
import logging
import torch
import torch.nn as nn
import numpy as np | 13,827 |
class ContextualDecoderLayer(nn.Module):
def __init__(
self,
size,
self_attn,
src_attn,
feed_forward,
dropout_rate,
normalize_before=True,
concat_after=False,
):
"""Construct an DecoderLayer object."""
super(ContextualDecoderLayer, self).__init__()
self.size = size
self.self_attn = self_attn
self.src_attn = src_attn
self.feed_forward = feed_forward
|
class ContextualDecoderLayer(nn.Module):
def __init__(
self,
size,
self_attn,
src_attn,
feed_forward,
dropout_rate,
normalize_before=True,
concat_after=False,
):
"""Construct an DecoderLayer object."""
super(ContextualDecoderLayer, self).__init__()
self.size = size
self.self_attn = self_attn
self.src_attn = src_attn
self.feed_forward = feed_forward | self.norm1 = LayerNorm(size) | 5 | 2023-10-07 02:00:40+00:00 | 16k |
longzw1997/Open-GroundingDino | models/GroundingDINO/groundingdino.py | [
{
"identifier": "box_ops",
"path": "groundingdino/util/box_ops.py",
"snippet": "def box_cxcywh_to_xyxy(x):\ndef box_xyxy_to_cxcywh(x):\ndef box_iou(boxes1, boxes2):\ndef generalized_box_iou(boxes1, boxes2):\ndef box_iou_pairwise(boxes1, boxes2):\ndef generalized_box_iou_pairwise(boxes1, boxes2):\ndef ma... | import copy
import torch
import torch.nn.functional as F
from typing import List
from torch import nn
from torchvision.ops.boxes import nms
from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
from groundingdino.util import box_ops, get_tokenlizer
from groundingdino.util.misc import (
NestedTensor,
accuracy,
get_world_size,
interpolate,
inverse_sigmoid,
is_dist_avail_and_initialized,
nested_tensor_from_tensor_list,
)
from groundingdino.util.utils import get_phrases_from_posmap
from groundingdino.util.visualizer import COCOVisualizer
from groundingdino.util.vl_utils import create_positive_map_from_span
from ..registry import MODULE_BUILD_FUNCS
from .backbone import build_backbone
from .bertwarper import (
BertModelWarper,
generate_masks_with_special_tokens,
generate_masks_with_special_tokens_and_transfer_map,
)
from .transformer import build_transformer
from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss
from .matcher import build_matcher
from pycocotools.coco import COCO | 12,492 | two_stage_type
)
if two_stage_type != "no":
if two_stage_bbox_embed_share:
assert dec_pred_bbox_embed_share
self.transformer.enc_out_bbox_embed = _bbox_embed
else:
self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
if two_stage_class_embed_share:
assert dec_pred_bbox_embed_share
self.transformer.enc_out_class_embed = _class_embed
else:
self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
self.refpoint_embed = None
self._reset_parameters()
def _reset_parameters(self):
# init input_proj
for proj in self.input_proj:
nn.init.xavier_uniform_(proj[0].weight, gain=1)
nn.init.constant_(proj[0].bias, 0)
def init_ref_points(self, use_num_queries):
self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
def forward(self, samples: NestedTensor, targets: List = None, **kw):
"""The forward expects a NestedTensor, which consists of:
- samples.tensor: batched images, of shape [batch_size x 3 x H x W]
- samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
It returns a dict with the following elements:
- "pred_logits": the classification logits (including no-object) for all queries.
Shape= [batch_size x num_queries x num_classes]
- "pred_boxes": The normalized boxes coordinates for all queries, represented as
(center_x, center_y, width, height). These values are normalized in [0, 1],
relative to the size of each individual image (disregarding possible padding).
See PostProcess for information on how to retrieve the unnormalized bounding box.
- "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
dictionnaries containing the two above keys for each decoder layer.
"""
if targets is None:
captions = kw["captions"]
else:
captions = [t["caption"] for t in targets]
# encoder texts
tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to(
samples.device
)
one_hot_token = tokenized
(
text_self_attention_masks,
position_ids,
cate_to_token_mask_list,
) = generate_masks_with_special_tokens_and_transfer_map(
tokenized, self.specical_tokens, self.tokenizer
)
if text_self_attention_masks.shape[1] > self.max_text_len:
text_self_attention_masks = text_self_attention_masks[
:, : self.max_text_len, : self.max_text_len
]
position_ids = position_ids[:, : self.max_text_len]
tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
# extract text embeddings
if self.sub_sentence_present:
tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
tokenized_for_encoder["attention_mask"] = text_self_attention_masks
tokenized_for_encoder["position_ids"] = position_ids
else:
tokenized_for_encoder = tokenized
bert_output = self.bert(**tokenized_for_encoder) # bs, 195, 768
encoded_text = self.feat_map(bert_output["last_hidden_state"]) # bs, 195, d_model
text_token_mask = tokenized.attention_mask.bool() # bs, 195
# text_token_mask: True for nomask, False for mask
# text_self_attention_masks: True for nomask, False for mask
if encoded_text.shape[1] > self.max_text_len:
encoded_text = encoded_text[:, : self.max_text_len, :]
text_token_mask = text_token_mask[:, : self.max_text_len]
position_ids = position_ids[:, : self.max_text_len]
text_self_attention_masks = text_self_attention_masks[
:, : self.max_text_len, : self.max_text_len
]
text_dict = {
"encoded_text": encoded_text, # bs, 195, d_model
"text_token_mask": text_token_mask, # bs, 195
"position_ids": position_ids, # bs, 195
"text_self_attention_masks": text_self_attention_masks, # bs, 195,195
}
if isinstance(samples, (list, torch.Tensor)):
samples = nested_tensor_from_tensor_list(samples)
features, poss = self.backbone(samples)
srcs = []
masks = []
for l, feat in enumerate(features):
src, mask = feat.decompose()
srcs.append(self.input_proj[l](src))
masks.append(mask)
assert mask is not None
if self.num_feature_levels > len(srcs):
_len_srcs = len(srcs)
for l in range(_len_srcs, self.num_feature_levels):
if l == _len_srcs:
src = self.input_proj[l](features[-1].tensors)
else:
src = self.input_proj[l](srcs[-1])
m = samples.mask
| # ------------------------------------------------------------------------
# Grounding DINO
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Conditional DETR model and criterion classes.
# Copyright (c) 2021 Microsoft. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
class GroundingDINO(nn.Module):
"""This is the Cross-Attention Detector module that performs object detection"""
def __init__(
self,
backbone,
transformer,
num_queries,
aux_loss=False,
iter_update=False,
query_dim=2,
num_feature_levels=1,
nheads=8,
# two stage
two_stage_type="no", # ['no', 'standard']
dec_pred_bbox_embed_share=True,
two_stage_class_embed_share=True,
two_stage_bbox_embed_share=True,
num_patterns=0,
dn_number=100,
dn_box_noise_scale=0.4,
dn_label_noise_ratio=0.5,
dn_labelbook_size=100,
text_encoder_type="bert-base-uncased",
sub_sentence_present=True,
max_text_len=256,
):
"""Initializes the model.
Parameters:
backbone: torch module of the backbone to be used. See backbone.py
transformer: torch module of the transformer architecture. See transformer.py
num_queries: number of object queries, ie detection slot. This is the maximal number of objects
Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
"""
super().__init__()
self.num_queries = num_queries
self.transformer = transformer
self.hidden_dim = hidden_dim = transformer.d_model
self.num_feature_levels = num_feature_levels
self.nheads = nheads
self.max_text_len = 256
self.sub_sentence_present = sub_sentence_present
# setting query dim
self.query_dim = query_dim
assert query_dim == 4
# for dn training
self.num_patterns = num_patterns
self.dn_number = dn_number
self.dn_box_noise_scale = dn_box_noise_scale
self.dn_label_noise_ratio = dn_label_noise_ratio
self.dn_labelbook_size = dn_labelbook_size
# bert
self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
self.bert.pooler.dense.weight.requires_grad_(False)
self.bert.pooler.dense.bias.requires_grad_(False)
self.bert = BertModelWarper(bert_model=self.bert)
self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
nn.init.constant_(self.feat_map.bias.data, 0)
nn.init.xavier_uniform_(self.feat_map.weight.data)
# freeze
# special tokens
self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
# prepare input projection layers
if num_feature_levels > 1:
num_backbone_outs = len(backbone.num_channels)
input_proj_list = []
for _ in range(num_backbone_outs):
in_channels = backbone.num_channels[_]
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
)
for _ in range(num_feature_levels - num_backbone_outs):
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
nn.GroupNorm(32, hidden_dim),
)
)
in_channels = hidden_dim
self.input_proj = nn.ModuleList(input_proj_list)
else:
assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
self.input_proj = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
]
)
self.backbone = backbone
self.aux_loss = aux_loss
self.box_pred_damping = box_pred_damping = None
self.iter_update = iter_update
assert iter_update, "Why not iter_update?"
# prepare pred layers
self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
# prepare class & box embed
_class_embed = ContrastiveEmbed()
_bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
if dec_pred_bbox_embed_share:
box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
else:
box_embed_layerlist = [
copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
]
class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
self.bbox_embed = nn.ModuleList(box_embed_layerlist)
self.class_embed = nn.ModuleList(class_embed_layerlist)
self.transformer.decoder.bbox_embed = self.bbox_embed
self.transformer.decoder.class_embed = self.class_embed
# two stage
self.two_stage_type = two_stage_type
assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
two_stage_type
)
if two_stage_type != "no":
if two_stage_bbox_embed_share:
assert dec_pred_bbox_embed_share
self.transformer.enc_out_bbox_embed = _bbox_embed
else:
self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
if two_stage_class_embed_share:
assert dec_pred_bbox_embed_share
self.transformer.enc_out_class_embed = _class_embed
else:
self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
self.refpoint_embed = None
self._reset_parameters()
def _reset_parameters(self):
# init input_proj
for proj in self.input_proj:
nn.init.xavier_uniform_(proj[0].weight, gain=1)
nn.init.constant_(proj[0].bias, 0)
def init_ref_points(self, use_num_queries):
self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
def forward(self, samples: NestedTensor, targets: List = None, **kw):
"""The forward expects a NestedTensor, which consists of:
- samples.tensor: batched images, of shape [batch_size x 3 x H x W]
- samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
It returns a dict with the following elements:
- "pred_logits": the classification logits (including no-object) for all queries.
Shape= [batch_size x num_queries x num_classes]
- "pred_boxes": The normalized boxes coordinates for all queries, represented as
(center_x, center_y, width, height). These values are normalized in [0, 1],
relative to the size of each individual image (disregarding possible padding).
See PostProcess for information on how to retrieve the unnormalized bounding box.
- "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
dictionnaries containing the two above keys for each decoder layer.
"""
if targets is None:
captions = kw["captions"]
else:
captions = [t["caption"] for t in targets]
# encoder texts
tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to(
samples.device
)
one_hot_token = tokenized
(
text_self_attention_masks,
position_ids,
cate_to_token_mask_list,
) = generate_masks_with_special_tokens_and_transfer_map(
tokenized, self.specical_tokens, self.tokenizer
)
if text_self_attention_masks.shape[1] > self.max_text_len:
text_self_attention_masks = text_self_attention_masks[
:, : self.max_text_len, : self.max_text_len
]
position_ids = position_ids[:, : self.max_text_len]
tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
# extract text embeddings
if self.sub_sentence_present:
tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
tokenized_for_encoder["attention_mask"] = text_self_attention_masks
tokenized_for_encoder["position_ids"] = position_ids
else:
tokenized_for_encoder = tokenized
bert_output = self.bert(**tokenized_for_encoder) # bs, 195, 768
encoded_text = self.feat_map(bert_output["last_hidden_state"]) # bs, 195, d_model
text_token_mask = tokenized.attention_mask.bool() # bs, 195
# text_token_mask: True for nomask, False for mask
# text_self_attention_masks: True for nomask, False for mask
if encoded_text.shape[1] > self.max_text_len:
encoded_text = encoded_text[:, : self.max_text_len, :]
text_token_mask = text_token_mask[:, : self.max_text_len]
position_ids = position_ids[:, : self.max_text_len]
text_self_attention_masks = text_self_attention_masks[
:, : self.max_text_len, : self.max_text_len
]
text_dict = {
"encoded_text": encoded_text, # bs, 195, d_model
"text_token_mask": text_token_mask, # bs, 195
"position_ids": position_ids, # bs, 195
"text_self_attention_masks": text_self_attention_masks, # bs, 195,195
}
if isinstance(samples, (list, torch.Tensor)):
samples = nested_tensor_from_tensor_list(samples)
features, poss = self.backbone(samples)
srcs = []
masks = []
for l, feat in enumerate(features):
src, mask = feat.decompose()
srcs.append(self.input_proj[l](src))
masks.append(mask)
assert mask is not None
if self.num_feature_levels > len(srcs):
_len_srcs = len(srcs)
for l in range(_len_srcs, self.num_feature_levels):
if l == _len_srcs:
src = self.input_proj[l](features[-1].tensors)
else:
src = self.input_proj[l](srcs[-1])
m = samples.mask | mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0] | 5 | 2023-10-14 02:20:31+00:00 | 16k |
Beckschen/3D-TransUNet | nn_transunet/trainer/network_trainer.py | [
{
"identifier": "SegmentationNetwork",
"path": "nn_transunet/networks/neural_network.py",
"snippet": "class SegmentationNetwork(NeuralNetwork):\n def __init__(self):\n super(NeuralNetwork, self).__init__()\n\n # if we have 5 pooling then our patch size must be divisible by 2**5\n ... | from _warnings import warn
from typing import Tuple
from batchgenerators.utilities.file_and_folder_operations import *
from nn_transunet.networks.neural_network import SegmentationNetwork
from sklearn.model_selection import KFold
from torch import nn
from torch.cuda.amp import GradScaler, autocast
from torch.optim.lr_scheduler import _LRScheduler
from ..trainer.loss_functions import ModelLossSemsegGatedCRF
from time import time, sleep
from collections import OrderedDict
from abc import abstractmethod
from datetime import datetime
from tqdm import trange
from ..utils.dist_utils import check_call_hdfs_command, mkdir_hdfs
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import sys
import torch.backends.cudnn as cudnn
import torch
import math
import matplotlib.pyplot as plt | 13,783 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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.
matplotlib.use("agg")
def maybe_to_torch(d):
if isinstance(d, list):
d = [maybe_to_torch(i) if not isinstance(i, torch.Tensor) else i for i in d]
elif not isinstance(d, torch.Tensor):
d = torch.from_numpy(d).float()
return d
def poly_lr(epoch, max_epochs, initial_lr, exponent=0.9):
return initial_lr * (1 - epoch / max_epochs) ** exponent
def warmup_poly_lr(epoch, max_epochs, warmup_epochs, initial_lr, exponent=0.9):
if epoch < warmup_epochs:
return initial_lr * (float(epoch) / float(max(1.0, warmup_epochs)))
epoch_rel = epoch - warmup_epochs
max_epochs_rel = max_epochs - warmup_epochs
return initial_lr * (1 - epoch_rel / max_epochs_rel) ** exponent
def to_cuda(data, non_blocking=True, gpu_id=0):
if isinstance(data, list):
data = [i.cuda(gpu_id, non_blocking=non_blocking) for i in data]
else:
data = data.cuda(gpu_id, non_blocking=non_blocking)
return data
class NetworkTrainer(object):
def __init__(self, deterministic=True, fp16=False):
"""
A generic class that can train almost any neural network (RNNs excluded). It provides basic functionality such
as the training loop, tracking of training and validation losses (and the target metric if you implement it)
Training can be terminated early if the validation loss (or the target metric if implemented) do not improve
anymore. This is based on a moving average (MA) of the loss/metric instead of the raw values to get more smooth
results.
What you need to override:
- __init__
- initialize
- run_online_evaluation (optional)
- finish_online_evaluation (optional)
- validate
- predict_test_case
"""
self.fp16 = fp16
self.amp_grad_scaler = None
if deterministic:
np.random.seed(12345)
torch.manual_seed(12345)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(12345)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
else:
cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
################# SET THESE IN self.initialize() ###################################
self.network: Tuple[SegmentationNetwork, nn.DataParallel] = None
self.optimizer = None
self.lr_scheduler = None
self.tr_gen = self.val_gen = None
self.was_initialized = False
self.initial_lr = 1e-2
################# SET THESE IN INIT ################################################
self.output_folder = None
self.fold = None
# self.loss = PartiallyCrossEntropyLoss()
self.loss = None
| # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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.
matplotlib.use("agg")
def maybe_to_torch(d):
if isinstance(d, list):
d = [maybe_to_torch(i) if not isinstance(i, torch.Tensor) else i for i in d]
elif not isinstance(d, torch.Tensor):
d = torch.from_numpy(d).float()
return d
def poly_lr(epoch, max_epochs, initial_lr, exponent=0.9):
return initial_lr * (1 - epoch / max_epochs) ** exponent
def warmup_poly_lr(epoch, max_epochs, warmup_epochs, initial_lr, exponent=0.9):
if epoch < warmup_epochs:
return initial_lr * (float(epoch) / float(max(1.0, warmup_epochs)))
epoch_rel = epoch - warmup_epochs
max_epochs_rel = max_epochs - warmup_epochs
return initial_lr * (1 - epoch_rel / max_epochs_rel) ** exponent
def to_cuda(data, non_blocking=True, gpu_id=0):
if isinstance(data, list):
data = [i.cuda(gpu_id, non_blocking=non_blocking) for i in data]
else:
data = data.cuda(gpu_id, non_blocking=non_blocking)
return data
class NetworkTrainer(object):
def __init__(self, deterministic=True, fp16=False):
"""
A generic class that can train almost any neural network (RNNs excluded). It provides basic functionality such
as the training loop, tracking of training and validation losses (and the target metric if you implement it)
Training can be terminated early if the validation loss (or the target metric if implemented) do not improve
anymore. This is based on a moving average (MA) of the loss/metric instead of the raw values to get more smooth
results.
What you need to override:
- __init__
- initialize
- run_online_evaluation (optional)
- finish_online_evaluation (optional)
- validate
- predict_test_case
"""
self.fp16 = fp16
self.amp_grad_scaler = None
if deterministic:
np.random.seed(12345)
torch.manual_seed(12345)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(12345)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
else:
cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
################# SET THESE IN self.initialize() ###################################
self.network: Tuple[SegmentationNetwork, nn.DataParallel] = None
self.optimizer = None
self.lr_scheduler = None
self.tr_gen = self.val_gen = None
self.was_initialized = False
self.initial_lr = 1e-2
################# SET THESE IN INIT ################################################
self.output_folder = None
self.fold = None
# self.loss = PartiallyCrossEntropyLoss()
self.loss = None | self.gatecrfloss = ModelLossSemsegGatedCRF() | 1 | 2023-10-11 05:19:25+00:00 | 16k |
AMAAI-Lab/Video2Music | train.py | [
{
"identifier": "compute_vevo_accuracy",
"path": "dataset/vevo_dataset.py",
"snippet": "def compute_vevo_accuracy(out, tgt):\n softmax = nn.Softmax(dim=-1)\n out = torch.argmax(softmax(out), dim=-1)\n\n out = out.flatten()\n tgt = tgt.flatten()\n\n mask = (tgt != CHORD_PAD)\n\n out = o... | import os
import csv
import shutil
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from torch.optim import Adam
from dataset.vevo_dataset import compute_vevo_accuracy, create_vevo_datasets
from model.music_transformer import MusicTransformer
from model.video_music_transformer import VideoMusicTransformer
from model.loss import SmoothCrossEntropyLoss
from utilities.constants import *
from utilities.device import get_device, use_cuda
from utilities.lr_scheduling import LrStepTracker, get_lr
from utilities.argument_funcs import parse_train_args, print_train_args, write_model_params
from utilities.run_model_vevo import train_epoch, eval_model
from torch.utils.tensorboard import SummaryWriter | 12,950 |
CSV_HEADER = ["Epoch", "Learn rate",
"Avg Train loss (total)", "Avg Train loss (chord)", "Avg Train loss (emotion)",
"Avg Eval loss (total)", "Avg Eval loss (chord)", "Avg Eval loss (emotion)"]
BASELINE_EPOCH = -1
version = VERSION
split_ver = SPLIT_VER
split_path = "split_" + split_ver
VIS_MODELS_ARR = [
"2d/clip_l14p"
]
# main
def main( vm = "" , isPrintArgs = True ):
args = parse_train_args()
if isPrintArgs:
|
CSV_HEADER = ["Epoch", "Learn rate",
"Avg Train loss (total)", "Avg Train loss (chord)", "Avg Train loss (emotion)",
"Avg Eval loss (total)", "Avg Eval loss (chord)", "Avg Eval loss (emotion)"]
BASELINE_EPOCH = -1
version = VERSION
split_ver = SPLIT_VER
split_path = "split_" + split_ver
VIS_MODELS_ARR = [
"2d/clip_l14p"
]
# main
def main( vm = "" , isPrintArgs = True ):
args = parse_train_args()
if isPrintArgs: | print_train_args(args) | 10 | 2023-10-13 09:06:24+00:00 | 16k |
RobotLocomotion/gcs-science-robotics | reproduction/bimanual/helpers.py | [
{
"identifier": "BezierGCS",
"path": "gcs/bezier.py",
"snippet": "class BezierGCS(BaseGCS):\n def __init__(self, regions, order, continuity, edges=None, hdot_min=1e-6, full_dim_overlap=False):\n BaseGCS.__init__(self, regions)\n\n self.order = order\n self.continuity = continuity... | import numpy as np
import os
import time
from copy import copy
from pydrake.common import FindResourceOrThrow
from pydrake.geometry import (
CollisionFilterDeclaration,
GeometrySet,
MeshcatVisualizer,
Rgba,
Role,
SceneGraph
)
from pydrake.math import RigidTransform, RollPitchYaw, RotationMatrix
from pydrake.multibody.inverse_kinematics import InverseKinematics
from pydrake.multibody.parsing import LoadModelDirectives, Parser, ProcessModelDirectives
from pydrake.multibody.plant import AddMultibodyPlantSceneGraph, MultibodyPlant
from pydrake.perception import PointCloud
from pydrake.solvers import MosekSolver, Solve
from pydrake.systems.analysis import Simulator
from pydrake.systems.framework import DiagramBuilder, LeafSystem
from pydrake.systems.primitives import TrajectorySource
from pydrake.systems.rendering import MultibodyPositionToGeometryPose
from gcs.bezier import BezierGCS
from gcs.linear import LinearGCS
from gcs.rounding import *
from reproduction.prm_comparison.helpers import set_transparency_of_models
from reproduction.util import * | 12,024 |
def filterCollsionGeometry(scene_graph, context):
filter_manager = scene_graph.collision_filter_manager(context)
inspector = scene_graph.model_inspector()
iiwa1 = [[], [], [], [], [], [], [], []]
iiwa2 = [[], [], [], [], [], [], [], []]
wsg1 = []
wsg2 = []
shelf = []
bins = [[], []]
table = []
for gid in inspector.GetGeometryIds(
GeometrySet(inspector.GetAllGeometryIds()), Role.kProximity):
gid_name = inspector.GetName(inspector.GetFrameId(gid))
if "iiwa_1::iiwa_link_" in gid_name:
link_num = gid_name[18]
iiwa1[int(link_num)].append(gid)
elif "iiwa_2::iiwa_link_" in gid_name:
link_num = gid_name[18]
iiwa2[int(link_num)].append(gid)
elif "wsg_1" in gid_name:
wsg1.append(gid)
elif "wsg_2" in gid_name:
wsg2.append(gid)
elif "shelves::" in gid_name:
shelf.append(gid)
elif "binR" in gid_name:
bins[0].append(gid)
elif "binL" in gid_name:
bins[1].append(gid)
elif "table" in gid_name:
table.append(gid)
else:
print("Geometry", gid_name, "not assigned to an object.")
filter_manager.Apply(CollisionFilterDeclaration().ExcludeWithin(
GeometrySet(iiwa1[0] + iiwa1[1] + iiwa1[2] + iiwa1[3] + shelf)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[1] + iiwa1[2]+ iiwa1[3]),
GeometrySet(iiwa1[4] + iiwa1[5])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[3] + iiwa1[4]), GeometrySet(iiwa1[6])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[2] + iiwa1[3] + iiwa1[4] + iiwa1[5] + iiwa1[6]),
GeometrySet(iiwa1[7] + wsg1)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[0] + iiwa1[0] + iiwa1[2]), GeometrySet(bins[0])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[0] + iiwa1[1] + iiwa1[2] + iiwa1[3] + iiwa1[4]),
GeometrySet(bins[1])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[0] + iiwa1[0] + iiwa1[2]), GeometrySet(table)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeWithin(
GeometrySet(iiwa2[0] + iiwa2[1] + iiwa2[2] + iiwa2[3] + shelf)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[1] + iiwa2[2]+ iiwa2[3]),
GeometrySet(iiwa2[4] + iiwa2[5])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[3] + iiwa2[4]), GeometrySet(iiwa2[6])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[2] + iiwa2[3] + iiwa2[4] + iiwa2[5] + iiwa2[6]),
GeometrySet(iiwa2[7] + wsg2)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[0] + iiwa2[0] + iiwa2[2]), GeometrySet(bins[1])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[0] + iiwa2[1] + iiwa2[2] + iiwa2[3] + iiwa2[4]),
GeometrySet(bins[0])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[0] + iiwa2[0] + iiwa2[2]), GeometrySet(table)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[0] + iiwa1[1]), GeometrySet(iiwa2[0] + iiwa2[1])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[2]), GeometrySet(iiwa2[0] + iiwa2[1])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[0] + iiwa1[1]), GeometrySet(iiwa2[2])))
pairs = scene_graph.get_query_output_port().Eval(context).inspector().GetCollisionCandidates()
print("Filtered collision pairs from",
len(inspector.GetCollisionCandidates()), "to", len(pairs))
# initial_guess = np.concatenate((q0, q0))
# min_dist = (0.01, 0.01)???
def runBimanualIK(plant, context, wsg1_id, wsg2_id, wsg1_pose, wsg2_pose,
initial_guess, min_dist=None):
hand_frame1 = plant.GetBodyByName("body", wsg1_id).body_frame()
hand_frame2 = plant.GetBodyByName("body", wsg2_id).body_frame()
ik = InverseKinematics(plant, context)
if min_dist is not None:
ik.AddMinimumDistanceConstraint(*min_dist)
ik.prog().AddBoundingBoxConstraint(plant.GetPositionLowerLimits(),
plant.GetPositionUpperLimits(), ik.q())
ik.prog().SetInitialGuess(ik.q(), initial_guess)
ik.prog().AddQuadraticCost((ik.q() - initial_guess).dot(ik.q() - initial_guess))
ik.AddPositionConstraint(hand_frame1, [0, 0, 0], plant.world_frame(),
wsg1_pose.translation(), wsg1_pose.translation())
ik.AddOrientationConstraint(hand_frame1, RotationMatrix(), plant.world_frame(),
wsg1_pose.rotation(), 0.001)
ik.AddPositionConstraint(hand_frame2, [0, 0, 0], plant.world_frame(),
wsg2_pose.translation(), wsg2_pose.translation())
ik.AddOrientationConstraint(hand_frame2, RotationMatrix(), plant.world_frame(),
wsg2_pose.rotation(), 0.001)
result = Solve(ik.prog())
return result.GetSolution(ik.q())
def visualizeConfig(diagram, plant, context, q):
plant_context = plant.GetMyMutableContextFromRoot(context)
plant.SetPositions(plant_context, q)
diagram.ForcedPublish(context)
def getLinearGcsPath(regions, sequence):
path = [sequence[0]]
run_time = 0.0
|
def getIkSeeds():
return {
"top_shelf/top_shelf": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.9]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.9])),
"top_shelf/shelf_1": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.9]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.65])),
"top_shelf/shelf_2": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.9]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.4])),
"top_shelf/bin_L": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.9]),
RigidTransform(RollPitchYaw(-np.pi/2+0.1, 0, np.pi), [0., 1.1, 0.3])),
"shelf_1/top_shelf": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.65]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.9])),
"shelf_1/shelf_1": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.65]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.65])),
"shelf_1/shelf_2": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.65]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.4])),
"shelf_1/bin_L": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.65]),
RigidTransform(RollPitchYaw(-np.pi/2+0.1, 0, np.pi), [0., 1.1, 0.3])),
"shelf_2/top_shelf": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.4]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.9])),
"shelf_2/shelf_1": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.4]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.65])),
"shelf_2/shelf_2": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.4]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.4])),
"shelf_2/bin_L": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.4]),
RigidTransform(RollPitchYaw(-np.pi/2+0.1, 0, np.pi), [0., 1.1, 0.3])),
"bin_R/top_shelf": (RigidTransform(RollPitchYaw(-np.pi/2+0.1, 0, -np.pi), [0.0, -0.6, 0.3]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.9])),
"bin_R/shelf_1": (RigidTransform(RollPitchYaw(-np.pi/2+0.1, 0, -np.pi), [0.0, -0.6, 0.3]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.65])),
"bin_R/shelf_2": (RigidTransform(RollPitchYaw(-np.pi/2+0.1, 0, -np.pi), [0.0, -0.6, 0.3]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.4])),
"bin_R/bin_L": (RigidTransform(RollPitchYaw(-np.pi/2+0.1, 0, -np.pi), [0.0, -0.6, 0.3]),
RigidTransform(RollPitchYaw(-np.pi/2+0.1, 0, np.pi), [0., 1.1, 0.3])),
"top_shelf/shelf_1_extract": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.9]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.5, 0.35, 0.65])),
"top_shelf/shelf_2_extract": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.9]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.5, 0.35, 0.4])),
"shelf_2_extract/top_shelf": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.5, 0.15, 0.4]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.9])),
"shelf_1_extract/top_shelf": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.5, 0.15, 0.65]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.35, 0.9])),
"top_shelf/shelf_1_cross": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.9]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2-0.3), [0.7, 0.15, 0.65])),
"cross_table/top_shelf_cross": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi), [0.4, 0.4, 0.2]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.15, 0.9])),
"shelf_2_cross/top_shelf_cross": (RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2+0.4), [0.7, 0.35, 0.4]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2-0.4), [0.7, 0.15, 0.9])),
}
def getConfigurationSeeds():
return {
"top_shelf/top_shelf": [0.37080011, 0.41394084, -0.16861973, -0.70789778, -0.37031516, 0.60412162, 0.39982981,
-0.37080019, 0.41394089, 0.16861988, -0.70789766, 0.37031506, 0.60412179, -0.39982996],
"top_shelf/shelf_1": [0.37080079, 0.41394132, -0.16862043, -0.70789679, -0.37031656, 0.60412327, 0.39982969,
-0.93496924, 0.46342534, 0.92801666, -1.45777635, -0.31061724, -0.0657716, -0.06019899],
"top_shelf/shelf_2": [0.37086448, 0.41394538, -0.16875166, -0.70789745, -0.37020563, 0.60411217, 0.399785,
-0.4416204 , 0.62965228, 0.20598405, -1.73324339, -0.41354372, -0.68738414, 0.17443976],
"top_shelf/bin_L": [0.37081989, 0.41394235, -0.16866012, -0.70789737, -0.37028201, 0.60411923, 0.39981634,
-0.89837331, -1.1576151 , 1.75505216, -1.37515153, 1.0676443 , 1.56371166, -0.64126346],
"shelf_1/top_shelf": [0.93496924, 0.46342534, -0.92801666, -1.45777635, 0.31061724, -0.0657716 , 0.06019899,
-0.37080079, 0.41394132, 0.16862043, -0.70789679, 0.37031656, 0.60412327, -0.39982969],
"shelf_1/shelf_1": [0.87224109, 0.43096634, -0.82223436, -1.45840049, 0.73813452, -0.08999384, -0.41624203,
-0.87556489, 0.43246906, 0.82766047, -1.45838515, -0.72259842, -0.0884963, 0.39840129],
"shelf_1/shelf_2": [0.93496866, 0.463425 , -0.92801564, -1.45777634, 0.3106235, -0.06577172, 0.06019173,
-0.44158858, 0.62964838, 0.20594112, -1.73324341, -0.41354987, -0.6873923 , 0.17446778],
"shelf_1/bin_L": [0.93496918, 0.46342531, -0.92801656, -1.45777637, 0.31061728, -0.06577167, 0.06019927,
-0.89837321, -1.15761746, 1.75504915, -1.37515113, 1.06764716, 1.56371454, -0.64126383],
"shelf_2/top_shelf": [0.4416204, 0.62965228, -0.20598405, -1.73324339, 0.41354372, -0.68738414, -0.17443976,
-0.37086448, 0.41394538, 0.16875166, -0.70789745, 0.37020563, 0.60411217, -0.399785],
"shelf_2/shelf_1": [0.44158858, 0.62964838, -0.20594112, -1.73324341, 0.41354987, -0.6873923, -0.17446778,
-0.93496866, 0.463425 , 0.92801564, -1.45777634, -0.3106235 , -0.06577172, -0.06019173],
"shelf_2/shelf_2": [0.44161313, 0.62965141, -0.20597435, -1.73324346, 0.41354447, -0.68738613, -0.17444557,
-0.4416132 , 0.62965142, 0.20597452, -1.73324348, -0.41354416, -0.68738609, 0.17444625],
"shelf_2/bin_L": [0.44161528, 0.62965169, -0.20597726, -1.73324347, 0.41354399, -0.68738565, -0.17444283,
-1.37292761, -0.68372976, 2.96705973, -1.41521783, 2.96705973, -1.11343251, -3.0140737 ],
"bin_R/top_shelf": [0.81207926, -1.25359738, -1.58098625, -1.5155474 , -1.32223687, 1.50549708, -2.38221725,
-0.37085114, 0.4139444 , 0.16872443, -0.70789757, 0.37022786, 0.60411401, -0.39979449],
"bin_R/shelf_1": [0.81207923, -1.25358454, -1.58100042, -1.51554769, -1.32222337, 1.50548369, -2.3822204 ,
-0.9349716 , 0.46342674, 0.92802082, -1.45777624, -0.31059455, -0.0657707 , -0.06022391],
"bin_R/shelf_2": [0.81207937, -1.25360462, -1.58097816, -1.51554761, -1.32224557, 1.50550485, -2.38221483,
-0.44166552, 0.62965782, 0.20604497, -1.7332434 , -0.41353464, -0.6873727 , 0.17439863],
"bin_R/bin_L": [-1.73637519, 0.6209681 , 0.24232887, -1.51538355, -0.17977474, 0.92618894, -3.01360257,
1.31861497, 0.72394333, 0.4044295 , -1.37509496, -0.27461997, 1.20038493, 0.18611701],
"neutral/neutral": [0.0, -0.2, 0, -1.2, 0, 1.6, 0.0, 0.0, -0.2, 0, -1.2, 0, 1.6, 0.0],
"neutral/shelf_1": [0.0, -0.2, 0, -1.2, 0, 1.6, 0.0,
-0.93496866, 0.463425 , 0.92801564, -1.45777634, -0.3106235 , -0.06577172, -0.06019173],
"neutral/shelf_2": [0.0, -0.2, 0, -1.2, 0, 1.6, 0.0,
-0.44166552, 0.62965782, 0.20604497, -1.7332434 , -0.41353464, -0.6873727 , 0.17439863],
"shelf_1/neutral": [0.93496924, 0.46342534, -0.92801666, -1.45777635, 0.31061724, -0.0657716 , 0.06019899,
0.0, -0.2, 0, -1.2, 0, 1.6, 0.0],
"shelf_2/neutral": [0.44161528, 0.62965169, -0.20597726, -1.73324347, 0.41354399, -0.68738565, -0.17444283,
0.0, -0.2, 0, -1.2, 0, 1.6, 0.0],
"shelf_2_cross/top_shelf_cross": [0.47500706, 0.72909874, 0.01397772, -1.52841372, 0.15392366, -0.591641, -0.12870521,
-0.48821156, 0.67762534, 0.02049926, -0.27420758, 0.10620709, 0.72215209, -0.09973172],
}
# Additional seed points not needed to connect the graph
# "neutral/shelf_1_extract": [ 0.0, -0.2, 0, -1.2, 0, 1.6, 0.0, -0.35486829, -0.10621117, -0.09276445, -1.94995786, 1.88826556, 0.46922151, -1.98267349],
# "neutral/shelf_2_extract": [ 0.0, -0.2, 0, -1.2, 0, 1.6, 0.0, 0.3078069 , 0.56765359, -0.86829439, -2.0943951 , 2.53950045, 1.09607546, -2.4169564],
# "shelf_1_extract/neutral": [-1.05527083, -0.43710629, 1.15648812, -1.95011062, 0.24422131, -0.07820216, 0.15872416, 0.0, -0.2, 0, -1.2, 0, 1.6, 0.0],
# "shelf_2_extract/neutral": [-0.30739053, 0.5673891 , 0.86772198, -2.0943951 , -2.53946773, 1.09586777, 2.41729532, 0.0, -0.2, 0, -1.2, 0, 1.6, 0.0],
# "cross_table/top_shelf_cross": [ 0.04655887, 0.97997658, 0.52004246, -1.91926412, -1.37518707, -0.88823968, 0.07674699, -0.5921624 , 0.83651867, 0.20513136, -0.00257881, 0.51748756, 0.92012332, -0.51686487],
def getDemoConfigurations():
return [
[0.0, -0.2, 0, -1.2, 0, 1.6, 0.0, 0.0, -0.2, 0, -1.2, 0, 1.6, 0.0],
[0.69312848, 0.36303784, -0.66625368, -1.49515991, 0.3230085, -0.10942887, -0.09496304,
-0.69312891, 0.36303794, 0.66625426, -1.49515975, -0.32300928, -0.10942832, 0.0949629],
[0.2014604, 0.66463495, 0.16799372, -1.66212763, -0.09131682, -0.64368844, -0.03645568,
-0.38777291, 0.56141139, -0.05760515, -0.47447495, 0.06515541, 0.63627899, -0.02552148],
[-1.8487163 , 0.71749397, 0.66464618, -1.4912954 , -0.52882233, 1.0096015 , -2.62844995,
1.43620829, 0.70451542, -0.01532988, -1.34999693, -0.00550105, 1.18684923, -0.14400234],
]
def generateDemoConfigurations(plant, context, wsg1_id, wsg2_id):
demo_q = [[0.0, -0.2, 0, -1.2, 0, 1.6, 0.0, 0.0, -0.2, 0, -1.2, 0, 1.6, 0.0]]
initial_guess = copy(demo_q[0])
demo_q.append(runBimanualIK(
plant, context, wsg1_id, wsg2_id,
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.10, 0.65]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2), [0.7, 0.40, 0.65]),
initial_guess, (0.01, 0.01)))
demo_q.append(runBimanualIK(
plant, context, wsg1_id, wsg2_id,
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2+0.4), [0.7, 0.25, 0.4]),
RigidTransform(RollPitchYaw(-np.pi+0.1, 0, np.pi/2-0.4), [0.7, 0.20, 0.9]),
initial_guess, None))
initial_guess[0] = -np.pi/2
initial_guess[7] = np.pi/2
demo_q.append(runBimanualIK(
plant, context, wsg1_id, wsg2_id,
RigidTransform(RollPitchYaw(-np.pi/2+0.1, 0, -np.pi), [0.09, -0.6, 0.3]),
RigidTransform(RollPitchYaw(-np.pi/2+0.1, 0, np.pi), [0.09, 1.1, 0.3]),
initial_guess, None))
return demo_q
def filterCollsionGeometry(scene_graph, context):
filter_manager = scene_graph.collision_filter_manager(context)
inspector = scene_graph.model_inspector()
iiwa1 = [[], [], [], [], [], [], [], []]
iiwa2 = [[], [], [], [], [], [], [], []]
wsg1 = []
wsg2 = []
shelf = []
bins = [[], []]
table = []
for gid in inspector.GetGeometryIds(
GeometrySet(inspector.GetAllGeometryIds()), Role.kProximity):
gid_name = inspector.GetName(inspector.GetFrameId(gid))
if "iiwa_1::iiwa_link_" in gid_name:
link_num = gid_name[18]
iiwa1[int(link_num)].append(gid)
elif "iiwa_2::iiwa_link_" in gid_name:
link_num = gid_name[18]
iiwa2[int(link_num)].append(gid)
elif "wsg_1" in gid_name:
wsg1.append(gid)
elif "wsg_2" in gid_name:
wsg2.append(gid)
elif "shelves::" in gid_name:
shelf.append(gid)
elif "binR" in gid_name:
bins[0].append(gid)
elif "binL" in gid_name:
bins[1].append(gid)
elif "table" in gid_name:
table.append(gid)
else:
print("Geometry", gid_name, "not assigned to an object.")
filter_manager.Apply(CollisionFilterDeclaration().ExcludeWithin(
GeometrySet(iiwa1[0] + iiwa1[1] + iiwa1[2] + iiwa1[3] + shelf)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[1] + iiwa1[2]+ iiwa1[3]),
GeometrySet(iiwa1[4] + iiwa1[5])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[3] + iiwa1[4]), GeometrySet(iiwa1[6])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[2] + iiwa1[3] + iiwa1[4] + iiwa1[5] + iiwa1[6]),
GeometrySet(iiwa1[7] + wsg1)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[0] + iiwa1[0] + iiwa1[2]), GeometrySet(bins[0])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[0] + iiwa1[1] + iiwa1[2] + iiwa1[3] + iiwa1[4]),
GeometrySet(bins[1])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[0] + iiwa1[0] + iiwa1[2]), GeometrySet(table)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeWithin(
GeometrySet(iiwa2[0] + iiwa2[1] + iiwa2[2] + iiwa2[3] + shelf)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[1] + iiwa2[2]+ iiwa2[3]),
GeometrySet(iiwa2[4] + iiwa2[5])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[3] + iiwa2[4]), GeometrySet(iiwa2[6])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[2] + iiwa2[3] + iiwa2[4] + iiwa2[5] + iiwa2[6]),
GeometrySet(iiwa2[7] + wsg2)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[0] + iiwa2[0] + iiwa2[2]), GeometrySet(bins[1])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[0] + iiwa2[1] + iiwa2[2] + iiwa2[3] + iiwa2[4]),
GeometrySet(bins[0])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa2[0] + iiwa2[0] + iiwa2[2]), GeometrySet(table)))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[0] + iiwa1[1]), GeometrySet(iiwa2[0] + iiwa2[1])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[2]), GeometrySet(iiwa2[0] + iiwa2[1])))
filter_manager.Apply(CollisionFilterDeclaration().ExcludeBetween(
GeometrySet(iiwa1[0] + iiwa1[1]), GeometrySet(iiwa2[2])))
pairs = scene_graph.get_query_output_port().Eval(context).inspector().GetCollisionCandidates()
print("Filtered collision pairs from",
len(inspector.GetCollisionCandidates()), "to", len(pairs))
# initial_guess = np.concatenate((q0, q0))
# min_dist = (0.01, 0.01)???
def runBimanualIK(plant, context, wsg1_id, wsg2_id, wsg1_pose, wsg2_pose,
initial_guess, min_dist=None):
hand_frame1 = plant.GetBodyByName("body", wsg1_id).body_frame()
hand_frame2 = plant.GetBodyByName("body", wsg2_id).body_frame()
ik = InverseKinematics(plant, context)
if min_dist is not None:
ik.AddMinimumDistanceConstraint(*min_dist)
ik.prog().AddBoundingBoxConstraint(plant.GetPositionLowerLimits(),
plant.GetPositionUpperLimits(), ik.q())
ik.prog().SetInitialGuess(ik.q(), initial_guess)
ik.prog().AddQuadraticCost((ik.q() - initial_guess).dot(ik.q() - initial_guess))
ik.AddPositionConstraint(hand_frame1, [0, 0, 0], plant.world_frame(),
wsg1_pose.translation(), wsg1_pose.translation())
ik.AddOrientationConstraint(hand_frame1, RotationMatrix(), plant.world_frame(),
wsg1_pose.rotation(), 0.001)
ik.AddPositionConstraint(hand_frame2, [0, 0, 0], plant.world_frame(),
wsg2_pose.translation(), wsg2_pose.translation())
ik.AddOrientationConstraint(hand_frame2, RotationMatrix(), plant.world_frame(),
wsg2_pose.rotation(), 0.001)
result = Solve(ik.prog())
return result.GetSolution(ik.q())
def visualizeConfig(diagram, plant, context, q):
plant_context = plant.GetMyMutableContextFromRoot(context)
plant.SetPositions(plant_context, q)
diagram.ForcedPublish(context)
def getLinearGcsPath(regions, sequence):
path = [sequence[0]]
run_time = 0.0 | gcs = LinearGCS(regions) | 1 | 2023-10-13 00:27:32+00:00 | 16k |
LeapLabTHU/Rank-DETR | projects/pnp_detr/configs/models/pnp_detr_r50.py | [
{
"identifier": "BasicStem",
"path": "detrex/modeling/backbone/resnet.py",
"snippet": "class BasicStem(CNNBlockBase):\n \"\"\"\n The standard ResNet stem (layers before the first residual block),\n with a conv, relu and max_pool.\n\n Args:\n norm (str or callable): norm after the firs... | from detectron2.config import LazyCall as L
from detrex.modeling.backbone import ResNet, BasicStem
from detrex.modeling.matcher import HungarianMatcher
from detrex.modeling.criterion.criterion import SetCriterion
from detrex.layers.position_embedding import PositionEmbeddingSine
from projects.pnp_detr.modeling import (
PnPDETR,
PnPDetrTransformer,
PnPDetrTransformerEncoder,
PnPDetrTransformerDecoder,
) | 11,842 |
model = L(PnPDETR)(
backbone=L(ResNet)(
stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"),
stages=L(ResNet.make_default_stages)(
depth=50,
stride_in_1x1=False,
norm="FrozenBN",
),
out_features=["res2", "res3", "res4", "res5"],
freeze_at=1,
),
in_features=["res5"],
in_channels=2048,
position_embedding=L(PositionEmbeddingSine)(
num_pos_feats=128,
temperature=10000,
normalize=True,
),
|
model = L(PnPDETR)(
backbone=L(ResNet)(
stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"),
stages=L(ResNet.make_default_stages)(
depth=50,
stride_in_1x1=False,
norm="FrozenBN",
),
out_features=["res2", "res3", "res4", "res5"],
freeze_at=1,
),
in_features=["res5"],
in_channels=2048,
position_embedding=L(PositionEmbeddingSine)(
num_pos_feats=128,
temperature=10000,
normalize=True,
), | transformer=L(PnPDetrTransformer)( | 8 | 2023-10-12 03:02:25+00:00 | 16k |
ByungKwanLee/Full-Segment-Anything | mask_generator.py | [
{
"identifier": "Sam",
"path": "modeling/sam.py",
"snippet": "class Sam(nn.Module):\n mask_threshold: float = 0.0\n image_format: str = \"RGB\"\n\n def __init__(\n self,\n image_encoder: ImageEncoderViT,\n prompt_encoder: PromptEncoder,\n mask_decoder: MaskDecoder,\n... | import numpy as np
import torch
import cv2 # type: ignore # noqa: F401
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing import Any, Dict, List, Optional, Tuple
from modeling import Sam
from predictor import SamPredictor
from utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
from pycocotools import mask as mask_utils # type: ignore # noqa: F401 | 10,848 | """
Using a SAM model, generates masks for the entire image.
Generates a grid of point prompts over the image, then filters
low quality and duplicate masks. The default settings are chosen
for SAM with a ViT-H backbone.
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
points_per_batch (int): Sets the number of points run simultaneously
by the model. Higher numbers may be faster but use more GPU memory.
pred_iou_thresh (float): A filtering threshold in [0,1], using the
model's predicted mask quality.
stability_score_thresh (float): A filtering threshold in [0,1], using
the stability of the mask under changes to the cutoff used to binarize
the model's mask predictions.
stability_score_offset (float): The amount to shift the cutoff when
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
crop_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
crop_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
to remove disconnected regions and holes in masks with area smaller
than min_mask_region_area. Requires opencv.
output_mode (str): The form masks are returned in. Can be 'binary_mask',
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
For large resolutions, 'binary_mask' may consume large amounts of
memory.
"""
assert (points_per_side is None) != (
point_grids is None
), "Exactly one of points_per_side or point_grid must be provided."
if points_per_side is not None:
self.point_grids = build_all_layer_point_grids(
points_per_side,
crop_n_layers,
crop_n_points_downscale_factor,
)
elif point_grids is not None:
self.point_grids = point_grids
else:
raise ValueError("Can't have both points_per_side and point_grid be None.")
assert output_mode in [
"binary_mask",
"uncompressed_rle",
"coco_rle",
], f"Unknown output_mode {output_mode}."
if output_mode == "coco_rle":
if min_mask_region_area > 0:
self.predictor = SamPredictor(model)
self.points_per_batch = points_per_batch
self.pred_iou_thresh = pred_iou_thresh
self.stability_score_thresh = stability_score_thresh
self.stability_score_offset = stability_score_offset
self.box_nms_thresh = box_nms_thresh
self.crop_n_layers = crop_n_layers
self.crop_nms_thresh = crop_nms_thresh
self.crop_overlap_ratio = crop_overlap_ratio
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
self.min_mask_region_area = min_mask_region_area
self.output_mode = output_mode
@torch.no_grad()
def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
"""
Generates masks for the given image.
Arguments:
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
Returns:
list(dict(str, any)): A list over records for masks. Each record is
a dict containing the following keys:
segmentation (dict(str, any) or np.ndarray): The mask. If
output_mode='binary_mask', is an array of shape HW. Otherwise,
is a dictionary containing the RLE.
bbox (list(float)): The box around the mask, in XYWH format.
area (int): The area in pixels of the mask.
predicted_iou (float): The model's own prediction of the mask's
quality. This is filtered by the pred_iou_thresh parameter.
point_coords (list(list(float))): The point coordinates input
to the model to generate this mask.
stability_score (float): A measure of the mask's quality. This
is filtered on using the stability_score_thresh parameter.
crop_box (list(float)): The crop of the image used to generate
the mask, given in XYWH format.
"""
# Generate masks
mask_data = self._generate_masks(image)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
mask_data = self.postprocess_small_regions(
mask_data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle":
| # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
class SamMaskGenerator:
def __init__(
self,
model: Sam,
points_per_side: Optional[int] = 32,
points_per_batch: int = 64,
pred_iou_thresh: float = 0.88,
stability_score_thresh: float = 0.95,
stability_score_offset: float = 1.0,
box_nms_thresh: float = 0.7,
crop_n_layers: int = 0,
crop_nms_thresh: float = 0.7,
crop_overlap_ratio: float = 512 / 1500,
crop_n_points_downscale_factor: int = 1,
point_grids: Optional[List[np.ndarray]] = None,
min_mask_region_area: int = 0,
output_mode: str = "binary_mask",
) -> None:
"""
Using a SAM model, generates masks for the entire image.
Generates a grid of point prompts over the image, then filters
low quality and duplicate masks. The default settings are chosen
for SAM with a ViT-H backbone.
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
points_per_batch (int): Sets the number of points run simultaneously
by the model. Higher numbers may be faster but use more GPU memory.
pred_iou_thresh (float): A filtering threshold in [0,1], using the
model's predicted mask quality.
stability_score_thresh (float): A filtering threshold in [0,1], using
the stability of the mask under changes to the cutoff used to binarize
the model's mask predictions.
stability_score_offset (float): The amount to shift the cutoff when
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
crop_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
crop_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
to remove disconnected regions and holes in masks with area smaller
than min_mask_region_area. Requires opencv.
output_mode (str): The form masks are returned in. Can be 'binary_mask',
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
For large resolutions, 'binary_mask' may consume large amounts of
memory.
"""
assert (points_per_side is None) != (
point_grids is None
), "Exactly one of points_per_side or point_grid must be provided."
if points_per_side is not None:
self.point_grids = build_all_layer_point_grids(
points_per_side,
crop_n_layers,
crop_n_points_downscale_factor,
)
elif point_grids is not None:
self.point_grids = point_grids
else:
raise ValueError("Can't have both points_per_side and point_grid be None.")
assert output_mode in [
"binary_mask",
"uncompressed_rle",
"coco_rle",
], f"Unknown output_mode {output_mode}."
if output_mode == "coco_rle":
if min_mask_region_area > 0:
self.predictor = SamPredictor(model)
self.points_per_batch = points_per_batch
self.pred_iou_thresh = pred_iou_thresh
self.stability_score_thresh = stability_score_thresh
self.stability_score_offset = stability_score_offset
self.box_nms_thresh = box_nms_thresh
self.crop_n_layers = crop_n_layers
self.crop_nms_thresh = crop_nms_thresh
self.crop_overlap_ratio = crop_overlap_ratio
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
self.min_mask_region_area = min_mask_region_area
self.output_mode = output_mode
@torch.no_grad()
def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
"""
Generates masks for the given image.
Arguments:
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
Returns:
list(dict(str, any)): A list over records for masks. Each record is
a dict containing the following keys:
segmentation (dict(str, any) or np.ndarray): The mask. If
output_mode='binary_mask', is an array of shape HW. Otherwise,
is a dictionary containing the RLE.
bbox (list(float)): The box around the mask, in XYWH format.
area (int): The area in pixels of the mask.
predicted_iou (float): The model's own prediction of the mask's
quality. This is filtered by the pred_iou_thresh parameter.
point_coords (list(list(float))): The point coordinates input
to the model to generate this mask.
stability_score (float): A measure of the mask's quality. This
is filtered on using the stability_score_thresh parameter.
crop_box (list(float)): The crop of the image used to generate
the mask, given in XYWH format.
"""
# Generate masks
mask_data = self._generate_masks(image)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
mask_data = self.postprocess_small_regions(
mask_data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle": | mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]] | 9 | 2023-10-13 20:07:42+00:00 | 16k |
sakemin/cog-musicgen-remixer | audiocraft/modules/conditioners.py | [
{
"identifier": "ChromaExtractor",
"path": "audiocraft/modules/chroma.py",
"snippet": "class ChromaExtractor(nn.Module):\n \"\"\"Chroma extraction and quantization.\n\n Args:\n sample_rate (int): Sample rate for the chroma extraction.\n n_chroma (int): Number of chroma bins for the c... | from collections import defaultdict
from copy import deepcopy
from dataclasses import dataclass, field
from itertools import chain
from pathlib import Path
from num2words import num2words
from transformers import RobertaTokenizer, T5EncoderModel, T5Tokenizer # type: ignore
from torch import nn
from torch.nn.utils.rnn import pad_sequence
from .chroma import ChromaExtractor
from .chord_chroma import ChordExtractor
from .streaming import StreamingModule
from .transformer import create_sin_embedding
from ..data.audio import audio_read
from ..data.audio_dataset import SegmentInfo
from ..data.audio_utils import convert_audio
from ..environment import AudioCraftEnvironment
from ..quantization import ResidualVectorQuantizer
from ..utils.autocast import TorchAutocast
from ..utils.cache import EmbeddingCache
from ..utils.utils import collate, hash_trick, length_to_mask, load_clap_state_dict, warn_once
from .btc.utils import chords
from demucs import pretrained
from audiocraft.data.audio_dataset import AudioDataset
from demucs.apply import apply_model
from demucs.audio import convert_audio
from demucs import pretrained
from audiocraft.data.audio_dataset import AudioDataset
from demucs.apply import apply_model
from demucs.audio import convert_audio
import logging
import math
import random
import re
import typing as tp
import warnings
import einops
import spacy
import torch
import torch.nn.functional as F
import numpy as np
import laion_clap # type: ignore | 13,735 | if match_len_on_eval:
self._use_masking = False
self.duration = duration
self.__dict__['demucs'] = pretrained.get_model('htdemucs').to(device)
stem_sources: list = self.demucs.sources # type: ignore
self.stem_indices = torch.LongTensor([stem_sources.index('vocals'), stem_sources.index('other')]).to(device)
self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma,
radix2_exp=radix2_exp, **kwargs).to(device)
self.chroma_len = self._get_chroma_len()
self.eval_wavs: tp.Optional[torch.Tensor] = self._load_eval_wavs(eval_wavs, n_eval_wavs)
self.cache = None
if cache_path is not None:
self.cache = EmbeddingCache(Path(cache_path) / 'wav', self.device,
compute_embed_fn=self._get_full_chroma_for_cache,
extract_embed_fn=self._extract_chroma_chunk)
def _downsampling_factor(self) -> int:
return self.chroma.winhop
def _load_eval_wavs(self, path: tp.Optional[str], num_samples: int) -> tp.Optional[torch.Tensor]:
"""Load pre-defined waveforms from a json.
These waveforms will be used for chroma extraction during evaluation.
This is done to make the evaluation on MusicCaps fair (we shouldn't see the chromas of MusicCaps).
"""
if path is None:
return None
logger.info(f"Loading evaluation wavs from {path}")
dataset: AudioDataset = AudioDataset.from_meta(
path, segment_duration=self.duration, min_audio_duration=self.duration,
sample_rate=self.sample_rate, channels=1)
if len(dataset) > 0:
eval_wavs = dataset.collater([dataset[i] for i in range(num_samples)]).to(self.device)
logger.info(f"Using {len(eval_wavs)} evaluation wavs for chroma-stem conditioner")
return eval_wavs
else:
raise ValueError("Could not find evaluation wavs, check lengths of wavs")
def reset_eval_wavs(self, eval_wavs: tp.Optional[torch.Tensor]) -> None:
self.eval_wavs = eval_wavs
def has_eval_wavs(self) -> bool:
return self.eval_wavs is not None
def _sample_eval_wavs(self, num_samples: int) -> torch.Tensor:
"""Sample wavs from a predefined list."""
assert self.eval_wavs is not None, "Cannot sample eval wavs as no eval wavs provided."
total_eval_wavs = len(self.eval_wavs)
out = self.eval_wavs
if num_samples > total_eval_wavs:
out = self.eval_wavs.repeat(num_samples // total_eval_wavs + 1, 1, 1)
return out[torch.randperm(len(out))][:num_samples]
def _get_chroma_len(self) -> int:
"""Get length of chroma during training."""
dummy_wav = torch.zeros((1, int(self.sample_rate * self.duration)), device=self.device)
dummy_chr = self.chroma(dummy_wav)
return dummy_chr.shape[1]
@torch.no_grad()
def _get_stemmed_wav(self, wav: torch.Tensor, sample_rate: int) -> torch.Tensor:
"""Get parts of the wav that holds the melody, extracting the main stems from the wav."""
with self.autocast:
wav = convert_audio(
wav, sample_rate, self.demucs.samplerate, self.demucs.audio_channels) # type: ignore
stems = apply_model(self.demucs, wav, device=self.device)
stems = stems[:, self.stem_indices] # extract relevant stems for melody conditioning
mix_wav = stems.sum(1) # merge extracted stems to single waveform
mix_wav = convert_audio(mix_wav, self.demucs.samplerate, self.sample_rate, 1) # type: ignore
return mix_wav
@torch.no_grad()
def _extract_chroma(self, wav: torch.Tensor) -> torch.Tensor:
"""Extract chroma features from the waveform."""
with self.autocast:
return self.chroma(wav)
@torch.no_grad()
def _compute_wav_embedding(self, wav: torch.Tensor, sample_rate: int) -> torch.Tensor:
"""Compute wav embedding, applying stem and chroma extraction."""
# avoid 0-size tensors when we are working with null conds
if wav.shape[-1] == 1:
return self._extract_chroma(wav)
stems = self._get_stemmed_wav(wav, sample_rate)
chroma = self._extract_chroma(stems)
return chroma
@torch.no_grad()
def _get_full_chroma_for_cache(self, path: tp.Union[str, Path], x: WavCondition, idx: int) -> torch.Tensor:
"""Extract chroma from the whole audio waveform at the given path."""
wav, sr = audio_read(path)
wav = wav[None].to(self.device)
wav = convert_audio(wav, sr, self.sample_rate, to_channels=1)
chroma = self._compute_wav_embedding(wav, self.sample_rate)[0]
return chroma
def _extract_chroma_chunk(self, full_chroma: torch.Tensor, x: WavCondition, idx: int) -> torch.Tensor:
"""Extract a chunk of chroma from the full chroma derived from the full waveform."""
wav_length = x.wav.shape[-1]
seek_time = x.seek_time[idx]
assert seek_time is not None, (
"WavCondition seek_time is required "
"when extracting chroma chunks from pre-computed chroma.")
full_chroma = full_chroma.float()
frame_rate = self.sample_rate / self._downsampling_factor()
target_length = int(frame_rate * wav_length / self.sample_rate)
index = int(frame_rate * seek_time)
out = full_chroma[index: index + target_length]
out = F.pad(out[None], (0, 0, 0, target_length - out.shape[0]))[0]
return out.to(self.device)
@torch.no_grad()
def _get_wav_embedding(self, x: WavCondition) -> torch.Tensor:
"""Get the wav embedding from the WavCondition.
The conditioner will either extract the embedding on-the-fly computing it from the condition wav directly
or will rely on the embedding cache to load the pre-computed embedding if relevant.
"""
sampled_wav: tp.Optional[torch.Tensor] = None
if not self.training and self.eval_wavs is not None:
| # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
logger = logging.getLogger(__name__)
TextCondition = tp.Optional[str] # a text condition can be a string or None (if doesn't exist)
ConditionType = tp.Tuple[torch.Tensor, torch.Tensor] # condition, mask
class WavCondition(tp.NamedTuple):
wav: torch.Tensor
length: torch.Tensor
sample_rate: tp.List[int]
path: tp.List[tp.Optional[str]] = []
seek_time: tp.List[tp.Optional[float]] = []
class WavChordTextCondition(tp.NamedTuple):
wav: tp.Union[torch.Tensor,str,tp.List[str]]
length: torch.Tensor
sample_rate: tp.List[int]
path: tp.List[tp.Optional[str]] = []
seek_time: tp.List[tp.Optional[float]] = []
bpm : tp.List[tp.Optional[tp.Union[int, float]]] = []
meter : tp.List[tp.Optional[int]] = []
class JointEmbedCondition(tp.NamedTuple):
wav: torch.Tensor
text: tp.List[tp.Optional[str]]
length: torch.Tensor
sample_rate: tp.List[int]
path: tp.List[tp.Optional[str]] = []
seek_time: tp.List[tp.Optional[float]] = []
@dataclass
class ConditioningAttributes:
text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
wav: tp.Dict[str, tp.Union[WavCondition,WavChordTextCondition]] = field(default_factory=dict)
joint_embed: tp.Dict[str, JointEmbedCondition] = field(default_factory=dict)
def __getitem__(self, item):
return getattr(self, item)
@property
def text_attributes(self):
return self.text.keys()
@property
def wav_attributes(self):
return self.wav.keys()
@property
def joint_embed_attributes(self):
return self.joint_embed.keys()
@property
def attributes(self):
return {
"text": self.text_attributes,
"wav": self.wav_attributes,
"joint_embed": self.joint_embed_attributes,
}
def to_flat_dict(self):
return {
**{f"text.{k}": v for k, v in self.text.items()},
**{f"wav.{k}": v for k, v in self.wav.items()},
**{f"joint_embed.{k}": v for k, v in self.joint_embed.items()}
}
@classmethod
def from_flat_dict(cls, x):
out = cls()
for k, v in x.items():
kind, att = k.split(".")
out[kind][att] = v
return out
class SegmentWithAttributes(SegmentInfo):
"""Base class for all dataclasses that are used for conditioning.
All child classes should implement `to_condition_attributes` that converts
the existing attributes to a dataclass of type ConditioningAttributes.
"""
def to_condition_attributes(self) -> ConditioningAttributes:
raise NotImplementedError()
def nullify_condition(condition: ConditionType, dim: int = 1):
"""Transform an input condition to a null condition.
The way it is done by converting it to a single zero vector similarly
to how it is done inside WhiteSpaceTokenizer and NoopTokenizer.
Args:
condition (ConditionType): A tuple of condition and mask (tuple[torch.Tensor, torch.Tensor])
dim (int): The dimension that will be truncated (should be the time dimension)
WARNING!: dim should not be the batch dimension!
Returns:
ConditionType: A tuple of null condition and mask
"""
assert dim != 0, "dim cannot be the batch dimension!"
assert isinstance(condition, tuple) and \
isinstance(condition[0], torch.Tensor) and \
isinstance(condition[1], torch.Tensor), "'nullify_condition' got an unexpected input type!"
cond, mask = condition
B = cond.shape[0]
last_dim = cond.dim() - 1
out = cond.transpose(dim, last_dim)
out = 0. * out[..., :1]
out = out.transpose(dim, last_dim)
mask = torch.zeros((B, 1), device=out.device).int()
assert cond.dim() == out.dim()
return out, mask
def nullify_wav(cond: tp.Union[WavCondition,WavChordTextCondition]) -> tp.Union[WavCondition,WavChordTextCondition]:
"""Transform a WavCondition to a nullified WavCondition.
It replaces the wav by a null tensor, forces its length to 0, and replaces metadata by dummy attributes.
Args:
cond (WavCondition): Wav condition with wav, tensor of shape [B, T].
Returns:
WavCondition: Nullified wav condition.
"""
if not isinstance(cond, WavChordTextCondition):
null_wav, _ = nullify_condition((cond.wav, torch.zeros_like(cond.wav)), dim=cond.wav.dim() - 1)
return WavCondition(
wav=null_wav,
length=torch.tensor([0] * cond.wav.shape[0], device=cond.wav.device),
sample_rate=cond.sample_rate,
path=[None] * cond.wav.shape[0],
seek_time=[None] * cond.wav.shape[0],
)
else:
return WavChordTextCondition(
wav=['N']* len(cond.wav),
length=torch.tensor([0] * len(cond.wav), device=cond.length.device),
sample_rate=cond.sample_rate,
path=[None],
seek_time=[None],
bpm = cond.bpm,
meter = cond.meter
)
def nullify_joint_embed(embed: JointEmbedCondition) -> JointEmbedCondition:
"""Nullify the joint embedding condition by replacing it by a null tensor, forcing its length to 0,
and replacing metadata by dummy attributes.
Args:
cond (JointEmbedCondition): Joint embedding condition with wav and text, wav tensor of shape [B, C, T].
"""
null_wav, _ = nullify_condition((embed.wav, torch.zeros_like(embed.wav)), dim=embed.wav.dim() - 1)
return JointEmbedCondition(
wav=null_wav, text=[None] * len(embed.text),
length=torch.LongTensor([0]).to(embed.wav.device),
sample_rate=embed.sample_rate,
path=[None] * embed.wav.shape[0],
seek_time=[0] * embed.wav.shape[0],
)
class Tokenizer:
"""Base tokenizer implementation
(in case we want to introduce more advances tokenizers in the future).
"""
def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
raise NotImplementedError()
class WhiteSpaceTokenizer(Tokenizer):
"""This tokenizer should be used for natural language descriptions.
For example:
["he didn't, know he's going home.", 'shorter sentence'] =>
[[78, 62, 31, 4, 78, 25, 19, 34],
[59, 77, 0, 0, 0, 0, 0, 0]]
"""
PUNCTUATION = "?:!.,;"
def __init__(self, n_bins: int, pad_idx: int = 0, language: str = "en_core_web_sm",
lemma: bool = True, stopwords: bool = True) -> None:
self.n_bins = n_bins
self.pad_idx = pad_idx
self.lemma = lemma
self.stopwords = stopwords
try:
self.nlp = spacy.load(language)
except IOError:
spacy.cli.download(language) # type: ignore
self.nlp = spacy.load(language)
@tp.no_type_check
def __call__(self, texts: tp.List[tp.Optional[str]],
return_text: bool = False) -> tp.Tuple[torch.Tensor, torch.Tensor]:
"""Take a list of strings and convert them to a tensor of indices.
Args:
texts (list[str]): List of strings.
return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False.
Returns:
tuple[torch.Tensor, torch.Tensor]:
- Indices of words in the LUT.
- And a mask indicating where the padding tokens are
"""
output, lengths = [], []
texts = deepcopy(texts)
for i, text in enumerate(texts):
# if current sample doesn't have a certain attribute, replace with pad token
if text is None:
output.append(torch.Tensor([self.pad_idx]))
lengths.append(0)
continue
# convert numbers to words
text = re.sub(r"(\d+)", lambda x: num2words(int(x.group(0))), text) # type: ignore
# normalize text
text = self.nlp(text) # type: ignore
# remove stopwords
if self.stopwords:
text = [w for w in text if not w.is_stop] # type: ignore
# remove punctuation
text = [w for w in text if w.text not in self.PUNCTUATION] # type: ignore
# lemmatize if needed
text = [getattr(t, "lemma_" if self.lemma else "text") for t in text] # type: ignore
texts[i] = " ".join(text)
lengths.append(len(text))
# convert to tensor
tokens = torch.Tensor([hash_trick(w, self.n_bins) for w in text])
output.append(tokens)
mask = length_to_mask(torch.IntTensor(lengths)).int()
padded_output = pad_sequence(output, padding_value=self.pad_idx).int().t()
if return_text:
return padded_output, mask, texts # type: ignore
return padded_output, mask
class NoopTokenizer(Tokenizer):
"""This tokenizer should be used for global conditioners such as: artist, genre, key, etc.
The difference between this and WhiteSpaceTokenizer is that NoopTokenizer does not split
strings, so "Jeff Buckley" will get it's own index. Whereas WhiteSpaceTokenizer will
split it to ["Jeff", "Buckley"] and return an index per word.
For example:
["Queen", "ABBA", "Jeff Buckley"] => [43, 55, 101]
["Metal", "Rock", "Classical"] => [0, 223, 51]
"""
def __init__(self, n_bins: int, pad_idx: int = 0):
self.n_bins = n_bins
self.pad_idx = pad_idx
def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
output, lengths = [], []
for text in texts:
# if current sample doesn't have a certain attribute, replace with pad token
if text is None:
output.append(self.pad_idx)
lengths.append(0)
else:
output.append(hash_trick(text, self.n_bins))
lengths.append(1)
tokens = torch.LongTensor(output).unsqueeze(1)
mask = length_to_mask(torch.IntTensor(lengths)).int()
return tokens, mask
class BaseConditioner(nn.Module):
"""Base model for all conditioner modules.
We allow the output dim to be different than the hidden dim for two reasons:
1) keep our LUTs small when the vocab is large;
2) make all condition dims consistent.
Args:
dim (int): Hidden dim of the model.
output_dim (int): Output dim of the conditioner.
"""
def __init__(self, dim: int, output_dim: int):
super().__init__()
self.dim = dim
self.output_dim = output_dim
self.output_proj = nn.Linear(dim, output_dim)
def tokenize(self, *args, **kwargs) -> tp.Any:
"""Should be any part of the processing that will lead to a synchronization
point, e.g. BPE tokenization with transfer to the GPU.
The returned value will be saved and return later when calling forward().
"""
raise NotImplementedError()
def forward(self, inputs: tp.Any) -> ConditionType:
"""Gets input that should be used as conditioning (e.g, genre, description or a waveform).
Outputs a ConditionType, after the input data was embedded as a dense vector.
Returns:
ConditionType:
- A tensor of size [B, T, D] where B is the batch size, T is the length of the
output embedding and D is the dimension of the embedding.
- And a mask indicating where the padding tokens.
"""
raise NotImplementedError()
class TextConditioner(BaseConditioner):
...
class LUTConditioner(TextConditioner):
"""Lookup table TextConditioner.
Args:
n_bins (int): Number of bins.
dim (int): Hidden dim of the model (text-encoder/LUT).
output_dim (int): Output dim of the conditioner.
tokenizer (str): Name of the tokenizer.
pad_idx (int, optional): Index for padding token. Defaults to 0.
"""
def __init__(self, n_bins: int, dim: int, output_dim: int, tokenizer: str, pad_idx: int = 0):
super().__init__(dim, output_dim)
self.embed = nn.Embedding(n_bins, dim)
self.tokenizer: Tokenizer
if tokenizer == 'whitespace':
self.tokenizer = WhiteSpaceTokenizer(n_bins, pad_idx=pad_idx)
elif tokenizer == 'noop':
self.tokenizer = NoopTokenizer(n_bins, pad_idx=pad_idx)
else:
raise ValueError(f"unrecognized tokenizer `{tokenizer}`.")
def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
device = self.embed.weight.device
tokens, mask = self.tokenizer(x)
tokens, mask = tokens.to(device), mask.to(device)
return tokens, mask
def forward(self, inputs: tp.Tuple[torch.Tensor, torch.Tensor]) -> ConditionType:
tokens, mask = inputs
embeds = self.embed(tokens)
embeds = self.output_proj(embeds)
embeds = (embeds * mask.unsqueeze(-1))
return embeds, mask
class T5Conditioner(TextConditioner):
"""T5-based TextConditioner.
Args:
name (str): Name of the T5 model.
output_dim (int): Output dim of the conditioner.
finetune (bool): Whether to fine-tune T5 at train time.
device (str): Device for T5 Conditioner.
autocast_dtype (tp.Optional[str], optional): Autocast dtype.
word_dropout (float, optional): Word dropout probability.
normalize_text (bool, optional): Whether to apply text normalization.
"""
MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
"google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
"google/flan-t5-xl", "google/flan-t5-xxl"]
MODELS_DIMS = {
"t5-small": 512,
"t5-base": 768,
"t5-large": 1024,
"t5-3b": 1024,
"t5-11b": 1024,
"google/flan-t5-small": 512,
"google/flan-t5-base": 768,
"google/flan-t5-large": 1024,
"google/flan-t5-3b": 1024,
"google/flan-t5-11b": 1024,
}
def __init__(self, name: str, output_dim: int, finetune: bool, device: str,
autocast_dtype: tp.Optional[str] = 'float32', word_dropout: float = 0.,
normalize_text: bool = False):
assert name in self.MODELS, f"Unrecognized t5 model name (should in {self.MODELS})"
super().__init__(self.MODELS_DIMS[name], output_dim)
self.device = device
self.name = name
self.finetune = finetune
self.word_dropout = word_dropout
if autocast_dtype is None or self.device == 'cpu':
self.autocast = TorchAutocast(enabled=False)
if self.device != 'cpu':
logger.warning("T5 has no autocast, this might lead to NaN")
else:
dtype = getattr(torch, autocast_dtype)
assert isinstance(dtype, torch.dtype)
logger.info(f"T5 will be evaluated with autocast as {autocast_dtype}")
self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
# Let's disable logging temporarily because T5 will vomit some errors otherwise.
# thanks https://gist.github.com/simon-weber/7853144
previous_level = logging.root.manager.disable
logging.disable(logging.ERROR)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
try:
self.t5_tokenizer = T5Tokenizer.from_pretrained(name)
t5 = T5EncoderModel.from_pretrained(name).train(mode=finetune)
finally:
logging.disable(previous_level)
if finetune:
self.t5 = t5
else:
# this makes sure that the t5 models is not part
# of the saved checkpoint
self.__dict__['t5'] = t5.to(device)
self.normalize_text = normalize_text
if normalize_text:
self.text_normalizer = WhiteSpaceTokenizer(1, lemma=True, stopwords=True)
def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]:
# if current sample doesn't have a certain attribute, replace with empty string
entries: tp.List[str] = [xi if xi is not None else "" for xi in x]
if self.normalize_text:
_, _, entries = self.text_normalizer(entries, return_text=True)
if self.word_dropout > 0. and self.training:
new_entries = []
for entry in entries:
words = [word for word in entry.split(" ") if random.random() >= self.word_dropout]
new_entries.append(" ".join(words))
entries = new_entries
empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""])
inputs = self.t5_tokenizer(entries, return_tensors='pt', padding=True).to(self.device)
mask = inputs['attention_mask']
mask[empty_idx, :] = 0 # zero-out index where the input is non-existant
return inputs
def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType:
mask = inputs['attention_mask']
with torch.set_grad_enabled(self.finetune), self.autocast:
embeds = self.t5(**inputs).last_hidden_state
embeds = self.output_proj(embeds.to(self.output_proj.weight))
embeds = (embeds * mask.unsqueeze(-1))
return embeds, mask
class WaveformConditioner(BaseConditioner):
"""Base class for all conditioners that take a waveform as input.
Classes that inherit must implement `_get_wav_embedding` that outputs
a continuous tensor, and `_downsampling_factor` that returns the down-sampling
factor of the embedding model.
Args:
dim (int): The internal representation dimension.
output_dim (int): Output dimension.
device (tp.Union[torch.device, str]): Device.
"""
def __init__(self, dim: int, output_dim: int, device: tp.Union[torch.device, str]):
super().__init__(dim, output_dim)
self.device = device
# if False no masking is done, used in ChromaStemConditioner when completing by periodicity a sample.
self._use_masking = True
def tokenize(self, x: WavCondition) -> WavCondition:
wav, length, sample_rate, path, seek_time = x
assert length is not None
return WavCondition(wav.to(self.device), length.to(self.device), sample_rate, path, seek_time)
def _get_wav_embedding(self, x: WavCondition) -> torch.Tensor:
"""Gets as input a WavCondition and returns a dense embedding."""
raise NotImplementedError()
def _downsampling_factor(self):
"""Returns the downsampling factor of the embedding model."""
raise NotImplementedError()
def forward(self, x: WavCondition) -> ConditionType:
"""Extract condition embedding and mask from a waveform and its metadata.
Args:
x (WavCondition): Waveform condition containing raw waveform and metadata.
Returns:
ConditionType: a dense vector representing the conditioning along with its mask
"""
wav, lengths, *_ = x
with torch.no_grad():
embeds = self._get_wav_embedding(x)
embeds = embeds.to(self.output_proj.weight)
embeds = self.output_proj(embeds)
if lengths is not None and self._use_masking:
lengths = lengths / self._downsampling_factor()
mask = length_to_mask(lengths, max_len=embeds.shape[1]).int() # type: ignore
else:
mask = torch.ones_like(embeds[..., 0])
embeds = (embeds * mask.unsqueeze(-1))
return embeds, mask
class ChromaStemConditioner(WaveformConditioner):
"""Chroma conditioner based on stems.
The ChromaStemConditioner uses DEMUCS to first filter out drums and bass, as
the drums and bass often dominate the chroma leading to the chroma features
not containing information about the melody.
Args:
output_dim (int): Output dimension for the conditioner.
sample_rate (int): Sample rate for the chroma extractor.
n_chroma (int): Number of chroma bins for the chroma extractor.
radix2_exp (int): Size of stft window for the chroma extractor (power of 2, e.g. 12 -> 2^12).
duration (int): duration used during training. This is later used for correct padding
in case we are using chroma as prefix.
match_len_on_eval (bool, optional): if True then all chromas are padded to the training
duration. Defaults to False.
eval_wavs (str, optional): path to a dataset manifest with waveform, this waveforms are used as
conditions during eval (for cases where we don't want to leak test conditions like MusicCaps).
Defaults to None.
n_eval_wavs (int, optional): limits the number of waveforms used for conditioning. Defaults to 0.
device (tp.Union[torch.device, str], optional): Device for the conditioner.
**kwargs: Additional parameters for the chroma extractor.
"""
def __init__(self, output_dim: int, sample_rate: int, n_chroma: int, radix2_exp: int,
duration: float, match_len_on_eval: bool = True, eval_wavs: tp.Optional[str] = None,
n_eval_wavs: int = 0, cache_path: tp.Optional[tp.Union[str, Path]] = None,
device: tp.Union[torch.device, str] = 'cpu', **kwargs):
super().__init__(dim=n_chroma, output_dim=output_dim, device=device)
self.autocast = TorchAutocast(enabled=device != 'cpu', device_type=self.device, dtype=torch.float32)
self.sample_rate = sample_rate
self.match_len_on_eval = match_len_on_eval
if match_len_on_eval:
self._use_masking = False
self.duration = duration
self.__dict__['demucs'] = pretrained.get_model('htdemucs').to(device)
stem_sources: list = self.demucs.sources # type: ignore
self.stem_indices = torch.LongTensor([stem_sources.index('vocals'), stem_sources.index('other')]).to(device)
self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma,
radix2_exp=radix2_exp, **kwargs).to(device)
self.chroma_len = self._get_chroma_len()
self.eval_wavs: tp.Optional[torch.Tensor] = self._load_eval_wavs(eval_wavs, n_eval_wavs)
self.cache = None
if cache_path is not None:
self.cache = EmbeddingCache(Path(cache_path) / 'wav', self.device,
compute_embed_fn=self._get_full_chroma_for_cache,
extract_embed_fn=self._extract_chroma_chunk)
def _downsampling_factor(self) -> int:
return self.chroma.winhop
def _load_eval_wavs(self, path: tp.Optional[str], num_samples: int) -> tp.Optional[torch.Tensor]:
"""Load pre-defined waveforms from a json.
These waveforms will be used for chroma extraction during evaluation.
This is done to make the evaluation on MusicCaps fair (we shouldn't see the chromas of MusicCaps).
"""
if path is None:
return None
logger.info(f"Loading evaluation wavs from {path}")
dataset: AudioDataset = AudioDataset.from_meta(
path, segment_duration=self.duration, min_audio_duration=self.duration,
sample_rate=self.sample_rate, channels=1)
if len(dataset) > 0:
eval_wavs = dataset.collater([dataset[i] for i in range(num_samples)]).to(self.device)
logger.info(f"Using {len(eval_wavs)} evaluation wavs for chroma-stem conditioner")
return eval_wavs
else:
raise ValueError("Could not find evaluation wavs, check lengths of wavs")
def reset_eval_wavs(self, eval_wavs: tp.Optional[torch.Tensor]) -> None:
self.eval_wavs = eval_wavs
def has_eval_wavs(self) -> bool:
return self.eval_wavs is not None
def _sample_eval_wavs(self, num_samples: int) -> torch.Tensor:
"""Sample wavs from a predefined list."""
assert self.eval_wavs is not None, "Cannot sample eval wavs as no eval wavs provided."
total_eval_wavs = len(self.eval_wavs)
out = self.eval_wavs
if num_samples > total_eval_wavs:
out = self.eval_wavs.repeat(num_samples // total_eval_wavs + 1, 1, 1)
return out[torch.randperm(len(out))][:num_samples]
def _get_chroma_len(self) -> int:
"""Get length of chroma during training."""
dummy_wav = torch.zeros((1, int(self.sample_rate * self.duration)), device=self.device)
dummy_chr = self.chroma(dummy_wav)
return dummy_chr.shape[1]
@torch.no_grad()
def _get_stemmed_wav(self, wav: torch.Tensor, sample_rate: int) -> torch.Tensor:
"""Get parts of the wav that holds the melody, extracting the main stems from the wav."""
with self.autocast:
wav = convert_audio(
wav, sample_rate, self.demucs.samplerate, self.demucs.audio_channels) # type: ignore
stems = apply_model(self.demucs, wav, device=self.device)
stems = stems[:, self.stem_indices] # extract relevant stems for melody conditioning
mix_wav = stems.sum(1) # merge extracted stems to single waveform
mix_wav = convert_audio(mix_wav, self.demucs.samplerate, self.sample_rate, 1) # type: ignore
return mix_wav
@torch.no_grad()
def _extract_chroma(self, wav: torch.Tensor) -> torch.Tensor:
"""Extract chroma features from the waveform."""
with self.autocast:
return self.chroma(wav)
@torch.no_grad()
def _compute_wav_embedding(self, wav: torch.Tensor, sample_rate: int) -> torch.Tensor:
"""Compute wav embedding, applying stem and chroma extraction."""
# avoid 0-size tensors when we are working with null conds
if wav.shape[-1] == 1:
return self._extract_chroma(wav)
stems = self._get_stemmed_wav(wav, sample_rate)
chroma = self._extract_chroma(stems)
return chroma
@torch.no_grad()
def _get_full_chroma_for_cache(self, path: tp.Union[str, Path], x: WavCondition, idx: int) -> torch.Tensor:
"""Extract chroma from the whole audio waveform at the given path."""
wav, sr = audio_read(path)
wav = wav[None].to(self.device)
wav = convert_audio(wav, sr, self.sample_rate, to_channels=1)
chroma = self._compute_wav_embedding(wav, self.sample_rate)[0]
return chroma
def _extract_chroma_chunk(self, full_chroma: torch.Tensor, x: WavCondition, idx: int) -> torch.Tensor:
"""Extract a chunk of chroma from the full chroma derived from the full waveform."""
wav_length = x.wav.shape[-1]
seek_time = x.seek_time[idx]
assert seek_time is not None, (
"WavCondition seek_time is required "
"when extracting chroma chunks from pre-computed chroma.")
full_chroma = full_chroma.float()
frame_rate = self.sample_rate / self._downsampling_factor()
target_length = int(frame_rate * wav_length / self.sample_rate)
index = int(frame_rate * seek_time)
out = full_chroma[index: index + target_length]
out = F.pad(out[None], (0, 0, 0, target_length - out.shape[0]))[0]
return out.to(self.device)
@torch.no_grad()
def _get_wav_embedding(self, x: WavCondition) -> torch.Tensor:
"""Get the wav embedding from the WavCondition.
The conditioner will either extract the embedding on-the-fly computing it from the condition wav directly
or will rely on the embedding cache to load the pre-computed embedding if relevant.
"""
sampled_wav: tp.Optional[torch.Tensor] = None
if not self.training and self.eval_wavs is not None: | warn_once(logger, "Using precomputed evaluation wavs!") | 15 | 2023-10-09 09:55:24+00:00 | 16k |
Texaser/MTN | nerf/network.py | [
{
"identifier": "trunc_exp",
"path": "activation.py",
"snippet": "class _trunc_exp(Function):\n def forward(ctx, x):\n def backward(ctx, g):\ndef biased_softplus(x, bias=0):"
},
{
"identifier": "NeRFRenderer",
"path": "nerf/renderer.py",
"snippet": "class NeRFRenderer(nn.Module):\n... | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from activation import trunc_exp
from .renderer import NeRFRenderer
from encoding import get_encoder
from .utils import safe_normalize | 13,514 |
# TODO: not sure about the details...
class ResBlock(nn.Module):
def __init__(self, dim_in, dim_out, bias=True):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.dense = nn.Linear(self.dim_in, self.dim_out, bias=bias)
self.norm = nn.LayerNorm(self.dim_out)
self.activation = nn.SiLU(inplace=True)
if self.dim_in != self.dim_out:
self.skip = nn.Linear(self.dim_in, self.dim_out, bias=False)
else:
self.skip = None
def forward(self, x):
# x: [B, C]
identity = x
out = self.dense(x)
out = self.norm(out)
if self.skip is not None:
identity = self.skip(identity)
out += identity
out = self.activation(out)
return out
class BasicBlock(nn.Module):
def __init__(self, dim_in, dim_out, bias=True):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.dense = nn.Linear(self.dim_in, self.dim_out, bias=bias)
self.activation = nn.ReLU(inplace=True)
def forward(self, x):
# x: [B, C]
out = self.dense(x)
out = self.activation(out)
return out
class MLP(nn.Module):
def __init__(self, dim_in, dim_out, dim_hidden, num_layers, bias=True, block=BasicBlock):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.dim_hidden = dim_hidden
self.num_layers = num_layers
net = []
for l in range(num_layers):
if l == 0:
net.append(BasicBlock(self.dim_in, self.dim_hidden, bias=bias))
elif l != num_layers - 1:
net.append(block(self.dim_hidden, self.dim_hidden, bias=bias))
else:
net.append(nn.Linear(self.dim_hidden, self.dim_out, bias=bias))
self.net = nn.ModuleList(net)
def forward(self, x):
for l in range(self.num_layers):
x = self.net[l](x)
return x
|
# TODO: not sure about the details...
class ResBlock(nn.Module):
def __init__(self, dim_in, dim_out, bias=True):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.dense = nn.Linear(self.dim_in, self.dim_out, bias=bias)
self.norm = nn.LayerNorm(self.dim_out)
self.activation = nn.SiLU(inplace=True)
if self.dim_in != self.dim_out:
self.skip = nn.Linear(self.dim_in, self.dim_out, bias=False)
else:
self.skip = None
def forward(self, x):
# x: [B, C]
identity = x
out = self.dense(x)
out = self.norm(out)
if self.skip is not None:
identity = self.skip(identity)
out += identity
out = self.activation(out)
return out
class BasicBlock(nn.Module):
def __init__(self, dim_in, dim_out, bias=True):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.dense = nn.Linear(self.dim_in, self.dim_out, bias=bias)
self.activation = nn.ReLU(inplace=True)
def forward(self, x):
# x: [B, C]
out = self.dense(x)
out = self.activation(out)
return out
class MLP(nn.Module):
def __init__(self, dim_in, dim_out, dim_hidden, num_layers, bias=True, block=BasicBlock):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.dim_hidden = dim_hidden
self.num_layers = num_layers
net = []
for l in range(num_layers):
if l == 0:
net.append(BasicBlock(self.dim_in, self.dim_hidden, bias=bias))
elif l != num_layers - 1:
net.append(block(self.dim_hidden, self.dim_hidden, bias=bias))
else:
net.append(nn.Linear(self.dim_hidden, self.dim_out, bias=bias))
self.net = nn.ModuleList(net)
def forward(self, x):
for l in range(self.num_layers):
x = self.net[l](x)
return x
| class NeRFNetwork(NeRFRenderer): | 1 | 2023-10-11 04:06:20+00:00 | 16k |
oracle/guardian-ai | guardian_ai/fairness/metrics/core.py | [
{
"identifier": "EqualizedOddsScorer",
"path": "guardian_ai/fairness/metrics/model.py",
"snippet": "class EqualizedOddsScorer(_ModelFairnessScorer):\n \"\"\"\n Measures the disparity of a model's true positive and false positive rates\n between subgroups and the rest of the subgroups.\n\n Th... | from guardian_ai.fairness.metrics.model import (
EqualizedOddsScorer,
ErrorRateScorer,
FalseDiscoveryRateScorer,
FalseNegativeRateScorer,
FalseOmissionRateScorer,
FalsePositiveRateScorer,
ModelStatisticalParityScorer,
TheilIndexScorer,
TruePositiveRateScorer,
equalized_odds,
error_rate,
false_discovery_rate,
false_negative_rate,
false_omission_rate,
false_positive_rate,
model_statistical_parity,
theil_index,
true_positive_rate,
)
from guardian_ai.utils.exception import GuardianAIValueError | 13,057 | #!/usr/bin/env python
# -*- coding: utf-8 -*--
# Copyright (c) 2023 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
"""Core for fairness metrics"""
fairness_scorers_dict = { # noqa N816
"statistical_parity": ModelStatisticalParityScorer,
"TPR": TruePositiveRateScorer,
"FPR": FalsePositiveRateScorer,
"FNR": FalseNegativeRateScorer,
"FOR": FalseOmissionRateScorer,
"FDR": FalseDiscoveryRateScorer,
"error_rate": ErrorRateScorer,
"equalized_odds": EqualizedOddsScorer,
"theil_index": TheilIndexScorer,
}
def _get_fairness_scorer(metric, protected_attributes, **kwargs): # noqa N802
if metric not in fairness_scorers_dict:
raise GuardianAIValueError(
f"{metric} is not a supported model fairness metric. Supported "
f"metrics are: {list(fairness_scorers_dict)}."
)
return fairness_scorers_dict[metric](protected_attributes, **kwargs)
fairness_metrics_dict = {
"statistical_parity": model_statistical_parity,
"TPR": true_positive_rate,
"FPR": false_positive_rate,
"FNR": false_negative_rate,
"FOR": false_omission_rate,
"FDR": false_discovery_rate,
"error_rate": error_rate,
"equalized_odds": equalized_odds,
| #!/usr/bin/env python
# -*- coding: utf-8 -*--
# Copyright (c) 2023 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
"""Core for fairness metrics"""
fairness_scorers_dict = { # noqa N816
"statistical_parity": ModelStatisticalParityScorer,
"TPR": TruePositiveRateScorer,
"FPR": FalsePositiveRateScorer,
"FNR": FalseNegativeRateScorer,
"FOR": FalseOmissionRateScorer,
"FDR": FalseDiscoveryRateScorer,
"error_rate": ErrorRateScorer,
"equalized_odds": EqualizedOddsScorer,
"theil_index": TheilIndexScorer,
}
def _get_fairness_scorer(metric, protected_attributes, **kwargs): # noqa N802
if metric not in fairness_scorers_dict:
raise GuardianAIValueError(
f"{metric} is not a supported model fairness metric. Supported "
f"metrics are: {list(fairness_scorers_dict)}."
)
return fairness_scorers_dict[metric](protected_attributes, **kwargs)
fairness_metrics_dict = {
"statistical_parity": model_statistical_parity,
"TPR": true_positive_rate,
"FPR": false_positive_rate,
"FNR": false_negative_rate,
"FOR": false_omission_rate,
"FDR": false_discovery_rate,
"error_rate": error_rate,
"equalized_odds": equalized_odds, | "theil_index": theil_index, | 16 | 2023-10-09 09:48:50+00:00 | 16k |
IST-DASLab/SparseFinetuning | llmfoundry/models/mpt/modeling_mpt.py | [
{
"identifier": "attn_bias_shape",
"path": "llmfoundry/models/layers/attention.py",
"snippet": "def attn_bias_shape(attn_impl: str, n_heads: int, seq_len: int, alibi: bool,\n prefix_lm: bool, causal: bool, use_sequence_id: bool):\n if attn_impl == 'flash':\n return None\n ... | import math
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Any, List, Mapping, MutableMapping, Optional, Tuple, Union
from composer.metrics import (InContextLearningLMAccuracy,
InContextLearningLMExpectedCalibrationError,
InContextLearningMCExpectedCalibrationError,
InContextLearningMultipleChoiceAccuracy,
InContextLearningQAAccuracy)
from composer.metrics.nlp import LanguageCrossEntropy, LanguagePerplexity
from composer.models import HuggingFaceModel
from composer.utils import dist
from omegaconf import DictConfig
from omegaconf import OmegaConf as om
from transformers import PreTrainedModel, PreTrainedTokenizerBase
from transformers.modeling_outputs import (BaseModelOutputWithPast,
CausalLMOutputWithPast)
from llmfoundry.models.layers.attention import attn_bias_shape, build_attn_bias
from llmfoundry.models.layers.blocks import MPTBlock
from llmfoundry.models.layers.custom_embedding import SharedEmbedding
from llmfoundry.models.layers.fc import FC_CLASS_REGISTRY as FC_CLASS_REGISTRY
from llmfoundry.models.layers.ffn import \
FFN_CLASS_REGISTRY as FFN_CLASS_REGISTRY
from llmfoundry.models.layers.ffn import MPTMLP as MPTMLP
from llmfoundry.models.layers.ffn import build_ffn as build_ffn
from llmfoundry.models.layers.norm import NORM_CLASS_REGISTRY
from llmfoundry.models.mpt.configuration_mpt import MPTConfig
from llmfoundry.models.utils.adapt_tokenizer import (
AutoTokenizerForMOD, # type: ignore (see note),
adapt_tokenizer_for_denoising, # type: ignore (see note)
)
from llmfoundry.models.utils.hf_prefixlm_converter import (
add_bidirectional_mask_if_missing, # type: ignore (see note)
convert_hf_causal_lm_to_prefix_lm, # type: ignore (see note)
)
from llmfoundry.models.utils.meta_init_context import \
init_empty_weights # type: ignore (see note)
from llmfoundry.models.utils.param_init_fns import (
generic_param_init_fn_, # type: ignore (see note)
MODEL_INIT_REGISTRY,
)
from llmfoundry.models.layers.flash_attn_triton import flash_attn_func as flash_attn_func
from flash_attn.losses.cross_entropy import CrossEntropyLoss as FusedCrossEntropyLoss # type: ignore # isort: skip | 11,923 | 'sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True '
+ 'and the model is in train mode.')
elif (self.attn_uses_sequence_id is False) and (sequence_id
is not None):
warnings.warn(
'MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. '
+
'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.'
)
S = input_ids.size(1)
assert (
S <= self.config.max_seq_len
), f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'
tok_emb = self.wte(input_ids) # type: ignore
if self.learned_pos_emb:
past_position = 0
if past_key_values is not None:
if len(past_key_values) != self.config.n_layers:
raise ValueError(
f'past_key_values must provide a past_key_value for each attention '
+
f'layer in the network ({len(past_key_values)=}; {self.config.n_layers=}).'
)
# For attn_impl: triton and flash the past key tensor spec is (batch, seq, dim).
# For attn_impl: torch the past key tensor spec is (batch, heads, head_dim, seq).
# Here we shift position embedding using the `seq` dim of the past key
past_position = past_key_values[0][0].size(1)
if self.attn_impl == 'torch':
past_position = past_key_values[0][0].size(3)
if S + past_position > self.config.max_seq_len:
raise ValueError(
f'Cannot forward input with past sequence length {past_position} and current sequence length '
+
f'{S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.'
)
pos = torch.arange(
past_position,
S + past_position,
dtype=torch.long,
device=input_ids.device,
).unsqueeze(0)
if attention_mask is not None:
# adjust the position indices to account for padding tokens
pos = torch.clamp(
pos - torch.cumsum((~attention_mask).to(torch.int32),
dim=1)[:, past_position:],
min=0,
)
pos_emb = self.wpe(pos) # type: ignore
x = tok_emb + pos_emb
else:
# ALiBi and NoPE use this path (RoPE will also use this path if / when enabled)
x = tok_emb
if self.embedding_fraction == 1:
x = self.emb_drop(x) # type: ignore
else:
# this implementation is proposed on page 7 of the GLM-130B paper https://arxiv.org/abs/2210.02414
x_shrunk = (x * self.embedding_fraction) + (
x.detach() * (1 - self.embedding_fraction))
assert isinstance(self.emb_drop, nn.Module) # pyright
x = self.emb_drop(x_shrunk)
attn_bias, attention_mask = self._attn_bias(
device=x.device,
dtype=torch.float32,
attention_mask=attention_mask,
prefix_mask=prefix_mask,
sequence_id=sequence_id,
)
# initialize the past key values cache if it should be used
if use_cache and past_key_values is None:
past_key_values = [() for _ in range(self.config.n_layers)
] # type: ignore
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for b_idx, block in enumerate(self.blocks): # type: ignore
if output_hidden_states:
assert all_hidden_states is not None # pyright
all_hidden_states = all_hidden_states + (x,)
past_key_value = (past_key_values[b_idx]
if past_key_values is not None else None)
x, attn_weights, past_key_value = block(
x,
past_key_value=past_key_value,
attn_bias=attn_bias,
attention_mask=attention_mask,
is_causal=self.is_causal,
)
if past_key_values is not None:
past_key_values[b_idx] = past_key_value
if output_attentions:
assert all_self_attns is not None # pyright
all_self_attns = all_self_attns + (attn_weights,)
x = self.norm_f(x) # type: ignore
# add hidden states from the last decoder layer
if output_hidden_states:
assert all_hidden_states is not None # pyright
all_hidden_states = all_hidden_states + (x,)
return BaseModelOutputWithPast(
last_hidden_state=x,
past_key_values=past_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
# Param Initialization, needed for device='meta' fast initialization
def param_init_fn(self, module: nn.Module):
init_fn_name = self.config.init_config['name']
| # Copyright 2022 MosaicML LLM Foundry authors
# SPDX-License-Identifier: Apache-2.0
"""A simple, flexible implementation of a GPT model.
Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
"""
# NOTE: All utils are imported directly even if unused so that
# HuggingFace can detect all the needed files to copy into its modules folder.
# Otherwise, certain modules are missing.
# isort: off
try:
except:
pass
# isort: on
class MPTPreTrainedModel(PreTrainedModel):
config_class = MPTConfig
base_model_prefix = 'model'
_no_split_modules = ['MPTBlock']
class MPTModel(MPTPreTrainedModel):
def __init__(self, config: MPTConfig):
config._validate_config()
super().__init__(config)
self.attn_impl = config.attn_config['attn_impl']
self.prefix_lm = config.attn_config['prefix_lm']
self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id']
self.alibi = config.attn_config['alibi']
self.alibi_bias_max = config.attn_config['alibi_bias_max']
self.learned_pos_emb = config.learned_pos_emb
if config.init_device == 'mixed':
if dist.get_local_rank() == 0:
config.init_device = 'cpu'
else:
config.init_device = 'meta'
if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys():
norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys())
raise NotImplementedError(
f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).'
)
norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()]
# CogView (https://arxiv.org/abs/2105.13290) and GLM-130B (https://arxiv.org/abs/2210.02414)
# both report this helping with stabilizing training
self.embedding_fraction = config.embedding_fraction
self.wte = SharedEmbedding(config.vocab_size,
config.d_model,
device=config.init_device)
if self.learned_pos_emb:
self.wpe = torch.nn.Embedding(config.max_seq_len,
config.d_model,
device=config.init_device)
self.emb_drop = nn.Dropout(config.emb_pdrop)
self.blocks = nn.ModuleList([
MPTBlock(
device=config.init_device,
**config.to_dict(),
) for _ in range(config.n_layers)
])
self.norm_f = norm_class(config.d_model, device=config.init_device)
if config.init_device != 'meta':
print(
f'You are using {config.init_device=}, but you can also use config.init_device="meta" with Composer + FSDP for fast initialization.'
)
self.apply(self.param_init_fn)
self.is_causal = not self.prefix_lm
# define attn mask
self._attn_bias_initialized = False
self.attn_bias = None
self.attn_bias_shape = attn_bias_shape(
self.attn_impl,
config.n_heads,
config.max_seq_len,
self.alibi,
prefix_lm=self.prefix_lm,
causal=self.is_causal,
use_sequence_id=self.attn_uses_sequence_id,
)
if config.no_bias:
for module in self.modules():
if hasattr(module, 'bias') and isinstance(
module.bias, nn.Parameter):
if config.verbose:
warnings.warn(
f'Removing bias ({module.bias}) from {module}.')
module.register_parameter('bias', None)
# Print verbose info
if config.verbose and config.verbose > 2:
print(self)
if 'verbose' not in self.config.init_config:
self.config.init_config['verbose'] = self.config.verbose
if self.config.init_config['verbose'] > 1:
init_fn_name = self.config.init_config['name']
warnings.warn(f'Using {init_fn_name} initialization.')
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, value: nn.Embedding):
self.wte = value
@torch.no_grad()
def _attn_bias(
self,
device: torch.device,
dtype: torch.dtype,
attention_mask: Optional[torch.ByteTensor] = None,
prefix_mask: Optional[torch.ByteTensor] = None,
sequence_id: Optional[torch.LongTensor] = None,
):
if not self._attn_bias_initialized:
if self.attn_bias_shape:
self.attn_bias = torch.zeros(self.attn_bias_shape,
device=device,
dtype=dtype)
self.attn_bias = build_attn_bias(
self.attn_impl,
self.attn_bias,
self.config.n_heads,
self.config.max_seq_len,
causal=self.is_causal,
alibi=self.alibi,
alibi_bias_max=self.alibi_bias_max,
)
self._attn_bias_initialized = True
# flash does not support prefix_lm and will incorporate any
# attention_mask inside the attention module
if self.attn_impl == 'flash':
return self.attn_bias, attention_mask
if self.attn_bias is not None:
# .to(*args, **kwargs) is a no-op if tensor is already on
# specified device or of specificed dtype
self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)
attn_bias = self.attn_bias
# If using torch or triton, we incorporate the prefix_mask (if appropriate)
if self.prefix_lm:
assert isinstance(attn_bias, torch.Tensor) # pyright
assert isinstance(prefix_mask, torch.Tensor) # pyright
attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
# If using torch or triton, we incorporate sequence_id (if appropriate)
if self.attn_uses_sequence_id and sequence_id is not None:
assert isinstance(attn_bias, torch.Tensor) # pyright
attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
# If using torch or triton, we incorporate attention_mask. This will output
# None in place of attention_mask since it will not be further needed in the
# attention modules.
if attention_mask is not None:
s_k = attention_mask.shape[-1]
if attn_bias is None:
attn_bias = torch.zeros((1, 1, 1, s_k),
device=device,
dtype=dtype)
else:
# clamp to 0 necessary for torch 2.0 compile()
_s_k = max(0, attn_bias.size(-1) - s_k)
attn_bias = attn_bias[:, :, :, _s_k:]
if prefix_mask is not None and (attention_mask.shape !=
prefix_mask.shape):
raise ValueError(
f'attention_mask shape={attention_mask.shape} ' +
f'and prefix_mask shape={prefix_mask.shape} are not equal.')
min_val = torch.finfo(attn_bias.dtype).min
attn_bias = attn_bias.masked_fill(
~attention_mask.view(-1, 1, 1, s_k), min_val)
return attn_bias, None
def _apply_prefix_mask(self, attn_bias: torch.Tensor,
prefix_mask: torch.Tensor):
s_k, s_q = attn_bias.shape[-2:]
if (s_k != self.config.max_seq_len) or (s_q != self.config.max_seq_len):
raise ValueError(
'attn_bias does not match the expected shape. ' +
f'The last two dimensions should both be {self.config.max_length} '
+ f'but are {s_k} and {s_q}.')
seq_len = prefix_mask.shape[-1]
if seq_len > self.config.max_seq_len:
raise ValueError(
f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}'
)
# select seq_len subset of attn mask
attn_bias = attn_bias[..., :seq_len, :seq_len]
# Mix the causal max and the bidirectional mask to get the full
# allowable attention (i.e. full = not accounting for padding yet)
causal = torch.tril(
torch.ones((seq_len, seq_len),
dtype=torch.bool,
device=prefix_mask.device)).view(1, 1, seq_len, seq_len)
prefix = prefix_mask.view(-1, 1, 1, seq_len)
cannot_attend = ~torch.logical_or(causal, prefix.bool())
min_val = torch.finfo(attn_bias.dtype).min
attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
return attn_bias
def _apply_sequence_id(self, attn_bias: torch.Tensor,
sequence_id: torch.LongTensor):
seq_len = sequence_id.shape[-1]
if seq_len > self.config.max_seq_len:
raise ValueError(
f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}'
)
# select seq_len subset of attn mask
attn_bias = attn_bias[..., :seq_len, :seq_len]
# Restrict attention to tokens that share the same value
# in sequence_id
cannot_attend = torch.logical_not(
torch.eq(
sequence_id.view(-1, seq_len, 1),
sequence_id.view(-1, 1, seq_len),
)).unsqueeze(1)
min_val = torch.finfo(attn_bias.dtype).min
attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
return attn_bias
def forward(
self,
input_ids: torch.LongTensor,
past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
attention_mask: Optional[torch.ByteTensor] = None,
prefix_mask: Optional[torch.ByteTensor] = None,
sequence_id: Optional[torch.LongTensor] = None,
return_dict: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
use_cache: Optional[bool] = None,
inputs_embeds: Optional[torch.Tensor] = None,
):
return_dict = (return_dict
if return_dict is not None else self.config.return_dict)
use_cache = (use_cache
if use_cache is not None else self.config.use_cache)
if attention_mask is not None:
attention_mask = attention_mask.bool(
) # type: ignore (TODO to figure out the right type here)
if prefix_mask is not None:
prefix_mask = prefix_mask.bool(
) # type: ignore (TODO to figure out the right type here)
# These args are passed in by keyword in huggingface's generate function
# https://github.com/huggingface/transformers/blob/68287689f2f0d8b7063c400230b3766987abf18d/src/transformers/generation/utils.py#L2201-L2206
# but have not yet been fully implemented in MPTModel
if not return_dict:
raise NotImplementedError(
'return_dict False is not implemented yet for MPT')
if output_attentions:
if self.attn_impl != 'torch':
raise NotImplementedError(
'output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.'
)
if (attention_mask is not None and
attention_mask[:, 0].sum() != attention_mask.shape[0] and
self.training):
raise NotImplementedError(
'MPT does not support training with left padding.')
if self.prefix_lm and prefix_mask is None:
raise ValueError(
'prefix_mask is a required argument when MPT is configured with prefix_lm=True.'
)
# Raise a not implemented error if input_embeds is not None (this is an arg in huggingface transformers and we need to support it for PEFT)
if inputs_embeds is not None:
raise NotImplementedError(
'inputs_embeds is not implemented for MPT.')
if self.training:
if self.attn_uses_sequence_id and sequence_id is None:
raise ValueError(
'sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True '
+ 'and the model is in train mode.')
elif (self.attn_uses_sequence_id is False) and (sequence_id
is not None):
warnings.warn(
'MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. '
+
'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.'
)
S = input_ids.size(1)
assert (
S <= self.config.max_seq_len
), f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'
tok_emb = self.wte(input_ids) # type: ignore
if self.learned_pos_emb:
past_position = 0
if past_key_values is not None:
if len(past_key_values) != self.config.n_layers:
raise ValueError(
f'past_key_values must provide a past_key_value for each attention '
+
f'layer in the network ({len(past_key_values)=}; {self.config.n_layers=}).'
)
# For attn_impl: triton and flash the past key tensor spec is (batch, seq, dim).
# For attn_impl: torch the past key tensor spec is (batch, heads, head_dim, seq).
# Here we shift position embedding using the `seq` dim of the past key
past_position = past_key_values[0][0].size(1)
if self.attn_impl == 'torch':
past_position = past_key_values[0][0].size(3)
if S + past_position > self.config.max_seq_len:
raise ValueError(
f'Cannot forward input with past sequence length {past_position} and current sequence length '
+
f'{S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.'
)
pos = torch.arange(
past_position,
S + past_position,
dtype=torch.long,
device=input_ids.device,
).unsqueeze(0)
if attention_mask is not None:
# adjust the position indices to account for padding tokens
pos = torch.clamp(
pos - torch.cumsum((~attention_mask).to(torch.int32),
dim=1)[:, past_position:],
min=0,
)
pos_emb = self.wpe(pos) # type: ignore
x = tok_emb + pos_emb
else:
# ALiBi and NoPE use this path (RoPE will also use this path if / when enabled)
x = tok_emb
if self.embedding_fraction == 1:
x = self.emb_drop(x) # type: ignore
else:
# this implementation is proposed on page 7 of the GLM-130B paper https://arxiv.org/abs/2210.02414
x_shrunk = (x * self.embedding_fraction) + (
x.detach() * (1 - self.embedding_fraction))
assert isinstance(self.emb_drop, nn.Module) # pyright
x = self.emb_drop(x_shrunk)
attn_bias, attention_mask = self._attn_bias(
device=x.device,
dtype=torch.float32,
attention_mask=attention_mask,
prefix_mask=prefix_mask,
sequence_id=sequence_id,
)
# initialize the past key values cache if it should be used
if use_cache and past_key_values is None:
past_key_values = [() for _ in range(self.config.n_layers)
] # type: ignore
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for b_idx, block in enumerate(self.blocks): # type: ignore
if output_hidden_states:
assert all_hidden_states is not None # pyright
all_hidden_states = all_hidden_states + (x,)
past_key_value = (past_key_values[b_idx]
if past_key_values is not None else None)
x, attn_weights, past_key_value = block(
x,
past_key_value=past_key_value,
attn_bias=attn_bias,
attention_mask=attention_mask,
is_causal=self.is_causal,
)
if past_key_values is not None:
past_key_values[b_idx] = past_key_value
if output_attentions:
assert all_self_attns is not None # pyright
all_self_attns = all_self_attns + (attn_weights,)
x = self.norm_f(x) # type: ignore
# add hidden states from the last decoder layer
if output_hidden_states:
assert all_hidden_states is not None # pyright
all_hidden_states = all_hidden_states + (x,)
return BaseModelOutputWithPast(
last_hidden_state=x,
past_key_values=past_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
# Param Initialization, needed for device='meta' fast initialization
def param_init_fn(self, module: nn.Module):
init_fn_name = self.config.init_config['name'] | MODEL_INIT_REGISTRY[init_fn_name]( | 16 | 2023-10-09 15:32:15+00:00 | 16k |
jiangjiechen/auction-arena | src/auctioneer_base.py | [
{
"identifier": "Bidder",
"path": "src/bidder_base.py",
"snippet": "class Bidder(BaseModel):\n name: str\n model_name: str \n budget: int \n desire: str\n plan_strategy: str\n temperature: float = 0.7\n overestimate_percent: int = 10\n correct_belief: bool\n enable_learning: b... | import re
import random
import inflect
from typing import List, Dict
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.callbacks import get_openai_callback
from pydantic import BaseModel
from collections import defaultdict
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
from .bidder_base import Bidder
from .human_bidder import HumanBidder
from .item_base import Item
from .prompt_base import PARSE_BID_INSTRUCTION | 12,958 | msg = f"Thank you! This is the {p.ordinal(bid_round)} round of bidding for this item:\n{bidding_history}\n\nNow we have ${self.highest_bid} from {self.highest_bidder.name} for {self.cur_item.name}. The minimum increase over this highest bid is ${int(self.cur_item.price * self.min_markup_pct)}. Do I have any advance on ${self.highest_bid}?"
return msg
def ask_for_rebid(self, fail_msg: str, bid_price: int):
return f"Your bid of ${bid_price} failed, because {fail_msg}: You must reconsider your bid."
def get_hammer_msg(self):
if self.highest_bidder is None:
return f"Since no one bid on {self.cur_item.name}, we'll move on to the next item."
else:
return f"Sold! {self.cur_item} to {self.highest_bidder} at ${self.highest_bid}! The true value for {self.cur_item} is ${self.cur_item.true_value}."# Thus {self.highest_bidder}'s profit by winning this item is ${self.cur_item.true_value - self.highest_bid}."
def check_hammer(self, bid_round: int):
# check if the item is sold
self.fail_to_sell = False
num_bid = self._num_bids_in_round(bid_round)
# highest_bidder has already been updated in record_bid().
# so when num_bid == 0 & highest_bidder is None, it means no one bid on this item
if self.highest_bidder is None:
if num_bid == 0:
# failed to sell, as there is no highest bidder
self.fail_to_sell = True
if self.enable_discount and bid_round < 3:
# lower the starting price by 50%. discoutn only applies to the first 3 rounds
self.cur_item.lower_price(0.5)
is_sold = False
else:
is_sold = True
else:
# won't happen
raise ValueError(f"highest_bidder is None but num_bid is {num_bid}")
else:
if self.prev_round_max_bid < 0 and num_bid == 1:
# only one bidder in the first round
is_sold = True
else:
self.prev_round_max_bid = self.highest_bid
is_sold = self._num_bids_in_round(bid_round) == 0
return is_sold
def _num_bids_in_round(self, bid_round: int):
# check if there is no bid in the current round
cnt = 0
for hist in self.bidding_history[bid_round]:
if hist['bid'] > 0:
cnt += 1
return cnt
def hammer_fall(self):
print(f'* Sold! {self.cur_item} (${self.cur_item.true_value}) goes to {self.highest_bidder} at ${self.highest_bid}.')
self.auction_logs[f"{self.cur_item.get_desc()}"].append({
'bidder': self.highest_bidder,
'bid': f"{self.highest_bid} (${self.cur_item.true_value})", # no need for the first $, as it will be added in the self.log()
'bid_round': 'Hammer price (true value)'})
self.cur_item = None
self.highest_bidder = None
self.highest_bid = -1
self.bidding_history = defaultdict(list)
self.prev_round_max_bid = -1
self.fail_to_sell = False
def end_auction(self):
return len(self.items_queue) == 0
def gather_all_status(self, bidders: List[Bidder]):
status = {}
for bidder in bidders:
status[bidder.name] = {
'profit': bidder.profit,
'items_won': bidder.items_won
}
return status
def parse_bid(self, text: str):
prompt = PARSE_BID_INSTRUCTION.format(response=text)
with get_openai_callback() as cb:
llm = ChatOpenAI(model='gpt-3.5-turbo-0613', temperature=0)
result = llm([HumanMessage(content=prompt)]).content
self.openai_cost += cb.total_cost
bid_number = re.findall(r'\$?\d+', result.replace(',', ''))
# find number in the result
if '-1' in result:
return -1
elif len(bid_number) > 0:
return int(bid_number[-1].replace('$', ''))
else:
print('* Rebid:', text)
return None
def log(self, bidder_personal_reports: list = [], show_model_name=True):
''' example
Apparatus H, starting at $1000.
1st bid:
Bidder 1 (gpt-3.5-turbo-16k-0613): $1200
Bidder 2 (gpt-3.5-turbo-16k-0613): $1100
Bidder 3 (gpt-3.5-turbo-16k-0613): Withdrawn
Bidder 4 (gpt-3.5-turbo-16k-0613): $1200
2nd bid:
Bidder 1 (gpt-3.5-turbo-16k-0613): Withdrawn
Bidder 2 (gpt-3.5-turbo-16k-0613): Withdrawn
Hammer price:
Bidder 4 (gpt-3.5-turbo-16k-0613): $1200
'''
markdown_output = "## Auction Log\n\n"
for i, (item, bids) in enumerate(self.auction_logs.items()):
markdown_output += f"### {i+1}. {item}\n\n"
cur_bid_round = -1
for i, bid in enumerate(bids):
if bid['bid_round'] != cur_bid_round:
cur_bid_round = bid['bid_round']
if isinstance(bid['bid_round'], int):
markdown_output += f"\n#### {p.ordinal(bid['bid_round']+1)} bid:\n\n"
else:
markdown_output += f"\n#### {bid['bid_round']}:\n\n"
bid_price = f"${bid['bid']}" if bid['bid'] != -1 else 'Withdrew'
|
p = inflect.engine()
class Auctioneer(BaseModel):
enable_discount: bool = False
items: List[Item] = []
cur_item: Item = None
highest_bidder: Bidder = None
highest_bid: int = -1
bidding_history = defaultdict(list) # history about the bidding war of one item
items_queue: List[Item] = [] # updates when a item is taken.
auction_logs = defaultdict(list) # history about the bidding war of all items
openai_cost = 0
prev_round_max_bid: int = -1
min_bid: int = 0
fail_to_sell = False
min_markup_pct = 0.1
class Config:
arbitrary_types_allowed = True
def init_items(self, items: List[Item]):
for item in items:
# reset discounted price
item.reset_price()
self.items = items
self.items_queue = items.copy()
def summarize_items_info(self):
desc = ''
for item in self.items:
desc += f"- {item.get_desc()}\n"
return desc.strip()
def present_item(self):
cur_item = self.items_queue.pop(0)
self.cur_item = cur_item
return cur_item
def shuffle_items(self):
random.shuffle(self.items)
self.items_queue = self.items.copy()
def record_bid(self, bid_info: dict, bid_round: int):
'''
Save the bidding history for each round, log the highest bidder and highest bidding
'''
# bid_info: {'bidder': xxx, 'bid': xxx, 'raw_msg': xxx}
self.bidding_history[bid_round].append(bid_info)
for hist in self.bidding_history[bid_round]:
if hist['bid'] > 0:
if self.highest_bid < hist['bid']:
self.highest_bid = hist['bid']
self.highest_bidder = hist['bidder']
elif self.highest_bid == hist['bid']:
# random if there's a tie
self.highest_bidder = random.choice([self.highest_bidder, hist['bidder']])
self.auction_logs[f"{self.cur_item.get_desc()}"].append(
{'bidder': bid_info['bidder'],
'bid': bid_info['bid'],
'bid_round': bid_round})
def _biddings_to_string(self, bid_round: int):
'''
Return a string that summarizes the bidding history in a round
'''
# bid_hist_text = '' if bid_round == 0 else f'- {self.highest_bidder}: ${self.highest_bid}\n'
bid_hist_text = ''
for js in self.bidding_history[bid_round]:
if js['bid'] < 0:
bid_hist_text += f"- {js['bidder']} withdrew\n"
else:
bid_hist_text += f"- {js['bidder']}: ${js['bid']}\n"
return bid_hist_text.strip()
def all_bidding_history_to_string(self):
bid_hist_text = ''
for bid_round in self.bidding_history:
bid_hist_text += f"Round {bid_round}:\n{self._biddings_to_string(bid_round)}\n\n"
return bid_hist_text.strip()
def ask_for_bid(self, bid_round: int):
'''
Ask for bid, return the message to be sent to bidders
'''
if self.highest_bidder is None:
if bid_round > 0:
msg = f"Seeing as we've had no takers at the initial price, we're going to lower the starting bid to ${self.cur_item.price} for {self.cur_item.name} to spark some interest! Do I have any takers?"
else:
remaining_items = [self.cur_item.name] + [item.name for item in self.items_queue]
msg = f"Attention, bidders! {len(remaining_items)} item(s) left, they are: {', '.join(remaining_items)}.\n\nNow, please bid on {self.cur_item}. The starting price for bidding for {self.cur_item} is ${self.cur_item.price}. Anyone interested in this item?"
else:
bidding_history = self._biddings_to_string(bid_round - 1)
msg = f"Thank you! This is the {p.ordinal(bid_round)} round of bidding for this item:\n{bidding_history}\n\nNow we have ${self.highest_bid} from {self.highest_bidder.name} for {self.cur_item.name}. The minimum increase over this highest bid is ${int(self.cur_item.price * self.min_markup_pct)}. Do I have any advance on ${self.highest_bid}?"
return msg
def ask_for_rebid(self, fail_msg: str, bid_price: int):
return f"Your bid of ${bid_price} failed, because {fail_msg}: You must reconsider your bid."
def get_hammer_msg(self):
if self.highest_bidder is None:
return f"Since no one bid on {self.cur_item.name}, we'll move on to the next item."
else:
return f"Sold! {self.cur_item} to {self.highest_bidder} at ${self.highest_bid}! The true value for {self.cur_item} is ${self.cur_item.true_value}."# Thus {self.highest_bidder}'s profit by winning this item is ${self.cur_item.true_value - self.highest_bid}."
def check_hammer(self, bid_round: int):
# check if the item is sold
self.fail_to_sell = False
num_bid = self._num_bids_in_round(bid_round)
# highest_bidder has already been updated in record_bid().
# so when num_bid == 0 & highest_bidder is None, it means no one bid on this item
if self.highest_bidder is None:
if num_bid == 0:
# failed to sell, as there is no highest bidder
self.fail_to_sell = True
if self.enable_discount and bid_round < 3:
# lower the starting price by 50%. discoutn only applies to the first 3 rounds
self.cur_item.lower_price(0.5)
is_sold = False
else:
is_sold = True
else:
# won't happen
raise ValueError(f"highest_bidder is None but num_bid is {num_bid}")
else:
if self.prev_round_max_bid < 0 and num_bid == 1:
# only one bidder in the first round
is_sold = True
else:
self.prev_round_max_bid = self.highest_bid
is_sold = self._num_bids_in_round(bid_round) == 0
return is_sold
def _num_bids_in_round(self, bid_round: int):
# check if there is no bid in the current round
cnt = 0
for hist in self.bidding_history[bid_round]:
if hist['bid'] > 0:
cnt += 1
return cnt
def hammer_fall(self):
print(f'* Sold! {self.cur_item} (${self.cur_item.true_value}) goes to {self.highest_bidder} at ${self.highest_bid}.')
self.auction_logs[f"{self.cur_item.get_desc()}"].append({
'bidder': self.highest_bidder,
'bid': f"{self.highest_bid} (${self.cur_item.true_value})", # no need for the first $, as it will be added in the self.log()
'bid_round': 'Hammer price (true value)'})
self.cur_item = None
self.highest_bidder = None
self.highest_bid = -1
self.bidding_history = defaultdict(list)
self.prev_round_max_bid = -1
self.fail_to_sell = False
def end_auction(self):
return len(self.items_queue) == 0
def gather_all_status(self, bidders: List[Bidder]):
status = {}
for bidder in bidders:
status[bidder.name] = {
'profit': bidder.profit,
'items_won': bidder.items_won
}
return status
def parse_bid(self, text: str):
prompt = PARSE_BID_INSTRUCTION.format(response=text)
with get_openai_callback() as cb:
llm = ChatOpenAI(model='gpt-3.5-turbo-0613', temperature=0)
result = llm([HumanMessage(content=prompt)]).content
self.openai_cost += cb.total_cost
bid_number = re.findall(r'\$?\d+', result.replace(',', ''))
# find number in the result
if '-1' in result:
return -1
elif len(bid_number) > 0:
return int(bid_number[-1].replace('$', ''))
else:
print('* Rebid:', text)
return None
def log(self, bidder_personal_reports: list = [], show_model_name=True):
''' example
Apparatus H, starting at $1000.
1st bid:
Bidder 1 (gpt-3.5-turbo-16k-0613): $1200
Bidder 2 (gpt-3.5-turbo-16k-0613): $1100
Bidder 3 (gpt-3.5-turbo-16k-0613): Withdrawn
Bidder 4 (gpt-3.5-turbo-16k-0613): $1200
2nd bid:
Bidder 1 (gpt-3.5-turbo-16k-0613): Withdrawn
Bidder 2 (gpt-3.5-turbo-16k-0613): Withdrawn
Hammer price:
Bidder 4 (gpt-3.5-turbo-16k-0613): $1200
'''
markdown_output = "## Auction Log\n\n"
for i, (item, bids) in enumerate(self.auction_logs.items()):
markdown_output += f"### {i+1}. {item}\n\n"
cur_bid_round = -1
for i, bid in enumerate(bids):
if bid['bid_round'] != cur_bid_round:
cur_bid_round = bid['bid_round']
if isinstance(bid['bid_round'], int):
markdown_output += f"\n#### {p.ordinal(bid['bid_round']+1)} bid:\n\n"
else:
markdown_output += f"\n#### {bid['bid_round']}:\n\n"
bid_price = f"${bid['bid']}" if bid['bid'] != -1 else 'Withdrew' | if isinstance(bid['bidder'], Bidder) or isinstance(bid['bidder'], HumanBidder): | 1 | 2023-10-08 09:30:57+00:00 | 16k |
SH1ROd/Bert-VITS2-Integration-train-txt-infer | train_ms.py | [
{
"identifier": "TextAudioSpeakerLoader",
"path": "data_utils.py",
"snippet": "class TextAudioSpeakerLoader(torch.utils.data.Dataset):\n \"\"\"\n 1) loads audio, speaker_id, text pairs\n 2) normalizes text and converts them to sequences of integers\n 3) computes spectrograms from... | import os
import json
import argparse
import itertools
import math
import torch
import shutil
import torch.multiprocessing as mp
import torch.distributed as dist
import logging
import commons
import utils
from torch import nn, optim
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 data_utils import (
TextAudioSpeakerLoader,
TextAudioSpeakerCollate,
DistributedBucketSampler
)
from models import (
SynthesizerTrn,
MultiPeriodDiscriminator,
DurationDiscriminator,
)
from losses import (
generator_loss,
discriminator_loss,
feature_loss,
kl_loss
)
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from text.symbols import symbols | 10,870 | _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
optim_g, skip_optimizer=not hps.cont)
_, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
optim_d, skip_optimizer=not hps.cont)
epoch_str = max(epoch_str, 1)
global_step = (epoch_str - 1) * len(train_loader)
except Exception as e:
print(e)
epoch_str = 1
global_step = 0
else:
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g,
optim_g, True)
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d,
optim_d, True)
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)
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.fp16_run)
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank == 0:
train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval],role=role)
else:
train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None, role=role)
scheduler_g.step()
scheduler_d.step()
if net_dur_disc is not None:
scheduler_dur_disc.step()
def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, role):
net_g, net_d, net_dur_disc = nets
optim_g, optim_d, optim_dur_disc = optims
scheduler_g, scheduler_d, scheduler_dur_disc = 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()
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) in tqdm(enumerate(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(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
speakers = speakers.cuda(rank, non_blocking=True)
tone = tone.cuda(rank, non_blocking=True)
language = language.cuda(rank, non_blocking=True)
bert = bert.cuda(rank, non_blocking=True)
with autocast(enabled=hps.train.fp16_run):
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_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, 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(
y_hat.squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax
)
y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
loss_disc_all = loss_disc
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach())
with autocast(enabled=False):
# TODO: I think need to mean using the mask, but for now, just mean all
loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
loss_dur_disc_all = loss_dur_disc
optim_dur_disc.zero_grad()
scaler.scale(loss_dur_disc_all).backward()
scaler.unscale_(optim_dur_disc)
grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None)
scaler.step(optim_dur_disc)
optim_d.zero_grad()
scaler.scale(loss_disc_all).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)
with autocast(enabled=hps.train.fp16_run):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
with autocast(enabled=False):
loss_dur = torch.sum(l_length.float())
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
| logging.getLogger('numba').setLevel(logging.WARNING)
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.set_float32_matmul_precision('medium')
global_step = 0
def main():
"""Assume Single Node Multi GPUs Training Only"""
assert torch.cuda.is_available(), "CPU training is not allowed."
n_gpus = torch.cuda.device_count()
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '65280'
hps = utils.get_hparams()
role=''
for t in hps.data.spk2id.items():
role=t[0]
if not hps.cont:
folder_path = f"./logs/{role}"
if not os.path.exists(folder_path):
os.makedirs(folder_path)
print(f"文件夹 '{role}' 已创建在 './logs/' 目录下。")
else:
print(f"文件夹 '{role}' 已经存在于 './logs/' 目录下。")
shutil.copy('./pretrained_models/D_0.pth',f'./logs/{role}/D_0.pth')
shutil.copy('./pretrained_models/G_0.pth',f'./logs/{role}/G_0.pth')
shutil.copy('./pretrained_models/DUR_0.pth',f'./logs/{role}/DUR_0.pth')
mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps, role))
def run(rank, n_gpus, hps, role):
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"))
dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(rank)
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=2, shuffle=False, pin_memory=True,
collate_fn=collate_fn, batch_sampler=train_sampler)
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 == True:
print("Using noise scaled MAS for VITS2")
use_noise_scaled_mas = True
mas_noise_scale_initial = 0.01
noise_scale_delta = 2e-6
else:
print("Using normal MAS for VITS1")
use_noise_scaled_mas = False
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 == True:
print("Using duration discriminator for VITS2")
use_duration_discriminator = True
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(rank)
if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True:
if hps.data.n_speakers == 0:
raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model")
use_spk_conditioned_encoder = True
else:
print("Using normal encoder for VITS1")
use_spk_conditioned_encoder = False
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(rank)
freeze_enc = getattr(hps.model, "freeze_enc", False)
if freeze_enc:
print("freeze encoder !!!")
for param in net_g.enc_p.parameters():
param.requires_grad = False
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(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)
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=[rank], find_unused_parameters=True)
net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
if net_dur_disc is not None:
net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True)
pretrain_dir = None
if pretrain_dir is None:
try:
if net_dur_disc is not None:
_, optim_dur_disc, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=not hps.cont)
_, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
optim_g, skip_optimizer=not hps.cont)
_, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
optim_d, skip_optimizer=not hps.cont)
epoch_str = max(epoch_str, 1)
global_step = (epoch_str - 1) * len(train_loader)
except Exception as e:
print(e)
epoch_str = 1
global_step = 0
else:
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g,
optim_g, True)
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d,
optim_d, True)
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)
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.fp16_run)
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank == 0:
train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval],role=role)
else:
train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None, role=role)
scheduler_g.step()
scheduler_d.step()
if net_dur_disc is not None:
scheduler_dur_disc.step()
def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, role):
net_g, net_d, net_dur_disc = nets
optim_g, optim_d, optim_dur_disc = optims
scheduler_g, scheduler_d, scheduler_dur_disc = 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()
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) in tqdm(enumerate(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(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
speakers = speakers.cuda(rank, non_blocking=True)
tone = tone.cuda(rank, non_blocking=True)
language = language.cuda(rank, non_blocking=True)
bert = bert.cuda(rank, non_blocking=True)
with autocast(enabled=hps.train.fp16_run):
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_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, 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(
y_hat.squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax
)
y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
loss_disc_all = loss_disc
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach())
with autocast(enabled=False):
# TODO: I think need to mean using the mask, but for now, just mean all
loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
loss_dur_disc_all = loss_dur_disc
optim_dur_disc.zero_grad()
scaler.scale(loss_dur_disc_all).backward()
scaler.unscale_(optim_dur_disc)
grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None)
scaler.step(optim_dur_disc)
optim_d.zero_grad()
scaler.scale(loss_disc_all).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)
with autocast(enabled=hps.train.fp16_run):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
if net_dur_disc is not None:
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
with autocast(enabled=False):
loss_dur = torch.sum(l_length.float())
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel | loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl | 9 | 2023-10-10 02:23:23+00:00 | 16k |
sakemin/cog-musicgen-chord | audiocraft/modules/conditioners.py | [
{
"identifier": "ChromaExtractor",
"path": "audiocraft/modules/chroma.py",
"snippet": "class ChromaExtractor(nn.Module):\n \"\"\"Chroma extraction and quantization.\n\n Args:\n sample_rate (int): Sample rate for the chroma extraction.\n n_chroma (int): Number of chroma bins for the c... | from collections import defaultdict
from copy import deepcopy
from dataclasses import dataclass, field
from itertools import chain
from pathlib import Path
from num2words import num2words
from transformers import RobertaTokenizer, T5EncoderModel, T5Tokenizer # type: ignore
from torch import nn
from torch.nn.utils.rnn import pad_sequence
from .chroma import ChromaExtractor
from .chord_chroma import ChordExtractor
from .streaming import StreamingModule
from .transformer import create_sin_embedding
from ..data.audio import audio_read
from ..data.audio_dataset import SegmentInfo
from ..data.audio_utils import convert_audio
from ..environment import AudioCraftEnvironment
from ..quantization import ResidualVectorQuantizer
from ..utils.autocast import TorchAutocast
from ..utils.cache import EmbeddingCache
from ..utils.utils import collate, hash_trick, length_to_mask, load_clap_state_dict, warn_once
from .btc.utils import chords
from demucs import pretrained
from audiocraft.data.audio_dataset import AudioDataset
from demucs.apply import apply_model
from demucs.audio import convert_audio
from demucs import pretrained
from audiocraft.data.audio_dataset import AudioDataset
from demucs.apply import apply_model
from demucs.audio import convert_audio
import logging
import math
import random
import re
import typing as tp
import warnings
import einops
import spacy
import torch
import torch.nn.functional as F
import numpy as np
import laion_clap # type: ignore | 13,447 | entries: tp.List[str] = [xi if xi is not None else "" for xi in x]
if self.normalize_text:
_, _, entries = self.text_normalizer(entries, return_text=True)
if self.word_dropout > 0. and self.training:
new_entries = []
for entry in entries:
words = [word for word in entry.split(" ") if random.random() >= self.word_dropout]
new_entries.append(" ".join(words))
entries = new_entries
empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""])
inputs = self.t5_tokenizer(entries, return_tensors='pt', padding=True).to(self.device)
mask = inputs['attention_mask']
mask[empty_idx, :] = 0 # zero-out index where the input is non-existant
return inputs
def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType:
mask = inputs['attention_mask']
with torch.set_grad_enabled(self.finetune), self.autocast:
embeds = self.t5(**inputs).last_hidden_state
embeds = self.output_proj(embeds.to(self.output_proj.weight))
embeds = (embeds * mask.unsqueeze(-1))
return embeds, mask
class WaveformConditioner(BaseConditioner):
"""Base class for all conditioners that take a waveform as input.
Classes that inherit must implement `_get_wav_embedding` that outputs
a continuous tensor, and `_downsampling_factor` that returns the down-sampling
factor of the embedding model.
Args:
dim (int): The internal representation dimension.
output_dim (int): Output dimension.
device (tp.Union[torch.device, str]): Device.
"""
def __init__(self, dim: int, output_dim: int, device: tp.Union[torch.device, str]):
super().__init__(dim, output_dim)
self.device = device
# if False no masking is done, used in ChromaStemConditioner when completing by periodicity a sample.
self._use_masking = True
def tokenize(self, x: WavCondition) -> WavCondition:
wav, length, sample_rate, path, seek_time = x
assert length is not None
return WavCondition(wav.to(self.device), length.to(self.device), sample_rate, path, seek_time)
def _get_wav_embedding(self, x: WavCondition) -> torch.Tensor:
"""Gets as input a WavCondition and returns a dense embedding."""
raise NotImplementedError()
def _downsampling_factor(self):
"""Returns the downsampling factor of the embedding model."""
raise NotImplementedError()
def forward(self, x: WavCondition) -> ConditionType:
"""Extract condition embedding and mask from a waveform and its metadata.
Args:
x (WavCondition): Waveform condition containing raw waveform and metadata.
Returns:
ConditionType: a dense vector representing the conditioning along with its mask
"""
wav, lengths, *_ = x
with torch.no_grad():
embeds = self._get_wav_embedding(x)
embeds = embeds.to(self.output_proj.weight)
embeds = self.output_proj(embeds)
if lengths is not None and self._use_masking:
lengths = lengths / self._downsampling_factor()
mask = length_to_mask(lengths, max_len=embeds.shape[1]).int() # type: ignore
else:
mask = torch.ones_like(embeds[..., 0])
embeds = (embeds * mask.unsqueeze(-1))
return embeds, mask
class ChromaStemConditioner(WaveformConditioner):
"""Chroma conditioner based on stems.
The ChromaStemConditioner uses DEMUCS to first filter out drums and bass, as
the drums and bass often dominate the chroma leading to the chroma features
not containing information about the melody.
Args:
output_dim (int): Output dimension for the conditioner.
sample_rate (int): Sample rate for the chroma extractor.
n_chroma (int): Number of chroma bins for the chroma extractor.
radix2_exp (int): Size of stft window for the chroma extractor (power of 2, e.g. 12 -> 2^12).
duration (int): duration used during training. This is later used for correct padding
in case we are using chroma as prefix.
match_len_on_eval (bool, optional): if True then all chromas are padded to the training
duration. Defaults to False.
eval_wavs (str, optional): path to a dataset manifest with waveform, this waveforms are used as
conditions during eval (for cases where we don't want to leak test conditions like MusicCaps).
Defaults to None.
n_eval_wavs (int, optional): limits the number of waveforms used for conditioning. Defaults to 0.
device (tp.Union[torch.device, str], optional): Device for the conditioner.
**kwargs: Additional parameters for the chroma extractor.
"""
def __init__(self, output_dim: int, sample_rate: int, n_chroma: int, radix2_exp: int,
duration: float, match_len_on_eval: bool = True, eval_wavs: tp.Optional[str] = None,
n_eval_wavs: int = 0, cache_path: tp.Optional[tp.Union[str, Path]] = None,
device: tp.Union[torch.device, str] = 'cpu', **kwargs):
super().__init__(dim=n_chroma, output_dim=output_dim, device=device)
self.autocast = TorchAutocast(enabled=device != 'cpu', device_type=self.device, dtype=torch.float32)
self.sample_rate = sample_rate
self.match_len_on_eval = match_len_on_eval
if match_len_on_eval:
self._use_masking = False
self.duration = duration
self.__dict__['demucs'] = pretrained.get_model('htdemucs').to(device)
stem_sources: list = self.demucs.sources # type: ignore
self.stem_indices = torch.LongTensor([stem_sources.index('vocals'), stem_sources.index('other')]).to(device)
self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma,
radix2_exp=radix2_exp, **kwargs).to(device)
self.chroma_len = self._get_chroma_len()
self.eval_wavs: tp.Optional[torch.Tensor] = self._load_eval_wavs(eval_wavs, n_eval_wavs)
self.cache = None
if cache_path is not None:
| # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
logger = logging.getLogger(__name__)
TextCondition = tp.Optional[str] # a text condition can be a string or None (if doesn't exist)
ConditionType = tp.Tuple[torch.Tensor, torch.Tensor] # condition, mask
class WavCondition(tp.NamedTuple):
wav: torch.Tensor
length: torch.Tensor
sample_rate: tp.List[int]
path: tp.List[tp.Optional[str]] = []
seek_time: tp.List[tp.Optional[float]] = []
class WavChordTextCondition(tp.NamedTuple):
wav: tp.Union[torch.Tensor,str,tp.List[str]]
length: torch.Tensor
sample_rate: tp.List[int]
path: tp.List[tp.Optional[str]] = []
seek_time: tp.List[tp.Optional[float]] = []
bpm : tp.List[tp.Optional[tp.Union[int, float]]] = []
meter : tp.List[tp.Optional[int]] = []
class JointEmbedCondition(tp.NamedTuple):
wav: torch.Tensor
text: tp.List[tp.Optional[str]]
length: torch.Tensor
sample_rate: tp.List[int]
path: tp.List[tp.Optional[str]] = []
seek_time: tp.List[tp.Optional[float]] = []
@dataclass
class ConditioningAttributes:
text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
wav: tp.Dict[str, tp.Union[WavCondition,WavChordTextCondition]] = field(default_factory=dict)
joint_embed: tp.Dict[str, JointEmbedCondition] = field(default_factory=dict)
def __getitem__(self, item):
return getattr(self, item)
@property
def text_attributes(self):
return self.text.keys()
@property
def wav_attributes(self):
return self.wav.keys()
@property
def joint_embed_attributes(self):
return self.joint_embed.keys()
@property
def attributes(self):
return {
"text": self.text_attributes,
"wav": self.wav_attributes,
"joint_embed": self.joint_embed_attributes,
}
def to_flat_dict(self):
return {
**{f"text.{k}": v for k, v in self.text.items()},
**{f"wav.{k}": v for k, v in self.wav.items()},
**{f"joint_embed.{k}": v for k, v in self.joint_embed.items()}
}
@classmethod
def from_flat_dict(cls, x):
out = cls()
for k, v in x.items():
kind, att = k.split(".")
out[kind][att] = v
return out
class SegmentWithAttributes(SegmentInfo):
"""Base class for all dataclasses that are used for conditioning.
All child classes should implement `to_condition_attributes` that converts
the existing attributes to a dataclass of type ConditioningAttributes.
"""
def to_condition_attributes(self) -> ConditioningAttributes:
raise NotImplementedError()
def nullify_condition(condition: ConditionType, dim: int = 1):
"""Transform an input condition to a null condition.
The way it is done by converting it to a single zero vector similarly
to how it is done inside WhiteSpaceTokenizer and NoopTokenizer.
Args:
condition (ConditionType): A tuple of condition and mask (tuple[torch.Tensor, torch.Tensor])
dim (int): The dimension that will be truncated (should be the time dimension)
WARNING!: dim should not be the batch dimension!
Returns:
ConditionType: A tuple of null condition and mask
"""
assert dim != 0, "dim cannot be the batch dimension!"
assert isinstance(condition, tuple) and \
isinstance(condition[0], torch.Tensor) and \
isinstance(condition[1], torch.Tensor), "'nullify_condition' got an unexpected input type!"
cond, mask = condition
B = cond.shape[0]
last_dim = cond.dim() - 1
out = cond.transpose(dim, last_dim)
out = 0. * out[..., :1]
out = out.transpose(dim, last_dim)
mask = torch.zeros((B, 1), device=out.device).int()
assert cond.dim() == out.dim()
return out, mask
def nullify_wav(cond: tp.Union[WavCondition,WavChordTextCondition]) -> tp.Union[WavCondition,WavChordTextCondition]:
"""Transform a WavCondition to a nullified WavCondition.
It replaces the wav by a null tensor, forces its length to 0, and replaces metadata by dummy attributes.
Args:
cond (WavCondition): Wav condition with wav, tensor of shape [B, T].
Returns:
WavCondition: Nullified wav condition.
"""
if not isinstance(cond, WavChordTextCondition):
null_wav, _ = nullify_condition((cond.wav, torch.zeros_like(cond.wav)), dim=cond.wav.dim() - 1)
return WavCondition(
wav=null_wav,
length=torch.tensor([0] * cond.wav.shape[0], device=cond.wav.device),
sample_rate=cond.sample_rate,
path=[None] * cond.wav.shape[0],
seek_time=[None] * cond.wav.shape[0],
)
else:
return WavChordTextCondition(
wav=['N']* len(cond.wav),
length=torch.tensor([0] * len(cond.wav), device=cond.length.device),
sample_rate=cond.sample_rate,
path=[None],
seek_time=[None],
bpm = cond.bpm,
meter = cond.meter
)
def nullify_joint_embed(embed: JointEmbedCondition) -> JointEmbedCondition:
"""Nullify the joint embedding condition by replacing it by a null tensor, forcing its length to 0,
and replacing metadata by dummy attributes.
Args:
cond (JointEmbedCondition): Joint embedding condition with wav and text, wav tensor of shape [B, C, T].
"""
null_wav, _ = nullify_condition((embed.wav, torch.zeros_like(embed.wav)), dim=embed.wav.dim() - 1)
return JointEmbedCondition(
wav=null_wav, text=[None] * len(embed.text),
length=torch.LongTensor([0]).to(embed.wav.device),
sample_rate=embed.sample_rate,
path=[None] * embed.wav.shape[0],
seek_time=[0] * embed.wav.shape[0],
)
class Tokenizer:
"""Base tokenizer implementation
(in case we want to introduce more advances tokenizers in the future).
"""
def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
raise NotImplementedError()
class WhiteSpaceTokenizer(Tokenizer):
"""This tokenizer should be used for natural language descriptions.
For example:
["he didn't, know he's going home.", 'shorter sentence'] =>
[[78, 62, 31, 4, 78, 25, 19, 34],
[59, 77, 0, 0, 0, 0, 0, 0]]
"""
PUNCTUATION = "?:!.,;"
def __init__(self, n_bins: int, pad_idx: int = 0, language: str = "en_core_web_sm",
lemma: bool = True, stopwords: bool = True) -> None:
self.n_bins = n_bins
self.pad_idx = pad_idx
self.lemma = lemma
self.stopwords = stopwords
try:
self.nlp = spacy.load(language)
except IOError:
spacy.cli.download(language) # type: ignore
self.nlp = spacy.load(language)
@tp.no_type_check
def __call__(self, texts: tp.List[tp.Optional[str]],
return_text: bool = False) -> tp.Tuple[torch.Tensor, torch.Tensor]:
"""Take a list of strings and convert them to a tensor of indices.
Args:
texts (list[str]): List of strings.
return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False.
Returns:
tuple[torch.Tensor, torch.Tensor]:
- Indices of words in the LUT.
- And a mask indicating where the padding tokens are
"""
output, lengths = [], []
texts = deepcopy(texts)
for i, text in enumerate(texts):
# if current sample doesn't have a certain attribute, replace with pad token
if text is None:
output.append(torch.Tensor([self.pad_idx]))
lengths.append(0)
continue
# convert numbers to words
text = re.sub(r"(\d+)", lambda x: num2words(int(x.group(0))), text) # type: ignore
# normalize text
text = self.nlp(text) # type: ignore
# remove stopwords
if self.stopwords:
text = [w for w in text if not w.is_stop] # type: ignore
# remove punctuation
text = [w for w in text if w.text not in self.PUNCTUATION] # type: ignore
# lemmatize if needed
text = [getattr(t, "lemma_" if self.lemma else "text") for t in text] # type: ignore
texts[i] = " ".join(text)
lengths.append(len(text))
# convert to tensor
tokens = torch.Tensor([hash_trick(w, self.n_bins) for w in text])
output.append(tokens)
mask = length_to_mask(torch.IntTensor(lengths)).int()
padded_output = pad_sequence(output, padding_value=self.pad_idx).int().t()
if return_text:
return padded_output, mask, texts # type: ignore
return padded_output, mask
class NoopTokenizer(Tokenizer):
"""This tokenizer should be used for global conditioners such as: artist, genre, key, etc.
The difference between this and WhiteSpaceTokenizer is that NoopTokenizer does not split
strings, so "Jeff Buckley" will get it's own index. Whereas WhiteSpaceTokenizer will
split it to ["Jeff", "Buckley"] and return an index per word.
For example:
["Queen", "ABBA", "Jeff Buckley"] => [43, 55, 101]
["Metal", "Rock", "Classical"] => [0, 223, 51]
"""
def __init__(self, n_bins: int, pad_idx: int = 0):
self.n_bins = n_bins
self.pad_idx = pad_idx
def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
output, lengths = [], []
for text in texts:
# if current sample doesn't have a certain attribute, replace with pad token
if text is None:
output.append(self.pad_idx)
lengths.append(0)
else:
output.append(hash_trick(text, self.n_bins))
lengths.append(1)
tokens = torch.LongTensor(output).unsqueeze(1)
mask = length_to_mask(torch.IntTensor(lengths)).int()
return tokens, mask
class BaseConditioner(nn.Module):
"""Base model for all conditioner modules.
We allow the output dim to be different than the hidden dim for two reasons:
1) keep our LUTs small when the vocab is large;
2) make all condition dims consistent.
Args:
dim (int): Hidden dim of the model.
output_dim (int): Output dim of the conditioner.
"""
def __init__(self, dim: int, output_dim: int):
super().__init__()
self.dim = dim
self.output_dim = output_dim
self.output_proj = nn.Linear(dim, output_dim)
def tokenize(self, *args, **kwargs) -> tp.Any:
"""Should be any part of the processing that will lead to a synchronization
point, e.g. BPE tokenization with transfer to the GPU.
The returned value will be saved and return later when calling forward().
"""
raise NotImplementedError()
def forward(self, inputs: tp.Any) -> ConditionType:
"""Gets input that should be used as conditioning (e.g, genre, description or a waveform).
Outputs a ConditionType, after the input data was embedded as a dense vector.
Returns:
ConditionType:
- A tensor of size [B, T, D] where B is the batch size, T is the length of the
output embedding and D is the dimension of the embedding.
- And a mask indicating where the padding tokens.
"""
raise NotImplementedError()
class TextConditioner(BaseConditioner):
...
class LUTConditioner(TextConditioner):
"""Lookup table TextConditioner.
Args:
n_bins (int): Number of bins.
dim (int): Hidden dim of the model (text-encoder/LUT).
output_dim (int): Output dim of the conditioner.
tokenizer (str): Name of the tokenizer.
pad_idx (int, optional): Index for padding token. Defaults to 0.
"""
def __init__(self, n_bins: int, dim: int, output_dim: int, tokenizer: str, pad_idx: int = 0):
super().__init__(dim, output_dim)
self.embed = nn.Embedding(n_bins, dim)
self.tokenizer: Tokenizer
if tokenizer == 'whitespace':
self.tokenizer = WhiteSpaceTokenizer(n_bins, pad_idx=pad_idx)
elif tokenizer == 'noop':
self.tokenizer = NoopTokenizer(n_bins, pad_idx=pad_idx)
else:
raise ValueError(f"unrecognized tokenizer `{tokenizer}`.")
def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
device = self.embed.weight.device
tokens, mask = self.tokenizer(x)
tokens, mask = tokens.to(device), mask.to(device)
return tokens, mask
def forward(self, inputs: tp.Tuple[torch.Tensor, torch.Tensor]) -> ConditionType:
tokens, mask = inputs
embeds = self.embed(tokens)
embeds = self.output_proj(embeds)
embeds = (embeds * mask.unsqueeze(-1))
return embeds, mask
class T5Conditioner(TextConditioner):
"""T5-based TextConditioner.
Args:
name (str): Name of the T5 model.
output_dim (int): Output dim of the conditioner.
finetune (bool): Whether to fine-tune T5 at train time.
device (str): Device for T5 Conditioner.
autocast_dtype (tp.Optional[str], optional): Autocast dtype.
word_dropout (float, optional): Word dropout probability.
normalize_text (bool, optional): Whether to apply text normalization.
"""
MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
"google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
"google/flan-t5-xl", "google/flan-t5-xxl"]
MODELS_DIMS = {
"t5-small": 512,
"t5-base": 768,
"t5-large": 1024,
"t5-3b": 1024,
"t5-11b": 1024,
"google/flan-t5-small": 512,
"google/flan-t5-base": 768,
"google/flan-t5-large": 1024,
"google/flan-t5-3b": 1024,
"google/flan-t5-11b": 1024,
}
def __init__(self, name: str, output_dim: int, finetune: bool, device: str,
autocast_dtype: tp.Optional[str] = 'float32', word_dropout: float = 0.,
normalize_text: bool = False):
assert name in self.MODELS, f"Unrecognized t5 model name (should in {self.MODELS})"
super().__init__(self.MODELS_DIMS[name], output_dim)
self.device = device
self.name = name
self.finetune = finetune
self.word_dropout = word_dropout
if autocast_dtype is None or self.device == 'cpu':
self.autocast = TorchAutocast(enabled=False)
if self.device != 'cpu':
logger.warning("T5 has no autocast, this might lead to NaN")
else:
dtype = getattr(torch, autocast_dtype)
assert isinstance(dtype, torch.dtype)
logger.info(f"T5 will be evaluated with autocast as {autocast_dtype}")
self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
# Let's disable logging temporarily because T5 will vomit some errors otherwise.
# thanks https://gist.github.com/simon-weber/7853144
previous_level = logging.root.manager.disable
logging.disable(logging.ERROR)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
try:
self.t5_tokenizer = T5Tokenizer.from_pretrained(name)
t5 = T5EncoderModel.from_pretrained(name).train(mode=finetune)
finally:
logging.disable(previous_level)
if finetune:
self.t5 = t5
else:
# this makes sure that the t5 models is not part
# of the saved checkpoint
self.__dict__['t5'] = t5.to(device)
self.normalize_text = normalize_text
if normalize_text:
self.text_normalizer = WhiteSpaceTokenizer(1, lemma=True, stopwords=True)
def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]:
# if current sample doesn't have a certain attribute, replace with empty string
entries: tp.List[str] = [xi if xi is not None else "" for xi in x]
if self.normalize_text:
_, _, entries = self.text_normalizer(entries, return_text=True)
if self.word_dropout > 0. and self.training:
new_entries = []
for entry in entries:
words = [word for word in entry.split(" ") if random.random() >= self.word_dropout]
new_entries.append(" ".join(words))
entries = new_entries
empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""])
inputs = self.t5_tokenizer(entries, return_tensors='pt', padding=True).to(self.device)
mask = inputs['attention_mask']
mask[empty_idx, :] = 0 # zero-out index where the input is non-existant
return inputs
def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType:
mask = inputs['attention_mask']
with torch.set_grad_enabled(self.finetune), self.autocast:
embeds = self.t5(**inputs).last_hidden_state
embeds = self.output_proj(embeds.to(self.output_proj.weight))
embeds = (embeds * mask.unsqueeze(-1))
return embeds, mask
class WaveformConditioner(BaseConditioner):
"""Base class for all conditioners that take a waveform as input.
Classes that inherit must implement `_get_wav_embedding` that outputs
a continuous tensor, and `_downsampling_factor` that returns the down-sampling
factor of the embedding model.
Args:
dim (int): The internal representation dimension.
output_dim (int): Output dimension.
device (tp.Union[torch.device, str]): Device.
"""
def __init__(self, dim: int, output_dim: int, device: tp.Union[torch.device, str]):
super().__init__(dim, output_dim)
self.device = device
# if False no masking is done, used in ChromaStemConditioner when completing by periodicity a sample.
self._use_masking = True
def tokenize(self, x: WavCondition) -> WavCondition:
wav, length, sample_rate, path, seek_time = x
assert length is not None
return WavCondition(wav.to(self.device), length.to(self.device), sample_rate, path, seek_time)
def _get_wav_embedding(self, x: WavCondition) -> torch.Tensor:
"""Gets as input a WavCondition and returns a dense embedding."""
raise NotImplementedError()
def _downsampling_factor(self):
"""Returns the downsampling factor of the embedding model."""
raise NotImplementedError()
def forward(self, x: WavCondition) -> ConditionType:
"""Extract condition embedding and mask from a waveform and its metadata.
Args:
x (WavCondition): Waveform condition containing raw waveform and metadata.
Returns:
ConditionType: a dense vector representing the conditioning along with its mask
"""
wav, lengths, *_ = x
with torch.no_grad():
embeds = self._get_wav_embedding(x)
embeds = embeds.to(self.output_proj.weight)
embeds = self.output_proj(embeds)
if lengths is not None and self._use_masking:
lengths = lengths / self._downsampling_factor()
mask = length_to_mask(lengths, max_len=embeds.shape[1]).int() # type: ignore
else:
mask = torch.ones_like(embeds[..., 0])
embeds = (embeds * mask.unsqueeze(-1))
return embeds, mask
class ChromaStemConditioner(WaveformConditioner):
"""Chroma conditioner based on stems.
The ChromaStemConditioner uses DEMUCS to first filter out drums and bass, as
the drums and bass often dominate the chroma leading to the chroma features
not containing information about the melody.
Args:
output_dim (int): Output dimension for the conditioner.
sample_rate (int): Sample rate for the chroma extractor.
n_chroma (int): Number of chroma bins for the chroma extractor.
radix2_exp (int): Size of stft window for the chroma extractor (power of 2, e.g. 12 -> 2^12).
duration (int): duration used during training. This is later used for correct padding
in case we are using chroma as prefix.
match_len_on_eval (bool, optional): if True then all chromas are padded to the training
duration. Defaults to False.
eval_wavs (str, optional): path to a dataset manifest with waveform, this waveforms are used as
conditions during eval (for cases where we don't want to leak test conditions like MusicCaps).
Defaults to None.
n_eval_wavs (int, optional): limits the number of waveforms used for conditioning. Defaults to 0.
device (tp.Union[torch.device, str], optional): Device for the conditioner.
**kwargs: Additional parameters for the chroma extractor.
"""
def __init__(self, output_dim: int, sample_rate: int, n_chroma: int, radix2_exp: int,
duration: float, match_len_on_eval: bool = True, eval_wavs: tp.Optional[str] = None,
n_eval_wavs: int = 0, cache_path: tp.Optional[tp.Union[str, Path]] = None,
device: tp.Union[torch.device, str] = 'cpu', **kwargs):
super().__init__(dim=n_chroma, output_dim=output_dim, device=device)
self.autocast = TorchAutocast(enabled=device != 'cpu', device_type=self.device, dtype=torch.float32)
self.sample_rate = sample_rate
self.match_len_on_eval = match_len_on_eval
if match_len_on_eval:
self._use_masking = False
self.duration = duration
self.__dict__['demucs'] = pretrained.get_model('htdemucs').to(device)
stem_sources: list = self.demucs.sources # type: ignore
self.stem_indices = torch.LongTensor([stem_sources.index('vocals'), stem_sources.index('other')]).to(device)
self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma,
radix2_exp=radix2_exp, **kwargs).to(device)
self.chroma_len = self._get_chroma_len()
self.eval_wavs: tp.Optional[torch.Tensor] = self._load_eval_wavs(eval_wavs, n_eval_wavs)
self.cache = None
if cache_path is not None: | self.cache = EmbeddingCache(Path(cache_path) / 'wav', self.device, | 10 | 2023-10-09 09:52:24+00:00 | 16k |
RVC-Project/Retrieval-based-Voice-Conversion | rvc/modules/vc/modules.py | [
{
"identifier": "Config",
"path": "rvc/configs/config.py",
"snippet": "class Config:\n def __new__(cls):\n if not hasattr(cls, \"_instance\"):\n cls._instance = super().__new__(cls)\n return cls._instance\n\n def __init__(self):\n self.device: str = \"cuda:0\"\n ... | import logging
import os
import traceback
import numpy as np
import soundfile as sf
import torch
from collections import OrderedDict
from io import BytesIO
from pathlib import Path
from rvc.configs.config import Config
from rvc.lib.audio import load_audio, wav2
from rvc.lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from rvc.modules.vc.pipeline import Pipeline
from rvc.modules.vc.utils import * | 11,768 |
logger: logging.Logger = logging.getLogger(__name__)
class VC:
def __init__(self):
self.n_spk: any = None
self.tgt_sr: int | None = None
self.net_g = None
|
logger: logging.Logger = logging.getLogger(__name__)
class VC:
def __init__(self):
self.n_spk: any = None
self.tgt_sr: int | None = None
self.net_g = None | self.pipeline: Pipeline | None = None | 7 | 2023-10-14 09:52:31+00:00 | 16k |
zhijie-group/LOVECon | video_diffusion/pipelines/stable_diffusion_controlnet.py | [
{
"identifier": "UNetPseudo3DConditionModel",
"path": "video_diffusion/models/unet_3d_condition.py",
"snippet": "class UNetPseudo3DConditionModel(ModelMixin, ConfigMixin):\n _supports_gradient_checkpointing = True\n\n @register_to_config\n def __init__(\n self,\n sample_size: Opti... | import inspect
import os, sys
import PIL
import torch
import numpy as np
import json
import diffusers
import bitsandbytes
from dataclasses import dataclass
from typing import Callable, List, Optional, Union,Dict,Any
from einops import rearrange
from tqdm import trange, 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
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from ..models.unet_3d_condition import UNetPseudo3DConditionModel
from ..models.controlnet_3d_condition import ControlNetPseudo3DModel
from video_diffusion.prompt_attention import attention_util
from accelerate import cpu_offload | 11,286 | # code mostly taken from https://github.com/huggingface/diffusers
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class SpatioTemporalStableDiffusionControlnetPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-video generation using Spatio-Temporal Stable Diffusion.
"""
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNetPseudo3DConditionModel,
| # code mostly taken from https://github.com/huggingface/diffusers
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class SpatioTemporalStableDiffusionControlnetPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-video generation using Spatio-Temporal Stable Diffusion.
"""
_optional_components = []
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNetPseudo3DConditionModel, | controlnet : ControlNetPseudo3DModel, | 1 | 2023-10-09 14:38:28+00:00 | 16k |
mlpc-ucsd/MaskCLIP | train_net.py | [
{
"identifier": "add_maskformer2_config",
"path": "maskclip/config.py",
"snippet": "def add_maskformer2_config(cfg):\n \"\"\"\n Add config for MASK_FORMER.\n \"\"\"\n # NOTE: configs from original maskformer\n # data config\n # select the dataset mapper\n cfg.INPUT.DATASET_MAPPER_NA... | from shapely.errors import ShapelyDeprecationWarning
from collections import OrderedDict
from typing import Any, Dict, List, Set
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog, build_detection_train_loader
from detectron2.engine import (
DefaultTrainer,
default_argument_parser,
default_setup,
launch,
)
from detectron2.evaluation import (
CityscapesInstanceEvaluator,
CityscapesSemSegEvaluator,
COCOEvaluator,
COCOPanopticEvaluator,
DatasetEvaluators,
LVISEvaluator,
SemSegEvaluator,
verify_results,
)
from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
from detectron2.solver.build import maybe_add_gradient_clipping
from detectron2.utils.logger import setup_logger
from maskclip import (
COCOInstanceNewBaselineDatasetMapper,
COCOPanopticNewBaselineDatasetMapper,
InstanceSegEvaluator,
MaskFormerInstanceDatasetMapper,
MaskFormerPanopticDatasetMapper,
MaskFormerSemanticDatasetMapper,
SemanticSegmentorWithTTA,
add_maskformer2_config,
)
import warnings
import copy
import itertools
import logging
import os
import torch
import detectron2.utils.comm as comm | 11,476 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
MaskFormer Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
try:
# ignore ShapelyDeprecationWarning from fvcore
warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)
except:
pass
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to MaskFormer.
"""
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each
builtin dataset. For your own dataset, you can simply create an
evaluator manually in your script and do not have to worry about the
hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
# semantic segmentation
if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]:
evaluator_list.append(
SemSegEvaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
)
)
# instance segmentation
if evaluator_type == "coco":
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
# panoptic segmentation
if evaluator_type in [
"coco_panoptic_seg",
"ade20k_panoptic_seg",
"cityscapes_panoptic_seg",
"mapillary_vistas_panoptic_seg",
]:
if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:
evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
# COCO
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Mapillary Vistas
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Cityscapes
if evaluator_type == "cityscapes_instance":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesInstanceEvaluator(dataset_name)
if evaluator_type == "cityscapes_sem_seg":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesSemSegEvaluator(dataset_name)
if evaluator_type == "cityscapes_panoptic_seg":
if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))
if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))
# ADE20K
if evaluator_type == "ade20k_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
# LVIS
if evaluator_type == "lvis":
return LVISEvaluator(dataset_name, output_dir=output_folder)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
@classmethod
def build_train_loader(cls, cfg):
# Semantic segmentation dataset mapper
if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic":
mapper = MaskFormerSemanticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Panoptic segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic":
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
MaskFormer Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
try:
# ignore ShapelyDeprecationWarning from fvcore
warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning)
except:
pass
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to MaskFormer.
"""
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each
builtin dataset. For your own dataset, you can simply create an
evaluator manually in your script and do not have to worry about the
hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
# semantic segmentation
if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]:
evaluator_list.append(
SemSegEvaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
)
)
# instance segmentation
if evaluator_type == "coco":
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
# panoptic segmentation
if evaluator_type in [
"coco_panoptic_seg",
"ade20k_panoptic_seg",
"cityscapes_panoptic_seg",
"mapillary_vistas_panoptic_seg",
]:
if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:
evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
# COCO
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Mapillary Vistas
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Cityscapes
if evaluator_type == "cityscapes_instance":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesInstanceEvaluator(dataset_name)
if evaluator_type == "cityscapes_sem_seg":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesSemSegEvaluator(dataset_name)
if evaluator_type == "cityscapes_panoptic_seg":
if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))
if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))
# ADE20K
if evaluator_type == "ade20k_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
# LVIS
if evaluator_type == "lvis":
return LVISEvaluator(dataset_name, output_dir=output_folder)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
@classmethod
def build_train_loader(cls, cfg):
# Semantic segmentation dataset mapper
if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic":
mapper = MaskFormerSemanticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Panoptic segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic": | mapper = MaskFormerPanopticDatasetMapper(cfg, True) | 4 | 2023-10-13 02:32:25+00:00 | 16k |
mlpc-ucsd/MasQCLIP | train_net.py | [
{
"identifier": "add_maskformer2_config",
"path": "masqclip/config.py",
"snippet": "def add_maskformer2_config(cfg):\n \"\"\"\n Add config for MASK_FORMER.\n \"\"\"\n # NOTE: configs from original maskformer\n # data config\n # select the dataset mapper\n cfg.INPUT.DATASET_MAPPER_NA... | import copy
import itertools
import logging
import os
import torch
import detectron2.utils.comm as comm
import warnings
from collections import OrderedDict
from typing import Any, Dict, List, Set
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog, build_detection_train_loader
from detectron2.engine import (
DefaultTrainer,
default_argument_parser,
default_setup,
launch,
)
from detectron2.evaluation import (
CityscapesInstanceEvaluator,
CityscapesSemSegEvaluator,
COCOEvaluator,
COCOPanopticEvaluator,
DatasetEvaluators,
LVISEvaluator,
SemSegEvaluator,
verify_results,
)
from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler
from detectron2.solver.build import maybe_add_gradient_clipping
from detectron2.utils.logger import setup_logger
from masqclip import (
COCOInstanceNewBaselineDatasetMapper,
COCOPanopticNewBaselineDatasetMapper,
InstanceSegEvaluator,
MaskFormerInstanceDatasetMapper,
MaskFormerPanopticDatasetMapper,
MaskFormerSemanticDatasetMapper,
SemanticSegmentorWithTTA,
add_maskformer2_config,
add_masqclip_config,
) | 12,249 | @classmethod
def build_train_loader(cls, cfg):
# Semantic segmentation dataset mapper
if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic":
mapper = MaskFormerSemanticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Panoptic segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic":
mapper = MaskFormerPanopticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Instance segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_instance":
mapper = MaskFormerInstanceDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# coco instance segmentation lsj new baseline
elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_instance_lsj":
mapper = COCOInstanceNewBaselineDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# coco panoptic segmentation lsj new baseline
elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_panoptic_lsj":
mapper = COCOPanopticNewBaselineDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
else:
mapper = None
return build_detection_train_loader(cfg, mapper=mapper)
@classmethod
def build_lr_scheduler(cls, cfg, optimizer):
"""
It now calls :func:`detectron2.solver.build_lr_scheduler`.
Overwrite it if you'd like a different scheduler.
"""
return build_lr_scheduler(cfg, optimizer)
@classmethod
def build_optimizer(cls, cfg, model):
weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM
weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED
defaults = {}
defaults["lr"] = cfg.SOLVER.BASE_LR
defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY
norm_module_types = (
torch.nn.BatchNorm1d,
torch.nn.BatchNorm2d,
torch.nn.BatchNorm3d,
torch.nn.SyncBatchNorm,
# NaiveSyncBatchNorm inherits from BatchNorm2d
torch.nn.GroupNorm,
torch.nn.InstanceNorm1d,
torch.nn.InstanceNorm2d,
torch.nn.InstanceNorm3d,
torch.nn.LayerNorm,
torch.nn.LocalResponseNorm,
)
params: List[Dict[str, Any]] = []
memo: Set[torch.nn.parameter.Parameter] = set()
for module_name, module in model.named_modules():
for module_param_name, value in module.named_parameters(recurse=False):
if not value.requires_grad:
continue
# Avoid duplicating parameters
if value in memo:
continue
memo.add(value)
hyperparams = copy.copy(defaults)
if "backbone" in module_name:
hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER
if (
"relative_position_bias_table" in module_param_name
or "absolute_pos_embed" in module_param_name
):
print(module_param_name)
hyperparams["weight_decay"] = 0.0
if isinstance(module, norm_module_types):
hyperparams["weight_decay"] = weight_decay_norm
if isinstance(module, torch.nn.Embedding):
hyperparams["weight_decay"] = weight_decay_embed
params.append({"params": [value], **hyperparams})
def maybe_add_full_model_gradient_clipping(optim):
# detectron2 doesn't have full model gradient clipping now
clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
enable = (
cfg.SOLVER.CLIP_GRADIENTS.ENABLED
and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
and clip_norm_val > 0.0
)
class FullModelGradientClippingOptimizer(optim):
def step(self, closure=None):
all_params = itertools.chain(*[x["params"] for x in self.param_groups])
torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
super().step(closure=closure)
return FullModelGradientClippingOptimizer if enable else optim
optimizer_type = cfg.SOLVER.OPTIMIZER
if optimizer_type == "SGD":
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM
)
elif optimizer_type == "ADAMW":
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
params, cfg.SOLVER.BASE_LR
)
else:
raise NotImplementedError(f"no optimizer type {optimizer_type}")
if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
optimizer = maybe_add_gradient_clipping(cfg, optimizer)
return optimizer
@classmethod
def test_with_TTA(cls, cfg, model):
logger = logging.getLogger("detectron2.trainer")
# In the end of training, run an evaluation with TTA.
logger.info("Running inference with test-time augmentation ...")
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
MasQCLIP Training Script.
"""
# MasQCLIP
warnings.filterwarnings("ignore")
class Trainer(DefaultTrainer):
"""
Extension of the Trainer class adapted to MaskFormer.
"""
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each
builtin dataset. For your own dataset, you can simply create an
evaluator manually in your script and do not have to worry about the
hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
# semantic segmentation
if evaluator_type in ["sem_seg", "ade20k_panoptic_seg"]:
evaluator_list.append(
SemSegEvaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
)
)
# instance segmentation
if evaluator_type == "coco":
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
# panoptic segmentation
if evaluator_type in [
"coco_panoptic_seg",
"ade20k_panoptic_seg",
"cityscapes_panoptic_seg",
"mapillary_vistas_panoptic_seg",
]:
if cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON:
evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
# COCO
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "coco_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Mapillary Vistas
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "mapillary_vistas_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
evaluator_list.append(SemSegEvaluator(dataset_name, distributed=True, output_dir=output_folder))
# Cityscapes
if evaluator_type == "cityscapes_instance":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesInstanceEvaluator(dataset_name)
if evaluator_type == "cityscapes_sem_seg":
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesSemSegEvaluator(dataset_name)
if evaluator_type == "cityscapes_panoptic_seg":
if cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesSemSegEvaluator(dataset_name))
if cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
assert (
torch.cuda.device_count() > comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
evaluator_list.append(CityscapesInstanceEvaluator(dataset_name))
# ADE20K
if evaluator_type == "ade20k_panoptic_seg" and cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON:
evaluator_list.append(InstanceSegEvaluator(dataset_name, output_dir=output_folder))
# LVIS
if evaluator_type == "lvis":
return LVISEvaluator(dataset_name, output_dir=output_folder)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(
dataset_name, evaluator_type
)
)
elif len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
@classmethod
def build_train_loader(cls, cfg):
# Semantic segmentation dataset mapper
if cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_semantic":
mapper = MaskFormerSemanticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Panoptic segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_panoptic":
mapper = MaskFormerPanopticDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# Instance segmentation dataset mapper
elif cfg.INPUT.DATASET_MAPPER_NAME == "mask_former_instance":
mapper = MaskFormerInstanceDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# coco instance segmentation lsj new baseline
elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_instance_lsj":
mapper = COCOInstanceNewBaselineDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
# coco panoptic segmentation lsj new baseline
elif cfg.INPUT.DATASET_MAPPER_NAME == "coco_panoptic_lsj":
mapper = COCOPanopticNewBaselineDatasetMapper(cfg, True)
return build_detection_train_loader(cfg, mapper=mapper)
else:
mapper = None
return build_detection_train_loader(cfg, mapper=mapper)
@classmethod
def build_lr_scheduler(cls, cfg, optimizer):
"""
It now calls :func:`detectron2.solver.build_lr_scheduler`.
Overwrite it if you'd like a different scheduler.
"""
return build_lr_scheduler(cfg, optimizer)
@classmethod
def build_optimizer(cls, cfg, model):
weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM
weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED
defaults = {}
defaults["lr"] = cfg.SOLVER.BASE_LR
defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY
norm_module_types = (
torch.nn.BatchNorm1d,
torch.nn.BatchNorm2d,
torch.nn.BatchNorm3d,
torch.nn.SyncBatchNorm,
# NaiveSyncBatchNorm inherits from BatchNorm2d
torch.nn.GroupNorm,
torch.nn.InstanceNorm1d,
torch.nn.InstanceNorm2d,
torch.nn.InstanceNorm3d,
torch.nn.LayerNorm,
torch.nn.LocalResponseNorm,
)
params: List[Dict[str, Any]] = []
memo: Set[torch.nn.parameter.Parameter] = set()
for module_name, module in model.named_modules():
for module_param_name, value in module.named_parameters(recurse=False):
if not value.requires_grad:
continue
# Avoid duplicating parameters
if value in memo:
continue
memo.add(value)
hyperparams = copy.copy(defaults)
if "backbone" in module_name:
hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER
if (
"relative_position_bias_table" in module_param_name
or "absolute_pos_embed" in module_param_name
):
print(module_param_name)
hyperparams["weight_decay"] = 0.0
if isinstance(module, norm_module_types):
hyperparams["weight_decay"] = weight_decay_norm
if isinstance(module, torch.nn.Embedding):
hyperparams["weight_decay"] = weight_decay_embed
params.append({"params": [value], **hyperparams})
def maybe_add_full_model_gradient_clipping(optim):
# detectron2 doesn't have full model gradient clipping now
clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
enable = (
cfg.SOLVER.CLIP_GRADIENTS.ENABLED
and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
and clip_norm_val > 0.0
)
class FullModelGradientClippingOptimizer(optim):
def step(self, closure=None):
all_params = itertools.chain(*[x["params"] for x in self.param_groups])
torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
super().step(closure=closure)
return FullModelGradientClippingOptimizer if enable else optim
optimizer_type = cfg.SOLVER.OPTIMIZER
if optimizer_type == "SGD":
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM
)
elif optimizer_type == "ADAMW":
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
params, cfg.SOLVER.BASE_LR
)
else:
raise NotImplementedError(f"no optimizer type {optimizer_type}")
if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
optimizer = maybe_add_gradient_clipping(cfg, optimizer)
return optimizer
@classmethod
def test_with_TTA(cls, cfg, model):
logger = logging.getLogger("detectron2.trainer")
# In the end of training, run an evaluation with TTA.
logger.info("Running inference with test-time augmentation ...") | model = SemanticSegmentorWithTTA(cfg, model) | 7 | 2023-10-13 02:43:53+00:00 | 16k |
Subsets and Splits
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have consistent code formatting levels across multiple scales (2k, 4k, 8k, 12k) and reveals the structured formatting patterns within these repositories.
SQL Console for tianyang/repobench_python_v1.1
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.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that have complete performance data across all seven code complexity levels, revealing consistent benchmarking patterns across different code sizes.
SQL Console for tianyang/repobench_python_v1.1
Identifies repositories that contain all 7 distinct quality levels (2k through 32k), revealing complete datasets that might be useful for comprehensive analysis.